Application of three-level linear mixed-effects model incorporating gene-age interactions for association analysis of longitudinal family data

Longitudinal studies that collect repeated measurements on the same subjects over time have long been considered as being more powerful and providing much better information on individual changes than cross-sectional data. We propose a three-level linear mixed-effects model for testing genetic main effects and gene-age interactions with longitudinal family data. The simulated Genetic Analysis Workshop 16 Problem 3 data sets were used to evaluate the method. Genome-wide association analyses were conducted based on cross-sectional data, i.e., each of the three single-visit data sets separately, and also on the longitudinal data, i.e., using data from all three visits simultaneously. Results from the analysis of coronary artery calcification phenotype showed that the longitudinal association tests were much more powerful than those based on single-visit data only. Gene-age interactions were evaluated under the same framework for detecting genetic effects that are modulated by age

The HERITAGE Family Study: A review of the effects of exercise training on cardiometabolic health, with insights into molecular transducers

The aim of the HERITAGE Family Study was to investigate individual differences in response to a standardized endurance exercise program, the role of familial aggregation, and the genetics of response levels of cardiorespiratory fitness and cardiovascular disease and diabetes risk factors. Here we summarize the findings and their potential implications for cardiometabolic health and cardiorespiratory fitness. It begins with overviews of background and planning, recruitment, testing and exercise program protocol, quality control measures, and other relevant organizational issues. A summary of findings is then provided on cardiorespiratory fitness, exercise hemodynamics, insulin and glucose metabolism, lipid and lipoprotein profiles, adiposity and abdominal visceral fat, blood levels of steroids and other hormones, markers of oxidative stress, skeletal muscle morphology and metabolic indicators, and resting metabolic rate. These summaries document the extent of the individual differences in response to a standardized and fully monitored endurance exercise program and document the importance of familial aggregation and heritability level for exercise response traits. Findings from genomic markers, muscle gene expression studies, and proteomic and metabolomics explorations are reviewed, along with lessons learned from a bioinformatics-driven analysis pipeline. The new opportunities being pursued in integrative -omics and physiology have extended considerably the expected life of HERITAGE and are being discussed in relation to the original conceptual model of the study

Comparison between single-marker analysis using Merlin and multi-marker analysis using LASSO for Framingham simulated data

We compared family-based single-marker association analysis using Merlin and multi-marker analysis using LASSO (least absolute shrinkage and selection operator) for the low-density lipoprotein phenotype at the first visit for all 200 replicates of the Genetic Analysis Workshop 16 Framingham simulated data sets. Using "answers," we selected single-nucleotide polymorphisms (SNPs) on chromosome 22 for comparison of results between single-marker and multi-marker analyses. For the major causal SNP rs2294207 on chromosome 22, both single-marker and multi-marker analyses provided similar results, indicating the importance of this SNP. For the 12 polygenic SNPs on the same chromosome, both single-marker and multi-marker analyses failed to provide statistically significant associations, indicating that their effects were too weak to be detected by either method. The main difference between the two methods was that for the 14 SNPs near the causal SNPs, p-values from Merlin were the next smallest, whereas LASSO often excluded these non-causal neighboring SNPs entirely from the first 10,000 models

Application of collapsing methods for continuous traits to the Genetic Analysis Workshop 17 exome sequence data

Genetic Analysis Workshop 17 used real sequence data from the 1000 Genomes Project and simulated phenotypes influenced by a large number of rare variants. Our aim is to evaluate the performance of various collapsing methods that were developed for analysis of multiple rare variants. We apply collapsing methods to continuous phenotypes Q1 and Q2 for all 200 replicates of the unrelated individuals data. Within each gene, we collapse (1) all SNPs, (2) all SNPs with minor allele frequency (MAF) < 0.05, and (3) nonsynonymous SNPs with MAF < 0.05. We consider two tests when collapsing variants: using the proportion of variants and using the presence/absence of any variant. We also compare our results to a single-marker analysis using PLINK. For phenotype Q1, the proportion test for collapsing rare nonsynonymous SNPs often performed the best. Two genes (FLT1 and KDR) had statistically significant results. A single-marker analysis using PLINK also provided statistically significant results for some SNPs within these two genes. For phenotype Q2, collapsing rare nonsynonymous SNPs performed the best, with almost no difference between proportion and presence tests. However, neither collapsing methods nor a single-marker analysis provided statistically significant results at the true genes for Q2. We also found that a large number of noncausal genes had high correlations with causal genes for Q1 and Q2, which may account for inflated false positives

Three Ontologies to Define Phenotype Measurement Data

Background: There is an increasing need to integrate phenotype measurement data across studies for both human studies and those involving model organisms. Current practices allow researchers to access only those data involved in a single experiment or multiple experiments utilizing the same protocol. Results: Three ontologies were created: Clinical Measurement Ontology, Measurement Method Ontology and Experimental Condition Ontology. These ontologies provided the framework for integration of rat phenotype data from multiple studies into a single resource as well as facilitated data integration from multiple human epidemiological studies into a centralized repository. Conclusion: An ontology based framework for phenotype measurement data affords the ability to successfully integrate vital phenotype data into critical resources, regardless of underlying technological structures allowing the user to easily query and retrieve data from multiple studies

Aggregate blood pressure responses to serial dietary sodium and potassium intervention: Defining responses using independent component analysis

BACKGROUND: Hypertension is a complex trait that often co-occurs with other conditions such as obesity and is affected by genetic and environmental factors. Aggregate indices such as principal components among these variables and their responses to environmental interventions may represent novel information that is potentially useful for genetic studies. RESULTS: In this study of families participating in the Genetic Epidemiology Network of Salt Sensitivity (GenSalt) Study, blood pressure (BP) responses to dietary sodium interventions are explored. Independent component analysis (ICA) was applied to 20 variables indexing obesity and BP measured at baseline and during low sodium, high sodium and high sodium plus potassium dietary intervention periods. A “heat map” protocol that classifies subjects based on risk for hypertension is used to interpret the extracted components. ICA and heat map suggest four components best describe the data: (1) systolic hypertension, (2) general hypertension, (3) response to sodium intervention and (4) obesity. The largest heritabilities are for the systolic (64 %) and general hypertension (56 %) components. There is a pattern of higher heritability for the component response to intervention (40–42 %) as compared to those for the traditional intervention responses computed as delta scores (24 %–40 %). CONCLUSIONS: In summary, the present study provides intermediate phenotypes that are heritable. Using these derived components may prove useful in gene discovery applications. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12863-015-0226-8) contains supplementary material, which is available to authorized users