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NHLBI to Fund Studies to Enhance Exome Sequencing Data

NEW YORK (GenomeWeb News) – The National Heart, Lung, and Blood Institute wants investigators to comb through data created by a recent exome sequencing effort to find important variants and fill in gaps in the dataset in order to make it more useful for disease studies.

Through a new grant funding opportunity, NHLBI will provide up to $1.5 million per year for researchers who sift through the data generated by the Grand Opportunities Exome Sequencing Project (GO ESP).

The GO ESP effort sequenced 7,500 samples from well-phenotyped populations to discover all the variations in exons and to provide the largest exome dataset for studying heart, lung, and blood diseases. Genotype data and detailed sequencing and phenotype data from the project also is being made available, and NHBLI plans to fund genomic investigators, statisticians, and biologists who will identify rare or lower frequency variants from these sets that may be related to diseases.

The primary phenotypes in the GO ESP dataset include myocardial infarction, extremes of low-density lipoprotein, extremes of blood pressure, early onset ischemic stroke, asthma, chronic obstructive pulmonary disease, and others of interest to NHLBI.

Beyond identifying variants, the institute also would like investigators to engage in multidisciplinary collaborations to fill in gaps and look at data related to ethnic diversity and phenotypes that require additional power, as well as looking at data in different ways and validating earlier findings.

Examples of the research projects NHLBI expects to support include, but are not limited to,
replication studies of rare variants identified in the GO ESP dataset selected for a certain primary phenotype; extension of datasets to increase ethnic diversity and identify rare variations involved in heart, lung, or blood diseases; extension of datasets to increase the power to identify more rare variants; identification and replication of variations that have significant, effective size in quantitative blood cell traits, hemostatic factors, inflammatory markers, peripheral artery disease and/or other phenotypes beyond the primary GO ESP phenotypes; and analysis of datasets by biological pathway or population genetics to identify variants involved in diseases.

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