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Gómez-Sánchez G, Alonso L, Pérez MÁ, Morán I, Torrents D, Berral JL. Exhaustive Variant Interaction Analysis using Multifactor Dimensionality Reduction. RESEARCH SQUARE 2023:rs.3.rs-3401025. [PMID: 37886566 PMCID: PMC10602162 DOI: 10.21203/rs.3.rs-3401025/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/28/2023]
Abstract
One of the main goals of human genetics is to understand the connections between genomic variation and the predisposition to develop a complex disorder. These disease-variant associations are usually studied in a single independent manner, disregarding the possible effect derived from the interaction between genomic variants. In particular, in a background of complex diseases, these interactions can be directly linked to the disorder and may play an important role in disease development. Although their study has been suggested to help to complete the understanding of the genetic bases of complex diseases, this still represents a big challenge due to large computing demands. Here, we have taken advantage of High-Performance Computing technologies to tackle this problem using a combination of machine learning methods and statistical approaches. As a result, we have created a containerized framework that uses Multifactor Dimensionality Reduction to detect pairs of variants associated with Type 2 Diabetes (T2D). This methodology has been tested in the Northwestern University NUgene project cohort using a dataset of 1,883,192 variant pairs with a certain degree of association with T2D. Out of the pairs studied, we have identified 104 significant pairs, two of which exhibit a potential functional relationship with T2D.
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Affiliation(s)
- Gonzalo Gómez-Sánchez
- Barcelona Supercomputing Center (BSC), Barcelona, Spain
- Universitat Politècnica de Catalunya - BarcelonaTECH, Barcelona, Spain
| | - Lorena Alonso
- Barcelona Supercomputing Center (BSC), Barcelona, Spain
| | | | - Ignasi Morán
- Barcelona Supercomputing Center (BSC), Barcelona, Spain
| | - David Torrents
- Barcelona Supercomputing Center (BSC), Barcelona, Spain
- Institut Català de Recerca i Estudis Avançats, Barcelona, Spain
| | - Josep Ll. Berral
- Barcelona Supercomputing Center (BSC), Barcelona, Spain
- Universitat Politècnica de Catalunya - BarcelonaTECH, Barcelona, Spain
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Yang CH, Hou MF, Chuang LY, Yang CS, Lin YD. Dimensionality reduction approach for many-objective epistasis analysis. Brief Bioinform 2023; 24:6858949. [PMID: 36458451 DOI: 10.1093/bib/bbac512] [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: 06/07/2022] [Revised: 10/07/2022] [Accepted: 10/26/2022] [Indexed: 12/04/2022] Open
Abstract
In epistasis analysis, single-nucleotide polymorphism-single-nucleotide polymorphism interactions (SSIs) among genes may, alongside other environmental factors, influence the risk of multifactorial diseases. To identify SSI between cases and controls (i.e. binary traits), the score for model quality is affected by different objective functions (i.e. measurements) because of potential disease model preferences and disease complexities. Our previous study proposed a multiobjective approach-based multifactor dimensionality reduction (MOMDR), with the results indicating that two objective functions could enhance SSI identification with weak marginal effects. However, SSI identification using MOMDR remains a challenge because the optimal measure combination of objective functions has yet to be investigated. This study extended MOMDR to the many-objective version (i.e. many-objective MDR, MaODR) by integrating various disease probability measures based on a two-way contingency table to improve the identification of SSI between cases and controls. We introduced an objective function selection approach to determine the optimal measure combination in MaODR among 10 well-known measures. In total, 6 disease models with and 40 disease models without marginal effects were used to evaluate the general algorithms, namely those based on multifactor dimensionality reduction, MOMDR and MaODR. Our results revealed that the MaODR-based three objective function model, correct classification rate, likelihood ratio and normalized mutual information (MaODR-CLN) exhibited the higher 6.47% detection success rates (Accuracy) than MOMDR and higher 17.23% detection success rates than MDR through the application of an objective function selection approach. In a Wellcome Trust Case Control Consortium, MaODR-CLN successfully identified the significant SSIs (P < 0.001) associated with coronary artery disease. We performed a systematic analysis to identify the optimal measure combination in MaODR among 10 objective functions. Our combination detected SSIs-based binary traits with weak marginal effects and thus reduced spurious variables in the score model. MOAI is freely available at https://sites.google.com/view/maodr/home.
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Affiliation(s)
- Cheng-Hong Yang
- Department of Information Management at the Tainan University of Technology, and at the Department of Electronic Engineering at National Kaohsiung of Science and Technology, Taiwan.,Biomedical Engineering, Kaohsiung Medical University, Taiwan
| | - Ming-Feng Hou
- Kaohsiung Medical University Hospital, and Professor at the Department of Surgery, College of Medicine, Kaohsiung Medical University, Kaohsiung, Taiwan
| | - Li-Yeh Chuang
- Department of Chemical Engineering & Institute of Biotechnology and Chemical Engineering at I-Shou University, Taiwan
| | - Cheng-San Yang
- Department of Plastic Surgery, and serves as the Medical Matters Secretary of Chia-Yi Christian Hospital, Taiwan
| | - Yu-Da Lin
- Department of Computer Science and Information Engineering, and at the National Penghu University of Science and Technology, Taiwan
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Yang CH, Huang HC, Hou MF, Chuang LY, Lin YD. Fuzzy-Based Multiobjective Multifactor Dimensionality Reduction for Epistasis Analysis. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023; 20:378-387. [PMID: 35061588 DOI: 10.1109/tcbb.2022.3144303] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
Epistasis detection is vital for understanding disease susceptibility in genetics. Multiobjective multifactor dimensionality reduction (MOMDR) was previously proposed to detect epistasis. MOMDR was performed using binary classification to distinguish the high-risk (H) and low-risk (L) groups to reduce multifactor dimensionality. However, the binary classification does not reflect the uncertainty of the H and L classification. In this study, we proposed an empirical fuzzy MOMDR (EFMOMDR) to address the limitations of binary classification using the degree of membership through an empirical fuzzy approach. The EFMOMDR can simultaneously consider two incorporated fuzzy-based measures, including correct classification rate and likelihood rate, and does not require parameter tuning. Simulation studies revealed that EFMOMDR has higher 7.14% detection success rates than MOMDR, indicating that the limitations of binary classification of MOMDR have been successfully improved by empirical fuzzy. Moreover, EFMOMDR was used to analyze coronary artery disease in the Wellcome Trust Case Control Consortium dataset.
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Lin YD, Lee YC, Chiang CP, Moi SH, Kan JY. MOAI: a multi-outcome interaction identification approach reveals an interaction between vaspin and carcinoembryonic antigen on colorectal cancer prognosis. Brief Bioinform 2021; 23:6398687. [PMID: 34661627 DOI: 10.1093/bib/bbab427] [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: 07/09/2021] [Revised: 09/14/2021] [Accepted: 09/18/2021] [Indexed: 11/12/2022] Open
Abstract
Identifying and characterizing the interaction between risk factors for multiple outcomes (multi-outcome interaction) has been one of the greatest challenges faced by complex multifactorial diseases. However, the existing approaches have several limitations in identifying the multi-outcome interaction. To address this issue, we proposed a multi-outcome interaction identification approach called MOAI. MOAI was motivated by the limitations of estimating the interaction simultaneously occurring in multi-outcomes and by the success of Pareto set filter operator for identifying multi-outcome interaction. MOAI permits the identification for the interaction of multiple outcomes and is applicable in population-based study designs. Our experimental results exhibited that the existing approaches are not effectively used to identify the multi-outcome interaction, whereas MOAI obviously exhibited superior performance in identifying multi-outcome interaction. We applied MOAI to identify the interaction between risk factors for colorectal cancer (CRC) in both metastases and mortality prognostic outcomes. An interaction between vaspin and carcinoembryonic antigen (CEA) was found, and the interaction indicated that patients with CRC characterized by higher vaspin (≥30%) and CEA (≥5) levels could simultaneously increase both metastases and mortality risk. The immunostaining evidence revealed that determined multi-outcome interaction could effectively identify the difference between non-metastases/survived and metastases/deceased patients, which offers multi-prognostic outcome risk estimation for CRC. To our knowledge, this is the first report of a multi-outcome interaction associated with a complex multifactorial disease. MOAI is freely available at https://sites.google.com/view/moaitool/home.
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Affiliation(s)
- Yu-Da Lin
- Department of Computer Science and Information Engineering, National Penghu University of Science and Technology, Magong, Penghu, 880011, Taiwan
| | - Yi-Chen Lee
- Department of Anatomy at Kaohsiung Medical University, Taiwan
| | - Chih-Po Chiang
- Division of Breast Oncology and Surgery, Department of Surgery, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung 80756, Taiwan
| | - Sin-Hua Moi
- Center of Cancer Program Development, E-Da Cancer Hospital, I-Shou University, Kaohsiung 824, Taiwan
| | - Jung-Yu Kan
- Division of Breast Oncology and Surgery, Department of Surgery, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung 80756, Taiwan
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An Empirical-Characteristic-Function-Based Change-Point Test for Detection of Multiple Distributional Changes. JOURNAL OF STATISTICAL THEORY AND PRACTICE 2021. [DOI: 10.1007/s42519-021-00170-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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Vasilopoulou C, Morris AP, Giannakopoulos G, Duguez S, Duddy W. What Can Machine Learning Approaches in Genomics Tell Us about the Molecular Basis of Amyotrophic Lateral Sclerosis? J Pers Med 2020; 10:E247. [PMID: 33256133 PMCID: PMC7712791 DOI: 10.3390/jpm10040247] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2020] [Revised: 11/21/2020] [Accepted: 11/23/2020] [Indexed: 02/07/2023] Open
Abstract
Amyotrophic Lateral Sclerosis (ALS) is the most common late-onset motor neuron disorder, but our current knowledge of the molecular mechanisms and pathways underlying this disease remain elusive. This review (1) systematically identifies machine learning studies aimed at the understanding of the genetic architecture of ALS, (2) outlines the main challenges faced and compares the different approaches that have been used to confront them, and (3) compares the experimental designs and results produced by those approaches and describes their reproducibility in terms of biological results and the performances of the machine learning models. The majority of the collected studies incorporated prior knowledge of ALS into their feature selection approaches, and trained their machine learning models using genomic data combined with other types of mined knowledge including functional associations, protein-protein interactions, disease/tissue-specific information, epigenetic data, and known ALS phenotype-genotype associations. The importance of incorporating gene-gene interactions and cis-regulatory elements into the experimental design of future ALS machine learning studies is highlighted. Lastly, it is suggested that future advances in the genomic and machine learning fields will bring about a better understanding of ALS genetic architecture, and enable improved personalized approaches to this and other devastating and complex diseases.
