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Liu Z, Turkmen AS, Lin S. Bayesian LASSO for population stratification correction in rare haplotype association studies. Stat Appl Genet Mol Biol 2024; 23:sagmb-2022-0034. [PMID: 38235525 PMCID: PMC10794901 DOI: 10.1515/sagmb-2022-0034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Accepted: 12/19/2023] [Indexed: 01/19/2024]
Abstract
Population stratification (PS) is one major source of confounding in both single nucleotide polymorphism (SNP) and haplotype association studies. To address PS, principal component regression (PCR) and linear mixed model (LMM) are the current standards for SNP associations, which are also commonly borrowed for haplotype studies. However, the underfitting and overfitting problems introduced by PCR and LMM, respectively, have yet to be addressed. Furthermore, there have been only a few theoretical approaches proposed to address PS specifically for haplotypes. In this paper, we propose a new method under the Bayesian LASSO framework, QBLstrat, to account for PS in identifying rare and common haplotypes associated with a continuous trait of interest. QBLstrat utilizes a large number of principal components (PCs) with appropriate priors to sufficiently correct for PS, while shrinking the estimates of unassociated haplotypes and PCs. We compare the performance of QBLstrat with the Bayesian counterparts of PCR and LMM and a current method, haplo.stats. Extensive simulation studies and real data analyses show that QBLstrat is superior in controlling false positives while maintaining competitive power for identifying true positives under PS.
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Affiliation(s)
- Zilu Liu
- Department of Statistics, The Ohio State University, Columbus, OH43210, USA
| | | | - Shili Lin
- Department of Statistics, The Ohio State University, Columbus, OH43210, USA
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2
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Boutry S, Helaers R, Lenaerts T, Vikkula M. Rare variant association on unrelated individuals in case-control studies using aggregation tests: existing methods and current limitations. Brief Bioinform 2023; 24:bbad412. [PMID: 37974506 DOI: 10.1093/bib/bbad412] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Revised: 10/14/2023] [Accepted: 10/28/2023] [Indexed: 11/19/2023] Open
Abstract
Over the past years, progress made in next-generation sequencing technologies and bioinformatics have sparked a surge in association studies. Especially, genome-wide association studies (GWASs) have demonstrated their effectiveness in identifying disease associations with common genetic variants. Yet, rare variants can contribute to additional disease risk or trait heterogeneity. Because GWASs are underpowered for detecting association with such variants, numerous statistical methods have been recently proposed. Aggregation tests collapse multiple rare variants within a genetic region (e.g. gene, gene set, genomic loci) to test for association. An increasing number of studies using such methods successfully identified trait-associated rare variants and led to a better understanding of the underlying disease mechanism. In this review, we compare existing aggregation tests, their statistical features and scope of application, splitting them into the five classical classes: burden, adaptive burden, variance-component, omnibus and other. Finally, we describe some limitations of current aggregation tests, highlighting potential direction for further investigations.
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Affiliation(s)
- Simon Boutry
- Human Molecular Genetics, de Duve Institute, University of Louvain, Avenue Hippocrate 74 (+5) bte B1.74.06, 1200 Brussels, Belgium
- Interuniversity Institute of Bioinformatics in Brussels, Université Libre de Bruxelles-Vrije Universiteit Brussels, 1050 Brussels, Belgium
| | - Raphaël Helaers
- Human Molecular Genetics, de Duve Institute, University of Louvain, Avenue Hippocrate 74 (+5) bte B1.74.06, 1200 Brussels, Belgium
| | - Tom Lenaerts
- Interuniversity Institute of Bioinformatics in Brussels, Université Libre de Bruxelles-Vrije Universiteit Brussels, 1050 Brussels, Belgium
- Machine Learning Group, Université Libre de Bruxelles, 1050 Brussels, Belgium
- Artificial Intelligence laboratory, Vrije Universiteit Brussel, 1050 Brussels, Belgium
| | - Miikka Vikkula
- Human Molecular Genetics, de Duve Institute, University of Louvain, Avenue Hippocrate 74 (+5) bte B1.74.06, 1200 Brussels, Belgium
- WELBIO department, WEL Research Institute, avenue Pasteur, 6, 1300 Wavre, Belgium
