Low-rank Latent Matrix-factor Prediction Modeling for Generalized High-dimensional Matrix-variate Regression

Motivated by diagnosing the COVID-19 disease using 2D image biomarkers from computed tomography (CT) scans, we propose a novel latent matrix-factor regression model to predict responses that may come from an exponential distribution family, where covariates include high-dimensional matrix-variate biomarkers. A latent generalized matrix regression (LaGMaR) is formulated, where the latent predictor is a low-dimensional matrix factor score extracted from the low-rank signal of the matrix variate through a cutting-edge matrix factor model. Unlike the general spirit of penalizing vectorization plus the necessity of tuning parameters in the literature, instead, our prediction modeling in LaGMaR conducts dimension reduction that respects the geometry characteristic of intrinsic two-dimensional structure of the matrix covariate and thus avoids iteration. This greatly relieves the computation burden, and meanwhile maintains structural information so that the latent matrix factor feature can perfectly replace the intractable matrix-variate owing to high-dimensionality. The estimation procedure of LaGMaR is subtly derived by transforming the bilinear form matrix factor model onto a high-dimensional vector factor model, so that the method of principle components can be applied. We establish bilinear-form consistency of the estimated matrix coefficient of the latent predictor and consistency of prediction. The proposed approach can be implemented conveniently. Through simulation experiments, prediction capability of LaGMaR is shown to outperform existing penalized methods under diverse scenarios of generalized matrix regressions. Through the application to a real COVID-19 dataset, the proposed approach is shown to predict efficiently the COVID-19

Maternal Serum Polychlorinated Biphenyl Concentrations across Critical Windows of Human Development

Background Few data are available on polychlorinated biphenyl (PCB) concentrations over critical windows of human reproduction and development inclusive of the periconception window. Objectives Our goal was to measure changes in PCB concentrations from preconception to pregnancy, through pregnancy, or after a year without becoming pregnant. Methods Seventy-nine women planning pregnancies were prospectively enrolled and followed for up to 12 menstrual cycles of attempting pregnancy. Blood specimens were obtained from participating women preconceptionally (n = 79), after a positive pregnancy test leading to a live birth (n = 54) or pregnancy loss (n= 10), at approximately 6 weeks postpartum (n = 53), and after 12 unsuccessful cycles (n = 9) for toxicologic analysis of 76 PCB congeners. We estimated overall and daily rate of change in PCB concentration (nanograms per gram serum) adjusting for relevant covariates, serum lipids, and baseline PCB concentration. Results Significant (p \u3c 0.0001) decreases in the mean overall and daily rate of change in PCB concentrations were observed between the preconception and first pregnancy samples for total (–1.012 and –0.034, respectively), estrogenic (–0.444 and –0.016, respectively), and antiestrogenic (–0.106 and –0.004, respectively) PCBs among women with live births. Similar significant decreases in total (–1.452 and –0.085), estrogenic (–0.647 and –0.040), and antiestrogenic (–0.093 and –0.004) PCB concentrations were seen for women with pregnancy losses. No significant changes were observed for PCB congener 153. Conclusions These data suggest that PCB concentrations may change during the periconception interval, questioning the stability of persistent compounds during this critical window

Robust joint analysis allowing for model uncertainty in two-stage genetic association studies

<p>Abstract</p> <p>Background</p> <p>The cost efficient two-stage design is often used in genome-wide association studies (GWASs) in searching for genetic loci underlying the susceptibility for complex diseases. Replication-based analysis, which considers data from each stage separately, often suffers from loss of efficiency. Joint test that combines data from both stages has been proposed and widely used to improve efficiency. However, existing joint analyses are based on test statistics derived under an assumed genetic model, and thus might not have robust performance when the assumed genetic model is not appropriate.</p> <p>Results</p> <p>In this paper, we propose joint analyses based on two robust tests, MERT and MAX3, for GWASs under a two-stage design. We developed computationally efficient procedures and formulas for significant level evaluation and power calculation. The performances of the proposed approaches are investigated through the extensive simulation studies and a real example. Numerical results show that the joint analysis based on the MAX3 test statistic has the best overall performance.</p> <p>Conclusions</p> <p>MAX3 joint analysis is the most robust procedure among the considered joint analyses, and we recommend using it in a two-stage genome-wide association study.</p

Maternal serum markers of lipid metabolism in relation to neonatal anthropometry

Objective: The objective of this study is to examine associations between lipids (high-density lipoprotein, low-density lipoprotein, total cholesterol, triglycerides and lipoprotein (a)) measured on average three time points during pregnancy and neonatal anthropometrics. Study design: Stored samples from a preeclampsia trial measured as part of a case-control study from five US centers (1992 to 1995) were used. The sample included women without pregnancy complications (n=136) and cases of gestational diabetes (n=93), abnormal glucose tolerance (AGT; n=76), gestational hypertension (n=170) and preeclampsia (n=177). Linear regression and linear mixed-effects models estimated adjusted associations between lipids and birth weight z-score, ponderal index (PI), length and head circumference. Results: Among women without complications, cross-sectional associations between total cholesterol measured at different gestational ages increased PI 2.23 to 2.55 kg m-3 per-unit increase in cholesterol. HDL was inversely associated with birth length (β\u27s=-2.21 and -2.56 cm). For gestational hypertension, triglycerides were associated with birth weight z-score (β\u27s=0.24 to 0.31). For preeclampsia, HDL was associated with lower birth weight z-scores (β\u27s=-0.49 and -0.82). Women with gestational diabetes or AGT had inconsistent associations. Examining the level changes across pregnancy, each 0.0037 mmol l-1 increase in HDL was associated with decreased birth weight z-score (β=-0.22), length (β=-0.24 cm) and head circumference (β=-0.24 cm), whereas each 0.028 mmol l-1 increase in triglycerides was associated with increased birth weight z-score (β=0.13) and head circumference (β=0.19 cm). Conclusions: Although associations varied by complications, in general, growth-promoting fuels such as total cholesterol and triglycerides were associated with increased neonatal size, whereas high HDL was associated with smaller size. Maternal HDL that failed to decrease over pregnancy was associated with smaller neonate size

The Cell Surface Estrogen Receptor, G Protein- Coupled Receptor 30 (GPR30), is Markedly Down Regulated During Breast Tumorigenesis

Background: GPR30 is a cell surface estrogen receptor that has been shown to mediate a number of non-genomic rapid effects of estrogen and appear to balance the signaling of estrogen and growth factors. In addition, progestins appear to use GPR30 for their actions. Therefore, GPR30 could play a critical role in hormonal regulation of breast epithelial cell integrity. Deregulation of the events mediated by GPR30 could contribute to tumorigenesis.Methods: To understand the role of GPR30 in the deregulation of estrogen signaling processes during breast carcinogenesis, we have undertaken this study to investigate its expression at mRNA levels in tumor tissues and their matched normal tissues. We compared its expression at mRNA levels by RT quantitative real-time PCR relative to GAPDH in ERα”—positive (n = 54) and ERα”—negative (n = 45) breast cancer tissues to their matched normal tissues.Results: We report here, for the first time, that GPR30 mRNA levels were significantly down-regulated in cancer tissues in comparison with their matched normal tissues (p 0.0001 by two sided paired t-test). The GPR30 expression levels were significantly lower in tumor tissues from patients (n = 29) who had lymph node metastasis in comparison with tumors from patients (n = 53) who were negative for lymph node metastasis (two sample t-test, p 0.02), but no association was found with ERα, PR and other tumor characteristics.Conclusions: Down-regulation of GPR30 could contribute to breast tumorigenesis and lymph node metastasis