Faculty Collaboration Database

Testing for rare variant associations in the presence of missing data. Genet Epidemiol 2013 Sep;37(6):529-38

Date

06/13/2013

Pubmed ID

23757187

Pubmed Central ID

PMC4459641

DOI

10.1002/gepi.21736

Scopus ID

2-s2.0-84881616126 (requires institutional sign-in at Scopus site)   18 Citations

Abstract

For studies of genetically complex diseases, many association methods have been developed to analyze rare variants. When variant calls are missing, naïve implementation of rare variant association (RVA) methods may lead to inflated type I error rates as well as a reduction in power. To overcome these problems, we developed extensions for four commonly used RVA tests. Data from the National Heart Lung and Blood Institute-Exome Sequencing Project were used to demonstrate that missing variant calls can lead to increased false-positive rates and that the extended RVA methods control type I error without reducing power. We suggest a combined strategy of data filtering based on variant and sample level missing genotypes along with implementation of these extended RVA tests.

Author List

Auer PL, Wang G, Leal SM

Author

Paul L. Auer PhD Professor in the Institute for Health and Equity department at Medical College of Wisconsin

MESH terms used to index this publication - Major topics in bold

Computer Simulation
Exome
Genetic Association Studies
Genetic Variation
Genotype
Humans
Models, Genetic
Receptor Protein-Tyrosine Kinases
Receptor, Melanocortin, Type 4