Signatures of natural selection on genetic variants affecting complex human traits

The Myth of the Genetically Sick African

Western medicine has an unfortunate history where it has been applied to address the health of African Americans. At its origins, it was aligned with the objectives of colonialism and chattel slavery. The degree to which medical “science” concerned itself with persons of African descent was to keep them alive for sale on the auction block, or to keep them healthy as they toiled to generate wealth for their European owners. Medicine in early America relied upon both dead and live African bodies to test its ideas to benefit Europeans. As medicine moved from quackery to a discipline based in science, its understanding of human biological variation was flawed. This was not a problem confined to medicine alone, but to the biological sciences in general. Biology had no solid theoretical basis until after 1859. As medicine further developed in the 20th century, it never doubted the difference between Europeans and Africans, and also asserted the innate inferiority of the latter. The genomic revolution in the latter 20th century produced tools that were deployed in a biomedical culture still mired in “racial” medicine. This lack of theoretical perspective still misdirects research associated with health disparity. In contrast to this is evolutionary medicine, which relies on a sound unification of evolutionary (ultimate) and physiological, cellular, and molecular (proximate) mechanisms. Utilizing the perspectives of evolutionary medicine is a prerequisite for an effective intervention in health disparity and finally dispelling the myth of the genetically sick African.

Human biological variation and the "normal"

Anatomically modern human being is a relatively young species (~300 000 years old) with small amounts of genetic variation contained within them. The vast majority of its existence was spent in Eastern Africa, migration out of the region began around 100 000 YBP. Sub-Saharan African populations have the greatest amount of human genetic variation. However, migration allowed populations to accumulate genomic variation associated with living in the arctic, higher altitudes, disease resistance, living on high fat or starchy foods, surviving toxic arsenic-rich environments, lactase persistence, changing skin pigmentation, gaining thicker hair, and changing height and body mass index. Understanding these aspects of human evolution forces us to reconsider our notion of the "normal." Thus, normal for our species includes having dark melanic skin, brown eyes, and brown tightly curled hair. Derived features include lighter skin (~10 000 YBP), blue eyes (~6000 YBP), and blond straight hair (~6000 YBP). Yet in reality, "normal" has no meaning for a species that inhabits such a broad geographic range. Natural selection and genetic drift have genetically differentiated human populations in ways that impact our morphological and physiological traits. The genomic differentiation is small and does not allow any unambiguous classification of human populations into biological races. Despite these now well-established facts of human variation, significant confusion associated with Eurocentric notions of the normal still persist in both the lay public and various professions such as biomedical research and clinical practice.

The Contextualized Genetics of Human Longevity: JACC Focus Seminar

The genetics of human longevity has long been studied, and in this regard, centenarians represent a very informative model. Centenarians are characterized by 2 main features: 1) the capability to avoid or postpone the major age-related diseases; and 2) a high level of heterogeneity of their phenotype. The first suggests that longevity and resistance to diseases are mediated by shared mechanisms, the latter that many strategies can be used to become long lived, likely as a result of variable genome-environment interactions. The authors suggest that the complexity of genome-environment interactions must be considered within an evolutionary and ecological perspective and that the concept of "risk allele" is highly context dependent, changing with age, time, and geography. Genes involved in both longevity and cardiovascular diseases, taken as a paradigmatic example of age-related diseases, as well as other emerging topics in genetics of longevity, such as micro-ribonucleic acid (miRNA) genetics, polygenic risk scores, environmental pollutants, and somatic mutations are discussed.

Genetic correlates of social stratification in Great Britain

Human DNA polymorphisms vary across geographic regions, with the most commonly observed variation reflecting distant ancestry differences. Here we investigate the geographic clustering of common genetic variants that influence complex traits in a sample of ~450,000 individuals from Great Britain. Of 33 traits analysed, 21 showed significant geographic clustering at the genetic level after controlling for ancestry, probably reflecting migration driven by socioeconomic status (SES). Alleles associated with educational attainment (EA) showed the most clustering, with EA-decreasing alleles clustering in lower SES areas such as coal mining areas. Individuals who leave coal mining areas carry more EA-increasing alleles on average than those in the rest of Great Britain. The level of geographic clustering is correlated with genetic associations between complex traits and regional measures of SES, health and cultural outcomes. Our results are consistent with the hypothesis that social stratification leaves visible marks in geographic arrangements of common allele frequencies and gene-environment correlations.

