scholarly journals Highly pleiotropic variants of human traits are enriched in genomic regions with strong background selection

2021 ◽  
Author(s):  
Irene Novo ◽  
Eugenio López-Cortegano ◽  
Armando Caballero
Keyword(s):  
Complex Traits ◽  
Rare Variants ◽  
Purifying Selection ◽  
Frequency Effect ◽  
Background Selection ◽  
The Mean ◽  
Using Data ◽  
Genomic Regions ◽  
Human Complex ◽  
Gwas Catalog

AbstractRecent studies have shown the ubiquity of pleiotropy for variants affecting human complex traits. These studies also show that rare variants tend to be less pleiotropic than common ones, suggesting that purifying natural selection acts against highly pleiotropic variants of large effect. Here, we investigate the mean frequency, effect size and recombination rate associated with pleiotropic variants, and focus particularly on whether highly pleiotropic variants are enriched in regions with putative strong background selection. We evaluate variants for 41 human traits using data from the NHGRI-EBI GWAS Catalog, as well as data from other three studies. Our results show that variants involving a higher degree of pleiotropy tend to be more common, have larger mean effect sizes, and contribute more to heritability than variants with a lower degree of pleiotropy. This is consistent with the fact that variants of large effect and frequency are more likely detected by GWAS. Using data from four different studies, we also show that more pleiotropic variants are enriched in genome regions with stronger background selection than less pleiotropic variants, suggesting that highly pleiotropic variants are subjected to strong purifying selection. From the above results, we hypothesized that a number of highly pleiotropic variants of low effect/frequency may pass undetected by GWAS.

scholarly journals Highly pleiotropic variants of human traits are enriched in genomic regions with strong background selection

2021 ◽  
Author(s):  
Irene Novo ◽  
Eugenio López-Cortegano ◽  
Armando Caballero
Keyword(s):  
Complex Traits ◽  
Rare Variants ◽  
Effect Sizes ◽  
Frequency Effect ◽  
Background Selection ◽  
The Mean ◽  
Using Data ◽  
Genomic Regions ◽  
Human Complex ◽  
Gwas Catalog

Abstract Recent studies have shown the ubiquity of pleiotropy for variants affecting human complex traits. These studies also show that rare variants tend to be less pleiotropic than common ones, suggesting that purifying natural selection acts against highly pleiotropic variants of large effect. Here we investigate the mean frequency, effect size and recombination rate associated with pleiotropic variants, and focus particularly on whether highly pleiotropic variants are enriched in regions with putative strong background selection. We evaluate variants for 41 human traits using data from the NHGRI-EBI GWAS Catalog, as well as data from other three studies. Our results show that variants involving a higher degree of pleiotropy tend to be more common, have larger mean effect sizes, and contribute more to heritability than variants with a lower degree of pleiotropy. Using data from four different studies, we show that more pleiotropic variants are enriched in genome regions with stronger background selection than less pleiotropic variants. Thus, we conclude that even though highly pleiotropic variants found so far have larger average effect sizes and frequencies than less pleiotropic ones, they are likely to be subjected to stronger background selection.

Regions of Lower Crossing Over Harbor More Rare Variants in African Populations ofDrosophila melanogaster

Genetics ◽  
2001 ◽  
Vol 158 (2) ◽  
pp. 657-665 ◽  
Author(s):  
Peter Andolfatto ◽  
Molly Przeworski
Keyword(s):  
Frequency Spectrum ◽  
Nucleotide Diversity ◽  
Rare Variants ◽  
Purifying Selection ◽  
Crossing Over ◽  
Background Selection ◽  
Deleterious Mutations ◽  
African Populations ◽  
Rapid Fixation ◽  
Parameter Values

AbstractA correlation between diversity levels and rates of recombination is predicted both by models of positive selection, such as hitchhiking associated with the rapid fixation of advantageous mutations, and by models of purifying selection against strongly deleterious mutations (commonly referred to as “background selection”). With parameter values appropriate for Drosophila populations, only the first class of models predicts a marked skew in the frequency spectrum of linked neutral variants, relative to a neutral model. Here, we consider 29 loci scattered throughout the Drosophila melanogaster genome. We show that, in African populations, a summary of the frequency spectrum of polymorphic mutations is positively correlated with the meiotic rate of crossing over. This pattern is demonstrated to be unlikely under a model of background selection. Models of weakly deleterious selection are not expected to produce both the observed correlation and the extent to which nucleotide diversity is reduced in regions of low (but nonzero) recombination. Thus, of existing models, hitchhiking due to the recurrent fixation of advantageous variants is the most plausible explanation for the data.

scholarly journals Evolution of polygenic traits under global vs local adaptation

Genetics ◽  
2022 ◽  
Vol 220 (1) ◽  
Author(s):  
Sam Yeaman
Keyword(s):  
Local Adaptation ◽  
Complex Traits ◽  
Genetic Architecture ◽  
Population Genomics ◽  
Natural Populations ◽  
Evolutionary Process ◽  
Frequency Effect ◽  
Theoretical Predictions ◽  
Using Data ◽  
And Migration

