scholarly journals Assessing Gene-Environment Interactions for Common and Rare Variants with Binary Traits Using Gene-Trait Similarity Regression

Genetics ◽  
2015 ◽  
Vol 199 (3) ◽  
pp. 695-710 ◽  
Author(s):  
Guolin Zhao ◽  
Rachel Marceau ◽  
Daowen Zhang ◽  
Jung-Ying Tzeng
Keyword(s):  
Rare Variants ◽  
Binary Traits ◽  
Gene Environment ◽  
Trait Similarity

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 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 Studying Gene and Gene-Environment Effects of Uncommon and Common Variants on Continuous Traits: A Marker-Set Approach Using Gene-Trait Similarity Regression

2011 ◽  
Vol 89 (2) ◽  
pp. 277-288 ◽  
Author(s):  
Jung-Ying Tzeng ◽  
Daowen Zhang ◽  
Monnat Pongpanich ◽  
Chris Smith ◽  
Mark I. McCarthy ◽  
...  
Keyword(s):  
Common Variants ◽  
Environment Effects ◽  
Gene Environment ◽  
Trait Similarity ◽  
Continuous Traits

The gene in its natural habitat: The importance of gene–trait interactions

2012 ◽  
Vol 24 (4) ◽  
pp. 1307-1318 ◽  
Author(s):  
Colin G. DeYoung ◽  
Rachel Clark
Keyword(s):  
Brain Function ◽  
Genetic Variant ◽  
Rare Variants ◽  
Single Gene ◽  
Natural Habitat ◽  
Trait Variation ◽  
Psychological Traits ◽  
Gene Environment ◽  
Main Effects ◽  
The Brain

AbstractDespite the substantial heritability of nearly all psychological traits, it has been difficult to identify specific genetic variants that account for more than a tiny percentage of genetic variance in phenotypes. Common explanations for this “missing heritability” include massive polygenicity, rare variants, epigenetics, epistasis, and gene–environment interactions. Gene–trait (G × T) interaction is another concept useful for understanding the lack of obvious genetic main effects. Both genes and environments are distal contributors to human behavior, but the brain is the proximal driver of behavior. The effect of any single genetic variant is dependent on the configuration of the brain in which it is expressed. One method to begin studying how single genes interact with variations in the rest of the brain is to investigate G × T interactions. A psychological trait reflects a characteristic pattern of psychological function (and, therefore, of brain function), which has its origin in the cumulative effects of both the genome and the environment. A trait therefore describes variation in the broad organismic context in which any single gene operates. We describe the nature and significance of G × T interactions for understanding psychopathology and normal trait variation, which are illustrated with empirical examples.

scholarly journals Does Accounting for Gene-Environment Interactions Help Uncover Association between Rare Variants and Complex Diseases?

2012 ◽  
Vol 74 (3-4) ◽  
pp. 205-214 ◽  
Author(s):  
Rémi Kazma ◽  
Niall J. Cardin ◽  
John S. Witte
Keyword(s):  
Rare Variants ◽  
Complex Diseases ◽  
Gene Environment

scholarly journals An evaluation of approaches for rare variant association analyses of binary traits in related samples

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ming-Huei Chen ◽  
Achilleas Pitsillides ◽  
Qiong Yang
Keyword(s):  
Logistic Regression ◽  
Rare Variants ◽  
Association Studies ◽  
Family Relationship ◽  
Genetic Association Studies ◽  
Error Rates ◽  
Ratio Test ◽  
Type I ◽  
Association Analyses ◽  
Binary Traits

AbstractRecognizing that family data provide unique advantage of identifying rare risk variants in genetic association studies, many cohorts with related samples have gone through whole genome sequencing in large initiatives such as the NHLBI Trans-Omics for Precision Medicine (TOPMed) program. Analyzing rare variants poses challenges for binary traits in that some genotype categories may have few or no observed events, causing bias and inflation in commonly used methods. Several methods have recently been proposed to better handle rare variants while accounting for family relationship, but their performances have not been thoroughly evaluated together. Here we compare several existing approaches including SAIGE but not limited to related samples using simulations based on the Framingham Heart Study samples and genotype data from Illumina HumanExome BeadChip where rare variants are the majority. We found that logistic regression with likelihood ratio test applied to related samples was the only approach that did not have inflated type I error rates in both single variant test (SVT) and gene-based tests, followed by Firth logistic regression that had inflation in its direction insensitive gene-based test at prevalence 0.01 only, applied to either related or unrelated samples, though theoretically logistic regression and Firth logistic regression do not account for relatedness in samples. SAIGE had inflation in SVT at prevalence 0.1 or lower and the inflation was eliminated with a minor allele count filter of 5. As for power, there was no approach that outperformed others consistently among all single variant tests and gene-based tests.

scholarly journals FastGWA-GLMM: a generalized linear mixed model association tool for biobank-scale data

2021 ◽  
Author(s):  
Jian Yang ◽  
Longda Jiang ◽  
Zhili Zheng
Keyword(s):  
Complex Traits ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Rare Variants ◽  
Sparse Matrix ◽  
Extreme Case ◽  
Generalized Linear Mixed Model ◽  
Binary Complex ◽  
Binary Traits ◽  
The Uk

Abstract Compared to linear mixed model-based genome-wide association (GWA) methods, generalized linear mixed model (GLMM)-based methods have better statistical properties when applied to binary traits but are computationally much slower. Here, leveraging efficient sparse matrix-based algorithms, we developed a GLMM-based GWA tool (called fastGWA-GLMM) that is orders of magnitude faster than the state-of-the-art tool (e.g., ~37 times faster when n=400,000) with more scalable memory usage. We show by simulation that the fastGWA-GLMM test-statistics of both common and rare variants are well-calibrated under the null, even for traits with an extreme case-control ratio (e.g., 0.1%). We applied fastGWA-GLMM to the UK Biobank data of 456,348 individuals, 11,842,647 variants and 2,989 binary traits (full summary statistics available at http://fastgwa.info/ukbimpbin) and identified 259 rare variants associated with 75 traits, demonstrating the use of imputed genotype data in a large cohort to discover rare variants for binary complex traits.

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