Methods of integrating data to uncover genotype-phenotype interactions

Marylyn D. Ritchie, Emily R. Holzinger, Ruowang Li, Sarah A. Pendergrass, Dokyoon Kim

Research output: Contribution to journalReview articlepeer-review

449 Scopus citations

Abstract

Recent technological advances have expanded the breadth of available omic data, from whole-genome sequencing data, to extensive transcriptomic, methylomic and metabolomic data. A key goal of analyses of these data is the identification of effective models that predict phenotypic traits and outcomes, elucidating important biomarkers and generating important insights into the genetic underpinnings of the heritability of complex traits. There is still a need for powerful and advanced analysis strategies to fully harness the utility of these comprehensive high-throughput data, identifying true associations and reducing the number of false associations. In this Review, we explore the emerging approaches for data integration-including meta-dimensional and multi-staged analyses-which aim to deepen our understanding of the role of genetics and genomics in complex outcomes. With the use and further development of these approaches, an improved understanding of the relationship between genomic variation and human phenotypes may be revealed.

Original languageEnglish (US)
Pages (from-to)85-97
Number of pages13
JournalNature Reviews Genetics
Volume16
Issue number2
DOIs
StatePublished - Jan 1 2015

All Science Journal Classification (ASJC) codes

  • Molecular Biology
  • Genetics
  • Genetics(clinical)

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