Estimating the mutation load in human genomes

Key Points Millions of new genetic variants have been discovered through sequencing studies and deposited in human genomic databases. Many of these, particularly rare variants, have been annotated as deleterious. Recent research has examined whether different human populations have a varying burden of deleterious alleles — a concept referred to as mutation load. Several recent studies have suggested that there is no significant difference among populations in the estimated number of deleterious alleles per individual. However, these analyses are sensitive to annotation prediction algorithms and summary statistics, leading to different, sometimes contradictory, interpretations. These calculations also involved a number of simplifying assumptions, including additive allelic effects, no epistasis and simple distributions of selection coefficients across deleterious variants and across populations. Following classical models of mutation load, we consider genotype frequencies in order to highlight how mutation load can change under different models of dominance. Additionally, genetic drift has shifted the allele frequency spectrum of deleterious variants such that the genomes of individuals in Out-of-Africa populations carry more common deleterious variants. The frequency distribution of deleterious variants has implications for characterizing the genetic architecture of diseases across populations. A large proportion of genetic variants in the human genome have been predicted to be deleterious. This Review examines the frequency and patterns of deleterious alleles in the human genome and considers recent studies with conflicting findings on whether the mutation load, or burden of deleterious alleles, differs across populations. Next-generation sequencing technology has facilitated the discovery of millions of genetic variants in human genomes. A sizeable fraction of these variants are predicted to be deleterious. Here, we review the pattern of deleterious alleles as ascertained in genome sequencing data sets and ask whether human populations differ in their predicted burden of deleterious alleles — a phenomenon known as mutation load. We discuss three demographic models that are predicted to affect mutation load and relate these models to the evidence (or the lack thereof) for variation in the efficacy of purifying selection in diverse human genomes. We also emphasize why accurate estimation of mutation load depends on assumptions regarding the distribution of