Population genetics models of common diseases

The number and frequency of susceptibility alleles for common diseases are important factors to consider in the efficient design of disease association studies. These quantities are the results of the joint effects of mutation, genetic drift and selection. Hence, population genetics models, informed by empirical knowledge about patterns of disease variation, can be used to make predictions about the allelic architecture of common disease susceptibility and to gain an overall understanding about the evolutionary origins of such diseases. Equilibrium models and empirical studies suggest a role for both rare and common variants. In addition, increasing evidence points to changes in selective pressures on susceptibility genes for common diseases; these findings are likely to form the basis for further modeling studies.     

What Can Genome-Wide Association Studies Tell Us about the Genetics of Common Disease

The success of genome-wide association studies relies on much of the risk of common diseases being due to common genetic variants; but evidence for this is inconclusive. The results of published genome-wide association studies are examined to see what can be learnt about the distribution of disease-associated variants and how this might influence future study design. Although replicated disease-associated variants tend to be very common and frequency is inversely correlated with estimated effect size, our simulations suggest that such observations are the result of power. We find that for studies conducted to date, the frequency and effect size of significantly associated alleles are likely to be similar to those of the underlying disease alleles that they represent. Little of the genetic variation of disease has been explained so far, but current studies are only adequately powered to detect very common alleles unless they greatly increase disease risk. Thus, although the truth of the common disease / common variant hypothesis remains undecided, recent successes suggest that there are many more common genetic disease-associated variants, requiring larger studies to be identified.     

Type 2 diabetes risk alleles demonstrate extreme directional differentiation among human populations, compared to other diseases

Many disease-susceptible SNPs exhibit significant disparity in ancestral and derived allele frequencies across worldwide populations. While previous studies have examined population differentiation of alleles at specific SNPs, global ethnic patterns of ensembles of disease risk alleles across human diseases are unexamined. To examine these patterns, we manually curated ethnic disease association data from 5,065 papers on human genetic studies representing 1,495 diseases, recording the precise risk alleles and their measured population frequencies and estimated effect sizes. We systematically compared the population frequencies of cross-ethnic risk alleles for each disease across 1,397 individuals from 11 HapMap populations, 1,064 individuals from 53 HGDP populations, and 49 individuals with whole-genome sequences from 10 populations. Type 2 diabetes (T2D) demonstrated extreme directional differentiation of risk allele frequencies across human populations, compared with null distributions of European-frequency matched control genomic alleles and risk alleles for other diseases. Most T2D risk alleles share a consistent pattern of decreasing frequencies along human migration into East Asia. Furthermore, we show that these patterns contribute to disparities in predicted genetic risk across 1,397 HapMap individuals, T2D genetic risk being consistently higher for individuals in the African populations and lower in the Asian populations, irrespective of the ethnicity considered in the initial discovery of risk alleles. We observed a similar pattern in the distribution of T2D Genetic Risk Scores, which are associated with an increased risk of developing diabetes in the Diabetes Prevention Program cohort, for the same individuals. This disparity may be attributable to the promotion of energy storage and usage appropriate to environments and inconsistent energy intake. Our results indicate that the differential frequencies of T2D risk alleles may contribute to the observed disparity in T2D incidence rates across ethnic populations.     

Worldwide population differentiation at disease-associated SNPs

Background

Recent genome-wide association (GWA) studies have provided compelling evidence of association between genetic variants and common complex diseases. These studies have made use of cases and controls almost exclusively from populations of European ancestry and little is known about the frequency of risk alleles in other populations. The present study addresses the transferability of disease associations across human populations by examining levels of population differentiation at disease-associated single nucleotide polymorphisms (SNPs).

Methods

We genotyped ~1000 individuals from 53 populations worldwide at 25 SNPs which show robust association with 6 complex human diseases (Crohn's disease, type 1 diabetes, type 2 diabetes, rheumatoid arthritis, coronary artery disease and obesity). Allele frequency differences between populations for these SNPs were measured using Fst. The Fst values for the disease-associated SNPs were compared to Fst values from 2750 random SNPs typed in the same set of individuals.

