Gene–environment interactions in human health
Gene–environment interactions (G× E), the interplay of genetic variation with environmental
factors, have a pivotal impact on human complex traits and diseases. Statistically, G× E can …
factors, have a pivotal impact on human complex traits and diseases. Statistically, G× E can …
A varying coefficient model to jointly test genetic and gene–environment interaction effects
Most human traits are influenced by the interplay between genetic and environmental
factors. Many statistical methods have been proposed to screen for gene-environment …
factors. Many statistical methods have been proposed to screen for gene-environment …
[HTML][HTML] A generalized kernel machine approach to identify higher-order composite effects in multi-view datasets, with application to adolescent brain development …
In recent years, a comprehensive study of complex disease with multi-view datasets (eg,
multi-omics and imaging scans) has been a focus and forefront in biomedical research …
multi-omics and imaging scans) has been a focus and forefront in biomedical research …
Pathological imaging‐assisted cancer gene–environment interaction analysis
Abstract Gene–environment (G–E) interactions have important implications for cancer
outcomes and phenotypes beyond the main G and E effects. Compared to main‐effect‐only …
outcomes and phenotypes beyond the main G and E effects. Compared to main‐effect‐only …
Identifying gene–environment interactions with robust marginal Bayesian variable selection
In high-throughput genetics studies, an important aim is to identify gene–environment
interactions associated with the clinical outcomes. Recently, multiple marginal penalization …
interactions associated with the clinical outcomes. Recently, multiple marginal penalization …
[HTML][HTML] VCSEL: Prioritizing SNP-set by penalized variance component selection
Single nucleotide polymorphism (SNP) set analysis aggregates both common and rare
variants and tests for association between phenotype (s) of interest and a set. However …
variants and tests for association between phenotype (s) of interest and a set. However …
Identification of gene–environment interactions with marginal penalization
S Zhang, Y Xue, Q Zhang, C Ma, M Wu… - Genetic …, 2020 - Wiley Online Library
Abstract Gene–environment (G–E) interaction analysis has been extensively conducted for
complex diseases. In marginal analysis, the common practice is to conduct likelihood‐based …
complex diseases. In marginal analysis, the common practice is to conduct likelihood‐based …
Composite Kernel Association Test (CKAT) for SNP-set joint assessment of genotype and genotype-by-treatment interaction in Pharmacogenetics studies
Motivation It is of substantial interest to discover novel genetic markers that influence drug
response in order to develop personalized treatment strategies that maximize therapeutic …
response in order to develop personalized treatment strategies that maximize therapeutic …
[PDF][PDF] Estimation and inference for high-dimensional nonparametric additive instrumental-variables regression
• When instruments and treatments are both high-dimensional, linear models have been
proposed (Lin et al., 2015; Zhu, 2018; Gold et al., 2020).• Nonlinear effects of the SNPs on …
proposed (Lin et al., 2015; Zhu, 2018; Gold et al., 2020).• Nonlinear effects of the SNPs on …
[图书][B] Generalization of kernel machine methods for association testing of multi-omics data
A Little - 2023 - search.proquest.com
Over the past couple of decades, genome-wide association studies (GWASs) have
successfully identified thousands of loci associated with complex traits and diseases in …
successfully identified thousands of loci associated with complex traits and diseases in …