Abstract Context Small for gestational age (SGA) can be the result of fetal growth restriction, which is associated with perinatal morbidity and mortality. Mechanisms that control prenatal growth are poorly understood. Objective The aim of the current study was to gain more insight into prenatal growth failure and determine an effective diagnostic approach in SGA newborns. We hypothesized that one or more copy number variations (CNVs) and disturbed methylation and sequence variants may be present in genes associated with fetal growth. Design A prospective cohort study of subjects with a low birth weight for gestational age. Setting The study was conducted at an academic pediatric research institute. Patients A total of 21 SGA newborns with a mean birth weight below the first centile and a control cohort of 24 appropriate-for-gestational-age newborns were studied. Interventions Array comparative genomic hybridization, genome-wide methylation studies, and exome sequencing were performed. Main Outcome Measures The numbers of CNVs, methylation disturbances, and sequence variants. Results The genetic analyses demonstrated three CNVs, one systematically disturbed methylation pattern, and one sequence variant explaining SGA. Additional methylation disturbances and sequence variants were present in 20 patients. In 19 patients, multiple abnormalities were found. Conclusion Our results confirm the influence of a large number of mechanisms explaining dysregulation of fetal growth. We concluded that CNVs, methylation disturbances, and sequence variants all contribute to prenatal growth failure. These genetic workups can be an effective diagnostic approach in SGA newborns. The process of human fetal growth is steered by fetal and maternal genetic factors that affect the intrauterine environment to ensure effective nutrient exchange between mother and fetus via the placenta. Small for gestational age (SGA) has been defined either as being below the 10th centile for weight at a given gestational age (GA) or as having a birth length or weight standard deviation score less than −2.0 (below the 2.3 centile) (1). SGA can be the result of fetal growth restriction (FGR), which is defined as a fetus being unable to reach its individual growth potential (2). FGR is associated with substantial perinatal morbidity and mortality (3), and babies with FGR can be predisposed to metabolic diseases later in life (4). Thirty to fifty percent of the variations in weight at birth can be explained by genetic or epigenetic causes (5), which include chromosome imbalances, sequence variants, and epigenetic disturbances. The London Dysmorphology Database contains >400 entities associated with prenatal growth failure (6). Numerous studies on epigenetic influences, especially DNA methylation disturbances, have also been performed (7). Despite this research, mechanisms behind prenatal growth failure are only poorly understood, at least in part because of the heterogeneous nature of growth disturbances. Consequently, an appropriate diagnostic workup for SGA newborns is not well established, and questions remain regarding the extent of genetic factor contributions, the optimal care pathway for the child, and how we provide adequate counseling to parents. The aim of the current study was to gain further insight into prenatal growth failure and determine whether a combination of genomic analyses is an effective diagnostic approach for SGA newborns. We used array comparative genomic hybridization (array-CGH) to detect copy number variations (CNVs), genome-wide methylation studies to uncover methylation disturbances, and “whole” exome sequencing (WES) to detect sequence variants in a cohort of SGA newborns. We hypothesized that CNVs explaining SGA may be found, that disturbed methylation may be present in genes known to be aberrantly methylated in low‒birth weight newborns, and that sequence variants may be present in genes targeted because of their known association with SGA. Methods Patients We selected 21 SGA newborns and their parents from the Baby Bio Bank (BBB) and Moore Cohort. The BBB contains biological samples and clinical data from 2515 pregnancies collected between 2000 and 2014. The Moore cohort consists of 319 trio samples collected from newborns and their parents between 1991 and 1994, including a small FGR cohort. Inclusion criteria for this study included weight at birth at or below the 10th centile, availability of parental samples, and absence of major structural malformations, in accordance with our aims to study newborns with severe intrauterine growth restriction (IUGR) without clues for a specific diagnosis. No preeclampsia/HELLP syndrome, maternal systemic disease, medication use during pregnancy, or maternal smoking was present, except for one mother (SGA4) who was a moderate smoker during pregnancy and one other mother (SGA3) who had preexisting essential hypertension for which she received treatment. SGA17 was a pregnancy termination at 22 weeks of gestation (reason unknown to us) and was