Results

On average, disease SNPs are not significantly more differentiated between populations than random SNPs in the genome. Risk allele frequencies, however, do show substantial variation across human populations and may contribute to differences in disease prevalence between populations. We demonstrate that, in some cases, risk allele frequency differences are unusually high compared to random SNPs and may be due to the action of local (i.e. geographically-restricted) positive natural selection. Moreover, some risk alleles were absent or fixed in a population, which implies that risk alleles identified in one population do not necessarily account for disease prevalence in all human populations.

Conclusion

Although differences in risk allele frequencies between human populations are not unusually large and are thus likely not due to positive local selection, there is substantial variation in risk allele frequencies between populations which may account for differences in disease prevalence between human populations.     

A genome-wide comparison of the functional properties of rare and common genetic variants in humans

One of the longest running debates in evolutionary biology concerns the kind of genetic variation that is primarily responsible for phenotypic variation in species. Here, we address this question for humans specifically from the perspective of population allele frequency of variants across the complete genome, including both coding and noncoding regions. We establish simple criteria to assess the likelihood that variants are functional based on their genomic locations and then use whole-genome sequence data from 29 subjects of European origin to assess the relationship between the functional properties of variants and their population allele frequencies. We find that for all criteria used to assess the likelihood that a variant is functional, the rarer variants are significantly more likely to be functional than the more common variants. Strikingly, these patterns disappear when we focus on only those variants in which the major alleles are derived. These analyses indicate that the majority of the genetic variation in terms of phenotypic consequence may result from a mutation-selection balance, as opposed to balancing selection, and have direct relevance to the study of human disease.     

Recent Mitochondrial DNA Mutations Increase the Risk of Developing Common Late-Onset Human Diseases

Mitochondrial DNA (mtDNA) is highly polymorphic at the population level, and specific mtDNA variants affect mitochondrial function. With emerging evidence that mitochondrial mechanisms are central to common human diseases, it is plausible that mtDNA variants contribute to the “missing heritability” of several complex traits. Given the central role of mtDNA genes in oxidative phosphorylation, the same genetic variants would be expected to alter the risk of developing several different disorders, but this has not been shown to date. Here we studied 38,638 individuals with 11 major diseases, and 17,483 healthy controls. Imputing missing variants from 7,729 complete mitochondrial genomes, we captured 40.41% of European mtDNA variation. We show that mtDNA variants modifying the risk of developing one disease also modify the risk of developing other diseases, thus providing independent replication of a disease association in different case and control cohorts. High-risk alleles were more common than protective alleles, indicating that mtDNA is not at equilibrium in the human population, and that recent mutations interact with nuclear loci to modify the risk of developing multiple common diseases.     

Power analysis for case–control association studies of samples with known family histories

Genome-wide case–control studies have been widely used to identify genetic variants that predispose to human diseases. Such studies are powerful in detecting common genetic variants with moderate effects, but quickly lose power as allele frequency and genotype relative risk decrease. Because patients with one or more affected relatives are more likely to inherit disease-predisposing alleles of a genetic disease than patients without family histories of the disease, sampling patients with affected relatives almost always increases the frequency of disease predisposing alleles in cases and improves the power of case–control association studies. This paper evaluates the power of case–control studies that select cases and/or controls according to their family histories of disease. Our results showed that this study design can dramatically increase the power of a case–control association study for a wide range of disease types. Because each additional affected relative of a patient reduces the required sample size roughly by a pair of case and control, inclusion of cases with affected relatives can dramatically decrease the required sample size and thus the cost of such studies.     

An evolutionary framework for common diseases: the ancestral-susceptibility model

Unlike rare mendelian diseases, which are due to new mutations (i.e. derived alleles), several alleles that increase the risk to common diseases are ancestral. Moreover, population genetics studies suggest that some derived alleles that protect against common diseases became advantageous recently. These observations can be explained within an evolutionary framework in which ancestral alleles reflect ancient adaptations to the lifestyle of ancient human populations, whereas the derived alleles were deleterious. However, with the shift in environment and lifestyle, the ancestral alleles now increase the risk of common diseases in modern populations. In this article, we develop an explicit evolutionary model and use population genetics simulations to investigate the expected haplotype structure and type of disease-association signals of ancestral risk alleles.     

