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 Comparison of haplotype-based tests for detecting gene–environment interactions with rare variants

2019 ◽  
Vol 21 (3) ◽  
pp. 851-862 ◽  
Author(s):  
Charalampos Papachristou ◽  
Swati Biswas
Keyword(s):  
Lung Cancer ◽  
Rare Variants ◽  
Practical Interest ◽  
Serum Triglyceride ◽  
Error Rates ◽  
Type I ◽  
Rare Haplotype ◽  
Data Set ◽  
Cancer Data ◽  
Gene Environment

Abstract Dissecting the genetic mechanism underlying a complex disease hinges on discovering gene–environment interactions (GXE). However, detecting GXE is a challenging problem especially when the genetic variants under study are rare. Haplotype-based tests have several advantages over the so-called collapsing tests for detecting rare variants as highlighted in recent literature. Thus, it is of practical interest to compare haplotype-based tests for detecting GXE including the recent ones developed specifically for rare haplotypes. We compare the following methods: haplo.glm, hapassoc, HapReg, Bayesian hierarchical generalized linear model (BhGLM) and logistic Bayesian LASSO (LBL). We simulate data under different types of association scenarios and levels of gene–environment dependence. We find that when the type I error rates are controlled to be the same for all methods, LBL is the most powerful method for detecting GXE. We applied the methods to a lung cancer data set, in particular, in region 15q25.1 as it has been suggested in the literature that it interacts with smoking to affect the lung cancer susceptibility and that it is associated with smoking behavior. LBL and BhGLM were able to detect a rare haplotype–smoking interaction in this region. We also analyzed the sequence data from the Dallas Heart Study, a population-based multi-ethnic study. Specifically, we considered haplotype blocks in the gene ANGPTL4 for association with trait serum triglyceride and used ethnicity as a covariate. Only LBL found interactions of haplotypes with race (Hispanic). Thus, in general, LBL seems to be the best method for detecting GXE among the ones we studied here. Nonetheless, it requires the most computation time.

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

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 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

Gene Environment Interactions in Women with Breast Cancer and Secondary Lung Cancer

2004 ◽  
Author(s):  
Meredith A. Tennis ◽  
Peter G. Shields
Keyword(s):  
Breast Cancer ◽  
Lung Cancer ◽  
Gene Environment

Gene Environment Interactions in Women With Breast and Secondary Lung Cancer

2005 ◽  
Author(s):  
Meredith A. Tennis ◽  
Peter G. Shields
Keyword(s):  
Lung Cancer ◽  
Gene Environment

scholarly journals Anatomical variants of pulmonary segments and uni-portal thoracoscopic segmentectomy for lung cancer in a patient with Kartagener syndrome: a case report

2021 ◽  
Author(s):  
Di Zhou ◽  
Ye Tian ◽  
Yao Lu ◽  
Xueying Yang
Keyword(s):  
Lung Cancer ◽  
Rare Variants ◽  
Sagittal Plane ◽  
Early Stage ◽  
Situs Inversus Totalis ◽  
Middle Lobe ◽  
The Body ◽  
Kartagener Syndrome ◽  
Early Stage Lung Cancer ◽  
Cancer Lesion

AbstractSitus inversus totalis (SIT) is an extremely uncommon congenital disease where the major organs of the body are transposed through the sagittal plane. Kartagener syndrome is a complication of SIT with immotility of bronchial cilia, bronchiectasis, and chronic sinusitis. There is no report describing patients with Kartagener syndrome who accept uni-portal segmentectomies for lung cancer in past studies. Here we report a 74-year-old female patient with both Kartagener syndrome and a small early-stage lung cancer lesion located in the apical segment of the left upper lobe (LS1). The pulmonary segment anatomy of the left upper lobe in this case, which had very rare variants, was presented and interpreted in detail. This patient underwent an anatomic segmentectomy to the LS1 and a partial excision to the left middle lobe with bronchiectasis through a single 3 cm length incision. We believe that the case can give surgeons some experience and inspiration.

Nonproportional hazards model with a frailty term for modeling subgroups with evidence of long‐term survivors: Application to a lung cancer dataset

2021 ◽  
Author(s):  
Amanda B. Gazon ◽  
Eder A. Milani ◽  
Alex L. Mota ◽  
Francisco Louzada ◽  
Vera L. D. Tomazella ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Cancer Dataset ◽  
Nonproportional Hazards ◽  
Hazards Model ◽  
Long Term Survivors

scholarly journals Effect of Occupational Exposures on Lung Cancer Susceptibility: A Study of Gene–Environment Interaction Analysis

2015 ◽  
Vol 24 (3) ◽  
pp. 570-579 ◽  
Author(s):  
Jyoti Malhotra ◽  
Samantha Sartori ◽  
Paul Brennan ◽  
David Zaridze ◽  
Neonila Szeszenia-Dabrowska ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Cancer Susceptibility ◽  
Interaction Analysis ◽  
Occupational Exposures ◽  
Environment Interaction ◽  
Gene Environment Interaction ◽  
Gene Environment ◽  
Lung Cancer Susceptibility

Logistic Principal Component Analysis for Rare Variants in Gene-Environment Interaction Analysis

2014 ◽  
Vol 11 (6) ◽  
pp. 1020-1028 ◽  
Author(s):  
Meng Lu ◽  
Hye-Seung Lee ◽  
David Hadley ◽  
Jianhua Z. Huang ◽  
Xiaoning Qian
Keyword(s):  
Principal Component Analysis ◽  
Rare Variants ◽  
Interaction Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Environment Interaction ◽  
Gene Environment Interaction ◽  
Gene Environment
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