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Data integration workflow for search of disease driving genes and genetic variants.

ABSTRACT: Comprehensive characterization of a gene's impact on phenotypes requires knowledge of the context of the gene. To address this issue we introduce a systematic data integration method Candidate Genes and SNPs (CANGES) that links SNP and linkage disequilibrium data to pathway- and protein-protein interaction information. It can be used as a knowledge discovery tool for the search of disease associated causative variants from genome-wide studies as well as to generate new hypotheses on synergistically functioning genes. We demonstrate the utility of CANGES by integrating pathway and protein-protein interaction data to identify putative functional variants for (i) the p53 gene and (ii) three glioblastoma multiforme (GBM) associated risk genes. For the GBM case, we further integrate the CANGES results with clinical and genome-wide data for 209 GBM patients and identify genes having effects on GBM patient survival. Our results show that selecting a focused set of genes can result in information beyond the traditional genome-wide association approaches. Taken together, holistic approach to identify possible interacting genes and SNPs with CANGES provides a means to rapidly identify networks for any set of genes and generate novel hypotheses. CANGES is available in http://csbi.ltdk.helsinki.fi/CANGES/

SUBMITTER: Karinen S

PROVIDER: S-EPMC3075259 | biostudies-literature | 2011 Apr

REPOSITORIES: biostudies-literature

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Data integration workflow for search of disease driving genes and genetic variants.

Karinen Sirkku S Heikkinen Tuomas T Nevanlinna Heli H Hautaniemi Sampsa S

PloS one 20110412 4

Comprehensive characterization of a gene's impact on phenotypes requires knowledge of the context of the gene. To address this issue we introduce a systematic data integration method Candidate Genes and SNPs (CANGES) that links SNP and linkage disequilibrium data to pathway- and protein-protein interaction information. It can be used as a knowledge discovery tool for the search of disease associated causative variants from genome-wide studies as well as to generate new hypotheses on synergistica ...[more]

PMID: 21533266

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Project description:The first decade of Genome Wide Association Studies (GWAS) has uncovered a wealth of disease-associated variants. Two important derivations will be the translation of this information into a multiscale understanding of pathogenic variants and leveraging existing data to increase the power of existing and future studies through prioritization. We explore edge prediction on heterogeneous networks--graphs with multiple node and edge types--for accomplishing both tasks. First we constructed a network with 18 node types--genes, diseases, tissues, pathophysiologies, and 14 MSigDB (molecular signatures database) collections--and 19 edge types from high-throughput publicly-available resources. From this network composed of 40,343 nodes and 1,608,168 edges, we extracted features that describe the topology between specific genes and diseases. Next, we trained a model from GWAS associations and predicted the probability of association between each protein-coding gene and each of 29 well-studied complex diseases. The model, which achieved 132-fold enrichment in precision at 10% recall, outperformed any individual domain, highlighting the benefit of integrative approaches. We identified pleiotropy, transcriptional signatures of perturbations, pathways, and protein interactions as influential mechanisms explaining pathogenesis. Our method successfully predicted the results (with AUROC = 0.79) from a withheld multiple sclerosis (MS) GWAS despite starting with only 13 previously associated genes. Finally, we combined our network predictions with statistical evidence of association to propose four novel MS genes, three of which (JAK2, REL, RUNX3) validated on the masked GWAS. Furthermore, our predictions provide biological support highlighting REL as the causal gene within its gene-rich locus. Users can browse all predictions online (http://het.io). Heterogeneous network edge prediction effectively prioritized genetic associations and provides a powerful new approach for data integration across multiple domains.

Project description:BackgroundGenetic variation databases provide invaluable information on the presence and frequency of genetic variants in the 'untargeted' human population, aggregated with the primary goal to facilitate the interpretation of clinically important variants. The presence of somatic variants in such databases can affect variant assessment in undiagnosed rare disease (RD) patients. Previously, the impact of somatic mosaicism was only considered in relation to two Mendelian disease-associated genes. Here, we expand the analyses to identify additional mosaicism-prone genes in blood-derived reference population databases.ResultsTo identify additional mosaicism-prone genes relevant to RDs, we focused on known/previously established ClinVar pathogenic and likely pathogenic single-nucleotide variants, residing in genes associated with early onset, severe autosomal dominant diseases. We asked whether any of these variants are present in a higher-than-expected frequency in the reference population databases and whether there is evidence of somatic origin (i.e., allelic imbalance) rather than germline heterozygosity (~ half of the reads supporting alternative allele). The mosaicism-prone genes identified were further categorized according to the processes they are involved in. Beyond the previously reported ASXL1 and DNMT3A, we identified 7 additional autosomal dominant RD-associated genes with known pathogenic single-nucleotide variants present in the reference population databases and good evidence of allelic imbalance: BRAF, CBL, FGFR3, IDH2, KRAS, PTPN11 and SETBP1. From this group of 9 genes, the majority (n = 7) was important for hematopoiesis. In addition, 4 of these genes were involved in cell proliferation. Further assessment of the known 156 hematopoietic genes led to identification of 48 genes (21 not yet associated with RDs) with at least some evidence of mosaicism detectable in reference population databases.ConclusionsThese results stress the importance of considering genes involved in hematopoiesis and cell proliferation when interpreting the presence and frequency of genetic variants in blood-derived reference population databases, both public and private. This is especially important when considering new variants of uncertain significance in known hematopoietic/cell proliferation RD genes and future novel gene-disease associations involving this class of genes.

Dataset Information

Data integration workflow for search of disease driving genes and genetic variants.

Publications

Data integration workflow for search of disease driving genes and genetic variants.

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