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1.  Detecting Gene-Environment Interactions in Genome-Wide Association Data 
Genetic epidemiology  2009;33(Suppl 1):S68-S73.
Despite the importance of gene-environment (G×E) interactions in the etiology of common diseases, little work has been done to develop methods for detecting these types of interactions in genome-wide association study data. This was the focus of Genetic Analysis Workshop 16 Group 10 contributions, which introduced a variety of new methods for the detection of G×E interactions in both case-control and family-based data using both cross-sectional and longitudinal study designs. Many of these contributions detected significant G×E interactions. Although these interactions have not yet been confirmed, the results suggest the importance of testing for interactions. Issues of sample size, quantifying the environmental exposure, longitudinal data analysis, family-based analysis, selection of the most powerful analysis method, population stratification, and computational expense with respect to testing G×E interactions are discussed.
doi:10.1002/gepi.20475
PMCID: PMC2924567  PMID: 19924704
GAW; case-control; family-based; cross-sectional; longitudinal; rheumatoid arthritis; Framingham Heart Study
2.  Association of 25-hydroxyvitamin D with Blood Pressure in Predominantly 25-hydroxyvitamin D Deficient Hispanic and African Americans 
American journal of hypertension  2009;22(8):867-870.
Background
Several observational studies have recently suggested an inverse association of circulating levels of vitamin D with blood pressure. These findings have been based mainly on Caucasian populations; whether this association also exists among Hispanic and African Americans has yet to be definitively determined. This study investigates the association of 25-hydroxyvitamin D (25[OH]D) with blood pressure in Hispanic and African Americans.
Methods
The data source for this study is the Insulin Resistance Atherosclerosis Family Study (IRASFS), which consists of Hispanic- and African-American families from three U.S. recruitment centers (n=1334). A variance components model was used to analyze the association of plasma 25[OH]D levels with blood pressure.
Results
An inverse association was found between 25[OH]D and both systolic (β for 10 ng/mL difference= −2.05; p<0.01) and diastolic (β for 10 ng/mL difference= −1.35; p<0.001) blood pressure in all populations combined, after adjusting for age, sex, ethnicity and season of blood draw. Further adjustment for body mass index (BMI) weakened this association (β for 10 ng/mL difference= −0.94; p=0.14 and β for 10 ng/mL difference = −0.64; p=0.09, respectively).
Conclusions
25[OH]D levels are significantly inversely associated with blood pressure in Hispanic and African Americans from the IRASFS. However, this association was not significant after adjustment for BMI. Further research is needed to determine the role of BMI in this association. Large, well-designed prospective studies of the effect of vitamin D supplementation on blood pressure may be warranted.
doi:10.1038/ajh.2009.88
PMCID: PMC2865679  PMID: 19444222
Vitamin D; 25-hydroxyvitamin D; blood pressure; hypertension; race; ethnic groups; Hispanic; African American
3.  The Challenge of Detecting Epistasis (G×G Interactions): Genetic Analysis Workshop 16 
Genetic epidemiology  2009;33(0 1):S58-S67.
Interest is increasing in epistasis as a possible source of the unexplained variance missed by genome-wide association studies. The Genetic Analysis Workshop 16 Group 9 participants evaluated a wide variety of classical and novel analytical methods for detecting epistasis, in both the statistical and machine learning paradigms, applied to both real and simulated data. Because the magnitude of epistasis is clearly relative to scale of penetrance, and therefore to some extent, to the choice of model framework, it is not surprising that strong interactions under one model might be minimized or even disappear entirely under a different modeling framework.
doi:10.1002/gepi.20474
PMCID: PMC3692280  PMID: 19924703
generalized linear model; machine learning methods
4.  Detecting gene-by-smoking interactions in a genome-wide association study of early-onset coronary heart disease using random forests 
BMC Proceedings  2009;3(Suppl 7):S88.
Background
Genome-wide association studies are often limited in their ability to attain their full potential due to the sheer volume of information created. We sought to use the random forest algorithm to identify single-nucleotide polymorphisms (SNPs) that may be involved in gene-by-smoking interactions related to the early-onset of coronary heart disease.
Methods
Using data from the Framingham Heart Study, our analysis used a case-only design in which the outcome of interest was age of onset of early coronary heart disease.
