Association Analyses of Repeated Measures on Triglyceride and High-Density Lipoprotein Levels: Insights from GAW20

Background: The GAW20 group formed on the theme of methods for association analyses of repeated measures comprised 4sets of investigators. The provided “real” data set included genotypes obtained from a human whole-genome association study based on longitudinal measurements of triglycerides (TGs) and high-density lipoprotein in addition to methylation levels before and after administration of fenofibrate. The simulated data set contained 200 replications of methylation levels and posttreatment TGs, mimicking the real data set. Results: The different investigators in the group focused on the statistical challenges unique to family-based association analyses of phenotypes measured longitudinally and applied a wide spectrum of statistical methods such as linear mixed models, generalized estimating equations, and quasi-likelihood–based regression models. This article discusses the varying strategies explored by the group’s investigators with the common goal of improving the power to detect association with repeated measures of a phenotype. Conclusions: Although it is difficult to identify a common message emanating from the different contributions because of the diversity in the issues addressed, the unifying theme of the contributions lie in the search for novel analytic strategies to circumvent the limitations of existing methodologies to detect genetic association

On Combining Family- and Population- Based Sequencing Data

Several statistical group-based approaches have been proposed to detect effects of variation within a gene for each of the population- and family-based designs. However, unified tests to combine gene-phenotype associations obtained from these 2 study designs are not yet well established. In this study, we investigated the efficient combination of population-based and family-based sequencing data to evaluate best practices using the Genetic Analysis Workshop 19 (GAW19) data set. Because one design employed whole genome sequencing and the other whole exome sequencing, we examined variants overlapping both data sets. We used the family-based sequence kernel association test (famSKAT) to analyze the family- and population-based data sets separately as well as with a combined data set. These were compared against meta-analysis. Using the combined data, we showed that famSKAT has high power to detect associations between diastolic and/or systolic blood pressures and the genes that have causal variants with large effect sizes, such as MAP4, TNN, and CGN. However, when there was a considerable difference in the powers between family- and population-based data, famSKAT with the combined data had lower power than that from the population-based data alone. The famSKAT test statistic for the combined data can be influenced by sample imbalance from the 2 designs. This underscores the importance of foresight in study design as, in this situation, the greatly lower sample size in the family-based data essentially serves to dilute signal. We observed inflated type I errors in our simulation study, largely when using population-based data, which might be a result of principal components failing to completely account for population admixture in this cohort

Automated Quality Control for Genome Wide Association Studies

This paper provides details on the necessary steps to assess and control data in genome wide association studies (GWAS) using genotype information on a large number of genetic markers for large number of individuals. Due to varied study designs and genotyping platforms between multiple sites/projects as well as potential genotyping errors, it is important to ensure high quality data. Scripts and directions are provided to facilitate others in this process

Causal Effect Estimation in Sequencing Studies: A Bayesian Method to Account for Confounder Adjustment Uncertainty

Estimating the causal effect of a single nucleotide variant (SNV) on clinical phenotypes is of interest in many genetic studies. The effect estimation may be confounded by other SNVs as a result of linkage disequilibrium as well as demographic and clinical characteristics. Because a large number of these other variables, which we call potential confounders, are collected, it is challenging to select and adjust for the variables that truly confound the causal effect. The Bayesian adjustment for confounding (BAC) method has been proposed as a general method to estimate the average causal effect in the presence of a large number of potential confounders under the assumption of no unmeasured confounders. In this paper, we explore the application of BAC in genetic studies using Genetic Analysis Workshop 19 exome sequencing data. Our results show that BAC can efficiently estimate the causal effect of genetic variants with adjustment for confounding. Consequently, BAC may serve as a useful tool for genome-wide association studies data analysis to effectively assess the causal effect of genetic variants and the impact of potential interventions

Comparing Performance of Non-Tree-Based and Tree-Based Association Mapping Methods

A central goal in the biomedical and biological sciences is to link variation in quantitative traits to locations along the genome (single nucleotide polymorphisms). Sequencing technology has rapidly advanced in recent decades, along with the statistical methodology to analyze genetic data. Two classes of association mapping methods exist: those that account for the evolutionary relatedness among individuals, and those that ignore the evolutionary relationships among individuals. While the former methods more fully use implicit information in the data, the latter methods are more flexible in the types of data they can handle. This study presents a comparison of the 2 types of association mapping methods when they are applied to simulated data

Genetics of \u3cem\u3ePICALM\u3c/em\u3e Expression and Alzheimer\u27s Disease

Novel Alzheimer\u27s disease (AD) risk factors have been identified by genome-wide association studies. Elucidating the mechanism underlying these factors is critical to the validation process and, by identifying rate-limiting steps in AD risk, may yield novel therapeutic targets. Here, we evaluated the association between the AD-associated polymorphism rs3851179 near PICALM, which encodes a clathrin-coated pit accessory protein. Immunostaining established that PICALM is expressed predominately in microvessels in human brain. Consistent with this finding, PICALM mRNA expression correlated with expression of the endothelial genes vWF and CD31. Additionally, we found that PICALM expression was modestly increased with the rs3851179A AD-protective allele. Analysis of PICALM isoforms found several isoforms lacking exons encoding elements previously identified as critical to PICALM function. Increased expression of the common isoform lacking exon 13 was also associated with the rs3851179A protective allele; this association was not apparent when this isoform was compared with total PICALM expression, indicating that the SNP is associated with total PICALM expression and not this isoform per se. Interestingly, PICALM lacking exons 2–4 was not associated with rs3851179 but was associated with rs592297, which is located in exon 5. Thus, our primary findings are that multiple PICALM isoforms are expressed in the human brain, that PICALM is robustly expressed in microvessels, and that expression of total PICALM is modestly correlated with the AD-associated SNP rs3851179. We interpret these results as suggesting that increased PICALM expression in the microvasculature may reduce AD risk

