scholarly journals GAS Power Calculator: web-based power calculator for genetic association studies

2017 ◽  
Author(s):  
Jennifer Li Johnson ◽  
Gonçalo R. Abecasis
Keyword(s):  
Genetic Association ◽  
Statistical Power ◽  
Association Studies ◽  
Genetic Association Studies ◽  
Web Interface ◽  
Genetic Studies ◽  
Web Based ◽  
Link Type ◽  
Interactive Tools ◽  
Research Findings

AbstractMotivation:Statistical power calculations are crucial in designing genetic association studies. They help guide tradeoffs between large sample sizes and detailed assessments of genotype and phenotype, help determine which studies are viable, and help interpret research findings. To facilitate widespread use of power analysis in the design and interpretation of genetic studies, it is important to enable users to calculate power and visualize the effect of different models and design choices in convenient, interactive tools that are easily accessible.Results:We developed the Genetic Association Study (GAS) Power Calculator to provide users with a simple interface that can be compute the power of genetic association studies in a convenient browser based interface.Availability:The GAS Power Calculator can be accessed from the web interface at http://csg.sph.umich.edu/abecasis/gas_power_calculator/. Source code is available at https://github.com/jenlij/GAS-power-calculator.

scholarly journals U-PASS: unified power analysis and forensics for qualitative traits in genetic association studies

2019 ◽  
Author(s):  
Zheng Gao ◽  
Jonathan Terhorst ◽  
Cristopher Van Hout ◽  
Stilian Stoev
Keyword(s):  
Genetic Association ◽  
Prospective Studies ◽  
Power Analysis ◽  
Association Studies ◽  
Genetic Association Studies ◽  
Web Based ◽  
Association Tests ◽  
Qualitative Traits ◽  
Link Type ◽  
Common Association

AbstractSummaryDespite the availability of existing calculators for statistical power analysis in genetic association studies, there has not been a model-invariant and test-independent tool that allows for both planning of prospective studies and systematic review of reported findings. In this work, we develop a web-based application U-PASS (Unified Power analysis of ASsociation Studies), implementing a unified framework for the analysis of common association tests for binary qualitative traits. The application quantifies the shared asymptotic power limits of the common association tests, and visualizes the fundamental statistical trade-off between risk allele frequency (RAF) and odds ratio (OR). The application also addresses the applicability of asymptotics-based power calculations in finite samples, and provides guidelines for single- SNP-based association tests. In addition to designing prospective studies, U-PASS enables researchers to retrospectively assess the statistical validity of previously reported associations.Availability and implementationU-PASS is available as a web-based R Shiny application at https://power.stat.lsa.umich.edu. Source code is available at https://github.com/Pill-GZ/[email protected] informationSupplementary data are available in the application.

scholarly journals Random Forests for Genetic Association Studies

2011 ◽  
Vol 10 (1) ◽  
Author(s):  
Benjamin A Goldstein ◽  
Eric C Polley ◽  
Farren B. S. Briggs
Keyword(s):  
Machine Learning ◽  
Genetic Association ◽  
Random Forests ◽  
Learning Algorithm ◽  
Association Studies ◽  
Genetic Association Studies ◽  
Machine Learning Algorithms ◽  
Computationally Efficient ◽  
Genetic Studies ◽  
Variable Importance Measures

The Random Forests (RF) algorithm has become a commonly used machine learning algorithm for genetic association studies. It is well suited for genetic applications since it is both computationally efficient and models genetic causal mechanisms well. With its growing ubiquity, there has been inconsistent and less than optimal use of RF in the literature. The purpose of this review is to breakdown the theoretical and statistical basis of RF so that practitioners are able to apply it in their work. An emphasis is placed on showing how the various components contribute to bias and variance, as well as discussing variable importance measures. Applications specific to genetic studies are highlighted. To provide context, RF is compared to other commonly used machine learning algorithms.

scholarly journals Power Estimation for Gene-Longevity Association Analysis Using Concordant Twins

2014 ◽  
Vol 2014 ◽  
pp. 1-8
Author(s):  
Qihua Tan ◽  
Jing Hua Zhao ◽  
Torben Kruse ◽  
Kaare Christensen
Keyword(s):  
Association Study ◽  
Genetic Association ◽  
Association Analysis ◽  
Statistical Power ◽  
Association Studies ◽  
Genetic Association Studies ◽  
Small Sample ◽  
Identical Twins ◽  
Human Longevity ◽  
Sample Sizes

Statistical power is one of the major concerns in genetic association studies. Related individuals such as twins are valuable samples for genetic studies because of their genetic relatedness. Phenotype similarity in twin pairs provides evidence of genetic control over the phenotype variation in a population. The genetic association study on human longevity, a complex trait that is under control of both genetic and environmental factors, has been confronted by the small sample sizes of longevity subjects which limit statistical power. Twin pairs concordant for longevity have increased probability for carrying beneficial genes and thus are useful samples for gene-longevity association analysis. We conducted a computer simulation to estimate the power of association study using longevity concordant twin pairs. We observed remarkable power increases in using singletons from longevity concordant twin pairs as cases in comparison with cases of sporadic proband. A similar power would require doubled sample sizes for fraternal twins than for identical twins who are concordant for longevity suggesting that longevity concordant identical twins are more efficient samples than fraternal twins. We also observed an approximate of 2- to 3-fold increase in sample sizes needed for longevity cutoff at age 90 as compared with that at age 95. Overall, our results showed high value of twins in genetic association studies on human longevity.

scholarly journals Assessing the quality of published genetic association studies in meta-analyses: the quality of genetic studies (Q-Genie) tool

