Details

Autor(en) / Beteiligte

• Zuk, Or
• Schaffner, Stephen F
• Samocha, Kaitlin
• Do, Ron
• Hechter, Eliana
• Kathiresan, Sekar
• Daly, Mark J
• Neale, Benjamin M
• Sunyaev, Shamil R
• Lander, Eric S

Titel

Searching for missing heritability: Designing rare variant association studies

Ist Teil von

• Proceedings of the National Academy of Sciences - PNAS, 2014-01, Vol.111 (4), p.E455-E464

Ort / Verlag

United States: National Academy of Sciences

Erscheinungsjahr

2014

Link zum Volltext

Quelle

MEDLINE

Beschreibungen/Notizen

• Genetic studies have revealed thousands of loci predisposing to hundreds of human diseases and traits, revealing important biological pathways and defining novel therapeutic hypotheses. However, the genes discovered to date typically explain less than half of the apparent heritability. Because efforts have largely focused on common genetic variants, one hypothesis is that much of the missing heritability is due to rare genetic variants. Studies of common variants are typically referred to as genomewide association studies, whereas studies of rare variants are often simply called sequencing studies. Because they are actually closely related, we use the terms common variant association study (CVAS) and rare variant association study (RVAS). In this paper, we outline the similarities and differences between RVAS and CVAS and describe a conceptual framework for the design of RVAS. We apply the framework to address key questions about the sample sizes needed to detect association, the relative merits of testing disruptive alleles vs. missense alleles, frequency thresholds for filtering alleles, the value of predictors of the functional impact of missense alleles, the potential utility of isolated populations, the value of gene-set analysis, and the utility of de novo mutations. The optimal design depends critically on the selection coefficient against deleterious alleles and thus varies across genes. The analysis shows that common variant and rare variant studies require similarly large sample collections. In particular, a well-powered RVAS should involve discovery sets with at least 25,000 cases, together with a substantial replication set.