Allelic Spectra of Risk SNPs Are Different for Environment/Lifestyle Dependent versus Independent Diseases

Genome-wide association studies (GWAS) have generated sufficient data to assess the role of selection in shaping allelic diversity of disease-associated SNPs. Negative selection against disease risk variants is expected to reduce their frequencies making them overrepresented in the group of minor (<50%) alleles. Indeed, we found that the overall proportion of risk alleles was higher among alleles with frequency <50% (minor alleles) compared to that in the group of major alleles. We hypothesized that negative selection may have different effects on environment (or lifestyle)-dependent versus environment (or lifestyle)-independent diseases. We used an environment/lifestyle index (ELI) to assess influence of environmental/lifestyle factors on disease etiology. ELI was defined as the number of publications mentioning “environment” or “lifestyle” AND disease per 1,000 disease-mentioning publications. We found that the frequency distributions of the risk alleles for the diseases with strong environmental/lifestyle components follow the distribution expected under a selectively neutral model, while frequency distributions of the risk alleles for the diseases with weak environmental/lifestyle influences is shifted to the lower values indicating effects of negative selection. We hypothesized that previously selectively neutral variants become risk alleles when environment changes. The hypothesis of ancestrally neutral, currently disadvantageous risk-associated alleles predicts that the distribution of risk alleles for the environment/lifestyle dependent diseases will follow a neutral model since natural selection has not had enough time to influence allele frequencies. The results of our analysis suggest that prediction of SNP functionality based on the level of evolutionary conservation may not be useful for SNPs associated with environment/lifestyle dependent diseases.     

Using Genetic Prediction from Known Complex Disease Loci to Guide the Design of Next-Generation Sequencing Experiments

A central focus of complex disease genetics after genome-wide association studies (GWAS) is to identify low frequency and rare risk variants, which may account for an important fraction of disease heritability unexplained by GWAS. A profusion of studies using next-generation sequencing are seeking such risk alleles. We describe how already-known complex trait loci (largely from GWAS) can be used to guide the design of these new studies by selecting cases, controls, or families who are most likely to harbor undiscovered risk alleles. We show that genetic risk prediction can select unrelated cases from large cohorts who are enriched for unknown risk factors, or multiply-affected families that are more likely to harbor high-penetrance risk alleles. We derive the frequency of an undiscovered risk allele in selected cases and controls, and show how this relates to the variance explained by the risk score, the disease prevalence and the population frequency of the risk allele. We also describe a new method for informing the design of sequencing studies using genetic risk prediction in large partially-genotyped families using an extension of the Inside-Outside algorithm for inference on trees. We explore several study design scenarios using both simulated and real data, and show that in many cases genetic risk prediction can provide significant increases in power to detect low-frequency and rare risk alleles. The same approach can also be used to aid discovery of non-genetic risk factors, suggesting possible future utility of genetic risk prediction in conventional epidemiology. Software implementing the methods in this paper is available in the R package Mangrove.     

Biased Gene Conversion Skews Allele Frequencies in Human Populations,Increasing the Disease Burden of Recessive Alleles

Gene conversion results in the nonreciprocal transfer of genetic information between two recombining sequences, and there is evidence that this process is biased toward G and C alleles. However, the strength of GC-biased gene conversion (gBGC) in human populations and its effects on hereditary disease have yet to be assessed on a genomic scale. Using high-coverage whole-genome sequences of African hunter-gatherers, agricultural populations, and primate outgroups, we quantified the effects of GC-biased gene conversion on population genomic data sets. We find that genetic distances (F_ST and population branch statistics) are modified by gBGC. In addition, the site frequency spectrum is left-shifted when ancestral alleles are favored by gBGC and right-shifted when derived alleles are favored by gBGC. Allele frequency shifts due to gBGC mimic the effects of natural selection. As expected, these effects are strongest in high-recombination regions of the human genome. By comparing the relative rates of fixation of unbiased and biased sites, the strength of gene conversion was estimated to be on the order of Nb ≈ 0.05 to 0.09. We also find that derived alleles favored by gBGC are much more likely to be homozygous than derived alleles at unbiased SNPs (+42.2% to 62.8%). This results in a curse of the converted, whereby gBGC causes substantial increases in hereditary disease risks. Taken together, our findings reveal that GC-biased gene conversion has important population genetic and public health implications.     

