Dataset Information

Semantic Modeling for SNPs Associated with Ethnic Disparities in HapMap Samples.

ABSTRACT: Single-nucleotide polymorphisms (SNPs) have been emerging out of the efforts to research human diseases and ethnic disparities. A semantic network is needed for in-depth understanding of the impacts of SNPs, because phenotypes are modulated by complex networks, including biochemical and physiological pathways. We identified ethnicity-specific SNPs by eliminating overlapped SNPs from HapMap samples, and the ethnicity-specific SNPs were mapped to the UCSC RefGene lists. Ethnicity-specific genes were identified as follows: 22 genes in the USA (CEU) individuals, 25 genes in the Japanese (JPT) individuals, and 332 genes in the African (YRI) individuals. To analyze the biologically functional implications for ethnicity-specific SNPs, we focused on constructing a semantic network model. Entities for the network represented by "Gene," "Pathway," "Disease," "Chemical," "Drug," "ClinicalTrials," "SNP," and relationships between entity-entity were obtained through curation. Our semantic modeling for ethnicity-specific SNPs showed interesting results in the three categories, including three diseases ("AIDS-associated nephropathy," "Hypertension," and "Pelvic infection"), one drug ("Methylphenidate"), and five pathways ("Hemostasis," "Systemic lupus erythematosus," "Prostate cancer," "Hepatitis C virus," and "Rheumatoid arthritis"). We found ethnicity-specific genes using the semantic modeling, and the majority of our findings was consistent with the previous studies - that an understanding of genetic variability explained ethnicity-specific disparities.

SUBMITTER: Kim H

PROVIDER: S-EPMC3990764 | biostudies-literature | 2014 Mar

REPOSITORIES: biostudies-literature

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Semantic Modeling for SNPs Associated with Ethnic Disparities in HapMap Samples.

Kim Hyoyoung H Yoo Won Gi WG Park Junhyung J Kim Heebal H Kang Byeong-Chul BC

Genomics & informatics 20140331 1

Single-nucleotide polymorphisms (SNPs) have been emerging out of the efforts to research human diseases and ethnic disparities. A semantic network is needed for in-depth understanding of the impacts of SNPs, because phenotypes are modulated by complex networks, including biochemical and physiological pathways. We identified ethnicity-specific SNPs by eliminating overlapped SNPs from HapMap samples, and the ethnicity-specific SNPs were mapped to the UCSC RefGene lists. Ethnicity-specific genes we ...[more]

PMID: 24748859

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Project description:With the completion of the HapMap project, a variety of computational algorithms and tools have been proposed for haplotype inference, tag SNP selection and genome-wide association studies. Simulated data are commonly used in evaluating these new developed approaches. In addition to simulations based on population models, empirical data generated by perturbing real data, has also been used because it may inherit specific properties from real data. However, there is no tool that is publicly available to generate large scale simulated variation data by taking into account knowledge from the HapMap project.A computer program (gs) was developed to quickly generate a large number of samples based on real data that are useful for a variety of purposes, including evaluating methods for haplotype inference, tag SNP selection and association studies. Two approaches have been implemented to generate dense SNP haplotype/genotype data that share similar local linkage disequilibrium (LD) patterns as those in human populations. The first approach takes haplotype pairs from samples as inputs, and the second approach takes patterns of haplotype block structures as inputs. Both quantitative and qualitative traits have been incorporated in the program. Phenotypes are generated based on a disease model, or based on the effect of a quantitative trait nucleotide, both of which can be specified by users. In addition to single-locus disease models, two-locus disease models have also been implemented that can incorporate any degree of epistasis. Users are allowed to specify all nine parameters in a 3 x 3 penetrance table. For several commonly used two-locus disease models, the program can automatically calculate penetrances based on the population prevalence and marginal effects of a disease that users can conveniently specify.The program gs can effectively generate large scale genetic and phenotypic variation data that can be used for evaluating new developed approaches. It is freely available from the authors' web site at http://www.eecs.case.edu/~jxl175/gs.html.

Project description:Accurate forecasting of cardiovascular disease mortality is crucial to guide policy and programming efforts. Prior forecasts often have not incorporated past trends in rates of reduction in cardiovascular disease mortality. This creates uncertainties about future trends in cardiovascular disease mortality and disparities.To forecast US cardiovascular disease mortality and disparities to 2030, we developed a hierarchical bayesian model to determine and incorporate prior age, period, and cohort effects from 1979 to 2012, stratified by age, sex, and race, which we combined with expected demographic shifts to 2030. Data sources included the National Vital Statistics System, Surveillance, Epidemiology, and End Results (SEER) single-year population estimates, and US Bureau of Statistics 2012 national population projections. We projected coronary disease and stroke deaths to 2030, first on the basis of constant age, period, and cohort effects at 2012 values, as is most commonly done (conventional), and then with the use of more rigorous projections incorporating expected trends in age, period, and cohort effects (trend based). We primarily evaluated absolute mortality. The conventional model projected total coronary and stroke deaths by 2030 to increase by ?18% (67 000 additional coronary deaths per year) and 50% (64 000 additional stroke deaths per year). Conversely, the trend-based model projected that coronary mortality would decrease by 2030 by ?27% (79 000 fewer deaths per year) and stroke mortality would remain unchanged (200 fewer deaths per year). Health disparities will be improved in stroke deaths but not coronary deaths.After prior mortality trends and expected demographic shifts are accounted for, total US coronary deaths are expected to decline, whereas stroke mortality will remain relatively constant. Health disparities in stroke but not coronary deaths will be improved but not eliminated. These age, period, and cohort approaches offer more plausible predictions than conventional estimates.

Dataset Information

Semantic Modeling for SNPs Associated with Ethnic Disparities in HapMap Samples.

Publications

Semantic Modeling for SNPs Associated with Ethnic Disparities in HapMap Samples.

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