Genetic Interactions with Sex Make a Relatively Small Contribution to the Heritability of Complex Traits in Mice

The extent to which sex-specific genetic effects contribute to phenotypic variation is largely unknown. We applied a novel Bayesian method, sparse partitioning, to detect gene by sex (GxS) and gene by gene (GxG) quantitative loci (QTLs) in 1,900 outbred heterogeneous stock mice. In an analysis of 55 phenotypes, we detected 16 GxS and 6 GxG QTLs. The increase in the amount of phenotypic variance explained by models including GxS was small, ranging from 0.14% to 4.30%. We conclude that GxS rarely make a large overall contribution to the heritability of phenotypes, however there are cases where these will be individually important.

[1]  Doug Speed,et al.  Improved heritability estimation from genome-wide SNPs. , 2012, American journal of human genetics.

[2]  E. Lander,et al.  The mystery of missing heritability: Genetic interactions create phantom heritability , 2012, Proceedings of the National Academy of Sciences.

[3]  Doug Speed,et al.  Sparse Partitioning: Nonlinear regression with binary or tertiary predictors, with application to association studies , 2011, 1101.0632.

[4]  Bjarni J. Vilhjálmsson,et al.  Genome-wide association study of 107 phenotypes in Arabidopsis thaliana inbred lines , 2010 .

[5]  William J. Astle,et al.  Population Structure and Cryptic Relatedness in Genetic Association Studies , 2009, 1010.4681.

[6]  J. Marchini,et al.  Introduction to the Special Issue: Genome-Wide Association Studies , 2009, 1010.4621.

[7]  Andrew S Greene,et al.  Chromosomal mapping of the genetic basis of hypertension and renal disease in FHH rats. , 2007, American journal of physiology. Renal physiology.

[8]  Nikolaos A Patsopoulos,et al.  Claims of sex differences: an empirical assessment in genetic associations. , 2007, JAMA.

[9]  Martin S. Taylor,et al.  Genome-wide genetic association of complex traits in heterogeneous stock mice , 2006, Nature Genetics.

[10]  M. Abney,et al.  The sex-specific genetic architecture of quantitative traits in humans , 2006, Nature Genetics.

[11]  K. Kendler,et al.  A Swedish national twin study of lifetime major depression. , 2006, The American journal of psychiatry.

[12]  C. Haley,et al.  QTLs for pre- and postweaning body weight and body composition in selected mice , 2004, Mammalian Genome.

[13]  Trudy F C Mackay,et al.  The genetic architecture of quantitative traits: lessons from Drosophila. , 2004, Current opinion in genetics & development.

[14]  E. Wakeland,et al.  The genetics of complex autoimmune diseases: non-MHC susceptibility genes , 2001, Nature Immunology.

[15]  K. Kendler,et al.  Genetic risk factors for major depression in men and women: similar or different heritabilities and same or partly distinct genes? , 2001, Psychological Medicine.

[16]  A. C. Collins,et al.  A method for fine mapping quantitative trait loci in outbred animal stocks. , 2000, Proceedings of the National Academy of Sciences of the United States of America.

[17]  L. Tarantino,et al.  Gender-influenced obesity QTLs identified in a cross involving the KK type II diabetes-prone mouse strain , 1999, Mammalian Genome.

[18]  T. van Wezel,et al.  Four new colon cancer susceptibility loci, Scc6 to Scc9 in the mouse. , 1999, Cancer research.

[19]  Robert Hitzemann,et al.  Identification of an Acute Ethanol Response Quantitative Trait Locus on Mouse Chromosome 2 , 1999, The Journal of Neuroscience.

[20]  J. Shendure,et al.  A major influence of sex-specific loci on alcohol preference in C57Bl/6 and DBA/2 inbred mice , 1998, Mammalian Genome.

[21]  C. Haley,et al.  Quantitative trait loci affecting body weight and fatness from a mouse line selected for extreme high growth. , 1998, Genetics.

[22]  Ritsert C. Jansen,et al.  Complex interactions of new quantitative trait loci, Sluc1, Sluc2, Sluc3, and Sluc4, that influence the susceptibility to lung cancer in the mouse , 1996, Nature Genetics.

[23]  S. R. Searle,et al.  Restricted Maximum Likelihood (REML) Estimation of Variance Components in the Mixed Model , 1976 .