Using Lifetime Risk Estimates in Personal Genomic Profiles: Estimation of Uncertainty

Personal genome tests are now offered direct-to-consumer (DTC) via genetic variants identified by genome-wide association studies (GWAS) for common diseases. Tests report risk estimates (age-specific and lifetime) for various diseases based on genotypes at multiple loci. However, uncertainty surrounding such risk estimates has not been systematically investigated. With breast cancer as an example, we examined the combined effect of uncertainties in population incidence rates, genotype frequency, effect sizes, and models of joint effects among genetic variants on lifetime risk estimates. We performed simulations to estimate lifetime breast cancer risk for carriers and noncarriers of genetic variants. We derived population-based cancer incidence rates from Surveillance, Epidemiology, and End Results (SEER) Program and comparative international data. We used data for non-Hispanic white women from 2003 to 2005. We derived genotype frequencies and effect sizes from published GWAS and meta-analyses. For a single genetic variant in FGFR2 gene (rs2981582), combination of uncertainty in these parameters produced risk estimates where upper and lower 95% simulation intervals differed by more than 3-fold. Difference in population incidence rates was the largest contributor to variation in risk estimates. For a panel of five genetic variants, estimated lifetime risk of developing breast cancer before age 80 for a woman that carried all risk variants ranged from 6.1% to 21%, depending on assumptions of additive or multiplicative joint effects and breast cancer incidence rates. Epidemiologic parameters involved in computation of disease risk have substantial uncertainty, and cumulative uncertainty should be properly recognized. Reliance on point estimates alone could be seriously misleading.     

High Trans-ethnic Replicability of GWAS Results Implies Common Causal Variants

Genome-wide association studies (GWAS) have detected many disease associations. However, the reported variants tend to explain small fractions of risk, and there are doubts about issues such as the portability of findings over different ethnic groups or the relative roles of rare versus common variants in the genetic architecture of complex disease. Studying the degree of sharing of disease-associated variants across populations can help in solving these issues. We present a comprehensive survey of GWAS replicability across 28 diseases. Most loci and SNPs discovered in Europeans for these conditions have been extensively replicated using peoples of European and East Asian ancestry, while the replication with individuals of African ancestry is much less common. We found a strong and significant correlation of Odds Ratios across Europeans and East Asians, indicating that underlying causal variants are common and shared between the two ancestries. Moreover, SNPs that failed to replicate in East Asians map into genomic regions where Linkage Disequilibrium patterns differ significantly between populations. Finally, we observed that GWAS with larger sample sizes have detected variants with weaker effects rather than with lower frequencies. Our results indicate that most GWAS results are due to common variants. In addition, the sharing of disease alleles and the high correlation in their effect sizes suggest that most of the underlying causal variants are shared between Europeans and East Asians and that they tend to map close to the associated marker SNPs.     

Evolution and disease converge in the mitochondrion

Mitochondrial DNA (mtDNA) mutations are long known to cause diseases but also underlie tremendous population divergence in humans. It was assumed that the two types of mutations differ in one major trait: functionality. However, evidence from disease association studies, cell culture and animal models support the functionality of common mtDNA genetic variants, leading to the hypothesis that disease-causing mutations and mtDNA genetic variants share considerable common features. Here we provide evidence showing that the two types of mutations obey the rules of evolution, including random genetic drift and natural selection. This similarity does not only converge at the principle level; rather, disease-causing mutations could recapitulate the ancestral DNA sequence state. Thus, the very same mutations could either mark ancient evolutionary changes or cause disease.     

Negative selection on BRCA1 susceptibility alleles sheds light on the population genetics of late-onset diseases and aging theory

