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( WanX. et al (2010) BOOST: a fast approach to detecting gene-gene interactions in genome-wide case-control studies. Am. J. Hum. Genet., 87, 325–340.20817139)
WanX. et al (2010) BOOST: a fast approach to detecting gene-gene interactions in genome-wide case-control studies. Am. J. Hum. Genet., 87, 325–340.20817139WanX. et al (2010) BOOST: a fast approach to detecting gene-gene interactions in genome-wide case-control studies. Am. J. Hum. Genet., 87, 325–340.20817139, WanX. et al (2010) BOOST: a fast approach to detecting gene-gene interactions in genome-wide case-control studies. Am. J. Hum. Genet., 87, 325–340.20817139
( KangH.M. et al (2008) Efficient control of population structure in model organism association mapping. Genetics, 178, 1709–1723.18385116)
KangH.M. et al (2008) Efficient control of population structure in model organism association mapping. Genetics, 178, 1709–1723.18385116KangH.M. et al (2008) Efficient control of population structure in model organism association mapping. Genetics, 178, 1709–1723.18385116, KangH.M. et al (2008) Efficient control of population structure in model organism association mapping. Genetics, 178, 1709–1723.18385116
( XuS. (2013) Mapping quantitative trait loci by controlling polygenic background effects. Genetics, 195, 1209–1222.24077303)
XuS. (2013) Mapping quantitative trait loci by controlling polygenic background effects. Genetics, 195, 1209–1222.24077303XuS. (2013) Mapping quantitative trait loci by controlling polygenic background effects. Genetics, 195, 1209–1222.24077303, XuS. (2013) Mapping quantitative trait loci by controlling polygenic background effects. Genetics, 195, 1209–1222.24077303
A. Upton, O. Trelles, J. Cornejo‐García, J. Perkins (2016)
Review: High-performance computing to detect epistasis in genome scale data setsBriefings in bioinformatics, 17 3
( GianolaD. et al (2009) Additive genetic variability and the Bayesian alphabet. Genetics, 183, 347–363.19620397)
GianolaD. et al (2009) Additive genetic variability and the Bayesian alphabet. Genetics, 183, 347–363.19620397GianolaD. et al (2009) Additive genetic variability and the Bayesian alphabet. Genetics, 183, 347–363.19620397, GianolaD. et al (2009) Additive genetic variability and the Bayesian alphabet. Genetics, 183, 347–363.19620397
J. Jarvis, J. Cheverud (2011)
Mapping the Epistatic Network Underlying Murine Reproductive Fatpad VariationGenetics, 187
( ChangC.C. et al (2015) Second-generation PLINK: rising to the challenge of larger and richer datasets. GigaScience, 4, 7.25722852)
ChangC.C. et al (2015) Second-generation PLINK: rising to the challenge of larger and richer datasets. GigaScience, 4, 7.25722852ChangC.C. et al (2015) Second-generation PLINK: rising to the challenge of larger and richer datasets. GigaScience, 4, 7.25722852, ChangC.C. et al (2015) Second-generation PLINK: rising to the challenge of larger and richer datasets. GigaScience, 4, 7.25722852
( FisherR.A. (1918) The correlation between relatives on the supposition of Mendelian. Philos. Trans. Royal Soc. Edinburgh, 52, 399–433.)
FisherR.A. (1918) The correlation between relatives on the supposition of Mendelian. Philos. Trans. Royal Soc. Edinburgh, 52, 399–433.FisherR.A. (1918) The correlation between relatives on the supposition of Mendelian. Philos. Trans. Royal Soc. Edinburgh, 52, 399–433., FisherR.A. (1918) The correlation between relatives on the supposition of Mendelian. Philos. Trans. Royal Soc. Edinburgh, 52, 399–433.
