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Recent innovations and in-depth aspects of post-genome wide association study (Post-GWAS) to understand the genetic basis of complex phenotypes. Heredity (Edinb) 2021; 127:485-497. [PMID: 34689168 PMCID: PMC8626474 DOI: 10.1038/s41437-021-00479-w] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2020] [Revised: 10/13/2021] [Accepted: 10/13/2021] [Indexed: 12/13/2022] Open
Abstract
In the past decade, the high throughput and low cost of sequencing/genotyping approaches have led to the accumulation of a large amount of data from genome-wide association studies (GWASs). The first aim of this review is to highlight how post-GWAS analysis can be used make sense of the obtained associations. Novel directions for integrating GWAS results with other resources, such as somatic mutation, metabolite-transcript, and transcriptomic data, are also discussed; these approaches can help us move beyond each individual data point and provide valuable information about complex trait genetics. In addition, cross-phenotype association tests, when the loci detected by GWASs have significant associations with multiple traits, are reviewed to provide biologically informative results for use in real-time applications. This review also discusses the challenges of identifying interactions between genetic mutations (epistasis) and mutations of loci affecting more than one trait (pleiotropy) as underlying causes of cross-phenotype associations; these challenges can be overcome using post-GWAS analysis. Genetic similarities between phenotypes that can be revealed using post-GWAS analysis are also discussed. In summary, different methodologies of post-GWAS analysis are now available, enhancing the value of information obtained from GWAS results, and facilitating application in both humans and nonhuman species. However, precise methods still need to be developed to overcome challenges in the field and uncover the genetic underpinnings of complex traits.
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52
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Reply to: On powerful GWAS in admixed populations. Nat Genet 2021; 53:1634-1635. [PMID: 34824479 DOI: 10.1038/s41588-021-00975-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2021] [Accepted: 10/22/2021] [Indexed: 11/08/2022]
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53
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Li B, Ritchie MD. From GWAS to Gene: Transcriptome-Wide Association Studies and Other Methods to Functionally Understand GWAS Discoveries. Front Genet 2021; 12:713230. [PMID: 34659337 PMCID: PMC8515949 DOI: 10.3389/fgene.2021.713230] [Citation(s) in RCA: 41] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2021] [Accepted: 07/27/2021] [Indexed: 12/12/2022] Open
Abstract
Since their inception, genome-wide association studies (GWAS) have identified more than a hundred thousand single nucleotide polymorphism (SNP) loci that are associated with various complex human diseases or traits. The majority of GWAS discoveries are located in non-coding regions of the human genome and have unknown functions. The valley between non-coding GWAS discoveries and downstream affected genes hinders the investigation of complex disease mechanism and the utilization of human genetics for the improvement of clinical care. Meanwhile, advances in high-throughput sequencing technologies reveal important genomic regulatory roles that non-coding regions play in the transcriptional activities of genes. In this review, we focus on data integrative bioinformatics methods that combine GWAS with functional genomics knowledge to identify genetically regulated genes. We categorize and describe two types of data integrative methods. First, we describe fine-mapping methods. Fine-mapping is an exploratory approach that calibrates likely causal variants underneath GWAS signals. Fine-mapping methods connect GWAS signals to potentially causal genes through statistical methods and/or functional annotations. Second, we discuss gene-prioritization methods. These are hypothesis generating approaches that evaluate whether genetic variants regulate genes via certain genetic regulatory mechanisms to influence complex traits, including colocalization, mendelian randomization, and the transcriptome-wide association study (TWAS). TWAS is a gene-based association approach that investigates associations between genetically regulated gene expression and complex diseases or traits. TWAS has gained popularity over the years due to its ability to reduce multiple testing burden in comparison to other variant-based analytic approaches. Multiple types of TWAS methods have been developed with varied methodological designs and biological hypotheses over the past 5 years. We dive into discussions of how TWAS methods differ in many aspects and the challenges that different TWAS methods face. Overall, TWAS is a powerful tool for identifying complex trait-associated genes. With the advent of single-cell sequencing, chromosome conformation capture, gene editing technologies, and multiplexing reporter assays, we are expecting a more comprehensive understanding of genomic regulation and genetically regulated genes underlying complex human diseases and traits in the future.
