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NHGRI to Fund Use of PhenX Data

By a GenomeWeb staff reporter

NEW YORK (GenomeWeb News) – The National Institutes of Health wants researchers to start using phenotype and environmental exposure knowledge and data developed through the PhenX Toolkit program in population-based genomic studies and will provide supplemental funding for such efforts.

The National Human Genome Research Institute and the Office of Behavioral and Social Sciences Research (OBSSR) will provide up to $700,000 next year for between four and eight administrative supplemental grants to stimulate the use of phenotypic and environmental measures from the NHGRI-funded PhenX program.

The PhenX Toolkit provides researchers ways to use high-priority measures and protocols in GWAS and other broad-based genomics studies, particularly for detecting genetic associations.

"The inclusion of measures from the PhenX Toolkit will enable researchers to broaden the scope of their studies, examine additional genetic and environmental factors contributing to human health and disease, and combine their studies with other investigators using the same measures to increase power and efficiency of genomic discovery," according to an NIH notice.

Under the program, NHGRI will provide up to $100,000 for NIH-funded researchers to incorporate eight to 10 PhenX measures into genomics studies that are aimed at identifying the genetic and environmental factors that contribute to health and development of complex disease.

The PhenX program, which was started in 2006, identified measures in 21 research domains such as demographics, anthropometrics, neurology, cancer, and social environments, for use in genome-wide association studies.

According to NHGRI, GWAS have identified hundreds of associations for genetic variants and human diseases, but most of these studies have had few phenotypic and exposure measures in common.

The institute also expects that developing and adopting standard phenotypic and exposure measures could facilitate the creation of larger, more comprehensive datasets for use in cross-study analysis, which could increase researchers' ability to detect associations and gene-gene and gene-environment interactions.

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