A. Mäki-Tanila, W. Hill (2014)
Influence of Gene Interaction on Complex Trait Variation with Multilocus ModelsGenetics, 198
Futao Zhang, E. Boerwinkle, M. Xiong (2014)
Epistasis analysis for quantitative traits by functional regression modelGenome Research, 24
( DoerksT. (2002) Systematic identification of novel protein domain families associated with nuclear functions. Genome Res., 12, 47–56.11779830)
DoerksT. (2002) Systematic identification of novel protein domain families associated with nuclear functions. Genome Res., 12, 47–56.11779830DoerksT. (2002) Systematic identification of novel protein domain families associated with nuclear functions. Genome Res., 12, 47–56.11779830, DoerksT. (2002) Systematic identification of novel protein domain families associated with nuclear functions. Genome Res., 12, 47–56.11779830
( LippertC. et al (2011) FaST linear mixed models for genome-wide association studies. Nat. Methods, 8, 833–835.21892150)
LippertC. et al (2011) FaST linear mixed models for genome-wide association studies. Nat. Methods, 8, 833–835.21892150LippertC. et al (2011) FaST linear mixed models for genome-wide association studies. Nat. Methods, 8, 833–835.21892150, LippertC. et al (2011) FaST linear mixed models for genome-wide association studies. Nat. Methods, 8, 833–835.21892150
C. Lippert, J. Listgarten, Y. Liu, C. Kadie, R. Davidson, D. Heckerman (2011)
FaST linear mixed models for genome-wide association studiesNature Methods, 8
( VanRadenP.M. (2008) Efficient methods to compute genomic predictions. J. Dairy Sci., 91, 4414–4423.18946147)
VanRadenP.M. (2008) Efficient methods to compute genomic predictions. J. Dairy Sci., 91, 4414–4423.18946147VanRadenP.M. (2008) Efficient methods to compute genomic predictions. J. Dairy Sci., 91, 4414–4423.18946147, VanRadenP.M. (2008) Efficient methods to compute genomic predictions. J. Dairy Sci., 91, 4414–4423.18946147
Thierry Schüpbach, I. Xenarios, S. Bergmann, K. Kapur (2010)
FastEpistasis: a high performance computing solution for quantitative trait epistasisBioinformatics, 26
H. Kang, N. Zaitlen, C. Wade, Andrew Kirby, D. Heckerman, M. Daly, E. Eskin (2008)
Efficient Control of Population Structure in Model Organism Association MappingGenetics, 178
S. Gabriel, S. Schaffner, Huy Nguyen, Jamie Moore, J. Roy, B. Blumenstiel, J. Higgins, M. Defelice, Amy Lochner, M. Faggart, S. Liu-Cordero, C. Rotimi, A. Adeyemo, R. Cooper, R. Ward, E. Lander, M. Daly, D. Altshuler (2002)
The Structure of Haplotype Blocks in the Human GenomeScience, 296
T. Meuwissen, B. Hayes, M. Goddard (2001)
Prediction of total genetic value using genome-wide dense marker maps.Genetics, 157 4
Xu (2003)
789Genetics, 163
( YangJ. et al (2014) Advantages and pitfalls in the application of mixed-model association methods. Nat. Genet., 46, 100–106.24473328)
YangJ. et al (2014) Advantages and pitfalls in the application of mixed-model association methods. Nat. Genet., 46, 100–106.24473328YangJ. et al (2014) Advantages and pitfalls in the application of mixed-model association methods. Nat. Genet., 46, 100–106.24473328, YangJ. et al (2014) Advantages and pitfalls in the application of mixed-model association methods. Nat. Genet., 46, 100–106.24473328
J. Bloom, Iulia Kotenko, Meru Sadhu, S. Treusch, F. Albert, L. Kruglyak (2015)
Genetic interactions contribute less than additive effects to quantitative trait variation in yeastNature Communications, 6
C. Lippert, J. Listgarten, R. Davidson, Jeff Baxter, Hoifung Poon, C. Kadie, D. Heckerman (2013)
CORRIGENDUM: An Exhaustive Epistatic SNP Association Analysis on Expanded Wellcome Trust DataScientific Reports, 3
( StrandenI., GarrickD.J. (2009) Technical note: derivation of equivalent computing algorithms for genomic predictions and reliabilities of animal merit. J. Dairy Sci., 92, 2971–2975.19448030)
