Gene–environment interaction: A variable selection perspective

F Zhou, J Ren, X Lu, S Ma, C Wu - Epistasis: Methods and Protocols, 2021 - Springer
Gene–environment interactions have important implications for elucidating the genetic basis
of complex diseases beyond the joint function of multiple genetic factors and their …

[HTML][HTML] Functional Phenotyping: Understanding the Dynamic Response of Plants to Drought Stress

S Mansoor, YS Chung - Current Plant Biology, 2024 - Elsevier
Drought stress, exacerbated by climate change, presents a critical global challenge
characterized by increasingly severe and prolonged drought events. This phenomenon …

Group inverse-gamma gamma shrinkage for sparse linear models with block-correlated regressors

J Boss, J Datta, X Wang, SK Park, J Kang… - Bayesian …, 2023 - projecteuclid.org
Heavy-tailed continuous shrinkage priors, such as the horseshoe prior, are widely used for
sparse estimation problems. However, there is limited work extending these priors to …

Semiparametric Bayesian variable selection for gene‐environment interactions

J Ren, F Zhou, X Li, Q Chen, H Zhang, S Ma… - Statistics in …, 2020 - Wiley Online Library
Many complex diseases are known to be affected by the interactions between genetic
variants and environmental exposures beyond the main genetic and environmental effects …

Bayesian longitudinal low-rank regression models for imaging genetic data from longitudinal studies

ZH Lu, Z Khondker, JG Ibrahim, Y Wang, H Zhu… - NeuroImage, 2017 - Elsevier
To perform a joint analysis of multivariate neuroimaging phenotypes and candidate genetic
markers obtained from longitudinal studies, we develop a Bayesian longitudinal low-rank …

[HTML][HTML] Minimizing the distortions in electrophysiological source imaging of cortical oscillatory activity via Spectral Structured Sparse Bayesian Learning

D Paz-Linares, E Gonzalez-Moreira… - Frontiers in …, 2023 - frontiersin.org
Oscillatory processes at all spatial scales and on all frequencies underpin brain function.
Electrophysiological Source Imaging (ESI) is the data-driven brain imaging modality that …

[PDF][PDF] Functional physiological phenotyping with functional mapping: A general framework to bridge the phenotype-genotype gap in plant physiology

AK Pandey, L Jiang, M Moshelion, SC Gosa, T Sun… - Iscience, 2021 - cell.com
The recent years have witnessed the emergence of high-throughput phenotyping
techniques. In particular, these techniques can characterize a comprehensive landscape of …

A Poisson reduced-rank regression model for association mapping in sequencing data

T Fitzgerald, A Jones, BE Engelhardt - BMC bioinformatics, 2022 - Springer
Background Single-cell RNA-sequencing (scRNA-seq) technologies allow for the study of
gene expression in individual cells. Often, it is of interest to understand how transcriptional …

Fast algorithms and theory for high-dimensional Bayesian varying coefficient models

R Bai, MR Boland, Y Chen - arXiv e-prints, 2019 - ui.adsabs.harvard.edu
Nonparametric varying coefficient (NVC) models are useful for modeling time-varying effects
on responses that are measured repeatedly. In this paper, we introduce the nonparametric …

Computational identification of genes modulating stem height–diameter allometry

L Jiang, M Ye, S Zhu, Y Zhai, M Xu… - Plant biotechnology …, 2016 - Wiley Online Library
The developmental variation in stem height with respect to stem diameter is related to a
broad range of ecological and evolutionary phenomena in trees, but the underlying genetic …