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Affiliation(s)
- Binglan Li
- Department of Biomedical Data Science, Stanford University, Stanford, CA, United States
| | - Marylyn D Ritchie
- Department of Genetics, University of Pennsylvania, Philadelphia, PA, United States.,Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, PA, United States
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54
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Adam Y, Samtal C, Brandenburg JT, Falola O, Adebiyi E. Performing post-genome-wide association study analysis: overview, challenges and recommendations. F1000Res 2021; 10:1002. [PMID: 35222990 PMCID: PMC8847724 DOI: 10.12688/f1000research.53962.1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 09/22/2021] [Indexed: 12/17/2022] Open
Abstract
Genome-wide association studies (GWAS) provide huge information on statistically significant single-nucleotide polymorphisms (SNPs) associated with various human complex traits and diseases. By performing GWAS studies, scientists have successfully identified the association of hundreds of thousands to millions of SNPs to a single phenotype. Moreover, the association of some SNPs with rare diseases has been intensively tested. However, classic GWAS studies have not yet provided solid, knowledgeable insight into functional and biological mechanisms underlying phenotypes or mechanisms of diseases. Therefore, several post-GWAS (pGWAS) methods have been recommended. Currently, there is no simple scientific document to provide a quick guide for performing pGWAS analysis. pGWAS is a crucial step for a better understanding of the biological machinery beyond the SNPs. Here, we provide an overview to performing pGWAS analysis and demonstrate the challenges behind each method. Furthermore, we direct readers to key articles for each pGWAS method and present the overall issues in pGWAS analysis. Finally, we include a custom pGWAS pipeline to guide new users when performing their research.
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Affiliation(s)
- Yagoub Adam
- Covenant University Bioinformatics Research (CUBRe), Covenant University, Ota, Ogun, 112233, Nigeria
| | - Chaimae Samtal
- Laboratory of Biotechnology, Environment, Agri-food and Health, Sidi Mohammed Ben Abdellah University, Fez, Fez-Meknes, 30000, Morocco
| | - Jean-tristan Brandenburg
- Sydney Brenner Institute for Molecular Bioscience (SBIMB), University of the Witwatersrand, Johannesburg, South Africa
| | - Oluwadamilare Falola
- Laboratory of Biotechnology, Environment, Agri-food and Health, Sidi Mohammed Ben Abdellah University, Fez, Fez-Meknes, 30000, Morocco
| | - Ezekiel Adebiyi
- Covenant University Bioinformatics Research (CUBRe), Covenant University, Ota, Ogun, 112233, Nigeria
- Computer & Information Sciences, Covenant University, Ota, Ogun, 112233, Nigeria
- Covenant Applied Informatics and Communication Africa Centre of Excellence, Covenant University, Ota, Ogun, 112233, Nigeria
- Applied Bioinformatics Division, German Cancer Center DKFZ - Heidelberg University, Heidelberg, Baden-Württemberg, 69120, Germany
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55
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Orrù V, Steri M, Cucca F, Fiorillo E. Application of Genetic Studies to Flow Cytometry Data and Its Impact on Therapeutic Intervention for Autoimmune Disease. Front Immunol 2021; 12:714461. [PMID: 34531863 PMCID: PMC8438121 DOI: 10.3389/fimmu.2021.714461] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2021] [Accepted: 08/13/2021] [Indexed: 12/03/2022] Open
Abstract
In recent years, systematic genome-wide association studies of quantitative immune cell traits, represented by circulating levels of cell subtypes established by flow cytometry, have revealed numerous association signals, a large fraction of which overlap perfectly with genetic signals associated with autoimmune diseases. By identifying further overlaps with association signals influencing gene expression and cell surface protein levels, it has also been possible, in several cases, to identify causal genes and infer candidate proteins affecting immune cell traits linked to autoimmune disease risk. Overall, these results provide a more detailed picture of how genetic variation affects the human immune system and autoimmune disease risk. They also highlight druggable proteins in the pathogenesis of autoimmune diseases; predict the efficacy and side effects of existing therapies; provide new indications for use for some of them; and optimize the research and development of new, more effective and safer treatments for autoimmune diseases. Here we review the genetic-driven approach that couples systematic multi-parametric flow cytometry with high-resolution genetics and transcriptomics to identify endophenotypes of autoimmune diseases for the development of new therapies.
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Affiliation(s)
- Valeria Orrù
- Institute for Genetic and Biomedical Research, National Research Council (CNR), Sardinia, Italy
| | - Maristella Steri
- Institute for Genetic and Biomedical Research, National Research Council (CNR), Sardinia, Italy
| | - Francesco Cucca
- Institute for Genetic and Biomedical Research, National Research Council (CNR), Sardinia, Italy.,Department of Biomedical Sciences, University of Sassari, Sassari, Italy
| | - Edoardo Fiorillo
- Institute for Genetic and Biomedical Research, National Research Council (CNR), Sardinia, Italy
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56
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Claringbould A, Zaugg JB. Enhancers in disease: molecular basis and emerging treatment strategies. Trends Mol Med 2021; 27:1060-1073. [PMID: 34420874 DOI: 10.1016/j.molmed.2021.07.012] [Citation(s) in RCA: 75] [Impact Index Per Article: 25.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2021] [Revised: 07/22/2021] [Accepted: 07/26/2021] [Indexed: 02/07/2023]
Abstract
Enhancers are genomic sequences that play a key role in regulating tissue-specific gene expression levels. An increasing number of diseases are linked to impaired enhancer function through chromosomal rearrangement, genetic variation within enhancers, or epigenetic modulation. Here, we review how these enhancer disruptions have recently been implicated in congenital disorders, cancers, and common complex diseases and address the implications for diagnosis and treatment. Although further fundamental research into enhancer function, target genes, and context is required, enhancer-targeting drugs and gene editing approaches show great therapeutic promise for a range of diseases.
