Recent advances in sequencing technology have presented both opportunities and challenges, with limited statistical power to detect a single causal rare variant with practical sample sizes. To overcome this, the contributors to Group 1 of Genetic Analysis Workshop 17 sought to develop methods to detect the combined signal of multiple causal rare variants in a biologically meaningful way. The contributors used genes, genome location proximity, or genetic pathways as the basic unit in combining the information from multiple variants. Weaknesses of the exome sequence data and the relative strengths and weaknesses of the five approaches are discussed.

Purpose

To investigate whether the association between physical activity and serum 25-hydroxyvitamin D (25(OH)D) concentrations is independent of sun exposure, body size, and other potential explanatory variables.

Methods

Using data from a sample of 1,343 postmenopausal women, from the Women’s Health Initiative, linear regression was used to examine the associations of duration (minutes/week) of recreational activity and of yard work with 25(OH)D concentrations (nmol/L).

Results

In age-adjusted analyses, positive associations were observed between 25(OH)D concentrations and both duration of recreational physical activity (β=0.71, SE(0.09), P<0.001) and yard work (β=0.36, SE(0.10), P=0.004). After further adjustment for vitamin D intake, self-reported sunlight exposure, waist circumference, and season of blood draw, 25(OH)D was significantly associated with recreational activity (β=0.21, SE(0.09), P=0.014) but not with yard work (β=0.18, SE(0.09), P=0.061). Interactions were observed between season and both recreational activity (Pinteraction=0.082) and yard work (Pinteraction=0.038) such that these activity-25(OH)D associations were greater during summer/fall compared to winter/spring. Self-reported sunlight exposure and measures of body size did not modify the associations.

Conclusion

The observed age-adjusted activity-25(OH)D associations were attenuated after adjusting for explanatory variables and were modified by season of blood draw. Adopting a lifestyle that incorporates outdoor physical activity during summer/fall, consuming recommended amounts of vitamin D, and maintaining a healthy weight may improve or maintain vitamin D status in postmenopausal women.

Next-generation sequencing technologies are rapidly changing the field of genetic epidemiology and enabling exploration of the full allele frequency spectrum underlying complex diseases. Although sequencing technologies have shifted our focus toward rare genetic variants, statistical methods traditionally used in genetic association studies are inadequate for estimating effects of low minor allele frequency variants. Four our study we use the Genetic Analysis Workshop 17 data from 697 unrelated individuals (genotypes for 24,487 autosomal variants from 3,205 genes). We apply a Bayesian hierarchical mixture model to identify genes associated with a simulated binary phenotype using a transformed genotype design matrix weighted by allele frequencies. A Metropolis Hasting algorithm is used to jointly sample each indicator variable and additive genetic effect pair from its conditional posterior distribution, and remaining parameters are sampled by Gibbs sampling. This method identified 58 genes with a posterior probability greater than 0.8 for being associated with the phenotype. One of these 58 genes, PIK3C2B was correctly identified as being associated with affected status based on the simulation process. This project demonstrates the utility of Bayesian hierarchical mixture models using a transformed genotype matrix to detect genes containing rare and common variants associated with a binary phenotype.

Vitamin D deficiency is associated with many adverse health outcomes. There are several well established environmental predictors of vitamin D concentrations, yet studies of the genetic determinants of vitamin D concentrations are in their infancy. Our objective was to conduct a pilot genome-wide association (GWA) study of 25-hydroxyvitamin D (25[OH]D) and 1,25-dihydroxyvitamin D (1,25[OH]2D) concentrations in a subset of 229 Hispanic subjects, followed by replication genotyping of 50 single nucleotide polymorphisms (SNPs) in the entire sample of 1,190 Hispanics from San Antonio, Texas and San Luis Valley, Colorado. Of the 309,200 SNPs that met all quality control criteria, three SNPs in high linkage disequilibrium (LD) with each other were significantly associated with 1,25[OH]2D (rs6680429, rs9970802, and rs10889028) at a Bonferroni corrected P-value threshold of 1.62 × 10−7, however none met the threshold for 25[OH]D. Of the 50 SNPs selected for replication genotyping, five for 25[OH]D (rs2806508, rs10141935, rs4778359, rs1507023, and rs9937918) and eight for 1,25[OH]2D (rs6680429, rs1348864, rs4559029, rs12667374, rs7781309, rs10505337, rs2486443, and rs2154175) were replicated in the entire sample of Hispanics (P < 0.01). In conclusion, we identified several SNPs that were associated with vitamin D metabolite concentrations in Hispanics. These candidate polymorphisms merit further investigation in independent populations and other ethnicities.

