Recent developments in statistical methods for GWAS and high-throughput sequencing association studies of complex traits

ABSTRACT

The advent of large-scale genetic studies has helped bring a new era of biomedical research on dissecting the genetic architecture of complex human disease. Genome-wide association studies (GWASs) and next-generation sequencing studies are two popular tools for identifying genetic variants that are associated with complex traits. This article overviews some of the most important statistical tools for analyzing data from these two types of studies, with an emphasis on single-SNP tests for common variants and region-based tests for rare variants. We compare various statistical methods for common and rare variants in humans, and describe some critical considerations to guide the choice of an analysis method. Also discussed are the related topics of sample ascertainment, missing heritability, and multiple testing correction, as well as some remaining analytical challenges presented by complex trait association mapping using genomic data obtained via high-throughput technologies.

KEYWORDS:

• Genome-wide association studies
• high-throughput sequencing
• complex traits
• association analysis
• statistical tests

Disclosure statement

No potential conflict of interest was reported by the authors.

ORCID

Duo Jiang  http://orcid.org/0000-0002-7103-4502

Additional information

Notes on contributors

Duo Jiang

Dr Duo Jiang is an Assistant Professor at the Department of Statistics at Oregon State University. She received her PhD degree in statistics from the University of Chicago in 2014. Her research is in the development of statistical and computational methods for genetics and genomics data, with a focus on complex trait association mapping.

Miaoyan Wang

Dr Miaoyan Wang is an Assistant Professor at the Department of Statistics at the University of Wisconsin-Madison. Miaoyan obtained her PhD degree in statistics from the University of Chicago in 2015. She was a Simons Math+X Postdoc at the University of Pennsylvania and a Chan-Zuckerberg Biohub Postdoc at UC Berkeley from 2015 to 2017. Her research focuses on genetic association analysis, high-dimensional statistics, and machine learning.