1
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Smail C, Ge B, Keever-Keigher MR, Schwendinger-Schreck C, Cheung WA, Johnston JJ, Barrett C, Feldman K, Cohen ASA, Farrow EG, Thiffault I, Grundberg E, Pastinen T. Complex trait associations in rare diseases and impacts on Mendelian variant interpretation. Nat Commun 2024; 15:8196. [PMID: 39294130 PMCID: PMC11411080 DOI: 10.1038/s41467-024-52407-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2024] [Accepted: 09/05/2024] [Indexed: 09/20/2024] Open
Abstract
Emerging evidence implicates common genetic variation - aggregated into polygenic scores (PGS) - in the onset and phenotypic presentation of rare diseases. Here, we comprehensively map individual polygenic liability for 1102 open-source PGS in a cohort of 3059 probands enrolled in the Genomic Answers for Kids (GA4K) rare disease study, revealing widespread associations between rare disease phenotypes and PGSs for common complex diseases and traits, blood protein levels, and brain and other organ morphological measurements. Using this resource, we demonstrate increased polygenic liability in probands with an inherited candidate disease variant (VUS) compared to unaffected carrier parents. Further, we show an enrichment for large-effect rare variants in putative core PGS genes for associated complex traits. Overall, our study supports and expands on previous findings of complex trait associations in rare diseases, implicates polygenic liability as a potential mechanism underlying variable penetrance of candidate causal variants, and provides a framework for identifying novel candidate rare disease genes.
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Affiliation(s)
- Craig Smail
- Genomic Medicine Center, Department of Pediatrics, Children's Mercy Kansas City, Kansas City, USA.
- UKMC School of Medicine, University of Missouri Kansas City, Kansas City, USA.
| | - Bing Ge
- Department of Human Genetics, McGill University, Montreal, Canada
| | - Marissa R Keever-Keigher
- Genomic Medicine Center, Department of Pediatrics, Children's Mercy Kansas City, Kansas City, USA
| | | | - Warren A Cheung
- Genomic Medicine Center, Department of Pediatrics, Children's Mercy Kansas City, Kansas City, USA
| | - Jeffrey J Johnston
- Genomic Medicine Center, Department of Pediatrics, Children's Mercy Kansas City, Kansas City, USA
| | - Cassandra Barrett
- Genomic Medicine Center, Department of Pediatrics, Children's Mercy Kansas City, Kansas City, USA
| | - Keith Feldman
- UKMC School of Medicine, University of Missouri Kansas City, Kansas City, USA
- Health Outcomes and Health Services Research, Department of Pediatrics, Children's Mercy Kansas City, Kansas City, USA
| | - Ana S A Cohen
- Genomic Medicine Center, Department of Pediatrics, Children's Mercy Kansas City, Kansas City, USA
- UKMC School of Medicine, University of Missouri Kansas City, Kansas City, USA
- Department of Pathology and Laboratory Medicine, Children's Mercy Kansas City, Kansas City, USA
| | - Emily G Farrow
- Genomic Medicine Center, Department of Pediatrics, Children's Mercy Kansas City, Kansas City, USA
- UKMC School of Medicine, University of Missouri Kansas City, Kansas City, USA
- Department of Pediatrics, Children's Mercy Kansas City, Kansas City, USA
| | - Isabelle Thiffault
- Genomic Medicine Center, Department of Pediatrics, Children's Mercy Kansas City, Kansas City, USA
- UKMC School of Medicine, University of Missouri Kansas City, Kansas City, USA
- Department of Pathology and Laboratory Medicine, Children's Mercy Kansas City, Kansas City, USA
| | - Elin Grundberg
- Genomic Medicine Center, Department of Pediatrics, Children's Mercy Kansas City, Kansas City, USA
- UKMC School of Medicine, University of Missouri Kansas City, Kansas City, USA
| | - Tomi Pastinen
- Genomic Medicine Center, Department of Pediatrics, Children's Mercy Kansas City, Kansas City, USA.
- UKMC School of Medicine, University of Missouri Kansas City, Kansas City, USA.
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2
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Domingo J, Minaeva M, Morris JA, Ghatan S, Ziosi M, Sanjana NE, Lappalainen T. Non-linear transcriptional responses to gradual modulation of transcription factor dosage. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.01.582837. [PMID: 38464330 PMCID: PMC10925300 DOI: 10.1101/2024.03.01.582837] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/12/2024]
Abstract
Genomic loci associated with common traits and diseases are typically non-coding and likely impact gene expression, sometimes coinciding with rare loss-of-function variants in the target gene. However, our understanding of how gradual changes in gene dosage affect molecular, cellular, and organismal traits is currently limited. To address this gap, we induced gradual changes in gene expression of four genes using CRISPR activation and inactivation. Downstream transcriptional consequences of dosage modulation of three master trans-regulators associated with blood cell traits (GFI1B, NFE2, and MYB) were examined using targeted single-cell multimodal sequencing. We showed that guide tiling around the TSS is the most effective way to modulate cis gene expression across a wide range of fold-changes, with further effects from chromatin accessibility and histone marks that differ between the inhibition and activation systems. Our single-cell data allowed us to precisely detect subtle to large gene expression changes in dozens of trans genes, revealing that many responses to dosage changes of these three TFs are non-linear, including non-monotonic behaviours, even when constraining the fold-changes of the master regulators to a copy number gain or loss. We found that the dosage properties are linked to gene constraint and that some of these non-linear responses are enriched for disease and GWAS genes. Overall, our study provides a straightforward and scalable method to precisely modulate gene expression and gain insights into its downstream consequences at high resolution.
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Affiliation(s)
| | - Mariia Minaeva
- Science for Life Laboratory, Department of Gene Technology, KTH Royal Institute of Technology, Stockholm, Sweden
| | - John A Morris
- New York Genome Center, New York, NY 10013, USA
- Department of Biology, New York University, New York, NY 10003, USA
| | - Sam Ghatan
- New York Genome Center, New York, NY 10013, USA
| | | | - Neville E Sanjana
- New York Genome Center, New York, NY 10013, USA
- Department of Biology, New York University, New York, NY 10003, USA
| | - Tuuli Lappalainen
- New York Genome Center, New York, NY 10013, USA
- Science for Life Laboratory, Department of Gene Technology, KTH Royal Institute of Technology, Stockholm, Sweden
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3
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Trabelsi N, Othman H, Bedhioufi H, Chouk H, El Mabrouk H, Mahdouani M, Gribaa M, Saad A, H'mida D. Is Tunisia ready for precision medicine? Challenges of medical genomics within a LMIC healthcare system. J Community Genet 2024; 15:339-350. [PMID: 39080231 PMCID: PMC11411033 DOI: 10.1007/s12687-024-00722-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2023] [Accepted: 07/16/2024] [Indexed: 09/19/2024] Open
Abstract
As one of the key tools on the precision medicine workbench, high-throughput genetic testing has enormous promise for improving healthcare outcomes. Tunisia has made tremendous progress in acquiring and implementing the technology in the clinical context. However, current utilization does not ensure the whole range of benefits that high-throughput genomic testing provides which impedes the country's ability to move forward into the new era of precision medicine. This issue is primarily related to the current state of Tunisia's healthcare ecosystem and the sociological attributes of its population, creating numerous challenges that must be addressed. In the current review, we aimed to identify and highlight these challenges that may be prevalent in other low and middle-income countries. Essentially, they fall into three main categories that include the socio-economic landscape in Tunisia, which prevents citizens from engaging in precision medicine activities; the current settings of the healthcare system that lack or miss key components for the successful implementation of precision medicine practices; and the inability of the current infrastructure and resources to handle the various challenges related to genomic data and metadata. We also propose five pillar solutions as a framework for addressing all of these challenges, which could strengthen Tunisia's capability for effective precision medicine implementation in today's clinical environment.
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Affiliation(s)
- Narjes Trabelsi
- Department of genetics, Farhat Hached University Hospital, Ibn El Jazzar St, Sousse, 4000, Tunisia
- Laboratory of cytogenetics, molecular genetics and reproductive biology (LR03SP02), Farhat Hached University Hospital, Ibn El Jazzar St, Sousse, 4000, Tunisia
- Faculty of Medicine of Sousse, Mohamed Qaroui St, Sousse, 4002, Tunisia
| | - Houcemeddine Othman
- Department of genetics, Farhat Hached University Hospital, Ibn El Jazzar St, Sousse, 4000, Tunisia
- Laboratory of cytogenetics, molecular genetics and reproductive biology (LR03SP02), Farhat Hached University Hospital, Ibn El Jazzar St, Sousse, 4000, Tunisia
- Sydney Brenner Institute for Molecular Bioscience, University of the Witwatersrand, 9 jubilee Road, Parktown, Johannesburg, 2193, South Africa
| | - Hafsi Bedhioufi
- Interdisciplinary Laboratory of University-Business Management (LIGUE), (LR99ES24), Higher Institute of Accounting and Business Administration (ISCAE), University of La Manouba, Manouba university campus, La Manouba, 2010, Tunisia
| | - Hamza Chouk
- Laboratory of cytogenetics, molecular genetics and reproductive biology (LR03SP02), Farhat Hached University Hospital, Ibn El Jazzar St, Sousse, 4000, Tunisia
- Higher Institute of Biotechnology of Monastir, Taher Haddad St, Monastir, 5000, Tunisia
| | - Haïfa El Mabrouk
- Laboratory of cytogenetics, molecular genetics and reproductive biology (LR03SP02), Farhat Hached University Hospital, Ibn El Jazzar St, Sousse, 4000, Tunisia
- Higher Institute of Biotechnology of Monastir, Taher Haddad St, Monastir, 5000, Tunisia
| | - Marwa Mahdouani
- Laboratory of cytogenetics, molecular genetics and reproductive biology (LR03SP02), Farhat Hached University Hospital, Ibn El Jazzar St, Sousse, 4000, Tunisia
- Higher Institute of Biotechnology of Monastir, Taher Haddad St, Monastir, 5000, Tunisia
| | - Moez Gribaa
- Department of genetics, Farhat Hached University Hospital, Ibn El Jazzar St, Sousse, 4000, Tunisia
- Laboratory of cytogenetics, molecular genetics and reproductive biology (LR03SP02), Farhat Hached University Hospital, Ibn El Jazzar St, Sousse, 4000, Tunisia
- Faculty of Medicine of Sousse, Mohamed Qaroui St, Sousse, 4002, Tunisia
| | - Ali Saad
- Department of genetics, Farhat Hached University Hospital, Ibn El Jazzar St, Sousse, 4000, Tunisia
- Laboratory of cytogenetics, molecular genetics and reproductive biology (LR03SP02), Farhat Hached University Hospital, Ibn El Jazzar St, Sousse, 4000, Tunisia
- Faculty of Medicine of Sousse, Mohamed Qaroui St, Sousse, 4002, Tunisia
| | - Dorra H'mida
- Department of genetics, Farhat Hached University Hospital, Ibn El Jazzar St, Sousse, 4000, Tunisia.
- Laboratory of cytogenetics, molecular genetics and reproductive biology (LR03SP02), Farhat Hached University Hospital, Ibn El Jazzar St, Sousse, 4000, Tunisia.
- Faculty of Medicine of Sousse, Mohamed Qaroui St, Sousse, 4002, Tunisia.
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4
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Giel AS, Bigge J, Schumacher J, Maj C, Dasmeh P. Analysis of Evolutionary Conservation, Expression Level, and Genetic Association at a Genome-wide Scale Reveals Heterogeneity Across Polygenic Phenotypes. Mol Biol Evol 2024; 41:msae115. [PMID: 38865495 PMCID: PMC11247350 DOI: 10.1093/molbev/msae115] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2023] [Revised: 03/22/2024] [Accepted: 05/03/2024] [Indexed: 06/14/2024] Open
Abstract
Understanding the expression level and evolutionary rate of associated genes with human polygenic diseases provides crucial insights into their disease-contributing roles. In this work, we leveraged genome-wide association studies (GWASs) to investigate the relationship between the genetic association and both the evolutionary rate (dN/dS) and expression level of human genes associated with the two polygenic diseases of schizophrenia and coronary artery disease. Our findings highlight a distinct variation in these relationships between the two diseases. Genes associated with both diseases exhibit a significantly greater variance in evolutionary rate compared to those implicated in monogenic diseases. Expanding our analyses to 4,756 complex traits in the GWAS atlas database, we unraveled distinct trait categories with a unique interplay among the evolutionary rate, expression level, and genetic association of human genes. In most polygenic traits, highly expressed genes were more associated with the polygenic phenotypes compared to lowly expressed genes. About 69% of polygenic traits displayed a negative correlation between genetic association and evolutionary rate, while approximately 30% of these traits showed a positive correlation between genetic association and evolutionary rate. Our results demonstrate the presence of a spectrum among complex traits, shaped by natural selection. Notably, at opposite ends of this spectrum, we find metabolic traits being more likely influenced by purifying selection, and immunological traits that are more likely shaped by positive selection. We further established the polygenic evolution portal (evopolygen.de) as a resource for investigating relationships and generating hypotheses in the field of human polygenic trait evolution.
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Affiliation(s)
- Ann-Sophie Giel
- Centre for Human Genetics, Marburg University, Marburg, Germany
| | - Jessica Bigge
- Centre for Human Genetics, Marburg University, Marburg, Germany
| | | | - Carlo Maj
- Centre for Human Genetics, Marburg University, Marburg, Germany
| | - Pouria Dasmeh
- Centre for Human Genetics, Marburg University, Marburg, Germany
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA, USA
- Institute for Evolutionary Biology and Environmental Studies, University of Zurich, Zurich, Switzerland
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5
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Sakaue S, Weinand K, Isaac S, Dey KK, Jagadeesh K, Kanai M, Watts GFM, Zhu Z, Brenner MB, McDavid A, Donlin LT, Wei K, Price AL, Raychaudhuri S. Tissue-specific enhancer-gene maps from multimodal single-cell data identify causal disease alleles. Nat Genet 2024; 56:615-626. [PMID: 38594305 PMCID: PMC11456345 DOI: 10.1038/s41588-024-01682-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Accepted: 02/07/2024] [Indexed: 04/11/2024]
Abstract
Translating genome-wide association study (GWAS) loci into causal variants and genes requires accurate cell-type-specific enhancer-gene maps from disease-relevant tissues. Building enhancer-gene maps is essential but challenging with current experimental methods in primary human tissues. Here we developed a nonparametric statistical method, SCENT (single-cell enhancer target gene mapping), that models association between enhancer chromatin accessibility and gene expression in single-cell or nucleus multimodal RNA sequencing and ATAC sequencing data. We applied SCENT to 9 multimodal datasets including >120,000 single cells or nuclei and created 23 cell-type-specific enhancer-gene maps. These maps were highly enriched for causal variants in expression quantitative loci and GWAS for 1,143 diseases and traits. We identified likely causal genes for both common and rare diseases and linked somatic mutation hotspots to target genes. We demonstrate that application of SCENT to multimodal data from disease-relevant human tissue enables the scalable construction of accurate cell-type-specific enhancer-gene maps, essential for defining noncoding variant function.
