Balancing selection maintains a form of ERAP2 that undergoes nonsense-mediated decay and affects antigen presentation.

A remarkable characteristic of the human major histocompatibility complex (MHC) is its extreme genetic diversity, which is maintained by balancing selection. In fact, the MHC complex remains one of the best-known examples of natural selection in humans, with well-established genetic signatures and biological mechanisms for the action of selection. Here, we present genetic and functional evidence that another gene with a fundamental role in MHC class I presentation, endoplasmic reticulum aminopeptidase 2 (ERAP2), has also evolved under balancing selection and contains a variant that affects antigen presentation. Specifically, genetic analyses of six human populations revealed strong and consistent signatures of balancing selection affecting ERAP2. This selection maintains two highly differentiated haplotypes (Haplotype A and Haplotype B), with frequencies 0.44 and 0.56, respectively. We found that ERAP2 expressed from Haplotype B undergoes differential splicing and encodes a truncated protein, leading to nonsense-mediated decay of the mRNA. To investigate the consequences of ERAP2 deficiency on MHC presentation, we correlated surface MHC class I expression with ERAP2 genotypes in primary lymphocytes. Haplotype B homozygotes had lower levels of MHC class I expressed on the surface of B cells, suggesting that naturally occurring ERAP2 deficiency affects MHC presentation and immune response. Interestingly, an ERAP2 paralog, endoplasmic reticulum aminopeptidase 1 (ERAP1), also shows genetic signatures of balancing selection. Together, our findings link the genetic signatures of selection with an effect on splicing and a cellular phenotype. Although the precise selective pressure that maintains polymorphism is unknown, the demonstrated differences between the ERAP2 splice forms provide important insights into the potential mechanism for the action of selection.

Inferring the joint demographic history of multiple populations from multidimensional SNP frequency data.

Demographic models built from genetic data play important roles in illuminating prehistorical events and serving as null models in genome scans for selection. We introduce an inference method based on the joint frequency spectrum of genetic variants within and between populations. For candidate models we numerically compute the expected spectrum using a diffusion approximation to the one-locus, two-allele Wright-Fisher process, involving up to three simultaneous populations. Our approach is a composite likelihood scheme, since linkage between neutral loci alters the variance but not the expectation of the frequency spectrum. We thus use bootstraps incorporating linkage to estimate uncertainties for parameters and significance values for hypothesis tests. Our method can also incorporate selection on single sites, predicting the joint distribution of selected alleles among populations experiencing a bevy of evolutionary forces, including expansions, contractions, migrations, and admixture. We model human expansion out of Africa and the settlement of the New World, using 5 Mb of noncoding DNA resequenced in 68 individuals from 4 populations (YRI, CHB, CEU, and MXL) by the Environmental Genome Project. We infer divergence between West African and Eurasian populations 140 thousand years ago (95% confidence interval: 40-270 kya). This is earlier than other genetic studies, in part because we incorporate migration. We estimate the European (CEU) and East Asian (CHB) divergence time to be 23 kya (95% c.i.: 17-43 kya), long after archeological evidence places modern humans in Europe. Finally, we estimate divergence between East Asians (CHB) and Mexican-Americans (MXL) of 22 kya (95% c.i.: 16.3-26.9 kya), and our analysis yields no evidence for subsequent migration. Furthermore, combining our demographic model with a previously estimated distribution of selective effects among newly arising amino acid mutations accurately predicts the frequency spectrum of nonsynonymous variants across three continental populations (YRI, CHB, CEU).

Assessing the evolutionary impact of amino acid mutations in the human genome.

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.

Localizing recent adaptive evolution in the human genome.

