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Amiri Roudbar M, Vahedi SM, Jin J, Jahangiri M, Lanjanian H, Habibi D, Masjoudi S, Riahi P, Fateh ST, Neshati F, Zahedi AS, Moazzam-Jazi M, Najd-Hassan-Bonab L, Mousavi SF, Asgarian S, Zarkesh M, Moghaddas MR, Tenesa A, Kazemnejad A, Vahidnezhad H, Hakonarson H, Azizi F, Hedayati M, Daneshpour MS, Akbarzadeh M. The effect of family structure on the still-missing heritability and genomic prediction accuracy of type 2 diabetes. Hum Genomics 2024; 18:98. [PMID: 39256828 PMCID: PMC11389528 DOI: 10.1186/s40246-024-00669-7] [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: 05/30/2024] [Accepted: 08/26/2024] [Indexed: 09/12/2024] Open
Abstract
This study aims to assess the effect of familial structures on the still-missing heritability estimate and prediction accuracy of Type 2 Diabetes (T2D) using pedigree estimated risk values (ERV) and genomic ERV. We used 11,818 individuals (T2D cases: 2,210) with genotype (649,932 SNPs) and pedigree information from the ongoing periodic cohort study of the Iranian population project. We considered three different familial structure scenarios, including (i) all families, (ii) all families with ≥ 1 generation, and (iii) families with ≥ 1 generation in which both case and control individuals are presented. Comprehensive simulation strategies were implemented to quantify the difference between estimates of [Formula: see text] and [Formula: see text]. A proportion of still-missing heritability in T2D could be explained by overestimation of pedigree-based heritability due to the presence of families with individuals having only one of the two disease statuses. Our research findings underscore the significance of including families with only case/control individuals in cohort studies. The presence of such family structures (as observed in scenarios i and ii) contributes to a more accurate estimation of disease heritability, addressing the underestimation that was previously overlooked in prior research. However, when predicting disease risk, the absence of these families (as seen in scenario iii) can yield the highest prediction accuracy and the strongest correlation with Polygenic Risk Scores. Our findings represent the first evidence of the important contribution of familial structure for heritability estimations and genomic prediction studies in T2D.
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Affiliation(s)
- Mahmoud Amiri Roudbar
- Department of Animal Science, Safiabad-Dezful Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education & Extension Organization, Dezful, Iran
| | - Seyed Milad Vahedi
- Department of Animal Science and Aquaculture, Dalhousie University, Bible Hill, NS, B2N5E3, Canada
| | - Jin Jin
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA, USA
| | - Mina Jahangiri
- Department of Biostatistics, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran
| | - Hossein Lanjanian
- Cellular and Molecular Endocrine Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Danial Habibi
- Cellular and Molecular Endocrine Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
- Department of Biostatistics and Epidemiology School of Public Health, Babol University of Medical Sciences, Babol, Iran
| | - Sajedeh Masjoudi
- Cellular and Molecular Endocrine Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Parisa Riahi
- Cellular and Molecular Endocrine Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | | | - Farideh Neshati
- Cellular and Molecular Endocrine Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Asiyeh Sadat Zahedi
- Cellular and Molecular Endocrine Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Maryam Moazzam-Jazi
- Cellular and Molecular Endocrine Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Leila Najd-Hassan-Bonab
- Cellular and Molecular Endocrine Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Seyedeh Fatemeh Mousavi
- Department of Animal Breeding and Genetics, Swedish University of Agricultural Sciences, Uppsala, Sweden
| | - Sara Asgarian
- Cellular and Molecular Endocrine Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Maryam Zarkesh
- Cellular and Molecular Endocrine Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Mohammad Reza Moghaddas
- Cellular and Molecular Endocrine Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Albert Tenesa
- MRC Human Genetics Unit, Institute of Genetics and Cancer, The University of Edinburgh, Edinburgh, UK
- The Roslin Institute, Royal (Dick) School of Veterinary Studies, The University of Edinburgh, Midlothian, UK
| | - Anoshirvan Kazemnejad
- Department of Biostatistics, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran
| | - Hassan Vahidnezhad
- Center for Applied Genomics (CAG), Children's Hospital of Philadelphia, 3615 Civic Center Blvd, Abramson Building, Philadelphia, PA, 19104, USA
- Department of Pediatrics, The Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Division of Human Genetics, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
| | - Hakon Hakonarson
- Center for Applied Genomics (CAG), Children's Hospital of Philadelphia, 3615 Civic Center Blvd, Abramson Building, Philadelphia, PA, 19104, USA
- Department of Pediatrics, The Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Division of Human Genetics, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
- Division of Pulmonary Medicine, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
- Faculty of Medicine, University of Iceland, Reykjavik, Iceland
| | - Fereidoun Azizi
- Endocrine Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Mehdi Hedayati
- Cellular and Molecular Endocrine Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Maryam Sadat Daneshpour
- Cellular and Molecular Endocrine Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
| | - Mahdi Akbarzadeh
- Cellular and Molecular Endocrine Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
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2
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Liu H, Guan L, Su X, Zhao L, Shu Q, Zhang J. A broken network of susceptibility genes in the monocytes of Crohn's disease patients. Life Sci Alliance 2024; 7:e202302394. [PMID: 38925865 PMCID: PMC11208737 DOI: 10.26508/lsa.202302394] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Revised: 06/15/2024] [Accepted: 06/17/2024] [Indexed: 06/28/2024] Open
Abstract
Genome-wide association studies have identified over 200 genetic loci associated with inflammatory bowel disease; however, the mechanism of such a large amount of susceptibility genes remains uncertain. In this study, we integrated bioinformatics analysis and two independent single-cell transcriptome datasets to investigate the expression network of 232 susceptibility genes in Crohn's disease (CD) patients and healthy controls. The study revealed that most of the susceptibility genes are specifically and strictly expressed in the monocytes of the human intestinal tract. The susceptibility genes established a network within the monocytes of health control. The robustness of a gene network may prevent disease onset that is influenced by the genetic and environmental alteration in the expression of susceptibility genes. In contrast, we showed a sparse network in pediatric/adult CD patients, suggesting the broken network contributed to the CD etiology. The network status of susceptibility genes at the single-cell level of monocytes provided novel insight into the etiology.
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Affiliation(s)
- Hankui Liu
- Hebei Industrial Technology Research Institute of Genomics in Maternal & Child Health, Clin Lab, BGI Genomics, Shijiazhuang, China
- BGI Genomics, Shenzhen, China
| | - Liping Guan
- Hebei Industrial Technology Research Institute of Genomics in Maternal & Child Health, Clin Lab, BGI Genomics, Shijiazhuang, China
- BGI Genomics, Shenzhen, China
- Department of Biology, University of Copenhagen, Copenhagen, Denmark
| | - Xi Su
- BGI Genomics, Shenzhen, China
- Department of Biology, University of Copenhagen, Copenhagen, Denmark
| | - Lijian Zhao
- Hebei Industrial Technology Research Institute of Genomics in Maternal & Child Health, Clin Lab, BGI Genomics, Shijiazhuang, China
- BGI Genomics, Shenzhen, China
- Hebei Medical University, Shijiazhuang, China
| | - Qing Shu
- Department of Gastroenterology, The First Affiliated Hospital of Shenzhen University, Shenzhen Second People's Hospital, Shenzhen, China
| | - Jianguo Zhang
- Hebei Industrial Technology Research Institute of Genomics in Maternal & Child Health, Clin Lab, BGI Genomics, Shijiazhuang, China
- BGI Research, Shenzhen, China
- Hebei Medical University, Shijiazhuang, China
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3
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Hoffmann M, Poschenrieder JM, Incudini M, Baier S, Fritz A, Maier A, Hartung M, Hoffmann C, Trummer N, Adamowicz K, Picciani M, Scheibling E, Harl MV, Lesch I, Frey H, Kayser S, Wissenberg P, Schwartz L, Hafner L, Acharya A, Hackl L, Grabert G, Lee SG, Cho G, Cloward ME, Jankowski J, Lee HK, Tsoy O, Wenke N, Pedersen AG, Bønnelykke K, Mandarino A, Melograna F, Schulz L, Climente-González H, Wilhelm M, Iapichino L, Wienbrandt L, Ellinghaus D, Van Steen K, Grossi M, Furth PA, Hennighausen L, Di Pierro A, Baumbach J, Kacprowski T, List M, Blumenthal DB. Network medicine-based epistasis detection in complex diseases: ready for quantum computing. Nucleic Acids Res 2024:gkae697. [PMID: 39175109 DOI: 10.1093/nar/gkae697] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2023] [Revised: 07/12/2024] [Accepted: 08/01/2024] [Indexed: 08/24/2024] Open
Abstract
Most heritable diseases are polygenic. To comprehend the underlying genetic architecture, it is crucial to discover the clinically relevant epistatic interactions (EIs) between genomic single nucleotide polymorphisms (SNPs) (1-3). Existing statistical computational methods for EI detection are mostly limited to pairs of SNPs due to the combinatorial explosion of higher-order EIs. With NeEDL (network-based epistasis detection via local search), we leverage network medicine to inform the selection of EIs that are an order of magnitude more statistically significant compared to existing tools and consist, on average, of five SNPs. We further show that this computationally demanding task can be substantially accelerated once quantum computing hardware becomes available. We apply NeEDL to eight different diseases and discover genes (affected by EIs of SNPs) that are partly known to affect the disease, additionally, these results are reproducible across independent cohorts. EIs for these eight diseases can be interactively explored in the Epistasis Disease Atlas (https://epistasis-disease-atlas.com). In summary, NeEDL demonstrates the potential of seamlessly integrated quantum computing techniques to accelerate biomedical research. Our network medicine approach detects higher-order EIs with unprecedented statistical and biological evidence, yielding unique insights into polygenic diseases and providing a basis for the development of improved risk scores and combination therapies.
