Genome-Wide Complex Trait Analysis (GCTA): Methods, Data Analyses, and Interpretations

Abstract

Genome-wide association studies (GWAS) have proven successful in identifying single nucleotide polymorphisms (SNPs) that affect the phenotypic variation in human complex diseases and traits [I]. GWAS was designed to uncover genes and pathways of medical importance to pinpoint the underlying molecular and genetic etiology of diseases but has been criticized for being unable to explain the heritability for most complex traits [2]. We have recently developed a method to estimate the proportion of additive genetic variance that can be captured by considering all the SNPs simultaneously without testing for association of any individual SNP with the trait [3]. We showed by analyses of GWAS data that a large proportion of heritability for quantitative traits such as height [3], body mass index [4], and cognitive ability [5, 6) and for diseases such as schizophrenia [7] can be explained by all the common SNPs. These results suggest that most heritability is hiding rather than missing [8] and that GWAS have not identified the SNPs that explain this proportion of the hidden heritability because the effect sizes of individual SNPs are too small to reach the stringent genome-wide significance level [ 3]. We forth er extended the method to partition the genetic variance onto chromosomes and genomic segments. We found that the variance attributed to a chromosome or a DNA segment is proportional to its length, in particular for height [4] and schizophrenia [7], and that SNPs located in genie regions explain more variation than those in intergenie regions. All the results are consistent with a pattern of polygenie inheritance for most complex traits.