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Affiliation(s)
- Saori Sakaue
- Center for Data Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Kathryn Weinand
- Center for Data Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Shakson Isaac
- Center for Data Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Kushal K Dey
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA, USA
| | - Karthik Jagadeesh
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA, USA
| | - Masahiro Kanai
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Computational and Integrative Biology, Massachusetts General Hospital, Boston, MA, USA
| | - Gerald F M Watts
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Zhu Zhu
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Michael B Brenner
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Andrew McDavid
- Department of Biostatistics and Computational Biology, University of Rochester Medical Center, Rochester, NY, USA
| | - Laura T Donlin
- Hospital for Special Surgery, New York, NY, USA
- Weill Cornell Medicine, New York, NY, USA
| | - Kevin Wei
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Alkes L Price
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA, USA
- Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA, USA
| | - Soumya Raychaudhuri
- Center for Data Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
- Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
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6
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Bellucci G, Buscarinu MC, Reniè R, Rinaldi V, Bigi R, Mechelli R, Romano S, Salvetti M, Ristori G. Disentangling multiple sclerosis phenotypes through Mendelian disorders: A network approach. Mult Scler 2024; 30:325-335. [PMID: 38333907 DOI: 10.1177/13524585241227119] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/10/2024]
Abstract
BACKGROUND The increasing knowledge about multiple sclerosis (MS) pathophysiology has reinforced the need for an improved description of disease phenotypes, connected to disease biology. Growing evidence indicates that complex diseases constitute phenotypical and genetic continuums with "simple," monogenic disorders, suggesting shared pathomechanisms. OBJECTIVES The objective of this study was to depict a novel MS phenotypical framework leveraging shared physiopathology with Mendelian diseases and to identify phenotype-specific candidate drugs. METHODS We performed an enrichment testing of MS-associated variants with Mendelian disorders genes. We defined a "MS-Mendelian network," further analyzed to define enriched phenotypic subnetworks and biological processes. Finally, a network-based drug screening was implemented. RESULTS Starting from 617 MS-associated loci, we showed a significant enrichment of monogenic diseases (p < 0.001). We defined an MS-Mendelian molecular network based on 331 genes and 486 related disorders, enriched in four phenotypic classes: neurologic, immunologic, metabolic, and visual. We prioritized a total of 503 drugs, of which 27 molecules active in 3/4 phenotypical subnetworks and 140 in subnetwork pairs. CONCLUSION The genetic architecture of MS contains the seeds of pathobiological multiplicities shared with immune, neurologic, metabolic and visual monogenic disorders. This result may inform future classifications of MS endophenotypes and support the development of new therapies in both MS and rare diseases.
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Affiliation(s)
- Gianmarco Bellucci
- Centre for Experimental Neurological Therapies (CENTERS), Department of Neurosciences, Mental Health and Sensory Organs, Sapienza University of Rome, Rome, Italy
| | - Maria Chiara Buscarinu
- Centre for Experimental Neurological Therapies (CENTERS), Department of Neurosciences, Mental Health and Sensory Organs, Sapienza University of Rome, Rome, Italy Neuroimmunology Unit, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) Fondazione Santa Lucia, Rome, Italy
| | - Roberta Reniè
- Centre for Experimental Neurological Therapies (CENTERS), Department of Neurosciences, Mental Health and Sensory Organs, Sapienza University of Rome, Rome, Italy
| | - Virginia Rinaldi
- Centre for Experimental Neurological Therapies (CENTERS), Department of Neurosciences, Mental Health and Sensory Organs, Sapienza University of Rome, Rome, Italy
| | - Rachele Bigi
- Centre for Experimental Neurological Therapies (CENTERS), Department of Neurosciences, Mental Health and Sensory Organs, Sapienza University of Rome, Rome, Italy
| | - Rosella Mechelli
- Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) San Raffaele Pisana, Rome, Italy San Raffaele Roma Open University, Rome, Italy
| | - Silvia Romano
- Centre for Experimental Neurological Therapies (CENTERS), Department of Neurosciences, Mental Health and Sensory Organs, Sapienza University of Rome, Rome, Italy
| | - Marco Salvetti
- Centre for Experimental Neurological Therapies (CENTERS), Department of Neurosciences, Mental Health and Sensory Organs, Sapienza University of Rome, Rome, Italy Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) Istituto Neurologico Mediterraneo Neuromed, Pozzilli, Italy
| | - Giovanni Ristori
- Centre for Experimental Neurological Therapies (CENTERS), Department of Neurosciences, Mental Health and Sensory Organs, Sapienza University of Rome, Rome, Italy Neuroimmunology Unit, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) Fondazione Santa Lucia, Rome, Italy
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7
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Smail C, Ge B, Keever-Keigher MR, Schwendinger-Schreck C, Cheung W, Johnston JJ, Barrett C, Feldman K, Cohen AS, Farrow EG, Thiffault I, Grundberg E, Pastinen T. Complex trait associations in rare diseases and impacts on Mendelian variant interpretation. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.01.10.24301111. [PMID: 38260377 PMCID: PMC10802745 DOI: 10.1101/2024.01.10.24301111] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Abstract
Emerging evidence implicates common genetic variation - aggregated into polygenic scores (PGS) - impacting the onset and phenotypic presentation of rare diseases. In this study, we quantified individual polygenic liability for 1,151 previously published PGS in a cohort of 2,374 probands enrolled in the Genomic Answers for Kids (GA4K) rare disease study, revealing widespread associations between rare disease phenotypes and PGSs for common complex diseases and traits, blood protein levels, and brain and other organ morphological measurements. We observed increased polygenic burden in probands with variants of unknown significance (VUS) compared to unaffected carrier parents. We further observed an enrichment in overlap between diagnostic and candidate rare disease genes and large-effect PGS genes. Overall, our study supports and expands on previous findings of complex trait associations in rare disease phenotypes and provides a framework for identifying novel candidate rare disease genes and in understanding variable penetrance of candidate Mendelian disease variants.
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Affiliation(s)
- Craig Smail
- Genomic Medicine Center, Department of Pediatrics, Children’s Mercy Kansas City, Kansas City, MO, USA
- UKMC School of Medicine, University of Missouri Kansas City, Kansas City, MO, USA
| | - Bing Ge
- Department of Human Genetics, McGill University, Montreal, Canada
| | - Marissa R. Keever-Keigher
- Genomic Medicine Center, Department of Pediatrics, Children’s Mercy Kansas City, Kansas City, MO, USA
| | - Carl Schwendinger-Schreck
- Genomic Medicine Center, Department of Pediatrics, Children’s Mercy Kansas City, Kansas City, MO, USA
| | - Warren Cheung
- Genomic Medicine Center, Department of Pediatrics, Children’s Mercy Kansas City, Kansas City, MO, USA
| | - Jeffrey J. Johnston
- Genomic Medicine Center, Department of Pediatrics, Children’s Mercy Kansas City, Kansas City, MO, USA
| | - Cassandra Barrett
- Genomic Medicine Center, Department of Pediatrics, Children’s Mercy Kansas City, Kansas City, MO, USA
| | | | - Keith Feldman
- UKMC School of Medicine, University of Missouri Kansas City, Kansas City, MO, USA
- Health Outcomes and Health Services Research, Department of Pediatrics, Children’s Mercy Kansas City, Kansas City, MO, USA
| | - Ana S.A. Cohen
- Genomic Medicine Center, Department of Pediatrics, Children’s Mercy Kansas City, Kansas City, MO, USA
- UKMC School of Medicine, University of Missouri Kansas City, Kansas City, MO, USA
- Department of Pathology and Laboratory Medicine, Children’s Mercy Kansas City, Kansas City, MO, USA
| | - Emily G. Farrow
- Genomic Medicine Center, Department of Pediatrics, Children’s Mercy Kansas City, Kansas City, MO, USA
- UKMC School of Medicine, University of Missouri Kansas City, Kansas City, MO, USA
- Department of Pediatrics, Children’s Mercy Kansas City, Kansas City, MO, USA
| | - Isabelle Thiffault
- Genomic Medicine Center, Department of Pediatrics, Children’s Mercy Kansas City, Kansas City, MO, USA
- UKMC School of Medicine, University of Missouri Kansas City, Kansas City, MO, USA
- Department of Pathology and Laboratory Medicine, Children’s Mercy Kansas City, Kansas City, MO, USA
| | - Elin Grundberg
- Genomic Medicine Center, Department of Pediatrics, Children’s Mercy Kansas City, Kansas City, MO, USA
- UKMC School of Medicine, University of Missouri Kansas City, Kansas City, MO, USA
| | - Tomi Pastinen
- Genomic Medicine Center, Department of Pediatrics, Children’s Mercy Kansas City, Kansas City, MO, USA
- UKMC School of Medicine, University of Missouri Kansas City, Kansas City, MO, USA
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8
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Ratajczak F, Joblin M, Hildebrandt M, Ringsquandl M, Falter-Braun P, Heinig M. Speos: an ensemble graph representation learning framework to predict core gene candidates for complex diseases. Nat Commun 2023; 14:7206. [PMID: 37938585 PMCID: PMC10632370 DOI: 10.1038/s41467-023-42975-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Accepted: 10/27/2023] [Indexed: 11/09/2023] Open
Abstract
Understanding phenotype-to-genotype relationships is a grand challenge of 21st century biology with translational implications. The recently proposed "omnigenic" model postulates that effects of genetic variation on traits are mediated by core-genes and -proteins whose activities mechanistically influence the phenotype, whereas peripheral genes encode a regulatory network that indirectly affects phenotypes via core gene products. Here, we develop a positive-unlabeled graph representation-learning ensemble-approach based on a nested cross-validation to predict core-like genes for diverse diseases using Mendelian disorder genes for training. Employing mouse knockout phenotypes for external validations, we demonstrate that core-like genes display several key properties of core genes: Mouse knockouts of genes corresponding to our most confident predictions give rise to relevant mouse phenotypes at rates on par with the Mendelian disorder genes, and all candidates exhibit core gene properties like transcriptional deregulation in disease and loss-of-function intolerance. Moreover, as predicted for core genes, our candidates are enriched for drug targets and druggable proteins. In contrast to Mendelian disorder genes the new core-like genes are enriched for druggable yet untargeted gene products, which are therefore attractive targets for drug development. Interpretation of the underlying deep learning model suggests plausible explanations for our core gene predictions in form of molecular mechanisms and physical interactions. Our results demonstrate the potential of graph representation learning for the interpretation of biological complexity and pave the way for studying core gene properties and future drug development.
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Affiliation(s)
- Florin Ratajczak
- Institute of Network Biology (INET), Molecular Targets and Therapeutics Center (MTTC), Helmholtz Munich, Neuherberg, Germany
| | | | | | | | - Pascal Falter-Braun
- Institute of Network Biology (INET), Molecular Targets and Therapeutics Center (MTTC), Helmholtz Munich, Neuherberg, Germany.
- Microbe-Host Interactions, Faculty of Biology, Ludwig-Maximilians-Universität München, Planegg-Martinsried, Germany.
| | - Matthias Heinig
- Institute of Computational Biology (ICB), Helmholtz Munich, Neuherberg, Germany.
- Department of Computer Science, TUM School of Computation, Information and Technology, Technical University of Munich, Garching, Germany.
- German Centre for Cardiovascular Research (DZHK), Munich Heart Association, Partner Site Munich, Berlin, Germany.
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Colvin A, Petukhova L. Inborn Errors of Immunity in Hidradenitis Suppurativa Pathogenesis and Disease Burden. J Clin Immunol 2023; 43:1040-1051. [PMID: 37204644 DOI: 10.1007/s10875-023-01518-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Accepted: 05/10/2023] [Indexed: 05/20/2023]
Abstract
Hidradenitis suppurativa (HS), also known as Verneuil's disease and acne inversa, is a prevalent, debilitating, and understudied inflammatory skin disease. It is marked by repeated bouts of pathological inflammation causing pain, hyperplasia, aberrant healing, and fibrosis. HS is difficult to manage and has many unmet medical needs. There is clinical and pharmacological evidence for extensive etiological heterogeneity with HS, suggesting that this clinical diagnosis is capturing a spectrum of disease entities. Human genetic studies provide robust insight into disease pathogenesis. They also can be used to resolve etiological heterogeneity and to identify drug targets. However, HS has not been extensively investigated with well-powered genetic studies. Here, we review what is known about its genetic architecture. We identify overlap in molecular, cellular, and clinical features between HS and inborn errors of immunity (IEI). This evidence indicates that HS may be an underrecognized component of IEI and suggests that undiagnosed IEI are present in HS cohorts. Inborn errors of immunity represent a salient opportunity for rapidly resolving the immunological landscape of HS pathogenesis, for prioritizing drug repurposing studies, and for improving the clinical management of HS.
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Affiliation(s)
- Annelise Colvin
- Vagelos College of Physicians and Surgeons, Columbia University, New York City, NY, USA
| | - Lynn Petukhova
- Department of Dermatology, Vagelos College of Physicians & Surgeons, Columbia University, New York City, NY, USA.
- Department of Epidemiology, Mailman School of Public Health, Columbia University, 722 West 168th Street, #527, York City, NY, USA.
