1
|
Waksmunski AR, Grunin M, Kinzy TG, Igo RP, Haines JL, Cooke Bailey JN. Statistical driver genes as a means to uncover missing heritability for age-related macular degeneration. BMC Med Genomics 2020; 13:95. [PMID: 32631374 PMCID: PMC7336430 DOI: 10.1186/s12920-020-00747-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2020] [Accepted: 06/22/2020] [Indexed: 11/26/2022] Open
Abstract
Background Age-related macular degeneration (AMD) is a progressive retinal disease contributing to blindness worldwide. Multiple estimates for AMD heritability (h2) exist; however, a substantial proportion of h2 is not attributable to known genomic loci. The International AMD Genomics Consortium (IAMDGC) gathered the largest dataset of advanced AMD (ADV) cases and controls available and identified 34 loci containing 52 independent risk variants defining known AMD h2. To better define AMD heterogeneity, we used Pathway Analysis by Randomization Incorporating Structure (PARIS) on the IAMDGC data and identified 8 statistical driver genes (SDGs), including 2 novel SDGs not discovered by the IAMDGC. We chose to further investigate these pathway-based risk genes and determine their contribution to ADV h2, as well as the differential ADV subtype h2. Methods We performed genomic-relatedness-based restricted maximum-likelihood (GREML) analyses on ADV, geographic atrophy (GA), and choroidal neovascularization (CNV) subtypes to investigate the h2 of genotyped variants on the full DNA array chip, 34 risk loci (n = 2758 common variants), 52 variants from the IAMDGC 2016 GWAS, and the 8 SDGs, specifically the novel 2 SDGs, PPARA and PLCG2. Results Via GREML, full chip h2 was 44.05% for ADV, 46.37% for GA, and 62.03% for CNV. The lead 52 variants’ h2 (ADV: 14.52%, GA: 8.02%, CNV: 13.62%) and 34 loci h2 (ADV: 13.73%, GA: 8.81%, CNV: 12.89%) indicate that known variants contribute ~ 14% to ADV h2. SDG variants account for a small percentage of ADV, GA, and CNV heritability, but estimates based on the combination of SDGs and the 34 known loci are similar to those calculated for known loci alone. We identified modest epistatic interactions among variants in the 2 SDGs and the 52 IAMDGC variants, including modest interactions between variants in PPARA and PLCG2. Conclusions Pathway analyses, which leverage biological relationships among genes in a pathway, may be useful in identifying additional loci that contribute to the heritability of complex disorders in a non-additive manner. Heritability analyses of these loci, especially amongst disease subtypes, may provide clues to the importance of specific genes to the genetic architecture of AMD.
Collapse
Affiliation(s)
- Andrea R Waksmunski
- Department of Genetics and Genome Sciences, Case Western Reserve University, Cleveland, OH, 44106, USA.,Cleveland Institute for Computational Biology, Case Western Reserve University, Cleveland, OH, 44106, USA.,Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, 44106, USA
| | - Michelle Grunin
- Cleveland Institute for Computational Biology, Case Western Reserve University, Cleveland, OH, 44106, USA.,Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, 44106, USA
| | - Tyler G Kinzy
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, 44106, USA
| | - Robert P Igo
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, 44106, USA
| | - Jonathan L Haines
- Department of Genetics and Genome Sciences, Case Western Reserve University, Cleveland, OH, 44106, USA.,Cleveland Institute for Computational Biology, Case Western Reserve University, Cleveland, OH, 44106, USA.,Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, 44106, USA
| | - Jessica N Cooke Bailey
- Cleveland Institute for Computational Biology, Case Western Reserve University, Cleveland, OH, 44106, USA. .,Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, 44106, USA.
| |
Collapse
|
2
|
Fifteen Years of Gene Set Analysis for High-Throughput Genomic Data: A Review of Statistical Approaches and Future Challenges. ENTROPY 2020; 22:e22040427. [PMID: 33286201 PMCID: PMC7516904 DOI: 10.3390/e22040427] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/24/2020] [Revised: 03/18/2020] [Accepted: 04/03/2020] [Indexed: 12/22/2022]
Abstract
Over the last decade, gene set analysis has become the first choice for gaining insights into underlying complex biology of diseases through gene expression and gene association studies. It also reduces the complexity of statistical analysis and enhances the explanatory power of the obtained results. Although gene set analysis approaches are extensively used in gene expression and genome wide association data analysis, the statistical structure and steps common to these approaches have not yet been comprehensively discussed, which limits their utility. In this article, we provide a comprehensive overview, statistical structure and steps of gene set analysis approaches used for microarrays, RNA-sequencing and genome wide association data analysis. Further, we also classify the gene set analysis approaches and tools by the type of genomic study, null hypothesis, sampling model and nature of the test statistic, etc. Rather than reviewing the gene set analysis approaches individually, we provide the generation-wise evolution of such approaches for microarrays, RNA-sequencing and genome wide association studies and discuss their relative merits and limitations. Here, we identify the key biological and statistical challenges in current gene set analysis, which will be addressed by statisticians and biologists collectively in order to develop the next generation of gene set analysis approaches. Further, this study will serve as a catalog and provide guidelines to genome researchers and experimental biologists for choosing the proper gene set analysis approach based on several factors.
Collapse
|
3
|
Waksmunski AR, Grunin M, Kinzy TG, Igo RP, Haines JL, Cooke Bailey JN. Pathway Analysis Integrating Genome-Wide and Functional Data Identifies PLCG2 as a Candidate Gene for Age-Related Macular Degeneration. Invest Ophthalmol Vis Sci 2019; 60:4041-4051. [PMID: 31560769 PMCID: PMC6779289 DOI: 10.1167/iovs.19-27827] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2019] [Accepted: 08/14/2019] [Indexed: 11/24/2022] Open
Abstract
Purpose Age-related macular degeneration (AMD) is the worldwide leading cause of blindness among the elderly. Although genome-wide association studies (GWAS) have identified AMD risk variants, their roles in disease etiology are not well-characterized, and they only explain a portion of AMD heritability. Methods We performed pathway analyses using summary statistics from the International AMD Genomics Consortium's 2016 GWAS and multiple pathway databases to identify biological pathways wherein genetic association signals for AMD may be aggregating. We determined which genes contributed most to significant pathway signals across the databases. We characterized these genes by constructing protein-protein interaction networks and performing motif analysis. Results We determined that eight genes (C2, C3, LIPC, MICA, NOTCH4, PLCG2, PPARA, and RAD51B) "drive" the statistical signals observed across pathways curated in the Kyoto Encyclopedia of Genes and Genomes (KEGG), Reactome, and Gene Ontology (GO) databases. We further refined our definition of statistical driver gene to identify PLCG2 as a candidate gene for AMD due to its significant gene-level signals (P < 0.0001) across KEGG, Reactome, GO, and NetPath pathways. Conclusions We performed pathway analyses on the largest available collection of advanced AMD cases and controls in the world. Eight genes strongly contributed to significant pathways from the three larger databases, and one gene (PLCG2) was central to significant pathways from all four databases. This is, to our knowledge, the first study to identify PLCG2 as a candidate gene for AMD based solely on genetic burden. Our findings reinforce the utility of integrating in silico genetic and biological pathway data to investigate the genetic architecture of AMD.
Collapse
Affiliation(s)
- Andrea R. Waksmunski
- Department of Genetics and Genome Sciences, Case Western Reserve University, Cleveland, Ohio, United States
- Cleveland Institute for Computational Biology, Case Western Reserve University, Cleveland, Ohio, United States
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, Ohio, United States
| | - Michelle Grunin
- Cleveland Institute for Computational Biology, Case Western Reserve University, Cleveland, Ohio, United States
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, Ohio, United States
| | - Tyler G. Kinzy
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, Ohio, United States
| | - Robert P. Igo
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, Ohio, United States
| | - Jonathan L. Haines
- Department of Genetics and Genome Sciences, Case Western Reserve University, Cleveland, Ohio, United States
- Cleveland Institute for Computational Biology, Case Western Reserve University, Cleveland, Ohio, United States
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, Ohio, United States
| | - Jessica N. Cooke Bailey
- Cleveland Institute for Computational Biology, Case Western Reserve University, Cleveland, Ohio, United States
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, Ohio, United States
| | - for the International Age-Related Macular Degeneration Genomics Consortium
- Department of Genetics and Genome Sciences, Case Western Reserve University, Cleveland, Ohio, United States
- Cleveland Institute for Computational Biology, Case Western Reserve University, Cleveland, Ohio, United States
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, Ohio, United States
| |
Collapse
|
4
|
White MJ, Yaspan BL, Veatch OJ, Goddard P, Risse-Adams OS, Contreras MG. Strategies for Pathway Analysis Using GWAS and WGS Data. ACTA ACUST UNITED AC 2018; 100:e79. [PMID: 30387919 DOI: 10.1002/cphg.79] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
Single-allele study designs, commonly used in genome-wide association studies (GWAS) as well as the more recently developed whole genome sequencing (WGS) studies, are a standard approach for investigating the relationship of common variation within the human genome to a given phenotype of interest. However, single-allele association results published for many GWAS studies represent only the tip of the iceberg for the information that can be extracted from these datasets. The primary analysis strategy for GWAS entails association analysis in which only the single nucleotide polymorphisms (SNPs) with the strongest p-values are declared statistically significant due to issues arising from multiple testing and type I errors. Factors such as locus heterogeneity, epistasis, and multiple genes conferring small effects contribute to the complexity of the genetic models underlying phenotype expression. Thus, many biologically meaningful associations having lower effect sizes at individual genes are overlooked, making it difficult to separate true associations from a sea of false-positive associations. Organizing these individual SNPs into biologically meaningful groups to look at the overall effects of minor perturbations to genes and pathways is desirable. This pathway-based approach provides researchers with insight into the functional foundations of the phenotype being studied and allows testing of various genetic scenarios. © 2018 by John Wiley & Sons, Inc.
Collapse
Affiliation(s)
- Marquitta J White
- Department of Medicine, Lung Biology Center, University of California San Francisco, San Francisco, California
| | | | - Olivia J Veatch
- Center for Sleep and Circadian Neurobiology, Department of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Pagé Goddard
- Department of Medicine, Lung Biology Center, University of California San Francisco, San Francisco, California
| | - Oona S Risse-Adams
- Department of Medicine, Lung Biology Center, University of California San Francisco, San Francisco, California
| | - Maria G Contreras
- Department of Medicine, Lung Biology Center, University of California San Francisco, San Francisco, California.,MARC, San Francisco State University, San Francisco, California
| |
Collapse
|
5
|
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.
Collapse
|
6
|
Cooke Bailey JN, Gharahkhani P, Kang JH, Butkiewicz M, Sullivan DA, Weinreb RN, Aschard H, Allingham RR, Ashley-Koch A, Lee RK, Moroi SE, Brilliant MH, Wollstein G, Schuman JS, Fingert JH, Budenz DL, Realini T, Gaasterland T, Scott WK, Singh K, Sit AJ, Igo RP, Song YE, Hark L, Ritch R, Rhee DJ, Vollrath D, Zack DJ, Medeiros F, Vajaranant TS, Chasman DI, Christen WG, Pericak-Vance MA, Liu Y, Kraft P, Richards JE, Rosner BA, Hauser MA, Craig JE, Burdon KP, Hewitt AW, Mackey DA, Haines JL, MacGregor S, Wiggs JL, Pasquale LR. Testosterone Pathway Genetic Polymorphisms in Relation to Primary Open-Angle Glaucoma: An Analysis in Two Large Datasets. Invest Ophthalmol Vis Sci 2018; 59:629-636. [PMID: 29392307 PMCID: PMC5795896 DOI: 10.1167/iovs.17-22708] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2017] [Accepted: 12/25/2017] [Indexed: 11/24/2022] Open
Abstract
Purpose Sex hormones may be associated with primary open-angle glaucoma (POAG), although the mechanisms are unclear. We previously observed that gene variants involved with estrogen metabolism were collectively associated with POAG in women but not men; here we assessed gene variants related to testosterone metabolism collectively and POAG risk. Methods We used two datasets: one from the United States (3853 cases and 33,480 controls) and another from Australia (1155 cases and 1992 controls). Both datasets contained densely called genotypes imputed to the 1000 Genomes reference panel. We used pathway- and gene-based approaches with Pathway Analysis by Randomization Incorporating Structure (PARIS) software to assess the overall association between a panel of single nucleotide polymorphisms (SNPs) in testosterone metabolism genes and POAG. In sex-stratified analyses, we evaluated POAG overall and POAG subtypes defined by maximum IOP (high-tension [HTG] or normal tension glaucoma [NTG]). Results In the US dataset, the SNP panel was not associated with POAG (permuted P = 0.77), although there was an association in the Australian sample (permuted P = 0.018). In both datasets, the SNP panel was associated with POAG in men (permuted P ≤ 0.033) and not women (permuted P ≥ 0.42), but in gene-based analyses, there was no consistency on the main genes responsible for these findings. In both datasets, the testosterone pathway association with HTG was significant (permuted P ≤ 0.011), but again, gene-based analyses showed no consistent driver gene associations. Conclusions Collectively, testosterone metabolism pathway SNPs were consistently associated with the high-tension subtype of POAG in two datasets.
