Polymorphisms Within RYR3 Gene Are Associated With Risk and Age at Onset of Hypertension, Diabetes, and Alzheimer’s Disease

Polymorphisms Within RYR3 Gene Are Associated With Risk and Age at Onset of Hypertension,... Abstract BACKGROUND Hypertension affects 33% of Americans while type 2 diabetes and Alzheimer’s disease (AD) affect 10% of Americans, respectively. Ryanodine receptor 3 gene (RYR3) codes for the RYR which functions to release stored endoplasmic reticulum calcium ions (Ca2+) to increase intracellular Ca2+ concentration. Increasing studies demonstrate that altered levels of intracellular Ca2+ affect cardiac contraction, insulin secretion, and neurodegeneration. In this study, we investigated associations of the RYR3 genetic variants with hypertension, AD, and diabetes. METHODS Family data sets were used to explore association of RYR3 polymorphisms with risk and age at onset (AAO) of hypertension, diabetes, and AD. RESULTS Family-based association tests using generalized estimating equations (FBAT–GEE) showed several unique or shared disease-1 associated variants in the RYR3 gene. Three single nuclear polymorphisms (SNPs; rs2033610, rs2596164, and rs2278317) are significantly associated with risk for hypertension, diabetes, and AD. Two SNPs (rs4780174 and rs7498093) are significantly associated with AAO of the 3 diseases. CONCLUSIONS RYR3 variants are associated with hypertension, diabetes, and AD. Replication of these results of this gene in these 3 complex traits may help to better understand the genetic basis of calcium-signaling gene, RYR3 in association with risk and AAO of these diseases. blood pressure, diabetes and Alzheimer’s disease, hypertension, RYR3 gene, shared genetic variants, SNP Worldwide, hypertension, type 2 diabetes, and Alzheimer’s disease (AD) are common public health problems estimated to cause millions of premature deaths. In recent decades, hypertension and diabetes, as vascular risk factors, increase the risk of cognitive impairment and dementia.1 Hypertension alone affects 33% of Americans presently. In 2015, diabetes was estimated to affect 9.4% of the US adult population. However, exclusively above 65 years, the percentage increases to 25%. Regarding AD, 10% of individuals above 65 years suffer from the disease. There is a close link among these 3 diseases based on their risk factors, causes, pathological bases,2–5 and genetic factors, including lipoprotein lipase gene in these 3 diseases.6 More evidence supports the link type 2 diabetes and hypertension with AD, the most common form of dementia. These 3 traits are caused by multiple susceptibility genes, whose effects are modulated by gene-environment and gene–gene interactions. Twin and family studies show low-to-moderate heritability for AD,7,8 hypertension,9 and diabetes.10 Mendelian hypertension has elucidated certain biological pathways contributing to hypertension over dietary salt intake or directly through increased peripheral vascular resistance. The Mendelian mutation/genes exercise large effects on blood pressure–related phenotypes. However, genome wide association study (GWAS) and meta-analysis for blood pressure have yielded many signals with small effects.11 Thus far, few loci have been validated. Both genetic strategies are necessary, and much remains to be explored. Genetic/genomic insights and analyses of Mendelian hypertension syndromes and GWAS on essential hypertension have contributed to the depth of understanding of the genetics origins of hypertension.12 The results from several large-scale studies have clearly shown that blood pressure–related single nuclear polymorphisms (SNPs) are associated with a difference in blood pressure and cardiovascular disease outcome.13 Type 2 diabetes is characterized by insulin resistance, obesity, and high blood pressure, each influenced by both genetic and environmental factors.14 Advanced diabetes is characterized with abnormal pancreatic islet β-cell function in presence of insulin.15 Insulin secretion is triggered by glucose, which is transported into the β-cells and metabolized. This increases the concentration of adenosine triphosphate (ATP), which in turn leads to closure of ATP-sensitive K+ channels and depolarization of cellular membrane. Depolarization activates voltage-gated Ca2+ channels, allowing entry of extracellular Ca2+ into β cells which triggers an even greater release of ER Ca2+ which is mediated by ryanodine receptors (RYRs). This suggests calcium-signaling pathway is involved in pathophysiology of diabetes. The third complex trait is AD. It is characterized clinically by progressive deterioration of cognitive functions,16 and physiologically by β-amyloid peptides (Aβ) aggregates and intracellular neurofibrillar tangles composed of hyperphosphorylated microtubule-associated tau protein. While aging is the major risk factor, a high number of cases are characterized by earlier onset and are inherited in an autosomal dominant manner.17,18 In neurons, higher Ca2+ concentration, released by RYRs, leads to the release of neurotransmitter at synaptic junctions and affects dendritic action potential.19 The results from research studies suggest adults with type 2 diabetes have a higher risk of late-onset AD. Numerous investigations have reported that risk for late-onset AD is increased in the presence of type 2 diabetes2,3 and hypertension.4 An estimated 54 million US adults have prediabetes and most of these people will develop type 2 diabetes within the next 10 years. Diabetes and hypertension raise the risk of heart disease and stroke. The pathophysiological factors include, but are not limited to, damaged blood vessels in the brain, increased insulin levels, unbalanced chemical changes, and high blood glucose. These changes may cause inflammation, damage brain cells, and increases risk for AD (https://www.alz.org/national/documents/latino_brochure_diabetes.pdf). A recent study focus on evaluation of amyloid-protective factors showed that hypertension, diabetes, and metabolic conditions were also associated with AD-like neurodegeneration.5 The critical roles of calcium-signaling pathway in all 3 phenotypes lead us to think shared genetic variants in the genes involved in calcium signaling may be associated with these complex traits. We are interested in gene RYR3 located at 15q13.3. The protein encoded by RYR3 (Gene ID: 6263) is a RYR, which functions to release calcium from the ER. As a large intracellular homotetrameric protein (>2 MDa) that comprises 4,780 amino acids,20,21 RYRs reside on the sarcoplasmic reticulum membrane and release Ca+2 from intracellular stores to regulate concentration.22 Increased genes and mutations were reported to be associated with these traits based on the results of GWAS, candidate genes, meta-analysis, and next-generation sequencing studies. In the cardiovascular system, Ca+2 is essential for cardiac muscle contraction and relaxation, and acts as a second messenger in signal transduction pathways. Complex mechanisms regulate intracellular free calcium levels in the heart and vasculature, and a failure of these systems to maintain normal Ca+2 homeostasis has been linked to hypertension and other cardiovascular disease outcomes.22 Studies of the RYR3 have reported association with AD. For example, a meta-analysis based on 4 GWAS identified RYR3 association with AD risk using generalized multifactor dimensionality reduction.23 Another study observed a significant interaction between RYR3 and CACNA1C (genes coding for calcium channels that mediate the influx of calcium ions into the cell upon membrane polarization) in all 3 independent data sets of Alzheimer’s Disease Neuroimaging Initiative cohorts.24 Functional studies of RYR3 suggest that upregulated RYR levels are found in human AD brains,25 and RYR3 isoforms are upregulated at early and late stages of AD in animal models.26 Limited study of shared SNPs has been reported in hypertension, diabetes, and AD. Based on previous findings and reasoning above, we hypothesized SNPs in RYR3 are involved in development of hypertension, diabetes, and AD. MATERIALS AND METHODS Subjects A family-based sample was available from the National Institute on Aging–Late Onset Alzheimer’s Disease (NIA-LOAD) family study: 2,545 individuals (1,266 cases including 1,070 with age at onset [AAO] values) were available for our current study. Family Study: GWAS for Susceptibility Loci—Study Accession: phs000168.v1.p1. This study is to identify and recruit families with 2 or more siblings with the late-onset form of Alzheimer’s disease and a cohort of unrelated, nondemented controls similar in age and ethnic background, and to make the samples, the clinical and genotyping data and preliminary analyses available to qualified investigators world-wide. Genotyping by the Center for Inherited Disease Research (CIDR) was performed using the Illumina Infinium II assay protocol with hybridization to Illumina Human 610Quadv1_B Beadchips. The details about these subjects were described elsewhere.27 Overall, 1,266 AD cases and 1,279 non-AD individuals (including 1,070 with AAO values) were from 1386 pedigrees (including 589 nuclear families) (Table 1). Table 1. Descriptive characteristics of cases and controls Variable AD patients Controls Hypertension Controls Diabetes Controls Sample size (n) 1,266 1,279 1,036 1,240 247 2,028 Sex  Male 435 466 382 462 120 727  Female 831 813 654 778 127 1,301 Mean AAO (years ± SD) 76.4 ± 6.7 – 60.3 ± 13.1 – 61.9 ± 12.2 – Median AAO (years) 77 – 60 – 60 – Range of age at onset (years) 50–98 – 22–91 – 35–89 – Variable AD patients Controls Hypertension Controls Diabetes Controls Sample size (n) 1,266 1,279 1,036 1,240 247 2,028 Sex  Male 435 466 382 462 120 727  Female 831 813 654 778 127 1,301 Mean AAO (years ± SD) 76.4 ± 6.7 – 60.3 ± 13.1 – 61.9 ± 12.2 – Median AAO (years) 77 – 60 – 60 – Range of age at onset (years) 50–98 – 22–91 – 35–89 – Abbreviations: AAO, age at onset; AD, Alzheimer’s disease. View Large Table 1. Descriptive characteristics of cases and controls Variable AD patients Controls Hypertension Controls Diabetes Controls Sample size (n) 1,266 1,279 1,036 1,240 247 2,028 Sex  Male 435 466 382 462 120 727  Female 831 813 654 778 127 1,301 Mean AAO (years ± SD) 76.4 ± 6.7 – 60.3 ± 13.1 – 61.9 ± 12.2 – Median AAO (years) 77 – 60 – 60 – Range of age at onset (years) 50–98 – 22–91 – 35–89 – Variable AD patients Controls Hypertension Controls Diabetes Controls Sample size (n) 1,266 1,279 1,036 1,240 247 2,028 Sex  Male 435 466 382 462 120 727  Female 831 813 654 778 127 1,301 Mean AAO (years ± SD) 76.4 ± 6.7 – 60.3 ± 13.1 – 61.9 ± 12.2 – Median AAO (years) 77 – 60 – 60 – Range of age at onset (years) 50–98 – 22–91 – 35–89 – Abbreviations: AAO, age at onset; AD, Alzheimer’s disease. View Large Hypertension is defined as systolic blood pressure of 140 mm Hg or greater, diastolic blood pressure of 90 mm Hg or greater, or taking antihypertensive medication (phs000168.v1.p1). There were a total of 1,036 patients with hypertension and 1,240 control subjects. Diabetes is defined by a history of diabetes or high blood sugar, or treatment of diabetes or high blood sugar reported by the subject. There are 247 patients with diabetes and 2,028 control subjects. There are 279 SNPs within the RYR3 gene available for patients with diseases and control subjects. Statistical methods Genotype quality control: Hardy–Weinberg equilibrium was tested for all SNPs using the controls by using HAPLOVIEW version 4.1 software.28 Minor allele frequency was determined for each SNP and pairwise linkage disequilibrium statistics (D′ and r2) were assessed using HAPMAP Caucasian data. Family-based study: family-based association analyses for 3 traits were performed using PBAT version 36.1,29 which can handle nuclear families with missing parental genotypes, extended pedigrees with missing genotypic information, analysis of SNPs, haplotype analysis, quantitative traits, and time-to-onset phenotypes. For the affection status of hypertension, diabetes, and AD, family-based association tests using generalized estimating equations (FBAT–GEE) was used to perform family-based association analysis For testing time-to-onset trait (AAO), FBAT–Wilcoxon statistics were employed.30 The AAO values for healthy siblings were censored and age at entry into the study was used. Haplotype analysis was conducted in 2-SNP or 3-SNP sliding windows. For multiple comparison, Bonferroni correction (α = 0.05/279 = 1.79 × 10−4) was used for statistical significance. Descriptive statistics were conducted with SAS 9.4 (SAS Institute, Cary, NC). Three genetic models were used (allele-dose, dominant, and recessive). In silico analysis We evaluated potential function of the disease-associated SNP. We examined if these variants were located within the regions of the gene that might have potential functional importance. The sequences containing the associated SNPs were examined for microRNA-binding sites, splicing sites, regulatory gene regions, and species-conserved regions using NIH-SNP Function Prediction (http://snpinfo.niehs.nih.gov/cgi-bin/snpinfo/snpfunc.cgi). To determine disease-associated SNPs with RYR3 expression levels in different human tissues, we used publicly available data from the Genotype-Tissue Expression (GTEx) project31 in which, there is RNA sequencing on brain tissue from healthy donors available, resulting in genotype and expression phenotype data for ~100–120 normal individuals in multiple different brain regions. The information about subjects and RNA quality can be found in the GTEx website (www.gtexportal.org). RESULTS Genotype quality control and descriptive statistics Out of 279 SNPs in RYR3, 3 SNPs with P <10−4 for Hardy–Weinberg equilibrium (HWE) were removed for further analysis. The demographic characteristics of the subjects in the study are shown in the Table 1. The mean AAO for AD, hypertension, and diabetes were 76.4, 60.3, and 61.9 years. Single-marker and haplotype analyses based of risk of hypertension, diabetes, and AD using FBAT–GEE We found shared disease-associated SNPs among these complex traits. A number of SNPs were associated with each individual disease (Table 2), 5 SNPs (rs16973062, rs2288609, rs965471, rs4780167, rs10519874, and rs7498093) were associated with diabetes and also survived after the correction for multiple testing using the Bonferroni correction. Single-marker analysis showed that 3 SNPs (rs2033610, rs2596164, and rs2278317, P < 0.05) were associated with hypertension, diabetes, and AD in various genetic models (additive, dominant, and/or, recessive) as well as 3 SNPs (rs1390158, rs11637619, and rs8037864) also showed associations with hypertension and AD and 2 SNPs (rs4780167 and rs8028974) showed associations with hypertension and diabetes (P < 0.05) without correction for multiple testing. Four disease-associated SNPs (rs965471, rs10519874, rs7498093, and rs17236525) were shared between diabetes and AD in various genetic models. These results suggest shared genetic susceptibility among these 3 diseases in the RYR3 gene (Table 2). Table 2. Single-marker analysis of risk of hypertension, diabetes, and AD based on FBAT–GEE (P < 0.05) SNP Positiona ALb MAFc HWEd Fam#e P-FBAT–GEEf for hypertension P-FBAT–GEEh for diabetes P-FBAT–GEEi for AD rs16973323 31694920 T 0.17 0.722 145 0.00379(a)0.00215(d,r)g 0.697(a) 0.516(d,r) 0.556(a) 0.466(d,r) rs2291736 31726787 A 0.21 0.438 186 0.0046(a) 0.0221(d,r) 0.534(a) 0.142(d,r) 0.875(a) 0.0534(d,r) rs4780118 31774633 C 0.29 0.704 216 0.00746(a) 0.0382(d,r) 0.703(a) 0.276(d,r) 0.194(a) 0.128(d,r) rs1390158 31744649 A 0.06 0.173 72 0.0151(a) 0.0437(d,r) 0.846(a) 0.223(d,r) 0.0459(a) 0.0207(d,r) rs11072471 31507915 C 0.27 0.740 208 0.0169(a) 0.0191(d,r) 0.143(a) 0.154(d,r) 0.403(a) 0.292(d,r) rs11637619 31743447 C 0.14 0.185 132 0.0171(a) 0.0255(d,r) 0.887(a) 0.155(d,r) 0.162(a) 0.041(d,r) rs2033610 31542203 T 0.47 0.031 260 0.0226(a) 0.0384(d,r) 0.0306(a) 0.0344(d,r) 0.098(a) 0.0245(d,r) rs2596164 31546180 G 0.47 0.041 258 0.0237(a) 0.0526(d,r) 0.0227(a) 0.0391(d,r) 0.00963(a) 0.0178(d,r) rs12441112 31921018 G 0.21 0.703 207 0.0266(a) 0.933(d,r) 0.876(a) 0.711(d,r) 0.519(a) 0.26(d,r) rs12906709 31797258 A 0.41 0.003 241 0.0293(a) 0.317(d,r) 0.725(a) 0.27(d,r) 0.231(a) 0.172(d,r) rs2339273 31528153 T 0.26 0.217 207 0.0301(a) 0.812(d,r) 0.244(a) 0.364(d,r) 0.349(a) 0.438(d,r) rs2088143 31770837 C 0.22 0.737 188 0.0344(a) 0.0636(d,r) 0.403(a) 0.369(d,r) 0.627(a) 0.173(d,r) rs8037864 31801713 G 0.21 0.178 173 0.036(a) 0.0846(d,r) 0.797(a) 0.374(d,r) 0.129(a) 0.0323(d,r) rs2278317 31848032 G 0.31 0.737 223 0.0385(a) 0.779(d,r) 0.106(a) 0.0208(d,r) 0.163(a) 0.0255(d,r) rs17236476 31784069 C 0.07 0.945 85 0.0392(a) 0.0267(d,r) 0.701(a) 0.223(d,r) 0.146(a) 0.081(d,r) rs8034012 31687997 C 0.37 0.239 239 0.0438(a) 0.101(d,r) 0.614(a) 0.541(d,r) 0.499(a) 0.576(d,r) rs16973062 31679589 C 0.06 0.523 70 0.0451(a) 0.0438(d,r) 0.884(a) 0.365(d,r) 0.376(a) 0.226(d,r) rs2288609 31821738 A 0.27 0.319 214 0.602(a) 0.0516(d,r) 6.82 × 10−5(a) 0.365(d,r) 0.50(a) 0.38(d,r) rs965471 31868429 G 0.34 0.361 213 0.982(a) 0.634(d,r) 1.21 × 10−4(a) 0.00229(d,r) 0.118(a) 