Results
Smoking status was dichotomized as ever versus never. The single SNP with the highest importance score assigned by random forests was rs2011345. This SNP was not associated with age alone in the control subjects. Using generalized estimating equations to adjust for sex and account for familial correlation, there was evidence of an interaction between rs2011345 and smoking status.
Conclusion
The results of this analysis suggest that random forests may be a useful tool for identifying SNPs taking part in gene-by-environment interactions in genome-wide association studies.
PMCID: PMC2795991  PMID: 20018084
5.  Classification tree for detection of single-nucleotide polymorphism (SNP)-by-SNP interactions related to heart disease: Framingham Heart Study 
BMC Proceedings  2009;3(Suppl 7):S83.
The aim of this study was to detect the effect of interactions between single-nucleotide polymorphisms (SNPs) on incidence of heart diseases. For this purpose, 2912 subjects with 350,160 SNPs from the Framingham Heart Study (FHS) were analyzed. PLINK was used to control quality and to select the 10,000 most significant SNPs. A classification tree algorithm, Generalized, Unbiased, Interaction Detection and Estimation (GUIDE), was employed to build a classification tree to detect SNP-by-SNP interactions for the selected 10 k SNPs. The classes generated by GUIDE were reexamined by a generalized estimating equations (GEE) model with the empirical variance after accounting for potential familial correlation. Overall, 17 classes were generated based on the splitting criteria in GUIDE. The prevalence of coronary heart disease (CHD) in class 16 (determined by SNPs rs1894035, rs7955732, rs2212596, and rs1417507) was the lowest (0.23%). Compared to class 16, all other classes except for class 288 (prevalence of 1.2%) had a significantly greater risk when analyzed using GEE model. This suggests the interactions of SNPs on these node paths are significant.
PMCID: PMC2795986  PMID: 20018079
6.  Detecting single-nucleotide polymorphism by single-nucleotide polymorphism interactions in rheumatoid arthritis using a two-step approach with machine learning and a Bayesian threshold least absolute shrinkage and selection operator (LASSO) model 
BMC Proceedings  2009;3(Suppl 7):S63.
The objective of this study was to detect interactions between relevant single-nucleotide polymorphisms (SNPs) associated with rheumatoid arthritis (RA). Data from Problem 1 of the Genetic Analysis Workshop 16 were used. These data consisted of 868 cases and 1,194 controls genotyped with the 500 k Illumina chip. First, machine learning methods were applied for preselecting SNPs. One hundred SNPs outside the HLA region and 1,500 SNPs in the HLA region were preselected using information-gain theory. The software weka was used to reduce colinearity and redundancy in the HLA region, resulting in a subset of 6 SNPs out of 1,500. In a second step, a parametric approach to account for interactions between SNPs in the HLA region, as well as HLA-nonHLA interactions was conducted using a Bayesian threshold least absolute shrinkage and selection operator (LASSO) model incorporating 2,560 covariates. This approach detected some main and interaction effects for SNPs in genes that have previously been associated with RA (e.g., rs2395175, rs660895, rs10484560, and rs2476601). Further, some other SNPs detected in this study may be considered in candidate gene studies.
PMCID: PMC2795964  PMID: 20018057
7.  Genome-wide association studies using single-nucleotide polymorphisms versus haplotypes: an empirical comparison with data from the North American Rheumatoid Arthritis Consortium 
BMC Proceedings  2009;3(Suppl 7):S35.
The high genomic density of the single-nucleotide polymorphism (SNP) sets that are typically surveyed in genome-wide association studies (GWAS) now allows the application of haplotype-based methods. Although the choice of haplotype-based vs. individual-SNP approaches is expected to affect the results of association studies, few empirical comparisons of method performance have been reported on the genome-wide scale in the same set of individuals. To measure the relative ability of the two strategies to detect associations, we used a large dataset from the North American Rheumatoid Arthritis Consortium to: 1) partition the genome into haplotype blocks, 2) associate haplotypes with disease, and 3) compare the results with individual-SNP association mapping. Although some associations were shared across methods, each approach uniquely identified several strong candidate regions. Our results suggest that the application of both haplotype-based and individual-SNP testing to GWAS should be adopted as a routine procedure.
PMCID: PMC2795933  PMID: 20018026

Results 1-7 (7)