Camille M Moore, Sean A Jacobson, Tasha E Fingerlin
{"title":"Power and Sample Size Calculations for Genetic Association Studies in the Presence of Genetic Model Misspecification.","authors":"Camille M Moore, Sean A Jacobson, Tasha E Fingerlin","doi":"10.1159/000508558","DOIUrl":null,"url":null,"abstract":"<p><strong>Introduction: </strong>When analyzing data from large-scale genetic association studies, such as targeted or genome-wide resequencing studies, it is common to assume a single genetic model, such as dominant or additive, for all tests of association between a given genetic variant and the phenotype. However, for many variants, the chosen model will result in poor model fit and may lack statistical power due to model misspecification.</p><p><strong>Objective: </strong>We develop power and sample size calculations for tests of gene and gene × environment interaction, allowing for misspecification of the true mode of genetic susceptibility.</p><p><strong>Methods: </strong>The power calculations are based on a likelihood ratio test framework and are implemented in an open-source R package (\"genpwr\").</p><p><strong>Results: </strong>We use these methods to develop an analysis plan for a resequencing study in idiopathic pulmonary fibrosis and show that using a 2-degree of freedom test can increase power to detect recessive genetic effects while maintaining power to detect dominant and additive effects.</p><p><strong>Conclusions: </strong>Understanding the impact of model misspecification can aid in study design and developing analysis plans that maximize power to detect a range of true underlying genetic effects. In particular, these calculations help identify when a multiple degree of freedom test or other robust test of association may be advantageous.</p>","PeriodicalId":13226,"journal":{"name":"Human Heredity","volume":"84 6","pages":"256-271"},"PeriodicalIF":1.1000,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1159/000508558","citationCount":"36","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Human Heredity","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1159/000508558","RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2020/7/28 0:00:00","PubModel":"Epub","JCR":"Q4","JCRName":"GENETICS & HEREDITY","Score":null,"Total":0}
引用次数: 36
Abstract
Introduction: When analyzing data from large-scale genetic association studies, such as targeted or genome-wide resequencing studies, it is common to assume a single genetic model, such as dominant or additive, for all tests of association between a given genetic variant and the phenotype. However, for many variants, the chosen model will result in poor model fit and may lack statistical power due to model misspecification.
Objective: We develop power and sample size calculations for tests of gene and gene × environment interaction, allowing for misspecification of the true mode of genetic susceptibility.
Methods: The power calculations are based on a likelihood ratio test framework and are implemented in an open-source R package ("genpwr").
Results: We use these methods to develop an analysis plan for a resequencing study in idiopathic pulmonary fibrosis and show that using a 2-degree of freedom test can increase power to detect recessive genetic effects while maintaining power to detect dominant and additive effects.
Conclusions: Understanding the impact of model misspecification can aid in study design and developing analysis plans that maximize power to detect a range of true underlying genetic effects. In particular, these calculations help identify when a multiple degree of freedom test or other robust test of association may be advantageous.
期刊介绍:
Gathering original research reports and short communications from all over the world, ''Human Heredity'' is devoted to methodological and applied research on the genetics of human populations, association and linkage analysis, genetic mechanisms of disease, and new methods for statistical genetics, for example, analysis of rare variants and results from next generation sequencing. The value of this information to many branches of medicine is shown by the number of citations the journal receives in fields ranging from immunology and hematology to epidemiology and public health planning, and the fact that at least 50% of all ''Human Heredity'' papers are still cited more than 8 years after publication (according to ISI Journal Citation Reports). Special issues on methodological topics (such as ‘Consanguinity and Genomics’ in 2014; ‘Analyzing Rare Variants in Complex Diseases’ in 2012) or reviews of advances in particular fields (‘Genetic Diversity in European Populations: Evolutionary Evidence and Medical Implications’ in 2014; ‘Genes and the Environment in Obesity’ in 2013) are published every year. Renowned experts in the field are invited to contribute to these special issues.