0.0326(d,r) rs4780167 31799489 G 0.41 0.905 266 0.111(a) 0.00388(d,r) 1.43 × 10−4(a) 1.22 × 10−4(d,r) 0.116(a)0.150(d,r) rs10519874 31863494 G 0.38 0.507 247 0.786(a) 0.77(d,r) 1.62 × 10−4(a) 0.365(d,r) 0.101(a) 0.0.0266(d,r) rs7498093 31808568 G 0.43 0.208 249 0.83151(a) 0.901(d,r) 1.63 × 10−4(a) 0.00208(d,r) 0.0494(a) 0.0254(d,r) rs10519873 31862443 A 0.36 0.749 243 0.869(a) 0.848(d,r) 2.38 × 10−4(a) 0.00292(d,r) 0.112(a) 0.068(d,r) rs8028974 31785766 G 0.39 0.865 245 0.453(a) 0.0175(d,r) 3.21 × 10−4(a) 0.00172(d,r) 0.748(a) 0.223(d,r) rs10519875 31864956 A 0.37 0.557 248 0.77(a) 0.749(d,r) 5.16 × 10−4(a) 0.00387(d,r) 0.14(a) 0.065(d,r) rs12440440 31829188 A 0.30 0.134 244 0.283(a) 0.368(d,r) 5.50 × 10−4(a) 0.00224(d,r) 0.522(a) 0.739(d,r) rs17236525 31813550 T 0.37 0.978 248 0.663(a) 0.679(d,r) 6.16 × 10−4(a) 0.0017(d,r) 0.0298(a) 8.17 × 10−4(d,r) rs7165052 31851908 T 0.43 0.823 258 0.825(a) 0.849(d,r) 7.13 × 10−4(a) 0.00659(d,r) 0.615(a) 0.574(d,r)g SNP Positiona ALb MAFc HWEd Fam#e P-FBAT–GEEf for hypertension P-FBAT–GEEh for diabetes P-FBAT–GEEi for AD rs16973323 31694920 T 0.17 0.722 145 0.00379(a)0.00215(d,r)g 0.697(a) 0.516(d,r) 0.556(a) 0.466(d,r) rs2291736 31726787 A 0.21 0.438 186 0.0046(a) 0.0221(d,r) 0.534(a) 0.142(d,r) 0.875(a) 0.0534(d,r) rs4780118 31774633 C 0.29 0.704 216 0.00746(a) 0.0382(d,r) 0.703(a) 0.276(d,r) 0.194(a) 0.128(d,r) rs1390158 31744649 A 0.06 0.173 72 0.0151(a) 0.0437(d,r) 0.846(a) 0.223(d,r) 0.0459(a) 0.0207(d,r) rs11072471 31507915 C 0.27 0.740 208 0.0169(a) 0.0191(d,r) 0.143(a) 0.154(d,r) 0.403(a) 0.292(d,r) rs11637619 31743447 C 0.14 0.185 132 0.0171(a) 0.0255(d,r) 0.887(a) 0.155(d,r) 0.162(a) 0.041(d,r) rs2033610 31542203 T 0.47 0.031 260 0.0226(a) 0.0384(d,r) 0.0306(a) 0.0344(d,r) 0.098(a) 0.0245(d,r) rs2596164 31546180 G 0.47 0.041 258 0.0237(a) 0.0526(d,r) 0.0227(a) 0.0391(d,r) 0.00963(a) 0.0178(d,r) rs12441112 31921018 G 0.21 0.703 207 0.0266(a) 0.933(d,r) 0.876(a) 0.711(d,r) 0.519(a) 0.26(d,r) rs12906709 31797258 A 0.41 0.003 241 0.0293(a) 0.317(d,r) 0.725(a) 0.27(d,r) 0.231(a) 0.172(d,r) rs2339273 31528153 T 0.26 0.217 207 0.0301(a) 0.812(d,r) 0.244(a) 0.364(d,r) 0.349(a) 0.438(d,r) rs2088143 31770837 C 0.22 0.737 188 0.0344(a) 0.0636(d,r) 0.403(a) 0.369(d,r) 0.627(a) 0.173(d,r) rs8037864 31801713 G 0.21 0.178 173 0.036(a) 0.0846(d,r) 0.797(a) 0.374(d,r) 0.129(a) 0.0323(d,r) rs2278317 31848032 G 0.31 0.737 223 0.0385(a) 0.779(d,r) 0.106(a) 0.0208(d,r) 0.163(a) 0.0255(d,r) rs17236476 31784069 C 0.07 0.945 85 0.0392(a) 0.0267(d,r) 0.701(a) 0.223(d,r) 0.146(a) 0.081(d,r) rs8034012 31687997 C 0.37 0.239 239 0.0438(a) 0.101(d,r) 0.614(a) 0.541(d,r) 0.499(a) 0.576(d,r) rs16973062 31679589 C 0.06 0.523 70 0.0451(a) 0.0438(d,r) 0.884(a) 0.365(d,r) 0.376(a) 0.226(d,r) rs2288609 31821738 A 0.27 0.319 214 0.602(a) 0.0516(d,r) 6.82 × 10−5(a) 0.365(d,r) 0.50(a) 0.38(d,r) rs965471 31868429 G 0.34 0.361 213 0.982(a) 0.634(d,r) 1.21 × 10−4(a) 0.00229(d,r) 0.118(a) 0.0326(d,r) rs4780167 31799489 G 0.41 0.905 266 0.111(a) 0.00388(d,r) 1.43 × 10−4(a) 1.22 × 10−4(d,r) 0.116(a)0.150(d,r) rs10519874 31863494 G 0.38 0.507 247 0.786(a) 0.77(d,r) 1.62 × 10−4(a) 0.365(d,r) 0.101(a) 0.0.0266(d,r) rs7498093 31808568 G 0.43 0.208 249 0.83151(a) 0.901(d,r) 1.63 × 10−4(a) 0.00208(d,r) 0.0494(a) 0.0254(d,r) rs10519873 31862443 A 0.36 0.749 243 0.869(a) 0.848(d,r) 2.38 × 10−4(a) 0.00292(d,r) 0.112(a) 0.068(d,r) rs8028974 31785766 G 0.39 0.865 245 0.453(a) 0.0175(d,r) 3.21 × 10−4(a) 0.00172(d,r) 0.748(a) 0.223(d,r) rs10519875 31864956 A 0.37 0.557 248 0.77(a) 0.749(d,r) 5.16 × 10−4(a) 0.00387(d,r) 0.14(a) 0.065(d,r) rs12440440 31829188 A 0.30 0.134 244 0.283(a) 0.368(d,r) 5.50 × 10−4(a) 0.00224(d,r) 0.522(a) 0.739(d,r) rs17236525 31813550 T 0.37 0.978 248 0.663(a) 0.679(d,r) 6.16 × 10−4(a) 0.0017(d,r) 0.0298(a) 8.17 × 10−4(d,r) rs7165052 31851908 T 0.43 0.823 258 0.825(a) 0.849(d,r) 7.13 × 10−4(a) 0.00659(d,r) 0.615(a) 0.574(d,r)g P values in bold are the ones retained statistical significant after correction for multiple testing using Bonferroni correction (a = 0.05/279 = 1.79 × 10−4) since a total of 279 SNPs in the RYR3 gene were used. Abbreviations: AD, Alzheimer’s disease; FBAT–GEE, family-based association tests using generalized estimating equations; SNP, single nuclear polymorphism. aPhysical position is based on NCBI Genome Build 36.3. bMinor allele. cMinor allele frequency. dP value of Hardy–Weinberg equilibrium test. eThe number of informative families for risk of hypertension using an additive model. fP value based on FBAT–GEE analysis for risk of hypertension. gLetters in parentheses indicate the genetic models used for analysis (a, additive; d, dominant; r, recessive model). hP value based on FBAT–GEE analysis for risk of diabetes. iP value based on FBAT–GEE analysis for risk of Alzheimer’s disease. View Large Table 2. Single-marker analysis of risk of hypertension, diabetes, and AD based on FBAT–GEE (P < 0.05) SNP Positiona ALb MAFc HWEd Fam#e P-FBAT–GEEf for hypertension P-FBAT–GEEh for diabetes P-FBAT–GEEi for AD rs16973323 31694920 T 0.17 0.722 145 0.00379(a)0.00215(d,r)g 0.697(a) 0.516(d,r) 0.556(a) 0.466(d,r) rs2291736 31726787 A 0.21 0.438 186 0.0046(a) 0.0221(d,r) 0.534(a) 0.142(d,r) 0.875(a) 0.0534(d,r) rs4780118 31774633 C 0.29 0.704 216 0.00746(a) 0.0382(d,r) 0.703(a) 0.276(d,r) 0.194(a) 0.128(d,r) rs1390158 31744649 A 0.06 0.173 72 0.0151(a) 0.0437(d,r) 0.846(a) 0.223(d,r) 0.0459(a) 0.0207(d,r) rs11072471 31507915 C 0.27 0.740 208 0.0169(a) 0.0191(d,r) 0.143(a) 0.154(d,r) 0.403(a) 0.292(d,r) rs11637619 31743447 C 0.14 0.185 132 0.0171(a) 0.0255(d,r) 0.887(a) 0.155(d,r) 0.162(a) 0.041(d,r) rs2033610 31542203 T 0.47 0.031 260 0.0226(a) 0.0384(d,r) 0.0306(a) 0.0344(d,r) 0.098(a) 0.0245(d,r) rs2596164 31546180 G 0.47 0.041 258 0.0237(a) 0.0526(d,r) 0.0227(a) 0.0391(d,r) 0.00963(a) 0.0178(d,r) rs12441112 31921018 G 0.21 0.703 207 0.0266(a) 0.933(d,r) 0.876(a) 0.711(d,r) 0.519(a) 0.26(d,r) rs12906709 31797258 A 0.41 0.003 241 0.0293(a) 0.317(d,r) 0.725(a) 0.27(d,r) 0.231(a) 0.172(d,r) rs2339273 31528153 T 0.26 0.217 207 0.0301(a) 0.812(d,r) 0.244(a) 0.364(d,r) 0.349(a) 0.438(d,r) rs2088143 31770837 C 0.22 0.737 188 0.0344(a) 0.0636(d,r) 0.403(a) 0.369(d,r) 0.627(a) 0.173(d,r) rs8037864 31801713 G 0.21 0.178 173 0.036(a) 0.0846(d,r) 0.797(a) 0.374(d,r) 0.129(a) 0.0323(d,r) rs2278317 31848032 G 0.31 0.737 223 0.0385(a) 0.779(d,r) 0.106(a) 0.0208(d,r) 0.163(a) 0.0255(d,r) rs17236476 31784069 C 0.07 0.945 85 0.0392(a) 0.0267(d,r) 0.701(a) 0.223(d,r) 0.146(a) 0.081(d,r) rs8034012 31687997 C 0.37 0.239 239 0.0438(a) 0.101(d,r) 0.614(a) 0.541(d,r) 0.499(a) 0.576(d,r) rs16973062 31679589 C 0.06 0.523 70 0.0451(a) 0.0438(d,r) 0.884(a) 0.365(d,r) 0.376(a) 0.226(d,r) rs2288609 31821738 A 0.27 0.319 214 0.602(a) 0.0516(d,r) 6.82 × 10−5(a) 0.365(d,r) 0.50(a) 0.38(d,r) rs965471 31868429 G 0.34 0.361 213 0.982(a) 0.634(d,r) 1.21 × 10−4(a) 0.00229(d,r) 0.118(a) 0.0326(d,r) rs4780167 31799489 G 0.41 0.905 266 0.111(a) 0.00388(d,r) 1.43 × 10−4(a) 1.22 × 10−4(d,r) 0.116(a)0.150(d,r) rs10519874 31863494 G 0.38 0.507 247 0.786(a) 0.77(d,r) 1.62 × 10−4(a) 0.365(d,r) 0.101(a) 0.0.0266(d,r) rs7498093 31808568 G 0.43 0.208 249 0.83151(a) 0.901(d,r) 1.63 × 10−4(a) 0.00208(d,r) 0.0494(a) 0.0254(d,r) rs10519873 31862443 A 0.36 0.749 243 0.869(a) 0.848(d,r) 2.38 × 10−4(a) 0.00292(d,r) 0.112(a) 0.068(d,r) rs8028974 31785766 G 0.39 0.865 245 0.453(a) 0.0175(d,r) 3.21 × 10−4(a) 0.00172(d,r) 0.748(a) 0.223(d,r) rs10519875 31864956 A 0.37 0.557 248 0.77(a) 0.749(d,r) 5.16 × 10−4(a) 0.00387(d,r) 0.14(a) 0.065(d,r) rs12440440 31829188 A 0.30 0.134 244 0.283(a) 0.368(d,r) 5.50 × 10−4(a) 0.00224(d,r) 0.522(a) 0.739(d,r) rs17236525 31813550 T 0.37 0.978 248 0.663(a) 0.679(d,r) 6.16 × 10−4(a) 0.0017(d,r) 0.0298(a) 8.17 × 10−4(d,r) rs7165052 31851908 T 0.43 0.823 258 0.825(a) 0.849(d,r) 7.13 × 10−4(a) 0.00659(d,r) 0.615(a) 0.574(d,r)g SNP Positiona ALb MAFc HWEd Fam#e P-FBAT–GEEf for hypertension P-FBAT–GEEh for diabetes P-FBAT–GEEi for AD rs16973323 31694920 T 0.17 0.722 145 0.00379(a)0.00215(d,r)g 0.697(a) 0.516(d,r) 0.556(a) 0.466(d,r) rs2291736 31726787 A 0.21 0.438 186 0.0046(a) 0.0221(d,r) 0.534(a) 0.142(d,r) 0.875(a) 0.0534(d,r) rs4780118 31774633 C 0.29 0.704 216 0.00746(a) 0.0382(d,r) 0.703(a) 0.276(d,r) 0.194(a) 0.128(d,r) rs1390158 31744649 A 0.06 0.173 72 0.0151(a) 0.0437(d,r) 0.846(a) 0.223(d,r) 0.0459(a) 0.0207(d,r) rs11072471 31507915 C 0.27 0.740 208 0.0169(a) 0.0191(d,r) 0.143(a) 0.154(d,r) 0.403(a) 0.292(d,r) rs11637619 31743447 C 0.14 0.185 132 0.0171(a) 0.0255(d,r) 0.887(a) 0.155(d,r) 0.162(a) 0.041(d,r) rs2033610 31542203 T 0.47 0.031 260 0.0226(a) 0.0384(d,r) 0.0306(a) 0.0344(d,r) 0.098(a) 0.0245(d,r) rs2596164 31546180 G 0.47 0.041 258 0.0237(a) 0.0526(d,r) 0.0227(a) 0.0391(d,r) 0.00963(a) 0.0178(d,r) rs12441112 31921018 G 0.21 0.703 207 0.0266(a) 0.933(d,r) 0.876(a) 0.711(d,r) 0.519(a) 0.26(d,r) rs12906709 31797258 A 0.41 0.003 241 0.0293(a) 0.317(d,r) 0.725(a) 0.27(d,r) 0.231(a) 0.172(d,r) rs2339273 31528153 T 0.26 0.217 207 0.0301(a) 0.812(d,r) 0.244(a) 0.364(d,r) 0.349(a) 0.438(d,r) rs2088143 31770837 C 0.22 0.737 188 0.0344(a) 0.0636(d,r) 0.403(a) 0.369(d,r) 0.627(a) 0.173(d,r) rs8037864 31801713 G 0.21 0.178 173 0.036(a) 0.0846(d,r) 0.797(a) 0.374(d,r) 0.129(a) 0.0323(d,r) rs2278317 31848032 G 0.31 0.737 223 0.0385(a) 0.779(d,r) 0.106(a) 0.0208(d,r) 0.163(a) 0.0255(d,r) rs17236476 31784069 C 0.07 0.945 85 0.0392(a) 0.0267(d,r) 0.701(a) 0.223(d,r) 0.146(a) 0.081(d,r) rs8034012 31687997 C 0.37 0.239 239 0.0438(a) 0.101(d,r) 0.614(a) 0.541(d,r) 0.499(a) 0.576(d,r) rs16973062 31679589 C 0.06 0.523 70 0.0451(a) 0.0438(d,r) 0.884(a) 0.365(d,r) 0.376(a) 0.226(d,r) rs2288609 31821738 A 0.27 0.319 214 0.602(a) 0.0516(d,r) 6.82 × 10−5(a) 0.365(d,r) 0.50(a) 0.38(d,r) rs965471 31868429 G 0.34 0.361 213 0.982(a) 0.634(d,r) 1.21 × 10−4(a) 0.00229(d,r) 0.118(a) 0.0326(d,r) rs4780167 31799489 G 0.41 0.905 266 0.111(a) 0.00388(d,r) 1.43 × 10−4(a) 1.22 × 10−4(d,r) 0.116(a)0.150(d,r) rs10519874 31863494 G 0.38 0.507 247 0.786(a) 0.77(d,r) 1.62 × 10−4(a) 0.365(d,r) 0.101(a) 0.0.0266(d,r) rs7498093 31808568 G 0.43 0.208 249 0.83151(a) 0.901(d,r) 1.63 × 10−4(a) 0.00208(d,r) 0.0494(a) 0.0254(d,r) rs10519873 31862443 A 0.36 0.749 243 0.869(a) 0.848(d,r) 2.38 × 10−4(a) 0.00292(d,r) 0.112(a) 0.068(d,r) rs8028974 31785766 G 0.39 0.865 245 0.453(a) 0.0175(d,r) 3.21 × 10−4(a) 0.00172(d,r) 0.748(a) 0.223(d,r) rs10519875 31864956 A 0.37 0.557 248 0.77(a) 0.749(d,r) 5.16 × 10−4(a) 0.00387(d,r) 0.14(a) 0.065(d,r) rs12440440 31829188 A 0.30 0.134 244 0.283(a) 0.368(d,r) 5.50 × 10−4(a) 0.00224(d,r) 0.522(a) 0.739(d,r) rs17236525 31813550 T 0.37 0.978 248 0.663(a) 0.679(d,r) 6.16 × 10−4(a) 0.0017(d,r) 0.0298(a) 8.17 × 10−4(d,r) rs7165052 31851908 T 0.43 0.823 258 0.825(a) 0.849(d,r) 7.13 × 10−4(a) 0.00659(d,r) 0.615(a) 0.574(d,r)g P values in bold are the ones retained statistical significant after correction for multiple testing using Bonferroni correction (a = 0.05/279 = 1.79 × 10−4) since a total of 279 SNPs in the RYR3 gene were used. Abbreviations: AD, Alzheimer’s disease; FBAT–GEE, family-based association tests using generalized estimating equations; SNP, single nuclear polymorphism. aPhysical position is based on NCBI Genome Build 36.3. bMinor allele. cMinor allele frequency. dP value of Hardy–Weinberg equilibrium test. eThe number of informative families for risk of hypertension using an additive model. fP value based on FBAT–GEE analysis for risk of hypertension. gLetters in parentheses indicate the genetic models used for analysis (a, additive; d, dominant; r, recessive model). hP value based on FBAT–GEE analysis for risk of diabetes. iP value based on FBAT–GEE analysis for risk of Alzheimer’s disease. View Large We also identified haplotypes in association with 2 of 3 traits, hypertension, and diabetes. The A-T haplotype from rs4780118 and rs11072471 (D′ = 0.87) and the A-C haplotype from rs2291736-rs937303 (D′ = 0.63) was significantly associated with hypertension in the family-data (P = 0.00254 and 0.00667, respectively) (Table 3). Moreover, we also observed the G-G haplotype from rs12906709–rs4780167 SNPs (P = 2.71 × 10−5), A-T haplotype of rs2288609–rs4780174 SNPs (P = 5.44 × 10−6), and the A-G haplotype from rs10519875–rs11855625 (P = 6.71 × 10−5) were significantly associated with diabetes in the family-data (Table 3). Table 3. Haplotypes associated with risk of hypertension and diabetes based on FBAT–GEE SNPs r2a Haplotypeb Frequencyc Fam#d P-FBAT–GEEe Hypertension  rs4780118–rs11072471 0.87 A–T 0.59 222 0.00254  rs2291736–rs937303 0.63 A–C 0.20 190 0.00667 Diabetes  rs12906709–rs4780167 0.44 G–G 0.33 282 2.71 × 10−5  rs2288609–rs4780174 0.41 A–T 0.27 216 5.44 × 10−6  rs10519875–rs11855625 0.34 A–G 0.32 266 6.71 × 10−5 SNPs r2a Haplotypeb Frequencyc Fam#d P-FBAT–GEEe Hypertension  rs4780118–rs11072471 0.87 A–T 0.59 222 0.00254  rs2291736–rs937303 0.63 A–C 0.20 190 0.00667 Diabetes  rs12906709–rs4780167 0.44 G–G 0.33 282 2.71 × 10−5  rs2288609–rs4780174 0.41 A–T 0.27 216 5.44 × 10−6  rs10519875–rs11855625 0.34 A–G 0.32 266 6.71 × 10−5 Abbreviations: FBAT–GEE, Family-based association tests using generalized estimating equations; SNP, single nuclear polymorphism. aLinkage disequilibrium measure (r2). bHaplotype inferred from 2 SNPs. cHaplotype frequency. dFam# refers to the number of informative families. eP value based on FBAT–GEE analysis. View Large Table 3. Haplotypes associated with risk of hypertension and diabetes based on FBAT–GEE SNPs r2a Haplotypeb Frequencyc Fam#d P-FBAT–GEEe Hypertension  rs4780118–rs11072471 0.87 A–T 0.59 222 0.00254  rs2291736–rs937303 0.63 A–C 0.20 190 0.00667 Diabetes  rs12906709–rs4780167 0.44 G–G 0.33 282 2.71 × 10−5  rs2288609–rs4780174 0.41 A–T 0.27 216 5.44 × 10−6  rs10519875–rs11855625 0.34 A–G 0.32 266 6.71 × 10−5 SNPs r2a Haplotypeb Frequencyc Fam#d P-FBAT–GEEe Hypertension  rs4780118–rs11072471 0.87 A–T 0.59 222 0.00254  rs2291736–rs937303 0.63 A–C 0.20 190 0.00667 Diabetes  rs12906709–rs4780167 0.44 G–G 0.33 282 2.71 × 10−5  rs2288609–rs4780174 0.41 A–T 0.27 216 5.44 × 10−6  rs10519875–rs11855625 0.34 A–G 0.32 266 6.71 × 10−5 Abbreviations: FBAT–GEE, Family-based association tests using generalized estimating equations; SNP, single nuclear polymorphism. aLinkage disequilibrium measure (r2). bHaplotype inferred from 2 SNPs. cHaplotype frequency. dFam# refers to the number of informative families. eP value based on FBAT–GEE analysis. View Large Single-marker analysis of age at onset of hypertension, diabetes, and AD in NIA-LOAD Family Study In addition to exam RYR3 SNPs in association with the risks of 3 traits, we also tested these SNPs in association with AAO. The most significant association was revealed for rs10519818 which was shared by patients with hypertension (P = 0.000227 at both dominant and recessive models) and patients with diabetes (P = 0.0119 at additive genetic model, Table 4). Another disease-associated SNPs shared by hypertension and diabetes was rs12909478. Moreover, we also observed disease-associated SNPs shared by 3 diseases, they were rs7498093 and rs4780174 at the various genetic models (Table 4). Three disease-associated SNPs (rs17236525, rs12440440, and rs4238567) were also shared by diabetes and AD at the different genetic models (Table 4). These results, once again support the disease-risk SNPs and haplotypes in associations with AAO of hypertension and diabetes. The P values of all disease-associated SNPs in Table 4 were before correction for multiple testing. A common haplotype of A–C from rs2291736-rs937303 was associated with hypertension observed in 190 families (P = 0.00667, Table 5) and the common haplotype of G–G of rs2288609–rs4780174 SNPs showed an association with diabetes (P = 5.68 × 10−3). Table 4. Single-marker analysis of age at onset of hypertension, diabetes, and Alzheimer’s disease based on FBAT–Wilcoxon test (P < 0.05) SNP Positiona ALb MAFc HWEd Fam#e P-FBAT–GEEf for hypertension P-FBAT–GEEh for diabetes P-FBAT–GEEi for AD rs10519818 31499690 C 0.12 0.228 62 0.00186(a)0.000227(d,r)g 0.0119(a) 0.0174(d,r) 0.573(a) 0.0943(d,r) rs2596230 31508018 C 0.12 0.275 65 0.00309(a) 0.00498(d,r) 0.113(a) 0.139(d,r) 0.946(a) 0.643(d,r) rs2018899 31546496 T 0.09 0.592 50 0.0419(a) 0.00484(d,r) 0.206(a) 0.298(d,r) 0.879(a) 0.836(d,r) rs7498093 31808568 G 0.42 0.208 125 0.255(a) 0.0381(d,r) 0.00273(a) 0.0138(d,r) 3.6 × 10−4(a) 0.00345(d,r) rs4780174 31822821 T 0.48 0.176 121 0.103(a) 0.0316(d,r) 0.00388(a) 0.0336(d,r) 0.00355(a) 0.00343(d,r) rs1036006 31900157 G 0.36 0.566 111 0.399(a) 0.406(d,r) 0.00457(a) 0.00599(d,r) 0.828(a) 0.589(d,r) rs12914825 31902573 T 0.09 0.203 52 0.679(a) 0.811(d,r) 0.0116(a) 0.00218(d,r) 0.926(a) 0.581(d,r) rs2288609 31821738 A 0.27 0.319 116 0.777(a) 0.0346(d,r) 0.0126(a) 0.0256(d,r) 0.0981(a) 0.24(d,r) rs17236525 31813550 T 0.37 0.978 119 0.152(a) 0.865(d,r) 0.0144(a) 0.0183(d,r) 0.00447(a) 0.00351(d,r) rs2059956 31910824 T 0.46 0.228 129 0.423(a) 0.509(d,r) 0.0197(a) 0.0159(d,r) 0.5(a) 0.397(d,r) rs12440440 31829188 A 0.30 0.134 130 0.468(a) 0.494(d,r) 0.0305(a) 0.556(d,r) 0.00256(a) 0.0153(d,r) rs11072687 31895202 T 0.27 0.983 100 0.193(a) 0.295(d,r) 0.0357(a) 0.0353(d,r) 0.799(a) 0.605(d,r) rs7165389 31589238 C 0.12 0.327 55 0.75(a) 