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1
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Fu B, Pazokitoroudi A, Xue A, Anand A, Anand P, Zaitlen N, Sankararaman S. A biobank-scale test of marginal epistasis reveals genome-wide signals of polygenic epistasis. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.09.10.557084. [PMID: 37745394 PMCID: PMC10515811 DOI: 10.1101/2023.09.10.557084] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/26/2023]
Abstract
The contribution of epistasis (interactions among genes or genetic variants) to human complex trait variation remains poorly understood. Methods that aim to explicitly identify pairs of genetic variants, usually single nucleotide polymorphisms (SNPs), associated with a trait suffer from low power due to the large number of hypotheses tested while also having to deal with the computational problem of searching over a potentially large number of candidate pairs. An alternate approach involves testing whether a single SNP modulates variation in a trait against a polygenic background. While overcoming the limitation of low power, such tests of polygenic or marginal epistasis (ME) are infeasible on Biobank-scale data where hundreds of thousands of individuals are genotyped over millions of SNPs. We present a method to test for ME of a SNP on a trait that is applicable to biobank-scale data. We performed extensive simulations to show that our method provides calibrated tests of ME. We applied our method to test for ME at SNPs that are associated with 53 quantitative traits across ≈ 300 K unrelated white British individuals in the UK Biobank (UKBB). Testing 15, 601 trait-loci associations that were significant in GWAS, we identified 16 trait-loci pairs across 12 traits that demonstrate strong evidence of ME signals (p-value p < 5 × 10 - 8 53 ). We further partitioned the significant ME signals across the genome to identify 6 trait-loci pairs with evidence of local (within-chromosome) ME while 15 show evidence of distal (cross-chromosome) ME. Across the 16 trait-loci pairs, we document that the proportion of trait variance explained by ME is about 12x as large as that explained by the GWAS effects on average (range: 0.59 to 43.89). Our results show, for the first time, evidence of interaction effects between individual genetic variants and overall polygenic background modulating complex trait variation.
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Affiliation(s)
- Boyang Fu
- Department of Computer Science, UCLA, Los Angeles, CA, USA
| | | | - Albert Xue
- Bioinformatics Interdepartmental Program, UCLA, Los Angeles, CA, USA
| | - Aakarsh Anand
- Department of Computer Science, UCLA, Los Angeles, CA, USA
| | - Prateek Anand
- Department of Computer Science, UCLA, Los Angeles, CA, USA
| | - Noah Zaitlen
- Department of Neurology, UCLA, Los Angeles, CA, USA
- Department of Computational Medicine, David Geffen School of Medicine, UCLA, Los Angeles, CA, USA
| | - Sriram Sankararaman
- Department of Computer Science, UCLA, Los Angeles, CA, USA
- Department of Computational Medicine, David Geffen School of Medicine, UCLA, Los Angeles, CA, USA
- Department of Human Genetics, David Geffen School of Medicine, UCLA, Los Angeles, CA, USA
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2
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Walakira A, Ocira J, Duroux D, Fouladi R, Moškon M, Rozman D, Van Steen K. Detecting gene-gene interactions from GWAS using diffusion kernel principal components. BMC Bioinformatics 2022; 23:57. [PMID: 35105309 PMCID: PMC8805268 DOI: 10.1186/s12859-022-04580-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Accepted: 01/18/2022] [Indexed: 11/10/2022] Open
Abstract
Genes and gene products do not function in isolation but as components of complex networks of macromolecules through physical or biochemical interactions. Dependencies of gene mutations on genetic background (i.e., epistasis) are believed to play a role in understanding molecular underpinnings of complex diseases such as inflammatory bowel disease (IBD). However, the process of identifying such interactions is complex due to for instance the curse of high dimensionality, dependencies in the data and non-linearity. Here, we propose a novel approach for robust and computationally efficient epistasis detection. We do so by first reducing dimensionality, per gene via diffusion kernel principal components (kpc). Subsequently, kpc gene summaries are used for downstream analysis including the construction of a gene-based epistasis network. We show that our approach is not only able to recover known IBD associated genes but also additional genes of interest linked to this difficult gastrointestinal disease.
