Grant Number:	5R03CA171011-03 Interpret this number
Primary Investigator:	Biswas, Swati
Organization:	University Of Texas Dallas
Project Title:	Identifying Rare Haplotype-Environment Interactions Using Logistic Bayesian Lasso
Fiscal Year:	2013

DESCRIPTION (provided by applicant): Rare variants have been heralded as key to uncovering \missing heritability" in complex diseases such as cancers. These variants can now be genotyped using next-generation sequencing technologies; nonetheless, rare haplotypes may also result from combination of common SNPs available from Genome-Wide Association Studies (GWAS). In this regard, there may be a great deal of treasure that are yet to be mined from the GWAS data to explore the common disease rare variant hypothesis. Recently, we have proposed an approach named Logistic Bayesian LASSO (LBL) to identify association with rare haplotypes in a case-control setting. LBL is an adaptation of the Bayesian counterpart of penalized regression approach LASSO. Our approach is able to weed out unassociated (especially common) haplotypes to achieve enough noise reduction so that the signals contained in the associated rare haplotypes can be more easily detected. Using LBL, we were able to implicate a specific rare haplotype for Age-related Macular Degeneration (AMD) in the Complement Factor H (CFH) gene for the first time. In addition to rare variants, gene-environment interaction (GXE) is believed to be another important contributor to missing heritability. LBL has a flexible framework that can incorporate non-genetic (environmental) covariates and gene- environment interactions. In this project we propose methods for exploring interactions between rare haplotypes and environmental factors in cancer epidemiology, rst in the setting of simple random sampling and then for stratified random sampling. We will develop methods both with and without the assumption of gene-environment independence. The methods will be extensively studied through simulations under a variety of settings. They will be applied to several cancer datasets available from NIH's database of Genotypes and Phenotypes (dbGaP) and the AMD data. Further, the method for stratified sampling will be used to analyze the NCI-sponsored Kidney Cancer Case-Control Study, wherein the controls were selected by stratified sampling using frequency matching with cases. We will implement the proposed methods in a well-documented user-friendly software and make it available to the larger scientific community.




Bivariate quantitative Bayesian LASSO for detecting association of rare haplotypes with two correlated continuous phenotypes.
Authors: Sajal I.H. , Biswas S. .
Source: Frontiers In Genetics, 2023; 14, p. 1104727.
EPub date: 2023-03-09 00:00:00.0.
PMID: 36968609
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Detecting rare haplotype association with two correlated phenotypes of binary and continuous types.
Authors: Yuan X. , Biswas S. .
Source: Statistics In Medicine, 2021-01-12 00:00:00.0; , .
EPub date: 2021-01-12 00:00:00.0.
PMID: 33438281
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Comparison of haplotype-based tests for detecting gene-environment interactions with rare variants.
Authors: Papachristou C. , Biswas S. .
Source: Briefings In Bioinformatics, 2020-05-21 00:00:00.0; 21(3), p. 851-862.
PMID: 31329820
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Bivariate logistic Bayesian LASSO for detecting rare haplotype association with two correlated phenotypes.
Authors: Yuan X. , Biswas S. .
Source: Genetic Epidemiology, 2019-09-23 00:00:00.0; , .
EPub date: 2019-09-23 00:00:00.0.
PMID: 31544985
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A Family-Based Rare Haplotype Association Method for Quantitative Traits.
Authors: Datta A.S. , Lin S. , Biswas S. .
Source: Human Heredity, 2018; 83(4), p. 175-195.
EPub date: 2019-02-21 00:00:00.0.
PMID: 30799419
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Logistic Bayesian Lasso For Genetic Association Analysis Of Data From Complex Sampling Designs
Authors: Zhang Y. , Hofmann J.N. , Purdue M.P. , Lin S. , Biswas S. .
Source: Journal Of Human Genetics, 2017-04-20 00:00:00.0; , .
PMID: 28424482
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Detecting rare and common haplotype-environment interaction under uncertainty of gene-environment independence assumption.
Authors: Zhang Y. , Lin S. , Biswas S. .
Source: Biometrics, 2017 Mar; 73(1), p. 344-355.
PMID: 27478935
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Association Of Rare Haplotypes On Ulk4 And Map4 Genes With Hypertension
Authors: Datta A.S. , Zhang Y. , Zhang L. , Biswas S. .
Source: Bmc Proceedings, 2016; 10(Suppl 7), p. 363-369.
PMID: 27980663
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Kullback-leibler Divergence For Detection Of Rare Haplotype Common Disease Association
Authors: Lin S. .
Source: European Journal Of Human Genetics : Ejhg, 2015 Nov; 23(11), p. 1558-65.
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Detecting associations of rare variants with common diseases: collapsing or haplotyping?
Authors: Wang M. , Lin S. .
Source: Briefings In Bioinformatics, 2015 Sep; 16(5), p. 759-68.
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An Improved Version Of Logistic Bayesian Lasso For Detecting Rare Haplotype-environment Interactions With Application To Lung Cancer
Authors: Zhang Y. , Biswas S. .
Source: Cancer Informatics, 2015; 14(Suppl 2), p. 11-6.
PMID: 25733797
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FamLBL: detecting rare haplotype disease association based on common SNPs using case-parent triads.
Authors: Wang M. , Lin S. .
Source: Bioinformatics (oxford, England), 2014-09-15 00:00:00.0; 30(18), p. 2611-8.
EPub date: 2014-09-15 00:00:00.0.
PMID: 24849576
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Population-based association and gene by environment interactions in Genetic Analysis Workshop 18.
Authors: Satten G.A. , Biswas S. , Papachristou C. , Turkmen A. , König I.R. .
Source: Genetic Epidemiology, 2014 Sep; 38 Suppl 1, p. S49-56.
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Detecting rare haplotype-environment interaction with logistic Bayesian LASSO.
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Source: Genetic Epidemiology, 2014 Jan; 38(1), p. 31-41.
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Evaluation Of Logistic Bayesian Lasso For Identifying Association With Rare Haplotypes
Authors: Biswas S. , Papachristou C. .
Source: Bmc Proceedings, 2014; 8(Suppl 1), p. S54.
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