Variance Components Models for Analysis of Big Family Data of Health Outcomes in the Lifelines Cohort Study.

Large multigenerational cohort studies offer powerful ways to study the hereditary effects on various health outcomes. However, accounting for complex kinship relations in big data structures can be methodologically challenging. The traditional kinship model is computationally infeasible when considering thousands of individuals. In this article, we propose a computationally efficient alternative that employs fractional relatedness of family members through a series of founding members. The primary goal of this study is to investigate whether the effect of determinants on health outcome variables differs with and without accounting for family structure. We compare a fixed-effects model without familial effects with several variance components models that account for heritability and shared environment structure. Our secondary goal is to apply the fractional relatedness model in a realistic setting. Lifelines is a three-generation cohort study investigating the biological, behavioral, and environmental determinants of healthy aging. We analyzed a sample of 89,353 participants from 32,452 reconstructed families. Our primary conclusion is that the effect of determinants on health outcome variables does not differ with and without accounting for family structure. However, accounting for family structure through fractional relatedness allows for estimating heritability in a computationally efficient way, showing some interesting differences between physical and mental quality of life heritability. We have shown through simulations that the proposed fractional relatedness model performs better than the standard kinship model, not only in terms of computational time and convenience of fitting using standard functions in R, but also in terms of bias of heritability estimates and coverage.

Confidence intervals for intraclass correlation coefficients in a nonlinear dose-response meta-analysis.

This work is motivated by a meta-analysis case study on antipsychotic medications. The Michaelis-Menten curve is employed to model the nonlinear relationship between the dose and D2 receptor occupancy across multiple studies. An intraclass correlation coefficient (ICC) is used to quantify the heterogeneity across studies. To interpret the size of heterogeneity, an accurate estimate of ICC and its confidence interval is required. The goal is to apply a recently proposed generic beta-approach for construction the confidence intervals on ICCs for linear mixed effects models to nonlinear mixed effects models using four estimation methods. These estimation methods are the maximum likelihood, second-order generalized estimating equations and two two-step procedures. The beta-approach is compared with a large sample normal approximation (delta method) and bootstrapping. The confidence intervals based on the delta method and the nonparametric percentile bootstrap with various resampling strategies failed in our settings. The beta-approach demonstrates good coverages with both two-step estimation methods and consequently, it is recommended for the computation of confidence interval for ICCs in nonlinear mixed effects models for small studies.

Probability genotype imputation method and integrated weighted lasso for QTL identification.

BACKGROUND: Many QTL studies have two common features: (1) often there is missing marker information, (2) among many markers involved in the biological process only a few are causal. In statistics, the second issue falls under the headings "sparsity" and "causal inference". The goal of this work is to develop a two-step statistical methodology for QTL mapping for markers with binary genotypes. The first step introduces a novel imputation method for missing genotypes. Outcomes of the proposed imputation method are probabilities which serve as weights to the second step, namely in weighted lasso. The sparse phenotype inference is employed to select a set of predictive markers for the trait of interest. RESULTS: Simulation studies validate the proposed methodology under a wide range of realistic settings. Furthermore, the methodology outperforms alternative imputation and variable selection methods in such studies. The methodology was applied to an Arabidopsis experiment, containing 69 markers for 165 recombinant inbred lines of a F8 generation. The results confirm previously identified regions, however several new markers are also found. On the basis of the inferred ROC behavior these markers show good potential for being real, especially for the germination trait Gmax. CONCLUSIONS: Our imputation method shows higher accuracy in terms of sensitivity and specificity compared to alternative imputation method. Also, the proposed weighted lasso outperforms commonly practiced multiple regression as well as the traditional lasso and adaptive lasso with three weighting schemes. This means that under realistic missing data settings this methodology can be used for QTL identification.

Zinc Single Atom Confinement Effects on Catalysis in 1T-Phase Molybdenum Disulfide.

Active sites are atomic sites within catalysts that drive reactions and are essential for catalysis. Spatially confining guest metals within active site microenvironments has been predicted to improve catalytic activity by altering the electronic states of active sites. Using the hydrogen evolution reaction (HER) as a model reaction, we show that intercalating zinc single atoms between layers of 1T-MoS2 (Zn SAs/1T-MoS2) enhances HER performance by decreasing the overpotential, charge transfer resistance, and kinetic barrier. The confined Zn atoms tetrahedrally coordinate to basal sulfur (S) atoms and expand the interlayer spacing of 1T-MoS2 by ∼3.4%. Under confinement, the Zn SAs donate electrons to coordinated S atoms, which lowers the free energy barrier of H* adsorption-desorption and enhances HER kinetics. In this work, which is applicable to all types of catalytic reactions and layered materials, HER performance is enhanced by controlling the coordination geometry and electronic states of transition metals confined within active-site microenvironments.

