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

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 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

scholarly journals Genome-wide haplotype association study identify the FGFR2 gene as a risk gene for Acute Myeloid Leukemia

Oncotarget ◽  
2016 ◽  
Vol 8 (5) ◽  
pp. 7891-7899 ◽  
Author(s):  
Hongchao Lv ◽  
Mingming Zhang ◽  
Zhenwei Shang ◽  
Jin Li ◽  
Shanshan Zhang ◽  
...  
Keyword(s):  
Acute Myeloid Leukemia ◽  
Association Study ◽  
Myeloid Leukemia ◽  
Haplotype Association ◽  
Risk Gene ◽  
Genome Wide ◽  
Fgfr2 Gene ◽  
Acute Myeloid

scholarly journals Strain screen and haplotype association mapping of wheel running in inbred mouse strains

2010 ◽  
Vol 109 (3) ◽  
pp. 623-634 ◽  
Author(s):  
J. Timothy Lightfoot ◽  
Larry Leamy ◽  
Daniel Pomp ◽  
Michael J. Turner ◽  
Anthony A. Fodor ◽  
...  
Keyword(s):  
Physical Activity ◽  
Association Mapping ◽  
Wheel Running ◽  
Association Studies ◽  
Mouse Strains ◽  
Haplotype Association ◽  
Male And Female ◽  
Female Mice ◽  
Running Activity ◽  
Wheel Running Activity

Previous genetic association studies of physical activity, in both animal and human models, have been limited in number of subjects and genetically homozygous strains used as well as number of genomic markers available for analysis. Expansion of the available mouse physical activity strain screens and the recently published dense single-nucleotide polymorphism (SNP) map of the mouse genome (≈8.3 million SNPs) and associated statistical methods allowed us to construct a more generalizable map of the quantitative trait loci (QTL) associated with physical activity. Specifically, we measured wheel running activity in male and female mice (average age 9 wk) in 41 inbred strains and used activity data from 38 of these strains in a haplotype association mapping analysis to determine QTL associated with activity. As seen previously, there was a large range of activity patterns among the strains, with the highest and lowest strains differing significantly in daily distance run (27.4-fold), duration of activity (23.6-fold), and speed (2.9-fold). On a daily basis, female mice ran further (24%), longer (13%), and faster (11%). Twelve QTL were identified, with three (on Chr. 12, 18, and 19) in both male and female mice, five specific to males, and four specific to females. Eight of the 12 QTL, including the 3 general QTL found for both sexes, fell into intergenic areas. The results of this study further support the findings of a moderate to high heritability of physical activity and add general genomic areas applicable to a large number of mouse strains that can be further mined for candidate genes associated with regulation of physical activity. Additionally, results suggest that potential genetic mechanisms arising from traditional noncoding regions of the genome may be involved in regulation of physical activity.

scholarly journals Identification of loci affecting sexually dimorphic patterns for height and recurrent laryngeal neuropathy risk in American Belgian Draft Horses

2018 ◽  
Vol 50 (12) ◽  
pp. 1051-1058 ◽  
Author(s):  
Samantha A. Brooks ◽  
John Stick ◽  
Ashley Braman ◽  
Katelyn Palermo ◽  
N. Edward Robinson ◽  
...  
Keyword(s):  
Adult Height ◽  
Growth Traits ◽  
Treatment Strategies ◽  
Unknown Etiology ◽  
Specific Gene ◽  
Sexually Dimorphic ◽  
Haplotype Association ◽  
Genome Wide ◽  
A Genome ◽  
Specific Locus

Equine recurrent laryngeal neuropathy (RLN) is a bilateral mononeuropathy with an unknown etiology. In Thoroughbreds (TB), we previously demonstrated that the haplotype association for height (LCORL/NCAPG locus on ECA3, which affects body size) and RLN was coincident. In the present study, we performed a genome-wide association scan (GWAS) for RLN in 458 American Belgian Draft Horses, a breed fixed for the LCORL/NCAPG risk alelle. In this breed, RLN risk is associated with sexually dimorphic differences in height, and we identified a novel locus contributing to height in a sex-specific manner: MYPN (ECA1). Yet this specific locus contributes little to RLN risk, suggesting that other growth traits correlated to height may underlie the correlation to this disease. Controlling for height, we identified a locus on ECA15 contributing to RLN risk specifically in males. These results suggest that loci with sex-specific gene expression play an important role in altering growth traits impacting RLN etiology, but not necessarily adult height. These newly identified genes are promising targets for novel preventative and treatment strategies.

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.

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