Joint analyses of disease and correlated quantitative phenotypes using next-generation sequencing data

Phillip E. Melton, Nathan Pankratz

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

The joint analysis of multiple disease phenotypes aims to increase statistical power and potentially identify pleiotropic genes involved in the biological development of common chronic diseases. As next-generation sequencing data become more common, it will be important to consider ways to maximize the ability to detect rare variants within the human genome. The two exome sequence data sets provided for analysis at Genetic Analysis Workshop 17 (GAW17) offered three quantitative phenotypes related to disease status in 200 simulated replicates for both families and unrelated individuals. Participants in Group 10 addressed the challenges and potential uses of next-generation sequencing data to identify causal variants through a broad range of statistical methods. These methods included investigating multiple phenotypes either through data reduction or joint methods, using family or unrelated individuals, and reducing the dimensionality inherent in these data. Most of the research teams regarded the use of multiple phenotypes as a means of increasing analytical power and as a way to clarify the biology of complex disease. Three major observations were gleaned from these Group 10 contributions. First, family and unrelated case-control samples are suited to finding different types of variants. In addition, collapsing either phenotypes or genotypes can reduce the dimensionality of the data and alleviate some of the problems of multiple testing. Finally, we were able to demonstrate in certain cases that performing a joint analysis of disease status and a quantitative trait can improve statistical power.

Original languageEnglish (US)
Pages (from-to)S67-S73
JournalGenetic epidemiology
Volume35
Issue numberSUPPL. 1
DOIs
StatePublished - Dec 5 2011

Keywords

  • Combined phenotype analysis
  • Dimensionality reduction
  • Next-generation sequencing
  • Quantitative traits
  • Rare variants

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