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Affiliation(s)
- Annique Claringbould
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, Meyerhofstraße 1, 69117 Heidelberg, Germany
| | - Judith B Zaugg
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, Meyerhofstraße 1, 69117 Heidelberg, Germany.
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57
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Fisher V, Sebastiani P, Cupples LA, Liu CT. ANNORE: Genetic fine mapping with functional annotation. Hum Mol Genet 2021; 31:32-40. [PMID: 34302344 DOI: 10.1093/hmg/ddab210] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2021] [Revised: 06/30/2021] [Accepted: 07/19/2021] [Indexed: 11/13/2022] Open
Abstract
Genome-wide association studies (GWAS) have successfully identified loci of the human genome implicated in numerous complex traits. However, the limitations of this study design make it difficult to identify specific causal variants or biological mechanisms of association. We propose a novel method, AnnoRE, which uses GWAS summary statistics, local correlation structure among genotypes, and functional annotation from external databases to prioritize the most plausible causal SNPs in each trait-associated locus. Our proposed method improves upon previous fine mapping approaches by estimating the effects of functional annotation from genome-wide summary statistics, allowing for the inclusion of many annotation categories. By implementing a multiple regression model with differential shrinkage via random effects, we avoid reductive assumptions on the number of causal SNPs per locus. Application of this method to a large GWAS meta-analysis of body mass index identified six loci with significant evidence in favor of one or more variants. In an additional 24 loci, one or two variants were strongly prioritized over others in the region. The use of functional annotation in genetic fine mapping studies helps to distinguish between variants in high LD, and to identify promising targets for follow-up studies.
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Affiliation(s)
- Virginia Fisher
- Department of Biostatistics, Boston University School of Public Health, Boston, MA 02118, USA
| | - Paola Sebastiani
- Department of Biostatistics, Boston University School of Public Health, Boston, MA 02118, USA.,Tufts Medical Center, Boston, MA 02111, USA
| | - L Adrienne Cupples
- Department of Biostatistics, Boston University School of Public Health, Boston, MA 02118, USA
| | - Ching-Ti Liu
- Department of Biostatistics, Boston University School of Public Health, Boston, MA 02118, USA
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58
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Quantitative neurogenetics: applications in understanding disease. Biochem Soc Trans 2021; 49:1621-1631. [PMID: 34282824 DOI: 10.1042/bst20200732] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2021] [Revised: 06/11/2021] [Accepted: 06/21/2021] [Indexed: 12/31/2022]
Abstract
Neurodevelopmental and neurodegenerative disorders (NNDs) are a group of conditions with a broad range of core and co-morbidities, associated with dysfunction of the central nervous system. Improvements in high throughput sequencing have led to the detection of putative risk genetic loci for NNDs, however, quantitative neurogenetic approaches need to be further developed in order to establish causality and underlying molecular genetic mechanisms of pathogenesis. Here, we discuss an approach for prioritizing the contribution of genetic risk loci to complex-NND pathogenesis by estimating the possible impacts of these loci on gene regulation. Furthermore, we highlight the use of a tissue-specificity gene expression index and the application of artificial intelligence (AI) to improve the interpretation of the role of genetic risk elements in NND pathogenesis. Given that NND symptoms are associated with brain dysfunction, risk loci with direct, causative actions would comprise genes with essential functions in neural cells that are highly expressed in the brain. Indeed, NND risk genes implicated in brain dysfunction are disproportionately enriched in the brain compared with other tissues, which we refer to as brain-specific expressed genes. In addition, the tissue-specificity gene expression index can be used as a handle to identify non-brain contexts that are involved in NND pathogenesis. Lastly, we discuss how using an AI approach provides the opportunity to integrate the biological impacts of risk loci to identify those putative combinations of causative relationships through which genetic factors contribute to NND pathogenesis.