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Affiliation(s)
- Andrew Walakira
- Centre for Functional Genomics and Bio-Chips, Institute for Biochemistry and Molecular Genetics, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
| | - Junior Ocira
- BIO3 - Laboratory for Systems Genetics, GIGA-R Medical Genomics, University of Liège, Liège, Belgium
| | - Diane Duroux
- BIO3 - Laboratory for Systems Genetics, GIGA-R Medical Genomics, University of Liège, Liège, Belgium
| | - Ramouna Fouladi
- BIO3 - Laboratory for Systems Genetics, GIGA-R Medical Genomics, University of Liège, Liège, Belgium
| | - Miha Moškon
- Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia
| | - Damjana Rozman
- Centre for Functional Genomics and Bio-Chips, Institute for Biochemistry and Molecular Genetics, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
| | - Kristel Van Steen
- BIO3 - Laboratory for Systems Genetics, GIGA-R Medical Genomics, University of Liège, Liège, Belgium
- BIO3 - Laboratory for Systems Medicine, Department of Human Genetics, KU Leuven, Leuven, Belgium
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3
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Wen J, Ford CT, Janies D, Shi X. A parallelized strategy for epistasis analysis based on Empirical Bayesian Elastic Net models. Bioinformatics 2020; 36:3803-3810. [PMID: 32227194 DOI: 10.1093/bioinformatics/btaa216] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2019] [Revised: 03/05/2020] [Accepted: 03/26/2020] [Indexed: 11/14/2022] Open
Abstract
MOTIVATION Epistasis reflects the distortion on a particular trait or phenotype resulting from the combinatorial effect of two or more genes or genetic variants. Epistasis is an important genetic foundation underlying quantitative traits in many organisms as well as in complex human diseases. However, there are two major barriers in identifying epistasis using large genomic datasets. One is that epistasis analysis will induce over-fitting of an over-saturated model with the high-dimensionality of a genomic dataset. Therefore, the problem of identifying epistasis demands efficient statistical methods. The second barrier comes from the intensive computing time for epistasis analysis, even when the appropriate model and data are specified. RESULTS In this study, we combine statistical techniques and computational techniques to scale up epistasis analysis using Empirical Bayesian Elastic Net (EBEN) models. Specifically, we first apply a matrix manipulation strategy for pre-computing the correlation matrix and pre-filter to narrow down the search space for epistasis analysis. We then develop a parallelized approach to further accelerate the modeling process. Our experiments on synthetic and empirical genomic data demonstrate that our parallelized methods offer tens of fold speed up in comparison with the classical EBEN method which runs in a sequential manner. We applied our parallelized approach to a yeast dataset, and we were able to identify both main and epistatic effects of genetic variants associated with traits such as fitness. AVAILABILITY AND IMPLEMENTATION The software is available at github.com/shilab/parEBEN.
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Affiliation(s)
- Jia Wen
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514, USA
| | - Colby T Ford
- Department of Bioinformatics and Genomics, College of Computing and Informatics.,School of Data Science, University of North Carolina at Charlotte, Charlotte, NC 28223, USA
| | - Daniel Janies
- Department of Bioinformatics and Genomics, College of Computing and Informatics
| | - Xinghua Shi
- Department of Computer and Information Sciences, Temple University, Philadelphia, PA 19122, USA
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4
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Blackburn TP. Depressive disorders: Treatment failures and poor prognosis over the last 50 years. Pharmacol Res Perspect 2019; 7:e00472. [PMID: 31065377 PMCID: PMC6498411 DOI: 10.1002/prp2.472] [Citation(s) in RCA: 77] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2018] [Revised: 02/11/2019] [Accepted: 03/18/2019] [Indexed: 12/12/2022] Open
Abstract
Depression like many diseases is pleiotropic but unlike cancer and Alzheimer's disease for example, is still largely stigmatized and falls into the dark shadows of human illness. The failure of depression to be in the spotlight for successful treatment options is inherent in the complexity of the disease(s), flawed clinical diagnosis, overgeneralization of the illness, inadequate and biased clinical trial design, restrictive and biased inclusion/exclusion criteria, lack of approved/robust biomarkers, expensive imaging technology along with few advances in neurobiological hypotheses in decades. Clinical trial studies summitted to the regulatory agencies (FDA/EMA) for approval, have continually failed to show significant differences between active and placebo. For decades, we have acknowledged this failure, despite vigorous debated by all stakeholders to provide adequate answers to this escalating problem, with only a few new antidepressants approved in the last 20 years with equivocal efficacy, little improvement in side effects or onset of efficacy. It is also clear that funding and initiatives for mental illness lags far behind other life-treating diseases. Thus, it is no surprise we have not achieved much success in the last 50 years in treating depression, but we are accountable for the many failures and suboptimal treatment. This review will therefore critically address where we have failed and how future advances in medical science offers a glimmer of light for the patient and aid our future understanding of the neurobiology and pathophysiology of the disease, enabling transformative therapies for the treatment of depressive disorders.