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Affiliation(s)
- Christina Vasilopoulou
- Northern Ireland Centre for Stratified Medicine, Altnagelvin Hospital Campus, Ulster University, Londonderry BT47 6SB, UK; (C.V.); (S.D.)
| | - Andrew P. Morris
- Centre for Genetics and Genomics Versus Arthritis, Centre for Musculoskeletal Research, Manchester Academic Health Science Centre, University of Manchester, Manchester M13 9PT, UK;
| | - George Giannakopoulos
- Institute of Informatics and Telecommunications, NCSR Demokritos, 153 10 Aghia Paraskevi, Greece;
- Science For You (SciFY) PNPC, TEPA Lefkippos-NCSR Demokritos, 27, Neapoleos, 153 41 Ag. Paraskevi, Greece
| | - Stephanie Duguez
- Northern Ireland Centre for Stratified Medicine, Altnagelvin Hospital Campus, Ulster University, Londonderry BT47 6SB, UK; (C.V.); (S.D.)
| | - William Duddy
- Northern Ireland Centre for Stratified Medicine, Altnagelvin Hospital Campus, Ulster University, Londonderry BT47 6SB, UK; (C.V.); (S.D.)
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Yang CH, Moi SH, Chuang LY, Chen JB. Higher-order clinical risk factor interaction analysis for overall mortality in maintenance hemodialysis patients. Ther Adv Chronic Dis 2020; 11:2040622320949060. [PMID: 33062235 PMCID: PMC7534064 DOI: 10.1177/2040622320949060] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2019] [Accepted: 07/20/2020] [Indexed: 01/21/2023] Open
Abstract
BACKGROUND AND AIMS In Taiwan, approximately 90% of patients with end-stage renal disease receive maintenance hemodialysis. Although studies have reported the survival predictability of multiclinical factors, the higher-order interactions among these factors have rarely been discussed. Conventional statistical approaches such as regression analysis are inadequate for detecting higher-order interactions. Therefore, this study integrated receiver operating characteristic, logistic regression, and balancing functions for adjusting the ratio in risk classes and classification errors for imbalanced cases and controls using multifactor-dimensionality reduction (MDR-ER) analyses to examine the impact of interaction effects between multiclinical factors on overall mortality in patients on maintenance hemodialysis. METERIALS AND METHODS In total, 781 patients who received outpatient hemodialysis dialysis three times per week before 1 January 2009 were included; their baseline clinical factor and mortality outcome data were retrospectively collected using an approved data protocol (201800595B0). RESULTS Consistent with conventional statistical approaches, the higher-order interaction model could indicate the impact of potential risk combination unique to patients on maintenance hemodialysis on the survival outcome, as described previously. Moreover, the MDR-based higher-order interaction model facilitated higher-order interaction effect detection among multiclinical factors and could determine more detailed mortality risk characteristics combinations. CONCLUSION Therefore, higher-order clinical risk interaction analysis is a reasonable strategy for detecting non-traditional risk factor interaction effects on survival outcome unique to patients on maintenance hemodialysis and thus clinically achieving whole-scale patient care.
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Affiliation(s)
- Cheng-Hong Yang
- Department of Electronic Engineering, National Kaohsiung University of Applied Sciences, Kaohsiung Ph.D. Program in Biomedical Engineering, Kaohsiung Medical University, Kaohsiung Drug Development and Value Creation Research Center, Kaohsiung Medical University, Kaohsiung
| | - Sin-Hua Moi
- Department of Chemical Engineering and Institute of Biotechnology and Chemical Engineering, I-Shou University, Kaohsiung
| | - Li-Yeh Chuang
- Department of Chemical Engineering and Institute of Biotechnology and Chemical Engineering, I-Shou University, Kaohsiung 84004
| | - Jin-Bor Chen
- Division of Nephrology, Department of Internal Medicine, Kaohsiung Chang Gung Memorial Hospital and Chang Gung University College of Medicine, 123 DaPei Rd, Niao Song Dist, Kaohsiung 83301
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Yang CH, Chuang LY, Lin YD. An improved fuzzy set-based multifactor dimensionality reduction for detecting epistasis. Artif Intell Med 2020; 102:101768. [PMID: 31980105 DOI: 10.1016/j.artmed.2019.101768] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2018] [Revised: 10/18/2019] [Accepted: 11/19/2019] [Indexed: 01/07/2023]
Abstract
OBJECTIVE Epistasis identification is critical for determining susceptibility to human genetic diseases. The rapid development of technology has enabled scalability to make multifactor dimensionality reduction (MDR) measurements an effective calculation tool that achieves superior detection. However, the classification of high-risk (H) or low-risk (L) groups in multidrug resistance operations calls for extensive research. METHODS AND MATERIAL In this study, an improved fuzzy sigmoid (FS) method using the membership degree in MDR (FSMDR) was proposed for solving the limitations of binary classification. The FS method combined with MDR measurements yielded an improved ability to distinguish similar frequencies of potential multifactor genotypes. RESULTS We compared our results with other MDR-based methods and FSMDR achieved superior detection rates on simulated data sets. The results indicated that the fuzzy classifications can provide insight into the uncertainty of H/L classification in MDR operation. CONCLUSION FSMDR successfully detected significant epistasis of coronary artery disease in the Wellcome Trust Case Control Consortium data set.
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Affiliation(s)
- Cheng-Hong Yang
- Department of Electronic Engineering, National Kaohsiung University of Science and Technology, No. 415, Jiangong Rd., Sanmin Dist., Kaohsiung City, 80778, Taiwan; Ph. D. Program in Biomedical Engineering, Kaohsiung Medical University, No. 100, Shih-Chuan 1st Rd., Kaohsiung, 80708, Taiwan.
| | - Li-Yeh Chuang
- Department of Chemical Engineering & Institute of Biotechnology and Chemical Engineering, I-Shou University, No.1, Sec. 1, Syuecheng Rd., Dashu District, Kaohsiung, 84001, Taiwan.
| | - Yu-Da Lin
- Department of Electronic Engineering, National Kaohsiung University of Science and Technology, No. 415, Jiangong Rd., Sanmin Dist., Kaohsiung City, 80778, Taiwan.
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Yang CH, Lin YD, Chuang LY. Class Balanced Multifactor Dimensionality Reduction to Detect Gene-Gene Interactions. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2020; 17:71-81. [PMID: 30040653 DOI: 10.1109/tcbb.2018.2858776] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
Detecting gene-gene interactions in single-nucleotide polymorphism data is vital for understanding disease susceptibility. However, existing approaches may be limited by the sample size in case-control studies. Herein, we propose a balance approach for the multifactor dimensionality reduction (BMDR) method to increase the accuracy of estimates of the prediction error rate in small samples. BMDR explicitly selects the best model by evaluating the average of prediction error rates over k-fold cross-validation without cross-validation consistency selection. In this study, we used several epistatic models with and without marginal effects under different parameter settings (heritability and minor allele frequencies) to evaluate the performance of existing approaches. Using simulated data sets, BMDR successfully detected gene-gene interactions, particularly for data sets with small sample sizes. A large data set was obtained from the Wellcome Trust Case Control Consortium, and results indicated that BMDR could effectively detect significant gene-gene interactions.
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Yang CH, Chuang LY, Lin YD. Multiobjective multifactor dimensionality reduction to detect SNP-SNP interactions. Bioinformatics 2019; 34:2228-2236. [PMID: 29471406 DOI: 10.1093/bioinformatics/bty076] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2017] [Accepted: 02/16/2018] [Indexed: 11/12/2022] Open
Abstract
Motivation Single-nucleotide polymorphism (SNP)-SNP interactions (SSIs) are popular markers for understanding disease susceptibility. Multifactor dimensionality reduction (MDR) can successfully detect considerable SSIs. Currently, MDR-based methods mainly adopt a single-objective function (a single measure based on contingency tables) to detect SSIs. However, generally, a single-measure function might not yield favorable results due to potential model preferences and disease complexities. Approach This study proposes a multiobjective MDR (MOMDR) method that is based on a contingency table of MDR as an objective function. MOMDR considers the incorporated measures, including correct classification and likelihood rates, to detect SSIs and adopts set theory to predict the most favorable SSIs with cross-validation consistency. MOMDR enables simultaneously using multiple measures to determine potential SSIs. Results Three simulation studies were conducted to compare the detection success rates of MOMDR and single-objective MDR (SOMDR), revealing that MOMDR had higher detection success rates than SOMDR. Furthermore, the Wellcome Trust Case Control Consortium dataset was analyzed by MOMDR to detect SSIs associated with coronary artery disease. Availability and implementation: MOMDR is freely available at https://goo.gl/M8dpDg. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Cheng-Hong Yang
- Department of Electronic Engineering, National Kaohsiung University of Applied Sciences, Kaohsiung, Taiwan.,Graduate Institute of Clinical Medicine, Kaohsiung Medical University, Kaohsiung, Taiwan
| | - Li-Yeh Chuang
- Department of Chemical Engineering and Institute of Biotechnology and Chemical Engineering, I-Shou University, Kaohsiung, Taiwan
| | - Yu-Da Lin
- Department of Electronic Engineering, National Kaohsiung University of Applied Sciences, Kaohsiung, Taiwan
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Van Steen K, Moore JH. How to increase our belief in discovered statistical interactions via large-scale association studies? Hum Genet 2019; 138:293-305. [PMID: 30840129 PMCID: PMC6483943 DOI: 10.1007/s00439-019-01987-w] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2018] [Accepted: 02/20/2019] [Indexed: 12/31/2022]
Abstract
The understanding that differences in biological epistasis may impact disease risk, diagnosis, or disease management stands in wide contrast to the unavailability of widely accepted large-scale epistasis analysis protocols. Several choices in the analysis workflow will impact false-positive and false-negative rates. One of these choices relates to the exploitation of particular modelling or testing strategies. The strengths and limitations of these need to be well understood, as well as the contexts in which these hold. This will contribute to determining the potentially complementary value of epistasis detection workflows and is expected to increase replication success with biological relevance. In this contribution, we take a recently introduced regression-based epistasis detection tool as a leading example to review the key elements that need to be considered to fully appreciate the value of analytical epistasis detection performance assessments. We point out unresolved hurdles and give our perspectives towards overcoming these.
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Affiliation(s)
- K Van Steen
- WELBIO, GIGA-R Medical Genomics-BIO3, University of Liège, Liege, Belgium.
- Department of Human Genetics, University of Leuven, Leuven, Belgium.
| | - J H Moore
- Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, USA
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Yang CH, Yang HS, Chuang LY. PBMDR: A particle swarm optimization-based multifactor dimensionality reduction for the detection of multilocus interactions. J Theor Biol 2018; 461:68-75. [PMID: 30296447 DOI: 10.1016/j.jtbi.2018.10.012] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2018] [Revised: 08/26/2018] [Accepted: 10/04/2018] [Indexed: 12/29/2022]
Abstract
Studies on multilocus interactions have mainly investigated the associations between genetic variations from the related genes and histopathological tumor characteristics in patients. However, currently, the identification and characterization of susceptibility genes for complex diseases remain a great challenge for geneticists. In this study, a particle swarm optimization (PSO)-based multifactor dimensionality reduction (MDR) approach was proposed, denoted by PBMDR. MDR was used to detect multilocus interactions based on the PSO algorithm. A test data set was simulated from the genotype frequencies of 26 SNPs from eight breast-cancer-related gene. In simulated disease models, we demonstrated that PBMDR outperforms existing global optimization algorithms in terms of its ability to explore and power to detect specific SNP-genotype combinations. In addition, the PBMDR algorithm was compared with other algorithms, including PSO and chaotic PSOs, and the results revealed that the PBMDR algorithm yielded higher accuracy and chi-square values than other algorithms did.