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10
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Wainberg M, Andrews SJ, Tripathy SJ. Shared genetic risk loci between Alzheimer's disease and related dementias, Parkinson's disease, and amyotrophic lateral sclerosis. Alzheimers Res Ther 2023; 15:113. [PMID: 37328865 PMCID: PMC10273745 DOI: 10.1186/s13195-023-01244-3] [Citation(s) in RCA: 19] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2023] [Accepted: 05/16/2023] [Indexed: 06/18/2023]
Abstract
BACKGROUND Genome-wide association studies (GWAS) have indicated moderate genetic overlap between Alzheimer's disease (AD) and related dementias (ADRD), Parkinson's disease (PD) and amyotrophic lateral sclerosis (ALS), neurodegenerative disorders traditionally considered etiologically distinct. However, the specific genetic variants and loci underlying this overlap remain almost entirely unknown. METHODS We leveraged state-of-the-art GWAS for ADRD, PD, and ALS. For each pair of disorders, we examined each of the GWAS hits for one disorder and tested whether they were also significant for the other disorder, applying Bonferroni correction for the number of variants tested. This approach rigorously controls the family-wise error rate for both disorders, analogously to genome-wide significance. RESULTS Eleven loci with GWAS hits for one disorder were also associated with one or both of the other disorders: one with all three disorders (the MAPT/KANSL1 locus), five with ADRD and PD (near LCORL, CLU, SETD1A/KAT8, WWOX, and GRN), three with ADRD and ALS (near GPX3, HS3ST5/HDAC2/MARCKS, and TSPOAP1), and two with PD and ALS (near GAK/TMEM175 and NEK1). Two of these loci (LCORL and NEK1) were associated with an increased risk of one disorder but decreased risk of another. Colocalization analysis supported a shared causal variant between ADRD and PD at the CLU, WWOX, and LCORL loci, between ADRD and ALS at the TSPOAP1 locus, and between PD and ALS at the NEK1 and GAK/TMEM175 loci. To address the concern that ADRD is an imperfect proxy for AD and that the ADRD and PD GWAS have overlapping participants (nearly all of which are from the UK Biobank), we confirmed that all our ADRD associations had nearly identical odds ratios in an AD GWAS that excluded the UK Biobank, and all but one remained nominally significant (p < 0.05) for AD. CONCLUSIONS In one of the most comprehensive investigations to date of pleiotropy between neurodegenerative disorders, we identify eleven genetic risk loci shared among ADRD, PD, and ALS. These loci support lysosomal/autophagic dysfunction (GAK/TMEM175, GRN, KANSL1), neuroinflammation/immunity (TSPOAP1), oxidative stress (GPX3, KANSL1), and the DNA damage response (NEK1) as transdiagnostic processes underlying multiple neurodegenerative disorders.
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Affiliation(s)
- Michael Wainberg
- Centre for Addiction and Mental Health, 250 College Street, Toronto, M5T 1R8, Canada
| | - Shea J Andrews
- Department of Psychiatry & Behavioral Sciences, University of California San Francisco, San Francisco, 94143, USA
| | - Shreejoy J Tripathy
- Centre for Addiction and Mental Health, 250 College Street, Toronto, M5T 1R8, Canada.
- Institute of Medical Sciences, University of Toronto, Toronto, M5S 1A8, Canada.
- Department of Psychiatry, University of Toronto, Toronto, M5T 1R8, Canada.
- Department of Physiology, University of Toronto, Toronto, M5S 1A8, Canada.
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11
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Kuwabara Y, York AJ, Lin SC, Sargent MA, Grimes KM, Pirruccello JP, Molkentin JD. A human FLII gene variant alters sarcomeric actin thin filament length and predisposes to cardiomyopathy. Proc Natl Acad Sci U S A 2023; 120:e2213696120. [PMID: 37126682 PMCID: PMC10175844 DOI: 10.1073/pnas.2213696120] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Accepted: 04/07/2023] [Indexed: 05/03/2023] Open
Abstract
To better understand the genetic basis of heart disease, we identified a variant in the Flightless-I homolog (FLII) gene that generates a R1243H missense change and predisposes to cardiac remodeling across multiple previous human genome-wide association studies (GWAS). Since this gene is of unknown function in the mammalian heart we generated gain- and loss-of-function genetically altered mice, as well as knock-in mice with the syntenic R1245H amino acid substitution, which showed that Flii protein binds the sarcomeric actin thin filament and influences its length. Deletion of Flii from the heart, or mice with the R1245H amino acid substitution, show cardiomyopathy due to shortening of the actin thin filaments. Mechanistically, Flii is a known actin binding protein that we show associates with tropomodulin-1 (TMOD1) to regulate sarcomere thin filament length. Indeed, overexpression of leiomodin-2 in the heart, which lengthens the actin-containing thin filaments, partially rescued disease due to heart-specific deletion of Flii. Collectively, the identified FLII human variant likely increases cardiomyopathy risk through an alteration in sarcomere structure and associated contractile dynamics, like other sarcomere gene-based familial cardiomyopathies.
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Affiliation(s)
- Yasuhide Kuwabara
- Department of Pediatrics, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH45229
| | - Allen J. York
- Department of Pediatrics, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH45229
| | - Suh-Chin Lin
- Department of Pediatrics, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH45229
| | - Michelle A. Sargent
- Department of Pediatrics, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH45229
| | - Kelly M. Grimes
- Department of Pediatrics, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH45229
| | - James P. Pirruccello
- Division of Cardiology, University of California San Francisco, San Francisco, CA94158
| | - Jeffery D. Molkentin
- Department of Pediatrics, Cincinnati Children’s Hospital and the University of Cincinnati, Cincinnati, OH45229
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12
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The emergence of genotypic divergence and future precision medicine applications. HANDBOOK OF CLINICAL NEUROLOGY 2023; 192:87-99. [PMID: 36796950 DOI: 10.1016/b978-0-323-85538-9.00013-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/16/2023]
Abstract
Genotypic divergence is a term adapted from population genetics and intimately linked to evolution. We use divergence here to emphasize the differences that set individuals apart in any cohort. The history of genetics is filled with descriptions of genotypic differences, but causal inference of interindividual biological variation has been scarce. We suggest that the practice of precision medicine requires a divergent approach, an approach dependent on the causal interpretation of previous convergent (and preliminary) knowledge in the field. This knowledge has relied on convergent descriptive syndromology (lumping), which has overemphasized a reductionistic gene determinism on the quest of seeking associations without causal understanding. Regulatory variants with small effect and somatic mutations are some of the modifying factors that lead to incomplete penetrance and intrafamilial variable expressivity often observed in apparently monogenic clinical disorders. A truly divergent approach to precision medicine requires splitting, that is, the consideration of different layers of genetic phenomena that interact causally in a nonlinear fashion. This chapter reviews convergences and divergences in genetics and genomics, aiming to discuss what can be causally understood to approximate the as-yet utopian lands of Precision Medicine for patients with neurodegenerative disorders.
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13
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Dattani S, Sham PC, Jermy BS, Coleman JRI, Howard DM, Lewis CM. Common and rare variant associations with latent traits underlying depression, bipolar disorder, and schizophrenia. Transl Psychiatry 2023; 13:46. [PMID: 36746926 PMCID: PMC9902570 DOI: 10.1038/s41398-023-02324-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Revised: 01/07/2023] [Accepted: 01/18/2023] [Indexed: 02/08/2023] Open
Abstract
Genetic studies in psychiatry have primarily focused on the effects of common genetic variants, but few have investigated the role of rare genetic variants, particularly for major depression. In order to explore the role of rare variants in the gap between estimates of single nucleotide polymorphism (SNP) heritability and twin study heritability, we examined the contribution of common and rare genetic variants to latent traits underlying psychiatric disorders using high-quality imputed genotype data from the UK Biobank. Using a pre-registered analysis, we used items from the UK Biobank Mental Health Questionnaire relevant to three psychiatric disorders: major depression (N = 134,463), bipolar disorder (N = 117,376) and schizophrenia (N = 130,013) and identified a general hierarchical factor for each that described participants' responses. We calculated participants' scores on these latent traits and conducted single-variant genetic association testing (MAF > 0.05%), gene-based burden testing and pathway association testing associations with these latent traits. We tested for enrichment of rare variants (MAF 0.05-1%) in genes that had been previously identified by common variant genome-wide association studies, and genes previously associated with Mendelian disorders having relevant symptoms. We found moderate genetic correlations between the latent traits in our study and case-control phenotypes in previous genome-wide association studies, and identified one common genetic variant (rs72657988, minor allele frequency = 8.23%, p = 1.01 × 10-9) associated with the general factor of schizophrenia, but no other single variants, genes or pathways passed significance thresholds in this analysis, and we did not find enrichment in previously identified genes.
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Affiliation(s)
- Saloni Dattani
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK.
- Department of Psychiatry, Li Ka Shing (LKS) Faculty of Medicine, University of Hong Kong, Hong Kong SAR, China.
| | - Pak C Sham
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
- NIHR Maudsley Biomedical Research Centre, South London and Maudsley NHS Trust, London, UK
- Department of Psychiatry, State Key Laboratory of Brain and Cognitive Sciences, The University of Hong Kong, Hong Kong SAR, China
- Centre for PanorOmic Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Bradley S Jermy
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
- NIHR Maudsley Biomedical Research Centre, South London and Maudsley NHS Trust, London, UK
| | - Jonathan R I Coleman
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
- NIHR Maudsley Biomedical Research Centre, South London and Maudsley NHS Trust, London, UK
| | - David M Howard
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
- Division of Psychiatry, University of Edinburgh, Royal Edinburgh Hospital, Edinburgh, UK
| | - Cathryn M Lewis
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
- Department of Medical and Molecular Genetics, Faculty of Life Sciences and Medicine, King's College London, London, UK
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14
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Babushkina NP, Kucher AN. Regulatory Potential of SNP Markers in Genes of DNA Repair Systems. Mol Biol 2023. [DOI: 10.1134/s002689332301003x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/03/2023]
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15
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Davies M, Jurynec MJ, Gomez-Alvarado F, Hu D, Feeley SE, Allen-Brady K, Tashjian RZ, Feeley BT. Current cellular and molecular biology techniques for the orthopedic surgeon-scientist. J Shoulder Elbow Surg 2023; 32:e11-e22. [PMID: 35988889 DOI: 10.1016/j.jse.2022.07.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Revised: 06/27/2022] [Accepted: 07/07/2022] [Indexed: 02/01/2023]
Affiliation(s)
- Michael Davies
- Department of Orthopedic Surgery, University of California, San Francisco, San Francisco, CA, USA
| | - Michael J Jurynec
- Department of Orthopaedics, University of Utah, Salt Lake City, UT, USA
| | - Francisco Gomez-Alvarado
- Department of Orthopedic Surgery, University of California, San Francisco, San Francisco, CA, USA
| | - Daniel Hu
- Department of Orthopedic Surgery, University of California, San Francisco, San Francisco, CA, USA
| | - Sonali E Feeley
- Department of Orthopedic Surgery, University of California, San Francisco, San Francisco, CA, USA
| | - Kristina Allen-Brady
- Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City, UT, USA
| | - Robert Z Tashjian
- Department of Orthopaedics, University of Utah, Salt Lake City, UT, USA.
| | - Brian T Feeley
- Department of Orthopedic Surgery, University of California, San Francisco, San Francisco, CA, USA
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16
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Connally NJ, Nazeen S, Lee D, Shi H, Stamatoyannopoulos J, Chun S, Cotsapas C, Cassa CA, Sunyaev SR. The missing link between genetic association and regulatory function. eLife 2022; 11:e74970. [PMID: 36515579 PMCID: PMC9842386 DOI: 10.7554/elife.74970] [Citation(s) in RCA: 47] [Impact Index Per Article: 23.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2021] [Accepted: 12/02/2022] [Indexed: 12/15/2022] Open
Abstract
The genetic basis of most traits is highly polygenic and dominated by non-coding alleles. It is widely assumed that such alleles exert small regulatory effects on the expression of cis-linked genes. However, despite the availability of gene expression and epigenomic datasets, few variant-to-gene links have emerged. It is unclear whether these sparse results are due to limitations in available data and methods, or to deficiencies in the underlying assumed model. To better distinguish between these possibilities, we identified 220 gene-trait pairs in which protein-coding variants influence a complex trait or its Mendelian cognate. Despite the presence of expression quantitative trait loci near most GWAS associations, by applying a gene-based approach we found limited evidence that the baseline expression of trait-related genes explains GWAS associations, whether using colocalization methods (8% of genes implicated), transcription-wide association (2% of genes implicated), or a combination of regulatory annotations and distance (4% of genes implicated). These results contradict the hypothesis that most complex trait-associated variants coincide with homeostatic expression QTLs, suggesting that better models are needed. The field must confront this deficit and pursue this 'missing regulation.'
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Affiliation(s)
- Noah J Connally
- Department of Biomedical Informatics, Harvard Medical SchoolBostonUnited States
- Brigham and Women’s Hospital, Division of Genetics, Harvard Medical SchoolBostonUnited States
- Program in Medical and Population Genetics, Broad Institute of MIT and HarvardCambridgeUnited States
| | - Sumaiya Nazeen
- Department of Biomedical Informatics, Harvard Medical SchoolBostonUnited States
- Brigham and Women’s Hospital, Division of Genetics, Harvard Medical SchoolBostonUnited States
- Brigham and Women’s Hospital, Department of Neurology, Harvard Medical SchoolBostonUnited States
| | - Daniel Lee
- Department of Biomedical Informatics, Harvard Medical SchoolBostonUnited States
- Brigham and Women’s Hospital, Division of Genetics, Harvard Medical SchoolBostonUnited States
- Program in Medical and Population Genetics, Broad Institute of MIT and HarvardCambridgeUnited States
| | - Huwenbo Shi
- Program in Medical and Population Genetics, Broad Institute of MIT and HarvardCambridgeUnited States
- Department of Epidemiology, Harvard T.H. Chan School of Public HealthBostonUnited States
| | | | - Sung Chun
- Division of Pulmonary Medicine, Boston Children’s HospitalBostonUnited States
| | - Chris Cotsapas
- Program in Medical and Population Genetics, Broad Institute of MIT and HarvardCambridgeUnited States
- Department of Neurology, Yale Medical SchoolNew HavenUnited States
- Department of Genetics, Yale Medical SchoolNew HavenUnited States
| | - Christopher A Cassa
- Brigham and Women’s Hospital, Division of Genetics, Harvard Medical SchoolBostonUnited States
- Program in Medical and Population Genetics, Broad Institute of MIT and HarvardCambridgeUnited States
| | - Shamil R Sunyaev
- Department of Biomedical Informatics, Harvard Medical SchoolBostonUnited States
- Brigham and Women’s Hospital, Division of Genetics, Harvard Medical SchoolBostonUnited States
- Program in Medical and Population Genetics, Broad Institute of MIT and HarvardCambridgeUnited States
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17
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Tabet D, Parikh V, Mali P, Roth FP, Claussnitzer M. Scalable Functional Assays for the Interpretation of Human Genetic Variation. Annu Rev Genet 2022; 56:441-465. [PMID: 36055970 DOI: 10.1146/annurev-genet-072920-032107] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Scalable sequence-function studies have enabled the systematic analysis and cataloging of hundreds of thousands of coding and noncoding genetic variants in the human genome. This has improved clinical variant interpretation and provided insights into the molecular, biophysical, and cellular effects of genetic variants at an astonishing scale and resolution across the spectrum of allele frequencies. In this review, we explore current applications and prospects for the field and outline the principles underlying scalable functional assay design, with a focus on the study of single-nucleotide coding and noncoding variants.
