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

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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An Improved Version of Logistic Bayesian LASSO for Detecting Rare Haplotype-Environment Interactions with Application to Lung Cancer

Cancer Informatics ◽

10.4137/cin.s17290 ◽

2015 ◽

Vol 14s2 ◽

pp. CIN.S17290 ◽

Cited By ~ 2

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.

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A unified method for rare variant analysis of gene-environment interactions

10.1101/570226 ◽

2019 ◽

Author(s):

Elise Lim ◽

Han Chen ◽

Josée Dupuis ◽

Ching-Ti Liu

Keyword(s):

Complex Traits ◽

Rare Variants ◽

Error Rates ◽

Advanced Technology ◽

Linear Mixed Effect Model ◽

Marginal Effect ◽

Type I ◽

Mixed Effect ◽

Gene Environment ◽

Unified Method

AbstractAdvanced technology in whole-genome sequencing has offered the opportunity to comprehensively investigate the genetic contribution, particularly rare variants, to complex traits. Many rare variants analysis methods have been developed to jointly model the marginal effect but methods to detect gene-environment (GE) interactions are underdeveloped. Identifying the modification effects of environmental factors on genetic risk poses a considerable challenge. To tackle this challenge, we develop a unified method to detect GE interactions of a set of rare variants using generalized linear mixed effect model. The proposed method can accommodate both binary and continuous traits in related or unrelated samples. Under this model, genetic main effects, sample relatedness and GE interactions are modeled as random effects. We adopt a kernel-based method to leverage the joint information across rare variants and implement variance component score tests to reduce the computational burden. Our simulation study shows that the proposed method maintains correct type I error rates and high power under various scenarios, such as differing the direction of main genotype and GE interaction effects and the proportion of causal variants in the model for both continuous and binary traits. We illustrate our method to test gene-based interaction with smoking on body mass index or overweight status in the Framingham Heart Study and replicate theCHRNB4gene association reported in previous large consortium meta-analysis of single nucleotide polymorphism (SNP)-smoking interaction. Our proposed set-based GE test is computationally efficient and is applicable to both binary and continuous phenotypes, while appropriately accounting for familial or cryptic relatedness.

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Testing gene-environment interactions for rare and/or common variants in sequencing association studies

10.1101/796540 ◽

2019 ◽

Author(s):

Zihan Zhao ◽

Jianjun Zhang ◽

Qiuying Sha ◽

Han Hao

Keyword(s):

Rare Variants ◽

Association Studies ◽

Error Rates ◽

Type I ◽

Common Variants ◽

Next Generation Sequencing Technology ◽

Gene Environment ◽

Variable Weight ◽

Protective Variants ◽

Weighted Combination

AbstractThe risk of many complex diseases is determined by a complex interplay of genetic and environmental factors. Advanced next generation sequencing technology makes identification of gene-environment (GE) interactions for both common and rare variants possible. However, most existing methods focus on testing the main effects of common and/or rare genetic variants. There are limited methods developed to test the effects of GE interactions for rare variants only or rare and common variants simultaneously. In this study, we develop novel approaches to test the effects of GE interactions of rare and/or common risk, and/or protective variants in sequencing association studies. We propose two approaches: 1) testing the effects of an optimally weighted combination of GE interactions for rare variants (TOW-GE); 2) testing the effects of a weighted combination of GE interactions for both rare and common variants (variable weight TOW-GE, VW-TOW-GE). Extensive simulation studies based on the Genetic Analysis Workshop 17 data show that the type I error rates of the proposed methods are well controlled. Compared to the existing interaction sequence kernel association test (ISKAT), TOW-GE is more powerful when there are GE interactions’ effects for rare risk and/or protective variants; VW-TOW-GE is more powerful when there are GE interactions’ effects for both rare and common risk and protective variants. Both TOW-GE and VW-TOW-GE are robust to the directions of effects of causal GE interactions. We demonstrate the applications of TOW-GE and VW-TOW-GE using an imputed data from the COPDGene Study.

