Estimation of narrow-sense heritability, h(2), from genome-wide SNPs genotyped in unrelated individuals has recently attracted interest and offers several advantages over traditional pedigree-based methods. With the use of this approach, it has been estimated that over half the heritability of human height can be attributed to the ~300,000 SNPs on a genome-wide genotyping array. In comparison, only 5%-10% can be explained by SNPs reaching genome-wide significance. We investigated via simulation the validity of several key assumptions underpinning the mixed-model analysis used in SNP-based h(2) estimation. Although we found that the method is reasonably robust to violations of four key assumptions, it can be highly sensitive to uneven linkage disequilibrium (LD) between SNPs: contributions to h(2) are overestimated from causal variants in regions of high LD and are underestimated in regions of low LD. The overall direction of the bias can be up or down depending on the genetic architecture of the trait, but it can be substantial in realistic scenarios. We propose a modified kinship matrix in which SNPs are weighted according to local LD. We show that this correction greatly reduces the bias and increases the precision of h(2) estimates. We demonstrate the impact of our method on the first seven diseases studied by the Wellcome Trust Case Control Consortium. Our LD adjustment revises downward the h(2) estimate for immune-related diseases, as expected because of high LD in the major-histocompatibility region, but increases it for some nonimmune diseases. To calculate our revised kinship matrix, we developed LDAK, software for computing LD-adjusted kinships.

Summary: Genetic data obtained on population samples convey information about their evolutionary history. Inference methods can extract part of this information but they require sophisticated statistical techniques that have been made available to the biologist community (through computer programs) only for simple and standard situations typically involving a small number of samples. We propose here a computer program (DIY ABC) for inference based on approximate Bayesian computation (ABC), in which scenarios can be customized by the user to fit many complex situations involving any number of populations and samples. Such scenarios involve any combination of population divergences, admixtures and population size changes. DIY ABC can be used to compare competing scenarios, estimate parameters for one or more scenarios and compute bias and precision measures for a given scenario and known values of parameters (the current version applies to unlinked microsatellite data). This article describes key methods used in the program and provides its main features. The analysis of one simulated and one real dataset, both with complex evolutionary scenarios, illustrates the main possibilities of DIY ABC. Availability: The software DIY ABC is freely available at http://www.montpellier.inra.fr/CBGP/diyabc. Contact:j.cornuet@imperial.ac.uk Supplementary information: Supplementary data are also available at http://www.montpellier.inra.fr/CBGP/diyabc