Estimating Mutation Load in Human Genomes
By B. Henn, Laura R. Botigué, Carlos D. Bustamante, Andrew G. Clark, Simon Gravel. 2015
256 citations recorded by the source corpus.
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 dominance and selection coefficients — quantities that remain poorly characterized for current genomic data sets.
Subfields: Materials Chemistry
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Citations in the corpus
- Quantifying unobserved protein-coding variants in human populations provides a roadmap for large-scale sequencing projects (2016, unknown): “Our empirical and mathematical analysis shows that UnseenEst provides accurate extrapolation of the SFS from current data to cohort sizes more than an order of magnitude larger (Supplementary Notes 1–6).”
- When Is Selection Effective? (2014, background): “[35] Henn BM, Botigué LR, Bustamante CD, Clark AG, Gravel S (2015) Estimating the mutation load in human genomes.”