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59
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Moon CY, Schilder BM, Raj T, Huang KL. Phenome-wide and expression quantitative trait locus associations of coronavirus disease 2019 genetic risk loci. iScience 2021; 24:102550. [PMID: 34027315 PMCID: PMC8129787 DOI: 10.1016/j.isci.2021.102550] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2020] [Revised: 03/24/2021] [Accepted: 05/13/2021] [Indexed: 12/15/2022] Open
Abstract
While several genes and clinical traits have been associated with higher risk of severe coronavirus disease 2019 (COVID-19), how host genetic variants may interact with these parameters and contribute to severe disease is still unclear. Herein, we performed phenome-wide association study, tissue and immune-cell-specific expression quantitative trait locus (eQTL)/splicing quantitative trait locus, and colocalization analyses for genetic risk loci suggestively associated with severe COVID-19 with respiratory failure. Thirteen phenotypes/traits were associated with the severe COVID-19-associated loci at the genome-wide significance threshold, including monocyte counts, fat metabolism traits, and fibrotic idiopathic interstitial pneumonia. In addition, we identified tissue and immune subtype-specific eQTL associations affecting 48 genes, including several ones that may directly impact host immune responses, colocalized with the severe COVID-19 genome-wide association study associations, and showed altered expression in single-cell transcriptomes. Collectively, our work demonstrates that host genetic variations associated with multiple genes and traits show genetic pleiotropy with severe COVID-19 and may inform disease etiology.
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Affiliation(s)
- Chang Yoon Moon
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Brian M. Schilder
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Nash Family Department of Neuroscience & Friedman Brain Institute, New York, NY, USA
- Ronald M. Loeb Center for Alzheimer's disease, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Estelle and Daniel Maggin Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Towfique Raj
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Nash Family Department of Neuroscience & Friedman Brain Institute, New York, NY, USA
- Ronald M. Loeb Center for Alzheimer's disease, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Estelle and Daniel Maggin Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Kuan-lin Huang
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
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60
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Su C, Argenziano M, Lu S, Pippin JA, Pahl MC, Leonard ME, Cousminer DL, Johnson ME, Lasconi C, Wells AD, Chesi A, Grant SFA. 3D promoter architecture re-organization during iPSC-derived neuronal cell differentiation implicates target genes for neurodevelopmental disorders. Prog Neurobiol 2021; 201:102000. [PMID: 33545232 PMCID: PMC8096691 DOI: 10.1016/j.pneurobio.2021.102000] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2020] [Revised: 12/07/2020] [Accepted: 01/23/2021] [Indexed: 12/27/2022]
Abstract
Neurodevelopmental disorders are thought to arise from interrupted development of the brain at an early age. Genome-wide association studies (GWAS) have identified hundreds of loci associated with susceptibility to neurodevelopmental disorders; however, which noncoding variants regulate which genes at these loci is often unclear. To implicate neuronal GWAS effector genes, we performed an integrated analysis of transcriptomics, epigenomics and chromatin conformation changes during the development from Induced pluripotent stem cell-derived neuronal progenitor cells (NPCs) into neurons using a combination of high-resolution promoter-focused Capture-C, ATAC-seq and RNA-seq. We observed that gene expression changes during the NPC-to-neuron transition were highly dependent on both promoter accessibility changes and long-range interactions which connect distal cis-regulatory elements (enhancer or silencers) to developmental-stage-specific genes. These genome-scale promoter-cis-regulatory-element atlases implicated 454 neurodevelopmental disorder-associated, putative causal variants mapping to 600 distal targets. These putative effector genes were significantly enriched for pathways involved in the regulation of neuronal development and chromatin organization, with 27 % expressed in a stage-specific manner. The intersection of open chromatin and chromatin conformation revealed development-stage-specific gene regulatory architectures during neuronal differentiation, providing a rich resource to aid characterization of the genetic and developmental basis of neurodevelopmental disorders.
