Type 2 diabetes genetic association database manually curated for the study design and odds ratio

Lim, Ji Eun; Hong, Kyung‐Won; Jin, Hyun‐Seok; Kim, Yang Seok; Park, Hun Kuk; Oh, Bermseok

doi:10.1186/1472-6947-10-76

Cited by 35 publications

(29 citation statements)

References 12 publications

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“…However, eQTL edges are able to capture the functional relationship between the SNPs and the genes in this N et P oco . Interestingly, some of the genes in this N et P oco are previously reported to be associated with T2D [52], while some may have links to T2D although no direct associations are previously reported. More precisely, Wong et al [53] show that SIRPA is a T1D risk gene in the non-obese diabetic mouse.…”

Section: Resultsmentioning

confidence: 99%

“…STC1 and LOXL2 are genes that are previously reported to be associated with T2D [52]. It is notable that TINAGL1 is involved in Glucose/Energy metabolism pathway and CHRNA9 is involved in Postsynaptic nicotinic acetylcholine receptors pathway with other genes such as CHRNA2 , CHRNA4 and CHRNA6 that are previously reported to be associated with T2D [52].…”

Section: Resultsmentioning

confidence: 99%

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PoCos: Population Covering Locus Sets for Risk Assessment in Complex Diseases

Ayati

Koyutürk

2016

PLoS Comput Biol

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Susceptibility loci identified by GWAS generally account for a limited fraction of heritability. Predictive models based on identified loci also have modest success in risk assessment and therefore are of limited practical use. Many methods have been developed to overcome these limitations by incorporating prior biological knowledge. However, most of the information utilized by these methods is at the level of genes, limiting analyses to variants that are in or proximate to coding regions. We propose a new method that integrates protein protein interaction (PPI) as well as expression quantitative trait loci (eQTL) data to identify sets of functionally related loci that are collectively associated with a trait of interest. We call such sets of loci “population covering locus sets” (PoCos). The contributions of the proposed approach are three-fold: 1) We consider all possible genotype models for each locus, thereby enabling identification of combinatorial relationships between multiple loci. 2) We develop a framework for the integration of PPI and eQTL into a heterogenous network model, enabling efficient identification of functionally related variants that are associated with the disease. 3) We develop a novel method to integrate the genotypes of multiple loci in a PoCo into a representative genotype to be used in risk assessment. We test the proposed framework in the context of risk assessment for seven complex diseases, type 1 diabetes (T1D), type 2 diabetes (T2D), psoriasis (PS), bipolar disorder (BD), coronary artery disease (CAD), hypertension (HT), and multiple sclerosis (MS). Our results show that the proposed method significantly outperforms individual variant based risk assessment models as well as the state-of-the-art polygenic score. We also show that incorporation of eQTL data improves the performance of identified POCOs in risk assessment. We also assess the biological relevance of PoCos for three diseases that have similar biological mechanisms and identify novel candidate genes. The resulting software is publicly available at http://compbio.case.edu/pocos/.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

PoCos: Population Covering Locus Sets for Risk Assessment in Complex Diseases

Ayati

Koyutürk

2016

PLoS Comput Biol

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“…[9][10][11] In addition, the following genomewide association tools were used: the Genetic Association Database, GAD (31) 39 All of the bioinformatics mining was verified by two independent experiments. Big data was downloaded two independent times and the output verified for consistency.…”

Section: Methodsmentioning

confidence: 99%

"Diabetes Associated Genes from the Dark Matter of the Human Proteome"

Delgado¹,

Br²,

Ao³

et al. 2014

MOJPB

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“…Using this definition of region of interest, we are able to map 23 loci involved in epistatic interactions identified on the WTCCC dataset to the region of interest of 14 genes. 6 loci out of these 14 loci are mapped to genes that are well known to be associated with T2D, including BAZ2B, TCF7L2, CDKN2B and CNTN1 [10]. Similary, on the eMERGE data set, 67 of epistatic loci are mapped to 49 genes and 16 of these genes are previously reported to be associated with T2D [22].…”

Section: Biological Relevance and Reproducibilitymentioning

confidence: 96%

Prioritization of genomic locus pairs for testing epistasis

Ayati

Koyutürk

2014

Proceedings of the 5th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics

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In recent years, genome-wide association studies (GWAS) have successfully identified loci that harbor genetic variants associated with complex diseases. However, susceptibility loci identified by GWAS so far generally account for a limited fraction of heritability in patient populations. More recently, there has been considerable attention on identifying epistatic interactions. However, the large number of pairs to be tested for epistasis poses significant challenges, in terms of both computational (run-time) and statistical (multiple hypothesis testing) considerations.In this paper, we propose a new method to reduce the number of tests required to identify epistatic pairs of genomic loci. The key idea of the proposed algorithm is to reduce the data by identifying sets of loci that may be complementary in their association with the disease. Namely, we identify population covering locus sets (PoCos), i.e., sets of loci that harbor at least one susceptibility allele in samples with the phenotype of interest. Then we compute representative genotypes for PoCos, and assess the significance of the interactions between pairs of PoCos. We use the results of this assessment to prioritize pairs of loci to be tested for epistasis. We test the proposed method on two independent GWAS data sets of Type 2 Diabetes (T2D). Our experimental results show that the proposed method reduces the number of hypotheses to be tested drastically, enabling efficient identification of more epistatic loci that are statistically significant. Moreover, some of the identified epistatic pairs of loci are reproducible between the two datasets. We also show that the proposed method outperforms an existing method for prioritization of locus pairs.

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Type 2 diabetes genetic association database manually curated for the study design and odds ratio

Cited by 35 publications

References 12 publications

PoCos: Population Covering Locus Sets for Risk Assessment in Complex Diseases

PoCos: Population Covering Locus Sets for Risk Assessment in Complex Diseases

"Diabetes Associated Genes from the Dark Matter of the Human Proteome"

Prioritization of genomic locus pairs for testing epistasis

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