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3
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Boutry S, Helaers R, Lenaerts T, Vikkula M. Excalibur: A new ensemble method based on an optimal combination of aggregation tests for rare-variant association testing for sequencing data. PLoS Comput Biol 2023; 19:e1011488. [PMID: 37708232 PMCID: PMC10522036 DOI: 10.1371/journal.pcbi.1011488] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Revised: 09/26/2023] [Accepted: 09/04/2023] [Indexed: 09/16/2023] Open
Abstract
The development of high-throughput next-generation sequencing technologies and large-scale genetic association studies produced numerous advances in the biostatistics field. Various aggregation tests, i.e. statistical methods that analyze associations of a trait with multiple markers within a genomic region, have produced a variety of novel discoveries. Notwithstanding their usefulness, there is no single test that fits all needs, each suffering from specific drawbacks. Selecting the right aggregation test, while considering an unknown underlying genetic model of the disease, remains an important challenge. Here we propose a new ensemble method, called Excalibur, based on an optimal combination of 36 aggregation tests created after an in-depth study of the limitations of each test and their impact on the quality of result. Our findings demonstrate the ability of our method to control type I error and illustrate that it offers the best average power across all scenarios. The proposed method allows for novel advances in Whole Exome/Genome sequencing association studies, able to handle a wide range of association models, providing researchers with an optimal aggregation analysis for the genetic regions of interest.
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Affiliation(s)
- Simon Boutry
- Human Molecular Genetics, de Duve Institute, University of Louvain, Brussels, Belgium
- Interuniversity Institute of Bioinformatics in Brussels, Université Libre de Bruxelles-Vrije Universiteit Brussels, Brussels, Belgium
| | - Raphaël Helaers
- Human Molecular Genetics, de Duve Institute, University of Louvain, Brussels, Belgium
| | - Tom Lenaerts
- Interuniversity Institute of Bioinformatics in Brussels, Université Libre de Bruxelles-Vrije Universiteit Brussels, Brussels, Belgium
- Machine Learning Group, Université Libre de Bruxelles, Brussels, Belgium
- Artificial Intelligence laboratory, Vrije Universiteit Brussel, Brussels, Belgium
| | - Miikka Vikkula
- Human Molecular Genetics, de Duve Institute, University of Louvain, Brussels, Belgium
- WELBIO department, WEL Research Institute, Wavre, Belgium
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4
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Sajal IH, Biswas S. Bivariate quantitative Bayesian LASSO for detecting association of rare haplotypes with two correlated continuous phenotypes. Front Genet 2023; 14:1104727. [PMID: 36968609 PMCID: PMC10033866 DOI: 10.3389/fgene.2023.1104727] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Accepted: 02/21/2023] [Indexed: 03/12/2023] Open
Abstract
In genetic association studies, the multivariate analysis of correlated phenotypes offers statistical and biological advantages compared to analyzing one phenotype at a time. The joint analysis utilizes additional information contained in the correlation and avoids multiple testing. It also provides an opportunity to investigate and understand shared genetic mechanisms of multiple phenotypes. Bivariate logistic Bayesian LASSO (LBL) was proposed earlier to detect rare haplotypes associated with two binary phenotypes or one binary and one continuous phenotype jointly. There is currently no haplotype association test available that can handle multiple continuous phenotypes. In this study, by employing the framework of bivariate LBL, we propose bivariate quantitative Bayesian LASSO (QBL) to detect rare haplotypes associated with two continuous phenotypes. Bivariate QBL removes unassociated haplotypes by regularizing the regression coefficients and utilizing a latent variable to model correlation between two phenotypes. We carry out extensive simulations to investigate the performance of bivariate QBL and compare it with that of a standard (univariate) haplotype association test, Haplo.score (applied twice to two phenotypes individually). Bivariate QBL performs better than Haplo.score in all simulations with varying degrees of power gain. We analyze Genetic Analysis Workshop 19 exome sequencing data on systolic and diastolic blood pressures and detect several rare haplotypes associated with the two phenotypes.
