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 Testing the effectiveness of principal components in adjusting for relatedness in genetic association studies

2019 ◽  
Author(s):  
Yiqi Yao ◽  
Alejandro Ochoa
Keyword(s):  
Population Structure ◽  
Family Structure ◽  
Genetic Association ◽  
Principal Components ◽  
Association Studies ◽  
Hybrid Approach ◽  
Genetic Association Studies ◽  
P Value ◽  
Type I ◽  
Sample Sizes

AbstractModern genetic association studies require modeling population structure and family relatedness in order to calculate correct statistics. Principal Components Analysis (PCA) is one of the most common approaches for modeling this population structure, but nowadays the Linear Mixed-Effects Model (LMM) is believed by many to be a superior model. Remarkably, previous comparisons have been limited by testing PCA without varying the number of principal components (PCs), by simulating unrealistically simple population structures, and by not always measuring both type-I error control and predictive power. In this work, we thoroughly evaluate PCA with varying number of PCs alongside LMM in various realistic scenarios, including admixture together with family structure, measuring both null p-value uniformity and the area under the precision-recall curves. We find that PCA performs as well as LMM when enough PCs are used and the sample size is large, and find a remarkable robustness to extreme number of PCs. However, we notice decreased performance for PCA relative to LMM when sample sizes are small and when there is family structure, although LMM performance is highly variable. Altogether, our work suggests that PCA is a favorable approach for association studies when sample sizes are large and no close relatives exist in the data, and a hybrid approach of LMM with PCs may be the best of both worlds.

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.

scholarly journals Asymptotic Relative Efficiencies of the Score and Robust Tests in Genetic Association Studies

2018 ◽  
Vol 9 (1) ◽  
pp. 26-41
Author(s):  
Ao Yuan ◽  
Ruzong Fan ◽  
Jinfeng Xu ◽  
Yuan Xue ◽  
Qizhai Li
Keyword(s):  
Association Study ◽  
Genetic Association ◽  
Genetic Association Study ◽  
Association Studies ◽  
Genetic Association Studies ◽  
Score Statistic ◽  
Test Statistic ◽  
Robust Test ◽  
Robust Tests ◽  
Parameter Values

Introduction:The score statisticZ(θ)and the maximin efficient robust test statisticZMERTare commonly used in genetic association study, but according to our knowledge there is no formal comparison of them.Methods:In this report, we compare the asymptotic behavior ofZ(θ)andZMERT, by computing their Asymptotic Relative Efficiencies (AREs) relative to each other. Four commonly used ARE measures, the Pitman ARE, Chernoff ARE, Hodges-Lehmann ARE and the Bahadur ARE are considered. Some modifications of these methods are made to simplify the computations. We found that the Chernoff, Hodges-Lehmann and Bahadur AREs are suitable for our setting.Results and Conclusion:Based on our study, the efficiencies of the two test statistic varies for different criterion used, and for different parameter values under the same criterion, so each test has its advantages and dis-advantages according to the criterion used and the parameters involved, which are described in the context. Numerical examples are given to illustrate the use of the two statistics in genetic association study.

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 Statistical significance in genetic association studies

2010 ◽  
Vol 33 (5) ◽  
pp. 266 ◽  
Author(s):  
Hui-Qi Qu ◽  
Matthew Tien ◽  
Constantin Polychronakos
Keyword(s):  
Association Study ◽  
Genetic Association ◽  
Genome Wide Association Study ◽  
Association Studies ◽  
Statistical Significance ◽  
Genetic Association Studies ◽  
Positive Association ◽  
Sampling Bias ◽  
Genetic Associations ◽  
Candidate Loci

Clinical & Investigative Medicine (CIM) is receiving an increasing number of reports of candidate-based association studies. The track record of such studies in the past has been poor: numerous genetic associations reported from candidate gene studies have not been replicated in later studies. The rise of the genome-wide association study (GWAS) is changing this situation. A well-designed GWAS may identify a number of candidate loci without bias by screening the whole human genome. Validating and fine-mapping the candidate loci from GWAS are required to clarify the genetic mechanisms. Thus, a candidate-based association study has become a well-directed effort, instead of searching for a needle in a haystack. In the post-GWAS era, exponential growth of candidate-based genetic association studies is expected. A pressing issue accompanying this new trend is the assessment of the validity of an association study. In this editorial, we illustrate the major cause of false positive association from random sampling bias by an empirical example, and emphasize the application of the probability theory in assessing the validity of a genetic association study.

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