Estimating the mutation load in human genomes

Journal name:
Nature Reviews Genetics
Volume:
16,
Pages:
333–343
Year published:
DOI:
doi:10.1038/nrg3931
Published online

Abstract

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.

At a glance

Figures

  1. Proportion of deleterious variants found in an individual's genome classified by their frequency in the population (common versus rare).
    Figure 1: Proportion of deleterious variants found in an individual's genome classified by their frequency in the population (common versus rare).

    We wanted to ascertain whether the deleterious portion of an individual's genome is mostly represented by rare or common variants. For the Yoruba (YRI) population in the 1000 Genomes Project, variants were assigned to three selection regimes (moderate, large and extreme), according to genomic evolutionary rate profiling (GERP) score categories in increasing order of phylogenetic conservation: 2:4, 4:6 and >6. The more conserved a site is, the more likely it is that a new allele is deleterious (Box 2). Deleterious single-nucleotide polymorphisms (SNPs) with a derived allele frequency lower than 5% within the population (shown in purple) are classified as 'rare' and the rest as 'common' (shown in blue). Almost 70% of the deleterious SNPs found in an individual genome are common, and most of them have a small predicted effect ('moderate'). Half of the rare SNPs also have a moderate effect, and half of them have a large effect, demonstrating how low-frequency, large-effect variants have not yet been purged by purifying selection.

  2. Differences in the site frequency spectrum across populations for deleterious and neutral variants.
    Figure 2: Differences in the site frequency spectrum across populations for deleterious and neutral variants.

    The site frequency spectrum (SFS) can be a powerful method for summarizing genomic data. The figure shows the SFSs for four populations, focusing on both low-frequency variants (minor allele frequency (MAF) <0.18; left panels) and nearly fixed variants (MAF >0.82; right panels). Derived variants were annotated with genomic evolutionary rate profiling (GERP) scores (see Supplementary information S1 (box)). In part a, we plot single-nucleotide polymorphisms (SNPs) that are predicted to have a 'large' deleterious effect (GERP >4). In part b, we plot SNPs that are predicted to have a 'neutral' effect (GERP <2). Using 1000 Genomes Project Phase 1 exome data34, we sampled 42 individuals from the Yoruba (YRI, Nigeria), Mexican (MXL, Mexico), Tuscan (TSI, Italy) and Japanese (JPT, Japan) populations. Only individuals sequenced on the same Agilent exome platform were compared here to avoid biases in target capture between platforms. Demography results in different SFS for each population. Neutral variants provide a null demographic model. The African YRI population have the highest number of rare deleterious variants, although the JPT and TSI populations have many more deleterious fixed variants, possibly owing to ancient founder effects resulting in the fixation by strong drift (also noted in Ref. 48). By comparing the difference between the neutral and deleterious SFS (see Supplementary information S2 (figure)), one can infer the impact of purifying selection. For example, non-African populations have a larger proportion of deleterious variants that are fixed than that seen neutrally.

  3. Schematic of different demographic models for the Out-of-Africa dispersal.
    Figure 3: Schematic of different demographic models for the Out-of-Africa dispersal.

    Three demographic models have been discussed in the context of changes in genetic load due to extreme genetic drift across different human populations. All three models allow for a severe Out-of-Africa bottleneck and recovery but with varying degrees of subsequent changes in population size. Coloured dots indicate allelic diversity; the width of the column is proportional to the effective population size (Ne). The bottom tube represents the ancestral African population size, with later events occurring in temporal sequence towards the top of the figure.

  4. Mutation load under an additive and a recessive model.
    Figure 4: Mutation load under an additive and a recessive model.

    Using the same data set as in Fig. 2, we computed the total mutation load2 for each population. Genomic evolutionary rate profiling (GERP) scores were annotated for whole-exome data. Variants were grouped into three categories according their GERP score (2:4, 4:6 and >6), corresponding to different biological functional effects. The more phylogenetically conserved a site is, the more likely it is that a new allele is deleterious and has a high GERP score (see Supplementary information S1 (box)). Within each category, three selection coefficients were assigned, using the inferred s coefficients in Boyko et al.47: s = −4.5 × 10−4, s = −4.5 × 10−3 and s = −1 × 10−2. The total mutation load is the sum of load for each locus2. The mutation load under an additive model is higher than the mutation load under a recessive model because the phenotypic effect of a variant is masked in the recessive homozygous state. Although only slight differences exist between populations for an additive model of dominance (~1.5%), strong differences occur under a recessive model because of the differential number of derived homozygotes among populations. JPT, Japanese (Japan); MXL, Mexican (Mexico); TSI, Tuscan (Italy); YRI, Yoruba (Nigeria).

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Author information

Affiliations

  1. Department of Ecology and Evolution, Stony Brook University, 650 Life Sciences Building, Stony Brook, New York 11794–5245, USA.

    • Brenna M. Henn &
    • Laura R. Botigué
  2. Stanford University School of Medicine, Department of Genetics, 291 Campus Drive, Stanford, California 94305, USA.

    • Carlos D. Bustamante
  3. Cornell University, Department of Molecular Biology and Genetics, 526 Campus Road, Ithaca, New York 14853–2703, USA.

    • Andrew G. Clark
  4. McGill University, Department of Human Genetics and Genome Quebec Innovation Centre, 740 Dr Penfield Avenue, Montreal, Quebec H3A 0G1, Canada.

    • Simon Gravel

Competing interests statement

The authors declare no competing interests.

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Author details

  • Brenna M. Henn

    Brenna M. Henn is a population geneticist specializing in the evolution of human genetic diversity. She is currently an assistant professor in the Department of Ecology and Evolution, Stony Brook University, New York, USA. She completed her graduate work in anthropology and postdoctoral work in human genomics at Stanford University, California, USA. Brenna M. Henn's homepage.

  • Laura R. Botigué

    Laura R. Botigué is a population geneticist focusing on the effect of demography on the genomic architecture of different human populations. She is currently a postdoctoral associate in the Henn Laboratory in the Department of Ecology and Evolution at Stony Brook University, New York, USA. She completed her graduate work in biomedicine at the Universitat Pompeu Fabra, Barcelona, Spain.

  • Carlos D. Bustamante

    Carlos D. Bustamante is a population geneticist who analyses genome-wide patterns of variation to address fundamental questions in biology, anthropology and medicine. He is Professor of Genetics at Stanford University School of Medicine, California, USA, and Co-Founding Director of the Stanford Center for Computational, Evolutionary, and Human Genomics (CEHG), California, USA.

  • Andrew G. Clark

    Andrew G. Clark is a population geneticist who studies several aspects of complex-trait genetics, including the impact of recent human demography on patterns of variation. He is the Jacob Gould Schurman Professor of Molecular Biology and Genetics at Cornell University, Ithaca, New York, USA.

  • Simon Gravel

    Simon Gravel is a population geneticist who specializes in statistical and mathematical models for interpreting population genomic data. He is a Sloan Fellow, holds the Canada Research Chair in Statistical and Population Genetics, and is an assistant professor at the Department of Human Genetics at McGill University, Montreal, Quebec, Canada.

Supplementary information

PDF files

  1. Supplementary information S1 (box) (171 KB)

    Variant Annotation Algorithms

  2. Supplementary information S2 (figure) (249 KB)

    Demographic history based on the site frequency spectrum and sharing of rare alleles.

  3. Supplementary information S3 (figure) (238 KB)

    Allele sharing versus allele frequency among European populations.

Additional data