The magnitude of negative selection on alleles involved in age-specific mortality decreases with age. This is the foundation of the evolutionary theory of senescence. Because of this decrease in negative selection with age, and because of the absence of reproduction after menopause, alleles involved in women's late-onset diseases are generally considered evolutionarily neutral. Recently, genetic and epidemiological data on alleles involved in late onset-diseases have become available. It is therefore timely to estimate selection on these alleles. Here, we estimate selection on BRCA1 alleles leading to susceptibility to late-onset breast and ovarian cancer. For this, we integrate estimates of the risk of developing a cancer for BRCA1-carriers into population genetics frameworks, and calculate selection coefficients on BRCA1 alleles for different demographic scenarios varying across the extent of human demography. We then explore the magnitude of negative selection on alleles leading to a diverse range of risk patterns, to capture a variety of late-onset diseases. We show that BRCA1 alleles may have been under significant negative selection during human history. Although the mean age of onset of the disease is long after menopause, variance in age of onset means that there are always enough cases occurring before the end of reproductive life to compromise the selective value of women carrying a susceptibility allele in BRCA1. This seems to be the case for an extended range of risk of onset functions varying both in mean and variance. This finding may explain the distribution of BRCA1 alleles' frequency, and also why alleles for many late-onset diseases, like certain familial forms of cancer, coronary artery diseases and Alzheimer dementia, are typically recent and rare. Finally, we discuss why the two most popular evolutionary theories of aging, mutation accumulation and antagonistic pleiotropy, may underestimate the effect of selection on survival at old ages.     

Natural Selection and Functional Potentials of Human Noncoding Elements Revealed by Analysis of Next Generation Sequencing Data

Noncoding DNA sequences (NCS) have attracted much attention recently due to their functional potentials. Here we attempted to reveal the functional roles of noncoding sequences from the point of view of natural selection that typically indicates the functional potentials of certain genomic elements. We analyzed nearly 37 million single nucleotide polymorphisms (SNPs) of Phase I data of the 1000 Genomes Project. We estimated a series of key parameters of population genetics and molecular evolution to characterize sequence variations of the noncoding genome within and between populations, and identified the natural selection footprints in NCS in worldwide human populations. Our results showed that purifying selection is prevalent and there is substantial constraint of variations in NCS, while positive selectionis more likely to be specific to some particular genomic regions and regional populations. Intriguingly, we observed larger fraction of non-conserved NCS variants with lower derived allele frequency in the genome, indicating possible functional gain of non-conserved NCS. Notably, NCS elements are enriched for potentially functional markers such as eQTLs, TF motif, and DNase I footprints in the genome. More interestingly, some NCS variants associated with diseases such as Alzheimer''s disease, Type 1 diabetes, and immune-related bowel disorder (IBD) showed signatures of positive selection, although the majority of NCS variants, reported as risk alleles by genome-wide association studies, showed signatures of negative selection. Our analyses provided compelling evidence of natural selection forces on noncoding sequences in the human genome and advanced our understanding of their functional potentials that play important roles in disease etiology and human evolution.     

Crohn's disease risk alleles on the NOD2 locus have been maintained by natural selection on standing variation

Risk alleles for complex diseases are widely spread throughout human populations. However, little is known about the geographic distribution and frequencies of risk alleles, which may contribute to differences in disease susceptibility and prevalence among populations. Here, we focus on Crohn's disease (CD) as a model for the evolutionary study of complex disease alleles. Recent genome-wide association studies and classical linkage analyses have identified more than 70 susceptible genomic regions for CD in Europeans, but only a few have been confirmed in non-European populations. Our analysis of eight European-specific susceptibility genes using HapMap data shows that at the NOD2 locus the CD-risk alleles are linked with a haplotype specific to CEU at a frequency that is significantly higher compared with the entire genome. We subsequently examined nine global populations and found that the CD-risk alleles spread through hitchhiking with a high-frequency haplotype (H1) exclusive to Europeans. To examine the neutrality of NOD2, we performed phylogenetic network analyses, coalescent simulation, protein structural prediction, characterization of mutation patterns, and estimations of population growth and time to most recent common ancestor (TMRCA). We found that while H1 was significantly prevalent in European populations, the H1 TMRCA predated human migration out of Africa. H1 is likely to have undergone negative selection because 1) the root of H1 genealogy is defined by a preexisting amino acid substitution that causes serious conformational changes to the NOD2 protein, 2) the haplotype has almost become extinct in Africa, and 3) the haplotype has not been affected by the recent European expansion reflected in the other haplotypes. Nevertheless, H1 has survived in European populations, suggesting that the haplotype is advantageous to this group. We propose that several CD-risk alleles, which destabilize and disrupt the NOD2 protein, have been maintained by natural selection on standing variation because the deleterious haplotype of NOD2 is advantageous in diploid individuals due to heterozygote advantage and/or intergenic interactions.     

