Genetic association studies and the effect of misclassification and selection bias in putative confounders

Genetic epidemiology studies often adjust for numerous potential confounders, yet the influences of confounder misclassification and selection bias are rarely considered. We used simulated data to evaluate the effect of confounder misclassification and selection bias in a case-control study of incident myocardial infarction. We show that putative confounders traditionally included in genetic association studies do not alter effect estimates, even when excessive levels of misclassification are incorporated. Conversely, selection bias resulting from covariates affected by the single-nucleotide polymorphism of interest can bias effect estimates upward or downward. These results support careful consideration of how well a study population represents the target population because selection bias may result even when associations are modest

Evaluation of population impact of candidate polymorphisms for coronary heart disease in the Framingham Heart Study Offspring Cohort

In order to evaluate the population impact of putative causal genetic variants over the life course of disease, we extended the static estimation of population-attributable risk fraction and developed a novel tool to evaluate how the population impact changes over time using the Framingham Heart Study Offspring Cohort data provided to the Genetic Analysis Workshop 16, Problem 2. A set of population-attributable risk fractions based on survival functions were estimated under the proportional hazards models. The development of this novel measure of population impact creates a more comprehensive estimate of population impact over the life course of disease, which may help us to better understand genetic susceptibility at the population level

Quantitative trait locus-specific genotype × alcoholism interaction on linkage for evoked electroencephalogram oscillations

We explored the evidence for a quantitative trait locus (QTL)-specific genotype × alcoholism interaction for an evoked electroencephalogram theta band oscillation (ERP) phenotype on a region of chromosome 7 in participants of the US Collaborative Study on the Genetics of Alcoholism. Among 901 participants with both genotype and phenotype data available, we performed variance component linkage analysis (SOLAR version 2.1.2) in the full sample and stratified by DSM-III-R and Feighner-definite alcoholism categories. The heritability of the ERP phenotype after adjusting for age and sex effects in the combined sample and in the alcoholism classification sub-groups ranged from 40% to 66%. Linkage on chromosome 7 was identified at 158 cM (LOD = 3.8) in the full sample and at 108 in the non-alcoholic subgroup (LOD = 3.1). Further, we detected QTL-specific genotype × alcoholism interaction at these loci. This work demonstrates the importance of considering the complexity of common complex traits in our search for genes that predispose to alcoholism

Longitudinal age-dependent effect on systolic blood pressure

Age-dependent genetic effects on susceptibility to hypertension have been documented. We present a novel variance-component method for the estimation of age-dependent genetic effects on longitudinal systolic blood pressure using 57,827 Affymetrix single-nucleotide polymorphisms (SNPs) on chromosomes 17-22 genotyped in 2,475 members of the Offspring Cohort of the Framingham Heart Study. We used the likelihood-ratio test statistic to test the main genetic effect, genotype-by-age interaction, and simultaneously, main genetic effect and genotype-by-age interactions (2 degrees of freedom (df) test) for each SNP. Applying Bonferroni correction, three SNPs were significantly associated with longitudinal blood pressure in the analysis of main genetic effects or in combined 2-df analyses. For the associations detected using the simultaneous 2-df test, neither main effects nor genotype-by-age interaction p-values reached genome-wide statistical significance. The value of the 2-df test for screening genetic interaction effects could not be established in this study

Use of longitudinal data in genetic studies in the genome-wide association studies era: summary of Group 14

Participants analyzed actual and simulated longitudinal data from the Framingham Heart Study for various metabolic and cardiovascular traits. The genetic information incorporated into these investigations ranged from selected single-nucleotide polymorphisms to genome-wide association arrays. Genotypes were incorporated using a broad range of methodological approaches including conditional logistic regression, linear mixed models, generalized estimating equations, linear growth curve estimation, growth modeling, growth mixture modeling, population attributable risk fraction based on survival functions under the proportional hazards models, and multivariate adaptive splines for the analysis of longitudinal data. The specific scientific questions addressed by these different approaches also varied, ranging from a more precise definition of the phenotype, bias reduction in control selection, estimation of effect sizes and genotype associated risk, to direct incorporation of genetic data into longitudinal modeling approaches and the exploration of population heterogeneity with regard to longitudinal trajectories. The group reached several overall conclusions: 1) The additional information provided by longitudinal data may be useful in genetic analyses. 2) The precision of the phenotype definition as well as control selection in nested designs may be improved, especially if traits demonstrate a trend over time or have strong age-of-onset effects. 3) Analyzing genetic data stratified for high-risk subgroups defined by a unique development over time could be useful for the detection of rare mutations in common multi-factorial diseases. 4) Estimation of the population impact of genomic risk variants could be more precise. The challenges and computational complexity demanded by genome-wide single-nucleotide polymorphism data were also discussed