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Affiliation(s)
- Cheng-Hong Yang
- Department of Electronic Engineering, National Kaohsiung University of Science and Technology, No.415, Jiangong Rd., Sanmin Dist., Kaohsiung City 80778, Taiwan.; Graduate Institute of Clinical Medicine, Kaohsiung Medical University, Kaohsiung City 80708, Taiwan..
| | - Huai-Shuo Yang
- Department of Electronic Engineering, National Kaohsiung University of Science and Technology, No.415, Jiangong Rd., Sanmin Dist., Kaohsiung City 80778, Taiwan
| | - Li-Yeh Chuang
- Department of Chemical Engineering & Institute of Biotechnology and Chemical Engineering, I-Shou University, No.1, Sec. 1, Syuecheng Rd., Dashu District, Kaohsiung City 84001, Taiwan..
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Yang CH, Lin YD, Chuang LY. Multiple-Criteria Decision Analysis-Based Multifactor Dimensionality Reduction for Detecting Gene-Gene Interactions. IEEE J Biomed Health Inform 2018; 23:416-426. [PMID: 29993963 DOI: 10.1109/jbhi.2018.2790951] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Gene-gene interactions (GGIs) are important markers for determining susceptibility to a disease. Multifactor dimensionality reduction (MDR) is a popular algorithm for detecting GGIs and primarily adopts the correct classification rate (CCR) to assess the quality of a GGI. However, CCR measurement alone may not successfully detect certain GGIs because of potential model preferences and disease complexities. In this study, multiple-criteria decision analysis (MCDA) based on MDR was named MCDA-MDR and proposed for detecting GGIs. MCDA facilitates MDR to simultaneously adopt multiple measures within the two-way contingency table of MDR to assess GGIs; the CCR and rule utility measure were employed. Cross-validation consistency was adopted to determine the most favorable GGIs among the Pareto sets. Simulation studies were conducted to compare the detection success rates of the MDR-only-based measure and MCDA-MDR, revealing that MCDA-MDR had superior detection success rates. The Wellcome Trust Case Control Consortium dataset was analyzed using MCDA-MDR to detect GGIs associated with coronary artery disease, and MCDA-MDR successfully detected numerous significant GGIs (p < 0.001). MCDA-MDR performance assessment revealed that the applied MCDA successfully enhanced the GGI detection success rate of the MDR-based method compared with MDR alone.
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Ritchie MD, Van Steen K. The search for gene-gene interactions in genome-wide association studies: challenges in abundance of methods, practical considerations, and biological interpretation. ANNALS OF TRANSLATIONAL MEDICINE 2018; 6:157. [PMID: 29862246 DOI: 10.21037/atm.2018.04.05] [Citation(s) in RCA: 58] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
One of the primary goals in this era of precision medicine is to understand the biology of human diseases and their treatment, such that each individual patient receives the best possible treatment for their disease based on their genetic and environmental exposures. One way to work towards achieving this goal is to identify the environmental exposures and genetic variants that are relevant to each disease in question, as well as the complex interplay between genes and environment. Genome-wide association studies (GWAS) have allowed for a greater understanding of the genetic component of many complex traits. However, these genetic effects are largely small and thus, our ability to use these GWAS finding for precision medicine is limited. As more and more GWAS have been performed, rather than focusing only on common single nucleotide polymorphisms (SNPs) and additive genetic models, many researchers have begun to explore alternative heritable components of complex traits including rare variants, structural variants, epigenetics, and genetic interactions. While genetic interactions are a plausible reality that could explain some of the heritabliy that has not yet been identified, especially when one considers the identification of genetic interactions in model organisms as well as our understanding of biological complexity, still there are significant challenges and considerations in identifying these genetic interactions. Broadly, these can be summarized in three categories: abundance of methods, practical considerations, and biological interpretation. In this review, we will discuss these important elements in the search for genetic interactions along with some potential solutions. While genetic interactions are theoretically understood to be important for complex human disease, the body of evidence is still building to support this component of the underlying genetic architecture of complex human traits. Our hope is that more sophisticated modeling approaches and more robust computational techniques will enable the community to identify these important genetic interactions and improve our ability to implement precision medicine in the future.
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Affiliation(s)
- Marylyn D Ritchie
- Department of Genetics, University of Pennsylvania, Philadelphia, PA, USA
| | - Kristel Van Steen
- WELBIO, GIGA-R Medical Genomics Unit - BIO3, University of Liège, Liège, Belgium.,Department of Human Genetics, University of Leuven, Leuven, Belgium
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Uppu S, Krishna A, Gopalan RP. A Review on Methods for Detecting SNP Interactions in High-Dimensional Genomic Data. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2018; 15:599-612. [PMID: 28060710 DOI: 10.1109/tcbb.2016.2635125] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
In this era of genome-wide association studies (GWAS), the quest for understanding the genetic architecture of complex diseases is rapidly increasing more than ever before. The development of high throughput genotyping and next generation sequencing technologies enables genetic epidemiological analysis of large scale data. These advances have led to the identification of a number of single nucleotide polymorphisms (SNPs) responsible for disease susceptibility. The interactions between SNPs associated with complex diseases are increasingly being explored in the current literature. These interaction studies are mathematically challenging and computationally complex. These challenges have been addressed by a number of data mining and machine learning approaches. This paper reviews the current methods and the related software packages to detect the SNP interactions that contribute to diseases. The issues that need to be considered when developing these models are addressed in this review. The paper also reviews the achievements in data simulation to evaluate the performance of these models. Further, it discusses the future of SNP interaction analysis.
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Moore JH, Andrews PC, Olson RS, Carlson SE, Larock CR, Bulhoes MJ, O'Connor JP, Greytak EM, Armentrout SL. Grid-based stochastic search for hierarchical gene-gene interactions in population-based genetic studies of common human diseases. BioData Min 2017; 10:19. [PMID: 28572842 PMCID: PMC5450417 DOI: 10.1186/s13040-017-0139-3] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2016] [Accepted: 05/18/2017] [Indexed: 11/18/2022] Open
Abstract
Background Large-scale genetic studies of common human diseases have focused almost exclusively on the independent main effects of single-nucleotide polymorphisms (SNPs) on disease susceptibility. These studies have had some success, but much of the genetic architecture of common disease remains unexplained. Attention is now turning to detecting SNPs that impact disease susceptibility in the context of other genetic factors and environmental exposures. These context-dependent genetic effects can manifest themselves as non-additive interactions, which are more challenging to model using parametric statistical approaches. The dimensionality that results from a multitude of genotype combinations, which results from considering many SNPs simultaneously, renders these approaches underpowered. We previously developed the multifactor dimensionality reduction (MDR) approach as a nonparametric and genetic model-free machine learning alternative. Approaches such as MDR can improve the power to detect gene-gene interactions but are limited in their ability to exhaustively consider SNP combinations in genome-wide association studies (GWAS), due to the combinatorial explosion of the search space. We introduce here a stochastic search algorithm called Crush for the application of MDR to modeling high-order gene-gene interactions in genome-wide data. The Crush-MDR approach uses expert knowledge to guide probabilistic searches within a framework that capitalizes on the use of biological knowledge to filter gene sets prior to analysis. Here we evaluated the ability of Crush-MDR to detect hierarchical sets of interacting SNPs using a biology-based simulation strategy that assumes non-additive interactions within genes and additivity in genetic effects between sets of genes within a biochemical pathway. Results We show that Crush-MDR is able to identify genetic effects at the gene or pathway level significantly better than a baseline random search with the same number of model evaluations. We then applied the same methodology to a GWAS for Alzheimer’s disease and showed base level validation that Crush-MDR was able to identify a set of interacting genes with biological ties to Alzheimer’s disease. Conclusions We discuss the role of stochastic search and cloud computing for detecting complex genetic effects in genome-wide data.
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Affiliation(s)
- Jason H Moore
- Department of Biostatistics and Epidemiology, Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, 19104 PA USA
| | - Peter C Andrews
- Department of Biostatistics and Epidemiology, Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, 19104 PA USA
| | - Randal S Olson
- Department of Biostatistics and Epidemiology, Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, 19104 PA USA
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Yang CH, Chuang LY, Lin YD. CMDR based differential evolution identifies the epistatic interaction in genome-wide association studies. Bioinformatics 2017; 33:2354-2362. [DOI: 10.1093/bioinformatics/btx163] [Citation(s) in RCA: 39] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2016] [Accepted: 03/21/2017] [Indexed: 12/31/2022] Open
Affiliation(s)
- Cheng-Hong Yang
- Department of Electronic Engineering, National Kaohsiung University of Applied Sciences, Kaohsiung, Taiwan
- Graduate Institute of Clinical Medicine, Kaohsiung Medical University, Kaohsiung, Taiwan
| | - Li-Yeh Chuang
- Department of Chemical Engineering and Institute of Biotechnology and Chemical Engineering, I-Shou University, Kaohsiung, Taiwan
| | - Yu-Da Lin
- Department of Electronic Engineering, National Kaohsiung University of Applied Sciences, Kaohsiung, Taiwan
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18
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Abstract
BACKGROUND Detection of gene-gene interaction (GGI) is a key challenge towards solving the problem of missing heritability in genetics. The multifactor dimensionality reduction (MDR) method has been widely studied for detecting GGIs. MDR reduces the dimensionality of multi-factor by means of binary classification into high-risk (H) or low-risk (L) groups. Unfortunately, this simple binary classification does not reflect the uncertainty of H/L classification. Thus, we proposed Fuzzy MDR to overcome limitations of binary classification by introducing the degree of membership of two fuzzy sets H/L. While Fuzzy MDR demonstrated higher power than that of MDR, its performance is highly dependent on the several tuning parameters. In real applications, it is not easy to choose appropriate tuning parameter values. RESULT In this work, we propose an empirical fuzzy MDR (EF-MDR) which does not require specifying tuning parameters values. Here, we propose an empirical approach to estimating the membership degree that can be directly estimated from the data. In EF-MDR, the membership degree is estimated by the maximum likelihood estimator of the proportion of cases(controls) in each genotype combination. We also show that the balanced accuracy measure derived from this new membership function is a linear function of the standard chi-square statistics. This relationship allows us to perform the standard significance test using p-values in the MDR framework without permutation. Through two simulation studies, the power of the proposed EF-MDR is shown to be higher than those of MDR and Fuzzy MDR. We illustrate the proposed EF-MDR by analyzing Crohn's disease (CD) and bipolar disorder (BD) in the Wellcome Trust Case Control Consortium (WTCCC) dataset. CONCLUSION We propose an empirical Fuzzy MDR for detecting GGI using the maximum likelihood of the proportion of cases(controls) as the membership degree of the genotype combination. The program written in R for EF-MDR is available at http://statgen.snu.ac.kr/software/EF-MDR .
