NEW YORK (GenomeWeb News) – The National Institutes of Health's Common Fund plans to provide $3.3 million in 2013 to support research projects analyzing data that will be generated by its Genotype-Tissue Expression (GTEx)resource.
The GTEx resource aims to identify regions of the genome that influence how genes are expressed. According to the funding notice, NIH intends to fund up to 8 awards next year for such research.
NIH said that by 2016 the GTEx program will include genetic variation data from around 900 post-mortem donors and gene expression measurements from more than 20,000 reference human tissues.
Genetic studies of both common and rare variants can lead to discoveries about the loci associated with many common diseases, such as heart disease, cancer, diabetes, and psychiatric conditions, among others. But risk analysis based on single SNPs can be difficult, because they may only have a minimal effect and be responsible for a small portion of an individual's disease risk, according to NIH.
By identifying genes whose expression is correlated with specific genetic variants known as expression quantitative trait loci (eQTLs), researchers can functionally characterize a disease-associated SNP and discover the underlying biological pathways involved in disease risk. Studying multiple tissues from the same individual could help scientists discover how eQTLs found in one tissue may be relevant in others, as well as the degree to which genetic influences on gene regulation may be tissue-specific.
GTEx is designed to provide such data sets. During its pilot project, from 2010 to 2012, around 1,000 tissue specimens were measured with both an Affymetrix GeneCHip gene expression array and with the Illumina HiSeq 2000. Going forward, NIH plans to measure samples using RNA-Seq on more than 20,000 tissues, and to analyze the samples' genetic variation using the Illumina Omni 5M and human exome array chips.
Applicants for this funding are expected to use the GTEx's SNP and tissue-specific gene expression data for a range of studies, such as developing or extending existing methods to integrate analysis of genotype and gene expression for eQTL identification; to make predictions about functional relevance of genetic variants; to use multiple datasets or multiple tissue types to reduce the number of false negative and false positive results; to integrate GTEx and other data types; to measure normal variation by comparing data sets for the same tissue type from different individuals; and to model and explore the influence of expression data from multiple tissues from each donor on statistical power to detect trans-eQTLs.