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Affiliation(s)
- Markus Hoffmann
- Data Science in Systems Biology, School of Life Sciences, Technical University of Munich, Freising, Germany
- Institute for Advanced Study (Lichtenbergstrasse 2 a) Technical University of Munich, D-85748 Garching, Germany
- National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD 20892, USA
| | - Julian M Poschenrieder
- Data Science in Systems Biology, School of Life Sciences, Technical University of Munich, Freising, Germany
- Institute for Computational Systems Biology, University of Hamburg, Germany
| | - Massimiliano Incudini
- Dipartimento di Informatica, Universit'a di Verona, Strada le Grazie 15 - 34137 Verona, Italy
| | - Sylvie Baier
- Data Science in Systems Biology, School of Life Sciences, Technical University of Munich, Freising, Germany
| | - Amelie Fritz
- Department of Health Technology, Section for Bioinformatics, Technical University of Denmark, DTU, 2800 Kgs. Lyngby, Denmark
- Copenhagen Prospective Studies on Asthma in Childhood (COPSAC), Herlev and Gentofte Hospital, University of Copenhagen, Copenhagen, Denmark
| | - Andreas Maier
- Institute for Computational Systems Biology, University of Hamburg, Germany
| | - Michael Hartung
- Institute for Computational Systems Biology, University of Hamburg, Germany
| | - Christian Hoffmann
- Institute for Computational Systems Biology, University of Hamburg, Germany
| | - Nico Trummer
- Data Science in Systems Biology, School of Life Sciences, Technical University of Munich, Freising, Germany
| | - Klaudia Adamowicz
- Institute for Computational Systems Biology, University of Hamburg, Germany
| | - Mario Picciani
- Computational Mass Spectrometry, Technical University of Munich, Freising, Germany
| | - Evelyn Scheibling
- Data Science in Systems Biology, School of Life Sciences, Technical University of Munich, Freising, Germany
| | - Maximilian V Harl
- Data Science in Systems Biology, School of Life Sciences, Technical University of Munich, Freising, Germany
- Department of Health Sciences and Technology, Neuroscience Center Zürich (ZNZ), Swiss Federal Institute of Technology (ETH Zürich), Zürich 8092, Switzerland
| | - Ingmar Lesch
- Data Science in Systems Biology, School of Life Sciences, Technical University of Munich, Freising, Germany
| | - Hunor Frey
- Data Science in Systems Biology, School of Life Sciences, Technical University of Munich, Freising, Germany
| | - Simon Kayser
- Data Science in Systems Biology, School of Life Sciences, Technical University of Munich, Freising, Germany
| | - Paul Wissenberg
- Data Science in Systems Biology, School of Life Sciences, Technical University of Munich, Freising, Germany
| | - Leon Schwartz
- Data Science in Systems Biology, School of Life Sciences, Technical University of Munich, Freising, Germany
| | - Leon Hafner
- Data Science in Systems Biology, School of Life Sciences, Technical University of Munich, Freising, Germany
- Institute for Advanced Study (Lichtenbergstrasse 2 a) Technical University of Munich, D-85748 Garching, Germany
| | - Aakriti Acharya
- Division Data Science in Biomedicine, Peter L. Reichertz Institute for Medical Informatics, Technische Universität Braunschweig and Hannover Medical School, Rebenring 56, 38106 Braunschweig, Germany
- Braunschweig Integrated Centre of Systems Biology (BRICS), Technische Universität Braunschweig, Rebenring 56, 38106 Braunschweig, Germany
| | - Lena Hackl
- Institute for Computational Systems Biology, University of Hamburg, Germany
| | - Gordon Grabert
- Division Data Science in Biomedicine, Peter L. Reichertz Institute for Medical Informatics, Technische Universität Braunschweig and Hannover Medical School, Rebenring 56, 38106 Braunschweig, Germany
- Braunschweig Integrated Centre of Systems Biology (BRICS), Technische Universität Braunschweig, Rebenring 56, 38106 Braunschweig, Germany
| | - Sung-Gwon Lee
- National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD 20892, USA
- School of Biological Sciences and Technology, Chonnam National University, Gwangju, Korea
| | - Gyuhyeok Cho
- Department of Chemistry, Gwangju Institute of Science and Technology, Gwangju, Korea
| | | | - Jakub Jankowski
- National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD 20892, USA
| | - Hye Kyung Lee
- National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD 20892, USA
| | - Olga Tsoy
- Institute for Computational Systems Biology, University of Hamburg, Germany
| | - Nina Wenke
- Institute for Computational Systems Biology, University of Hamburg, Germany
| | - Anders Gorm Pedersen
- Department of Health Technology, Section for Bioinformatics, Technical University of Denmark, DTU, 2800 Kgs. Lyngby, Denmark
| | - Klaus Bønnelykke
- Copenhagen Prospective Studies on Asthma in Childhood (COPSAC), Herlev and Gentofte Hospital, University of Copenhagen, Copenhagen, Denmark
| | - Antonio Mandarino
- International Centre for Theory of Quantum Technologies, University of Gdańsk, 80-309 Gdańsk, Poland
| | - Federico Melograna
- BIO3 - Systems Genetics; GIGA-R Medical Genomics, University of Liège, Liège, Belgium
- BIO3 - Systems Medicine; Department of Human Genetics, KU Leuven, Leuven, Belgium
| | - Laura Schulz
- Leibniz Supercomputing Centre of the Bavarian Academy of Sciences and Humanities (LRZ), Garching b. München, Germany
| | | | - Mathias Wilhelm
- Computational Mass Spectrometry, Technical University of Munich, Freising, Germany
- Munich Data Science Institute (MDSI), Technical University of Munich, Garching, Germany
| | - Luigi Iapichino
- Leibniz Supercomputing Centre of the Bavarian Academy of Sciences and Humanities (LRZ), Garching b. München, Germany
| | - Lars Wienbrandt
- Institute of Clinical Molecular Biology, Christian Albrechts University of Kiel, Kiel, Germany
| | - David Ellinghaus
- Institute of Clinical Molecular Biology, Christian Albrechts University of Kiel, Kiel, Germany
| | - Kristel Van Steen
- BIO3 - Systems Genetics; GIGA-R Medical Genomics, University of Liège, Liège, Belgium
- BIO3 - Systems Medicine; Department of Human Genetics, KU Leuven, Leuven, Belgium
| | - Michele Grossi
- European Organization for Nuclear Research (CERN), Geneva1211, Switzerland
| | - Priscilla A Furth
- Departments of Oncology & Medicine, Georgetown University, Washington, DC, USA
| | - Lothar Hennighausen
- Institute for Advanced Study (Lichtenbergstrasse 2 a) Technical University of Munich, D-85748 Garching, Germany
- National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD 20892, USA
| | - Alessandra Di Pierro
- Dipartimento di Informatica, Universit'a di Verona, Strada le Grazie 15 - 34137 Verona, Italy
| | - Jan Baumbach
- Institute for Computational Systems Biology, University of Hamburg, Germany
- Computational BioMedicine Lab, University of Southern Denmark, Denmark
| | - Tim Kacprowski
- Department of Health Sciences and Technology, Neuroscience Center Zürich (ZNZ), Swiss Federal Institute of Technology (ETH Zürich), Zürich 8092, Switzerland
- Division Data Science in Biomedicine, Peter L. Reichertz Institute for Medical Informatics, Technische Universität Braunschweig and Hannover Medical School, Rebenring 56, 38106 Braunschweig, Germany
| | - Markus List
- Data Science in Systems Biology, School of Life Sciences, Technical University of Munich, Freising, Germany
- Biomedical Network Science Lab, Department Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
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4
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Curman P, Jebril W, Larsson H, Bachar-Wikström E, Cederlöf M, Wikström JD. Increased risk of depression and anxiety in individuals with Darier disease. Br J Dermatol 2024; 191:462-463. [PMID: 38757923 DOI: 10.1093/bjd/ljae195] [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: 01/25/2024] [Revised: 04/30/2024] [Accepted: 05/04/2024] [Indexed: 05/18/2024]
Abstract
Patients with Darier disease have an increased risk of depression and anxiety, which agrees with patterns of increased prescription of antidepressants and anxiolytics in people with the disease.
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Affiliation(s)
- Philip Curman
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- Dermatology and Venereology Division, Department of Medicine (Solna), Karolinska Institutet, Stockholm, Sweden
- Dermato-Venereology Clinic, Karolinska University Hospital, Stockholm, Sweden
| | - William Jebril
- Dermatology and Venereology Division, Department of Medicine (Solna), Karolinska Institutet, Stockholm, Sweden
- Dermato-Venereology Clinic, Karolinska University Hospital, Stockholm, Sweden
| | - Henrik Larsson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- School of Medical Sciences, Faculty of Medicine and Health, Örebro University, Örebro, Sweden
| | - Etty Bachar-Wikström
- Dermatology and Venereology Division, Department of Medicine (Solna), Karolinska Institutet, Stockholm, Sweden
| | - Martin Cederlöf
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- School of Medical Sciences, Faculty of Medicine and Health, Örebro University, Örebro, Sweden
| | - Jakob D Wikström
- Dermatology and Venereology Division, Department of Medicine (Solna), Karolinska Institutet, Stockholm, Sweden
- Dermato-Venereology Clinic, Karolinska University Hospital, Stockholm, Sweden
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5
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Larue F, Rouan L, Pot D, Rami JF, Luquet D, Beurier G. Linking genetic markers and crop model parameters using neural networks to enhance genomic prediction of integrative traits. FRONTIERS IN PLANT SCIENCE 2024; 15:1393965. [PMID: 39139722 PMCID: PMC11319263 DOI: 10.3389/fpls.2024.1393965] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/11/2024] [Accepted: 07/04/2024] [Indexed: 08/15/2024]
Abstract
Introduction Predicting the performance (yield or other integrative traits) of cultivated plants is complex because it involves not only estimating the genetic value of the candidates to selection, the interactions between the genotype and the environment (GxE) but also the epistatic interactions between genomic regions for a given trait, and the interactions between the traits contributing to the integrative trait. Classical Genomic Prediction (GP) models mostly account for additive effects and are not suitable to estimate non-additive effects such as epistasis. Therefore, the use of machine learning and deep learning methods has been previously proposed to model those non-linear effects. Methods In this study, we propose a type of Artificial Neural Network (ANN) called Convolutional Neural Network (CNN) and compare it to two classical GP regression methods for their ability to predict an integrative trait of sorghum: aboveground fresh weight accumulation. We also suggest that the use of a crop growth model (CGM) can enhance predictions of integrative traits by decomposing them into more heritable intermediate traits. Results The results show that CNN outperformed both LASSO and Bayes C methods in accuracy, suggesting that CNN are better suited to predict integrative traits. Furthermore, the predictive ability of the combined CGM-GP approach surpassed that of GP without the CGM integration, irrespective of the regression method used. Discussion These results are consistent with recent works aiming to develop Genome-to-Phenotype models and advocate for the use of non-linear prediction methods, and the use of combined CGM-GP to enhance the prediction of crop performances.