0.617(d,r) 0.0365(a) 0.0706(d,r) 0.627(a) 0.574(d,r) rs4780144 31741944 C 0.05 0.015 23 0.136(a) 0.136(d,r) 0.0389(a) 0.0389(d,r) 0.603(a) 0.621(d,r) rs12909478 31616542 T 0.19 0.831 76 0.389(a) 0.0269(d,r) 0.0407(a) 0.0288(d,r) 0.988(a) 0.84(d,r) rs4238567 31714681 T 0.49 0.064 129 0.834(a) 0.865(d,r) 0.0411(a) 0.473(d,r) 0.00443(a) 0.0043(d,r) rs12901404 31862592 C 0.14 0.719 64 0.511(a) 0.786(d,r) 0.0498(a) 0.0737(d,r) 0.628(a) 0.543(d,r) SNP Positiona ALb MAFc HWEd Fam#e P-FBAT–GEEf for hypertension P-FBAT–GEEh for diabetes P-FBAT–GEEi for AD rs10519818 31499690 C 0.12 0.228 62 0.00186(a)0.000227(d,r)g 0.0119(a) 0.0174(d,r) 0.573(a) 0.0943(d,r) rs2596230 31508018 C 0.12 0.275 65 0.00309(a) 0.00498(d,r) 0.113(a) 0.139(d,r) 0.946(a) 0.643(d,r) rs2018899 31546496 T 0.09 0.592 50 0.0419(a) 0.00484(d,r) 0.206(a) 0.298(d,r) 0.879(a) 0.836(d,r) rs7498093 31808568 G 0.42 0.208 125 0.255(a) 0.0381(d,r) 0.00273(a) 0.0138(d,r) 3.6 × 10−4(a) 0.00345(d,r) rs4780174 31822821 T 0.48 0.176 121 0.103(a) 0.0316(d,r) 0.00388(a) 0.0336(d,r) 0.00355(a) 0.00343(d,r) rs1036006 31900157 G 0.36 0.566 111 0.399(a) 0.406(d,r) 0.00457(a) 0.00599(d,r) 0.828(a) 0.589(d,r) rs12914825 31902573 T 0.09 0.203 52 0.679(a) 0.811(d,r) 0.0116(a) 0.00218(d,r) 0.926(a) 0.581(d,r) rs2288609 31821738 A 0.27 0.319 116 0.777(a) 0.0346(d,r) 0.0126(a) 0.0256(d,r) 0.0981(a) 0.24(d,r) rs17236525 31813550 T 0.37 0.978 119 0.152(a) 0.865(d,r) 0.0144(a) 0.0183(d,r) 0.00447(a) 0.00351(d,r) rs2059956 31910824 T 0.46 0.228 129 0.423(a) 0.509(d,r) 0.0197(a) 0.0159(d,r) 0.5(a) 0.397(d,r) rs12440440 31829188 A 0.30 0.134 130 0.468(a) 0.494(d,r) 0.0305(a) 0.556(d,r) 0.00256(a) 0.0153(d,r) rs11072687 31895202 T 0.27 0.983 100 0.193(a) 0.295(d,r) 0.0357(a) 0.0353(d,r) 0.799(a) 0.605(d,r) rs7165389 31589238 C 0.12 0.327 55 0.75(a) 0.617(d,r) 0.0365(a) 0.0706(d,r) 0.627(a) 0.574(d,r) rs4780144 31741944 C 0.05 0.015 23 0.136(a) 0.136(d,r) 0.0389(a) 0.0389(d,r) 0.603(a) 0.621(d,r) rs12909478 31616542 T 0.19 0.831 76 0.389(a) 0.0269(d,r) 0.0407(a) 0.0288(d,r) 0.988(a) 0.84(d,r) rs4238567 31714681 T 0.49 0.064 129 0.834(a) 0.865(d,r) 0.0411(a) 0.473(d,r) 0.00443(a) 0.0043(d,r) rs12901404 31862592 C 0.14 0.719 64 0.511(a) 0.786(d,r) 0.0498(a) 0.0737(d,r) 0.628(a) 0.543(d,r) Abbreviations: AD, Alzheimer’s disease; FBAT–GEE, family-based association tests using generalized estimating equations; SNP, single nuclear polymorphism. aPhysical position is based on NCBI Genome Build 36.3. bMinor allele. cMinor allele frequency. dP value of Hardy-Weinberg equilibrium test. eThe number of informative families for age at onset of hypertension using an additive model. fP value based on FBAT–Wilcoxon test for age at onset of hypertension. gLetters in parentheses indicate the genetic models used for analysis (a, additive; d, dominant; r, recessive model). hP value based on FBAT–Wilcoxon test for age at onset of diabetes. iP value based on FBAT–Wilcoxon test for age at onset of Alzheimer’s disease. View Large Table 4. Single-marker analysis of age at onset of hypertension, diabetes, and Alzheimer’s disease based on FBAT–Wilcoxon test (P < 0.05) SNP Positiona ALb MAFc HWEd Fam#e P-FBAT–GEEf for hypertension P-FBAT–GEEh for diabetes P-FBAT–GEEi for AD rs10519818 31499690 C 0.12 0.228 62 0.00186(a)0.000227(d,r)g 0.0119(a) 0.0174(d,r) 0.573(a) 0.0943(d,r) rs2596230 31508018 C 0.12 0.275 65 0.00309(a) 0.00498(d,r) 0.113(a) 0.139(d,r) 0.946(a) 0.643(d,r) rs2018899 31546496 T 0.09 0.592 50 0.0419(a) 0.00484(d,r) 0.206(a) 0.298(d,r) 0.879(a) 0.836(d,r) rs7498093 31808568 G 0.42 0.208 125 0.255(a) 0.0381(d,r) 0.00273(a) 0.0138(d,r) 3.6 × 10−4(a) 0.00345(d,r) rs4780174 31822821 T 0.48 0.176 121 0.103(a) 0.0316(d,r) 0.00388(a) 0.0336(d,r) 0.00355(a) 0.00343(d,r) rs1036006 31900157 G 0.36 0.566 111 0.399(a) 0.406(d,r) 0.00457(a) 0.00599(d,r) 0.828(a) 0.589(d,r) rs12914825 31902573 T 0.09 0.203 52 0.679(a) 0.811(d,r) 0.0116(a) 0.00218(d,r) 0.926(a) 0.581(d,r) rs2288609 31821738 A 0.27 0.319 116 0.777(a) 0.0346(d,r) 0.0126(a) 0.0256(d,r) 0.0981(a) 0.24(d,r) rs17236525 31813550 T 0.37 0.978 119 0.152(a) 0.865(d,r) 0.0144(a) 0.0183(d,r) 0.00447(a) 0.00351(d,r) rs2059956 31910824 T 0.46 0.228 129 0.423(a) 0.509(d,r) 0.0197(a) 0.0159(d,r) 0.5(a) 0.397(d,r) rs12440440 31829188 A 0.30 0.134 130 0.468(a) 0.494(d,r) 0.0305(a) 0.556(d,r) 0.00256(a) 0.0153(d,r) rs11072687 31895202 T 0.27 0.983 100 0.193(a) 0.295(d,r) 0.0357(a) 0.0353(d,r) 0.799(a) 0.605(d,r) rs7165389 31589238 C 0.12 0.327 55 0.75(a) 0.617(d,r) 0.0365(a) 0.0706(d,r) 0.627(a) 0.574(d,r) rs4780144 31741944 C 0.05 0.015 23 0.136(a) 0.136(d,r) 0.0389(a) 0.0389(d,r) 0.603(a) 0.621(d,r) rs12909478 31616542 T 0.19 0.831 76 0.389(a) 0.0269(d,r) 0.0407(a) 0.0288(d,r) 0.988(a) 0.84(d,r) rs4238567 31714681 T 0.49 0.064 129 0.834(a) 0.865(d,r) 0.0411(a) 0.473(d,r) 0.00443(a) 0.0043(d,r) rs12901404 31862592 C 0.14 0.719 64 0.511(a) 0.786(d,r) 0.0498(a) 0.0737(d,r) 0.628(a) 0.543(d,r) SNP Positiona ALb MAFc HWEd Fam#e P-FBAT–GEEf for hypertension P-FBAT–GEEh for diabetes P-FBAT–GEEi for AD rs10519818 31499690 C 0.12 0.228 62 0.00186(a)0.000227(d,r)g 0.0119(a) 0.0174(d,r) 0.573(a) 0.0943(d,r) rs2596230 31508018 C 0.12 0.275 65 0.00309(a) 0.00498(d,r) 0.113(a) 0.139(d,r) 0.946(a) 0.643(d,r) rs2018899 31546496 T 0.09 0.592 50 0.0419(a) 0.00484(d,r) 0.206(a) 0.298(d,r) 0.879(a) 0.836(d,r) rs7498093 31808568 G 0.42 0.208 125 0.255(a) 0.0381(d,r) 0.00273(a) 0.0138(d,r) 3.6 × 10−4(a) 0.00345(d,r) rs4780174 31822821 T 0.48 0.176 121 0.103(a) 0.0316(d,r) 0.00388(a) 0.0336(d,r) 0.00355(a) 0.00343(d,r) rs1036006 31900157 G 0.36 0.566 111 0.399(a) 0.406(d,r) 0.00457(a) 0.00599(d,r) 0.828(a) 0.589(d,r) rs12914825 31902573 T 0.09 0.203 52 0.679(a) 0.811(d,r) 0.0116(a) 0.00218(d,r) 0.926(a) 0.581(d,r) rs2288609 31821738 A 0.27 0.319 116 0.777(a) 0.0346(d,r) 0.0126(a) 0.0256(d,r) 0.0981(a) 0.24(d,r) rs17236525 31813550 T 0.37 0.978 119 0.152(a) 0.865(d,r) 0.0144(a) 0.0183(d,r) 0.00447(a) 0.00351(d,r) rs2059956 31910824 T 0.46 0.228 129 0.423(a) 0.509(d,r) 0.0197(a) 0.0159(d,r) 0.5(a) 0.397(d,r) rs12440440 31829188 A 0.30 0.134 130 0.468(a) 0.494(d,r) 0.0305(a) 0.556(d,r) 0.00256(a) 0.0153(d,r) rs11072687 31895202 T 0.27 0.983 100 0.193(a) 0.295(d,r) 0.0357(a) 0.0353(d,r) 0.799(a) 0.605(d,r) rs7165389 31589238 C 0.12 0.327 55 0.75(a) 0.617(d,r) 0.0365(a) 0.0706(d,r) 0.627(a) 0.574(d,r) rs4780144 31741944 C 0.05 0.015 23 0.136(a) 0.136(d,r) 0.0389(a) 0.0389(d,r) 0.603(a) 0.621(d,r) rs12909478 31616542 T 0.19 0.831 76 0.389(a) 0.0269(d,r) 0.0407(a) 0.0288(d,r) 0.988(a) 0.84(d,r) rs4238567 31714681 T 0.49 0.064 129 0.834(a) 0.865(d,r) 0.0411(a) 0.473(d,r) 0.00443(a) 0.0043(d,r) rs12901404 31862592 C 0.14 0.719 64 0.511(a) 0.786(d,r) 0.0498(a) 0.0737(d,r) 0.628(a) 0.543(d,r) Abbreviations: AD, Alzheimer’s disease; FBAT–GEE, family-based association tests using generalized estimating equations; SNP, single nuclear polymorphism. aPhysical position is based on NCBI Genome Build 36.3. bMinor allele. cMinor allele frequency. dP value of Hardy-Weinberg equilibrium test. eThe number of informative families for age at onset of hypertension using an additive model. fP value based on FBAT–Wilcoxon test for age at onset of hypertension. gLetters in parentheses indicate the genetic models used for analysis (a, additive; d, dominant; r, recessive model). hP value based on FBAT–Wilcoxon test for age at onset of diabetes. iP value based on FBAT–Wilcoxon test for age at onset of Alzheimer’s disease. View Large Table 5. Haplotypes associated with age at onset of hypertension and diabetes based on FBAT–Wilcoxon test SNPs r2a Haplotypeb Frequencyc Fam#d P-FBAT–GEEe Hypertension  rs2596240–rs8023659 0.30 A–G 0.03 22 0.00888  rs2291736–rs937303 0.63 A–C 0.20 190 0.00667 Diabetes  rs16970801–rs16970823 0.70 C–A 0.06 20 0.00949  rs2288609–rs4780174 0.41 G–G 0.43 44 0.00568 SNPs r2a Haplotypeb Frequencyc Fam#d P-FBAT–GEEe Hypertension  rs2596240–rs8023659 0.30 A–G 0.03 22 0.00888  rs2291736–rs937303 0.63 A–C 0.20 190 0.00667 Diabetes  rs16970801–rs16970823 0.70 C–A 0.06 20 0.00949  rs2288609–rs4780174 0.41 G–G 0.43 44 0.00568 Abbreviations: FBAT–GEE, Family-based association tests using generalized estimating equations; SNP, single nuclear polymorphism. aLinkage disequilibrium measure (r2). bHaplotype inferred from 2 SNPs. cHaplotype frequency. dFam# refers to the number of informative families. eP value based on FBAT–Wilcoxon test. View Large Table 5. Haplotypes associated with age at onset of hypertension and diabetes based on FBAT–Wilcoxon test SNPs r2a Haplotypeb Frequencyc Fam#d P-FBAT–GEEe Hypertension  rs2596240–rs8023659 0.30 A–G 0.03 22 0.00888  rs2291736–rs937303 0.63 A–C 0.20 190 0.00667 Diabetes  rs16970801–rs16970823 0.70 C–A 0.06 20 0.00949  rs2288609–rs4780174 0.41 G–G 0.43 44 0.00568 SNPs r2a Haplotypeb Frequencyc Fam#d P-FBAT–GEEe Hypertension  rs2596240–rs8023659 0.30 A–G 0.03 22 0.00888  rs2291736–rs937303 0.63 A–C 0.20 190 0.00667 Diabetes  rs16970801–rs16970823 0.70 C–A 0.06 20 0.00949  rs2288609–rs4780174 0.41 G–G 0.43 44 0.00568 Abbreviations: FBAT–GEE, Family-based association tests using generalized estimating equations; SNP, single nuclear polymorphism. aLinkage disequilibrium measure (r2). bHaplotype inferred from 2 SNPs. cHaplotype frequency. dFam# refers to the number of informative families. eP value based on FBAT–Wilcoxon test. View Large Shared disease-associated SNPs and haplotype between the risk and AAO We observed different disease-associated SNPs with disease risk and AAO; however, in the current study, we found several SNPs were associated not only with the disease risk, but also with AAO. SNP rs12440440 was found to be associated with diabetes risk (Table 2) and AAO for both diabetes and AD (Table 4). While SNP, rs17236525 was associated with both risk and AAO for diabetes and AD (Table 2 and 4). SNP rs2288609 was found to increase susceptibility for AAO and risk of diabetes. Finally, rs7498093 was observed association with risk of diabetes and AD as well as AAO for 3 diseases at various genetic models (Table 2 and 4). In silico analysis To test if the associated SNPs were located at regulatory gene regions and species-conserved regions, we used NIH-SNP Function Prediction. We found hypertension-associated SNPs, rs11072471 and rs16973323, were located at the species-conserved region and the gene regulatory region, respectively, which suggests the regions containing biological function.32 Having established strong associations of disease-associated SNPs with AAO and risk of AD, we tested if the genotype at these SNPs are associated with levels of gene expression in various tissues, including brain and adrenal gland based on the data from the Genotype-Tissue Expression. We hypothesized the effects of the SNP genotypes on these 3 disease-risks may reflect genotype-based differences in levels of the gene expression. To investigate this, we analyzed recently released GTEx Consortium data. In postmortem samples from 100 to 120 normal individuals from the GTEx data set, among the SNPs we tested, the homozygous genotypes (T/T) for minor alleles of all 3 diseases (AD, hypertension and diabetes) associated SNP, rs2033610 were shown significantly decreased gene expression as compared with C/C and C/T genotypes in the adrenal glands (P = 2.6 × 10−7, Figure 1a). For rs2596164, GG genotype carriers showed the lowest levels of expression (P = 2.8 × 10−7) as compared with AG and AA genotypes in the adrenal glands (G is minor allele, Figure 1b). Finally, hypertension- and AD-associated SNP, rs8037864, was also shown statistically significantly decreased gene expression of homozygous GG genotype as compared with T/G and T/T genotypes in the cells of transformed fibroblasts (P = 4.7 × 10−5). Figure 1. View largeDownload slide Based on the GTEx Consortium data of postmortem samples from 100 to 120 normal individuals,the homozygous genotypes (T/T) for minor alleles of all three diseases (AD, hypertension and diabetes) associated SNP, rs2033610 were shown significantly decreased gene expression as compared with C/C and C/T genotypes in the adrenal glands (P = 2.6 x 10-7, [a]). For rs2596164 GG genotype carriers showed the lowest levels of expression (P = 2.8 x 10−7) as compared with AG and AA genotypes in the adrenal glands (G is Pminor allele, [b]). (a) Genotypes at 3 disease risk variant, rs2033610, were also associates with gene expression in the adrenal glands in eQTLBoxplot (P = 2.6 × 10−7) from the GTEx Consortium, with TT (Home Ref, N = 30) genotype carriers showing the lowest levels of expression. Medians and interquartile ranges are indicated. CT genotype (Het, N = 57) and CC genotype (Homo Alt, N = 39) are shown. (b) Genotypes at 3 disease risk variant, rs2596164, of the RYR3 gene were also associates with gene expression in the adrenal glands in eQTLBoxplot (P = 2.6 × 10−7) from the GTEx Consortium, with GG (Home Ref, N = 30) genotype carriers showing the lowest levels of expression. Medians and interquartile ranges are indicated. AG genotype (Het, N = 57) and AA genotype (Homo Alt, N = 39) are shown. Figure 1. View largeDownload slide Based on the GTEx Consortium data of postmortem samples from 100 to 120 normal individuals,the homozygous genotypes (T/T) for minor alleles of all three diseases (AD, hypertension and diabetes) associated SNP, rs2033610 were shown significantly decreased gene expression as compared with C/C and C/T genotypes in the adrenal glands (P = 2.6 x 10-7, [a]). For rs2596164 GG genotype carriers showed the lowest levels of expression (P = 2.8 x 10−7) as compared with AG and AA genotypes in the adrenal glands (G is Pminor allele, [b]). (a) Genotypes at 3 disease risk variant, rs2033610, were also associates with gene expression in the adrenal glands in eQTLBoxplot (P = 2.6 × 10−7) from the GTEx Consortium, with TT (Home Ref, N = 30) genotype carriers showing the lowest levels of expression. Medians and interquartile ranges are indicated. CT genotype (Het, N = 57) and CC genotype (Homo Alt, N = 39) are shown. (b) Genotypes at 3 disease risk variant, rs2596164, of the RYR3 gene were also associates with gene expression in the adrenal glands in eQTLBoxplot (P = 2.6 × 10−7) from the GTEx Consortium, with GG (Home Ref, N = 30) genotype carriers showing the lowest levels of expression. Medians and interquartile ranges are indicated. AG genotype (Het, N = 57) and AA genotype (Homo Alt, N = 39) are shown. DISCUSSION Supporting our hypothesis, we identified unique and shared SNPs of RYR3 with hypertension, diabetes, and AD. In addition, the results of haplotype analyses and disease-risk SNP function predictions of RYR3 support the role of its variants in these complex and multifactorial diseases. Furthermore, hypertension risk SNPs rs11072471 and rs16973323 were found located at the species-well-conserved region indicating functional importance of RYR3, which supports how important it is to identify potentially important conserved noncoding sequences in association with these complex diseases. A recent study reports an identification of a major quantitative trait locus on chromosome 15q26 for systolic blood pressure (A logarithm of the odds [LOD] = 3.36), which is close to RYR gene location (15q13.3) in Mexican-Americans.33 Furthermore, 2 disease-associated SNPs (rs2033610 and rs2596164) also showed alterations of gene expression in the adrenal gland tissue, which plays an important role in secreting hormones that regulate both blood sugar and pressure. Additionally, a recent study has confirmed their previous findings of gene–gene interactions of RYR3 and CACNA1C in late-onset of AD using endo-phenotype analysis.34 Overall, results strongly imply the existence of shared SNPs associated of the RYR3 gene across these 3 diseases. A number of previous studies identified common genes and variants among diabetes, hypertension, and other phenotypes,35 e.g., lipoprotein lipase gene is associated with hypertension, AD, type 2 diabetes, and coronary heart disease.6 There might be a pathophysiology mechanism of sharing genetic variants for these 3 traits, as previous studies suggest that high glucose level and high blood pressure affect AD through multiple mechanisms, including reduction in cerebral blood flow36 and breakdown of the blood–brain barrier.37 A recent study (39) has also showed that excess sugar in the blood can lead to organ and even brain damage, which can lead to dementia as well as early onset of AD. It has been demonstrated that alterations of calcium (Ca2+)-signaling pathway are involved in hypertension, diabetes, and AD based on functional studies.24,25 To the best of our knowledge, this is the first study of investigating the association of RYR3 gene with the risk and AAO of hypertension, diabetes, and AD. The strengths of this study include the comparatively medium sample size used which is of relatively large size for this type of study. The sample was also ethnically homogenous (US European decent), which gives indication that within this ethnic community the same genetic evidence may be replicated. Multiple analyses were performed using single-marker analysis FBAT–GEE and FBAT–Wilcoxon. The RYRs associated variants were also supported by the results of haplotype and