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Affiliation(s)
- Sangseob Leem
- Department of Statistics, Seoul National University, Seoul, 08826 South Korea
| | - Taesung Park
- Department of Statistics, Seoul National University, Seoul, 08826 South Korea
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19
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Xu HM, Xu LF, Hou TT, Luo LF, Chen GB, Sun XW, Lou XY. GMDR: Versatile Software for Detecting Gene-Gene and Gene-Environ- ment Interactions Underlying Complex Traits. Curr Genomics 2016; 17:396-402. [PMID: 28479868 PMCID: PMC5320543 DOI: 10.2174/1389202917666160513102612] [Citation(s) in RCA: 41] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2015] [Revised: 04/10/2015] [Accepted: 04/15/2015] [Indexed: 11/22/2022] Open
Abstract
Identification of multifactor gene-gene (G×G) and gene-environment (G×E) interactions underlying complex traits poses one of the great challenges to today’s genetic study. Development of the generalized multifactor dimensionality reduction (GMDR) method provides a practicable solution to problems in detection of interactions. To exploit the opportunities brought by the availability of diverse data, it is in high demand to develop the corresponding GMDR software that can handle a breadth of phenotypes, such as continuous, count, dichotomous, polytomous nominal, ordinal, survival and multivariate, and various kinds of study designs, such as unrelated case-control, family-based and pooled unrelated and family samples, and also allows adjustment for covariates. We developed a versatile GMDR package to implement this serial of GMDR analyses for various scenarios (e.g., unified analysis of unrelated and family samples) and large-scale (e.g., genome-wide) data. This package includes other desirable features such as data management and preprocessing. Permutation testing strategies are also built in to evaluate the threshold or empirical p values. In addition, its performance is scalable to the computational resources. The software is available at http://www.soph.uab.edu/ssg/software or http://ibi.zju.edu.cn/software.
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Affiliation(s)
- Hai-Ming Xu
- Institute of Bioinformatics and Institute of Crop Science, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou, P.R. China.,Research Center for Air Pollution and Health, Zhejiang University, Hangzhou, P.R. China
| | - Li-Feng Xu
- Institute of Computer Application Technology, College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, P.R. China
| | - Ting-Ting Hou
- Institute of Bioinformatics and Institute of Crop Science, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou, P.R. China
| | - Lin-Feng Luo
- Institute of Computer Application Technology, College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, P.R. China
| | - Guo-Bo Chen
- Queensland Brain Institute, University of Queensland, St Lucia, Queensland, Australia
| | - Xi-Wei Sun
- Sir Run Run Shaw Hospital and Institute of Translational Medicine, School of Medicine, Zhejiang University, Hangzhou, P.R. China
| | - Xiang-Yang Lou
- Department of Biostatistics and Bioinformatics, Tulane University, New Orleans, Louisiana, USA
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20
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Simon PHG, Sylvestre MP, Tremblay J, Hamet P. Key Considerations and Methods in the Study of Gene-Environment Interactions. Am J Hypertens 2016; 29:891-9. [PMID: 27037711 DOI: 10.1093/ajh/hpw021] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2015] [Accepted: 02/08/2016] [Indexed: 12/16/2022] Open
Abstract
With increased involvement of genetic data in most epidemiological investigations, gene-environment (G × E) interactions now stand as a topic, which must be meticulously assessed and thoroughly understood. The level, mode, and outcomes of interactions between environmental factors and genetic traits have the capacity to modulate disease risk. These must, therefore, be carefully evaluated as they have the potential to offer novel insights on the "missing heritability problem", reaching beyond our current limitations. First, we review a definition of G × E interactions. We then explore how concepts such as the early manifestation of the genetic components of a disease, the heterogeneity of complex traits, the clear definition of epidemiological strata, and the effect of varying physiological conditions can affect our capacity to detect (or miss) G × E interactions. Lastly, we discuss the shortfalls of regression models to study G × E interactions and how other methods such as the ReliefF algorithm, pattern recognition methods, or the LASSO (Least Absolute Shrinkage and Selection Operator) method can enable us to more adequately model G × E interactions. Overall, we present the elements to consider and a path to follow when studying genetic determinants of disease in order to uncover potential G × E interactions.
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Affiliation(s)
- Paul H G Simon
- CHUM Research Center, Centre hospitalier de l'Université de Montréal, Montréal, Québec, Canada
| | - Marie-Pierre Sylvestre
- CHUM Research Center, Centre hospitalier de l'Université de Montréal, Montréal, Québec, Canada
| | - Johanne Tremblay
- CHUM Research Center, Centre hospitalier de l'Université de Montréal, Montréal, Québec, Canada
| | - Pavel Hamet
- CHUM Research Center, Centre hospitalier de l'Université de Montréal, Montréal, Québec, Canada.
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Yang CH, Lin YD, Yen CY, Chuang LY, Chang HW. A systematic gene-gene and gene-environment interaction analysis of DNA repair genes XRCC1, XRCC2, XRCC3, XRCC4, and oral cancer risk. OMICS-A JOURNAL OF INTEGRATIVE BIOLOGY 2016; 19:238-47. [PMID: 25831063 DOI: 10.1089/omi.2014.0121] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Oral cancer is the sixth most common cancer worldwide with a high mortality rate. Biomarkers that anticipate susceptibility, prognosis, or response to treatments are much needed. Oral cancer is a polygenic disease involving complex interactions among genetic and environmental factors, which require multifaceted analyses. Here, we examined in a dataset of 103 oral cancer cases and 98 controls from Taiwan the association between oral cancer risk and the DNA repair genes X-ray repair cross-complementing group (XRCCs) 1-4, and the environmental factors of smoking, alcohol drinking, and betel quid (BQ) chewing. We employed logistic regression, multifactor dimensionality reduction (MDR), and hierarchical interaction graphs for analyzing gene-gene (G×G) and gene-environment (G×E) interactions. We identified a significantly elevated risk of the XRCC2 rs2040639 heterozygous variant among smokers [adjusted odds ratio (OR) 3.7, 95% confidence interval (CI)=1.1-12.1] and alcohol drinkers [adjusted OR=5.7, 95% CI=1.4-23.2]. The best two-factor based G×G interaction of oral cancer included the XRCC1 rs1799782 and XRCC2 rs2040639 [OR=3.13, 95% CI=1.66-6.13]. For the G×E interaction, the estimated OR of oral cancer for two (drinking-BQ chewing), three (XRCC1-XRCC2-BQ chewing), four (XRCC1-XRCC2-age-BQ chewing), and five factors (XRCC1-XRCC2-age-drinking-BQ chewing) were 32.9 [95% CI=14.1-76.9], 31.0 [95% CI=14.0-64.7], 49.8 [95% CI=21.0-117.7] and 82.9 [95% CI=31.0-221.5], respectively. Taken together, the genotypes of XRCC1 rs1799782 and XRCC2 rs2040639 DNA repair genes appear to be significantly associated with oral cancer. These were enhanced by exposure to certain environmental factors. The observations presented here warrant further research in larger study samples to examine their relevance for routine clinical care in oncology.
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Affiliation(s)
- Cheng-Hong Yang
- 1 Department of Electronic Engineering, National Kaohsiung University of Applied Sciences , Kaohsiung, Taiwan
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22
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Upton A, Trelles O, Cornejo-García JA, Perkins JR. Review: High-performance computing to detect epistasis in genome scale data sets. Brief Bioinform 2015; 17:368-79. [PMID: 26272945 DOI: 10.1093/bib/bbv058] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2015] [Indexed: 11/14/2022] Open
Abstract
It is becoming clear that most human diseases have a complex etiology that cannot be explained by single nucleotide polymorphisms (SNPs) or simple additive combinations; the general consensus is that they are caused by combinations of multiple genetic variations. The limited success of some genome-wide association studies is partly a result of this focus on single genetic markers. A more promising approach is to take into account epistasis, by considering the association of multiple SNP interactions with disease. However, as genomic data continues to grow in resolution, and genome and exome sequencing become more established, the number of combinations of variants to consider increases rapidly. Two potential solutions should be considered: the use of high-performance computing, which allows us to consider a larger number of variables, and heuristics to make the solution more tractable, essential in the case of genome sequencing. In this review, we look at different computational methods to analyse epistatic interactions within disease-related genetic data sets created by microarray technology. We also review efforts to use epistatic analysis results to produce biomarkers for diagnostic tests and give our views on future directions in this field in light of advances in sequencing technology and variants in non-coding regions.
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23
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Yang CH, Lin YD, Yang CS, Chuang LY. An efficiency analysis of high-order combinations of gene-gene interactions using multifactor-dimensionality reduction. BMC Genomics 2015; 16:489. [PMID: 26126977 PMCID: PMC4487567 DOI: 10.1186/s12864-015-1717-8] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2015] [Accepted: 06/24/2015] [Indexed: 12/21/2022] Open
Abstract
Background Multifactor dimensionality reduction (MDR) is widely used to analyze interactions of genes to determine the complex relationship between diseases and polymorphisms in humans. However, the astronomical number of high-order combinations makes MDR a highly time-consuming process which can be difficult to implement for multiple tests to identify more complex interactions between genes. This study proposes a new framework, named fast MDR (FMDR), which is a greedy search strategy based on the joint effect property. Results Six models with different minor allele frequencies (MAFs) and different sample sizes were used to generate the six simulation data sets. A real data set was obtained from the mitochondrial D-loop of chronic dialysis patients. Comparison of results from the simulation data and real data sets showed that FMDR identified significant gene–gene interaction with less computational complexity than the MDR in high-order interaction analysis. Conclusion FMDR improves the MDR difficulties associated with the computational loading of high-order SNPs and can be used to evaluate the relative effects of each individual SNP on disease susceptibility. FMDR is freely available at http://bioinfo.kmu.edu.tw/FMDR.rar. Electronic supplementary material The online version of this article (doi:10.1186/s12864-015-1717-8) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Cheng-Hong Yang
- Department of Electronic Engineering, National Kaohsiung University of Applied Sciences, Kaohsiung, Taiwan.
| | - Yu-Da Lin
- Department of Electronic Engineering, National Kaohsiung University of Applied Sciences, Kaohsiung, Taiwan.
| | - Cheng-San Yang
- Department of Plastic Surgery, Chia-Yi Christian Hospital, Chiayi, Taiwan.
| | - Li-Yeh Chuang
- Department of Chemical Engineering & Institute of Biotechnology and Chemical Engineering, I-Shou University, Kaohsiung, Taiwan.
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24
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Beam AL, Motsinger-Reif A, Doyle J. Bayesian neural networks for detecting epistasis in genetic association studies. BMC Bioinformatics 2014; 15:368. [PMID: 25413600 PMCID: PMC4256933 DOI: 10.1186/s12859-014-0368-0] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2014] [Accepted: 10/30/2014] [Indexed: 12/02/2022] Open
Abstract
Background Discovering causal genetic variants from large genetic association studies poses many difficult challenges. Assessing which genetic markers are involved in determining trait status is a computationally demanding task, especially in the presence of gene-gene interactions. Results A non-parametric Bayesian approach in the form of a Bayesian neural network is proposed for use in analyzing genetic association studies. Demonstrations on synthetic and real data reveal they are able to efficiently and accurately determine which variants are involved in determining case-control status. By using graphics processing units (GPUs) the time needed to build these models is decreased by several orders of magnitude. In comparison with commonly used approaches for detecting interactions, Bayesian neural networks perform very well across a broad spectrum of possible genetic relationships. Conclusions The proposed framework is shown to be a powerful method for detecting causal SNPs while being computationally efficient enough to handle large datasets. Electronic supplementary material The online version of this article (doi:10.1186/s12859-014-0368-0) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Andrew L Beam
- Center for Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
| | - Alison Motsinger-Reif
- Bioinformatics Research Center, North Carolina State University, Raleigh, NC, USA. .,Department of Statistics, North Carolina State University, Raleigh, NC, USA.
| | - Jon Doyle
- Department of Computer Science, North Carolina State University, Raleigh, NC, USA.
