Project FIT was a two-year multi-component nutrition and physical activity intervention delivered in ethnically-diverse low-income elementary schools in Grand Rapids, MI. This paper reports effects on children's nutrition outcomes and process evaluation of the school component. A quasi-experimental design was utilized. 3rd, 4th and 5th-grade students (Yr 1 baseline: N = 410; Yr 2 baseline: N = 405; age range: 7.5-12.6 years) were measured in the fall and spring over the two-year intervention. Ordinal logistic, mixed effect models and generalized estimating equations were fitted, and the robust standard errors were utilized. Primary outcomes favoring the intervention students were found regarding consumption of fruits, vegetables and whole grain bread during year 2. Process evaluation revealed that implementation of most intervention components increased during year 2. Project FIT resulted in small but beneficial effects on consumption of fruits, vegetables, and whole grain bread in ethnically diverse low-income elementary school children.

Affymetrix SNP arrays have been widely used for single-nucleotide polymorphism (SNP) genotype calling and DNA copy number variation inference. Although numerous methods have achieved high accuracy in these fields, most studies have paid little attention to the modeling of hybridization of probes to off-target allele sequences, which can affect the accuracy greatly. In this study, we address this issue and demonstrate that hybridization with mismatch nucleotides (HWMMN) occurs in all SNP probe-sets and has a critical effect on the estimation of allelic concentrations (ACs). We study sequence binding through binding free energy and then binding affinity, and develop a probe intensity composite representation (PICR) model. The PICR model allows the estimation of ACs at a given SNP through statistical regression. Furthermore, we demonstrate with cell-line data of known true copy numbers that the PICR model can achieve reasonable accuracy in copy number estimation at a single SNP locus, by using the ratio of the estimated AC of each sample to that of the reference sample, and can reveal subtle genotype structure of SNPs at abnormal loci. We also demonstrate with HapMap data that the PICR model yields accurate SNP genotype calls consistently across samples, laboratories and even across array platforms.

Motivation The use of human genome discoveries and other established factors to build an accurate risk prediction model is an essential step toward precision medicine. While multi-layer high-dimensional omics data provide unprecedented data resources for prediction studies, their corresponding analytical methods are much less developed. Results We present a multi-kernel penalized linear mixed model with adaptive lasso (MKpLMM), a predictive modeling framework that extends the standard linear mixed models widely used in genomic risk prediction, for multi-omics data analysis. MKpLMM can capture not only the predictive effects from each layer of omics data but also their interactions via using multiple kernel functions. It adopts a data-driven approach to select predictive regions as well as predictive layers of omics data, and achieves robust selection performance. Through extensive simulation studies, the analyses of PET-imaging outcomes from the Alzheimer’s Disease Neuroimaging Initiative study, and the analyses of 64 drug responses, we demonstrate that MKpLMM consistently outperforms competing methods in phenotype prediction. Availability and implementation The R-package is available at https://github.com/YaluWen/OmicPred. Supplementary information Supplementary data are available at Bioinformatics online.

It is available at https://github.com/DMU-lilab/GetisDMR CONTACTS: y.wen@auckland.ac.nz or zhiguangli@dlmedu.edu.cnSupplementary information: Supplementary data are available at Bioinformatics online.

New USDA nutrition standards for à la carte and vending will likely increase the healthfulness of middle school children's diets.

Linear mixed models (LMMs) and their extensions have been widely used for high‐dimensional genomic data analyses. While LMMs hold great promise for risk prediction research, the high dimensionality of the data and different effect sizes of genomic regions bring great analytical and computational challenges. In this work, we present a multikernel linear mixed model with adaptive lasso (KLMM‐AL) to predict phenotypes using high‐dimensional genomic data. We develop two algorithms for estimating parameters from our model and also establish the asymptotic properties of LMM with adaptive lasso when only one dependent observation is available. The proposed KLMM‐AL can account for heterogeneous effect sizes from different genomic regions, capture both additive and nonadditive genetic effects, and adaptively and efficiently select predictive genomic regions and their corresponding effects. Through simulation studies, we demonstrate that KLMM‐AL outperforms most of existing methods. Moreover, KLMM‐AL achieves high sensitivity and specificity of selecting predictive genomic regions. KLMM‐AL is further illustrated by an application to the sequencing dataset obtained from the Alzheimer's disease neuroimaging initiative.

The Michigan Healthy School Action Tools (HSAT) is an online self-assessment and action planning process for schools seeking to improve their health policies and practices. The School Nutrition Advances Kids study, a 2-year quasi-experimental intervention with low-income middle schools, evaluated whether completing the HSAT with a facilitator assistance and small grant funding resulted in (1) improvements in school nutrition practices and policies and (2) improvements in student dietary intake. A total of 65 low-income Michigan middle schools participated in the study. The Block Youth Food Frequency Questionnaire was completed by 1,176 seventh-grade students at baseline and in eighth grade (during intervention). Schools reported nutrition-related policies and practices/education using the School Environment and Policy Survey. Schools completing the HSAT were compared to schools that did not complete the HSAT with regard to number of policy and practice changes and student dietary intake. Schools that completed the HSAT made significantly more nutrition practice/education changes than schools that did not complete the HSAT, and students in those schools made dietary improvements in fruit, fiber, and cholesterol intake. The Michigan HSAT process is an effective strategy to initiate improvements in nutrition policies and practices within schools, and to improve student dietary intake.

Motivation Accurate disease risk prediction is essential for precision medicine. Existing models either assume that diseases are caused by groups of predictors with small-to-moderate effects or a few isolated predictors with large effects. Their performance can be sensitive to the underlying disease mechanisms, which are usually unknown in advance. Results We developed a Bayesian linear mixed model (BLMM), where genetic effects were modelled using a hybrid of the sparsity regression and linear mixed model with multiple random effects. The parameters in BLMM were inferred through a computationally efficient variational Bayes algorithm. The proposed method can resemble the shape of the true effect size distributions, captures the predictive effects from both common and rare variants, and is robust against various disease models. Through extensive simulations and the application to a whole-genome sequencing dataset obtained from the Alzheimer’s Disease Neuroimaging Initiatives, we have demonstrated that BLMM has better prediction performance than existing methods and can detect variables and/or genetic regions that are predictive. Availabilityand implementation The R-package is available at https://github.com/yhai943/BLMM. Supplementary information Supplementary data are available at Bioinformatics online.