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 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 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 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 Fine scale human genetic structure in three regions of Cameroon reveals episodic diversifying selection

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Kevin K. Esoh ◽  
Tobias O. Apinjoh ◽  
Steven G. Nyanjom ◽  
Ambroise Wonkam ◽  
Emile R. Chimusa ◽  
...  
Keyword(s):  
Genetic Structure ◽  
Ethnic Groups ◽  
Genetic Association ◽  
Association Studies ◽  
Genetic Association Studies ◽  
Genomic Region ◽  
Sub Saharan Africa ◽  
Genome Wide Association Studies ◽  
Fine Scale ◽  
Sub Saharan

AbstractInferences from genetic association studies rely largely on the definition and description of the underlying populations that highlight their genetic similarities and differences. The clustering of human populations into subgroups (population structure) can significantly confound disease associations. This study investigated the fine-scale genetic structure within Cameroon that may underlie disparities observed with Cameroonian ethnicities in malaria genome-wide association studies in sub-Saharan Africa. Genotype data of 1073 individuals from three regions and three ethnic groups in Cameroon were analyzed using measures of genetic proximity to ascertain fine-scale genetic structure. Model-based clustering revealed distinct ancestral proportions among the Bantu, Semi-Bantu and Foulbe ethnic groups, while haplotype-based coancestry estimation revealed possible longstanding and ongoing sympatric differentiation among individuals of the Foulbe ethnic group, and their Bantu and Semi-Bantu counterparts. A genome scan found strong selection signatures in the HLA gene region, confirming longstanding knowledge of natural selection on this genomic region in African populations following immense disease pressure. Signatures of selection were also observed in the HBB gene cluster, a genomic region known to be under strong balancing selection in sub-Saharan Africa due to its co-evolution with malaria. This study further supports the role of evolution in shaping genomes of Cameroonian populations and reveals fine-scale hierarchical structure among and within Cameroonian ethnicities that may impact genetic association studies in the country.

scholarly journals Evaluation of genome-wide power of genetic association studies based on empirical data from the HapMap project

2007 ◽  
Vol 16 (20) ◽  
pp. 2494-2505 ◽  
Author(s):  
Yasuhito Nannya ◽  
Kenjiro Taura ◽  
Mineo Kurokawa ◽  
Shigeru Chiba ◽  
Seishi Ogawa
Keyword(s):  
Genetic Association ◽  
Empirical Data ◽  
Association Studies ◽  
Genetic Association Studies ◽  
Hapmap Project ◽  
Genome Wide
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