LBL-package: LBL: Logistic and QUantitative Bayesian Lasso for Detecting...

In mxw010/LBL: Logistic and Quantitative Bayesian Lasso for identifying genetic association between common diseases and rare haplotypes

Description

LBL uses the Bayesian LASSO framework to detect association between a phenotype and haplotypes given the (unphased) genotypes of individuals.

Details

LBL uses Bayesian Lasso to detect rare haplotypes that are associated with common diseases. The current implementation considers dichotomous and quantitative traits. A future release will include survival traits. LBL is capable of handling different study designs: this version of the software is capable of handling independent cases and controls, case-parent trios and a mixture of both (provided that the family data is independent of the case-control data). The QBLbs function is designed for quantitative traits while also controlling for population stratification using principal components (PCs).

A function from the hapassoc package is first used to acquire all compatible haplotypes. The posterior samples are then obtained via Markov Chain Monte Carlo (MCMC) algorithm and inference on the parameters of interest can be carried out (Bayes Factor, Credible Interval, etc.) based on these posterior samples.

Functions

LBL: MCMC algorithm to obtain posterior samples for independent case-control data. famLBL: MCMC algorithm to obtain posterior samples for case-parent trio data. cLBL: MCMC algorithm to obtain posterior samples for combined data. QBLbs: MCMC algorithm to obtain posterior samples for a quantitative trait.

LBL_summary provides model summary (in the form of list) based on posterior samples. print_LBL_summary prints model summary in a user-friendly format from the list result of LBL_summary.

Author(s)

Swati Biswas, Meng Wang, Xiaofei Zhou, Han Zhang, Shuang Xia, Yuan Zhang, and Shili Lin <[e-mail]>

References

Biswas S. and Lin S. (2012). Logistic Bayesian LASSO for identifying association with rare haplotypes and application to age-related macular degeneration. Biometrics, 68(2): 587-97.

Wang, M. and Lin, S. (2014). FamLBL: detecting rare haplotype disease association based on common SNPs using case-parent triads. Bioinformatics, 30(18), 2611-2618.

Zhou, X., Wang, M., and Lin, S. (2019). cLBL: Combined logistic Bayesian LASSO for detecting rare associated haplotypes using independent case, control and family trio data. Manuscript.

mxw010/LBL documentation built on Sept. 26, 2021, 3:44 a.m.