included because of the markedly low weight for GA without malformations or other clues for a specific diagnosis. A control cohort of appropriate-for-gestational-age newborns (n = 24) was selected from the Preeclampsia And Nonpreeclampsia Database (PANDA) on the basis of birth weight for GA closest to the 50th centile and an equal distribution of GA and mode of delivery in relation to the SGA cases. The PANDA Biobank collected placental biopsies, umbilical cord blood samples, and maternal blood samples between 2006 and 2010 from 400 women with either preeclampsia or normotensive pregnancies. The standard deviation scores of weight at birth were calculated using the 1990 British growth references (8) for the British cases and the 1991 reference data for the Dutch controls (9). Analyses of demographic data using descriptive statistics were performed in IBM SPSS Statistics, version 22. Ethical approval was obtained for all studies (BBB Research Ethics Committee references: 09/H0405/30 and 09/h0405/30+5; Moore cohort reference: 2001/6029; PANDA Biobank AMC2005_133). Targeted genes We performed literature searches on (1) genes known to be aberrantly methylated in SGA (Supplemental Table 1), (2) genes known to be involved in regulation of DNA methylation (Supplemental Table 2), and (3) genes in which sequence variants are associated with disorders with SGA as part of the phenotype (Supplemental Table 3). These genes are referred to as targeted genes. DNA isolation DNA was obtained from biopsies from the fetal side of the placenta near the umbilical cord insertion. DNA from parental blood samples and the cases were extracted using DNEasy Blood and Tissue Kit (Qiagen, Venlo, Netherlands). DNA from the control samples was biopsied from the maternal site of the placenta and extracted according to the Gentra protocol (Qiagen). To minimize the risk of maternal blood contamination, placental biopsy specimens were washed in phosphate-buffered saline and stored in RNAlater. To verify that no maternal DNA contamination had occured, clustering of male samples and female samples was investigated by principal component analysis. Array CGH The array-CGH analysis was performed using Agilent 180K oligo-array (Agilent, Santa Clara, CA), with 13-kb overall median probe spacing and a GRCh37/hg19 browser. Standard methods were used for labeling and hybridization. Samples were hybridized against a pool of 40 healthy sex-matched human reference samples. Data were analyzed with Genomic Workbench 6.5 (Agilent) and Cartagenia [BENCHlab CNV v5.0 (r6643); Agilent]. Genome-wide methylation array Bisulfite conversion of genomic DNA was performed using the EZ DNA Methylation Kit (Zymo Research, CA). Converted DNA samples were randomized across one batch and hybridized on an Infinium Human Methylation 450K BeadChip array (Illumina, Inc., CA), carried out by a certified Illumina service provider (ServiceXS, Leiden, The Netherlands). The 450K BeadChip applies both Infinium I and II assays and examines >450.000 C-phosphate-G (CpG) sites across the genome. Because of the bisulfite conversion, the array recognized methylated and unmethylated loci and expressed the degree of methylation in β-values, ranging from 0 (fully unmethylated) to 1 (fully methylated). Quality control of the Illumina 450k assay (Illumina) was performed using MethylAid (10). Raw data provided by ServiceXS were used for statistical analysis. A file containing the β-value methylation data, including annotation, was produced by GenomeStudio (Illumina). Methylation data from GenomeStudio and sample phenotype data were exported to the R statistical analysis environment (R version 2.15.2) (http://www.r-project.org), where a single-sample analysis (11) was performed. This allowed analysis of genome-wide methylation data in small sample sizes, in which each case is individually compared with a control cohort. The method combines the Illumina Methylation Analyzer package (version 3.2.1) and the Crawford-Howell t test (11). The Illumina Methylation Analyzer package performs a basic quality control and preprocesses methylation data. Any CpG sites with missing values and samples with >75% CpG sites having a P value >0.05, CpG sites where >75% samples had detection P values >1e−5, probes on the X and Y chromosomes, and probes containing single nucleotide polymorphisms (SNPs) were removed. The β-values were converted to M-values by logit transformation (12). Quantile normalization was used to reduce unwanted technical variations across samples. Peak correction (13) was applied to correct differences between Infinium I‒ and Infinium II‒type assays. Because all cases and controls were hybridized on the same batch, no batch correction was required. Differences between preprocessed M-values of all single cases and those of controls were determined using the Crawford-Howell t test. Given the large number of significantly differentially methylated probes in our patients resulting from the single-sample analysis, a script in Python (version 2.7) (https://www.python.org/) was