title: The benefits of selecting phenotype-specific variants for applications of mixed models in genomics.
abstract: Applications of linear mixed models (LMMs) to problems in genomics include phenotype prediction, correction for confounding in genome-wide association studies, estimation of narrow sense heritability, and testing sets of variants (e.g., rare variants) for association. In each of these applications, the LMM uses a genetic similarity matrix, which encodes the pairwise similarity between every two individuals in a cohort. Although ideally these similarities would be estimated using strictly variants relevant to the given phenotype, the identity of such variants is typically unknown. Consequently, relevant variants are excluded and irrelevant variants are included, both having deleterious effects. For each application of the LMM, we review known effects and describe new effects showing how variable selection can be used to mitigate them.
authors: eScience Group, Microsoft Research, Los Angeles, CA 90024, United States. lippert@microsoft.com
authors: Christoph
authors: C
authors: Lippert
authors: Gerald
authors: G
authors: Quon
authors: Eun Yong
authors: EY
authors: Kang
authors: Carl M
authors: CM
authors: Kadie
authors: Jennifer
authors: J
authors: Listgarten
authors: David
authors: D
authors: Heckerman
mesh_terms: D014644:Genetic Variation; D055106:Genome-Wide Association Study; D023281:Genomics; D006801:Humans; D008962:Models, Theoretical; D010641:Phenotype; D019655:Quantitative Trait, Heritable; D012641:Selection, Genetic
references: Genetica. 1995;96(1-2):3-12
references: 7607457
references: PLoS Genet. 2011 Apr;7(4):e1002051
references: 21552331
references: Nat Genet. 2013 May;45(5):470-1
references: 23619783
references: Hum Genet. 2012 Oct;131(10):1655-64
references: 22821350
references: PLoS Genet. 2012;8(11):e1003032
references: 23144628
references: Genet Sel Evol. 2009 Dec 31;41:56
references: 20043835
references: Genet Epidemiol. 2008 Apr;32(3):215-26
references: 18161052
references: Nat Methods. 2011 Sep 04;8(10):833-5
references: 21892150
references: Nat Genet. 2010 Apr;42(4):348-54
references: 20208533
references: Nucleic Acids Res. 2013 Feb 1;41(4):2095-104
references: 23303775
references: Genetics. 2001 Apr;157(4):1819-29
references: 11290733
references: Nat Rev Genet. 2013 Jan;14(1):1-2
references: 23165185
references: Genet Epidemiol. 2012 Apr;36(3):214-24
references: 22714935
references: Am J Hum Genet. 2012 Dec 7;91(6):1011-21
references: 23217325
references: Nat Genet. 2010 Jul;42(7):565-9
references: 20562875
references: Nat Genet. 2006 Feb;38(2):203-8
references: 16380716
references: Proc Natl Acad Sci U S A. 2012 Jan 24;109(4):1193-8
references: 22223662
references: Eur J Hum Genet. 2011 Jul;19(7):807-12
references: 21407268
references: PLoS Genet. 2010 May 13;6(5):e1000952
references: 20485568
references: Nat Genet. 2012 Feb 05;44(3):243-6
references: 22306651
references: Nat Methods. 2012 May 30;9(6):525-6
references: 22669648
references: Genet Res (Camb). 2009 Feb;91(1):47-60
references: 19220931
references: Biometrics. 1999 Dec;55(4):997-1004
references: 11315092
references: Genetics. 2008 Mar;178(3):1709-23
references: 18385116
references: Am J Hum Genet. 2011 Jul 15;89(1):82-93
references: 21737059
references: Bioinformatics. 2013 Jun 15;29(12):1526-33
references: 23599503