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Affiliation(s)
- Florian Larue
- Centre de Coopération Internationale en Recherche Agronomique pour le Développement (CIRAD), Unité Mixte de Recherche, Institut Amélioration Génétique et Adaptation des Plantes méditerranéennes et Tropicales (UMR AGAP), Montpellier, France
- Unité Mixte de Recherche, Institut Amélioration Génétique et Adaptation des Plantes méditerranéennes et Tropicales (UMR AGAP), Université Montpellier, Centre de Coopération Internationale en Recherche Agronomique pour le Développement (CIRAD), Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement (INRA), Institut Agro, Montpellier, France
| | - Lauriane Rouan
- Centre de Coopération Internationale en Recherche Agronomique pour le Développement (CIRAD), Unité Mixte de Recherche, Institut Amélioration Génétique et Adaptation des Plantes méditerranéennes et Tropicales (UMR AGAP), Montpellier, France
- Unité Mixte de Recherche, Institut Amélioration Génétique et Adaptation des Plantes méditerranéennes et Tropicales (UMR AGAP), Université Montpellier, Centre de Coopération Internationale en Recherche Agronomique pour le Développement (CIRAD), Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement (INRA), Institut Agro, Montpellier, France
| | - David Pot
- Centre de Coopération Internationale en Recherche Agronomique pour le Développement (CIRAD), Unité Mixte de Recherche, Institut Amélioration Génétique et Adaptation des Plantes méditerranéennes et Tropicales (UMR AGAP), Montpellier, France
- Unité Mixte de Recherche, Institut Amélioration Génétique et Adaptation des Plantes méditerranéennes et Tropicales (UMR AGAP), Université Montpellier, Centre de Coopération Internationale en Recherche Agronomique pour le Développement (CIRAD), Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement (INRA), Institut Agro, Montpellier, France
| | - Jean-François Rami
- Centre de Coopération Internationale en Recherche Agronomique pour le Développement (CIRAD), Unité Mixte de Recherche, Institut Amélioration Génétique et Adaptation des Plantes méditerranéennes et Tropicales (UMR AGAP), Montpellier, France
- Unité Mixte de Recherche, Institut Amélioration Génétique et Adaptation des Plantes méditerranéennes et Tropicales (UMR AGAP), Université Montpellier, Centre de Coopération Internationale en Recherche Agronomique pour le Développement (CIRAD), Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement (INRA), Institut Agro, Montpellier, France
| | - Delphine Luquet
- Centre de Coopération Internationale en Recherche Agronomique pour le Développement (CIRAD), Unité Mixte de Recherche, Institut Amélioration Génétique et Adaptation des Plantes méditerranéennes et Tropicales (UMR AGAP), Montpellier, France
- Unité Mixte de Recherche, Institut Amélioration Génétique et Adaptation des Plantes méditerranéennes et Tropicales (UMR AGAP), Université Montpellier, Centre de Coopération Internationale en Recherche Agronomique pour le Développement (CIRAD), Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement (INRA), Institut Agro, Montpellier, France
| | - Grégory Beurier
- Centre de Coopération Internationale en Recherche Agronomique pour le Développement (CIRAD), Unité Mixte de Recherche, Institut Amélioration Génétique et Adaptation des Plantes méditerranéennes et Tropicales (UMR AGAP), Montpellier, France
- Unité Mixte de Recherche, Institut Amélioration Génétique et Adaptation des Plantes méditerranéennes et Tropicales (UMR AGAP), Université Montpellier, Centre de Coopération Internationale en Recherche Agronomique pour le Développement (CIRAD), Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement (INRA), Institut Agro, Montpellier, France
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6
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Chitra U, Arnold BJ, Raphael BJ. Quantifying higher-order epistasis: beware the chimera. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.07.17.603976. [PMID: 39071303 PMCID: PMC11275791 DOI: 10.1101/2024.07.17.603976] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/30/2024]
Abstract
Epistasis, or interactions in which alleles at one locus modify the fitness effects of alleles at other loci, plays a fundamental role in genetics, protein evolution, and many other areas of biology. Epistasis is typically quantified by computing the deviation from the expected fitness under an additive or multiplicative model using one of several formulae. However, these formulae are not all equivalent. Importantly, one widely used formula - which we call the chimeric formula - measures deviations from a multiplicative fitness model on an additive scale, thus mixing two measurement scales. We show that for pairwise interactions, the chimeric formula yields a different magnitude, but the same sign (synergistic vs. antagonistic) of epistasis compared to the multiplicative formula that measures both fitness and deviations on a multiplicative scale. However, for higher-order interactions, we show that the chimeric formula can have both different magnitude and sign compared to the multiplicative formula - thus confusing negative epistatic interactions with positive interactions, and vice versa. We resolve these inconsistencies by deriving fundamental connections between the different epistasis formulae and the parameters of the multivariate Bernoulli distribution . Our results demonstrate that the additive and multiplicative epistasis formulae are more mathematically sound than the chimeric formula. Moreover, we demonstrate that the mathematical issues with the chimeric epistasis formula lead to markedly different biological interpretations of real data. Analyzing multi-gene knockout data in yeast, multi-way drug interactions in E. coli , and deep mutational scanning (DMS) of several proteins, we find that 10 - 60% of higher-order interactions have a change in sign with the multiplicative or additive epistasis formula. These sign changes result in qualitatively different findings on functional divergence in the yeast genome, synergistic vs. antagonistic drug interactions, and and epistasis between protein mutations. In particular, in the yeast data, the more appropriate multiplicative formula identifies nearly 500 additional negative three-way interactions, thus extending the trigenic interaction network by 25%.
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Mészáros S, Piroska M, Leel-Őssy T, Tárnoki ÁD, Tárnoki DL, Jokkel Z, Szabó H, Hosszú É, Csupor E, Kollár R, Kézdi Á, Tabák ÁG, Horváth C. Genetic and environmental determinants of bone quality: a cross-sectional analysis of the Hungarian Twin Registry. GeroScience 2024:10.1007/s11357-024-01265-2. [PMID: 38955996 DOI: 10.1007/s11357-024-01265-2] [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: 05/07/2024] [Accepted: 06/25/2024] [Indexed: 07/04/2024] Open
Abstract
There is abundant evidence that bone mineral content is highly heritable, while the heritability of bone quality (i.e. trabecular bone score [TBS] and quantitative ultrasound index [QUI]) is rarely investigated. We aimed to disentangle the role of genetic, shared and unique environmental factors on TBS and QUI among Hungarian twins. Our study includes 82 twin (48 monozygotic, 33 same-sex dizygotic) pairs from the Hungarian Twin Registry. TBS was determined by DXA, QUI by calcaneal bone ultrasound. To estimate the genetic and environmental effects, we utilized ACE-variance decomposition. For the unadjusted model of TBS, an AE model provided the best fit with > 80% additive genetic heritability. Adjustment for age, sex, BMI and smoking status improved model fit with 48.0% of total variance explained by independent variables. Furthermore, there was a strong dominant genetic effect (73.7%). In contrast, unadjusted and adjusted models for QUI showed an AE structure. Adjustments improved model fit and 25.7% of the total variance was explained by independent variables. Altogether 70-90% of the variance in QUI was related to additive genetic influences. We found a strong genetic heritability of bone quality in unadjusted models. Half of the variance of TBS was explained by age, sex and BMI. Furthermore, the adjusted model suggested that the genetic component of TBS could be dominant or an epistasis could be present. In contrast, independent variables explained only a quarter of the variance of QUI and the additive heritability explained more than half of all the variance.
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Affiliation(s)
- Szilvia Mészáros
- Department of Internal Medicine and Oncology, Faculty of Medicine, Semmelweis University, Budapest, Hungary.
| | - Márton Piroska
- Medical Imaging Centre, Faculty of Medicine, Semmelweis University, Budapest, Hungary
| | - Tamás Leel-Őssy
- Department of Internal Medicine and Oncology, Faculty of Medicine, Semmelweis University, Budapest, Hungary
| | - Ádám Domonkos Tárnoki
- Medical Imaging Centre, Faculty of Medicine, Semmelweis University, Budapest, Hungary
- Hungarian Twin Registry, Budapest, Hungary
| | - Dávid László Tárnoki
- Medical Imaging Centre, Faculty of Medicine, Semmelweis University, Budapest, Hungary
- Hungarian Twin Registry, Budapest, Hungary
| | - Zsófia Jokkel
- Medical Imaging Centre, Faculty of Medicine, Semmelweis University, Budapest, Hungary
| | - Helga Szabó
- Medical Imaging Centre, Faculty of Medicine, Semmelweis University, Budapest, Hungary
| | - Éva Hosszú
- 2nd Department of Pediatrics, Faculty of Medicine, Semmelweis University, Budapest, Hungary
| | - Emőke Csupor
- Health Service, Buda Castle Local Authorities, Budapest, Hungary
| | - Réka Kollár
- Department of Internal Medicine and Oncology, Faculty of Medicine, Semmelweis University, Budapest, Hungary
| | - Árpád Kézdi
- Department of Internal Medicine and Oncology, Faculty of Medicine, Semmelweis University, Budapest, Hungary
- Károly Rácz Conservative Medicine Division, Doctoral College, Semmelweis University, Budapest, Hungary
| | - Ádám G Tabák
- Department of Internal Medicine and Oncology, Faculty of Medicine, Semmelweis University, Budapest, Hungary
- Department of Public Health, Faculty of Medicine, Semmelweis University, Budapest, Hungary
- UCL Brain Sciences, University College London, London, UK
| | - Csaba Horváth
- Department of Internal Medicine and Oncology, Faculty of Medicine, Semmelweis University, Budapest, Hungary
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Al-kaabi M, Deshpande P, Firth M, Pavlos R, Chopra A, Basiri H, Currenti J, Alves E, Kalams S, Fellay J, Phillips E, Mallal S, John M, Gaudieri S. Epistatic interaction between ERAP2 and HLA modulates HIV-1 adaptation and disease outcome in an Australian population. PLoS Pathog 2024; 20:e1012359. [PMID: 38980912 PMCID: PMC11259285 DOI: 10.1371/journal.ppat.1012359] [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: 10/17/2023] [Revised: 07/19/2024] [Accepted: 06/19/2024] [Indexed: 07/11/2024] Open
Abstract
A strong genetic predictor of outcome following untreated HIV-1 infection is the carriage of specific alleles of human leukocyte antigens (HLAs) that present viral epitopes to T cells. Residual variation in outcome measures may be attributed, in part, to viral adaptation to HLA-restricted T cell responses. Variants of the endoplasmic reticulum aminopeptidases (ERAPs) influence the repertoire of T cell epitopes presented by HLA alleles as they trim pathogen-derived peptide precursors to optimal lengths for antigen presentation, along with other functions unrelated to antigen presentation. We investigated whether ERAP variants influence HLA-associated HIV-1 adaptation with demonstrable effects on overall HIV-1 disease outcome. Utilizing host and viral data of 249 West Australian individuals with HIV-1 subtype B infection, we identified a novel association between two linked ERAP2 single nucleotide polymorphisms (SNPs; rs2248374 and rs2549782) with plasma HIV RNA concentration (viral load) (P adjusted = 0.0024 for both SNPs). Greater HLA-associated HIV-1 adaptation in the HIV-1 Gag gene correlated significantly with higher viral load, lower CD4+ T cell count and proportion; P = 0.0103, P = 0.0061, P = 0.0061, respectively). When considered together, there was a significant interaction between the two ERAP2 SNPs and HLA-associated HIV-1 adaptation on viral load (P = 0.0111). In a comprehensive multivariate model, addition of ERAP2 haplotypes and HLA associated adaptation as an interaction term to known HLA and CCR5 determinants and demographic factors, increased the explanatory variance of population viral load from 17.67% to 45.1% in this dataset. These effects were not replicated in publicly available datasets with comparably sized cohorts, suggesting that any true global epistasis may be dependent on specific HLA-ERAP allelic combinations. Our data raises the possibility that ERAP2 variants may shape peptide repertoires presented to HLA class I-restricted T cells to modulate the degree of viral adaptation within individuals, in turn contributing to disease variability at the population level. Analyses of other populations and experimental studies, ideally with locally derived ERAP genotyping and HLA-specific viral adaptations are needed to elucidate this further.