used for further filtering of data. Probes with a β-value difference of at least 20%, an adjusted P value <0.05, and a minimum of three differentially methylated probes within 2000 base pairs, allowing for reduction of false-positive findings, were selected for hypermethylated and hypomethylated probes. Probes without gene annotation were removed from further analysis. Genes found to be hypermethylated and hypomethylated at the same time in the same patient were removed. First, genome-wide methylation patterns in SGA newborns were analyzed against the previously reported literature (Supplemental Table 1). Second, other genes that were differentially methylated in more than five patients were selected. To investigate the significance of the present methylation findings, we analyzed the cohort of controls as if they were cases: The results in a single control were analyzed against the remaining controls, and this was performed for each control. We carried out this analysis for the candidate genes as well as for the untargeted genes differentially methylated in more than five controls. For the permutation analysis, the fraction of probes showing significant differential methylation (P values below the threshold of 1e−2) were compared between the 50 candidate genes and 50 randomly selected genes within the same sample. This random selection was carried out 1000 times per sample, and the mean value was generated for comparison. The fraction of the probes having significant differential methylation was expected to be higher in the 50 candidate genes than in the 50 randomly selected samples. If it was significantly higher, the permutation P value would be <0.05, making them more likely candidates. Exome sequencing WES was performed by BGI (Hong Kong). In total, 41 samples were analyzed using the Agilent SureSelect Human All Exon V5 (50M) Kit (Agilent) and high-throughput sequencing technology of Complete Genomics at 100× coverage. The samples consisted of 10 trios from newborns with the lowest birth weights and their parents (SGA1, SGA3, SGA6, and SGA15 through SGA21) and 11 singletons of the remaining newborns. For each sample, BGI analyzed and provided reads, results of mappings, and basic bioinformatics analysis (including alignment and assessment, SNP and InDel calling, basic annotation and statistics, and SNP validation). At our institution, data were further annotated, including pathogenicity prediction data, allowing for subsequent filtering of variants. Variants with “high” and “moderate” impact (Ensembl Variation – Predicted data, ensemble.org) mutation types (SO terms), 1K genome minor allele frequency (MAF) <0.05, ExAC allele frequency <0.05, read depth ≥30 and quality score ≥30 were selected for further examination. Variants with known nonpathogenic significance and a combined SIFT and PolyPhen prediction of “tolerated” and “benign” were discarded. Subsequently, we checked variants in targeted genes known to cause low birth weight (Supplemental Table 3) and determined the likelihood of pathogenicity. Ethnicity-specific MAFs were obtained from 1000 Genome, ExAC, and GO-ESP databases. Second, potential de novo variants were selected and verified in the Integrative Genomics Viewer (Broad Institue, Cambridge, MA) in the 10 patients for whom sequencing results from both newborns and parents were available. Lastly, homozygous and compound heterozygous mutations were analyzed. All variants in genes discussed in the Results and Discussion have been validated by Sanger sequencing. Results Patients All 21 SGA cases (SGA1 through SGA21) had a birth weight for GA below the 3.4 centile; 19 were below the 2.3 centile, and 14 patients were below the first centile. Table 1 shows other demographics of the study group and the control samples. Separate clustering of male cases and control samples from female samples was confirmed, indicating that no maternal DNA contamination was measured (Supplemental Fig. 1). Table 1. Demographics of 21 SGA Newborns and 24 Controls Appropriate for GA Patient ID Sex GA BW (g) BW (centile) BW (SDS) Ethnicity Mode of Delivery Cases SGA1 Female 33.00 1220 0.41 −2.64 Caucasian Cesarean section SGA2 Female 38.00 1980 0.51 −2.57 African Vaginal SGA3 Female 33.71 640 4.7E-5 −4.91 South American Cesarean section SGA4 Female 39.00 2435 3.36 −1.83 Caribbean Cesarean section SGA5 Female 34.00 1350 0.59 −2.52 Asian Cesarean section SGA6 Female 39.57 2120 0.22 −2.85 Caucasian Vaginal SGA7 Male 38.00 2080 0.69 −2.46 South American Vaginal SGA8 Male 38.00 2140 1.04 −2.31 Caucasian Vaginal SGA9 Male 34.43 1543 1.04 −2.31 Caucasian Cesarean section SGA10 Male 39.57 2320 0.62 −2.50 Caucasian Vaginal SGA11 Female 38.57 2180 1.10 −2.29 African Cesarean section SGA12 Female 39.00 2385 2.56 −1.95 African Cesarean section SGA13 Male 38.57 2280 1.36 −2.21 Asian Cesarean section SGA14 Female 37.14 2017 1.83 −2.09 Caribbean Vaginal SGA15 Female 31.71 474 3.14E-4 −4.52 Caucasian Cesarean section SGA16 Male 39.00 2090 0.24 −2.82 African Cesarean section SGA17 Male 