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Epistasis detectably alters correlations between genomic sites in a narrow parameter window. PLoS One 2019; 14:e0214036. [PMID: 31150393 PMCID: PMC6544209 DOI: 10.1371/journal.pone.0214036] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2019] [Accepted: 05/18/2019] [Indexed: 01/12/2023] Open
Abstract
Different genomic sites evolve inter-dependently due to the combined action of epistasis, defined as a non-multiplicative contribution of alleles at different loci to genome fitness, and the physical linkage of different loci in genome. Both epistasis and linkage, partially compensated by recombination, cause correlations between allele frequencies at the loci (linkage disequilibrium, LD). The interaction and competition between epistasis and linkage are not fully understood, nor is their relative sensitivity to recombination. Modeling an adapting population in the presence of random mutation, natural selection, pairwise epistasis, and random genetic drift, we compare the contributions of epistasis and linkage. For this end, we use a panel of haplotype-based measures of LD and their various combinations calculated for epistatic and non-epistatic pairs separately. We compute the optimal percentages of detected and false positive pairs in a one-time sample of a population of moderate size. We demonstrate that true interacting pairs can be told apart in a sufficiently short genome within a narrow window of time and parameters. Outside of this parameter region, unless the population is extremely large, shared ancestry of individual sequences generates pervasive stochastic LD for non-interacting pairs masking true epistatic associations. In the presence of sufficiently strong recombination, linkage effects decrease faster than those of epistasis, and the detection of epistasis improves. We demonstrate that the epistasis component of locus association can be isolated, at a single time point, by averaging haplotype frequencies over multiple independent populations. These results demonstrate the existence of fundamental restrictions on the protocols for detecting true interactions in DNA sequence sets.
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Pedruzzi G, Barlukova A, Rouzine IM. Evolutionary footprint of epistasis. PLoS Comput Biol 2018; 14:e1006426. [PMID: 30222748 PMCID: PMC6177197 DOI: 10.1371/journal.pcbi.1006426] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2018] [Revised: 10/09/2018] [Accepted: 08/09/2018] [Indexed: 11/18/2022] Open
Abstract
Variation of an inherited trait across a population cannot be explained by additive contributions of relevant genes, due to epigenetic effects and biochemical interactions (epistasis). Detecting epistasis in genomic data still represents a significant challenge that requires a better understanding of epistasis from the mechanistic point of view. Using a standard Wright-Fisher model of bi-allelic asexual population, we study how compensatory epistasis affects the process of adaptation. The main result is a universal relationship between four haplotype frequencies of a single site pair in a genome, which depends only on the epistasis strength of the pair defined regarding Darwinian fitness. We demonstrate the existence, at any time point, of a quasi-equilibrium between epistasis and disorder (entropy) caused by random genetic drift and mutation. We verify the accuracy of these analytic results by Monte-Carlo simulation over a broad range of parameters, including the topology of the interacting network. Thus, epistasis assists the evolutionary transit through evolutionary hurdles leaving marks at the level of haplotype disequilibrium. The method allows determining selection coefficient for each site and the epistasis strength of each pair from a sequence set. The resulting ability to detect clusters of deleterious mutation close to full compensation is essential for biomedical applications. These findings help to understand the role of epistasis in multiple compensatory mutations in viral resistance to antivirals and immune response.