Collapse
Affiliation(s)
- Jessica N. Cooke Bailey
- Department of Population and Quantitative Health Sciences, Case Western Reserve University School of Medicine, Cleveland, Ohio, United States
- Institute for Computational Biology, Case Western Reserve University School of Medicine, Cleveland, Ohio, United States
| | - Puya Gharahkhani
- Statistical Genetics, QIMR Berghofer Medical Research Institute, Royal Brisbane Hospital, Brisbane, Australia
| | - Jae H. Kang
- Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, United States
| | - Mariusz Butkiewicz
- Department of Population and Quantitative Health Sciences, Case Western Reserve University School of Medicine, Cleveland, Ohio, United States
- Institute for Computational Biology, Case Western Reserve University School of Medicine, Cleveland, Ohio, United States
| | - David A. Sullivan
- Schepens Eye Research Institute, Massachusetts Eye and Ear Infirmary, Harvard Medical School, Boston, Massachusetts, United States
| | - Robert N. Weinreb
- Department of Ophthalmology, Hamilton Glaucoma Center and Shiley Eye Institute, University of California at San Diego, La Jolla, California, United States
| | - Hugues Aschard
- Department of Epidemiology, Harvard T. H. Chan School of Public Health, Harvard Medical School, Boston, Massachusetts, United States
| | - R. Rand Allingham
- Department of Ophthalmology, Duke University Medical Center, Durham, North Carolina, United States
| | - Allison Ashley-Koch
- Department of Medicine, Duke University Medical Center, Durham, North Carolina, United States
| | - Richard K. Lee
- Bascom Palmer Eye Institute, University of Miami Miller School of Medicine, Miami, Florida, United States
| | - Sayoko E. Moroi
- Department of Ophthalmology and Visual Sciences, University of Michigan, Ann Arbor, Michigan, United States
| | - Murray H. Brilliant
- Center for Human Genetics, Marshfield Clinic Research Institute, Marshfield, Wisconsin, United States
| | - Gadi Wollstein
- Department of Ophthalmology, NYU Langone Medical Center, NYU School of Medicine, New York, New York, United States
| | - Joel S. Schuman
- Department of Ophthalmology, NYU Langone Medical Center, NYU School of Medicine, New York, New York, United States
| | - John H. Fingert
- Departments of Ophthalmology and Anatomy/Cell Biology, University of Iowa, College of Medicine, Iowa City, Iowa, United States
| | - Donald L. Budenz
- Department of Ophthalmology, University of North Carolina, Chapel Hill, North Carolina, United States
| | - Tony Realini
- Department of Ophthalmology, WVU Eye Institute, Morgantown, West Virginia, United States
| | - Terry Gaasterland
- Scripps Genome Center, University of California at San Diego, San Diego, California, United States
| | - William K. Scott
- Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, Florida, United States
| | - Kuldev Singh
- Department of Ophthalmology, Stanford University, Palo Alto, California, United States
| | - Arthur J. Sit
- Department of Ophthalmology, Mayo Clinic, Rochester, Minnesota, United States
| | - Robert P. Igo
- Department of Population and Quantitative Health Sciences, Case Western Reserve University School of Medicine, Cleveland, Ohio, United States
| | - Yeunjoo E. Song
- Department of Population and Quantitative Health Sciences, Case Western Reserve University School of Medicine, Cleveland, Ohio, United States
- Institute for Computational Biology, Case Western Reserve University School of Medicine, Cleveland, Ohio, United States
| | - Lisa Hark
- Wills Eye Hospital, Glaucoma Research Center, Philadelphia, Pennsylvania, United States
| | - Robert Ritch
- Einhorn Clinical Research Center, New York Eye and Ear Infirmary of Mount Sinai, New York, New York, United States
| | - Douglas J. Rhee
- Department of Ophthalmology, Case Western Reserve University School of Medicine, Cleveland, Ohio, United States
| | - Douglas Vollrath
- Department of Genetics, Stanford University, Palo Alto, California, United States
| | - Donald J. Zack
- Wilmer Eye Institute, Johns Hopkins University Hospital, Baltimore, Maryland, United States
| | - Felipe Medeiros
- Department of Ophthalmology, Hamilton Glaucoma Center and Shiley Eye Institute, University of California at San Diego, La Jolla, California, United States
| | - Thasarat S. Vajaranant
- Department of Ophthalmology, University of Illinois College of Medicine at Chicago, Chicago, Illinois, United States
| | - Daniel I. Chasman
- Division of Preventive Medicine, Brigham and Women's Hospital, Boston, Massachusetts, United States
| | - William G. Christen
- Division of Preventive Medicine, Brigham and Women's Hospital, Boston, Massachusetts, United States
| | - Margaret A. Pericak-Vance
- Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, Florida, United States
| | - Yutao Liu
- Department of Cellular Biology and Anatomy, Augusta University, Augusta, Georgia, United States
| | - Peter Kraft
- Department of Epidemiology, Harvard T. H. Chan School of Public Health, Harvard Medical School, Boston, Massachusetts, United States
- Department of Biostatistics, Harvard T. H. Chan School of Public Health, Harvard Medical School, Boston, Massachusetts, United States
| | - Julia E. Richards
- Department of Ophthalmology and Visual Sciences, University of Michigan, Ann Arbor, Michigan, United States
| | - Bernard A. Rosner
- Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, United States
- Department of Biostatistics, Harvard T. H. Chan School of Public Health, Harvard Medical School, Boston, Massachusetts, United States
| | - Michael A. Hauser
- Department of Ophthalmology, Duke University Medical Center, Durham, North Carolina, United States
- Department of Medicine, Duke University Medical Center, Durham, North Carolina, United States
| | - Jamie E. Craig
- Department of Ophthalmology, Flinders University, Adelaide, SA, Australia
| | - Kathryn P. Burdon
- School of Medicine, Menzies Research Institute of Tasmania, Hobart, Australia
| | - Alex W. Hewitt
- Centre for Ophthalmology and Visual Science, Lions Eye Institute, University of Western Australia, Perth, Australia
| | - David A. Mackey
- School of Medicine, Menzies Research Institute of Tasmania, Hobart, Australia
- Centre for Ophthalmology and Visual Science, Lions Eye Institute, University of Western Australia, Perth, Australia
| | - Jonathan L. Haines
- Department of Population and Quantitative Health Sciences, Case Western Reserve University School of Medicine, Cleveland, Ohio, United States
- Institute for Computational Biology, Case Western Reserve University School of Medicine, Cleveland, Ohio, United States
| | - Stuart MacGregor
- Statistical Genetics, QIMR Berghofer Medical Research Institute, Royal Brisbane Hospital, Brisbane, Australia
| | - Janey L. Wiggs
- Department of Ophthalmology, Mass Eye and Ear Infirmary, Harvard Medical School, Boston, Massachusetts, United States
| | - Louis R. Pasquale
- Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, United States
- Department of Ophthalmology, Mass Eye and Ear Infirmary, Harvard Medical School, Boston, Massachusetts, United States
| | - for the Australian and New Zealand Registry of Advanced Glaucoma (ANZRAG) Consortium
- Department of Population and Quantitative Health Sciences, Case Western Reserve University School of Medicine, Cleveland, Ohio, United States
- Institute for Computational Biology, Case Western Reserve University School of Medicine, Cleveland, Ohio, United States
- Statistical Genetics, QIMR Berghofer Medical Research Institute, Royal Brisbane Hospital, Brisbane, Australia
- Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, United States
- Schepens Eye Research Institute, Massachusetts Eye and Ear Infirmary, Harvard Medical School, Boston, Massachusetts, United States
- Department of Ophthalmology, Hamilton Glaucoma Center and Shiley Eye Institute, University of California at San Diego, La Jolla, California, United States
- Department of Epidemiology, Harvard T. H. Chan School of Public Health, Harvard Medical School, Boston, Massachusetts, United States
- Department of Ophthalmology, Duke University Medical Center, Durham, North Carolina, United States
- Department of Medicine, Duke University Medical Center, Durham, North Carolina, United States
- Bascom Palmer Eye Institute, University of Miami Miller School of Medicine, Miami, Florida, United States
- Department of Ophthalmology and Visual Sciences, University of Michigan, Ann Arbor, Michigan, United States
- Center for Human Genetics, Marshfield Clinic Research Institute, Marshfield, Wisconsin, United States
- Department of Ophthalmology, NYU Langone Medical Center, NYU School of Medicine, New York, New York, United States
- Departments of Ophthalmology and Anatomy/Cell Biology, University of Iowa, College of Medicine, Iowa City, Iowa, United States
- Department of Ophthalmology, University of North Carolina, Chapel Hill, North Carolina, United States
- Department of Ophthalmology, WVU Eye Institute, Morgantown, West Virginia, United States
- Scripps Genome Center, University of California at San Diego, San Diego, California, United States
- Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, Florida, United States
- Department of Ophthalmology, Stanford University, Palo Alto, California, United States
- Department of Ophthalmology, Mayo Clinic, Rochester, Minnesota, United States
- Wills Eye Hospital, Glaucoma Research Center, Philadelphia, Pennsylvania, United States
- Einhorn Clinical Research Center, New York Eye and Ear Infirmary of Mount Sinai, New York, New York, United States
- Department of Ophthalmology, Case Western Reserve University School of Medicine, Cleveland, Ohio, United States
- Department of Genetics, Stanford University, Palo Alto, California, United States
- Wilmer Eye Institute, Johns Hopkins University Hospital, Baltimore, Maryland, United States
- Department of Ophthalmology, University of Illinois College of Medicine at Chicago, Chicago, Illinois, United States
- Division of Preventive Medicine, Brigham and Women's Hospital, Boston, Massachusetts, United States
- Department of Cellular Biology and Anatomy, Augusta University, Augusta, Georgia, United States
- Department of Biostatistics, Harvard T. H. Chan School of Public Health, Harvard Medical School, Boston, Massachusetts, United States
- Department of Ophthalmology, Flinders University, Adelaide, SA, Australia
- School of Medicine, Menzies Research Institute of Tasmania, Hobart, Australia
- Centre for Ophthalmology and Visual Science, Lions Eye Institute, University of Western Australia, Perth, Australia
- Department of Ophthalmology, Mass Eye and Ear Infirmary, Harvard Medical School, Boston, Massachusetts, United States
| |
Collapse
|
7
|
Genome-wide association and pathway analysis of left ventricular function after anthracycline exposure in adults. Pharmacogenet Genomics 2018; 27:247-254. [PMID: 28542097 DOI: 10.1097/fpc.0000000000000284] [Citation(s) in RCA: 45] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
BACKGROUND Anthracyclines are important chemotherapeutic agents, but their use is limited by cardiotoxicity. Candidate gene and genome-wide studies have identified putative risk loci for overt cardiotoxicity and heart failure, but there has been no comprehensive assessment of genomic variation influencing the intermediate phenotype of anthracycline-related changes in left ventricular (LV) function. The purpose of this study was to identify genetic factors influencing changes in LV function after anthracycline chemotherapy. METHODS We conducted a genome-wide association study (GWAS) of change in LV function after anthracycline exposure in 385 patients identified from BioVU, a resource linking DNA samples to de-identified electronic medical record data. Variants with P values less than 1×10 were independently tested for replication in a cohort of 181 anthracycline-exposed patients from a prospective clinical trial. Pathway analysis was performed to assess combined effects of multiple genetic variants. RESULTS Both cohorts were middle-aged adults of predominantly European descent. Among 11 candidate loci identified in discovery GWAS, one single nucleotide polymorphism near PR domain containing 2, with ZNF domain (PRDM2), rs7542939, had a combined P value of 6.5×10 in meta-analysis. Eighteen Kyoto Encyclopedia of Gene and Genomes pathways showed strong enrichment for variants associated with the primary outcome. Identified pathways related to DNA repair, cellular metabolism, and cardiac remodeling. CONCLUSION Using genome-wide association we identified a novel candidate susceptibility locus near PRDM2. Variation in genes belonging to pathways related to DNA repair, metabolism, and cardiac remodeling may influence changes in LV function after anthracycline exposure.
Collapse
|
8
|
Wei J, Li M, Gao F, Zeng R, Liu G, Li K. Multiple analyses of large-scale genome-wide association study highlight new risk pathways in lumbar spine bone mineral density. Oncotarget 2017; 7:31429-39. [PMID: 27119226 PMCID: PMC5058768 DOI: 10.18632/oncotarget.8948] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2015] [Accepted: 03/29/2016] [Indexed: 11/25/2022] Open
Abstract
Osteoporosis is a common human complex disease. It is mainly characterized by low bone mineral density (BMD) and low-trauma osteoporotic fractures (OF). Until now, a large proportion of heritability has yet to be explained. The existing large-scale genome-wide association studies (GWAS) provide strong support for the investigation of osteoporosis mechanisms using pathway analysis. Recent findings showed that different risk pathways may be involved in BMD in different tissues. Here, we conducted multiple pathway analyses of a large-scale lumbar spine BMD GWAS dataset (2,468,080 SNPs and 31,800 samples) using two published gene-based analysis software including ProxyGeneLD and the PLINK. Using BMD genes from ProxyGeneLD, we identified 51 significant KEGG pathways with adjusted P<0.01. Using BMD genes from PLINK, we identified 38 significant KEGG pathways with adjusted P<0.01. Interestingly, 33 pathways are shared in both methods. In summary, we not only identified the known risk pathway such as Wnt signaling, in which the top GWAS variants are significantly enriched, but also highlight some new risk pathways. Interestingly, evidence from further supports the involvement of these pathways in MBD.
Collapse
Affiliation(s)
- Jinsong Wei
- Department of Orthopedic Surgery, Affiliated Hospital of Guangdong Medical University, Zhanjiang, China
| | - Ming Li
- Departmentof Endocrinology and Metabolism, The Fourth Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China
| | - Feng Gao
- Department of Trauma and Emergency Surgeon, The Second Affiliated Hospital, Harbin Medical University, Harbin, China
| | - Rong Zeng
- Department of Orthopedic Surgery, Affiliated Hospital of Guangdong Medical University, Zhanjiang, China
| | - Guiyou Liu
- Genome Analysis Laboratory, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, China
| | - Keshen Li
- Institute of Neurology, Affiliated Hospital of Guangdong Medical University, Zhanjiang, China.,Stroke Center, Neurology & Neurosurgery Division, The Clinical Medicine Research Institute & The First Affiliated Hospital, Jinan University, Guangzhou, China
| |
Collapse
|
9
|
Reilly J, Gallagher L, Chen JL, Leader G, Shen S. Bio-collections in autism research. Mol Autism 2017; 8:34. [PMID: 28702161 PMCID: PMC5504648 DOI: 10.1186/s13229-017-0154-8] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2017] [Accepted: 06/23/2017] [Indexed: 01/06/2023] Open
Abstract
Autism spectrum disorder (ASD) is a group of complex neurodevelopmental disorders with diverse clinical manifestations and symptoms. In the last 10 years, there have been significant advances in understanding the genetic basis for ASD, critically supported through the establishment of ASD bio-collections and application in research. Here, we summarise a selection of major ASD bio-collections and their associated findings. Collectively, these include mapping ASD candidate genes, assessing the nature and frequency of gene mutations and their association with ASD clinical subgroups, insights into related molecular pathways such as the synapses, chromatin remodelling, transcription and ASD-related brain regions. We also briefly review emerging studies on the use of induced pluripotent stem cells (iPSCs) to potentially model ASD in culture. These provide deeper insight into ASD progression during development and could generate human cell models for drug screening. Finally, we provide perspectives concerning the utilities of ASD bio-collections and limitations, and highlight considerations in setting up a new bio-collection for ASD research.
Collapse
Affiliation(s)
- Jamie Reilly
- Regenerative Medicine Institute, School of Medicine, BioMedical Sciences Building, National University of Ireland (NUI), Galway, Ireland
| | - Louise Gallagher
- Trinity Translational Medicine Institute and Department of Psychiatry, Trinity Centre for Health Sciences, St. James Hospital Street, Dublin 8, Ireland
| | - June L Chen
- Department of Special Education, Faculty of Education, East China Normal University, Shanghai, 200062 China
| | - Geraldine Leader
- Irish Centre for Autism and Neurodevelopmental Research (ICAN), Department of Psychology, National University of Ireland Galway, University Road, Galway, Ireland
| | - Sanbing Shen
- Regenerative Medicine Institute, School of Medicine, BioMedical Sciences Building, National University of Ireland (NUI), Galway, Ireland
| |
Collapse
|
10
|
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.
Collapse
|
11
|
Kao PYP, Leung KH, Chan LWC, Yip SP, Yap MKH. Pathway analysis of complex diseases for GWAS, extending to consider rare variants, multi-omics and interactions. Biochim Biophys Acta Gen Subj 2016; 1861:335-353. [PMID: 27888147 DOI: 10.1016/j.bbagen.2016.11.030] [Citation(s) in RCA: 36] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2016] [Revised: 10/17/2016] [Accepted: 11/19/2016] [Indexed: 12/20/2022]
Abstract
BACKGROUND Genome-wide association studies (GWAS) is a major method for studying the genetics of complex diseases. Finding all sequence variants to explain fully the aetiology of a disease is difficult because of their small effect sizes. To better explain disease mechanisms, pathway analysis is used to consolidate the effects of multiple variants, and hence increase the power of the study. While pathway analysis has previously been performed within GWAS only, it can now be extended to examining rare variants, other "-omics" and interaction data. SCOPE OF REVIEW 1. Factors to consider in the choice of software for GWAS pathway analysis. 2. Examples of how pathway analysis is used to analyse rare variants, other "-omics" and interaction data. MAJOR CONCLUSIONS To choose appropriate software tools, factors for consideration include covariate compatibility, null hypothesis, one- or two-step analysis required, curation method of gene sets, size of pathways, and size of flanking regions to define gene boundaries. For rare variants, analysis performance depends on consistency between assumed and actual effect distribution of variants. Integration of other "-omics" data and interaction can better explain gene functions. GENERAL SIGNIFICANCE Pathway analysis methods will be more readily used for integration of multiple sources of data, and enable more accurate prediction of phenotypes.