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Yao J, Zhang Z, Huang X, Guo Y. Blocker displacement amplification mediated PCR based screen-printed carbon electrode biosensor and lateral flow strip strategy for CYP2C19*2 genotyping. Biosens Bioelectron 2022; 207:114138. [PMID: 35334330 DOI: 10.1016/j.bios.2022.114138] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2021] [Revised: 02/03/2022] [Accepted: 02/24/2022] [Indexed: 01/26/2023]
Abstract
Single nucleotide variants in CYP2C19*2 are associated with clopidogrel resistance in coronary heart disease. In order the guidance the dosage of drug and personalized medicine, blocker displacement amplification was first used to specific amplify G site and A site alleles. For electrochemical strategy, forward primers were labeled electrochemical active methyl blue and ferrocene, generates signals on -0.26 for G site and 0.22 V for A site. For lateral flow strip assay, primers with specific modification were used to generates unique color in test line 1 for G site and test line 2 for A site. In conclusion, we developed a sensitive screen-printed carbon electrodes based electrochemical sensor and gold nano particle based lateral flow strip assay strategy to successfully genotyping CYP2C19*2 GG, GA and AA genotype. The proposed method can realize CYP2C19*2 analysis from multiple biological samples including whole blood, buccal swab, saliva and hair root, and showed good consistency with Sequencing. Due to the fact our proposed strategy merely relies on thermal cycler instrument and visual strip detection, this platform shows great potential in source-limited regions genotyping.
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Affiliation(s)
- Juan Yao
- Department of Laboratory Medicine, The Affiliated Traditional Chinese Medicine Hospital of Southwest Medical University, Luzhou, PR China
| | - Zhang Zhang
- Key Laboratory of Laboratory Medical Diagnostics of Education, Department of Laboratory Medicine, Chongqing Medical University, Chongqing, PR China.
| | - Xiaoling Huang
- Department of Laboratory Medicine, The Affiliated Traditional Chinese Medicine Hospital of Southwest Medical University, Luzhou, PR China
| | - Yongcan Guo
- Department of Laboratory Medicine, The Affiliated Traditional Chinese Medicine Hospital of Southwest Medical University, Luzhou, PR China.
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6
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Yuan X, Biswas S. Detecting rare haplotype association with two correlated phenotypes of binary and continuous types. Stat Med 2021; 40:1877-1900. [PMID: 33438281 DOI: 10.1002/sim.8877] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2020] [Revised: 11/18/2020] [Accepted: 12/25/2020] [Indexed: 11/10/2022]
Abstract
Multiple correlated traits/phenotypes are often collected in genetic association studies and they may share a common genetic mechanism. Joint analysis of correlated phenotypes has well-known advantages over one-at-a-time analysis including gain in power and better understanding of genetic etiology. However, when the phenotypes are of discordant types such as binary and continuous, the joint modeling is more challenging. Another research area of current interest is discovery of rare genetic variants. Currently there is no method available for detecting association of rare (or common) haplotypes with multiple discordant phenotypes jointly. Our goal is to fill this gap specifically for two discordant phenotypes. We consider a rare haplotype association method for a binary phenotype, logistic Bayesian LASSO (univariate LBL) and its extension for two correlated binary phenotypes (bivariate LBL-2B). Under this framework, we propose a haplotype association test with binary and continuous phenotypes jointly (bivariate LBL-BC). Specifically, we use a latent variable to induce correlation between the two phenotypes. We carry out extensive simulations to investigate bivariate LBL-BC and compare it with univariate LBL and bivariate LBL-2B. In most settings, bivariate LBL-BC performs the best. In only two situations, bivariate LBL-BC has similar performance-when the two phenotypes are (1) weakly or not correlated and the target haplotype affects the binary phenotype only and (2) strongly positively correlated and the target haplotype affects both phenotypes in positive direction. Finally, we apply the method to a data set on lung cancer and nicotine dependence and detect several haplotypes including a rare one.
