scholarly journals Quantifying posterior effect size distribution of susceptibility loci by common summary statistics

2020 ◽  
Vol 44 (4) ◽  
pp. 339-351
Author(s):  
Olga A. Vsevolozhskaya ◽  
Dmitri V. Zaykin
Keyword(s):  
Size Distribution ◽  
Effect Size ◽  
Summary Statistics ◽  
Susceptibility Loci ◽  
Effect Size Distribution

scholarly journals The distribution of common-variant effect sizes

2020 ◽  
Author(s):  
Luke Jen O’Connor
Keyword(s):  
Sample Size ◽  
Size Distribution ◽  
Effect Size ◽  
Genetic Architecture ◽  
Common Variant ◽  
Effect Sizes ◽  
Summary Statistics ◽  
Genome Wide ◽  
Variant Effect ◽  
Effect Size Distribution

AbstractThe genetic effect-size distribution describes the number of variants that affect disease risk and the range of their effect sizes. Accurate estimates of this distribution would provide insights into genetic architecture and set sample-size targets for future genome-wide association studies. We developed Fourier Mixture Regression (FMR) to estimate common-variant effect-size distributions from GWAS summary statistics. We validated FMR in simulations and in analyses of UK Biobank data, using interim-release summary statistics (max N=145k) to predict the results of the full release (N=460k). Analyzing summary statistics for 10 diseases (avg Neff=169k) and 22 other traits, we estimated the sample size required for genome-wide significant SNPs to explain 50% of SNP-heritability. For most diseases the requisite number of cases is 100k-1M, an attainable number; ten times more would be required to explain 90% of heritability. In well-powered GWAS, genome-wide significance is a conservative threshold, and loci at less stringent thresholds have true positive rates that remain close to 1 if confounding is controlled. Analyzing the shape of the effect-size distribution, we estimate that heritability accumulates across many thousands of SNPs with a wide range of effect sizes: the largest effects (at the 90th percentile of heritability) are 100 times larger than the smallest (10th percentile), and while the midpoint of this range varies across traits, its size is similar. These results suggest attainable sample size targets for future GWAS, and they underscore the complexity of genetic architecture.

scholarly journals An effect size distribution analysis of heart rate variability studies: Recommendations for reporting the magnitude of group differences

2016 ◽  
Author(s):  
Daniel S. Quintana
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Size Distribution ◽  
Effect Size ◽  
Case Control ◽  
The Body ◽  
Effect Sizes ◽  
Group Differences ◽  
Distribution Analysis ◽  
Effect Size Distribution

AbstractThe calculation of heart rate variability (HRV) is a popular tool used to investigate differences in cardiac autonomic control between population samples. When interpreting effect sizes to quantify the magnitude of group differences, researchers typically use Cohen's guidelines of small (0.2), medium (0.5), and large (0.8) effects. However, these guidelines were only proposed for use when the effect size distribution (ESD) was unknown. Despite the availability of effect sizes from hundreds of HRV studies, researchers still largely rely on Cohen's guidelines to interpret effect sizes. This article describes an ESD analysis of 297 HRV effect sizes from case-control studies, revealing that the 25th, 50th, and 75th effect size percentiles correspond with effect sizes of 0.25, 0.5, and 0.84, respectively. The ESD for separate clinical groups are also presented. The data suggests that Cohen's guidelines underestimate the magnitude of small and large effect sizes for the body of HRV case-control research. Therefore, to better reflect observed HRV effect sizes, the data suggest that effect sizes of 0.25, 0.5, and 0.85 should be interpreted as small, medium, and large effects. Researchers are encouraged to use the ESD dataset or their own collected datasets in tandem with the provided analysis script to perform bespoke ESD analyses relevant to their specific research area.

scholarly journals Effect-Size Estimation Using Semiparametric Hierarchical Mixture Models in Disease-Association Studies with Neuroimaging Data

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Ryo Emoto ◽  
Atsushi Kawaguchi ◽  
Kunihiko Takahashi ◽  
Shigeyuki Matsui
Keyword(s):  
Size Distribution ◽  
Mixture Models ◽  
Effect Size ◽  
Association Studies ◽  
Disease Association ◽  
Spatial Dependency ◽  
Size Estimation ◽  
Neuroimaging Data ◽  
Hierarchical Mixture Models ◽  
Effect Size Distribution

In disease-association studies using neuroimaging data, evaluating the biological or clinical significance of individual associations requires not only detection of disease-associated areas of the brain but also estimation of the magnitudes of the associations or effect sizes for individual brain areas. In this paper, we propose a model-based framework for voxel-based inferences under spatial dependency in neuroimaging data. Specifically, we employ hierarchical mixture models with a hidden Markov random field structure to incorporate the spatial dependency between voxels. A nonparametric specification is proposed for the effect size distribution to flexibly estimate the underlying effect size distribution. Simulation experiments demonstrate that compared with a naive estimation method, the proposed methods can substantially reduce the selection bias in the effect size estimates of the selected voxels with the greatest observed associations. An application to neuroimaging data from an Alzheimer’s disease study is provided.

scholarly journals Estimation of effect size distribution from genome-wide association studies and implications for future discoveries

