scholarly journals Estimating inflation in GWAS summary statistics due to variance distortion from cryptic relatedness

2017 ◽  
Author(s):  
Dominic Holland ◽  
Chun-Chieh Fan ◽  
Oleksandr Frei ◽  
Alexey A. Shadrin ◽  
Olav B. Smeland ◽  
...  
Keyword(s):  
Association Studies ◽  
Control Procedure ◽  
Genome Wide Association Studies ◽  
Summary Statistics ◽  
Single Nucleotide ◽  
Genome Wide ◽  
Order Of Magnitude ◽  
Inflation Factor ◽  
Cryptic Relatedness ◽  
Strength Of Association

AbstractCryptic relatedness is inherently a feature of large genome-wide association studies (GWAS), and can give rise to considerable inflation in summary statistics for single nucleotide polymorphism (SNP) associations with phenotypes. It has proven difficult to disentangle these inflationary effects from true polygenic effects. Here we present results of a model that enables estimation of polygenicity, mean strength of association, and residual inflation in GWAS summary statistics. We show that there is substantial residual inflation in recent large GWAS of height and schizophrenia; correcting for this reduces the number of independent genome-wide significant loci from the reported values of 697 for height and 108 for schizophrenia to 368 and 61, respectively. In contrast, a larger GWAS of educational attainment shows no residual inflation. Additionally, we find that height has a relatively low polygenicity, with approximately 8k SNPs having causal association, more than an order of magnitude less than has been reported. The residual inflation in GWAS summary statistics can be corrected using the standard genomic control procedure with the estimated residual inflation factor.

scholarly journals CAUSALdb: a database for disease/trait causal variants identified using summary statistics of genome-wide association studies

2019 ◽  
Author(s):  
Jianhua Wang ◽  
Dandan Huang ◽  
Yao Zhou ◽  
Hongcheng Yao ◽  
Huanhuan Liu ◽  
...  
Keyword(s):  
Fine Mapping ◽  
Genetic Variants ◽  
Association Studies ◽  
Complex Trait ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Summary Statistics ◽  
Genome Wide ◽  
Credible Sets ◽  
Causal Variants

Abstract Genome-wide association studies (GWASs) have revolutionized the field of complex trait genetics over the past decade, yet for most of the significant genotype-phenotype associations the true causal variants remain unknown. Identifying and interpreting how causal genetic variants confer disease susceptibility is still a big challenge. Herein we introduce a new database, CAUSALdb, to integrate the most comprehensive GWAS summary statistics to date and identify credible sets of potential causal variants using uniformly processed fine-mapping. The database has six major features: it (i) curates 3052 high-quality, fine-mappable GWAS summary statistics across five human super-populations and 2629 unique traits; (ii) estimates causal probabilities of all genetic variants in GWAS significant loci using three state-of-the-art fine-mapping tools; (iii) maps the reported traits to a powerful ontology MeSH, making it simple for users to browse studies on the trait tree; (iv) incorporates highly interactive Manhattan and LocusZoom-like plots to allow visualization of credible sets in a single web page more efficiently; (v) enables online comparison of causal relations on variant-, gene- and trait-levels among studies with different sample sizes or populations and (vi) offers comprehensive variant annotations by integrating massive base-wise and allele-specific functional annotations. CAUSALdb is freely available at http://mulinlab.org/causaldb.

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.

scholarly journals A publicly available repository of ROH islands reveals signatures of selection in different livestock and pet species

2021 ◽  
Vol 53 (1) ◽  
Author(s):  
Wim Gorssen ◽  
Roel Meyermans ◽  
Steven Janssens ◽  
Nadine Buys
Keyword(s):  
Association Studies ◽  
Genome Wide Association Studies ◽  
Single Nucleotide ◽  
Future Studies ◽  
Snp Data ◽  
Animal Populations ◽  
Genome Wide ◽  
Starting Point ◽  
Different Populations ◽  
Signatures Of Selection

Abstract Background Runs of homozygosity (ROH) have become the state-of-the-art method for analysis of inbreeding in animal populations. Moreover, ROH are suited to detect signatures of selection via ROH islands and are used in other applications, such as genomic prediction and genome-wide association studies (GWAS). Currently, a vast amount of single nucleotide polymorphism (SNP) data is available online, but most of these data have never been used for ROH analysis. Therefore, we performed a ROH analysis on large medium-density SNP datasets in eight animal species (cat, cattle, dog, goat, horse, pig, sheep and water buffalo; 442 different populations) and make these results publicly available. Results The results include an overview of ROH islands per population and a comparison of the incidence of these ROH islands among populations from the same species, which can assist researchers when studying other (livestock) populations or when looking for similar signatures of selection. We were able to confirm many known ROH islands, for example signatures of selection for the myostatin (MSTN) gene in sheep and horses. However, our results also included multiple other ROH islands, which are common to many populations and not identified to date (e.g. on chromosomes D4 and E2 in cats and on chromosome 6 in sheep). Conclusions We are confident that our repository of ROH islands is a valuable reference for future studies. The discovered ROH island regions represent a unique starting point for new studies or can be used as a reference for future studies. Furthermore, we encourage authors to add their population-specific ROH findings to our repository.