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Affiliation(s)
- Xiaochen Yuan
- Department of Mathematical Sciences, University of Texas at Dallas, Richardson, Texas, USA
| | - Swati Biswas
- Department of Mathematical Sciences, University of Texas at Dallas, Richardson, Texas, USA
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7
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Huang J, Pallotti S, Zhou Q, Kleber M, Xin X, King DA, Napolioni V. PERHAPS: Paired-End short Reads-based HAPlotyping from next-generation Sequencing data. Brief Bioinform 2020; 22:6025504. [PMID: 33285565 DOI: 10.1093/bib/bbaa320] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2020] [Revised: 10/11/2020] [Accepted: 10/19/2020] [Indexed: 11/13/2022] Open
Abstract
The identification of rare haplotypes may greatly expand our knowledge in the genetic architecture of both complex and monogenic traits. To this aim, we developed PERHAPS (Paired-End short Reads-based HAPlotyping from next-generation Sequencing data), a new and simple approach to directly call haplotypes from short-read, paired-end Next Generation Sequencing (NGS) data. To benchmark this method, we considered the APOE classic polymorphism (*1/*2/*3/*4), since it represents one of the best examples of functional polymorphism arising from the haplotype combination of two Single Nucleotide Polymorphisms (SNPs). We leveraged the big Whole Exome Sequencing (WES) and SNP-array data obtained from the multi-ethnic UK BioBank (UKBB, N=48,855). By applying PERHAPS, based on piecing together the paired-end reads according to their FASTQ-labels, we extracted the haplotype data, along with their frequencies and the individual diplotype. Concordance rates between WES directly called diplotypes and the ones generated through statistical pre-phasing and imputation of SNP-array data are extremely high (>99%), either when stratifying the sample by SNP-array genotyping batch or self-reported ethnic group. Hardy-Weinberg Equilibrium tests and the comparison of obtained haplotype frequencies with the ones available from the 1000 Genome Project further supported the reliability of PERHAPS. Notably, we were able to determine the existence of the rare APOE*1 haplotype in two unrelated African subjects from UKBB, supporting its presence at appreciable frequency (approximatively 0.5%) in the African Yoruba population. Despite acknowledging some technical shortcomings, PERHAPS represents a novel and simple approach that will partly overcome the limitations in direct haplotype calling from short read-based sequencing.
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Affiliation(s)
- Jie Huang
- Department of Global Health, School of Public Health, Peking University, Beijing, China
| | - Stefano Pallotti
- Genetics and Animal Breeding Group, School of Pharmacy, University of Camerino, Italy
| | - Qianling Zhou
- Department of Maternal and Child Health, School of Public Health, Peking University, Beijing, China
| | - Marcus Kleber
- Department of Medicine, Medical Faculty of Mannheim, University of Heidelberg, Mannheim, Germany and at SYNLAB MVZ Humangenetik Mannheim, Mannheim, Germany
| | | | - Daniel A King
- Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Valerio Napolioni
- School of Biosciences and Veterinary Medicine, University of Camerino, Camerino, Italy
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8
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Yuan X, Biswas S. Bivariate logistic Bayesian LASSO for detecting rare haplotype association with two correlated phenotypes. Genet Epidemiol 2019; 43:996-1017. [PMID: 31544985 DOI: 10.1002/gepi.22258] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2019] [Revised: 07/31/2019] [Accepted: 08/09/2019] [Indexed: 11/08/2022]
Abstract
In genetic association studies, joint modeling of related traits/phenotypes can utilize the correlation between them and thereby provide more power and uncover additional information about genetic etiology. Moreover, detecting rare genetic variants are of current scientific interest as a key to missing heritability. Logistic Bayesian LASSO (LBL) has been proposed recently to detect rare haplotype variants using case-control data, that is, a single binary phenotype. As there is currently no haplotype association method that can handle multiple binary phenotypes, we extend LBL to fill this gap. We develop a bivariate model by using a latent variable to induce correlation between the two outcomes. We carry out extensive simulations to investigate the bivariate LBL and compare with the univariate LBL. The bivariate LBL performs better or similar to the univariate LBL in most settings. It has the highest gain in power when a haplotype is associated with both traits and it affects at least one trait in a direction opposite to the direction of the correlation between the traits. We analyze two data sets-Genetic Analysis Workshop 19 sequence data on systolic and diastolic blood pressures and a genome-wide association data set on lung cancer and smoking and detect several associated rare haplotypes.
