Impact of erroneous marker data on the accuracy of narrow-sense heritability

Genomic relationship matrices computed from single nucleotide polymorphism (SNP) data are now widely used to estimate narrow-sense heritability (h2), yet the impact of genotyping error on these estimates is not well understood. We used stochastic simulation and supporting algebra to examine this impact and its interplay with marker density. Starting from a diploid founder population with 300 additive quantitative trait loci, we simulated SNP panels with densities ranging from 6.25 to 50 SNPs per cM and traits with true h2 of either 0.2 or 0.6. Genotypes were then altered at error rates ε=0-1 under three error kernels. For each of 100 simulation replicates, we calculated the genomic relationship matrix using VanRaden's method and estimated h2 with restricted maximum likelihood (REML). In the absence of error, low-density marker panels underestimated h2. Sparse panels were also the most tolerant up to ε≈0.1 yet still underestimated h2. Conversely, the densest panel recovered the true h2 when ε=0, but even a small error ε>0.01 caused an upward bias. The analysis reveals that all distortions are attributable to: (i) a shift in the mean off-diagonal elements of the genomic relationship matrix with magnitude (1-ε)2 and (ii) a change in the ratio between the mean diagonal and mean off-diagonal elements of the genomic relationship matrix. When ε≳0.6, every kernel pushed h2 toward zero. Thus, even modest genotyping error can inflate or deflate additive genetic variance estimates. SNP panels therefore require rigorous laboratory quality control, error-aware imputation, and statistical models that account for genotype uncertainty when estimating h2.