scholarly journals Challenges of Adjusting Single-Nucleotide Polymorphism Effect Sizes for Linkage Disequilibrium

2021 ◽  
pp. 1-11
Author(s):  
Valentina Escott-Price ◽  
Karl Michael Schmidt
Keyword(s):  
Linkage Disequilibrium ◽  
Association Studies ◽  
Statistical Significance ◽  
Ordinary Least Squares ◽  
Effect Sizes ◽  
Risk Scores ◽  
Genome Wide Association Studies ◽  
Single Nucleotide ◽  
Genome Wide ◽  
Tikhonov Regularisation

<b><i>Background:</i></b> Genome-wide association studies (GWAS) were successful in identifying SNPs showing association with disease, but their individual effect sizes are small and require large sample sizes to achieve statistical significance. Methods of post-GWAS analysis, including gene-based, gene-set and polygenic risk scores, combine the SNP effect sizes in an attempt to boost the power of the analyses. To avoid giving undue weight to SNPs in linkage disequilibrium (LD), the LD needs to be taken into account in these analyses. <b><i>Objectives:</i></b> We review methods that attempt to adjust the effect sizes (β<i>-</i>coefficients) of summary statistics, instead of simple LD pruning. <b><i>Methods:</i></b> We subject LD adjustment approaches to a mathematical analysis, recognising Tikhonov regularisation as a framework for comparison. <b><i>Results:</i></b> Observing the similarity of the processes involved with the more straightforward Tikhonov-regularised ordinary least squares estimate for multivariate regression coefficients, we note that current methods based on a Bayesian model for the effect sizes effectively provide an implicit choice of the regularisation parameter, which is convenient, but at the price of reduced transparency and, especially in smaller LD blocks, a risk of incomplete LD correction. <b><i>Conclusions:</i></b> There is no simple answer to the question which method is best, but where interpretability of the LD adjustment is essential, as in research aiming at identifying the genomic aetiology of disorders, our study suggests that a more direct choice of mild regularisation in the correction of effect sizes may be preferable.

scholarly journals Estimating Effect Sizes and Expected Replication Probabilities from GWAS Summary Statistics

2015 ◽  
Author(s):  
Dominic Holland ◽  
Yunpeng Wang ◽  
Wesley K Thompson ◽  
Andrew Schork ◽  
Chi-Hua Chen ◽  
...  
Keyword(s):  
Association Studies ◽  
Significant Snps ◽  
Effect Sizes ◽  
Genome Wide Association Studies ◽  
Summary Statistics ◽  
Sample Sizes ◽  
Genetic Components ◽  
Complex Phenotypes ◽  
Genome Wide ◽  
Z Scores

Genome-wide Association Studies (GWAS) result in millions of summary statistics (``z-scores'') for single nucleotide polymorphism (SNP) associations with phenotypes. These rich datasets afford deep insights into the nature and extent of genetic contributions to complex phenotypes such as psychiatric disorders, which are understood to have substantial genetic components that arise from very large numbers of SNPs. The complexity of the datasets, however, poses a significant challenge to maximizing their utility. This is reflected in a need for better understanding the landscape of z-scores, as such knowledge would enhance causal SNP and gene discovery, help elucidate mechanistic pathways, and inform future study design. Here we present a parsimonious methodology for modeling effect sizes and replication probabilities that does not require raw genotype data, relying only on summary statistics from GWAS substudies, and a scheme allowing for direct empirical validation. We show that modeling z-scores as a mixture of Gaussians is conceptually appropriate, in particular taking into account ubiquitous non-null effects that are likely in the datasets due to weak linkage disequilibrium with causal SNPs. The four-parameter model allows for estimating the degree of polygenicity of the phenotype -- the proportion of SNPs (after uniform pruning, so that large LD blocks are not over-represented) likely to be in strong LD with causal/mechanistically associated SNPs -- and predicting the proportion of chip heritability explainable by genome wide significant SNPs in future studies with larger sample sizes. We apply the model to recent GWAS of schizophrenia (N=82,315) and additionally, for purposes of illustration, putamen volume (N=12,596), with approximately 9.3 million SNP z-scores in both cases. We show that, over a broad range of z-scores and sample sizes, the model accurately predicts expectation estimates of true effect sizes and replication probabilities in multistage GWAS designs. We estimate the degree to which effect sizes are over-estimated when based on linear regression association coefficients. We estimate the polygenicity of schizophrenia to be 0.037 and the putamen to be 0.001, while the respective sample sizes required to approach fully explaining the chip heritability are 106and 105. The model can be extended to incorporate prior knowledge such as pleiotropy and SNP annotation. The current findings suggest that the model is applicable to a broad array of complex phenotypes and will enhance understanding of their genetic architectures.

