scholarly journals Dissecting dynamics and differences of selective pressures in the evolution of human pigmentation

2018 ◽  
Author(s):  
Xin Huang ◽  
Yungang He ◽  
Sijia Wang ◽  
Li Jin
Keyword(s):  
Natural Selection ◽  
Ultraviolet Radiation ◽  
Complex Traits ◽  
Complex Trait ◽  
Color Variation ◽  
Human Populations ◽  
Selective Pressures ◽  
Multiple Genes ◽  
Dark Pigmentation ◽  
Time Periods

AbstractHuman pigmentation is a highly diverse and complex trait among populations, and has drawn particular attention from both academic and non-academic investigators for thousands of years. Previous studies detected selection signals in several human pigmentation genes, but few studies have integrated contribution from multiple genes to the evolution of human pigmentation. Moreover, none has quantified selective pressures on human pigmentation over epochs and between populations. Here, we dissect dynamics and differences of selective pressures during different periods and between distinct populations with new approaches. We propose a new model with multiple populations to estimate historical selective pressures by summarizing selective pressures on multiple genes. We use genotype data of 19 genes associated with human pigmentation from 17 datasets, and obtain data for 2346 individuals of six representative population groups from worldwide. Our results quantify selective pressures on light pigmentation not only in modern Europeans (0.0249/generation) but also in proto-Eurasians (0.00665/generation). Our results also support several derived alleles associated with human dark pigmentation may under directional selection by quantifying differences of selective pressures between populations. Our study provides a first attempt to quantitatively investigate the dynamics of selective pressures during different time periods in the evolution of human pigmentation, and may facilitate studies of the evolution of other complex traits.Author SummaryThe color variation of human skin, hair, and eye is affected by multiple genes with different roles. This diversity may be shaped by natural selection and adapted for ultraviolet radiation in different environments around the world. As human populations migrated out from Africa, the ultraviolet radiation in the environment they encountered also changed. It is possible that the selective pressures on human pigmentation varied throughout human evolutionary history. In this study, we develop a new approach and estimate historical selective pressures on light pigmentation not only in modern Europeans but also in proto-Eurasians. To our best knowledge, this is the first study that quantifies selective pressures during different time periods in the evolution of human pigmentation. Besides, we provide statistical evidence to support several genes associated with human dark pigmentation may be favored by natural selection. Thus, natural selection may not only affect light pigmentation in Eurasians, but also influence dark pigmentation in Africans.

scholarly journals Dissecting dynamics and differences of selective pressures in the evolution of human pigmentation

Biology Open ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. bio056523
Author(s):  
Xin Huang ◽  
Sijia Wang ◽  
Li Jin ◽  
Yungang He
Keyword(s):  
Natural Selection ◽  
Complex Trait ◽  
Directional Selection ◽  
Genotype Data ◽  
Population Groups ◽  
Selective Pressures ◽  
African Populations ◽  
The World ◽  
Dark Pigmentation ◽  
Time Periods

ABSTRACTHuman pigmentation is a highly diverse and complex trait among populations and has drawn particular attention from both academic and non-academic investigators for thousands of years. Previous studies detected selection signals in several human pigmentation genes, but few studies have integrated contribution from multiple genes to the evolution of human pigmentation. Moreover, none has quantified selective pressures on human pigmentation over epochs and between populations. Here, we dissect dynamics and differences of selective pressures during different periods and between distinct populations with new approaches. We use genotype data of 19 genes associated with human pigmentation from 17 publicly available datasets and obtain data for 2346 individuals of six representative population groups from across the world. Our results quantify the strength of natural selection on light pigmentation not only in modern Europeans (0.0259/generation) but also in proto-Eurasians (0.00650/generation). Our results also suggest that several derived alleles associated with human dark pigmentation may be under positive directional selection in some African populations. Our study provides the first attempt to quantitatively investigate the dynamics of selective pressures during different time periods in the evolution of human pigmentation.This article has an associated First Person interview with the first author of the article.

scholarly journals Natural selection influenced the genetic architecture of brain structure, behavioral and neuropsychiatric traits