Abstract Observations about the number, frequency, effect size, and genomic distribution of alleles associated with complex traits must be interpreted in light of evolutionary process. These characteristics, which constitute a trait’s genetic architecture, can dramatically affect evolutionary outcomes in applications from agriculture to medicine, and can provide a window into how evolution works. Here, I review theoretical predictions about the evolution of genetic architecture under spatially homogeneous, global adaptation as compared with spatially heterogeneous, local adaptation. Due to the tension between divergent selection and migration, local adaptation can favor “concentrated” genetic architectures that are enriched for alleles of larger effect, clustered in a smaller number of genomic regions, relative to expectations under global adaptation. However, the evolution of such architectures may be limited by many factors, including the genotypic redundancy of the trait, mutation rate, and temporal variability of environment. I review the circumstances in which predictions differ for global vs local adaptation and discuss where progress can be made in testing hypotheses using data from natural populations and lab experiments. As the field of comparative population genomics expands in scope, differences in architecture among traits and species will provide insights into how evolution works, and such differences must be interpreted in light of which kind of selection has been operating.

scholarly journals Background selection and biased gene conversion affect more than 95% of the human genome and bias demographic inferences

eLife ◽  
2018 ◽  
Vol 7 ◽  
Author(s):  
Fanny Pouyet ◽  
Simon Aeschbacher ◽  
Alexandre Thiéry ◽  
Laurent Excoffier
Keyword(s):  
Gene Conversion ◽  
Purifying Selection ◽  
Genomic Diversity ◽  
Background Selection ◽  
Recombination Rates ◽  
Biased Gene Conversion ◽  
Synonymous Sites ◽  
History Of ◽  
Demographic Inference ◽  
Genomic Regions

Disentangling the effect on genomic diversity of natural selection from that of demography is notoriously difficult, but necessary to properly reconstruct the history of species. Here, we use high-quality human genomic data to show that purifying selection at linked sites (i.e. background selection, BGS) and GC-biased gene conversion (gBGC) together affect as much as 95% of the variants of our genome. We find that the magnitude and relative importance of BGS and gBGC are largely determined by variation in recombination rate and base composition. Importantly, synonymous sites and non-transcribed regions are also affected, albeit to different degrees. Their use for demographic inference can lead to strong biases. However, by conditioning on genomic regions with recombination rates above 1.5 cM/Mb and mutation types (C↔G, A↔T), we identify a set of SNPs that is mostly unaffected by BGS or gBGC, and that avoids these biases in the reconstruction of human history.

scholarly journals Using gene genealogies to localize rare variants associated with complex traits in diploid populations

2017 ◽  
Author(s):  
Charith B. Karunarathna ◽  
Jinko Graham
Keyword(s):  
Complex Traits ◽  
Rare Variants ◽  
Causal Variant ◽  
Mantel Test ◽  
Diploid Population ◽  
Exact Test ◽  
Causal Variants ◽  
Genomic Regions ◽  
Intermediate Performance ◽  
Diploid Populations

AbstractBackground and AimsMany methods can detect trait association with causal variants in candidate genomic regions; however, a comparison of their ability to localize causal variants is lacking. We extend a previous study of the detection abilities of these methods to a comparison of their localization abilities.MethodsThrough coalescent simulation, we compare several popular association methods. Cases and controls are sampled from a diploid population to mimic human studies. As benchmarks for comparison, we include two methods that cluster phenotypes on the true genealogical trees, a naive Mantel test considered previously in haploid populations and an extension that takes into account whether case haplotypes carry a causal variant. We first work through a simulated dataset to illustrate the methods. We then perform a simulation study to score the localization and detection properties.ResultsIn our simulations, the association signal was localized least precisely by the naive Mantel test and most precisely by its extension. Most other approaches had intermediate performance similar to the single-variant Fisher’s-exact test.ConclusionsOur results confirm earlier findings in haploid populations about potential gains in performance from genealogy-based approaches. They also highlight differences between haploid and diploid populations when localizing and detecting causal variants.

scholarly journals Background selection and biased gene conversion affect more than 95% of the human genome and bias demographic inferences

2019 ◽  
Author(s):  
Fanny Pouyet
Keyword(s):  
Gene Conversion ◽  
Purifying Selection ◽  
Genomic Diversity ◽  
Background Selection ◽  
Recombination Rates ◽  
Biased Gene Conversion ◽  
Synonymous Sites ◽  
History Of ◽  
Demographic Inference ◽  
Genomic Regions

Disentangling the effect on genomic diversity of natural selection from that of demography is notoriously difficult, but necessary to properly reconstruct the history of species. Here, we use high-quality human genomic data to show that purifying selection at linked sites (i.e. background selection, BGS) and GC-biased gene conversion (gBGC) together affect as much as 95% of the variants of our genome. We find that the magnitude and relative importance of BGS and gBGC are largely determined by variation in recombination rate and base composition. Importantly, synonymous sites and non-transcribed regions are also affected, albeit to different degrees. Their use for demographic inference can lead to strong biases. However, by conditioning on genomic regions with recombination rates above 1.5 cM/Mb and mutation types (C↔G, A↔T), we identify a set of SNPs that is mostly unaffected by BGS or gBGC, and that avoids these biases in the reconstruction of human history.