StrandenI., GarrickD.J. (2009) Technical note: derivation of equivalent computing algorithms for genomic predictions and reliabilities of animal merit. J. Dairy Sci., 92, 2971–2975.19448030StrandenI., GarrickD.J. (2009) Technical note: derivation of equivalent computing algorithms for genomic predictions and reliabilities of animal merit. J. Dairy Sci., 92, 2971–2975.19448030, StrandenI., GarrickD.J. (2009) Technical note: derivation of equivalent computing algorithms for genomic predictions and reliabilities of animal merit. J. Dairy Sci., 92, 2971–2975.19448030
( SuG. et al (2012) Estimating additive and non-additive genetic variances and predicting genetic merits using genome-wide dense single nucleotide polymorphism markers. PloS One, 7, e45293.23028912)
SuG. et al (2012) Estimating additive and non-additive genetic variances and predicting genetic merits using genome-wide dense single nucleotide polymorphism markers. PloS One, 7, e45293.23028912SuG. et al (2012) Estimating additive and non-additive genetic variances and predicting genetic merits using genome-wide dense single nucleotide polymorphism markers. PloS One, 7, e45293.23028912, SuG. et al (2012) Estimating additive and non-additive genetic variances and predicting genetic merits using genome-wide dense single nucleotide polymorphism markers. PloS One, 7, e45293.23028912
( YuJ. et al (2006) A unified mixed-model method for association mapping that accounts for multiple levels of relatedness. Nat. Genet., 38, 203–208.16380716)
YuJ. et al (2006) A unified mixed-model method for association mapping that accounts for multiple levels of relatedness. Nat. Genet., 38, 203–208.16380716YuJ. et al (2006) A unified mixed-model method for association mapping that accounts for multiple levels of relatedness. Nat. Genet., 38, 203–208.16380716, YuJ. et al (2006) A unified mixed-model method for association mapping that accounts for multiple levels of relatedness. Nat. Genet., 38, 203–208.16380716
T. Doerks, R. Copley, J. Schultz, C. Ponting, P. Bork (2002)
Systematic identification of novel protein domain families associated with nuclear functions.Genome research, 12 1
( JiangY., ReifJ.C. (2015) Modeling epistasis in genomic selection. Genetics, 201, 759–768.26219298)
JiangY., ReifJ.C. (2015) Modeling epistasis in genomic selection. Genetics, 201, 759–768.26219298JiangY., ReifJ.C. (2015) Modeling epistasis in genomic selection. Genetics, 201, 759–768.26219298, JiangY., ReifJ.C. (2015) Modeling epistasis in genomic selection. Genetics, 201, 759–768.26219298
Xiaolei Liu, Meng Huang, B. Fan, E. Buckler, Zhiwu Zhang (2016)
Iterative Usage of Fixed and Random Effect Models for Powerful and Efficient Genome-Wide Association StudiesPLoS Genetics, 12
Lang Wu, A. Goldstein, Kai Yu, Xiaohong Yang, K. Rabe, A. Arslan, F. Canzian, B. Wolpin, R. Stolzenberg-Solomon, L. Amundadottir, G. Petersen (2014)
Variants Associated with Susceptibility to Pancreatic Cancer and Melanoma Do Not Reciprocally Affect RiskCancer Epidemiology, Biomarkers & Prevention, 23
( MeuwissenT.H. et al (2001) Prediction of total genetic value using genome-wide dense marker maps. Genetics, 157, 1819–1829.11290733)
MeuwissenT.H. et al (2001) Prediction of total genetic value using genome-wide dense marker maps. Genetics, 157, 1819–1829.11290733MeuwissenT.H. et al (2001) Prediction of total genetic value using genome-wide dense marker maps. Genetics, 157, 1819–1829.11290733, MeuwissenT.H. et al (2001) Prediction of total genetic value using genome-wide dense marker maps. Genetics, 157, 1819–1829.11290733
Xiaoping Zhou, M. Stephens (2012)
Genome-wide Efficient Mixed Model Analysis for Association StudiesNature genetics, 44
Lippert (2013)
1099.Sci. Rep, 3
( ShenX. et al (2013) A novel generalized ridge regression method for quantitative genetics. Genetics, 193, 1255–1268.23335338)
ShenX. et al (2013) A novel generalized ridge regression method for quantitative genetics. Genetics, 193, 1255–1268.23335338ShenX. et al (2013) A novel generalized ridge regression method for quantitative genetics. Genetics, 193, 1255–1268.23335338, ShenX. et al (2013) A novel generalized ridge regression method for quantitative genetics. Genetics, 193, 1255–1268.23335338
C. Henderson (1985)
Best Linear Unbiased Prediction of Nonadditive Genetic Merits in Noninbred PopulationsJournal of Animal Science, 60