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25
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Li P, Guo M, Wang C, Liu X, Zou Q. An overview of SNP interactions in genome-wide association studies. Brief Funct Genomics 2014; 14:143-55. [PMID: 25241224 DOI: 10.1093/bfgp/elu036] [Citation(s) in RCA: 80] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022] Open
Abstract
With the recent explosion in high-throughput genotyping technology, the amount and quality of single-nucleotide polymorphism (SNP) data has increased exponentially. Therefore, the identification of SNP interactions that are associated with common diseases is playing an increasing and important role in interpreting the genetic basis of disease susceptibility and in devising new diagnostic tests and treatments. However, because these data sets are large, although they typically have small sample sizes and low signal-to-noise ratios, there has been no major breakthrough despite many efforts, making this a major focus in the field of bioinformatics. In this article, we review the two main aspects of SNP interaction studies in recent years-the simulation and identification of SNP interactions-and then discuss the principles, efficiency and differences between these methods.
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26
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Zhang H, Wheeler W, Wang Z, Taylor PR, Yu K. A fast and powerful tree-based association test for detecting complex joint effects in case-control studies. Bioinformatics 2014; 30:2171-8. [PMID: 24794927 DOI: 10.1093/bioinformatics/btu186] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
MOTIVATION Multivariate tests derived from the logistic regression model are widely used to assess the joint effect of multiple predictors on a disease outcome in case-control studies. These tests become less optimal if the joint effect cannot be approximated adequately by the additive model. The tree-structure model is an attractive alternative, as it is more apt to capture non-additive effects. However, the tree model is used most commonly for prediction and seldom for hypothesis testing, mainly because of the computational burden associated with the resampling-based procedure required for estimating the significance level. RESULTS We designed a fast algorithm for building the tree-structure model and proposed a robust TREe-based Association Test (TREAT) that incorporates an adaptive model selection procedure to identify the optimal tree model representing the joint effect. We applied TREAT as a multilocus association test on >20 000 genes/regions in a study of esophageal squamous cell carcinoma (ESCC) and detected a highly significant novel association between the gene CDKN2B and ESCC ([Formula: see text]). We also demonstrated, through simulation studies, the power advantage of TREAT over other commonly used tests. AVAILABILITY AND IMPLEMENTATION The package TREAT is freely available for download at http://www.hanzhang.name/softwares/treat, implemented in C++ and R and supported on 64-bit Linux and 64-bit MS Windows. CONTACT yuka@mail.nih.gov SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Han Zhang
- Biostatistics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD 20850, USA, Information Management Services, Inc., Silver Spring, Maryland 20904, USA, and Cancer Genomics Research Laboratory, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Gaithersburg, Maryland 20877, USA
| | - William Wheeler
- Biostatistics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD 20850, USA, Information Management Services, Inc., Silver Spring, Maryland 20904, USA, and Cancer Genomics Research Laboratory, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Gaithersburg, Maryland 20877, USA
| | - Zhaoming Wang
- Biostatistics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD 20850, USA, Information Management Services, Inc., Silver Spring, Maryland 20904, USA, and Cancer Genomics Research Laboratory, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Gaithersburg, Maryland 20877, USABiostatistics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD 20850, USA, Information Management Services, Inc., Silver Spring, Maryland 20904, USA, and Cancer Genomics Research Laboratory, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Gaithersburg, Maryland 20877, USA
| | - Philip R Taylor
- Biostatistics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD 20850, USA, Information Management Services, Inc., Silver Spring, Maryland 20904, USA, and Cancer Genomics Research Laboratory, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Gaithersburg, Maryland 20877, USA
| | - Kai Yu
- Biostatistics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD 20850, USA, Information Management Services, Inc., Silver Spring, Maryland 20904, USA, and Cancer Genomics Research Laboratory, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Gaithersburg, Maryland 20877, USA
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27
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Sluga D, Curk T, Zupan B, Lotric U. Heterogeneous computing architecture for fast detection of SNP-SNP interactions. BMC Bioinformatics 2014; 15:216. [PMID: 24964802 PMCID: PMC4230497 DOI: 10.1186/1471-2105-15-216] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2013] [Accepted: 06/19/2014] [Indexed: 12/04/2022] Open
Abstract
Background The extent of data in a typical genome-wide association study (GWAS) poses considerable computational challenges to software tools for gene-gene interaction discovery. Exhaustive evaluation of all interactions among hundreds of thousands to millions of single nucleotide polymorphisms (SNPs) may require weeks or even months of computation. Massively parallel hardware within a modern Graphic Processing Unit (GPU) and Many Integrated Core (MIC) coprocessors can shorten the run time considerably. While the utility of GPU-based implementations in bioinformatics has been well studied, MIC architecture has been introduced only recently and may provide a number of comparative advantages that have yet to be explored and tested. Results We have developed a heterogeneous, GPU and Intel MIC-accelerated software module for SNP-SNP interaction discovery to replace the previously single-threaded computational core in the interactive web-based data exploration program SNPsyn. We report on differences between these two modern massively parallel architectures and their software environments. Their utility resulted in an order of magnitude shorter execution times when compared to the single-threaded CPU implementation. GPU implementation on a single Nvidia Tesla K20 runs twice as fast as that for the MIC architecture-based Xeon Phi P5110 coprocessor, but also requires considerably more programming effort. Conclusions General purpose GPUs are a mature platform with large amounts of computing power capable of tackling inherently parallel problems, but can prove demanding for the programmer. On the other hand the new MIC architecture, albeit lacking in performance reduces the programming effort and makes it up with a more general architecture suitable for a wider range of problems.
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Affiliation(s)
| | | | | | - Uros Lotric
- Faculty of Computer and Information Science, University of Ljubljana, Trzaska 25, SI 1000 Ljubljana, SI, Slovenia.
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28
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Costa FF. Big data in biomedicine. Drug Discov Today 2014; 19:433-40. [DOI: 10.1016/j.drudis.2013.10.012] [Citation(s) in RCA: 115] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2013] [Revised: 09/05/2013] [Accepted: 10/16/2013] [Indexed: 12/15/2022]
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Abstract
Modern high-throughput assays yield detailed characterizations of the genomic, transcriptomic, and proteomic states of biological samples, enabling us to probe the molecular mechanisms that regulate hematopoiesis or give rise to hematological disorders. At the same time, the high dimensionality of the data and the complex nature of biological interaction networks present significant analytical challenges in identifying causal variations and modeling the underlying systems biology. In addition to identifying significantly disregulated genes and proteins, integrative analysis approaches that allow the investigation of these single genes within a functional context are required. This chapter presents a survey of current computational approaches for the statistical analysis of high-dimensional data and the development of systems-level models of cellular signaling and regulation. Specifically, we focus on multi-gene analysis methods and the integration of expression data with domain knowledge (such as biological pathways) and other gene-wise information (e.g., sequence or methylation data) to identify novel functional modules in the complex cellular interaction network.
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Affiliation(s)
- Rosemary Braun
- Biostatistics Division, Department of Preventive Medicine and Northwestern Institute on Complex Systems, Northwestern University, 680 N. Lake Shore Dr., Suite 1400, 60611, Chicago, IL, USA,
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30
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Chen GK, Guo Y. Discovering epistasis in large scale genetic association studies by exploiting graphics cards. Front Genet 2013; 4:266. [PMID: 24348518 PMCID: PMC3848199 DOI: 10.3389/fgene.2013.00266] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2013] [Accepted: 11/16/2013] [Indexed: 11/13/2022] Open
Abstract
Despite the enormous investments made in collecting DNA samples and generating germline variation data across thousands of individuals in modern genome-wide association studies (GWAS), progress has been frustratingly slow in explaining much of the heritability in common disease. Today's paradigm of testing independent hypotheses on each single nucleotide polymorphism (SNP) marker is unlikely to adequately reflect the complex biological processes in disease risk. Alternatively, modeling risk as an ensemble of SNPs that act in concert in a pathway, and/or interact non-additively on log risk for example, may be a more sensible way to approach gene mapping in modern studies. Implementing such analyzes genome-wide can quickly become intractable due to the fact that even modest size SNP panels on modern genotype arrays (500k markers) pose a combinatorial nightmare, require tens of billions of models to be tested for evidence of interaction. In this article, we provide an in-depth analysis of programs that have been developed to explicitly overcome these enormous computational barriers through the use of processors on graphics cards known as Graphics Processing Units (GPU). We include tutorials on GPU technology, which will convey why they are growing in appeal with today's numerical scientists. One obvious advantage is the impressive density of microprocessor cores that are available on only a single GPU. Whereas high end servers feature up to 24 Intel or AMD CPU cores, the latest GPU offerings from nVidia feature over 2600 cores. Each compute node may be outfitted with up to 4 GPU devices. Success on GPUs varies across problems. However, epistasis screens fare well due to the high degree of parallelism exposed in these problems. Papers that we review routinely report GPU speedups of over two orders of magnitude (>100x) over standard CPU implementations.
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Affiliation(s)
- Gary K Chen
- Division of Biostatics, Department of Preventive Medicine, University of Southern California Los Angeles, CA, USA
| | - Yunfei Guo
- Division of Biostatics, Department of Preventive Medicine, University of Southern California Los Angeles, CA, USA ; Zilkha Neurogenetic Institute, University of Southern California Los Angeles, CA, USA
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31
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Abstract
Background It has been hypothesized that multivariate analysis and systematic detection of epistatic interactions between explanatory genotyping variables may help resolve the problem of "missing heritability" currently observed in genome-wide association studies (GWAS). However, even the simplest bivariate analysis is still held back by significant statistical and computational challenges that are often addressed by reducing the set of analysed markers. Theoretically, it has been shown that combinations of loci may exist that show weak or no effects individually, but show significant (even complete) explanatory power over phenotype when combined. Reducing the set of analysed SNPs before bivariate analysis could easily omit such critical loci. Results We have developed an exhaustive bivariate GWAS analysis methodology that yields a manageable subset of candidate marker pairs for subsequent analysis using other, often more computationally expensive techniques. Our model-free filtering approach is based on classification using ROC curve analysis, an alternative to much slower regression-based modelling techniques. Exhaustive analysis of studies containing approximately 450,000 SNPs and 5,000 samples requires only 2 hours using a desktop CPU or 13 minutes using a GPU (Graphics Processing Unit). We validate our methodology with analysis of simulated datasets as well as the seven Wellcome Trust Case-Control Consortium datasets that represent a wide range of real life GWAS challenges. We have identified SNP pairs that have considerably stronger association with disease than their individual component SNPs that often show negligible effect univariately. When compared against previously reported results in the literature, our methods re-detect most significant SNP-pairs and additionally detect many pairs absent from the literature that show strong association with disease. The high overlap suggests that our fast analysis could substitute for some slower alternatives. Conclusions We demonstrate that the proposed methodology is robust, fast and capable of exhaustive search for epistatic interactions using a standard desktop computer. First, our implementation is significantly faster than timings for comparable algorithms reported in the literature, especially as our method allows simultaneous use of multiple statistical filters with low computing time overhead. Second, for some diseases, we have identified hundreds of SNP pairs that pass formal multiple test (Bonferroni) correction and could form a rich source of hypotheses for follow-up analysis. Availability A web-based version of the software used for this analysis is available at http://bioinformatics.research.nicta.com.au/gwis.
