Evolution of Complex Traits in Human Populations

2017 ◽  
pp. 165-186
Author(s):  
Carolina Medina-Gomez ◽  
Oscar Lao ◽  
Fernando Rivadeneira
Keyword(s):  
Complex Traits ◽  
Human 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 Modeling epistasis in mice and yeast using the proportion of two or more distinct genetic backgrounds: evidence for “polygenic epistasis”

2019 ◽  
Author(s):  
Christoph D. Rau ◽  
Natalia M. Gonzales ◽  
Joshua S. Bloom ◽  
Danny Park ◽  
Julien Ayroles ◽  
...  
Keyword(s):  
Gene Expression ◽  
Complex Traits ◽  
Statistical Tests ◽  
Inbred Mice ◽  
Statistical Test ◽  
Genetic Ancestry ◽  
Human Populations ◽  
Epistatic Interactions ◽  
Variable Effect ◽  
The Impact

AbstractBackgroundThe majority of quantitative genetic models used to map complex traits assume that alleles have similar effects across all individuals. Significant evidence suggests, however, that epistatic interactions modulate the impact of many alleles. Nevertheless, identifying epistatic interactions remains computationally and statistically challenging. In this work, we address some of these challenges by developing a statistical test for polygenic epistasis that determines whether the effect of an allele is altered by the global genetic ancestry proportion from distinct progenitors.ResultsWe applied our method to data from mice and yeast. For the mice, we observed 49 significant genotype-by-ancestry interaction associations across 14 phenotypes as well as over 1,400 Bonferroni-corrected genotype-by-ancestry interaction associations for mouse gene expression data. For the yeast, we observed 92 significant genotype-by-ancestry interactions across 38 phenotypes. Given this evidence of epistasis, we test for and observe evidence of rapid selection pressure on ancestry specific polymorphisms within one of the cohorts, consistent with epistatic selection.ConclusionsUnlike our prior work in human populations, we observe widespread evidence of ancestry-modified SNP effects, perhaps reflecting the greater divergence present in crosses using mice and yeast.Author SummaryMany statistical tests which link genetic markers in the genome to differences in traits rely on the assumption that the same polymorphism will have identical effects in different individuals. However, there is substantial evidence indicating that this is not the case. Epistasis is the phenomenon in which multiple polymorphisms interact with one another to amplify or negate each other’s effects on a trait. We hypothesized that individual SNP effects could be changed in a polygenic manner, such that the proportion of as genetic ancestry, rather than specific markers, might be used to capture epistatic interactions. Motivated by this possibility, we develop a new statistical test that allowed us to examine the genome to identify polymorphisms which have different effects depending on the ancestral makeup of each individual. We use our test in two different populations of inbred mice and a yeast panel and demonstrate that these sorts of variable effect polymorphisms exist in 14 different physical traits in mice and 38 phenotypes in yeast as well as in murine gene expression. We use the term “polygenic epistasis” to distinguish these interactions from the more conventional two- or multi-locus interactions.

scholarly journals The HapMap Resource is Providing New Insights into Ourselves and its Application to Pharmacogenomics

2008 ◽  
Vol 2 ◽  
pp. BBI.S455 ◽  
Author(s):  
Wei Zhang ◽  
Mark J. Ratain ◽  
M. Eileen Dolan
Keyword(s):  
Gene Expression ◽  
Complex Traits ◽  
Drug Response ◽  
Human Populations ◽  
International Hapmap Project ◽  
Hapmap Project ◽  
Phenotypic Data ◽  
Human Lymphoblastoid Cell ◽  
Human Lymphoblastoid Cell Lines ◽  
Japanese Ancestry

The exploration of quantitative variation in complex traits such as gene expression and drug response in human populations has become one of the major priorities for medical genetics. The International HapMap Project provides a key resource of genotypic data on human lymphoblastoid cell lines derived from four major world populations of European, African, Chinese and Japanese ancestry for researchers to associate with various phenotypic data to find genes affecting health, disease and response to drugs. Recent progress in dissecting genetic contribution to natural variation in gene expression within and among human populations and variation in drug response are two examples in which researchers have utilized the HapMap resource. The HapMap Project provides new insights into the human genome and has applicability to pharmacogenomics studies leading to personalized medicine.

scholarly journals Polygenic Adaptation has Impacted Multiple Anthropometric Traits

2017 ◽  
Author(s):  
Jeremy J. Berg ◽  
Xinjun Zhang ◽  
Graham Coop
Keyword(s):  
Complex Traits ◽  
Association Studies ◽  
Gwas Data ◽  
Human Populations ◽  
Genome Wide Association Studies ◽  
Uk Biobank ◽  
Link Type ◽  
Polygenic Scores ◽  
Polygenic Adaptation ◽  
The Uk

