title: Methods of integrating data to uncover genotype-phenotype interactions.
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.
authors.affiliation: Department of Biochemistry and Molecular Biology, Center for Systems Genomics, The Pennsylvania State University, University Park, Pennsylvania 16802, USA.
authors.forename: Marylyn D
authors.initials: MD
authors.lastname: Ritchie
authors.affiliation: National Human Genome Research Institute, Inherited Disease Research Branch, Baltimore, Maryland 21224, USA.
authors.forename: Emily R
authors.initials: ER
authors.lastname: Holzinger
authors.affiliation: Department of Biochemistry and Molecular Biology, Center for Systems Genomics, The Pennsylvania State University, University Park, Pennsylvania 16802, USA.
authors.forename: Ruowang
authors.initials: R
authors.lastname: Li
authors.affiliation: Department of Biochemistry and Molecular Biology, Center for Systems Genomics, The Pennsylvania State University, University Park, Pennsylvania 16802, USA.
authors.forename: Sarah A
authors.initials: SA
authors.lastname: Pendergrass
authors.affiliation: Department of Biochemistry and Molecular Biology, Center for Systems Genomics, The Pennsylvania State University, University Park, Pennsylvania 16802, USA.
authors.forename: Dokyoon
authors.initials: D
authors.lastname: Kim
mesh_terms: D003627:Data Interpretation, Statistical; D014644:Genetic Variation; D005838:Genotype; D006801:Humans; D040582:Inheritance Patterns / Q000502:physiology*; D015201:Meta-Analysis as Topic; D008954:Models, Biological; D010641:Phenotype; D049490:Systems Biology / Q000379:methods*