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Kuo CY, Chen TY, Kao PH, Huang W, Cho CR, Lai YS, Yiang GT, Kao CF. Genetic Pathways and Functional Subnetworks for the Complex Nature of Bipolar Disorder in Genome-Wide Association Study. Front Mol Neurosci 2021; 14:772584. [PMID: 34880727 PMCID: PMC8645771 DOI: 10.3389/fnmol.2021.772584] [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: 09/08/2021] [Accepted: 10/08/2021] [Indexed: 11/19/2022] Open
Abstract
Bipolar disorder is a complex psychiatric trait that is also recognized as a high substantial heritability from a worldwide distribution. The success in identifying susceptibility loci for bipolar disorder (BPD) has been limited due to its complex genetic architecture. Growing evidence from association studies including genome-wide association (GWA) studies points to the need of improved analytic strategies to pinpoint the missing heritability for BPD. More importantly, many studies indicate that BPD has a strong association with dementia. We conducted advanced pathway analytics strategies to investigate synergistic effects of multilocus within biologically functional pathways, and further demonstrated functional effects among proteins in subnetworks to examine mechanisms underlying the complex nature of bipolarity using a GWA dataset for BPD. We allowed bipolar susceptible loci to play a role that takes larger weights in pathway-based analytic approaches. Having significantly informative genes identified from enriched pathways, we further built function-specific subnetworks of protein interactions using MetaCore. The gene-wise scores (i.e., minimum p-value) were corrected for the gene-length, and the results were corrected for multiple tests using Benjamini and Hochberg’s method. We found 87 enriched pathways that are significant for BPD; of which 36 pathways were reported. Most of them are involved with several metabolic processes, neural systems, immune system, molecular transport, cellular communication, and signal transduction. Three significant and function-related subnetworks with multiple hotspots were reported to link with several Gene Ontology processes for BPD. Our comprehensive pathway-network frameworks demonstrated that the use of prior knowledge is promising to facilitate our understanding between complex psychiatric disorders (e.g., BPD) and dementia for the access to the connection and clinical implications, along with the development and progression of dementia.
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Affiliation(s)
- Chan-Yen Kuo
- Department of Research, Taipei Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, New Taipei, Taiwan.,Department of Nursing, Cardinal Tien College of Healthcare and Management, New Taipei, Taiwan
| | - Tsu-Yi Chen
- Department of Emergency Medicine, Taipei Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, New Taipei, Taiwan.,Department of Emergency Medicine, School of Medicine, Tzu Chi University, Hualien, Taiwan
| | - Pei-Hsiu Kao
- Department of Agronomy, College of Agriculture and Natural Resources, National Chung Hsing University, Taichung, Taiwan
| | - Winifred Huang
- School of Management, University of Bath, Bath, United Kingdom
| | - Chun-Ruei Cho
- Department of Agronomy, College of Agriculture and Natural Resources, National Chung Hsing University, Taichung, Taiwan
| | - Ya-Syuan Lai
- Department of Agronomy, College of Agriculture and Natural Resources, National Chung Hsing University, Taichung, Taiwan
| | - Giou-Teng Yiang
- Department of Emergency Medicine, Taipei Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, New Taipei, Taiwan.,Department of Emergency Medicine, School of Medicine, Tzu Chi University, Hualien, Taiwan
| | - Chung-Feng Kao
- Department of Agronomy, College of Agriculture and Natural Resources, National Chung Hsing University, Taichung, Taiwan.,Advanced Plant Biotechnology Center, National Chung Hsing University, Taichung, Taiwan
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2
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Lopes I, Altab G, Raina P, de Magalhães JP. Gene Size Matters: An Analysis of Gene Length in the Human Genome. Front Genet 2021; 12:559998. [PMID: 33643374 PMCID: PMC7905317 DOI: 10.3389/fgene.2021.559998] [Citation(s) in RCA: 68] [Impact Index Per Article: 22.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2020] [Accepted: 01/06/2021] [Indexed: 12/23/2022] Open
Abstract
While it is expected for gene length to be associated with factors such as intron number and evolutionary conservation, we are yet to understand the connections between gene length and function in the human genome. In this study, we show that, as expected, there is a strong positive correlation between gene length, transcript length, and protein size as well as a correlation with the number of genetic variants and introns. Among tissue-specific genes, we find that the longest transcripts tend to be expressed in the blood vessels, nerves, thyroid, cervix uteri, and the brain, while the smallest transcripts tend to be expressed in the pancreas, skin, stomach, vagina, and testis. We report, as shown previously, that natural selection suppresses changes for genes with longer transcripts and promotes changes for genes with smaller transcripts. We also observe that genes with longer transcripts tend to have a higher number of co-expressed genes and protein-protein interactions, as well as more associated publications. In the functional analysis, we show that bigger transcripts are often associated with neuronal development, while smaller transcripts tend to play roles in skin development and in the immune system. Furthermore, pathways related to cancer, neurons, and heart diseases tend to have genes with longer transcripts, with smaller transcripts being present in pathways related to immune responses and neurodegenerative diseases. Based on our results, we hypothesize that longer genes tend to be associated with functions that are important in the early development stages, while smaller genes tend to play a role in functions that are important throughout the whole life, like the immune system, which requires fast responses.
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Affiliation(s)
| | | | | | - João Pedro de Magalhães
- Integrative Genomics of Ageing Group, Institute of Ageing and Chronic Disease, University of Liverpool, Liverpool, United Kingdom
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3
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Raymond B, Yengo L, Costilla R, Schrooten C, Bouwman AC, Hayes BJ, Veerkamp RF, Visscher PM. Using prior information from humans to prioritize genes and gene-associated variants for complex traits in livestock. PLoS Genet 2020; 16:e1008780. [PMID: 32925905 PMCID: PMC7514049 DOI: 10.1371/journal.pgen.1008780] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2020] [Revised: 09/24/2020] [Accepted: 07/21/2020] [Indexed: 01/13/2023] Open
Abstract
Genome-Wide Association Studies (GWAS) in large human cohorts have identified thousands of loci associated with complex traits and diseases. For identifying the genes and gene-associated variants that underlie complex traits in livestock, especially where sample sizes are limiting, it may help to integrate the results of GWAS for equivalent traits in humans as prior information. In this study, we sought to investigate the usefulness of results from a GWAS on human height as prior information for identifying the genes and gene-associated variants that affect stature in cattle, using GWAS summary data on samples sizes of 700,000 and 58,265 for humans and cattle, respectively. Using Fisher's exact test, we observed a significant proportion of cattle stature-associated genes (30/77) that are also associated with human height (odds ratio = 5.1, p = 3.1e-10). Result of randomized sampling tests showed that cattle orthologs of human height-associated genes, hereafter referred to as candidate genes (C-genes), were more enriched for cattle stature GWAS signals than random samples of genes in the cattle genome (p = 0.01). Randomly sampled SNPs within the C-genes also tend to explain more genetic variance for cattle stature (up to 13.2%) than randomly sampled SNPs within random cattle genes (p = 0.09). The most significant SNPs from a cattle GWAS for stature within the C-genes did not explain more genetic variance for cattle stature than the most significant SNPs within random cattle genes (p = 0.87). Altogether, our findings support previous studies that suggest a similarity in the genetic regulation of height across mammalian species. However, with the availability of a powerful GWAS for stature that combined data from 8 cattle breeds, prior information from human-height GWAS does not seem to provide any additional benefit with respect to the identification of genes and gene-associated variants that affect stature in cattle.