2020 ◽  
Author(s):  
Frank R Wendt ◽  
Gita A Pathak ◽  
Cassie Overstreet ◽  
Daniel S Tylee ◽  
Joel Gelernter ◽  
...  
Keyword(s):  
Natural Selection ◽  
Complex Traits ◽  
Association Studies ◽  
Model Fit ◽  
Human Populations ◽  
Genome Wide Association Studies ◽  
Loss Of Function ◽  
Risk Alleles ◽  
Genome Wide ◽  
Improved Model

AbstractNatural selection has shaped the phenotypic characteristics of human populations. Genome-wide association studies (GWAS) have elucidated contributions of thousands of common variants with small effects on an individual’s predisposition to complex traits (polygenicity), as well as wide-spread sharing of risk alleles across traits in the human phenome (pleiotropy). It remains unclear how the pervasive effects of natural selection influence polygenicity in brain-related traits. We investigate these effects by annotating the genome with measures of background (BGS) and positive selection, indications of Neanderthal introgression, measures of functional significance including loss-of-function (LoF) intolerant and genic regions, and genotype networks in 75 brain-related traits. Evidence of natural selection was determined using binary annotations of top 2%, 1%, and 0.5% of selection scores genome-wide. We detected enrichment (q<0.05) of SNP-heritability at loci with elevated BGS (7 phenotypes) and in genic (34 phenotypes) and LoF-intolerant regions (67 phenotypes). BGS (top 2%) significantly predicted effect size variance for trait-associated loci (σ2 parameter) in 75 brain-related traits (β=4.39×10−5, p=1.43×10−5, model r2=0.548). By including the number of DSM-5 diagnostic combinations per psychiatric disorder, we substantially improved model fit (σ2 ~ BTop2% × Genic × diagnostic combinations; model r2=0.661). We show that GWAS with larger variance in risk locus effect sizes are collectively predicted by the effects of loci under strong BGS and in regulatory regions of the genome. We further show that diagnostic complexity exacerbates this relationship and perhaps dampens the ability to detect psychiatric risk loci.

scholarly journals Widespread signatures of negative selection in the genetic architecture of human complex traits

2017 ◽  
Author(s):  
Jian Zeng ◽  
Ronald de Vlaming ◽  
Yang Wu ◽  
Matthew R Robinson ◽  
Luke Lloyd-Jones ◽  
...  
Keyword(s):  
Gene Expression ◽  
Natural Selection ◽  
Effect Size ◽  
Complex Traits ◽  
Negative Selection ◽  
Complex Trait ◽  
Mixed Linear Model ◽  
Phenotypic Variance ◽  
The Relationship ◽  
Human Complex

AbstractEstimation of the joint distribution of effect size and minor allele frequency (MAF) for genetic variants is important for understanding the genetic basis of complex trait variation and can be used to detect signature of natural selection. We develop a Bayesian mixed linear model that simultaneously estimates SNP-based heritability, polygenicity (i.e. the proportion of SNPs with nonzero effects) and the relationship between effect size and MAF for complex traits in conventionally unrelated individuals using genome-wide SNP data. We apply the method to 28 complex traits in the UK Biobank data (N = 126,752), and show that on average across 28 traits, 6% of SNPs have nonzero effects, which in total explain 22% of phenotypic variance. We detect significant (p < 0.05/28 =1.8×10−3) signatures of natural selection for 23 out of 28 traits including reproductive, cardiovascular, and anthropometric traits, as well as educational attainment. We further apply the method to 27,869 gene expression traits (N = 1,748), and identify 30 genes that show significant (p < 2.3×10−6) evidence of natural selection. All the significant estimates of the relationship between effect size and MAF in either complex traits or gene expression traits are consistent with a model of negative selection, as confirmed by forward simulation. We conclude that natural selection acts pervasively on human complex traits shaping genetic variation in the form of negative selection.