Background

Evidence-based public health requires the existence of reliable information systems for priority setting and evaluation of interventions. Existing data systems in the United States are either too crude (e.g., vital statistics), rely on administrative data (e.g., Medicare) or, because of their national scope (e.g., NHANES), lack the discriminatory power to assess specific needs and to evaluate community health activities at the state and local level. This manuscript describes the rationale and methods of the Survey of the Health of Wisconsin (SHOW), a novel infrastructure for population health research.

Methods/Design

The program consists of a series of independent annual surveys gathering health-related data on representative samples of state residents and communities. Two-stage cluster sampling is used to select households and recruit approximately 800-1,000 adult participants (21-74 years old) each year. Recruitment and initial interviews are done at the household; additional interviews and physical exams are conducted at permanent or mobile examination centers. Individual survey data include physical, mental, and oral health history, health literacy, demographics, behavioral, lifestyle, occupational, and household characteristics as well as health care access and utilization. The physical exam includes blood pressure, anthropometry, bioimpedance, spirometry, urine collection and blood draws. Serum, plasma, and buffy coats (for DNA extraction) are stored in a biorepository for future studies. Every household is geocoded for linkage with existing contextual data including community level measures of the social and physical environment; local neighborhood characteristics are also recorded using an audit tool. Participants are re-contacted bi-annually by phone for health history updates.

Discussion

SHOW generates data to assess health disparities across state communities as well as trends on prevalence of health outcomes and determinants. SHOW also serves as a platform for ancillary epidemiologic studies and for studies to evaluate the effect of community-specific interventions. It addresses key gaps in our current data resources and increases capacity for etiologic, applied and translational population health research. It is hoped that this program will serve as a model to better support evidence-based public health, facilitate intervention evaluation research, and ultimately help improve health throughout the state and nation.

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.

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.

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.

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.

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.

Background

Genes have been found to influence the age of onset of several diseases and traits. The occurrence of many chronic diseases, obesity included, appears to be strongly age-dependent. However, an analysis of potential age of onset genes for obesity has yet to be reported. There are at least two analytic methods for determining an age of onset gene. The first is to consider a person affected if they possess the trait before a certain age (an early age of onset phenotype). The second is to define the phenotype based on the residual from a survival analysis.

Results

No regions provided evidence for linkage at the more stringent level of p < 0.001. However, five regions showed consistent suggestive evidence for linkage (one marker with p < 0.01 and a second contiguous marker at p < 0.05). These regions were chromosome 1 (280–294 cM) and chromosome 16 (56–64 cM) for overweight using the survival analysis residual method and chromosome 13 (102–122 cM), chromosome 17 (127–138 cM), and chromosome 19 (23–47 cM) for obese before age 35.

Conclusion

Only one region (chromosome 19 at 23–47 cM) showed somewhat consistent results between the two analytic methods. Potential reasons for inconsistent results between the two methods, as well as their strengths and weaknesses, are discussed. The use of both methods together to explore the genetics of the age of onset of a trait may prove to be beneficial in determining a gene that is linked only to an early age of onset phenotype versus one that determines age of onset through all age groups.

The relationship between elevated blood pressure and cardiovascular and cerebrovascular disease risk is well accepted. Both systolic and diastolic hypertension are associated with this risk increase, but systolic blood pressure appears to be a more important determinant of cardiovascular risk than diastolic blood pressure. Subjects for this study are derived from the Framingham Heart Study data set. Each subject had five records of clinical data of which systolic blood pressure, age, height, gender, weight, and hypertension treatment were selected to characterize the phenotype in this analysis.

We modeled systolic blood pressure as a function of age using a mixed modeling methodology that enabled us to characterize the phenotype for each individual as the individual's deviation from the population average rate of change in systolic blood pressure for each year of age while controlling for gender, body mass index, and hypertension treatment. Significant (p = 0.00002) evidence for linkage was found between this normalized phenotype and a region on chromosome 1. Similar linkage results were obtained when we estimated the phenotype while excluding values obtained during hypertension treatment. The use of linear mixed models to define phenotypes is a methodology that allows for the adjustment of the main factor by covariates. Future work should be done in the area of combining this phenotype estimation directly with the linkage analysis so that the error in estimating the phenotype can be properly incorporated into the genetic analysis, which, at present, assumes that the phenotype is measured (or estimated) without error.

Genetic Analysis Workshop 17 (GAW17) provided a platform for evaluating existing statistical genetic methods and for developing novel methods to analyze rare variants that modulate complex traits. In this article, we present an overview of the 1000 Genomes Project exome data and simulated phenotype data that were distributed to GAW17 participants for analyses, the different issues addressed by the participants, and the process of preparation of manuscripts resulting from the discussions during the workshop.

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.