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Affiliation(s)
- Biaty Raymond
- Animal Breeding and Genomics, Wageningen University and Research, Wageningen, The Netherlands
- Biometris, Wageningen University and Research, Wageningen, The Netherlands
- * E-mail:
| | - Loic Yengo
- Institute for Molecular Bioscience, University of Queensland, St. Lucia, Australia
| | - Roy Costilla
- Queensland Alliance for Agriculture and Food Innovation, University of Queensland, St. Lucia, Australia
| | | | - Aniek C. Bouwman
- Animal Breeding and Genomics, Wageningen University and Research, Wageningen, The Netherlands
| | - Ben J. Hayes
- Queensland Alliance for Agriculture and Food Innovation, University of Queensland, St. Lucia, Australia
| | - Roel F. Veerkamp
- Animal Breeding and Genomics, Wageningen University and Research, Wageningen, The Netherlands
| | - Peter M. Visscher
- Institute for Molecular Bioscience, University of Queensland, St. Lucia, Australia
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4
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Harrison BR, Wang L, Gajda E, Hoffman EV, Chung BY, Pletcher SD, Raftery D, Promislow DEL. The metabolome as a link in the genotype-phenotype map for peroxide resistance in the fruit fly, Drosophila melanogaster. BMC Genomics 2020; 21:341. [PMID: 32366330 PMCID: PMC7199327 DOI: 10.1186/s12864-020-6739-1] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2019] [Accepted: 04/15/2020] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND Genetic association studies that seek to explain the inheritance of complex traits typically fail to explain a majority of the heritability of the trait under study. Thus, we are left with a gap in the map from genotype to phenotype. Several approaches have been used to fill this gap, including those that attempt to map endophenotype such as the transcriptome, proteome or metabolome, that underlie complex traits. Here we used metabolomics to explore the nature of genetic variation for hydrogen peroxide (H2O2) resistance in the sequenced inbred Drosophila Genetic Reference Panel (DGRP). RESULTS We first studied genetic variation for H2O2 resistance in 179 DGRP lines and along with identifying the insulin signaling modulator u-shaped and several regulators of feeding behavior, we estimate that a substantial amount of phenotypic variation can be explained by a polygenic model of genetic variation. We then profiled a portion of the aqueous metabolome in subsets of eight 'high resistance' lines and eight 'low resistance' lines. We used these lines to represent collections of genotypes that were either resistant or sensitive to the stressor, effectively modeling a discrete trait. Across the range of genotypes in both populations, flies exhibited surprising consistency in their metabolomic signature of resistance. Importantly, the resistance phenotype of these flies was more easily distinguished by their metabolome profiles than by their genotypes. Furthermore, we found a metabolic response to H2O2 in sensitive, but not in resistant genotypes. Metabolomic data further implicated at least two pathways, glycogen and folate metabolism, as determinants of sensitivity to H2O2. We also discovered a confounding effect of feeding behavior on assays involving supplemented food. CONCLUSIONS This work suggests that the metabolome can be a point of convergence for genetic variation influencing complex traits, and can efficiently elucidate mechanisms underlying trait variation.
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Affiliation(s)
- Benjamin R Harrison
- Department of Pathology, University of Washington School of Medicine, Seattle, WA, 98195, USA.
| | - Lu Wang
- Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, WA, 98105, USA
| | - Erika Gajda
- Department of Pathology, University of Washington School of Medicine, Seattle, WA, 98195, USA
| | - Elise V Hoffman
- Department of Pathology, University of Washington School of Medicine, Seattle, WA, 98195, USA
| | - Brian Y Chung
- Department of Molecular and Integrative Physiology, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Scott D Pletcher
- Department of Molecular and Integrative Physiology, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Daniel Raftery
- Northwest Metabolomics Research Center, Department of Anesthesiology & Pain Medicine, University of Washington, Seattle, WA, 98195, USA
| | - Daniel E L Promislow
- Department of Pathology, University of Washington School of Medicine, Seattle, WA, 98195, USA
- Department of Biology, University of Washington, Seattle, WA, 98195, USA
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5
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Gouy A, Excoffier L. Polygenic Patterns of Adaptive Introgression in Modern Humans Are Mainly Shaped by Response to Pathogens. Mol Biol Evol 2020; 37:1420-1433. [DOI: 10.1093/molbev/msz306] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
AbstractAnatomically modern humans carry many introgressed variants from other hominins in their genomes. Some of them affect their phenotype and can thus be negatively or positively selected. Several individual genes have been proposed to be the subject of adaptive introgression, but the possibility of polygenic adaptive introgression has not been extensively investigated yet. In this study, we analyze archaic introgression maps with refined functional enrichment methods to find signals of polygenic adaptation of introgressed variants. We first apply a method to detect sets of connected genes (subnetworks) within biological pathways that present higher-than-expected levels of archaic introgression. We then introduce and apply a new statistical test to distinguish between epistatic and independent selection in gene sets of present-day humans. We identify several known targets of adaptive introgression, and we show that they belong to larger networks of introgressed genes. After correction for genetic linkage, we find that signals of polygenic adaptation are mostly explained by independent and potentially sequential selection episodes. However, we also find some gene sets where introgressed variants present significant signals of epistatic selection. Our results confirm that archaic introgression has facilitated local adaptation, especially in immunity related and metabolic functions and highlight its involvement in a coordinated response to pathogens out of Africa.
