Methods for the Analysis and Interpretation for Rare Variants Associated with Complex Traits
Year: 2019
Type: review
Abstract: With the advent of Next Generation Sequencing (NGS) technologies, whole genome and whole exome DNA sequencing has become affordable for routine genetic studies. Coupled with improved genotyping arrays and genotype imputation methodologies, it is increasingly feasible to obtain rare genetic variant information in large datasets. Such datasets allow researchers to gain a more complete understanding of the genetic architecture of complex traits caused by rare variants. State-of-the-art statistical methods for the statistical genetics analysis of sequence-based association, including efficient algorithms for association analysis in biobank-scale datasets, gene-association tests, meta-analysis, fine mapping methods that integrate functional genomic dataset, and phenome-wide association studies (PheWAS), are reviewed here. These methods are expected to be highly useful for next generation statistical genetics analysis in the era of precision medicine. © 2019 by John Wiley & Sons, Inc.
DOI: 10.1002/cphg.83
Authors: J. Dylan Weissenkampen, Yu Jiang, Scott Eckert, Bibo Jiang, Bingshan Li, Dajiang J. Liu
Institutions: Pennsylvania State University, Vanderbilt University
Language: English
Subfield: Genetics
Domain: Life Sciences
Open Access status: green
Awards: Method to Enhance the Rare Variant Genetic Architecture Analysis in Meta-Analyses, Tools for integrative genomics and disease association study for the X chromosome, Analysis, Validation and Resource Creation for Genome Sequencing of Complex Diseases, Penn State Biomedical Big Data to Knowledge (B2D2K) Training Program, Methods to Unveil the Genetic Architecture for Nicotine Dependence via NGS data
OpenAlex ID: W2921159822