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Affiliation(s)
- Chun Su
- Division of Human Genetics, The Children's Hospital of Philadelphia, 3615 Civic Center Boulevard, Philadelphia, PA, United States
| | - Mariana Argenziano
- Heart Institute, University of South Florida, 560 Channelside Dr, Tampa FL 33602, United States
| | - Sumei Lu
- Division of Human Genetics, The Children's Hospital of Philadelphia, 3615 Civic Center Boulevard, Philadelphia, PA, United States
| | - James A Pippin
- Division of Human Genetics, The Children's Hospital of Philadelphia, 3615 Civic Center Boulevard, Philadelphia, PA, United States
| | - Matthew C Pahl
- Division of Human Genetics, The Children's Hospital of Philadelphia, 3615 Civic Center Boulevard, Philadelphia, PA, United States
| | - Michelle E Leonard
- Division of Human Genetics, The Children's Hospital of Philadelphia, 3615 Civic Center Boulevard, Philadelphia, PA, United States
| | - Diana L Cousminer
- Division of Human Genetics, The Children's Hospital of Philadelphia, 3615 Civic Center Boulevard, Philadelphia, PA, United States
| | - Matthew E Johnson
- Division of Human Genetics, The Children's Hospital of Philadelphia, 3615 Civic Center Boulevard, Philadelphia, PA, United States
| | - Chiara Lasconi
- Division of Human Genetics, The Children's Hospital of Philadelphia, 3615 Civic Center Boulevard, Philadelphia, PA, United States
| | - Andrew D Wells
- Department of Pathology, The Children's Hospital of Philadelphia, 3615 Civic Center Boulevard, Philadelphia, PA, United States; Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, 3615 Civic Center Boulevard, Philadelphia, PA, United States
| | - Alessandra Chesi
- Division of Human Genetics, The Children's Hospital of Philadelphia, 3615 Civic Center Boulevard, Philadelphia, PA, United States
| | - Struan F A Grant
- Division of Human Genetics, The Children's Hospital of Philadelphia, 3615 Civic Center Boulevard, Philadelphia, PA, United States; Division of Diabetes and Endocrinology, The Children's Hospital of Philadelphia, 3615 Civic Center Boulevard, Philadelphia, PA, United States; Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, 3615 Civic Center Boulevard, Philadelphia, PA, United States; Department of Genetics, Perelman School of Medicine, University of Pennsylvania, 3615 Civic Center Boulevard, Philadelphia, PA, United States.
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61
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Levran O, Randesi M, Adelson M, Kreek MJ. OPRD1 SNPs associated with opioid addiction are cis-eQTLs for the phosphatase and actin regulator 4 gene, PHACTR4, a mediator of cytoskeletal dynamics. Transl Psychiatry 2021; 11:316. [PMID: 34031368 PMCID: PMC8144180 DOI: 10.1038/s41398-021-01439-y] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/11/2021] [Revised: 04/29/2021] [Accepted: 05/06/2021] [Indexed: 12/16/2022] Open
Abstract
Several OPRD1 intronic variants were associated with opioid addiction (OD) in a population-specific manner. This follow-up study aims to further characterize the OPRD1 haplotype pattern of the risk variants in different populations and apply in silico analysis to identify potential causal variants. A population-specific haplotype pattern was revealed based on six OPRD1 eQTL SNPs and five common haplotypes were identified in a sample of European ancestry (CEU). A European-specific haplotype ('Hap 3') that includes SNPs previously associated with OD and is tagged by SNP rs2236861 is more common in subjects with OD. It is quite common (10%) in CEU but is absent in the African sample (YRI) and extends upstream of OPRD1. SNP rs2236857 is most probably a non-causal variant in LD with the causal SNP/s in a population-specific manner. The study provides an explanation for the lack of association in African Americans, despite its high frequency in this population. OD samples homozygous for 'Hap 3' were reanalyzed using a denser coverage of the region and revealed at least 25 potentially regulatory SNPs in high LD. Notably, GTEx data indicate that some of the SNPs are eQTLs for the upstream phosphatase and actin regulator 4 (PHACTR4), in the cortex, and others are eQTLs for OPRD1 and the upstream lncRNA ENSG00000270605, in the cerebellum. The study highlights the limitation of single SNP analysis and the sensitivity of association studies of OPRD1 to a genetic background. It proposes a long-range functional connection between OPRD1 and PHACTR4. PHACTR4, a mediator of cytoskeletal dynamics, may contribute to drug addiction by modulating synaptic plasticity.
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Affiliation(s)
- Orna Levran
- The Laboratory of the Biology of Addictive Diseases, The Rockefeller University, New York, NY, USA.