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Affiliation(s)
- Daniel Tabet
- Donnelly Centre, Department of Molecular Genetics, and Department of Computer Science, University of Toronto, Toronto, Ontario, Canada;
- Lunenfeld-Tanenbaum Research Institute, Sinai Health, Toronto, Ontario, Canada
| | - Victoria Parikh
- Center for Inherited Cardiovascular Disease, Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, California, USA
| | - Prashant Mali
- Department of Bioengineering, University of California, San Diego, California, USA
| | - Frederick P Roth
- Donnelly Centre, Department of Molecular Genetics, and Department of Computer Science, University of Toronto, Toronto, Ontario, Canada;
- Lunenfeld-Tanenbaum Research Institute, Sinai Health, Toronto, Ontario, Canada
| | - Melina Claussnitzer
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
- Center for Genomic Medicine and Endocrine Division, Massachusetts General Hospital, Boston, Massachusetts, USA
- Harvard Medical School, Harvard University, Boston, Massachusetts, USA;
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18
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Dapas M, Dunaif A. Deconstructing a Syndrome: Genomic Insights Into PCOS Causal Mechanisms and Classification. Endocr Rev 2022; 43:927-965. [PMID: 35026001 PMCID: PMC9695127 DOI: 10.1210/endrev/bnac001] [Citation(s) in RCA: 87] [Impact Index Per Article: 43.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/15/2021] [Indexed: 01/16/2023]
Abstract
Polycystic ovary syndrome (PCOS) is among the most common disorders in women of reproductive age, affecting up to 15% worldwide, depending on the diagnostic criteria. PCOS is characterized by a constellation of interrelated reproductive abnormalities, including disordered gonadotropin secretion, increased androgen production, chronic anovulation, and polycystic ovarian morphology. It is frequently associated with insulin resistance and obesity. These reproductive and metabolic derangements cause major morbidities across the lifespan, including anovulatory infertility and type 2 diabetes (T2D). Despite decades of investigative effort, the etiology of PCOS remains unknown. Familial clustering of PCOS cases has indicated a genetic contribution to PCOS. There are rare Mendelian forms of PCOS associated with extreme phenotypes, but PCOS typically follows a non-Mendelian pattern of inheritance consistent with a complex genetic architecture, analogous to T2D and obesity, that reflects the interaction of susceptibility genes and environmental factors. Genomic studies of PCOS have provided important insights into disease pathways and have indicated that current diagnostic criteria do not capture underlying differences in biology associated with different forms of PCOS. We provide a state-of-the-science review of genetic analyses of PCOS, including an overview of genomic methodologies aimed at a general audience of non-geneticists and clinicians. Applications in PCOS will be discussed, including strengths and limitations of each study. The contributions of environmental factors, including developmental origins, will be reviewed. Insights into the pathogenesis and genetic architecture of PCOS will be summarized. Future directions for PCOS genetic studies will be outlined.
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Affiliation(s)
- Matthew Dapas
- Department of Human Genetics, University of Chicago, Chicago, IL, USA
| | - Andrea Dunaif
- Division of Endocrinology, Diabetes and Bone Disease, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
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19
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Pagnamenta AT, Diaz-Gonzalez F, Banos-Pinero B, Ferla MP, Toosi MB, Calder AD, Karimiani EG, Doosti M, Wainwright A, Wordsworth P, Bailey K, Ejeskär K, Lester T, Maroofian R, Heath KE, Tajsharghi H, Shears D, Taylor JC. Variable skeletal phenotypes associated with biallelic variants in PRKG2. J Med Genet 2022; 59:947-950. [PMID: 34782440 PMCID: PMC9554069 DOI: 10.1136/jmedgenet-2021-108027] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2021] [Accepted: 10/08/2021] [Indexed: 11/30/2022]
Affiliation(s)
- Alistair T Pagnamenta
- NIHR Biomedical Research Centre, Oxford, Oxfordshire, UK
- Wellcome Centre for Human Genetics, Oxford University, Oxford, Oxfordshire, UK
| | - Francisca Diaz-Gonzalez
- INGEMM, IdiPAZ and Skeletal Dysplasia Multidisciplinary Unit (UMDE, ERN-BOND), Hospital Universitario La Paz, Madrid, Spain
| | - Benito Banos-Pinero
- Oxford Genetics Laboratories, Oxford University Hospitals NHS Foundation Trust, Oxford, Oxfordshire, UK
| | - Matteo P Ferla
- NIHR Biomedical Research Centre, Oxford, Oxfordshire, UK
- Wellcome Centre for Human Genetics, Oxford University, Oxford, Oxfordshire, UK
| | - Mehran B Toosi
- Department of Pediatric Neurology, Ghaem Hospital, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Alistair D Calder
- Radiology Department, Great Ormond Street Hospital for Children NHS Foundation Trust, London, UK
| | - Ehsan G Karimiani
- Genetics Research Centre, Molecular and Clinical Sciences Institute, St. George's, University of London, London, UK
- Next Generation Genetic Polyclinic, Razavi International Hospital, Mashhad, Iran
| | - Mohammad Doosti
- Next Generation Genetic Polyclinic, Razavi International Hospital, Mashhad, Iran
| | - Andrew Wainwright
- Department of Paediatrics, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - Paul Wordsworth
- NIHR Biomedical Research Centre, Oxford, Oxfordshire, UK
- Wellcome Centre for Human Genetics, Oxford University, Oxford, Oxfordshire, UK
- Department of Paediatrics, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - Kathryn Bailey
- Department of Paediatrics, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - Katarina Ejeskär
- School of Health Sciences, Translational Medicine, University of Skövde, Skövde, Sweden
| | - Tracy Lester
- Oxford Genetics Laboratories, Oxford University Hospitals NHS Foundation Trust, Oxford, Oxfordshire, UK
| | - Reza Maroofian
- Department of Neuromuscular Disorders, Queen Square Institute of Neurology, UCL, London, UK
| | - Karen E Heath
- INGEMM, IdiPAZ and Skeletal Dysplasia Multidisciplinary Unit (UMDE, ERN-BOND), Hospital Universitario La Paz, Madrid, Spain
- CIBERER, ISCIII, Madrid, Spain
| | - Homa Tajsharghi
- School of Health Sciences, Translational Medicine, University of Skövde, Skövde, Sweden
| | - Deborah Shears
- Oxford Centre for Genomic Medicine, Oxford University Hospitals NHS Foundation Trust, Oxford, Oxfordshire, UK
| | - Jenny C Taylor
- NIHR Biomedical Research Centre, Oxford, Oxfordshire, UK
- Wellcome Centre for Human Genetics, Oxford University, Oxford, Oxfordshire, UK
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20
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Gavile CM, Kazmers NH, Novak KA, Meeks HD, Yu Z, Thomas JL, Hansen C, Barker T, Jurynec MJ. Familial Clustering and Genetic Analysis of Severe Thumb Carpometacarpal Joint Osteoarthritis in a Large Statewide Cohort. J Hand Surg Am 2022; 47:923-933. [PMID: 36184273 PMCID: PMC9547951 DOI: 10.1016/j.jhsa.2022.08.004] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/24/2022] [Revised: 07/29/2022] [Accepted: 08/04/2022] [Indexed: 02/02/2023]
Abstract
PURPOSE Our goals were to identify individuals who required surgery for thumb carpometacarpal (CMC) joint osteoarthritis (OA), determine if CMC joint OA clusters in families, define the magnitude of familial risk of CMC joint OA, identify risk factors associated with CMC joint OA, and identify rare genetic variants that segregate with familial CMC joint OA. METHODS We searched the Utah Population Database to identify a cohort of CMC joint OA patients who required surgery. Affected individuals were mapped to pedigrees to identify high-risk families with excess clustering of CMC joint OA. Cox regression models were used to calculate familial risk of CMC joint OA in related individuals. Risk factors were evaluated using logistic regression models. Whole exome sequencing was used to identify rare coding variants associated with familial CMC joint OA. RESULTS We identified 550 pedigrees with excess clustering of severe CMC joint OA. The relative risk of CMC joint OA requiring surgical treatment was elevated significantly in first- and third-degree relatives of affected individuals, and significant associations with advanced age, female sex, obesity, and tobacco use were observed. We discovered candidate genes that dominantly segregate with severe CMC joint OA in 4 independent families, including a rare variant in Chondroitin Sulfate Synthase 3 (CHSY3). CONCLUSIONS Familial clustering of severe CMC joint OA was observed in a statewide population. Our data indicate that genetic and environmental factors contribute to the disease process, further highlighting the multifactorial nature of the disease. Genomic analyses suggest distinct biological processes are involved in CMC joint OA pathogenesis. CLINICAL RELEVANCE Awareness of associated comorbidities may guide the diagnosis of CMC joint OA in at-risk populations and help identify individuals who may not do well with nonoperative treatment. Further pursuit of the genes associated with severe CMC joint OA may lead to assays for detection of early stages of disease and have therapeutic potential.
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Affiliation(s)
| | | | - Kendra A Novak
- Department of Orthopaedics, University of Utah, Salt Lake City, UT
| | - Huong D Meeks
- Huntsman Cancer Institute, Utah Population Database, University of Utah, Salt Lake City, UT
| | - Zhe Yu
- Huntsman Cancer Institute, Utah Population Database, University of Utah, Salt Lake City, UT
| | - Joy L Thomas
- Intermountain Healthcare, Precision Genomics, St. George, UT
| | - Channing Hansen
- Intermountain Healthcare, Biorepository, South Salt Lake City, UT
| | - Tyler Barker
- Department of Orthopaedics, University of Utah, Salt Lake City, UT; Intermountain Healthcare, Precision Genomics, Murray, UT; Department of Nutrition and Integrative Physiology, University of Utah, Salt Lake City, UT
| | - Michael J Jurynec
- Department of Orthopaedics, University of Utah, Salt Lake City, UT; Department of Human Genetics, University of Utah, Salt Lake City, UT.
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21
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Nazarenko MS, Sleptcov AA, Puzyrev VP. “Mendelian Code” in the Genetic Structure of Common Multifactorial Diseases. RUSS J GENET+ 2022. [DOI: 10.1134/s1022795422100052] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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22
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Jurynec MJ, Gavile CM, Honeggar M, Ma Y, Veerabhadraiah SR, Novak KA, Hoshijima K, Kazmers NH, Grunwald DJ. NOD/RIPK2 signalling pathway contributes to osteoarthritis susceptibility. Ann Rheum Dis 2022; 81:1465-1473. [PMID: 35732460 PMCID: PMC9474725 DOI: 10.1136/annrheumdis-2022-222497] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2022] [Accepted: 06/07/2022] [Indexed: 12/21/2022]
Abstract
OBJECTIVES How inflammatory signalling contributes to osteoarthritis (OA) susceptibility is undetermined. An allele encoding a hyperactive form of the Receptor Interacting Protein Kinase 2 (RIPK2) proinflammatory signalling intermediate has been associated with familial OA. To test whether altered nucleotide-binding oligomerisation domain (NOD)/RIPK2 pathway activity causes heightened OA susceptibility, we investigated whether variants affecting additional pathway components are associated with familial OA. To determine whether the Ripk2104Asp disease allele is sufficient to account for the familial phenotype, we determined the effect of the allele on mice. METHODS Genomic analysis of 150 independent families with dominant inheritance of OA affecting diverse joints was used to identify coding variants that segregated strictly with occurrence of OA. Genome editing was used to introduce the OA-associated RIPK2 (p.Asn104Asp) allele into the genome of inbred mice. The consequences of the Ripk2104Asp disease allele on physiology and OA susceptibility in mice were measured by histology, immunohistochemistry, serum cytokine levels and gene expression. RESULTS We identified six novel variants affecting components of the NOD/RIPK2 inflammatory signalling pathway that are associated with familial OA affecting the hand, shoulder or foot. The Ripk2104Asp allele acts dominantly to alter basal physiology and response to trauma in the mouse knee. Whereas the knees of uninjured Ripk2Asp104 mice appear normal histologically, the joints exhibit a set of marked gene expression changes reminiscent of overt OA. Although the Ripk2104Asp mice lack evidence of chronically elevated systemic inflammation, they do exhibit significantly increased susceptibility to post-traumatic OA (PTOA). CONCLUSIONS Two types of data support the hypothesis that altered NOD/RIPK2 signalling confers susceptibility to OA.
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Affiliation(s)
- Michael J Jurynec
- Department of Orthopaedics, University of Utah Health, Salt Lake City, Utah, USA
- Department of Human Genetics, University of Utah Health, Salt Lake City, Utah, USA
| | - Catherine M Gavile
- Department of Orthopaedics, University of Utah Health, Salt Lake City, Utah, USA
| | - Matthew Honeggar
- Department of Orthopaedics, University of Utah Health, Salt Lake City, Utah, USA
| | - Ying Ma
- Department of Orthopaedics, University of Utah Health, Salt Lake City, Utah, USA
| | | | - Kendra A Novak
- Department of Orthopaedics, University of Utah Health, Salt Lake City, Utah, USA
| | - Kazuyuki Hoshijima
- Department of Human Genetics, University of Utah Health, Salt Lake City, Utah, USA
| | - Nikolas H Kazmers
- Department of Orthopaedics, University of Utah Health, Salt Lake City, Utah, USA
| | - David J Grunwald
- Department of Human Genetics, University of Utah Health, Salt Lake City, Utah, USA
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23
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Zhang MJ, Hou K, Dey KK, Sakaue S, Jagadeesh KA, Weinand K, Taychameekiatchai A, Rao P, Pisco AO, Zou J, Wang B, Gandal M, Raychaudhuri S, Pasaniuc B, Price AL. Polygenic enrichment distinguishes disease associations of individual cells in single-cell RNA-seq data. Nat Genet 2022; 54:1572-1580. [PMID: 36050550 PMCID: PMC9891382 DOI: 10.1038/s41588-022-01167-z] [Citation(s) in RCA: 59] [Impact Index Per Article: 29.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2021] [Accepted: 07/19/2022] [Indexed: 02/03/2023]
Abstract
Single-cell RNA sequencing (scRNA-seq) provides unique insights into the pathology and cellular origin of disease. We introduce single-cell disease relevance score (scDRS), an approach that links scRNA-seq with polygenic disease risk at single-cell resolution, independent of annotated cell types. scDRS identifies cells exhibiting excess expression across disease-associated genes implicated by genome-wide association studies (GWASs). We applied scDRS to 74 diseases/traits and 1.3 million single-cell gene-expression profiles across 31 tissues/organs. Cell-type-level results broadly recapitulated known cell-type-disease associations. Individual-cell-level results identified subpopulations of disease-associated cells not captured by existing cell-type labels, including T cell subpopulations associated with inflammatory bowel disease, partially characterized by their effector-like states; neuron subpopulations associated with schizophrenia, partially characterized by their spatial locations; and hepatocyte subpopulations associated with triglyceride levels, partially characterized by their higher ploidy levels. Genes whose expression was correlated with the scDRS score across cells (reflecting coexpression with GWAS disease-associated genes) were strongly enriched for gold-standard drug target and Mendelian disease genes.