Identifying genomic locations that have experienced selective sweeps is an important first step toward understanding the molecular basis of adaptive evolution. Using statistical methods that account for the confounding effects of population demography, recombination rate variation, and single-nucleotide polymorphism ascertainment, while also providing fine-scale estimates of the position of the selected site, we analyzed a genomic dataset of 1.2 million human single-nucleotide polymorphisms genotyped in African-American, European-American, and Chinese samples. We identify 101 regions of the human genome with very strong evidence (p < 10(-5)) of a recent selective sweep and where our estimate of the position of the selective sweep falls within 100 kb of a known gene. Within these regions, genes of biological interest include genes in pigmentation pathways, components of the dystrophin protein complex, clusters of olfactory receptors, genes involved in nervous system development and function, immune system genes, and heat shock genes. We also observe consistent evidence of selective sweeps in centromeric regions. In general, we find that recent adaptation is strikingly pervasive in the human genome, with as much as 10% of the genome affected by linkage to a selective sweep.

Global dissemination of a single mutation conferring white pericarp in rice.

Here we report that the change from the red seeds of wild rice to the white seeds of cultivated rice (Oryza sativa) resulted from the strong selective sweep of a single mutation, a frame-shift deletion within the Rc gene that is found in 97.9% of white rice varieties today. A second mutation, also within Rc, is present in less than 3% of white accessions surveyed. Haplotype analysis revealed that the predominant mutation originated in the japonica subspecies and crossed both geographic and sterility barriers to move into the indica subspecies. A little less than one Mb of japonica DNA hitchhiked with the rc allele into most indica varieties, suggesting that other linked domestication alleles may have been transferred from japonica to indica along with white pericarp color. Our finding provides evidence of active cultural exchange among ancient farmers over the course of rice domestication coupled with very strong, positive selection for a single white allele in both subspecies of O. sativa.

Genome-wide patterns of nucleotide polymorphism in domesticated rice.

Domesticated Asian rice (Oryza sativa) is one of the oldest domesticated crop species in the world, having fed more people than any other plant in human history. We report the patterns of DNA sequence variation in rice and its wild ancestor, O. rufipogon, across 111 randomly chosen gene fragments, and use these to infer the evolutionary dynamics that led to the origins of rice. There is a genome-wide excess of high-frequency derived single nucleotide polymorphisms (SNPs) in O. sativa varieties, a pattern that has not been reported for other crop species. We developed several alternative models to explain contemporary patterns of polymorphisms in rice, including a (i) selectively neutral population bottleneck model, (ii) bottleneck plus migration model, (iii) multiple selective sweeps model, and (iv) bottleneck plus selective sweeps model. We find that a simple bottleneck model, which has been the dominant demographic model for domesticated species, cannot explain the derived nucleotide polymorphism site frequency spectrum in rice. Instead, a bottleneck model that incorporates selective sweeps, or a more complex demographic model that includes subdivision and gene flow, are more plausible explanations for patterns of variation in domesticated rice varieties. If selective sweeps are indeed the explanation for the observed nucleotide data of domesticated rice, it suggests that strong selection can leave its imprint on genome-wide polymorphism patterns, contrary to expectations that selection results only in a local signature of variation.

Context dependence, ancestral misidentification, and spurious signatures of natural selection.

Population genetic analyses often use polymorphism data from one species, and orthologous genomic sequences from closely related outgroup species. These outgroup sequences are frequently used to identify ancestral alleles at segregating sites and to compare the patterns of polymorphism and divergence. Inherent in such studies is the assumption of parsimony, which posits that the ancestral state of each single nucleotide polymorphism (SNP) is the allele that matches the orthologous site in the outgroup sequence, and that all nucleotide substitutions between species have been observed. This study tests the effect of violating the parsimony assumption when mutation rates vary across sites and over time. Using a context-dependent mutation model that accounts for elevated mutation rates at CpG dinucleotides, increased propensity for transitional versus transversional mutations, as well as other directional and contextual mutation biases estimated along the human lineage, we show (using both simulations and a theoretical model) that enough unobserved substitutions could have occurred since the divergence of human and chimpanzee to cause many statistical tests to spuriously reject neutrality. Moreover, using both the chimpanzee and rhesus macaque genomes to parsimoniously identify ancestral states causes a large fraction of the data to be removed while not completely alleviating problem. By constructing a novel model of the context-dependent mutation process, we can correct polymorphism data for the effect of ancestral misidentification using a single outgroup.