22.00 236 0.13 −3.00 Caucasian Termination of pregnancy SGA18 Male 36.00 1600 0.21 −2.86 Caucasian Cesarean section SGA19 Male 37.00 1782 0.26 −2.80 Caucasian Cesarean section SGA20 Male 40.00 1874 0.01 −3.69 Caucasian Vaginal SGA21 Male 40.00 2220 0.20 −2.88 Caucasian Vaginal Mean ± SD — 36.49 ± 4.14 1760 ± 640 0.78 ± 0.88 −2.76 ± 0.77 — — Controls Mean ± SD — 37.48 ± 4.10 2953 ± 926 53.83 ± 15.51 0.10 ± 0.42 — — Patient ID Sex GA BW (g) BW (centile) BW (SDS) Ethnicity Mode of Delivery Cases SGA1 Female 33.00 1220 0.41 −2.64 Caucasian Cesarean section SGA2 Female 38.00 1980 0.51 −2.57 African Vaginal SGA3 Female 33.71 640 4.7E-5 −4.91 South American Cesarean section SGA4 Female 39.00 2435 3.36 −1.83 Caribbean Cesarean section SGA5 Female 34.00 1350 0.59 −2.52 Asian Cesarean section SGA6 Female 39.57 2120 0.22 −2.85 Caucasian Vaginal SGA7 Male 38.00 2080 0.69 −2.46 South American Vaginal SGA8 Male 38.00 2140 1.04 −2.31 Caucasian Vaginal SGA9 Male 34.43 1543 1.04 −2.31 Caucasian Cesarean section SGA10 Male 39.57 2320 0.62 −2.50 Caucasian Vaginal SGA11 Female 38.57 2180 1.10 −2.29 African Cesarean section SGA12 Female 39.00 2385 2.56 −1.95 African Cesarean section SGA13 Male 38.57 2280 1.36 −2.21 Asian Cesarean section SGA14 Female 37.14 2017 1.83 −2.09 Caribbean Vaginal SGA15 Female 31.71 474 3.14E-4 −4.52 Caucasian Cesarean section SGA16 Male 39.00 2090 0.24 −2.82 African Cesarean section SGA17 Male 22.00 236 0.13 −3.00 Caucasian Termination of pregnancy SGA18 Male 36.00 1600 0.21 −2.86 Caucasian Cesarean section SGA19 Male 37.00 1782 0.26 −2.80 Caucasian Cesarean section SGA20 Male 40.00 1874 0.01 −3.69 Caucasian Vaginal SGA21 Male 40.00 2220 0.20 −2.88 Caucasian Vaginal Mean ± SD — 36.49 ± 4.14 1760 ± 640 0.78 ± 0.88 −2.76 ± 0.77 — — Controls Mean ± SD — 37.48 ± 4.10 2953 ± 926 53.83 ± 15.51 0.10 ± 0.42 — — Abbreviations: BW, birth weight; SD, standard deviation; SDS, standard deviation score. View Large Array-CGH The array-CGH yielded abnormalities in three patients. Patient SGA1 showed a mosaic trisomy of chromosome 16 in 70% of the cells [arr(19) 16p13.3q24(64,381-90,163,114)×2∼3]. Another mosaic imbalance, mosaic Turner syndrome, was seen in patient SGA11 [arr Xp22.33q28(61,091-155,009,479)×1∼2]. Patient SGA17 had a deletion of 11p13-p14.1 [arr(h19) 11p14.1p13(29,663,942-33,400,789)×1] causing WAGR syndrome (Wilms tumor, aniridia, genital anomalies, and mental retardation). Genome-wide methylation Quality control of the Illumina 450k assay showed no failed samples for bisulfite conversion, hybridization, and overall methylation threshold. Table 2 shows methylation changes in genes known to be aberrantly methylated in low‒birth weight newborns, which we targeted first (see Methods and Supplemental Table 1). Differential methylation was seen in 12 patients, of whom nine had differential methylation in more than one gene. Because patient SGA3 showed an extensively aberrant methylation profile (Fig. 1), the results for this patient are presented separately. Subsequently, all genes found in an untargeted study to be differentially methylated in five or more patients were analyzed (Supplemental Table 4), showing 28 hypermethylated genes and six hypomethylated genes. Analysis of our control cohort showed 45 differentially methylated genes present in more than five individuals. Of the 34 genes resulting from the case analysis, 12 genes total (35%) appeared to be the same genes. SGA3 showed differential methylation in 26 targeted genes known to be aberrantly methylated in low‒birth weight newborns (Supplemental Table 5). A possible explanation for this extensively disturbed methylation pattern in SGA3 is an alteration in a gene known to be involved in (the regulation of) DNA methylation (see Methods and Supplemental Table 2). Of these, four were hypermethylated and 11 were hypomethylated (Supplemental Table 6). Table 2. Differential Methylation in Genes Known To Be Aberrantly Methylated in Low‒Birth Weight Newborns Patient ID Gene Chromosome (MapInfo) Control β-Value (mean) Case β-Value (mean) No. of Probes Main Gene Function(s) and Influence(s) on Fetal Growth a Comparison With Control Cases Hypermethylation Direction of Methylation No. of Controls SGA11 CDKN1C 11 (2905931-2907008) 0.14 0.41 5 Imprinted gene in 11p15.5 region, highly expressed in the placenta; upregulation associated with IUGR placentas, loss of function associated with Beckwith-Wiedemann syndrome, and gain of function with Silver-Russell syndromeb Hypermethylation 1 SGA15 11 (2906667-2907073) 0.21 0.48 4 SGA4 FGF14 13 (103052362-103052943) 0.16 0.44 3 Hypomethylation associated with SGA or FGR — — SGA13 GNAS; GNASAS 20 (57414162-57414539) 0.62 0.83 3 Hypomethylation of GNASAS associated with SGA; decreased expression of GNAS observed in IUGR placentas — — SGA1 FOXP1 3 (71631050-71631744) 0.09 0.45 4 Increased methylation associated with FGR Hypermethylation 1 SGA7 3 (71631050-71631744) 0.09 0.46 4 SGA14 NPR3 5 (32710614-32711429) 0.24 0.49 4 Hypermethylation associated with FGR Hypermethylation 3 SGA20 5 (32710231-32711517) 0.12 0.39 6 SGA14 NR3C1 5 (142784522-142785258) 0.22 0.47 3 Differential methylation in this glucocorticoid receptor in the placenta correlated with birth weight Hypermethylation 3 SGA11 TBX15 1 (119530600-119530702) 0.28 0.58 3 Promotor hypomethylation leads to TBX15 decrease in FGR placentas Both 3c SGA14 1 (119530600-119531093) 0.31 0.55 3 SGA15 1 (119530600-119530702) 0.28 0.64 3 SGA17 1 (119530048-119530932) 0.35 0.58 4 SGA20 1 (119530600-119530702) 0.28 0.57 3 SGA21 1 (119530600-119531093) 0.30 0.61 4 SGA7 WNT2 7 (116963193-116963502) 0.18 0.52 5 WNT2 promoter methylation in placenta associated with low birth weight Both 6c SGA11 7 (116962950-116964012) 0.19 0.51 7 SGA13 7 (116962950-116963502) 0.17 0.48 6 SGA16 7 (116962950-116963502) 0.17 0.48 6 SGA10 ZIC1; 3 (147125714-147127662) 0.30 0.58 5 Decreased methylation associated with SGA or FGR Both 6c SGA15 ZIC4 3 (147125712-147126206) 0.43 0.66 6 SGA21 3 (147126763-147127662) 0.21 0.57 6 Hypomethylation SGA17 IGF2AS; INS-IGF2; IGF2 11 (2162406-2162616) 0.44 0.17 5 IGF2 imprinted and highly expressed in the placenta, hypomethylation of H19/IGF2 control region associated with FGR; INS-IGF2 involved in growth and metabolism; IGF2AS imprinted and expressed in antisense to IGF2 — — SGA13 KCNQ1; KCNQ1OT1 11(2721207-2721383) 0.49 0.24 4 Upregulated KCNQ1 and loss of KCNQ1OT1 associated with IUGR; genetic variants of KCNQ1 associated with Beckwith-Wiedemann syndrome — — SGA1 TBX15 1 (119526060-119527377) 0.68 0.42 4 Promotor hypomethylation leads to TBX15 decrease in FGR placentas Both 1c SGA17 WNT2 7 (116964012-116964802) 0.35 0.11 4 WNT2 promoter methylation in placenta associated with low birth weight Both 2c Patient ID Gene Chromosome (MapInfo) Control β-Value (mean) Case β-Value (mean) No. of Probes Main Gene Function(s) and Influence(s) on Fetal Growth a Comparison With Control Cases Hypermethylation Direction of Methylation No. of Controls SGA11 CDKN1C 11 (2905931-2907008) 0.14 0.41 5 Imprinted gene in 11p15.5 region, highly expressed in the placenta; upregulation associated with IUGR placentas, loss of function associated with Beckwith-Wiedemann syndrome, and gain of function with Silver-Russell syndromeb Hypermethylation 1 SGA15 11 (2906667-2907073) 0.21 0.48 4 SGA4 FGF14 13 (103052362-103052943) 0.16 0.44 3 Hypomethylation associated with SGA or FGR — — SGA13 GNAS; GNASAS 20 (57414162-57414539) 0.62 0.83 3 Hypomethylation of GNASAS associated with SGA; decreased expression of GNAS observed in IUGR placentas — — SGA1 FOXP1 3 (71631050-71631744) 0.09 0.45 4 Increased methylation associated with FGR Hypermethylation 1 SGA7 3 (71631050-71631744) 0.09 0.46 4 SGA14 NPR3 5 (32710614-32711429) 0.24 0.49 4 Hypermethylation associated with FGR Hypermethylation 3 SGA20 5 (32710231-32711517) 0.12 0.39 6 SGA14 NR3C1 5 (142784522-142785258) 0.22 0.47 3 Differential methylation in this glucocorticoid receptor in the placenta correlated with birth weight Hypermethylation 3 SGA11 TBX15 1 (119530600-119530702) 0.28 0.58 3 Promotor hypomethylation leads to TBX15 decrease in FGR placentas Both 3c SGA14 1 (119530600-119531093) 0.31 0.55 3 SGA15 1 (119530600-119530702) 0.28 0.64 3 SGA17 1 (119530048-119530932) 0.35 0.58 4 SGA20 1 (119530600-119530702) 0.28 0.57 3 SGA21 1 (119530600-119531093) 0.30 0.61 4 SGA7 WNT2 7 (116963193-116963502) 0.18 0.52 5 WNT2 promoter methylation in placenta associated with low birth weight Both 6c SGA11 7 (116962950-116964012) 0.19 0.51 7 SGA13 7 (116962950-116963502) 0.17 0.48 6 SGA16 7 (116962950-116963502) 0.17 0.48 6 SGA10 ZIC1; 3 (147125714-147127662) 0.30 0.58 5 Decreased methylation associated with SGA or FGR Both 6c SGA15 ZIC4 3 (147125712-147126206) 0.43 0.66 6 SGA21 3 (147126763-147127662) 0.21 0.57 6 Hypomethylation SGA17 IGF2AS; INS-IGF2; IGF2 11 (2162406-2162616) 0.44 0.17 5 IGF2 imprinted and highly expressed in the placenta, hypomethylation of H19/IGF2 control region associated with FGR; INS-IGF2 involved in growth and metabolism; IGF2AS imprinted and expressed in antisense to IGF2 — — SGA13 KCNQ1; KCNQ1OT1 11(2721207-2721383) 0.49 0.24 4 Upregulated KCNQ1 and loss of KCNQ1OT1 associated with IUGR; genetic variants of KCNQ1 associated with Beckwith-Wiedemann syndrome — — SGA1 TBX15 1 (119526060-119527377) 0.68 0.42 4 Promotor hypomethylation leads to TBX15 decrease in FGR placentas Both 1c SGA17 WNT2 7 (116964012-116964802) 0.35 0.11 4 WNT2 promoter methylation in placenta associated with low birth weight Both 2c a For references, see Supplemental Table 1. b Clinical diagnosis uncertain because of unavailable detailed phenotyping. c Controls with methylation in same direction as case. View Large Figure 1. View largeDownload slide Number of differentially methylated probes per patient. Total number of differentially methylated probes per patient out of 485,577 interrogated probes, after single-case analysis and further probe filtering (see Methods). An extensively disturbed methylation profile is evident in patient SGA3, and patient SGA15 had more hypermethylated probes