2010 ◽  
Vol 42 (7) ◽  
pp. 570-575 ◽  
Author(s):  
Ju-Hyun Park ◽  
Sholom Wacholder ◽  
Mitchell H Gail ◽  
Ulrike Peters ◽  
Kevin B Jacobs ◽  
...  
Keyword(s):  
Size Distribution ◽  
Effect Size ◽  
Association Studies ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Effect Size Distribution

scholarly journals Semi-parametric empirical Bayes factor for genome-wide association studies

2021 ◽  
Author(s):  
Junji Morisawa ◽  
Takahiro Otani ◽  
Jo Nishino ◽  
Ryo Emoto ◽  
Kunihiko Takahashi ◽  
...  
Keyword(s):  
Factor Analysis ◽  
Size Distribution ◽  
Effect Size ◽  
Empirical Bayes ◽  
Bayes Factor ◽  
Association Studies ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Effect Size Distribution

AbstractBayes factor analysis has the attractive property of accommodating the risks of both false negatives and false positives when identifying susceptibility gene variants in genome-wide association studies (GWASs). For a particular SNP, the critical aspect of this analysis is that it incorporates the probability of obtaining the observed value of a statistic on disease association under the alternative hypotheses of non-null association. An approximate Bayes factor (ABF) was proposed by Wakefield (Genetic Epidemiology 2009;33:79–86) based on a normal prior for the underlying effect-size distribution. However, misspecification of the prior can lead to failure in incorporating the probability under the alternative hypothesis. In this paper, we propose a semi-parametric, empirical Bayes factor (SP-EBF) based on a nonparametric effect-size distribution estimated from the data. Analysis of several GWAS datasets revealed the presence of substantial numbers of SNPs with small effect sizes, and the SP-EBF attributed much greater significance to such SNPs than the ABF. Overall, the SP-EBF incorporates an effect-size distribution that is estimated from the data, and it has the potential to improve the accuracy of Bayes factor analysis in GWASs.

scholarly journals Factors influencing the effect size distribution of adaptive substitutions

2016 ◽  
Vol 283 (1828) ◽  
pp. 20153065 ◽  
Author(s):  
Emily L. Dittmar ◽  
Christopher G. Oakley ◽  
Jeffrey K. Conner ◽  
Billie A. Gould ◽  
Douglas W. Schemske
Keyword(s):  
Size Distribution ◽  
Effect Size ◽  
Evolutionary Biology ◽  
Empirical Work ◽  
Effect Sizes ◽  
Recent Theory ◽  
Early Theory ◽  
Evolutionary Forces ◽  
New Research ◽  
Effect Size Distribution

The distribution of effect sizes of adaptive substitutions has been central to evolutionary biology since the modern synthesis. Early theory proposed that because large-effect mutations have negative pleiotropic consequences, only small-effect mutations contribute to adaptation. More recent theory suggested instead that large-effect mutations could be favoured when populations are far from their adaptive peak. Here we suggest that the distributions of effect sizes are expected to differ among study systems, reflecting the wide variation in evolutionary forces and ecological conditions experienced in nature. These include selection, mutation, genetic drift, gene flow, and other factors such as the degree of pleiotropy, the distance to the phenotypic optimum, whether the optimum is stable or moving, and whether new mutation or standing genetic variation provides the source of adaptive alleles. Our goal is to review how these factors might affect the distribution of effect sizes and to identify new research directions. Until more theory and empirical work is available, we feel that it is premature to make broad generalizations about the effect size distribution of adaptive substitutions important in nature.

scholarly journals Mendelian Randomization Analysis Using Mixture Models (MRMix) for Genetic Effect-Size-Distribution Leads to Robust Estimation of Causal Effects

2018 ◽  
Author(s):  
Guanghao Qi ◽  
Nilanjan Chatterjee
Keyword(s):  
Breast Cancer ◽  
Size Distribution ◽  
Robust Estimation ◽  
Effect Size ◽  
Detrimental Effect ◽  
Mendelian Randomization ◽  
Causal Effect ◽  
Causal Effects ◽  
Randomization Analysis ◽  
Effect Size Distribution

AbstractWe propose a novel method for robust estimation of causal effects in two-sample Mendelian randomization analysis using potentially large number of genetic instruments. We consider a “working model” for bi-variate effect-size distribution across pairs of traits in the form of normal-mixtures which assumes existence of a fraction of the genetic markers that are valid instruments, i.e. they have only direct effect on one trait, while other markers can have potentially correlated, direct and indirect effects, or have no effects at all. We show that model motivates a simple method for estimating causal effect (θ) through a procedure for maximizing the probability concentration of the residuals, , at the “null” component of a two-component normal-mixture model. Simulation studies showed that MRMix provides nearly unbiased or/and substantially more robust estimates of causal effects compared to alternative methods under various scenarios. Further, the studies showed that MRMix is sensitive to direction and can achieve much higher efficiency (up to 3–4 fold) relative to other comparably robust estimators. We applied the proposed methods for conducting MR analysis using largest publicly available datasets across a number of risk-factors and health outcomes. Notable findings included identification of causal effects of genetically determined BMI and ageat-menarche, which have relationship among themselves, on the risk of breast cancer; detrimental effect of HDL on the risk of breast cancer; no causal effect of HDL and triglycerides on the risk of coronary artery disease; a strong detrimental effect of BMI, but no causal effect of years of education, on the risk of major depressive disorder.

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