Guidelines for large-scale sequence-based association studies: Lessons learned from whole-exome sequencing

Multifactorial traits include not only many complex diseases but also general parameters (e.g. height, body mass index), as well as drug adverse effects, drug efficacy, and drug toxicity. Massive parallel whole-genome sequencing (WGS) data have ushered in a new era in human genetics. These data are now being used to understand the role of rare variants in multifactorial traits and to advance the goals of “precision medicine” (the almost-meaningless buzz word for 2o16). The technological and computing advances that have enabled us to generate WGS data on thousands of individuals have also outpaced our ability to perform analyses in scientifically and statistically rigorous and thoughtful ways.

The past several years have witnessed the application of whole-exome sequencing (WES) to complex traits and diseases. From the analysis of NHLBI Exome  Sequencing Project (ESP) data, not only have a number of important disease-and complex-trait-association findings emerged, but our collective experience offers some valuable lessons for WGS initiatives. These include caveats associated with generating automated pipelines for quality-control and analysis of rare variants; the importance of studying minority populations; sample-size requirements, efficient study-designs for identifying rare-variant associations; and the significance of incidental findings in population-based genetic research. With the ESP as an example, authors [see attached article] offer guidance and a framework on how to conduct a large-scale association study in the present-day era of WGS.

Am J Hum Genet 2o16 Oct 6; 99: 791–801

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