Using Gene Expression to Improve the Power of Genome-Wide Association Analysis | Zendy

Yen-Yi Ho | Zendy; Emily C. Baechler | Zendy; Ward Ortmann | Zendy; Timothy W. Behrens | Zendy; Robert Graham | Zendy; Tushar Bhangale | Zendy; Wei Pan | Zendy

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Using Gene Expression to Improve the Power of Genome-Wide Association Analysis

Author(s) -

Yen-Yi Ho,

Emily C. Baechler,

Ward Ortmann,

Timothy W. Behrens,

Robert Graham,

Tushar Bhangale,

Wei Pan

Publication year - 2014

Publication title -

human heredity

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.423

H-Index - 62

eISSN - 1423-0062

pISSN - 0001-5652

DOI - 10.1159/000362837

Subject(s) - genome wide association study , computational biology , expression quantitative trait loci , biology , type i and type ii errors , genetic association , computer science , weighting , missing heritability problem , genetics , data mining , gene , single nucleotide polymorphism , statistics , mathematics , genotype , medicine , radiology

Genome-wide association (GWA) studies have reported susceptible regions in the human genome for many common diseases and traits; however, these loci only explain a minority of trait heritability. To boost the power of a GWA study, substantial research endeavors have been focused on integrating other available genomic information in the analysis. Advances in high through-put technologies have generated a wealth of genomic data and made combining SNP and gene expression data become feasible.

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