ReviewCancer informatics2015
An Improved Version of Logistic Bayesian LASSO for Detecting Rare Haplotype-Environment Interactions with Application to Lung Cancer.
Review in Cancer informatics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Bayesian Approaches in Exploring Gene-environment and Gene-gene Interactions: A Comprehensive Review.Cancer genomics & proteomics · 2023Review
- A Comprehensive Survey on the Progress, Process, and Challenges of Lung Cancer Detection and Classification.Journal of healthcare engineering · 2022Review
- Establishment of a regression model of bone metabolism markers for the diagnosis of bone metastases in lung cancer.World journal of surgical oncology · 2021Article
- Comparison of haplotype-based tests for detecting gene-environment interactions with rare variants.Briefings in bioinformatics · 2020Review
- Improving five-year survival prediction via multitask learning across HPV-related cancers.PloS one · 2020Article
- Bivariate logistic Bayesian LASSO for detecting rare haplotype association with two correlated phenotypes.Genetic epidemiology · 2019Article
- Genome-Wide Gene-Environment Interaction Analysis Using Set-Based Association Tests.Frontiers in genetics · 2018Article
- Logistic Bayesian LASSO for genetic association analysis of data from complex sampling designs.Journal of human genetics · 2017Article
- Detecting rare and common haplotype-environment interaction under uncertainty of gene-environment independence assumption.Biometrics · 2017Article
- Are Randomized Controlled Trials the (G)old Standard? From Clinical Intelligence to Prescriptive Analytics.Journal of medical Internet research · 2016Article
- Comparison of haplotype-based statistical tests for disease association with rare and common variants.Briefings in bioinformatics · 2016Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
The importance of haplotype association and gene-environment interactions (GxE) in the context of rare variants has been underlined in voluminous literature. Recently, a software based on logistic Bayesian LASSO (LBL) was proposed for detecting GxE, where G is a rare (or common) haplotype variant (rHTV)-it is called LBL-GxE. However, it required relatively long computation time and could handle only one environmental covariate with two levels. Here we propose an improved version of LBL-GxE, which is not only computationally faster but can also handle multiple covariates, each with multiple levels. We also discuss details of the software, including input, output, and some options. We apply LBL-GxE to a lung cancer dataset and find a rare haplotype with protective effect for current smokers. Our results indicate that LBL-GxE, especially with the improvements proposed here, is a useful and computationally viable tool for investigating rare haplotype interactions.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.