AbstractOur understanding of the genetic basis of human adaptation is biased toward loci of large pheno-typic effect. Genome wide association studies (GWAS) now enable the study of genetic adaptation in polygenic phenotypes. We test for polygenic adaptation among 187 world-wide human populations using polygenic scores constructed from GWAS of 34 complex traits. We identify signals of polygenic adaptation for anthropometric traits including height, infant head circumference (IHC), hip circumference and waist-to-hip ratio (WHR). Analysis of ancient DNA samples indicates that a north-south cline of height within Europe and and a west-east cline across Eurasia can be traced to selection for increased height in two late Pleistocene hunter gatherer populations living in western and west-central Eurasia. Our observation that IHC and WHR follow a latitudinal cline in Western Eurasia support the role of natural selection driving Bergmann’s Rule in humans, consistent with thermoregulatory adaptation in response to latitudinal temperature variation.Author’s Note on Failure to ReplicateAfter this preprint was posted, the UK Biobank dataset was released, providing a new and open GWAS resource. When attempting to replicate the height selection results from this preprint using GWAS data from the UK Biobank, we discovered that we could not. In subsequent analyses, we determined that both the GIANT consortium height GWAS data, as well as another dataset that was used for replication, were impacted by stratification issues that created or at a minimum substantially inflated the height selection signals reported here. The results of this second investigation, written together with additional coauthors, have now been published (https://elifesciences.org/articles/39725 along with another paper by a separate group of authors, showing similar issues https://elifesciences.org/articles/39702). A preliminary investigation shows that the other non-height based results may suffer from similar issues. We stand by the theory and statistical methods reported in this paper, and the paper can be cited for these results. However, we have shown that the data on which the major empirical results were based are not sound, and so should be treated with caution until replicated.

scholarly journals Detecting gene subnetworks under selection in biological pathways

2017 ◽  
Author(s):  
Alexandre Gouy ◽  
Joséphine T. Daub ◽  
Laurent Excoffier
Keyword(s):  
Complex Traits ◽  
Gene Networks ◽  
High Throughput Sequencing ◽  
Biological Pathways ◽  
General Idea ◽  
Human Populations ◽  
Sequencing Technologies ◽  
Genetic Components ◽  
Evolutionary Features ◽  
Gene Network Analysis

ABSTRACTAdvances in high throughput sequencing technologies have created a gap between data production and functional data analysis. Indeed, phenotypes result from interactions between numerous genes, but traditional methods treat loci independently, missing important knowledge brought by network-level emerging properties. Therefore, evidencing selection acting on multiple genes affecting the evolution of complex traits remains challenging. In this context, gene network analysis provides a powerful framework to study the evolution of adaptive traits and facilitates the interpretation of genome-wide data. To tackle this problem, we developed a method to analyse gene networks that is suitable to evidence polygenic selection. The general idea is to search biological pathways for subnetworks of genes that directly interact with each other and that present unusual evolutionary features. Subnetwork search is a typical combinatorial optimization problem that we solve using a simulated annealing approach. We have applied our methodology to find signals of adaptation to high-altitude in human populations. We show that this adaptation has a clear polygenic basis and is influenced by many genetic components. Our approach improves on classical tests for selection based on single genes by identifying both new candidate genes and new biological processes involved in adaptation to altitude.

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 A simple test identifies selection on complex traits in breeding and experimentally-evolved populations

2017 ◽  
Author(s):  
Tim Beissinger ◽  
Jochen Kruppa ◽  
David Cavero ◽  
Ngoc-Thuy Ha ◽  
Malena Erbe ◽  
...  
Keyword(s):  
Complex Traits ◽  
Association Studies ◽  
Human Populations ◽  
Frequency Change ◽  
Genome Wide Association Studies ◽  
Breeding Populations ◽  
Large Numbers ◽  
Genome Wide ◽  
Single Time Point ◽  
Time Point

AbstractImportant traits in agricultural, natural, and human populations are increasingly being shown to be under the control of many genes that individually contribute only a small proportion of genetic variation. However, the majority of modern tools in quantitative and population genetics, including genome wide association studies and selection mapping protocols, are designed to identify individual genes with large effects. We have developed an approach to identify traits that have been under selection and are controlled by large numbers of loci. In contrast to existing methods, our technique utilizes additive effects estimates from all available markers, and relates these estimates to allele frequency change over time. Using this information, we generate a composite statistic, denoted Ĝ, which can be used to test for significant evidence of selection on a trait. Our test requires pre- and post-selection genotypic data but only a single time point with phenotypic information. Simulations demonstrate that Ĝ is powerful for identifying selection, particularly in situations where the trait being tested is controlled by many genes, which is precisely the scenario where classical approaches for selection mapping are least powerful. We apply this test to breeding populations of maize and chickens, where we demonstrate the successful identification of selection on traits that are documented to have been under selection.