than the other patients. Figure 1. View largeDownload slide Number of differentially methylated probes per patient. Total number of differentially methylated probes per patient out of 485,577 interrogated probes, after single-case analysis and further probe filtering (see Methods). An extensively disturbed methylation profile is evident in patient SGA3, and patient SGA15 had more hypermethylated probes than the other patients. In addition, WES data were checked for sequence variants in genes involved in regulating DNA methylation (Supplemental Table 6), showing a heterozygous missense mutation in MPHOSPH8 (p.Asp460Tyr). The same variant, with a known MAF of 2% to 3% (rs75390100), was found in two other patients (SGA2 and SGA15). This high MAF excludes this variant as being the sole (Mendelian) cause of the IUGR, but we cannot exclude that it contributes to a polygenic or multifactorial origin of the IUGR. Because of an administrative error, three samples (SGA5, SGA9, and SGA12) could not be included in the genome-wide methylation analysis. Analysis of the control cohort yielded a total of eight targeted genes (CDKN1C, NPR3, NR3C1, FOXP1, H19, TBX15, WNT2, and ZIC1) that were differentially methylated. Seven of those genes were also found in our case analysis with a comparable number of individuals and methylation in the same direction as in the cases (or both hypermethylation and hypomethylation). The permutation analysis showed the probes within the 50 candidate genes from our study (Table 2 and Supplemental Table 5) had a consistently higher fraction of probes below the threshold value than did the randomly selected genes, except for four patients (Supplemental Table 7). In addition, seven of 18 samples had P values <0.05. Exome sequencing Exome sequencing without filtering yielded >70,000 single-nucleotide variants and ∼5000 InDel variants in the 21 patients studied. After filtering (see Methods), we first evaluated sequence variants in genes that, when mutated, are known to be associated with disorders in which a low birth weight is part of the phenotype (Supplemental Table 3). This targeted analysis yielded potentially pathogenic heterozygous variants in 32 genes, one homozygous variant, and two compound heterozygous variants (Supplemental Table 8). In this targeted gene panel, no de novo variants were identified in newborns for whom sequencing data of the parents were available. In patients for whom no WES was performed in their parents, variants were sequenced by Sanger in parents and showed inheritance of all variants from one or both parents. Second, de novo variants in untargeted genes were analyzed in silico (see Methods). Two denovo single-nucleotide variants were predicted to be potentially pathogenic (Supplemental Table 8). Third, we analyzed all WES data for homozygous variants in untargeted genes and found three homozygous missense mutations of potential interest (Supplemental Table 8). Finally, we evaluated data for compound heterozygous mutations in untargeted genes and found one compound heterozygous variant (Supplemental Table 8). All variants described have been validated by Sanger sequencing. The recommendation of the American College of Medical Genetics and Genomics was followed in interpreting variants. Discussion In the current study, we investigated 21 SGA newborns using a combination of array-CGH, genome-wide methylation array, and exome sequencing. In four patients (19%), we found a genetic abnormality that likely contributed to their low birth weight. Three CNVs (14%) were detected in the present cohort, a relatively higher number than in patients with SGA or short stature in previous reports (14). Mosaic trisomy 16 is known to lead to a high risk of prenatal abnormalities (15), frequently including SGA, and can thus be considered a valid explanation for SGA in patient SGA1. Patient SGA11 showed mosaicism for monosomy X. About 50% of individuals with Turner syndrome have a mosaic karyotype (16), and it typically includes a low birth weight. In patient SGA17, an 11p14.1-p13 deletion, as seen in WAGR syndrome, was identified. This syndrome features reduced intrauterine growth as a known phenotype (17). As hypothesized in advance, methylation disturbances in several genes known to be aberrantly methylated in low‒birth weight newborns were found. In general, more hypermethylation than hypomethylation was found in the present cohort. A methylation abnormality potentially involved in SGA was detected in 13 patients, of whom five showed differential methylation in several imprinted genes from the 11p15.5 imprinted region associated with FGR: CDKN1C, KCNQ1, IGF2AS, INS, and IGF2 (18). However, when each control was analyzed vs the remaining controls, similar to the case analyses, similar differential methylation of the majority of these genes were found in control individuals. This suggests that these findings are not significant as a Mendelian cause for disturbed intrauterine growth. On the other hand, the control vs controls analyses also showed that