RT Journal Article
SR Electronic
T1 Explaining Missing Heritability Using Gaussian Process Regression
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 040576
DO 10.1101/040576
A1 Kevin Sharp
A1 Wim Wiegerinck
A1 Alejandro Arias-Vasquez
A1 Barbara Franke
A1 Jonathan Marchini
A1 Cornelis A. Albers
A1 Hilbert J. Kappen
YR 2016
UL http://biorxiv.org/content/early/2016/02/22/040576.abstract
AB For many traits and common human diseases, causal loci uncovered by genetic association studies account for little of the known heritable variation. Such ‘missing heritability’ may be due to the effect of non-additive interactions between multiple loci, but this has been little explored and difficult to test using existing parametric approaches. We propose a Bayesian non-parametric Gaussian Process Regression model, for identifying associated loci in the presence of interactions of arbitrary order. We analysed 46 quantitative yeast phenotypes and found that over 70% of the total known missing heritability could be explained using common genetic variants, many without significant marginal effects. Additional analysis of an immunological rat phenotype identified a three SNP interaction model providing a significantly better fit (p-value 9.0e-11) than the null model incorporating only the single marginally significant SNP. This new approach, called GPMM, represents a significant advance in approaches to understanding the missing heritability problem with potentially important implications for studies of complex, quantitative traits.