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.

scholarly journals Signals of polygenic adaptation on height have been overestimated due to uncorrected population structure in genome-wide association studies

2018 ◽  
Author(s):  
Mashaal Sohail ◽  
Robert M. Maier ◽  
Andrea Ganna ◽  
Alex Bloemendal ◽  
Alicia R. Martin ◽  
...  
Keyword(s):  
Population Structure ◽  
Association Studies ◽  
Meta Analysis ◽  
Human Populations ◽  
Genome Wide Association Studies ◽  
Multiple Traits ◽  
Large Numbers ◽  
Genome Wide ◽  
Polygenic Adaptation ◽  
The Uk

AbstractGenetic predictions of height differ among human populations and these differences are too large to be explained by genetic drift. This observation has been interpreted as evidence of polygenic adaptation. Differences across populations were detected using SNPs genome-wide significantly associated with height, and many studies also found that the signals grew stronger when large numbers of subsignificant SNPs were analyzed. This has led to excitement about the prospect of analyzing large fractions of the genome to detect subtle signals of selection and claims of polygenic adaptation for multiple traits. Polygenic adaptation studies of height have been based on SNP effect size measurements in the GIANT Consortium meta-analysis. Here we repeat the height analyses in the UK Biobank, a much more homogeneously designed study. Our results show that polygenic adaptation signals based on large numbers of SNPs below genome-wide significance are extremely sensitive to biases due to uncorrected population structure.

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 Polygenic adaptation on height is overestimated due to uncorrected stratification in genome-wide association studies

eLife ◽  
2019 ◽  
Vol 8 ◽  
Author(s):  
Mashaal Sohail ◽  
Robert M Maier ◽  
Andrea Ganna ◽  
Alex Bloemendal ◽  
Alicia R Martin ◽  
...  
Keyword(s):  
Population Stratification ◽  
Association Studies ◽  
Editorial Note ◽  
Human Populations ◽  
Genome Wide Association Studies ◽  
Multiple Traits ◽  
Large Numbers ◽  
Genome Wide ◽  
Polygenic Scores ◽  
Polygenic Adaptation

Genetic predictions of height differ among human populations and these differences have been interpreted as evidence of polygenic adaptation. These differences were first detected using SNPs genome-wide significantly associated with height, and shown to grow stronger when large numbers of sub-significant SNPs were included, leading to excitement about the prospect of analyzing large fractions of the genome to detect polygenic adaptation for multiple traits. Previous studies of height have been based on SNP effect size measurements in the GIANT Consortium meta-analysis. Here we repeat the analyses in the UK Biobank, a much more homogeneously designed study. We show that polygenic adaptation signals based on large numbers of SNPs below genome-wide significance are extremely sensitive to biases due to uncorrected population stratification. More generally, our results imply that typical constructions of polygenic scores are sensitive to population stratification and that population-level differences should be interpreted with caution.Editorial note: This article has been through an editorial process in which the authors decide how to respond to the issues raised during peer review. The Reviewing Editor's assessment is that all the issues have been addressed (<xref ref-type="decision-letter" rid="SA1">see decision letter</xref>).

scholarly journals Genetics of complex traits: prediction of phenotype, identification of causal polymorphisms and genetic architecture

2016 ◽  
Vol 283 (1835) ◽  
pp. 20160569 ◽  
Author(s):  
M. E. Goddard ◽  
K. E. Kemper ◽  
I. M. MacLeod ◽  
A. J. Chamberlain ◽  
B. J. Hayes
Keyword(s):  
Complex Traits ◽  
Genetic Architecture ◽  
Quantitative Traits ◽  
Association Studies ◽  
Genome Wide Association Studies ◽  
Nucleotide Polymorphisms ◽  
Crop Breeding ◽  
Single Nucleotide ◽  
Genome Wide ◽  
Phenotype Identification

Complex or quantitative traits are important in medicine, agriculture and evolution, yet, until recently, few of the polymorphisms that cause variation in these traits were known. Genome-wide association studies (GWAS), based on the ability to assay thousands of single nucleotide polymorphisms (SNPs), have revolutionized our understanding of the genetics of complex traits. We advocate the analysis of GWAS data by a statistical method that fits all SNP effects simultaneously, assuming that these effects are drawn from a prior distribution. We illustrate how this method can be used to predict future phenotypes, to map and identify the causal mutations, and to study the genetic architecture of complex traits. The genetic architecture of complex traits is even more complex than previously thought: in almost every trait studied there are thousands of polymorphisms that explain genetic variation. Methods of predicting future phenotypes, collectively known as genomic selection or genomic prediction, have been widely adopted in livestock and crop breeding, leading to increased rates of genetic improvement.

Comprehensive evaluation of mapping complex traits in wheat using genome-wide association studies

2021 ◽  
Vol 42 (1) ◽  
Author(s):  
Dinesh K. Saini ◽  
Yuvraj Chopra ◽  
Jagmohan Singh ◽  
Karansher S. Sandhu ◽  
Anand Kumar ◽  
...  
Keyword(s):  
Complex Traits ◽  
Comprehensive Evaluation ◽  
Association Studies ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Mapping Complex Traits

Abstract 1443: Systems-Based Analysis of HDL Metabolism in a Mouse Intercross between Strains CAST and C57BL6/J (CxB)

Circulation ◽  
2008 ◽  
Vol 118 (suppl_18) ◽  
Author(s):  
Margarete Mehrabian ◽  
Charles Farber ◽  
Peter Langfelder ◽  
Anatole Ghazalpour ◽  
Zhiqiang Zhou ◽  
...  
Keyword(s):  
Complex Traits ◽  
Association Studies ◽  
Plasma Lipid ◽  
Heart Association ◽  
Hdl Cholesterol ◽  
Chow Diet ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Genomic Study ◽  
Hdl Metabolism