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Affiliation(s)
- Gabriele Pedruzzi
- Sorbonne Université, Institute de Biologie Paris-Seine, Laboratoire de Biologie Computationelle et Quantitative, Paris, France
| | - Ayuna Barlukova
- Sorbonne Université, Institute de Biologie Paris-Seine, Laboratoire de Biologie Computationelle et Quantitative, Paris, France
| | - Igor M. Rouzine
- Sorbonne Université, Institute de Biologie Paris-Seine, Laboratoire de Biologie Computationelle et Quantitative, Paris, France
- * E-mail:
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7
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Stanfill AG, Starlard-Davenport A. Primer in Genetics and Genomics, Article 7-Multifactorial Concepts: Gene-Gene Interactions. Biol Res Nurs 2018. [PMID: 29514459 DOI: 10.1177/1099800418761098] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Most common disorders affecting human health are not attributable to simple Mendelian (single-gene) inheritance patterns. Rather, the risk of developing a complex disease is often the result of interactions across genes, whereby one gene modifies the phenotype of another gene. These types of interactions can occur between two or more genes and are referred to as epistasis. There are five major types of epistatic interactions, but in human genetics, additive epistasis is most often discussed and includes both positive and negative subtypes. Detecting epistatic interactions can be quite difficult because seemingly unrelated genes can interact with and influence each other. As a result of this complexity, statistical geneticists are constantly developing new methods to enhance detection, but there are disadvantages to each proposed method. In this article, we explore the concept of epistasis, discuss different types of epistatic interactions, and provide a brief introduction to statistical methods researchers use to uncover sets of epistatic interactions. Then, we consider Alzheimer's disease as an exemplar for a disease with epistatic effects. Finally, we provide helpful resources, where nurses can learn more about epistasis in order to incorporate these methods into their own program of research.
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Affiliation(s)
- Ansley Grimes Stanfill
- 1 Department of Acute and Tertiary Care, College of Nursing, University of Tennessee Health Science Center, Memphis, TN, USA.,2 Department of Genetics, Genomics, and Informatics, College of Medicine, University of Tennessee Health Science Center, Memphis, TN, USA
| | - Athena Starlard-Davenport
- 2 Department of Genetics, Genomics, and Informatics, College of Medicine, University of Tennessee Health Science Center, Memphis, TN, USA
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8
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Bessonov K, Gusareva ES, Van Steen K. A cautionary note on the impact of protocol changes for genome-wide association SNP × SNP interaction studies: an example on ankylosing spondylitis. Hum Genet 2015; 134:761-73. [DOI: 10.1007/s00439-015-1560-7] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2015] [Accepted: 04/26/2015] [Indexed: 12/11/2022]
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9
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Talluri R, Shete S. Evaluating methods for modeling epistasis networks with application to head and neck cancer. Cancer Inform 2015; 14:17-23. [PMID: 25733798 PMCID: PMC4332043 DOI: 10.4137/cin.s17289] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2014] [Revised: 01/05/2015] [Accepted: 01/06/2015] [Indexed: 11/23/2022] Open
Abstract
Epistasis helps to explain how multiple single-nucleotide polymorphisms (SNPs) interact to cause disease. A variety of tools have been developed to detect epistasis. In this article, we explore the strengths and weaknesses of an information theory approach for detecting epistasis and compare it to the logistic regression approach through simulations. We consider several scenarios to simulate the involvement of SNPs in an epistasis network with respect to linkage disequilibrium patterns among them and the presence or absence of main and interaction effects. We conclude that the information theory approach more efficiently detects interaction effects when main effects are absent, whereas, in general, the logistic regression approach is appropriate in all scenarios but results in higher false positives. We compute epistasis networks for SNPs in the FSD1L gene using a two-phase head and neck cancer genome-wide association study involving 2,185 cases and 4,507 controls to demonstrate the practical application of the methods.
