title: Testing for rare variant associations in the presence of missing data.
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.
authors.affiliation: Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA.
authors.forename: Paul L
authors.initials: PL
authors.lastname: Auer
authors.forename: Gao
authors.initials: G
authors.lastname: Wang
authors.forename: Suzanne M
authors.initials: SM
authors.lastname: Leal
mesh_terms: D000077548:Anaplastic Lymphoma Kinase; D003198:Computer Simulation; D059472:Exome; D056726:Genetic Association Studies; D014644:Genetic Variation; D005838:Genotype; D006801:Humans; D008957:Models, Genetic; D020794:Receptor Protein-Tyrosine Kinases / Q000235:genetics; D044105:Receptor, Melanocortin, Type 4 / Q000235:genetics
chemical_list: C470072:MC4R protein, human; D044105:Receptor, Melanocortin, Type 4; D000077548:Anaplastic Lymphoma Kinase; D020794:Receptor Protein-Tyrosine Kinases
keywords: complex disease; next-generation sequencing; rare variant association studies