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Affiliation(s)
- Alexandre Gouy
- Institute of Ecology and Evolution, University of Berne, Berne 3012, Switzerland
- Swiss Institute of Bioinformatics, Lausanne 1015, Switzerland
| | - Laurent Excoffier
- Institute of Ecology and Evolution, University of Berne, Berne 3012, Switzerland
- Swiss Institute of Bioinformatics, Lausanne 1015, Switzerland
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6
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Rodríguez-López ML, Martínez-Magaña JJ, Cabrera-Mendoza B, Genis-Mendoza AD, García-Dolores F, López-Armenta M, Flores G, Vázquez-Roque RA, Nicolini H. Exploratory analysis of genetic variants influencing molecular traits in cerebral cortex of suicide completers. Am J Med Genet B Neuropsychiatr Genet 2020; 183:26-37. [PMID: 31418530 DOI: 10.1002/ajmg.b.32752] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/24/2019] [Revised: 05/13/2019] [Accepted: 07/09/2019] [Indexed: 12/28/2022]
Abstract
Genetic factors have been implicated in suicidal behavior. It has been suggested that one of the roles of genetic factors in suicide could be represented by the effect of genetic variants on gene expression regulation. Alteration in the expression of genes participating in multiple biological systems in the suicidal brain has been demonstrated, so it is imperative to identify genetic variants that could influence gene expression or its regulatory mechanisms. In this study, we integrated DNA methylation, gene expression, and genotype data from the prefrontal cortex of suicides to identify genetic variants that could be factors in the regulation of gene expression, generally called quantitative trait locus (xQTLs). We identify 6,224 methylation quantitative trait loci and 2,239 expression quantitative trait loci (eQTLs) in the prefrontal cortex of suicide completers. The xQTLs identified influence the expression of genes involved in neurodevelopment and cell organization. Two of the eQTLs identified (rs8065311 and rs1019238) were previously associated with cannabis dependence, highlighting a candidate genetic variant for the increased suicide risk in subjects with substance use disorders. Our findings suggest that genetic variants may regulate gene expression in the prefrontal cortex of suicides through the modulation of promoter and enhancer activity, and to a lesser extent, binding transcription factors.
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Affiliation(s)
- Mariana L Rodríguez-López
- Genomics of Psychiatric and Neurodegenerative Diseases Laboratory, National Institute of Genomic Medicine (INMEGEN), Mexico City, Mexico
| | - José J Martínez-Magaña
- Genomics of Psychiatric and Neurodegenerative Diseases Laboratory, National Institute of Genomic Medicine (INMEGEN), Mexico City, Mexico
| | - Brenda Cabrera-Mendoza
- Genomics of Psychiatric and Neurodegenerative Diseases Laboratory, National Institute of Genomic Medicine (INMEGEN), Mexico City, Mexico
| | - Alma D Genis-Mendoza
- Genomics of Psychiatric and Neurodegenerative Diseases Laboratory, National Institute of Genomic Medicine (INMEGEN), Mexico City, Mexico.,Psychiatric Care Services, Child Psychiatric Hospital Dr. Juan N Navarro, CDMX, Mexico
| | | | | | - Gonzalo Flores
- Neuropsychiatry Laboratory, Institute of Physiology, Meritorious Autonomous University of Puebla, Puebla, Mexico
| | - Rubén A Vázquez-Roque
- Neuropsychiatry Laboratory, Institute of Physiology, Meritorious Autonomous University of Puebla, Puebla, Mexico
| | - Humberto Nicolini
- Genomics of Psychiatric and Neurodegenerative Diseases Laboratory, National Institute of Genomic Medicine (INMEGEN), Mexico City, Mexico.,Carracci Medical Group, CDMX, Mexico
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7
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Ostrom QT, Coleman W, Huang W, Rubin JB, Lathia JD, Berens ME, Speyer G, Liao P, Wrensch MR, Eckel-Passow JE, Armstrong G, Rice T, Wiencke JK, McCoy LS, Hansen HM, Amos CI, Bernstein JL, Claus EB, Houlston RS, Il’yasova D, Jenkins RB, Johansen C, Lachance DH, Lai RK, Merrell RT, Olson SH, Sadetzki S, Schildkraut JM, Shete S, Andersson U, Rajaraman P, Chanock SJ, Linet MS, Wang Z, Yeager M, Melin B, Bondy ML, Barnholtz-Sloan JS. Sex-specific gene and pathway modeling of inherited glioma risk. Neuro Oncol 2019; 21:71-82. [PMID: 30124908 PMCID: PMC6303471 DOI: 10.1093/neuonc/noy135] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
Background To date, genome-wide association studies (GWAS) have identified 25 risk variants for glioma, explaining 30% of heritable risk. Most histologies occur with significantly higher incidence in males, and this difference is not explained by currently known risk factors. A previous GWAS identified sex-specific glioma risk variants, and this analysis aims to further elucidate risk variation by sex using gene- and pathway-based approaches. Methods Results from the Glioma International Case-Control Study were used as a testing set, and results from 3 GWAS were combined via meta-analysis and used as a validation set. Using summary statistics for nominally significant autosomal SNPs (P < 0.01 in a previous meta-analysis) and nominally significant X-chromosome SNPs (P < 0.01), 3 algorithms (Pascal, BimBam, and GATES) were used to generate gene scores, and Pascal was used to generate pathway scores. Results were considered statistically significant in the discovery set when P < 3.3 × 10-6 and in the validation set when P < 0.001 in 2 of 3 algorithms. Results Twenty-five genes within 5 regions and 19 genes within 6 regions reached statistical significance in at least 2 of 3 algorithms in males and females, respectively. EGFR was significantly associated with all glioma and glioblastoma in males only and a female-specific association in TERT, all of which remained nominally significant after conditioning on known risk loci. There were nominal associations with the BioCarta telomeres pathway in both males and females. Conclusions These results provide additional evidence that there may be differences by sex in genetic risk for glioma. Additional analyses may further elucidate the biological processes through which this risk is conferred.