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Affiliation(s)
- Xiaochen Yuan
- Department of Mathematical Sciences, University of Texas at Dallas, Richardson, Texas
| | - Swati Biswas
- Department of Mathematical Sciences, University of Texas at Dallas, Richardson, Texas
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9
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Papachristou C, Biswas S. Comparison of haplotype-based tests for detecting gene-environment interactions with rare variants. Brief Bioinform 2019; 21:851-862. [PMID: 31329820 DOI: 10.1093/bib/bbz031] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2018] [Revised: 02/06/2019] [Accepted: 02/28/2019] [Indexed: 11/13/2022] Open
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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Affiliation(s)
| | - Swati Biswas
- Department of Mathematical Sciences, University of Texas at Dallas, Richardson, TX, USA
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Datta AS, Lin S, Biswas S. A Family-Based Rare Haplotype Association Method for Quantitative Traits. Hum Hered 2019; 83:175-195. [PMID: 30799419 DOI: 10.1159/000493543] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2018] [Accepted: 09/07/2018] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND The variants identified in genome-wide association studies account for only a small fraction of disease heritability. A key to this "missing heritability" is believed to be rare variants. Specifically, we focus on rare haplotype variant (rHTV). The existing methods for detecting rHTV are mostly population-based, and as such, are susceptible to population stratification and admixture, leading to an inflated false-positive rate. Family-based methods are more robust in this respect. METHODS We propose a method for detecting rHTVs associated with quantitative traits called family-based quantitative Bayesian LASSO (famQBL). FamQBL can analyze any type of pedigree and is based on a mixed model framework. We regularize the haplotype effects using Bayesian LASSO and estimate the posterior distributions using Markov chain Monte Carlo methods. RESULTS We conduct simulation studies, including analyses of Genetic Analysis Workshop 18 simulated data, to study the properties of famQBL and compare with a standard family-based haplotype association test implemented in FBAT (family-based association test) software. We find famQBL to be more powerful than FBAT with well-controlled false-positive rates. We also apply famQBL to the Framingham Heart Study data and detect an rHTV associated with diastolic blood pressure. CONCLUSION FamQBL can help uncover rHTVs associated with quantitative traits.
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Affiliation(s)
- Ananda S Datta
- Department of Mathematical Sciences, University of Texas at Dallas, Richardson, Texas, USA
| | - Shili Lin
- Department of Statistics, The Ohio State University, Columbus, Ohio, USA
| | - Swati Biswas
- Department of Mathematical Sciences, University of Texas at Dallas, Richardson, Texas, USA,
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Affiliation(s)
| | - Philip Erhart
- Department of Vascular and Endovascular Surgery, University Hospital Heidelberg, Heidelberg, Germany
| | - Bowang Chen
- Department of Biology, Southern University of Science and Technology, Shenzhen, China
| | - Manja Kloss
- Department of Neurology, University Hospital Heidelberg, Heidelberg, Germany
| | - Stefan T. Engelter
- Neurorehabilitation Unit, University of Basel and University Center for Medicine of Aging, Felix Platter Hospital, Basel, Switzerland
- Department of Neurology and Stroke Center, University Hospital Basel, Basel, Switzerland
| | - John W. Cole
- Department of Neurology, Veterans Affairs Medical Center and University of Maryland School of Medicine, Baltimore, USA
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12
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Zhou X, Wang M, Zhang H, Stewart WCL, Lin S. Logistic Bayesian LASSO for detecting association combining family and case-control data. BMC Proc 2018; 12:54. [PMID: 30263052 PMCID: PMC6156907 DOI: 10.1186/s12919-018-0139-4] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022] Open
Abstract
Because of the limited information from the GAW20 samples when only case-control or trio data are considered, we propose eLBL, an extension of the Logistic Bayesian LASSO (least absolute shrinkage and selection operator) methodology so that both types of data can be analyzed jointly in the hope of obtaining an increased statistical power, especially for detecting association between rare haplotypes and complex diseases. The methodology is further extended to account for familial correlation among the case-control individuals and the trios. A 2-step analysis strategy was taken to first perform a genome-wise single single-nucleotide polymorphism (SNP) search using the Monte Carlo pedigree disequilibrium test (MCPDT) to determine interesting regions for the Adult Treatment Panel (ATP) binary trait. Then eLBL was applied to haplotype blocks covering the flagged SNPs in Step 1. Several significantly associated haplotypes were identified; most are in blocks contained in protein coding genes that appear to be relevant for metabolic syndrome. The results are further substantiated with a Type I error study and by an additional analysis using the triglyceride measurements directly as a quantitative trait.