scholarly journals Inferring the Nature of Missing Heritability in Human Traits Using Data from the GWAS Catalog

Genetics ◽  
2019 ◽  
Vol 212 (3) ◽  
pp. 891-904 ◽  
Author(s):  
Eugenio López-Cortegano ◽  
Armando Caballero
Keyword(s):  
Complex Traits ◽  
Statistical Power ◽  
Association Studies ◽  
Genome Wide Association Studies ◽  
Missing Heritability ◽  
Narrow Sense Heritability ◽  
Additive Contribution ◽  
Heritability Estimates ◽  
Using Data ◽  
Gwas Catalog

Thousands of genes responsible for many diseases and other common traits in humans have been detected by Genome Wide Association Studies (GWAS) in the last decade. However, candidate causal variants found so far usually explain only a small fraction of the heritability estimated by family data. The most common explanation for this observation is that the missing heritability corresponds to variants, either rare or common, with very small effect, which pass undetected due to a lack of statistical power. We carried out a meta-analysis using data from the NHGRI-EBI GWAS Catalog in order to explore the observed distribution of locus effects for a set of 42 complex traits and to quantify their contribution to narrow-sense heritability. With the data at hand, we were able to predict the expected distribution of locus effects for 16 traits and diseases, their expected contribution to heritability, and the missing number of loci yet to be discovered to fully explain the familial heritability estimates. Our results indicate that, for 6 out of the 16 traits, the additive contribution of a great number of loci is unable to explain the familial (broad-sense) heritability, suggesting that the gap between GWAS and familial estimates of heritability may not ever be closed for these traits. In contrast, for the other 10 traits, the additive contribution of hundreds or thousands of loci yet to be found could potentially explain the familial heritability estimates, if this were the case. Computer simulations are used to illustrate the possible contribution from nonadditive genetic effects to the gap between GWAS and familial estimates of heritability.

scholarly journals Use of genomic models to study genetic control of environmental variance

2011 ◽  
Vol 93 (2) ◽  
pp. 125-138 ◽  
Author(s):  
YE YANG ◽  
OLE F. CHRISTENSEN ◽  
DANIEL SORENSEN
Keyword(s):  
Complex Traits ◽  
Simulated Data ◽  
Environmental Variance ◽  
Mcmc Algorithms ◽  
Mean And Variance ◽  
Fat Thickness ◽  
The Mean ◽  
Genomic Regions ◽  
Accuracy Of Prediction ◽  
Qtl Effects

SummaryVast amount of genetic marker information is being used to obtain insight into the genetic architecture of complex traits, for locating genomic regions (quantitative trait loci (QTL)) affecting disease and for enhancing the accuracy of prediction of genetic values in selection programmes. The genomic model commonly found in the literature, with marker effects affecting mean only, is extended to investigate putative effects at the level of the environmental variance. Two classes of models are proposed and their behaviour, studied using simulated data, indicates that they are capable of detecting genetic variation at the level of mean and variance. Implementation is via Markov chain Monte Carlo (McMC) algorithms. The models are compared in terms of a measure of global fit, in their ability to detect QTL effects and in terms of their predictive power. The models are subsequently fitted to back fat thickness data in pigs. The analysis of back fat thickness shows that the data support genomic models with effects on the mean but not on the variance. The relative sizes of experiment necessary to detect effects on mean and variance is discussed and an extension of the McMC algorithm is proposed.

scholarly journals Evolution and Functional Impact of Rare Coding Variation from Deep Sequencing of Human Exomes

Science ◽  
2012 ◽  
Vol 337 (6090) ◽  
pp. 64-69 ◽  
Author(s):  
Jacob A. Tennessen ◽  
Abigail W. Bigham ◽  
Timothy D. O’Connor ◽  
Wenqing Fu ◽  
Eimear E. Kenny ◽  
...  
Keyword(s):  
Protein Function ◽  
Complex Traits ◽  
Rare Variants ◽  
Purifying Selection ◽  
Single Nucleotide Variants ◽  
Single Nucleotide ◽  
Protein Coding ◽  
Protein Coding Genes ◽  
Functional Variants ◽  
A Minor

As a first step toward understanding how rare variants contribute to risk for complex diseases, we sequenced 15,585 human protein-coding genes to an average median depth of 111× in 2440 individuals of European (n = 1351) and African (n = 1088) ancestry. We identified over 500,000 single-nucleotide variants (SNVs), the majority of which were rare (86% with a minor allele frequency less than 0.5%), previously unknown (82%), and population-specific (82%). On average, 2.3% of the 13,595 SNVs each person carried were predicted to affect protein function of ~313 genes per genome, and ~95.7% of SNVs predicted to be functionally important were rare. This excess of rare functional variants is due to the combined effects of explosive, recent accelerated population growth and weak purifying selection. Furthermore, we show that large sample sizes will be required to associate rare variants with complex traits.

Sign in / Sign up

Export Citation Format

Share Document