Impact of genotyping errors on statistical power of association tests in genomic analyses: A case study. Issue 2 (26th December 2016)

• Record Type: Journal Article
• Authors: Hou, Lin; Sun, Ning; Mane, Shrikant; Sayward, Fred; Rajeevan, Nallakkandi; Cheung, Kei‐Hoi; Cho, Kelly; Pyarajan, Saiju; Aslan, Mihaela; Miller, Perry; Harvey, Philip D.; Gaziano, J. Michael; Concato, John; Zhao, Hongyu
• Is Part Of: Genetic epidemiology. Volume 41:Issue 2(2017)
• Page Start: 152
• Page End: 162
• Publication Date: 2016-12-26
• Subjects: genome wide association test -- genotyping -- genotyping error -- sequencing -- statistical power; Genetic epidemiology -- Periodicals; Heredity -- Periodicals; Medical geography -- Periodicals; 614
• DOI: 10.1002/gepi.22027
• Languages: English
• ISSNs: 0741-0395

Abstract

ABSTRACT: A key step in genomic studies is to assess high throughput measurements across millions of markers for each participant's DNA, either using microarrays or sequencing techniques. Accurate genotype calling is essential for downstream statistical analysis of genotype‐phenotype associations, and next generation sequencing (NGS) has recently become a more common approach in genomic studies. How the accuracy of variant calling in NGS‐based studies affects downstream association analysis has not, however, been studied using empirical data in which both microarrays and NGS were available. In this article, we investigate the impact of variant calling errors on the statistical power to identify associations between single nucleotides and disease, and on associations between multiple rare variants and disease. Both differential and nondifferential genotyping errors are considered. Our results show that the power of burden tests for rare variants is strongly influenced by the specificity in variant calling, but is rather robust with regard to sensitivity. By using the variant calling accuracies estimated from a substudy of a Cooperative Studies Program project conducted by the Department of Veterans Affairs, we show that the power of association tests is mostly retained with commonly adopted variant calling pipelines. An R package, GWAS.PC, is provided to accommodate power analysis that takes account of genotyping errors (http://zhaocenter.org/software/ ).