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Affiliation(s)
- Marwah Al-kaabi
- School of Human Sciences, University of Western Australia, Crawley, Australia
| | - Pooja Deshpande
- School of Human Sciences, University of Western Australia, Crawley, Australia
- Institute for Immunology and Infectious Diseases, Murdoch University, Murdoch, Australia
| | - Martin Firth
- School of Physics, Mathematics and Computing, Department of Mathematics and Statistics, University of Western Australia, Crawley, Australia
| | - Rebecca Pavlos
- Institute for Immunology and Infectious Diseases, Murdoch University, Murdoch, Australia
| | - Abha Chopra
- Institute for Immunology and Infectious Diseases, Murdoch University, Murdoch, Australia
| | - Hamed Basiri
- School of Human Sciences, University of Western Australia, Crawley, Australia
| | - Jennifer Currenti
- School of Human Sciences, University of Western Australia, Crawley, Australia
| | - Eric Alves
- School of Human Sciences, University of Western Australia, Crawley, Australia
| | - Spyros Kalams
- Division of Infectious Diseases, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America
| | - Jacques Fellay
- School of Life Sciences, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
- Swiss HIV Cohort Study, Zurich, Switzerland
| | - Elizabeth Phillips
- Institute for Immunology and Infectious Diseases, Murdoch University, Murdoch, Australia
- Division of Infectious Diseases, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America
| | - Simon Mallal
- Institute for Immunology and Infectious Diseases, Murdoch University, Murdoch, Australia
- Division of Infectious Diseases, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America
| | - Mina John
- School of Human Sciences, University of Western Australia, Crawley, Australia
- Institute for Immunology and Infectious Diseases, Murdoch University, Murdoch, Australia
- Department of Clinical Immunology, Royal Perth Hospital, Perth, Australia
| | - Silvana Gaudieri
- School of Human Sciences, University of Western Australia, Crawley, Australia
- Institute for Immunology and Infectious Diseases, Murdoch University, Murdoch, Australia
- Division of Infectious Diseases, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America
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von Maydell D, Wright S, Bonner JM, Staab C, Spitaleri A, Liu L, Pao PC, Yu CJ, Scannail AN, Li M, Boix CA, Mathys H, Leclerc G, Menchaca GS, Welch G, Graziosi A, Leary N, Samaan G, Kellis M, Tsai LH. Single-cell atlas of ABCA7 loss-of-function reveals impaired neuronal respiration via choline-dependent lipid imbalances. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.09.05.556135. [PMID: 38979214 PMCID: PMC11230156 DOI: 10.1101/2023.09.05.556135] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/10/2024]
Abstract
Loss-of-function (LoF) variants in the lipid transporter ABCA7 significantly increase the risk of Alzheimer's disease (odds ratio ∼2), yet the pathogenic mechanisms and the neural cell types affected by these variants remain largely unknown. Here, we performed single-nuclear RNA sequencing of 36 human post-mortem samples from the prefrontal cortex of 12 ABCA7 LoF carriers and 24 matched non-carrier control individuals. ABCA7 LoF was associated with gene expression changes in all major cell types. Excitatory neurons, which expressed the highest levels of ABCA7, showed transcriptional changes related to lipid metabolism, mitochondrial function, cell cycle-related pathways, and synaptic signaling. ABCA7 LoF-associated transcriptional changes in neurons were similarly perturbed in carriers of the common AD missense variant ABCA7 p.Ala1527Gly (n = 240 controls, 135 carriers), indicating that findings from our study may extend to large portions of the at-risk population. Consistent with ABCA7's function as a lipid exporter, lipidomic analysis of isogenic iPSC-derived neurons (iNs) revealed profound intracellular triglyceride accumulation in ABCA7 LoF, which was accompanied by a relative decrease in phosphatidylcholine abundance. Metabolomic and biochemical analyses of iNs further indicated that ABCA7 LoF was associated with disrupted mitochondrial bioenergetics that suggested impaired lipid breakdown by uncoupled respiration. Treatment of ABCA7 LoF iNs with CDP-choline (a rate-limiting precursor of phosphatidylcholine synthesis) reduced triglyceride accumulation and restored mitochondrial function, indicating that ABCA7 LoF-induced phosphatidylcholine dyshomeostasis may directly disrupt mitochondrial metabolism of lipids. Treatment with CDP-choline also rescued intracellular amyloid β -42 levels in ABCA7 LoF iNs, further suggesting a link between ABCA7 LoF metabolic disruptions in neurons and AD pathology. This study provides a detailed transcriptomic atlas of ABCA7 LoF in the human brain and mechanistically links ABCA7 LoF-induced lipid perturbations to neuronal energy dyshomeostasis. In line with a growing body of evidence, our study highlights the central role of lipid metabolism in the etiology of Alzheimer's disease.
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Brito-Robinson T, Ayinuola YA, Ploplis VA, Castellino FJ. Plasminogen missense variants and their involvement in cardiovascular and inflammatory disease. Front Cardiovasc Med 2024; 11:1406953. [PMID: 38984351 PMCID: PMC11231438 DOI: 10.3389/fcvm.2024.1406953] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2024] [Accepted: 06/06/2024] [Indexed: 07/11/2024] Open
Abstract
Human plasminogen (PLG), the zymogen of the fibrinolytic protease, plasmin, is a polymorphic protein with two widely distributed codominant alleles, PLG/Asp453 and PLG/Asn453. About 15 other missense or non-synonymous single nucleotide polymorphisms (nsSNPs) of PLG show major, yet different, relative abundances in world populations. Although the existence of these relatively abundant allelic variants is generally acknowledged, they are often overlooked or assumed to be non-pathogenic. In fact, at least half of those major variants are classified as having conflicting pathogenicity, and it is unclear if they contribute to different molecular phenotypes. From those, PLG/K19E and PLG/A601T are examples of two relatively abundant PLG variants that have been associated with PLG deficiencies (PD), but their pathogenic mechanisms are unclear. On the other hand, approximately 50 rare and ultra-rare PLG missense variants have been reported to cause PD as homozygous or compound heterozygous variants, often leading to a debilitating disease known as ligneous conjunctivitis. The true abundance of PD-associated nsSNPs is unknown since they can remain undetected in heterozygous carriers. However, PD variants may also contribute to other diseases. Recently, the ultra-rare autosomal dominant PLG/K311E has been found to be causative of hereditary angioedema (HAE) with normal C1 inhibitor. Two other rare pathogenic PLG missense variants, PLG/R153G and PLG/V709E, appear to affect platelet function and lead to HAE, respectively. Herein, PLG missense variants that are abundant and/or clinically relevant due to association with disease are examined along with their world distribution. Proposed molecular mechanisms are discussed when known or can be reasonably assumed.
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Affiliation(s)
| | | | | | - Francis J. Castellino
- Department of Chemistry and Biochemistry and the W.M. Keck Center for Transgene Research, University of Notre Dame, Notre Dame, IN, United States
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11
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Kraemer D, Terumalai D, Famiglietti ML, Filges I, Joset P, Koller S, Maurer F, Meier S, Nouspikel T, Sanz J, Zweier C, Abramowicz M, Berger W, Cichon S, Schaller A, Superti-Furga A, Barbié V, Rauch A. SwissGenVar: A Platform for Clinical-Grade Interpretation of Genetic Variants to Foster Personalized Healthcare in Switzerland. J Pers Med 2024; 14:648. [PMID: 38929869 PMCID: PMC11204794 DOI: 10.3390/jpm14060648] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2024] [Revised: 05/28/2024] [Accepted: 06/11/2024] [Indexed: 06/28/2024] Open
Abstract
Large-scale next-generation sequencing (NGS) germline testing is technically feasible today, but variant interpretation represents a major bottleneck in analysis workflows. This includes extensive variant prioritization, annotation, and time-consuming evidence curation. The scale of the interpretation problem is massive, and variants of uncertain significance (VUSs) are a challenge to personalized medicine. This challenge is further compounded by the complexity and heterogeneity of the standards used to describe genetic variants and the associated phenotypes when searching for relevant information to support clinical decision making. To address this, all five Swiss academic institutions for Medical Genetics joined forces with the Swiss Institute of Bioinformatics (SIB) to create SwissGenVar as a user-friendly nationwide repository and sharing platform for genetic variant data generated during routine diagnostic procedures and research sequencing projects. Its aim is to provide a protected environment for expert evidence sharing about individual variants to harmonize and upscale their significance interpretation at the clinical grade according to international standards. To corroborate the clinical assessment, the variant-related data will be combined with consented high-quality clinical information. Broader visibility will be achieved by interfacing with international databases, thus supporting global initiatives in personalized healthcare.