Families or Unrelated: The Evolving Debate in Genetic Association Studies

To help uncover the genetic determinants of complex disease, a scientist often designs an association study using either unrelated subjects or family members within pedigrees. But which of these two subject recruitment paradigms is preferable? This editorial addresses the debate over the relative merits of family- and population-based genetic association studies. We begin by briefly recounting the evolution of genetic epidemiology and the rich crossroads of statistics and genetics. We then detail the arguments for the two aforementioned paradigms in recent and current applications. Finally, we speculate on how the debate may progress with the emergence of next-generation sequencing technologies

Longitudinal Trajectories of Cholesterol from Midlife through Late Life According to Apolipoprotein E Allele Status

Background: Previous research indicates that total cholesterol levels increase with age during young adulthood and middle age and decline with age later in life. This is attributed to changes in diet, body composition, medication use, physical activity, and hormone levels. In the current study we utilized data from the Framingham Heart Study Original Cohort to determine if variations in apolipoprotein E (APOE), a gene involved in regulating cholesterol homeostasis, influence trajectories of total cholesterol, HDL cholesterol, and total: HDL cholesterol ratio from midlife through late life. Methods: Cholesterol trajectories from midlife through late life were modeled using generalized additive mixed models and mixed-effects regression models. Results: APOE e2+ subjects had lower total cholesterol levels, higher HDL cholesterol levels, and lower total: HDL cholesterol ratios from midlife to late life compared to APOE e3 and APOE e4+ subjects. Statistically significant differences in life span cholesterol trajectories according to gender and use of cholesterol-lowering medications were also detected. Conclusion: The findings from this research provide evidence that variations in APOE modify trajectories of serum cholesterol from midlife to late life. In order to efficiently modify cholesterol through the life span, it is important to take into account APOE allele status

Openness to Change: Experiential and Demographic Components of Change in Local Health Department Leaders

BACKGROUND: During the 2008-2010 economic recession, Kentucky local health department (LHD) leaders utilized innovative strategies to maintain their programs. A characteristic of innovative strategy is leader openness to change. Leader demographical research in for-profit organizations has yielded valuable insight into leader openness to change. For LHD leaders, the nature of the association between leader demographic and organizational characteristics on leader openness to change is unknown. The objectives of this study are to identify variation in openness to change by leaders\u27 demographic and organizational characteristics and to characterize the underlying relationships. MATERIALS AND METHODS: The study utilized Spearman rank correlations test to determine relationships between leader openness to change (ACQ) and leader and LHD characteristics. To identify differences in the distribution of ACQ scores, Wilcoxon-Mann-Whitney and Kruskal-Wallis non-parametric tests were used, and to adjust for potential confounding, linear regression analysis was performed. DATA: Local health department leaders in the Commonwealth of Kentucky were the unit of analysis. Expenditure and revenue data were available from the state health department. National census data were utilized for county level population estimates. A cross-sectional survey was performed of KY LHD leaders\u27 observable attributes relating to age, gender, race, educational background, leadership experience, and openness to change. RESULTS: Leaders had relatively high openness to change scores. Spearman correlations between leader ACQ and departmental 2012-2013 revenue and expenditures were statistically significant, as were the differences observed in ACQ by gender and the educational level of the leader. Differences in ACQ score by education level and agency revenue were significant even after adjusting for potential confounders. The analyses imply that there are underlying relationships between leader and LHD characteristics based on leader openness to change

Openness to Change: Experiential and Demographic Components of Change in Local Health Department Leaders

BACKGROUND: During the 2008-2010 economic recession, Kentucky local health department (LHD) leaders utilized innovative strategies to maintain their programs. A characteristic of innovative strategy is leader openness to change. Leader demographical research in for-profit organizations has yielded valuable insight into leader openness to change. For LHD leaders, the nature of the association between leader demographic and organizational characteristics on leader openness to change is unknown. The objectives of this study are to identify variation in openness to change by leaders\u27 demographic and organizational characteristics and to characterize the underlying relationships. MATERIALS AND METHODS: The study utilized Spearman rank correlations test to determine relationships between leader openness to change (ACQ) and leader and LHD characteristics. To identify differences in the distribution of ACQ scores, Wilcoxon-Mann-Whitney and Kruskal-Wallis non-parametric tests were used, and to adjust for potential confounding, linear regression analysis was performed. DATA: Local health department leaders in the Commonwealth of Kentucky were the unit of analysis. Expenditure and revenue data were available from the state health department. National census data were utilized for county level population estimates. A cross-sectional survey was performed of KY LHD leaders\u27 observable attributes relating to age, gender, race, educational background, leadership experience, and openness to change. RESULTS: Leaders had relatively high openness to change scores. Spearman correlations between leader ACQ and departmental 2012-2013 revenue and expenditures were statistically significant, as were the differences observed in ACQ by gender and the educational level of the leader. Differences in ACQ score by education level and agency revenue were significant even after adjusting for potential confounders. The analyses imply that there are underlying relationships between leader and LHD characteristics based on leader openness to change