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Jin N, Rong J, Chen X, Huang L, Ma H. Exploring T-cell exhaustion features in Acute myocardial infarction for a Novel Diagnostic model and new therapeutic targets by bio-informatics and machine learning. BMC Cardiovasc Disord 2024; 24:272. [PMID: 38783198 PMCID: PMC11118734 DOI: 10.1186/s12872-024-03907-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2023] [Accepted: 04/29/2024] [Indexed: 05/25/2024] Open
Abstract
BACKGROUND T-cell exhaustion (TEX), a condition characterized by impaired T-cell function, has been implicated in numerous pathological conditions, but its role in acute myocardial Infarction (AMI) remains largely unexplored. This research aims to identify and characterize all TEX-related genes for AMI diagnosis. METHODS By integrating gene expression profiles, differential expression analysis, gene set enrichment analysis, protein-protein interaction networks, and machine learning algorithms, we were able to decipher the molecular mechanisms underlying TEX and its significant association with AMI. In addition, we investigated the diagnostic validity of the leading TEX-related genes and their interactions with immune cell profiles. Different types of candidate small molecule compounds were ultimately matched with TEX-featured genes in the "DrugBank" database to serve as potential therapeutic medications for future TEX-AMI basic research. RESULTS We screened 1725 differentially expressed genes (DEGs) from 80 AMI samples and 71 control samples, identifying 39 differential TEX-related transcripts in total. Functional enrichment analysis identified potential biological functions and signaling pathways associated with the aforementioned genes. We constructed a TEX signature containing five hub genes with favorable prognostic performance using machine learning algorithms. In addition, the prognostic performance of the nomogram of these five hub genes was adequate (AUC between 0.815 and 0.995). Several dysregulated immune cells were also observed. Finally, six small molecule compounds which could be the future therapeutic for TEX in AMI were discovered. CONCLUSION Five TEX diagnostic feature genes, CD48, CD247, FCER1G, TNFAIP3, and FCGRA, were screened in AMI. Combining these genes may aid in the early diagnosis and risk prediction of AMI, as well as the evaluation of immune cell infiltration and the discovery of new therapeutics.
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Affiliation(s)
- Nake Jin
- Department of Cardiology, The Second Affiliated Hospital, School of Medicine, Zhejiang University, State Key Laboratory of Transvascular Implantation Devices, Cardiovascular Key Laboratory of Zhejiang Province, Hangzhou, 310009, Zhejiang, China
- Department of Cardiology, Ningbo Hangzhou Bay Hospital, Ningbo, 315300, Zhejiang, China
| | - Jiacheng Rong
- Department of Cardiology, Ningbo Hangzhou Bay Hospital, Ningbo, 315300, Zhejiang, China
| | - Xudong Chen
- Department of Cardiology, Ningbo Hangzhou Bay Hospital, Ningbo, 315300, Zhejiang, China
| | - Lei Huang
- Department of Cardiology, Ningbo Hangzhou Bay Hospital, Ningbo, 315300, Zhejiang, China
| | - Hong Ma
- Department of Cardiology, The Second Affiliated Hospital, School of Medicine, Zhejiang University, State Key Laboratory of Transvascular Implantation Devices, Cardiovascular Key Laboratory of Zhejiang Province, Hangzhou, 310009, Zhejiang, China.
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Singhal P, Verma SS, Ritchie MD. Gene Interactions in Human Disease Studies-Evidence Is Mounting. Annu Rev Biomed Data Sci 2023; 6:377-395. [PMID: 37196359 DOI: 10.1146/annurev-biodatasci-102022-120818] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/19/2023]
Abstract
Despite monumental advances in molecular technology to generate genome sequence data at scale, there is still a considerable proportion of heritability in most complex diseases that remains unexplained. Because many of the discoveries have been single-nucleotide variants with small to moderate effects on disease, the functional implication of many of the variants is still unknown and, thus, we have limited new drug targets and therapeutics. We, and many others, posit that one primary factor that has limited our ability to identify novel drug targets from genome-wide association studies may be due to gene interactions (epistasis), gene-environment interactions, network/pathway effects, or multiomic relationships. We propose that many of these complex models explain much of the underlying genetic architecture of complex disease. In this review, we discuss the evidence from multiple research avenues, ranging from pairs of alleles to multiomic integration studies and pharmacogenomics, that supports the need for further investigation of gene interactions (or epistasis) in genetic and genomic studies of human disease. Our goal is to catalog the mounting evidence for epistasis in genetic studies and the connections between genetic interactions and human health and disease that could enable precision medicine of the future.
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Affiliation(s)
- Pankhuri Singhal
- Genetics and Epigenetics Graduate Group, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - Shefali Setia Verma
- Department of Pathology and Laboratory Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - Marylyn D Ritchie
- Department of Genetics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA;
- Penn Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, Pennsylvania, USA
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Ookawa T, Nomura T, Kamahora E, Jiang M, Ochiai Y, Samadi AF, Yamaguchi T, Adachi S, Katsura K, Motobayashi T. Pyramiding of multiple strong-culm genes originating from indica and tropical japonica to the temperate japonica rice. Sci Rep 2022; 12:15400. [PMID: 36100633 PMCID: PMC9470567 DOI: 10.1038/s41598-022-19768-3] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2022] [Accepted: 09/05/2022] [Indexed: 11/16/2022] Open
Abstract
Severe lodging has recurrently occurred at strong typhoon's hitting in recent climate change. The identification of quantitative trait loci and their responsible genes associated with a strong culm and their pyramiding are important for developing high-yielding varieties with a superior lodging resistance. To evaluate the effects of four strong-culm genes on lodging resistance, the temperate japonica near isogenic line (NIL) with the introgressed SCM1 or SCM2 locus of the indica variety, Habataki and the other NIL with the introgeressed SCM3 or SCM4 locus of the tropical japonica variety, Chugoku 117 were developed. Then, we developed the pyramiding lines with double,triple and quadruple combinations derived from step-by-step crosses among NIL-SCM1-NIL-SCM4. Quadruple pyramiding line (NIL-SCM1 + 2 + 3 + 4) showed the largest culm diameter and the highest culm strength among the combinations and increased spikelet number due to the pleiotropic effects of these genes. Pyramiding of strong culm genes resulted in much increased culm thickness, culm strength and spikelet number due to their additive effect. SCM1 mainly contributed to enhance their pyramiding effect. These results in this study suggest the importance of identifying the combinations of superior alleles of strong culm genes among natural variation and pyramiding these genes for improving high-yielding varieties with a superior lodging resistance.