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Affiliation(s)
- Rajesh Talluri
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Sanjay Shete
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
- Department of Epidemiology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
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10
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Abstract
Genome-wide association studies (GWASs) have become the focus of the statistical analysis of complex traits in humans, successfully shedding light on several aspects of genetic architecture and biological aetiology. Single-nucleotide polymorphisms (SNPs) are usually modelled as having additive, cumulative and independent effects on the phenotype. Although evidently a useful approach, it is often argued that this is not a realistic biological model and that epistasis (that is, the statistical interaction between SNPs) should be included. The purpose of this Review is to summarize recent directions in methodology for detecting epistasis and to discuss evidence of the role of epistasis in human complex trait variation. We also discuss the relevance of epistasis in the context of GWASs and potential hazards in the interpretation of statistical interaction terms.
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11
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Capasso M, Calabrese FM, Iolascon A, Mellerup E. Combinations of genetic data in a study of neuroblastoma risk genotypes. Cancer Genet 2014; 207:94-7. [PMID: 24726319 DOI: 10.1016/j.cancergen.2014.02.004] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2013] [Revised: 02/09/2014] [Accepted: 02/10/2014] [Indexed: 11/26/2022]
Abstract
Analysis of combinations of genetic changes that occur exclusively in patients may be a supplementary strategy to the single-locus strategy used in many genetic studies. The genotypes of 16 SNPs within susceptibility loci for neuroblastoma (NB) were analyzed in a previous study. In the present study, combinations of these genotypes have been analyzed. The theoretical number of combinations of 3 SNP genotypes taken from 16 SNPs is 15,120. Of these, 14,307 were found in 370 patients and 803 controls; 12,772 combinations were common to both patients and controls; 1,213 were found in controls only; and 322 combinations were found in patients only. Among the latter, a cluster of 24 combinations was found to be significantly associated with NB (P < 0.00001).
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Affiliation(s)
- Mario Capasso
- Department of Molecular Medicine and Medical Biotechnologies, University of Napoli Federico II, Naples, Italy; CEINGE (Centro Ingegneria Genetica) Advanced Biotechnologies, Naples, Italy
| | | | - Achille Iolascon
- Department of Molecular Medicine and Medical Biotechnologies, University of Napoli Federico II, Naples, Italy; CEINGE (Centro Ingegneria Genetica) Advanced Biotechnologies, Naples, Italy
| | - Erling Mellerup
- Laboratory of Neuropsychiatry, Department of Neuroscience and Pharmacology, Faculty of Health, University of Copenhagen, Copenhagen, Denmark.
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12
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Abstract
Integrative genomics studies have greatly advanced our understanding of cardiovascular pathophysiology over the last decade. Here, we highlight the strengths and challenges of this cutting-edge approach and provide examples where novel insights have arisen through the integration of multi-level genomic information and cardiac physiology. Going forward, the integration of comprehensive next-generation sequencing data sets with quantitative phenotypes at the molecular, cellular, and whole-heart level using advanced modelling approaches provides an unprecedented opportunity for cardiovascular science.
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Affiliation(s)
- James S Ware
- MRC Clinical Sciences Centre, Imperial Centre for Translational and Experimental Medicine, Imperial College London, Du Cane Road, London, W12 0NN, UK
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13
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Hothorn LA, Libiger O, Gerhard D. Model-specific tests on variance heterogeneity for detection of potentially interacting genetic loci. BMC Genet 2012; 13:59. [PMID: 22808950 PMCID: PMC3549778 DOI: 10.1186/1471-2156-13-59] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2011] [Accepted: 07/18/2012] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Trait variances among genotype groups at a locus are expected to differ in the presence of an interaction between this locus and another locus or environment. A simple maximum test on variance heterogeneity can thus be used to identify potentially interacting single nucleotide polymorphisms (SNPs). RESULTS We propose a multiple contrast test for variance heterogeneity that compares the mean of Levene residuals for each genotype group with their average as an alternative to a global Levene test. We applied this test to a Bogalusa Heart Study dataset to screen for potentially interacting SNPs across the whole genome that influence a number of quantitative traits. A user-friendly implementation of this method is available in the R statistical software package multcomp. CONCLUSIONS We show that the proposed multiple contrast test of model-specific variance heterogeneity can be used to test for potential interactions between SNPs and unknown alleles, loci or covariates and provide valuable additional information compared with traditional tests. Although the test is statistically valid for severely unbalanced designs, care is needed in interpreting the results at loci with low allele frequencies.