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32
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Abstract
Genome-wide association studies (GWASs) and other high-throughput initiatives have led to an information explosion in human genetics and genetic epidemiology. Conversion of this wealth of new information about genomic variation to knowledge about public health and human biology will depend critically on the complexity of the genotype to phenotype mapping relationship. We review here computational approaches to genetic analysis that embrace, rather than ignore, the complexity of human health. We focus on multifactor dimensionality reduction (MDR) as an approach for modeling one of these complexities: epistasis or gene-gene interaction.
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Affiliation(s)
- Qinxin Pan
- Computational Genetics Laboratory, Dartmouth Medical School, Dartmouth College, Lebanon, NH, USA
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Applications of multifactor dimensionality reduction to genome-wide data using the R package 'MDR'. Methods Mol Biol 2013; 1019:479-98. [PMID: 23756907 DOI: 10.1007/978-1-62703-447-0_23] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
This chapter describes how to use the R package 'MDR' to search and identify gene-gene interactions in high-dimensional data and illustrates applications for exploratory analysis of multi-locus models by providing specific examples.
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34
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Kruppa J, Ziegler A, König IR. Risk estimation and risk prediction using machine-learning methods. Hum Genet 2012; 131:1639-54. [PMID: 22752090 PMCID: PMC3432206 DOI: 10.1007/s00439-012-1194-y] [Citation(s) in RCA: 81] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2012] [Accepted: 06/14/2012] [Indexed: 01/02/2023]
Abstract
After an association between genetic variants and a phenotype has been established, further study goals comprise the classification of patients according to disease risk or the estimation of disease probability. To accomplish this, different statistical methods are required, and specifically machine-learning approaches may offer advantages over classical techniques. In this paper, we describe methods for the construction and evaluation of classification and probability estimation rules. We review the use of machine-learning approaches in this context and explain some of the machine-learning algorithms in detail. Finally, we illustrate the methodology through application to a genome-wide association analysis on rheumatoid arthritis.
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Affiliation(s)
- Jochen Kruppa
- Institut für Medizininsche Biometrie und Statistik, Universität zu Lübeck, Universitätsklinikum Schleswig-Holstein, Campus Lübeck, Maria-Goeppert-Str. 1, 23562 Lübeck, Germany
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Kim NC, Andrews PC, Asselbergs FW, Frost HR, Williams SM, Harris BT, Read C, Askland KD, Moore JH. Gene ontology analysis of pairwise genetic associations in two genome-wide studies of sporadic ALS. BioData Min 2012; 5:9. [PMID: 22839596 PMCID: PMC3463436 DOI: 10.1186/1756-0381-5-9] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2012] [Accepted: 07/13/2012] [Indexed: 12/14/2022] Open
Abstract
BACKGROUND It is increasingly clear that common human diseases have a complex genetic architecture characterized by both additive and nonadditive genetic effects. The goal of the present study was to determine whether patterns of both additive and nonadditive genetic associations aggregate in specific functional groups as defined by the Gene Ontology (GO). RESULTS We first estimated all pairwise additive and nonadditive genetic effects using the multifactor dimensionality reduction (MDR) method that makes few assumptions about the underlying genetic model. Statistical significance was evaluated using permutation testing in two genome-wide association studies of ALS. The detection data consisted of 276 subjects with ALS and 271 healthy controls while the replication data consisted of 221 subjects with ALS and 211 healthy controls. Both studies included genotypes from approximately 550,000 single-nucleotide polymorphisms (SNPs). Each SNP was mapped to a gene if it was within 500 kb of the start or end. Each SNP was assigned a p-value based on its strongest joint effect with the other SNPs. We then used the Exploratory Visual Analysis (EVA) method and software to assign a p-value to each gene based on the overabundance of significant SNPs at the α = 0.05 level in the gene. We also used EVA to assign p-values to each GO group based on the overabundance of significant genes at the α = 0.05 level. A GO category was determined to replicate if that category was significant at the α = 0.05 level in both studies. We found two GO categories that replicated in both studies. The first, 'Regulation of Cellular Component Organization and Biogenesis', a GO Biological Process, had p-values of 0.010 and 0.014 in the detection and replication studies, respectively. The second, 'Actin Cytoskeleton', a GO Cellular Component, had p-values of 0.040 and 0.046 in the detection and replication studies, respectively. CONCLUSIONS Pathway analysis of pairwise genetic associations in two GWAS of sporadic ALS revealed a set of genes involved in cellular component organization and actin cytoskeleton, more specifically, that were not reported by prior GWAS. However, prior biological studies have implicated actin cytoskeleton in ALS and other motor neuron diseases. This study supports the idea that pathway-level analysis of GWAS data may discover important associations not revealed using conventional one-SNP-at-a-time approaches.
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Affiliation(s)
- Nora Chung Kim
- Institute for Quantitative Biomedical Sciences, Department of Genetics, Dartmouth Medical School, One Medical Center Dr., Lebanon, NH 03756, USA
| | - Peter C Andrews
- Institute for Quantitative Biomedical Sciences, Department of Genetics, Dartmouth Medical School, One Medical Center Dr., Lebanon, NH 03756, USA
| | - Folkert W Asselbergs
- Department of Cardiology, Division Heart & Lungs, University Medical Center Utrecht, Utrecht, the Netherlands
| | - H Robert Frost
- Institute for Quantitative Biomedical Sciences, Department of Genetics, Dartmouth Medical School, One Medical Center Dr., Lebanon, NH 03756, USA
| | - Scott M Williams
- Institute for Quantitative Biomedical Sciences, Department of Genetics, Dartmouth Medical School, One Medical Center Dr., Lebanon, NH 03756, USA
| | - Brent T Harris
- Department of Neurology, 4000 Reservoir Rd, Georgetown University Medical Center, Washington, DC 20057, USA
| | - Cynthia Read
- Department of Psychiatry and Human Behavior, Butler Hospital, Brown University, 345 Blackstone Blvd, Providence, RI 02906, USA
| | - Kathleen D Askland
- Department of Psychiatry and Human Behavior, Butler Hospital, Brown University, 345 Blackstone Blvd, Providence, RI 02906, USA
| | - Jason H Moore
- Institute for Quantitative Biomedical Sciences, Department of Genetics, Dartmouth Medical School, One Medical Center Dr., Lebanon, NH 03756, USA
- Department of Psychiatry and Human Behavior, Butler Hospital, Brown University, 345 Blackstone Blvd, Providence, RI 02906, USA
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Abstract
Long-range gene–gene interactions are biologically compelling models for disease genetics and can provide insights on relevant mechanisms and pathways. Despite considerable effort, rigorous interaction mapping in humans has remained prohibitively difficult due to computational and statistical limitations. We introduce a novel algorithmic approach to find long-range interactions in common diseases using a standard two-locus test that contrasts the linkage disequilibrium between SNPs in cases and controls. Our ultrafast method overcomes the computational burden of a genome × genome scan by using a novel randomization technique that requires 10× to 100× fewer tests than a brute-force approach. By sampling small groups of cases and highlighting combinations of alleles carried by all individuals in the group, this algorithm drastically trims the universe of combinations while simultaneously guaranteeing that all statistically significant pairs are reported. Our implementation can comprehensively scan large data sets (2K cases, 3K controls, 500K SNPs) to find all candidate pairwise interactions (LD-contrast ) in a few hours—a task that typically took days or weeks to complete by methods running on equivalent desktop computers. We applied our method to the Wellcome Trust bipolar disorder data and found a significant interaction between SNPs located within genes encoding two calcium channel subunits: RYR2 on chr1q43 and CACNA2D4 on chr12p13 (LD-contrast test, ). We replicated this pattern of interchromosomal LD between the genes in a separate bipolar data set from the GAIN project, demonstrating an example of gene–gene interaction that plays a role in the largely uncharted genetic landscape of bipolar disorder.
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Affiliation(s)
- Snehit Prabhu
- Department of Computer Science, Columbia University, New York, NY 10027, USA
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Lvovs D, Favorova O, Favorov A. A Polygenic Approach to the Study
of Polygenic Diseases. Acta Naturae 2012; 4:59-71. [PMID: 23150804 PMCID: PMC3491892] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022] Open
Abstract
Polygenic diseases are caused by the joint contribution of a number of independently acting or interacting polymorphic genes; the individual contribution of each gene may be small or even unnoticeable. The carriage of certain combinations of genes can determine the occurrence of clinically heterogeneous forms of the disease and treatment efficacy. This review describes the approaches used in a polygenic analysis of data in medical genomics, in particular, pharmacogenomics, aimed at identifying the cumulative effect of genes. This effect may result from the summation of gains of different genes or be caused by the epistatic interaction between the genes. Both cases are undoubtedly of great interest in investigating the nature of polygenic diseases. The means that allow one to discriminate between these two possibilities are discussed. The methods for searching for combinations of alleles of different genes associated with the polygenic phenotypic traits of the disease, as well as the methods for presenting and validating the results, are described and compared. An attempt is made to evaluate the applicability of the existing methods to an epistasis analysis. The results obtained by the authors using the APSampler software are described and summarized.