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Affiliation(s)
- Quinn T Ostrom
- Case Comprehensive Cancer Center, Case Western Reserve University School of Medicine, Cleveland, Ohio, USA
- Department of Medicine, Section of Epidemiology and Population Sciences, Dan L. Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, Texas, USA
- Department of Population and Quantitative Health Sciences, Case Western Reserve University School of Medicine, Cleveland, Ohio, USA
| | | | - William Huang
- Case Western Reserve University, Cleveland, Ohio, USA
| | - Joshua B Rubin
- Department of Pediatrics, Washington University School of Medicine, St Louis, Missouri, USA; Department of Neuroscience, Washington University School of Medicine, St Louis, Missouri, USA
| | - Justin D Lathia
- Department of Stem Cell Biology and Regenerative Medicine, Cleveland Clinic Foundation, Cleveland, Ohio, USA
| | - Michael E Berens
- Cancer and Cell Biology Division, The Translational Genomics Research Institute, Phoenix, Arizona, USA
| | - Gil Speyer
- Cancer and Cell Biology Division, The Translational Genomics Research Institute, Phoenix, Arizona, USA
| | - Peter Liao
- Case Comprehensive Cancer Center, Case Western Reserve University School of Medicine, Cleveland, Ohio, USA
| | - Margaret R Wrensch
- Department of Neurological Surgery, School of Medicine, University of California San Francisco, San Francisco, California, USA
| | - Jeanette E Eckel-Passow
- Division of Biomedical Statistics and Informatics, Mayo Clinic College of Medicine, Rochester, Minnesota, USA
| | - Georgina Armstrong
- Department of Medicine, Section of Epidemiology and Population Sciences, Dan L. Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, Texas, USA
| | - Terri Rice
- Department of Neurological Surgery, School of Medicine, University of California San Francisco, San Francisco, California, USA
| | - John K Wiencke
- Department of Neurological Surgery, School of Medicine, University of California San Francisco, San Francisco, California, USA
| | - Lucie S McCoy
- Department of Neurological Surgery, School of Medicine, University of California San Francisco, San Francisco, California, USA
| | - Helen M Hansen
- Department of Neurological Surgery, School of Medicine, University of California San Francisco, San Francisco, California, USA
| | - Christopher I Amos
- Institute for Clinical and Translational Research, Dan L. Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, Texas, USA
| | - Jonine L Bernstein
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Elizabeth B Claus
- School of Public Health, Yale University, New Haven, Connecticut, USA
- Department of Neurosurgery, Brigham and Women’s Hospital, Boston, Massachusetts, USA
| | - Richard S Houlston
- Division of Genetics and Epidemiology, The Institute of Cancer Research, Sutton, Surrey, United Kingdom
| | - Dora Il’yasova
- Department of Epidemiology and Biostatistics, School of Public Health, Georgia State University, Atlanta, Georgia, USA
- Cancer Control and Prevention Program, Department of Community and Family Medicine, Duke University Medical Center, Durham, North Carolina, USA
- Duke Cancer Institute, Duke University Medical Center, Durham, North Carolina, USA
| | - Robert B Jenkins
- Department of Laboratory Medicine and Pathology, Mayo Clinic Comprehensive Cancer Center, Mayo Clinic, Rochester, Minnesota, USA
| | - Christoffer Johansen
- Oncology Clinic, Finsen Center, Rigshospitalet and Survivorship Research Unit, The Danish Cancer Society Research Center, Copenhagen, Denmark
| | - Daniel H Lachance
- Department of Neurology, Mayo Clinic Comprehensive Cancer Center, Mayo Clinic, Rochester, Minnesota, USA
| | - Rose K Lai
- Departments of Neurology and Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California, USA
| | - Ryan T Merrell
- Department of Neurology, NorthShore University HealthSystem, Evanston, Illinois, USA
| | - Sara H Olson
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Siegal Sadetzki
- Cancer and Radiation Epidemiology Unit, Gertner Institute, Chaim Sheba Medical Center, Tel Hashomer, Israel
- Department of Epidemiology and Preventive Medicine, School of Public Health, Sackler Faculty of Medicine, Tel-Aviv University, Tel-Aviv, Israel
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Joellen M Schildkraut
- Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, Virginia, USA
| | | | - Ulrika Andersson
- Department of Radiation Sciences, Faculty of Medicine, Umeå University, Umeå, Sweden
| | - Preetha Rajaraman
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, Maryland, USA
| | - Stephen J Chanock
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, Maryland, USA
- Core Genotyping Facility, National Cancer Institute, SAIC-Frederick, Inc, Gaithersburg, Maryland, USA
| | - Martha S Linet
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, Maryland, USA
| | - Zhaoming Wang
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, Maryland, USA
- Core Genotyping Facility, National Cancer Institute, SAIC-Frederick, Inc, Gaithersburg, Maryland, USA
- Department of Computational Biology, St. Jude Children’s Research Hospital, Memphis, Tennessee, USA
| | - Meredith Yeager
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, Maryland, USA
- Core Genotyping Facility, National Cancer Institute, SAIC-Frederick, Inc, Gaithersburg, Maryland, USA
| | - Beatrice Melin
- Department of Radiation Sciences, Faculty of Medicine, Umeå University, Umeå, Sweden
| | - Melissa L Bondy
- Department of Medicine, Section of Epidemiology and Population Sciences, Dan L. Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, Texas, USA
| | - Jill S Barnholtz-Sloan
- Case Comprehensive Cancer Center, Case Western Reserve University School of Medicine, Cleveland, Ohio, USA
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8
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Liu J, Chen J, Perrone-Bizzozero NI, Turner JA, Calhoun VD. Regional enrichment analyses on genetic profiles for schizophrenia and bipolar disorder. Schizophr Res 2018; 192:240-246. [PMID: 28442247 PMCID: PMC5651209 DOI: 10.1016/j.schres.2017.04.033] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/03/2016] [Revised: 04/14/2017] [Accepted: 04/16/2017] [Indexed: 01/28/2023]
Abstract
Both schizophrenia (SZ) and bipolar disorder (BD) are highly heritable psychiatric disorders. The significant genomic risk loci are of great importance but with no guarantee of known functional impact and they cannot totally explain the genetic inheritance. In this study we present regional enrichment analyses across the genome, aiming to strike a balance between individual risk loci and integrated regional effects. Chromosomes were partitioned into 2 million base-pair regions (indicated by an underscore sign in the cytogenetic bands) on which enrichment tests are performed. In the discovery phase, we leverage the Psychiatric Genomics Consortium SZ and BD initial association test results for European Ancestry (EA) population and dbGAP SNP data for African Ancestry (AA) population. 78 and 48 regions show significantly enriched associations with SZ and BD respectively in the EA population, and nine are in common including MHC, 3p21.1, 7p22.3_2, 2q32.3_2, 8q24.3_4, and 19q13.33_1. The most unique SZ associated region is 1p21.3_3, while the most unique BD associated region is 6q25.2_1. For the AA population fewer regions are discovered with only 10% overlapping with that of EA population. A replication test using Wellcome Trust Case Control Consortium data for EA population verified 9% of the SZ enriched regions and 40% of the BD enriched regions. In summary, we showed that regional enrichment analyses produce reliable genetic association profiles using about one tenth of samples compared to single base-pair genome wide association approach. The identified association regions will be useful for further genetic functional studies.
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Affiliation(s)
- Jingyu Liu
- The Mind Research Network, Albuquerque, NM, USA; Dept. of Electrical Engineering, University of New Mexico, Albuquerque, NM, USA.
| | - Jiayu Chen
- The Mind Research Network, Albuquerque, NM, USA
| | | | - Jessica A Turner
- The Mind Research Network, Albuquerque, NM, USA; Psychology Department and Neuroscience Institute, Georgia State University, Atlanta, GA, USA
| | - Vince D Calhoun
- The Mind Research Network, Albuquerque, NM, USA; Dept. of Electrical Engineering, University of New Mexico, Albuquerque, NM, USA; Dept. of Neurosciences, University of New Mexico School of Medicine, Albuquerque, NM, USA
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9
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Greene CS, Himmelstein DS. Genetic Association-Guided Analysis of Gene Networks for the Study of Complex Traits. ACTA ACUST UNITED AC 2017; 9:179-84. [PMID: 27094199 DOI: 10.1161/circgenetics.115.001181] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2015] [Accepted: 03/08/2016] [Indexed: 12/29/2022]
Affiliation(s)
- Casey S Greene
- From the Systems Pharmacology and Translational Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia (C.S.G.); and Biological and Medical Informatics, University of California, San Francisco (D.S.H.).
| | - Daniel S Himmelstein
- From the Systems Pharmacology and Translational Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia (C.S.G.); and Biological and Medical Informatics, University of California, San Francisco (D.S.H.)