| | - Matthew Randesi
- grid.134907.80000 0001 2166 1519The Laboratory of the Biology of Addictive Diseases, The Rockefeller University, New York, NY USA
| | - Miriam Adelson
- grid.134907.80000 0001 2166 1519The Laboratory of the Biology of Addictive Diseases, The Rockefeller University, New York, NY USA ,Dr. Miriam and Sheldon G. Adelson Clinic for Drug Abuse Treatment and Research, Las Vegas, NV USA
| | - Mary Jeanne Kreek
- grid.134907.80000 0001 2166 1519The Laboratory of the Biology of Addictive Diseases, The Rockefeller University, New York, NY USA
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62
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Feng Y, McQuillan MA, Tishkoff SA. Evolutionary genetics of skin pigmentation in African populations. Hum Mol Genet 2021; 30:R88-R97. [PMID: 33438000 PMCID: PMC8117430 DOI: 10.1093/hmg/ddab007] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2020] [Revised: 01/07/2021] [Accepted: 01/07/2021] [Indexed: 12/14/2022] Open
Abstract
Skin color is a highly heritable human trait, and global variation in skin pigmentation has been shaped by natural selection, migration and admixture. Ethnically diverse African populations harbor extremely high levels of genetic and phenotypic diversity, and skin pigmentation varies widely across Africa. Recent genome-wide genetic studies of skin pigmentation in African populations have advanced our understanding of pigmentation biology and human evolutionary history. For example, novel roles in skin pigmentation for loci near MFSD12 and DDB1 have recently been identified in African populations. However, due to an underrepresentation of Africans in human genetic studies, there is still much to learn about the evolutionary genetics of skin pigmentation. Here, we summarize recent progress in skin pigmentation genetics in Africans and discuss the importance of including more ethnically diverse African populations in future genetic studies. In addition, we discuss methods for functional validation of adaptive variants related to skin pigmentation.
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Affiliation(s)
- Yuanqing Feng
- Department of Genetics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Michael A McQuillan
- Department of Genetics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Sarah A Tishkoff
- Department of Genetics, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Biology, University of Pennsylvania, Philadelphia, PA 19104, USA
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63
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Zhu A, Matoba N, Wilson EP, Tapia AL, Li Y, Ibrahim JG, Stein JL, Love MI. MRLocus: Identifying causal genes mediating a trait through Bayesian estimation of allelic heterogeneity. PLoS Genet 2021; 17:e1009455. [PMID: 33872308 PMCID: PMC8084342 DOI: 10.1371/journal.pgen.1009455] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2020] [Revised: 04/29/2021] [Accepted: 02/26/2021] [Indexed: 11/18/2022] Open
Abstract
Expression quantitative trait loci (eQTL) studies are used to understand the regulatory function of non-coding genome-wide association study (GWAS) risk loci, but colocalization alone does not demonstrate a causal relationship of gene expression affecting a trait. Evidence for mediation, that perturbation of gene expression in a given tissue or developmental context will induce a change in the downstream GWAS trait, can be provided by two-sample Mendelian Randomization (MR). Here, we introduce a new statistical method, MRLocus, for Bayesian estimation of the gene-to-trait effect from eQTL and GWAS summary data for loci with evidence of allelic heterogeneity, that is, containing multiple causal variants. MRLocus makes use of a colocalization step applied to each nearly-LD-independent eQTL, followed by an MR analysis step across eQTLs. Additionally, our method involves estimation of the extent of allelic heterogeneity through a dispersion parameter, indicating variable mediation effects from each individual eQTL on the downstream trait. Our method is evaluated against other state-of-the-art methods for estimation of the gene-to-trait mediation effect, using an existing simulation framework. In simulation, MRLocus often has the highest accuracy among competing methods, and in each case provides more accurate estimation of uncertainty as assessed through interval coverage. MRLocus is then applied to five candidate causal genes for mediation of particular GWAS traits, where gene-to-trait effects are concordant with those previously reported. We find that MRLocus's estimation of the causal effect across eQTLs within a locus provides useful information for determining how perturbation of gene expression or individual regulatory elements will affect downstream traits. The MRLocus method is implemented as an R package available at https://mikelove.github.io/mrlocus.
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Affiliation(s)
- Anqi Zhu
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
| | - Nana Matoba
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
- UNC Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
| | - Emma P. Wilson
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
| | - Amanda L. Tapia
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
| | - Yun Li
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
- Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
| | - Joseph G. Ibrahim
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
| | - Jason L. Stein
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
- UNC Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
| | - Michael I. Love
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
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Rao S, Yao Y, Bauer DE. Editing GWAS: experimental approaches to dissect and exploit disease-associated genetic variation. Genome Med 2021; 13:41. [PMID: 33691767 PMCID: PMC7948363 DOI: 10.1186/s13073-021-00857-3] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2020] [Accepted: 02/12/2021] [Indexed: 12/17/2022] Open
Abstract
Genome-wide association studies (GWAS) have uncovered thousands of genetic variants that influence risk for human diseases and traits. Yet understanding the mechanisms by which these genetic variants, mainly noncoding, have an impact on associated diseases and traits remains a significant hurdle. In this review, we discuss emerging experimental approaches that are being applied for functional studies of causal variants and translational advances from GWAS findings to disease prevention and treatment. We highlight the use of genome editing technologies in GWAS functional studies to modify genomic sequences, with proof-of-principle examples. We discuss the challenges in interrogating causal variants, points for consideration in experimental design and interpretation of GWAS locus mechanisms, and the potential for novel therapeutic opportunities. With the accumulation of knowledge of functional genetics, therapeutic genome editing based on GWAS discoveries will become increasingly feasible.