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Investigation of the international comparability of population-based routine hospital data set derived comorbidity scores for patients with lung cancer

Thorax ◽

10.1136/thoraxjnl-2017-210362 ◽

2017 ◽

Vol 73 (4) ◽

pp. 339-349 ◽

Cited By ~ 4

Author(s):

Margreet Lüchtenborg ◽

Eva J A Morris ◽

Daniela Tataru ◽

Victoria H Coupland ◽

Andrew Smith ◽

...

Keyword(s):

Lung Cancer ◽

Cancer Survival ◽

Population Based ◽

Data Sets ◽

Hospital Data ◽

Data Set ◽

Discharge Data ◽

International Differences ◽

Cancer Data ◽

Comorbidity Scores

IntroductionThe International Cancer Benchmarking Partnership (ICBP) identified significant international differences in lung cancer survival. Differing levels of comorbid disease across ICBP countries has been suggested as a potential explanation of this variation but, to date, no studies have quantified its impact. This study investigated whether comparable, robust comorbidity scores can be derived from the different routine population-based cancer data sets available in the ICBP jurisdictions and, if so, use them to quantify international variation in comorbidity and determine its influence on outcome.MethodsLinked population-based lung cancer registry and hospital discharge data sets were acquired from nine ICBP jurisdictions in Australia, Canada, Norway and the UK providing a study population of 233 981 individuals. For each person in this cohort Charlson, Elixhauser and inpatient bed day Comorbidity Scores were derived relating to the 4–36 months prior to their lung cancer diagnosis. The scores were then compared to assess their validity and feasibility of use in international survival comparisons.ResultsIt was feasible to generate the three comorbidity scores for each jurisdiction, which were found to have good content, face and concurrent validity. Predictive validity was limited and there was evidence that the reliability was questionable.ConclusionThe results presented here indicate that interjurisdictional comparability of recorded comorbidity was limited due to probable differences in coding and hospital admission practices in each area. Before the contribution of comorbidity on international differences in cancer survival can be investigated an internationally harmonised comorbidity index is required.

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Considering Both Statistical and Clinical Significance

International Journal of Statistics and Probability ◽

10.5539/ijsp.v5n5p16 ◽

2016 ◽

Vol 5 (5) ◽

pp. 16 ◽

Cited By ~ 1

Author(s):

Guolong Zhao

Keyword(s):

Clinical Significance ◽

Null Hypothesis ◽

Type I Error ◽

Statistical Significance ◽

Error Rates ◽

Type I ◽

Data Set ◽

Type I Error Rates ◽

Empirical Coverage ◽

Superiority Trials

To evaluate a drug, statistical significance alone is insufficient and clinical significance is also necessary. This paper explains how to analyze clinical data with considering both statistical and clinical significance. The analysis is practiced by combining a confidence interval under null hypothesis with that under non-null hypothesis. The combination conveys one of the four possible results: (i) both significant, (ii) only significant in the former, (iii) only significant in the latter or (iv) neither significant. The four results constitute a quadripartite procedure. Corresponding tests are mentioned for describing Type I error rates and power. The empirical coverage is exhibited by Monte Carlo simulations. In superiority trials, the four results are interpreted as clinical superiority, statistical superiority, non-superiority and indeterminate respectively. The interpretation is opposite in inferiority trials. The combination poses a deflated Type I error rate, a decreased power and an increased sample size. The four results may helpful for a meticulous evaluation of drugs. Of these, non-superiority is another profile of equivalence and so it can also be used to interpret equivalence. This approach may prepare a convenience for interpreting discordant cases. Nevertheless, a larger data set is usually needed. An example is taken from a real trial in naturally acquired influenza.