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10
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Verardo LL, Silva FF, Lopes MS, Madsen O, Bastiaansen JWM, Knol EF, Kelly M, Varona L, Lopes PS, Guimarães SEF. Revealing new candidate genes for reproductive traits in pigs: combining Bayesian GWAS and functional pathways. Genet Sel Evol 2016; 48:9. [PMID: 26830357 PMCID: PMC4736284 DOI: 10.1186/s12711-016-0189-x] [Citation(s) in RCA: 48] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2015] [Accepted: 01/20/2016] [Indexed: 12/18/2022] Open
Abstract
Background Reproductive traits such as number of stillborn piglets (SB) and number of teats (NT) have been evaluated in many genome-wide association studies (GWAS). Most of these GWAS were performed under the assumption that these traits were normally distributed. However, both SB and NT are discrete (e.g. count) variables. Therefore, it is necessary to test for better fit of other appropriate statistical models based on discrete distributions. In addition, although many GWAS have been performed, the biological meaning of the identified candidate genes, as well as their functional relationships still need to be better understood. Here, we performed and tested a Bayesian treatment of a GWAS model assuming a Poisson distribution for SB and NT in a commercial pig line. To explore the biological role of the genes that underlie SB and NT and identify the most likely candidate genes, we used the most significant single nucleotide polymorphisms (SNPs), to collect related genes and generated gene-transcription factor (TF) networks. Results Comparisons of the Poisson and Gaussian distributions showed that the Poisson model was appropriate for SB, while the Gaussian was appropriate for NT. The fitted GWAS models indicated 18 and 65 significant SNPs with one and nine quantitative trait locus (QTL) regions within which 18 and 57 related genes were identified for SB and NT, respectively. Based on the related TF, we selected the most representative TF for each trait and constructed a gene-TF network of gene-gene interactions and identified new candidate genes. Conclusions Our comparative analyses showed that the Poisson model presented the best fit for SB. Thus, to increase the accuracy of GWAS, counting models should be considered for this kind of trait. We identified multiple candidate genes (e.g. PTP4A2, NPHP1, and CYP24A1 for SB and YLPM1, SYNDIG1L, TGFB3, and VRTN for NT) and TF (e.g. NF-κB and KLF4 for SB and SOX9 and ELF5 for NT), which were consistent with known newborn survival traits (e.g. congenital heart disease in fetuses and kidney diseases and diabetes in the mother) and mammary gland biology (e.g. mammary gland development and body length). Electronic supplementary material The online version of this article (doi:10.1186/s12711-016-0189-x) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Lucas L Verardo
- Department of Animal Science, Universidade Federal de Viçosa, Viçosa, 36570000, Brazil. .,Animal Breeding and Genomics Centre, Wageningen University, 6700 AH, Wageningen, The Netherlands.
| | - Fabyano F Silva
- Department of Animal Science, Universidade Federal de Viçosa, Viçosa, 36570000, Brazil.
| | - Marcos S Lopes
- Animal Breeding and Genomics Centre, Wageningen University, 6700 AH, Wageningen, The Netherlands. .,Topigs Norsvin, Research Center, 6641 SZ, Beuningen, The Netherlands.
| | - Ole Madsen
- Animal Breeding and Genomics Centre, Wageningen University, 6700 AH, Wageningen, The Netherlands.
| | - John W M Bastiaansen
- Animal Breeding and Genomics Centre, Wageningen University, 6700 AH, Wageningen, The Netherlands.
| | - Egbert F Knol
- Topigs Norsvin, Research Center, 6641 SZ, Beuningen, The Netherlands.
| | - Mathew Kelly
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St Lucia, QLD, 4072, Australia.
| | - Luis Varona
- Departamento de Anatomía, Embriología y Genética, Universidad de Zaragoza, 50013, Saragossa, Spain.
| | - Paulo S Lopes
- Department of Animal Science, Universidade Federal de Viçosa, Viçosa, 36570000, Brazil.
| | - Simone E F Guimarães
- Department of Animal Science, Universidade Federal de Viçosa, Viçosa, 36570000, Brazil.
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11
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Hägg S, Ganna A, Van Der Laan SW, Esko T, Pers TH, Locke AE, Berndt SI, Justice AE, Kahali B, Siemelink MA, Pasterkamp G, Strachan DP, Speliotes EK, North KE, Loos RJF, Hirschhorn JN, Pawitan Y, Ingelsson E. Gene-based meta-analysis of genome-wide association studies implicates new loci involved in obesity. Hum Mol Genet 2015; 24:6849-60. [PMID: 26376864 PMCID: PMC4643645 DOI: 10.1093/hmg/ddv379] [Citation(s) in RCA: 50] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2015] [Accepted: 09/10/2015] [Indexed: 11/12/2022] Open
Abstract
To date, genome-wide association studies (GWASs) have identified >100 loci with single variants associated with body mass index (BMI). This approach may miss loci with high allelic heterogeneity; therefore, the aim of the present study was to use gene-based meta-analysis to identify regions with high allelic heterogeneity to discover additional obesity susceptibility loci. We included GWAS data from 123 865 individuals of European descent from 46 cohorts in Stage 1 and Metabochip data from additional 103 046 individuals from 43 cohorts in Stage 2, all within the Genetic Investigation of ANthropometric Traits (GIANT) consortium. Each cohort was tested for association between ∼2.4 million (Stage 1) or ∼200 000 (Stage 2) imputed or genotyped single variants and BMI, and summary statistics were subsequently meta-analyzed in 17 941 genes. We used the 'VErsatile Gene-based Association Study' (VEGAS) approach to assign variants to genes and to calculate gene-based P-values based on simulations. The VEGAS method was applied to each cohort separately before a gene-based meta-analysis was performed. In Stage 1, two known (FTO and TMEM18) and six novel (PEX2, MTFR2, SSFA2, IARS2, CEP295 and TXNDC12) loci were associated with BMI (P < 2.8 × 10(-6) for 17 941 gene tests). We confirmed all loci, and six of them were gene-wide significant in Stage 2 alone. We provide biological support for the loci by pathway, expression and methylation analyses. Our results indicate that gene-based meta-analysis of GWAS provides a useful strategy to find loci of interest that were not identified in standard single-marker analyses due to high allelic heterogeneity.