A method for genome-wide genealogy estimation for thousands of samples

Knowledge of genome-wide genealogies for thousands of individuals would simplify most evolutionary analyses for humans and other species, but has remained computationally infeasible. We have developed a method, Relate, scaling to >10,000 sequences while simultaneously estimating branch lengths, mutational ages and variable historical population sizes, as well as allowing for data errors. Application to 1,000 Genomes Project haplotypes produces joint genealogical histories for 26 human populations. Highly diverged lineages are present in all groups, but most frequent in Africa. Outside Africa, these mainly reflect ancient introgression from groups related to Neanderthals and Denisovans, while African signals instead reflect unknown events unique to that continent. Our approach allows more powerful inferences of natural selection than has previously been possible. We identify multiple regions under strong positive selection, and multi-allelic traits including hair color, body mass index and blood pressure, showing strong evidence of directional selection, varying among human groups.

Detecting Signatures of Positive Selection along Defined Branches of a Population Tree Using LSD

Identifying the genomic basis underlying local adaptation is paramount to evolutionary biology, and bears many applications in the fields of conservation biology, crop, and animal breeding, as well as personalized medicine. Although many approaches have been developed to detect signatures of positive selection within single populations and population pairs, the increasing wealth of high-throughput sequencing data requires improved methods capable of handling multiple, and ideally large number of, populations in a single analysis. In this study, we introduce LSD (levels of exclusively shared differences), a fast and flexible framework to perform genome-wide selection scans, along the internal and external branches of a given population tree. We use forward simulations to demonstrate that LSD can identify branches targeted by positive selection with remarkable sensitivity and specificity. We illustrate a range of potential applications by analyzing data from the 1000 Genomes Project and uncover a list of adaptive candidates accompanying the expansion of anatomically modern humans out of Africa and their spread to Europe.

Global genetic differentiation of complex traits shaped by natural selection in humans

There are mean differences in complex traits among global human populations. We hypothesize that part of the phenotypic differentiation is due to natural selection. To address this hypothesis, we assess the differentiation in allele frequencies of trait-associated SNPs among African, Eastern Asian, and European populations for ten complex traits using data of large sample size (up to ~405,000). We show that SNPs associated with height ([Formula: see text]), waist-to-hip ratio ([Formula: see text]), and schizophrenia ([Formula: see text]) are significantly more differentiated among populations than matched "control" SNPs, suggesting that these trait-associated SNPs have undergone natural selection. We further find that SNPs associated with height ([Formula: see text]) and schizophrenia ([Formula: see text]) show significantly higher variance in linkage disequilibrium (LD) scores across populations than control SNPs. Our results support the hypothesis that natural selection has shaped the genetic differentiation of complex traits, such as height and schizophrenia, among worldwide populations.

Human Genetic Adaptation to High Altitudes: Current Status and Future Prospects

The question of whether human populations have adapted genetically to high altitude has been of interest since studies began there in the early 1900s. Initially there was debate as to whether genetic adaptation to high altitude has taken place based, in part, on disciplinary orientation and the sources of evidence being considered. Studies centered on short-term responses, termed acclimatization, and the developmental changes occurring across lifetimes. A paradigm shift occurred with the advent of single nucleotide polymorphism (SNP) technologies and statistical methods for detecting evidence of natural selection, resulting in an exponential rise in the number of publications reporting genetic adaptation. Reviewed here are the various kinds of evidence by which adaptation to high altitude has been assessed and which have led to widespread acceptance of the idea that genetic adaptation to high altitude has occurred. While methodological and other challenges remain for determining the specific gene or genes involved and the physiological mechanisms by which they are exerting their effects, considerable progress has been realized as shown by recent studies in Tibetans, Andeans and Ethiopians. Further advances are anticipated with the advent of new statistical methods, whole-genome sequencing and other molecular techniques for finer-scale genetic mapping, and greater intradisciplinary and interdisciplinary collaboration to identify the functional consequences of the genes or gene regions implicated and the time scales involved.

Genomic and genetic studies of systemic sclerosis: A systematic review

Systemic sclerosis is an autoimmune rheumatic disease characterised by fibrosis, vasculopathy and inflammation. The exact aetiology of SSc remains unknown but evidences show that various genetic factors may be involved. This review aimed to assess HLA alleles/non-HLA polymorphisms, microsatellites and chromosomal abnormalities that have thus far been associated with SSc. PubMed, Embase and Scopus databases were searched up to July 29, 2015 using a combination of search-terms. Articles retrieved were evaluated based on set exclusion and inclusion criteria. A total of 150 publications passed the filters. HLA and non-HLA studies showed that particular alleles in the HLA-DRB1, HLA-DQB1, HLA-DQA1, HLA-DPB1 genes and variants in STAT4, IRF5 and CD247 are frequently associated with SSc. Non-HLA genes analysis was performed using the PANTHER and STRING₁₀ databases. PANTHER classification revealed that inflammation mediated by chemokine and cytokine, interleukin and integrin signalling pathways are among the common extracted pathways associated with SSc. STRING₁₀ analysis showed that NFKB1, CSF3R, STAT4, IFNG, PRL and ILs are the main "hubs" of interaction network of the non-HLA genes associated with SSc. This study gathers data of valid genetic factors associated with SSc and discusses the possible interactions of implicated molecules.