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Affiliation(s)
- Dennis Kraemer
- Institute of Medical Genetics (IMG), University of Zurich (UZH), Wagistrasse 12, CH-8952 Zurich, Switzerland;
| | - Dillenn Terumalai
- Swiss Institute of Bioinformatics (SIB), Clinical Bioinformatics, CH-1202 Geneva, Switzerland; (D.T.); (V.B.)
| | | | - Isabel Filges
- Medical Genetics, Institute of Medical Genetics and Pathology, University Hospital Basel and University of Basel, CH-4031 Basel, Switzerland; (I.F.); (P.J.); (S.M.); (S.C.)
| | - Pascal Joset
- Medical Genetics, Institute of Medical Genetics and Pathology, University Hospital Basel and University of Basel, CH-4031 Basel, Switzerland; (I.F.); (P.J.); (S.M.); (S.C.)
| | - Samuel Koller
- Institute of Medical Molecular Genetics (IMMG), University of Zurich (UZH), Wagistrasse 12, CH-8952 Zurich, Switzerland; (S.K.); (W.B.)
| | - Fabienne Maurer
- Division of Genetic Medicine, Lausanne University Hospital (CHUV), CH-1011 Lausanne, Switzerland; (F.M.); (A.S.-F.)
| | - Stéphanie Meier
- Medical Genetics, Institute of Medical Genetics and Pathology, University Hospital Basel and University of Basel, CH-4031 Basel, Switzerland; (I.F.); (P.J.); (S.M.); (S.C.)
| | - Thierry Nouspikel
- Genetic Medicine Division, Diagnostics Department/Center for Genomic Medicine, Geneva University Hospitals (HUG), 1206 Geneva, Switzerland; (T.N.); (M.A.)
| | - Javier Sanz
- Department of Human Genetics, Inselspital, Bern University Hospital, CH-3010 Bern, Switzerland; (J.S.); (C.Z.); (A.S.)
| | - Christiane Zweier
- Department of Human Genetics, Inselspital, Bern University Hospital, CH-3010 Bern, Switzerland; (J.S.); (C.Z.); (A.S.)
| | - Marc Abramowicz
- Genetic Medicine Division, Diagnostics Department/Center for Genomic Medicine, Geneva University Hospitals (HUG), 1206 Geneva, Switzerland; (T.N.); (M.A.)
| | - Wolfgang Berger
- Institute of Medical Molecular Genetics (IMMG), University of Zurich (UZH), Wagistrasse 12, CH-8952 Zurich, Switzerland; (S.K.); (W.B.)
- Neuroscience Center Zurich (ZNZ), University and ETH Zurich, CH-8057 Zurich, Switzerland
- Zurich Center for Integrative Human Physiology (ZIHP), University of Zurich, CH-8057 Zurich, Switzerland
| | - Sven Cichon
- Medical Genetics, Institute of Medical Genetics and Pathology, University Hospital Basel and University of Basel, CH-4031 Basel, Switzerland; (I.F.); (P.J.); (S.M.); (S.C.)
| | - André Schaller
- Department of Human Genetics, Inselspital, Bern University Hospital, CH-3010 Bern, Switzerland; (J.S.); (C.Z.); (A.S.)
| | - Andrea Superti-Furga
- Division of Genetic Medicine, Lausanne University Hospital (CHUV), CH-1011 Lausanne, Switzerland; (F.M.); (A.S.-F.)
| | - Valérie Barbié
- Swiss Institute of Bioinformatics (SIB), Clinical Bioinformatics, CH-1202 Geneva, Switzerland; (D.T.); (V.B.)
| | - Anita Rauch
- Institute of Medical Genetics (IMG), University of Zurich (UZH), Wagistrasse 12, CH-8952 Zurich, Switzerland;
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Bakhashab S, Batarfi AA, Alhartani MM, Turki R, Mady W. Genetic Association Between Polycystic Ovary Syndrome and the APOA5 rs662799 and PLIN1 rs894160 Metabolic Variants in the Western Saudi Population: A Case-Control Study. Biomark Insights 2024; 19:11772719241258585. [PMID: 38887365 PMCID: PMC11181890 DOI: 10.1177/11772719241258585] [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: 12/26/2023] [Accepted: 05/15/2024] [Indexed: 06/20/2024] Open
Abstract
Background Polycystic ovary syndrome (PCOS) is a common endocrinological condition affecting women of reproductive age, associated with insulin resistance and obesity. PCOS pathogenesis is complex and multifactorial, involving genetic and environmental factors. Objectives This study aimed to determine and compare genotype and allele frequencies of single nucleotide polymorphisms (SNPs) in the apolipoprotein A5 (APOA5; rs662799) and perilipin 1 (PLIN1; rs894160, rs1052700 and rs6496589) genes in Western Saudi women to investigate their association with PCOS and its clinical characteristics. Design and methods This was a case-control study conducted on women with (n = 104) and without (n = 87) PCOS. The SNPs were genotyped using TaqMan genotyping assays. Results Significant and direct associations were detected between PCOS susceptibility and APOA5 SNP rs662799 and PLIN1 SNP rs894160 (P < .001). For APOA5 SNP rs662799, women with the A allele were more likely to have PCOS (relative risk [RR] = 1.348, odds ratio [OR] = 2.313, P < .001) and hypertriglyceridaemia (OR = 17.0, P = .5) than women with the G allele. For PLIN1 SNP rs894160, women with the T allele were more likely to have PCOS than women with the C allele (RR = 8.043, OR = 7.427, P < .001). For PLIN1 SNP rs1052700, women with the TT genotype were more likely to have hyperandrogenism (OR = 29.75, P = .02) and an irregular period (OR = 0.07, P = .040) than women with the AT genotype. Conclusion We identified novel alleles and genotypes contributing to the genetic risk of PCOS in the Western Saudi population.
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Affiliation(s)
- Sherin Bakhashab
- Department of Biochemistry, Faculty of Science, King Abdulaziz University, Jeddah, Saudi Arabia
- Center of Excellence in Genomic Medicine Research, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Asma A Batarfi
- Department of Biochemistry, Faculty of Science, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Mahinar M Alhartani
- College of Medicine and Surgery, Batterjee Medical College, Jeddah, Saudi Arabia
| | - Rola Turki
- Department of Obstetrics and Gynaecology, King Abdulaziz University Hospital, Jeddah, Saudi Arabia
| | - Wessam Mady
- Center of Excellence in Genomic Medicine Research, King Abdulaziz University, Jeddah, Saudi Arabia
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Wang J, Gu R, Kong X, Luan S, Luo YLL. Genome-wide association studies (GWAS) and post-GWAS analyses of impulsivity: A systematic review. Prog Neuropsychopharmacol Biol Psychiatry 2024; 132:110986. [PMID: 38430953 DOI: 10.1016/j.pnpbp.2024.110986] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Revised: 01/30/2024] [Accepted: 02/28/2024] [Indexed: 03/05/2024]
Abstract
Impulsivity is related to a host of mental and behavioral problems. It is a complex construct with many different manifestations, most of which are heritable. The genetic compositions of these impulsivity manifestations, however, remain unclear. A number of genome-wide association studies (GWAS) and post-GWAS analyses have tried to address this issue. We conducted a systematic review of all GWAS and post-GWAS analyses of impulsivity published up to December 2023. Available data suggest that single nucleotide polymorphisms (SNPs) in more than a dozen of genes (e.g., CADM2, CTNNA2, GPM6B) are associated with different measures of impulsivity at genome-wide significant levels. Post-GWAS analyses further show that different measures of impulsivity are subject to different degrees of genetic influence, share few genetic variants, and have divergent genetic overlap with basic personality traits such as extroversion and neuroticism, cognitive ability, psychiatric disorders, substance use, and obesity. These findings shed light on controversies in the conceptualization and measurement of impulsivity, while providing new insights on the underlying mechanisms that yoke impulsivity to psychopathology.
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Affiliation(s)
- Jiaqi Wang
- Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, 16 Lincui Road, Beijing 100101, China; Department of Psychology, University of Chinese Academy of Sciences, 16 Lincui Road, Beijing 100101, China
| | - Ruolei Gu
- Department of Psychology, University of Chinese Academy of Sciences, 16 Lincui Road, Beijing 100101, China; Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences, 16 Lincui Road, Beijing 100101, China
| | - Xiangzhen Kong
- Department of Psychology and Behavioral Sciences, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, China; Department of Psychiatry of Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, 3 Qingchundong Road, Hangzhou 310016, China
| | - Shenghua Luan
- Department of Psychology, University of Chinese Academy of Sciences, 16 Lincui Road, Beijing 100101, China; Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences, 16 Lincui Road, Beijing 100101, China
| | - Yu L L Luo
- Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, 16 Lincui Road, Beijing 100101, China; Department of Psychology, University of Chinese Academy of Sciences, 16 Lincui Road, Beijing 100101, China.
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14
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Taş G, Westerdijk T, Postma E, Veldink JH, Schönhuth A, Balvert M. Computing linkage disequilibrium aware genome embeddings using autoencoders. Bioinformatics 2024; 40:btae326. [PMID: 38775680 PMCID: PMC11208726 DOI: 10.1093/bioinformatics/btae326] [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: 11/24/2023] [Revised: 04/23/2024] [Accepted: 05/17/2024] [Indexed: 06/28/2024] Open
Abstract
MOTIVATION The completion of the genome has paved the way for genome-wide association studies (GWAS), which explained certain proportions of heritability. GWAS are not optimally suited to detect non-linear effects in disease risk, possibly hidden in non-additive interactions (epistasis). Alternative methods for epistasis detection using, e.g. deep neural networks (DNNs) are currently under active development. However, DNNs are constrained by finite computational resources, which can be rapidly depleted due to increasing complexity with the sheer size of the genome. Besides, the curse of dimensionality complicates the task of capturing meaningful genetic patterns for DNNs; therefore necessitates dimensionality reduction. RESULTS We propose a method to compress single nucleotide polymorphism (SNP) data, while leveraging the linkage disequilibrium (LD) structure and preserving potential epistasis. This method involves clustering correlated SNPs into haplotype blocks and training per-block autoencoders to learn a compressed representation of the block's genetic content. We provide an adjustable autoencoder design to accommodate diverse blocks and bypass extensive hyperparameter tuning. We applied this method to genotyping data from Project MinE, and achieved 99% average test reconstruction accuracy-i.e. minimal information loss-while compressing the input to nearly 10% of the original size. We demonstrate that haplotype-block based autoencoders outperform linear Principal Component Analysis (PCA) by approximately 3% chromosome-wide accuracy of reconstructed variants. To the extent of our knowledge, our approach is the first to simultaneously leverage haplotype structure and DNNs for dimensionality reduction of genetic data. AVAILABILITY AND IMPLEMENTATION Data are available for academic use through Project MinE at https://www.projectmine.com/research/data-sharing/, contingent upon terms and requirements specified by the source studies. Code is available at https://github.com/gizem-tas/haploblock-autoencoders.