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Affiliation(s)
- Taiichiro Ookawa
- Graduate School of Agriculture, Tokyo University of Agriculture and Technology, 3-5-8 Saiwai-cho, Fuchu, Tokyo, 183-8509, Japan.
| | - Tomohiro Nomura
- Graduate School of Agriculture, Tokyo University of Agriculture and Technology, 3-5-8 Saiwai-cho, Fuchu, Tokyo, 183-8509, Japan
| | - Eri Kamahora
- Graduate School of Agriculture, Tokyo University of Agriculture and Technology, 3-5-8 Saiwai-cho, Fuchu, Tokyo, 183-8509, Japan
| | - Mingjin Jiang
- Rice Research Institute of Guizhou Academy of Agricultural Science, Guiyang, 550006, Guizhou, China
| | - Yusuke Ochiai
- Graduate School of Agriculture, Tokyo University of Agriculture and Technology, 3-5-8 Saiwai-cho, Fuchu, Tokyo, 183-8509, Japan
| | - Ahmad Fahim Samadi
- Graduate School of Agriculture, Tokyo University of Agriculture and Technology, 3-5-8 Saiwai-cho, Fuchu, Tokyo, 183-8509, Japan
| | - Takuya Yamaguchi
- Toyama Prefectural Tonami Agriculture and Forestry Promotion Center, 1-7 Saiwai-cho, Tonami, Toyama, 939-1386, Japan
| | - Shunsuke Adachi
- Graduate School of Agriculture, Tokyo University of Agriculture and Technology, 3-5-8 Saiwai-cho, Fuchu, Tokyo, 183-8509, Japan
| | - Keisuke Katsura
- Graduate School of Agriculture, Tokyo University of Agriculture and Technology, 3-5-8 Saiwai-cho, Fuchu, Tokyo, 183-8509, Japan
| | - Takashi Motobayashi
- Graduate School of Agriculture, Tokyo University of Agriculture and Technology, 3-5-8 Saiwai-cho, Fuchu, Tokyo, 183-8509, Japan
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Dondi R, Hosseinzadeh MM, Guzzi PH. A novel algorithm for finding top-k weighted overlapping densest connected subgraphs in dual networks. APPLIED NETWORK SCIENCE 2021; 6:40. [PMID: 34124340 PMCID: PMC8179714 DOI: 10.1007/s41109-021-00381-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/26/2021] [Accepted: 05/20/2021] [Indexed: 06/12/2023]
Abstract
The use of networks for modelling and analysing relations among data is currently growing. Recently, the use of a single networks for capturing all the aspects of some complex scenarios has shown some limitations. Consequently, it has been proposed to use Dual Networks (DN), a pair of related networks, to analyse complex systems. The two graphs in a DN have the same set of vertices and different edge sets. Common subgraphs among these networks may convey some insights about the modelled scenarios. For instance, the detection of the Top-k Densest Connected subgraphs, i.e. a set k subgraphs having the largest density in the conceptual network which are also connected in the physical network, may reveal set of highly related nodes. After proposing a formalisation of the approach, we propose a heuristic to find a solution, since the problem is computationally hard. A set of experiments on synthetic and real networks is also presented to support our approach.
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Affiliation(s)
- Riccardo Dondi
- Department of Science, University of Bergamo, Bergamo, Italy
| | | | - Pietro H. Guzzi
- Department of Surgical and Medical Sciences, Magna Graecia University, Catanzaro, Italy
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Li H, Jiang S, Li C, Liu L, Lin Z, He H, Deng XW, Zhang Z, Wang X. The hybrid protein interactome contributes to rice heterosis as epistatic effects. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2020; 102:116-128. [PMID: 31736145 DOI: 10.1111/tpj.14616] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/05/2019] [Revised: 10/27/2019] [Accepted: 11/01/2019] [Indexed: 05/15/2023]
Abstract
Heterosis is the phenomenon in which hybrid progeny exhibits superior traits in comparison with those of their parents. Genomic variations between the two parental genomes may generate epistasis interactions, which is one of the genetic hypotheses explaining heterosis. We postulate that protein-protein interactions specific to F1 hybrids (F1 -specific PPIs) may occur when two parental genomes combine, as the proteome of each parent may supply novel interacting partners. To test our assumption, an inter-subspecies hybrid interactome was simulated by in silico PPI prediction between rice japonica (cultivar Nipponbare) and indica (cultivar 9311). Four-thousand, six-hundred and twelve F1 -specific PPIs accounting for 20.5% of total PPIs in the hybrid interactome were found. Genes participating in F1 -specific PPIs tend to encode metabolic enzymes and are generally localized in genomic regions harboring metabolic gene clusters. To test the genetic effect of F1 -specific PPIs in heterosis, genomic selection analysis was performed for trait prediction with additive, dominant and epistatic effects separately considered in the model. We found that the removal of single nucleotide polymorphisms associated with F1 -specific PPIs reduced prediction accuracy when epistatic effects were considered in the model, but no significant changes were observed when additive or dominant effects were considered. In summary, genomic divergence widely dispersed between japonica and indica rice may generate F1 -specific PPIs, part of which may accumulatively contribute to heterosis according to our computational analysis. These candidate F1 -specific PPIs, especially for those involved in metabolic biosynthesis pathways, are worthy of experimental validation when large-scale protein interactome datasets are generated in hybrid rice in the future.