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Affiliation(s)
- D. Lvovs
- Scientific Center of Russian Federation Research Institute for Genetics
and Selection of Industrial Microorganisms “Genetika”, 1-st Dorozny
proezd, 1, Moscow, Russia, 113545
| | - O.O. Favorova
- N.I. Pirogov Russian National Research Medical University, Ostrovityanova
Str., 1, Moscow, Russia, 117437
- Russian Cardiology Research and Production Complex, 3-rd Cherepkovskaya
Str., 15a, Moscow, Russia, 121552
| | - A.V. Favorov
- Scientific Center of Russian Federation Research Institute for Genetics
and Selection of Industrial Microorganisms “Genetika”, 1-st Dorozny
proezd, 1, Moscow, Russia, 113545
- Vavilov Institute of General Genetics, Russian Academy of Sciences,
Moscow, Gubkin Str., 3, Moscow, Russia, 117809
- Oncology Biostatistics and Bioinformatics, Johns Hopkins School of
Medicine, 550 North Broadway, Baltimore, MD 21205, US
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Gyenesei A, Moody J, Semple CAM, Haley CS, Wei WH. High-throughput analysis of epistasis in genome-wide association studies with BiForce. ACTA ACUST UNITED AC 2012; 28:1957-64. [PMID: 22618535 PMCID: PMC3400955 DOI: 10.1093/bioinformatics/bts304] [Citation(s) in RCA: 43] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Abstract
MOTIVATION Gene-gene interactions (epistasis) are thought to be important in shaping complex traits, but they have been under-explored in genome-wide association studies (GWAS) due to the computational challenge of enumerating billions of single nucleotide polymorphism (SNP) combinations. Fast screening tools are needed to make epistasis analysis routinely available in GWAS. RESULTS We present BiForce to support high-throughput analysis of epistasis in GWAS for either quantitative or binary disease (case-control) traits. BiForce achieves great computational efficiency by using memory efficient data structures, Boolean bitwise operations and multithreaded parallelization. It performs a full pair-wise genome scan to detect interactions involving SNPs with or without significant marginal effects using appropriate Bonferroni-corrected significance thresholds. We show that BiForce is more powerful and significantly faster than published tools for both binary and quantitative traits in a series of performance tests on simulated and real datasets. We demonstrate BiForce in analysing eight metabolic traits in a GWAS cohort (323 697 SNPs, >4500 individuals) and two disease traits in another (>340 000 SNPs, >1750 cases and 1500 controls) on a 32-node computing cluster. BiForce completed analyses of the eight metabolic traits within 1 day, identified nine epistatic pairs of SNPs in five metabolic traits and 18 SNP pairs in two disease traits. BiForce can make the analysis of epistasis a routine exercise in GWAS and thus improve our understanding of the role of epistasis in the genetic regulation of complex traits. AVAILABILITY AND IMPLEMENTATION The software is free and can be downloaded from http://bioinfo.utu.fi/BiForce/. CONTACT wenhua.wei@igmm.ed.ac.uk SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Attila Gyenesei
- Finnish Microarray and Sequencing Centre, Turku Centre for Biotechnology, University of Turku and Åbo Akademi University, 20520, Turku, Finland
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High-order SNP combinations associated with complex diseases: efficient discovery, statistical power and functional interactions. PLoS One 2012; 7:e33531. [PMID: 22536319 PMCID: PMC3334940 DOI: 10.1371/journal.pone.0033531] [Citation(s) in RCA: 42] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2011] [Accepted: 02/10/2012] [Indexed: 11/19/2022] Open
Abstract
There has been increased interest in discovering combinations of single-nucleotide polymorphisms (SNPs) that are strongly associated with a phenotype even if each SNP has little individual effect. Efficient approaches have been proposed for searching two-locus combinations from genome-wide datasets. However, for high-order combinations, existing methods either adopt a brute-force search which only handles a small number of SNPs (up to few hundreds), or use heuristic search that may miss informative combinations. In addition, existing approaches lack statistical power because of the use of statistics with high degrees-of-freedom and the huge number of hypotheses tested during combinatorial search. Due to these challenges, functional interactions in high-order combinations have not been systematically explored. We leverage discriminative-pattern-mining algorithms from the data-mining community to search for high-order combinations in case-control datasets. The substantially improved efficiency and scalability demonstrated on synthetic and real datasets with several thousands of SNPs allows the study of several important mathematical and statistical properties of SNP combinations with order as high as eleven. We further explore functional interactions in high-order combinations and reveal a general connection between the increase in discriminative power of a combination over its subsets and the functional coherence among the genes comprising the combination, supported by multiple datasets. Finally, we study several significant high-order combinations discovered from a lung-cancer dataset and a kidney-transplant-rejection dataset in detail to provide novel insights on the complex diseases. Interestingly, many of these associations involve combinations of common variations that occur in small fractions of population. Thus, our approach is an alternative methodology for exploring the genetics of rare diseases for which the current focus is on individually rare variations.
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Mechanic LE, Chen HS, Amos CI, Chatterjee N, Cox NJ, Divi RL, Fan R, Harris EL, Jacobs K, Kraft P, Leal SM, McAllister K, Moore JH, Paltoo DN, Province MA, Ramos EM, Ritchie MD, Roeder K, Schaid DJ, Stephens M, Thomas DC, Weinberg CR, Witte JS, Zhang S, Zöllner S, Feuer EJ, Gillanders EM. Next generation analytic tools for large scale genetic epidemiology studies of complex diseases. Genet Epidemiol 2011; 36:22-35. [PMID: 22147673 DOI: 10.1002/gepi.20652] [Citation(s) in RCA: 48] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Abstract
Over the past several years, genome-wide association studies (GWAS) have succeeded in identifying hundreds of genetic markers associated with common diseases. However, most of these markers confer relatively small increments of risk and explain only a small proportion of familial clustering. To identify obstacles to future progress in genetic epidemiology research and provide recommendations to NIH for overcoming these barriers, the National Cancer Institute sponsored a workshop entitled "Next Generation Analytic Tools for Large-Scale Genetic Epidemiology Studies of Complex Diseases" on September 15-16, 2010. The goal of the workshop was to facilitate discussions on (1) statistical strategies and methods to efficiently identify genetic and environmental factors contributing to the risk of complex disease; and (2) how to develop, apply, and evaluate these strategies for the design, analysis, and interpretation of large-scale complex disease association studies in order to guide NIH in setting the future agenda in this area of research. The workshop was organized as a series of short presentations covering scientific (gene-gene and gene-environment interaction, complex phenotypes, and rare variants and next generation sequencing) and methodological (simulation modeling and computational resources and data management) topic areas. Specific needs to advance the field were identified during each session and are summarized.
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Affiliation(s)
- Leah E Mechanic
- Epidemiology and Genetics Research Program, Division of Cancer Control and Population Sciences, National Cancer Institute, NIH, Bethesda, Maryland 20892, USA.
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Oki NO, Motsinger-Reif AA. Multifactor dimensionality reduction as a filter-based approach for genome wide association studies. Front Genet 2011; 2:80. [PMID: 22303374 PMCID: PMC3268633 DOI: 10.3389/fgene.2011.00080] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2011] [Accepted: 10/26/2011] [Indexed: 11/13/2022] Open
Abstract
Advances in genotyping technology and the multitude of genetic data available now provide a vast amount of data that is proving to be useful in the quest for a better understanding of human genetic diseases through the study of genetic variation. This has led to the development of approaches such as genome wide association studies (GWAS) designed specifically for interrogating variants across the genome for association with disease, typically by testing single locus, univariate associations. More recently it has been accepted that epistatic (interaction) effects may also be great contributors to these genetic effects, and GWAS methods are now being applied to find epistatic effects. The challenge for these methods still remain in prioritization and interpretation of results, as it has also become standard for initial findings to be independently investigated in replication cohorts or functional studies. This is motivating the development and implementation of filter-based approaches to prioritize variants found to be significant in a discovery stage for follow-up for replication. Such filters must be able to detect both univariate and interactive effects. In the current study we present and evaluate the use of multifactor dimensionality reduction (MDR) as such a filter, with simulated data and a wide range of effect sizes. Additionally, we compare the performance of the MDR filter to a similar filter approach using logistic regression (LR), the more traditional approach used in GWAS analysis, as well as evaporative cooling (EC)-another prominent machine learning filtering method. The results of our simulation study show that MDR is an effective method for such prioritization, and that it can detect main effects, and interactions with or without marginal effects. Importantly, it performed as well as EC and LR for main effect models. It also significantly outperforms LR for various two-locus epistatic models, while it has equivalent results as EC for the epistatic models. The results of this study demonstrate the potential of MDR as a filter to detect gene-gene interactions in GWAS studies.
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Affiliation(s)
- Noffisat O. Oki
- Bioinformatics Research Center, North Carolina State UniversityRaleigh, NC, USA
| | - Alison A. Motsinger-Reif
- Bioinformatics Research Center, North Carolina State UniversityRaleigh, NC, USA
- Department of Statistics, North Carolina State UniversityRaleigh, NC, USA
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Gilbert-Diamond D, Moore JH. Analysis of gene-gene interactions. CURRENT PROTOCOLS IN HUMAN GENETICS 2011; Chapter 1:Unit1.14. [PMID: 21735376 PMCID: PMC4086055 DOI: 10.1002/0471142905.hg0114s70] [Citation(s) in RCA: 32] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
The goal of this unit is to introduce gene-gene interactions (epistasis) as a significant complicating factor in the search for disease susceptibility genes. This unit begins with an overview of gene-gene interactions and why they are likely to be common. Then, it reviews several statistical and computational methods for detecting and characterizing genes with effects that are dependent on other genes. The focus of this unit is genetic association studies of discrete and quantitative traits because most of the methods for detecting gene-gene interactions have been developed specifically for these study designs.
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Affiliation(s)
- Diane Gilbert-Diamond
- Computational Genetics Laboratory, Departments of Genetics and Community and Family Medicine, Dartmouth Medical School, Lebanon, New Hampshire, USA
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43
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Braun R, Buetow K. Pathways of distinction analysis: a new technique for multi-SNP analysis of GWAS data. PLoS Genet 2011; 7:e1002101. [PMID: 21695280 PMCID: PMC3111473 DOI: 10.1371/journal.pgen.1002101] [Citation(s) in RCA: 41] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2010] [Accepted: 03/28/2011] [Indexed: 02/07/2023] Open
Abstract
Genome-wide association studies (GWAS) have become increasingly common due to advances in technology and have permitted the identification of differences in single nucleotide polymorphism (SNP) alleles that are associated with diseases. However, while typical GWAS analysis techniques treat markers individually, complex diseases (cancers, diabetes, and Alzheimers, amongst others) are unlikely to have a single causative gene. Thus, there is a pressing need for multi–SNP analysis methods that can reveal system-level differences in cases and controls. Here, we present a novel multi–SNP GWAS analysis method called Pathways of Distinction Analysis (PoDA). The method uses GWAS data and known pathway–gene and gene–SNP associations to identify pathways that permit, ideally, the distinction of cases from controls. The technique is based upon the hypothesis that, if a pathway is related to disease risk, cases will appear more similar to other cases than to controls (or vice versa) for the SNPs associated with that pathway. By systematically applying the method to all pathways of potential interest, we can identify those for which the hypothesis holds true, i.e., pathways containing SNPs for which the samples exhibit greater within-class similarity than across classes. Importantly, PoDA improves on existing single–SNP and SNP–set enrichment analyses, in that it does not require the SNPs in a pathway to exhibit independent main effects. This permits PoDA to reveal pathways in which epistatic interactions drive risk. In this paper, we detail the PoDA method and apply it to two GWAS: one of breast cancer and the other of liver cancer. The results obtained strongly suggest that there exist pathway-wide genomic differences that contribute to disease susceptibility. PoDA thus provides an analytical tool that is complementary to existing techniques and has the power to enrich our understanding of disease genomics at the systems-level. We present a novel method for multi–SNP analysis of genome-wide association studies. The method is motivated by the intuition that, if a set of SNPs is associated with disease, cases and controls will exhibit more within-group similarity than across-group similarity for the SNPs in the set of interest. Our method, Pathways of Distinction Analysis (PoDA), uses GWAS data and known pathway–gene and gene–SNP associations to identify pathways that permit the distinction of cases from controls. By systematically applying the method to all pathways of potential interest, we can identify pathways containing SNPs for which the cases and controls are distinguished and infer those pathways' role in disease. We detail the PoDA method and describe its results in breast and liver cancer GWAS data, demonstrating its utility as a method for systems-level analysis of GWAS data.
