Henn Brenna M, Botigué Laura R, Bustamante Carlos D, Clark Andrew G, Gravel Simon
Department of Ecology and Evolution, Stony Brook University, 650 Life Sciences Building, Stony Brook, New York 11794-5245, USA.
Stanford University School of Medicine, Department of Genetics, 291 Campus Drive, Stanford, California 94305, USA.
Nat Rev Genet. 2015 Jun;16(6):333-43. doi: 10.1038/nrg3931. Epub 2015 May 12.
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.
下一代测序技术推动了人类基因组中数以百万计基因变异的发现。预计这些变异中有相当一部分是有害的。在此，我们回顾了在基因组测序数据集中确定的有害等位基因模式，并探讨人类群体在预测的有害等位基因负担方面是否存在差异——这一现象被称为突变负荷。我们讨论了三种预计会影响突变负荷的人口统计学模型，并将这些模型与不同人类基因组中纯化选择效率差异的证据（或缺乏证据的情况）联系起来。我们还强调了为什么准确估计突变负荷取决于关于显性和选择系数分布的假设——而对于当前的基因组数据集，这些数量仍未得到很好的表征。
