scholarly journals Bivariate logistic Bayesian LASSO for detecting rare haplotype association with two correlated phenotypes

2019 ◽  
Vol 43 (8) ◽  
pp. 996-1017
Author(s):  
Xiaochen Yuan ◽  
Swati Biswas
Keyword(s):  
Rare Haplotype ◽  
Bayesian Lasso ◽  
Haplotype Association ◽  
Logistic Bayesian Lasso

scholarly journals An Improved Version of Logistic Bayesian LASSO for Detecting Rare Haplotype-Environment Interactions with Application to Lung Cancer

2015 ◽  
Vol 14s2 ◽  
pp. CIN.S17290 ◽  
Author(s):  
Yuan Zhang ◽  
Swati Biswas
Keyword(s):  
Lung Cancer ◽  
Rare Variants ◽  
Computation Time ◽  
Rare Haplotype ◽  
Cancer Dataset ◽  
Bayesian Lasso ◽  
Haplotype Association ◽  
Gene Environment ◽  
Multiple Covariates ◽  
Logistic Bayesian Lasso

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.

scholarly journals Detecting Rare Haplotype-Environment Interaction With Logistic Bayesian LASSO

2013 ◽  
Vol 38 (1) ◽  
pp. 31-41 ◽  
Author(s):  
Swati Biswas ◽  
Shuang Xia ◽  
Shili Lin
Keyword(s):  
Environment Interaction ◽  
Rare Haplotype ◽  
Bayesian Lasso ◽  
Logistic Bayesian Lasso

scholarly journals Logistic Bayesian LASSO for detecting association combining family and case-control data

2018 ◽  
Vol 12 (S9) ◽  
Author(s):  
Xiaofei Zhou ◽  
Meng Wang ◽  
Han Zhang ◽  
William C. L. Stewart ◽  
Shili Lin
Keyword(s):  
Case Control ◽  
Control Data ◽  
Bayesian Lasso ◽  
Logistic Bayesian Lasso

scholarly journals A Family-Based Rare Haplotype Association Method for Quantitative Traits

2018 ◽  
Vol 83 (4) ◽  
pp. 175-195 ◽  
Author(s):  
Ananda S. Datta ◽  
Shili Lin ◽  
Swati Biswas
Keyword(s):  
Quantitative Traits ◽  
Rare Haplotype ◽  
Haplotype Association ◽  
Association Method ◽  
Family Based

Detecting rare haplotype association with two correlated phenotypes of binary and continuous types

2021 ◽  
Author(s):  
Xiaochen Yuan ◽  
Swati Biswas
Keyword(s):  
Rare Haplotype ◽  
Haplotype Association

Detecting rare haplotypes associated with complex diseases using both population and family data: Combined logistic Bayesian Lasso

2020 ◽  
Vol 29 (11) ◽  
pp. 3340-3350
Author(s):  
Xiaofei Zhou ◽  
Meng Wang ◽  
Shili Lin
Keyword(s):  
Rare Variants ◽  
Complex Diseases ◽  
Population Data ◽  
Real Data ◽  
Case Control ◽  
Study Data ◽  
Family Data ◽  
Bayesian Lasso ◽  
Biologically Relevant ◽  
Logistic Bayesian Lasso

Haplotype-based association methods have been developed to understand the genetic architecture of complex diseases. Compared to single-variant-based methods, haplotype methods are thought to be more biologically relevant, since there are typically multiple non-independent genetic variants involved in complex diseases, and the use of haplotypes implicitly accounts for non-independence caused by linkage disequilibrium. In recent years, with the focus moving from common to rare variants, haplotype-based methods have also evolved accordingly to uncover the roles of rare haplotypes. One particular approach is regularization-based, with the use of Bayesian least absolute shrinkage and selection operator (Lasso) as an example. This type of methods has been developed for either case-control population data (the logistic Bayesian Lasso (LBL)) or family data (family-triad-based logistic Bayesian Lasso (famLBL)). In some situations, both family data and case-control data are available; therefore, it would be a waste of resources if only one of them could be analyzed. To make full usage of available data to increase power, we propose a unified approach that can combine both case-control and family data (combined logistic Bayesian Lasso (cLBL)). Through simulations, we characterized the performance of cLBL and showed the advantage of cLBL over existing methods. We further applied cLBL to the Framingham Heart Study data to demonstrate its utility in real data applications.

scholarly journals Evaluation of logistic Bayesian LASSO for identifying association with rare haplotypes

2014 ◽  
Vol 8 (S1) ◽  
Author(s):  
Swati Biswas ◽  
Charalampos Papachristou
Keyword(s):  
Bayesian Lasso ◽  
Logistic Bayesian Lasso

scholarly journals Logistic Bayesian LASSO for genetic association analysis of data from complex sampling designs

2017 ◽  
Vol 62 (9) ◽  
pp. 819-829 ◽  
Author(s):  
Yuan Zhang ◽  
Jonathan N Hofmann ◽  
Mark P Purdue ◽  
Shili Lin ◽  
Swati Biswas
Keyword(s):  
Genetic Association ◽  
Association Analysis ◽  
Genetic Association Analysis ◽  
Bayesian Lasso ◽  
Complex Sampling ◽  
Complex Sampling Designs ◽  
Logistic Bayesian Lasso
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