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Affiliation(s)
- Martin Jinye Zhang
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
| | - Kangcheng Hou
- Bioinformatics Interdepartmental Program, University of California, Los Angeles, Los Angeles, CA, USA.
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.
- Department of Computational Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.
| | - Kushal K Dey
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Saori Sakaue
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Data Sciences, Brigham and Women's Hospital, Boston, MA, USA
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Karthik A Jagadeesh
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Kathryn Weinand
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Data Sciences, Brigham and Women's Hospital, Boston, MA, USA
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Aris Taychameekiatchai
- Department of Medicine and Liver Center, University of California, San Francisco, San Francisco, CA, USA
- Developmental and Stem Cell Biology Graduate Program, University of California, San Francisco, San Francisco, CA, USA
| | - Poorvi Rao
- Department of Medicine and Liver Center, University of California, San Francisco, San Francisco, CA, USA
| | | | - James Zou
- Chan Zuckerberg Biohub, San Francisco, CA, USA
- Department of Electrical Engineering, Stanford University, Palo Alto, CA, USA
- Department of Biomedical Data Science, Stanford University, Palo Alto, CA, USA
| | - Bruce Wang
- Department of Medicine and Liver Center, University of California, San Francisco, San Francisco, CA, USA
| | - Michael Gandal
- Department of Psychiatry, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Program in Neurobehavioral Genetics, Semel Institute, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Soumya Raychaudhuri
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Data Sciences, Brigham and Women's Hospital, Boston, MA, USA
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Centre for Genetics and Genomics Versus Arthritis, Centre for Musculoskeletal Research, Manchester Academic Health Science Centre, The University of Manchester, Manchester, UK
| | - Bogdan Pasaniuc
- Bioinformatics Interdepartmental Program, University of California, Los Angeles, Los Angeles, CA, USA.
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.
- Department of Computational Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.
| | - Alkes L Price
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
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24
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Das SS. Mendel paved the path toward understanding genetic diseases. EGYPTIAN JOURNAL OF MEDICAL HUMAN GENETICS 2022. [DOI: 10.1186/s43042-022-00339-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
Abstract
Background
July 20th, 2022, marks the 200th anniversary of the “Father of Genetics,” Gregor Mendel’s birth. His experiments with pea plants established the fundamental principles of genetic inheritance.
Main text
In this article, a succinct review of literature is hereby done to answer two key questions: (1) How Mendel’s principles of genetic inheritance helped us understand Mendelian disorders? and (2) How the study of Mendelian disorders can help us understand complex diseases?
Conclusion
This literature review concludes that continued effort to understand the genetic basis of Mendelian disorders will improve our understanding and treatment strategies for complex diseases.
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25
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Kingdom R, Wright CF. Incomplete Penetrance and Variable Expressivity: From Clinical Studies to Population Cohorts. Front Genet 2022; 13:920390. [PMID: 35983412 PMCID: PMC9380816 DOI: 10.3389/fgene.2022.920390] [Citation(s) in RCA: 77] [Impact Index Per Article: 38.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2022] [Accepted: 06/09/2022] [Indexed: 12/20/2022] Open
Abstract
The same genetic variant found in different individuals can cause a range of diverse phenotypes, from no discernible clinical phenotype to severe disease, even among related individuals. Such variants can be said to display incomplete penetrance, a binary phenomenon where the genotype either causes the expected clinical phenotype or it does not, or they can be said to display variable expressivity, in which the same genotype can cause a wide range of clinical symptoms across a spectrum. Both incomplete penetrance and variable expressivity are thought to be caused by a range of factors, including common variants, variants in regulatory regions, epigenetics, environmental factors, and lifestyle. Many thousands of genetic variants have been identified as the cause of monogenic disorders, mostly determined through small clinical studies, and thus, the penetrance and expressivity of these variants may be overestimated when compared to their effect on the general population. With the wealth of population cohort data currently available, the penetrance and expressivity of such genetic variants can be investigated across a much wider contingent, potentially helping to reclassify variants that were previously thought to be completely penetrant. Research into the penetrance and expressivity of such genetic variants is important for clinical classification, both for determining causative mechanisms of disease in the affected population and for providing accurate risk information through genetic counseling. A genotype-based definition of the causes of rare diseases incorporating information from population cohorts and clinical studies is critical for our understanding of incomplete penetrance and variable expressivity. This review examines our current knowledge of the penetrance and expressivity of genetic variants in rare disease and across populations, as well as looking into the potential causes of the variation seen, including genetic modifiers, mosaicism, and polygenic factors, among others. We also considered the challenges that come with investigating penetrance and expressivity.
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Affiliation(s)
| | - Caroline F. Wright
- Institute of Biomedical and Clinical Science, Royal Devon & Exeter Hospital, University of Exeter Medical School, Exeter, United Kingdom
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26
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Spendlove SJ, Bondhus L, Lluri G, Sul JH, Arboleda VA. Polygenic risk scores of endo-phenotypes identify the effect of genetic background in congenital heart disease. HGG ADVANCES 2022; 3:100112. [PMID: 35599848 PMCID: PMC9118152 DOI: 10.1016/j.xhgg.2022.100112] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Accepted: 04/19/2022] [Indexed: 01/28/2023] Open
Abstract
Congenital heart disease (CHD) is a rare structural defect that occurs in ∼1% of live births. Studies on CHD genetic architecture have identified pathogenic single-gene mutations in less than 30% of cases. Single-gene mutations often show incomplete penetrance and variable expressivity. Therefore, we hypothesize that genetic background may play a role in modulating disease expression. Polygenic risk scores (PRSs) aggregate effects of common genetic variants to investigate whether, cumulatively, these variants are associated with disease penetrance or severity. However, the major limitations in this field have been in generating sufficient sample sizes for these studies. Here we used CHD-phenotype matched genome-wide association study (GWAS) summary statistics from the UK Biobank (UKBB) as our base study and whole-genome sequencing data from the CHD cohort (n1 = 711 trios, n2 = 362 European trios) of the Gabriella Miller Kids First dataset as our target study to develop PRSs for CHD. PRSs estimated using a GWAS for heart valve problems and heart murmur explain 2.5% of the variance in case-control status of CHD (all SNVs, p = 7.90 × 10-3; fetal cardiac SNVs, p = 8.00 × 10-3) and 1.8% of the variance in severity of CHD (fetal cardiac SNVs, p = 6.20 × 10-3; all SNVs, p = 0.015). These results show that common variants captured in CHD phenotype-matched GWASs have a modest but significant contribution to phenotypic expression of CHD. Further exploration of the cumulative effect of common variants is necessary for understanding the complex genetic etiology of CHD and other rare diseases.
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Affiliation(s)
- Sarah J. Spendlove
- Interdepartmental Bioinformatics Program, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Leroy Bondhus
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Gentian Lluri
- Ahmanson/UCLA Adult Congenital Heart Disease Center, Division of Cardiology, Department of Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Jae Hoon Sul
- Interdepartmental Bioinformatics Program, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Psychiatry, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Valerie A. Arboleda
- Interdepartmental Bioinformatics Program, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Computational Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
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27
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Dey KK, Gazal S, van de Geijn B, Kim SS, Nasser J, Engreitz JM, Price AL. SNP-to-gene linking strategies reveal contributions of enhancer-related and candidate master-regulator genes to autoimmune disease. CELL GENOMICS 2022; 2:100145. [PMID: 35873673 PMCID: PMC9306342 DOI: 10.1016/j.xgen.2022.100145] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/23/2020] [Revised: 04/03/2021] [Accepted: 05/27/2022] [Indexed: 12/11/2022]
Abstract
We assess contributions to autoimmune disease of genes whose regulation is driven by enhancer regions (enhancer-related) and genes that regulate other genes in trans (candidate master-regulator). We link these genes to SNPs using several SNP-to-gene (S2G) strategies and apply heritability analyses to draw three conclusions about 11 autoimmune/blood-related diseases/traits. First, several characterizations of enhancer-related genes using functional genomics data are informative for autoimmune disease heritability after conditioning on a broad set of regulatory annotations. Second, candidate master-regulator genes defined using trans-eQTL in blood are also conditionally informative for autoimmune disease heritability. Third, integrating enhancer-related and master-regulator gene sets with protein-protein interaction (PPI) network information magnified their disease signal. The resulting PPI-enhancer gene score produced >2-fold stronger heritability signal and >2-fold stronger enrichment for drug targets, compared with the recently proposed enhancer domain score. In each case, functionally informed S2G strategies produced 4.1- to 13-fold stronger disease signals than conventional window-based strategies.
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Affiliation(s)
- Kushal K. Dey
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
| | - Steven Gazal
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
| | - Bryce van de Geijn
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
- Genentech, South San Francisco, CA 94080, USA
| | - Samuel Sungil Kim
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Joseph Nasser
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Jesse M. Engreitz
- Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA
- BASE Initiative, Betty Irene Moore Children’s Heart Center, Lucile Packard Children’s Hospital, Stanford University School of Medicine, Stanford, CA 94304, USA
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Alkes L. Price
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
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28
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Basmanav FB, Betz RC. Translational impact of omics studies in alopecia areata: recent advances and future perspectives. Expert Rev Clin Immunol 2022; 18:845-857. [PMID: 35770930 DOI: 10.1080/1744666x.2022.2096590] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
INTRODUCTION Alopecia areata (AA) is a non-scarring, hair loss disorder and a common autoimmune-mediated disease with an estimated lifetime risk of about 2%. To date, the treatment of AA is mainly based on suppression or stimulation of the immune response. Genomics and transcriptomics studies generated important insights into the underlying pathophysiology, enabled discovery of molecular disease signatures, which were used in some of the recent clinical trials to monitor drug response and substantiated the consideration of new therapeutic modalities for the treatment of AA such as abatacept, dupilumab, ustekinumab and Janus Kinase (JAK) inhibitors. AREAS COVERED In this review, genomics and transcriptomics studies in AA are discussed in detail with particular emphasis on their past and prospective translational impacts. Microbiome studies are also briefly introduced. EXPERT OPINION The generation of large datasets using the new high-throughput technologies has revolutionized medical research and AA has also benefited from the wave of omics studies. However, the limitations associated with JAK inhibitors and clinical heterogeneity in AA patients underscore the necessity for continuing omics research in AA for discovery of novel therapeutic modalities and development of clinical tools for precision medicine.
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Affiliation(s)
- F Buket Basmanav
- Medical Faculty & University Hospital Bonn, Institute of Human Genetics, University of Bonn, Bonn, Germany
| | - Regina C Betz
- Medical Faculty & University Hospital Bonn, Institute of Human Genetics, University of Bonn, Bonn, Germany
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29
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Mallard TT, Karlsson Linnér R, Grotzinger AD, Sanchez-Roige S, Seidlitz J, Okbay A, de Vlaming R, Meddens SFW, Palmer AA, Davis LK, Tucker-Drob EM, Kendler KS, Keller MC, Koellinger PD, Harden KP. Multivariate GWAS of psychiatric disorders and their cardinal symptoms reveal two dimensions of cross-cutting genetic liabilities. CELL GENOMICS 2022; 2:S2666-979X(22)00073-8. [PMID: 35812988 PMCID: PMC9264403 DOI: 10.1016/j.xgen.2022.100140] [Citation(s) in RCA: 30] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/13/2020] [Revised: 10/25/2021] [Accepted: 05/10/2022] [Indexed: 02/07/2023]
Abstract
Understanding which biological pathways are specific versus general across diagnostic categories and levels of symptom severity is critical to improving nosology and treatment of psychopathology. Here, we combine transdiagnostic and dimensional approaches to genetic discovery for the first time, conducting a novel multivariate genome-wide association study of eight psychiatric symptoms and disorders broadly related to mood disturbance and psychosis. We identify two transdiagnostic genetic liabilities that distinguish between common forms of psychopathology versus rarer forms of serious mental illness. Biological annotation revealed divergent genetic architectures that differentially implicated prenatal neurodevelopment and neuronal function and regulation. These findings inform psychiatric nosology and biological models of psychopathology, as they suggest that the severity of mood and psychotic symptoms present in serious mental illness may reflect a difference in kind rather than merely in degree.
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Affiliation(s)
- Travis T. Mallard
- Department of Psychology, University of Texas at Austin, Austin, TX, USA
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Richard Karlsson Linnér
- Department of Economics, School of Business and Economics, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
- Autism and Developmental Medicine Institute, Geisinger, Lewisburg, PA, USA
| | | | - Sandra Sanchez-Roige
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Jakob Seidlitz
- Department of Child and Adolescent Psychiatry and Behavioral Science, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
| | - Aysu Okbay
- Department of Economics, School of Business and Economics, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Ronald de Vlaming
- Department of Economics, School of Business and Economics, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - S. Fleur W. Meddens
- Erasmus University Rotterdam Institute for Behavior and Biology, Erasmus School of Economics, Erasmus University Rotterdam, Rotterdam, the Netherlands
| | - Bipolar Disorder Working Group of the Psychiatric Genomics Consortium
- Department of Psychology, University of Texas at Austin, Austin, TX, USA
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Economics, School of Business and Economics, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
- Autism and Developmental Medicine Institute, Geisinger, Lewisburg, PA, USA
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Child and Adolescent Psychiatry and Behavioral Science, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
- Erasmus University Rotterdam Institute for Behavior and Biology, Erasmus School of Economics, Erasmus University Rotterdam, Rotterdam, the Netherlands
- Institute for Genomic Medicine, University of California San Diego, La Jolla, CA, USA
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
- Population Research Center, University of Texas at Austin, Austin, TX, USA
- Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA, USA
- Department of Psychiatry, Medical College of Virginia/Virginia Commonwealth University, Richmond, VA, USA
- Institute for Behavioral Genetics, University of Colorado Boulder, Boulder, CO, USA
- Department of Psychology and Neuroscience, University of Colorado Boulder, Boulder, CO, USA
| | - Abraham A. Palmer
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
- Institute for Genomic Medicine, University of California San Diego, La Jolla, CA, USA
| | - Lea K. Davis
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Elliot M. Tucker-Drob
- Department of Psychology, University of Texas at Austin, Austin, TX, USA
- Population Research Center, University of Texas at Austin, Austin, TX, USA
| | - Kenneth S. Kendler
- Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA, USA
- Department of Psychiatry, Medical College of Virginia/Virginia Commonwealth University, Richmond, VA, USA
| | - Matthew C. Keller
- Institute for Behavioral Genetics, University of Colorado Boulder, Boulder, CO, USA
- Department of Psychology and Neuroscience, University of Colorado Boulder, Boulder, CO, USA
| | - Philipp D. Koellinger
- Department of Economics, School of Business and Economics, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - K. Paige Harden
- Department of Psychology, University of Texas at Austin, Austin, TX, USA
- Population Research Center, University of Texas at Austin, Austin, TX, USA
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30
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Gazal S, Weissbrod O, Hormozdiari F, Dey KK, Nasser J, Jagadeesh KA, Weiner DJ, Shi H, Fulco CP, O'Connor LJ, Pasaniuc B, Engreitz JM, Price AL. Combining SNP-to-gene linking strategies to identify disease genes and assess disease omnigenicity. Nat Genet 2022; 54:827-836. [PMID: 35668300 PMCID: PMC9894581 DOI: 10.1038/s41588-022-01087-y] [Citation(s) in RCA: 63] [Impact Index Per Article: 31.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2021] [Accepted: 04/27/2022] [Indexed: 02/04/2023]
Abstract
Disease-associated single-nucleotide polymorphisms (SNPs) generally do not implicate target genes, as most disease SNPs are regulatory. Many SNP-to-gene (S2G) linking strategies have been developed to link regulatory SNPs to the genes that they regulate in cis. Here, we developed a heritability-based framework for evaluating and combining different S2G strategies to optimize their informativeness for common disease risk. Our optimal combined S2G strategy (cS2G) included seven constituent S2G strategies and achieved a precision of 0.75 and a recall of 0.33, more than doubling the recall of any individual strategy. We applied cS2G to fine-mapping results for 49 UK Biobank diseases/traits to predict 5,095 causal SNP-gene-disease triplets (with S2G-derived functional interpretation) with high confidence. We further applied cS2G to provide an empirical assessment of disease omnigenicity; we determined that the top 1% of genes explained roughly half of the SNP heritability linked to all genes and that gene-level architectures vary with variant allele frequency.