Collapse
Affiliation(s)
- Patrick Y P Kao
- Centre for Myopia Research, School of Optometry, The Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Kim Hung Leung
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Lawrence W C Chan
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Shea Ping Yip
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, China.
| | - Maurice K H Yap
- Centre for Myopia Research, School of Optometry, The Hong Kong Polytechnic University, Hong Kong SAR, China
| |
Collapse
|
12
|
Bronson PG, Chang D, Bhangale T, Seldin MF, Ortmann W, Ferreira RC, Urcelay E, Pereira LF, Martin J, Plebani A, Lougaris V, Friman V, Freiberger T, Litzman J, Thon V, Pan-Hammarström Q, Hammarström L, Graham RR, Behrens TW. Common variants at PVT1, ATG13-AMBRA1, AHI1 and CLEC16A are associated with selective IgA deficiency. Nat Genet 2016; 48:1425-1429. [PMID: 27723758 PMCID: PMC5086090 DOI: 10.1038/ng.3675] [Citation(s) in RCA: 54] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2015] [Accepted: 08/24/2016] [Indexed: 12/18/2022]
Abstract
Selective immunoglobulin A deficiency (IgAD) is the most common primary immunodeficiency in Europeans. Our genome-wide association study (GWAS) meta-analysis of 1,635 patients with IgAD and 4,852 controls identified four new significant (P < 5 × 10-8) loci and association with a rare IFIH1 variant (p.Ile923Val). Peak new variants (PVT1, P = 4.3 × 10-11; ATG13-AMBRA1, P = 6.7 × 10-10; AHI1, P = 8.4 × 10-10; CLEC16A, P = 1.4 × 10-9) overlapped with autoimmune markers (3/4) and correlated with 21 putative regulatory variants, including expression quantitative trait loci (eQTLs) for AHI1 and DEXI and DNase hypersensitivity sites in FOXP3+ regulatory T cells. Pathway analysis of the meta-analysis results showed striking association with the KEGG pathway for IgA production (pathway P < 0.0001), with 22 of the 30 annotated pathway genes containing at least one variant with P ≤ 0.05 in the IgAD meta-analysis. These data suggest that a complex network of genetic effects, including genes known to influence the biology of IgA production, contributes to IgAD.
Collapse
Affiliation(s)
- Paola G. Bronson
- Department of Human Genetics, Genentech, Inc., South San
Francisco, CA, USA
| | - Diana Chang
- Department of Human Genetics, Genentech, Inc., South San
Francisco, CA, USA
| | - Tushar Bhangale
- Department of Bioinformatics and Computational Biology,
Genentech, Inc., South San Francisco, CA, USA
| | - Michael F. Seldin
- Department of Biochemistry, School of Medicine, University
of California, Davis, CA, USA
| | - Ward Ortmann
- Department of Human Genetics, Genentech, Inc., South San
Francisco, CA, USA
| | - Ricardo C. Ferreira
- Juvenile Diabetes Research Foundation/Wellcome Trust
Diabetes and Inflammation Laboratory, Cambridge Institute for Medical Research,
Cambridge, UK
| | - Elena Urcelay
- Department of Immunology, Instituto de Investigación
Sanitaria del Hospital Clínico San Carlos, IdISSC, Madrid, Spain
| | | | - Javier Martin
- Instituto de Parasitología y Biomedicina
López-Neyra, CSIC, Granada, Spain
| | - Alessandro Plebani
- Pediatrics Clinic, Department of Clinical and Experimental
Sciences, University of Brescia, Spedali Civili di Brescia, Italy
- Institute for Molecular Medicine, A. Nocivelli, Department
of Clinical and Experimental Sciences, University of Brescia, Spedali Civili di
Brescia, Italy
| | - Vassilios Lougaris
- Pediatrics Clinic, Department of Clinical and Experimental
Sciences, University of Brescia, Spedali Civili di Brescia, Italy
- Institute for Molecular Medicine, A. Nocivelli, Department
of Clinical and Experimental Sciences, University of Brescia, Spedali Civili di
Brescia, Italy
| | - Vanda Friman
- Department of Infectious Diseases, University of
Gothenburg, Gothenburg, Sweden
| | - Tomáš Freiberger
- Molecular Genetics Laboratory, Centre for Cardiovascular
Surgery and Transplantation, Brno, Czech Republic
- Central European Institute of Technology, Masaryk
University, Brno, Czech Republic
| | - Jiri Litzman
- Department of Clinical Immunology and Allergy, Faculty of
Medicine, Masaryk University, St. Anne’s Univ. Hospital, Brno, Czech
Republic
| | - Vojtech Thon
- Department of Clinical Immunology and Allergy, Faculty of
Medicine, Masaryk University, St. Anne’s Univ. Hospital, Brno, Czech
Republic
- Research Centre for Toxic Compounds in the Environment,
Faculty of Science, Masaryk University, Brno, Czech Republic
| | - Qiang Pan-Hammarström
- Division of Clinical Immunology & Transfusion
Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Lennart Hammarström
- Division of Clinical Immunology & Transfusion
Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Robert R. Graham
- Department of Human Genetics, Genentech, Inc., South San
Francisco, CA, USA
| | - Timothy W. Behrens
- Department of Human Genetics, Genentech, Inc., South San
Francisco, CA, USA
| |
Collapse
|
13
|
Restrepo NA, Butkiewicz M, McGrath JA, Crawford DC. Shared Genetic Etiology of Autoimmune Diseases in Patients from a Biorepository Linked to De-identified Electronic Health Records. Front Genet 2016; 7:185. [PMID: 27812365 PMCID: PMC5071319 DOI: 10.3389/fgene.2016.00185] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2016] [Accepted: 10/03/2016] [Indexed: 01/15/2023] Open
Abstract
Autoimmune diseases represent a significant medical burden affecting up to 5–8% of the U.S. population. While genetics is known to play a role, studies of common autoimmune diseases are complicated by phenotype heterogeneity, limited sample sizes, and a single disease approach. Here we performed a targeted genetic association study for cases of multiple sclerosis (MS), rheumatoid arthritis (RA), and Crohn's disease (CD) to assess which common genetic variants contribute individually and pleiotropically to disease risk. Joint modeling and pathway analysis combining the three phenotypes were performed to identify common underlying mechanisms of risk of autoimmune conditions. European American cases of MS, RA, and CD, (n = 119, 53, and 129, respectively) and 1924 controls were identified using de-identified electronic health records (EHRs) through a combination of International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) billing codes, Current Procedural Terminology (CPT) codes, medication lists, and text matching. As expected, hallmark SNPs in MS, such as DQA1 rs9271366 (OR = 1.91; p = 0.008), replicated in the present study. Both MS and CD were associated with TIMMDC1 rs2293370 (OR = 0.27, p = 0.01; OR = 0.25, p = 0.02; respectively). Additionally, PDE2A rs3781913 was significantly associated with both CD and RA (OR = 0.46, p = 0.02; OR = 0.32, p = 0.02; respectively). Joint modeling and pathway analysis identified variants within the KEGG NOD-like receptor signaling pathway and Shigellosis pathway as being correlated with the combined autoimmune phenotype. Our study replicated previously-reported genetic associations for MS and CD in a population derived from de-identified EHRs. We found evidence to support a shared genetic etiology between CD/MS and CD/RA outside of the major histocompatibility complex region and identified KEGG pathways indicative of a bacterial pathogenesis risk for autoimmunity in a joint model. Future work to elucidate this shared etiology will be key in the development of risk models as envisioned in the era of precision medicine.
Collapse
Affiliation(s)
- Nicole A Restrepo
- Department of Epidemiology and Biostatistics, Case Western Reserve University Cleveland, OH, USA
| | - Mariusz Butkiewicz
- Department of Epidemiology and Biostatistics, Case Western Reserve University Cleveland, OH, USA
| | - Josephine A McGrath
- Vanderbilt Eye Institute, Vanderbilt University Medical Center Nashville, TN, USA
| | - Dana C Crawford
- Department of Epidemiology and Biostatistics, Case Western Reserve UniversityCleveland, OH, USA; Institute for Computational Biology, Case Western Reserve UniversityCleveland, OH, USA
| |
Collapse
|
14
|
Khawaja AP, Cooke Bailey JN, Kang JH, Allingham RR, Hauser MA, Brilliant M, Budenz DL, Christen WG, Fingert J, Gaasterland D, Gaasterland T, Kraft P, Lee RK, Lichter PR, Liu Y, Medeiros F, Moroi SE, Richards JE, Realini T, Ritch R, Schuman JS, Scott WK, Singh K, Sit AJ, Vollrath D, Wollstein G, Zack DJ, Zhang K, Pericak-Vance M, Weinreb RN, Haines JL, Pasquale LR, Wiggs JL. Assessing the Association of Mitochondrial Genetic Variation With Primary Open-Angle Glaucoma Using Gene-Set Analyses. Invest Ophthalmol Vis Sci 2016; 57:5046-5052. [PMID: 27661856 PMCID: PMC5040191 DOI: 10.1167/iovs.16-20017] [Citation(s) in RCA: 36] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2016] [Accepted: 07/27/2016] [Indexed: 12/18/2022] Open
Abstract
PURPOSE Recent studies indicate that mitochondrial proteins may contribute to the pathogenesis of primary open-angle glaucoma (POAG). In this study, we examined the association between POAG and common variations in gene-encoding mitochondrial proteins. METHODS We examined genetic data from 3430 POAG cases and 3108 controls derived from the combination of the GLAUGEN and NEIGHBOR studies. We constructed biological-system coherent mitochondrial nuclear-encoded protein gene-sets by intersecting the MitoCarta database with the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. We examined the mitochondrial gene-sets for association with POAG and with normal-tension glaucoma (NTG) and high-tension glaucoma (HTG) subsets using Pathway Analysis by Randomization Incorporating Structure. RESULTS We identified 22 KEGG pathways with significant mitochondrial protein-encoding gene enrichment, belonging to six general biological classes. Among the pathway classes, mitochondrial lipid metabolism was associated with POAG overall (P = 0.013) and with NTG (P = 0.0006), and mitochondrial carbohydrate metabolism was associated with NTG (P = 0.030). Examining the individual KEGG pathway mitochondrial gene-sets, fatty acid elongation and synthesis and degradation of ketone bodies, both lipid metabolism pathways, were significantly associated with POAG (P = 0.005 and P = 0.002, respectively) and NTG (P = 0.0004 and P < 0.0001, respectively). Butanoate metabolism, a carbohydrate metabolism pathway, was significantly associated with POAG (P = 0.004), NTG (P = 0.001), and HTG (P = 0.010). CONCLUSIONS We present an effective approach for assessing the contributions of mitochondrial genetic variation to open-angle glaucoma. Our findings support a role for mitochondria in POAG pathogenesis and specifically point to lipid and carbohydrate metabolism pathways as being important.
Collapse
Affiliation(s)
- Anthony P. Khawaja
- National Institute for Health Research (NIHR) Biomedical Research Centre, Moorfields Eye Hospital National Health Service (NHS) Foundation Trust, and University College London (UCL) Institute of Ophthalmology, London, United Kingdom
| | - Jessica N. Cooke Bailey
- Department of Epidemiology and Biostatistics, Institute for Computational Biology, Case Western Reserve University School of Medicine, Cleveland, Ohio, United States
| | - Jae Hee Kang
- Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, United States
| | - R. Rand Allingham
- Department of Ophthalmology, Duke University Medical Center, Durham, North Carolina, United States
| | - Michael A. Hauser
- Department of Ophthalmology, Duke University Medical Center, Durham, North Carolina, United States
- Department of Medicine, Duke University Medical Center, Durham, North Carolina, United States
| | - Murray Brilliant
- Marshfield Clinic Research Foundation, Marshfield, Wisconsin, United States
| | - Donald L. Budenz
- Department of Ophthalmology, University of North Carolina, Chapel Hill, North Carolina, United States
| | - William G. Christen
- Division of Preventive Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, United States
| | - John Fingert
- Department of Ophthalmology, University of Iowa, College of Medicine, Iowa City, Iowa, United States
- Stephen A. Wynn Institute for Vision Research, Iowa City, Iowa, United States
| | | | - Terry Gaasterland
- Scripps Genome Center, University of California at San Diego, San Diego, California, United States
| | - Peter Kraft
- Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, United States
- Program in Genetic Epidemiology and Statistical Genetics, Harvard School of Public Health, Boston, Massachusetts, United States
| | - Richard K. Lee
- Bascom Palmer Eye Institute, University of Miami Miller School of Medicine, Miami, Florida, United States
| | - Paul R. Lichter
- Department of Ophthalmology and Visual Sciences, University of Michigan, Ann Arbor, Michigan, United States
| | - Yutao Liu
- Department of Cellular Biology and Anatomy, Medical College of Georgia, Augusta University, Augusta, Georgia, United States
- James and Jean Culver Vision Discovery Institute, Medical College of Georgia, Augusta University, Augusta, Georgia, United States
| | - Felipe Medeiros
- Hamilton Glaucoma Center, Shiley Eye Institute, University of California, San Diego, San Diego, California, United States
| | - Syoko E. Moroi
- Department of Ophthalmology and Visual Sciences, University of Michigan, Ann Arbor, Michigan, United States
| | - Julia E. Richards
- Department of Ophthalmology and Visual Sciences, University of Michigan, Ann Arbor, Michigan, United States
- Department of Epidemiology, University of Michigan, Ann Arbor, Michigan, United States
| | - Tony Realini
- Department of Ophthalmology, West Virginia University Eye Institute, Morgantown, West Virginia, United States
| | - Robert Ritch
- Einhorn Clinical Research Center, Department of Ophthalmology, New York Eye and Ear Infirmary of Mount Sinai, New York, New York, United States
| | - Joel S. Schuman
- Department of Ophthalmology, New York University, New York, New York, United States
| | - William K. Scott
- Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, Florida, United States
| | - Kuldev Singh
- Department of Ophthalmology, Stanford University School of Medicine, Palo Alto, California, United States
| | - Arthur J. Sit
- Department of Ophthalmology, Mayo Clinic, Rochester, Minnesota, United States
| | - Douglas Vollrath
- Department of Genetics, Stanford University School of Medicine, Palo Alto, California, United States
| | - Gadi Wollstein
- Department of Ophthalmology, University of Pittsburgh, Pittsburgh, Pennsylvania, United States
| | - Donald J. Zack
- Wilmer Eye Institute, Johns Hopkins University Hospital, Baltimore, Maryland, United States
| | - Kang Zhang
- Hamilton Glaucoma Center, Shiley Eye Institute, University of California, San Diego, San Diego, California, United States
| | - Margaret Pericak-Vance
- Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, Florida, United States
| | - Robert N. Weinreb
- Hamilton Glaucoma Center, Shiley Eye Institute, University of California, San Diego, San Diego, California, United States
| | - Jonathan L. Haines
- Department of Epidemiology and Biostatistics, Institute for Computational Biology, Case Western Reserve University School of Medicine, Cleveland, Ohio, United States
| | - Louis R. Pasquale
- Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, United States
- Department of Ophthalmology, Harvard Medical School, Massachusetts Eye and Ear Infirmary, Boston, Massachusetts, United States
| | - Janey L. Wiggs
- Department of Ophthalmology, Harvard Medical School, Massachusetts Eye and Ear Infirmary, Boston, Massachusetts, United States
| |
Collapse
|
15
|
Hoffman JD, van Grinsven MJJP, Li C, Brantley M, McGrath J, Agarwal A, Scott WK, Schwartz SG, Kovach J, Pericak-Vance M, Sanchez CI, Haines JL. Genetic Association Analysis of Drusen Progression. Invest Ophthalmol Vis Sci 2016; 57:2225-31. [PMID: 27116550 PMCID: PMC4849854 DOI: 10.1167/iovs.15-18571] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2015] [Accepted: 02/08/2016] [Indexed: 11/24/2022] Open
Abstract
PURPOSE Age-related macular degeneration is a common form of vision loss affecting older adults. The etiology of AMD is multifactorial and is influenced by environmental and genetic risk factors. In this study, we examine how 19 common risk variants contribute to drusen progression, a hallmark of AMD pathogenesis. METHODS Exome chip data was made available through the International AMD Genomics Consortium (IAMDGC). Drusen quantification was carried out with color fundus photographs using an automated drusen detection and quantification algorithm. A genetic risk score (GRS) was calculated per subject by summing risk allele counts at 19 common genetic risk variants weighted by their respective effect sizes. Pathway analysis of drusen progression was carried out with the software package Pathway Analysis by Randomization Incorporating Structure. RESULTS We observed significant correlation with drusen baseline area and the GRS in the age-related eye disease study (AREDS) dataset (ρ = 0.175, P = 0.006). Measures of association were not statistically significant between drusen progression and the GRS (P = 0.54). Pathway analysis revealed the cell adhesion molecules pathway as the most highly significant pathway associated with drusen progression (corrected P = 0.02). CONCLUSIONS In this study, we explored the potential influence of known common AMD genetic risk factors on drusen progression. Our results from the GRS analysis showed association of increasing genetic burden (from 19 AMD associated loci) to baseline drusen load but not drusen progression in the AREDS dataset while pathway analysis suggests additional genetic contributors to AMD risk.
