Identifying Relationships among Genomic Disease Regions: Predicting Genes at Pathogenic SNP Associations and Rare Deletions

By Soumya Raychaudhuri, Robert M. Plenge, E. Rossin, Aylwin Ng, Shaun Purcell, P. Sklar, E. Scolnick, Ramnik J. Xavier, David Altshuler, Mark J. Daly. PLoS Genetics, 2009

468 citations recorded by the source corpus.

Translating a set of disease regions into insight about pathogenic mechanisms requires not only the ability to identify the key disease genes within them, but also the biological relationships among those key genes. Here we describe a statistical method, Gene Relationships Among Implicated Loci (GRAIL), that takes a list of disease regions and automatically assesses the degree of relatedness of implicated genes using 250,000 PubMed abstracts. We first evaluated GRAIL by assessing its ability to identify subsets of highly related genes in common pathways from validated lipid and height SNP associations from recent genome-wide studies. We then tested GRAIL, by assessing its ability to separate true disease regions from many false positive disease regions in two separate practical applications in human genetics. First, we took 74 nominally associated Crohn's disease SNPs and applied GRAIL to identify a subset of 13 SNPs with highly related genes. Of these, ten convincingly validated in follow-up genotyping; genotyping results for the remaining three were inconclusive. Next, we applied GRAIL to 165 rare deletion events seen in schizophrenia cases (less than one-third of which are contributing to disease risk). We demonstrate that GRAIL is able to identify a subset of 16 deletions containing highly related genes; many of these genes are expressed in the central nervous system and play a role in neuronal synapses. GRAIL offers a statistically robust approach to identifying functionally related genes from across multiple disease regions—that likely represent key disease pathways. An online version of this method is available for public use (http://www.broad.mit.edu/mpg/grail/).

Subfields: Genetics

View Paper: https://www.semanticscholar.org/paper/800cdc844372bdccdb9f30d9dc0b4902ed7dbca2

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Citations in the corpus

Twelve type 2 diabetes susceptibility loci identified through large-scale association analysis (2010, background): “Using GRAIL 49 , we found that genes within T2D-associated regions showed evidence of increased connectivity within PubMed abstracts, though this largely reflects shared roles in monogenic or syndromic diabetes (involving HNF1A, HNF1B and WFS1).”
Selecting causal genes from genome-wide association studies via functionally-coherent subnetworks (2014, result): “Despite sharing only 9 loci (among 26 and 17 total in the two analyses, respectively), the separate analyses both identified genes involved in diabetes-related biological functions, including “glucose homeostasis,” “pancreas development” and “insulin secretion” ( Supplementary Tables 3 and 5 ).”
Biological interpretation of genome-wide association studies using predicted gene functions (2015, unknown): “Primary biliary cirrhosis [23, 13] Ventricular conduction [24, 2] Mean corpuscular volume [25, 1] Chronic kidney disease [26, 1] Corneal structure [27, 4] Phospholipid levels (plasma) [30, 1] Triglycerides [33, 4] Mean platelet volume [37, 2] Type 1 diabetes [39, 3] LDL cholesterol [40, 4] Red blood”
Meta-Analysis Increases to 71 the Tally of Confirmed Crohn’s Disease Susceptibility Loci (2010, methodology): “717-S1">Supplementary Table 5 ) and (iii) use of GRAI”
Rare de novo variants associated with autism implicate a large functional network of genes involved in formation and function of synapses (2011, methodology): “We and others have previously used various network-based methods to analyze genetic data from rare and common diseases (Feldman et al., 2008; Franke et al., 2006; Iossifov et al., 2008, 2009; Lango Allen et al., 2010; Raychaudhuri et al., 2009).”
Multiple common variants for celiac disease influencing immune gene expression (2010, unknown): “To obtain more insight into the functional relatedness of the celiac disease risk loci, we applied GRAIL, a statistical tool that uses text mining of PubMed abstracts to annotate candidate genes from loci associated with common disease ris”
Human metabolic individuality in biomedical and pharmaceutical research (2011, unknown): “8 3 10(219) Glutaryl-CoA(1) is an intermediate in the metabolism of lysine(1) and tryptophan Deficiencies in glutaryl-CoA dehydrogenase are linked to metabolic disorders KLKB1 rs4253252 Bradykinin, des-arg(9) 6.”