Association of tissue promoter methylation levels of APC, TGFß2, HOXD3 and RASSF1A with prostate cancer progression.

Aberrant promoter methylation is known to silence tumor-suppressor genes in prostate cancer (PCa). We correlated quantitative promoter methylation levels of APC, TGFß2 and RASSF1A in 219 radical prostatectomies diagnosed between 1998 and 2001 with clinicopathological follow-up data available including Gleason Pattern (GP), Gleason Score (GS) and pathological stage and explored their potential in predicting biochemical recurrence using univariate and multivariate analyses. We observed that the average methylation levels of APC increased significantly from GS ≤ 6 to GS7, and pT2 to pT3a, and that of TGFß2 increased from GS ≤ 6 to GS7, but not for RASSF1A. PCa samples were also stratified into high methylation (HM) and low methylation (LM) groups based on the PMR scores of all cases analyzed for each marker. The HM frequency of APC was greater in pT3a than pT2, and in GS ≥ 8 than GS ≤ 6. The HM frequency also increased significantly from GP3 to GP4 for APC, TGFß2 and RASSF1A. APC methylation level was a significant predictor of biochemical recurrence in univariate analysis (p-value = 0.028). Finally, we combined methylation data of these three genes with the previously reported novel methylation biomarker HOXD3. Quantitative methylation assessment of a multiplex panel of markers, consisting of APC, HOXD3 and TGFß2, outperforms any single marker for the prediction of biochemical recurrence (p-value = 0.017). Our study demonstrated that quantitative increase in promoter methylation levels of APC, HOXD3 and TGFß2 are associated with PCa progression.

DNA methylation of HOXD3 as a marker of prostate cancer progression.

DNA methylation in gene promoters causes gene silencing and is a common event in cancer development and progression. The ability of aberrant methylation events to serve as diagnostic and prognostic markers is being appreciated for many cancers, including prostate cancer. Using quantitative MethyLight technology, we evaluated the relationship between HOXD3 methylation and clinicopathological parameters including biochemical recurrence, pathological stage, Gleason score (GS), and Gleason pattern in a series of 232 radical prostatectomies performed between 1998 and 2001. HOXD3 methylation was significantly greater in GS 7 cancers vs GS < or = 6 cancers (P-value <0.001) as well as pT3/pT4 vs pT2 cancers (P-value <0.001). The proportion of cases with high methylation in GS 7 vs < or = GS 6 and pT3/pT4 vs pT2 were also significantly different (P-values=0.002 and 0.005, respectively). There were also significant increases in methylation from Gleason pattern 2-3 and from pattern 3 to 4/5 (paired t-test P-values=0.01 and <0.001, respectively), whereas methylation from lymph node metastases was decreased when compared with matched tumor tissue (P-value=0.029). HOXD3 methylation was associated with biochemical recurrence in univariate analysis (P-value=0.043) and showed evidence for interaction with pathological stage as a predictor variable in Cox regression analysis (P-value=0.028). The results indicate that HOXD3 methylation distinguishes low-grade prostate cancers from intermediate and high-grade ones and may also have prognostic value when considered together with pathological stage.

Confidence intervals for intraclass correlation coefficients in variance components models.

Confidence intervals for intraclass correlation coefficients in agreement studies with continuous outcomes are model-specific and no generic approach exists. This paper provides two generic approaches for intraclass correlation coefficients of the form [Formula: see text] The first approach uses Satterthwaite's approximation and an F-distribution. The second approach uses the first and second moments of the intraclass correlation coefficient estimate in combination with a Beta distribution. Both approaches are based on the restricted maximum likelihood estimates for the variance components involved. Simulation studies are conducted to examine the coverage probabilities of the confidence intervals for agreement studies with a mix of small sample sizes. Two different three-way variance components models and balanced and unbalanced one-way random effects models are investigated. The proposed approaches are compared with other approaches developed for these specific models. The approach based on the F-distribution provides acceptable coverage probabilities, but the approach based on the Beta distribution results in accurate coverages for most settings in both balanced and unbalanced designs. A real agreement study is provided to illustrate the approaches.