in-silico analyses. We used a family-based design, which can reduce the type 1 error rate arising from population stratification. Especially, the FBAT–GEE approach in the PBAT software can easily be adapted to scenarios with multiple offspring per family and missing parental information, and testing for linkage disequilibrium under the assumption of linkage.38 We are also aware of some limitations: among disease-associated SNPs, only 5 diabetes-risk SNPs were retained after multiple testing using the Bonferroni correction, other unique and shared SNPs did not; however, it is well known that Bonferroni correction is a very conservative method. Moreover, we cannot exclude contribution from environmental or behavioral factors or other nongenetic correlations. Additional replication of these results is also necessary. Finally, our current findings might be spurious or subject to type I error. Future confirmatory studies of the RYR3 in these 3 traits in independent samples or targeted genome sequencing of the gene for these diseases may provide an opportunity to dissect the genetic complexity of this gene more accurately. In conclusion, we provide genetic evidence of unique and shared disease-associated RYR3 polymorphisms among hypertension, diabetes, and AD. Suggesting an etiologic relationship between them, calcium disturbances and certain calcium-signaling pathways may be involved in pathophysiology of these traits. These results will provide a basis for replication in larger samples and/or other populations to elucidate the potential role of these genetic variants. DISCLOSURE The authors declared no conflict of interest. ACKNOWLEDGMENTS We acknowledge the NIH GWAS Data Repository, the Contributing Investigator(s) who contributed the phenotype data and DNA samples from his/her original study and the primary funding organization that supported the contributing study “Multi-Site Collaborative Study for Genotype-Phenotype Associations in Alzheimer’s disease and longitudinal follow-up of Genotype-Phenotype Associations in Alzheimer’s disease and Neuroimaging component of Genotype-Phenotype Associations in Alzheimer’s disease”. The genotypic and associated phenotypic data used in the study, “Multi-Site Collaborative Study for Genotype-Phenotype Associations in Alzheimer’s Disease (GenADA)” were provided by the GlaxoSmithKline, R&D Limited. The data sets used for analyses described in this manuscript were obtained from dbGaP at http://www.ncbi.nlm.nih.gov/gap through dbGaP accession number phs000219.v1.p1. We also acknowledge Pedro Hinojosa, a UTRGV BMED student for his contribution on preparation of manuscript. A part of funding sources is from Dr Chun Xu’s UTRGV start-up fund. REFERENCES 1. Campos-Peña V , Toral-Rios D , Becerril-Pérez F , Sánchez-Torres C , Delgado-Namorado Y , Torres-Ossorio E , Franco-Bocanegra D , Carvajal K . Metabolic syndrome as a risk factor for Alzheimer’s disease: is Aβ a crucial factor in both pathologies ? Antioxid Redox Signal 2017 ; 26 : 542 – 560 . Google Scholar CrossRef Search ADS PubMed 2. Cheng D , Noble J , Tang MX , Schupf N , Mayeux R , Luchsinger JA . Type 2 diabetes and late-onset Alzheimer’s disease . Dement Geriatr Cogn Disord 2011 ; 31 : 424 – 430 . Google Scholar CrossRef Search ADS PubMed 3. Luchsinger JA , Tang MX , Stern Y , Shea S , Mayeux R . Diabetes mellitus and risk of Alzheimer’s disease and dementia with stroke in a multiethnic cohort . Am J Epidemiol 2001 ; 154 : 635 – 641 . Google Scholar CrossRef Search ADS PubMed 4. Tosto G , Bird TD , Bennett DA , Boeve BF , Brickman AM , Cruchaga C , Faber K , Foroud TM , Farlow M , Goate AM , Graff-Radford NR , Lantigua R , Manly J , Ottman R , Rosenberg R , Schaid DJ , Schupf N , Stern Y , Sweet RA , Mayeux R ; National Institute on Aging Late-Onset Alzheimer Disease/National Cell Repository for Alzheimer Disease (NIA-LOAD/NCRAD) Family Study Group . The role of cardiovascular risk factors and stroke in familial Alzheimer disease . JAMA Neurol 2016 ; 73 : 1231 – 1237 . Google Scholar CrossRef Search ADS PubMed 5. Vemuri P , Knopman DS , Lesnick TG , Przybelski SA , Mielke MM , Graff-Radford J , Murray ME , Roberts RO , Vassilaki M , Lowe VJ , Machulda MM , Jones DT , Petersen RC , Jack CR Jr . Evaluation of amyloid protective factors and Alzheimer disease neurodegeneration protective factors in elderly individuals . JAMA Neurol 2017 ; 74 : 718 – 726 . Google Scholar CrossRef Search ADS PubMed 6. Xie C , Wang ZC , Liu XF , Yang MS . The common biological basis for common complex diseases: evidence from lipoprotein lipase gene . Eur J Hum Genet 2010 ; 18 : 3 – 7 . Google Scholar CrossRef Search ADS PubMed 7. Cuyvers E , Sleegers K . Genetic variations underlying Alzheimer’s disease: evidence from genome-wide association studies and beyond . Lancet Neurol 2016 ; 15 : 857 – 868 . Google Scholar CrossRef Search ADS PubMed 8. Guerreiro R , Hardy J . Genetics of Alzheimer’s disease . Neurotherapeutics 2014 ; 11 : 732 – 737 . Google Scholar CrossRef Search ADS PubMed 9. Ehret GB , Caulfield MJ . Genes for blood pressure: an opportunity to understand hypertension . Eur Heart J 2013 ; 34 : 951 – 961 . Google Scholar CrossRef Search ADS PubMed 10. Stančáková A , Laakso M . Genetics of type 2 diabetes . Endocr Dev 2016 ; 31 : 203 – 220 . Google Scholar CrossRef Search ADS PubMed 11. Luft FC . What have we learned from the genetics of hypertension ? Med Clin North Am 2017 ; 101 : 195 – 206 . Google Scholar CrossRef Search ADS PubMed 12. Dodoo SN , Benjamin IJ . Genomic approaches to hypertension . Cardiol Clin 2017 ; 35 : 185 – 196 . Google Scholar CrossRef Search ADS PubMed 13. Ehret GB , Munroe PB , Rice KM , Bochud M , Johnson AD , Chasman DI , Smith AV , Tobin MD , Verwoert GC , Hwang SJ , Pihur V , Vollenweider P , O’Reilly PF , Amin N , Bragg-Gresham JL , Teumer A , Glazer NL , Launer L , Zhao JH , Aulchenko Y , Heath S , Sõber S , Parsa A , Luan J , Arora P , Dehghan A , Zhang F , Lucas G , Hicks AA , Jackson AU , Peden JF , Tanaka T , Wild SH , Rudan I , Igl W , Milaneschi Y , Parker AN , Fava C , Chambers JC , Fox ER , Kumari M , Go MJ , van der Harst P , Kao WH , Sjögren M , Vinay DG , Alexander M , Tabara Y , Shaw-Hawkins S , Whincup PH , Liu Y , Shi G , Kuusisto J , Tayo B , Seielstad M , Sim X , Nguyen KD , Lehtimäki T , Matullo G , Wu Y , Gaunt TR , Onland-Moret NC , Cooper MN , Platou CG , Org E , Hardy R , Dahgam S , Palmen J , Vitart V , Braund PS , Kuznetsova T , Uiterwaal CS , Adeyemo A , Palmas W , Campbell H , Ludwig B , Tomaszewski M , Tzoulaki I , Palmer ND , Aspelund T , Garcia M , Chang YP , O’Connell JR , Steinle NI , Grobbee DE , Arking DE , Kardia SL , Morrison AC , Hernandez D , Najjar S , McArdle WL , Hadley D , Brown MJ , Connell JM , Hingorani AD , Day IN , Lawlor DA , Beilby JP , Lawrence RW , Clarke R , Hopewell JC , Ongen H , Dreisbach AW , Li Y , Young JH , Bis JC , Kähönen M , Viikari J , Adair LS , Lee NR , Chen MH , Olden M , Pattaro C , Bolton JA , Köttgen A , Bergmann S , Mooser V , Chaturvedi N , Frayling TM , Islam M , Jafar TH , Erdmann J , Kulkarni SR , Bornstein SR , Grässler J , Groop L , Voight BF , Kettunen J , Howard P , Taylor A , Guarrera S , Ricceri F , Emilsson V , Plump A , Barroso I , Khaw KT , Weder AB , Hunt SC , Sun YV , Bergman RN , Collins FS , Bonnycastle LL , Scott LJ , Stringham HM , Peltonen L , Perola M , Vartiainen E , Brand SM , Staessen JA , Wang TJ , Burton PR , Soler Artigas M , Dong Y , Snieder H , Wang X , Zhu H , Lohman KK , Rudock ME , Heckbert SR , Smith NL , Wiggins KL , Doumatey A , Shriner D , Veldre G , Viigimaa M , Kinra S , Prabhakaran D , Tripathy V , Langefeld CD , Rosengren A , Thelle DS , Corsi AM , Singleton A , Forrester T , Hilton G , McKenzie CA , Salako T , Iwai N , Kita Y , Ogihara T , Ohkubo T , Okamura T , Ueshima H , Umemura S , Eyheramendy S , Meitinger T , Wichmann HE , Cho YS , Kim HL , Lee JY , Scott J , Sehmi JS , Zhang W , Hedblad B , Nilsson P , Smith GD , Wong A , Narisu N , Stančáková A , Raffel LJ , Yao J , Kathiresan S , O’Donnell CJ , Schwartz SM , Ikram MA , Longstreth WT Jr , Mosley TH , Seshadri S , Shrine NR , Wain LV , Morken MA , Swift AJ , Laitinen J , Prokopenko I , Zitting P , Cooper JA , Humphries SE , Danesh J , Rasheed A , Goel A , Hamsten A , Watkins H , Bakker SJ , van Gilst WH , Janipalli CS , Mani KR , Yajnik CS , Hofman A , Mattace-Raso FU , Oostra BA , Demirkan A , Isaacs A , Rivadeneira F , Lakatta EG , Orru M , Scuteri A , Ala-Korpela M , Kangas AJ , Lyytikäinen LP , Soininen P , Tukiainen T , Würtz P , Ong RT , Dörr M , Kroemer HK , Völker U , Völzke H , Galan P , Hercberg S , Lathrop M , Zelenika D , Deloukas P , Mangino M , Spector TD , Zhai G , Meschia JF , Nalls MA , Sharma P , Terzic J , Kumar MV , Denniff M , Zukowska-Szczechowska E , Wagenknecht LE , Fowkes FG , Charchar FJ , Schwarz PE , Hayward C , Guo X , Rotimi C , Bots ML , Brand E , Samani NJ , Polasek O , Talmud PJ , Nyberg F , Kuh D , Laan M , Hveem K , Palmer LJ , van der Schouw YT , Casas JP , Mohlke KL , Vineis P , Raitakari O , Ganesh SK , Wong TY , Tai ES , Cooper RS , Laakso M , Rao DC , Harris TB , Morris RW , Dominiczak AF , Kivimaki M , Marmot MG , Miki T , Saleheen D , Chandak GR , Coresh J , Navis G , Salomaa V , Han BG , Zhu X , Kooner JS , Melander O , Ridker PM , Bandinelli S , Gyllensten UB , Wright AF , Wilson JF , Ferrucci L , Farrall M , Tuomilehto J , Pramstaller PP , Elosua R , Soranzo N , Sijbrands EJ , Altshuler D , Loos RJ , Shuldiner AR , Gieger C , Meneton P , Uitterlinden AG , Wareham NJ , Gudnason V , Rotter JI , Rettig R , Uda M , Strachan DP , Witteman JC , Hartikainen AL , Beckmann JS , Boerwinkle E , Vasan RS , Boehnke M , Larson MG , Järvelin MR , Psaty BM , Abecasis GR , Chakravarti A , Elliott P , van Duijn CM , Newton-Cheh C , Levy D , Caulfield MJ , Johnson T ; International Consortium for Blood Pressure Genome-Wide Association Studies; CARDIoGRAM consortium; CKDGen Consortium; KidneyGen Consortium; EchoGen consortium; CHARGE-HF consortium . Genetic variants in novel pathways influence blood pressure and cardiovascular disease risk . Nature 2011 ; 478 : 103 – 109 . Google Scholar CrossRef Search ADS PubMed 14. National Cholesterol Education Program Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults . Third report of the national cholesterol education program (NCEP) expert panel on detection, evaluation, and treatment of high blood cholesterol in adults (Adult Treatment Panel III) final report . Circulation 2002 ; 106 : 3143 – 3421 . PubMed 15. Fonseca VA . Defining and characterizing the progression of type 2 diabetes . Diabetes Care 2009 ; 32 ( Suppl 2 ): S151 – S156 . Google Scholar CrossRef Search ADS PubMed 16. Lindeboom J , Weinstein H . Neuropsychology of cognitive ageing, minimal cognitive impairment, Alzheimer’s disease, and vascular cognitive impairment . Eur J Pharmacol 2004 ; 490 : 83 – 86 . Google Scholar CrossRef Search ADS PubMed 17. St George-Hyslop PH , Tanzi RE , Haines JL , Polinsky RJ , Farrer L , Myers RH , Gusella JF . Molecular genetics of familial Alzheimer’s disease . Eur Neurol 1989 ; 29 ( Suppl 3 ): 25 – 27 . Google Scholar CrossRef Search ADS PubMed 18. Tanzi RE , Bertram L . Twenty years of the Alzheimer’s disease amyloid hypothesis: a genetic perspective . Cell 2005 ; 120 : 545 – 555 . Google Scholar CrossRef Search ADS PubMed 19. Del Prete D , Checler F , Chami M . Ryanodine receptors: physiological function and deregulation in Alzheimer disease . Mol Neurodegener 2014 ; 9 : 21 . Google Scholar CrossRef Search ADS PubMed 20. Leeb T , Giese A , Al-Bayati H , Rettenberger G , Brenig B . Assignment of the porcine ryanodine receptor 3 gene (RYR3) to chromosome 7q22–>q23 . Cytogenet Cell Genet 1998 ; 83 : 244 – 245 . Google Scholar CrossRef Search ADS PubMed 21. Sorrentino V , Giannini G , Malzac P , Mattei MG . Localization of a novel ryanodine receptor gene (RYR3) to human chromosome 15q14-q15 by in situ hybridization . Genomics 1993 ; 18 : 163 – 165 . Google Scholar CrossRef Search ADS PubMed 22. Cartwright EJ , Oceandy D , Austin C , Neyses L . Ca2+ signalling in cardiovascular disease: the role of the plasma membrane calcium pumps . Sci China Life Sci 2011 ; 54 : 691 – 698 . Google Scholar CrossRef Search ADS PubMed 23. Sun J , Song F , Wang J , Han G , Bai Z , Xie B , Feng X , Jia J , Duan Y , Lei H . Hidden risk genes with high-order intragenic epistasis in Alzheimer’s disease . J Alzheimers Dis 2014 ; 41 : 1039 – 1056 . Google Scholar CrossRef Search ADS PubMed 24. Koran ME , Hohman TJ , Thornton-Wells TA . Genetic interactions found between calcium channel genes modulate amyloid load measured by positron emission tomography . Hum Genet 2014 ; 133 : 85 – 93 . Google Scholar CrossRef Search ADS PubMed 25. Kelliher M , Fastbom J , Cowburn RF , Bonkale W , Ohm TG , Ravid R , Sorrentino V , O’Neill C . Alterations in the ryanodine receptor calcium release channel correlate with Alzheimer’s disease neurofibrillary and beta-amyloid pathologies . Neuroscience 1999 ; 92 : 499 – 513 . Google Scholar CrossRef Search ADS PubMed 26. Supnet C , Noonan C , Richard K , Bradley J , Mayne M . Up-regulation of the type 3 ryanodine receptor is neuroprotective in the TgCRND8 mouse model of Alzheimer’s disease . J Neurochem 2010 ; 112 : 356 – 365 . Google Scholar CrossRef Search ADS PubMed 27. Lee JH , Cheng R , Graff-Radford N , Foroud T , Mayeux R ; National Institute on Aging Late-Onset Alzheimer’s Disease Family Study Group . Analyses of the national institute on aging late-onset Alzheimer’s disease family study: implication of additional loci . Arch Neurol 2008 ; 65 : 1518 – 1526 . Google Scholar CrossRef Search ADS PubMed 28. Barrett JC , Fry B , Maller J , Daly MJ . Haploview: analysis and visualization of LD and haplotype maps . Bioinformatics 2005 ; 21 : 263 – 265 . Google Scholar CrossRef Search ADS PubMed 29. Van Steen K , Lange C . PBAT: a comprehensive software package for genome-wide association analysis of complex family-based studies . Hum Genomics 2005 ; 2 : 67 – 69 . Google Scholar CrossRef Search ADS PubMed 30. Lange C , DeMeo D , Silverman EK , Weiss ST , Laird NM . PBAT: tools for family-based association studies . Am J Hum Genet 2004 ; 74 : 367 – 369 . Google Scholar CrossRef Search ADS PubMed 31. Melé M , Ferreira PG , Reverter F , DeLuca DS , Monlong J , Sammeth M , Young TR , Goldmann JM , Pervouchine DD , Sullivan TJ , Johnson R , Segrè AV , Djebali S , Niarchou A , Wright FA , Lappalainen T , Calvo M , Getz G , Dermitzakis ET , Ardlie KG , Guigó R ; GTEx Consortium . Human genomics. The human transcriptome across tissues and individuals . Science 2015 ; 348 : 660 – 665 . Google Scholar CrossRef Search ADS PubMed 32. Hardison RC . Conserved noncoding sequences are reliable guides to regulatory elements . Trends Genet 2000 ; 16 : 369 – 372 . Google Scholar CrossRef Search ADS PubMed 33. Montasser ME , Shimmin LC , Hanis CL , Boerwinkle E , Hixson JE . Gene by smoking interaction in hypertension: identification of a major quantitative trait locus on chromosome 15q for systolic blood pressure in Mexican-Americans . J Hypertens 2009 ; 27 : 491 – 501 . Google Scholar CrossRef Search ADS PubMed 34. Hohman TJ , Bush WS , Jiang L , Brown-Gentry KD , Torstenson ES , Dudek SM , Mukherjee S , Naj A , Kunkle BW , Ritchie MD , Martin ER , Schellenberg GD , Mayeux R , Farrer LA , Pericak-Vance MA , Haines JL , Thornton-Wells TA ; Alzheimer’s Disease Genetics Consortium . Discovery of gene-gene interactions across multiple independent data sets of late onset Alzheimer disease from the Alzheimer disease genetics consortium . Neurobiol Aging 2016 ; 38 : 141 – 150 . Google Scholar CrossRef Search ADS PubMed 35. Andreassen OA , McEvoy LK , Thompson WK , Wang Y , Reppe S , Schork AJ , Zuber V , Barrett-Connor E , Gautvik K , Aukrust P , Karlsen TH , Djurovic S , Desikan RS , Dale AM ; International Consortium for Blood Pressure Genome-Wide Association Studies, Genetic Factors for Osteoporosis Consortium . Identifying common genetic variants in blood pressure due to polygenic pleiotropy with associated phenotypes . Hypertension 2014 ; 63 : 819 – 826 . Google Scholar CrossRef Search ADS PubMed 36. Bangen KJ , Nation DA , Clark LR , Harmell AL , Wierenga CE , Dev SI , Delano-Wood L , Zlatar ZZ , Salmon DP , Liu TT , Bondi MW . Interactive effects of vascular risk burden and advanced age on cerebral blood flow . Front Aging Neurosci 2014 ; 6 : 159 . Google Scholar CrossRef Search ADS PubMed 37. Kalaria RN . Vascular basis for brain degeneration: faltering controls and risk factors for dementia . Nutr Rev 2010 ; 68 ( Suppl 2 ): S74 – S87 . Google Scholar CrossRef Search ADS PubMed 38. Lange C , Silverman EK , Xu X , Weiss ST , Laird NM . A multivariate family-based association test using generalized estimating equations: FBAT-GEE . Biostatistics 2003 ; 4 : 195 – 206 . Google Scholar CrossRef Search ADS PubMed © Published by Oxford University Press on behalf of American Journal of Hypertension Ltd 2018. This work is written by (a) US Government employees(s) and is in the public domain in the US. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png American Journal of Hypertension Oxford University Press