Genome-wide identification of cis DNA methylation quantitative trait loci in three Southeast Asian Populations

2021 ◽  
Author(s):  
Irfahan Kassam ◽  
Sili Tan ◽  
Fei Fei Gan ◽  
Woei-Yuh Saw ◽  
Linda Wei-Lin Tan ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Complex Traits ◽  
Epigenetic Modification ◽  
Complex Trait ◽  
Southeast Asian ◽  
Human Populations ◽  
Significance Level ◽  
Asian Populations ◽  
Using Data ◽  
N 93

Abstract DNA methylation (DNAm) is an epigenetic modification that acts to regulate gene transcription, is essential for cellular processes and plays an important role in complex traits and disease. Variation in DNAm levels is influenced by both genetic and environmental factors. Several studies have examined the extent to which common genetic variation influences DNAm (i.e. mQTLs), however, an improved understanding of mQTLs across diverse human populations is needed to increase their utility in integrative genomic studies in order to further our understanding of complex trait and disease biology. Here, we systematically examine cis-mQTLs in three Southeast Asian populations in the Singapore Integrative Omics (iOmics) Study, comprised of Chinese (n = 93), Indians (n = 83) and Malays (n = 78). A total of 24 851 cis-mQTL probes were associated with at least one SNP in meta- and ethnicity-specific analyses at a stringent significance level. These cis-mQTL probes show significant differences in local SNP heritability between the ethnicities, enrichment in functionally relevant regions using data from the Roadmap Epigenomics Mapping Consortium and are associated with nearby genes and complex traits due to pleiotropy. Importantly, DNAm prediction performance and the replication of cis-mQTLs both within iOmics and between two independent mQTL studies in European and Bangladeshi individuals is best when the genetic distance between the ethnicities is small, with differences in cis-mQTLs likely due to differences in allele frequency and linkage disequilibrium. This study highlights the importance of, and opportunities from, extending investigation of the genetic control of DNA methylation to Southeast Asian populations.

A Primer of Population Genetics and Genomics

2020 ◽  
Author(s):  
Daniel L. Hartl
Keyword(s):  
Population Genetics ◽  
Complex Traits ◽  
Evolutionary Biology ◽  
Knowledge Retention ◽  
Learning By Doing ◽  
Human Populations ◽  
Gene Drive ◽  
Molecular Population Genetics ◽  
Genetics And Genomics ◽  
And Mathematics

A Primer of Population Genetics and Genomics, 4th edition, has been completely revised and updated to provide a concise but comprehensive introduction to the basic concepts of population genetics and genomics. Recent textbooks have tended to focus on such specialized topics as the coalescent, molecular evolution, human population genetics, or genomics. This primer bucks that trend by encouraging a broader familiarity with, and understanding of, population genetics and genomics as a whole. The overview ranges from mating systems through the causes of evolution, molecular population genetics, and the genomics of complex traits. Interwoven are discussions of ancient DNA, gene drive, landscape genetics, identifying risk factors for complex diseases, the genomics of adaptation and speciation, and other active areas of research. The principles are illuminated by numerous examples from a wide variety of animals, plants, microbes, and human populations. The approach also emphasizes learning by doing, which in this case means solving numerical or conceptual problems. The rationale behind this is that the use of concepts in problem-solving lead to deeper understanding and longer knowledge retention. This accessible, introductory textbook is aimed principally at students of various levels and abilities (from senior undergraduate to postgraduate) as well as practising scientists in the fields of population genetics, ecology, evolutionary biology, computational biology, bioinformatics, biostatistics, physics, and mathematics.

scholarly journals Leveraging Multiple Layers of Data To Predict Drosophila Complex Traits

2020 ◽  
Vol 10 (12) ◽  
pp. 4599-4613
Author(s):  
Fabio Morgante ◽  
Wen Huang ◽  
Peter Sørensen ◽  
Christian Maltecca ◽  
Trudy F. C. Mackay
Keyword(s):  
Precision Agriculture ◽  
Complex Traits ◽  
Prediction Accuracy ◽  
Cellular Response ◽  
Complex Trait ◽  
Sources Of Information ◽  
Starvation Resistance ◽  
Expression Levels ◽  
Chill Coma ◽  
Additional Layer