Collapse
Affiliation(s)
- Joshua D. Hoffman
- Center for Human Genetics Research, Vanderbilt University, Nashville, Tennessee, United States
| | | | - Chun Li
- Department of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, Ohio, United States
| | - Milam Brantley
- Department of Ophthalmology and Visual Sciences, Vanderbilt University, Nashville, Tennessee, United States
| | - Josephine McGrath
- Center for Human Genetics Research, Vanderbilt University, Nashville, Tennessee, United States
| | - Anita Agarwal
- Department of Ophthalmology and Visual Sciences, Vanderbilt University, Nashville, Tennessee, United States
| | - William K. Scott
- John P. Hussman Institute for Human Genomics, Miller School of Medicine, University of Miami, Miami, Florida, United States
| | - Stephen G. Schwartz
- Ophthalmology, Bascom Palmer Eye Institute, Retina Center of Naples, Naples, Florida, United States
| | - Jaclyn Kovach
- Bascom Palmer Eye Institute, Miller School of Medicine, University of Miami, Miami, Florida, United States
| | - Margaret Pericak-Vance
- John P. Hussman Institute for Human Genomics, Miller School of Medicine, University of Miami, Miami, Florida, United States
| | - Clara I. Sanchez
- Diagnostic Image Analysis Group, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Jonathan L. Haines
- Center for Human Genetics Research, Vanderbilt University, Nashville, Tennessee, United States
- Institute for Computational Biology, Case Western Reserve University, Cleveland, Ohio, United States
| |
Collapse
|
16
|
Butkiewicz M, Cooke Bailey JN, Frase A, Dudek S, Yaspan BL, Ritchie MD, Pendergrass SA, Haines JL. Pathway analysis by randomization incorporating structure-PARIS: an update. ACTA ACUST UNITED AC 2016; 32:2361-3. [PMID: 27153576 DOI: 10.1093/bioinformatics/btw130] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2015] [Accepted: 03/03/2016] [Indexed: 01/11/2023]
Abstract
MOTIVATION We present an update to the pathway enrichment analysis tool 'Pathway Analysis by Randomization Incorporating Structure (PARIS)' that determines aggregated association signals generated from genome-wide association study results. Pathway-based analyses highlight biological pathways associated with phenotypes. PARIS uses a unique permutation strategy to evaluate the genomic structure of interrogated pathways, through permutation testing of genomic features, thus eliminating many of the over-testing concerns arising with other pathway analysis approaches. RESULTS We have updated PARIS to incorporate expanded pathway definitions through the incorporation of new expert knowledge from multiple database sources, through customized user provided pathways, and other improvements in user flexibility and functionality. AVAILABILITY AND IMPLEMENTATION PARIS is freely available to all users at https://ritchielab.psu.edu/software/paris-download CONTACT jnc43@case.edu SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Collapse
Affiliation(s)
- Mariusz Butkiewicz
- Department of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, OH, USA Institute for Computational Biology, Case Western Reserve University, Cleveland, OH, USA
| | - Jessica N Cooke Bailey
- Department of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, OH, USA Institute for Computational Biology, Case Western Reserve University, Cleveland, OH, USA
| | - Alex Frase
- Biomedical and Translational Informatics Program, Geisinger Health System, Danville, PA, USA
| | - Scott Dudek
- Biomedical and Translational Informatics Program, Geisinger Health System, Danville, PA, USA
| | - Brian L Yaspan
- Department of Human Genetics, Genentech, Inc, South San Francisco, CA, USA
| | - Marylyn D Ritchie
- Biomedical and Translational Informatics Program, Geisinger Health System, Danville, PA, USA
| | - Sarah A Pendergrass
- Biomedical and Translational Informatics Program, Geisinger Health System, Danville, PA, USA
| | - Jonathan L Haines
- Department of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, OH, USA Institute for Computational Biology, Case Western Reserve University, Cleveland, OH, USA
| |
Collapse
|
17
|
Seldin MF. The genetics of human autoimmune disease: A perspective on progress in the field and future directions. J Autoimmun 2015; 64:1-12. [PMID: 26343334 PMCID: PMC4628839 DOI: 10.1016/j.jaut.2015.08.015] [Citation(s) in RCA: 67] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2015] [Accepted: 08/23/2015] [Indexed: 12/18/2022]
Abstract
Progress in defining the genetics of autoimmune disease has been dramatically enhanced by large scale genetic studies. Genome-wide approaches, examining hundreds or for some diseases thousands of cases and controls, have been implemented using high throughput genotyping and appropriate algorithms to provide a wealth of data over the last decade. These studies have identified hundreds of non-HLA loci as well as further defining HLA variations that predispose to different autoimmune diseases. These studies to identify genetic risk loci are also complemented by progress in gene expression studies including definition of expression quantitative trait loci (eQTL), various alterations in chromatin structure including histone marks, DNase I sensitivity, repressed chromatin regions as well as transcript factor binding sites. Integration of this information can partially explain why particular variations can alter proclivity to autoimmune phenotypes. Despite our incomplete knowledge base with only partial definition of hereditary factors and possible functional connections, this progress has and will continue to facilitate a better understanding of critical pathways and critical changes in immunoregulation. Advances in defining and understanding functional variants potentially can lead to both novel therapeutics and personalized medicine in which therapeutic approaches are chosen based on particular molecular phenotypes and genomic alterations.
Collapse
Affiliation(s)
- Michael F Seldin
- Department of Biochemistry and Molecular Medicine, University of California, Davis, Tupper Hall Room 4453, Davis, CA 95616, USA; Division of Rheumatology and Allergy, Department of Medicine, University of California, Davis, Tupper Hall Room 4453, Davis, CA 95616, USA.
| |
Collapse
|
18
|
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.
Collapse
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.
| |
Collapse
|
19
|
Pendergrass SA, Verma A, Okula A, Hall MA, Crawford DC, Ritchie MD. Phenome-Wide Association Studies: Embracing Complexity for Discovery. Hum Hered 2015. [PMID: 26201697 DOI: 10.1159/000381851] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022] Open
Abstract
The inherent complexity of biological systems can be leveraged for a greater understanding of the impact of genetic architecture on outcomes, traits, and pharmacological response. The genome-wide association study (GWAS) approach has well-developed methods and relatively straight-forward methodologies; however, the bigger picture of the impact of genetic architecture on phenotypic outcome still remains to be elucidated even with an ever-growing number of GWAS performed. Greater consideration of the complexity of biological processes, using more data from the phenome, exposome, and diverse -omic resources, including considering the interplay of pleiotropy and genetic interactions, may provide additional leverage for making the most of the incredible wealth of information available for study. Here, we describe how incorporating greater complexity into analyses through the use of additional phenotypic data and widespread deployment of phenome-wide association studies may provide new insights into genetic factors influencing diseases, traits, and pharmacological response.
Collapse
Affiliation(s)
- Sarah A Pendergrass
- Biomedical and Translational Informatics Program, Geisinger Health System, Danville, Pa., USA
| | | | | | | | | | | |
Collapse
|
20
|
Himmelstein DS, Baranzini SE. Heterogeneous Network Edge Prediction: A Data Integration Approach to Prioritize Disease-Associated Genes. PLoS Comput Biol 2015; 11:e1004259. [PMID: 26158728 PMCID: PMC4497619 DOI: 10.1371/journal.pcbi.1004259] [Citation(s) in RCA: 91] [Impact Index Per Article: 10.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2014] [Accepted: 03/26/2015] [Indexed: 12/13/2022] Open
Abstract
The first decade of Genome Wide Association Studies (GWAS) has uncovered a wealth of disease-associated variants. Two important derivations will be the translation of this information into a multiscale understanding of pathogenic variants and leveraging existing data to increase the power of existing and future studies through prioritization. We explore edge prediction on heterogeneous networks—graphs with multiple node and edge types—for accomplishing both tasks. First we constructed a network with 18 node types—genes, diseases, tissues, pathophysiologies, and 14 MSigDB (molecular signatures database) collections—and 19 edge types from high-throughput publicly-available resources. From this network composed of 40,343 nodes and 1,608,168 edges, we extracted features that describe the topology between specific genes and diseases. Next, we trained a model from GWAS associations and predicted the probability of association between each protein-coding gene and each of 29 well-studied complex diseases. The model, which achieved 132-fold enrichment in precision at 10% recall, outperformed any individual domain, highlighting the benefit of integrative approaches. We identified pleiotropy, transcriptional signatures of perturbations, pathways, and protein interactions as influential mechanisms explaining pathogenesis. Our method successfully predicted the results (with AUROC = 0.79) from a withheld multiple sclerosis (MS) GWAS despite starting with only 13 previously associated genes. Finally, we combined our network predictions with statistical evidence of association to propose four novel MS genes, three of which (JAK2, REL, RUNX3) validated on the masked GWAS. Furthermore, our predictions provide biological support highlighting REL as the causal gene within its gene-rich locus. Users can browse all predictions online (http://het.io). Heterogeneous network edge prediction effectively prioritized genetic associations and provides a powerful new approach for data integration across multiple domains. For complex human diseases, identifying the genes harboring susceptibility variants has taken on medical importance. Disease-associated genes provide clues for elucidating disease etiology, predicting disease risk, and highlighting therapeutic targets. Here, we develop a method to predict whether a given gene and disease are associated. To capture the multitude of biological entities underlying pathogenesis, we constructed a heterogeneous network, containing multiple node and edge types. We built on a technique developed for social network analysis, which embraces disparate sources of data to make predictions from heterogeneous networks. Using the compendium of associations from genome-wide studies, we learned the influential mechanisms underlying pathogenesis. Our findings provide a novel perspective about the existence of pervasive pleiotropy across complex diseases. Furthermore, we suggest transcriptional signatures of perturbations are an underutilized resource amongst prioritization approaches. For multiple sclerosis, we demonstrated our ability to prioritize future studies and discover novel susceptibility genes. Researchers can use these predictions to increase the statistical power of their studies, to suggest the causal genes from a set of candidates, or to generate evidence-based experimental hypothesis.
Collapse
Affiliation(s)
- Daniel S. Himmelstein
- Biological & Medical Informatics, University of California, San Francisco, San Francisco, California, United States of America
| | - Sergio E. Baranzini
- Biological & Medical Informatics, University of California, San Francisco, San Francisco, California, United States of America
- Department of Neurology, University of California, San Francisco, San Francisco, California, United States of America
- Institute for Human Genetics, University of California, San Francisco, San Francisco, California, United States of America
- * E-mail:
| |
Collapse
|
21
|
Bioinformatics tools for discovery and functional analysis of single nucleotide polymorphisms. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2015; 827:287-310. [PMID: 25387971 DOI: 10.1007/978-94-017-9245-5_17] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Abstract
With the high speed DNA sequencing of genome, databases of genome data continue to grow, and the understanding of genetic variation between individuals grows as well. Single nucleotide polymorphisms (SNPs), a main type of genetic variation, are increasingly important resource for understanding the structure and function of the human genome and become a valuable resource for investigating the genetic basis of disease. During the past years, in addition to experimental approaches to characterize specific variants, intense bioinformatics techniques were applied to understand effects of these genetic changes. In the genetics studies, one intends to understand the molecular basis of disease, and computational methods are becoming increasingly important for SNPs selection, prediction and understanding the downstream effects of genetic variation. The review provides systematic information on the available resources and methods for SNPs discovery and analysis. We also report some new results on DNA sequence-based prediction of SNPs in human cytochrome P450, which serves as an example of computational methods to predict and discovery SNPs. Additionally, annotation and prediction of functional SNPs, as well as a comprehensive list of existing tools and online recourses, are reviewed and described.
Collapse
|
22
|
Li J, Shi M, Ma Z, Zhao S, Euskirchen G, Ziskin J, Urban A, Hallmayer J, Snyder M. Integrated systems analysis reveals a molecular network underlying autism spectrum disorders. Mol Syst Biol 2014; 10:774. [PMID: 25549968 PMCID: PMC4300495 DOI: 10.15252/msb.20145487] [Citation(s) in RCA: 94] [Impact Index Per Article: 9.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/04/2022] Open
Abstract
Autism is a complex disease whose etiology remains elusive. We integrated previously and newly generated data and developed a systems framework involving the interactome, gene expression and genome sequencing to identify a protein interaction module with members strongly enriched for autism candidate genes. Sequencing of 25 patients confirmed the involvement of this module in autism, which was subsequently validated using an independent cohort of over 500 patients. Expression of this module was dichotomized with a ubiquitously expressed subcomponent and another subcomponent preferentially expressed in the corpus callosum, which was significantly affected by our identified mutations in the network center. RNA-sequencing of the corpus callosum from patients with autism exhibited extensive gene mis-expression in this module, and our immunochemical analysis showed that the human corpus callosum is predominantly populated by oligodendrocyte cells. Analysis of functional genomic data further revealed a significant involvement of this module in the development of oligodendrocyte cells in mouse brain. Our analysis delineates a natural network involved in autism, helps uncover novel candidate genes for this disease and improves our understanding of its molecular pathology.
Collapse
Affiliation(s)
- Jingjing Li
- Department of Genetics, Stanford Center for Genomics and Personalized Medicine Stanford University School of Medicine, Stanford, CA, USA
| | - Minyi Shi
- Department of Genetics, Stanford Center for Genomics and Personalized Medicine Stanford University School of Medicine, Stanford, CA, USA
| | - Zhihai Ma
- Department of Genetics, Stanford Center for Genomics and Personalized Medicine Stanford University School of Medicine, Stanford, CA, USA
| | - Shuchun Zhao
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
| | - Ghia Euskirchen
- Department of Genetics, Stanford Center for Genomics and Personalized Medicine Stanford University School of Medicine, Stanford, CA, USA
| | - Jennifer Ziskin
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
| | - Alexander Urban
- Department of Psychiatry & Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA
| | - Joachim Hallmayer
- Department of Psychiatry & Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA
| | - Michael Snyder
- Department of Genetics, Stanford Center for Genomics and Personalized Medicine Stanford University School of Medicine, Stanford, CA, USA
| |
Collapse
|
23
|
Chhibber A, Kroetz DL, Tantisira KG, McGeachie M, Cheng C, Plenge R, Stahl E, Sadee W, Ritchie MD, Pendergrass SA. Genomic architecture of pharmacological efficacy and adverse events. Pharmacogenomics 2014; 15:2025-48. [PMID: 25521360 PMCID: PMC4308414 DOI: 10.2217/pgs.14.144] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022] Open
Abstract
The pharmacokinetic and pharmacodynamic disciplines address pharmacological traits, including efficacy and adverse events. Pharmacogenomics studies have identified pervasive genetic effects on treatment outcomes, resulting in the development of genetic biomarkers for optimization of drug therapy. Pharmacogenomics-based tests are already being applied in clinical decision making. However, despite substantial progress in identifying the genetic etiology of pharmacological response, current biomarker panels still largely rely on single gene tests with a large portion of the genetic effects remaining to be discovered. Future research must account for the combined effects of multiple genetic variants, incorporate pathway-based approaches, explore gene-gene interactions and nonprotein coding functional genetic variants, extend studies across ancestral populations, and prioritize laboratory characterization of molecular mechanisms. Because genetic factors can play a key role in drug response, accurate biomarker tests capturing the main genetic factors determining treatment outcomes have substantial potential for improving individual clinical care.