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Affiliation(s)
- Rosemary Braun
- Laboratory of Population Genetics, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, United States of America
- * E-mail:
| | - Kenneth Buetow
- Laboratory of Population Genetics, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, United States of America
- Center for Biomedical Informatics and Information Technology, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, United States of America
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44
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Yung LS, Yang C, Wan X, Yu W. GBOOST: a GPU-based tool for detecting gene-gene interactions in genome-wide case control studies. ACTA ACUST UNITED AC 2011; 27:1309-10. [PMID: 21372087 DOI: 10.1093/bioinformatics/btr114] [Citation(s) in RCA: 116] [Impact Index Per Article: 8.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
Abstract
MOTIVATION Collecting millions of genetic variations is feasible with the advanced genotyping technology. With a huge amount of genetic variations data in hand, developing efficient algorithms to carry out the gene-gene interaction analysis in a timely manner has become one of the key problems in genome-wide association studies (GWAS). Boolean operation-based screening and testing (BOOST), a recent work in GWAS, completes gene-gene interaction analysis in 2.5 days on a desktop computer. Compared with central processing units (CPUs), graphic processing units (GPUs) are highly parallel hardware and provide massive computing resources. We are, therefore, motivated to use GPUs to further speed up the analysis of gene-gene interactions. RESULTS We implement the BOOST method based on a GPU framework and name it GBOOST. GBOOST achieves a 40-fold speedup compared with BOOST. It completes the analysis of Wellcome Trust Case Control Consortium Type 2 Diabetes (WTCCC T2D) genome data within 1.34 h on a desktop computer equipped with Nvidia GeForce GTX 285 display card. AVAILABILITY GBOOST code is available at http://bioinformatics.ust.hk/BOOST.html#GBOOST.
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Affiliation(s)
- Ling Sing Yung
- Laboratory for Bioinformatics and Computational Biology, Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China.
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Wang Z, Wang Y, Tan KL, Wong L, Agrawal D. eCEO: an efficient Cloud Epistasis cOmputing model in genome-wide association study. Bioinformatics 2011; 27:1045-51. [PMID: 21367868 DOI: 10.1093/bioinformatics/btr091] [Citation(s) in RCA: 23] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
MOTIVATION Recent studies suggested that a combination of multiple single nucleotide polymorphisms (SNPs) could have more significant associations with a specific phenotype. However, to discover epistasis, the epistatic interactions of SNPs, in a large number of SNPs, is a computationally challenging task. We are, therefore, motivated to develop efficient and effective solutions for identifying epistatic interactions of SNPs. RESULTS In this article, we propose an efficient Cloud-based Epistasis cOmputing (eCEO) model for large-scale epistatic interaction in genome-wide association study (GWAS). Given a large number of combinations of SNPs, our eCEO model is able to distribute them to balance the load across the processing nodes. Moreover, our eCEO model can efficiently process each combination of SNPs to determine the significance of its association with the phenotype. We have implemented and evaluated our eCEO model on our own cluster of more than 40 nodes. The experiment results demonstrate that the eCEO model is computationally efficient, flexible, scalable and practical. In addition, we have also deployed our eCEO model on the Amazon Elastic Compute Cloud. Our study further confirms its efficiency and ease of use in a public cloud. AVAILABILITY The source code of eCEO is available at http://www.comp.nus.edu.sg/~wangzk/eCEO.html. CONTACT wangzhengkui@nus.edu.sg.
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Affiliation(s)
- Zhengkui Wang
- NUS Graduate School for Integrative Sciences and Engineering, Department of Computer Science, School of Computing, National University of Singapore, Singapore and Department of Computer Science, University of California, Santa Barbara, 93106-5110, USA.
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Comparison of information-theoretic to statistical methods for gene-gene interactions in the presence of genetic heterogeneity. BMC Genomics 2010; 11:487. [PMID: 20815886 PMCID: PMC2996983 DOI: 10.1186/1471-2164-11-487] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2010] [Accepted: 09/03/2010] [Indexed: 11/10/2022] Open
Abstract
Background Multifactorial diseases such as cancer and cardiovascular diseases are caused by the complex interplay between genes and environment. The detection of these interactions remains challenging due to computational limitations. Information theoretic approaches use computationally efficient directed search strategies and thus provide a feasible solution to this problem. However, the power of information theoretic methods for interaction analysis has not been systematically evaluated. In this work, we compare power and Type I error of an information-theoretic approach to existing interaction analysis methods. Methods The k-way interaction information (KWII) metric for identifying variable combinations involved in gene-gene interactions (GGI) was assessed using several simulated data sets under models of genetic heterogeneity driven by susceptibility increasing loci with varying allele frequency, penetrance values and heritability. The power and proportion of false positives of the KWII was compared to multifactor dimensionality reduction (MDR), restricted partitioning method (RPM) and logistic regression. Results The power of the KWII was considerably greater than MDR on all six simulation models examined. For a given disease prevalence at high values of heritability, the power of both RPM and KWII was greater than 95%. For models with low heritability and/or genetic heterogeneity, the power of the KWII was consistently greater than RPM; the improvements in power for the KWII over RPM ranged from 4.7% to 14.2% at for α = 0.001 in the three models at the lowest heritability values examined. KWII performed similar to logistic regression. Conclusions Information theoretic models are flexible and have excellent power to detect GGI under a variety of conditions that characterize complex diseases.
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Payne JL, Sinnott-Armstrong NA, Moore JH. Exploiting graphics processing units for computational biology and bioinformatics. Interdiscip Sci 2010; 2:213-20. [PMID: 20658333 DOI: 10.1007/s12539-010-0002-4] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2010] [Revised: 05/17/2010] [Accepted: 05/18/2010] [Indexed: 10/19/2022]
Abstract
Advances in the video gaming industry have led to the production of low-cost, high-performance graphics processing units (GPUs) that possess more memory bandwidth and computational capability than central processing units (CPUs), the standard workhorses of scientific computing. With the recent release of generalpurpose GPUs and NVIDIA's GPU programming language, CUDA, graphics engines are being adopted widely in scientific computing applications, particularly in the fields of computational biology and bioinformatics. The goal of this article is to concisely present an introduction to GPU hardware and programming, aimed at the computational biologist or bioinformaticist. To this end, we discuss the primary differences between GPU and CPU architecture, introduce the basics of the CUDA programming language, and discuss important CUDA programming practices, such as the proper use of coalesced reads, data types, and memory hierarchies. We highlight each of these topics in the context of computing the all-pairs distance between instances in a dataset, a common procedure in numerous disciplines of scientific computing. We conclude with a runtime analysis of the GPU and CPU implementations of the all-pairs distance calculation. We show our final GPU implementation to outperform the CPU implementation by a factor of 1700.
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Affiliation(s)
- Joshua L Payne
- Computational Genetics Laboratory, Department of Genetics, Dartmouth Medical School, Lebanon, NH 03756, USA.
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Machine learning techniques for single nucleotide polymorphism--disease classification models in schizophrenia. Molecules 2010; 15:4875-89. [PMID: 20657396 PMCID: PMC6257637 DOI: 10.3390/molecules15074875] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2010] [Revised: 07/08/2010] [Accepted: 07/09/2010] [Indexed: 11/16/2022] Open
Abstract
Single nucleotide polymorphisms (SNPs) can be used as inputs in disease computational studies such as pattern searching and classification models. Schizophrenia is an example of a complex disease with an important social impact. The multiple causes of this disease create the need of new genetic or proteomic patterns that can diagnose patients using biological information. This work presents a computational study of disease machine learning classification models using only single nucleotide polymorphisms at the HTR2A and DRD3 genes from Galician (Northwest Spain) schizophrenic patients. These classification models establish for the first time, to the best knowledge of the authors, a relationship between the sequence of the nucleic acid molecule and schizophrenia (Quantitative Genotype – Disease Relationships) that can automatically recognize schizophrenia DNA sequences and correctly classify between 78.3–93.8% of schizophrenia subjects when using datasets which include simulated negative subjects and a linear artificial neural network.
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Schüpbach T, Xenarios I, Bergmann S, Kapur K. FastEpistasis: a high performance computing solution for quantitative trait epistasis. ACTA ACUST UNITED AC 2010; 26:1468-9. [PMID: 20375113 PMCID: PMC2872003 DOI: 10.1093/bioinformatics/btq147] [Citation(s) in RCA: 78] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
MOTIVATION Genome-wide association studies have become widely used tools to study effects of genetic variants on complex diseases. While it is of great interest to extend existing analysis methods by considering interaction effects between pairs of loci, the large number of possible tests presents a significant computational challenge. The number of computations is further multiplied in the study of gene expression quantitative trait mapping, in which tests are performed for thousands of gene phenotypes simultaneously. RESULTS We present FastEpistasis, an efficient parallel solution extending the PLINK epistasis module, designed to test for epistasis effects when analyzing continuous phenotypes. Our results show that the algorithm scales with the number of processors and offers a reduction in computation time when several phenotypes are analyzed simultaneously. FastEpistasis is capable of testing the association of a continuous trait with all single nucleotide polymorphism (SNP) pairs from 500 000 SNPs, totaling 125 billion tests, in a population of 5000 individuals in 29, 4 or 0.5 days using 8, 64 or 512 processors. AVAILABILITY FastEpistasis is open source and available free of charge only for non-commercial users from http://www.vital-it.ch/software/FastEpistasis.
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Affiliation(s)
- Thierry Schüpbach
- Vital-IT Group, Molecular Modeling Group, Swiss Institute of Bioinformatics, Lausanne, Switzerland
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Moore JH. Detecting, characterizing, and interpreting nonlinear gene-gene interactions using multifactor dimensionality reduction. ADVANCES IN GENETICS 2010; 72:101-16. [PMID: 21029850 DOI: 10.1016/b978-0-12-380862-2.00005-9] [Citation(s) in RCA: 46] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
Abstract
Human health is a complex process that is dependent on many genes, many environmental factors and chance events that are perhaps not measurable with current technology or are simply unknowable. Success in the design and execution of population-based association studies to identify those genetic and environmental factors that play an important role in human disease will depend on our ability to embrace, rather that ignore, complexity in the genotype to phenotype mapping relationship for any given human ecology. We review here three general computational challenges that must be addressed. First, data mining and machine learning methods are needed to model nonlinear interactions between multiple genetic and environmental factors. Second, filter and wrapper methods are needed to identify attribute interactions in large and complex solution landscapes. Third, visualization methods are needed to help interpret computational models and results. We provide here an overview of the multifactor dimensionality reduction (MDR) method that was developed for addressing each of these challenges.
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Affiliation(s)
- Jason H Moore
- Institute for Quantitative Biomedical Sciences, Departments of Genetics and Community and Family Medicine, Dartmouth Medical School, Lebanon, New Hampshire, USA
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