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.

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 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 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 A scalable method for estimating the regional polygenicity of complex traits

2020 ◽  
Author(s):  
Ruth Johnson ◽  
Kathryn S. Burch ◽  
Kangcheng Hou ◽  
Mario Paciuc ◽  
Bogdan Pasaniuc ◽  
...  
Keyword(s):  
Complex Traits ◽  
Genetic Architecture ◽  
Human Genetics ◽  
Effect Sizes ◽  
Marginal Effect ◽  
High Cholesterol ◽  
Genome Wide ◽  
Unbiased Estimates ◽  
Genomic Regions ◽  
Insight Into

AbstractA key question in human genetics is understanding the proportion of SNPs modulating a particular phenotype or the proportion of susceptibility SNPs for a disease, termed polygenicity. Previous studies have observed that complex traits tend to be highly polygenic, opposing the previous belief that only a handful of SNPs contribute to a trait. Beyond these genome-wide estimates, the distribution of polygenicity across genomic regions as well as the genomic factors that affect regional polygenicity remain poorly understood. A reason for this gap is that methods for estimating polygenicity utilize SNP effect sizes from GWAS. However, estimating regional polygenicity from GWAS effect sizes involves untangling the correlation between SNPs due to LD, leading to intractable computations for even a small number of SNPs. In this work, we propose a scalable method, BEAVR, to estimate the regional polygenicity of a trait given marginal effect sizes from GWAS and LD information. We implement a Gibbs sampler to estimate the posterior distribution of the regional polygenicity and derive a fast, algorithmic update to circumvent the computational bottlenecks associated with LD. The runtime of our algorithm is 𝒪(MK) for M SNPs and K susceptibility SNPs, where the number of susceptibility SNPs is typically K ≪ M. By modeling the full LD structure, we show that BEAVR provides unbiased estimates of polygenicity compared to previous methods that only partially model LD. Finally, we show how estimates of regional polygenicity for BMI, eczema, and high cholesterol provide insight into the regional genetic architecture of each trait.

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 Quantification of frequency-dependent genetic architectures and action of negative selection in 25 UK Biobank traits

2017 ◽  
Author(s):  
Armin P Schoech ◽  
Daniel Jordan ◽  
Po-Ru Loh ◽  
Steven Gazal ◽  
Luke O’Connor ◽  
...  
Keyword(s):  
Rare Variant ◽  
Complex Traits ◽  
Negative Selection ◽  
Rare Variants ◽  
Effect Sizes ◽  
Significant Heterogeneity ◽  
Uk Biobank ◽  
Model Framework ◽  
Allele Effect ◽  
Variant Effect

AbstractUnderstanding the role of rare variants is important in elucidating the genetic basis of human diseases and complex traits. It is widely believed that negative selection can cause rare variants to have larger per-allele effect sizes than common variants. Here, we develop a method to estimate the minor allele frequency (MAF) dependence of SNP effect sizes. We use a model in which per-allele effect sizes have variance proportional to [p(1−p)]α, where p is the MAF and negative values of α imply larger effect sizes for rare variants. We estimate α by maximizing its profile likelihood in a linear mixed model framework using imputed genotypes, including rare variants (MAF >0.07%). We applied this method to 25 UK Biobank diseases and complex traits (N = 113,851). All traits produced negative α estimates with 20 significantly negative, implying larger rare variant effect sizes. The inferred best-fit distribution of true α values across traits had mean −0.38 (s.e. 0.02) and standard deviation 0.08 (s.e. 0.03), with statistically significant heterogeneity across traits (P = 0.0014). Despite larger rare variant effect sizes, we show that for most traits analyzed, rare variants (MAF <1%) explain less than 10% of total SNP-heritability. Using evolutionary modeling and forward simulations, we validated the α model of MAF-dependent trait effects and estimated the level of coupling between fitness effects and trait effects. Based on this analysis an average genome-wide negative selection coefficient on the order of 10−4 or stronger is necessary to explain the α values that we inferred.

scholarly journals Leveraging molecular QTL to understand the genetic architecture of diseases and complex traits

2017 ◽  
Author(s):  
Farhad Hormozdiari ◽  
Steven Gazal ◽  
Bryce van de Geijn ◽  
Hilary Finucane ◽  
Chelsea J.-T. Ju ◽  
...  
Keyword(s):  
Complex Traits ◽  
Genetic Architecture ◽  
Genetic Correlations ◽  
Complex Trait ◽  
Effect Sizes ◽  
Joint Analysis ◽  
Loss Of Function ◽  
Tissue Specific ◽  
Functional Annotations ◽  
Using Data

AbstractThere is increasing evidence that many GWAS risk loci are molecular QTL for gene ex-pression (eQTL), histone modification (hQTL), splicing (sQTL), and/or DNA methylation (meQTL). Here, we introduce a new set of functional annotations based on causal posterior prob-abilities (CPP) of fine-mapped molecular cis-QTL, using data from the GTEx and BLUEPRINT consortia. We show that these annotations are very strongly enriched for disease heritability across 41 independent diseases and complex traits (average N = 320K): 5.84x for GTEx eQTL, and 5.44x for eQTL, 4.27-4.28x for hQTL (H3K27ac and H3K4me1), 3.61x for sQTL and 2.81x for meQTL in BLUEPRINT (all P ≤ 1.39e-10), far higher than enrichments obtained using stan-dard functional annotations that include all significant molecular cis-QTL (1.17-1.80x). eQTL annotations that were obtained by meta-analyzing all 44 GTEx tissues generally performed best, but tissue-specific blood eQTL annotations produced stronger enrichments for autoimmune dis-eases and blood cell traits and tissue-specific brain eQTL annotations produced stronger enrich-ments for brain-related diseases and traits, despite high cis-genetic correlations of eQTL effect sizes across tissues. Notably, eQTL annotations restricted to loss-of-function intolerant genes from ExAC were even more strongly enriched for disease heritability (17.09x; vs. 5.84x for all genes; P = 4.90e-17 for difference). All molecular QTL except sQTL remained significantly enriched for disease heritability in a joint analysis conditioned on each other and on a broad set of functional annotations from previous studies, implying that each of these annotations is uniquely informative for disease and complex trait architectures.

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.

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