Polymorphisms Within RYR3 Gene Are Associated With Risk and Age at Onset of Hypertension, Diabetes, and Alzheimer’s Disease

Loading next page...
 
/lp/ou_press/polymorphisms-within-ryr3-gene-are-associated-with-risk-and-age-at-gg5Fkyphvh
Publisher
Oxford University Press
Copyright
© Published by Oxford University Press on behalf of American Journal of Hypertension Ltd 2018.
ISSN
0895-7061
eISSN
1941-7225
D.O.I.
10.1093/ajh/hpy046
Publisher site
See Article on Publisher Site

Abstract

Abstract BACKGROUND Hypertension affects 33% of Americans while type 2 diabetes and Alzheimer’s disease (AD) affect 10% of Americans, respectively. Ryanodine receptor 3 gene (RYR3) codes for the RYR which functions to release stored endoplasmic reticulum calcium ions (Ca2+) to increase intracellular Ca2+ concentration. Increasing studies demonstrate that altered levels of intracellular Ca2+ affect cardiac contraction, insulin secretion, and neurodegeneration. In this study, we investigated associations of the RYR3 genetic variants with hypertension, AD, and diabetes. METHODS Family data sets were used to explore association of RYR3 polymorphisms with risk and age at onset (AAO) of hypertension, diabetes, and AD. RESULTS Family-based association tests using generalized estimating equations (FBAT–GEE) showed several unique or shared disease-1 associated variants in the RYR3 gene. Three single nuclear polymorphisms (SNPs; rs2033610, rs2596164, and rs2278317) are significantly associated with risk for hypertension, diabetes, and AD. Two SNPs (rs4780174 and rs7498093) are significantly associated with AAO of the 3 diseases. CONCLUSIONS RYR3 variants are associated with hypertension, diabetes, and AD. Replication of these results of this gene in these 3 complex traits may help to better understand the genetic basis of calcium-signaling gene, RYR3 in association with risk and AAO of these diseases. blood pressure, diabetes and Alzheimer’s disease, hypertension, RYR3 gene, shared genetic variants, SNP Worldwide, hypertension, type 2 diabetes, and Alzheimer’s disease (AD) are common public health problems estimated to cause millions of premature deaths. In recent decades, hypertension and diabetes, as vascular risk factors, increase the risk of cognitive impairment and dementia.1 Hypertension alone affects 33% of Americans presently. In 2015, diabetes was estimated to affect 9.4% of the US adult population. However, exclusively above 65 years, the percentage increases to 25%. Regarding AD, 10% of individuals above 65 years suffer from the disease. There is a close link among these 3 diseases based on their risk factors, causes, pathological bases,2–5 and genetic factors, including lipoprotein lipase gene in these 3 diseases.6 More evidence supports the link type 2 diabetes and hypertension with AD, the most common form of dementia. These 3 traits are caused by multiple susceptibility genes, whose effects are modulated by gene-environment and gene–gene interactions. Twin and family studies show low-to-moderate heritability for AD,7,8 hypertension,9 and diabetes.10 Mendelian hypertension has elucidated certain biological pathways contributing to hypertension over dietary salt intake or directly through increased peripheral vascular resistance. The Mendelian mutation/genes exercise large effects on blood pressure–related phenotypes. However, genome wide association study (GWAS) and meta-analysis for blood pressure have yielded many signals with small effects.11 Thus far, few loci have been validated. Both genetic strategies are necessary, and much remains to be explored. Genetic/genomic insights and analyses of Mendelian hypertension syndromes and GWAS on essential hypertension have contributed to the depth of understanding of the genetics origins of hypertension.12 The results from several large-scale studies have clearly shown that blood pressure–related single nuclear polymorphisms (SNPs) are associated with a difference in blood pressure and cardiovascular disease outcome.13 Type 2 diabetes is characterized by insulin resistance, obesity, and high blood pressure, each influenced by both genetic and environmental factors.14 Advanced diabetes is characterized with abnormal pancreatic islet β-cell function in presence of insulin.15 Insulin secretion is triggered by glucose, which is transported into the β-cells and metabolized. This increases the concentration of adenosine triphosphate (ATP), which in turn leads to closure of ATP-sensitive K+ channels and depolarization of cellular membrane. Depolarization activates voltage-gated Ca2+ channels, allowing entry of extracellular Ca2+ into β cells which triggers an even greater release of ER Ca2+ which is mediated by ryanodine receptors (RYRs). This suggests calcium-signaling pathway is involved in pathophysiology of diabetes. The third complex trait is AD. It is characterized clinically by progressive deterioration of cognitive functions,16 and physiologically by β-amyloid peptides (Aβ) aggregates and intracellular neurofibrillar tangles composed of hyperphosphorylated microtubule-associated tau protein. While aging is the major risk factor, a high number of cases are characterized by earlier onset and are inherited in an autosomal dominant manner.17,18 In neurons, higher Ca2+ concentration, released by RYRs, leads to the release of neurotransmitter at synaptic junctions and affects dendritic action potential.19 The results from research studies suggest adults with type 2 diabetes have a higher risk of late-onset AD. Numerous investigations have reported that risk for late-onset AD is increased in the presence of type 2 diabetes2,3 and hypertension.4 An estimated 54 million US adults have prediabetes and most of these people will develop type 2 diabetes within the next 10 years. Diabetes and hypertension raise the risk of heart disease and stroke. The pathophysiological factors include, but are not limited to, damaged blood vessels in the brain, increased insulin levels, unbalanced chemical changes, and high blood glucose. These changes may cause inflammation, damage brain cells, and increases risk for AD (https://www.alz.org/national/documents/latino_brochure_diabetes.pdf). A recent study focus on evaluation of amyloid-protective factors showed that hypertension, diabetes, and metabolic conditions were also associated with AD-like neurodegeneration.5 The critical roles of calcium-signaling pathway in all 3 phenotypes lead us to think shared genetic variants in the genes involved in calcium signaling may be associated with these complex traits. We are interested in gene RYR3 located at 15q13.3. The protein encoded by RYR3 (Gene ID: 6263) is a RYR, which functions to release calcium from the ER. As a large intracellular homotetrameric protein (>2 MDa) that comprises 4,780 amino acids,20,21 RYRs reside on the sarcoplasmic reticulum membrane and release Ca+2 from intracellular stores to regulate concentration.22 Increased genes and mutations were reported to be associated with these traits based on the results of GWAS, candidate genes, meta-analysis, and next-generation sequencing studies. In the cardiovascular system, Ca+2 is essential for cardiac muscle contraction and relaxation, and acts as a second messenger in signal transduction pathways. Complex mechanisms regulate intracellular free calcium levels in the heart and vasculature, and a failure of these systems to maintain normal Ca+2 homeostasis has been linked to hypertension and other cardiovascular disease outcomes.22 Studies of the RYR3 have reported association with AD. For example, a meta-analysis based on 4 GWAS identified RYR3 association with AD risk using generalized multifactor dimensionality reduction.23 Another study observed a significant interaction between RYR3 and CACNA1C (genes coding for calcium channels that mediate the influx of calcium ions into the cell upon membrane polarization) in all 3 independent data sets of Alzheimer’s Disease Neuroimaging Initiative cohorts.24 Functional studies of RYR3 suggest that upregulated RYR levels are found in human AD brains,25 and RYR3 isoforms are upregulated at early and late stages of AD in animal models.26 Limited study of shared SNPs has been reported in hypertension, diabetes, and AD. Based on previous findings and reasoning above, we hypothesized SNPs in RYR3 are involved in development of hypertension, diabetes, and AD. MATERIALS AND METHODS Subjects A family-based sample was available from the National Institute on Aging–Late Onset Alzheimer’s Disease (NIA-LOAD) family study: 2,545 individuals (1,266 cases including 1,070 with age at onset [AAO] values) were available for our current study. Family Study: GWAS for Susceptibility Loci—Study Accession: phs000168.v1.p1. This study is to identify and recruit families with 2 or more siblings with the late-onset form of Alzheimer’s disease and a cohort of unrelated, nondemented controls similar in age and ethnic background, and to make the samples, the clinical and genotyping data and preliminary analyses available to qualified investigators world-wide. Genotyping by the Center for Inherited Disease Research (CIDR) was performed using the Illumina Infinium II assay protocol with hybridization to Illumina Human 610Quadv1_B Beadchips. The details about these subjects were described elsewhere.27 Overall, 1,266 AD cases and 1,279 non-AD individuals (including 1,070 with AAO values) were from 1386 pedigrees (including 589 nuclear families) (Table 1). Table 1. Descriptive characteristics of cases and controls Variable AD patients Controls Hypertension Controls Diabetes Controls Sample size (n) 1,266 1,279 1,036 1,240 247 2,028 Sex  Male 435 466 382 462 120 727  Female 831 813 654 778 127 1,301 Mean AAO (years ± SD) 76.4 ± 6.7 – 60.3 ± 13.1 – 61.9 ± 12.2 – Median AAO (years) 77 – 60 – 60 – Range of age at onset (years) 50–98 – 22–91 – 35–89 – Variable AD patients Controls Hypertension Controls Diabetes Controls Sample size (n) 1,266 1,279 1,036 1,240 247 2,028 Sex  Male 435 466 382 462 120 727  Female 831 813 654 778 127 1,301 Mean AAO (years ± SD) 76.4 ± 6.7 – 60.3 ± 13.1 – 61.9 ± 12.2 – Median AAO (years) 77 – 60 – 60 – Range of age at onset (years) 50–98 – 22–91 – 35–89 – Abbreviations: AAO, age at onset; AD, Alzheimer’s disease. View Large Table 1. Descriptive characteristics of cases and controls Variable AD patients Controls Hypertension Controls Diabetes Controls Sample size (n) 1,266 1,279 1,036 1,240 247 2,028 Sex  Male 435 466 382 462 120 727  Female 831 813 654 778 127 1,301 Mean AAO (years ± SD) 76.4 ± 6.7 – 60.3 ± 13.1 – 61.9 ± 12.2 – Median AAO (years) 77 – 60 – 60 – Range of age at onset (years) 50–98 – 22–91 – 35–89 – Variable AD patients Controls Hypertension Controls Diabetes Controls Sample size (n) 1,266 1,279 1,036 1,240 247 2,028 Sex  Male 435 466 382 462 120 727  Female 831 813 654 778 127 1,301 Mean AAO (years ± SD) 76.4 ± 6.7 – 60.3 ± 13.1 – 61.9 ± 12.2 – Median AAO (years) 77 – 60 – 60 – Range of age at onset (years) 50–98 – 22–91 – 35–89 – Abbreviations: AAO, age at onset; AD, Alzheimer’s disease. View Large Hypertension is defined as systolic blood pressure of 140 mm Hg or greater, diastolic blood pressure of 90 mm Hg or greater, or taking antihypertensive medication (phs000168.v1.p1). There were a total of 1,036 patients with hypertension and 1,240 control subjects. Diabetes is defined by a history of diabetes or high blood sugar, or treatment of diabetes or high blood sugar reported by the subject. There are 247 patients with diabetes and 2,028 control subjects. There are 279 SNPs within the RYR3 gene available for patients with diseases and control subjects. Statistical methods Genotype quality control: Hardy–Weinberg equilibrium was tested for all SNPs using the controls by using HAPLOVIEW version 4.1 software.28 Minor allele frequency was determined for each SNP and pairwise linkage disequilibrium statistics (D′ and r2) were assessed using HAPMAP Caucasian data. Family-based study: family-based association analyses for 3 traits were performed using PBAT version 36.1,29 which can handle nuclear families with missing parental genotypes, extended pedigrees with missing genotypic information, analysis of SNPs, haplotype analysis, quantitative traits, and time-to-onset phenotypes. For the affection status of hypertension, diabetes, and AD, family-based association tests using generalized estimating equations (FBAT–GEE) was used to perform family-based association analysis For testing time-to-onset trait (AAO), FBAT–Wilcoxon statistics were employed.30 The AAO values for healthy siblings were censored and age at entry into the study was used. Haplotype analysis was conducted in 2-SNP or 3-SNP sliding windows. For multiple comparison, Bonferroni correction (α = 0.05/279 = 1.79 × 10−4) was used for statistical significance. Descriptive statistics were conducted with SAS 9.4 (SAS Institute, Cary, NC). Three genetic models were used (allele-dose, dominant, and recessive). In silico analysis We evaluated potential function of the disease-associated SNP. We examined if these variants were located within the regions of the gene that might have potential functional importance. The sequences containing the associated SNPs were examined for microRNA-binding sites, splicing sites, regulatory gene regions, and species-conserved regions using NIH-SNP Function Prediction (http://snpinfo.niehs.nih.gov/cgi-bin/snpinfo/snpfunc.cgi). To determine disease-associated SNPs with RYR3 expression levels in different human tissues, we used publicly available data from the Genotype-Tissue Expression (GTEx) project31 in which, there is RNA sequencing on brain tissue from healthy donors available, resulting in genotype and expression phenotype data for ~100–120 normal individuals in multiple different brain regions. The information about subjects and RNA quality can be found in the GTEx website (www.gtexportal.org). RESULTS Genotype quality control and descriptive statistics Out of 279 SNPs in RYR3, 3 SNPs with P <10−4 for Hardy–Weinberg equilibrium (HWE) were removed for further analysis. The demographic characteristics of the subjects in the study are shown in the Table 1. The mean AAO for AD, hypertension, and diabetes were 76.4, 60.3, and 61.9 years. Single-marker and haplotype analyses based of risk of hypertension, diabetes, and AD using FBAT–GEE We found shared disease-associated SNPs among these complex traits. A number of SNPs were associated with each individual disease (Table 2), 5 SNPs (rs16973062, rs2288609, rs965471, rs4780167, rs10519874, and rs7498093) were associated with diabetes and also survived after the correction for multiple testing using the Bonferroni correction. Single-marker analysis showed that 3 SNPs (rs2033610, rs2596164, and rs2278317, P < 0.05) were associated with hypertension, diabetes, and AD in various genetic models (additive, dominant, and/or, recessive) as well as 3 SNPs (rs1390158, rs11637619, and rs8037864) also showed associations with hypertension and AD and 2 SNPs (rs4780167 and rs8028974) showed associations with hypertension and diabetes (P < 0.05) without correction for multiple testing. Four disease-associated SNPs (rs965471, rs10519874, rs7498093, and rs17236525) were shared between diabetes and AD in various genetic models. These results suggest shared genetic susceptibility among these 3 diseases in the RYR3 gene (Table 2). Table 2. Single-marker analysis of risk of hypertension, diabetes, and AD based on FBAT–GEE (P < 0.05) SNP Positiona ALb MAFc HWEd Fam#e P-FBAT–GEEf for hypertension P-FBAT–GEEh for diabetes P-FBAT–GEEi for AD rs16973323 31694920 T 0.17 0.722 145 0.00379(a)0.00215(d,r)g 0.697(a) 0.516(d,r) 0.556(a) 0.466(d,r) rs2291736 31726787 A 0.21 0.438 186 0.0046(a) 0.0221(d,r) 0.534(a) 0.142(d,r) 0.875(a) 0.0534(d,r) rs4780118 31774633 C 0.29 0.704 216 0.00746(a) 0.0382(d,r) 0.703(a) 0.276(d,r) 0.194(a) 0.128(d,r) rs1390158 31744649 A 0.06 0.173 72 0.0151(a) 0.0437(d,r) 0.846(a) 0.223(d,r) 0.0459(a) 0.0207(d,r) rs11072471 31507915 C 0.27 0.740 208 0.0169(a) 0.0191(d,r) 0.143(a) 0.154(d,r) 0.403(a) 0.292(d,r) rs11637619 31743447 C 0.14 0.185 132 0.0171(a) 0.0255(d,r) 0.887(a) 0.155(d,r) 0.162(a) 0.041(d,r) rs2033610 31542203 T 0.47 0.031 260 0.0226(a) 0.0384(d,r) 0.0306(a) 0.0344(d,r) 0.098(a) 0.0245(d,r) rs2596164 31546180 G 0.47 0.041 258 0.0237(a) 0.0526(d,r) 0.0227(a) 0.0391(d,r) 0.00963(a) 0.0178(d,r) rs12441112 31921018 G 0.21 0.703 207 0.0266(a) 0.933(d,r) 0.876(a) 0.711(d,r) 0.519(a) 0.26(d,r) rs12906709 31797258 A 0.41 0.003 241 0.0293(a) 0.317(d,r) 0.725(a) 0.27(d,r) 0.231(a) 0.172(d,r) rs2339273 31528153 T 0.26 0.217 207 0.0301(a) 0.812(d,r) 0.244(a) 0.364(d,r) 0.349(a) 0.438(d,r) rs2088143 31770837 C 0.22 0.737 188 0.0344(a) 0.0636(d,r) 0.403(a) 0.369(d,r) 0.627(a) 0.173(d,r) rs8037864 31801713 G 0.21 0.178 173 0.036(a) 0.0846(d,r) 0.797(a) 0.374(d,r) 0.129(a) 0.0323(d,r) rs2278317 31848032 G 0.31 0.737 223 0.0385(a) 0.779(d,r) 0.106(a) 0.0208(d,r) 0.163(a) 0.0255(d,r) rs17236476 31784069 C 0.07 0.945 85 0.0392(a) 0.0267(d,r) 0.701(a) 0.223(d,r) 0.146(a) 0.081(d,r) rs8034012 31687997 C 0.37 0.239 239 0.0438(a) 0.101(d,r) 0.614(a) 0.541(d,r) 0.499(a) 0.576(d,r) rs16973062 31679589 C 0.06 0.523 70 0.0451(a) 0.0438(d,r) 0.884(a) 0.365(d,r) 0.376(a) 0.226(d,r) rs2288609 31821738 A 0.27 0.319 214 0.602(a) 0.0516(d,r) 6.82 × 10−5(a) 0.365(d,r) 0.50(a) 0.38(d,r) rs965471 31868429 G 0.34 0.361 213 0.982(a) 0.634(d,r) 1.21 × 10−4(a) 0.00229(d,r) 0.118(a) 0.0326(d,r) rs4780167 31799489 G 0.41 0.905 266 0.111(a) 0.00388(d,r) 1.43 × 10−4(a) 1.22 × 10−4(d,r) 0.116(a)0.150(d,r) rs10519874 31863494 G 0.38 0.507 247 0.786(a) 0.77(d,r) 1.62 × 10−4(a) 0.365(d,r) 0.101(a) 0.0.0266(d,r) rs7498093 31808568 G 0.43 0.208 249 0.83151(a) 0.901(d,r) 1.63 × 10−4(a) 0.00208(d,r) 0.0494(a) 0.0254(d,r) rs10519873 31862443 A 0.36 0.749 243 0.869(a) 0.848(d,r) 2.38 × 10−4(a) 0.00292(d,r) 0.112(a) 0.068(d,r) rs8028974 31785766 G 0.39 0.865 245 0.453(a) 0.0175(d,r) 3.21 × 10−4(a) 0.00172(d,r) 0.748(a) 0.223(d,r) rs10519875 31864956 A 0.37 0.557 248 0.77(a) 0.749(d,r) 5.16 × 10−4(a) 0.00387(d,r) 0.14(a) 0.065(d,r) rs12440440 31829188 A 0.30 0.134 244 0.283(a) 0.368(d,r) 5.50 × 10−4(a) 0.00224(d,r) 0.522(a) 0.739(d,r) rs17236525 31813550 T 0.37 0.978 248 0.663(a) 0.679(d,r) 6.16 × 10−4(a) 0.0017(d,r) 0.0298(a) 8.17 × 10−4(d,r) rs7165052 31851908 T 0.43 0.823 258 0.825(a) 0.849(d,r) 7.13 × 10−4(a) 0.00659(d,r) 0.615(a) 0.574(d,r)g SNP Positiona ALb MAFc HWEd Fam#e P-FBAT–GEEf for hypertension P-FBAT–GEEh for diabetes P-FBAT–GEEi for AD rs16973323 31694920 T 0.17 0.722 145 0.00379(a)0.00215(d,r)g 0.697(a) 0.516(d,r) 0.556(a) 0.466(d,r) rs2291736 31726787 A 0.21 0.438 186 0.0046(a) 0.0221(d,r) 0.534(a) 0.142(d,r) 0.875(a) 0.0534(d,r) rs4780118 31774633 C 0.29 0.704 216 0.00746(a) 0.0382(d,r) 0.703(a) 0.276(d,r) 0.194(a) 0.128(d,r) rs1390158 31744649 A 0.06 0.173 72 0.0151(a) 0.0437(d,r) 0.846(a) 0.223(d,r) 0.0459(a) 0.0207(d,r) rs11072471 31507915 C 0.27 0.740 208 0.0169(a) 0.0191(d,r) 0.143(a) 0.154(d,r) 0.403(a) 0.292(d,r) rs11637619 31743447 C 0.14 0.185 132 0.0171(a) 0.0255(d,r) 0.887(a) 0.155(d,r) 0.162(a) 0.041(d,r) rs2033610 31542203 T 0.47 0.031 260 0.0226(a) 0.0384(d,r) 0.0306(a) 0.0344(d,r) 0.098(a) 0.0245(d,r) rs2596164 31546180 G 0.47 0.041 258 0.0237(a) 0.0526(d,r) 0.0227(a) 0.0391(d,r) 0.00963(a) 0.0178(d,r) rs12441112 31921018 G 0.21 0.703 207 0.0266(a) 0.933(d,r) 0.876(a) 0.711(d,r) 0.519(a) 0.26(d,r) rs12906709 31797258 A 0.41 0.003 241 0.0293(a) 0.317(d,r) 0.725(a) 0.27(d,r) 0.231(a) 0.172(d,r) rs2339273 31528153 T 0.26 0.217 207 0.0301(a) 0.812(d,r) 0.244(a) 0.364(d,r) 0.349(a) 0.438(d,r) rs2088143 31770837 C 0.22 0.737 188 0.0344(a) 0.0636(d,r) 0.403(a) 0.369(d,r) 0.627(a) 0.173(d,r) rs8037864 31801713 G 0.21 0.178 173 0.036(a) 0.0846(d,r) 0.797(a) 0.374(d,r) 0.129(a) 0.0323(d,r) rs2278317 31848032 G 0.31 0.737 223 0.0385(a) 0.779(d,r) 0.106(a) 0.0208(d,r) 0.163(a) 0.0255(d,r) rs17236476 31784069 C 0.07 0.945 85 0.0392(a) 0.0267(d,r) 0.701(a) 0.223(d,r) 0.146(a) 0.081(d,r) rs8034012 31687997 C 0.37 0.239 239 0.0438(a) 0.101(d,r) 0.614(a) 0.541(d,r) 0.499(a) 0.576(d,r) rs16973062 31679589 C 0.06 0.523 70 0.0451(a) 0.0438(d,r) 0.884(a) 0.365(d,r) 0.376(a) 0.226(d,r) rs2288609 31821738 A 0.27 0.319 214 0.602(a) 0.0516(d,r) 6.82 × 10−5(a) 0.365(d,r) 0.50(a) 0.38(d,r) rs965471 31868429 G 0.34 0.361 213 0.982(a) 0.634(d,r) 1.21 × 10−4(a) 0.00229(d,r) 0.118(a) 0.0326(d,r) rs4780167 31799489 G 0.41 0.905 266 0.111(a) 0.00388(d,r) 1.43 × 10−4(a) 1.22 × 10−4(d,r) 0.116(a)0.150(d,r) rs10519874 31863494 G 0.38 0.507 247 0.786(a) 0.77(d,r) 1.62 × 10−4(a) 0.365(d,r) 0.101(a) 0.0.0266(d,r) rs7498093 31808568 G 0.43 0.208 249 0.83151(a) 0.901(d,r) 1.63 × 10−4(a) 0.00208(d,r) 0.0494(a) 0.0254(d,r) rs10519873 31862443 A 0.36 0.749 243 0.869(a) 0.848(d,r) 2.38 × 10−4(a) 0.00292(d,r) 0.112(a) 0.068(d,r) rs8028974 31785766 G 0.39 0.865 245 0.453(a) 0.0175(d,r) 3.21 × 10−4(a) 0.00172(d,r) 0.748(a) 0.223(d,r) rs10519875 31864956 A 0.37 0.557 248 0.77(a) 0.749(d,r) 5.16 × 10−4(a) 0.00387(d,r) 0.14(a) 0.065(d,r) rs12440440 31829188 A 0.30 0.134 244 0.283(a) 0.368(d,r) 5.50 × 10−4(a) 0.00224(d,r) 0.522(a) 0.739(d,r) rs17236525 31813550 T 0.37 0.978 248 0.663(a) 0.679(d,r) 6.16 × 10−4(a) 0.0017(d,r) 0.0298(a) 8.17 × 10−4(d,r) rs7165052 31851908 T 0.43 0.823 258 0.825(a) 0.849(d,r) 7.13 × 10−4(a) 0.00659(d,r) 0.615(a) 0.574(d,r)g P values in bold are the ones retained statistical significant after correction for multiple testing using Bonferroni correction (a = 0.05/279 = 1.79 × 10−4) since a total of 279 SNPs in the RYR3 gene were used. Abbreviations: AD, Alzheimer’s disease; FBAT–GEE, family-based association tests using generalized estimating equations; SNP, single nuclear polymorphism. aPhysical position is based on NCBI Genome Build 36.3. bMinor allele. cMinor allele frequency. dP value of Hardy–Weinberg equilibrium test. eThe number of informative families for risk of hypertension using an additive model. fP value based on FBAT–GEE analysis for risk of hypertension. gLetters in parentheses indicate the genetic models used for analysis (a, additive; d, dominant; r, recessive model). hP value based on FBAT–GEE analysis for risk of diabetes. iP value based on FBAT–GEE analysis for risk of Alzheimer’s disease. View Large Table 2. Single-marker analysis of risk of hypertension, diabetes, and AD based on FBAT–GEE (P < 0.05) SNP Positiona ALb MAFc HWEd Fam#e P-FBAT–GEEf for hypertension P-FBAT–GEEh for diabetes P-FBAT–GEEi for AD rs16973323 31694920 T 0.17 0.722 145 0.00379(a)0.00215(d,r)g 0.697(a) 0.516(d,r) 0.556(a) 0.466(d,r) rs2291736 31726787 A 0.21 0.438 186 0.0046(a) 0.0221(d,r) 0.534(a) 0.142(d,r) 0.875(a) 0.0534(d,r) rs4780118 31774633 C 0.29 0.704 216 0.00746(a) 0.0382(d,r) 0.703(a) 0.276(d,r) 0.194(a) 0.128(d,r) rs1390158 31744649 A 0.06 0.173 72 0.0151(a) 0.0437(d,r) 0.846(a) 0.223(d,r) 0.0459(a) 0.0207(d,r) rs11072471 31507915 C 0.27 0.740 208 0.0169(a) 0.0191(d,r) 0.143(a) 0.154(d,r) 0.403(a) 0.292(d,r) rs11637619 31743447 C 0.14 0.185 132 0.0171(a) 0.0255(d,r) 0.887(a) 0.155(d,r) 0.162(a) 0.041(d,r) rs2033610 31542203 T 0.47 0.031 260 0.0226(a) 0.0384(d,r) 0.0306(a) 0.0344(d,r) 0.098(a) 0.0245(d,r) rs2596164 31546180 G 0.47 0.041 258 0.0237(a) 0.0526(d,r) 0.0227(a) 0.0391(d,r) 0.00963(a) 0.0178(d,r) rs12441112 31921018 G 0.21 0.703 207 0.0266(a) 0.933(d,r) 0.876(a) 0.711(d,r) 0.519(a) 0.26(d,r) rs12906709 31797258 A 0.41 0.003 241 0.0293(a) 0.317(d,r) 0.725(a) 0.27(d,r) 0.231(a) 0.172(d,r) rs2339273 31528153 T 0.26 0.217 207 0.0301(a) 0.812(d,r) 0.244(a) 0.364(d,r) 0.349(a) 0.438(d,r) rs2088143 31770837 C 0.22 0.737 188 0.0344(a) 0.0636(d,r) 0.403(a) 0.369(d,r) 0.627(a) 0.173(d,r) rs8037864 31801713 G 0.21 0.178 173 0.036(a) 0.0846(d,r) 0.797(a) 0.374(d,r) 0.129(a) 0.0323(d,r) rs2278317 31848032 G 0.31 0.737 223 0.0385(a) 0.779(d,r) 0.106(a) 0.0208(d,r) 0.163(a) 0.0255(d,r) rs17236476 31784069 C 0.07 0.945 85 0.0392(a) 0.0267(d,r) 0.701(a) 0.223(d,r) 0.146(a) 0.081(d,r) rs8034012 31687997 C 0.37 0.239 239 0.0438(a) 0.101(d,r) 0.614(a) 0.541(d,r) 0.499(a) 0.576(d,r) rs16973062 31679589 C 0.06 0.523 70 0.0451(a) 0.0438(d,r) 0.884(a) 0.365(d,r) 0.376(a) 0.226(d,r) rs2288609 31821738 A 0.27 0.319 214 0.602(a) 0.0516(d,r) 6.82 × 10−5(a) 0.365(d,r) 0.50(a) 0.38(d,r) rs965471 31868429 G 0.34 0.361 213 0.982(a) 0.634(d,r) 1.21 × 10−4(a) 0.00229(d,r) 0.118(a) 0.0326(d,r) rs4780167 31799489 G 0.41 0.905 266 0.111(a) 0.00388(d,r) 1.43 × 10−4(a) 1.22 × 10−4(d,r) 0.116(a)0.150(d,r) rs10519874 31863494 G 0.38 0.507 247 0.786(a) 0.77(d,r) 1.62 × 10−4(a) 0.365(d,r) 0.101(a) 0.0.0266(d,r) rs7498093 31808568 G 0.43 0.208 249 0.83151(a) 0.901(d,r) 1.63 × 10−4(a) 0.00208(d,r) 0.0494(a) 0.0254(d,r) rs10519873 31862443 A 0.36 0.749 243 0.869(a) 0.848(d,r) 2.38 × 10−4(a) 0.00292(d,r) 0.112(a) 0.068(d,r) rs8028974 31785766 G 0.39 0.865 245 0.453(a) 0.0175(d,r) 3.21 × 10−4(a) 0.00172(d,r) 0.748(a) 0.223(d,r) rs10519875 31864956 A 0.37 0.557 248 0.77(a) 0.749(d,r) 5.16 × 10−4(a) 0.00387(d,r) 0.14(a) 0.065(d,r) rs12440440 31829188 A 0.30 0.134 244 0.283(a) 0.368(d,r) 5.50 × 10−4(a) 0.00224(d,r) 0.522(a) 0.739(d,r) rs17236525 31813550 T 0.37 0.978 248 0.663(a) 0.679(d,r) 6.16 × 10−4(a) 0.0017(d,r) 0.0298(a) 8.17 × 10−4(d,r) rs7165052 31851908 T 0.43 0.823 258 0.825(a) 0.849(d,r) 7.13 × 10−4(a) 0.00659(d,r) 0.615(a) 0.574(d,r)g SNP Positiona ALb MAFc HWEd Fam#e P-FBAT–GEEf for hypertension P-FBAT–GEEh for diabetes P-FBAT–GEEi for AD rs16973323 31694920 T 0.17 0.722 145 0.00379(a)0.00215(d,r)g 0.697(a) 0.516(d,r) 0.556(a) 0.466(d,r) rs2291736 31726787 A 0.21 0.438 186 0.0046(a) 0.0221(d,r) 0.534(a) 0.142(d,r) 0.875(a) 0.0534(d,r) rs4780118 31774633 C 0.29 0.704 216 0.00746(a) 0.0382(d,r) 0.703(a) 0.276(d,r) 0.194(a) 0.128(d,r) rs1390158 31744649 A 0.06 0.173 72 0.0151(a) 0.0437(d,r) 0.846(a) 0.223(d,r) 0.0459(a) 0.0207(d,r) rs11072471 31507915 C 0.27 0.740 208 0.0169(a) 0.0191(d,r) 0.143(a) 0.154(d,r) 0.403(a) 0.292(d,r) rs11637619 31743447 C 0.14 0.185 132 0.0171(a) 0.0255(d,r) 0.887(a) 0.155(d,r) 0.162(a) 0.041(d,r) rs2033610 31542203 T 0.47 0.031 260 0.0226(a) 0.0384(d,r) 0.0306(a) 0.0344(d,r) 0.098(a) 0.0245(d,r) rs2596164 31546180 G 0.47 0.041 258 0.0237(a) 0.0526(d,r) 0.0227(a) 0.0391(d,r) 0.00963(a) 0.0178(d,r) rs12441112 31921018 G 0.21 0.703 207 0.0266(a) 0.933(d,r) 0.876(a) 0.711(d,r) 0.519(a) 0.26(d,r) rs12906709 31797258 A 0.41 0.003 241 0.0293(a) 0.317(d,r) 0.725(a) 0.27(d,r) 0.231(a) 0.172(d,r) rs2339273 31528153 T 0.26 0.217 207 0.0301(a) 0.812(d,r) 0.244(a) 0.364(d,r) 0.349(a) 0.438(d,r) rs2088143 31770837 C 0.22 0.737 188 0.0344(a) 0.0636(d,r) 0.403(a) 0.369(d,r) 0.627(a) 0.173(d,r) rs8037864 31801713 G 0.21 0.178 173 0.036(a) 0.0846(d,r) 0.797(a) 0.374(d,r) 0.129(a) 0.0323(d,r) rs2278317 31848032 G 0.31 0.737 223 0.0385(a) 0.779(d,r) 0.106(a) 0.0208(d,r) 0.163(a) 0.0255(d,r) rs17236476 31784069 C 0.07 0.945 85 0.0392(a) 0.0267(d,r) 0.701(a) 0.223(d,r) 0.146(a) 0.081(d,r) rs8034012 31687997 C 0.37 0.239 239 0.0438(a) 0.101(d,r) 0.614(a) 0.541(d,r) 0.499(a) 0.576(d,r) rs16973062 31679589 C 0.06 0.523 70 0.0451(a) 0.0438(d,r) 0.884(a) 0.365(d,r) 0.376(a) 0.226(d,r) rs2288609 31821738 A 0.27 0.319 214 0.602(a) 0.0516(d,r) 6.82 × 10−5(a) 0.365(d,r) 0.50(a) 0.38(d,r) rs965471 31868429 G 0.34 0.361 213 0.982(a) 0.634(d,r) 1.21 × 10−4(a) 0.00229(d,r) 0.118(a) 0.0326(d,r) rs4780167 31799489 G 0.41 0.905 266 0.111(a) 0.00388(d,r) 1.43 × 10−4(a) 1.22 × 10−4(d,r) 0.116(a)0.150(d,r) rs10519874 31863494 G 0.38 0.507 247 0.786(a) 0.77(d,r) 1.62 × 10−4(a) 0.365(d,r) 0.101(a) 0.0.0266(d,r) rs7498093 31808568 G 0.43 0.208 249 0.83151(a) 0.901(d,r) 1.63 × 10−4(a) 0.00208(d,r) 0.0494(a) 0.0254(d,r) rs10519873 31862443 A 0.36 0.749 243 0.869(a) 0.848(d,r) 2.38 × 10−4(a) 0.00292(d,r) 0.112(a) 0.068(d,r) rs8028974 31785766 G 0.39 0.865 245 0.453(a) 0.0175(d,r) 3.21 × 10−4(a) 0.00172(d,r) 0.748(a) 0.223(d,r) rs10519875 31864956 A 0.37 0.557 248 0.77(a) 0.749(d,r) 5.16 × 10−4(a) 0.00387(d,r) 0.14(a) 0.065(d,r) rs12440440 31829188 A 0.30 0.134 244 0.283(a) 0.368(d,r) 5.50 × 10−4(a) 0.00224(d,r) 0.522(a) 0.739(d,r) rs17236525 31813550 T 0.37 0.978 248 0.663(a) 0.679(d,r) 6.16 × 10−4(a) 0.0017(d,r) 0.0298(a) 8.17 × 10−4(d,r) rs7165052 31851908 T 0.43 0.823 258 0.825(a) 0.849(d,r) 7.13 × 10−4(a) 0.00659(d,r) 0.615(a) 0.574(d,r)g P values in bold are the ones retained statistical significant after correction for multiple testing using Bonferroni correction (a = 0.05/279 = 1.79 × 10−4) since a total of 279 SNPs in the RYR3 gene were used. Abbreviations: AD, Alzheimer’s disease; FBAT–GEE, family-based association tests using generalized estimating equations; SNP, single nuclear polymorphism. aPhysical position is based on NCBI Genome Build 36.3. bMinor allele. cMinor allele frequency. dP value of Hardy–Weinberg equilibrium test. eThe number of informative families for risk of hypertension using an additive model. fP value based on FBAT–GEE analysis for risk of hypertension. gLetters in parentheses indicate the genetic models used for analysis (a, additive; d, dominant; r, recessive model). hP value based on FBAT–GEE analysis for risk of diabetes. iP value based on FBAT–GEE analysis for risk of Alzheimer’s disease. View Large We also identified haplotypes in association with 2 of 3 traits, hypertension, and diabetes. The A-T haplotype from rs4780118 and rs11072471 (D′ = 0.87) and the A-C haplotype from rs2291736-rs937303 (D′ = 0.63) was significantly associated with hypertension in the family-data (P = 0.00254 and 0.00667, respectively) (Table 3). Moreover, we also observed the G-G haplotype from rs12906709–rs4780167 SNPs (P = 2.71 × 10−5), A-T haplotype of rs2288609–rs4780174 SNPs (P = 5.44 × 10−6), and the A-G haplotype from rs10519875–rs11855625 (P = 6.71 × 10−5) were significantly associated with diabetes in the family-data (Table 3). Table 3. Haplotypes associated with risk of hypertension and diabetes based on FBAT–GEE SNPs r2a Haplotypeb Frequencyc Fam#d P-FBAT–GEEe Hypertension  rs4780118–rs11072471 0.87 A–T 0.59 222 0.00254  rs2291736–rs937303 0.63 A–C 0.20 190 0.00667 Diabetes  rs12906709–rs4780167 0.44 G–G 0.33 282 2.71 × 10−5  rs2288609–rs4780174 0.41 A–T 0.27 216 5.44 × 10−6  rs10519875–rs11855625 0.34 A–G 0.32 266 6.71 × 10−5 SNPs r2a Haplotypeb Frequencyc Fam#d P-FBAT–GEEe Hypertension  rs4780118–rs11072471 0.87 A–T 0.59 222 0.00254  rs2291736–rs937303 0.63 A–C 0.20 190 0.00667 Diabetes  rs12906709–rs4780167 0.44 G–G 0.33 282 2.71 × 10−5  rs2288609–rs4780174 0.41 A–T 0.27 216 5.44 × 10−6  rs10519875–rs11855625 0.34 A–G 0.32 266 6.71 × 10−5 Abbreviations: FBAT–GEE, Family-based association tests using generalized estimating equations; SNP, single nuclear polymorphism. aLinkage disequilibrium measure (r2). bHaplotype inferred from 2 SNPs. cHaplotype frequency. dFam# refers to the number of informative families. eP value based on FBAT–GEE analysis. View Large Table 3. Haplotypes associated with risk of hypertension and diabetes based on FBAT–GEE SNPs r2a Haplotypeb Frequencyc Fam#d P-FBAT–GEEe Hypertension  rs4780118–rs11072471 0.87 A–T 0.59 222 0.00254  rs2291736–rs937303 0.63 A–C 0.20 190 0.00667 Diabetes  rs12906709–rs4780167 0.44 G–G 0.33 282 2.71 × 10−5  rs2288609–rs4780174 0.41 A–T 0.27 216 5.44 × 10−6  rs10519875–rs11855625 0.34 A–G 0.32 266 6.71 × 10−5 SNPs r2a Haplotypeb Frequencyc Fam#d P-FBAT–GEEe Hypertension  rs4780118–rs11072471 0.87 A–T 0.59 222 0.00254  rs2291736–rs937303 0.63 A–C 0.20 190 0.00667 Diabetes  rs12906709–rs4780167 0.44 G–G 0.33 282 2.71 × 10−5  rs2288609–rs4780174 0.41 A–T 0.27 216 5.44 × 10−6  rs10519875–rs11855625 0.34 A–G 0.32 266 6.71 × 10−5 Abbreviations: FBAT–GEE, Family-based association tests using generalized estimating equations; SNP, single nuclear polymorphism. aLinkage disequilibrium measure (r2). bHaplotype inferred from 2 SNPs. cHaplotype frequency. dFam# refers to the number of informative families. eP value based on FBAT–GEE analysis. View Large Single-marker analysis of age at onset of hypertension, diabetes, and AD in NIA-LOAD Family Study In addition to exam RYR3 SNPs in association with the risks of 3 traits, we also tested these SNPs in association with AAO. The most significant association was revealed for rs10519818 which was shared by patients with hypertension (P = 0.000227 at both dominant and recessive models) and patients with diabetes (P = 0.0119 at additive genetic model, Table 4). Another disease-associated SNPs shared by hypertension and diabetes was rs12909478. Moreover, we also observed disease-associated SNPs shared by 3 diseases, they were rs7498093 and rs4780174 at the various genetic models (Table 4). Three disease-associated SNPs (rs17236525, rs12440440, and rs4238567) were also shared by diabetes and AD at the different genetic models (Table 4). These results, once again support the disease-risk SNPs and haplotypes in associations with AAO of hypertension and diabetes. The P values of all disease-associated SNPs in Table 4 were before correction for multiple testing. A common haplotype of A–C from rs2291736-rs937303 was associated with hypertension observed in 190 families (P = 0.00667, Table 5) and the common haplotype of G–G of rs2288609–rs4780174 SNPs showed an association with diabetes (P = 5.68 × 10−3). Table 4. Single-marker analysis of age at onset of hypertension, diabetes, and Alzheimer’s disease based on FBAT–Wilcoxon test (P < 0.05) SNP Positiona ALb MAFc HWEd Fam#e P-FBAT–GEEf for hypertension P-FBAT–GEEh for diabetes P-FBAT–GEEi for AD rs10519818 31499690 C 0.12 0.228 62 0.00186(a)0.000227(d,r)g 0.0119(a) 0.0174(d,r) 0.573(a) 0.0943(d,r) rs2596230 31508018 C 0.12 0.275 65 0.00309(a) 0.00498(d,r) 0.113(a) 0.139(d,r) 0.946(a) 0.643(d,r) rs2018899 31546496 T 0.09 0.592 50 0.0419(a) 0.00484(d,r) 0.206(a) 0.298(d,r) 0.879(a) 0.836(d,r) rs7498093 31808568 G 0.42 0.208 125 0.255(a) 0.0381(d,r) 0.00273(a) 0.0138(d,r) 3.6 × 10−4(a) 0.00345(d,r) rs4780174 31822821 T 0.48 0.176 121 0.103(a) 0.0316(d,r) 0.00388(a) 0.0336(d,r) 0.00355(a) 0.00343(d,r) rs1036006 31900157 G 0.36 0.566 111 0.399(a) 0.406(d,r) 0.00457(a) 0.00599(d,r) 0.828(a) 0.589(d,r) rs12914825 31902573 T 0.09 0.203 52 0.679(a) 0.811(d,r) 0.0116(a) 0.00218(d,r) 0.926(a) 0.581(d,r) rs2288609 31821738 A 0.27 0.319 116 0.777(a) 0.0346(d,r) 0.0126(a) 0.0256(d,r) 0.0981(a) 0.24(d,r) rs17236525 31813550 T 0.37 0.978 119 0.152(a) 0.865(d,r) 0.0144(a) 0.0183(d,r) 0.00447(a) 0.00351(d,r) rs2059956 31910824 T 0.46 0.228 129 0.423(a) 0.509(d,r) 0.0197(a) 0.0159(d,r) 0.5(a) 0.397(d,r) rs12440440 31829188 A 0.30 0.134 130 0.468(a) 0.494(d,r) 0.0305(a) 0.556(d,r) 0.00256(a) 0.0153(d,r) rs11072687 31895202 T 0.27 0.983 100 0.193(a) 0.295(d,r) 0.0357(a) 0.0353(d,r) 0.799(a) 0.605(d,r) rs7165389 31589238 C 0.12 0.327 55 0.75(a) 0.617(d,r) 0.0365(a) 0.0706(d,r) 0.627(a) 0.574(d,r) rs4780144 31741944 C 0.05 0.015 23 0.136(a) 0.136(d,r) 0.0389(a) 0.0389(d,r) 0.603(a) 0.621(d,r) rs12909478 31616542 T 0.19 0.831 76 0.389(a) 0.0269(d,r) 0.0407(a) 0.0288(d,r) 0.988(a) 0.84(d,r) rs4238567 31714681 T 0.49 0.064 129 0.834(a) 0.865(d,r) 0.0411(a) 0.473(d,r) 0.00443(a) 0.0043(d,r) rs12901404 31862592 C 0.14 0.719 64 0.511(a) 0.786(d,r) 0.0498(a) 0.0737(d,r) 