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Affiliation(s)
- Steven Gazal
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
- Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
- Broad Institute of MIT and Harvard, Cambridge, MA, USA.
| | - Omer Weissbrod
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Farhad Hormozdiari
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Kushal K Dey
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Joseph Nasser
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Karthik A Jagadeesh
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | | | - Huwenbo Shi
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Charles P Fulco
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA
- Bristol Myers Squibb, Cambridge, MA, USA
| | | | - Bogdan Pasaniuc
- Departments of Computational Medicine, Human Genetics, Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Jesse M Engreitz
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
- BASE Initiative, Betty Irene Moore Children's Heart Center, Lucile Packard Children's Hospital, Stanford University School of Medicine, Stanford, CA, USA
| | - Alkes L Price
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
- Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
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31
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Burch KS, Hou K, Ding Y, Wang Y, Gazal S, Shi H, Pasaniuc B. Partitioning gene-level contributions to complex-trait heritability by allele frequency identifies disease-relevant genes. Am J Hum Genet 2022; 109:692-709. [PMID: 35271803 PMCID: PMC9069080 DOI: 10.1016/j.ajhg.2022.02.012] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2021] [Accepted: 02/15/2022] [Indexed: 11/15/2022] Open
Abstract
Recent works have shown that SNP heritability-which is dominated by low-effect common variants-may not be the most relevant quantity for localizing high-effect/critical disease genes. Here, we introduce methods to estimate the proportion of phenotypic variance explained by a given assignment of SNPs to a single gene ("gene-level heritability"). We partition gene-level heritability by minor allele frequency (MAF) to find genes whose gene-level heritability is explained exclusively by "low-frequency/rare" variants (0.5% ≤ MAF < 1%). Applying our method to ∼16K protein-coding genes and 25 quantitative traits in the UK Biobank (N = 290K "White British"), we find that, on average across traits, ∼2.5% of nonzero-heritability genes have a rare-variant component and only ∼0.8% (327 gene-trait pairs) have heritability exclusively from rare variants. Of these 327 gene-trait pairs, 114 (35%) were not detected by existing gene-level association testing methods. The additional genes we identify are significantly enriched for known disease genes, and we find several examples of genes that have been previously implicated in phenotypically related Mendelian disorders. Notably, the rare-variant component of gene-level heritability exhibits trends different from those of common-variant gene-level heritability. For example, while total gene-level heritability increases with gene length, the rare-variant component is significantly larger among shorter genes; the cumulative distributions of gene-level heritability also vary across traits and reveal differences in the relative contributions of rare/common variants to overall gene-level polygenicity. While nonzero gene-level heritability does not imply causality, if interpreted in the correct context, gene-level heritability can reveal useful insights into complex-trait genetic architecture.
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Affiliation(s)
- Kathryn S Burch
- Bioinformatics Interdepartmental Program, University of California, Los Angeles, Los Angeles, CA 90095, USA; Department of Computational Medicine, David Geffen School of Medicine at University of California, Los Angeles, Los Angeles, CA 90095, USA.
| | - Kangcheng Hou
- Bioinformatics Interdepartmental Program, University of California, Los Angeles, Los Angeles, CA 90095, USA; Department of Computational Medicine, David Geffen School of Medicine at University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Yi Ding
- Bioinformatics Interdepartmental Program, University of California, Los Angeles, Los Angeles, CA 90095, USA; Department of Computational Medicine, David Geffen School of Medicine at University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Yifei Wang
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine at University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Steven Gazal
- Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
| | - Huwenbo Shi
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA; Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; OMNI Bioinformatics, Genentech, 1 DNA Way, South San Francisco, CA 94080, USA
| | - Bogdan Pasaniuc
- Bioinformatics Interdepartmental Program, University of California, Los Angeles, Los Angeles, CA 90095, USA; Department of Pathology and Laboratory Medicine, David Geffen School of Medicine at University of California, Los Angeles, Los Angeles, CA 90095, USA; Department of Human Genetics, David Geffen School of Medicine at University of California, Los Angeles, Los Angeles, CA 90095, USA; Department of Computational Medicine, David Geffen School of Medicine at University of California, Los Angeles, Los Angeles, CA 90095, USA.
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32
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Weiner DJ, Gazal S, Robinson EB, O'Connor LJ. Partitioning gene-mediated disease heritability without eQTLs. Am J Hum Genet 2022; 109:405-416. [PMID: 35143757 PMCID: PMC8948166 DOI: 10.1016/j.ajhg.2022.01.010] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2021] [Accepted: 01/13/2022] [Indexed: 12/30/2022] Open
Abstract
Unknown SNP-to-gene regulatory architecture complicates efforts to link noncoding GWAS associations with genes implicated by sequencing or functional studies. eQTLs are often used to link SNPs to genes, but expression in bulk tissue explains a small fraction of disease heritability. A simple but successful approach has been to link SNPs with nearby genes via base pair windows, but genes may often be regulated by SNPs outside their window. We propose the abstract mediation model (AMM) to estimate (1) the fraction of heritability mediated by the closest or kth-closest gene to each SNP and (2) the mediated heritability enrichment of a gene set (e.g., genes with rare-variant associations). AMM jointly estimates these quantities by matching the decay in SNP enrichment with distance from genes in the gene set. Across 47 complex traits and diseases, we estimate that the closest gene to each SNP mediates 27% (SE: 6%) of heritability and that a substantial fraction is mediated by genes outside the ten closest. Mendelian disease genes are strongly enriched for common-variant heritability; for example, just 21 dyslipidemia genes mediate 25% of LDL heritability (211× enrichment, p = 0.01). Among brain-related traits, genes involved in neurodevelopmental disorders are only about 4× enriched, but gene expression patterns are highly informative, as they have detectable differences in per-gene heritability even among weakly brain-expressed genes.
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Affiliation(s)
- Daniel J Weiner
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
| | - Steven Gazal
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA 02115, USA; Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
| | - Elise B Robinson
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA 02115, USA
| | - Luke J O'Connor
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
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33
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Fang H, Knight JC. Priority index: database of genetic targets in immune-mediated disease. Nucleic Acids Res 2022; 50:D1358-D1367. [PMID: 34751399 PMCID: PMC8728240 DOI: 10.1093/nar/gkab994] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2021] [Revised: 10/07/2021] [Accepted: 10/19/2021] [Indexed: 11/12/2022] Open
Abstract
We describe a comprehensive and unique database 'Priority index' (Pi; http://pi.well.ox.ac.uk) of prioritized genes encoding potential therapeutic targets that encompasses all major immune-mediated diseases. We provide targets at the gene level, each receiving a 5-star rating supported by: genomic evidence arising from disease genome-wide associations and functional immunogenomics, annotation evidence using ontologies restricted to genes with genomic evidence, and network evidence from protein interactions. Target genes often act together in related molecular pathways. The underlying Pi approach is unique in identifying a network of highly rated genes that mediate pathway crosstalk. In the Pi website, disease-centric pages are specially designed to enable the users to browse a complete list of prioritized genes and also a manageable list of nodal genes at the pathway crosstalk level; both switchable by clicks. Moreover, target genes are cross-referenced and supported using additional information, particularly regarding tractability, including druggable pockets viewed in 3D within protein structures. Target genes highly rated across diseases suggest drug repurposing opportunity, while genes in a particular disease reveal disease-specific targeting potential. To facilitate the ease of such utility, cross-disease comparisons involving multiple diseases are also supported. This facility, together with the faceted search, enhances integrative mining of the Pi resource to accelerate early-stage therapeutic target identification and validation leveraging human genetics.
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Affiliation(s)
- Hai Fang
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Centre for Translational Medicine at Shanghai, Ruijin Hospital affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Julian C Knight
- Wellcome Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK
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Kondratyev NV, Alfimova MV, Golov AK, Golimbet VE. Bench Research Informed by GWAS Results. Cells 2021; 10:3184. [PMID: 34831407 PMCID: PMC8623533 DOI: 10.3390/cells10113184] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2021] [Revised: 11/11/2021] [Accepted: 11/11/2021] [Indexed: 12/15/2022] Open
Abstract
Scientifically interesting as well as practically important phenotypes often belong to the realm of complex traits. To the extent that these traits are hereditary, they are usually 'highly polygenic'. The study of such traits presents a challenge for researchers, as the complex genetic architecture of such traits makes it nearly impossible to utilise many of the usual methods of reverse genetics, which often focus on specific genes. In recent years, thousands of genome-wide association studies (GWAS) were undertaken to explore the relationships between complex traits and a large number of genetic factors, most of which are characterised by tiny effects. In this review, we aim to familiarise 'wet biologists' with approaches for the interpretation of GWAS results, to clarify some issues that may seem counterintuitive and to assess the possibility of using GWAS results in experiments on various complex traits.
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Affiliation(s)
| | | | - Arkadiy K. Golov
- Mental Health Research Center, 115522 Moscow, Russia; (M.V.A.); (A.K.G.); (V.E.G.)
- Institute of Gene Biology, Russian Academy of Sciences, 119334 Moscow, Russia
| | - Vera E. Golimbet
- Mental Health Research Center, 115522 Moscow, Russia; (M.V.A.); (A.K.G.); (V.E.G.)
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35
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Vysotskiy M, Zhong X, Miller-Fleming TW, Zhou D, Cox NJ, Weiss LA. Integration of genetic, transcriptomic, and clinical data provides insight into 16p11.2 and 22q11.2 CNV genes. Genome Med 2021; 13:172. [PMID: 34715901 PMCID: PMC8557010 DOI: 10.1186/s13073-021-00972-1] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2021] [Accepted: 09/16/2021] [Indexed: 11/24/2022] Open
Abstract
BACKGROUND Deletions and duplications of the multigenic 16p11.2 and 22q11.2 copy number variant (CNV) regions are associated with brain-related disorders including schizophrenia, intellectual disability, obesity, bipolar disorder, and autism spectrum disorder (ASD). The contribution of individual CNV genes to each of these identified phenotypes is unknown, as well as the contribution of these CNV genes to other potentially subtler health implications for carriers. Hypothesizing that DNA copy number exerts most effects via impacts on RNA expression, we attempted a novel in silico fine-mapping approach in non-CNV carriers using both GWAS and biobank data. METHODS We first asked whether gene expression level in any individual gene in the CNV region alters risk for a known CNV-associated behavioral phenotype(s). Using transcriptomic imputation, we performed association testing for CNV genes within large genotyped cohorts for schizophrenia, IQ, BMI, bipolar disorder, and ASD. Second, we used a biobank containing electronic health data to compare the medical phenome of CNV carriers to controls within 700,000 individuals in order to investigate the full spectrum of health effects of the CNVs. Third, we used genotypes for over 48,000 individuals within the biobank to perform phenome-wide association studies between imputed expressions of individual 16p11.2 and 22q11.2 genes and over 1500 health traits. RESULTS Using large genotyped cohorts, we found individual genes within 16p11.2 associated with schizophrenia (TMEM219, INO80E, YPEL3), BMI (TMEM219, SPN, TAOK2, INO80E), and IQ (SPN), using conditional analysis to identify upregulation of INO80E as the driver of schizophrenia, and downregulation of SPN and INO80E as increasing BMI. We identified both novel and previously observed over-represented traits within the electronic health records of 16p11.2 and 22q11.2 CNV carriers. In the phenome-wide association study, we found seventeen significant gene-trait pairs, including psychosis (NPIPB11, SLX1B) and mood disorders (SCARF2), and overall enrichment of mental traits. CONCLUSIONS Our results demonstrate how integration of genetic and clinical data aids in understanding CNV gene function and implicates pleiotropy and multigenicity in CNV biology.
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Affiliation(s)
- Mikhail Vysotskiy
- Department of Psychiatry and Behavioral Sciences, University of California San Francisco, 513 Parnassus Ave., Health Sciences East 9th floor HSE901E, San Francisco, CA, 94143, USA
- Institute for Human Genetics, University of California San Francisco, San Francisco, CA, 94143, USA
- Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA, 94143, USA
- Pharmaceutical Sciences and Pharmacogenomics Graduate Program, University of California San Francisco, San Francisco, CA, 94143, USA
| | - Xue Zhong
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, 37232, USA
- Vanderbilt Genetics Institute, Nashville, TN, 37232, USA
| | - Tyne W Miller-Fleming
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, 37232, USA
- Vanderbilt Genetics Institute, Nashville, TN, 37232, USA
| | - Dan Zhou
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, 37232, USA
- Vanderbilt Genetics Institute, Nashville, TN, 37232, USA
| | - Nancy J Cox
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, 37232, USA
- Vanderbilt Genetics Institute, Nashville, TN, 37232, USA
| | - Lauren A Weiss
- Department of Psychiatry and Behavioral Sciences, University of California San Francisco, 513 Parnassus Ave., Health Sciences East 9th floor HSE901E, San Francisco, CA, 94143, USA.
- Institute for Human Genetics, University of California San Francisco, San Francisco, CA, 94143, USA.
- Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA, 94143, USA.
