RT Journal
A1 Schaub, Marc A.
A1 Boyle, Alan P.
A1 Kundaje, Anshul
A1 Batzoglou, Serafim
A1 Snyder, Michael
T1 Linking disease associations with regulatory information in the human genome
JF Genome Research 
JO Genome Research 
YR 2012 
FD September 01 
VO 22 
IS 9 
SP 1748 
OP 1759 
DO 10.1101/gr.136127.111 
UL http://genome.cshlp.org/content/22/9/1748.abstract 
AB Genome-wide association studies have been successful in identifying single nucleotide polymorphisms (SNPs) associated with a large number of phenotypes. However, an associated SNP is likely part of a larger region of linkage disequilibrium. This makes it difficult to precisely identify the SNPs that have a biological link with the phenotype. We have systematically investigated the association of multiple types of ENCODE data with disease-associated SNPs and show that there is significant enrichment for functional SNPs among the currently identified associations. This enrichment is strongest when integrating multiple sources of functional information and when highest confidence disease-associated SNPs are used. We propose an approach that integrates multiple types of functional data generated by the ENCODE Consortium to help identify “functional SNPs” that may be associated with the disease phenotype. Our approach generates putative functional annotations for up to 80% of all previously reported associations. We show that for most associations, the functional SNP most strongly supported by experimental evidence is a SNP in linkage disequilibrium with the reported association rather than the reported SNP itself. Our results show that the experimental data sets generated by the ENCODE Consortium can be successfully used to suggest functional hypotheses for variants associated with diseases and other phenotypes.