Heterogeneity in familial clustering of metabolic syndrome components in the multiethnic GENNID study.

OBJECTIVE: Metabolic syndrome (MetS) is defined by clustering of cardiometabolic components, which may be present in different combinations. The authors evaluated clustering in individuals and extended families within and across ancestry groups. METHODS: The prevalence of different combinations of MetS components (high fasting glucose, low high-density lipoprotein cholesterol, high triglycerides, high blood pressure, and abdominal obesity) was estimated in 1651 individuals (340 families) self-reporting as European American (EA), Hispanic/Mexican American (MA), African American (AA), and Japanese American (JA). Odds ratios were estimated using logistic regression with generalized estimating equations comparing individual MetS components, number, and combinations of components for each ancestry group versus EA. RESULTS: Clustering of all five components (Combination #16) was more prevalent in EA (29.9%) and MA (25.2%) individuals than in AA (18.7%) and JA (15.5%) individuals. Compared with EA individuals, AA individuals were 64% and 66% less likely to have high triglycerides and low high-density lipoprotein cholesterol, whereas JA individuals were 85% and 56% less likely to have abdominal obesity and high blood pressure, respectively. Compared with EA individuals, the odds of having two, four, or five components were at least 77% lower in JA individuals, whereas the odds of having three, four, or five components were at least 3.79 times greater in MA individuals. CONCLUSIONS: Understanding heterogeneity in MetS clustering may identify factors important in reducing health disparities.

On Sample Size and Power Calculation for Variant Set-Based Association Tests.

Sample size and power calculations are an important part of designing new sequence-based association studies. The recently developed SEQPower and SPS programs adopted computationally intensive Monte Carlo simulations to empirically estimate power for a series of variant set association (VSA) test methods including the sequence kernel association test (SKAT). It is desirable to develop methods that can quickly and accurately compute power without intensive Monte Carlo simulations. We will show that the computed power for SKAT based on the existing analytical approach could be inflated especially for small significance levels, which are often of primary interest for large-scale whole genome and exome sequencing projects. We propose a new χ(2) -approximation-based approach to accurately and efficiently compute sample size and power. In addition, we propose and implement a more accurate "exact" method to compute power, which is more efficient than the Monte Carlo approach though generally involves more computations than the χ(2) approximation method. The exact approach could produce very accurate results and be used to verify alternative approximation approaches. We implement the proposed methods in publicly available R programs that can be readily adapted when planning sequencing projects.

Information assessment on predicting protein-protein interactions.

BACKGROUND: Identifying protein-protein interactions is fundamental for understanding the molecular machinery of the cell. Proteome-wide studies of protein-protein interactions are of significant value, but the high-throughput experimental technologies suffer from high rates of both false positive and false negative predictions. In addition to high-throughput experimental data, many diverse types of genomic data can help predict protein-protein interactions, such as mRNA expression, localization, essentiality, and functional annotation. Evaluations of the information contributions from different evidences help to establish more parsimonious models with comparable or better prediction accuracy, and to obtain biological insights of the relationships between protein-protein interactions and other genomic information. RESULTS: Our assessment is based on the genomic features used in a Bayesian network approach to predict protein-protein interactions genome-wide in yeast. In the special case, when one does not have any missing information about any of the features, our analysis shows that there is a larger information contribution from the functional-classification than from expression correlations or essentiality. We also show that in this case alternative models, such as logistic regression and random forest, may be more effective than Bayesian networks for predicting interactions. CONCLUSIONS: In the restricted problem posed by the complete-information subset, we identified that the MIPS and Gene Ontology (GO) functional similarity datasets as the dominating information contributors for predicting the protein-protein interactions under the framework proposed by Jansen et al. Random forests based on the MIPS and GO information alone can give highly accurate classifications. In this particular subset of complete information, adding other genomic data does little for improving predictions. We also found that the data discretizations used in the Bayesian methods decreased classification performance.