Collapse
Affiliation(s)
- Aparna Chhibber
- Department of Bioengineering & Therapeutic Sciences, Department of Pharmaceutical Chemistry, University of California San Francisco, San Francisco, CA,USA
| | - Deanna L Kroetz
- Department of Bioengineering & Therapeutic Sciences, Department of Pharmaceutical Chemistry, University of California San Francisco, San Francisco, CA,USA
| | - Kelan G Tantisira
- Department of Medicine, Brigham & Women's Hospital, Harvard Medical School, Cambridge, MA, USA
| | - Michael McGeachie
- Department of Medicine, Brigham & Women's Hospital, Harvard Medical School, Cambridge, MA, USA
| | - Cheng Cheng
- Department of Biostatistics, St Jude Children's Research Hospital, Memphis, TN, USA
| | - Robert Plenge
- Division of Rheumatology, Immunology & Allergy, Division of Genetics, Brigham & Women's Hospital, Harvard Medical School, Cambridge, MA, USA
| | - Eli Stahl
- Department of Genetics & Genomic Sciences, Mount Sinai Hospital, New York, NY, USA
| | - Wolfgang Sadee
- Center for Pharmacogenomics, College of Medicine, The Ohio State University, Columbus, OH, USA
| | - Marylyn D Ritchie
- Department of Biochemistry & Molecular Biology, Center for Systems Genomics, Eberly College of Science, The Huck Institutes of the Life Sciences, The Pennsylvania State University, University Park, PA 16801, USA
| | - Sarah A Pendergrass
- Department of Biochemistry & Molecular Biology, Center for Systems Genomics, Eberly College of Science, The Huck Institutes of the Life Sciences, The Pennsylvania State University, University Park, PA 16801, USA
| |
Collapse
|
24
|
Correia C, Oliveira G, Vicente AM. Protein interaction networks reveal novel autism risk genes within GWAS statistical noise. PLoS One 2014; 9:e112399. [PMID: 25409314 PMCID: PMC4237351 DOI: 10.1371/journal.pone.0112399] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2013] [Accepted: 10/15/2014] [Indexed: 11/19/2022] Open
Abstract
Genome-wide association studies (GWAS) for Autism Spectrum Disorder (ASD) thus far met limited success in the identification of common risk variants, consistent with the notion that variants with small individual effects cannot be detected individually in single SNP analysis. To further capture disease risk gene information from ASD association studies, we applied a network-based strategy to the Autism Genome Project (AGP) and the Autism Genetics Resource Exchange GWAS datasets, combining family-based association data with Human Protein-Protein interaction (PPI) data. Our analysis showed that autism-associated proteins at higher than conventional levels of significance (P<0.1) directly interact more than random expectation and are involved in a limited number of interconnected biological processes, indicating that they are functionally related. The functionally coherent networks generated by this approach contain ASD-relevant disease biology, as demonstrated by an improved positive predictive value and sensitivity in retrieving known ASD candidate genes relative to the top associated genes from either GWAS, as well as a higher gene overlap between the two ASD datasets. Analysis of the intersection between the networks obtained from the two ASD GWAS and six unrelated disease datasets identified fourteen genes exclusively present in the ASD networks. These are mostly novel genes involved in abnormal nervous system phenotypes in animal models, and in fundamental biological processes previously implicated in ASD, such as axon guidance, cell adhesion or cytoskeleton organization. Overall, our results highlighted novel susceptibility genes previously hidden within GWAS statistical "noise" that warrant further analysis for causal variants.
Collapse
Affiliation(s)
- Catarina Correia
- Departamento de Promoção da Saúde e Doenças não Transmissíveis, Instituto Nacional de Saúde Doutor Ricardo Jorge, 1649-016 Lisboa, Portugal
- Center for Biodiversity, Functional & Integrative Genomics, Faculty of Sciences, University of Lisbon, 1749-016 Lisboa, Portugal
- Instituto Gulbenkian de Ciência, 2780-156 Oeiras, Portugal
| | - Guiomar Oliveira
- Unidade Neurodesenvolvimento e Autismo, Centro de Desenvolvimento, Hospital Pediátrico (HP) do Centro Hospitalar e Universitário de Coimbra (CHUC), 3000-602 Coimbra, Portugal
- Centro de Investigação e Formação Clinica do HP-CHUC, 3000-602 Coimbra, Portugal
- Faculdade de Medicina da Universidade de Coimbra, 3000-548 Coimbra, Portugal
| | - Astrid M. Vicente
- Departamento de Promoção da Saúde e Doenças não Transmissíveis, Instituto Nacional de Saúde Doutor Ricardo Jorge, 1649-016 Lisboa, Portugal
- Center for Biodiversity, Functional & Integrative Genomics, Faculty of Sciences, University of Lisbon, 1749-016 Lisboa, Portugal
- Instituto Gulbenkian de Ciência, 2780-156 Oeiras, Portugal
- * E-mail:
| |
Collapse
|
25
|
Cooke Bailey JN, Yaspan BL, Pasquale LR, Hauser MA, Kang JH, Loomis SJ, Brilliant M, Budenz DL, Christen WG, Fingert J, Gaasterland D, Gaasterland T, Kraft P, Lee RK, Lichter PR, Liu Y, McCarty CA, Moroi SE, Richards JE, Realini T, Schuman JS, Scott WK, Singh K, Sit AJ, Vollrath D, Wollstein G, Zack DJ, Zhang K, Pericak-Vance MA, Allingham RR, Weinreb RN, Haines JL, Wiggs JL. Hypothesis-independent pathway analysis implicates GABA and acetyl-CoA metabolism in primary open-angle glaucoma and normal-pressure glaucoma. Hum Genet 2014; 133:1319-30. [PMID: 25037249 PMCID: PMC4273559 DOI: 10.1007/s00439-014-1468-7] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2014] [Accepted: 07/08/2014] [Indexed: 12/15/2022]
Abstract
Primary open-angle glaucoma (POAG) is a leading cause of blindness worldwide. Using genome-wide association single-nucleotide polymorphism data from the Glaucoma Genes and Environment study and National Eye Institute Glaucoma Human Genetics Collaboration comprising 3,108 cases and 3,430 controls, we assessed biologic pathways as annotated in the KEGG database for association with risk of POAG. After correction for genic overlap among pathways, we found 4 pathways, butanoate metabolism (hsa00650), hematopoietic cell lineage (hsa04640), lysine degradation (hsa00310) and basal transcription factors (hsa03022) related to POAG with permuted p < 0.001. In addition, the human leukocyte antigen (HLA) gene family was significantly associated with POAG (p < 0.001). In the POAG subset with normal-pressure glaucoma (NPG), the butanoate metabolism pathway was also significantly associated (p < 0.001) as well as the MAPK and Hedgehog signaling pathways (hsa04010 and hsa04340), glycosaminoglycan biosynthesis-heparan sulfate pathway (hsa00534) and the phenylalanine, tyrosine and tryptophan biosynthesis pathway (hsa0400). The butanoate metabolism pathway overall, and specifically the aspects of the pathway that contribute to GABA and acetyl-CoA metabolism, was the only pathway significantly associated with both POAG and NPG. Collectively these results implicate GABA and acetyl-CoA metabolism in glaucoma pathogenesis, and suggest new potential therapeutic targets.
Collapse
Affiliation(s)
| | - Brian L. Yaspan
- Center for Human Genetics Research, Vanderbilt University, Nashville, TN, USA
| | - Louis R. Pasquale
- Department of Ophthalmology, Harvard Medical School, Massachusetts Eye and Ear Infirmary, Boston, MA, USA
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Michael A. Hauser
- Department of Ophthalmology, Duke University Medical Center, Durham, NC, USA
- Department of Medicine, Duke University Medical Center, Durham, NC, USA
| | - Jae H. Kang
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Stephanie J. Loomis
- Department of Ophthalmology, Harvard Medical School, Massachusetts Eye and Ear Infirmary, Boston, MA, USA
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Murray Brilliant
- Center for Human Genetics, Marshfield Clinic Research Foundation, Marshfield, WI, USA
| | - Donald L. Budenz
- Department of Ophthalmology, University of North Carolina, Chapel Hill, NC, USA
| | | | - John Fingert
- Department of Ophthalmology, College of Medicine, University of Iowa, Iowa City, IO, USA
- Department of A natomy/Cell Biology, College of Medicine, University of Iowa, Iowa City, IO, USA
| | | | - Terry Gaasterland
- Scripps Genome Center, University of California at San Diego, San Diego, CA, USA
| | - Peter Kraft
- Department of Biostatistics, Harvard School of Public Health, Boston, MA, USA
| | - Richard K. Lee
- Bascom Palmer Eye Institute, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Paul R. Lichter
- Department of Ophthalmology and V isual Sciences, University of Michigan, Ann Arbor, MI, USA
| | - Yutao Liu
- Department of Ophthalmology, Duke University Medical Center, Durham, NC, USA
- Department of Medicine, Duke University Medical Center, Durham, NC, USA
| | | | - Sayoko E. Moroi
- Department of Ophthalmology and V isual Sciences, University of Michigan, Ann Arbor, MI, USA
| | - Julia E. Richards
- Department of Ophthalmology and V isual Sciences, University of Michigan, Ann Arbor, MI, USA
| | - Tony Realini
- Department of Ophthalmology, WVU Eye Institute, Morgantown, WV, USA
| | - Joel S. Schuman
- Department of Ophthalmology, UPMC Eye Center, University of Pittsburgh, Pittsburgh, PA, USA
| | - William K. Scott
- Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Kuldev Singh
- Department of Ophthalmology, Stanford University, Palo Alto, CA, USA
| | - Arthur J. Sit
- Department of Ophthalmology, Mayo Clinic, Rochester, MN, USA
| | | | - Gadi Wollstein
- Department of Ophthalmology, UPMC Eye Center, University of Pittsburgh, Pittsburgh, PA, USA
| | - Donald J. Zack
- Wilmer Eye Institute, Johns Hopkins University Hospital, Baltimore, MD, USA
| | - Kang Zhang
- Department of Ophthalmology, Hamilton Eye Center, University of California, San Diego, CA, USA
| | | | - R. Rand Allingham
- Department of Ophthalmology, Duke University Medical Center, Durham, NC, USA
| | - Robert N. Weinreb
- Department of Ophthalmology, Hamilton Eye Center, University of California, San Diego, CA, USA
| | - Jonathan L. Haines
- Center for Human Genetics Research, Vanderbilt University, Nashville, TN, USA
| | - Janey L. Wiggs
- Department of Ophthalmology, Harvard Medical School, Massachusetts Eye and Ear Infirmary, Boston, MA, USA
| |
Collapse
|
26
|
Wang L, Matsushita T, Madireddy L, Mousavi P, Baranzini SE. PINBPA: cytoscape app for network analysis of GWAS data. ACTA ACUST UNITED AC 2014; 31:262-4. [PMID: 25260698 DOI: 10.1093/bioinformatics/btu644] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
UNLABELLED Protein interaction network-based pathway analysis (PINBPA) for genome-wide association studies (GWAS) has been developed as a Cytoscape app, to enable analysis of GWAS data in a network fashion. Users can easily import GWAS summary-level data, draw Manhattan plots, define blocks, prioritize genes with random walk with restart, detect enriched subnetworks and test the significance of subnetworks via a user-friendly interface. AVAILABILITY AND IMPLEMENTATION PINBPA app is freely available in Cytoscape app store. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Collapse
Affiliation(s)
- Lili Wang
- School of Computing, Queen's University, 25 Union Street, Goodwin Hall, Kingston, Ontario K7L 3N6, Canada and Department of Neurology, University of California San Francisco, 675 Nelson Rising Lane, Room 215, San Francisco, CA 94158, USA
| | - Takuya Matsushita
- School of Computing, Queen's University, 25 Union Street, Goodwin Hall, Kingston, Ontario K7L 3N6, Canada and Department of Neurology, University of California San Francisco, 675 Nelson Rising Lane, Room 215, San Francisco, CA 94158, USA
| | - Lohith Madireddy
- School of Computing, Queen's University, 25 Union Street, Goodwin Hall, Kingston, Ontario K7L 3N6, Canada and Department of Neurology, University of California San Francisco, 675 Nelson Rising Lane, Room 215, San Francisco, CA 94158, USA
| | - Parvin Mousavi
- School of Computing, Queen's University, 25 Union Street, Goodwin Hall, Kingston, Ontario K7L 3N6, Canada and Department of Neurology, University of California San Francisco, 675 Nelson Rising Lane, Room 215, San Francisco, CA 94158, USA
| | - Sergio E Baranzini
- School of Computing, Queen's University, 25 Union Street, Goodwin Hall, Kingston, Ontario K7L 3N6, Canada and Department of Neurology, University of California San Francisco, 675 Nelson Rising Lane, Room 215, San Francisco, CA 94158, USA
| |
Collapse
|
27
|
Genome-wide association studies: getting to pathogenesis, the role of inflammation/complement in age-related macular degeneration. Cold Spring Harb Perspect Med 2014; 4:a017186. [PMID: 25213188 DOI: 10.1101/cshperspect.a017186] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
Age-related macular degeneration (AMD) is a chronic, degenerative, and significant cause of visual impairment and blindness in the elderly. Genetic and epidemiological studies have confirmed that AMD has a strong genetic component, which has encouraged the application of increasingly sophisticated genetic techniques to uncover the important underlying genetic variants. Although various genes and pathways have been implicated in the risk for AMD, complement activation has been emphasized repeatedly throughout the literature as having a major role both physiologically and genetically in susceptibility to and pathogenesis of this disease. This article explores the research efforts that brought about the discovery and characterization of the role of inflammatory and immune processes (specifically complement) in AMD. The focus herein is on the genetic evidence for the role of complement in AMD as supported specifically by genome-wide association (GWA) studies, which interrogate hundreds of thousands of variants across the genome in a hypothesis-free approach, and other genetic interrogation methods.
Collapse
|
28
|
Mooney MA, Nigg JT, McWeeney SK, Wilmot B. Functional and genomic context in pathway analysis of GWAS data. Trends Genet 2014; 30:390-400. [PMID: 25154796 DOI: 10.1016/j.tig.2014.07.004] [Citation(s) in RCA: 86] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2014] [Revised: 07/18/2014] [Accepted: 07/18/2014] [Indexed: 02/07/2023]
Abstract
Gene set analysis (GSA) is a promising tool for uncovering the polygenic effects associated with complex diseases. However, the available techniques reflect a wide variety of hypotheses about how genetic effects interact to contribute to disease susceptibility. The lack of consensus about the best way to perform GSA has led to confusion in the field and has made it difficult to compare results across methods. A clear understanding of the various choices made during GSA - such as how gene sets are defined, how single-nucleotide polymorphisms (SNPs) are assigned to genes, and how individual SNP-level effects are aggregated to produce gene- or pathway-level effects - will improve the interpretability and comparability of results across methods and studies. In this review we provide an overview of the various data sources used to construct gene sets and the statistical methods used to test for gene set association, as well as provide guidelines for ensuring the comparability of results.
Collapse
Affiliation(s)
- Michael A Mooney
- Division of Bioinformatics and Computational Biology, Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, OR, USA; OHSU Knight Cancer Institute, Portland, OR, USA
| | - Joel T Nigg
- Division of Psychology, Department of Psychiatry, Oregon Health & Science University, Portland, OR, USA; Department of Behavioral Neuroscience, Oregon Health & Science University, Portland, OR, USA
| | - Shannon K McWeeney
- Division of Bioinformatics and Computational Biology, Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, OR, USA; Oregon Clinical and Translational Research Institute, Portland, OR, USA; OHSU Knight Cancer Institute, Portland, OR, USA.
| | - Beth Wilmot
- Division of Bioinformatics and Computational Biology, Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, OR, USA; Oregon Clinical and Translational Research Institute, Portland, OR, USA; OHSU Knight Cancer Institute, Portland, OR, USA
| |
Collapse
|
29
|
The impact of the human genome project on complex disease. Genes (Basel) 2014; 5:518-35. [PMID: 25032678 PMCID: PMC4198915 DOI: 10.3390/genes5030518] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2014] [Revised: 06/03/2014] [Accepted: 06/24/2014] [Indexed: 02/06/2023] Open
Abstract
In the decade that has passed since the initial release of the Human Genome, numerous advancements in science and technology within and beyond genetics and genomics have been encouraged and enhanced by the availability of this vast and remarkable data resource. Progress in understanding three common, complex diseases: age-related macular degeneration (AMD), Alzheimer's disease (AD), and multiple sclerosis (MS), are three exemplars of the incredible impact on the elucidation of the genetic architecture of disease. The approaches used in these diseases have been successfully applied to numerous other complex diseases. For example, the heritability of AMD was confirmed upon the release of the first genome-wide association study (GWAS) along with confirmatory reports that supported the findings of that state-of-the art method, thus setting the foundation for future GWAS in other heritable diseases. Following this seminal discovery and applying it to other diseases including AD and MS, the genetic knowledge of AD expanded far beyond the well-known APOE locus and now includes more than 20 loci. MS genetics saw a similar increase beyond the HLA loci and now has more than 100 known risk loci. Ongoing and future efforts will seek to define the remaining heritability of these diseases; the next decade could very well hold the key to attaining this goal.