45 Partitioning SNP Heritability in Related Individuals

2021 ◽  
Vol 99 (Supplement_3) ◽  
pp. 24-24
Author(s):  
Jicai Jiang ◽  
Li Ma ◽  
Jeffrey O’Connell
Keyword(s):  
Complex Traits ◽  
Sequence Data ◽  
Whole Genome Sequence ◽  
Unbiased Estimation ◽  
Human Populations ◽  
Functional Annotations ◽  
Long Span ◽  
Order Of Magnitude ◽  
Genomic Studies ◽  
Residual Variances

Abstract Partitioning SNP heritability by many functional annotations has been a successful tool for understanding the genetic architecture of complex traits in human genetic studies. Similar analyses are being extended to animal research, as (imputed) whole-genome sequence data of many individuals and various functional annotations have become available in livestock animals. Though many approaches have been developed for heritability partition (e.g., LDSC and HE-reg), they are mostly based on approximations tailored to human populations and few can produce statistically efficient estimates for animal genomic studies where individuals are often related. To tackle this issue, we present a stochastic MINQUE (Minimum Norm Quadratic Unbiased Estimation) approach for partitioning SNP heritability, which we refer to as MPH. We provide a theoretical analysis comparing LDSC and HE-reg with REML and MPH and demonstrate what LDSC and HE-reg (and similar methods) take advantage of in their approximations: sparse relationships between individuals and relatively weak linkage disequilibrium. We also show that our method is mathematically equivalent to the MC-REML approach implemented in BOLT. MPH has three key features. First, it is comparable to genomic REML in terms of accuracy, while being at least one order of magnitude faster than GCTA and BOLT and using only ~1/4 of memory as much as GCTA, when applied to sequence data and many variance components (or functional annotation categories). Second, it can do weighted analyses if residual variances are unequal (such as DYD). Third, it works for many overlapping functional annotations. Using simulations based on a human pedigree and a dairy cattle pedigree, we illustrate the benefits of our method for partitioning SNP heritability in pedigree-based studies. We also demonstrate that it is feasible to efficiently partition SNP heritability for animal genomes with strong, long-span LD. MPH is freely available at https://jiang18.github.io/mph.

scholarly journals The Power of Association Studies to Detect the Contribution of Candidate Genetic Loci to Variation in Complex Traits

1999 ◽  
Vol 9 (8) ◽  
pp. 720-731 ◽  
Author(s):  
Anthony D. Long ◽  
Charles H. Langley
Keyword(s):  
Complex Traits ◽  
Statistical Power ◽  
Association Studies ◽  
Random Mating ◽  
Human Populations ◽  
Transmission Disequilibrium ◽  
Linkage Disequilibria ◽  
Sufficient Power ◽  
Study Test ◽  
Parameter Values

The statistical power of five association study test statistics (two haplotype-based tests, two marker-based tests, and the Transmission Disequilibrium Test–Q5) to detect single nucleotide polymorphism (SNP)/phenotype associations in a linkage–disequilibrium-based candidate gene scan employing a number of SNPs is examined. Power is estimated as a function of realistic parameters expected to affect the likelihood of detecting a significant association: the number of SNPs examined, the scaled recombination size of the region examined, the proportion of variance in the trait attributable to a hidden causative polymorphism within the region, and the number of individuals or families examined. For the different combinations of parameter values, power is estimated from a large number of realizations of a simulated coalescent describing a single random mating population with mutation, random genetic drift, and recombination. This explicit population genetics model results in a distribution of DNA marker heterozygosities and linkage disequilibria that are likely to resemble those expected in actual population samples. The study concludes that (1) marker-based permutation tests are more powerful than simple haplotype-based tests, (2) there is sufficient power to detect the presence of causative polymorphisms of small effect if on the order of 500 individuals are sampled, (3) greater power is achieved by increasing the sample size than by increasing the number of polymorphisms, (4) association studies are generally more powerful than transmission disequilibrium-based tests, and (5) for the range of parameters considered association studies have a low repeatability unless sample sizes are on the order of 500 individuals. Estimates of 4Nc for a number of gene regions and human populations will be of use in determining the density of SNPs that are likely to be required for successful association studies.

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