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NIH to Commit $11.3M for New Methods to Study Variants in Non-Protein-Coding Regions

NEW YORK (GenomeWeb News) – The National Institutes of Health plans to award $11.3 million to fund a number of research projects that will develop computational approaches for interpreting sequence variants found in the non-protein-coding regions of the human genome.

The aim is to spur researchers to develop and apply new methods that will make it possible to begin to trim down the number of genomic variants that are known to be contributing to diseases and other traits, NIH said in a funding announcement earlier this week.

NIH expects that these methods will be used to analyze whole-genome sequence data by integrating data sets for genome function, phenotypes, or patterns in variation, to pare down the set of variants that are considered candidates for affecting function and disease risk.

Funded by the National Human Genome Research Institute, the National Cancer Institute, and the National Institute on Drug Abuse, these awards will support up to 15 projects in fiscal years 2015 and 2016, and will provide up to $500,000 per-year to each award.

Although many researchers are trying to predict the organismal effects of variants using exon sequencing, these new studies will tackle the "more challenging problem" of deciphering the impacts of variants in non-coding regions, which are highly complex, NIH said. This complexity is largely caused by linkage disequilibrium, which occurs when multiple genes, genomic elements, and variants in a region are statistically associated with a particular trait or disease. Although many elements may be statistically associated with the trait, there may only be one element that is actually causing of the trait.

Functional differences are also problematic for creating causal links between variants and traits, because although some variants are found in putative regulatory elements it is generally not known precisely how they affect molecular function. Integrating genetic data with genome function data and similar data sets could help narrow down the number of candidates that might be affecting traits, NIH said.

"For example, several variants in a region may have similar associations with a disease, but the set of variants could be narrowed by including only ones that are conserved among species and that have histone modifications indicative of regulatory elements," NIH said in the funding announcement. "Or variants associated with a disease might be found near several genes, but the set of variants affecting organismal function could be narrowed if RNA expression levels in tissues relevant to that disease differ for only one of these genes."

Projects receiving funding could involve efforts to use genome-wide association data with other omics data types to narrow the sets of variants that potentially affect organismal function; use data on chromatin structure to predict where insertions or deletions affect gene regulation; use data on transcription factor binding and RNA expression to predict how variants and enhancers affect gene regulation; use patterns of variation in populations and species to find genomic regions that have undergone selection; and use data on epigenomic marks to assess how epigenomic variability correlates with disease risk.