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Affiliation(s)
- Gizem Taş
- Department of Econometrics and Operations Research, Tilburg University, Tilburg 5037AB, The Netherlands
| | - Timo Westerdijk
- Department of Neurology, University Medical Center Utrecht, Utrecht 3584CX, The Netherlands
| | - Eric Postma
- Department of Cognitive Science and Artificial Intelligence, Tilburg University, Tilburg 5037AB, The Netherlands
| | - Jan H Veldink
- Department of Neurology, University Medical Center Utrecht, Utrecht 3584CX, The Netherlands
| | | | - Marleen Balvert
- Department of Econometrics and Operations Research, Tilburg University, Tilburg 5037AB, The Netherlands
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15
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Almodóvar-Payá C, Guardiola-Ripoll M, Giralt-López M, Oscoz-Irurozqui M, Canales-Rodríguez EJ, Madre M, Soler-Vidal J, Ramiro N, Callado LF, Arias B, Gallego C, Pomarol-Clotet E, Fatjó-Vilas M. NRN1 epistasis with BDNF and CACNA1C: mediation effects on symptom severity through neuroanatomical changes in schizophrenia. Brain Struct Funct 2024; 229:1299-1315. [PMID: 38720004 PMCID: PMC11147852 DOI: 10.1007/s00429-024-02793-5] [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: 12/27/2023] [Accepted: 03/19/2024] [Indexed: 06/05/2024]
Abstract
The expression of Neuritin-1 (NRN1), a neurotrophic factor crucial for neurodevelopment and synaptic plasticity, is enhanced by the Brain Derived Neurotrophic Factor (BDNF). Although the receptor of NRN1 remains unclear, it is suggested that NRN1's activation of the insulin receptor (IR) pathway promotes the transcription of the calcium voltage-gated channel subunit alpha1 C (CACNA1C). These three genes have been independently associated with schizophrenia (SZ) risk, symptomatology, and brain differences. However, research on how they synergistically modulate these phenotypes is scarce. We aimed to study whether the genetic epistasis between these genes affects the risk and clinical presentation of the disorder via its effect on brain structure. First, we tested the epistatic effect of NRN1 and BDNF or CACNA1C on (i) the risk for SZ, (ii) clinical symptoms severity and functionality (onset, PANSS, CGI and GAF), and (iii) brain cortical structure (thickness, surface area and volume measures estimated using FreeSurfer) in a sample of 86 SZ patients and 89 healthy subjects. Second, we explored whether those brain clusters influenced by epistatic effects mediate the clinical profiles. Although we did not find a direct epistatic impact on the risk, our data unveiled significant effects on the disorder's clinical presentation. Specifically, the NRN1-rs10484320 x BDNF-rs6265 interplay influenced PANSS general psychopathology, and the NRN1-rs4960155 x CACNA1C-rs1006737 interaction affected GAF scores. Moreover, several interactions between NRN1 SNPs and BDNF-rs6265 significantly influenced the surface area and cortical volume of the frontal, parietal, and temporal brain regions within patients. The NRN1-rs10484320 x BDNF-rs6265 epistasis in the left lateral orbitofrontal cortex fully mediated the effect on PANSS general psychopathology. Our study not only adds clinical significance to the well-described molecular relationship between NRN1 and BDNF but also underscores the utility of deconstructing SZ into biologically validated brain-imaging markers to explore their mediation role in the path from genetics to complex clinical manifestation.
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Affiliation(s)
- Carmen Almodóvar-Payá
- FIDMAG Germanes Hospitalàries Research Foundation, Barcelona, Spain
- Departament de Biologia Evolutiva, Ecologia i Ciències Ambientals, Facultat de Biologia, Universitat de Barcelona, Barcelona, Spain
- CIBERSAM (Biomedical Research Network in Mental Health), Instituto de Salud Carlos III, Madrid, Spain
| | - Maria Guardiola-Ripoll
- FIDMAG Germanes Hospitalàries Research Foundation, Barcelona, Spain
- CIBERER (Biomedical Research Network in Rare Diseases), Instituto de Salud Carlos III, Madrid, Spain
| | - Maria Giralt-López
- Department of Child and Adolescent Psychiatry, Germans Trias i Pujol University Hospital (HUGTP), Barcelona, Spain
- Department of Psychiatry and Legal Medicine, Faculty of Medicine, Autonomous University of Barcelona (UAB), Barcelona, Spain
| | - Maitane Oscoz-Irurozqui
- FIDMAG Germanes Hospitalàries Research Foundation, Barcelona, Spain
- Red de Salud Mental de Gipuzkoa, Osakidetza-Basque Health Service, Gipuzkoa, Spain
| | - Erick Jorge Canales-Rodríguez
- FIDMAG Germanes Hospitalàries Research Foundation, Barcelona, Spain
- CIBERSAM (Biomedical Research Network in Mental Health), Instituto de Salud Carlos III, Madrid, Spain
- Signal Processing Laboratory (LTS5), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Mercè Madre
- FIDMAG Germanes Hospitalàries Research Foundation, Barcelona, Spain
- Mental Health, IR SANT PAU, Hospital de la Santa Creu i Sant Pau, Universitat Autònoma Barcelona, Barcelona, Spain
| | - Joan Soler-Vidal
- FIDMAG Germanes Hospitalàries Research Foundation, Barcelona, Spain
- CIBERSAM (Biomedical Research Network in Mental Health), Instituto de Salud Carlos III, Madrid, Spain
- Hospital Benito Menni, Germanes Hospitalàries, Sant Boi de Llobregat, Barcelona, Spain
| | - Núria Ramiro
- Hospital San Rafael, Germanes Hospitalàries, Barcelona, Spain
| | - Luis F Callado
- CIBERSAM (Biomedical Research Network in Mental Health), Instituto de Salud Carlos III, Madrid, Spain
- Department of Pharmacology, University of the Basque Country (UPV/EHU), Bizkaia, Spain
- BioBizkaia Health Research Institute, Bizkaia, Spain
| | - Bárbara Arias
- Departament de Biologia Evolutiva, Ecologia i Ciències Ambientals, Facultat de Biologia, Universitat de Barcelona, Barcelona, Spain
- CIBERSAM (Biomedical Research Network in Mental Health), Instituto de Salud Carlos III, Madrid, Spain
- Institut de Biomedicina de la Universitat de Barcelona (IBUB), Barcelona, Spain
| | - Carme Gallego
- Department of Cells and Tissues, Molecular Biology Institute of Barcelona (IBMB-CSIC), Barcelona, Spain
| | - Edith Pomarol-Clotet
- FIDMAG Germanes Hospitalàries Research Foundation, Barcelona, Spain
- CIBERSAM (Biomedical Research Network in Mental Health), Instituto de Salud Carlos III, Madrid, Spain
| | - Mar Fatjó-Vilas
- FIDMAG Germanes Hospitalàries Research Foundation, Barcelona, Spain.
- Departament de Biologia Evolutiva, Ecologia i Ciències Ambientals, Facultat de Biologia, Universitat de Barcelona, Barcelona, Spain.
- CIBERSAM (Biomedical Research Network in Mental Health), Instituto de Salud Carlos III, Madrid, Spain.
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16
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Lin Z, Luo W, Zhang K, Dai S. Environmental and Microbial Factors in Inflammatory Bowel Disease Model Establishment: A Review Partly through Mendelian Randomization. Gut Liver 2024; 18:370-390. [PMID: 37814898 PMCID: PMC11096900 DOI: 10.5009/gnl230179] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/21/2023] [Revised: 07/09/2023] [Accepted: 07/24/2023] [Indexed: 10/11/2023] Open
Abstract
Inflammatory bowel disease (IBD) is a complex condition resulting from environmental, microbial, immunologic, and genetic factors. With the advancement of Mendelian randomization research in IBD, we have gained new insights into the relationship between these factors and IBD. Many animal models of IBD have been developed using different methods, but few studies have attempted to model IBD by combining environmental factors and microbial factors. In this review, we examine how environmental factors and microbial factors affect the development and progression of IBD, and how they interact with each other and with the intestinal microbiota. We also summarize the current methods for creating animal models of IBD and compare their advantages and disadvantages. Based on the latest findings from Mendelian randomization studies on the role of environmental factors in IBD, we discuss which environmental and microbial factors could be used to construct a more realistic and reliable IBD experimental model. We propose that animal models of IBD should consider both environmental and microbial factors to better mimic human IBD pathogenesis and to reveal the underlying mechanisms of IBD at the immune and genetic levels. We highlight the importance of environmental and microbial factors in IBD pathogenesis and offer new perspectives and suggestions for improving experimental animal modeling. Our goal is to create a model that closely resembles the clinical picture of IBD.
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Affiliation(s)
- Zesheng Lin
- The First Clinical Medical School, Southern Medical University, Guangzhou, China
| | - Wenjing Luo
- The Second Clinical Medical School, Southern Medical University, Guangzhou, China
| | - Kaijun Zhang
- Department of Gastroenterology, Guangdong Provincial Geriatrics Institute, Guangzhou, ChinaNational Key Clinical Specialty, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
| | - Shixue Dai
- Department of Gastroenterology, Guangdong Provincial Geriatrics Institute, Guangzhou, ChinaNational Key Clinical Specialty, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
- Department of Gastroenterology, Geriatric Center, National Regional Medical Center, Ganzhou Hospital Affiliated to Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Ganzhou, China
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17
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Wang Y, Chan YW. Rare-variant association study unveils the Achilles' heel for HCC. CELL GENOMICS 2024; 4:100558. [PMID: 38723605 PMCID: PMC11099380 DOI: 10.1016/j.xgen.2024.100558] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/12/2024] [Revised: 04/15/2024] [Accepted: 04/15/2024] [Indexed: 05/15/2024]
Abstract
In this issue of Cell Genomics, Wang, Liu, Zuo, Wang, et al.1 investigate rare variants in hepatocellular carcinoma (HCC) by performing the first rare-variant association study (RVAS) in a Chinese population cohort. It uncovers BRCAness phenotypes associated with the NRDE2-p.N377I variant, suggesting PARP inhibitors as a promising therapeutic approach for certain HCC patients.
