title: KNOWLEDGE DRIVEN BINNING AND PHEWAS ANALYSIS IN MARSHFIELD PERSONALIZED MEDICINE RESEARCH PROJECT USING BIOBIN.
abstract: Next-generation sequencing technology has presented an opportunity for rare variant discovery and association of these variants with disease. To address the challenges of rare variant analysis, multiple statistical methods have been developed for combining rare variants to increase statistical power for detecting associations. BioBin is an automated tool that expands on collapsing/binning methods by performing multi-level variant aggregation with a flexible, biologically informed binning strategy using an internal biorepository, the Library of Knowledge (LOKI). The databases within LOKI provide variant details, regional annotations and pathway interactions which can be used to generate bins of biologically-related variants, thereby increasing the power of any subsequent statistical test. In this study, we expand the framework of BioBin to incorporate statistical tests, including a dispersion-based test, SKAT, thereby providing the option of performing a unified collapsing and statistical rare variant analysis in one tool. Extensive simulation studies performed on gene-coding regions showed a Bin-KAT analysis to have greater power than BioBin-regression in all simulated conditions, including variants influencing the phenotype in the same direction, a scenario where burden tests often retain greater power. The use of Madsen- Browning variant weighting increased power in the burden analysis to that equitable with Bin-KAT; but overall Bin-KAT retained equivalent or higher power under all conditions. Bin-KAT was applied to a study of 82 pharmacogenes sequenced in the Marshfield Personalized Medicine Research Project (PMRP). We looked for association of these genes with 9 different phenotypes extracted from the electronic health record. This study demonstrates that Bin-KAT is a powerful tool for the identification of genes harboring low frequency variants for complex phenotypes.
authors.affiliation: Department of Biochemistry, Microbiology and Molecular Biology, The Pennsylvania State University, University Park, PA, USA.
authors.forename: Anna O
authors.initials: AO
authors.lastname: Basile
authors.forename: John R
authors.initials: JR
authors.lastname: Wallace
authors.forename: Peggy
authors.initials: P
authors.lastname: Peissig
authors.forename: Catherine A
authors.initials: CA
authors.lastname: McCarty
authors.forename: Murray
authors.initials: M
authors.lastname: Brilliant
authors.forename: Marylyn D
authors.initials: MD
authors.lastname: Ritchie
mesh_terms: D019295:Computational Biology / Q000379:methods / Q000706:statistics & numerical data; D003198:Computer Simulation; D030541:Databases, Genetic / Q000706:statistics & numerical data; D014644:Genetic Variation; D055106:Genome-Wide Association Study / Q000706:statistics & numerical data*; D059014:High-Throughput Nucleotide Sequencing / Q000706:statistics & numerical data; D006801:Humans; D051188:Knowledge Bases; D008957:Models, Genetic; D015233:Models, Statistical; D010597:Pharmacogenetics / Q000706:statistics & numerical data; D010641:Phenotype; D057285:Precision Medicine / Q000706:statistics & numerical data; D012984:Software