The ability to accurately predict complex trait phenotypes from genetic and genomic data are critical for the implementation of personalized medicine and precision agriculture; however, prediction accuracy for most complex traits is currently low. Here, we used data on whole genome sequences, deep RNA sequencing, and high quality phenotypes for three quantitative traits in the ∼200 inbred lines of the Drosophila melanogaster Genetic Reference Panel (DGRP) to compare the prediction accuracies of gene expression and genotypes for three complex traits. We found that expression levels (r = 0.28 and 0.38, for females and males, respectively) provided higher prediction accuracy than genotypes (r = 0.07 and 0.15, for females and males, respectively) for starvation resistance, similar prediction accuracy for chill coma recovery (null for both models and sexes), and lower prediction accuracy for startle response (r = 0.15 and 0.14 for female and male genotypes, respectively; and r = 0.12 and 0.11, for females and male transcripts, respectively). Models including both genotype and expression levels did not outperform the best single component model. However, accuracy increased considerably for all the three traits when we included gene ontology (GO) category as an additional layer of information for both genomic variants and transcripts. We found strongly predictive GO terms for each of the three traits, some of which had a clear plausible biological interpretation. For example, for starvation resistance in females, GO:0033500 (r = 0.39 for transcripts) and GO:0032870 (r = 0.40 for transcripts), have been implicated in carbohydrate homeostasis and cellular response to hormone stimulus (including the insulin receptor signaling pathway), respectively. In summary, this study shows that integrating different sources of information improved prediction accuracy and helped elucidate the genetic architecture of three Drosophila complex phenotypes.

Trends in temperate Australian grass breeding and selection

2003 ◽  
Vol 54 (3) ◽  
pp. 211 ◽  
Author(s):  
Rex Oram ◽  
Greg Lodge
Keyword(s):  
Natural Selection ◽  
Introduced Species ◽  
Complex Traits ◽  
Environmental Changes ◽  
Breeding Systems ◽  
Ecological Knowledge ◽  
Native Grasses ◽  
Species Diversification ◽  
Cultivar Development ◽  
Grass Breeding

Current trends in grass cultivar development are reviewed, with respect to the range of species involved, and the objectives and methodology within each species. Extrapolations and predictions are made about future directions and methodologies. It is assumed that selection will necessarily cater for the following environmental changes: (1) higher year-round temperatures, higher variability of rainfall incidence, and lower total winter and spring rainfall along the south of the continent; (2) higher nutrient and lime inputs as land utilisation intensifies; and (3) the grazing management requirements of the important pasture components will be increasingly defined and met in practice.The 'big four' species, perennial ryegrass, phalaris, cocksfoot and tall fescue, will continue to be the most widely sown species in temperate regions for many decades, with the latter 3 increasing most in area and genetic differentiation. However, species diversification will continue, especially with native grasses, legumes, and shrubs from fertile regions of Australia and exotics from little-explored parts of the world, such as South Africa, western North and South America, coastal Caucasus, and Iraq–Iran. By contrast, the recent high rate of species diversification in the tropics and subtropics will probably give way to a much lower rate of cultivar development by refinement and diversification within the established species. Domestication of native grasses will continue for amenity, recreational, land protection, and grazing purposes. As seed harvesting technologies and ecological knowledge improve, natural stands will become increasingly important as local sources of seed. It is suggested that many native grasses have been greatly changed by natural selection so as to withstand strong competition from introduced species under conditions of higher soil fertility and grazing pressure. Conversely, some introduced species are being selected consciously and naturally to persist in regions with irregular rainfall and less fertile soils. Therefore, the distinction between native and introduced grasses may be disappearing, and many populations of native species could now be as foreign to the habitats of pre-European settlement as are populations of introduced species that have been evolving here for 50–200 years. Methods used for genetic improvement will continue to be selection among both overseas accessions and the many native and introduced populations that have responded to natural selection in Australia. As well, there will be deliberate recurrent crossing and selection programs in both native and introduced species for specific purposes and environments. Increasingly, molecular biology methods will complement traditional ones, at first by the provision of DNA markers to assist the selection of complex traits, and for proving distinctness to obtain Plant Breeders' Rights for new cultivars. Later, genetic engineering will be used to manipulate nutritive value, resistance to fungal and viral diseases, and breeding systems, especially cytoplasmic male sterility and apomixis, to utilise heterosis in hybrid cultivars of grasses, particularly for dairying and intensive meat production.Areas where the practice and management of grass breeding and selection programs could be improved are highlighted throughout the review, and reiterated in a concluding statement. Most problems appear to stem from inadequate training in population ecology, population genetics, evolution, and quantitative inheritance.