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Affiliation(s)
- Hong Li
- National Maize Improvement Center, College of Agronomy and Biotechnology, China Agricultural University, Beijing, 100193, China
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing, 100193, China
- Key Laboratory of Tropical Biological Resources of Ministry of Education, School of Life and Pharmaceutical Sciences, Hainan University, Haikou, 570228, China
| | - Shuqin Jiang
- National Maize Improvement Center, College of Agronomy and Biotechnology, China Agricultural University, Beijing, 100193, China
| | - Chen Li
- Rice Research Institute, Guangdong Academy of Agricultural Sciences, Guangzhou, 510640, China
| | - Lei Liu
- Beijing Key Laboratory of Plant Resources Research and Development, School of Sciences, Beijing Technology and Business University, Beijing, 100048, China
| | - Zechuan Lin
- State Key Laboratory of Protein and Plant Gene Research, Peking-Tsinghua Center for Life Sciences, School of Advanced Agriculture Sciences and School of Life Sciences, Peking University, Beijing, China
| | - Hang He
- State Key Laboratory of Protein and Plant Gene Research, Peking-Tsinghua Center for Life Sciences, School of Advanced Agriculture Sciences and School of Life Sciences, Peking University, Beijing, China
| | - Xing-Wang Deng
- State Key Laboratory of Protein and Plant Gene Research, Peking-Tsinghua Center for Life Sciences, School of Advanced Agriculture Sciences and School of Life Sciences, Peking University, Beijing, China
| | - Ziding Zhang
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing, 100193, China
| | - Xiangfeng Wang
- National Maize Improvement Center, College of Agronomy and Biotechnology, China Agricultural University, Beijing, 100193, China
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Choi S, Lee S, Kim Y, Hwang H, Park T. HisCoM-GGI: Hierarchical structural component analysis of gene-gene interactions. J Bioinform Comput Biol 2018; 16:1840026. [PMID: 30567476 DOI: 10.1142/s0219720018400267] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Although genome-wide association studies (GWAS) have successfully identified thousands of single nucleotide polymorphisms (SNPs) associated with common diseases, these observations are limited for fully explaining "missing heritability". Determining gene-gene interactions (GGI) are one possible avenue for addressing the missing heritability problem. While many statistical approaches have been proposed to detect GGI, most of these focus primarily on SNP-to-SNP interactions. While there are many advantages of gene-based GGI analyses, such as reducing the burden of multiple-testing correction, and increasing power by aggregating multiple causal signals across SNPs in specific genes, only a few methods are available. In this study, we proposed a new statistical approach for gene-based GGI analysis, "Hierarchical structural CoMponent analysis of Gene-Gene Interactions" (HisCoM-GGI). HisCoM-GGI is based on generalized structured component analysis, and can consider hierarchical structural relationships between genes and SNPs. For a pair of genes, HisCoM-GGI first effectively summarizes all possible pairwise SNP-SNP interactions into a latent variable, from which it then performs GGI analysis. HisCoM-GGI can evaluate both gene-level and SNP-level interactions. Through simulation studies, HisCoM-GGI demonstrated higher statistical power than existing gene-based GGI methods, in analyzing a GWAS of a Korean population for identifying GGI associated with body mass index. Resultantly, HisCoM-GGI successfully identified 14 potential GGI, two of which, (NCOR2 <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mo>×</mml:mo></mml:math> SPOCK1) and (LINGO2 <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mo>×</mml:mo></mml:math> ZNF385D) were successfully replicated in independent datasets. We conclude that HisCoM-GGI method may be a valuable tool for genome to identify GGI in missing heritability, allowing us to better understand the biological genetic mechanisms of complex traits. We conclude that HisCoM-GGI method may be a valuable tool for genome to identify GGI in missing heritability, allowing us to better understand biological genetic mechanisms of complex traits. An implementation of HisCoM-GGI can be downloaded from the website ( http://statgen.snu.ac.kr/software/hiscom-ggi ).
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Affiliation(s)
- Sungkyoung Choi
- Department of Pharmacology, Yonsei University College of Medicine, 50-1 Yonsei-ro Seodaemun-gu, Seoul 03722, Republic of Korea
| | - Sungyoung Lee
- Center for Precision Medicine, Seoul National University Hospital, 71 Daehak-ro Jongno-gu, Seoul 03082, Republic of Korea
| | - Yongkang Kim
- Department of Statistics, Seoul National University, 1 Gwanak-ro Gwanak-gu, Seoul 08826, Republic of Korea.,Department of Psychology, McGill University, 2001 Avenue McGill College, Montreal, Quebec H3A 1G1, Canada
| | - Heungsun Hwang
- Department of Psychology, McGill University, 2001 Avenue McGill College, Montreal, Quebec H3A 1G1, Canada
| | - Taesung Park
- Department of Statistics, Seoul National University, 1 Gwanak-ro Gwanak-gu, Seoul 08826, Republic of Korea.,Interdisciplinary Program in Bioinformatics, Seoul National University, 1 Gwanak-ro Gwanak-gu, Seoul 08826, Republic of Korea
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Verma SS, Ritchie MD. Another Round of "Clue" to Uncover the Mystery of Complex Traits. Genes (Basel) 2018; 9:E61. [PMID: 29370075 PMCID: PMC5852557 DOI: 10.3390/genes9020061] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2017] [Revised: 12/19/2017] [Accepted: 01/15/2018] [Indexed: 12/13/2022] Open
Abstract
A plethora of genetic association analyses have identified several genetic risk loci. Technological and statistical advancements have now led to the identification of not only common genetic variants, but also low-frequency variants, structural variants, and environmental factors, as well as multi-omics variations that affect the phenotypic variance of complex traits in a population, thus referred to as complex trait architecture. The concept of heritability, or the proportion of phenotypic variance due to genetic inheritance, has been studied for several decades, but its application is mainly in addressing the narrow sense heritability (or additive genetic component) from Genome-Wide Association Studies (GWAS). In this commentary, we reflect on our perspective on the complexity of understanding heritability for human traits in comparison to model organisms, highlighting another round of clues beyond GWAS and an alternative approach, investigating these clues comprehensively to help in elucidating the genetic architecture of complex traits.