Genome-Wide Association Studies in an Isolated Founder Population from the Pacific Island of Kosrae

It has been argued that the limited genetic diversity and reduced allelic heterogeneity observed in isolated founder populations facilitates discovery of loci contributing to both Mendelian and complex disease. A strong founder effect, severe isolation, and substantial inbreeding have dramatically reduced genetic diversity in natives from the island of Kosrae, Federated States of Micronesia, who exhibit a high prevalence of obesity and other metabolic disorders. We hypothesized that genetic drift and possibly natural selection on Kosrae might have increased the frequency of previously rare genetic variants with relatively large effects, making these alleles readily detectable in genome-wide association analysis. However, mapping in large, inbred cohorts introduces analytic challenges, as extensive relatedness between subjects violates the assumptions of independence upon which traditional association test statistics are based. We performed genome-wide association analysis for 15 quantitative traits in 2,906 members of the Kosrae population, using novel approaches to manage the extreme relatedness in the sample. As positive controls, we observe association to known loci for plasma cholesterol, triglycerides, and C-reactive protein and to a compelling candidate loci for thyroid stimulating hormone and fasting plasma glucose. We show that our study is well powered to detect common alleles explaining ≥5% phenotypic variance. However, no such large effects were observed with genome-wide significance, arguing that even in such a severely inbred population, common alleles typically have modest effects. Finally, we show that a majority of common variants discovered in Caucasians have indistinguishable effect sizes on Kosrae, despite the major differences in population genetics and environment.     

Geographical distribution and evolutionary history of organophosphate-resistant Ace alleles in the olive fly (Bactrocera oleae)

Acetylcholinesterase (Ace) is the molecular target of organophosphate (OP) insecticides, and two mutations that confer different levels of OP insensitivity have previously been identified in the olive fly, Bactrocera oleae. Numerous sensitive and two insensitive alleles (including one convergent acquisition) are described from the entire worldwide distribution of the fly. Most of the variation is harbored in the native range of the species and in the Middle East and consists of numerous low-frequency sensitive alleles. The insensitive alleles likely came to high frequency more recently in the Mediterranean region or in the Middle East, reaching frequencies as high as 100% in some populations, and determined a corresponding decline in overall genetic variation. We hypothesize that the major force that shaped the current distribution of resistant and non-resistant acetylcholinesterase alleles is natural selection, likely responsible for the high frequency of insensitive alleles in areas where organophosphates have been used extensively. We also discuss a role for historical contingency, that can explain why sensitive alleles are absent altogether in the species ancestral range and present in areas of recent expansion, such as California, despite the limited use of OPs.     

Linkage analysis in the next-generation sequencing era

Linkage analysis was developed to detect excess co-segregation of the putative alleles underlying a phenotype with the alleles at a marker locus in family data. Many different variations of this analysis and corresponding study design have been developed to detect this co-segregation. Linkage studies have been shown to have high power to detect loci that have alleles (or variants) with a large effect size, i.e. alleles that make large contributions to the risk of a disease or to the variation of a quantitative trait. However, alleles with a large effect size tend to be rare in the population. In contrast, association studies are designed to have high power to detect common alleles which tend to have a small effect size for most diseases or traits. Although genome-wide association studies have been successful in detecting many new loci with common alleles of small effect for many complex traits, these common variants often do not explain a large proportion of disease risk or variation of the trait. In the past, linkage studies were successful in detecting regions of the genome that were likely to harbor rare variants with large effect for many simple Mendelian diseases and for many complex traits. However, identifying the actual sequence variant(s) responsible for these linkage signals was challenging because of difficulties in sequencing the large regions implicated by each linkage peak. Current 'next-generation' DNA sequencing techniques have made it economically feasible to sequence all exons or the whole genomes of a reasonably large number of individuals. Studies have shown that rare variants are quite common in the general population, and it is now possible to combine these new DNA sequencing methods with linkage studies to identify rare causal variants with a large effect size. A brief review of linkage methods is presented here with examples of their relevance and usefulness for the interpretation of whole-exome and whole-genome sequence data.