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Trans-ethnic meta-analysis of rare variants in sequencing association studies

Biostatistics ◽

10.1093/biostatistics/kxz061 ◽

2019 ◽

Author(s):

Jingchunzi Shi ◽

Michael Boehnke ◽

Seunggeun Lee

Keyword(s):

Rare Variants ◽

Sequence Data ◽

Association Studies ◽

Meta Analysis ◽

Error Rates ◽

Efficient Estimation ◽

Gene Region ◽

Type I ◽

Test Statistic ◽

Different Populations

Summary Trans-ethnic meta-analysis is a powerful tool for detecting novel loci in genetic association studies. However, in the presence of heterogeneity among different populations, existing gene-/region-based rare variants meta-analysis methods may be unsatisfactory because they do not consider genetic similarity or dissimilarity among different populations. In response, we propose a score test under the modified random effects model for gene-/region-based rare variants associations. We adapt the kernel regression framework to construct the model and incorporate genetic similarities across populations into modeling the heterogeneity structure of the genetic effect coefficients. We use a resampling-based copula method to approximate asymptotic distribution of the test statistic, enabling efficient estimation of p-values. Simulation studies show that our proposed method controls type I error rates and increases power over existing approaches in the presence of heterogeneity. We illustrate our method by analyzing T2D-GENES consortium exome sequence data to explore rare variant associations with several traits.

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A new efficient method to detect genetic interactions for lung cancer GWAS

BMC Medical Genomics ◽

10.1186/s12920-020-00807-9 ◽

2020 ◽

Vol 13 (1) ◽

Author(s):

Jennifer Luyapan ◽

Xuemei Ji ◽

Siting Li ◽

Xiangjun Xiao ◽

Dakai Zhu ◽

...

Keyword(s):

Lung Cancer ◽

Dimensionality Reduction ◽

Multifactor Dimensionality Reduction ◽

Disease Onset ◽

Genetic Interactions ◽

Survival Outcomes ◽

Type I ◽

Genome Wide Association Studies ◽

Data Set ◽

Genome Wide

Abstract Background Genome-wide association studies (GWAS) have proven successful in predicting genetic risk of disease using single-locus models; however, identifying single nucleotide polymorphism (SNP) interactions at the genome-wide scale is limited due to computational and statistical challenges. We addressed the computational burden encountered when detecting SNP interactions for survival analysis, such as age of disease-onset. To confront this problem, we developed a novel algorithm, called the Efficient Survival Multifactor Dimensionality Reduction (ES-MDR) method, which used Martingale Residuals as the outcome parameter to estimate survival outcomes, and implemented the Quantitative Multifactor Dimensionality Reduction method to identify significant interactions associated with age of disease-onset. Methods To demonstrate efficacy, we evaluated this method on two simulation data sets to estimate the type I error rate and power. Simulations showed that ES-MDR identified interactions using less computational workload and allowed for adjustment of covariates. We applied ES-MDR on the OncoArray-TRICL Consortium data with 14,935 cases and 12,787 controls for lung cancer (SNPs = 108,254) to search over all two-way interactions to identify genetic interactions associated with lung cancer age-of-onset. We tested the best model in an independent data set from the OncoArray-TRICL data. Results Our experiment on the OncoArray-TRICL data identified many one-way and two-way models with a single-base deletion in the noncoding region of BRCA1 (HR 1.24, P = 3.15 × 10–15), as the top marker to predict age of lung cancer onset. Conclusions From the results of our extensive simulations and analysis of a large GWAS study, we demonstrated that our method is an efficient algorithm that identified genetic interactions to include in our models to predict survival outcomes.

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Three New Methods for Analysis of Answer Changes

Educational and Psychological Measurement ◽

10.1177/0013164416632287 ◽

2016 ◽

Vol 77 (1) ◽

pp. 54-81 ◽

Cited By ~ 6

Author(s):

Sandip Sinharay ◽

Matthew S. Johnson

Keyword(s):

Normal Distribution ◽

Null Hypothesis ◽

Type I Error ◽

Error Rates ◽

Standard Normal Distribution ◽

Type I ◽

Data Set ◽

Continuity Correction ◽

Type I Error Rates ◽

Standard Normal

In a pioneering research article, Wollack and colleagues suggested the “erasure detection index” (EDI) to detect test tampering. The EDI can be used with or without a continuity correction and is assumed to follow the standard normal distribution under the null hypothesis of no test tampering. When used without a continuity correction, the EDI often has inflated Type I error rates. When used with a continuity correction, the EDI has satisfactory Type I error rates, but smaller power compared with the EDI without a continuity correction. This article suggests three methods for detecting test tampering that do not rely on the assumption of a standard normal distribution under the null hypothesis. It is demonstrated in a detailed simulation study that the performance of each suggested method is slightly better than that of the EDI. The EDI and the suggested methods were applied to a real data set. The suggested methods, although more computation intensive than the EDI, seem to be promising in detecting test tampering.