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Affiliation(s)
- Shefali Setia Verma
- The Huck Institute of Life Sciences, The Pennsylvania State University, University Park, PA 16802, USA.
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.
| | - Marylyn D Ritchie
- The Huck Institute of Life Sciences, The Pennsylvania State University, University Park, PA 16802, USA.
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.
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Skryabin NA, Vasilyev SA, Lebedev IN. Epigenetic silencing of genomic structural variations. RUSS J GENET+ 2017. [DOI: 10.1134/s1022795417100106] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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Musameh MD, Wang WYS, Nelson CP, Lluís-Ganella C, Debiec R, Subirana I, Elosua R, Balmforth AJ, Ball SG, Hall AS, Kathiresan S, Thompson JR, Lucas G, Samani NJ, Tomaszewski M. Analysis of gene-gene interactions among common variants in candidate cardiovascular genes in coronary artery disease. PLoS One 2015; 10:e0117684. [PMID: 25658981 PMCID: PMC4320092 DOI: 10.1371/journal.pone.0117684] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2013] [Accepted: 12/30/2014] [Indexed: 11/19/2022] Open
Abstract
Objective Only a small fraction of coronary artery disease (CAD) heritability has been explained by common variants identified to date. Interactions between genes of importance to cardiovascular regulation may account for some of the missing heritability of CAD. This study aimed to investigate the role of gene-gene interactions in common variants in candidate cardiovascular genes in CAD. Approach and Results 2,101 patients with CAD from the British Heart Foundation Family Heart Study and 2,426 CAD-free controls were included in the discovery cohort. All subjects were genotyped with the Illumina HumanCVD BeadChip enriched for genes and pathways relevant to the cardiovascular system and disease. The primary analysis in the discovery cohort examined pairwise interactions among 913 common (minor allele frequency >0.1) independent single nucleotide polymorphisms (SNPs) with at least nominal association with CAD in single locus analysis. A secondary exploratory interaction analysis was performed among all 11,332 independent common SNPs surviving quality control criteria. Replication analyses were conducted in 2,967 patients and 3,075 controls from the Myocardial Infarction Genetics Consortium. None of the interactions amongst 913 SNPs analysed in the primary analysis was statistically significant after correction for multiple testing (required P<1.2x10-7). Similarly, none of the pairwise gene-gene interactions in the secondary analysis reached statistical significance after correction for multiple testing (required P = 7.8x10-10). None of 36 suggestive interactions from the primary analysis or 31 interactions from the secondary analysis was significant in the replication cohort. Our study had 80% power to detect odds ratios > 1.7 for common variants in the primary analysis. Conclusions Moderately large additive interactions between common SNPs in genes relevant to cardiovascular disease do not appear to play a major role in genetic predisposition to CAD. The role of genetic interactions amongst less common SNPs and with medium and small magnitude effects remain to be investigated.
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Affiliation(s)
- Muntaser D. Musameh
- Department of Cardiovascular Sciences, University of Leicester, Glenfield Hospital, Leicester, United Kingdom
- NIHR Leicester Cardiovascular Biomedical Research Unit, Glenfield Hospital, Leicester, United Kingdom
- * E-mail:
| | - William Y. S. Wang
- School of Medicine, University of Queensland, Brisbane, Queensland, Australia
| | - Christopher P. Nelson
- Department of Cardiovascular Sciences, University of Leicester, Glenfield Hospital, Leicester, United Kingdom
- NIHR Leicester Cardiovascular Biomedical Research Unit, Glenfield Hospital, Leicester, United Kingdom
| | | | - Radoslaw Debiec
- Department of Cardiovascular Sciences, University of Leicester, Glenfield Hospital, Leicester, United Kingdom
- NIHR Leicester Cardiovascular Biomedical Research Unit, Glenfield Hospital, Leicester, United Kingdom
| | - Isaac Subirana
- Cardiovascular Epidemiology and Genetics, IMIM, Barcelona, Spain
- Epidemiology and Public Health Network (CIBERESP), Barcelona, Spain
| | - Roberto Elosua
- Cardiovascular Epidemiology and Genetics, IMIM, Barcelona, Spain
| | - Anthony J. Balmforth
- Division of Epidemiology, LIGHT, School of Medicine, University of Leeds, Leeds, United Kingdom
| | - Stephen G. Ball
- University of Leeds, MCRC, Leeds Institute of Genetics, Health and Therapeutics, Leeds, United Kingdom
| | - Alistair S. Hall
- Division of Epidemiology, LIGHT, School of Medicine, University of Leeds, Leeds, United Kingdom
| | - Sekar Kathiresan
- The Broad Institute of MIT and Harvard, Cambridge, Massachusetts, United States of America
| | - John R. Thompson
- Department of Cardiovascular Sciences, University of Leicester, Glenfield Hospital, Leicester, United Kingdom
| | - Gavin Lucas
- Cardiovascular Epidemiology and Genetics, IMIM, Barcelona, Spain
| | - Nilesh J. Samani
- Department of Cardiovascular Sciences, University of Leicester, Glenfield Hospital, Leicester, United Kingdom
- NIHR Leicester Cardiovascular Biomedical Research Unit, Glenfield Hospital, Leicester, United Kingdom
| | - Maciej Tomaszewski
- Department of Cardiovascular Sciences, University of Leicester, Glenfield Hospital, Leicester, United Kingdom
- NIHR Leicester Cardiovascular Biomedical Research Unit, Glenfield Hospital, Leicester, United Kingdom
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Ma L, Keinan A, Clark AG. Biological knowledge-driven analysis of epistasis in human GWAS with application to lipid traits. Methods Mol Biol 2015; 1253:35-45. [PMID: 25403526 DOI: 10.1007/978-1-4939-2155-3_3] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Abstract
While the importance of epistasis is well established, specific gene-gene interactions have rarely been identified in human genome-wide association studies (GWAS), mainly due to low power associated with such interaction tests. In this chapter, we integrate biological knowledge and human GWAS data to reveal epistatic interactions underlying quantitative lipid traits, which are major risk factors for coronary artery disease. To increase power to detect interactions, we only tested pairs of SNPs filtered by prior biological knowledge, including GWAS results, protein-protein interactions (PPIs), and pathway information. Using published GWAS and 9,713 European Americans (EA) from the Atherosclerosis Risk in Communities (ARIC) study, we identified an interaction between HMGCR and LIPC affecting high-density lipoprotein cholesterol (HDL-C) levels. We then validated this interaction in additional multiethnic cohorts from ARIC, the Framingham Heart Study, and the Multi-Ethnic Study of Atherosclerosis. Both HMGCR and LIPC are involved in the metabolism of lipids and lipoproteins, and LIPC itself has been marginally associated with HDL-C. Furthermore, no significant interaction was detected using PPI and pathway information, mainly due to the stringent significance level required after correcting for the large number of tests conducted. These results suggest the potential of biological knowledge-driven approaches to detect epistatic interactions in human GWAS, which may hold the key to exploring the role gene-gene interactions play in connecting genotypes and complex phenotypes in future GWAS.
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Affiliation(s)
- Li Ma
- Department of Animal and Avian Sciences, University of Maryland, Bldg 142, College Park, MD, 20742, USA,
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Hill SY, Wang S, Carter H, McDermott MD, Zezza N, Stiffler S. Amygdala Volume in Offspring from Multiplex for Alcohol Dependence Families: The Moderating Influence of Childhood Environment and 5-HTTLPR Variation. ACTA ACUST UNITED AC 2015; Suppl 1. [PMID: 25285331 DOI: 10.4172/2329-6488.s1-001] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Abstract
BACKGROUND The increased susceptibility for developing alcohol dependence seen in offspring from families with alcohol dependence may be related to structural and functional differences in brain circuits that influence emotional processing. Early childhood environment, genetic variation in the serotonin transporter-linked polymorphic region (5-HTTLPR) of the SLCA4 gene and allelic variation in the Brain Derived Neurotrophic Factor (BDNF) gene have each been reported to be related to volumetric differences in the temporal lobe especially the amygdala. METHODS Magnetic resonance imaging was used to obtain amygdala volumes for 129 adolescent/young adult individuals who were either High-Risk (HR) offspring from families with multiple cases of alcohol dependence (N=71) or Low-Risk (LR) controls (N=58). Childhood family environment was measured prospectively using age-appropriate versions of the Family Environment Scale during a longitudinal follow-up study. The subjects were genotyped for Brain-Derived Neurotrophic Factor (BDNF) Val66Met and the serotonin transporter polymorphism (5-HTTLPR). Two family environment scale scores (Cohesion and Conflict), genotypic variation, and their interaction were tested for their association with amygdala volumes. Personal and prenatal exposure to alcohol and drugs were considered in statistical analyses in order to more accurately determine the effects of familial risk group differences. RESULTS Amygdala volume was reduced in offspring from families with multiple alcohol dependent members in comparison to offspring from control families. High-Risk offspring who were carriers of the S variant of the 5-HTTLPR polymorphism had reduced amygdala volume in comparison to those with an LL genotype. Larger amygdala volume was associated with greater family cohesion but only in Low-Risk control offspring. CONCLUSIONS Familial risk for alcohol dependence is an important predictor of amygdala volume even when removing cases with significant personal exposure and covarying for prenatal exposure effects. The present study provides new evidence that amygdala volume is modified by 5-HTTLPR variation in High-Risk families.
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Génin E, Coustet B, Allanore Y, Ito I, Teruel M, Constantin A, Schaeverbeke T, Ruyssen-Witrand A, Tohma S, Cantagrel A, Vittecoq O, Barnetche T, Le Loët X, Fardellone P, Furukawa H, Meyer O, Fernández-Gutiérrez B, Balsa A, González-Gay MA, Chiocchia G, Tsuchiya N, Martin J, Dieudé P. Epistatic interaction between BANK1 and BLK in rheumatoid arthritis: results from a large trans-ethnic meta-analysis. PLoS One 2013; 8:e61044. [PMID: 23646104 PMCID: PMC3639995 DOI: 10.1371/journal.pone.0061044] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2012] [Accepted: 03/05/2013] [Indexed: 12/22/2022] Open
Abstract
BACKGROUND BANK1 and BLK belong to the pleiotropic autoimmune genes; recently, epistasis between BANK1 and BLK was detected in systemic lupus erythematosus. Although BLK has been reproducibly identified as a risk factor in rheumatoid arthritis (RA), reports are conflicting about the contribution of BANK1 to RA susceptibility. To ascertain the real impact of BANK1 on RA genetic susceptibility, we performed a large meta-analysis including our original data and tested for an epistatic interaction between BANK1 and BLK in RA susceptibility. PATIENTS AND METHODS We investigated data for 1,915 RA patients and 1,915 ethnically matched healthy controls genotyped for BANK1 rs10516487 and rs3733197 and BLK rs13277113. The association of each SNP and RA was tested by logistic regression. Multivariate analysis was then used with an interaction term to test for an epistatic interaction between the SNPs in the 2 genes. RESULTS None of the SNPs tested individually was significantly associated with RA in the genotyped samples. However, we detected an epistatic interaction between BANK1 rs3733197 and BLK rs13277113 (P(interaction) = 0.037). In individuals carrying the BLK rs13277113 GG genotype, presence of the BANK1 rs3733197 G allele increased the risk of RA (odds ratio 1.21 [95% confidence interval 1.04-1.41], P = 0.015. Combining our results with those of all other studies in a large trans-ethnic meta-analysis revealed an association of the BANK1 rs3733197 G allele and RA (1.11 [1.02-1.21], P = 0.012). CONCLUSION This study confirms BANK1 as an RA susceptibility gene and for the first time provides evidence for epistasis between BANK1 and BLK in RA. Our results illustrate the concept of pleiotropic epistatic interaction, suggesting that BANK1 and BLK might play a role in RA pathogenesis.
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Affiliation(s)
- Emmanuelle Génin
- Institut National de la Santé et de la Recherche Médicale UMR-S946, Univ Paris Diderot, Paris, France
| | - Baptiste Coustet
- Rheumatology Department, Bichat Hospital, Assistance Publique Hôpitaux de Paris, Univ Paris Diderot, Paris, France
| | - Yannick Allanore
- Rheumatology Department A, Cochin Hospital, Assistance Publique Hôpitaux de Paris, Univ Paris Descartes, Paris, France
- Institut National de la Santé et de la Recherche Médicale UMRS-S1016, Univ Paris Descartes, Cochin Hospital, Paris, France
| | - Ikue Ito
- Faculty of Medicine, University of Tsukuba, Tsukuba, Japan
| | - Maria Teruel
- Instituto de Parasitologia y Biomedicina Lopez-Neyra, Consejo Superior de Investigaciones Científicas, Granada, Spain
| | - Arnaud Constantin
- UMR 1027, Institut National de la Santé et de la Recherche Médicale, Toulouse III University and Rheumatology Department, Purpan Hospital, CHU Toulouse, Toulouse, France
| | - Thierry Schaeverbeke
- Rheumatology Department, Pellegrin Hospital, Bordeaux Selagen University, Bordeaux, France
| | - Adeline Ruyssen-Witrand
- UMR 1027, Institut National de la Santé et de la Recherche Médicale, Toulouse III University and Rheumatology Department, Purpan Hospital, CHU Toulouse, Toulouse, France
| | - Shigeto Tohma
- Clinical Research Center for Allergy and Rheumatology, Sagamihara Hospital, National Hospital Organization, Sagamihara, Kanagawa, Japan
| | - Alain Cantagrel
- UMR 1027, Institut National de la Santé et de la Recherche Médicale, Toulouse III University and Rheumatology Department, Purpan Hospital, CHU Toulouse, Toulouse, France
| | - Olivier Vittecoq
- Rheumatology Department, CHU de Rouen-Hôpitaux de Rouen, Institut National de la Santé et de la Recherche Médicale U905, Institute for Research and Innovation in Biomedicine, Rouen University, Rouen, France
| | - Thomas Barnetche
- Rheumatology Department, Pellegrin Hospital, Bordeaux Selagen University, Bordeaux, France
| | - Xavier Le Loët
- Rheumatology Department, CHU de Rouen-Hôpitaux de Rouen, Institut National de la Santé et de la Recherche Médicale U905, Institute for Research and Innovation in Biomedicine, Rouen University, Rouen, France
| | - Patrice Fardellone
- Rheumatology Department, Amiens Teaching Hospital, University of Picardie - Jules Verne, Amiens, France
| | - Hiroshi Furukawa
- Clinical Research Center for Allergy and Rheumatology, Sagamihara Hospital, National Hospital Organization, Sagamihara, Kanagawa, Japan
| | - Olivier Meyer
- Rheumatology Department, Bichat Hospital, Assistance Publique Hôpitaux de Paris, Univ Paris Diderot, Paris, France
| | | | | | | | - Gilles Chiocchia
- Institut National de la Santé et de la Recherche Médicale UMRS-S1016, Univ Paris Descartes, Cochin Hospital, Paris, France
| | | | - Javier Martin
- Instituto de Parasitologia y Biomedicina Lopez-Neyra, Consejo Superior de Investigaciones Científicas, Granada, Spain
| | - Philippe Dieudé
- Rheumatology Department, Bichat Hospital, Assistance Publique Hôpitaux de Paris, Univ Paris Diderot, Paris, France
- Institut National de la Santé et de la Recherche Médicale U699, Bichat Faculty of Medicine, Univ Paris Diderot, Paris, France
- * E-mail:
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Liu Y, Maxwell S, Feng T, Zhu X, Elston RC, Koyutürk M, Chance MR. Gene, pathway and network frameworks to identify epistatic interactions of single nucleotide polymorphisms derived from GWAS data. BMC SYSTEMS BIOLOGY 2012; 6 Suppl 3:S15. [PMID: 23281810 PMCID: PMC3524014 DOI: 10.1186/1752-0509-6-s3-s15] [Citation(s) in RCA: 35] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
Background Interactions among genomic loci (also known as epistasis) have been suggested as one of the potential sources of missing heritability in single locus analysis of genome-wide association studies (GWAS). The computational burden of searching for interactions is compounded by the extremely low threshold for identifying significant p-values due to multiple hypothesis testing corrections. Utilizing prior biological knowledge to restrict the set of candidate SNP pairs to be tested can alleviate this problem, but systematic studies that investigate the relative merits of integrating different biological frameworks and GWAS data have not been conducted. Results We developed four biologically based frameworks to identify pairwise interactions among candidate SNP pairs as follows: (1) for each human protein-coding gene, a set of SNPs associated with that gene was constructed providing a gene-based interaction model, (2) for each known biological pathway, a set of SNPs associated with the genes in the pathway was constructed providing a pathway-based interaction model, (3) a set of SNPs associated with genes in a disease-related subnetwork provides a network-based interaction model, and (4) a framework is based on the function of SNPs. The last approach uses expression SNPs (eSNPs or eQTLs), which are SNPs or loci that have defined effects on the abundance of transcripts of other genes. We constructed pairs of eSNPs and SNPs located in the target genes whose expression is regulated by eSNPs. For all four frameworks the SNP sets were exhaustively tested for pairwise interactions within the sets using a traditional logistic regression model after excluding genes that were previously identified to associate with the trait. Using previously published GWAS data for type 2 diabetes (T2D) and the biologically based pair-wise interaction modeling, we identify twelve genes not seen in the previous single locus analysis. Conclusion We present four approaches to detect interactions associated with complex diseases. The results show our approaches outperform the traditional single locus approaches in detecting genes that previously did not reach significance; the results also provide novel drug targets and biomarkers relevant to the underlying mechanisms of disease.
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Affiliation(s)
- Yu Liu
- Center for Proteomics and Bioinformatics, Case Western Reserve University, Cleveland, OH, USA
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Zhao W, Wineinger NE, Tiwari HK, Mosley TH, Broeckel U, Arnett DK, Kardia SLR, Kabagambe EK, Sun YV. Copy number variations associated with obesity-related traits in African Americans: a joint analysis between GENOA and HyperGEN. Obesity (Silver Spring) 2012; 20:2431-7. [PMID: 22836685 PMCID: PMC3484176 DOI: 10.1038/oby.2012.162] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Obesity is a highly heritable trait and a growing public health problem. African Americans (AAs) are a genetically diverse, yet understudied population with a high prevalence of obesity (BMI >30 kg/m(2)). Recent studies based upon single-nucleotide polymorphisms (SNPs) have identified genetic markers associated with obesity. However, a large proportion of the heritability of obesity remains unexplained. Copy number variation (CNV) has been cited as a possible source of missing heritability in common diseases such as obesity. We conducted a CNV genome-wide association study of BMI in two African-American cohorts from Genetic Epidemiology Network of Arteriopathy (GENOA) and Hypertension Genetic Epidemiology Network (HyperGEN). We performed independent and identical association analyses in each study, then combined the results in a meta-analysis. We identified three CNVs associated with BMI, obesity, and other obesity-related traits after adjusting for multiple testing. These CNVs overlap the PARK2, GYPA, and SGCZ genes. Our results suggest that CNV may play a role in the etiology of obesity in AAs.
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Affiliation(s)
- Wei Zhao
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI
| | - Nathan E. Wineinger
- Department of Biostatistics, University of Alabama at Birmingham, Birmingham, AL
- Scripps Translational Science Institute, Scripps Health, San Diego, CA
| | - Hemant K. Tiwari
- Department of Biostatistics, University of Alabama at Birmingham, Birmingham, AL
| | - Thomas H. Mosley
- Department of Medicine, University of Mississippi Medical Center, Jackson, MS
| | - Ulrich Broeckel
- Department of Pediatrics and Medicine & Human and Molecular Genetics Center, Medical College of Wisconsin, Milwaukee, WI
| | - Donna K. Arnett
- Department of Epidemiology, University of Alabama at Birmingham, Birmingham, AL
| | - Sharon L. R. Kardia
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI
| | - Edmond K. Kabagambe
- Department of Epidemiology, University of Alabama at Birmingham, Birmingham, AL
| | - Yan V. Sun
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI
- Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, GA
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15
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Integration of biological networks and pathways with genetic association studies. Hum Genet 2012; 131:1677-86. [PMID: 22777728 DOI: 10.1007/s00439-012-1198-7] [Citation(s) in RCA: 34] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2012] [Accepted: 06/27/2012] [Indexed: 12/13/2022]
Abstract
Millions of genetic variants have been assessed for their effects on the trait of interest in genome-wide association studies (GWAS). The complex traits are affected by a set of inter-related genes. However, the typical GWAS only examine the association of a single genetic variant at a time. The individual effects of a complex trait are usually small, and the simple sum of these individual effects may not reflect the holistic effect of the genetic system. High-throughput methods enable genomic studies to produce a large amount of data to expand the knowledge base of the biological systems. Biological networks and pathways are built to represent the functional or physical connectivity among genes. Integrated with GWAS data, the network- and pathway-based methods complement the approach of single genetic variant analysis, and may improve the power to identify trait-associated genes. Taking advantage of the biological knowledge, these approaches are valuable to interpret the functional role of the genetic variants, and to further understand the molecular mechanism influencing the traits. The network- and pathway-based methods have demonstrated their utilities, and will be increasingly important to address a number of challenges facing the mainstream GWAS.
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Bebek G, Koyutürk M, Price ND, Chance MR. Network biology methods integrating biological data for translational science. Brief Bioinform 2012; 13:446-59. [PMID: 22390873 PMCID: PMC3404396 DOI: 10.1093/bib/bbr075] [Citation(s) in RCA: 51] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2011] [Revised: 11/29/2011] [Indexed: 12/29/2022] Open
Abstract
The explosion of biomedical data, both on the genomic and proteomic side as well as clinical data, will require complex integration and analysis to provide new molecular variables to better understand the molecular basis of phenotype. Currently, much data exist in silos and is not analyzed in frameworks where all data are brought to bear in the development of biomarkers and novel functional targets. This is beginning to change. Network biology approaches, which emphasize the interactions between genes, proteins and metabolites provide a framework for data integration such that genome, proteome, metabolome and other -omics data can be jointly analyzed to understand and predict disease phenotypes. In this review, recent advances in network biology approaches and results are identified. A common theme is the potential for network analysis to provide multiplexed and functionally connected biomarkers for analyzing the molecular basis of disease, thus changing our approaches to analyzing and modeling genome- and proteome-wide data.
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Knowledge-driven analysis identifies a gene-gene interaction affecting high-density lipoprotein cholesterol levels in multi-ethnic populations. PLoS Genet 2012; 8:e1002714. [PMID: 22654671 PMCID: PMC3359971 DOI: 10.1371/journal.pgen.1002714] [Citation(s) in RCA: 56] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2012] [Accepted: 03/30/2012] [Indexed: 12/17/2022] Open
Abstract
Total cholesterol, low-density lipoprotein cholesterol, triglyceride, and high-density lipoprotein cholesterol (HDL-C) levels are among the most important risk factors for coronary artery disease. We tested for gene–gene interactions affecting the level of these four lipids based on prior knowledge of established genome-wide association study (GWAS) hits, protein–protein interactions, and pathway information. Using genotype data from 9,713 European Americans from the Atherosclerosis Risk in Communities (ARIC) study, we identified an interaction between HMGCR and a locus near LIPC in their effect on HDL-C levels (Bonferroni corrected Pc = 0.002). Using an adaptive locus-based validation procedure, we successfully validated this gene–gene interaction in the European American cohorts from the Framingham Heart Study (Pc = 0.002) and the Multi-Ethnic Study of Atherosclerosis (MESA; Pc = 0.006). The interaction between these two loci is also significant in the African American sample from ARIC (Pc = 0.004) and in the Hispanic American sample from MESA (Pc = 0.04). Both HMGCR and LIPC are involved in the metabolism of lipids, and genome-wide association studies have previously identified LIPC as associated with levels of HDL-C. However, the effect on HDL-C of the novel gene–gene interaction reported here is twice as pronounced as that predicted by the sum of the marginal effects of the two loci. In conclusion, based on a knowledge-driven analysis of epistasis, together with a new locus-based validation method, we successfully identified and validated an interaction affecting a complex trait in multi-ethnic populations. Genome-wide association studies (GWAS) have identified many loci associated with complex human traits or diseases. However, the fraction of heritable variation explained by these loci is often relatively low. Gene–gene interactions might play a significant role in complex traits or diseases and are one of the many possible factors contributing to the missing heritability. However, to date only a few interactions have been found and validated in GWAS due to the limited power caused by the need for multiple-testing correction for the very large number of tests conducted. Here, we used three types of prior knowledge, known GWAS hits, protein–protein interactions, and pathway information, to guide our search for gene–gene interactions affecting four lipid levels. We identified an interaction between HMGCR and a locus near LIPC in their effect on high-density lipoprotein cholesterol (HDL-C) and another pair of loci that interact in their effect on low-density lipoprotein cholesterol (LDL-C). We validated the interaction on HDL-C in a number of independent multiple-ethnic populations, while the interaction underlying LDL-C did not validate. The prior knowledge-driven searching approach and a locus-based validation procedure show the potential for dissecting and validating gene–gene interactions in current and future GWAS.
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Kapur K, Schüpbach T, Xenarios I, Kutalik Z, Bergmann S. Comparison of strategies to detect epistasis from eQTL data. PLoS One 2011; 6:e28415. [PMID: 22205949 PMCID: PMC3242756 DOI: 10.1371/journal.pone.0028415] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2011] [Accepted: 11/07/2011] [Indexed: 11/26/2022] Open
Abstract
Genome-wide association studies have been instrumental in identifying genetic variants associated with complex traits such as human disease or gene expression phenotypes. It has been proposed that extending existing analysis methods by considering interactions between pairs of loci may uncover additional genetic effects. However, the large number of possible two-marker tests presents significant computational and statistical challenges. Although several strategies to detect epistasis effects have been proposed and tested for specific phenotypes, so far there has been no systematic attempt to compare their performance using real data. We made use of thousands of gene expression traits from linkage and eQTL studies, to compare the performance of different strategies. We found that using information from marginal associations between markers and phenotypes to detect epistatic effects yielded a lower false discovery rate (FDR) than a strategy solely using biological annotation in yeast, whereas results from human data were inconclusive. For future studies whose aim is to discover epistatic effects, we recommend incorporating information about marginal associations between SNPs and phenotypes instead of relying solely on biological annotation. Improved methods to discover epistatic effects will result in a more complete understanding of complex genetic effects.
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Affiliation(s)
- Karen Kapur
- Department of Medical Genetics, University of Lausanne, Lausanne, Switzerland.
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Wong L. Using Biological Networks in Protein Function Prediction and Gene Expression Analysis. ACTA ACUST UNITED AC 2011. [DOI: 10.1080/15427951.2011.604561] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/15/2022]
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Castillejo-López C, Delgado-Vega AM, Wojcik J, Kozyrev SV, Thavathiru E, Wu YY, Sánchez E, Pöllmann D, López-Egido JR, Fineschi S, Domínguez N, Lu R, James JA, Merrill JT, Kelly JA, Kaufman KM, Moser KL, Gilkeson G, Frostegård J, Pons-Estel BA, D'Alfonso S, Witte T, Callejas JL, Harley JB, Gaffney PM, Martin J, Guthridge JM, Alarcón-Riquelme ME. Genetic and physical interaction of the B-cell systemic lupus erythematosus-associated genes BANK1 and BLK. Ann Rheum Dis 2011; 71:136-42. [PMID: 21978998 DOI: 10.1136/annrheumdis-2011-200085] [Citation(s) in RCA: 59] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
OBJECTIVES Altered signalling in B cells is a predominant feature of systemic lupus erythematosus (SLE). The genes BANK1 and BLK were recently described as associated with SLE. BANK1 codes for a B-cell-specific cytoplasmic protein involved in B-cell receptor signalling and BLK codes for an Src tyrosine kinase with important roles in B-cell development. To characterise the role of BANK1 and BLK in SLE, a genetic interaction analysis was performed hypothesising that genetic interactions could reveal functional pathways relevant to disease pathogenesis. METHODS The GPAT16 method was used to analyse the gene-gene interactions of BANK1 and BLK. Confocal microscopy was used to investigate co-localisation, and immunoprecipitation was used to verify the physical interaction of BANK1 and BLK. RESULTS Epistatic interactions between BANK1 and BLK polymorphisms associated with SLE were observed in a discovery set of 279 patients and 515 controls from northern Europe. A meta-analysis with 4399 European individuals confirmed the genetic interactions between BANK1 and BLK. As BANK1 was identified as a binding partner of the Src tyrosine kinase LYN, the possibility that BANK1 and BLK could also show a protein-protein interaction was tested. The co-immunoprecipitation and co-localisation of BLK and BANK1 were demonstrated. In a Daudi cell line and primary naive B cells endogenous binding was enhanced upon B-cell receptor stimulation using anti-IgM antibodies. CONCLUSION This study shows a genetic interaction between BANK1 and BLK, and demonstrates that these molecules interact physically. The results have important consequences for the understanding of SLE and other autoimmune diseases and identify a potential new signalling pathway.
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Affiliation(s)
- Casimiro Castillejo-López
- Correspondence to Marta E Alarcón-Riquelme, Arthritis and Clinical Immunology Program, Oklahoma Medical Research Foundation, 825 NE 13th Street, Oklahoma City, OK 73102, USA.
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