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Affiliation(s)
- Shuquan Rao
- Division of Hematology/Oncology, Boston Children's Hospital; Department of Pediatric Oncology, Dana-Farber Cancer Institute; Harvard Stem Cell Institute; Broad Institute; Department of Pediatrics, Harvard Medical School, Boston, MA, USA.
| | - Yao Yao
- Division of Hematology/Oncology, Boston Children's Hospital; Department of Pediatric Oncology, Dana-Farber Cancer Institute; Harvard Stem Cell Institute; Broad Institute; Department of Pediatrics, Harvard Medical School, Boston, MA, USA
- School of Basic Medicine, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Daniel E Bauer
- Division of Hematology/Oncology, Boston Children's Hospital; Department of Pediatric Oncology, Dana-Farber Cancer Institute; Harvard Stem Cell Institute; Broad Institute; Department of Pediatrics, Harvard Medical School, Boston, MA, USA.
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Frontiers in Dissecting and Managing Brassica Diseases: From Reference-Based RGA Candidate Identification to Building Pan-RGAomes. Int J Mol Sci 2020; 21:ijms21238964. [PMID: 33255840 PMCID: PMC7728316 DOI: 10.3390/ijms21238964] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2020] [Revised: 11/23/2020] [Accepted: 11/23/2020] [Indexed: 02/06/2023] Open
Abstract
The Brassica genus contains abundant economically important vegetable and oilseed crops, which are under threat of diseases caused by fungal, bacterial and viral pathogens. Resistance gene analogues (RGAs) are associated with quantitative and qualitative disease resistance and the identification of candidate RGAs associated with disease resistance is crucial for understanding the mechanism and management of diseases through breeding. The availability of Brassica genome assemblies has greatly facilitated reference-based quantitative trait loci (QTL) mapping for disease resistance. In addition, pangenomes, which characterise both core and variable genes, have been constructed for B. rapa, B. oleracea and B. napus. Genome-wide characterisation of RGAs using conserved domains and motifs in reference genomes and pangenomes reveals their clustered arrangements and presence of structural variations. Here, we comprehensively review RGA identification in important Brassica genome and pangenome assemblies. Comparison of the RGAs in QTL between resistant and susceptible individuals allows for efficient identification of candidate disease resistance genes. However, the reference-based QTL mapping and RGA candidate identification approach is restricted by the under-represented RGA diversity characterised in the limited number of Brassica assemblies. The species-wide repertoire of RGAs make up the pan-resistance gene analogue genome (pan-RGAome). Building a pan-RGAome, through either whole genome resequencing or resistance gene enrichment sequencing, would effectively capture RGA diversity, greatly expanding breeding resources that can be utilised for crop improvement.
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Post-GWAS knowledge gap: the how, where, and when. NPJ PARKINSONS DISEASE 2020; 6:23. [PMID: 32964108 PMCID: PMC7481221 DOI: 10.1038/s41531-020-00125-y] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/23/2020] [Accepted: 08/13/2020] [Indexed: 01/05/2023]
Abstract
Genetic risk for complex diseases very rarely reflects only Mendelian-inherited phenotypes where single-gene mutations can be followed in families by linkage analysis. More commonly, a large set of low-penetrance, small effect-size variants combine to confer risk; they are normally revealed in genome-wide association studies (GWAS), which compare large population groups. Whereas Mendelian inheritance points toward disease mechanisms arising from the mutated genes, in the case of GWAS signals, the effector proteins and even general risk mechanism are mostly unknown. Instead, the utility of GWAS currently lies primarily in predictive and diagnostic information. Although an amazing body of GWAS-based knowledge now exists, we advocate for more funding towards the exploration of the fundamental biology in post-GWAS studies; this research will bring us closer to causality and risk gene identification. Using Parkinson's Disease as an example, we ask, how, where, and when do risk loci contribute to disease?
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Calender A, Weichhart T, Valeyre D, Pacheco Y. Current Insights in Genetics of Sarcoidosis: Functional and Clinical Impacts. J Clin Med 2020; 9:E2633. [PMID: 32823753 PMCID: PMC7465171 DOI: 10.3390/jcm9082633] [Citation(s) in RCA: 39] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2020] [Revised: 08/05/2020] [Accepted: 08/11/2020] [Indexed: 12/17/2022] Open
Abstract
Sarcoidosis is a complex disease that belongs to the vast group of autoinflammatory disorders, but the etiological mechanisms of which are not known. At the crosstalk of environmental, infectious, and genetic factors, sarcoidosis is a multifactorial disease that requires a multidisciplinary approach for which genetic research, in particular, next generation sequencing (NGS) tools, has made it possible to identify new pathways and propose mechanistic hypotheses. Codified treatments for the disease cannot always respond to the most progressive forms and the identification of new genetic and metabolic tracks is a challenge for the future management of the most severe patients. Here, we review the current knowledge regarding the genes identified by both genome wide association studies (GWAS) and whole exome sequencing (WES), as well the connection of these pathways with the current research on sarcoidosis immune-related disorders.