Context-dependent mutation rates may cause spurious signatures of a fixation bias favoring higher GC-content in humans.

Understanding the proximate and ultimate causes underlying the evolution of nucleotide composition in mammalian genomes is of fundamental interest to the study of molecular evolution. Comparative genomics studies have revealed that many more substitutions occur from G and C nucleotides to A and T nucleotides than the reverse, suggesting that mammalian genomes are not at equilibrium for base composition. Analysis of human polymorphism data suggests that mutations that increase GC-content tend to be at much higher frequencies than those that decrease or preserve GC-content when the ancestral allele is inferred via parsimony using the chimpanzee genome. These observations have been interpreted as evidence for a fixation bias in favor of G and C alleles due to either positive natural selection or biased gene conversion. Here, we test the robustness of this interpretation to violations of the parsimony assumption using a data set of 21,488 noncoding single nucleotide polymorphisms (SNPs) discovered by the National Institute of Environmental Health Sciences (NIEHS) SNPs project via direct resequencing of n = 95 individuals. Applying standard nonparametric and parametric population genetic approaches, we replicate the signatures of a fixation bias in favor of G and C alleles when the ancestral base is assumed to be the base found in the chimpanzee outgroup. However, upon taking into account the probability of misidentifying the ancestral state of each SNP using a context-dependent mutation model, the corrected distribution of SNP frequencies for GC-content increasing SNPs are nearly indistinguishable from the patterns observed for other types of mutations, suggesting that the signature of fixation bias is a spurious artifact of the parsimony assumption.

Ascertainment bias in studies of human genome-wide polymorphism.

Large-scale SNP genotyping studies rely on an initial assessment of nucleotide variation to identify sites in the DNA sequence that harbor variation among individuals. This "SNP discovery" sample may be quite variable in size and composition, and it has been well established that properties of the SNPs that are found are influenced by the discovery sampling effort. The International HapMap project relied on nearly any piece of information available to identify SNPs-including BAC end sequences, shotgun reads, and differences between public and private sequences-and even made use of chimpanzee data to confirm human sequence differences. In addition, the ascertainment criteria shifted from using only SNPs that had been validated in population samples, to double-hit SNPs, to finally accepting SNPs that were singletons in small discovery samples. In contrast, Perlegen's primary discovery was a resequencing-by-hybridization effort using the 24 people of diverse origin in the Polymorphism Discovery Resource. Here we take these two data sets and contrast two basic summary statistics, heterozygosity and F(ST), as well as the site frequency spectra, for 500-kb windows spanning the genome. The magnitude of disparity between these samples in these measures of variability indicates that population genetic analysis on the raw genotype data is ill advised. Given the knowledge of the discovery samples, we perform an ascertainment correction and show how the post-correction data are more consistent across these studies. However, discrepancies persist, suggesting that the heterogeneity in the SNP discovery process of the HapMap project resulted in a data set resistant to complete ascertainment correction. Ascertainment bias will likely erode the power of tests of association between SNPs and complex disorders, but the effect will likely be small, and perhaps more importantly, it is unlikely that the bias will introduce false-positive inferences.

Simultaneous inference of selection and population growth from patterns of variation in the human genome.

Natural selection and demographic forces can have similar effects on patterns of DNA polymorphism. Therefore, to infer selection from samples of DNA sequences, one must simultaneously account for demographic effects. Here we take a model-based approach to this problem by developing predictions for patterns of polymorphism in the presence of both population size change and natural selection. If data are available from different functional classes of variation, and a priori information suggests that mutations in one of those classes are selectively neutral, then the putatively neutral class can be used to infer demographic parameters, and inferences regarding selection on other classes can be performed given demographic parameter estimates. This procedure is more robust to assumptions regarding the true underlying demography than previous approaches to detecting and analyzing selection. We apply this method to a large polymorphism data set from 301 human genes and find (i) widespread negative selection acting on standing nonsynonymous variation, (ii) that the fitness effects of nonsynonymous mutations are well predicted by several measures of amino acid exchangeability, especially site-specific methods, and (iii) strong evidence for very recent population growth.