A recent meta-analysis of three large genome-wide association studies for HDL cholesterol levels revealed several highly significant associations, but altogether these explained less than 10% of the population variance of HDL. Since HDL levels are highly heritable, with heritability estimated at 50–70% in many studies, there are clearly many additional genes, and probably complex genetic and environmental interactions, involved in HDL metabolism. Thus, if “personalized medicine” is to become a reality, these complex factors must be addressed. Combined genetic-genomic approaches have rejuvenated the analysis of complex traits using mouse models, and here report an integrative genomic study of HDL in a large mouse cross. We previously reported the identification of loci associated with HDL cholesterol concentrations using a CXB F2 intercross. We have now generated a much larger CXB cross, consisting of 438 mice, and have integrated genome wide gene expression analysis of liver and adipose with quantitative trait locus (QTL) mapping and causality modeling. These studies were carried out on mice fed a low fat, chow diet and then switched to a high fat, ’Western’ diet. QTL analysis on the clinical traits using R/QTL (http://cran.r-project.org/) revealed a complex inheritance pattern with significant LOD scores at 9 loci, on chromosomes 1,2,4,5,8,9,10,16,18. Of these loci, 6 (chr: 1,4,5,10,16,18) were seen to be involved in genetic-dietary regulation of HDL cholesterol. Expression QTLs (eQTL) were determined using Agilent microarrays for 23,624 transcripts. Genes expressed within a 1-LOD support interval or correlated with HDL (p<2.7E-11) in both adipose and liver were identified. Using Network Edge Orienting (NEO) methods, causal relationships between the identified genes, related QTL peak markers and HDL levels were accessed. The genes were then ranked based on the NEO scores. In liver the highest ranked genes were associated with mitochondrial, ER and golgi trafficking. In adipose, on the other hand, pathways associated with cell signaling, transcription regulation and protein ubiquitation were predicted to be causal for HDL levels. In conclusion, our results reveal a large number of novel pathways and candidate genes for plasma lipid metabolism. This research has received full or partial funding support from the American Heart Association, AHA Western States Affiliate (California, Nevada & Utah).

scholarly journals GWAS of three molecular traits highlights core genes and pathways alongside a highly polygenic background

2020 ◽  
Author(s):  
Nasa Sinnott-Armstrong ◽  
Sahin Naqvi ◽  
Manuel Rivas ◽  
Jonathan K Pritchard
Keyword(s):  
Complex Traits ◽  
Genetic Basis ◽  
Association Studies ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Biological Processes ◽  
Uk Biobank ◽  
The Core ◽  
Genome Wide ◽  
Core Genes

SummaryGenome-wide association studies (GWAS) have been used to study the genetic basis of a wide variety of complex diseases and other traits. However, for most traits it remains difficult to interpret what genes and biological processes are impacted by the top hits. Here, as a contrast, we describe UK Biobank GWAS results for three molecular traits—urate, IGF-1, and testosterone—that are biologically simpler than most diseases, and for which we know a great deal in advance about the core genes and pathways. Unlike most GWAS of complex traits, for all three traits we find that most top hits are readily interpretable. We observe huge enrichment of significant signals near genes involved in the relevant biosynthesis, transport, or signaling pathways. We show how GWAS data illuminate the biology of variation in each trait, including insights into differences in testosterone regulation between females and males. Meanwhile, in other respects the results are reminiscent of GWAS for more-complex traits. In particular, even these molecular traits are highly polygenic, with most of the variance coming not from core genes, but from thousands to tens of thousands of variants spread across most of the genome. Given that diseases are often impacted by many distinct biological processes, including these three, our results help to illustrate why so many variants can affect risk for any given disease.

scholarly journals Better estimation of SNP heritability from summary statistics provides a new understanding of the genetic architecture of complex traits

2018 ◽  
Author(s):  
Doug Speed ◽  
David J Balding
Keyword(s):  
Complex Traits ◽  
Genetic Architecture ◽  
Large Scale ◽  
Association Studies ◽  
Genome Wide Association Studies ◽  
Summary Statistics ◽  
Confounding Bias ◽  
Conserved Regions ◽  
Genome Wide ◽  
Variation Explained

LD Score Regression (LDSC) has been widely applied to the results of genome-wide association studies. However, its estimates of SNP heritability are derived from an unrealistic model in which each SNP is expected to contribute equal heritability. As a consequence, LDSC tends to over-estimate confounding bias, under-estimate the total phenotypic variation explained by SNPs, and provide misleading estimates of the heritability enrichment of SNP categories. Therefore, we present SumHer, software for estimating SNP heritability from summary statistics using more realistic heritability models. After demonstrating its superiority over LDSC, we apply SumHer to the results of 24 large-scale association studies (average sample size 121 000). First we show that these studies have tended to substantially over-correct for confounding, and as a result the number of genome-wide significant loci has under-reported by about 20%. Next we estimate enrichment for 24 categories of SNPs defined by functional annotations. A previous study using LDSC reported that conserved regions were 13-fold enriched, and found a further twelve categories with above 2-fold enrichment. By contrast, our analysis using SumHer finds that conserved regions are only 1.6-fold (SD 0.06) enriched, and that no category has enrichment above 1.7-fold. SumHer provides an improved understanding of the genetic architecture of complex traits, which enables more efficient analysis of future genetic data.

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