D. Gianola, G. Campos, W. Hill, E. Manfredi, R. Fernando (2009)
Additive Genetic Variability and the Bayesian AlphabetGenetics, 183
Jian Yang, N. Zaitlen, M. Goddard, P. Visscher, A. Price (2014)
Advantages and pitfalls in the application of mixed-model association methodsNature Genetics, 46
Shizhong Xu (2003)
Estimating polygenic effects using markers of the entire genome.Genetics, 163 2
Yong Jiang, J. Reif (2015)
Modeling Epistasis in Genomic SelectionGenetics, 201
P. Burton, D. Clayton, L. Cardon, N. Craddock, P. Deloukas, A. Duncanson, D. Kwiatkowski, M. McCarthy, W. Ouwehand, N. Samani, J. Todd, P. Donnelly, J. Barrett, D. Davison, D. Easton, David Evans, H. Leung, J. Marchini, A. Morris, C. Spencer, M. Tobin, A. Attwood, J. Boorman, B. Cant, Ursula Everson, Judith Hussey, J. Jolley, A. Knight, K. Koch, Elizabeth Meech, S. Nutland, C. Prowse, H. Stevens, N. Taylor, G. Walters, N. Walker, N. Watkins, T. Winzer, Richard Jones, W. McArdle, S. Ring, D. Strachan, M. Pembrey, G. Breen, D. Clair, S. Caesar, K. Gordon-Smith, L. Jones, C. Fraser, E. Green, D. Grozeva, M. Hamshere, P. Holmans, I. Jones, G. Kirov, V. Moskvina, I. Nikolov, M. O’Donovan, M. Owen, D. Collier, A. Elkin, A. Farmer, R. Williamson, P. McGuffin, A. Young, I. Ferrier, S. Ball, A. Balmforth, J. Barrett, D. Bishop, M. Iles, A. Maqbool, N. Yuldasheva, A. Hall, P. Braund, R. Dixon, M. Mangino, S. Stevens, J. Thompson, F. Bredin, M. Tremelling, M. Parkes, H. Drummond, C. Lees, E. Nimmo, J. Satsangi, S. Fisher, A. Forbes, C. Lewis, Clive Onnie, N. Prescott, J. Sanderson, C. Mathew, J. Barbour, M. Mohiuddin, C. Todhunter, J. Mansfield, T. Ahmad, Fraser Cummings, D. Jewell, J. Webster, Morris Brown, G. Lathrop, J. Connell, A. Dominiczak, C. Marcano, B. Burke, R. Dobson, J. Gungadoo, Kate Lee, P. Munroe, S. Newhouse, Abiodun Onipinla, C. Wallace, M. Xue, M. Caulfield, M. Farrall, A. Barton, I. Bruce, Hannah Donovan, S. Eyre, Paul Gilbert, S. Hider, A. Hinks, S. John, C. Potter, A. Silman, D. Symmons, W. Thomson, Jane Worthington, D. Dunger, B. Widmer, T. Frayling, R. Freathy, H. Lango, J. Perry, B. Shields, M. Weedon, A. Hattersley, G. Hitman, M. Walker, K. Elliott, C. Groves, C. Lindgren, N. Rayner, N. Timpson, E. Zeggini, M. Newport, G. Sirugo, Emily Lyons, F. Vannberg, A. Hill, L. Bradbury, C. Farrar, J. Pointon, Paul Wordsworth, M. Brown, J. Franklyn, J. Heward, M. Simmonds, S. Gough, S. Seal, M. Stratton, N. Rahman, M. Ban, A. Goris, S. Sawcer, Alastair Compston, D. Conway, M. Jallow, K. Rockett, S. Bumpstead, Amy Chaney, K. Downes, Mohammed Ghori, R. Gwilliam, S. Hunt, M. Inouye, Andrew Keniry, E. King, R. McGinnis, Simon Potter, R. Ravindrarajah, P. Whittaker, Claire Widden, D. Withers, Niall Cardin, T. Ferreira, Joanne Pereira-Gale, Ingileif Hallgrímsdóttir, Bryan Howie, Z. Su, Y. Teo, Damjan Vukcevic, D. Bentley, A. Compston (2007)
Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controlsNature, 447
( GabrielS.B. et al (2002) The structure of haplotype blocks in the human genome. Science, 296, 2225–2229.12029063)
GabrielS.B. et al (2002) The structure of haplotype blocks in the human genome. Science, 296, 2225–2229.12029063GabrielS.B. et al (2002) The structure of haplotype blocks in the human genome. Science, 296, 2225–2229.12029063, GabrielS.B. et al (2002) The structure of haplotype blocks in the human genome. Science, 296, 2225–2229.12029063
D. Speed, D. Balding (2014)
MultiBLUP: improved SNP-based prediction for complex traitsGenome Research, 24
( UptonA. et al (2016) Review: high-performance computing to detect epistasis in genome scale data sets. Brief. Bioinf., 17, 368–379.)
UptonA. et al (2016) Review: high-performance computing to detect epistasis in genome scale data sets. Brief. Bioinf., 17, 368–379.UptonA. et al (2016) Review: high-performance computing to detect epistasis in genome scale data sets. Brief. Bioinf., 17, 368–379., UptonA. et al (2016) Review: high-performance computing to detect epistasis in genome scale data sets. Brief. Bioinf., 17, 368–379.
