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Probability theory-based SNP association study method for identifying susceptibility loci and genetic disease models in human case-control data.

ABSTRACT: One of the most challenging points in studying human common complex diseases is to search for both strong and weak susceptibility single-nucleotide polymorphisms (SNPs) and identify forms of genetic disease models. Currently, a number of methods have been proposed for this purpose. Many of them have not been validated through applications into various genome datasets, so their abilities are not clear in real practice. In this paper, we present a novel SNP association study method based on probability theory, called ProbSNP. The method firstly detects SNPs by evaluating their joint probabilities in combining with disease status and selects those with the lowest joint probabilities as susceptibility ones, and then identifies some forms of genetic disease models through testing multiple-locus interactions among the selected SNPs. The joint probabilities of combined SNPs are estimated by establishing Gaussian distribution probability density functions, in which the related parameters (i.e., mean value and standard deviation) are evaluated based on allele and haplotype frequencies. Finally, we test and validate the method using various genome datasets. We find that ProbSNP has shown remarkable success in the applications to both simulated genome data and real genome-wide data.

SUBMITTER: Yuan X

PROVIDER: S-EPMC3029504 | biostudies-literature | 2010 Dec

REPOSITORIES: biostudies-literature

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Probability theory-based SNP association study method for identifying susceptibility loci and genetic disease models in human case-control data.

Yuan Xiguo X Zhang Junying J Wang Yue Y

IEEE transactions on nanobioscience 20100913 4

One of the most challenging points in studying human common complex diseases is to search for both strong and weak susceptibility single-nucleotide polymorphisms (SNPs) and identify forms of genetic disease models. Currently, a number of methods have been proposed for this purpose. Many of them have not been validated through applications into various genome datasets, so their abilities are not clear in real practice. In this paper, we present a novel SNP association study method based on probab ...[more]

PMID: 20840904

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Project description:BackgroundGenome-wide association studies (GWAS) have identified more than 40 colorectal cancer susceptibility loci, but only a small fraction of heritability was explained. To account for missing heritability, we investigated gene-environment interactions (G × Es) between GWAS-identified single-nucleotide polymorphisms (SNPs) and established risk or protective factors for colorectal cancer using both case-only and case-control study designs.MethodsData on 703 colorectal cancer cases and 1406 healthy controls from the National Cancer Center in Korea were used. We tested interactions between 31 GWAS-identified SNPs and 13 established risk or protective factors for colorectal cancer (family history, body mass index, history of colorectal polyps, inflammatory bowel disease, and diabetes mellitus, alcohol drinking, smoking, regular exercise, regular aspirin use, postmenopausal hormone replace therapy, red meat and processed meat intake, and dairy consumption). Logistic regression models were used to assess G × Es for colorectal cancer risk.ResultsThe SNP rs4444235 at 14q22.2 interacted with regular exercise in colorectal cancer (pcase-only = 2.4 × 10- 3, pcase-control = 1.5 × 10- 3). The risk allele (C) of rs4444235 increased the risk of colorectal cancer in regularly exercising individuals (OR = 1.47, 95% CI = 1.02-2.10) but decreased the risk in non-exercising individuals (OR = 0.76, 95% CI = 0.62-0.94). Furthermore, the G × E between the SNP rs2423279 at 20p12.3 and regular aspirin use was statistically significant (pcase-only = 7.7 × 10- 3, pcase-control = 1.6 × 10- 3). The additive effect of the risk allele (T) of rs2423279 on colorectal cancer risk was increased among regular aspirin users (OR = 4.62, 95% CI = 1.97-10.80).ConclusionOur results suggest that SNP rs4444235 at 14q22.2 and SNP rs2423279 at 20p12.3 may interact with regular exercise and aspirin use in colorectal carcinogenesis.

Project description:BackgroundGenetic association studies are conducted to discover genetic loci that contribute to an inherited trait, identify the variants behind these associations and ascertain their functional role in determining the phenotype. To date, functional annotations of the genetic variants have rarely played more than an indirect role in assessing evidence for association. Here, we demonstrate how these data can be systematically integrated into an association study's analysis plan.ResultsWe developed a Bayesian statistical model for the prior probability of phenotype-genotype association that incorporates data from past association studies and publicly available functional annotation data regarding the susceptibility variants under study. The model takes the form of a binary regression of association status on a set of annotation variables whose coefficients were estimated through an analysis of associated SNPs in the GWAS Catalog (GC). The functional predictors examined included measures that have been demonstrated to correlate with the association status of SNPs in the GC and some whose utility in this regard is speculative: summaries of the UCSC Human Genome Browser ENCODE super-track data, dbSNP function class, sequence conservation summaries, proximity to genomic variants in the Database of Genomic Variants and known regulatory elements in the Open Regulatory Annotation database, PolyPhen-2 probabilities and RegulomeDB categories. Because we expected that only a fraction of the annotations would contribute to predicting association, we employed a penalized likelihood method to reduce the impact of non-informative predictors and evaluated the model's ability to predict GC SNPs not used to construct the model. We show that the functional data alone are predictive of a SNP's presence in the GC. Further, using data from a genome-wide study of ovarian cancer, we demonstrate that their use as prior data when testing for association is practical at the genome-wide scale and improves power to detect associations.ConclusionsWe show how diverse functional annotations can be efficiently combined to create 'functional signatures' that predict the a priori odds of a variant's association to a trait and how these signatures can be integrated into a standard genome-wide-scale association analysis, resulting in improved power to detect truly associated variants.

Dataset Information

Probability theory-based SNP association study method for identifying susceptibility loci and genetic disease models in human case-control data.

Publications

Probability theory-based SNP association study method for identifying susceptibility loci and genetic disease models in human case-control data.

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