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Affiliation(s)
- Yin Wang
- School of Biological Sciences, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
| | - Ying Wai Chan
- School of Biological Sciences, The University of Hong Kong, Pokfulam, Hong Kong SAR, China.
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18
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Siraj L, Castro RI, Dewey H, Kales S, Nguyen TTL, Kanai M, Berenzy D, Mouri K, Wang QS, McCaw ZR, Gosai SJ, Aguet F, Cui R, Vockley CM, Lareau CA, Okada Y, Gusev A, Jones TR, Lander ES, Sabeti PC, Finucane HK, Reilly SK, Ulirsch JC, Tewhey R. Functional dissection of complex and molecular trait variants at single nucleotide resolution. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.05.592437. [PMID: 38766054 PMCID: PMC11100724 DOI: 10.1101/2024.05.05.592437] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/22/2024]
Abstract
Identifying the causal variants and mechanisms that drive complex traits and diseases remains a core problem in human genetics. The majority of these variants have individually weak effects and lie in non-coding gene-regulatory elements where we lack a complete understanding of how single nucleotide alterations modulate transcriptional processes to affect human phenotypes. To address this, we measured the activity of 221,412 trait-associated variants that had been statistically fine-mapped using a Massively Parallel Reporter Assay (MPRA) in 5 diverse cell-types. We show that MPRA is able to discriminate between likely causal variants and controls, identifying 12,025 regulatory variants with high precision. Although the effects of these variants largely agree with orthogonal measures of function, only 69% can plausibly be explained by the disruption of a known transcription factor (TF) binding motif. We dissect the mechanisms of 136 variants using saturation mutagenesis and assign impacted TFs for 91% of variants without a clear canonical mechanism. Finally, we provide evidence that epistasis is prevalent for variants in close proximity and identify multiple functional variants on the same haplotype at a small, but important, subset of trait-associated loci. Overall, our study provides a systematic functional characterization of likely causal common variants underlying complex and molecular human traits, enabling new insights into the regulatory grammar underlying disease risk.
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Affiliation(s)
- Layla Siraj
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Program in Biophysics, Harvard Graduate School of Arts and Sciences, Boston, MA, USA
- Harvard-Massachusetts Institute of Technology MD/PhD Program, Harvard Medical School, Boston, MA, USA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
| | | | | | | | | | - Masahiro Kanai
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA USA
- Center for Computational and Integrative Biology, Massachusetts General Hospital, Boston, MA, USA
| | | | | | - Qingbo S. Wang
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA USA
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
- Department of Genome Informatics, Graduate School of Medicine, the University of Tokyo, Tokyo, Japan
| | | | - Sager J. Gosai
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Program in Biological and Biomedical Sciences, Harvard Medical School, Boston, MA, USA
- Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA, USA
- Howard Hughes Medical Institute, Chevy Chase, MD, USA
| | - François Aguet
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Illumina Artificial Intelligence Laboratory, Illumina, San Diego, CA, USA
| | - Ran Cui
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA USA
- The Novo Nordisk Foundation Center for Genomic Mechanisms of Disease, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | | | - Caleb A. Lareau
- Program in Computational and Systems Biology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Yukinori Okada
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
- Department of Genome Informatics, Graduate School of Medicine, the University of Tokyo, Tokyo, Japan
- Laboratory for Systems Genetics, RIKEN Center for Integrative Medical Sciences, Kanagawa, Japan
| | - Alexander Gusev
- Harvard Medical School and Dana-Farber Cancer Institute, Boston, MA, USA
| | - Thouis R. Jones
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
- The Novo Nordisk Foundation Center for Genomic Mechanisms of Disease, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Eric S. Lander
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Department of Biology, MIT, Cambridge, MA, USA
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA
| | - Pardis C. Sabeti
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA, USA
- Howard Hughes Medical Institute, Chevy Chase, MD, USA
| | - Hilary K. Finucane
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA USA
| | - Steven K. Reilly
- Department of Genetics, Yale School of Medicine, New Haven, CT, USA
- Wu Tsai Institute, Yale University, New Haven, CT, USA
| | - Jacob C. Ulirsch
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA USA
- Program in Biological and Biomedical Sciences, Harvard Medical School, Boston, MA, USA
- Illumina Artificial Intelligence Laboratory, Illumina, San Diego, CA, USA
| | - Ryan Tewhey
- The Jackson Laboratory, Bar Harbor, ME, USA
- Graduate School of Biomedical Sciences and Engineering, University of Maine, Orono, ME, USA
- Graduate School of Biomedical Sciences, Tufts University School of Medicine, Boston, MA, USA
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19
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Bhuiyan MSA, Kim YK, Lee DH, Chung Y, Lee DJ, Kang JM, Lee SH. Evaluation of non-additive genetic effects on carcass and meat quality traits in Korean Hanwoo cattle using genomic models. Animal 2024; 18:101152. [PMID: 38701710 DOI: 10.1016/j.animal.2024.101152] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2023] [Revised: 03/26/2024] [Accepted: 03/29/2024] [Indexed: 05/05/2024] Open
Abstract
The traditional genetic evaluation methods generally consider additive genetic effects only and often ignore non-additive (dominance and epistasis) effects that may have contributed to genetic variation of complex traits of livestock species. The available dense single nucleotide polymorphisms (SNPs) panels offer to investigate the potential benefits of including non-additive genetic effects in the genomic evaluation models. Data from 16 971 genotyped (Illumina Bovine 50 K SNP chip) Korean Hanwoo cattle were used to estimate genetic variance components and prediction accuracy of genomic breeding values (GEBVs) for four carcass and meat quality traits: carcass weight (CWT), eye muscle area (EMA), back fat thickness (BFT) and marbling score (MS). Five different genetic models were evaluated through including additive, dominance and epistatic interactions (additive by additive, A × A; additive by dominance, A × D and dominance by dominance, D × D) successively in the models. The estimates of additive genetic variances and narrow sense heritabilities (ha2) were found similar across the evaluated models and traits except when additive interaction (A × A) was included. The dominance variance estimates relative to phenotypic variance ranged from 1.7-3.4% for CWT and MS traits, whereas, they were close to zero for EMA and BFT traits. The magnitude of A × A epistatic heritability (haa2) ranged between 14.8 and 27.7% in all traits. However, heritability estimates for A × D and D × D epistatic interactions (had2 and hdd2) were quite low compared to haa2 and were contributed only 0.0-9.7% of the total phenotypic variation. In general, broad sense heritability (hG2) estimates were almost twice (ranging between 0.54 and 0.68) the ha2 for all of the investigated traits. The inclusion of dominance effects did not improve the prediction accuracy of GEBV but improved 2.0-3.0% when epistatic effects were included in the model. More importantly, rank correlation revealed that partitioning of variance components considering dominance and epistatic effects in the model would enable to re-rank of top animals with better prediction of GEBV. The present result suggests that dominance and epistatic effects could be included in the genomic evaluation model for better estimates of variance components and more accurate prediction of GEBV for carcass and meat quality traits in Korean Hanwoo cattle.
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Affiliation(s)
- M S A Bhuiyan
- Division of Animal and Dairy Science, Chungnam National University, Daejeon 34134, Republic of Korea
| | - Y K Kim
- Division of Animal and Dairy Science, Chungnam National University, Daejeon 34134, Republic of Korea; Quantomic Research & Solution, Yuseong-gu, Daejeon 34134, Republic of Korea
| | - D H Lee
- Division of Animal and Dairy Science, Chungnam National University, Daejeon 34134, Republic of Korea; Quantomic Research & Solution, Yuseong-gu, Daejeon 34134, Republic of Korea
| | - Y Chung
- Division of Animal and Dairy Science, Chungnam National University, Daejeon 34134, Republic of Korea
| | - D J Lee
- Division of Animal and Dairy Science, Chungnam National University, Daejeon 34134, Republic of Korea
| | - J M Kang
- Division of Animal and Dairy Science, Chungnam National University, Daejeon 34134, Republic of Korea
| | - S H Lee
- Division of Animal and Dairy Science, Chungnam National University, Daejeon 34134, Republic of Korea.
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20
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Hajiaghabozorgi M, Fischbach M, Albrecht M, Wang W, Myers CL. BridGE: a pathway-based analysis tool for detecting genetic interactions from GWAS. Nat Protoc 2024; 19:1400-1435. [PMID: 38514837 PMCID: PMC11311251 DOI: 10.1038/s41596-024-00954-8] [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: 08/08/2022] [Accepted: 11/22/2023] [Indexed: 03/23/2024]
Abstract
Genetic interactions have the potential to modulate phenotypes, including human disease. In principle, genome-wide association studies (GWAS) provide a platform for detecting genetic interactions; however, traditional methods for identifying them, which tend to focus on testing individual variant pairs, lack statistical power. In this protocol, we describe a novel computational approach, called Bridging Gene sets with Epistasis (BridGE), for discovering genetic interactions between biological pathways from GWAS data. We present a Python-based implementation of BridGE along with instructions for its application to a typical human GWAS cohort. The major stages include initial data processing and quality control, construction of a variant-level genetic interaction network, measurement of pathway-level genetic interactions, evaluation of statistical significance using sample permutations and generation of results in a standardized output format. The BridGE software pipeline includes options for running the analysis on multiple cores and multiple nodes for users who have access to computing clusters or a cloud computing environment. In a cluster computing environment with 10 nodes and 100 GB of memory per node, the method can be run in less than 24 h for typical human GWAS cohorts. Using BridGE requires knowledge of running Python programs and basic shell script programming experience.
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Affiliation(s)
- Mehrad Hajiaghabozorgi
- Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN, USA
| | - Mathew Fischbach
- Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN, USA
- Graduate Program in Bioinformatics and Computational Biology (BICB), University of Minnesota, Minneapolis, MN, USA
| | - Michael Albrecht
- Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN, USA
| | - Wen Wang
- Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN, USA.
| | - Chad L Myers
- Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN, USA.
- Graduate Program in Bioinformatics and Computational Biology (BICB), University of Minnesota, Minneapolis, MN, USA.