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Affiliation(s)
- Sara Hägg
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden, Molecular Epidemiology and Science for Life Laboratory, Department of Medical Sciences, Uppsala University, SE-751 41 Uppsala, Sweden
| | - Andrea Ganna
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA, Analytical and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Sander W Van Der Laan
- Laboratory of Experimental Cardiology, Division of Heart and Lungs, University Medical Center Utrecht, Utrecht 3584 CX, The Netherlands
| | - Tonu Esko
- Divisions of Endocrinology and Genetics and Center for Basic and Translational Obesity Research, Boston Children's Hospital, Boston, MA 02115, USA, Broad Institute of the Massachusetts Institute of Technology and Harvard University, Cambridge, MA 02142, USA, Department of Genetics, Harvard Medical School, Boston, MA 02115, USA, Estonian Genome Center, University of Tartu, Tartu 51010, Estonia
| | - Tune H Pers
- Divisions of Endocrinology and Genetics and Center for Basic and Translational Obesity Research, Boston Children's Hospital, Boston, MA 02115, USA, Broad Institute of the Massachusetts Institute of Technology and Harvard University, Cambridge, MA 02142, USA, Department of Genetics, Harvard Medical School, Boston, MA 02115, USA, Department of Epidemiology Research, Statens Serum Institut, Copenhagen, Denmark, Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen, Copenhagen, Denmark
| | - Adam E Locke
- Center for Statistical Genetics, Department of Biostatistics
| | - Sonja I Berndt
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | | | - Bratati Kahali
- Department of Internal Medicine, Division of Gastroenterology, Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Marten A Siemelink
- Laboratory of Experimental Cardiology, Division of Heart and Lungs, University Medical Center Utrecht, Utrecht 3584 CX, The Netherlands
| | - Gerard Pasterkamp
- Laboratory of Experimental Cardiology, Division of Heart and Lungs, University Medical Center Utrecht, Utrecht 3584 CX, The Netherlands, Laboratory of Clinical Chemistry and Hematology, Division Laboratories & Pharmacy, UMC Utrecht, Utrecht, The Netherlands
| | | | - Elizabeth K Speliotes
- Department of Internal Medicine, Division of Gastroenterology, Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Kari E North
- Department of Epidemiology, Carolina Center for Genome Sciences, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Ruth J F Loos
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Addenbrooke's Hospital, Hills Road, Cambridge CB2 0QQ, UK, The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA, The Genetics of Obesity and Related Metabolic Traits Program, The Mindich Child Health and Development Institute
| | - Joel N Hirschhorn
- Divisions of Endocrinology and Genetics and Center for Basic and Translational Obesity Research, Boston Children's Hospital, Boston, MA 02115, USA, Broad Institute of the Massachusetts Institute of Technology and Harvard University, Cambridge, MA 02142, USA, Department of Genetics, Harvard Medical School, Boston, MA 02115, USA
| | - Yudi Pawitan
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Erik Ingelsson
- Molecular Epidemiology and Science for Life Laboratory, Department of Medical Sciences, Uppsala University, SE-751 41 Uppsala, Sweden, Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK and Department of Medicine, Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA
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12
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Pearlson GD, Liu J, Calhoun VD. An introductory review of parallel independent component analysis (p-ICA) and a guide to applying p-ICA to genetic data and imaging phenotypes to identify disease-associated biological pathways and systems in common complex disorders. Front Genet 2015; 6:276. [PMID: 26442095 PMCID: PMC4561364 DOI: 10.3389/fgene.2015.00276] [Citation(s) in RCA: 60] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2015] [Accepted: 08/17/2015] [Indexed: 11/26/2022] Open
Abstract
Complex inherited phenotypes, including those for many common medical and psychiatric diseases, are most likely underpinned by multiple genes contributing to interlocking molecular biological processes, along with environmental factors (Owen et al., 2010). Despite this, genotyping strategies for complex, inherited, disease-related phenotypes mostly employ univariate analyses, e.g., genome wide association. Such procedures most often identify isolated risk-related SNPs or loci, not the underlying biological pathways necessary to help guide the development of novel treatment approaches. This article focuses on the multivariate analysis strategy of parallel (i.e., simultaneous combination of SNP and neuroimage information) independent component analysis (p-ICA), which typically yields large clusters of functionally related SNPs statistically correlated with phenotype components, whose overall molecular biologic relevance is inferred subsequently using annotation software suites. Because this is a novel approach, whose details are relatively new to the field we summarize its underlying principles and address conceptual questions regarding interpretation of resulting data and provide practical illustrations of the method.
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Affiliation(s)
- Godfrey D Pearlson
- The Olin Neuropsychiatry Research Center, Institute of Living, Hartford CT, USA ; Department of Neurobiology, Yale School of Medicine, Yale University, New Haven CT, USA ; Department of Psychiatry, Yale School of Medicine, Yale University, New Haven CT, USA
| | - Jingyu Liu
- Department of Electrical and Computer Engineering, and The Mind Research Network, The University of New Mexico, Albuquerque NM, USA
| | - Vince D Calhoun
- Department of Psychiatry, Yale School of Medicine, Yale University, New Haven CT, USA ; Department of Electrical and Computer Engineering, and The Mind Research Network, The University of New Mexico, Albuquerque NM, USA
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13
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Yeh FC, Kao CF, Kuo PH. Explore the Features of Brain-Derived Neurotrophic Factor in Mood Disorders. PLoS One 2015; 10:e0128605. [PMID: 26091093 PMCID: PMC4474832 DOI: 10.1371/journal.pone.0128605] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2014] [Accepted: 04/28/2015] [Indexed: 11/19/2022] Open
Abstract
Objectives Brain-derived neurotrophic factor (BDNF) plays important roles in neuronal survival and differentiation; however, the effects of BDNF on mood disorders remain unclear. We investigated BDNF from the perspective of various aspects of systems biology, including its molecular evolution, genomic studies, protein functions, and pathway analysis. Methods We conducted analyses examining sequences, multiple alignments, phylogenetic trees and positive selection across 12 species and several human populations. We summarized the results of previous genomic and functional studies of pro-BDNF and mature-BDNF (m-BDNF) found in a literature review. We identified proteins that interact with BDNF and performed pathway-based analysis using large genome-wide association (GWA) datasets obtained for mood disorders. Results BDNF is encoded by a highly conserved gene. The chordate BDNF genes exhibit an average of 75% identity with the human gene, while vertebrate orthologues are 85.9%-100% identical to human BDNF. No signs of recent positive selection were found. Associations between BDNF and mood disorders were not significant in most of the genomic studies (e.g., linkage, association, gene expression, GWA), while relationships between serum/plasma BDNF level and mood disorders were consistently reported. Pro-BDNF is important in the response to stress; the literature review suggests the necessity of studying both pro- and m-BDNF with regard to mood disorders. In addition to conventional pathway analysis, we further considered proteins that interact with BDNF (I-Genes) and identified several biological pathways involved with BDNF or I-Genes to be significantly associated with mood disorders. Conclusions Systematically examining the features and biological pathways of BDNF may provide opportunities to deepen our understanding of the mechanisms underlying mood disorders.
