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

Abstract

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.

Effects of Linked Selective Sweeps on Demographic Inference and Model Selection

“…This is likely an unrealistic scenario in human populations, where positive selection is perhaps less common (Hernandez et al 2011). However, some have argued that selection may be pervasive in the human genome as well (Boyko et al 2008;Enard et al 2014), and certainly humans show many adaptations to local environments (e.g., Li et al 2007;Perry et al 2007;Tishkoff et al 2007;Barreiro et al 2008;Bryk et al 2008). Thus while it seems unlikely that every site in the genome would be affected by linked selection, some currently unknown subset of sites must be, and thus we should expect demographic estimation to be subject to some degree of bias (perhaps negligible in well-designed studies; see below).…”

Inferring causality and functional significance of human coding DNA variants

“…Therefore, absence in a moderate number of controls would support pathogenicity. The following factors suggest that this is a stark under-estimate for human populations: (i) human population growth which has resulted in an excess of rare alleles, (ii) selection against moderately deleterious alleles, and (iii) human migrations which have resulted in rare alleles not seen in multiple controls (38)(39)(40)(41)(42)(43)(44). As seen from Figure 1, a more complex population genetics model incorporating population growth and natural selection (40) but not migration predicts that there is .1% chance that a benign variant observed in a single patient would not be detected in as many as 10 000 controls.…”

Positional conservation and amino acids shape the correct diagnosis and population frequencies of benign and damaging personal amino acid mutations

“…These in silico predictions are of great interest in detecting variants for Mendelian and complex diseases, in prioritizing polymorphisms for experimental research in humans and other species, and in analyzing data from genome-wide association studies (e.g., Rudd et al 2005;Bhatti et al 2006;Kryukov et al 2007;Doniger et al 2008). Using various prediction tools, up to one-fourth of nonsynonymous mutations have been diagnosed to be not strictly neutral and are thus thought to harbor signatures of negative or positive selection (Yampolsky et al 2005;Eyre-Walker et al 2006;Levy et al 2007;Shastry 2007;Bentley et al 2008;Boyko et al 2008;Wang et al 2008;Wheeler et al 2008).…”