H. Kang, J. Sul, S. Service, N. Zaitlen, Sit-yee Kong, N. Freimer, C. Sabatti, E. Eskin (2010)
Variance component model to account for sample structure in genome-wide association studiesNature Genetics, 42
A. Legarra, I. Aguilar, I. Misztal (2009)
A relationship matrix including full pedigree and genomic information.Journal of dairy science, 92 9
( ZhangF. et al (2014) Epistasis analysis for quantitative traits by functional regression model. Genome Res., 24, 989–998.24803592)
ZhangF. et al (2014) Epistasis analysis for quantitative traits by functional regression model. Genome Res., 24, 989–998.24803592ZhangF. et al (2014) Epistasis analysis for quantitative traits by functional regression model. Genome Res., 24, 989–998.24803592, ZhangF. et al (2014) Epistasis analysis for quantitative traits by functional regression model. Genome Res., 24, 989–998.24803592
Simon Forsberg, J. Bloom, Meru Sadhu, L. Kruglyak, Ö. Carlborg (2016)
Accounting for genetic interactions improves modeling of individual quantitative trait phenotypes in yeastNature genetics, 49
G. Su, O. Christensen, T. Ostersen, M. Henryon, M. Lund (2012)
Estimating Additive and Non-Additive Genetic Variances and Predicting Genetic Merits Using Genome-Wide Dense Single Nucleotide Polymorphism MarkersPLoS ONE, 7
O. Christensen, M. Lund (2010)
Genomic prediction when some animals are not genotypedGenetics, Selection, Evolution : GSE, 42
Fisher (1918)
399
( HendersonC. (1985) Best linear unbiased prediction of nonadditive genetic merits. J. Anim. Sci., 60, 111–117.)
HendersonC. (1985) Best linear unbiased prediction of nonadditive genetic merits. J. Anim. Sci., 60, 111–117.HendersonC. (1985) Best linear unbiased prediction of nonadditive genetic merits. J. Anim. Sci., 60, 111–117., HendersonC. (1985) Best linear unbiased prediction of nonadditive genetic merits. J. Anim. Sci., 60, 111–117.
H. Bickeböller, J. Bailey, J. Beyene, R. Cantor, H. Cordell, R. Culverhouse, C. Engelman, D. Fardo, Saurabh Ghosh, I. König, J. Bermejo, P. Melton, Stephanie Santorico, G. Satten, Lei Sun, N. Tintle, A. Ziegler, J. Maccluer, L. Almasy (2014)
Genetic Analysis Workshop 18: Methods and strategies for analyzing human sequence and phenotype data in members of extended pedigreesBMC Proceedings, 8
Xia Shen, Moudud Alam, F. Fikse, L. Rönnegård (2013)
A Novel Generalized Ridge Regression Method for Quantitative GeneticsGenetics, 193
(1949)
Estimation of changes in herd environment
( ZhangZ. et al (2010) Mixed linear model approach adapted for genome-wide association studies. Nat. Genet., 42, 355–360.20208535)
ZhangZ. et al (2010) Mixed linear model approach adapted for genome-wide association studies. Nat. Genet., 42, 355–360.20208535ZhangZ. et al (2010) Mixed linear model approach adapted for genome-wide association studies. Nat. Genet., 42, 355–360.20208535, ZhangZ. et al (2010) Mixed linear model approach adapted for genome-wide association studies. Nat. Genet., 42, 355–360.20208535
T. Mackay, J. Moore (2014)
Why epistasis is important for tackling complex human disease geneticsGenome Medicine, 6
( SpeedD., BaldingD.J. (2014) MultiBLUP: improved SNP-based prediction for complex traits. Genome Res., 24, 1550–1557.24963154)
SpeedD., BaldingD.J. (2014) MultiBLUP: improved SNP-based prediction for complex traits. Genome Res., 24, 1550–1557.24963154SpeedD., BaldingD.J. (2014) MultiBLUP: improved SNP-based prediction for complex traits. Genome Res., 24, 1550–1557.24963154, SpeedD., BaldingD.J. (2014) MultiBLUP: improved SNP-based prediction for complex traits. Genome Res., 24, 1550–1557.24963154
Author Henderson (1975)
Best linear unbiased estimation and prediction under a selection model.Biometrics, 31 2
L. Penrose
THE CORRELATION BETWEEN RELATIVES ON THE SUPPOSITION OF MENDELIAN INHERITANCE
( WuL. et al (2014) Variants associated with susceptibility to pancreatic cancer and melanoma do not reciprocally affect risk. Cancer Epidemiol. Biomark. Prevent. Publ. Am. Assoc. Cancer Res. Cosponsored Am. Soc. Prevent. Oncol., 23, 1121–1124.)