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Affiliation(s)
- Fan-Chi Yeh
- Department of Public Health & Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan
| | - Chung-Feng Kao
- Department of Public Health & Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan
| | - Po-Hsiu Kuo
- Department of Public Health & Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan
- Research Center for Genes, Environment and Human Health, National Taiwan University, Taipei, Taiwan
- * E-mail:
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14
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Morota G, Peñagaricano F, Petersen JL, Ciobanu DC, Tsuyuzaki K, Nikaido I. An application of MeSH enrichment analysis in livestock. Anim Genet 2015; 46:381-7. [PMID: 26036323 PMCID: PMC5032990 DOI: 10.1111/age.12307] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/01/2015] [Indexed: 01/01/2023]
Abstract
An integral part of functional genomics studies is to assess the enrichment of specific biological terms in lists of genes found to be playing an important role in biological phenomena. Contrasting the observed frequency of annotated terms with those of the background is at the core of overrepresentation analysis (ORA). Gene Ontology (GO) is a means to consistently classify and annotate gene products and has become a mainstay in ORA. Alternatively, Medical Subject Headings (MeSH) offers a comprehensive life science vocabulary including additional categories that are not covered by GO. Although MeSH is applied predominantly in human and model organism research, its full potential in livestock genetics is yet to be explored. In this study, MeSH ORA was evaluated to discern biological properties of identified genes and contrast them with the results obtained from GO enrichment analysis. Three published datasets were employed for this purpose, representing a gene expression study in dairy cattle, the use of SNPs for genome‐wide prediction in swine and the identification of genomic regions targeted by selection in horses. We found that several overrepresented MeSH annotations linked to these gene sets share similar concepts with those of GO terms. Moreover, MeSH yielded unique annotations, which are not directly provided by GO terms, suggesting that MeSH has the potential to refine and enrich the representation of biological knowledge. We demonstrated that MeSH can be regarded as another choice of annotation to draw biological inferences from genes identified via experimental analyses. When used in combination with GO terms, our results indicate that MeSH can enhance our functional interpretations for specific biological conditions or the genetic basis of complex traits in livestock species.
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Affiliation(s)
- G Morota
- Department of Animal Science, University of Nebraska, Lincoln, NE, USA
| | - F Peñagaricano
- Department of Animal Sciences, University of Florida, Gainesville, FL, USA.,University of Florida Genetics Institute, University of Florida, Gainesville, FL, USA
| | - J L Petersen
- Department of Animal Science, University of Nebraska, Lincoln, NE, USA
| | - D C Ciobanu
- Department of Animal Science, University of Nebraska, Lincoln, NE, USA
| | - K Tsuyuzaki
- Department of Medicinal and Life Science, Faculty of Pharmaceutical Sciences, Tokyo University of Science, 2641 Yamazaki, Noda, Chiba, Japan.,Bioinformatics Research Unit, Advanced Center for Computing and Communication, RIKEN, 2-1 Hirosawa, Wako, Saitama, Japan
| | - I Nikaido
- Bioinformatics Research Unit, Advanced Center for Computing and Communication, RIKEN, 2-1 Hirosawa, Wako, Saitama, Japan
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15
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Novel candidate genes putatively involved in stress fracture predisposition detected by whole-exome sequencing. Genet Res (Camb) 2015; 96:e004. [PMID: 25023003 DOI: 10.1017/s001667231400007x] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022] Open
Abstract
While genetic factors in all likelihood contribute to stress fracture (SF) pathogenesis, a few studies focusing on candidate genes have previously been reported. The objective of this study is to gain better understanding on the genetic basis of SF in a gene-naive manner. Exome sequence capture followed by massive parallel sequencing of two pooled DNA samples from Israeli combat soldiers was employed: cases with high grade SF and ethnically matched healthy controls. The resulting sequence variants were individually verified using the Sequenom™ platform and the contribution of the genetic alterations was validated in a second cohort of cases and controls. In the discovery set that included DNA pool of cases (n = 34) and controls (n = 60), a total of 1174 variants with >600 reads/variant/DNA pool were identified, and 146 (in 127 genes) of these exhibited statistically significant (P < 0·05) different rates between SF cases and controls after multiple comparisons correction. Subsequent validation of these 146 sequence variants individually in a total of 136 SF cases and 127 controls using the Sequenom™ platform validated 20/146 variants. Of these, three missense mutations (rs7426114, rs4073918, rs3752135 in the NEB, SLC6A18 and SIGLEC12 genes, respectively) and three synonymous mutations (rs2071856, rs2515941, rs716745 in the ELFN2, GRK4, LRRC55 genes) displayed significant different rates in SF cases compared with controls. Exome sequencing seemingly unravelled novel candidate genes as involved in SF pathogenesis and predisposition.
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16
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Kao CF, Chuang LC, Kuo PH. Risk and information evaluation of prioritized genes for complex traits: application to bipolar disorder. Am J Med Genet B Neuropsychiatr Genet 2014; 165B:596-606. [PMID: 25123107 DOI: 10.1002/ajmg.b.32263] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/11/2013] [Accepted: 06/26/2014] [Indexed: 01/01/2023]
Abstract
Many susceptibility genes for complex traits were identified without conclusive findings. There is a strong need to integrate rapidly accumulated genomic data from multi-dimensional platforms, and to conduct risk evaluation for potential therapeutic and diagnostic usages. We set up an algorithm to computationally search for optimal weight-vector for various data sources, while minimized potential noises. Through gene-prioritization framework, combined scores for the resulting prioritized gene-set were calculated using a genome-wide association (GWA) dataset, following with evaluation using weighted genetic risk score and risk-attributed information using an independent GWA dataset. The significance of association of GWA data was corrected for gene length. Enriched functional pathways were identified for the prioritized gene-set using the Gene Ontology analysis. We illustrated our framework with bipolar disorder. 233 prioritized genes were identified from 10,830 candidates that curated from six platforms. The prioritized genes were significantly enriched (P(adjusted) < 1 × 10(-5)) in 18 biological functions and molecular mechanisms including membrane, synaptic transmission, transmission of nerve impulse, integral to membrane, and plasma membrane. Our risk evaluation demonstrated higher weighted genetic risk score in bipolar patients than controls (P-values ranged from 0.002 to 3.8 × 10(-6)). Substantial risk-information (71%) was extracted from prioritized genes for bipolar illness than other candidate-gene sets. Our evidence-based prioritized gene-set provides opportunity to explore the complex network and to conduct follow-up basic and clinical studies for complex traits.