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36
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Johnson R, Burch KS, Hou K, Paciuc M, Pasaniuc B, Sankararaman S. Estimation of regional polygenicity from GWAS provides insights into the genetic architecture of complex traits. PLoS Comput Biol 2021; 17:e1009483. [PMID: 34673766 PMCID: PMC8562817 DOI: 10.1371/journal.pcbi.1009483] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2021] [Revised: 11/02/2021] [Accepted: 09/27/2021] [Indexed: 11/18/2022] Open
Abstract
The number of variants that have a non-zero effect on a trait (i.e. polygenicity) is a fundamental parameter in the study of the genetic architecture of a complex trait. Although many previous studies have investigated polygenicity at a genome-wide scale, a detailed understanding of how polygenicity varies across genomic regions is currently lacking. In this work, we propose an accurate and scalable statistical framework to estimate regional polygenicity for a complex trait. We show that our approach yields approximately unbiased estimates of regional polygenicity in simulations across a wide-range of various genetic architectures. We then partition the polygenicity of anthropometric and blood pressure traits across 6-Mb genomic regions (N = 290K, UK Biobank) and observe that all analyzed traits are highly polygenic: over one-third of regions harbor at least one causal variant for each of the traits analyzed. Additionally, we observe wide variation in regional polygenicity: on average across all traits, 48.9% of regions contain at least 5 causal SNPs, 5.44% of regions contain at least 50 causal SNPs. Finally, we find that heritability is proportional to polygenicity at the regional level, which is consistent with the hypothesis that heritability enrichments are largely driven by the variation in the number of causal SNPs. The proportion of SNPs with nonzero effects on a trait, or polygenicity, is a key quantity used to describe the genetic architecture of a complex trait. Furthermore, identifying the specific genomic regions that contribute to trait variation requires an understanding of how the number of causal SNPs varies across regions of the genome (regional polygenicity). In this work, we propose a statistical framework to estimate regional polygenicity for a complex trait using marginal effect sizes from GWAS and LD information. We demonstrate in simulation and empirical data that our approach accurately and efficiently estimates regional polygenicity. We find that SNP-heritability is proportional to polygenicity both on the genome-wide and regional scale, suggesting that the observed differences in heritability across traits stem from differences in the underlying number of causal SNPs.
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Affiliation(s)
- Ruth Johnson
- Department of Computer Science, University of California, Los Angeles, California, United States of America
- * E-mail: (RJ); (SS)
| | - Kathryn S. Burch
- Bioinformatics Interdepartmental Program, University of California, Los Angeles, California, United States of America
| | - Kangcheng Hou
- Bioinformatics Interdepartmental Program, University of California, Los Angeles, California, United States of America
| | - Mario Paciuc
- Department of Statistics, Rice University, Houston, Texas, United States of America
| | - Bogdan Pasaniuc
- Bioinformatics Interdepartmental Program, University of California, Los Angeles, California, United States of America
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, California, United States of America
- Department of Human Genetics, University of California, Los Angeles, California, United States of America
- Department of Computational Medicine, David Geffen School of Medicine, University of California, Los Angeles, California, United States of America
| | - Sriram Sankararaman
- Department of Computer Science, University of California, Los Angeles, California, United States of America
- Bioinformatics Interdepartmental Program, University of California, Los Angeles, California, United States of America
- Department of Human Genetics, University of California, Los Angeles, California, United States of America
- Department of Computational Medicine, David Geffen School of Medicine, University of California, Los Angeles, California, United States of America
- * E-mail: (RJ); (SS)
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37
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Yates J, Gutiérrez-Sacristán A, Jouhet V, LeBlanc K, Esteves C, DeSain TN, Benik N, Stedman J, Palmer N, Mellon G, Kohane I, Avillach P. Finding commonalities in rare diseases through the undiagnosed diseases network. J Am Med Inform Assoc 2021; 28:1694-1702. [PMID: 34009343 PMCID: PMC8324228 DOI: 10.1093/jamia/ocab050] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2020] [Accepted: 03/05/2021] [Indexed: 11/14/2022] Open
Abstract
Objective When studying any specific rare disease, heterogeneity and scarcity of affected individuals has historically hindered investigators from discerning on what to focus to understand and diagnose a disease. New nongenomic methodologies must be developed that identify similarities in seemingly dissimilar conditions. Materials and Methods This observational study analyzes 1042 patients from the Undiagnosed Diseases Network (2015-2019), a multicenter, nationwide research study using phenotypic data annotated by specialized staff using Human Phenotype Ontology terms. We used Louvain community detection to cluster patients linked by Jaccard pairwise similarity and 2 support vector classifier to assign new cases. We further validated the clusters’ most representative comorbidities using a national claims database (67 million patients). Results Patients were divided into 2 groups: those with symptom onset before 18 years of age (n = 810) and at 18 years of age or older (n = 232) (average symptom onset age: 10 [interquartile range, 0-14] years). For 810 pediatric patients, we identified 4 statistically significant clusters. Two clusters were characterized by growth disorders, and developmental delay enriched for hypotonia presented a higher likelihood of diagnosis. Support vector classifier showed 0.89 balanced accuracy (0.83 for Human Phenotype Ontology terms only) on test data. Discussions To set the framework for future discovery, we chose as our endpoint the successful grouping of patients by phenotypic similarity and provide a classification tool to assign new patients to those clusters. Conclusion This study shows that despite the scarcity and heterogeneity of patients, we can still find commonalities that can potentially be harnessed to uncover new insights and targets for therapy.
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Affiliation(s)
- Josephine Yates
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA
| | | | - Vianney Jouhet
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA
| | - Kimberly LeBlanc
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA
| | - Cecilia Esteves
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA
| | | | - Thomas N DeSain
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA
| | - Nick Benik
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA
| | - Jason Stedman
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA
| | - Nathan Palmer
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA
| | - Guillaume Mellon
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA
| | - Isaac Kohane
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA
| | - Paul Avillach
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA
- Corresponding Author: Paul Avillach, MD, PhD, 10 Shattuck Street, 02115 Boston, MA, USA;
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Abstract
Disease classification, or nosology, was historically driven by careful examination of clinical features of patients. As technologies to measure and understand human phenotypes advanced, so too did classifications of disease, and the advent of genetic data has led to a surge in genetic subtyping in the past decades. Although the fundamental process of refining disease definitions and subtypes is shared across diverse fields, each field is driven by its own goals and technological expertise, leading to inconsistent and conflicting definitions of disease subtypes. Here, we review several classical and recent subtypes and subtyping approaches and provide concrete definitions to delineate subtypes. In particular, we focus on subtypes with distinct causal disease biology, which are of primary interest to scientists, and subtypes with pragmatic medical benefits, which are of primary interest to physicians. We propose genetic heterogeneity as a gold standard for establishing biologically distinct subtypes of complex polygenic disease. We focus especially on methods to find and validate genetic subtypes, emphasizing common pitfalls and how to avoid them.
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Affiliation(s)
- Andy Dahl
- Section of Genetic Medicine, Department of Medicine, University of Chicago, Chicago, Illinois 60637, USA; .,Department of Neurology, University of California, Los Angeles, California 90024, USA; .,Department of Computational Medicine, University of California, Los Angeles, California 90095, USA
| | - Noah Zaitlen
- Department of Neurology, University of California, Los Angeles, California 90024, USA; .,Department of Computational Medicine, University of California, Los Angeles, California 90095, USA
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Zhu X, Duren Z, Wong WH. Modeling regulatory network topology improves genome-wide analyses of complex human traits. Nat Commun 2021; 12:2851. [PMID: 33990562 PMCID: PMC8121952 DOI: 10.1038/s41467-021-22588-0] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2020] [Accepted: 03/03/2021] [Indexed: 01/22/2023] Open
Abstract
Genome-wide association studies (GWAS) have cataloged many significant associations between genetic variants and complex traits. However, most of these findings have unclear biological significance, because they often have small effects and occur in non-coding regions. Integration of GWAS with gene regulatory networks addresses both issues by aggregating weak genetic signals within regulatory programs. Here we develop a Bayesian framework that integrates GWAS summary statistics with regulatory networks to infer genetic enrichments and associations simultaneously. Our method improves upon existing approaches by explicitly modeling network topology to assess enrichments, and by automatically leveraging enrichments to identify associations. Applying this method to 18 human traits and 38 regulatory networks shows that genetic signals of complex traits are often enriched in interconnections specific to trait-relevant cell types or tissues. Prioritizing variants within enriched networks identifies known and previously undescribed trait-associated genes revealing biological and therapeutic insights.
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Affiliation(s)
- Xiang Zhu
- Department of Statistics, The Pennsylvania State University, University Park, PA, 16802, USA. .,Huck Institutes of the Life Sciences, The Pennsylvania State University, University Park, PA, 16802, USA. .,Department of Statistics, Stanford University, Stanford, CA, 94305, USA.
| | - Zhana Duren
- Department of Statistics, Stanford University, Stanford, CA, 94305, USA.,Center for Human Genetics, Department of Genetics and Biochemistry, Clemson University, Greenwood, SC, 29646, USA
| | - Wing Hung Wong
- Department of Statistics, Stanford University, Stanford, CA, 94305, USA. .,Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, CA, 94305, USA.
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40
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Findley AS, Monziani A, Richards AL, Rhodes K, Ward MC, Kalita CA, Alazizi A, Pazokitoroudi A, Sankararaman S, Wen X, Lanfear DE, Pique-Regi R, Gilad Y, Luca F. Functional dynamic genetic effects on gene regulation are specific to particular cell types and environmental conditions. eLife 2021; 10:e67077. [PMID: 33988505 PMCID: PMC8248987 DOI: 10.7554/elife.67077] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2021] [Accepted: 05/13/2021] [Indexed: 12/14/2022] Open
Abstract
Genetic effects on gene expression and splicing can be modulated by cellular and environmental factors; yet interactions between genotypes, cell type, and treatment have not been comprehensively studied together. We used an induced pluripotent stem cell system to study multiple cell types derived from the same individuals and exposed them to a large panel of treatments. Cellular responses involved different genes and pathways for gene expression and splicing and were highly variable across contexts. For thousands of genes, we identified variable allelic expression across contexts and characterized different types of gene-environment interactions, many of which are associated with complex traits. Promoter functional and evolutionary features distinguished genes with elevated allelic imbalance mean and variance. On average, half of the genes with dynamic regulatory interactions were missed by large eQTL mapping studies, indicating the importance of exploring multiple treatments to reveal previously unrecognized regulatory loci that may be important for disease.
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Affiliation(s)
- Anthony S Findley
- Center for Molecular Medicine and Genetics, Wayne State UniversityDetroitUnited States
| | - Alan Monziani
- Center for Molecular Medicine and Genetics, Wayne State UniversityDetroitUnited States
| | - Allison L Richards
- Center for Molecular Medicine and Genetics, Wayne State UniversityDetroitUnited States
| | - Katherine Rhodes
- Department of Human Genetics, University of ChicagoChicagoUnited States
| | - Michelle C Ward
- Department of Medicine, University of ChicagoChicagoUnited States
| | - Cynthia A Kalita
- Center for Molecular Medicine and Genetics, Wayne State UniversityDetroitUnited States
| | - Adnan Alazizi
- Center for Molecular Medicine and Genetics, Wayne State UniversityDetroitUnited States
| | | | - Sriram Sankararaman
- Department of Computer Science, UCLALos AngelesUnited States
- Department of Human Genetics, UCLALos AngelesUnited States
- Department of Computational Medicine, UCLALos AngelesUnited States
| | - Xiaoquan Wen
- Department of Biostatistics, University of MichiganAnn ArborUnited States
| | - David E Lanfear
- Center for Individualized and Genomic Medicine Research, Henry Ford HospitalDetroitUnited States
| | - Roger Pique-Regi
- Center for Molecular Medicine and Genetics, Wayne State UniversityDetroitUnited States
- Department of Obstetrics and Gynecology, Wayne State UniversityDetroitUnited States
| | - Yoav Gilad
- Department of Human Genetics, University of ChicagoChicagoUnited States
- Department of Medicine, University of ChicagoChicagoUnited States
| | - Francesca Luca
- Center for Molecular Medicine and Genetics, Wayne State UniversityDetroitUnited States
- Department of Obstetrics and Gynecology, Wayne State UniversityDetroitUnited States
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41
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Babushkina NP, Postrigan AE, Kucher AN. Involvement of Variants in the Genes Encoding BRCA1-Associated Genome Surveillance Complex (BASC) in the Development of Human Common Diseases. Mol Biol 2021. [DOI: 10.1134/s0026893321020047] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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Sobczyk MK, Gaunt TR, Paternoster L. MendelVar: gene prioritization at GWAS loci using phenotypic enrichment of Mendelian disease genes. Bioinformatics 2021; 37:1-8. [PMID: 33836063 PMCID: PMC8034535 DOI: 10.1093/bioinformatics/btaa1096] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2020] [Revised: 11/30/2020] [Accepted: 01/08/2021] [Indexed: 11/26/2022] Open
Abstract
Motivation Gene prioritization at human GWAS loci is challenging due to linkage-disequilibrium and long-range gene regulatory mechanisms. However, identifying the causal gene is crucial to enable identification of potential drug targets and better understanding of molecular mechanisms. Mapping GWAS traits to known phenotypically relevant Mendelian disease genes near a locus is a promising approach to gene prioritization. Results We present MendelVar, a comprehensive tool that integrates knowledge from four databases on Mendelian disease genes with enrichment testing for a range of associated functional annotations such as Human Phenotype Ontology, Disease Ontology and variants from ClinVar. This open web-based platform enables users to strengthen the case for causal importance of phenotypically matched candidate genes at GWAS loci. We demonstrate the use of MendelVar in post-GWAS gene annotation for type 1 diabetes, type 2 diabetes, blood lipids and atopic dermatitis. Availability and implementation MendelVar is freely available at https://mendelvar.mrcieu.ac.uk Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- M K Sobczyk
- MRC Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, Bristol BS8 2BN, UK
| | - T R Gaunt
- MRC Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, Bristol BS8 2BN, UK
| | - L Paternoster
- MRC Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, Bristol BS8 2BN, UK
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43
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Tukker AM, Royal CD, Bowman AB, McAllister KA. The Impact of Environmental Factors on Monogenic Mendelian Diseases. Toxicol Sci 2021; 181:3-12. [PMID: 33677604 PMCID: PMC8599782 DOI: 10.1093/toxsci/kfab022] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Environmental factors and gene-environment interactions modify the variable expressivity, progression, severity, and onset of some classic (monogenic) Mendelian-inherited genetic diseases. Cystic fibrosis, Huntington disease, Parkinson's disease, and sickle cell disease are examples of well-known Mendelian disorders that are influenced by exogenous exposures. Environmental factors may act by direct or indirect mechanisms to modify disease severity, timing, and presentation, including through epigenomic influences, protein misfolding, miRNA alterations, transporter activity, and mitochondrial effects. Because pathological features of early-onset Mendelian diseases can mimic later onset complex diseases, we propose that studies of environmental exposure vulnerabilities using monogenic model systems of rare Mendelian diseases have high potential to provide insight into complex disease phenotypes arising from multi-genetic/multi-toxicant interactions. Mendelian disorders can be modeled by homologous mutations in animal model systems with strong recapitulation of human disease etiology and natural history, providing an important advantage for study of these diseases. Monogenic high penetrant mutations are ideal for toxicant challenge studies with a wide variety of environmental stressors, because background genetic variability may be less able to alter the relatively strong phenotype driving disease-causing mutations. These models promote mechanistic understandings of gene-environment interactions and biological pathways relevant to both Mendelian and related sporadic complex disease outcomes by creating a sensitized background for relevant environmental risk factors. Additionally, rare disease communities are motivated research participants, creating the potential of strong research allies among rare Mendelian disease advocacy groups and disease registries and providing a variety of translational opportunities that are under-utilized in genetic or environmental health science.