Collapse
|
30
|
Kang JH, Loomis SJ, Yaspan BL, Bailey JC, Weinreb RN, Lee RK, Lichter PR, Budenz DL, Liu Y, Realini T, Gaasterland D, Gaasterland T, Friedman DS, McCarty CA, Moroi SE, Olson L, Schuman JS, Singh K, Vollrath D, Wollstein G, Zack DJ, Brilliant M, Sit AJ, Christen WG, Fingert J, Forman JP, Buys ES, Kraft P, Zhang K, Allingham RR, Pericak-Vance MA, Richards JE, Hauser MA, Haines JL, Wiggs JL, Pasquale LR. Vascular tone pathway polymorphisms in relation to primary open-angle glaucoma. Eye (Lond) 2014; 28:662-71. [PMID: 24603425 PMCID: PMC4058608 DOI: 10.1038/eye.2014.42] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2013] [Accepted: 01/24/2014] [Indexed: 01/28/2023] Open
Abstract
AIMS Vascular perfusion may be impaired in primary open-angle glaucoma (POAG); thus, we evaluated a panel of markers in vascular tone-regulating genes in relation to POAG. METHODS We used Illumina 660W-Quad array genotype data and pooled P-values from 3108 POAG cases and 3430 controls from the combined National Eye Institute Glaucoma Human Genetics Collaboration consortium and Glaucoma Genes and Environment studies. Using information from previous literature and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, we compiled single-nucleotide polymorphisms (SNPs) in 186 vascular tone-regulating genes. We used the 'Pathway Analysis by Randomization Incorporating Structure' analysis software, which performed 1000 permutations to compare the overall pathway and selected genes with comparable randomly generated pathways and genes in their association with POAG. RESULTS The vascular tone pathway was not associated with POAG overall or POAG subtypes, defined by the type of visual field loss (early paracentral loss (n=224 cases) or only peripheral loss (n=993 cases)) (permuted P≥0.20). In gene-based analyses, eight were associated with POAG overall at permuted P<0.001: PRKAA1, CAV1, ITPR3, EDNRB, GNB2, DNM2, HFE, and MYL9. Notably, six of these eight (the first six listed) code for factors involved in the endothelial nitric oxide synthase activity, and three of these six (CAV1, ITPR3, and EDNRB) were also associated with early paracentral loss at P<0.001, whereas none of the six genes reached P<0.001 for peripheral loss only. DISCUSSION Although the assembled vascular tone SNP set was not associated with POAG, genes that code for local factors involved in setting vascular tone were associated with POAG.
Collapse
MESH Headings
- AMP-Activated Protein Kinases/genetics
- Aged
- Case-Control Studies
- Caveolin 1/genetics
- Dynamin II
- Dynamins/genetics
- Endothelium, Vascular/metabolism
- Female
- GTP-Binding Proteins/genetics
- Genetic Predisposition to Disease
- Genotype
- Glaucoma, Open-Angle/genetics
- Glaucoma, Open-Angle/physiopathology
- Humans
- Inositol 1,4,5-Trisphosphate Receptors/genetics
- Intraocular Pressure
- Male
- Middle Aged
- Muscle, Smooth, Vascular/physiology
- Nitric Oxide Synthase Type III/genetics
- Polymorphism, Single Nucleotide
- Receptor, Endothelin B
- Receptors, Endothelin/genetics
- Signal Transduction/genetics
Collapse
Affiliation(s)
- J H Kang
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - S J Loomis
- Department of Ophthalmology, Mass Eye and Ear, Boston, MA, USA
| | | | - J C Bailey
- Center for Human Genetics Research, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - R N Weinreb
- Department of Ophthalmology and Hamilton Glaucoma Center, University of California at San Diego, San Diego, CA, USA
| | - R K Lee
- Bascom Palmer Eye Institute and Human Genomics, University of Miami Miller School of Medicine, Miami, FL, USA
| | - P R Lichter
- Department of Ophthalmology and Visual Sciences, University of Michigan, Ann Arbor, MI, USA
| | - D L Budenz
- Department of Ophthalmology, University of North Carolina, Chapel Hill, NC, USA
| | - Y Liu
- Department of Ophthalmology, Duke University Medical Center, Durham, NC, USA
- Department of Medicine, Duke University Medical Center, Durham, NC, USA
| | - T Realini
- Department of Ophthalmology, West Virginia University Eye Institute, Morgantown, WV, USA
| | | | - T Gaasterland
- Scripps Genome Center, University of California at San Diego, San Diego, CA, USA
| | - D S Friedman
- Wilmer Eye Institute, Johns Hopkins University Hospital, Baltimore, MD, USA
| | - C A McCarty
- Essentia Institute of Rural Health, Duluth, MN, USA
| | - S E Moroi
- Department of Ophthalmology and Visual Sciences, University of Michigan, Ann Arbor, MI, USA
| | - L Olson
- Center for Human Genetics Research, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - J S Schuman
- Department of Ophthalmology, UPMC Eye Center, University of Pittsburgh, Pittsburgh, PA, USA
| | - K Singh
- Department of Ophthalmology, Stanford University, Palo Alto, CA, USA
| | - D Vollrath
- Department of Genetics, Stanford University, Palo Alto, CA, USA
| | - G Wollstein
- Department of Ophthalmology, UPMC Eye Center, University of Pittsburgh, Pittsburgh, PA, USA
| | - D J Zack
- Wilmer Eye Institute, Johns Hopkins University Hospital, Baltimore, MD, USA
| | - M Brilliant
- Center for Human Genetics, Marshfield Clinic Research Foundation, Marshfield, WI, USA
| | - A J Sit
- Department of Ophthalmology, Mayo Clinic, Rochester, MN, USA
| | - W G Christen
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - J Fingert
- Departments of Ophthalmology and Anatomy/Cell Biology, University of Iowa, College of Medicine, Iowa City, IA, USA
| | - J P Forman
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - E S Buys
- Department of Anesthesia, Critical Care, and Pain Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - P Kraft
- Department of Biostatistics and Epidemiology, Harvard School of Public Health, Boston, MA, USA
| | - K Zhang
- Department of Ophthalmology and Hamilton Glaucoma Center, University of California at San Diego, San Diego, CA, USA
| | - R R Allingham
- Department of Ophthalmology, Duke University Medical Center, Durham, NC, USA
| | - M A Pericak-Vance
- Bascom Palmer Eye Institute and Human Genomics, University of Miami Miller School of Medicine, Miami, FL, USA
| | - J E Richards
- Department of Ophthalmology and Visual Sciences, University of Michigan, Ann Arbor, MI, USA
| | - M A Hauser
- Department of Ophthalmology, Duke University Medical Center, Durham, NC, USA
- Department of Medicine, Duke University Medical Center, Durham, NC, USA
| | - J L Haines
- Center for Human Genetics Research, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - J L Wiggs
- Department of Ophthalmology, Mass Eye and Ear, Boston, MA, USA
| | - L R Pasquale
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Ophthalmology, Mass Eye and Ear, Boston, MA, USA
| |
Collapse
|
31
|
Abstract
Inflammatory bowel disease includes ulcerative colitis and Crohn's disease, which are both inflammatory disorders of the gastrointestinal tract. Both types of inflammatory bowel disease have a complex etiology, resulting from a genetically determined susceptibility interacting with environmental factors, including the diet and gut microbiota. Genome Wide Association Studies have implicated more than 160 single-nucleotide polymorphisms in disease susceptibility. Consideration of the different pathways suggested to be involved implies that specific dietary interventions are likely to be appropriate, dependent upon the nature of the genes involved. Epigenetics and the gut microbiota are also responsive to dietary interventions. Nutrigenetics may lead to personalized nutrition for disease prevention and treatment, while nutrigenomics may help to understand the nature of the disease and individual response to nutrients.
Collapse
Affiliation(s)
- Lynnette R Ferguson
- Discipline of Nutrition, Faculty of Medical & Health Sciences, The University of Auckland, Private Bag 92019, Auckland, New Zealand and Nutrigenomics New Zealand, Auckland, New Zealand.
| |
Collapse
|
32
|
Incorporating prior knowledge to increase the power of genome-wide association studies. Methods Mol Biol 2014; 1019:519-41. [PMID: 23756909 DOI: 10.1007/978-1-62703-447-0_25] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/10/2023]
Abstract
Typical methods of analyzing genome-wide single nucleotide variant (SNV) data in cases and controls involve testing each variant's genotypes separately for phenotype association, and then using a substantial multiple-testing penalty to minimize the rate of false positives. This approach, however, can result in low power for modestly associated SNVs. Furthermore, simply looking at the most associated SNVs may not directly yield biological insights about disease etiology. SNVset methods attempt to address both limitations of the traditional approach by testing biologically meaningful sets of SNVs (e.g., genes or pathways). The number of tests run in a SNVset analysis is typically much lower (hundreds or thousands instead of millions) than in a traditional analysis, so the false-positive rate is lower. Additionally, by testing SNVsets that are biologically meaningful finding a significant set may more quickly yield insights into disease etiology.In this chapter we summarize the short history of SNVset testing and provide an overview of the many recently proposed methods. Furthermore, we provide detailed step-by-step instructions on how to perform a SNVset analysis, including a substantial number of practical tips and questions that researchers should consider before undertaking a SNVset analysis. Lastly, we describe a companion R package (snvset) that implements recently proposed SNVset methods. While SNVset testing is a new approach, with many new methods still being developed and many open questions, the promise of the approach is worth serious consideration when considering analytic methods for GWAS.
Collapse
|
33
|
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/.
Collapse
Affiliation(s)
- Yo Son Park
- Division of Biostatistics and Bioinformatics, Institute of Population Health Sciences, National Health Research Institutes, Zhunan, Miaoli, Taiwan.
| | | | | | | | | |
Collapse
|
34
|
Abstract
The autism spectrum disorders (ASD) are characterized by impairments in social interaction and stereotyped behaviors. For the majority of individuals with ASD, the causes of the disorder remain unknown; however, in up to 25% of cases, a genetic cause can be identified. Chromosomal rearrangements as well as rare and de novo copy-number variants are present in ∼10-20% of individuals with ASD, compared with 1-2% in the general population and/or unaffected siblings. Rare and de novo coding-sequence mutations affecting neuronal genes have also been identified in ∼5-10% of individuals with ASD. Common variants such as single-nucleotide polymorphisms seem to contribute to ASD susceptibility, but, taken individually, their effects appear to be small. Despite a heterogeneous genetic landscape, the genes implicated thus far-which are involved in chromatin remodeling, metabolism, mRNA translation, and synaptic function-seem to converge in common pathways affecting neuronal and synaptic homeostasis. Animal models developed to study these genes should lead to a better understanding of the diversity of the genetic landscapes of ASD.
Collapse
Affiliation(s)
- Guillaume Huguet
- Human Genetics and Cognitive Functions Unit, Institut Pasteur, 75015 Paris, France;
| | | | | |
Collapse
|
35
|
Pasquale LR, Loomis SJ, Weinreb RN, Kang JH, Yaspan BL, Bailey JC, Gaasterland D, Gaasterland T, Lee RK, Scott WK, Lichter PR, Budenz DL, Liu Y, Realini T, Friedman DS, McCarty CA, Moroi SE, Olson L, Schuman JS, Singh K, Vollrath D, Wollstein G, Zack DJ, Brilliant M, Sit AJ, Christen WG, Fingert J, Kraft P, Zhang K, Allingham RR, Pericak-Vance MA, Richards JE, Hauser MA, Haines JL, Wiggs JL. Estrogen pathway polymorphisms in relation to primary open angle glaucoma: an analysis accounting for gender from the United States. Mol Vis 2013; 19:1471-81. [PMID: 23869166 PMCID: PMC3712669] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2013] [Accepted: 07/05/2013] [Indexed: 11/15/2022] Open
Abstract
PURPOSE Circulating estrogen levels are relevant in glaucoma phenotypic traits. We assessed the association between an estrogen metabolism single nucleotide polymorphism (SNP) panel in relation to primary open angle glaucoma (POAG), accounting for gender. METHODS We included 3,108 POAG cases and 3,430 controls of both genders from the Glaucoma Genes and Environment (GLAUGEN) study and the National Eye Institute Glaucoma Human Genetics Collaboration (NEIGHBOR) consortium genotyped on the Illumina 660W-Quad platform. We assessed the relation between the SNP panels representative of estrogen metabolism and POAG using pathway- and gene-based approaches with the Pathway Analysis by Randomization Incorporating Structure (PARIS) software. PARIS executes a permutation algorithm to assess statistical significance relative to the pathways and genes of comparable genetic architecture. These analyses were performed using the meta-analyzed results from the GLAUGEN and NEIGHBOR data sets. We evaluated POAG overall as well as two subtypes of POAG defined as intraocular pressure (IOP) ≥22 mmHg (high-pressure glaucoma [HPG]) or IOP <22 mmHg (normal pressure glaucoma [NPG]) at diagnosis. We conducted these analyses for each gender separately and then jointly in men and women. RESULTS Among women, the estrogen SNP pathway was associated with POAG overall (permuted p=0.006) and HPG (permuted p<0.001) but not NPG (permuted p=0.09). Interestingly, there was no relation between the estrogen SNP pathway and POAG when men were considered alone (permuted p>0.99). Among women, gene-based analyses revealed that the catechol-O-methyltransferase gene showed strong associations with HTG (permuted gene p≤0.001) and NPG (permuted gene p=0.01). CONCLUSIONS The estrogen SNP pathway was associated with POAG among women.
Collapse
Affiliation(s)
- Louis R Pasquale
- Department of Ophthalmology, Mass Eye & Ear Infirmary, Harvard Medical School, Boston, MA 02114, USA.
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
Collapse
|
36
|
Neurobiology meets genomic science: the promise of human-induced pluripotent stem cells. Dev Psychopathol 2013; 24:1443-51. [PMID: 23062309 DOI: 10.1017/s095457941200082x] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
The recent introduction of the induced pluripotent stem cell technology has made possible the derivation of neuronal cells from somatic cells obtained from human individuals. This in turn has opened new areas of investigation that can potentially bridge the gap between neuroscience and psychopathology. For the first time we can study the cell biology and genetics of neurons derived from any individual. Furthermore, by recapitulating in vitro the developmental steps whereby stem cells give rise to neuronal cells, we can now hope to understand factors that control typical and atypical development. We can begin to explore how human genes and their variants are transcribed into messenger RNAs within developing neurons and how these gene transcripts control the biology of developing cells. Thus, human-induced pluripotent stem cells have the potential to uncover not only what aspects of development are uniquely human but also variations in the series of events necessary for normal human brain development that predispose to psychopathology.
