Assessing the Evolutionary Impact of Amino Acid Mutations in the Human Genome

By A. Boyko, S. Williamson, Amit R. Indap, Jeremiah D. Degenhardt, Ryan D. Hernandez, K. Lohmueller, M. Adams, S. Schmidt, J. Sninsky, Shamil R. Sunyaev and 4 more. 2008

675 citations recorded by the source corpus.

Quantifying the distribution of fitness effects among newly arising mutations in the human genome is key to resolving important debates in medical and evolutionary genetics. Here, we present a method for inferring this distribution using Single Nucleotide Polymorphism (SNP) data from a population with non-stationary demographic history (such as that of modern humans). Application of our method to 47,576 coding SNPs found by direct resequencing of 11,404 protein coding-genes in 35 individuals (20 European Americans and 15 African Americans) allows us to assess the relative contribution of demographic and selective effects to patterning amino acid variation in the human genome. We find evidence of an ancient population expansion in the sample with African ancestry and a relatively recent bottleneck in the sample with European ancestry. After accounting for these demographic effects, we find strong evidence for great variability in the selective effects of new amino acid replacing mutations. In both populations, the patterns of variation are consistent with a leptokurtic distribution of selection coefficients (e.g., gamma or log-normal) peaked near neutrality. Specifically, we predict 27–29% of amino acid changing (nonsynonymous) mutations are neutral or nearly neutral (|s|<0.01%), 30–42% are moderately deleterious (0.01%<|s|<1%), and nearly all the remainder are highly deleterious or lethal (|s|>1%). Our results are consistent with 10–20% of amino acid differences between humans and chimpanzees having been fixed by positive selection with the remainder of differences being neutral or nearly neutral. Our analysis also predicts that many of the alleles identified via whole-genome association mapping may be selectively neutral or (formerly) positively selected, implying that deleterious genetic variation affecting disease phenotype may be missed by this widely used approach for mapping genes underlying complex traits.

Subfields: Genetics, Molecular Biology

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Citations in the corpus

Predicting Functional Effect of Human Missense Mutations Using PolyPhen-2 (2013, positive): “As we demonstrated earlier (Sunyaev et al., 2001; Boyko et al., 2008), these predictions correlate with the effect of natural selection, demonstrated by an excess of rare alleles among alleles that are predicted to be functional.”
The Genetics of Human Adaptation: Hard Sweeps, Soft Sweeps, and Polygenic Adaptation (2010, neutral): “The conclusion that adaptive selection may be widespread is further bolstered by similar results for other organisms, especially Drosophila [6], and also by recent estimates that 10-20% of amino acid replacements on the human lineage have been driven by positive selection [33].”
A Comparison of Models to Infer the Distribution of Fitness Effects of New Mutations (2013, methodology): “Boyko et al. (2008) also fitted several types of distributions and combinations of continuous distributions and discrete fixed effects when inferring the DFE for amino acid-changing mutations in humans.”
Effective population size and patterns of molecular evolution and variation (2009, background): “There is increasingly strong evidence for such bottleneck effects in both huma”
Estimating Mutation Load in Human Genomes (2015, methodology): “Within each category, three selection coefficients were assigned, using the inferred s coefficients in Boyko et al.(47): s = –4.”
Genome-Wide Inference of Ancestral Recombination Graphs (2013, methodology): “Q)= 8 >> >> >< >> >> >: 1 if R n i =0/;Tn i =T i 1 1 A( w) j P(s j jw;s k;T i 1 ;Q) if R i =(w;s k); (x;s j) s.t. T i =SPR(T ;w;s k;x;s j); s k s j s K 0 otherwise; (12) where P(s j jw;s k;Tn i 1 ;Q) is given by equations9–11. 22 Initial Local Tree The DSMC begins by”
Spontaneous Mutation Accumulation Studies in Evolutionary Genetics (2009, background): “…been assumed that the distribution of mutational effects for fitness is strongly leptokurtic (Kimura 1983, Robertson 1967), and this has been corroborated by analysis of the frequency distribution of polymorphisms segregating within populations (Boyko et al. 2008, Eyre-Walker & Keightley 2007).”
Approximation to the Distribution of Fitness Effects across Functional Categories in Human Segregating Polymorphisms (2014, neutral): “These studies have relied on fitting a 70 demographic moel to the neutral class of sites and then fitting the DFE of new mutations to a probability 71 distribution, typically an exponential or gamma distribution [9,13] to the class of sites that are putatively 72 under selection (e.”
Positional conservation and amino acids shape the correct diagnosis and population frequencies of benign and damaging personal amino acid mutations. (2009, methodology): “Still, these methods offer up to 80% accuracy for mutations in genes implicated in Mendelian diseases (for reviews, see Bhatti et al. 2006; Ng and Henikoff 2006; Bromberg and Rost 2007; Shastry 2007; Tian et al. 2007).”
Demographic history and rare allele sharing among human populations (2011, background): “The one-population site frequency spectrum (SFS) is a staple of population genetics and is commonly used to reveal broad patterns of selection (2, 3) and demography (3–5).”
Searching for missing heritability: Designing rare variant association studies (2014, background): “Hypomorphic alleles will typically be common rather than rare owing to weaker selection (s estimated in the range of 1010) (13, 29, 59) and rare hypomorphic alleles will increase detection power only modestly, owing to their smaller effect size (SI Appendix, Section 5.”
Estimating the Selective Effects of Heterozygous Protein Truncating Variants from Human Exome Data (2017, background): “(6) shet ~ Gamma and shet ~ InvGamma, and chose the model with the highest likelihood, which is shet ~ IG.”