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Affiliation(s)
- Ludwig A Hothorn
- Institute of Biostatistics, Leibniz University Hannover, D-30419 Hannover, Germany.
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Aschard H, Lutz S, Maus B, Duell EJ, Fingerlin TE, Chatterjee N, Kraft P, Van Steen K. Challenges and opportunities in genome-wide environmental interaction (GWEI) studies. Hum Genet 2012; 131:1591-613. [PMID: 22760307 DOI: 10.1007/s00439-012-1192-0] [Citation(s) in RCA: 107] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2012] [Accepted: 06/11/2012] [Indexed: 02/03/2023]
Abstract
The interest in performing gene-environment interaction studies has seen a significant increase with the increase of advanced molecular genetics techniques. Practically, it became possible to investigate the role of environmental factors in disease risk and hence to investigate their role as genetic effect modifiers. The understanding that genetics is important in the uptake and metabolism of toxic substances is an example of how genetic profiles can modify important environmental risk factors to disease. Several rationales exist to set up gene-environment interaction studies and the technical challenges related to these studies-when the number of environmental or genetic risk factors is relatively small-has been described before. In the post-genomic era, it is now possible to study thousands of genes and their interaction with the environment. This brings along a whole range of new challenges and opportunities. Despite a continuing effort in developing efficient methods and optimal bioinformatics infrastructures to deal with the available wealth of data, the challenge remains how to best present and analyze genome-wide environmental interaction (GWEI) studies involving multiple genetic and environmental factors. Since GWEIs are performed at the intersection of statistical genetics, bioinformatics and epidemiology, usually similar problems need to be dealt with as for genome-wide association gene-gene interaction studies. However, additional complexities need to be considered which are typical for large-scale epidemiological studies, but are also related to "joining" two heterogeneous types of data in explaining complex disease trait variation or for prediction purposes.
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Affiliation(s)
- Hugues Aschard
- Department of Epidemiology, Harvard School of Public Health, Boston, MA, USA.
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15
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McEachin RC, Cavalcoli JD. Overlap of genetic influences in phenotypes classically categorized as psychiatric vs medical disorders. World J Med Genet 2011; 1:4-10. [DOI: 10.5496/wjmg.v1.i1.4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Abstract
Psychiatric disorders have traditionally been segregated from medical disorders in terms of drugs, treatment, insurance coverage and training of clinicians. This segregation is consistent with the long-standing observation that there are inherent differences between psychiatric disorders (diseases relating to thoughts, feelings and behavior) and medical disorders (diseases relating to physical processes). However, these differences are growing less distinct as we improve our understanding of the roles of epistasis and pleiotropy in medical genetics. Both psychiatric and medical disorders are predisposed in part by genetic variation, and psychiatric disorders tend to be comorbid with medical disorders. One hypothesis on this interaction posits that certain combinations of genetic variants (epistasis) influence psychiatric disorders due to their impact on the brain, but the associated genes are also expressed in other tissues so the same groups of variants influence medical disorders (pleiotropy). The observation that psychiatric and medical disorders may interact is not novel. Equally, both epistasis and pleiotropy are fundamental concepts in medical genetics. However, we are just beginning to understand how genetic variation can influence both psychiatric and medical disorders. In our recent work, we have discovered gene networks significantly associated with psychiatric and substance use disorders. Invariably, these networks are also significantly associated with medical disorders. Recognizing how genetic variation can influence both psychiatric and medical disorders will help us to understand the etiology of the individual and comorbid disease phenotypes, predict and minimize side effects in drug and other treatments, and help to reduce stigma associated with psychiatric disorders.
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16
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Garcia-Garcia M, Barceló F, Clemente I, Escera C. COMT and ANKK1 gene–gene interaction modulates contextual updating of mental representations. Neuroimage 2011; 56:1641-7. [DOI: 10.1016/j.neuroimage.2011.02.053] [Citation(s) in RCA: 24] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2010] [Revised: 02/14/2011] [Accepted: 02/17/2011] [Indexed: 11/16/2022] Open
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