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Affiliation(s)
- Anke M Tukker
- School of Health Sciences, Purdue University, West Lafayette, Indiana 47907-2051
| | - Charmaine D Royal
- Departments of African and African American Studies, Biology, Global Health, and Family Medicine and Community Health and Center on Genomics, Race, Identity, Difference, Duke University, Durham, North Carolina 27708
| | - Aaron B Bowman
- School of Health Sciences, Purdue University, West Lafayette, Indiana 47907-2051
| | - Kimberly A McAllister
- Genes Environment and Health Branch, Division of Extramural Research and Training, National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina 27709
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Abstract
The known genetic architecture of blood pressure now comprises >30 genes, with rare variants resulting in monogenic forms of hypertension or hypotension and >1,477 common single-nucleotide polymorphisms (SNPs) being associated with the blood pressure phenotype. Monogenic blood pressure syndromes predominantly involve the renin-angiotensin-aldosterone system and the adrenal glucocorticoid pathway, with a smaller fraction caused by neuroendocrine tumours of the sympathetic and parasympathetic nervous systems. The SNPs identified in genome-wide association studies (GWAS) as being associated with the blood pressure phenotype explain only approximately 27% of the 30-50% estimated heritability of blood pressure, and the effect of each SNP on the blood pressure phenotype is small. A paucity of SNPs from GWAS are mapped to known genes causing monogenic blood pressure syndromes. For example, a GWAS signal mapped to the gene encoding uromodulin has been shown to affect blood pressure by influencing sodium homeostasis, and the effects of another GWAS signal were mediated by endothelin. However, the majority of blood pressure-associated SNPs show pleiotropic associations. Unravelling these associations can potentially help us to understand the underlying biological pathways. In this Review, we appraise the current knowledge of blood pressure genomics, explore the causal pathways for hypertension identified in Mendelian randomization studies and highlight the opportunities for drug repurposing and pharmacogenomics for the treatment of hypertension.
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Affiliation(s)
- Sandosh Padmanabhan
- BHF Glasgow Cardiovascular Research Centre, Institute of Cardiovascular and Medical Sciences, University of Glasgow, Glasgow, UK
| | - Anna F Dominiczak
- BHF Glasgow Cardiovascular Research Centre, Institute of Cardiovascular and Medical Sciences, University of Glasgow, Glasgow, UK.
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45
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Haukka J, Sandholm N, Valo E, Forsblom C, Harjutsalo V, Cole JB, McGurnaghan SJ, Colhoun HM, Groop PH. Novel Linkage Peaks Discovered for Diabetic Nephropathy in Individuals With Type 1 Diabetes. Diabetes 2021; 70:986-995. [PMID: 33414249 PMCID: PMC8928864 DOI: 10.2337/db20-0158] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/13/2020] [Accepted: 01/01/2021] [Indexed: 11/13/2022]
Abstract
Genome-wide association studies (GWAS) and linkage studies have had limited success in identifying genome-wide significantly linked regions or risk loci for diabetic nephropathy (DN) in individuals with type 1 diabetes (T1D). As GWAS cohorts have grown, they have also included more documented and undocumented familial relationships. Here we computationally inferred and manually curated pedigrees in a study cohort of >6,000 individuals with T1D and their relatives without diabetes. We performed a linkage study for 177 pedigrees consisting of 452 individuals with T1D and their relatives using a genome-wide genotyping array with >300,000 single nucleotide polymorphisms and PSEUDOMARKER software. Analysis resulted in genome-wide significant linkage peaks on eight chromosomal regions from five chromosomes (logarithm of odds score >3.3). The highest peak was localized at the HLA region on chromosome 6p, but whether the peak originated from T1D or DN remained ambiguous. Of other significant peaks, the chromosome 4p22 region was localized on top of ARHGAP24, a gene associated with focal segmental glomerulosclerosis, suggesting this gene may play a role in DN as well. Furthermore, rare variants have been associated with DN and chronic kidney disease near the 4q25 peak, localized on top of CCSER1.
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Affiliation(s)
- Jani Haukka
- Folkhälsan Institute of Genetics, Folkhälsan Research Center, Helsinki, Finland
- Nephrology, Abdominal Center, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
- Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Niina Sandholm
- Folkhälsan Institute of Genetics, Folkhälsan Research Center, Helsinki, Finland
- Nephrology, Abdominal Center, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
- Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Erkka Valo
- Folkhälsan Institute of Genetics, Folkhälsan Research Center, Helsinki, Finland
- Nephrology, Abdominal Center, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
- Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Carol Forsblom
- Folkhälsan Institute of Genetics, Folkhälsan Research Center, Helsinki, Finland
- Nephrology, Abdominal Center, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
- Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Valma Harjutsalo
- Folkhälsan Institute of Genetics, Folkhälsan Research Center, Helsinki, Finland
- Nephrology, Abdominal Center, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
- Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki, Helsinki, Finland
- Chronic Disease Prevention Unit, National Institute for Health and Welfare, Helsinki, Finland
| | - Joanne B. Cole
- Division of Endocrinology, Department of Pediatrics, Boston Children’s Hospital, Boston, MA
- Programs in Metabolism, Broad Institute, Cambridge, MA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA
- Programs in Medical and Population Genetics, Broad Institute, Cambridge, MA
| | - Stuart J. McGurnaghan
- Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, U.K
| | - Helen M. Colhoun
- Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, U.K
| | - Per-Henrik Groop
- Folkhälsan Institute of Genetics, Folkhälsan Research Center, Helsinki, Finland
- Nephrology, Abdominal Center, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
- Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki, Helsinki, Finland
- Department of Diabetes, Central Clinical School, Monash University, Melbourne, Australia
- Corresponding author: Per-Henrik Groop,
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47
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Hernandez LM, Kim M, Hoftman GD, Haney JR, de la Torre-Ubieta L, Pasaniuc B, Gandal MJ. Transcriptomic Insight Into the Polygenic Mechanisms Underlying Psychiatric Disorders. Biol Psychiatry 2021; 89:54-64. [PMID: 32792264 PMCID: PMC7718368 DOI: 10.1016/j.biopsych.2020.06.005] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/26/2020] [Revised: 05/15/2020] [Accepted: 06/03/2020] [Indexed: 12/20/2022]
Abstract
Over the past decade, large-scale genetic studies have successfully identified hundreds of genetic variants robustly associated with risk for psychiatric disorders. However, mechanistic insight and clinical translation continue to lag the pace of risk variant identification, hindered by the sheer number of targets and their predominant noncoding localization, as well as pervasive pleiotropy and incomplete penetrance. Successful next steps require identification of "causal" genetic variants and their proximal biological consequences; placing variants within biologically defined functional contexts, reflecting specific molecular pathways, cell types, circuits, and developmental windows; and characterizing the downstream, convergent neurobiological impact of polygenicity within an individual. Here, we discuss opportunities and challenges of high-throughput transcriptomic profiling in the human brain, and how transcriptomic approaches can help pinpoint mechanisms underlying genetic risk for psychiatric disorders at a scale necessary to tackle daunting levels of polygenicity. These include transcriptome-wide association studies for risk gene prioritization through integration of genome-wide association studies with expression quantitative trait loci. We outline transcriptomic results that inform our understanding of the brain-level molecular pathology of psychiatric disorders, including autism spectrum disorder, bipolar disorder, major depressive disorder, and schizophrenia. Finally, we discuss systems-level approaches for integration of distinct genetic, genomic, and phenotypic levels, including combining spatially resolved gene expression and human neuroimaging maps. Results highlight the importance of understanding gene expression (dys)regulation across human brain development as a major contributor to psychiatric disease pathogenesis, from common variants acting as expression quantitative trait loci to rare variants enriched for gene expression regulatory pathways.
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Affiliation(s)
- Leanna M Hernandez
- Department of Psychiatry, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California; Intellectual and Developmental Disabilities Research Center, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California
| | - Minsoo Kim
- Department of Psychiatry, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California; Intellectual and Developmental Disabilities Research Center, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California; Program in Neurobehavioral Genetics, Semel Institute, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California; Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California
| | - Gil D Hoftman
- Department of Psychiatry, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California
| | - Jillian R Haney
- Department of Psychiatry, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California; Intellectual and Developmental Disabilities Research Center, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California
| | - Luis de la Torre-Ubieta
- Department of Psychiatry, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California; Intellectual and Developmental Disabilities Research Center, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California
| | - Bogdan Pasaniuc
- Program in Neurobehavioral Genetics, Semel Institute, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California; Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California; Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California
| | - Michael J Gandal
- Department of Psychiatry, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California; Intellectual and Developmental Disabilities Research Center, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California; Program in Neurobehavioral Genetics, Semel Institute, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California; Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California.
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48
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Kim SS, Dey KK, Weissbrod O, Márquez-Luna C, Gazal S, Price AL. Improving the informativeness of Mendelian disease-derived pathogenicity scores for common disease. Nat Commun 2020; 11:6258. [PMID: 33288751 PMCID: PMC7721881 DOI: 10.1038/s41467-020-20087-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2020] [Accepted: 11/09/2020] [Indexed: 02/08/2023] Open
Abstract
Despite considerable progress on pathogenicity scores prioritizing variants for Mendelian disease, little is known about the utility of these scores for common disease. Here, we assess the informativeness of Mendelian disease-derived pathogenicity scores for common disease and improve upon existing scores. We first apply stratified linkage disequilibrium (LD) score regression to evaluate published pathogenicity scores across 41 common diseases and complex traits (average N = 320K). Several of the resulting annotations are informative for common disease, even after conditioning on a broad set of functional annotations. We then improve upon published pathogenicity scores by developing AnnotBoost, a machine learning framework to impute and denoise pathogenicity scores using a broad set of functional annotations. AnnotBoost substantially increases the informativeness for common disease of both previously uninformative and previously informative pathogenicity scores, implying that Mendelian and common disease variants share similar properties. The boosted scores also produce improvements in heritability model fit and in classifying disease-associated, fine-mapped SNPs. Our boosted scores may improve fine-mapping and candidate gene discovery for common disease.
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Affiliation(s)
- Samuel S Kim
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, 02142, USA.
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA.
| | - Kushal K Dey
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA
| | - Omer Weissbrod
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA
| | - Carla Márquez-Luna
- Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Steven Gazal
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA
| | - Alkes L Price
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA.
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA.
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA.
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49
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A phenome-wide association study of 26 mendelian genes reveals phenotypic expressivity of common and rare variants within the general population. PLoS Genet 2020; 16:e1008802. [PMID: 33226994 PMCID: PMC7735621 DOI: 10.1371/journal.pgen.1008802] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2019] [Revised: 12/14/2020] [Accepted: 04/27/2020] [Indexed: 02/06/2023] Open
Abstract
The clinical evaluation of a genetic syndrome relies upon recognition of a characteristic pattern of signs or symptoms to guide targeted genetic testing for confirmation of the diagnosis. However, individuals displaying a single phenotype of a complex syndrome may not meet criteria for clinical diagnosis or genetic testing. Here, we present a phenome-wide association study (PheWAS) approach to systematically explore the phenotypic expressivity of common and rare alleles in genes associated with four well-described syndromic diseases (Alagille (AS), Marfan (MS), DiGeorge (DS), and Noonan (NS) syndromes) in the general population. Using human phenotype ontology (HPO) terms, we systematically mapped 60 phenotypes related to AS, MS, DS and NS in 337,198 unrelated white British from the UK Biobank (UKBB) based on their hospital admission records, self-administrated questionnaires, and physiological measurements. We performed logistic regression adjusting for age, sex, and the first 5 genetic principal components, for each phenotype and each variant in the target genes (JAG1, NOTCH2 FBN1, PTPN1 and RAS-opathy genes, and genes in the 22q11.2 locus) and performed a gene burden test. Overall, we observed multiple phenotype-genotype correlations, such as the association between variation in JAG1, FBN1, PTPN11 and SOS2 with diastolic and systolic blood pressure; and pleiotropy among multiple variants in syndromic genes. For example, rs11066309 in PTPN11 was significantly associated with a lower body mass index, an increased risk of hypothyroidism and a smaller size for gestational age, all in concordance with NS-related phenotypes. Similarly, rs589668 in FBN1 was associated with an increase in body height and blood pressure, and a reduced body fat percentage as observed in Marfan syndrome. Our findings suggest that the spectrum of associations of common and rare variants in genes involved in syndromic diseases can be extended to individual phenotypes within the general population. Standard medical evaluation of genetic syndromes relies upon recognizing a characteristic pattern of signs or symptoms to guide targeted genetic testing for confirmation of the diagnosis. This may lead to missing diagnoses in patients with silent or a low expressed form of the syndrome. Here we take advantage of a rich electronic health record, various phenotypic measurements, and genetic information in 337,198 unrelated white British from the UKBB, to study the relation between single syndromic disease phenotypes and genes related to syndromic disease. We show multiple phenotype-genotype associations in concordance with phenotypes variations found in syndromic diseases. For example, we show that a commonly found variant in FBN1 was associated with high standing/sitting height ratio and reduced body fat percentage as observed in individuals with Marfan syndrome. Our findings suggest that common and rare alleles in syndromic disease genes are causative of individual component phenotypes present in a general population; further research is needed to characterize the pleiotropic effect of alleles in syndromic genes in persons without the syndromic disease.
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50
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Abstract
Human genetic studies of diseases that are multifactorial and prevalent have generated a wealth of knowledge about the genetic architecture of chronic diseases. Generalizable attributes are shaping the development of models to explain how the human genome influences our health and can be leveraged to improve it. Importantly, both rare and common genetic variants contribute to disease risk and provide complementary information. Although initial genetic studies of alopecia areata have yielded insight with high clinical impact, there remains a number of important unanswered questions pertaining to disease biology and patient care that could be addressed by further genetic investigations.
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Affiliation(s)
- Lynn Petukhova
- Department of Dermatology, College of Physicians and Surgeons, Columbia University, New York, New York, USA; Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, New York, USA.
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