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Dynamic Scan Procedure for Detecting Rare-Variant Association Regions in Whole Genome Sequencing Studies

10.1101/552950 ◽

2019 ◽

Author(s):

Zilin Li ◽

Xihao Li ◽

Yaowu Liu ◽

Jincheng Shen ◽

Han Chen ◽

...

Keyword(s):

Whole Genome Sequencing ◽

Genome Sequencing ◽

Rare Variant ◽

Type I Error ◽

Rare Variants ◽

Error Rates ◽

Type I ◽

Whole Genome ◽

Rare Variant Association ◽

Dynamic Scan

AbstractWhole genome sequencing (WGS) studies are being widely conducted to identify rare variants associated with human diseases and disease-related traits. Classical single-marker association analyses for rare variants have limited power, and variant-set based analyses are commonly used to analyze rare variants. However, existing variant-set based approaches need to pre-specify genetic regions for analysis, and hence are not directly applicable to WGS data due to the large number of intergenic and intron regions that consist of a massive number of non-coding variants. The commonly used sliding window method requires pre-specifying fixed window sizes, which are often unknown as a priori, are difficult to specify in practice and are subject to limitations given genetic association region sizes are likely to vary across the genome and phenotypes. We propose a computationally-efficient and dynamic scan statistic method (Scan the Genome (SCANG)) for analyzing WGS data that flexibly detects the sizes and the locations of rare-variants association regions without the need of specifying a prior fixed window size. The proposed method controls the genome-wise type I error rate and accounts for the linkage disequilibrium among genetic variants. It allows the detected rare variants association region sizes to vary across the genome. Through extensive simulated studies that consider a wide variety of scenarios, we show that SCANG substantially outperforms several alternative rare-variant association detection methods while controlling for the genome-wise type I error rates. We illustrate SCANG by analyzing the WGS lipids data from the Atherosclerosis Risk in Communities (ARIC) study.

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Test Gene-Environment Interactions for Multiple Traits in Sequencing Association Studies

10.1101/710574 ◽

2019 ◽

Author(s):

Jianjun Zhang ◽

Qiuying Sha ◽

Han Hao ◽

Shuanglin Zhang ◽

Xiaoyi Raymond Gao ◽

...

Keyword(s):

Association Studies ◽

Complex Diseases ◽

Error Rates ◽

Phenotypic Trait ◽

Type I ◽

Environment Interaction ◽

Multiple Traits ◽

Gene Environment ◽

Variable Weight ◽

Protective Variants

AbstractThe risk of many complex diseases is determined by a complex interplay of genetic and environmental factors. Data on multiple traits is often collected for many complex diseases in order to obtain a better understanding of the diseases. Examination of gene-environment interactions (GxEs) for multiple traits can yield valuable insights about the etiology of the disease and increase power in detecting disease associated genes. Most existing methods focus on testing gene-environment interaction (GxE) for a single trait. In this study, we develop novel approaches to test GxEs for multiple traits in sequencing association studies. We first perform transformation of multiple traits by using either principle component analysis or standardization analysis. Then, we detect the effect of GxE for each transferred phenotypic trait using novel proposed tests: testing the effect of an optimallyweighted combination of GxE (TOW-GE) and/or variable weight TOW-GE (VW-TOW-GE). Finally, we employ the Fisher’s combination test to combine the p-values of TOW-GE and/or VW-TOW-GE. Extensive simulation studies based on the Genetic Analysis Workshop 17 data show that the type I error rates of the proposed methods are well controlled. Compared to the existing interaction sequence kernel association test (ISKAT), TOW-GE is more powerful when there are only rare risk and protective variants; VW-TOW-GE is more powerful when there are both rare and common risk and protective variants. Both TOW-GE and VW-TOW-GE are robust to directions of effects of causal GxEs. Application to the COPDGene Study demonstrates that our proposed methods are very powerful.

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