Collapse
|
37
|
Anney R, Klei L, Pinto D, Almeida J, Bacchelli E, Baird G, Bolshakova N, Bölte S, Bolton PF, Bourgeron T, Brennan S, Brian J, Casey J, Conroy J, Correia C, Corsello C, Crawford EL, de Jonge M, Delorme R, Duketis E, Duque F, Estes A, Farrar P, Fernandez BA, Folstein SE, Fombonne E, Gilbert J, Gillberg C, Glessner JT, Green A, Green J, Guter SJ, Heron EA, Holt R, Howe JL, Hughes G, Hus V, Igliozzi R, Jacob S, Kenny GP, Kim C, Kolevzon A, Kustanovich V, Lajonchere CM, Lamb JA, Law-Smith M, Leboyer M, Le Couteur A, Leventhal BL, Liu XQ, Lombard F, Lord C, Lotspeich L, Lund SC, Magalhaes TR, Mantoulan C, McDougle CJ, Melhem NM, Merikangas A, Minshew NJ, Mirza GK, Munson J, Noakes C, Nygren G, Papanikolaou K, Pagnamenta AT, Parrini B, Paton T, Pickles A, Posey DJ, Poustka F, Ragoussis J, Regan R, Roberts W, Roeder K, Roge B, Rutter ML, Schlitt S, Shah N, Sheffield VC, Soorya L, Sousa I, Stoppioni V, Sykes N, Tancredi R, Thompson AP, Thomson S, Tryfon A, Tsiantis J, Van Engeland H, Vincent JB, Volkmar F, Vorstman JAS, Wallace S, Wing K, Wittemeyer K, Wood S, Zurawiecki D, Zwaigenbaum L, Bailey AJ, Battaglia A, Cantor RM, Coon H, Cuccaro ML, Dawson G, Ennis S, Freitag CM, Geschwind DH, Haines JL, Klauck SM, McMahon WM, Maestrini E, Miller J, Monaco AP, Nelson SF, Nurnberger JI, Oliveira G, Parr JR, Pericak-Vance MA, Piven J, Schellenberg GD, Scherer SW, Vicente AM, Wassink TH, Wijsman EM, Betancur C, Buxbaum JD, Cook EH, Gallagher L, Gill M, Hallmayer J, Paterson AD, Sutcliffe JS, Szatmari P, Vieland VJ, Hakonarson H, Devlin B. Individual common variants exert weak effects on the risk for autism spectrum disorders. Hum Mol Genet 2012; 21:4781-92. [PMID: 22843504 PMCID: PMC3471395 DOI: 10.1093/hmg/dds301] [Citation(s) in RCA: 254] [Impact Index Per Article: 21.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2012] [Revised: 07/13/2012] [Accepted: 07/19/2012] [Indexed: 11/13/2022] Open
Abstract
While it is apparent that rare variation can play an important role in the genetic architecture of autism spectrum disorders (ASDs), the contribution of common variation to the risk of developing ASD is less clear. To produce a more comprehensive picture, we report Stage 2 of the Autism Genome Project genome-wide association study, adding 1301 ASD families and bringing the total to 2705 families analysed (Stages 1 and 2). In addition to evaluating the association of individual single nucleotide polymorphisms (SNPs), we also sought evidence that common variants, en masse, might affect the risk. Despite genotyping over a million SNPs covering the genome, no single SNP shows significant association with ASD or selected phenotypes at a genome-wide level. The SNP that achieves the smallest P-value from secondary analyses is rs1718101. It falls in CNTNAP2, a gene previously implicated in susceptibility for ASD. This SNP also shows modest association with age of word/phrase acquisition in ASD subjects, of interest because features of language development are also associated with other variation in CNTNAP2. In contrast, allele scores derived from the transmission of common alleles to Stage 1 cases significantly predict case status in the independent Stage 2 sample. Despite being significant, the variance explained by these allele scores was small (Vm< 1%). Based on results from individual SNPs and their en masse effect on risk, as inferred from the allele score results, it is reasonable to conclude that common variants affect the risk for ASD but their individual effects are modest.
Collapse
Affiliation(s)
- Richard Anney
- Autism Genetics Group, Department of Psychiatry, School of Medicine, Trinity College, Dublin 8, Ireland
| | - Lambertus Klei
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA, 15232, USA
| | - Dalila Pinto
- The Centre for Applied Genomics and Program in Genetics and Genomic Biology, The Hospital for Sick Children and Department of Molecular Genetics, University of Toronto, Toronto, ON, CanadaM5G 1L7
| | - Joana Almeida
- Hospital Pediátrico de Coimbra, 3000–076 Coimbra, Portugal
| | - Elena Bacchelli
- Department of Biology, University of Bologna, 40126 Bologna, Italy
| | - Gillian Baird
- Guy's and St Thomas' NHS Trust & King's College, London SE1 9RT, UK
| | - Nadia Bolshakova
- Autism Genetics Group, Department of Psychiatry, School of Medicine, Trinity College, Dublin 8, Ireland
| | - Sven Bölte
- Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, J.W. Goethe University Frankfurt, 60528 Frankfurt, Germany
| | | | - Thomas Bourgeron
- Human Genetics and Cognitive Functions, Institut Pasteur and
- University Paris Diderot-Paris 7, CNRS URA 2182, Fondation FondaMental, 75015 Paris, France
| | - Sean Brennan
- Autism Genetics Group, Department of Psychiatry, School of Medicine, Trinity College, Dublin 8, Ireland
| | - Jessica Brian
- Autism Research Unit, The Hospital for Sick Children and Bloorview Kids Rehabilitation, University of Toronto, Toronto, ON, CanadaM5G 1Z8
| | - Jillian Casey
- School of Medicine, Medical Science University College, Dublin 4, Ireland
| | - Judith Conroy
- School of Medicine, Medical Science University College, Dublin 4, Ireland
| | - Catarina Correia
- Instituto Nacional de Saude Dr Ricardo Jorge and Instituto Gulbenkian de Cîencia, 1649-016 Lisbon, Portugal
- BioFIG—Center for Biodiversity, Functional and Integrative Genomics, Campus da FCUL, C2.2.12, Campo Grande, 1749-016 Lisboa, Portugal
| | - Christina Corsello
- Department of Psychology, University of Michigan, Ann Arbor, MI 48109, USA
| | - Emily L. Crawford
- Department of Molecular Physiology and Biophysics, Vanderbilt Kennedy Center, and Centers for Human Genetics Research and Molecular Neuroscience and
| | - Maretha de Jonge
- Department of Child Psychiatry, University Medical Center, Utrecht, 3508 GA, The Netherlands
| | - Richard Delorme
- Child and Adolescent Psychiatry, APHP, Hôpital Robert Debré, 75019 Paris, France
| | - Eftichia Duketis
- Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, J.W. Goethe University Frankfurt, 60528 Frankfurt, Germany
| | | | | | - Penny Farrar
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK
| | - Bridget A. Fernandez
- Disciplines of Genetics and Medicine, Memorial University of Newfoundland,St John's, NL, CanadaA1B 3V6
| | - Susan E. Folstein
- Department of Psychiatry, University of Miami School of Medicine, Miami, FL 33136, USA
| | - Eric Fombonne
- Division of Psychiatry, McGill University, Montreal, QC, CanadaH3A 1A1
| | - John Gilbert
- The John P. Hussman Institute for Human Genomics, University of Miami, Miami, FL 33101, USA
| | - Christopher Gillberg
- Gillberg Neuropsychiatry Centre, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Joseph T. Glessner
- The Center for Applied Genomics, Division of Human Genetics, The Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Andrew Green
- School of Medicine, Medical Science University College, Dublin 4, Ireland
| | - Jonathan Green
- Academic Department of Child Psychiatry, University of Manchester, Manchester M9 7AA, UK
| | - Stephen J. Guter
- Department of Psychiatry, Institute for Juvenile Research, University of Illinois at Chicago, Chicago, IL 60608, USA
| | - Elizabeth A. Heron
- Autism Genetics Group, Department of Psychiatry, School of Medicine, Trinity College, Dublin 8, Ireland
| | - Richard Holt
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK
| | - Jennifer L. Howe
- The Centre for Applied Genomics and Program in Genetics and Genomic Biology, The Hospital for Sick Children and Department of Molecular Genetics, University of Toronto, Toronto, ON, CanadaM5G 1L7
| | - Gillian Hughes
- Autism Genetics Group, Department of Psychiatry, School of Medicine, Trinity College, Dublin 8, Ireland
| | - Vanessa Hus
- Department of Psychology, University of Michigan, Ann Arbor, MI 48109, USA
| | - Roberta Igliozzi
- BioFIG—Center for Biodiversity, Functional and Integrative Genomics, Campus da FCUL, C2.2.12, Campo Grande, 1749-016 Lisboa, Portugal
| | - Suma Jacob
- Department of Psychiatry, Institute for Juvenile Research, University of Illinois at Chicago, Chicago, IL 60608, USA
| | - Graham P. Kenny
- Autism Genetics Group, Department of Psychiatry, School of Medicine, Trinity College, Dublin 8, Ireland
| | - Cecilia Kim
- The Center for Applied Genomics, Division of Human Genetics, The Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Alexander Kolevzon
- The Seaver Autism Center for Research and Treatment, Department of Psychiatry, The Friedman Brain Institute, Mount Sinai School of Medicine, New York NY 10029, USA
| | - Vlad Kustanovich
- Autism Genetic Resource Exchange, Autism Speaks, Los Angeles, CA 90036-4234, USA
| | - Clara M. Lajonchere
- Autism Genetic Resource Exchange, Autism Speaks, Los Angeles, CA 90036-4234, USA
| | | | - Miriam Law-Smith
- Autism Genetics Group, Department of Psychiatry, School of Medicine, Trinity College, Dublin 8, Ireland
| | - Marion Leboyer
- Department of Psychiatry, Groupe hospitalier Henri Mondor-Albert Chenevier, INSERM U995, AP-HP; University Paris 12, Fondation FondaMental, Créteil 94000, France
| | - Ann Le Couteur
- Institutes of Neuroscience and Health and Society, Newcastle University, Newcastle upon Tyne NE1 7RU, UK
| | - Bennett L. Leventhal
- Nathan Kline Institute for Psychiatric Research (NKI), 140 Old Orangeburg Road, Orangeburg, NY 10962, USA
- Department of Child and Adolescent Psychiatry, New York University, NYU Child Study Center, New York, NY 10016, USA
| | - Xiao-Qing Liu
- Department of Obstetrics, Gynecology, and Reproductive Sciences, University of Manitoba, Winnipeg, MB, Canada
| | - Frances Lombard
- Autism Genetics Group, Department of Psychiatry, School of Medicine, Trinity College, Dublin 8, Ireland
| | - Catherine Lord
- Center for Autism and the Developing Brain, Weill Cornell Medical College, White Plains, NY, USA
| | - Linda Lotspeich
- Department of Psychiatry, Division of Child and Adolescent Psychiatry and Child Development, Stanford University School of Medicine, Stanford, CA 94304, USA
| | - Sabata C. Lund
- Department of Molecular Physiology and Biophysics, Vanderbilt Kennedy Center, and Centers for Human Genetics Research and Molecular Neuroscience and
| | - Tiago R. Magalhaes
- Instituto Nacional de Saude Dr Ricardo Jorge and Instituto Gulbenkian de Cîencia, 1649-016 Lisbon, Portugal
- BioFIG—Center for Biodiversity, Functional and Integrative Genomics, Campus da FCUL, C2.2.12, Campo Grande, 1749-016 Lisboa, Portugal
| | - Carine Mantoulan
- Centre d'Eudes et de Recherches en Psychopathologie, University de Toulouse Le Mirail, Toulouse 31200, France
| | - Christopher J. McDougle
- Department of Psychiatry, Indiana University School of Medicine, Indianapolis, IN 46202, USA
| | - Nadine M. Melhem
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA, 15232, USA
| | - Alison Merikangas
- Autism Genetics Group, Department of Psychiatry, School of Medicine, Trinity College, Dublin 8, Ireland
| | - Nancy J. Minshew
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA, 15232, USA
- Department of Neurology, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213, USA
| | - Ghazala K. Mirza
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK
| | - Jeff Munson
- Department of Psychiatry and Behavioral Sciences
| | - Carolyn Noakes
- Autism Research Unit, The Hospital for Sick Children and Bloorview Kids Rehabilitation, University of Toronto, Toronto, ON, CanadaM5G 1Z8
| | - Gudrun Nygren
- Gillberg Neuropsychiatry Centre, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Katerina Papanikolaou
- University Department of Child Psychiatry, Athens University, Medical School, Agia Sophia Children's Hospital, 115 27 Athens, Greece
| | | | - Barbara Parrini
- Stella Maris Institute for Child and Adolescent Neuropsychiatry, 56128 Calambrone (Pisa), Italy
| | - Tara Paton
- The Centre for Applied Genomics and Program in Genetics and Genomic Biology, The Hospital for Sick Children and Department of Molecular Genetics, University of Toronto, Toronto, ON, CanadaM5G 1L7
| | - Andrew Pickles
- Department of Medicine, School of Epidemiology and Health Science, University of Manchester, Manchester M13 9PT, UK
| | - David J. Posey
- Department of Psychiatry, Indiana University School of Medicine, Indianapolis, IN 46202, USA
| | - Fritz Poustka
- Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, J.W. Goethe University Frankfurt, 60528 Frankfurt, Germany
| | - Jiannis Ragoussis
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK
| | - Regina Regan
- School of Medicine, Medical Science University College, Dublin 4, Ireland
| | - Wendy Roberts
- Autism Research Unit, The Hospital for Sick Children and Bloorview Kids Rehabilitation, University of Toronto, Toronto, ON, CanadaM5G 1Z8
| | - Kathryn Roeder
- Department of Statistics, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Bernadette Roge
- Centre d'Eudes et de Recherches en Psychopathologie, University de Toulouse Le Mirail, Toulouse 31200, France
| | - Michael L. Rutter
- MRC Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, King's College, London SE5 8AF, UK
| | - Sabine Schlitt
- Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, J.W. Goethe University Frankfurt, 60528 Frankfurt, Germany
| | - Naisha Shah
- School of Medicine, Medical Science University College, Dublin 4, Ireland
| | - Val C. Sheffield
- Department of Pediatrics and Howard Hughes Medical Institute Carver College of Medicine, University of Iowa, Iowa City, IA 52242, USA
| | - Latha Soorya
- The Seaver Autism Center for Research and Treatment, Department of Psychiatry, The Friedman Brain Institute, Mount Sinai School of Medicine, New York NY 10029, USA
| | - Inês Sousa
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK
| | - Vera Stoppioni
- Neuropsichiatria Infantile, Ospedale Santa Croce, 61032 Fano, Italy
| | - Nuala Sykes
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK
| | - Raffaella Tancredi
- Stella Maris Institute for Child and Adolescent Neuropsychiatry, 56128 Calambrone (Pisa), Italy
| | - Ann P. Thompson
- Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, ON, CanadaL8N 3Z5
| | - Susanne Thomson
- Department of Molecular Physiology and Biophysics, Vanderbilt Kennedy Center, and Centers for Human Genetics Research and Molecular Neuroscience and
| | - Ana Tryfon
- The Seaver Autism Center for Research and Treatment, Department of Psychiatry, The Friedman Brain Institute, Mount Sinai School of Medicine, New York NY 10029, USA
| | - John Tsiantis
- University Department of Child Psychiatry, Athens University, Medical School, Agia Sophia Children's Hospital, 115 27 Athens, Greece
| | - Herman Van Engeland
- Department of Child Psychiatry, University Medical Center, Utrecht, 3508 GA, The Netherlands
| | - John B. Vincent
- Centre for Addiction and Mental Health, Clarke Institute and Department of Psychiatry, University of Toronto, Toronto, ON, CanadaM5G 1X8
| | - Fred Volkmar
- Child Study Centre, Yale University, New Haven, CT 06520, USA
| | - JAS Vorstman
- Department of Psychiatry, Rudolf Magnus Institute of Neuroscience, University Medical Center Utrecht, Utrecht 3584 CX, The Netherlands
| | - Simon Wallace
- Department of Psychiatry, University of Oxford, Warneford Hospital, Headington, Oxford, OX3 7JX, UK
| | - Kirsty Wing
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK
| | - Kerstin Wittemeyer
- Department of Psychiatry, University of Oxford, Warneford Hospital, Headington, Oxford, OX3 7JX, UK
| | - Shawn Wood
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA, 15232, USA
| | - Danielle Zurawiecki
- The Seaver Autism Center for Research and Treatment, Department of Psychiatry, The Friedman Brain Institute, Mount Sinai School of Medicine, New York NY 10029, USA
| | - Lonnie Zwaigenbaum
- Department of Pediatrics, University of Alberta, Edmonton, AB, CanadaT6G 2J3
| | - Anthony J. Bailey
- BC Mental Health and Addictions Research Unit, University of British Columbia, Vancouver, BC, CanadaV5Z4H4
| | - Agatino Battaglia
- Stella Maris Institute for Child and Adolescent Neuropsychiatry, 56128 Calambrone (Pisa), Italy
| | | | - Hilary Coon