WuL. et al (2014) Variants associated with susceptibility to pancreatic cancer and melanoma do not reciprocally affect risk. Cancer Epidemiol. Biomark. Prevent. Publ. Am. Assoc. Cancer Res. Cosponsored Am. Soc. Prevent. Oncol., 23, 1121–1124.WuL. et al (2014) Variants associated with susceptibility to pancreatic cancer and melanoma do not reciprocally affect risk. Cancer Epidemiol. Biomark. Prevent. Publ. Am. Assoc. Cancer Res. Cosponsored Am. Soc. Prevent. Oncol., 23, 1121–1124., WuL. et al (2014) Variants associated with susceptibility to pancreatic cancer and melanoma do not reciprocally affect risk. Cancer Epidemiol. Biomark. Prevent. Publ. Am. Assoc. Cancer Res. Cosponsored Am. Soc. Prevent. Oncol., 23, 1121–1124.
Jianming Yu, G. Pressoir, W. Briggs, I. Bi, M. Yamasaki, J. Doebley, M. McMullen, B. Gaut, D. Nielsen, J. Holland, S. Kresovich, E. Buckler (2006)
A unified mixed-model method for association mapping that accounts for multiple levels of relatednessNature Genetics, 38
Henderson (1949)
706J. Dairy Sci, 32
( ForsbergS.K. et al (2017) Accounting for genetic interactions improves modeling of individual quantitative trait phenotypes in yeast. Nat. Genet., 49, 497–503.28250458)
ForsbergS.K. et al (2017) Accounting for genetic interactions improves modeling of individual quantitative trait phenotypes in yeast. Nat. Genet., 49, 497–503.28250458ForsbergS.K. et al (2017) Accounting for genetic interactions improves modeling of individual quantitative trait phenotypes in yeast. Nat. Genet., 49, 497–503.28250458, ForsbergS.K. et al (2017) Accounting for genetic interactions improves modeling of individual quantitative trait phenotypes in yeast. Nat. Genet., 49, 497–503.28250458
( KruijerW. et al (2015) Marker-based estimation of heritability in immortal populations. Genetics, 199, 379–398.25527288)
KruijerW. et al (2015) Marker-based estimation of heritability in immortal populations. Genetics, 199, 379–398.25527288KruijerW. et al (2015) Marker-based estimation of heritability in immortal populations. Genetics, 199, 379–398.25527288, KruijerW. et al (2015) Marker-based estimation of heritability in immortal populations. Genetics, 199, 379–398.25527288
( Maki-TanilaA., HillW.G. (2014) Influence of gene interaction on complex trait variation with multilocus models. Genetics, 198, 355–367.24990992)
Maki-TanilaA., HillW.G. (2014) Influence of gene interaction on complex trait variation with multilocus models. Genetics, 198, 355–367.24990992Maki-TanilaA., HillW.G. (2014) Influence of gene interaction on complex trait variation with multilocus models. Genetics, 198, 355–367.24990992, Maki-TanilaA., HillW.G. (2014) Influence of gene interaction on complex trait variation with multilocus models. Genetics, 198, 355–367.24990992
W. Kruijer, M. Boer, M. Malosetti, P. Flood, B. Engel, Rik Kooke, Joost Keurentjes, F. Eeuwijk (2014)
Marker-Based Estimation of Heritability in Immortal PopulationsGenetics, 199
( LiuX. et al (2016) Iterative usage of fixed and random effect models for powerful and efficient genome-wide association studies. PLoS Genet., 12, e1005767.26828793)
LiuX. et al (2016) Iterative usage of fixed and random effect models for powerful and efficient genome-wide association studies. PLoS Genet., 12, e1005767.26828793LiuX. et al (2016) Iterative usage of fixed and random effect models for powerful and efficient genome-wide association studies. PLoS Genet., 12, e1005767.26828793, LiuX. et al (2016) Iterative usage of fixed and random effect models for powerful and efficient genome-wide association studies. PLoS Genet., 12, e1005767.26828793
X. Wan, Can Yang, Qiang Yang, H. Xue, Xiaodan Fan, N. Tang, Weichuan Yu (2010)
BOOST: A fast approach to detecting gene-gene interactions in genome-wide case-control studiesAmerican journal of human genetics, 87 3
( ZhouX., StephensM. (2012) Genome-wide efficient mixed-model analysis for association studies. Nat. Genet., 44, 821–824.22706312)
ZhouX., StephensM. (2012) Genome-wide efficient mixed-model analysis for association studies. Nat. Genet., 44, 821–824.22706312ZhouX., StephensM. (2012) Genome-wide efficient mixed-model analysis for association studies. Nat. Genet., 44, 821–824.22706312, ZhouX., StephensM. (2012) Genome-wide efficient mixed-model analysis for association studies. Nat. Genet., 44, 821–824.22706312
Christopher Chang, C. Chow, L. Tellier, S. Vattikuti, S. Purcell, James Lee (2014)
Second-generation PLINK: rising to the challenge of larger and richer datasetsGigaScience, 4
Shizhong Xu (2013)
Mapping Quantitative Trait Loci by Controlling Polygenic Background EffectsGenetics, 195
( ChristensenO.F., LundM.S. (2010) Genomic prediction when some animals are not genotyped. Genet. Select. Evol. GSE, 42, 2.)