the differential methylation of KCNQ1, IGF2AS, INS, and IGF2 was solely differently methylated in the case series, which increases the likelihood (but does not prove) that these contribute to FGR. In addition, probes within the candidate genes had a consistently higher fraction of probes below the threshold value compared with the randomly selected genes in the majority of cases, as shown in the permutation analysis. Seven of 19 samples showed a permutation P value <0.05, when only one would be expected by chance. Therefore, these results were consistent with the overall findings of this study: The 50 candidate genes showed differences in a small proportion of patients, some of whom also carried potential genetic variants. Methylation disturbances in untargeted genes were considered when the disturbance was present in at least five patients. Such changes were detected in 34 genes; six of these (PIK3R1, DIXDC1, ESRRG, TBX15, GGT1, and FGF8) appeared to be of specific interest in view of their known functions. However, given that analysis of the control cohort yielded a similar amount of genes, including overlap in 12 genes between the cases and controls, these results are unlikely to be significant, at least when considered as Mendelian causes for the FGR. Although promoter methylation is generally associated with reduced gene expression and methylation of a gene itself typically with increased gene expression (19), DNA methylation differs pre- and postnatally both quantitatively and in terms of CpG vs non-CpG methylation in utero. Hypermethylation does not necessarily lead to decreased transcription and hypomethylation to increased transcription. Therefore, understanding of the consequences of the direction of differential methylation remains uncertain. RNA expression studies should provide insight into the consequences of the hypermethylation and hypomethylation detected in candidate genes in our study. In contrast to the generally more frequent hypermethylation profile in the present cohort, patient SGA3 showed a predominant hypomethylation pattern and an extensively disturbed methylation profile. First, an external epigenetic influence, such as tobacco smoke or infectious pathogens, could be the cause of this observation (20). The essential hypertension of SGA3’s mother may be of importance in this respect. Second, a mutation in genes regulating DNA methylation, such as the DNMTs and TETs (21), could theoretically cause widespread DNA methylation disturbances. Also, maternal mutations in so-called “maternal-effect genes,” such as NLRP5, NLRP7 I, and KHDC3L (22), can cause multilocus imprinting disturbances in their offspring, usually resulting in hypomethylation at multiple loci and seen primarily in female offspring (23). We were unable to detect any of these mutations in SGA3. Lastly, disturbed methylation of 15 genes known to be involved in (regulation of) DNA methylation was present in SGA3. The abnormal methylation of DNMT1, DNMT3B, TET1, UHRF1, and ZFP57 may be of special interest, as abnormal methylation of one of these may have had an extensive subsequent effect. The evaluation of exome sequencing, targeted for genes associated with disorders in which a low birth weight is part of the phenotype, uncovered 37 sequence variants in 35 genes. When evaluating the results, we took into account the variability of pattern of inheritance of variants in a single gene and the possibility that variants may not act in a Mendelian manner but can also act in a polygenic or multifactorial manner. Each reported finding may be involved in FGR because, if mutated, these genes can cause malformation syndromes, skeletal dysplasias, and endocrine disorders. Our analyses showed several sequence variants that are plausible candidates for causality, whereas the majority remained of uncertain significance. One strong candidate is the splice acceptor variant in SOS1 (c.3347-1G>A). This variant has been described previously in patients with Noonan syndrome and IUGR (24). Given the earlier reported patients and the low MAF, this variant deserves further investigation to confirm its potential pathogenic nature. When the different sequence variants for potential pathogenicity are assessed, it is important to stress that, in our opinion, it is likely that the cause of FGR will not be monogenic but rather polygenic in most patients. This implies that there will be changes in either one or more genes and/or in the methylation pattern, such that each individually will not cause a major health problem (and will be present in controls as well). In this multifactorial model, combinations of a series of such changes lead to disturbed growth. The size of the current study is too limited to reveal complex interactions; however, to provide comparison with future studies, sequence and differential methylation data are available in the Supplemental Tables. We found de novo mutations in two untargeted genes. MTUS1 is a tumor suppressor gene controlling cell proliferation; however, no function interfering