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Affiliation(s)
- Alain Calender
- Department of Molecular and Medical genetics, Hospices Civils de Lyon, University Hospital, 69500 Bron, France;
- CNRS UMR 5305, Tissue Biology and Therapeutic Engineering Laboratory, University Claude Bernard Lyon 1, 69007 Lyon, France
| | - Thomas Weichhart
- Center for Pathobiochemistry and Genetics, Institute of Medical Genetics, Medical University of Vienna, 1090 Vienna, Austria;
| | - Dominique Valeyre
- INSERM UMR 1272, Department of Pulmonology, Avicenne Hospital, University Sorbonne Paris Nord, Saint Joseph Hospital, AP-HP, 75014 Paris, France;
| | - Yves Pacheco
- Department of Molecular and Medical genetics, Hospices Civils de Lyon, University Hospital, 69500 Bron, France;
- CNRS UMR 5305, Tissue Biology and Therapeutic Engineering Laboratory, University Claude Bernard Lyon 1, 69007 Lyon, France
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Abstract
Atrial fibrillation is a common heart rhythm disorder that leads to an increased risk for stroke and heart failure. Atrial fibrillation is a complex disease with both environmental and genetic risk factors that contribute to the arrhythmia. Over the last decade, rapid progress has been made in identifying the genetic basis for this common condition. In this review, we provide an overview of the primary types of genetic analyses performed for atrial fibrillation, including linkage studies, genome-wide association studies, and studies of rare coding variation. With these results in mind, we aim to highlighting the existing knowledge gaps and future directions for atrial fibrillation genetics research.
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Affiliation(s)
- Carolina Roselli
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, MA, USA
- Department of Cardiology, University of Groningen, University Medical Center Groningen Groningen, the Netherlands
| | - Michiel Rienstra
- Department of Cardiology, University of Groningen, University Medical Center Groningen Groningen, the Netherlands
| | - Patrick T. Ellinor
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, MA, USA
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
- Cardiac Arrhythmia Service, Massachusetts General Hospital, Boston, MA, USA
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69
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Zheng Q, Ma Y, Chen S, Che Q, Chen D. The Integrated Landscape of Biological Candidate Causal Genes in Coronary Artery Disease. Front Genet 2020; 11:320. [PMID: 32373157 PMCID: PMC7186505 DOI: 10.3389/fgene.2020.00320] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2019] [Accepted: 03/18/2020] [Indexed: 12/27/2022] Open
Abstract
Background Genome-wide association studies (GWASs) have identified more than 150 genetic loci that demonstrate robust association with coronary artery disease (CAD). In contrast to the success of GWAS, the translation from statistical signals to biological mechanism and exploration of causal genes for drug development remain difficult, owing to the complexity of gene regulatory and linkage disequilibrium patterns. We aim to prioritize the plausible causal genes for CAD at a genome-wide level. Methods We integrated the latest GWAS summary statistics with other omics data from different layers and utilized eight different computational methods to predict CAD potential causal genes. The prioritized candidate genes were further characterized by pathway enrichment analysis, tissue-specific expression analysis, and pathway crosstalk analysis. Results Our analysis identified 55 high-confidence causal genes for CAD, among which 15 genes (LPL, COL4A2, PLG, CDKN2B, COL4A1, FES, FLT1, FN1, IL6R, LPA, PCSK9, PSRC1, SMAD3, SWAP70, and VAMP8) ranked the highest priority because of consistent evidence from different data-driven approaches. GO analysis showed that these plausible causal genes were enriched in lipid metabolic and extracellular regions. Tissue-specific enrichment analysis revealed that these genes were significantly overexpressed in adipose and liver tissues. Further, KEGG and crosstalk analysis also revealed several key pathways involved in the pathogenesis of CAD. Conclusion Our study delineated the landscape of CAD potential causal genes and highlighted several biological processes involved in CAD pathogenesis. Further studies and experimental validations of these genes may shed light on mechanistic insights into CAD development and provide potential drug targets for future therapeutics.
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Affiliation(s)
- Qiwen Zheng
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
| | - Yujia Ma
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
| | - Si Chen
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
| | - Qianzi Che
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
| | - Dafang Chen
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
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