Assessing the collective disease association of multiple genomic loci

@article{Ayati2015AssessingTC,
  title={Assessing the collective disease association of multiple genomic loci},
  author={Marzieh Ayati and Mehmet Koyut{\"u}rk},
  journal={Proceedings of the 6th ACM Conference on Bioinformatics, Computational Biology and Health Informatics},
  year={2015}
}
  • M. Ayati, Mehmet Koyutürk
  • Published 9 September 2015
  • Biology
  • Proceedings of the 6th ACM Conference on Bioinformatics, Computational Biology and Health Informatics
Genome-wide association studies (GWAS) facilitate large-scale identification of genomic variants that are associated with complex traits. However, susceptibility loci identified by GWAS so far generally account for a limited fraction of the genotypic variation in patient populations. Predictive models based on identified loci also have modest success in risk assessment and therefore are of limited practical use. In this paper, we propose a new method to identify sets of loci that are… 
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References

SHOWING 1-10 OF 72 REFERENCES
Prioritization of genomic locus pairs for testing epistasis
TLDR
The proposed method reduces the number of hypotheses to be tested drastically, enabling efficient identification of more epistatic loci that are statistically significant and some of the identified epistatic pairs of loci are reproducible between the two datasets.
SNP-based pathway enrichment analysis for genome-wide association studies
TLDR
The SNP-based pathway enrichment method described here offers a new alternative approach for analysing GWAS data, and is able to identify statistically significant pathways, and importantly, pathways that can be replicated in large genetically distinct samples.
Using biological networks to search for interacting loci in genome-wide association studies
TLDR
This study reports four significant cases of epistasis between unlinked loci, in susceptibility to Crohn's disease, bipolar disorder, hypertension and rheumatoid arthritis, using the experimental knowledge on biological networks to narrow the search for two-locus epistasis.
Pathway and network-based analysis of genome-wide association studies in multiple sclerosis
TLDR
A pathway-oriented analysis of two GWAS in MS that takes into account all SNPs with nominal evidence of association (P < 0.05) and reports here for the first time the potential involvement of neural pathways in MS susceptibility.
iLOCi: a SNP interaction prioritization technique for detecting epistasis in genome-wide association studies
TLDR
A novel SNP interaction prioritization algorithm, named iLOCi (Interacting Loci), which accounts for marker dependencies separately in case and control groups and can provide a more complete understanding of the genetic basis underlying complex disease.
Meta-analysis of genome-wide association data and large-scale replication identifies additional susceptibility loci for type 2 diabetes
TLDR
The results illustrate the value of large discovery and follow-up samples for gaining further insights into the inherited basis of T2D, and detect at least six previously unknown loci with robust evidence for association.
Gene, pathway and network frameworks to identify epistatic interactions of single nucleotide polymorphisms derived from GWAS data
TLDR
Four biologically based frameworks to detect interactions associated with complex diseases outperform the traditional single locus approaches in detecting genes that previously did not reach significance and provide novel drug targets and biomarkers relevant to the underlying mechanisms of disease.
Recent methods for polygenic analysis of genome-wide data implicate an important effect of common variants on cardiovascular disease risk
TLDR
The results of this study imply that common SNPs explain a large amount of the variation in the Framingham Risk Score and suggest that future, better-powered genome-wide association studies will uncover more risk variants that will help to elucidate the genetic architecture of cardiovascular disease.
TEAM: efficient two-locus epistasis tests in human genome-wide association study
TLDR
This article proposes an efficient algorithm, TEAM, which significantly speeds up epistasis detection for human GWAS, and has broader applicability and is more efficient than existing methods for large sample study.
An integrative functional genomics approach for discovering biomarkers in schizophrenia.
TLDR
Pilot data is presented to support methods of investigation such as the use of eQTLs to annotate GWASs of SZ, which could be applied to the field of biomarker discovery.
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