with fetal growth is known, so the meaning of this variant remains uncertain. The protein encoded by LZTS2 acts as a tumor suppressor and is involved in regulating embryonic development by the Wnt signaling pathway. Homozygous mutations were found in four genes, all of uncertain significance. One patient was homozygous for the p.Val316Ala variant in MTHFD1. MTHFD1 is important for folate metabolism and embryonic development, and a mutation in this gene has been associated with fetal hypotrophy. Given the contradictory classifications by the prediction programs, the significance of this variant is uncertain. Compound heterozygous variants of interest were found in two targeted genes and one untargeted gene, all likely benign or of uncertain significance. Our results do not indicate the presence of a single, unifying theme explaining the dysregulation of fetal growth and confirm previous findings that growth in utero is influenced by a large number of genes. We demonstrated that there was no predominant type of genetic abnormality present in SGA newborns; CNVs, methylation disturbances, and sequence variants may all contribute in part to the phenotype. In 19 patients, combinations of a CNV, (multiple) sequence variants, and (multiple) methylation disturbances were present. Each of these will require detailed and sophisticated investigations to better understand their significance. Our results mirror those of a similar study in children with postnatal growth failure (25), which used a similar approach in evaluating variants in genes known to be associated with short stature as well as studying variants in other, untargeted genes (25). The latter authors highlighted the multitude of genetic causes for short stature and the complexity of interpretation of variants and their pathogenicity, which resembled the observations in the current study. We acknowledge the limitations of the current study. The size of our cohort is small, and the power to draw general conclusions is limited. The genetic heterogeneity within the present cohort also appears high. We therefore used an individual-based data analysis approach for the methylation study to enable suitable data analysis. Use of filtering strategies based on population allele frequencies, as in the current study, limited the ability to determine the pathogenicity of WES variants because such data lack individual phenotypic data. Ideally, for future studies our appropriate-for-gestational-age newborns should be sequenced to serve as a control population to allow determining pathogenicity of combinations of variants detected by exome sequencing. Furthermore, we had no access to clinical follow-up data of the presently studied cohort. The large number of variants of uncertain significance inhibited investigating each individual variant extensively; ideally, each variant would require a separate, detailed study. We conclude that CNVs, methylation disturbances, and sequence variants may all contribute in part to prenatal growth failure. This study shows that genetic disturbances in SGA are complex and likely polygenic. The results of these studies in individual patients may have important consequences for care and counseling of patients and their families. Further research is needed to determine whether such genetic workups can become an effective diagnostic approach in SGA newborns. Abbreviations: Abbreviations: array-CGH array comparative genomic hybridization BBB Baby Bio Bank CNV copy number variation CpG C-phosphate-G FGR fetal growth restriction GA gestational age IUGR intrauterine growth restriction MAF minor allele frequency SGA small for gestational age SNP single nucleotide polymorphism WES whole exome sequencing Acknowledgments We thank Jessica Glebbeek, Patricia Schmidt, Sander de Jong, Femke van Sinderen, and Jet Bliek for their help in the laboratory. Financial Support: The current study was made possible by grants from the following institutions: Tergooi Grant for Support of Scientific Research (to S.E.S.), KNAW Ter Meulen Fund (to S.E.S.), Jo Kolk Study Fund (to S.E.S.), ZonMW Rare Disease Network Grant, the Baby Bio Bank (to S.E.S.), Wellbeing of Women (to G.E.M.), and a Biomedical Research Centre Grant (to G.E.M.). All sponsors are gratefully acknowledged. Sponsors had no involvement in any stage of the study design, data collection, data analyses, or interpretation of the data or in the decision to publish the study results. Disclosure Summary: The authors have nothing to disclose. References 1. Clayton PE, Cianfarani S, Czernichow P, Johannsson G, Rapaport R, Rogol A. Management of the child born small for gestational age through to adulthood: a consensus statement of the International Societies of Pediatric Endocrinology and the Growth Hormone Research Society. J Clin Endocrinol Metab . 2007; 92( 3): 804– 810. 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Journal of Clinical Endocrinology and Metabolism – Oxford University Press
Published: Mar 1, 2018
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