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21
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Bass AJ, Bian S, Wingo AP, Wingo TS, Cutler DJ, Epstein MP. Identifying latent genetic interactions in genome-wide association studies using multiple traits. Genome Med 2024; 16:62. [PMID: 38664839 PMCID: PMC11044415 DOI: 10.1186/s13073-024-01329-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Accepted: 04/02/2024] [Indexed: 04/28/2024] Open
Abstract
The "missing" heritability of complex traits may be partly explained by genetic variants interacting with other genes or environments that are difficult to specify, observe, and detect. We propose a new kernel-based method called Latent Interaction Testing (LIT) to screen for genetic interactions that leverages pleiotropy from multiple related traits without requiring the interacting variable to be specified or observed. Using simulated data, we demonstrate that LIT increases power to detect latent genetic interactions compared to univariate methods. We then apply LIT to obesity-related traits in the UK Biobank and detect variants with interactive effects near known obesity-related genes (URL: https://CRAN.R-project.org/package=lit ).
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Affiliation(s)
- Andrew J Bass
- Department of Human Genetics, Emory University, Atlanta, GA, 30322, USA.
| | - Shijia Bian
- Department of Biostatistics and Bioinformatics, Emory University, Atlanta, GA, 30322, USA
| | - Aliza P Wingo
- Department of Psychiatry, Emory University, Atlanta, GA, 30322, USA
| | - Thomas S Wingo
- Department of Human Genetics, Emory University, Atlanta, GA, 30322, USA
- Department of Neurology, Emory University, Atlanta, GA, 30322, USA
| | - David J Cutler
- Department of Human Genetics, Emory University, Atlanta, GA, 30322, USA
| | - Michael P Epstein
- Department of Human Genetics, Emory University, Atlanta, GA, 30322, USA.
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22
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Zhu X, Yang Y, Lorincz-Comi N, Li G, Bentley AR, de Vries PS, Brown M, Morrison AC, Rotimi CN, Gauderman WJ, Rao DC, Aschard H. An approach to identify gene-environment interactions and reveal new biological insight in complex traits. Nat Commun 2024; 15:3385. [PMID: 38649715 PMCID: PMC11035594 DOI: 10.1038/s41467-024-47806-3] [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: 09/22/2023] [Accepted: 04/10/2024] [Indexed: 04/25/2024] Open
Abstract
There is a long-standing debate about the magnitude of the contribution of gene-environment interactions to phenotypic variations of complex traits owing to the low statistical power and few reported interactions to date. To address this issue, the Gene-Lifestyle Interactions Working Group within the Cohorts for Heart and Aging Research in Genetic Epidemiology Consortium has been spearheading efforts to investigate G × E in large and diverse samples through meta-analysis. Here, we present a powerful new approach to screen for interactions across the genome, an approach that shares substantial similarity to the Mendelian randomization framework. We identify and confirm 5 loci (6 independent signals) interacted with either cigarette smoking or alcohol consumption for serum lipids, and empirically demonstrate that interaction and mediation are the major contributors to genetic effect size heterogeneity across populations. The estimated lower bound of the interaction and environmentally mediated heritability is significant (P < 0.02) for low-density lipoprotein cholesterol and triglycerides in Cross-Population data. Our study improves the understanding of the genetic architecture and environmental contributions to complex traits.
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Affiliation(s)
- Xiaofeng Zhu
- Department of Population and Quantitative Health Sciences, School of Medicine, Case Western Reserve University, Cleveland, OH, USA.
| | - Yihe Yang
- Department of Population and Quantitative Health Sciences, School of Medicine, Case Western Reserve University, Cleveland, OH, USA
| | - Noah Lorincz-Comi
- Department of Population and Quantitative Health Sciences, School of Medicine, Case Western Reserve University, Cleveland, OH, USA
| | - Gen Li
- Department of Population and Quantitative Health Sciences, School of Medicine, Case Western Reserve University, Cleveland, OH, USA
| | - Amy R Bentley
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Paul S de Vries
- Human Genetics Center, Department of Epidemiology, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Michael Brown
- Human Genetics Center, Department of Epidemiology, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Alanna C Morrison
- Human Genetics Center, Department of Epidemiology, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Charles N Rotimi
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - W James Gauderman
- Division of Biostatistics, Department of Population and Public Health Sciences, University of Southern California, Los Angeles, CA, USA
| | - Dabeeru C Rao
- Center for Biostatistics and Data Science, Institute for Informatics, Data Science and Biostatistics, Washington University School of Medicine, St. Louis, MO, USA
| | - Hugues Aschard
- Institut Pasteur, Université Paris Cité, Department of Computational Biology, F-75015, Paris, France
- Program in Genetic Epidemiology and Statistical Genetics, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA
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23
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Durvasula A, Price AL. Distinct explanations underlie gene-environment interactions in the UK Biobank. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2023.09.22.23295969. [PMID: 37790574 PMCID: PMC10543037 DOI: 10.1101/2023.09.22.23295969] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/05/2023]
Abstract
The role of gene-environment (GxE) interaction in disease and complex trait architectures is widely hypothesized, but currently unknown. Here, we apply three statistical approaches to quantify and distinguish three different types of GxE interaction for a given trait and E variable. First, we detect locus-specific GxE interaction by testing for genetic correlation r g < 1 across E bins. Second, we detect genome-wide effects of the E variable on genetic variance by leveraging polygenic risk scores (PRS) to test for significant PRSxE in a regression of phenotypes on PRS, E, and PRSxE, together with differences in SNP-heritability across E bins. Third, we detect genome-wide proportional amplification of genetic and environmental effects as a function of the E variable by testing for significant PRSxE with no differences in SNP-heritability across E bins. Simulations show that these approaches achieve high sensitivity and specificity in distinguishing these three GxE scenarios. We applied our framework to 33 UK Biobank traits (25 quantitative traits and 8 diseases; average N = 325 K ) and 10 E variables spanning lifestyle, diet, and other environmental exposures. First, we identified 19 trait-E pairs with r g significantly < 1 (FDR<5%) (average r g = 0.95 ); for example, white blood cell count had r g = 0.95 (s.e. 0.01) between smokers and non-smokers. Second, we identified 28 trait-E pairs with significant PRSxE and significant SNP-heritability differences across E bins; for example, BMI had a significant PRSxE for physical activity (P=4.6e-5) with 5% larger SNP-heritability in the largest versus smallest quintiles of physical activity (P=7e-4). Third, we identified 15 trait-E pairs with significant PRSxE with no SNP-heritability differences across E bins; for example, waist-hip ratio adjusted for BMI had a significant PRSxE effect for time spent watching television (P=5e-3) with no SNP-heritability differences. Across the three scenarios, 8 of the trait-E pairs involved disease traits, whose interpretation is complicated by scale effects. Analyses using biological sex as the E variable produced additional significant findings in each of the three scenarios. Overall, we infer a significant contribution of GxE and GxSex effects to complex trait and disease variance.
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Affiliation(s)
- Arun Durvasula
- Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- Department of Genetics, Harvard Medical School, Cambridge, MA, USA
- Department of Human Evolutionary Biology, Harvard University, Cambridge, MA, USA
- 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
| | - 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
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24
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Li Q, Bian J, Qian Y, Kossinna P, Gau C, Gordon PMK, Zhou X, Guo X, Yan J, Wu J, Long Q. An expression-directed linear mixed model discovering low-effect genetic variants. Genetics 2024; 226:iyae018. [PMID: 38314848 DOI: 10.1093/genetics/iyae018] [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: 11/29/2023] [Revised: 11/29/2023] [Accepted: 01/05/2024] [Indexed: 02/07/2024] Open
Abstract
Detecting genetic variants with low-effect sizes using a moderate sample size is difficult, hindering downstream efforts to learn pathology and estimating heritability. In this work, by utilizing informative weights learned from training genetically predicted gene expression models, we formed an alternative approach to estimate the polygenic term in a linear mixed model. Our linear mixed model estimates the genetic background by incorporating their relevance to gene expression. Our protocol, expression-directed linear mixed model, enables the discovery of subtle signals of low-effect variants using moderate sample size. By applying expression-directed linear mixed model to cohorts of around 5,000 individuals with either binary (WTCCC) or quantitative (NFBC1966) traits, we demonstrated its power gain at the low-effect end of the genetic etiology spectrum. In aggregate, the additional low-effect variants detected by expression-directed linear mixed model substantially improved estimation of missing heritability. Expression-directed linear mixed model moves precision medicine forward by accurately detecting the contribution of low-effect genetic variants to human diseases.
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Affiliation(s)
- Qing Li
- Department of Biochemistry & Molecular Biology, University of Calgary, Calgary T2N 1N4, Canada
| | - Jiayi Bian
- Department of Mathematics and Statistics, University of Calgary, Calgary T2N 1N4, Canada
| | - Yanzhao Qian
- Department of Mathematics and Statistics, University of Calgary, Calgary T2N 1N4, Canada
| | - Pathum Kossinna
- Department of Biochemistry & Molecular Biology, University of Calgary, Calgary T2N 1N4, Canada
| | - Cooper Gau
- Department of Mathematics and Statistics, University of Calgary, Calgary T2N 1N4, Canada
| | - Paul M K Gordon
- Alberta Children's Hospital Research Institute, University of Calgary, Calgary T2N 1N4, Canada
| | - Xiang Zhou
- School of Public Health, University of Michigan, Ann Arbor 48109, USA
| | - Xingyi Guo
- Department of Medicine & Biomedical Informatics, Vanderbilt University Medical Center, Nashville 37203, USA
| | - Jun Yan
- Physiology and Pharmacology, University of Calgary, Calgary T2N 1N4, Canada
- Hotchkiss Brain Institute, University of Calgary, Calgary T2N 1N4, Canada
| | - Jingjing Wu
- Department of Mathematics and Statistics, University of Calgary, Calgary T2N 1N4, Canada
| | - Quan Long
- Department of Biochemistry & Molecular Biology, University of Calgary, Calgary T2N 1N4, Canada
- Department of Mathematics and Statistics, University of Calgary, Calgary T2N 1N4, Canada
- Alberta Children's Hospital Research Institute, University of Calgary, Calgary T2N 1N4, Canada
- Hotchkiss Brain Institute, University of Calgary, Calgary T2N 1N4, Canada
- Department of Medical Genetics, University of Calgary, Calgary T2N 1N4, Canada
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25
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Tsouris A, