1
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Chen Z, Liang H, Wei P. Data-adaptive and pathway-based tests for association studies between somatic mutations and germline variations in human cancers. Genet Epidemiol 2023; 47:617-636. [PMID: 37822029 DOI: 10.1002/gepi.22537] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2022] [Revised: 07/22/2023] [Accepted: 09/18/2023] [Indexed: 10/13/2023]
Abstract
Cancer is a disease driven by a combination of inherited genetic variants and somatic mutations. Recently available large-scale sequencing data of cancer genomes have provided an unprecedented opportunity to study the interactions between them. However, previous studies on this topic have been limited by simple, low statistical power tests such as Fisher's exact test. In this paper, we design data-adaptive and pathway-based tests based on the score statistic for association studies between somatic mutations and germline variations. Previous research has shown that two single-nucleotide polymorphism (SNP)-set-based association tests, adaptive sum of powered score (aSPU) and data-adaptive pathway-based (aSPUpath) tests, increase the power in genome-wide association studies (GWASs) with a single disease trait in a case-control study. We extend aSPU and aSPUpath to multi-traits, that is, somatic mutations of multiple genes in a cohort study, allowing extensive information aggregation at both SNP and gene levels.p $p$ -values from different parameters assuming varying genetic architecture are combined to yield data-adaptive tests for somatic mutations and germline variations. Extensive simulations show that, in comparison with some commonly used methods, our data-adaptive somatic mutations/germline variations tests can be applied to multiple germline SNPs/genes/pathways, and generally have much higher statistical powers while maintaining the appropriate type I error. The proposed tests are applied to a large-scale real-world International Cancer Genome Consortium whole genome sequencing data set of 2583 subjects, detecting more significant and biologically relevant associations compared with the other existing methods on both gene and pathway levels. Our study has systematically identified the associations between various germline variations and somatic mutations across different cancer types, which potentially provides valuable utility for cancer risk prediction, prognosis, and therapeutics.
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Affiliation(s)
- Zhongyuan Chen
- Division of Biostatistics, Medical College of Wisconsin, Milwaukee, Wisconsin, USA
| | - Han Liang
- Department of Bioinformatics and Computational Biology, MD Anderson Cancer Center, Houston, Texas, USA
| | - Peng Wei
- Department of Biostatistics, MD Anderson Cancer Center, Houston, Texas, USA
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2
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Carlin DE, Larsen SJ, Sirupurapu V, Cho MH, Silverman EK, Baumbach J, Ideker T. Hierarchical association of COPD to principal genetic components of biological systems. PLoS One 2023; 18:e0286064. [PMID: 37228113 DOI: 10.1371/journal.pone.0286064] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2021] [Accepted: 05/08/2023] [Indexed: 05/27/2023] Open
Abstract
Many disease-causing genetic variants converge on common biological functions and pathways. Precisely how to incorporate pathway knowledge in genetic association studies is not yet clear, however. Previous approaches employ a two-step approach, in which a regular association test is first performed to identify variants associated with the disease phenotype, followed by a test for functional enrichment within the genes implicated by those variants. Here we introduce a concise one-step approach, Hierarchical Genetic Analysis (Higana), which directly computes phenotype associations against each function in the large hierarchy of biological functions documented by the Gene Ontology. Using this approach, we identify risk genes and functions for Chronic Obstructive Pulmonary Disease (COPD), highlighting microtubule transport, muscle adaptation, and nicotine receptor signaling pathways. Microtubule transport has not been previously linked to COPD, as it integrates genetic variants spread over numerous genes. All associations validate strongly in a second COPD cohort.
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Affiliation(s)
- Daniel E Carlin
- Department of Medicine, Division of Genetics, University of California San Diego, La Jolla, CA, United States of America
| | | | - Vikram Sirupurapu
- Department of Medicine, Division of Genetics, University of California San Diego, La Jolla, CA, United States of America
| | - Michael H Cho
- Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, MA, United States of America
| | - Edwin K Silverman
- Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, MA, United States of America
| | - Jan Baumbach
- Department of Computational Systems Biology, University of Hamburg, Hamburg, Germany
| | - Trey Ideker
- Department of Medicine, Division of Genetics, University of California San Diego, La Jolla, CA, United States of America
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3
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Caudai C, Galizia A, Geraci F, Le Pera L, Morea V, Salerno E, Via A, Colombo T. AI applications in functional genomics. Comput Struct Biotechnol J 2021; 19:5762-5790. [PMID: 34765093 PMCID: PMC8566780 DOI: 10.1016/j.csbj.2021.10.009] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2021] [Revised: 10/05/2021] [Accepted: 10/05/2021] [Indexed: 12/13/2022] Open
Abstract
We review the current applications of artificial intelligence (AI) in functional genomics. The recent explosion of AI follows the remarkable achievements made possible by "deep learning", along with a burst of "big data" that can meet its hunger. Biology is about to overthrow astronomy as the paradigmatic representative of big data producer. This has been made possible by huge advancements in the field of high throughput technologies, applied to determine how the individual components of a biological system work together to accomplish different processes. The disciplines contributing to this bulk of data are collectively known as functional genomics. They consist in studies of: i) the information contained in the DNA (genomics); ii) the modifications that DNA can reversibly undergo (epigenomics); iii) the RNA transcripts originated by a genome (transcriptomics); iv) the ensemble of chemical modifications decorating different types of RNA transcripts (epitranscriptomics); v) the products of protein-coding transcripts (proteomics); and vi) the small molecules produced from cell metabolism (metabolomics) present in an organism or system at a given time, in physiological or pathological conditions. After reviewing main applications of AI in functional genomics, we discuss important accompanying issues, including ethical, legal and economic issues and the importance of explainability.
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Affiliation(s)
- Claudia Caudai
- CNR, Institute of Information Science and Technologies “A. Faedo” (ISTI), Pisa, Italy
| | - Antonella Galizia
- CNR, Institute of Applied Mathematics and Information Technologies (IMATI), Genoa, Italy
| | - Filippo Geraci
- CNR, Institute for Informatics and Telematics (IIT), Pisa, Italy
| | - Loredana Le Pera
- CNR, Institute of Biomembranes, Bioenergetics and Molecular Biotechnologies (IBIOM), Bari, Italy
- CNR, Institute of Molecular Biology and Pathology (IBPM), Rome, Italy
| | - Veronica Morea
- CNR, Institute of Molecular Biology and Pathology (IBPM), Rome, Italy
| | - Emanuele Salerno
- CNR, Institute of Information Science and Technologies “A. Faedo” (ISTI), Pisa, Italy
| | - Allegra Via
- CNR, Institute of Molecular Biology and Pathology (IBPM), Rome, Italy
| | - Teresa Colombo
- CNR, Institute of Molecular Biology and Pathology (IBPM), Rome, Italy
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4
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Lonjou C, Eon-Marchais S, Truong T, Dondon MG, Karimi M, Jiao Y, Damiola F, Barjhoux L, Le Gal D, Beauvallet J, Mebirouk N, Cavaciuti E, Chiesa J, Floquet A, Audebert-Bellanger S, Giraud S, Frebourg T, Limacher JM, Gladieff L, Mortemousque I, Dreyfus H, Lejeune-Dumoulin S, Lasset C, Venat-Bouvet L, Bignon YJ, Pujol P, Maugard CM, Luporsi E, Bonadona V, Noguès C, Berthet P, Delnatte C, Gesta P, Lortholary A, Faivre L, Buecher B, Caron O, Gauthier-Villars M, Coupier I, Mazoyer S, Monraz LC, Kondratova M, Kuperstein I, Guénel P, Barillot E, Stoppa-Lyonnet D, Andrieu N, Lesueur F. Gene- and pathway-level analyses of iCOGS variants highlight novel signaling pathways underlying familial breast cancer susceptibility. Int J Cancer 2021; 148:1895-1909. [PMID: 33368296 PMCID: PMC9290690 DOI: 10.1002/ijc.33457] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2020] [Revised: 11/20/2020] [Accepted: 12/07/2020] [Indexed: 12/17/2022]
Abstract
Single‐nucleotide polymorphisms (SNPs) in over 180 loci have been associated with breast cancer (BC) through genome‐wide association studies involving mostly unselected population‐based case‐control series. Some of them modify BC risk of women carrying a BRCA1 or BRCA2 (BRCA1/2) mutation and may also explain BC risk variability in BC‐prone families with no BRCA1/2 mutation. Here, we assessed the contribution of SNPs of the iCOGS array in GENESIS consisting of BC cases with no BRCA1/2 mutation and a sister with BC, and population controls. Genotyping data were available for 1281 index cases, 731 sisters with BC, 457 unaffected sisters and 1272 controls. In addition to the standard SNP‐level analysis using index cases and controls, we performed pedigree‐based association tests to capture transmission information in the sibships. We also performed gene‐ and pathway‐level analyses to maximize the power to detect associations with lower‐frequency SNPs or those with modest effect sizes. While SNP‐level analyses identified 18 loci, gene‐level analyses identified 112 genes. Furthermore, 31 Kyoto Encyclopedia of Genes and Genomes and 7 Atlas of Cancer Signaling Network pathways were highlighted (false discovery rate of 5%). Using results from the “index case‐control” analysis, we built pathway‐derived polygenic risk scores (PRS) and assessed their performance in the population‐based CECILE study and in a data set composed of GENESIS‐affected sisters and CECILE controls. Although these PRS had poor predictive value in the general population, they performed better than a PRS built using our SNP‐level findings, and we found that the joint effect of family history and PRS needs to be considered in risk prediction models.
What's new?
Genetic studies have identified more than 180 single‐nucleotide polymorphisms (SNPs) associated with breast cancer susceptibility, but these studies are reaching their limits. Here, the authors evaluated SNPs in the iCOGS genotyping array using a multilevel approach, including single variant, gene, and pathway analyses. They measured the contribution of the SNPs to breast cancer in patients who have a sister with breast cancer but do not carry a BRCA1/2 mutation. They showed that a pathway‐derived polygenic risk score performed poorly in the general population, and that the best predictive model must include family history.
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Affiliation(s)
- Christine Lonjou
- Inserm, U900, Institut Curie, Paris, France.,Mines ParisTech, Fontainebleau, France.,PSL Research University, Paris, France
| | - Séverine Eon-Marchais
- Inserm, U900, Institut Curie, Paris, France.,Mines ParisTech, Fontainebleau, France.,PSL Research University, Paris, France
| | - Thérèse Truong
- Université Paris-Saclay, UVSQ, Inserm, CESP, Villejuif, France.,Inserm U1018, CESP, Team Exposome and Heredity, Villejuif, France
| | - Marie-Gabrielle Dondon
- Inserm, U900, Institut Curie, Paris, France.,Mines ParisTech, Fontainebleau, France.,PSL Research University, Paris, France
| | - Mojgan Karimi
- Université Paris-Saclay, UVSQ, Inserm, CESP, Villejuif, France.,Inserm U1018, CESP, Team Exposome and Heredity, Villejuif, France
| | - Yue Jiao
- Inserm, U900, Institut Curie, Paris, France.,Mines ParisTech, Fontainebleau, France.,PSL Research University, Paris, France
| | | | - Laure Barjhoux
- Department of BioPathology, Centre Léon Bérard, Lyon, France
| | - Dorothée Le Gal
- Inserm, U900, Institut Curie, Paris, France.,Mines ParisTech, Fontainebleau, France.,PSL Research University, Paris, France
| | - Juana Beauvallet
- Inserm, U900, Institut Curie, Paris, France.,Mines ParisTech, Fontainebleau, France.,PSL Research University, Paris, France
| | - Noura Mebirouk
- Inserm, U900, Institut Curie, Paris, France.,Mines ParisTech, Fontainebleau, France.,PSL Research University, Paris, France
| | - Eve Cavaciuti
- Inserm, U900, Institut Curie, Paris, France.,Mines ParisTech, Fontainebleau, France.,PSL Research University, Paris, France
| | | | | | | | - Sophie Giraud
- Service de Génétique, Hospices Civils de Lyon, Groupement Hospitalier Est, Bron, France
| | - Thierry Frebourg
- Département de Génétique, Hôpital Universitaire de Rouen, Rouen, France
| | | | - Laurence Gladieff
- Service d'Oncologie Médicale, Institut Claudius Regaud-IUCT-Oncopole, Toulouse, France
| | | | - Hélène Dreyfus
- Clinique Sainte Catherine, Avignon, France.,Département de Génétique, CHU de Grenoble, Hôpital Couple-Enfant, Grenoble, France
| | | | - Christine Lasset
- Université Claude Bernard Lyon 1, Villeurbanne, France.,CNRS UMR 5558, Lyon, France.,Centre Léon Bérard, Unité de Prévention et Epidémiologie Génétique, Lyon, France
| | | | - Yves-Jean Bignon
- Département d'Oncogénétique, Université Clermont Auvergne, UMR INSERM, U1240, Centre Jean Perrin, Clermont Ferrand, France
| | - Pascal Pujol
- Hôpital Arnaud de Villeneuve, CHU Montpellier, Service de Génétique Médicale et Oncogénétique, Montpellier, France.,INSERM 896, CRCM Val d'Aurelle, Montpellier, France
| | - Christine M Maugard
- Département d'Oncobiologie, LBBM, Hôpitaux Universitaires de Strasbourg, Génétique Oncologique Moléculaire, UF1422, Strasbourg, France.,Hôpitaux Universitaires de Strasbourg, UF6948 Génétique Oncologique Clinique, Évaluation Familiale et Suivi, Strasbourg, France
| | - Elisabeth Luporsi
- ICL Alexis Vautrin, Unité d'Oncogénétique, Vandœuvre-lès-Nancy, France
| | - Valérie Bonadona
- Université Claude Bernard Lyon 1, Villeurbanne, France.,CNRS UMR 5558, Lyon, France.,Centre Léon Bérard, Unité de Prévention et Epidémiologie Génétique, Lyon, France
| | - Catherine Noguès
- Département d'Anticipation et de Suivi des Cancers, Oncogénétique Clinique, Institut Paoli-Calmettes, Marseille, France.,Aix Marseille University, INSERM, IRD, SESSTIM, Marseille, France
| | - Pascaline Berthet
- Département de Biopathologie, Centre François Baclesse, Oncogénétique, Caen, France
| | - Capucine Delnatte
- Institut de Cancérologie de l'Ouest, Unité d'Oncogénétique, Saint Herblain, France
| | - Paul Gesta
- CH Georges Renon, Service d'Oncogénétique Régional Poitou-Charentes, Niort, France
| | - Alain Lortholary
- Centre Catherine de Sienne, Service d'Oncologie Médicale, Nantes, France
| | - Laurence Faivre
- Institut GIMI, CHU de Dijon, Hôpital d'Enfants, Dijon, France.,Oncogénétique, Centre de Lutte contre le Cancer Georges François Leclerc, Dijon, France
| | | | - Olivier Caron
- Département de Médecine Oncologique, Gustave Roussy, Villejuif, France
| | | | - Isabelle Coupier
- Hôpital Arnaud de Villeneuve, CHU Montpellier, Service de Génétique Médicale et Oncogénétique, Montpellier, France.,INSERM 896, CRCM Val d'Aurelle, Montpellier, France
| | - Sylvie Mazoyer
- Equipe GENDEV, Centre de Recherche en Neurosciences de Lyon, Inserm U1028, CNRS UMR5292, Université Lyon 1, Université St Etienne, Lyon, France
| | - Luis-Cristobal Monraz
- Inserm, U900, Institut Curie, Paris, France.,Mines ParisTech, Fontainebleau, France.,PSL Research University, Paris, France
| | - Maria Kondratova
- Inserm, U900, Institut Curie, Paris, France.,Mines ParisTech, Fontainebleau, France.,PSL Research University, Paris, France
| | - Inna Kuperstein
- Inserm, U900, Institut Curie, Paris, France.,Mines ParisTech, Fontainebleau, France.,PSL Research University, Paris, France
| | - Pascal Guénel
- Université Paris-Saclay, UVSQ, Inserm, CESP, Villejuif, France.,Inserm U1018, CESP, Team Exposome and Heredity, Villejuif, France
| | - Emmanuel Barillot
- Inserm, U900, Institut Curie, Paris, France.,Mines ParisTech, Fontainebleau, France.,PSL Research University, Paris, France
| | - Dominique Stoppa-Lyonnet
- Institut Curie, Service de Génétique, Paris, France.,Inserm, U830, Université Paris-Descartes, Paris, France
| | - Nadine Andrieu
- Inserm, U900, Institut Curie, Paris, France.,Mines ParisTech, Fontainebleau, France.,PSL Research University, Paris, France
| | - Fabienne Lesueur
- Inserm, U900, Institut Curie, Paris, France.,Mines ParisTech, Fontainebleau, France.,PSL Research University, Paris, France
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5
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Lee E, Eum SY, Slifer SH, Martin ER, Takita C, Wright JL, Hines RB, Hu JJ. Association Between Polymorphisms in DNA Damage Repair Genes and Radiation Therapy-Induced Early Adverse Skin Reactions in a Breast Cancer Population: A Polygenic Risk Score Approach. Int J Radiat Oncol Biol Phys 2020; 106:948-957. [PMID: 32007367 DOI: 10.1016/j.ijrobp.2019.12.021] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2019] [Revised: 12/16/2019] [Accepted: 12/19/2019] [Indexed: 12/12/2022]
Abstract
PURPOSE Genetic variations in DNA damage repair (DDR) genes may influence radiation therapy (RT)-induced acute normal tissue toxicity in patients with breast cancer. Identifying an individual or multiple single-nucleotide polymorphisms (SNPs) associated with RT-induced early adverse skin reactions (EASR) is critical for precision medicine in radiation oncology. METHODS AND MATERIALS At the completion of RT, EASR was assessed using the Oncology Nursing Society scale (0-6) in 416 patients with breast cancer, and Oncology Nursing Society score ≥4 was considered RT-induced EASR. PLINK set-based tests and subsequent individual SNP association analyses were conducted to identify genes and SNPs associated with EASR among the 53 DDR genes and 1968 SNPs. A weighted polygenic risk score (PRS) model was constructed to ascertain the association between the joint effect of risk alleles and EASR. RESULTS The study population consisted of 264 Hispanic whites, 86 blacks or African Americans, 55 non-Hispanic whites, and 11 others. A total of 115 patients (27.6%) developed EASR. Five genes (ATM, CHEK1, ERCC2, RAD51C, and TGFB1) were significantly associated with RT-induced EASR. Nine SNPs within these 5 genes were further identified: ATM rs61915066, CHEK1 rs11220184, RAD51C rs302877, rs405684, TBFB1 rs4803455, rs2241714, and ERCC2 rs60152947, rs10404465, rs1799786. In a multivariable-adjusted PRS model, patients in a higher quartile of PRS were more likely to develop EASR compared with patients in the lowest quartile (ORq2 vs.q1 = 1.94, 95% CI, 0.86-4.39; ORq3 vs.q1 = 3.46, 95% CI, 1.57-7.63; ORq4 vs.q1 = 8.64, 95% CI, 3.92-19.02; and Ptrend < .0001). CONCLUSIONS We newly identified the associations between 9 SNPs in ATM, CHEK1, RAD51C, TGFB1, and ERCC2 and RT-induced EASR. PRS modeling showed its potential in identifying populations at risk. Multiple SNPs in DDR genes may jointly contribute to interindividual variation in RT-induced EASR. Validation in an independent external cohort is required to determine the clinical significance of these predictive biomarkers.
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Affiliation(s)
- Eunkyung Lee
- Department of Health Sciences, University of Central Florida College of Health Professions and Sciences, Orlando, Florida.
| | - Sung Y Eum
- Department of Biochemistry and Molecular Biology, University of Miami Miller School of Medicine, Miami, Florida
| | - Susan H Slifer
- Center for Genetic Epidemiology and Statistical Genetics, John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, Florida
| | - Eden R Martin
- Dr. John T. Macdonald Department of Human Genetics, Center for Genetic Epidemiology and Statistical Genetics, John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, Florida
| | - Cristiane Takita
- Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, Florida; Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, Florida
| | - Jean L Wright
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Baltimore, Maryland
| | - Robert B Hines
- Department of Population Health Sciences, University of Central Florida College of Medicine, Orlando, Florida
| | - Jennifer J Hu
- Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, Florida; Department of Public Health Sciences, University of Miami Miller School of Medicine, Miami, Florida.
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6
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Yang Y, Basu S, Zhang L. A Bayesian hierarchical variable selection prior for pathway-based GWAS using summary statistics. Stat Med 2019; 39:724-739. [PMID: 31777110 DOI: 10.1002/sim.8442] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2019] [Revised: 10/27/2019] [Accepted: 11/10/2019] [Indexed: 12/23/2022]
Abstract
While genome-wide association studies (GWASs) have been widely used to uncover associations between diseases and genetic variants, standard SNP-level GWASs often lack the power to identify SNPs that individually have a moderate effect size but jointly contribute to the disease. To overcome this problem, pathway-based GWASs methods have been developed as an alternative strategy that complements SNP-level approaches. We propose a Bayesian method that uses the generalized fused hierarchical structured variable selection prior to identify pathways associated with the disease using SNP-level summary statistics. Our prior has the flexibility to take in pathway structural information so that it can model the gene-level correlation based on prior biological knowledge, an important feature that makes it appealing compared to existing pathway-based methods. Using simulations, we show that our method outperforms competing methods in various scenarios, particularly when we have pathway structural information that involves complex gene-gene interactions. We apply our method to the Wellcome Trust Case Control Consortium Crohn's disease GWAS data, demonstrating its practical application to real data.
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Affiliation(s)
- Yi Yang
- Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, Minnesota
| | - Saonli Basu
- Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, Minnesota
| | - Lin Zhang
- Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, Minnesota
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7
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Horgusluoglu-Moloch E, Risacher SL, Crane PK, Hibar D, Thompson PM, Saykin AJ, Nho K. Genome-wide association analysis of hippocampal volume identifies enrichment of neurogenesis-related pathways. Sci Rep 2019; 9:14498. [PMID: 31601890 PMCID: PMC6787090 DOI: 10.1038/s41598-019-50507-3] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2018] [Accepted: 09/09/2019] [Indexed: 01/04/2023] Open
Abstract
Adult neurogenesis occurs in the dentate gyrus of the hippocampus during adulthood and contributes to sustaining the hippocampal formation. To investigate whether neurogenesis-related pathways are associated with hippocampal volume, we performed gene-set enrichment analysis using summary statistics from a large-scale genome-wide association study (N = 13,163) of hippocampal volume from the Enhancing Neuro Imaging Genetics through Meta-Analysis (ENIGMA) Consortium and two year hippocampal volume changes from baseline in cognitively normal individuals from Alzheimer's Disease Neuroimaging Initiative Cohort (ADNI). Gene-set enrichment analysis of hippocampal volume identified 44 significantly enriched biological pathways (FDR corrected p-value < 0.05), of which 38 pathways were related to neurogenesis-related processes including neurogenesis, generation of new neurons, neuronal development, and neuronal migration and differentiation. For genes highly represented in the significantly enriched neurogenesis-related pathways, gene-based association analysis identified TESC, ACVR1, MSRB3, and DPP4 as significantly associated with hippocampal volume. Furthermore, co-expression network-based functional analysis of gene expression data in the hippocampal subfields, CA1 and CA3, from 32 normal controls showed that distinct co-expression modules were mostly enriched in neurogenesis related pathways. Our results suggest that neurogenesis-related pathways may be enriched for hippocampal volume and that hippocampal volume may serve as a potential phenotype for the investigation of human adult neurogenesis.
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Grants
- UL1 TR001108 NCATS NIH HHS
- R01 CA129769 NCI NIH HHS
- R35 CA197289 NCI NIH HHS
- P50 GM115318 NIGMS NIH HHS
- R01 AG019771 NIA NIH HHS
- P30 AG010133 NIA NIH HHS
- R03 AG054936 NIA NIH HHS
- U01 AG024904 NIA NIH HHS
- UL1 TR002369 NCATS NIH HHS
- R01 LM011360 NLM NIH HHS
- U54 EB020403 NIBIB NIH HHS
- K01 AG049050 NIA NIH HHS
- R01 LM012535 NLM NIH HHS
- CIHR
- NLM R01 LM012535, NIA R03 AG054936, NIA R01 AG19771, NIA P30 AG10133, NLM R01 LM011360, NSF IIS-1117335, DOD W81XWH-14-2-0151, NCAA 14132004, NIGMS P50GM115318, NCATS UL1 TR001108, NIA K01 AG049050, the Alzheimer’s Association, the Indiana Clinical and Translational Science Institute, and the IU Health-IU School of Medicine Strategic Neuroscience Research Initiative.
- ENIGMA was supported in part by a Consortium grant (U54EB020403 to PMT) from the NIH Institutes contributing to the Big Data to Knowledge (BD2K) Initiative, including the NIBIB and NCI.
- Data collection and sharing for this project was funded by the Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie, Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Cogstate; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer’s Therapeutic Research Institute at the University of Southern California. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California. Additional support for data analysis was provided by NLM R01 LM012535, NIA R03 AG054936, NIA R01 AG19771, NIA P30 AG10133, NLM R01 LM011360, NSF IIS-1117335, DOD W81XWH-14-2-0151, NCAA 14132004, NIGMS P50GM115318, NCATS UL1 TR001108, NIA K01 AG049050, the Alzheimer’s Association, the Indiana Clinical and Translational Science Institute, and the IU Health-IU School of Medicine Strategic Neuroscience Research Initiative.
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Affiliation(s)
- Emrin Horgusluoglu-Moloch
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA
- Department of Genetics and Genomic Sciences, Icahn Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Shannon L Risacher
- Center for Neuroimaging, Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA
- Indiana Alzheimer Disease Center, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Paul K Crane
- Department of Medicine, University of Washington, School of Medicine, Seattle, WA, USA
| | - Derrek Hibar
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, USC Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- Neuroscience Biomarkers, Janssen Research and Development, LLC, San Diego, CA, USA
| | - Paul M Thompson
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, USC Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Andrew J Saykin
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA.
- Center for Neuroimaging, Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA.
- Indiana Alzheimer Disease Center, Indiana University School of Medicine, Indianapolis, IN, USA.
- Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN, USA.
| | - Kwangsik Nho
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA.
- Center for Neuroimaging, Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA.
- Indiana Alzheimer Disease Center, Indiana University School of Medicine, Indianapolis, IN, USA.
- Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN, USA.
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A non-invasive diagnostic model of immunoglobulin A nephropathy and serological markers for evaluating disease severity. Chin Med J (Engl) 2019; 132:647-652. [PMID: 30855344 PMCID: PMC6416104 DOI: 10.1097/cm9.0000000000000121] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022] Open
Abstract
BACKGROUND Immunoglobulin A nephropathy (IgAN) is the most common pathological type of glomerular disease. Kidney biopsy, the gold standard for IgAN diagnosis, has not been routinely applied in hospitals worldwide due to its invasion nature. Thus, we aim to establish a non-invasive diagnostic model and determine markers to evaluate disease severity by analyzing the serological parameters and pathological stages of patients with IgAN. METHODS A total of 272 biopsy-diagnosed IgAN inpatients and 518 non-IgA nephropathy inpatients from the Department of Nephrology of Chinese People's Liberation Army General Hospital were recruited for this study. Routine blood examination, blood coagulation testing, immunoglobulin-complement testing, and clinical biochemistry testing were conducted and pathological stages were analyzed according to Lee grading system. The serological parameters and pathological stages were analyzed. The receiver operating characteristic (ROC) analysis was performed to estimate the diagnostic value of the clinical factors. Logistic regression was used to establish the diagnostic model. RESULTS There were 15 significantly different serological parameters between the IgAN and non-IgAN groups (all P < 0.05). The ROC analysis was performed to measure the diagnostic value for IgAN of these parameters and the results showed that the area under the ROC curve (AUC) of total protein (TP), total cholesterol (TC), fibrinogen (FIB), D-dimer (D2), immunoglobulin A (IgA), and immunoglobulin G (IgG) were more than 0.70. The AUC of the "TC + FIB + D2 + IgA + age" combination was 0.86, with a sensitivity of 85.98% and a specificity of 73.85%. Pathological grades of I, II, III, IV, and V accounted for 2.21%, 17.65%, 62.50%, 11.76%, and 5.88%, respectively, with grade III being the most prevalent. The levels of urea nitrogen (UN) (13.57 ± 5.95 vs. 6.06 ± 3.63, 5.92 ± 2.97, 5.41 ± 1.73, and 8.41 ± 3.72 mmol/L, respectively) and creatinine (Cr) (292.19 ± 162.21 vs. 80.42 ± 24.75, 103.79 ± 72.72, 96.41 ± 33.79, and 163.04 ± 47.51 μmol/L, respectively) were significantly higher in grade V than in the other grades, and the levels of TP (64.45 ± 7.56, 67.16 ± 6.94, 63.22 ± 8.56, and 61.41 ± 10.86 vs. 37.47 ± 5.6 mg/d, respectively), direct bilirubin (DB) (2.34 ± 1.23, 2.58 ± 1.40, 1.91 ± 0.97, and 1.81 ± 1.44 vs. 0.74 ± 0.57 μmol/L, respectively), and IgA (310.35 ± 103.78, 318.48 ± 107.54, 292.58 ± 81.85, and 323.29 ± 181.67 vs. 227.17 ± 68.12 g/L, respectively) were significantly increased in grades II-V compared with grade I (all P < 0.05). CONCLUSIONS The established diagnostic model that combined multiple factors (TC, FIB, D2, IgA, and age) might be used for IgAN non-invasive diagnosis. TP, DB, IgA, Cr, and UN have the potential to be used to evaluate IgAN disease severity.
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Sun R, Hui S, Bader GD, Lin X, Kraft P. Powerful gene set analysis in GWAS with the Generalized Berk-Jones statistic. PLoS Genet 2019; 15:e1007530. [PMID: 30875371 PMCID: PMC6436759 DOI: 10.1371/journal.pgen.1007530] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2018] [Revised: 03/27/2019] [Accepted: 02/28/2019] [Indexed: 11/19/2022] Open
Abstract
A common complementary strategy in Genome-Wide Association Studies (GWAS) is to perform Gene Set Analysis (GSA), which tests for the association between one phenotype of interest and an entire set of Single Nucleotide Polymorphisms (SNPs) residing in selected genes. While there exist many tools for performing GSA, popular methods often include a number of ad-hoc steps that are difficult to justify statistically, provide complicated interpretations based on permutation inference, and demonstrate poor operating characteristics. Additionally, the lack of gold standard gene set lists can produce misleading results and create difficulties in comparing analyses even across the same phenotype. We introduce the Generalized Berk-Jones (GBJ) statistic for GSA, a permutation-free parametric framework that offers asymptotic power guarantees in certain set-based testing settings. To adjust for confounding introduced by different gene set lists, we further develop a GBJ step-down inference technique that can discriminate between gene sets driven to significance by single genes and those demonstrating group-level effects. We compare GBJ to popular alternatives through simulation and re-analysis of summary statistics from a large breast cancer GWAS, and we show how GBJ can increase power by incorporating information from multiple signals in the same gene. In addition, we illustrate how breast cancer pathway analysis can be confounded by the frequency of FGFR2 in pathway lists. Our approach is further validated on two other datasets of summary statistics generated from GWAS of height and schizophrenia.
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Affiliation(s)
- Ryan Sun
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America
| | - Shirley Hui
- The Donnelly Center, University of Toronto, Toronto, Ontario, Canada
| | - Gary D. Bader
- The Donnelly Center, University of Toronto, Toronto, Ontario, Canada
| | - Xihong Lin
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America
| | - Peter Kraft
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America
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10
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Wallace HJ, Cadby G, Melton PE, Wood FM, Falder S, Crowe MM, Martin LJ, Marlow K, Ward SV, Fear MW. Genetic influence on scar height and pliability after burn injury in individuals of European ancestry: A prospective cohort study. Burns 2018; 45:567-578. [PMID: 30595539 DOI: 10.1016/j.burns.2018.10.027] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2018] [Revised: 08/15/2018] [Accepted: 10/04/2018] [Indexed: 12/26/2022]
Abstract
After similar extent of injury there is considerable variability in scarring between individuals, in part due to genetic factors. This study aimed to identify genetic variants associated with scar height and pliability after burn injury. An exome-wide array association study and gene pathway analysis were performed on a prospective cohort of 665 patients treated for burn injury. Outcomes were scar height (SH) and scar pliability (SP) sub-scores of the modified Vancouver Scar Scale (mVSS). DNA was genotyped using the Infinium® HumanCoreExome-24 BeadChip. Associations between genetic variants (single nucleotide polymorphisms) and SH and SP were estimated using an additive genetic model adjusting for age, sex, number of surgical procedures and % total body surface area of burn in subjects of European ancestry. No individual genetic variants achieved the cut-off threshold of significance. Gene regions were analysed for spatially correlated single nucleotide polymorphisms and significant regions identified using comb-p software. This gene list was subject to gene pathway analysis to find which biological process terms were over-represented. Using this approach biological processes related to the nervous system and cell adhesion were the predominant gene pathways associated with both SH and SP. This study suggests genes associated with innervation may be important in scar fibrosis. Further studies using similar and larger datasets will be essential to validate these findings.
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Affiliation(s)
- Hilary J Wallace
- Burn Injury Research Unit, School of Biomedical Sciences, Faculty of Health and Medical Sciences, The University of Western Australia, Perth, Australia; School of Medicine, The University of Notre Dame Australia, Fremantle, Australia.
| | - Gemma Cadby
- Centre for Genetic Origins of Health and Disease, Faculty of Health and Medical Sciences, The University of Western Australia and Faculty of Health Science, Curtin University, Perth, Australia
| | - Phillip E Melton
- Centre for Genetic Origins of Health and Disease, Faculty of Health and Medical Sciences, The University of Western Australia and Faculty of Health Science, Curtin University, Perth, Australia; School of Pharmacy and Biomedical Sciences, Faculty of Health Science, Curtin University, Perth, Australia
| | - Fiona M Wood
- Burn Injury Research Unit, School of Biomedical Sciences, Faculty of Health and Medical Sciences, The University of Western Australia, Perth, Australia; Burns Service of Western Australia, Princess Margaret Hospital for Children and Fiona Stanley Hospital, Perth, Australia
| | - Sian Falder
- Alder Hey Children's NHS Foundation Trust, Liverpool, UK
| | - Margaret M Crowe
- Burns Service of Western Australia, Princess Margaret Hospital for Children and Fiona Stanley Hospital, Perth, Australia
| | - Lisa J Martin
- Burns Service of Western Australia, Princess Margaret Hospital for Children and Fiona Stanley Hospital, Perth, Australia
| | - Karen Marlow
- Alder Hey Children's NHS Foundation Trust, Liverpool, UK
| | - Sarah V Ward
- Centre for Genetic Origins of Health and Disease, Faculty of Health and Medical Sciences, The University of Western Australia and Faculty of Health Science, Curtin University, Perth, Australia
| | - Mark W Fear
- Burn Injury Research Unit, School of Biomedical Sciences, Faculty of Health and Medical Sciences, The University of Western Australia, Perth, Australia
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11
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Seyerle AA, Sitlani CM, Noordam R, Gogarten SM, Li J, Li X, Evans DS, Sun F, Laaksonen MA, Isaacs A, Kristiansson K, Highland HM, Stewart JD, Harris TB, Trompet S, Bis JC, Peloso GM, Brody JA, Broer L, Busch EL, Duan Q, Stilp AM, O'Donnell CJ, Macfarlane PW, Floyd JS, Kors JA, Lin HJ, Li-Gao R, Sofer T, Méndez-Giráldez R, Cummings SR, Heckbert SR, Hofman A, Ford I, Li Y, Launer LJ, Porthan K, Newton-Cheh C, Napier MD, Kerr KF, Reiner AP, Rice KM, Roach J, Buckley BM, Soliman EZ, de Mutsert R, Sotoodehnia N, Uitterlinden AG, North KE, Lee CR, Gudnason V, Stürmer T, Rosendaal FR, Taylor KD, Wiggins KL, Wilson JG, Chen YD, Kaplan RC, Wilhelmsen K, Cupples LA, Salomaa V, van Duijn C, Jukema JW, Liu Y, Mook-Kanamori DO, Lange LA, Vasan RS, Smith AV, Stricker BH, Laurie CC, Rotter JI, Whitsel EA, Psaty BM, Avery CL. Pharmacogenomics study of thiazide diuretics and QT interval in multi-ethnic populations: the cohorts for heart and aging research in genomic epidemiology. THE PHARMACOGENOMICS JOURNAL 2018; 18:215-226. [PMID: 28719597 PMCID: PMC5773415 DOI: 10.1038/tpj.2017.10] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/20/2016] [Revised: 01/14/2017] [Accepted: 03/09/2017] [Indexed: 12/23/2022]
Abstract
Thiazide diuretics, commonly used antihypertensives, may cause QT interval (QT) prolongation, a risk factor for highly fatal and difficult to predict ventricular arrhythmias. We examined whether common single-nucleotide polymorphisms (SNPs) modified the association between thiazide use and QT or its component parts (QRS interval, JT interval) by performing ancestry-specific, trans-ethnic and cross-phenotype genome-wide analyses of European (66%), African American (15%) and Hispanic (19%) populations (N=78 199), leveraging longitudinal data, incorporating corrected standard errors to account for underestimation of interaction estimate variances and evaluating evidence for pathway enrichment. Although no loci achieved genome-wide significance (P<5 × 10-8), we found suggestive evidence (P<5 × 10-6) for SNPs modifying the thiazide-QT association at 22 loci, including ion transport loci (for example, NELL1, KCNQ3). The biologic plausibility of our suggestive results and simulations demonstrating modest power to detect interaction effects at genome-wide significant levels indicate that larger studies and innovative statistical methods are warranted in future efforts evaluating thiazide-SNP interactions.
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Affiliation(s)
- A A Seyerle
- Department of Epidemiology, University of North Carolina, Chapel Hill, NC, USA
- Division of Epidemiology and Community Health, University of Minnesota, Minneapolis, MN, USA
| | - C M Sitlani
- Department of Medicine, University of Washington, Seattle, WA, USA
| | - R Noordam
- Department of Epidemiology, Erasmus MC-University Medical Center Rotterdam, Rotterdam, The Netherlands
- Department of Gerontology and Geriatrics, Leiden University Medical Center, Leiden, The Netherlands
| | - S M Gogarten
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - J Li
- Department of Medicine, Division of Cardiovascular Medicine, Stanford University School of Medicine, Palo Alto, CA, USA
| | - X Li
- Institute for Translational Genomics and Population Sciences, Department of Pediatrics, Los Angeles Biomedical Research Institute at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - D S Evans
- California Pacific Medical Center Research Institute, San Francisco, CA, USA
| | - F Sun
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - M A Laaksonen
- Department of Health, THL-National Institute for Health and Welfare, Helsinki, Finland
| | - A Isaacs
- Department of Epidemiology, Erasmus MC-University Medical Center Rotterdam, Rotterdam, The Netherlands
- CARIM School of Cardiovascular Diseases, Maastricht Centre for Systems Biology (MaCSBio), and Department of Biochemistry, Maastricht University, Maastricht, The Netherlands
| | - K Kristiansson
- Department of Health, THL-National Institute for Health and Welfare, Helsinki, Finland
| | - H M Highland
- Department of Epidemiology, University of North Carolina, Chapel Hill, NC, USA
| | - J D Stewart
- Department of Epidemiology, University of North Carolina, Chapel Hill, NC, USA
- Carolina Population Center, University of North Carolina, Chapel Hill, NC, USA
| | - T B Harris
- Laboratory of Epidemiology, Demography, and Biometry, National Institute on Aging, Bethesda, MD, USA
| | - S Trompet
- Department of Gerontology and Geriatrics, Leiden University Medical Center, Leiden, The Netherlands
- Department of Cardiology, Leiden University Medical Center, Leiden, The Netherlands
| | - J C Bis
- Department of Medicine, University of Washington, Seattle, WA, USA
| | - G M Peloso
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - J A Brody
- Department of Medicine, University of Washington, Seattle, WA, USA
| | - L Broer
- Department of Internal Medicine, Erasmus MC-University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - E L Busch
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Q Duan
- Department of Genetics, University of North Carolina, Chapel Hill, NC, USA
| | - A M Stilp
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - C J O'Donnell
- Department of Medicine, Harvard University, Boston, MA, USA
- National Heart, Lung, and Blood Institute Framingham Heart Study, Framingham, MA, USA
- Cardiology Section, Boston Veterans Administration Healthcare, Boston, MA, USA
| | - P W Macfarlane
- Institute of Health and Wellbeing, University of Glasgow, Glasgow, UK
| | - J S Floyd
- Department of Medicine, University of Washington, Seattle, WA, USA
- Department of Epidemiology, University of Washington, Seattle, WA, USA
| | - J A Kors
- Department of Medical Informatics, Erasmus MC-University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - H J Lin
- Institute for Translational Genomics and Population Sciences, Department of Pediatrics, Los Angeles Biomedical Research Institute at Harbor-UCLA Medical Center, Torrance, CA, USA
- Division of Medical Genetics, Department of Pediatrics, Harbor-UCLA Medical Center, Torrance, CA, USA
| | - R Li-Gao
- Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, The Netherlands
| | - T Sofer
- Department of Medicine, University of Washington, Seattle, WA, USA
| | - R Méndez-Giráldez
- Department of Epidemiology, University of North Carolina, Chapel Hill, NC, USA
| | - S R Cummings
- California Pacific Medical Center Research Institute, San Francisco, CA, USA
| | - S R Heckbert
- Department of Epidemiology, University of Washington, Seattle, WA, USA
| | - A Hofman
- Department of Epidemiology, Erasmus MC-University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - I Ford
- Robertson Center for Biostatistics, University of Glasgow, Glasgow, UK
| | - Y Li
- Department of Genetics, University of North Carolina, Chapel Hill, NC, USA
- Department of Biostatistics, University of North Carolina, Chapel Hill, NC, USA
- Department of Computer Science, University of North Carolina, Chapel Hill, NC, USA
| | - L J Launer
- Laboratory of Epidemiology, Demography, and Biometry, National Institute on Aging, Bethesda, MD, USA
| | - K Porthan
- Division of Cardiology, Heart and Lung Center, Helsinki University Central Hospital, Helsinki, Finland
| | - C Newton-Cheh
- Institute of Health and Wellbeing, University of Glasgow, Glasgow, UK
- Center for Human Genetic Research, Massachusetts General Hospital, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute, Cambridge, MA, USA
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
| | - M D Napier
- Department of Epidemiology, University of North Carolina, Chapel Hill, NC, USA
| | - K F Kerr
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - A P Reiner
- Department of Epidemiology, University of Washington, Seattle, WA, USA
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - K M Rice
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - J Roach
- Research Computing Center, University of North Carolina, Chapel Hill, NC, USA
| | - B M Buckley
- Department of Pharmacology and Therapeutics, University College Cork, Cork, Ireland
| | - E Z Soliman
- Epidemiology Cardiology Research Center (EPICARE), Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - R de Mutsert
- Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, The Netherlands
| | - N Sotoodehnia
- Department of Epidemiology, University of Washington, Seattle, WA, USA
- Division of Cardiology, University of Washington, Seattle, WA, USA
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA
| | - A G Uitterlinden
- Department of Internal Medicine, Erasmus MC-University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - K E North
- Department of Epidemiology, University of North Carolina, Chapel Hill, NC, USA
| | - C R Lee
- Division of Pharmacotherapy and Experimental Therapeutics, Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, USA
| | - V Gudnason
- Icelandic Heart Association, Kopavogur, Iceland
- Department of Medicine, University of Iceland, Reykjavik, Iceland
| | - T Stürmer
- Department of Epidemiology, University of North Carolina, Chapel Hill, NC, USA
- Center for Pharmacoepidemiology, University of North Carolina, Chapel Hill, NC, USA
| | - F R Rosendaal
- Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, The Netherlands
| | - K D Taylor
- Institute for Translational Genomics and Population Sciences, Department of Pediatrics, Los Angeles Biomedical Research Institute at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - K L Wiggins
- Department of Medicine, University of Washington, Seattle, WA, USA
| | - J G Wilson
- Department of Physiology and Biophysics, University of Mississippi Medical Center, Jackson, MS, USA
| | - Y-Di Chen
- Institute for Translational Genomics and Population Sciences, Department of Pediatrics, Los Angeles Biomedical Research Institute at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - R C Kaplan
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, USA
| | - K Wilhelmsen
- Department of Genetics, University of North Carolina, Chapel Hill, NC, USA
- The Renaissance Computing Institute, Chapel Hill, NC, USA
| | - L A Cupples
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
- National Heart, Lung, and Blood Institute Framingham Heart Study, Framingham, MA, USA
| | - V Salomaa
- Department of Health, THL-National Institute for Health and Welfare, Helsinki, Finland
| | - C van Duijn
- Department of Epidemiology, Erasmus MC-University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - J W Jukema
- Department of Cardiology, Leiden University Medical Center, Leiden, The Netherlands
- Durrer Center for Cardiogenetic Research, Amsterdam, The Netherlands
- Interuniversity Cardiology Institute of the Netherlands, Utrecht, The Netherlands
| | - Y Liu
- Department of Epidemiology and Prevention, Division of Public Health Sciences, Wake Forest University, Winston-Salem, NC, USA
| | - D O Mook-Kanamori
- Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, The Netherlands
- Department of Public Health and Primary Care, Leiden University Medical Center, Leiden, the Netherlands
- Department of BESC, Epidemiology Section, King Faisal Specialist Hospital and Research Centre, Riyadh, Saudi Arabia
| | - L A Lange
- Department of Genetics, University of North Carolina, Chapel Hill, NC, USA
| | - R S Vasan
- National Heart, Lung, and Blood Institute Framingham Heart Study, Framingham, MA, USA
- Division of Preventive Medicine and Epidemiology, Department of Epidemiology, Boston University School of Medicine, Boston, MA, USA
| | - A V Smith
- Icelandic Heart Association, Kopavogur, Iceland
- Department of Medicine, University of Iceland, Reykjavik, Iceland
| | - B H Stricker
- Department of Epidemiology, Erasmus MC-University Medical Center Rotterdam, Rotterdam, The Netherlands
- Inspectorate of Health Care, Utrecht, The Netherlands
| | - C C Laurie
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - J I Rotter
- Institute for Translational Genomics and Population Sciences, Department of Pediatrics, Los Angeles Biomedical Research Institute at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - E A Whitsel
- Department of Epidemiology, University of North Carolina, Chapel Hill, NC, USA
- Department of Medicine, University of North Carolina, Chapel Hill, NC, USA
| | - B M Psaty
- Department of Medicine, University of Washington, Seattle, WA, USA
- Department of Epidemiology, University of Washington, Seattle, WA, USA
- Department of Health Services, University of Washington, Seattle, WA, USA
- Group Health Research Institute, Group Health Cooperative, Seattle, WA, USA
| | - C L Avery
- Department of Epidemiology, University of North Carolina, Chapel Hill, NC, USA
- Carolina Population Center, University of North Carolina, Chapel Hill, NC, USA
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12
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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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Cirillo E, Parnell LD, Evelo CT. A Review of Pathway-Based Analysis Tools That Visualize Genetic Variants. Front Genet 2017; 8:174. [PMID: 29163640 PMCID: PMC5681904 DOI: 10.3389/fgene.2017.00174] [Citation(s) in RCA: 42] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2017] [Accepted: 10/24/2017] [Indexed: 01/04/2023] Open
Abstract
Pathway analysis is a powerful method for data analysis in genomics, most often applied to gene expression analysis. It is also promising for single-nucleotide polymorphism (SNP) data analysis, such as genome-wide association study data, because it allows the interpretation of variants with respect to the biological processes in which the affected genes and proteins are involved. Such analyses support an interactive evaluation of the possible effects of variations on function, regulation or interaction of gene products. Current pathway analysis software often does not support data visualization of variants in pathways as an alternate method to interpret genetic association results, and specific statistical methods for pathway analysis of SNP data are not combined with these visualization features. In this review, we first describe the visualization options of the tools that were identified by a literature review, in order to provide insight for improvements in this developing field. Tool evaluation was performed using a computational epistatic dataset of gene–gene interactions for obesity risk. Next, we report the necessity to include in these tools statistical methods designed for the pathway-based analysis with SNP data, expressly aiming to define features for more comprehensive pathway-based analysis tools. We conclude by recognizing that pathway analysis of genetic variations data requires a sophisticated combination of the most useful and informative visual aspects of the various tools evaluated.
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Affiliation(s)
- Elisa Cirillo
- Department of Bioinformatics - BiGCaT, Maastricht University, Maastricht, Netherlands
| | - Laurence D Parnell
- Jean Mayer-USDA Human Nutrition Research Center on Aging at Tufts University, Agricultural Research Service, USDA, Boston, MA, United States
| | - Chris T Evelo
- Department of Bioinformatics - BiGCaT, Maastricht University, Maastricht, Netherlands
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14
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Mozhui K, Snively BM, Rapp SR, Wallace RB, Williams RW, Johnson KC. Genetic Analysis of Mitochondrial Ribosomal Proteins and Cognitive Aging in Postmenopausal Women. Front Genet 2017; 8:127. [PMID: 28983317 PMCID: PMC5613226 DOI: 10.3389/fgene.2017.00127] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2017] [Accepted: 09/04/2017] [Indexed: 11/24/2022] Open
Abstract
Genes encoding mitochondrial ribosomal proteins (MRPs) have been linked to aging and longevity in model organisms (i.e., mice, Caenorhabditis elegans). Here we evaluated if the MRPs have conserved effects on aging traits in humans. We utilized data from 4,504 participants of the Women's Health Initiative Memory Study (WHIMS) who had both longitudinal cognitive data and genetic data. Two aging phenotypes were considered: (1) gross lifespan (time to all-cause mortality), and (2) cognitive aging (longitudinal rate of change in modified mini-mental state scores). We tested genetic association with variants in 78 members of the MRP gene family. Genetic association tests were done at the single nucleotide polymorphism (SNP) level, and at gene-set level using two distinct procedures (GATES and MAGMA). We included SNPs in APOE and adjusted the tests for the APOE-ε4 allele, a known risk factor for dementia. The strongest association signal is for the known cognitive aging SNP, rs429358, in APOE (p-value = 5 × 10-28 for cognitive aging; p-value = 0.03 for survival). We found no significant association between the MRPs and survival time. For cognitive aging, we detected SNP level association for rs189661478 in MRPL23 (p-value < 9 × 10-6). Furthermore, the gene-set analysis showed modest but significant association between the MRP family and cognitive aging. In conclusion, our results indicate a potential pathway-level association between the MRPs and cognitive aging that is independent of the APOE locus. We however did not detect association between the MRPs and lifespan.
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Affiliation(s)
- Khyobeni Mozhui
- Department of Preventive Medicine, University of Tennessee Health Science CenterMemphis, TN, United States
- Department of Genetics, Genomics and Informatics, University of Tennessee Health Science CenterMemphis, TN, United States
| | - Beverly M. Snively
- Department of Biostatistical Sciences, Wake Forest University School of MedicineWinston-Salem, NC, United States
| | - Stephen R. Rapp
- Department of Psychiatry and Behavioral Medicine, Wake Forest University School of MedicineWinston-Salem, NC, United States
| | - Robert B. Wallace
- Department of Epidemiology, University of Iowa College of Public HealthIowa City, IA, United States
| | - Robert W. Williams
- Department of Genetics, Genomics and Informatics, University of Tennessee Health Science CenterMemphis, TN, United States
| | - Karen C. Johnson
- Department of Preventive Medicine, University of Tennessee Health Science CenterMemphis, TN, United States
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15
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Gui H, Kwan JS, Sham PC, Cherny SS, Li M. Sharing of Genes and Pathways Across Complex Phenotypes: A Multilevel Genome-Wide Analysis. Genetics 2017; 206:1601-1609. [PMID: 28495956 PMCID: PMC5500153 DOI: 10.1534/genetics.116.198150] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2016] [Accepted: 04/20/2017] [Indexed: 12/15/2022] Open
Abstract
Evidence from genome-wide association studies (GWAS) suggest that pleiotropic effects on human complex phenotypes are very common. Recently, an atlas of genetic correlations among complex phenotypes has broadened our understanding of human diseases and traits. Here, we examine genetic overlap, from a gene-centric perspective, among the same 24 phenotypes previously investigated for genetic correlations. After adopting the multilevel pipeline (freely available at http://grass.cgs.hku.hk/limx/kgg/), which includes intragenic single nucleotide polymorphisms (SNPs), genes, and gene-sets, to estimate genetic similarities across phenotypes, a large amount of sharing of several biologically related phenotypes was confirmed. In addition, significant genetic overlaps were also found among phenotype pairs that were previously unidentified by SNP-level approaches. All these pairs with new genetic links are supported by earlier epidemiological evidence, although only a few of them have pleiotropic genes in the GWAS Catalog. Hence, our gene and gene-set analyses are able to provide new insights into cross-phenotype connections. The investigation on genetic sharing at three different levels presents a complementary picture of how common DNA sequence variations contribute to disease comorbidities and trait manifestations.
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Affiliation(s)
- Hongsheng Gui
- Center for Genomic Sciences, University of Hong Kong, Hong Kong SAR, China
- Center for Health Policy and Health Services Research, Henry Ford Health System, Detroit, Michigan 48202
| | - Johnny S Kwan
- Department of Psychiatry, University of Hong Kong, Hong Kong SAR, China
| | - Pak C Sham
- Center for Genomic Sciences, University of Hong Kong, Hong Kong SAR, China
- Department of Psychiatry, University of Hong Kong, Hong Kong SAR, China
- The State Key Laboratory of Brain and Cognitive Sciences, University of Hong Kong, Hong Kong SAR, China
| | - Stacey S Cherny
- Center for Genomic Sciences, University of Hong Kong, Hong Kong SAR, China
- Department of Psychiatry, University of Hong Kong, Hong Kong SAR, China
- The State Key Laboratory of Brain and Cognitive Sciences, University of Hong Kong, Hong Kong SAR, China
| | - Miaoxin Li
- Center for Genomic Sciences, University of Hong Kong, Hong Kong SAR, China
- Department of Psychiatry, University of Hong Kong, Hong Kong SAR, China
- Department of Medical Genetics, Center for Genome Research, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, 510275 China
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16
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Aterido A, Julià A, Carreira P, Blanco R, López-Longo JJ, Venegas JJP, Olivé À, Andreu JL, Aguirre-Zamorano MÁ, Vela P, Nolla JM, Marenco-de la Fuente JL, Zea A, Pego JM, Freire M, Díez E, López-Lasanta M, López-Corbeto M, Palau N, Tortosa R, Gelpí JL, Absher D, Myers RM, Fernández-Nebro A, Marsal S. Genome-wide pathway analysis identifies VEGF pathway association with oral ulceration in systemic lupus erythematosus. Arthritis Res Ther 2017; 19:138. [PMID: 28619073 PMCID: PMC5471877 DOI: 10.1186/s13075-017-1345-6] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2017] [Accepted: 05/22/2017] [Indexed: 02/06/2023] Open
Abstract
Background Systemic lupus erythematosus (SLE) is a genetically complex rheumatic disease characterized by heterogeneous clinical manifestations of unknown etiology. Recent studies have suggested the existence of a genetic basis for SLE heterogeneity. The objective of the present study was to identify new genetic variation associated with the clinically relevant phenotypes in SLE. Methods A two-stage pathway-based approach was used to identify the genetic variation associated with the main clinical phenotypes in SLE. In the discovery stage, 482 SLE patients were genotyped using Illumina Human Quad610 microarrays. Association between 798 reference genetic pathways from the Molecular Signatures Database and 11 SLE phenotypes was tested using the set-based method implemented in PLINK software. Pathways significantly associated after multiple test correction were subsequently tested for replication in an independent cohort of 425 SLE patients. Using an in silico approach, we analyzed the functional effects of common SLE therapies on the replicated genetic pathways. The association of known SLE risk variants with the development of the clinical phenotypes was also analyzed. Results In the discovery stage, we found a significant association between the vascular endothelial growth factor (VEGF) pathway and oral ulceration (P value for false discovery rate (PFDR) < 0.05), and between the negative regulation signaling pathway of retinoic acid inducible gene-I/melanoma differentiation associated gene 5 and the production of antinuclear antibodies (PFDR < 0.05). In the replication stage, we validated the association between the VEGF pathway and oral ulceration. Therapies commonly used to treat mucocutaneous phenotypes in SLE were found to strongly influence VEGF pathway gene expression (P = 4.60e-4 to 5.38e-14). Analysis of known SLE risk loci identified a strong association between PTPN22 and the risk of hematologic disorder and with the development of antinuclear antibodies. Conclusions The present study has identified VEGF genetic pathway association with the risk of oral ulceration in SLE. New therapies targeting the VEGF pathway could be more effective in reducing the severity of this phenotype. These findings represent a first step towards the understanding of the genetic basis of phenotype heterogeneity in SLE. Electronic supplementary material The online version of this article (doi:10.1186/s13075-017-1345-6) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Adrià Aterido
- Rheumatology Research Group, Vall d'Hebron Research Institute, Barcelona, 08035, Spain.,Department of Experimental and Health Sciences, Universitat Pompeu Fabra, Barcelona, 08005, Spain
| | - Antonio Julià
- Rheumatology Research Group, Vall d'Hebron Research Institute, Barcelona, 08035, Spain.
| | - Patricia Carreira
- Rheumatology Department, Hospital Universitario 12 de Octubre, Madrid, 28041, Spain
| | - Ricardo Blanco
- Rheumatology Department, Hospital Universitario Marqués de Valdecilla, Santander, 39008, Spain
| | | | | | - Àlex Olivé
- Rheumatology Department, Hospital Universitari Germans Trias i Pujol, Badalona, 08916, Spain
| | - José Luís Andreu
- Rheumatology Department, Hospital Universitario Puerta de Hierro, Madrid, 28222, Spain
| | | | - Paloma Vela
- Rheumatology Department, Hospital General Universitario de Alicante, Alicante, 03010, Spain
| | - Joan M Nolla
- Rheumatology Department, Hospital Universitari de Bellvitge, Barcelona, 08907, Spain
| | | | - Antonio Zea
- Rheumatology Department, Hospital Universitario Ramón y Cajal, 28034, Madrid, Spain
| | - José María Pego
- Instituto de Investigación Biomédica de Vigo, Ourense y Pontevedra, 36204, Spain
| | - Mercedes Freire
- Rheumatology Department, Hospital Universitario A Coruña, A Coruña, 15006, Spain
| | - Elvira Díez
- Rheumatology Department, Hospital Complejo Asistencial Universitario de León, León, 24001, Spain
| | - María López-Lasanta
- Rheumatology Research Group, Vall d'Hebron Research Institute, Barcelona, 08035, Spain
| | - Mireia López-Corbeto
- Rheumatology Research Group, Vall d'Hebron Research Institute, Barcelona, 08035, Spain
| | - Núria Palau
- Rheumatology Research Group, Vall d'Hebron Research Institute, Barcelona, 08035, Spain
| | - Raül Tortosa
- Rheumatology Research Group, Vall d'Hebron Research Institute, Barcelona, 08035, Spain
| | - Josep Lluís Gelpí
- Life Sciences, Barcelona Supercomputing Centre, Barcelona, 08034, Spain
| | - Devin Absher
- HudsonAlpha Institute for Biotechnology, Huntsville, Alabama, 35806, USA
| | - Richard M Myers
- HudsonAlpha Institute for Biotechnology, Huntsville, Alabama, 35806, USA
| | - Antonio Fernández-Nebro
- Rheumatology Department, Hospital Regional Universitario de Málaga, Málaga, 29010, Spain. .,Instituto de Investigación Biomédica de Málaga, Universidad de Málaga, Málaga, 29010, Spain.
| | - Sara Marsal
- Rheumatology Research Group, Vall d'Hebron Research Institute, Barcelona, 08035, Spain
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17
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Improving the detection of pathways in genome-wide association studies by combined effects of SNPs from Linkage Disequilibrium blocks. Sci Rep 2017; 7:3512. [PMID: 28615668 PMCID: PMC5471232 DOI: 10.1038/s41598-017-03826-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2016] [Accepted: 05/05/2017] [Indexed: 01/31/2023] Open
Abstract
Genome-wide association studies (GWAS) have successfully identified single variants associated with diseases. To increase the power of GWAS, gene-based and pathway-based tests are commonly employed to detect more risk factors. However, the gene- and pathway-based association tests may be biased towards genes or pathways containing a large number of single-nucleotide polymorphisms (SNPs) with small P-values caused by high linkage disequilibrium (LD) correlations. To address such bias, numerous pathway-based methods have been developed. Here we propose a novel method, DGAT-path, to divide all SNPs assigned to genes in each pathway into LD blocks, and to sum the chi-square statistics of LD blocks for assessing the significance of the pathway by permutation tests. The method was proven robust with the type I error rate >1.6 times lower than other methods. Meanwhile, the method displays a higher power and is not biased by the pathway size. The applications to the GWAS summary statistics for schizophrenia and breast cancer indicate that the detected top pathways contain more genes close to associated SNPs than other methods. As a result, the method identified 17 and 12 significant pathways containing 20 and 21 novel associated genes, respectively for two diseases. The method is available online by http://sparks-lab.org/server/DGAT-path.
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18
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Abstract
The rapid increase in loci discovered in genome-wide association studies has created a need to understand the biological implications of these results. Gene-set analysis provides a means of gaining such understanding, but the statistical properties of gene-set analysis are not well understood, which compromises our ability to interpret its results. In this Analysis article, we provide an extensive statistical evaluation of the core structure that is inherent to all gene- set analyses and we examine current implementations in available tools. We show which factors affect valid and successful detection of gene sets and which provide a solid foundation for performing and interpreting gene-set analysis.
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19
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Hu Y, Tanaka T, Zhu J, Guan W, Wu JHY, Psaty BM, McKnight B, King IB, Sun Q, Richard M, Manichaikul A, Frazier-Wood AC, Kabagambe EK, Hopkins PN, Ordovas JM, Ferrucci L, Bandinelli S, Arnett DK, Chen YDI, Liang S, Siscovick DS, Tsai MY, Rich SS, Fornage M, Hu FB, Rimm EB, Jensen MK, Lemaitre RN, Mozaffarian D, Steffen LM, Morris AP, Li H, Lin X. Discovery and fine-mapping of loci associated with MUFAs through trans-ethnic meta-analysis in Chinese and European populations. J Lipid Res 2017; 58:974-981. [PMID: 28298293 PMCID: PMC5408616 DOI: 10.1194/jlr.p071860] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2016] [Revised: 02/17/2017] [Indexed: 11/20/2022] Open
Abstract
MUFAs are unsaturated FAs with one double bond and are derived from endogenous synthesis and dietary intake. Accumulating evidence has suggested that plasma and erythrocyte MUFA levels are associated with cardiometabolic disorders, including CVD, T2D, and metabolic syndrome (MS). Previous genome-wide association studies (GWASs) have identified seven loci for plasma and erythrocyte palmitoleic and oleic acid levels in populations of European origin. To identify additional MUFA-associated loci and the potential functional variant at each locus, we performed ethnic-specific GWAS meta-analyses and trans-ethnic meta-analyses in more than 15,000 participants of Chinese and European ancestry. We identified novel genome-wide significant associations for vaccenic acid at FADS1/2 and PKD2L1 [log10(Bayes factor) ≥ 8.07] and for gondoic acid at FADS1/2 and GCKR [log10(Bayes factor) ≥ 6.22], and also observed improved fine-mapping resolutions at FADS1/2 and GCKR loci. The greatest improvement was observed at GCKR, where the number of variants in the 99% credible set was reduced from 16 (covering 94.8 kb) to 5 (covering 19.6 kb, including a missense variant rs1260326) after trans-ethnic meta-analysis. We also confirmed the previously reported associations of PKD2L1, FADS1/2, GCKR, and HIF1AN with palmitoleic acid and of FADS1/2 and LPCAT3 with oleic acid in the Chinese-specific GWAS and the trans-ethnic meta-analyses. Pathway-based analyses suggested that the identified loci were in unsaturated FA metabolism and signaling pathways. Our findings provide novel insight into the genetic basis relevant to MUFA metabolism and biology.
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Affiliation(s)
- Yao Hu
- The Key Laboratory of Nutrition and Metabolism, Institute for Nutritional Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, University of the Chinese Academy of Sciences, Shanghai, People's Republic of China
| | - Toshiko Tanaka
- Translational Gerontology Branch, National Institute on Aging, Baltimore, MD
| | - Jingwen Zhu
- The Key Laboratory of Nutrition and Metabolism, Institute for Nutritional Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, University of the Chinese Academy of Sciences, Shanghai, People's Republic of China
| | - Weihua Guan
- Division of Biostatistics University of Minnesota, Minneapolis, MN
| | - Jason H Y Wu
- George Institute for Global Health, Sydney Medical School, University of Sydney, Sydney, New South Wales, Australia
| | - Bruce M Psaty
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA
- Group Health Research Institute, Group Health Cooperative, Seattle, WA
| | - Barbara McKnight
- Department of Biostatistics, University of Washington, Seattle, WA
| | - Irena B King
- Department of Internal Medicine, University of New Mexico, Albuquerque, NM
| | - Qi Sun
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA
- Department of Nutrition, Harvard T. H. Chan School of Public Health, Harvard University, Cambridge, MA
| | - Melissa Richard
- Institute of Molecular Medicine, University of Texas Health Science Center at Houston, Houston, TX
| | - Ani Manichaikul
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA
- Biostatistics Section, Department of Public Health Sciences, University of Virginia, Charlottesville, VA
| | - Alexis C Frazier-Wood
- USDA Children's Nutrition Research Center, Department of Pediatrics, Baylor College of Medicine, Houston, TX
| | - Edmond K Kabagambe
- Division of Epidemiology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN
| | - Paul N Hopkins
- Department of Internal Medicine, University of Utah, Salt Lake City, UT
| | - Jose M Ordovas
- Nutrition and Genomics Laboratory, Jean Mayer-USDA Human Nutrition Research Center on Aging, Tufts University, Boston, MA
- Department of Epidemiology and Population Genetics, National Center for Cardiovascular Investigation, Madrid, Spain
| | - Luigi Ferrucci
- Translational Gerontology Branch, National Institute on Aging, Baltimore, MD
| | | | - Donna K Arnett
- Department of Epidemiology, University of Alabama at Birmingham, Birmingham, AL
| | - Yii-Der I Chen
- Institute for Translational Genomics and Population Sciences, Los Angeles BioMedical Research Institute at Harbor-UCLA Medical Center, Torrance, CA
| | - Shuang Liang
- Department of Laboratory Medicine and Pathology, University of Minnesota, Minneapolis, MN
| | - David S Siscovick
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA
- Department of Epidemiology, School of Public Health, University of Washington, Seattle, WA
- New York Academy of Medicine, New York, NY
| | - Michael Y Tsai
- Department of Laboratory Medicine and Pathology, University of Minnesota, Minneapolis, MN
| | - Stephen S Rich
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA
| | - Myriam Fornage
- Institute of Molecular Medicine, University of Texas Health Science Center at Houston, Houston, TX
| | - Frank B Hu
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA
- Department of Nutrition, Harvard T. H. Chan School of Public Health, Harvard University, Cambridge, MA
| | - Eric B Rimm
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA
- Department of Nutrition, Harvard T. H. Chan School of Public Health, Harvard University, Cambridge, MA
| | - Majken K Jensen
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA
- Department of Nutrition, Harvard T. H. Chan School of Public Health, Harvard University, Cambridge, MA
| | - Rozenn N Lemaitre
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA
| | - Dariush Mozaffarian
- Friedman School of Nutrition Science and Policy, Tufts University, Boston, MA
| | - Lyn M Steffen
- Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, MN
| | - Andrew P Morris
- Genetic and Genomic Epidemiology Unit, Wellcome Trust Centre for Human Genetics, Oxford, United Kingdom
| | - Huaixing Li
- The Key Laboratory of Nutrition and Metabolism, Institute for Nutritional Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, University of the Chinese Academy of Sciences, Shanghai, People's Republic of China
| | - Xu Lin
- The Key Laboratory of Nutrition and Metabolism, Institute for Nutritional Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, University of the Chinese Academy of Sciences, Shanghai, People's Republic of China
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20
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Bogenpohl JW, Mignogna KM, Smith ML, Miles MF. Integrative Analysis of Genetic, Genomic, and Phenotypic Data for Ethanol Behaviors: A Network-Based Pipeline for Identifying Mechanisms and Potential Drug Targets. Methods Mol Biol 2017; 1488:531-549. [PMID: 27933543 PMCID: PMC5152688 DOI: 10.1007/978-1-4939-6427-7_26] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
Complex behavioral traits, such as alcohol abuse, are caused by an interplay of genetic and environmental factors, producing deleterious functional adaptations in the central nervous system. The long-term behavioral consequences of such changes are of substantial cost to both the individual and society. Substantial progress has been made in the last two decades in understanding elements of brain mechanisms underlying responses to ethanol in animal models and risk factors for alcohol use disorder (AUD) in humans. However, treatments for AUD remain largely ineffective and few medications for this disease state have been licensed. Genome-wide genetic polymorphism analysis (GWAS) in humans, behavioral genetic studies in animal models and brain gene expression studies produced by microarrays or RNA-seq have the potential to produce nonbiased and novel insight into the underlying neurobiology of AUD. However, the complexity of such information, both statistical and informational, has slowed progress toward identifying new targets for intervention in AUD. This chapter describes one approach for integrating behavioral, genetic, and genomic information across animal model and human studies. The goal of this approach is to identify networks of genes functioning in the brain that are most relevant to the underlying mechanisms of a complex disease such as AUD. We illustrate an example of how genomic studies in animal models can be used to produce robust gene networks that have functional implications, and to integrate such animal model genomic data with human genetic studies such as GWAS for AUD. We describe several useful analysis tools for such studies: ComBAT, WGCNA, and EW_dmGWAS. The end result of this analysis is a ranking of gene networks and identification of their cognate hub genes, which might provide eventual targets for future therapeutic development. Furthermore, this combined approach may also improve our understanding of basic mechanisms underlying gene x environmental interactions affecting brain functioning in health and disease.
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Affiliation(s)
- James W Bogenpohl
- Department of Pharmacology and Toxicology, VCU Alcohol Research Center, Virginia Commonwealth University, 980613, Richmond, VA, 23298, USA
| | - Kristin M Mignogna
- Department of Psychiatry, VCU Alcohol Research Center, Virginia Commonwealth University, Richmond, VA, 23298, USA
| | - Maren L Smith
- Department of Human and Molecular Genetics, VCU Alcohol Research Center, Virginia Commonwealth University, Richmond, VA, 23298, USA
| | - Michael F Miles
- Department of Pharmacology and Toxicology, VCU Alcohol Research Center, Virginia Commonwealth University, 980613, Richmond, VA, 23298, USA.
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21
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Kwak IY, Pan W. Gene- and pathway-based association tests for multiple traits with GWAS summary statistics. Bioinformatics 2017; 33:64-71. [PMID: 27592708 PMCID: PMC5198520 DOI: 10.1093/bioinformatics/btw577] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2016] [Revised: 08/08/2016] [Accepted: 08/29/2016] [Indexed: 11/15/2022] Open
Abstract
To identify novel genetic variants associated with complex traits and to shed new insights on underlying biology, in addition to the most popular single SNP-single trait association analysis, it would be useful to explore multiple correlated (intermediate) traits at the gene- or pathway-level by mining existing single GWAS or meta-analyzed GWAS data. For this purpose, we present an adaptive gene-based test and a pathway-based test for association analysis of multiple traits with GWAS summary statistics. The proposed tests are adaptive at both the SNP- and trait-levels; that is, they account for possibly varying association patterns (e.g. signal sparsity levels) across SNPs and traits, thus maintaining high power across a wide range of situations. Furthermore, the proposed methods are general: they can be applied to mixed types of traits, and to Z-statistics or P-values as summary statistics obtained from either a single GWAS or a meta-analysis of multiple GWAS. Our numerical studies with simulated and real data demonstrated the promising performance of the proposed methods. AVAILABILITY AND IMPLEMENTATION The methods are implemented in R package aSPU, freely and publicly available at: https://cran.r-project.org/web/packages/aSPU/ CONTACT: weip@biostat.umn.eduSupplementary information: Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Il-Youp Kwak
- Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN, USA
| | - Wei Pan
- Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN, USA
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22
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Mooney MA, McWeeney SK, Faraone SV, Hinney A, Hebebrand J, Nigg JT, Wilmot B. Pathway analysis in attention deficit hyperactivity disorder: An ensemble approach. Am J Med Genet B Neuropsychiatr Genet 2016; 171:815-26. [PMID: 27004716 PMCID: PMC4983253 DOI: 10.1002/ajmg.b.32446] [Citation(s) in RCA: 37] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/29/2016] [Accepted: 03/07/2016] [Indexed: 12/21/2022]
Abstract
Despite a wealth of evidence for the role of genetics in attention deficit hyperactivity disorder (ADHD), specific and definitive genetic mechanisms have not been identified. Pathway analyses, a subset of gene-set analyses, extend the knowledge gained from genome-wide association studies (GWAS) by providing functional context for genetic associations. However, there are numerous methods for association testing of gene sets and no real consensus regarding the best approach. The present study applied six pathway analysis methods to identify pathways associated with ADHD in two GWAS datasets from the Psychiatric Genomics Consortium. Methods that utilize genotypes to model pathway-level effects identified more replicable pathway associations than methods using summary statistics. In addition, pathways implicated by more than one method were significantly more likely to replicate. A number of brain-relevant pathways, such as RhoA signaling, glycosaminoglycan biosynthesis, fibroblast growth factor receptor activity, and pathways containing potassium channel genes, were nominally significant by multiple methods in both datasets. These results support previous hypotheses about the role of regulation of neurotransmitter release, neurite outgrowth and axon guidance in contributing to the ADHD phenotype and suggest the value of cross-method convergence in evaluating pathway analysis results. © 2016 Wiley Periodicals, Inc.
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Affiliation(s)
- Michael A. Mooney
- Division of Bioinformatics & Computational Biology, Department of Medical Informatics & Clinical Epidemiology, Oregon Health & Science University, Portland, Oregon,OHSU Knight Cancer Institute, Portland, Oregon
| | - Shannon K. McWeeney
- Division of Bioinformatics & Computational Biology, Department of Medical Informatics & Clinical Epidemiology, Oregon Health & Science University, Portland, Oregon,OHSU Knight Cancer Institute, Portland, Oregon,Oregon Clinical and Translational Research Institute, Portland, Oregon
| | - Stephen V. Faraone
- Departments of Psychiatry and Neuroscience & Physiology, State University of New York, Syracuse, New York,K.G. Jebsen Centre for Neuropsychiatric Disorders, Department of Biomedicine, University of Bergen, Bergen, Norway
| | - Anke Hinney
- Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, University Hospital Essen, University of Duisburg-Essen, Essen, Germany
| | - Johannes Hebebrand
- Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, University Hospital Essen, University of Duisburg-Essen, Essen, Germany
| | | | | | - Joel T. Nigg
- Division of Psychology, Department of Psychiatry, Oregon Health & Science University, Portland, Oregon,Department of Behavioral Neuroscience, Oregon Health & Science University, Portland, Oregon
| | - Beth Wilmot
- Division of Bioinformatics & Computational Biology, Department of Medical Informatics & Clinical Epidemiology, Oregon Health & Science University, Portland, Oregon,OHSU Knight Cancer Institute, Portland, Oregon,Oregon Clinical and Translational Research Institute, Portland, Oregon,Correspondence to: Beth Wilmot, Ph.D., Division of Bioinformatics & Computational Biology, Department of Medical Informatics & Clinical Epidemiology, Oregon Health & Science University, 3181 SW Sam Jackson Park Rd., Mail code: CR145, Portland, OR 97239.
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23
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Kwak IY, Pan W. Adaptive gene- and pathway-trait association testing with GWAS summary statistics. Bioinformatics 2016; 32:1178-84. [PMID: 26656570 PMCID: PMC5860182 DOI: 10.1093/bioinformatics/btv719] [Citation(s) in RCA: 46] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2015] [Revised: 11/24/2015] [Accepted: 11/29/2015] [Indexed: 11/12/2022] Open
Abstract
BACKGROUND Gene- and pathway-based analyses offer a useful alternative and complement to the usual single SNP-based analysis for GWAS. On the other hand, most existing gene- and pathway-based tests are not highly adaptive, and/or require the availability of individual-level genotype and phenotype data. It would be desirable to have highly adaptive tests applicable to summary statistics for single SNPs. This has become increasingly important given the popularity of large-scale meta-analyses of multiple GWASs and the practical availability of either single GWAS or meta-analyzed GWAS summary statistics for single SNPs. RESULTS We extend two adaptive tests for gene- and pathway-level association with a univariate trait to the case with GWAS summary statistics without individual-level genotype and phenotype data. We use the WTCCC GWAS data to evaluate and compare the proposed methods and several existing methods. We further illustrate their applications to a meta-analyzed dataset to identify genes and pathways associated with blood pressure, demonstrating the potential usefulness of the proposed methods. The methods are implemented in R package aSPU, freely and publicly available. AVAILABILITY AND IMPLEMENTATION https://cran.r-project.org/web/packages/aSPU/ CONTACT: weip@biostat.umn.edu SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Il-Youp Kwak
- Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN 55455, USA
| | - Wei Pan
- Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN 55455, USA
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24
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Genome-Wide Pathway Analysis Identifies Genetic Pathways Associated with Psoriasis. J Invest Dermatol 2015; 136:593-602. [PMID: 26743605 DOI: 10.1016/j.jid.2015.11.026] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2015] [Revised: 10/27/2015] [Accepted: 11/12/2015] [Indexed: 11/22/2022]
Abstract
Psoriasis is a chronic inflammatory disease with a complex genetic architecture. To date, the psoriasis heritability is only partially explained. However, there is increasing evidence that the missing heritability in psoriasis could be explained by multiple genetic variants of low effect size from common genetic pathways. The objective of this study was to identify new genetic variation associated with psoriasis risk at the pathway level. We genotyped 598,258 single nucleotide polymorphisms in a discovery cohort of 2,281 case-control individuals from Spain. We performed a genome-wide pathway analysis using 1,053 reference biological pathways. A total of 14 genetic pathways (PFDR ≤ 2.55 × 10(-2)) were found to be significantly associated with psoriasis risk. Using an independent validation cohort of 7,353 individuals from the UK, a total of 6 genetic pathways were significantly replicated (PFDR ≤ 3.46 × 10(-2)). We found genetic pathways that had not been previously associated with psoriasis risk such as retinol metabolism (Pcombined = 1.84 × 10(-4)), the transport of inorganic ions and amino acids (Pcombined = 1.57 × 10(-7)), and post-translational protein modification (Pcombined = 1.57 × 10(-7)). In the latter pathway, MGAT5 showed a strong network centrality, and its association with psoriasis risk was further validated in an additional case-control cohort of 3,429 individuals (P < 0.05). These findings provide insights into the biological mechanisms associated with psoriasis susceptibility.
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Pathway-based variant enrichment analysis on the example of dilated cardiomyopathy. Hum Genet 2015; 135:31-40. [PMID: 26547721 DOI: 10.1007/s00439-015-1609-7] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2015] [Accepted: 10/30/2015] [Indexed: 10/22/2022]
Abstract
Genome-wide association (GWA) studies have significantly contributed to the understanding of human genetic variation and its impact on clinical traits. Frequently only a limited number of highly significant associations were considered as biologically relevant. Increasingly, network analysis of affected genes is used to explore the potential role of the genetic background on disease mechanisms. Instead of first determining affected genes or calculating scores for genes and performing pathway analysis on the gene level, we integrated both steps and directly calculated enrichment on the genetic variant level. The respective approach has been tested on dilated cardiomyopathy (DCM) GWA data as showcase. To compute significance values, 5000 permutation tests were carried out and p values were adjusted for multiple testing. For 282 KEGG pathways, we computed variant enrichment scores and significance values. Of these, 65 were significant. Surprisingly, we discovered the "nucleotide excision repair" and "tuberculosis" pathways to be most significantly associated with DCM (p = 10(-9)). The latter pathway is driven by genes of the HLA-D antigen group, a finding that closely resembles previous discoveries made by expression quantitative trait locus analysis in the context of DCM-GWA. Next, we implemented a sub-network-based analysis, which searches for affected parts of KEGG, however, independent on the pre-defined pathways. Here, proteins of the contractile apparatus of cardiac cells as well as the FAS sub-network were found to be affected by common polymorphisms in DCM. In this work, we performed enrichment analysis directly on variants, leveraging the potential to discover biological information in thousands of published GWA studies. The applied approach is cutoff free and considers a ranked list of genetic variants as input.
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Mooney MA, Wilmot B. Gene set analysis: A step-by-step guide. Am J Med Genet B Neuropsychiatr Genet 2015; 168:517-27. [PMID: 26059482 PMCID: PMC4638147 DOI: 10.1002/ajmg.b.32328] [Citation(s) in RCA: 53] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/16/2015] [Accepted: 05/20/2015] [Indexed: 12/21/2022]
Abstract
To maximize the potential of genome-wide association studies, many researchers are performing secondary analyses to identify sets of genes jointly associated with the trait of interest. Although methods for gene-set analyses (GSA), also called pathway analyses, have been around for more than a decade, the field is still evolving. There are numerous algorithms available for testing the cumulative effect of multiple SNPs, yet no real consensus in the field about the best way to perform a GSA. This paper provides an overview of the factors that can affect the results of a GSA, the lessons learned from past studies, and suggestions for how to make analysis choices that are most appropriate for different types of data. © 2015 Wiley Periodicals, Inc.
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Affiliation(s)
- Michael A. Mooney
- Department of Medical Informatics & Clinical Epidemiology, Division of Bioinformatics & Computational Biology, Oregon Health & Science University, Portland, Oregon,OHSU Knight Cancer Institute, Portland, Oregon
| | - Beth Wilmot
- Department of Medical Informatics & Clinical Epidemiology, Division of Bioinformatics & Computational Biology, Oregon Health & Science University, Portland, Oregon,OHSU Knight Cancer Institute, Portland, Oregon,Oregon Clinical and Translational Research Institute, Portland, Oregon,Correspondence to: Beth Wilmot, Department of Medical Informatics & Clinical Epidemiology, Division of Bioinformatics & Computational Biology, Oregon Health & Science University, Portland, OR 97239.
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Pan W, Kwak IY, Wei P. A Powerful Pathway-Based Adaptive Test for Genetic Association with Common or Rare Variants. Am J Hum Genet 2015; 97:86-98. [PMID: 26119817 DOI: 10.1016/j.ajhg.2015.05.018] [Citation(s) in RCA: 54] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2015] [Accepted: 05/21/2015] [Indexed: 12/11/2022] Open
Abstract
In spite of the success of genome-wide association studies (GWASs), only a small proportion of heritability for each complex trait has been explained by identified genetic variants, mainly SNPs. Likely reasons include genetic heterogeneity (i.e., multiple causal genetic variants) and small effect sizes of causal variants, for which pathway analysis has been proposed as a promising alternative to the standard single-SNP-based analysis. A pathway contains a set of functionally related genes, each of which includes multiple SNPs. Here we propose a pathway-based test that is adaptive at both the gene and SNP levels, thus maintaining high power across a wide range of situations with varying numbers of the genes and SNPs associated with a trait. The proposed method is applicable to both common variants and rare variants and can incorporate biological knowledge on SNPs and genes to boost statistical power. We use extensively simulated data and a WTCCC GWAS dataset to compare our proposal with several existing pathway-based and SNP-set-based tests, demonstrating its promising performance and its potential use in practice.
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Li J, Sham PC, Song Y, Li M. SPS: A Simulation Tool for Calculating Power of Set-Based Genetic Association Tests. Genet Epidemiol 2015; 39:395-7. [DOI: 10.1002/gepi.21898] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2014] [Revised: 02/13/2015] [Accepted: 02/23/2015] [Indexed: 01/06/2023]
Affiliation(s)
- Jiang Li
- Department of Biochemistry; the University of Hong Kong; Pokfulam Hong Kong
| | - Pak Chung Sham
- Department of Psychiatry; the University of Hong Kong; Pokfulam Hong Kong
- The Centre for Genomic Sciences; the University of Hong Kong; Pokfulam Hong Kong
- State Key Laboratory for Cognitive and Brain Sciences; the University of Hong Kong; Pokfulam Hong Kong
- The Centre for Reproduction; Development and Growth; the University of Hong Kong; Pokfulam Hong Kong
| | - Youqiang Song
- Department of Biochemistry; the University of Hong Kong; Pokfulam Hong Kong
- The Centre for Genomic Sciences; the University of Hong Kong; Pokfulam Hong Kong
- The Centre for Reproduction; Development and Growth; the University of Hong Kong; Pokfulam Hong Kong
| | - Miaoxin Li
- Department of Psychiatry; the University of Hong Kong; Pokfulam Hong Kong
- The Centre for Genomic Sciences; the University of Hong Kong; Pokfulam Hong Kong
- State Key Laboratory for Cognitive and Brain Sciences; the University of Hong Kong; Pokfulam Hong Kong
- The Centre for Reproduction; Development and Growth; the University of Hong Kong; Pokfulam Hong Kong
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JAG: A Computational Tool to Evaluate the Role of Gene-Sets in Complex Traits. Genes (Basel) 2015; 6:238-51. [PMID: 26110313 PMCID: PMC4488663 DOI: 10.3390/genes6020238] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2014] [Accepted: 04/27/2015] [Indexed: 12/25/2022] Open
Abstract
Gene-set analysis has been proposed as a powerful tool to deal with the highly polygenic architecture of complex traits, as well as with the small effect sizes typically found in GWAS studies for complex traits. We developed a tool, Joint Association of Genetic variants (JAG), which can be applied to Genome Wide Association (GWA) data and tests for the joint effect of all single nucleotide polymorphisms (SNPs) located in a user-specified set of genes or biological pathway. JAG assigns SNPs to genes and incorporates self-contained and/or competitive tests for gene-set analysis. JAG uses permutation to evaluate gene-set significance, which implicitly controls for linkage disequilibrium, sample size, gene size, the number of SNPs per gene and the number of genes in the gene-set. We conducted a power analysis using the Wellcome Trust Case Control Consortium (WTCCC) Crohn’s disease data set and show that JAG correctly identifies validated gene-sets for Crohn’s disease and has more power than currently available tools for gene-set analysis. JAG is a powerful, novel tool for gene-set analysis, and can be freely downloaded from the CTG Lab website.
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ZUO L, ZHANG CK, SAYWARD FG, CHEUNG KH, WANG K, KRYSTAL JH, ZHAO H, LUO X. Gene-based and pathway-based genome-wide association study of alcohol dependence. SHANGHAI ARCHIVES OF PSYCHIATRY 2015; 27:111-8. [PMID: 26120261 PMCID: PMC4466852 DOI: 10.11919/j.issn.1002-0829.215031] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/01/2015] [Accepted: 03/23/2015] [Indexed: 11/03/2022]
Abstract
BACKGROUND The organization of risk genes within signaling pathways may provide clues about the converging neurobiological effects of risk genes for alcohol dependence. AIM Identify risk genes and risk gene pathways for alcohol dependence. METHODS We conducted a pathway-based genome-wide association study (GWAS) of alcohol dependence using a gene-set-rich analytic approach. Approximately one million genetic markers were tested in the discovery sample which included 1409 European-American (EA) alcohol dependent individuals and 1518 EA healthy comparison subjects. An additional 681 African-American (AA) cases and 508 AA healthy subjects served as the replication sample. RESULTS We identified several genome-wide replicable risk genes and risk pathways that were significantly associated with alcohol dependence. After applying the Bonferroni correction for multiple testing, the 'cellextracellular matrix interactions' pathway (p<2.0E-4 in EAs) and the PXN gene (which encodes paxillin) (p=3.9E-7 in EAs) within this pathway were the most promising risk factors for alcohol dependence. There were also two nominally replicable pathways enriched in alcohol dependence-related genes in both EAs (0.015≤p≤0.035) and AAs (0.025≤p≤0.050): the 'Na+/Cl- dependent neurotransmitter transporters' pathway and the 'other glycan degradation' pathway. CONCLUSION These findings provide new evidence highlighting several genes and biological signaling processes that may be related to the risk for alcohol dependence.
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Affiliation(s)
- Lingjun ZUO
- Department of Psychiatry, Yale University School of
Medicine, New Haven, CT, United States
| | - Clarence K. ZHANG
- Department of Epidemiology and Public Health, Yale
University School of Medicine, New Haven, CT, United States
- Biostatistics Resource, Keck Laboratory, Department of
Genetics, Yale University School of Medicine, New Haven, CT, United States
| | - Frederick G. SAYWARD
- Center for Medical Informatics, Yale University School of
Medicine, New Haven, CT, United States
- Cooperative Studies Program Coordinating Center, VA
Connecticut Healthcare System, West Haven, CT, United States
| | - Kei-Hoi CHEUNG
- Center for Medical Informatics, Yale University School of
Medicine, New Haven, CT, United States
| | - Kesheng WANG
- Department of Biostatistics and Epidemiology, College of
Public Health, East Tennessee State University, Johnson City, TN, United
States
| | - John H. KRYSTAL
- Department of Psychiatry, Yale University School of
Medicine, New Haven, CT, United States
| | - Hongyu ZHAO
- Department of Epidemiology and Public Health, Yale
University School of Medicine, New Haven, CT, United States
| | - Xingguang LUO
- Department of Psychiatry, Yale University School of
Medicine, New Haven, CT, United States
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Malhotra J, Sartori S, Brennan P, Zaridze D, Szeszenia-Dabrowska N, Świątkowska B, Rudnai P, Lissowska J, Fabianova E, Mates D, Bencko V, Gaborieau V, Stücker I, Foretova L, Janout V, Boffetta P. Effect of occupational exposures on lung cancer susceptibility: a study of gene-environment interaction analysis. Cancer Epidemiol Biomarkers Prev 2015; 24:570-9. [PMID: 25583949 DOI: 10.1158/1055-9965.epi-14-1143-t] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
BACKGROUND Occupational exposures are known risk factors for lung cancer. Role of genetically determined host factors in occupational exposure-related lung cancer is unclear. METHODS We used genome-wide association (GWA) data from a case-control study conducted in 6 European countries from 1998 to 2002 to identify gene-occupation interactions and related pathways for lung cancer risk. GWA analysis was performed for each exposure using logistic regression and interaction term for genotypes, and exposure was included in this model. Both SNP-based and gene-based interaction P values were calculated. Pathway analysis was performed using three complementary methods, and analyses were adjusted for multiple comparisons. We analyzed 312,605 SNPs and occupational exposure to 70 agents from 1,802 lung cancer cases and 1,725 cancer-free controls. RESULTS Mean age of study participants was 60.1 ± 9.1 years and 75% were male. Largest number of significant associations (P ≤ 1 × 10(-5)) at SNP level was demonstrated for nickel, brick dust, concrete dust, and cement dust, and for brick dust and cement dust at the gene-level (P ≤ 1 × 10(-4)). Approximately 14 occupational exposures showed significant gene-occupation interactions with pathways related to response to environmental information processing via signal transduction (P < 0.001 and FDR < 0.05). Other pathways that showed significant enrichment were related to immune processes and xenobiotic metabolism. CONCLUSION Our findings suggest that pathways related to signal transduction, immune process, and xenobiotic metabolism may be involved in occupational exposure-related lung carcinogenesis. IMPACT Our study exemplifies an integrative approach using pathway-based analysis to demonstrate the role of genetic variants in occupational exposure-related lung cancer susceptibility. Cancer Epidemiol Biomarkers Prev; 24(3); 570-9. ©2015 AACR.
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Affiliation(s)
- Jyoti Malhotra
- Icahn School of Medicine at Mount Sinai, New York, New York.
| | | | - Paul Brennan
- International Agency for Research on Cancer, Lyon, France
| | | | | | - Beata Świątkowska
- Department of Epidemiology, The Nofer Institute of Occupational Medicine, Lodz, Poland
| | - Peter Rudnai
- National Institute of Environmental Health, Budapest, Hungary
| | - Jolanta Lissowska
- M. Sklodowska-Curie Memorial Cancer Center and Institute of Oncology, Warsaw, Poland
| | - Eleonora Fabianova
- Department of Occupational Health, Specialized State Health Institute, Banska Bystrica, Slovakia
| | - Dana Mates
- National Institute of Public Health, Bucharest, Romania
| | - Vladimir Bencko
- Institute of Hygiene and Epidemiology, Charles University, First Faculty of Medicine, Prague, Czech Republic
| | | | - Isabelle Stücker
- Centre for Research in Epidemiology and Population Health, INSERM, Villejuif, France
| | - Lenka Foretova
- Department of Cancer Epidemiology and Genetics, Masaryk Memorial Cancer Institute and MF MU Brno, Brno, Czech Republic
| | - Vladimir Janout
- Department of Preventive Medicine, Faculty of Medicine, Palacky University, Olomouc, Czech Republic
| | - Paolo Boffetta
- Icahn School of Medicine at Mount Sinai, New York, New York
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Quan B, Qi X, Yu Z, Jiang Y, Liao M, Wang G, Feng R, Zhang L, Chen Z, Jiang Q, Liu G. Pathway analysis of genome-wide association study and transcriptome data highlights new biological pathways in colorectal cancer. Mol Genet Genomics 2014; 290:603-10. [PMID: 25362561 DOI: 10.1007/s00438-014-0945-y] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2014] [Accepted: 10/17/2014] [Indexed: 12/11/2022]
Abstract
Colorectal cancer (CRC) is a common malignancy that meets the definition of a complex disease. Genome-wide association study (GWAS) has identified several loci of weak predictive value in CRC, however, these do not fully explain the occurrence risk. Recently, gene set analysis has allowed enhanced interpretation of GWAS data in CRC, identifying a number of metabolic pathways as important for disease pathogenesis. Whether there are other important pathways involved in CRC, however, remains unclear. We present a systems analysis of KEGG pathways in CRC using (1) a human CRC GWAS dataset and (2) a human whole transcriptome CRC case-control expression dataset. Analysis of the GWAS dataset revealed significantly enriched KEGG pathways related to metabolism, immune system and diseases, cellular processes, environmental information processing, genetic information processing, and neurodegenerative diseases. Altered gene expression was confirmed in these pathways using the transcriptome dataset. Taken together, these findings not only confirm previous work in this area, but also highlight new biological pathways whose deregulation is critical for CRC. These results contribute to our understanding of disease-causing mechanisms and will prove useful for future genetic and functional studies in CRC.
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Affiliation(s)
- Baoku Quan
- Department of General Surgery, The First Hospital of Harbin, Harbin, China
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Backes C, Rühle F, Stoll M, Haas J, Frese K, Franke A, Lieb W, Wichmann HE, Weis T, Kloos W, Lenhof HP, Meese E, Katus H, Meder B, Keller A. Systematic permutation testing in GWAS pathway analyses: identification of genetic networks in dilated cardiomyopathy and ulcerative colitis. BMC Genomics 2014; 15:622. [PMID: 25052024 PMCID: PMC4223581 DOI: 10.1186/1471-2164-15-622] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2014] [Accepted: 07/17/2014] [Indexed: 12/20/2022] Open
Abstract
Background Genome wide association studies (GWAS) are applied to identify genetic loci, which are associated with complex traits and human diseases. Analogous to the evolution of gene expression analyses, pathway analyses have emerged as important tools to uncover functional networks of genome-wide association data. Usually, pathway analyses combine statistical methods with a priori available biological knowledge. To determine significance thresholds for associated pathways, correction for multiple testing and over-representation permutation testing is applied. Results We systematically investigated the impact of three different permutation test approaches for over-representation analysis to detect false positive pathway candidates and evaluate them on genome-wide association data of Dilated Cardiomyopathy (DCM) and Ulcerative Colitis (UC). Our results provide evidence that the gold standard - permuting the case–control status – effectively improves specificity of GWAS pathway analysis. Although permutation of SNPs does not maintain linkage disequilibrium (LD), these permutations represent an alternative for GWAS data when case–control permutations are not possible. Gene permutations, however, did not add significantly to the specificity. Finally, we provide estimates on the required number of permutations for the investigated approaches. Conclusions To discover potential false positive functional pathway candidates and to support the results from standard statistical tests such as the Hypergeometric test, permutation tests of case control data should be carried out. The most reasonable alternative was case–control permutation, if this is not possible, SNP permutations may be carried out. Our study also demonstrates that significance values converge rapidly with an increasing number of permutations. By applying the described statistical framework we were able to discover axon guidance, focal adhesion and calcium signaling as important DCM-related pathways and Intestinal immune network for IgA production as most significant UC pathway.
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Affiliation(s)
| | | | | | | | | | | | | | | | | | | | | | | | | | | | - Andreas Keller
- Chair for Clinical Bioinformatics, Saarland University, Saarbrücken, Germany.
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Courtenay MD, Cade W, Schwartz SG, Kovach JL, Agarwal A, Wang G, Haines JL, Pericak-Vance MA, Scott WK. Set-based joint test of interaction between SNPs in the VEGF pathway and exogenous estrogen finds association with age-related macular degeneration. Invest Ophthalmol Vis Sci 2014; 55:IOVS-14-14494. [PMID: 25015356 PMCID: PMC4126792 DOI: 10.1167/iovs.14-14494] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2014] [Accepted: 06/27/2014] [Indexed: 11/24/2022] Open
Abstract
Purpose:Age-Related Macular Degeneration (AMD) is the leading cause of irreversible visual loss in developed countries. Its etiology includes genetic and environmental factors. Although VEGFA variants are associated with AMD, the joint action of variants within the VEGF pathway and their interaction with non-genetic factors has not been investigated. Methods:Affymetrix 6.0 chipsets were used to genotype 668,238 SNPs in 1,207 AMD cases and 686 controls. Environmental exposures were collected by questionnaire. A set-based test was conducted using the chi-square statistic at each SNP derived from Kraft's 2df joint test. Pathway and gene-based test statistics were calculated as the mean of all independent SNP statistics. Phenotype labels were permuted 10,000 times to generate an empirical p-value. Results: While a main effect of the VEGF pathway was not identified, the pathway was associated with neovascular AMD in women when accounting for birth control pill (BCP) use (P= 0.017). Analysis of VEGF's subpathways found that SNPs in the Proliferation subpathway were associated with neovascular AMD (P=0.029) when accounting for BCP use. Nominally significant genes within this subpathway were also observed. Stratification by BCP use revealed novel significant genetic effects in women who had taken BCPs. Conclusions: These results illustrate that some AMD genetic risk factors may only be revealed when considering complex relationships among risk factors. This shows the utility of exploring pathways of previously associated genes to find novel effects. It also demonstrates the importance of incorporating environmental exposures in tests of genetic association at the SNP, gene, or pathway level.
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Affiliation(s)
- Monique D Courtenay
- Human Genetics and Genomics, University of Miami Miller School Medicine, 1501 NW 10th Ave, Miami, FL, 33136, United States
| | - William Cade
- John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, 1501 NW 10 Ave, BRB-314 (M860), Miami, Florida, 33136, United States
| | - Stephen G Schwartz
- Ophthalmology, Bascom Palmer Eye Institute, Retina Center of Naples, 311 9th Street North, Naples, Florida, 34102, United States of America
| | - Jaclyn L Kovach
- Bascom Palmer Eye Institute, University of Miami Miller School of Medicine, 311 9th St N, Naples, FL, 34102, United States of America
| | - Anita Agarwal
- VEI, Vanderbilt University, 2311 Pierce avenue, Nashville, Tennessee, 37232-8808, United States of America
| | - Gaofeng Wang
- Human Genetics, University of Miami Miller School of Medicine, 1501 NW 10th Avenue; BRB 525, Miami, Florida, 33136, United States
| | - Jonathan L Haines
- Department of Epidemiology & Biostatistics, Case Western Reserve University, 2-529 Wolstein Research Building, 2103 Cornell Road, Cleveland, Ohio, 44106, United States
| | - Margaret A Pericak-Vance
- John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, 1501 NW 10th Avenue, BRB-314 (M860), Miami, Florida, 33136, United States of America
| | - Wiliam K Scott
- Dr. John T. Macdonald Foundation Department of Human Genetics, John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, 1501 NW 10 Ave., Biomedical Research Building (BRB) # 414, Miami, Florida, 33136, United States
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Büchel F, Mittag F, Wrzodek C, Zell A, Gasser T, Sharma M. Integrative pathway-based approach for genome-wide association studies: identification of new pathways for rheumatoid arthritis and type 1 diabetes. PLoS One 2013; 8:e78577. [PMID: 24205270 PMCID: PMC3808349 DOI: 10.1371/journal.pone.0078577] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2013] [Accepted: 09/14/2013] [Indexed: 12/19/2022] Open
Abstract
Genome-wide association studies (GWAS) led to the identification of numerous novel loci for a number of complex diseases. Pathway-based approaches using genotypic data provide tangible leads which cannot be identified by single marker approaches as implemented in GWAS. The available pathway analysis approaches mainly differ in the employed databases and in the applied statistics for determining the significance of the associated disease markers. So far, pathway-based approaches using GWAS data failed to consider the overlapping of genes among different pathways or the influence of protein–interactions. We performed a multistage integrative pathway (MIP) analysis on three common diseases - Crohn's disease (CD), rheumatoid arthritis (RA) and type 1 diabetes (T1D) - incorporating genotypic, pathway, protein- and domain-interaction data to identify novel associations between these diseases and pathways. Additionally, we assessed the sensitivity of our method by studying the influence of the most significant SNPs on the pathway analysis by removing those and comparing the corresponding pathway analysis results. Apart from confirming many previously published associations between pathways and RA, CD and T1D, our MIP approach was able to identify three new associations between disease phenotypes and pathways. This includes a relation between the influenza-A pathway and RA, as well as a relation between T1D and the phagosome and toxoplasmosis pathways. These results provide new leads to understand the molecular underpinnings of these diseases. The developed software herein used is available at http://www.cogsys.cs.uni-tuebingen.de/software/GWASPathwayIdentifier/index.htm.
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Affiliation(s)
- Finja Büchel
- Center for Bioinformatics Tuebingen (ZBIT), University of Tuebingen, Tübingen, Germany
| | - Florian Mittag
- Center for Bioinformatics Tuebingen (ZBIT), University of Tuebingen, Tübingen, Germany
| | - Clemens Wrzodek
- Center for Bioinformatics Tuebingen (ZBIT), University of Tuebingen, Tübingen, Germany
| | - Andreas Zell
- Center for Bioinformatics Tuebingen (ZBIT), University of Tuebingen, Tübingen, Germany
| | - Thomas Gasser
- Department for Neurodegenerative Diseases, Hertie Institute for Clinical Brain Research, University of Tübingen, and DZNE, German Centre for Neurodegenerative Diseases, Tübingen, Germany
| | - Manu Sharma
- Department for Neurodegenerative Diseases, Hertie Institute for Clinical Brain Research, University of Tübingen, and DZNE, German Centre for Neurodegenerative Diseases, Tübingen, Germany
- Institute for Clinical Epidemiology and Applied Biometry, University of Tübingen, Tübingen, Germany
- * E-mail:
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Liu G, Jiang Y, Chen X, Zhang R, Ma G, Feng R, Zhang L, Liao M, Miao Y, Chen Z, Zeng R, Li K. Measles contributes to rheumatoid arthritis: evidence from pathway and network analyses of genome-wide association studies. PLoS One 2013; 8:e75951. [PMID: 24204584 PMCID: PMC3799991 DOI: 10.1371/journal.pone.0075951] [Citation(s) in RCA: 27] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2013] [Accepted: 08/19/2013] [Indexed: 12/27/2022] Open
Abstract
Growing evidence from epidemiological studies indicates the association between rheumatoid arthritis (RA) and measles. However, the exact mechanism for this association is still unclear now. We consider that the strong association between both diseases may be caused by shared genetic pathways. We performed a pathway analysis of large-scale RA genome-wide association studies (GWAS) dataset with 5,539 cases and 20,169 controls of European descent. Meanwhile, we evaluated our findings using previously identified RA loci, protein-protein interaction network and previous results from pathway analysis of RA and other autoimmune diseases GWAS. We confirmed four pathways including Cytokine-cytokine receptor interaction, Jak-STAT signaling, T cell receptor signaling and Cell adhesion molecules. Meanwhile, we highlighted for the first time the involvement of Measles and Intestinal immune network for IgA production pathways in RA. Our results may explain the strong association between RA and measles, which may be caused by the shared genetic pathway. We believe that our results will be helpful for future genetic studies in RA pathogenesis and may significantly assist in the development of therapeutic strategies.
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Affiliation(s)
- Guiyou Liu
- Institute of Neurology, Guangdong Medical College, Zhanjiang, China
- Genome Analysis Laboratory, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, China
| | - Yongshuai Jiang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Xiaoguang Chen
- Key Laboratory of Sweetpotato Biology and Genetic Breeding, Sweetpotato Research Institute, Chinese Academy of Agricultural Sciences, Xuzhou, Jiangsu, China
| | - Ruijie Zhang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Guoda Ma
- Institute of Neurology, Guangdong Medical College, Zhanjiang, China
| | - Rennan Feng
- Department of Nutrition and Food Hygiene, School of Public Health, Harbin Medical University, Harbin, China
| | - Liangcai Zhang
- Department of Bioinformatics and Computational Biology, Division of Quantitative Sciences, The University of Texas M.D. Anderson Cancer Center, Houston, Texas, United States of America
| | - Mingzhi Liao
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Yingbo Miao
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Zugen Chen
- Department of Human Genetics, University of California Los Angeles, Los Angeles, California, United States of America
| | - Rong Zeng
- Department of Orthopedic Surgery, Affiliated Hospital of Guangdong Medical College, Zhanjiang, China
- * E-mail: (RZ); (KL)
| | - Keshen Li
- Institute of Neurology, Guangdong Medical College, Zhanjiang, China
- * E-mail: (RZ); (KL)
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Park YS, Schmidt M, Martin ER, Pericak-Vance MA, Chung RH. Pathway-PDT: a flexible pathway analysis tool for nuclear families. BMC Bioinformatics 2013; 14:267. [PMID: 24006871 PMCID: PMC3844459 DOI: 10.1186/1471-2105-14-267] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2013] [Accepted: 08/30/2013] [Indexed: 01/22/2023] Open
Abstract
Background Pathway analysis based on Genome-Wide Association Studies (GWAS) data has become popular as a secondary analysis strategy. Although many pathway analysis tools have been developed for case–control studies, there is no tool that can use all information from raw genotypes in general nuclear families. We developed Pathway-PDT, which uses the framework of Pedigree Disequilibrium Test (PDT) for general family data, to perform pathway analysis based on raw genotypes in family-based GWAS. Results Simulation results showed that Pathway-PDT is more powerful than the p-value based method, ALIGATOR. Pathway-PDT also can be more powerful than the PLINK set-based test when analyzing general nuclear families with multiple siblings or missing parents. Additionally, Pathway-PDT has a flexible and convenient user interface, which allows users to modify their analysis parameters as well as to apply various types of gene and pathway definitions. Conclusions The Pathway-PDT method is implemented in C++ with POSIX threads and is computationally feasible for pathway analysis with large scale family GWAS datasets. The Windows binary along with Makefile and source codes for the Linux are available at https://sourceforge.net/projects/pathway-pdt/.
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Affiliation(s)
- Yo Son Park
- Division of Biostatistics and Bioinformatics, Institute of Population Health Sciences, National Health Research Institutes, Zhunan, Miaoli, Taiwan.
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Verschuren JJW, Trompet S, Sampietro ML, Heijmans BT, Koch W, Kastrati A, Houwing-Duistermaat JJ, Slagboom PE, Quax PHA, Jukema JW. Pathway analysis using genome-wide association study data for coronary restenosis--a potential role for the PARVB gene. PLoS One 2013; 8:e70676. [PMID: 23950981 PMCID: PMC3739784 DOI: 10.1371/journal.pone.0070676] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2013] [Accepted: 06/21/2013] [Indexed: 12/20/2022] Open
Abstract
Background Coronary restenosis after percutaneous coronary intervention (PCI) still remains a significant limitation of the procedure. The causative mechanisms of restenosis have not yet been fully identified. The goal of the current study was to perform gene-set analysis of biological pathways related to inflammation, proliferation, vascular function and transcriptional regulation on coronary restenosis to identify novel genes and pathways related to this condition. Methods The GENetic DEterminants of Restenosis (GENDER) databank contains genotypic data of 556,099SNPs of 295 cases with restenosis and 571 matched controls. Fifty-four pathways, related to known restenosis-related processes, were selected. Gene-set analysis was performed using PLINK, GRASS and ALIGATOR software. Pathways with a p<0.01 were fine-mapped and significantly associated SNPs were analyzed in an independent replication cohort. Results Six pathways (cell-extracellular matrix (ECM) interactions pathway, IL2 signaling pathway, IL6 signaling pathway, platelet derived growth factor pathway, vitamin D receptor pathway and the mitochondria pathway) were significantly associated in one or two of the software packages. Two SNPs in the cell-ECM interactions pathway were replicated in an independent restenosis cohort. No replication was obtained for the other pathways. Conclusion With these results we demonstrate a potential role of the cell-ECM interactions pathway in the development of coronary restenosis. These findings contribute to the increasing knowledge of the genetic etiology of restenosis formation and could serve as a hypothesis-generating effort for further functional studies.
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Affiliation(s)
| | - Stella Trompet
- Department of Cardiology, Leiden University Medical Center, Leiden, The Netherlands
- Department of Gerontology and Geriatrics, Leiden University Medical Center, Leiden, The Netherlands
| | - M. Lourdes Sampietro
- Department Human Genetics, Leiden University Medical Center, Leiden, The Netherlands
- Interuniversity Cardiology Institute of the Netherlands (ICIN), Utrecht, The Netherlands
| | - Bastiaan T. Heijmans
- Molecular Epidemiology, Leiden University Medical Center, Leiden, The Netherlands
- Netherlands Consortium for Healthy Ageing, Leiden University Medical Center, Leiden, The Netherlands
| | - Werner Koch
- Deutsches Herzzentrum München, Technische Universität München, Munich, Germany
| | - Adnan Kastrati
- Deutsches Herzzentrum München, Technische Universität München, Munich, Germany
| | | | - P. Eline Slagboom
- Molecular Epidemiology, Leiden University Medical Center, Leiden, The Netherlands
- Netherlands Consortium for Healthy Ageing, Leiden University Medical Center, Leiden, The Netherlands
| | - Paul H. A. Quax
- Department of Vascular Surgery, Leiden University Medical Center, Leiden, The Netherlands
| | - J. Wouter Jukema
- Department of Cardiology, Leiden University Medical Center, Leiden, The Netherlands
- Netherlands Consortium for Healthy Ageing, Leiden University Medical Center, Leiden, The Netherlands
- Durrer Center for Cardiogenetic Research, Amsterdam, The Netherlands
- Interuniversity Cardiology Institute of the Netherlands (ICIN), Utrecht, The Netherlands
- * E-mail:
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Genome-wide pathway analysis of memory impairment in the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort implicates gene candidates, canonical pathways, and networks. Brain Imaging Behav 2013; 6:634-48. [PMID: 22865056 DOI: 10.1007/s11682-012-9196-x] [Citation(s) in RCA: 50] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Memory deficits are prominent features of mild cognitive impairment (MCI) and Alzheimer's disease (AD). The genetic architecture underlying these memory deficits likely involves the combined effects of multiple genetic variants operative within numerous biological pathways. In order to identify functional pathways associated with memory impairment, we performed a pathway enrichment analysis on genome-wide association data from 742 Alzheimer's Disease Neuroimaging Initiative (ADNI) participants. A composite measure of memory was generated as the phenotype for this analysis by applying modern psychometric theory to item-level data from the ADNI neuropsychological test battery. Using the GSA-SNP software tool, we identified 27 canonical, expertly-curated pathways with enrichment (FDR-corrected p-value < 0.05) against this composite memory score. Processes classically understood to be involved in memory consolidation, such as neurotransmitter receptor-mediated calcium signaling and long-term potentiation, were highly represented among the enriched pathways. In addition, pathways related to cell adhesion, neuronal differentiation and guided outgrowth, and glucose- and inflammation-related signaling were also enriched. Among genes that were highly-represented in these enriched pathways, we found indications of coordinated relationships, including one large gene set that is subject to regulation by the SP1 transcription factor, and another set that displays co-localized expression in normal brain tissue along with known AD risk genes. These results 1) demonstrate that psychometrically-derived composite memory scores are an effective phenotype for genetic investigations of memory impairment and 2) highlight the promise of pathway analysis in elucidating key mechanistic targets for future studies and for therapeutic interventions.
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Non-homologous end-joining pathway associated with occurrence of myocardial infarction: gene set analysis of genome-wide association study data. PLoS One 2013; 8:e56262. [PMID: 23457540 PMCID: PMC3574159 DOI: 10.1371/journal.pone.0056262] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2012] [Accepted: 01/07/2013] [Indexed: 01/28/2023] Open
Abstract
PURPOSE DNA repair deficiencies have been postulated to play a role in the development and progression of cardiovascular disease (CVD). The hypothesis is that DNA damage accumulating with age may induce cell death, which promotes formation of unstable plaques. Defects in DNA repair mechanisms may therefore increase the risk of CVD events. We examined whether the joints effect of common genetic variants in 5 DNA repair pathways may influence the risk of CVD events. METHODS The PLINK set-based test was used to examine the association to myocardial infarction (MI) of the DNA repair pathway in GWAS data of 866 subjects of the GENetic DEterminants of Restenosis (GENDER) study and 5,244 subjects of the PROspective Study of Pravastatin in the Elderly at Risk (PROSPER) study. We included the main DNA repair pathways (base excision repair, nucleotide excision repair, mismatch repair, homologous recombination and non-homologous end-joining (NHEJ)) in the analysis. RESULTS The NHEJ pathway was associated with the occurrence of MI in both GENDER (P = 0.0083) and PROSPER (P = 0.014). This association was mainly driven by genetic variation in the MRE11A gene (PGENDER = 0.0001 and PPROSPER = 0.002). The homologous recombination pathway was associated with MI in GENDER only (P = 0.011), for the other pathways no associations were observed. CONCLUSION This is the first study analyzing the joint effect of common genetic variation in DNA repair pathways and the risk of CVD events, demonstrating an association between the NHEJ pathway and MI in 2 different cohorts.
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Kar SP, Seldin MF, Chen W, Lu E, Hirschfield GM, Invernizzi P, Heathcote J, Cusi D, Gershwin ME, Siminovitch KA, Amos CI. Pathway-based analysis of primary biliary cirrhosis genome-wide association studies. Genes Immun 2013; 14:179-86. [PMID: 23392275 DOI: 10.1038/gene.2013.1] [Citation(s) in RCA: 46] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
Genome-wide association studies (GWAS) have successfully identified several loci associated with primary biliary cirrhosis (PBC) risk. Pathway analysis complements conventional GWAS analysis. We applied the recently developed linear combination test for pathways to datasets drawn from independent PBC GWAS in Italian and Canadian subjects. Of the Kyoto Encyclopedia of Genes and Genomes and BioCarta pathways tested, 25 pathways in the Italian dataset (449 cases, 940 controls) and 26 pathways in the Canadian dataset (530 cases, 398 controls) were associated with PBC susceptibility (P<0.05). After correcting for multiple comparisons, only the eight most significant pathways in the Italian dataset had FDR <0.25 with tumor necrosis factor/stress-related signaling emerging as the top pathway (P=7.38 × 10⁻⁴, FDR=0.18). Two pathways, phosphatidylinositol signaling and hedgehog signaling, were replicated in both datasets (P<0.05), and subjected to two additional complementary pathway tests. Both pathway signals remained significant in the Italian dataset on modified gene set enrichment analysis (P<0.05). In both GWAS, variants nominally associated with PBC were significantly overrepresented in the phosphatidylinositol pathway (Fisher exact P<0.05). These results point to established and novel pathway-level associations with inherited predisposition to PBC that, on further independent replication and functional validation, may provide fresh insights into PBC etiology.
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Affiliation(s)
- S P Kar
- Department of Genetics, University of Texas MD Anderson Cancer Center, Houston, TX, USA
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Guo X, Liu Z, Wang X, Zhang H. Genetic association test for multiple traits at gene level. Genet Epidemiol 2013; 37:122-9. [PMID: 23032486 PMCID: PMC3524409 DOI: 10.1002/gepi.21688] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2012] [Revised: 08/21/2012] [Accepted: 09/07/2012] [Indexed: 01/09/2023]
Abstract
Genome-wide association studies (GWASs) at the gene level are commonly used to understand biological mechanisms underlying complex diseases. In general, one response or outcome is used to present a disease of interest in such studies. In this study, we consider a multiple traits association test from the gene level. We propose and examine a class of test statistics that summarizes the association information between single nucleotide polymorphisms (SNPs) and each of the traits. Our simulation studies demonstrate the advantage of gene-based multiple traits association tests when multiple traits share common genes. Using our proposed tests, we reanalyze the dataset from the Study of Addiction: Genetics and Environment (SAGE). Our result validates previous findings while presenting stronger evidence for consideration of multiple traits.
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Affiliation(s)
- Xiaobo Guo
- Department of Biostatistics, Yale University School of Medicine, New Haven, CT, USA
- Department of Statistical Science, School of Mathematics and Computational Science, Sun Yat-Sen University, Guangzhou, China
| | - Zhifa Liu
- Department of Biostatistics, Yale University School of Medicine, New Haven, CT, USA
| | - Xueqin Wang
- Department of Statistical Science, School of Mathematics and Computational Science, Sun Yat-Sen University, Guangzhou, China
- Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou, China
| | - Heping Zhang
- Department of Biostatistics, Yale University School of Medicine, New Haven, CT, USA
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Jia P, Liu Y, Zhao Z. Integrative pathway analysis of genome-wide association studies and gene expression data in prostate cancer. BMC SYSTEMS BIOLOGY 2012; 6 Suppl 3:S13. [PMID: 23281744 PMCID: PMC3524313 DOI: 10.1186/1752-0509-6-s3-s13] [Citation(s) in RCA: 27] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/23/2023]
Abstract
BACKGROUND Pathway analysis of large-scale omics data assists us with the examination of the cumulative effects of multiple functionally related genes, which are difficult to detect using the traditional single gene/marker analysis. So far, most of the genomic studies have been conducted in a single domain, e.g., by genome-wide association studies (GWAS) or microarray gene expression investigation. A combined analysis of disease susceptibility genes across multiple platforms at the pathway level is an urgent need because it can reveal more reliable and more biologically important information. RESULTS We performed an integrative pathway analysis of a GWAS dataset and a microarray gene expression dataset in prostate cancer. We obtained a comprehensive pathway annotation set from knowledge-based public resources, including KEGG pathways and the prostate cancer candidate gene set, and gene sets specifically defined based on cross-platform information. By leveraging on this pathway collection, we first searched for significant pathways in the GWAS dataset using four methods, which represent two broad groups of pathway analysis approaches. The significant pathways identified by each method varied greatly, but the results were more consistent within each method group than between groups. Next, we conducted a gene set enrichment analysis of the microarray gene expression data and found 13 pathways with cross-platform evidence, including "Fc gamma R-mediated phagocytosis" (P GWAS = 0.003, P expr < 0.001, and P combined = 6.18 × 10(-8)), "regulation of actin cytoskeleton" (P GWAS = 0.003, P expr = 0.009, and P combined = 3.34 × 10(-4)), and "Jak-STAT signaling pathway" (P GWAS = 0.001, P expr = 0.084, and P combined = 8.79 × 10(-4)). CONCLUSIONS Our results provide evidence at both the genetic variation and expression levels that several key pathways might have been involved in the pathological development of prostate cancer. Our framework that employs gene expression data to facilitate pathway analysis of GWAS data is not only feasible but also much needed in studying complex disease.
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Affiliation(s)
- Peilin Jia
- Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, TN, USA
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Harari O, Wang JC, Bucholz K, Edenberg HJ, Heath A, Martin NG, Pergadia ML, Montgomery G, Schrage A, Bierut LJ, Madden PF, Goate AM. Pathway analysis of smoking quantity in multiple GWAS identifies cholinergic and sensory pathways. PLoS One 2012; 7:e50913. [PMID: 23227220 PMCID: PMC3515482 DOI: 10.1371/journal.pone.0050913] [Citation(s) in RCA: 11] [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: 06/05/2012] [Accepted: 10/25/2012] [Indexed: 11/25/2022] Open
Abstract
Cigarette smoking is a common addiction that increases the risk for many diseases, including lung cancer and chronic obstructive pulmonary disease. Genome-wide association studies (GWAS) have successfully identified and validated several susceptibility loci for nicotine consumption and dependence. However, the trait variance explained by these genes is only a small fraction of the estimated genetic risk. Pathway analysis complements single marker methods by including biological knowledge into the evaluation of GWAS, under the assumption that causal variants lie in functionally related genes, enabling the evaluation of a broad range of signals. Our approach to the identification of pathways enriched for multiple genes associated with smoking quantity includes the analysis of two studies and the replication of common findings in a third dataset. This study identified pathways for the cholinergic receptors, which included SNPs known to be genome-wide significant; as well as novel pathways, such as genes involved in the sensory perception of smell, that do not contain any single SNP that achieves that stringent threshold.
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Affiliation(s)
- Oscar Harari
- Department of Psychiatry, Washington University School of Medicine, St. Louis, Missouri, United States of America
| | - Jen-Chyong Wang
- Department of Psychiatry, Washington University School of Medicine, St. Louis, Missouri, United States of America
| | - Kathleen Bucholz
- Department of Psychiatry, Washington University School of Medicine, St. Louis, Missouri, United States of America
| | - Howard J. Edenberg
- Department of Biochemistry and Molecular Biology, School of Medicine, Indiana University, Indianapolis, Indiana, United States of America
| | - Andrew Heath
- Department of Psychiatry, Washington University School of Medicine, St. Louis, Missouri, United States of America
| | - Nicholas G. Martin
- Genetic Epidemiology, Queensland Institute of Medical Research, Brisbane, Australia
| | - Michele L. Pergadia
- Department of Psychiatry, Washington University School of Medicine, St. Louis, Missouri, United States of America
| | - Grant Montgomery
- Molecular Epidemology, Queensland Institute of Medical Research, Brisbane, Australia
| | - Andrew Schrage
- Department of Psychiatry, Washington University School of Medicine, St. Louis, Missouri, United States of America
| | - Laura J. Bierut
- Department of Psychiatry, Washington University School of Medicine, St. Louis, Missouri, United States of America
| | - Pamela F. Madden
- Department of Psychiatry, Washington University School of Medicine, St. Louis, Missouri, United States of America
| | - Alison M. Goate
- Department of Psychiatry, Washington University School of Medicine, St. Louis, Missouri, United States of America
- * E-mail:
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Systematic testing of literature reported genetic variation associated with coronary restenosis: results of the GENDER Study. PLoS One 2012; 7:e42401. [PMID: 22879966 PMCID: PMC3411750 DOI: 10.1371/journal.pone.0042401] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2012] [Accepted: 07/05/2012] [Indexed: 12/13/2022] Open
Abstract
Background Coronary restenosis after percutaneous coronary intervention still remains a significant problem, despite all medical advances. Unraveling the mechanisms leading to restenosis development remains challenging. Many studies have identified genetic markers associated with restenosis, but consistent replication of the reported markers is scarce. The aim of the current study was to analyze the joined effect of previously in literature reported candidate genes for restenosis in the GENetic DEterminants of Restenosis (GENDER) databank. Methodology/Principal Findings Candidate genes were selected using a MEDLINE search including the terms ‘genetic polymorphism’ and ‘coronary restenosis’. The final set included 36 genes. Subsequently, all single nucleotide polymorphisms (SNPs) in the genomic region of these genes were analyzed in GENDER using set-based analysis in PLINK. The GENDER databank contains genotypic data of 2,571,586 SNPs of 295 cases with restenosis and 571 matched controls. The set, including all 36 literature reported genes, was, indeed, significantly associated with restenosis, p = 0.024 in the GENDER study. Subsequent analyses of the individual genes demonstrated that the observed association of the complete set was determined by 6 of the 36 genes. Conclusion Despite overt inconsistencies in literature, with regard to individual candidate gene studies, this is the first study demonstrating that the joint effect of all these genes together, indeed, is associated with restenosis.
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Gao J, Wang Y, Dong Z, Yan Z, Jia X, Tian Y. A novel differential diagnostic model based on multiple biological parameters for immunoglobulin A nephropathy. BMC Med Inform Decis Mak 2012; 12:58. [PMID: 22738421 PMCID: PMC3488968 DOI: 10.1186/1472-6947-12-58] [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: 10/23/2011] [Accepted: 06/27/2012] [Indexed: 12/14/2022] Open
Abstract
BACKGROUND Immunoglobulin A nephropathy (IgAN) is the most common form of glomerulonephritis in China. An accurate diagnosis of IgAN is dependent on renal biopsies, and there is lack of non-invasive and practical classification methods for discriminating IgAN from other primary kidney diseases. The objective of this study was to develop a classification model for the auxiliary diagnosis of IgAN using multiparameter analysis with various biological parameters. METHODS To establish an optimal classification model, 121 cases (58 IgAN vs. 63 non-IgAN) were recruited and statistically analyzed. The model was then validated in another 180 cases. RESULTS Of the 57 biological parameters, there were 16 parameters that were significantly different (P < 0.05) between IgAN and non-IgAN. The combination of fibrinogen, serum immunoglobulin A level, and manifestation was found to be significant in predicting IgAN. The validation accuracies of the logistic regression and discriminant analysis models were 77.5 and 77.0%, respectively at a predictive probability cut-off of 0.5, and 81.1 and 79.9%, respectively, at a predictive probability cut-off of 0.40. When the predicted probability of the equation containing the combination of fibrinogen, serum IgA level, and manifestation was more than 0.59, a patient had at least an 85.0% probability of having IgAN. When the predicted probability was lower than 0.26, a patient had at least an 88.5% probability of having non-IgAN. The results of the net reclassification improvement certificated serum Immunoglobulin A and fibrinogen had classification power for discriminating IgAN from non-IgAN. CONCLUSIONS These models possess potential clinical applications in distinguishing IgAN from other primary kidney diseases.
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Affiliation(s)
- Jing Gao
- Department of Clinical Biochemistry, State Key Laboratory of Kidney Disease, Chinese PLA General Hospital, Beijing 100853, China
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Pathway analysis of genomic data: concepts, methods, and prospects for future development. Trends Genet 2012; 28:323-32. [PMID: 22480918 DOI: 10.1016/j.tig.2012.03.004] [Citation(s) in RCA: 215] [Impact Index Per Article: 17.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2012] [Revised: 03/02/2012] [Accepted: 03/07/2012] [Indexed: 12/31/2022]
Abstract
Genome-wide data sets are increasingly being used to identify biological pathways and networks underlying complex diseases. In particular, analyzing genomic data through sets defined by functional pathways offers the potential of greater power for discovery and natural connections to biological mechanisms. With the burgeoning availability of next-generation sequencing, this is an opportune moment to revisit strategies for pathway-based analysis of genomic data. Here, we synthesize relevant concepts and extant methodologies to guide investigators in study design and execution. We also highlight ongoing challenges and proposed solutions. As relevant analytical strategies mature, pathways and networks will be ideally placed to integrate data from diverse -omics sources to harness the extensive, rich information related to disease and treatment mechanisms.
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