Genome partitioning of genetic variation for complex traits using common SNPs

Abstract

We estimate and partition genetic variation for height, body mass index (BMI), von Willebrand factor and QT interval (QTi) using 586,898 SNPs genotyped on 11,586 unrelated individuals. We estimate that ∼45%, ∼17%, ∼25% and ∼21% of the variance in height, BMI, von Willebrand factor and QTi, respectively, can be explained by all autosomal SNPs and a further ∼0.5-1% can be explained by X chromosome SNPs. We show that the variance explained by each chromosome is proportional to its length, and that SNPs in or near genes explain more variation than SNPs between genes. We propose a new approach to estimate variation due to cryptic relatedness and population stratification. Our results provide further evidence that a substantial proportion of heritability is captured by common SNPs, that height, BMI and QTi are highly polygenic traits, and that the additive variation explained by a part of the genome is approximately proportional to the total length of DNA contained within genes therein.

Figure 1

Estimate of the variance explained by each chromosome for height, BMI, vWF and QTi by the joint analysis using unrelated individuals against chromosome length. The numbers in the circles/squares are the chromosome numbers.

Figure 2

Estimates of the variance explained by genic and intergenic regions on each chromosome for height by the joint analysis using 11,586 unrelated individuals. The genic region is defined as ± 0 Kb (a), ± 20 Kb (b) and ± 50 Kb (c) of the 3′ and 5′ UTRs. Error bars are the standard errors of the estimates. $h_{\mathrm{Gg}}^2$ and $h_{\mathrm{Gi}}^2$ are the variances explained by all the genic and intergenic SNPs across the whole genome. P(observed vs. expected): goodness-of-fit test of the estimated $h_{\mathrm{Gg}}^2/h_{\mathrm{G}}^2$ against that expected from the coverage of genic regions.

Figure 3

Difference between the estimates of variance explained by each chromosome by the separate ( $h_{\mathrm{C}}^2(\mathrm{sep})$) and joint ( $h_{\mathrm{C}}^2$) analyses for height, BMI, vWF and QTi against chromosome length. All: using all the individuals in the entire sample. Unrelated: using unrelated individuals after excluding one of each pair of individuals with an estimate of genetic relationship > 0.025.

Figure 4

The sum of variance explained by the GWAS associated SNPs on each chromosome in the GIANT meta-analysis (MA) of height against the estimate of variance explained by each chromosome for height by the joint analysis using the combined data of 11,586 unrelated individuals in the present study. The variance explained by GWAS loci in the GIANT MA was calculated based on the result of its replication study.