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Affiliation(s)
- Xiaofei Zhou
- 1Department of Statistics, The Ohio State University, 1958 Neil Avenue, Columbus, OH 43210 USA
| | - Meng Wang
- 2Battelle Center for Mathematical Medicine, Nationwide Children's Hospital Research Institute, 700 Childrens Drive, Columbus, OH 43205 USA
| | - Han Zhang
- 1Department of Statistics, The Ohio State University, 1958 Neil Avenue, Columbus, OH 43210 USA
| | - William C L Stewart
- 1Department of Statistics, The Ohio State University, 1958 Neil Avenue, Columbus, OH 43210 USA.,2Battelle Center for Mathematical Medicine, Nationwide Children's Hospital Research Institute, 700 Childrens Drive, Columbus, OH 43205 USA
| | - Shili Lin
- 1Department of Statistics, The Ohio State University, 1958 Neil Avenue, Columbus, OH 43210 USA
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Zhang Y, Hofmann JN, Purdue MP, Lin S, Biswas S. Logistic Bayesian LASSO for genetic association analysis of data from complex sampling designs. J Hum Genet 2017; 62:819-829. [PMID: 28424482 PMCID: PMC5572548 DOI: 10.1038/jhg.2017.43] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2016] [Revised: 03/21/2017] [Accepted: 03/22/2017] [Indexed: 01/20/2023]
Abstract
Detecting gene-environment interactions with rare variants is critical in dissecting the etiology of common diseases. Interactions with rare haplotype variants (rHTVs) are of particular interest. At the same time, complex sampling designs, such as stratified random sampling, are becoming increasingly popular for designing case-control studies, especially for recruiting controls. The US Kidney Cancer Study (KCS) is an example, wherein all available cases were included while the controls at each site were randomly selected from the population by frequency matching with cases based on age, sex and race. There is currently no rHTV association method that can account for such a complex sampling design. To fill this gap, we consider logistic Bayesian LASSO (LBL), an existing rHTV approach for case-control data, and show that its model can easily accommodate the complex sampling design. We study two extensions that include stratifying variables either as main effects only or with additional modeling of their interactions with haplotypes. We conduct extensive simulation studies to compare the complex sampling methods with the original LBL methods. We find that, when there is no interaction between haplotype and stratifying variables, both extensions perform well while the original LBL methods lead to inflated type I error rates. However, when such an interaction exists, it is necessary to include the interaction effect in the model to control the type I error rate. Finally, we analyze the KCS data and find a significant interaction between (current) smoking and a specific rHTV in the N-acetyltransferase 2 gene.
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Affiliation(s)
- Yuan Zhang
- Department of Mathematical Sciences, University of Texas at Dallas, Richardson, TX, USA
| | - Jonathan N Hofmann
- Occupational and Environmental Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
| | - Mark P Purdue
- Occupational and Environmental Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
| | - Shili Lin
- Department of Statistics, The Ohio State University, Columbus, OH, USA
| | - Swati Biswas
- Department of Mathematical Sciences, University of Texas at Dallas, Richardson, TX, USA
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14
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Datta AS, Zhang Y, Zhang L, Biswas S. Association of rare haplotypes on ULK4 and MAP4 genes with hypertension. BMC Proc 2016; 10:363-369. [PMID: 27980663 PMCID: PMC5133474 DOI: 10.1186/s12919-016-0057-2] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023] Open
Abstract
Several variants have been implicated earlier on ULK4 and MAP4 genes on chromosome 3 to be associated with hypertension. As a natural follow-up step, we explore association of haplotypes in those genes. We consider the Genetic Analysis Workshop 19 real data on unrelated individuals and analyze haplotype blocks of 5 single-nucleotide polymorphisms through a sliding window approach. We apply 4 haplotype association methods-haplo.score, haplo.glm, hapassoc, and logistic Bayesian LASSO (LBL)-and for comparison, sequence kernel association test (SKAT) and its variants. We find several rare haplotype blocks to be associated. To get an idea about the false-positive proportions, we also analyzed the data after permuting the case-control status of individuals. We found that LBL, unlike the other methods, maintains low false-positive rates in presence of rare haplotypes. Thus, we conclude that the haplotypes found to be associated by LBL are more likely to be true positive. SKAT and its variants did not find significance on either gene.