BMC Genetics ◽  
2015 ◽  
Vol 16 (1) ◽  
Author(s):  
Zahra N. Sohani ◽  
David Meyre ◽  
Russell J. de Souza ◽  
Philip G. Joseph ◽  
Mandark Gandhi ◽  
...  
Keyword(s):  
Genetic Association ◽  
Association Studies ◽  
Genetic Association Studies ◽  
Genetic Studies ◽  
Meta Analyses

scholarly journals A Web-based database of genetic association studies in cutaneous melanoma enhanced with network-driven data exploration tools

Database ◽  
2014 ◽  
Vol 2014 (0) ◽  
pp. bau101-bau101 ◽  
Author(s):  
E. I. Athanasiadis ◽  
K. Antonopoulou ◽  
F. Chatzinasiou ◽  
C. M. Lill ◽  
M. M. Bourdakou ◽  
...  
Keyword(s):  
Genetic Association ◽  
Cutaneous Melanoma ◽  
Association Studies ◽  
Genetic Association Studies ◽  
Data Exploration ◽  
Web Based

scholarly journals Improving statistical power in severe malaria genetic association studies by augmenting phenotypic precision

2021 ◽  
Author(s):  
James A Watson ◽  
Carolyne M Ndila ◽  
Sophie Uyoga ◽  
Alex W Macharia ◽  
Gideon Nyutu ◽  
...  
Keyword(s):  
Severe Malaria ◽  
Genetic Association ◽  
Statistical Power ◽  
Association Studies ◽  
Genetic Association Studies ◽  
Diagnostic Model ◽  
Genome Wide Association Studies ◽  
Case Control Studies ◽  
False Discovery Rates ◽  
Population Controls

Severe falciparum malaria has substantially affected human evolution. Genetic association studies of patients with clinically defined severe malaria and matched population controls have helped characterise human genetic susceptibility to severe malaria, but phenotypic imprecision compromises discovered associations. In areas of high malaria transmission the diagnosis of severe malaria in young children and, in particular, the distinction from bacterial sepsis, is imprecise. We developed a probabilistic diagnostic model of severe malaria using platelet and white count data. Under this model we re-analysed clinical and genetic data from 2,220 Kenyan children with clinically defined severe malaria and 3,940 population controls, adjusting for phenotype mis-labelling. Our model, validated by the distribution of sickle trait, estimated that approximately one third of cases did not have severe malaria. We propose a data-tilting approach for case-control studies with phenotype mis-labelling and show that this reduces false discovery rates and improves statistical power in genome-wide association studies.

scholarly journals U-PASS: unified power analysis and forensics for qualitative traits in genetic association studies

2019 ◽  
Vol 36 (3) ◽  
pp. 974-975 ◽  
Author(s):  
Zheng Gao ◽  
Jonathan Terhorst ◽  
Cristopher V Van Hout ◽  
Stilian Stoev
Keyword(s):  
Genetic Association ◽  
Prospective Studies ◽  
Power Analysis ◽  
Statistical Power ◽  
Association Studies ◽  
Genetic Association Studies ◽  
Supplementary Information ◽  
Association Tests ◽  
Qualitative Traits ◽  
Common Association

Abstract Summary Despite the availability of existing calculators for statistical power analysis in genetic association studies, there has not been a model-invariant and test-independent tool that allows for both planning of prospective studies and systematic review of reported findings. In this work, we develop a web-based application U-PASS (Unified Power analysis of ASsociation Studies), implementing a unified framework for the analysis of common association tests for binary qualitative traits. The application quantifies the shared asymptotic power limits of the common association tests, and visualizes the fundamental statistical trade-off between risk allele frequency and odds ratio. The application also addresses the applicability of asymptotics-based power calculations in finite samples, and provides guidelines for single-SNP-based association tests. In addition to designing prospective studies, U-PASS enables researchers to retrospectively assess the statistical validity of previously reported associations. Availability and implementation U-PASS is an open-source R Shiny application. A live instance is hosted at https://power.stat.lsa.umich.edu. Source is available on https://github.com/Pill-GZ/U-PASS. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Improving statistical power in severe malaria genetic association studies by augmenting phenotypic precision

eLife ◽  
2021 ◽  
Vol 10 ◽  
Author(s):  
James A Watson ◽  
Carolyne M Ndila ◽  
Sophie Uyoga ◽  
Alexander Macharia ◽  
Gideon Nyutu ◽  
...  
Keyword(s):  
Severe Malaria ◽  
Genetic Association ◽  
Statistical Power ◽  
Association Studies ◽  
Genetic Association Studies ◽  
Diagnostic Model ◽  
Genome Wide Association Studies ◽  
Case Control Studies ◽  
False Discovery Rates ◽  
Population Controls

Severe falciparum malaria has substantially affected human evolution. Genetic association studies of patients with clinically defined severe malaria and matched population controls have helped characterise human genetic susceptibility to severe malaria, but phenotypic imprecision compromises discovered associations. In areas of high malaria transmission the diagnosis of severe malaria in young children and, in particular, the distinction from bacterial sepsis, is imprecise. We developed a probabilistic diagnostic model of severe malaria using platelet and white count data. Under this model we re-analysed clinical and genetic data from 2,220 Kenyan children with clinically defined severe malaria and 3,940 population controls, adjusting for phenotype mis-labelling. Our model, validated by the distribution of sickle trait, estimated that approximately one third of cases did not have severe malaria. We propose a data-tilting approach for case-control studies with phenotype mis-labelling and show that this reduces false discovery rates and improves statistical power in genome-wide association studies.

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