- Psychiatry Department, University of Utah Medical School, Salt Lake City, UT 84108, USA
| | - Michael L. Cuccaro
- The John P. Hussman Institute for Human Genomics, University of Miami, Miami, FL 33101, USA
| | | | - Sean Ennis
- School of Medicine, Medical Science University College, Dublin 4, Ireland
| | - Christine M. Freitag
- Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, J.W. Goethe University Frankfurt, 60528 Frankfurt, Germany
| | - Daniel H. Geschwind
- Department of Neurology, Los Angeles School of Medicine, University of California, Los Angeles, CA 90095, USA
| | - Jonathan L. Haines
- Center for Human Genetics Research, Vanderbilt University Medical Centre, Nashville, TN 37232, USA
| | - Sabine M. Klauck
- Division of Molecular Genome Analysis, German Cancer Research Center (DKFZ), Heidelberg 69120, Germany
| | - William M. McMahon
- Psychiatry Department, University of Utah Medical School, Salt Lake City, UT 84108, USA
| | - Elena Maestrini
- Department of Biology, University of Bologna, 40126 Bologna, Italy
| | - Judith Miller
- Psychiatry Department, University of Utah Medical School, Salt Lake City, UT 84108, USA
| | - Anthony P. Monaco
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK
- Office of the President, Tufts University, Boston, MA, USA
| | | | - John I. Nurnberger
- Department of Psychiatry, Indiana University School of Medicine, Indianapolis, IN 46202, USA
| | | | - Jeremy R. Parr
- Institutes of Neuroscience and Health and Society, Newcastle University, Newcastle upon Tyne NE1 7RU, UK
| | | | - Joseph Piven
- Carolina Institute for Developmental Disabilities, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599-3366, USA
| | - Gerard D. Schellenberg
- Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Stephen W. Scherer
- The Centre for Applied Genomics and Program in Genetics and Genomic Biology, The Hospital for Sick Children and Department of Molecular Genetics, University of Toronto, Toronto, ON, CanadaM5G 1L7
| | - Astrid M. Vicente
- Instituto Nacional de Saude Dr Ricardo Jorge and Instituto Gulbenkian de Cîencia, 1649-016 Lisbon, Portugal
- BioFIG—Center for Biodiversity, Functional and Integrative Genomics, Campus da FCUL, C2.2.12, Campo Grande, 1749-016 Lisboa, Portugal
| | - Thomas H. Wassink
- Department of Psychiatry, Carver College of Medicine, Iowa City, IA 52242, USA
| | - Ellen M. Wijsman
- Department of Biostatistics and
- Department of Medicine, University of Washington, Seattle, WA 98195, USA
| | - Catalina Betancur
- INSERM U952
- CNRS UMR 7224 and
- UPMC Univ Paris 06, Paris 75005, France and
| | - Joseph D. Buxbaum
- The Seaver Autism Center for Research and Treatment, Department of Psychiatry, The Friedman Brain Institute, Mount Sinai School of Medicine, New York NY 10029, USA
| | - Edwin H. Cook
- Department of Psychiatry, Institute for Juvenile Research, University of Illinois at Chicago, Chicago, IL 60608, USA
| | - Louise Gallagher
- Autism Genetics Group, Department of Psychiatry, School of Medicine, Trinity College, Dublin 8, Ireland
| | - Michael Gill
- Autism Genetics Group, Department of Psychiatry, School of Medicine, Trinity College, Dublin 8, Ireland
| | - Joachim Hallmayer
- Department of Psychiatry, Division of Child and Adolescent Psychiatry and Child Development, Stanford University School of Medicine, Stanford, CA 94304, USA
| | - Andrew D. Paterson
- The Centre for Applied Genomics and Program in Genetics and Genomic Biology, The Hospital for Sick Children and Department of Molecular Genetics, University of Toronto, Toronto, ON, CanadaM5G 1L7
| | - James S. Sutcliffe
- Department of Molecular Physiology and Biophysics, Vanderbilt Kennedy Center, and Centers for Human Genetics Research and Molecular Neuroscience and
| | - Peter Szatmari
- Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, ON, CanadaL8N 3Z5
| | - Veronica J. Vieland
- Battelle Center for Mathematical Medicine, The Research Institute at Nationwide Children's Hospital and The Ohio State University, Columbus, OH 43205, USA
| | - Hakon Hakonarson
- The Center for Applied Genomics, Division of Human Genetics, The Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA
- Department of Pediatrics, Children's Hospital of Philadelphia, University of Pennsylvania School of Medicine, Philadelphia, PA 19104, USA
| | - Bernie Devlin
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA, 15232, USA
| |
Collapse
|
38
|
Davis LK, Gamazon ER, Kistner-Griffin E, Badner JA, Liu C, Cook EH, Sutcliffe JS, Cox NJ. Loci nominally associated with autism from genome-wide analysis show enrichment of brain expression quantitative trait loci but not lymphoblastoid cell line expression quantitative trait loci. Mol Autism 2012; 3:3. [PMID: 22591576 PMCID: PMC3484025 DOI: 10.1186/2040-2392-3-3] [Citation(s) in RCA: 29] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2012] [Accepted: 03/22/2012] [Indexed: 01/26/2023] Open
Abstract
Background Autism spectrum disorder is a severe early onset neurodevelopmental disorder with high heritability but significant heterogeneity. Traditional genome-wide approaches to test for an association of common variants with autism susceptibility risk have met with limited success. However, novel methods to identify moderate risk alleles in attainable sample sizes are now gaining momentum. Methods In this study, we utilized publically available genome-wide association study data from the Autism Genome Project and annotated the results (P <0.001) for expression quantitative trait loci present in the parietal lobe (GSE35977), cerebellum (GSE35974) and lymphoblastoid cell lines (GSE7761). We then performed a test of enrichment by comparing these results to simulated data conditioned on minor allele frequency to generate an empirical P-value indicating statistically significant enrichment of expression quantitative trait loci in top results from the autism genome-wide association study. Results Our findings show a global enrichment of brain expression quantitative trait loci, but not lymphoblastoid cell line expression quantitative trait loci, among top single nucleotide polymorphisms from an autism genome-wide association study. Additionally, the data implicates individual genes SLC25A12, PANX1 and PANX2 as well as pathways previously implicated in autism. Conclusions These findings provide supportive rationale for the use of annotation-based approaches to genome-wide association studies.
Collapse
Affiliation(s)
- Lea K Davis
- Section of Genetic Medicine, Department of Medicine, University of Chicago, Chicago, IL 60637, USA.
| | | | | | | | | | | | | | | |
Collapse
|
39
|
Wiggs JL, Yaspan BL, Hauser MA, Kang JH, Allingham RR, Olson LM, Abdrabou W, Fan BJ, Wang DY, Brodeur W, Budenz DL, Caprioli J, Crenshaw A, Crooks K, Delbono E, Doheny KF, Friedman DS, Gaasterland D, Gaasterland T, Laurie C, Lee RK, Lichter PR, Loomis S, Liu Y, Medeiros FA, McCarty C, Mirel D, Moroi SE, Musch DC, Realini A, Rozsa FW, Schuman JS, Scott K, Singh K, Stein JD, Trager EH, Vanveldhuisen P, Vollrath D, Wollstein G, Yoneyama S, Zhang K, Weinreb RN, Ernst J, Kellis M, Masuda T, Zack D, Richards JE, Pericak-Vance M, Pasquale LR, Haines JL. Common variants at 9p21 and 8q22 are associated with increased susceptibility to optic nerve degeneration in glaucoma. PLoS Genet 2012; 8:e1002654. [PMID: 22570617 PMCID: PMC3343074 DOI: 10.1371/journal.pgen.1002654] [Citation(s) in RCA: 226] [Impact Index Per Article: 18.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2012] [Accepted: 03/01/2012] [Indexed: 01/07/2023] Open
Abstract
Optic nerve degeneration caused by glaucoma is a leading cause of blindness worldwide. Patients affected by the normal-pressure form of glaucoma are more likely to harbor risk alleles for glaucoma-related optic nerve disease. We have performed a meta-analysis of two independent genome-wide association studies for primary open angle glaucoma (POAG) followed by a normal-pressure glaucoma (NPG, defined by intraocular pressure (IOP) less than 22 mmHg) subgroup analysis. The single-nucleotide polymorphisms that showed the most significant associations were tested for association with a second form of glaucoma, exfoliation-syndrome glaucoma. The overall meta-analysis of the GLAUGEN and NEIGHBOR dataset results (3,146 cases and 3,487 controls) identified significant associations between two loci and POAG: the CDKN2BAS region on 9p21 (rs2157719 [G], OR = 0.69 [95%CI 0.63-0.75], p = 1.86×10⁻¹⁸), and the SIX1/SIX6 region on chromosome 14q23 (rs10483727 [A], OR = 1.32 [95%CI 1.21-1.43], p = 3.87×10⁻¹¹). In sub-group analysis two loci were significantly associated with NPG: 9p21 containing the CDKN2BAS gene (rs2157719 [G], OR = 0.58 [95% CI 0.50-0.67], p = 1.17×10⁻¹²) and a probable regulatory region on 8q22 (rs284489 [G], OR = 0.62 [95% CI 0.53-0.72], p = 8.88×10⁻¹⁰). Both NPG loci were also nominally associated with a second type of glaucoma, exfoliation syndrome glaucoma (rs2157719 [G], OR = 0.59 [95% CI 0.41-0.87], p = 0.004 and rs284489 [G], OR = 0.76 [95% CI 0.54-1.06], p = 0.021), suggesting that these loci might contribute more generally to optic nerve degeneration in glaucoma. Because both loci influence transforming growth factor beta (TGF-beta) signaling, we performed a genomic pathway analysis that showed an association between the TGF-beta pathway and NPG (permuted p = 0.009). These results suggest that neuro-protective therapies targeting TGF-beta signaling could be effective for multiple forms of glaucoma.
Collapse
Affiliation(s)
- Janey L Wiggs
- Department of Ophthalmology, Harvard Medical School, Massachusetts Eye and Ear Infirmary, Boston, Massachusetts, United States of America.
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
Collapse
|
40
|
Newschaffer CJ, Croen LA, Fallin MD, Hertz-Picciotto I, Nguyen DV, Lee NL, Berry CA, Farzadegan H, Hess HN, Landa RJ, Levy SE, Massolo ML, Meyerer SC, Mohammed SM, Oliver MC, Ozonoff S, Pandey J, Schroeder A, Shedd-Wise KM. Infant siblings and the investigation of autism risk factors. J Neurodev Disord 2012; 4:7. [PMID: 22958474 PMCID: PMC3436647 DOI: 10.1186/1866-1955-4-7] [Citation(s) in RCA: 82] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/06/2011] [Accepted: 04/18/2012] [Indexed: 12/31/2022] Open
Abstract
Infant sibling studies have been at the vanguard of autism spectrum disorders (ASD) research over the past decade, providing important new knowledge about the earliest emerging signs of ASD and expanding our understanding of the developmental course of this complex disorder. Studies focused on siblings of children with ASD also have unrealized potential for contributing to ASD etiologic research. Moving targeted time of enrollment back from infancy toward conception creates tremendous opportunities for optimally studying risk factors and risk biomarkers during the pre-, peri- and neonatal periods. By doing so, a traditional sibling study, which already incorporates close developmental follow-up of at-risk infants through the third year of life, is essentially reconfigured as an enriched-risk pregnancy cohort study. This review considers the enriched-risk pregnancy cohort approach of studying infant siblings in the context of current thinking on ASD etiologic mechanisms. It then discusses the key features of this approach and provides a description of the design and implementation strategy of one major ASD enriched-risk pregnancy cohort study: the Early Autism Risk Longitudinal Investigation (EARLI).
Collapse
Affiliation(s)
- Craig J Newschaffer
- Department of Epidemiology and Biostatistics, Drexel School of Public Health, 1505 Race Street, Mail Stop 1033, Philadelphia, PA 19102, USA
| | - Lisa A Croen
- Kaiser Permanente Division of Research, 2000 Broadway, Oakland, CA 94612, USA
| | - M Daniele Fallin
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, 615 N Wolfe Street, Baltimore, MD 21205, USA
| | - Irva Hertz-Picciotto
- Department of Public Health Sciences, University of California, Davis, CA 95616, USA
| | - Danh V Nguyen
- Department of Public Health Sciences, University of California, Davis, CA 95616, USA
| | - Nora L Lee
- Department of Epidemiology and Biostatistics, Drexel School of Public Health, 1505 Race Street, Mail Stop 1033, Philadelphia, PA 19102, USA
| | - Carmen A Berry
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, 615 N Wolfe Street, Baltimore, MD 21205, USA
| | - Homayoon Farzadegan
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, 615 N Wolfe Street, Baltimore, MD 21205, USA
| | - H Nicole Hess
- Kaiser Permanente San Jose Medical Center, 6620 Via Del Oro, San Jose, CA 95119, USA
| | - Rebecca J Landa
- Kennedy Krieger Institute, 3901 Greenspring Avenue, 2nd Floor, Baltimore, MD 21211, USA
| | - Susan E Levy
- Center for Autism Research, The Children's Hospital of Philadelphia, 3535 Market Street, Suite 860, Philadelphia, PA 19104, USA
| | - Maria L Massolo
- Kaiser Permanente Division of Research, 2000 Broadway, Oakland, CA 94612, USA
| | - Stacey C Meyerer
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, 615 N Wolfe Street, Baltimore, MD 21205, USA
| | - Sandra M Mohammed
- Department of Public Health Sciences, University of California, Davis, CA 95616, USA
| | - McKenzie C Oliver
- Department of Public Health Sciences, University of California, Davis, CA 95616, USA
| | - Sally Ozonoff
- The MIND Institute, UC Davis Medical Center, 2825 50th Street, Sacramento, CA 95817, USA
| | - Juhi Pandey
- Center for Autism Research, The Children's Hospital of Philadelphia, 3535 Market Street, Suite 860, Philadelphia, PA 19104, USA
| | - Adam Schroeder
- Department of Public Health Sciences, University of California, Davis, CA 95616, USA
| | - Kristine M Shedd-Wise
- Department of Public Health Sciences, University of California, Davis, CA 95616, USA
| |
Collapse
|
41
|
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.
Collapse
|
42
|
Abstract
Genome-wide association studies (GWAS) are a standard approach for investigating the relationship of common variation within the human genome to a given phenotype of interest. However, single-allele association results published for many GWAS studies represent only the tip of the iceberg for the information that can be extracted from these datasets. The primary analysis strategy for GWAS entails association analysis in which only the single nucleotide polymorphisms (SNPs) with the strongest p values are declared statistically significant due to issues arising from multiple testing and type I error concerns. Factors such as locus heterogeneity, epistasis, and multiple genes conferring small effects contribute to the complexity of the genetic models underlying phenotype expression. Thus, many biologically meaningful associations having lower effect sizes at individual genes are overlooked, as they are difficult to separate from a sea of false-positive associations. Organizing these individual SNPs into biologically meaningful groups to look at overall effects of minor perturbations to genes and pathways is desirable. This pathway-based approach provides researchers with insight into the functional foundations of the phenotype being studied and allows testing of various genetic scenarios.
Collapse
Affiliation(s)
- Brian L Yaspan
- Center for Human Genetics Research, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | | |
Collapse
|
43
|
Abstract
Autism spectrum disorders (ASD) are important neuropsychiatric disorders, currently estimated to affect approximately 1% of children, with considerable emotional and financial costs. Significant collaborative effort has been made over the last 15 years in an attempt to unravel the genetic mechanisms underlying these conditions. This has led to important discoveries, both of the roles of specific genes, as well as larger scale chromosomal copy number changes. Here, we summarize some of the latest genetic findings in the field of ASD and attempt to link them with the results of pathophysiological studies to provide an overall picture of at least one of the mechanisms by which ASD may develop.
Collapse
Affiliation(s)
- Richard Holt
- The Wellcome Trust Centre for Human Genetics, University of Oxford, UK
| | | |
Collapse
|