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Affiliation(s)
- Ananda S. Datta
- Department of Mathematical Sciences, University of Texas at Dallas, Richardson, TX USA
| | - Yuan Zhang
- Department of Mathematical Sciences, University of Texas at Dallas, Richardson, TX USA
| | - Lei Zhang
- Department of Mathematical Sciences, University of Texas at Dallas, Richardson, TX USA
| | - Swati Biswas
- Department of Mathematical Sciences, University of Texas at Dallas, Richardson, TX USA
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15
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Zhang Y, Lin S, Biswas S. Detecting rare and common haplotype-environment interaction under uncertainty of gene-environment independence assumption. Biometrics 2016; 73:344-355. [PMID: 27478935 DOI: 10.1111/biom.12567] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2015] [Revised: 05/01/2016] [Accepted: 06/01/2016] [Indexed: 11/28/2022]
Abstract
Finding rare variants and gene-environment interactions (GXE) is critical in dissecting complex diseases. We consider the problem of detecting GXE where G is a rare haplotype and E is a nongenetic factor. Such methods typically assume G-E independence, which may not hold in many applications. A pertinent example is lung cancer-there is evidence that variants on Chromosome 15q25.1 interact with smoking to affect the risk. However, these variants are associated with smoking behavior rendering the assumption of G-E independence inappropriate. With the motivation of detecting GXE under G-E dependence, we extend an existing approach, logistic Bayesian LASSO, which assumes G-E independence (LBL-GXE-I) by modeling G-E dependence through a multinomial logistic regression (referred to as LBL-GXE-D). Unlike LBL-GXE-I, LBL-GXE-D controls type I error rates in all situations; however, it has reduced power when G-E independence holds. To control type I error without sacrificing power, we further propose a unified approach, LBL-GXE, to incorporate uncertainty in the G-E independence assumption by employing a reversible jump Markov chain Monte Carlo method. Our simulations show that LBL-GXE has power similar to that of LBL-GXE-I when G-E independence holds, yet has well-controlled type I errors in all situations. To illustrate the utility of LBL-GXE, we analyzed a lung cancer dataset and found several significant interactions in the 15q25.1 region, including one between a specific rare haplotype and smoking.
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Affiliation(s)
- Yuan Zhang
- Department of Mathematical Sciences, University of Texas at Dallas, Richardson, Texas 75080, U.S.A
| | - Shili Lin
- Department of Statistics, The Ohio State University, Columbus, Ohio 43210, U.S.A
| | - Swati Biswas
- Department of Mathematical Sciences, University of Texas at Dallas, Richardson, Texas 75080, U.S.A
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Datta AS, Biswas S. Comparison of haplotype-based statistical tests for disease association with rare and common variants. Brief Bioinform 2015; 17:657-71. [PMID: 26338417 DOI: 10.1093/bib/bbv072] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2015] [Indexed: 01/26/2023] Open
Abstract
Recent literature has highlighted the advantages of haplotype association methods for detecting rare variants associated with common diseases. As several new haplotype association methods have been proposed in the past few years, a comparison of new and standard methods is important and timely for guidance to the practitioners. We consider nine methods-Haplo.score, Haplo.glm, Hapassoc, Bayesian hierarchical Generalized Linear Model (BhGLM), Logistic Bayesian LASSO (LBL), regularized GLM (rGLM), Haplotype Kernel Association Test, wei-SIMc-matching and Weighted Haplotype and Imputation-based Tests. These can be divided into two types-individual haplotype-specific tests and global tests depending on whether there is just one overall test for a haplotype region (global) or there is an individual test for each haplotype in the region. Haplo.score is the only method that tests for both; Haplo.glm, Hapassoc, BhGLM and LBL are individual haplotype-specific, while the rest are global tests. For comparison, we also apply a popular collapsing method-Sequence Kernel Association Test (SKAT) and its two variants-SKAT-O (Optimal) and SKAT-C (Combined). We carry out an extensive comparison on our simulated data sets as well as on the Genetic Analysis Workshop (GAW) 18 simulated data. Further, we apply the methods to GAW18 real hypertension data and Dallas Heart Study sequence data. We find that LBL, Haplo.score (global test) and rGLM perform well over the scenarios considered here. Also, haplotype methods are more powerful (albeit more computationally intensive) than SKAT and its variants in scenarios where multiple causal variants act interactively to produce haplotype effects.
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