0.628(a) 0.543(d,r) SNP Positiona ALb MAFc HWEd Fam#e P-FBAT–GEEf for hypertension P-FBAT–GEEh for diabetes P-FBAT–GEEi for AD rs10519818 31499690 C 0.12 0.228 62 0.00186(a)0.000227(d,r)g 0.0119(a) 0.0174(d,r) 0.573(a) 0.0943(d,r) rs2596230 31508018 C 0.12 0.275 65 0.00309(a) 0.00498(d,r) 0.113(a) 0.139(d,r) 0.946(a) 0.643(d,r) rs2018899 31546496 T 0.09 0.592 50 0.0419(a) 0.00484(d,r) 0.206(a) 0.298(d,r) 0.879(a) 0.836(d,r) rs7498093 31808568 G 0.42 0.208 125 0.255(a) 0.0381(d,r) 0.00273(a) 0.0138(d,r) 3.6 × 10−4(a) 0.00345(d,r) rs4780174 31822821 T 0.48 0.176 121 0.103(a) 0.0316(d,r) 0.00388(a) 0.0336(d,r) 0.00355(a) 0.00343(d,r) rs1036006 31900157 G 0.36 0.566 111 0.399(a) 0.406(d,r) 0.00457(a) 0.00599(d,r) 0.828(a) 0.589(d,r) rs12914825 31902573 T 0.09 0.203 52 0.679(a) 0.811(d,r) 0.0116(a) 0.00218(d,r) 0.926(a) 0.581(d,r) rs2288609 31821738 A 0.27 0.319 116 0.777(a) 0.0346(d,r) 0.0126(a) 0.0256(d,r) 0.0981(a) 0.24(d,r) rs17236525 31813550 T 0.37 0.978 119 0.152(a) 0.865(d,r) 0.0144(a) 0.0183(d,r) 0.00447(a) 0.00351(d,r) rs2059956 31910824 T 0.46 0.228 129 0.423(a) 0.509(d,r) 0.0197(a) 0.0159(d,r) 0.5(a) 0.397(d,r) rs12440440 31829188 A 0.30 0.134 130 0.468(a) 0.494(d,r) 0.0305(a) 0.556(d,r) 0.00256(a) 0.0153(d,r) rs11072687 31895202 T 0.27 0.983 100 0.193(a) 0.295(d,r) 0.0357(a) 0.0353(d,r) 0.799(a) 0.605(d,r) rs7165389 31589238 C 0.12 0.327 55 0.75(a) 0.617(d,r) 0.0365(a) 0.0706(d,r) 0.627(a) 0.574(d,r) rs4780144 31741944 C 0.05 0.015 23 0.136(a) 0.136(d,r) 0.0389(a) 0.0389(d,r) 0.603(a) 0.621(d,r) rs12909478 31616542 T 0.19 0.831 76 0.389(a) 0.0269(d,r) 0.0407(a) 0.0288(d,r) 0.988(a) 0.84(d,r) rs4238567 31714681 T 0.49 0.064 129 0.834(a) 0.865(d,r) 0.0411(a) 0.473(d,r) 0.00443(a) 0.0043(d,r) rs12901404 31862592 C 0.14 0.719 64 0.511(a) 0.786(d,r) 0.0498(a) 0.0737(d,r) 0.628(a) 0.543(d,r) Abbreviations: AD, Alzheimer’s disease; FBAT–GEE, family-based association tests using generalized estimating equations; SNP, single nuclear polymorphism. aPhysical position is based on NCBI Genome Build 36.3. bMinor allele. cMinor allele frequency. dP value of Hardy-Weinberg equilibrium test. eThe number of informative families for age at onset of hypertension using an additive model. fP value based on FBAT–Wilcoxon test for age at onset of hypertension. gLetters in parentheses indicate the genetic models used for analysis (a, additive; d, dominant; r, recessive model). hP value based on FBAT–Wilcoxon test for age at onset of diabetes. iP value based on FBAT–Wilcoxon test for age at onset of Alzheimer’s disease. View Large Table 4. Single-marker analysis of age at onset of hypertension, diabetes, and Alzheimer’s disease based on FBAT–Wilcoxon test (P < 0.05) SNP Positiona ALb MAFc HWEd Fam#e P-FBAT–GEEf for hypertension P-FBAT–GEEh for diabetes P-FBAT–GEEi for AD rs10519818 31499690 C 0.12 0.228 62 0.00186(a)0.000227(d,r)g 0.0119(a) 0.0174(d,r) 0.573(a) 0.0943(d,r) rs2596230 31508018 C 0.12 0.275 65 0.00309(a) 0.00498(d,r) 0.113(a) 0.139(d,r) 0.946(a) 0.643(d,r) rs2018899 31546496 T 0.09 0.592 50 0.0419(a) 0.00484(d,r) 0.206(a) 0.298(d,r) 0.879(a) 0.836(d,r) rs7498093 31808568 G 0.42 0.208 125 0.255(a) 0.0381(d,r) 0.00273(a) 0.0138(d,r) 3.6 × 10−4(a) 0.00345(d,r) rs4780174 31822821 T 0.48 0.176 121 0.103(a) 0.0316(d,r) 0.00388(a) 0.0336(d,r) 0.00355(a) 0.00343(d,r) rs1036006 31900157 G 0.36 0.566 111 0.399(a) 0.406(d,r) 0.00457(a) 0.00599(d,r) 0.828(a) 0.589(d,r) rs12914825 31902573 T 0.09 0.203 52 0.679(a) 0.811(d,r) 0.0116(a) 0.00218(d,r) 0.926(a) 0.581(d,r) rs2288609 31821738 A 0.27 0.319 116 0.777(a) 0.0346(d,r) 0.0126(a) 0.0256(d,r) 0.0981(a) 0.24(d,r) rs17236525 31813550 T 0.37 0.978 119 0.152(a) 0.865(d,r) 0.0144(a) 0.0183(d,r) 0.00447(a) 0.00351(d,r) rs2059956 31910824 T 0.46 0.228 129 0.423(a) 0.509(d,r) 0.0197(a) 0.0159(d,r) 0.5(a) 0.397(d,r) rs12440440 31829188 A 0.30 0.134 130 0.468(a) 0.494(d,r) 0.0305(a) 0.556(d,r) 0.00256(a) 0.0153(d,r) rs11072687 31895202 T 0.27 0.983 100 0.193(a) 0.295(d,r) 0.0357(a) 0.0353(d,r) 0.799(a) 0.605(d,r) rs7165389 31589238 C 0.12 0.327 55 0.75(a) 0.617(d,r) 0.0365(a) 0.0706(d,r) 0.627(a) 0.574(d,r) rs4780144 31741944 C 0.05 0.015 23 0.136(a) 0.136(d,r) 0.0389(a) 0.0389(d,r) 0.603(a) 0.621(d,r) rs12909478 31616542 T 0.19 0.831 76 0.389(a) 0.0269(d,r) 0.0407(a) 0.0288(d,r) 0.988(a) 0.84(d,r) rs4238567 31714681 T 0.49 0.064 129 0.834(a) 0.865(d,r) 0.0411(a) 0.473(d,r) 0.00443(a) 0.0043(d,r) rs12901404 31862592 C 0.14 0.719 64 0.511(a) 0.786(d,r) 0.0498(a) 0.0737(d,r) 0.628(a) 0.543(d,r) SNP Positiona ALb MAFc HWEd Fam#e P-FBAT–GEEf for hypertension P-FBAT–GEEh for diabetes P-FBAT–GEEi for AD rs10519818 31499690 C 0.12 0.228 62 0.00186(a)0.000227(d,r)g 0.0119(a) 0.0174(d,r) 0.573(a) 0.0943(d,r) rs2596230 31508018 C 0.12 0.275 65 0.00309(a) 0.00498(d,r) 0.113(a) 0.139(d,r) 0.946(a) 0.643(d,r) rs2018899 31546496 T 0.09 0.592 50 0.0419(a) 0.00484(d,r) 0.206(a) 0.298(d,r) 0.879(a) 0.836(d,r) rs7498093 31808568 G 0.42 0.208 125 0.255(a) 0.0381(d,r) 0.00273(a) 0.0138(d,r) 3.6 × 10−4(a) 0.00345(d,r) rs4780174 31822821 T 0.48 0.176 121 0.103(a) 0.0316(d,r) 0.00388(a) 0.0336(d,r) 0.00355(a) 0.00343(d,r) rs1036006 31900157 G 0.36 0.566 111 0.399(a) 0.406(d,r) 0.00457(a) 0.00599(d,r) 0.828(a) 0.589(d,r) rs12914825 31902573 T 0.09 0.203 52 0.679(a) 0.811(d,r) 0.0116(a) 0.00218(d,r) 0.926(a) 0.581(d,r) rs2288609 31821738 A 0.27 0.319 116 0.777(a) 0.0346(d,r) 0.0126(a) 0.0256(d,r) 0.0981(a) 0.24(d,r) rs17236525 31813550 T 0.37 0.978 119 0.152(a) 0.865(d,r) 0.0144(a) 0.0183(d,r) 0.00447(a) 0.00351(d,r) rs2059956 31910824 T 0.46 0.228 129 0.423(a) 0.509(d,r) 0.0197(a) 0.0159(d,r) 0.5(a) 0.397(d,r) rs12440440 31829188 A 0.30 0.134 130 0.468(a) 0.494(d,r) 0.0305(a) 0.556(d,r) 0.00256(a) 0.0153(d,r) rs11072687 31895202 T 0.27 0.983 100 0.193(a) 0.295(d,r) 0.0357(a) 0.0353(d,r) 0.799(a) 0.605(d,r) rs7165389 31589238 C 0.12 0.327 55 0.75(a) 0.617(d,r) 0.0365(a) 0.0706(d,r) 0.627(a) 0.574(d,r) rs4780144 31741944 C 0.05 0.015 23 0.136(a) 0.136(d,r) 0.0389(a) 0.0389(d,r) 0.603(a) 0.621(d,r) rs12909478 31616542 T 0.19 0.831 76 0.389(a) 0.0269(d,r) 0.0407(a) 0.0288(d,r) 0.988(a) 0.84(d,r) rs4238567 31714681 T 0.49 0.064 129 0.834(a) 0.865(d,r) 0.0411(a) 0.473(d,r) 0.00443(a) 0.0043(d,r) rs12901404 31862592 C 0.14 0.719 64 0.511(a) 0.786(d,r) 0.0498(a) 0.0737(d,r) 0.628(a) 0.543(d,r) Abbreviations: AD, Alzheimer’s disease; FBAT–GEE, family-based association tests using generalized estimating equations; SNP, single nuclear polymorphism. aPhysical position is based on NCBI Genome Build 36.3. bMinor allele. cMinor allele frequency. dP value of Hardy-Weinberg equilibrium test. eThe number of informative families for age at onset of hypertension using an additive model. fP value based on FBAT–Wilcoxon test for age at onset of hypertension. gLetters in parentheses indicate the genetic models used for analysis (a, additive; d, dominant; r, recessive model). hP value based on FBAT–Wilcoxon test for age at onset of diabetes. iP value based on FBAT–Wilcoxon test for age at onset of Alzheimer’s disease. View Large Table 5. Haplotypes associated with age at onset of hypertension and diabetes based on FBAT–Wilcoxon test SNPs r2a Haplotypeb Frequencyc Fam#d P-FBAT–GEEe Hypertension  rs2596240–rs8023659 0.30 A–G 0.03 22 0.00888  rs2291736–rs937303 0.63 A–C 0.20 190 0.00667 Diabetes  rs16970801–rs16970823 0.70 C–A 0.06 20 0.00949  rs2288609–rs4780174 0.41 G–G 0.43 44 0.00568 SNPs r2a Haplotypeb Frequencyc Fam#d P-FBAT–GEEe Hypertension  rs2596240–rs8023659 0.30 A–G 0.03 22 0.00888  rs2291736–rs937303 0.63 A–C 0.20 190 0.00667 Diabetes  rs16970801–rs16970823 0.70 C–A 0.06 20 0.00949  rs2288609–rs4780174 0.41 G–G 0.43 44 0.00568 Abbreviations: FBAT–GEE, Family-based association tests using generalized estimating equations; SNP, single nuclear polymorphism. aLinkage disequilibrium measure (r2). bHaplotype inferred from 2 SNPs. cHaplotype frequency. dFam# refers to the number of informative families. eP value based on FBAT–Wilcoxon test. View Large Table 5. Haplotypes associated with age at onset of hypertension and diabetes based on FBAT–Wilcoxon test SNPs r2a Haplotypeb Frequencyc Fam#d P-FBAT–GEEe Hypertension  rs2596240–rs8023659 0.30 A–G 0.03 22 0.00888  rs2291736–rs937303 0.63 A–C 0.20 190 0.00667 Diabetes  rs16970801–rs16970823 0.70 C–A 0.06 20 0.00949  rs2288609–rs4780174 0.41 G–G 0.43 44 0.00568 SNPs r2a Haplotypeb Frequencyc Fam#d P-FBAT–GEEe Hypertension  rs2596240–rs8023659 0.30 A–G 0.03 22 0.00888  rs2291736–rs937303 0.63 A–C 0.20 190 0.00667 Diabetes  rs16970801–rs16970823 0.70 C–A 0.06 20 0.00949  rs2288609–rs4780174 0.41 G–G 0.43 44 0.00568 Abbreviations: FBAT–GEE, Family-based association tests using generalized estimating equations; SNP, single nuclear polymorphism. aLinkage disequilibrium measure (r2). bHaplotype inferred from 2 SNPs. cHaplotype frequency. dFam# refers to the number of informative families. eP value based on FBAT–Wilcoxon test. View Large Shared disease-associated SNPs and haplotype between the risk and AAO We observed different disease-associated SNPs with disease risk and AAO; however, in the current study, we found several SNPs were associated not only with the disease risk, but also with AAO. SNP rs12440440 was found to be associated with diabetes risk (Table 2) and AAO for both diabetes and AD (Table 4). While SNP, rs17236525 was associated with both risk and AAO for diabetes and AD (Table 2 and 4). SNP rs2288609 was found to increase susceptibility for AAO and risk of diabetes. Finally, rs7498093 was observed association with risk of diabetes and AD as well as AAO for 3 diseases at various genetic models (Table 2 and 4). In silico analysis To test if the associated SNPs were located at regulatory gene regions and species-conserved regions, we used NIH-SNP Function Prediction. We found hypertension-associated SNPs, rs11072471 and rs16973323, were located at the species-conserved region and the gene regulatory region, respectively, which suggests the regions containing biological function.32 Having established strong associations of disease-associated SNPs with AAO and risk of AD, we tested if the genotype at these SNPs are associated with levels of gene expression in various tissues, including brain and adrenal gland based on the data from the Genotype-Tissue Expression. We hypothesized the effects of the SNP genotypes on these 3 disease-risks may reflect genotype-based differences in levels of the gene expression. To investigate this, we analyzed recently released GTEx Consortium data. In postmortem samples from 100 to 120 normal individuals from the GTEx data set, among the SNPs we tested, the homozygous genotypes (T/T) for minor alleles of all 3 diseases (AD, hypertension and diabetes) associated SNP, rs2033610 were shown significantly decreased gene expression as compared with C/C and C/T genotypes in the adrenal glands (P = 2.6 × 10−7, Figure 1a). For rs2596164, GG genotype carriers showed the lowest levels of expression (P = 2.8 × 10−7) as compared with AG and AA genotypes in the adrenal glands (G is minor allele, Figure 1b). Finally, hypertension- and AD-associated SNP, rs8037864, was also shown statistically significantly decreased gene expression of homozygous GG genotype as compared with T/G and T/T genotypes in the cells of transformed fibroblasts (P = 4.7 × 10−5). Figure 1. View largeDownload slide Based on the GTEx Consortium data of postmortem samples from 100 to 120 normal individuals,the homozygous genotypes (T/T) for minor alleles of all three diseases (AD, hypertension and diabetes) associated SNP, rs2033610 were shown significantly decreased gene expression as compared with C/C and C/T genotypes in the adrenal glands (P = 2.6 x 10-7, [a]). For rs2596164 GG genotype carriers showed the lowest levels of expression (P = 2.8 x 10−7) as compared with AG and AA genotypes in the adrenal glands (G is Pminor allele, [b]). (a) Genotypes at 3 disease risk variant, rs2033610, were also associates with gene expression in the adrenal glands in eQTLBoxplot (P = 2.6 × 10−7) from the GTEx Consortium, with TT (Home Ref, N = 30) genotype carriers showing the lowest levels of expression. Medians and interquartile ranges are indicated. CT genotype (Het, N = 57) and CC genotype (Homo Alt, N = 39) are shown. (b) Genotypes at 3 disease risk variant, rs2596164, of the RYR3 gene were also associates with gene expression in the adrenal glands in eQTLBoxplot (P = 2.6 × 10−7) from the GTEx Consortium, with GG (Home Ref, N = 30) genotype carriers showing the lowest levels of expression. Medians and interquartile ranges are indicated. AG genotype (Het, N = 57) and AA genotype (Homo Alt, N = 39) are shown. Figure 1. View largeDownload slide Based on the GTEx Consortium data of postmortem samples from 100 to 120 normal individuals,the homozygous genotypes (T/T) for minor alleles of all three diseases (AD, hypertension and diabetes) associated SNP, rs2033610 were shown significantly decreased gene expression as compared with C/C and C/T genotypes in the adrenal glands (P = 2.6 x 10-7, [a]). For rs2596164 GG genotype carriers showed the lowest levels of expression (P = 2.8 x 10−7) as compared with AG and AA genotypes in the adrenal glands (G is Pminor allele, [b]). (a) Genotypes at 3 disease risk variant, rs2033610, were also associates with gene expression in the adrenal glands in eQTLBoxplot (P = 2.6 × 10−7) from the GTEx Consortium, with TT (Home Ref, N = 30) genotype carriers showing the lowest levels of expression. Medians and interquartile ranges are indicated. CT genotype (Het, N = 57) and CC genotype (Homo Alt, N = 39) are shown. (b) Genotypes at 3 disease risk variant, rs2596164, of the RYR3 gene were also associates with gene expression in the adrenal glands in eQTLBoxplot (P = 2.6 × 10−7) from the GTEx Consortium, with GG (Home Ref, N = 30) genotype carriers showing the lowest levels of expression. Medians and interquartile ranges are indicated. AG genotype (Het, N = 57) and AA genotype (Homo Alt, N = 39) are shown. DISCUSSION Supporting our hypothesis, we identified unique and shared SNPs of RYR3 with hypertension, diabetes, and AD. In addition, the results of haplotype analyses and disease-risk SNP function predictions of RYR3 support the role of its variants in these complex and multifactorial diseases. Furthermore, hypertension risk SNPs rs11072471 and rs16973323 were found located at the species-well-conserved region indicating functional importance of RYR3, which supports how important it is to identify potentially important conserved noncoding sequences in association with these complex diseases. A recent study reports an identification of a major quantitative trait locus on chromosome 15q26 for systolic blood pressure (A logarithm of the odds [LOD] = 3.36), which is close to RYR gene location (15q13.3) in Mexican-Americans.33 Furthermore, 2 disease-associated SNPs (rs2033610 and rs2596164) also showed alterations of gene expression in the adrenal gland tissue, which plays an important role in secreting hormones that regulate both blood sugar and pressure. Additionally, a recent study has confirmed their previous findings of gene–gene interactions of RYR3 and CACNA1C in late-onset of AD using endo-phenotype analysis.34 Overall, results strongly imply the existence of shared SNPs associated of the RYR3 gene across these 3 diseases. A number of previous studies identified common genes and variants among diabetes, hypertension, and other phenotypes,35 e.g., lipoprotein lipase gene is associated with hypertension, AD, type 2 diabetes, and coronary heart disease.6 There might be a pathophysiology mechanism of sharing genetic variants for these 3 traits, as previous studies suggest that high glucose level and high blood pressure affect AD through multiple mechanisms, including reduction in cerebral blood flow36 and breakdown of the blood–brain barrier.37 A recent study (39) has also showed that excess sugar in the blood can lead to organ and even brain damage, which can lead to dementia as well as early onset of AD. It has been demonstrated that alterations of calcium (Ca2+)-signaling pathway are involved in hypertension, diabetes, and AD based on functional studies.24,25 To the best of our knowledge, this is the first study of investigating the association of RYR3 gene with the risk and AAO of hypertension, diabetes, and AD. The strengths of this study include the comparatively medium sample size used which is of relatively large size for this type of study. The sample was also ethnically homogenous (US European decent), which gives indication that within this ethnic community the same genetic evidence may be replicated. Multiple analyses were performed using single-marker analysis FBAT–GEE and FBAT–Wilcoxon. The RYRs associated variants were also supported by the results of haplotype and in-silico analyses. We used a family-based design, which can reduce the type 1 error rate arising from population stratification. Especially, the FBAT–GEE approach in the PBAT software can easily be adapted to scenarios with multiple offspring per family and missing parental information, and testing for linkage disequilibrium under the assumption of linkage.38 We are also aware of some limitations: among disease-associated SNPs, only 5 diabetes-risk SNPs were retained after multiple testing using the Bonferroni correction, other unique and shared SNPs did not; however, it is well known that Bonferroni correction is a very conservative method. Moreover, we cannot exclude contribution from environmental or behavioral factors or other nongenetic correlations. Additional