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.

scholarly journals Partitioning heritability by functional category using GWAS summary statistics

2015 ◽  
Author(s):  
Hilary Kiyo Finucane ◽  
Brendan Bulik-Sullivan ◽  
Alexander Gusev ◽  
Gosia Trynka ◽  
Yakir Reshef ◽  
...  
Keyword(s):  
Association Studies ◽  
Smoking Behavior ◽  
Complex Diseases ◽  
New Method ◽  
Age At Menarche ◽  
Genome Wide Association Studies ◽  
Summary Statistics ◽  
Cell Type ◽  
Genome Wide ◽  
Cell Type Specific

Recent work has demonstrated that some functional categories of the genome contribute disproportionately to the heritability of complex diseases. Here, we analyze a broad set of functional elements, including cell-type-specific elements, to estimate their polygenic contributions to heritability in genome-wide association studies (GWAS) of 17 complex diseases and traits spanning a total of 1.3 million phenotype measurements. To enable this analysis, we introduce a new method for partitioning heritability from GWAS summary statistics while controlling for linked markers. This new method is computationally tractable at very large sample sizes, and leverages genome-wide information. Our results include a large enrichment of heritability in conserved regions across many traits; a very large immunological disease-specific enrichment of heritability in FANTOM5 enhancers; and many cell-type-specific enrichments including significant enrichment of central nervous system cell types in body mass index, age at menarche, educational attainment, and smoking behavior. These results demonstrate that GWAS can aid in understanding the biological basis of disease and provide direction for functional follow-up.

scholarly journals Impact of Pre and Post Variant Filtration Strategies on Imputation

2020 ◽  
Author(s):  
Celine Charon ◽  
Rodrigue Allodji ◽  
Vincent Meyer ◽  
Jean-François Deleuze
Keyword(s):  
Quality Control ◽  
Rare Variants ◽  
Association Studies ◽  
Genome Wide Association Studies ◽  
Nucleotide Polymorphisms ◽  
Direct Effects ◽  
Single Nucleotide Variants ◽  
Single Nucleotide ◽  
Genome Wide ◽  
Conservative Post

Abstract Quality control methods for genome-wide association studies and fine mapping are commonly used for imputation, however, they result in loss of many single nucleotide polymorphisms (SNPs). To investigate the consequences of filtration on imputation, we studied the direct effects on the number of markers, their allele frequencies, imputation quality scores and post-filtration events. We pre-phrased 1,031 genotyped individuals from diverse ethnicities and compared the imputed variants to 1,089 NCBI recorded individuals for additional validation.Without variant pre-filtration based on quality control (QC), we observed no impairment in the imputation of SNPs that failed QC whereas with pre-filtration there was an overall loss of information. Significant differences between frequencies with and without pre-filtration were found only in the range of very rare (5E-04-1E-03) and rare variants (1E-03-5E-03) (p < 1E-04). Increasing the post-filtration imputation quality score from 0.3 to 0.8 reduced the number of single nucleotide variants (SNVs) <0.001 2.5 fold with or without QC pre-filtration and halved the number of very rare variants (5E-04). As a result, to maintain confidence and enough SNVs, we propose here a 2-step post-filtration approach to increase the number of very rare and rare variants compared to conservative post-filtration methods.

scholarly journals Reproducibility in the UK Biobank of Genome-Wide Significant Signals Discovered in Earlier Genome-wide Association Studies

2020 ◽  
Author(s):  
Jack W. O’Sullivan ◽  
John P. A. Ioannidis
Keyword(s):  
Effect Size ◽  
Association Studies ◽  
Genome Wide Association ◽  
P Value ◽  
Genome Wide Association Studies ◽  
Uk Biobank ◽  
Single Nucleotide ◽  
Genome Wide ◽  
The Uk ◽  
Open Question

AbstractWith the establishment of large biobanks, discovery of single nucleotide polymorphism (SNPs) that are associated with various phenotypes has been accelerated. An open question is whether SNPs identified with genome-wide significance in earlier genome-wide association studies (GWAS) are replicated also in later GWAS conducted in biobanks. To address this question, the authors examined a publicly available GWAS database and identified two, independent GWAS on the same phenotype (an earlier, “discovery” GWAS and a later, replication GWAS done in the UK biobank). The analysis evaluated 136,318,924 SNPs (of which 6,289 had reached p<5e-8 in the discovery GWAS) from 4,397,962 participants across nine phenotypes. The overall replication rate was 85.0% and it was lower for binary than for quantitative phenotypes (58.1% versus 94.8% respectively). There was a18.0% decrease in SNP effect size for binary phenotypes, but a 12.0% increase for quantitative phenotypes. Using the discovery SNP effect size, phenotype trait (binary or quantitative), and discovery p-value, we built and validated a model that predicted SNP replication with area under the Receiver Operator Curve = 0.90. While non-replication may often reflect lack of power rather than genuine false-positive findings, these results provide insights about which discovered associations are likely to be seen again across subsequent GWAS.

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