Interpreting SNP heritability in admixed populations

Abstract

SNP heritability (h²_snp) is defined as the proportion of phenotypic variance explained by genotyped SNPs and is believed to be a lower bound of heritability (h²), being equal to it if all causal variants are known. Despite the simple intuition behind h²_snp, its interpretation and equivalence to h² is unclear, particularly in the presence of population structure and assortative mating. It is well known that population structure can lead to inflation in h²_snp estimates. Here we use analytical theory and simulations to demonstrate that h²_snp estimated with genome-wide restricted maximum likelihood (GREML) can be biased in admixed populations, even in the absence of confounding and even if all causal variants are known. This is because admixture generates linkage disequilibrium (LD), which contributes to the genetic variance, and therefore to heritability. GREML implicitly assumes this component is zero, which may not be true, particularly for traits under divergent or stabilizing selection in the source populations, leading under- or over-estimates of h²_snp relative to h². For the same reason, GREML estimates of local ancestry heritability (h²_γ) will also be biased. We describe the bias in ĥ²_snp and ĥ²_γ as a function of admixture history and the genetic architecture of the trait and show that it can be recovered under some conditions. We discuss the correct interpretation of ĥ²_snpin admixed populations and its implication for genome-wide association and polygenic prediction.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

* Additional analyses showing bias in h2snp can be recovered by alternate scaling of the GRM.