Phenotypic, genetic, and genome-wide structure in the metabolic syndrome

BACKGROUND: Insulin resistance, obesity, dyslipidemia, and high blood pressure characterize the metabolic syndrome. In an effort to explore the utility of different multivariate methods of data reduction to better understand the genetic influences on the aggregation of metabolic syndrome phenotypes, we calculated phenotypic, genetic, and genome-wide LOD score correlation matrices using five traits (total cholesterol, high density lipoprotein cholesterol, triglycerides, systolic blood pressure, and body mass index) from the Framingham Heart Study data set prepared for the Genetic Analysis Workshop 13, clinic visits 10 and 1 for the original and offspring cohorts, respectively. We next applied factor analysis to summarize the relationship between these phenotypes. RESULTS: Factors generated from the genetic correlation matrix explained the most variation. Factors extracted using the other matrices followed a different pattern and suggest distinct effects. CONCLUSIONS: Given these results, different methods of multivariate data reduction may provide unique clues on the clustering of this complex syndrome

Angiotensin II type 1 receptor polymorphisms and susceptibility to hypertension: A HuGE review

The angiotensin II type 1 receptor (AGTR1) plays an integral role in blood pressure control, and is implicated in the pathogenesis of hypertension. Polymorphisms within this gene have been extensively studied in association with hypertension; however, findings are conflicting. To clarify these data, we conducted a systematic review of association studies of AGTR1 polymorphisms and hypertension, and performed a meta-analysis of the rs5186 variant. Results show that the currently available literature is too heterogeneous to draw meaningful conclusions. The definition of hypertension and gender composition of individual studies helps to explain this heterogeneity. Although the structure and splicing pattern of AGTR1 would suggest a likely effect of polymorphisms within the promoter region on gene function, few studies have been conducted thus far. In conclusion, there is insufficient evidence that polymorphisms in the AGTR1 gene are risk factors for hypertension. However, most studies are inadequately powered, and larger well-designed studies of haplotypes are warranted

Correction: A whole genome association study of mother-to-child transmission of HIV in Malawi

A correction to: Bonnie R Joubert, Ethan M Lange, Nora Franceschini, Victor Mwapasa, Kari E North, Steven R Meshnick andthe NIAID Center for HIV/AIDS Vaccine Immunology. A whole genome association study of mother-to-child transmission of HIV in Malawi. Genome Medicine 2010, 2:17

A whole genome association study of mother-to-child transmission of HIV in Malawi

Abstract: Background: More than 300,000 children are newly infected with HIV each year, predominantly through mother-to-child transmission (HIV MTCT). Identification of host genetic traits associated with transmission may more clearly explain the mechanisms of HIV MTCT and further the development of a vaccine to protect infants from infection. Associations between transmission and a selection of genes or single nucleotide polymorphisms (SNP)s may give an incomplete picture of HIV MTCT etiology. Thus, this study employed a genome-wide association approach to identify novel variants associated with HIV MTCT. Methods: We conducted a nested case-control study of HIV MTCT using infants of HIV(+) mothers, drawn from a cohort study of malaria and HIV in pregnancy in Blantyre, Malawi. Whole genome scans (650,000 SNPs genotyped using Illumina genotyping assays) were obtained for each infant. Logistic regression was used to evaluate the association between each SNP and HIV MTCT. Results: Genotype results were available for 100 HIV(+) infants (at birth, 6, or 12 weeks) and 126 HIV(-) infants (at birth, 6, and 12 weeks). We identified 9 SNPs within 6 genes with a P-value <5 × 10-5 associated with the risk of transmission, in either unadjusted or adjusted by maternal HIV viral load analyses. Carriers of the rs8069770 variant allele were associated with a lower risk of HIV MTCT (odds ratio = 0.27, 95% confidence interval = 0.14, 0.51), where rs8069770 is located within HS3ST3A1, a gene involved in heparan sulfate biosynthesis. Interesting associations for SNPs located within or near genes involved in pregnancy and development, innate immunological response, or HIV protein interactions were also observed. Conclusions: This study used a genome-wide approach to identify novel variants associated with the risk of HIV MTCT in order to gain new insights into HIV MTCT etiology. Replication of this work using a larger sample size will help us to differentiate true positive findings

Challenges and strategies for recruitment of minorities to clinical research and trials

Minority populations are largely absent from clinical research trials. The neglect of these populations has become increasingly apparent, with escalating cancer burdens and chronic disease. The challenges to recruitment of minorities in the United States are multiple including trust or lack thereof. Keys to successful recruitment are responding to community issues, its history, beliefs, and its social and economic pressures. The strategy we have used in many low-income, sometimes remote, communities is to recruit staff from the same community and train them in the required basic research methods. They are the first line of communication. After our arrival in the Texas Rio Grande Valley in 2001, we applied these principles learned over years of global research, to studies of chronic diseases. Beginning in 2004, we recruited and trained a team of local women who enrolled in a cohort of over five thousand Mexican Americans from randomly selected households. This cohort is being followed, and the team has remained, acquiring not only advanced skills (ultrasound, FibroScan, retinal photos, measures of cognition, etc.) but capacity to derive key health information. Currently, we are participating in multiple funded studies, including an NIH clinical trial, liver disease, obesity, and diabetes using multiomics aimed at developing precision medicine approaches to chronic disease prevention and treatment