ChristensenO.F., LundM.S. (2010) Genomic prediction when some animals are not genotyped. Genet. Select. Evol. GSE, 42, 2.ChristensenO.F., LundM.S. (2010) Genomic prediction when some animals are not genotyped. Genet. Select. Evol. GSE, 42, 2., ChristensenO.F., LundM.S. (2010) Genomic prediction when some animals are not genotyped. Genet. Select. Evol. GSE, 42, 2.
P. VanRaden (2008)
Efficient methods to compute genomic predictions.Journal of dairy science, 91 11
( LegarraA. et al (2009) A relationship matrix including full pedigree and genomic information. J. Dairy Sci., 92, 4656–4663.19700729)
LegarraA. et al (2009) A relationship matrix including full pedigree and genomic information. J. Dairy Sci., 92, 4656–4663.19700729LegarraA. et al (2009) A relationship matrix including full pedigree and genomic information. J. Dairy Sci., 92, 4656–4663.19700729, LegarraA. et al (2009) A relationship matrix including full pedigree and genomic information. J. Dairy Sci., 92, 4656–4663.19700729
( BickebollerH. et al (2014) Genetic Analysis Workshop 18: methods and strategies for analyzing human sequence and phenotype data in members of extended pedigrees. BMC Proc., 8, S1.)
BickebollerH. et al (2014) Genetic Analysis Workshop 18: methods and strategies for analyzing human sequence and phenotype data in members of extended pedigrees. BMC Proc., 8, S1.BickebollerH. et al (2014) Genetic Analysis Workshop 18: methods and strategies for analyzing human sequence and phenotype data in members of extended pedigrees. BMC Proc., 8, S1., BickebollerH. et al (2014) Genetic Analysis Workshop 18: methods and strategies for analyzing human sequence and phenotype data in members of extended pedigrees. BMC Proc., 8, S1.
( HendersonC. (1949) Estimation of changes in herd environment. J. Dairy Sci, 32, 706–715.)
HendersonC. (1949) Estimation of changes in herd environment. J. Dairy Sci, 32, 706–715.HendersonC. (1949) Estimation of changes in herd environment. J. Dairy Sci, 32, 706–715., HendersonC. (1949) Estimation of changes in herd environment. J. Dairy Sci, 32, 706–715.
