Identity-by-descent-based heritability analysis in the Northern Finland Birth Cohort

Abstract

For most complex traits, only a small proportion of heritability is explained by statistically significant associations from genome-wide association studies (GWAS). In order to determine how much heritability can potentially be explained through larger GWAS, several different approaches for estimating total narrow-sense heritability from GWAS data have recently been proposed. These methods include variance components with relatedness estimates from allele-sharing, variance components with relatedness estimates from identity-by-descent (IBD) segments, and regression of phenotypic correlation on relatedness estimates from IBD segments. These methods have not previously been compared on real or simulated data. We analyze the narrow-sense heritability of nine metabolic traits in the Northern Finland Birth Cohort (NFBC) using these methods. We find substantial estimated heritability for several traits, including LDL cholesterol (54 % heritability), HDL cholesterol (46 % heritability), and fasting glucose levels (39 % heritability). Estimates of heritability from the regression-based approach are much lower than variance component estimates in these data, which may be due to the presence of strong population structure. We also investigate the accuracy of the competing approaches using simulated phenotypes based on genotype data from the NFBC. The simulation results substantiate the downward bias of the regression-based approach in the presence of population structure.

Fig. 1

Rate of IBD segment detection across the autosomes in the NFBC data. Chromosomes are ordered left to right from 1 to 22. The IBD rate at a SNP is the number of pairs of individuals with an IBD segment covering the SNP, divided by the number of pairs of individuals in the data set. The upper horizontal line is the 95th percentile and is the cut-off for determining high IBD rate regions for the simulation study in Section 1.3 of the Appendix (Electronic supplementary material). The lower horizontal line is the 0.1th percentile and is the cut-off for weighting; regions with IBD rate below this threshold are excluded from the weighted estimates of relatedness. These low rates occur at positions with large gaps in the SNPs and/or poorer quality genotypes, such as telomeres and centromeres

Fig. 2

The potential effect of extreme population structure on regression-based heritability estimation. This figure illustrates an extreme scenario in which the heritability estimates of the method of Zuk et al. can be biased. In this scenario there are two sub-populations that have equal heritability. One sub-population has a higher rate of relatedness than the other. Pairs of individuals across populations have zero relatedness. Products of normalized trait values for pairs of individuals are shown on the y-axis, while relatedness values for the pairs are shown on the x-axis. It can be seen that, depending on the range of relatedness values used, the overall fitted slope will tend to be less than the heritability