replication of these results is also necessary. Finally, our current findings might be spurious or subject to type I error. Future confirmatory studies of the RYR3 in these 3 traits in independent samples or targeted genome sequencing of the gene for these diseases may provide an opportunity to dissect the genetic complexity of this gene more accurately. In conclusion, we provide genetic evidence of unique and shared disease-associated RYR3 polymorphisms among hypertension, diabetes, and AD. Suggesting an etiologic relationship between them, calcium disturbances and certain calcium-signaling pathways may be involved in pathophysiology of these traits. These results will provide a basis for replication in larger samples and/or other populations to elucidate the potential role of these genetic variants. DISCLOSURE The authors declared no conflict of interest. ACKNOWLEDGMENTS We acknowledge the NIH GWAS Data Repository, the Contributing Investigator(s) who contributed the phenotype data and DNA samples from his/her original study and the primary funding organization that supported the contributing study “Multi-Site Collaborative Study for Genotype-Phenotype Associations in Alzheimer’s disease and longitudinal follow-up of Genotype-Phenotype Associations in Alzheimer’s disease and Neuroimaging component of Genotype-Phenotype Associations in Alzheimer’s disease”. The genotypic and associated phenotypic data used in the study, “Multi-Site Collaborative Study for Genotype-Phenotype Associations in Alzheimer’s Disease (GenADA)” were provided by the GlaxoSmithKline, R&D Limited. The data sets used for analyses described in this manuscript were obtained from dbGaP at http://www.ncbi.nlm.nih.gov/gap through dbGaP accession number phs000219.v1.p1. We also acknowledge Pedro Hinojosa, a UTRGV BMED student for his contribution on preparation of manuscript. A part of funding sources is from Dr Chun Xu’s UTRGV start-up fund. REFERENCES 1. Campos-Peña V , Toral-Rios D , Becerril-Pérez F , Sánchez-Torres C , Delgado-Namorado Y , Torres-Ossorio E , Franco-Bocanegra D , Carvajal K . Metabolic syndrome as a risk factor for Alzheimer’s disease: is Aβ a crucial factor in both pathologies ? Antioxid Redox Signal 2017 ; 26 : 542 – 560 . Google Scholar CrossRef Search ADS PubMed 2. Cheng D , Noble J , Tang MX , Schupf N , Mayeux R , Luchsinger JA . Type 2 diabetes and late-onset Alzheimer’s disease . Dement Geriatr Cogn Disord 2011 ; 31 : 424 – 430 . Google Scholar CrossRef Search ADS PubMed 3. Luchsinger JA , Tang MX , Stern Y , Shea S , Mayeux R . Diabetes mellitus and risk of Alzheimer’s disease and dementia with stroke in a multiethnic cohort . Am J Epidemiol 2001 ; 154 : 635 – 641 . Google Scholar CrossRef Search ADS PubMed 4. Tosto G , Bird TD , Bennett DA , Boeve BF , Brickman AM , Cruchaga C , Faber K , Foroud TM , Farlow M , Goate AM , Graff-Radford NR , Lantigua R , Manly J , Ottman R , Rosenberg R , Schaid DJ , Schupf N , Stern Y , Sweet RA , Mayeux R ; National Institute on Aging Late-Onset Alzheimer Disease/National Cell Repository for Alzheimer Disease (NIA-LOAD/NCRAD) Family Study Group . The role of cardiovascular risk factors and stroke in familial Alzheimer disease . JAMA Neurol 2016 ; 73 : 1231 – 1237 . Google Scholar CrossRef Search ADS PubMed 5. Vemuri P , Knopman DS , Lesnick TG , Przybelski SA , Mielke MM , Graff-Radford J , Murray ME , Roberts RO , Vassilaki M , Lowe VJ , Machulda MM , Jones DT , Petersen RC , Jack CR Jr . Evaluation of amyloid protective factors and Alzheimer disease neurodegeneration protective factors in elderly individuals . JAMA Neurol 2017 ; 74 : 718 – 726 . Google Scholar CrossRef Search ADS PubMed 6. Xie C , Wang ZC , Liu XF , Yang MS . The common biological basis for common complex diseases: evidence from lipoprotein lipase gene . Eur J Hum Genet 2010 ; 18 : 3 – 7 . Google Scholar CrossRef Search ADS PubMed 7. Cuyvers E , Sleegers K . Genetic variations underlying Alzheimer’s disease: evidence from genome-wide association studies and beyond . Lancet Neurol 2016 ; 15 : 857 – 868 . Google Scholar CrossRef Search ADS PubMed 8. Guerreiro R , Hardy J . Genetics of Alzheimer’s disease . Neurotherapeutics 2014 ; 11 : 732 – 737 . Google Scholar CrossRef Search ADS PubMed 9. Ehret GB , Caulfield MJ . Genes for blood pressure: an opportunity to understand hypertension . Eur Heart J 2013 ; 34 : 951 – 961 . Google Scholar CrossRef Search ADS PubMed 10. Stančáková A , Laakso M . Genetics of type 2 diabetes . Endocr Dev 2016 ; 31 : 203 – 220 . Google Scholar CrossRef Search ADS PubMed 11. Luft FC . What have we learned from the genetics of hypertension ? Med Clin North Am 2017 ; 101 : 195 – 206 . Google Scholar CrossRef Search ADS PubMed 12. Dodoo SN , Benjamin IJ . Genomic approaches to hypertension . Cardiol Clin 2017 ; 35 : 185 – 196 . Google Scholar CrossRef Search ADS PubMed 13. Ehret GB , Munroe PB , Rice KM , Bochud M , Johnson AD , Chasman DI , Smith AV , Tobin MD , Verwoert GC , Hwang SJ , Pihur V , Vollenweider P , O’Reilly PF , Amin N , Bragg-Gresham JL , Teumer A , Glazer NL , Launer L , Zhao JH , Aulchenko Y , Heath S , Sõber S , Parsa A , Luan J , Arora P , Dehghan A , Zhang F , Lucas G , Hicks AA , Jackson AU , Peden JF , Tanaka T , Wild SH , Rudan I , Igl W , Milaneschi Y , Parker AN , Fava C , Chambers JC , Fox ER , Kumari M , Go MJ , van der Harst P , Kao WH , Sjögren M , Vinay DG , Alexander M , Tabara Y , Shaw-Hawkins S , Whincup PH , Liu Y , Shi G , Kuusisto J , Tayo B , Seielstad M , Sim X , Nguyen KD , Lehtimäki T , Matullo G , Wu Y , Gaunt TR , Onland-Moret NC , Cooper MN , Platou CG , Org E , Hardy R , Dahgam S , Palmen J , Vitart V , Braund PS , Kuznetsova T , Uiterwaal CS , Adeyemo A , Palmas W , Campbell H , Ludwig B , Tomaszewski M , Tzoulaki I , Palmer ND , Aspelund T , Garcia M , Chang YP , O’Connell JR , Steinle NI , Grobbee DE , Arking DE , Kardia SL , Morrison AC , Hernandez D , Najjar S , McArdle WL , Hadley D , Brown MJ , Connell JM , Hingorani AD , Day IN , Lawlor DA , Beilby JP , Lawrence RW , Clarke R , Hopewell JC , Ongen H , Dreisbach AW , Li Y , Young JH , Bis JC , Kähönen M , Viikari J , Adair LS , Lee NR , Chen MH , Olden M , Pattaro C , Bolton JA , Köttgen A , Bergmann S , Mooser V , Chaturvedi N , Frayling TM , Islam M , Jafar TH , Erdmann J , Kulkarni SR , Bornstein SR , Grässler J , Groop L , Voight BF , Kettunen J , Howard P , Taylor A , Guarrera S , Ricceri F , Emilsson V , Plump A , Barroso I , Khaw KT , Weder AB , Hunt SC , Sun YV , Bergman RN , Collins FS , Bonnycastle LL , Scott LJ , Stringham HM , Peltonen L , Perola M , Vartiainen E , Brand SM , Staessen JA , Wang TJ , Burton PR , Soler Artigas M , Dong Y , Snieder H , Wang X , Zhu H , Lohman KK , Rudock ME , Heckbert SR , Smith NL , Wiggins KL , Doumatey A , Shriner D , Veldre G , Viigimaa M , Kinra S , Prabhakaran D , Tripathy V , Langefeld CD , Rosengren A , Thelle DS , Corsi AM , Singleton A , Forrester T , Hilton G , McKenzie CA , Salako T , Iwai N , Kita Y , Ogihara T , Ohkubo T , Okamura T , Ueshima H , Umemura S , Eyheramendy S , Meitinger T , Wichmann HE , Cho YS , Kim HL , Lee JY , Scott J , Sehmi JS , Zhang W , Hedblad B , Nilsson P , Smith GD , Wong A , Narisu N , Stančáková A , Raffel LJ , Yao J , Kathiresan S , O’Donnell CJ , Schwartz SM , Ikram MA , Longstreth WT Jr , Mosley TH , Seshadri S , Shrine NR , Wain LV , Morken MA , Swift AJ , Laitinen J , Prokopenko I , Zitting P , Cooper JA , Humphries SE , Danesh J , Rasheed A , Goel A , Hamsten A , Watkins H , Bakker SJ , van Gilst WH , Janipalli CS , Mani KR , Yajnik CS , Hofman A , Mattace-Raso FU , Oostra BA , Demirkan A , Isaacs A , Rivadeneira F , Lakatta EG , Orru M , Scuteri A , Ala-Korpela M , Kangas AJ , Lyytikäinen LP , Soininen P , Tukiainen T , Würtz P , Ong RT , Dörr M , Kroemer HK , Völker U , Völzke H , Galan P , Hercberg S , Lathrop M , Zelenika D , Deloukas P , Mangino M , Spector TD , Zhai G , Meschia JF , Nalls MA , Sharma P , Terzic J , Kumar MV , Denniff M , Zukowska-Szczechowska E , Wagenknecht LE , Fowkes FG , Charchar FJ , Schwarz PE , Hayward C , Guo X , Rotimi C , Bots ML , Brand E , Samani NJ , Polasek O , Talmud PJ , Nyberg F , Kuh D , Laan M , Hveem K , Palmer LJ , van der Schouw YT , Casas JP , Mohlke KL , Vineis P , Raitakari O , Ganesh SK , Wong TY , Tai ES , Cooper RS , Laakso M , Rao DC , Harris TB , Morris RW , Dominiczak AF , Kivimaki M , Marmot MG , Miki T , Saleheen D , Chandak GR , Coresh J , Navis G , Salomaa V , Han BG , Zhu X , Kooner JS , Melander O , Ridker PM , Bandinelli S , Gyllensten UB , Wright AF , Wilson JF , Ferrucci L , Farrall M , Tuomilehto J , Pramstaller PP , Elosua R , Soranzo N , Sijbrands EJ , Altshuler D , Loos RJ , Shuldiner AR , Gieger C , Meneton P , Uitterlinden AG , Wareham NJ , Gudnason V , Rotter JI , Rettig R , Uda M , Strachan DP , Witteman JC , Hartikainen AL , Beckmann JS , Boerwinkle E , Vasan RS , Boehnke M , Larson MG , Järvelin MR , Psaty BM , Abecasis GR , Chakravarti A , Elliott P , van Duijn CM , Newton-Cheh C , Levy D , Caulfield MJ , Johnson T ; International Consortium for Blood Pressure Genome-Wide Association Studies; CARDIoGRAM consortium; CKDGen Consortium; KidneyGen Consortium; EchoGen consortium; CHARGE-HF consortium . Genetic variants in novel pathways influence blood pressure and cardiovascular disease risk . Nature 2011 ; 478 : 103 – 109 . Google Scholar CrossRef Search ADS PubMed 14. National Cholesterol Education Program Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults . Third report of the national cholesterol education program (NCEP) expert panel on detection, evaluation, and treatment of high blood cholesterol in adults (Adult Treatment Panel III) final report . Circulation 2002 ; 106 : 3143 – 3421 . PubMed 15. Fonseca VA . Defining and characterizing the progression of type 2 diabetes . Diabetes Care 2009 ; 32 ( Suppl 2 ): S151 – S156 . Google Scholar CrossRef Search ADS PubMed 16. Lindeboom J , Weinstein H . Neuropsychology of cognitive ageing, minimal cognitive impairment, Alzheimer’s disease, and vascular cognitive impairment . Eur J Pharmacol 2004 ; 490 : 83 – 86 . Google Scholar CrossRef Search ADS PubMed 17. St George-Hyslop PH , Tanzi RE , Haines JL , Polinsky RJ , Farrer L , Myers RH , Gusella JF . Molecular genetics of familial Alzheimer’s disease . Eur Neurol 1989 ; 29 ( Suppl 3 ): 25 – 27 . Google Scholar CrossRef Search ADS PubMed 18. Tanzi RE , Bertram L . Twenty years of the Alzheimer’s disease amyloid hypothesis: a genetic perspective . Cell 2005 ; 120 : 545 – 555 . Google Scholar CrossRef Search ADS PubMed 19. Del Prete D , Checler F , Chami M . Ryanodine receptors: physiological function and deregulation in Alzheimer disease . Mol Neurodegener 2014 ; 9 : 21 . Google Scholar CrossRef Search ADS PubMed 20. Leeb T , Giese A , Al-Bayati H , Rettenberger G , Brenig B . Assignment of the porcine ryanodine receptor 3 gene (RYR3) to chromosome 7q22–>q23 . Cytogenet Cell Genet 1998 ; 83 : 244 – 245 . Google Scholar CrossRef Search ADS PubMed 21. Sorrentino V , Giannini G , Malzac P , Mattei MG . Localization of a novel ryanodine receptor gene (RYR3) to human chromosome 15q14-q15 by in situ hybridization . Genomics 1993 ; 18 : 163 – 165 . Google Scholar CrossRef Search ADS PubMed 22. Cartwright EJ , Oceandy D , Austin C , Neyses L . Ca2+ signalling in cardiovascular disease: the role of the plasma membrane calcium pumps . Sci China Life Sci 2011 ; 54 : 691 – 698 . Google Scholar CrossRef Search ADS PubMed 23. Sun J , Song F , Wang J , Han G , Bai Z , Xie B , Feng X , Jia J , Duan Y , Lei H . Hidden risk genes with high-order intragenic epistasis in Alzheimer’s disease . J Alzheimers Dis 2014 ; 41 : 1039 – 1056 . Google Scholar CrossRef Search ADS PubMed 24. Koran ME , Hohman TJ , Thornton-Wells TA . Genetic interactions found between calcium channel genes modulate amyloid load measured by positron emission tomography . Hum Genet 2014 ; 133 : 85 – 93 . Google Scholar CrossRef Search ADS PubMed 25. Kelliher M , Fastbom J , Cowburn RF , Bonkale W , Ohm TG , Ravid R , Sorrentino V , O’Neill C . Alterations in the ryanodine receptor calcium release channel correlate with Alzheimer’s disease neurofibrillary and beta-amyloid pathologies . Neuroscience 1999 ; 92 : 499 – 513 . Google Scholar CrossRef Search ADS PubMed 26. Supnet C , Noonan C , Richard K , Bradley J , Mayne M . Up-regulation of the type 3 ryanodine receptor is neuroprotective in the TgCRND8 mouse model of Alzheimer’s disease . J Neurochem 2010 ; 112 : 356 – 365 . Google Scholar CrossRef Search ADS PubMed 27. Lee JH , Cheng R , Graff-Radford N , Foroud T , Mayeux R ; National Institute on Aging Late-Onset Alzheimer’s Disease Family Study Group . Analyses of the national institute on aging late-onset Alzheimer’s disease family study: implication of additional loci . Arch Neurol 2008 ; 65 : 1518 – 1526 . Google Scholar CrossRef Search ADS PubMed 28. Barrett JC , Fry B , Maller J , Daly MJ . Haploview: analysis and visualization of LD and haplotype maps . Bioinformatics 2005 ; 21 : 263 – 265 . Google Scholar CrossRef Search ADS PubMed 29. Van Steen K , Lange C . PBAT: a comprehensive software package for genome-wide association analysis of complex family-based studies . Hum Genomics 2005 ; 2 : 67 – 69 . Google Scholar CrossRef Search ADS PubMed 30. Lange C , DeMeo D , Silverman EK , Weiss ST , Laird NM . PBAT: tools for family-based association studies . Am J Hum Genet 2004 ; 74 : 367 – 369 . Google Scholar CrossRef Search ADS PubMed 31. Melé M , Ferreira PG , Reverter F , DeLuca DS , Monlong J , Sammeth M , Young TR , Goldmann JM , Pervouchine DD , Sullivan TJ , Johnson R , Segrè AV , Djebali S , Niarchou A , Wright FA , Lappalainen T , Calvo M , Getz G , Dermitzakis ET , Ardlie KG , Guigó R ; GTEx Consortium . Human genomics. The human transcriptome across tissues and individuals . Science 2015 ; 348 : 660 – 665 . Google Scholar CrossRef Search ADS PubMed 32. Hardison RC . Conserved noncoding sequences are reliable guides to regulatory elements . Trends Genet 2000 ; 16 : 369 – 372 . Google Scholar CrossRef Search ADS PubMed 33. Montasser ME , Shimmin LC , Hanis CL , Boerwinkle E , Hixson JE . Gene by smoking interaction in hypertension: identification of a major quantitative trait locus on chromosome 15q for systolic blood pressure in Mexican-Americans . J Hypertens 2009 ; 27 : 491 – 501 . Google Scholar CrossRef Search ADS PubMed 34. Hohman TJ , Bush WS , Jiang L , Brown-Gentry KD , Torstenson ES , Dudek SM , Mukherjee S , Naj A , Kunkle BW , Ritchie MD , Martin ER , Schellenberg GD , Mayeux R , Farrer LA , Pericak-Vance MA , Haines JL , Thornton-Wells TA ; Alzheimer’s Disease Genetics Consortium . Discovery of gene-gene interactions across multiple independent data sets of late onset Alzheimer disease from the Alzheimer disease genetics consortium . Neurobiol Aging 2016 ; 38 : 141 – 150 . Google Scholar CrossRef Search ADS PubMed 35. Andreassen OA , McEvoy LK , Thompson WK , Wang Y , Reppe S , Schork AJ , Zuber V , Barrett-Connor E , Gautvik K , Aukrust P , Karlsen TH , Djurovic S , Desikan RS , Dale AM ; International Consortium for Blood Pressure Genome-Wide Association Studies, Genetic Factors for Osteoporosis Consortium . Identifying common genetic variants in blood pressure due to polygenic pleiotropy with associated phenotypes . Hypertension 2014 ; 63 : 819 – 826 . Google Scholar CrossRef Search ADS PubMed 36. Bangen KJ , Nation DA , Clark LR , Harmell AL , Wierenga CE , Dev SI , Delano-Wood L , Zlatar ZZ , Salmon DP , Liu TT , Bondi MW . Interactive effects of vascular risk burden and advanced age on cerebral blood flow . Front Aging Neurosci 2014 ; 6 : 159 . Google Scholar CrossRef Search ADS PubMed 37. Kalaria RN . Vascular basis for brain degeneration: faltering controls and risk factors for dementia . Nutr Rev 2010 ; 68 ( Suppl 2 ): S74 – S87 . Google Scholar CrossRef Search ADS PubMed 38. Lange C , Silverman EK , Xu X , Weiss ST , Laird NM . A multivariate family-based association test using generalized estimating equations: FBAT-GEE . Biostatistics 2003 ; 4 : 195 – 206 . Google Scholar CrossRef Search ADS PubMed © Published by Oxford University Press on behalf of American Journal of Hypertension Ltd 2018. This work is written by (a) US Government employees(s) and is in the public domain in the US.

Journal

American Journal of HypertensionOxford University Press

Published: Mar 26, 2018

There are no references for this article.

You’re reading a free preview. Subscribe to read the entire article.


DeepDyve is your
personal research library

It’s your single place to instantly
discover and read the research
that matters to you.

Enjoy affordable access to
over 18 million articles from more than
15,000 peer-reviewed journals.

All for just $49/month

Explore the DeepDyve Library

Search

Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly

Organize

Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place.

Access

Get unlimited, online access to over 18 million full-text articles from more than 15,000 scientific journals.

Your journals are on DeepDyve

Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.

All the latest content is available, no embargo periods.

See the journals in your area

DeepDyve

Freelancer

DeepDyve

Pro

Price

FREE

$49/month
$360/year

Save searches from
Google Scholar,
PubMed

Create lists to
organize your research

Export lists, citations

Read DeepDyve articles

Abstract access only

Unlimited access to over
18 million full-text articles

Print

20 pages / month

PDF Discount

20% off