Submitted by ja607 on
Title | Methods for the Analysis and Interpretation for Rare Variants Associated with Complex Traits. |
Publication Type | Journal Article |
Year of Publication | 2019 |
Authors | J Weissenkampen, D, Jiang, Y, Eckert, S, Jiang, B, Li, B, Liu, DJ |
Journal | Curr Protoc Hum Genet |
Volume | 101 |
Issue | 1 |
Pagination | e83 |
Date Published | 2019 04 |
ISSN | 1934-8258 |
Keywords | Algorithms, Genetic Predisposition to Disease, Genome, Human, Genome-Wide Association Study, Genotype, High-Throughput Nucleotide Sequencing, Humans, Multifactorial Inheritance, Phenotype, Polymorphism, Single Nucleotide, Whole Exome Sequencing, Whole Genome Sequencing |
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 |
Alternate Journal | Curr Protoc Hum Genet |
PubMed ID | 30849219 |
PubMed Central ID | PMC6455968 |
Grant List | R01 GM126479 / GM / NIGMS NIH HHS / United States R01 HG008983 / HG / NHGRI NIH HHS / United States R21 DA040177 / DA / NIDA NIH HHS / United States U01 HG009086 / HG / NHGRI NIH HHS / United States |