Computational prediction of the phenotypic propensities of noncoding single nucleotide variants typically combines annotation of genomic, functional and evolutionary attributes into a single score. Here, we evaluate if the claimed excellent accuracies of these predictions translate into high rates of success in addressing questions important in biological research, such as fine mapping causal variants, distinguishing pathogenic allele(s) at a given position, and prioritizing variants for genetic risk assessment. A significant disconnect is found to exist between the statistical modelling and biological performance of predictive approaches. We discuss fundamental reasons underlying these deficiencies and suggest that future improvements of computational predictions need to address confounding of allelic, positional and regional effects as well as imbalance of the proportion of true positive variants in candidate lists.

The human genome contains hundreds of thousands of missense mutations. However, only a handful of these variants are known to be adaptive, which implies that adaptation through protein sequence change is an extremely rare phenomenon in human evolution. Alternatively, existing methods may lack the power to pinpoint adaptive variation. We have developed and applied an Evolutionary Probability Approach (EPA) to discover candidate adaptive polymorphisms (CAPs) through the discordance between allelic evolutionary probabilities and their observed frequencies in human populations. EPA reveals thousands of missense CAPs, which suggest that a large number of previously optimal alleles experienced a reversal of fortune in the human lineage. We explored nonadaptive mechanisms to explain CAPs, including the effects of demography, mutation rate variability, and negative and positive selective pressures in modern humans. Many nonadaptive hypotheses were tested, but failed to explain the data, which suggests that a large proportion of CAP alleles have increased in frequency due to beneficial selection. This suggestion is supported by the fact that a vast majority of adaptive missense variants discovered previously in humans are CAPs, and hundreds of CAP alleles are protective in genotype–phenotype association data. Our integrated phylogenomic and population genetic EPA approach predicts the existence of thousands of nonneutral candidate variants in the human proteome. We expect this collection to be enriched in beneficial variation. The EPA approach can be applied to discover candidate adaptive variation in any protein, population, or species for which allele frequency data and reliable multispecies alignments are available.

34The human genome contains hundreds of thousands of missense mutations. However, only 35 a handful of these variants are known to be adaptive, which implies that adaptation through 36 protein sequence change is an extremely rare phenomenon in human evolution. 37Alternatively, existing methods may lack the power to pinpoint adaptive variation. We have 38 developed and applied an Evolutionary Probability Approach (EPA) to discover candidate 39 adaptive polymorphisms (CAPs) through the discordance between allelic evolutionary 40 probabilities and their observed frequencies in human populations. EPA reveals thousands 41 of missense CAPs, which suggest that a large number of previously optimal alleles had 42 experienced a reversal of fortune in the human lineage. We explored non-adaptive 43 mechanisms to explain CAPs, including the effects of demography, mutation rate 44 variability, and negative and positive selective pressures in modern humans. Our analyses 45 suggest that a large proportion of CAP alleles have increased in frequency due to beneficial 46 selection. This conclusion is supported by the facts that a vast majority of adaptive 47 missense variants discovered previously in humans are CAPs, and that hundreds of CAP 48 alleles are protective in genotype-phenotype association data. Our integrated 49 phylogenomic and population genetic EPA approach predicts the existence of thousands of 50 signatures of non-neutral evolution in the human proteome. We expect this collection to be 51 enriched in beneficial variation. EPA approach can be applied to discover candidate 52 adaptive variation in any protein, population, or species for which allele frequency data 53 and reliable multispecies alignments are available. 54 55

Genetic differences between species and within populations are two sides of the same coin under the neutral theory of molecular evolution. This theory posits that a vast majority of evolutionary substitutions, which appear as differences between species, are (nearly) neutral, that is, these substitutions are permitted without a significantly adverse impact on a species' survival. We refer to them as evolutionarily permissible (ePerm) variation. Evolutionary permissibility of any possible variant can be inferred from multispecies sequence alignments by applying sophisticated statistical methods to the evolutionary tree of species. Here, we explore the evolutionary permissibility of amino acid variants associated with genetic diseases and those observed in personal exomes. Consistent with the predictions of the neutral theory, disease associated amino acid variants are rarely ePerm, much more biochemically radical, and found predominantly at more conserved positions than their non-disease counterparts. Only 10% of amino acid mutations are ePerm, but these variants rise to become two-thirds of all substitutions in the human lineage (a 6-fold enrichment). In contrast, only a minority of the variants in a personal exome are ePerm, a seemingly counterintuitive pattern that results from a combination of mutational and evolutionary processes that are, in fact, broadly consistent with the neutral theory. Evolutionarily forbidden variants outnumber detrimental variants in individual exomes and may play an underappreciated role in protecting against disease. We discuss these observations and conclude that the long-term evolutionary history of species can illuminate functional biomedical properties of variation present in personal exomes.