( JarvisJ.P., CheverudJ.M. (2011) Mapping the epistatic network underlying murine reproductive fatpad variation. Genetics, 187, 597–610.21115969)
JarvisJ.P., CheverudJ.M. (2011) Mapping the epistatic network underlying murine reproductive fatpad variation. Genetics, 187, 597–610.21115969JarvisJ.P., CheverudJ.M. (2011) Mapping the epistatic network underlying murine reproductive fatpad variation. Genetics, 187, 597–610.21115969, JarvisJ.P., CheverudJ.M. (2011) Mapping the epistatic network underlying murine reproductive fatpad variation. Genetics, 187, 597–610.21115969
( XuS. (2003) Estimating polygenic effects using markers of the entire genome. Genetics, 163, 789–801.12618414)
XuS. (2003) Estimating polygenic effects using markers of the entire genome. Genetics, 163, 789–801.12618414XuS. (2003) Estimating polygenic effects using markers of the entire genome. Genetics, 163, 789–801.12618414, XuS. (2003) Estimating polygenic effects using markers of the entire genome. Genetics, 163, 789–801.12618414
( Wellcome Trust Case Control Consortium (2007) Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature, 447, 661–678.17554300)
Wellcome Trust Case Control Consortium (2007) Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature, 447, 661–678.17554300Wellcome Trust Case Control Consortium (2007) Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature, 447, 661–678.17554300, Wellcome Trust Case Control Consortium (2007) Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature, 447, 661–678.17554300
( HendersonC. (1975) Best linear unbiased estimation and prediction under a selection model. Biometrics, 31, 423–447.1174616)
HendersonC. (1975) Best linear unbiased estimation and prediction under a selection model. Biometrics, 31, 423–447.1174616HendersonC. (1975) Best linear unbiased estimation and prediction under a selection model. Biometrics, 31, 423–447.1174616, HendersonC. (1975) Best linear unbiased estimation and prediction under a selection model. Biometrics, 31, 423–447.1174616
( SchupbachT. et al (2010) FastEpistasis: a high performance computing solution for quantitative trait epistasis. Bioinformatics, 26, 1468–1469.20375113)
SchupbachT. et al (2010) FastEpistasis: a high performance computing solution for quantitative trait epistasis. Bioinformatics, 26, 1468–1469.20375113SchupbachT. et al (2010) FastEpistasis: a high performance computing solution for quantitative trait epistasis. Bioinformatics, 26, 1468–1469.20375113, SchupbachT. et al (2010) FastEpistasis: a high performance computing solution for quantitative trait epistasis. Bioinformatics, 26, 1468–1469.20375113
Meuwissen (2001)
1819Genetics, 157
Zhiwu Zhang, E. Ersoz, Chao-Qiang Lai, R. Todhunter, H. Tiwari, M. Gore, Peter Bradbury, Jianming Yu, D. Arnett, J. Ordovás, E. Buckler (2010)
Mixed linear model approach adapted for genome-wide association studiesNature Genetics, 42
( BloomJ.S. et al (2015) Genetic interactions contribute less than additive effects to quantitative trait variation in yeast. Nat. Commun., 6, 8712.26537231)
BloomJ.S. et al (2015) Genetic interactions contribute less than additive effects to quantitative trait variation in yeast. Nat. Commun., 6, 8712.26537231BloomJ.S. et al (2015) Genetic interactions contribute less than additive effects to quantitative trait variation in yeast. Nat. Commun., 6, 8712.26537231, BloomJ.S. et al (2015) Genetic interactions contribute less than additive effects to quantitative trait variation in yeast. Nat. Commun., 6, 8712.26537231
I. Strandén, D. Garrick, D. Garrick (2009)
Technical note: Derivation of equivalent computing algorithms for genomic predictions and reliabilities of animal merit.Journal of dairy science, 92 6
( MackayT.F., MooreJ.H. (2014) Why epistasis is important for tackling complex human disease genetics. Genome Med., 6, 124.25031624)
MackayT.F., MooreJ.H. (2014) Why epistasis is important for tackling complex human disease genetics. Genome Med., 6, 124.25031624MackayT.F., MooreJ.H. (2014) Why epistasis is important for tackling complex human disease genetics. Genome Med., 6, 124.25031624, MackayT.F., MooreJ.H. (2014) Why epistasis is important for tackling complex human disease genetics. Genome Med., 6, 124.25031624
MotivationEpistasis provides a feasible way for probing potential genetic mechanism of complex traits. However, time-consuming computation challenges successful detection of interaction in practice, especially when linear mixed model (LMM) is used to control type I error in the presence of population structure and cryptic relatedness.ResultsA rapid epistatic mixed-model association analysis (REMMA) method was developed to overcome computational limitation. This method first estimates individuals’ epistatic effects by an extended genomic best linear unbiased prediction (EG-BLUP) model with additive and epistatic kinship matrix, then pairwise interaction effects are obtained by linear retransformations of individuals’ epistatic effects. Simulation studies showed that REMMA could control type I error and increase statistical power in detecting epistatic QTNs in comparison with existing LMM-based FaST-LMM. We applied REMMA to two real datasets, a mouse dataset and the Wellcome Trust Case Control Consortium (WTCCC) data. Application to the mouse data further confirmed the performance of REMMA in controlling type I error. For the WTCCC data, we found most epistatic QTNs for type 1 diabetes (T1D) located in a major histocompatibility complex (MHC) region, from which a large interacting network with 12 hub genes (interacting with ten or more genes) was established.Availability and implementationOur REMMA method can be freely accessed at https://github.com/chaoning/REMMA.Supplementary informationSupplementary data are available at Bioinformatics online.
Bioinformatics – Oxford University Press
Published: Jan 12, 2018
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