The Impact of Evolutionary Driving Forces on Human Complex Diseases: A Population Genetics Approach

Investigating the molecular evolution of human genome has paved the way to understand genetic adaptation of humans to the environmental changes and corresponding complex diseases. In this review, we discussed the historical origin of genetic diversity among human populations, the evolutionary driving forces that can affect genetic diversity among populations, and the effects of human movement into new environments and gene flow on population genetic diversity. Furthermore, we presented the role of natural selection on genetic diversity and complex diseases. Then we reviewed the disadvantageous consequences of historical selection events in modern time and their relation to the development of complex diseases. In addition, we discussed the effect of consanguinity on the incidence of complex diseases in human populations. Finally, we presented the latest information about the role of ancient genes acquired from interbreeding with ancient hominids in the development of complex diseases.

Inference of Evolutionary Forces Acting on Human Biological Pathways

Because natural selection is likely to act on multiple genes underlying a given phenotypic trait, we study here the potential effect of ongoing and past selection on the genetic diversity of human biological pathways. We first show that genes included in gene sets are generally under stronger selective constraints than other genes and that their evolutionary response is correlated. We then introduce a new procedure to detect selection at the pathway level based on a decomposition of the classical McDonald-Kreitman test extended to multiple genes. This new test, called 2DNS, detects outlier gene sets and takes into account past demographic effects and evolutionary constraints specific to gene sets. Selective forces acting on gene sets can be easily identified by a mere visual inspection of the position of the gene sets relative to their two-dimensional null distribution. We thus find several outlier gene sets that show signals of positive, balancing, or purifying selection but also others showing an ancient relaxation of selective constraints. The principle of the 2DNS test can also be applied to other genomic contrasts. For instance, the comparison of patterns of polymorphisms private to African and non-African populations reveals that most pathways show a higher proportion of nonsynonymous mutations in non-Africans than in Africans, potentially due to different demographic histories and selective pressures.

A population genetic signal of polygenic adaptation

Adaptation in response to selection on polygenic phenotypes may occur via subtle allele frequencies shifts at many loci. Current population genomic techniques are not well posed to identify such signals. In the past decade, detailed knowledge about the specific loci underlying polygenic traits has begun to emerge from genome-wide association studies (GWAS). Here we combine this knowledge from GWAS with robust population genetic modeling to identify traits that may have been influenced by local adaptation. We exploit the fact that GWAS provide an estimate of the additive effect size of many loci to estimate the mean additive genetic value for a given phenotype across many populations as simple weighted sums of allele frequencies. We use a general model of neutral genetic value drift for an arbitrary number of populations with an arbitrary relatedness structure. Based on this model, we develop methods for detecting unusually strong correlations between genetic values and specific environmental variables, as well as a generalization of comparisons to test for over-dispersion of genetic values among populations. Finally we lay out a framework to identify the individual populations or groups of populations that contribute to the signal of overdispersion. These tests have considerably greater power than their single locus equivalents due to the fact that they look for positive covariance between like effect alleles, and also significantly outperform methods that do not account for population structure. We apply our tests to the Human Genome Diversity Panel (HGDP) dataset using GWAS data for height, skin pigmentation, type 2 diabetes, body mass index, and two inflammatory bowel disease datasets. This analysis uncovers a number of putative signals of local adaptation, and we discuss the biological interpretation and caveats of these results.

The process of adaptation is of fundamental importance in evolutionary biology. Within the last few decades, genotyping technologies and new statistical methods have given evolutionary biologists the ability to identify individual regions of the genome that are likely to have been important in this process. When adaptation occurs in traits that are underwritten by many genes, however, the genetic signals left behind are more diffuse, and no individual region of the genome is likely to show strong signatures of selection. Identifying this signature therefore requires a detailed annotation of sites associated with a particular phenotype. Here we develop and implement a suite of statistical methods to integrate this sort of annotation from genome wide association studies with allele frequency data from many populations, providing a powerful way to identify the signal of adaptation in polygenic traits. We apply our methods to test for the impact of selection on human height, skin pigmentation, body mass index, type 2 diabetes risk, and inflammatory bowel disease risk. We find relatively strong signals for height and skin pigmentation, moderate signals for inflammatory bowel disease, and comparatively little evidence for body mass index and type 2 diabetes risk.