scholarly journals Dynamics of natural selection in two generations (on the example of the population of the Kirovograd region)

2021 ◽  
Vol 29 ◽  
pp. 152-156
Author(s):  
K. K. Kovleva ◽  
N.A. Kozak
Keyword(s):  
Natural Selection ◽  
First Generation ◽  
Second Generation ◽  
Selection Index ◽  
Genetic Load ◽  
Modern Medicine ◽  
Reproductive Age ◽  
Human Populations ◽  
Medical Histories ◽  
Total Selection

Aim. In connection with the success of modern medicine, the pressure of natural selection in various civilized human populations is weakening, which leads to the accumulation of a genetic load. The purpose of this work was to trace the change in the intensity of natural selection among population of the Kirovograd region in two successive generations. Methods. The collection of material was carried out in 2020 and 2021. Anonymous questionnaires were conducted and medical histories of women of post-reproductive age of the Kirovograd region were studied. The first generation included 40 women born in 1937–1959; the second generation consists of 273 women born in 1960–1981. Results. The total selection index was 0.27 in the first generation, and 0.37 in the second generation. The percentage of women who have not had pregnancies increased from the first generation to the second from 2.5 to 3.7, respectively. Conclusions. The index of total selection in the Kirovograd region population for one generation increased by almost one and a half times (from 0.27 to 0.37), as well as the index of differential fertility (from 0.25 to 0.35). Keywords: reproductive characteristics, Kirovograd population, Crow's index, selection, generations.

scholarly journals Opportunities and limits of combining microbiome and genome data for complex trait prediction

2020 ◽  
Author(s):  
Miguel Pérez-Enciso ◽  
Laura M. Zingaretti ◽  
Yuliaxis Ramayo-Caldas ◽  
Gustavo de los Campos
Keyword(s):  
Complex Traits ◽  
Variance Components ◽  
Complex Trait ◽  
Parameter Estimates ◽  
Phenotypic Variance ◽  
Genome Data ◽  
Individual Snps ◽  
Simulation Strategy ◽  
Microbiome Data ◽  
Trait Prediction

AbstractThe analysis and prediction of complex traits using microbiome data combined with host genomic information is a topic of utmost interest. However, numerous questions remain to be answered: How useful can the microbiome be for complex trait prediction? Are microbiability estimates reliable? Can the underlying biological links between the host’s genome, microbiome, and the phenome be recovered? Here, we address these issues by (i) developing a novel simulation strategy that uses real microbiome and genotype data as input, and (ii) proposing a variance-component approach which, in the spirit of mediation analyses, quantifies the proportion of phenotypic variance explained by genome and microbiome, and dissects it into direct and indirect effects. The proposed simulation approach can mimic a genetic link between the microbiome and SNP data via a permutation procedure that retains the distributional properties of the data. Results suggest that microbiome data could significantly improve phenotype prediction accuracy, irrespective of whether some abundances are under direct genetic control by the host or not. Overall, random-effects linear methods appear robust for variance components estimation, despite the highly leptokurtic distribution of microbiota abundances. Nevertheless, we observed that accuracy depends in part on the number of microorganisms’ taxa influencing the trait of interest. While we conclude that overall genome-microbiome-links can be characterized via variance components, we are less optimistic about the possibility of identifying the causative effects, i.e., individual SNPs affecting abundances; power at this level would require much larger sample sizes than the ones typically available for genome-microbiome-phenome data.Author summaryThe microbiome consists of the microorganisms that live in a particular environment, including those in our organism. There is consistent evidence that these communities play an important role in numerous traits of relevance, including disease susceptibility or feed efficiency. Moreover, it has been shown that the microbiome can be relatively stable throughout an individual’s life and that is affected by the host genome. These reasons have prompted numerous studies to determine whether and how the microbiome can be used for prediction of complex phenotypes, either using microbiome alone or in combination with host’s genome data. However, numerous questions remain to be answered such as the reliability of parameter estimates, or which is the underlying relationship between microbiome, genome, and phenotype. The few available empirical studies do not provide a clear answer to these problems. Here we address these issues by developing a novel simulation strategy and we show that, although the microbiome can significantly help in prediction, it will be difficult to retrieve the actual biological basis of interactions between the microbiome and the trait.

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