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Affiliation(s)
- Chung-Feng Kao
- Department of Public Health & Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan
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17
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Liu J, Calhoun VD. A review of multivariate analyses in imaging genetics. Front Neuroinform 2014; 8:29. [PMID: 24723883 PMCID: PMC3972473 DOI: 10.3389/fninf.2014.00029] [Citation(s) in RCA: 69] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2013] [Accepted: 03/04/2014] [Indexed: 12/13/2022] Open
Abstract
Recent advances in neuroimaging technology and molecular genetics provide the unique opportunity to investigate genetic influence on the variation of brain attributes. Since the year 2000, when the initial publication on brain imaging and genetics was released, imaging genetics has been a rapidly growing research approach with increasing publications every year. Several reviews have been offered to the research community focusing on various study designs. In addition to study design, analytic tools and their proper implementation are also critical to the success of a study. In this review, we survey recent publications using data from neuroimaging and genetics, focusing on methods capturing multivariate effects accommodating the large number of variables from both imaging data and genetic data. We group the analyses of genetic or genomic data into either a priori driven or data driven approach, including gene-set enrichment analysis, multifactor dimensionality reduction, principal component analysis, independent component analysis (ICA), and clustering. For the analyses of imaging data, ICA and extensions of ICA are the most widely used multivariate methods. Given detailed reviews of multivariate analyses of imaging data available elsewhere, we provide a brief summary here that includes a recently proposed method known as independent vector analysis. Finally, we review methods focused on bridging the imaging and genetic data by establishing multivariate and multiple genotype-phenotype-associations, including sparse partial least squares, sparse canonical correlation analysis, sparse reduced rank regression and parallel ICA. These methods are designed to extract latent variables from both genetic and imaging data, which become new genotypes and phenotypes, and the links between the new genotype-phenotype pairs are maximized using different cost functions. The relationship between these methods along with their assumptions, advantages, and limitations are discussed.
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Affiliation(s)
- Jingyu Liu
- The Mind Research Network and Lovelace Biomedical and Environmental Research InstituteAlbuquerque, NM, USA
- Department of Electrical and Computer Engineering, University of New MexicoAlbuquerque, NM, USA
| | - Vince D. Calhoun
- The Mind Research Network and Lovelace Biomedical and Environmental Research InstituteAlbuquerque, NM, USA
- Department of Electrical and Computer Engineering, University of New MexicoAlbuquerque, NM, USA
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18
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Fernández RM, Bleda M, Luzón-Toro B, García-Alonso L, Arnold S, Sribudiani Y, Besmond C, Lantieri F, Doan B, Ceccherini I, Lyonnet S, Hofstra RMW, Chakravarti A, Antiñolo G, Dopazo J, Borrego S. Pathways systematically associated to Hirschsprung's disease. Orphanet J Rare Dis 2013; 8:187. [PMID: 24289864 PMCID: PMC3879038 DOI: 10.1186/1750-1172-8-187] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2013] [Accepted: 11/19/2013] [Indexed: 02/08/2023] Open
Abstract
Despite it has been reported that several loci are involved in Hirschsprung's disease, the molecular basis of the disease remains yet essentially unknown. The study of collective properties of modules of functionally-related genes provides an efficient and sensitive statistical framework that can overcome sample size limitations in the study of rare diseases. Here, we present the extension of a previous study of a Spanish series of HSCR trios to an international cohort of 162 HSCR trios to validate the generality of the underlying functional basis of the Hirschsprung's disease mechanisms previously found. The Pathway-Based Analysis (PBA) confirms a strong association of gene ontology (GO) modules related to signal transduction and its regulation, enteric nervous system (ENS) formation and other processes related to the disease. In addition, network analysis recovers sub-networks significantly associated to the disease, which contain genes related to the same functionalities, thus providing an independent validation of these findings. The functional profiles of association obtained for patients populations from different countries were compared to each other. While gene associations were different at each series, the main functional associations were identical in all the five populations. These observations would also explain the reported low reproducibility of associations of individual disease genes across populations.
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Affiliation(s)
- Raquel M Fernández
- Department of Genetics, Reproduction and Fetal Medicine, Institute of Biomedicine of Seville (IBIS), University Hospital Virgen del Rocío/CSIC/University of Seville, Av. Manuel Siurot s/n, Seville, 41013, Spain
- Centre for Biomedical Network Research on Rare Diseases (CIBERER), Valencia, Spain
| | - Marta Bleda
- Centre for Biomedical Network Research on Rare Diseases (CIBERER), Valencia, Spain
- Department of Computational Genomics, Centro de Investigación Príncipe Felipe (CIPF), c/Eduardo Primo Yufera, 3, Valencia, 46012, Spain
| | - Berta Luzón-Toro
- Department of Genetics, Reproduction and Fetal Medicine, Institute of Biomedicine of Seville (IBIS), University Hospital Virgen del Rocío/CSIC/University of Seville, Av. Manuel Siurot s/n, Seville, 41013, Spain
- Centre for Biomedical Network Research on Rare Diseases (CIBERER), Valencia, Spain
| | - Luz García-Alonso
- Department of Computational Genomics, Centro de Investigación Príncipe Felipe (CIPF), c/Eduardo Primo Yufera, 3, Valencia, 46012, Spain
| | - Stacey Arnold
- Center for Complex Disease Genomics, McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Yunia Sribudiani
- Department of Medical Genetics, University of Groningen, Groningen, The Netherlands
| | - Claude Besmond
- INSERM U-781, AP-HP Hôpital Necker-Enfants Malades, Paris, France
| | | | - Betty Doan
- Center for Complex Disease Genomics, McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | | | | | - Robert MW Hofstra
- Department of Medical Genetics, University of Groningen, Groningen, The Netherlands
| | - Aravinda Chakravarti
- Center for Complex Disease Genomics, McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Guillermo Antiñolo
- Department of Genetics, Reproduction and Fetal Medicine, Institute of Biomedicine of Seville (IBIS), University Hospital Virgen del Rocío/CSIC/University of Seville, Av. Manuel Siurot s/n, Seville, 41013, Spain
- Centre for Biomedical Network Research on Rare Diseases (CIBERER), Valencia, Spain
| | - Joaquín Dopazo
- Centre for Biomedical Network Research on Rare Diseases (CIBERER), Valencia, Spain
- Department of Computational Genomics, Centro de Investigación Príncipe Felipe (CIPF), c/Eduardo Primo Yufera, 3, Valencia, 46012, Spain
- Functional Genomics Node (INB), CIPF, Valencia, Spain
| | - Salud Borrego
- Department of Genetics, Reproduction and Fetal Medicine, Institute of Biomedicine of Seville (IBIS), University Hospital Virgen del Rocío/CSIC/University of Seville, Av. Manuel Siurot s/n, Seville, 41013, Spain
- Centre for Biomedical Network Research on Rare Diseases (CIBERER), Valencia, Spain
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