Identification of Genetic Association of Multiple Rare Variants Using Collapsing Methods

• Authors: Yan Sun, Emory University; Yun Ju Sung, Washington University; Nathan Tintle, Dordt College; Andreas Ziegler, Universität zu Lübeck
• Journal Title: Genetic Epidemiology
• Volume: Volume 35, Number Suppl 1
• Publisher: Wiley: 12 months | 2011-11-29, Pages S101-S106
• Type of Work: Article | Post-print: After Peer Review
• Publisher DOI: http://doi.org/10.1002/gepi.20658
• Subjects: Health Sciences, Epidemiology; Biology, Biostatistics
• Keywords: 1000 Genomes Project; association; collapsing methods; next-generation sequencing

Abstract

Next-generation sequencing technology allows investigation of both common and rare variants in humans. Exomes are sequenced on the population level or in families to further study the genetics of human diseases. Genetic Analysis Workshop 17 (GAW17) provided exomic data from the 1000 Genomes Project and simulated phenotypes. These data enabled evaluations of existing and newly developed statistical methods for rare variant sequence analysis for which standard statistical methods fail because of the rareness of the alleles. Various alternative approaches have been proposed that overcome the rareness problem by combining multiple rare variants within a gene. These approaches are termed collapsing methods, and our GAW17 group focused on studying the performance of existing and novel collapsing methods using rare variants. All tested methods performed similarly, as measured by type I error and power. Inflated type I error fractions were consistently observed and might be caused by gametic phase disequilibrium between causal and noncausal rare variants in this relatively small sample as well as by population stratification. Incorporating prior knowledge, such as appropriate covariates and information on functionality of SNPs, increased the power of detecting associated genes. Overall, collapsing rare variants can increase the power of identifying disease-associated genes. However, studying genetic associations of rare variants remains a challenging task that requires further development and improvement in data collection, management, analysis, and computation.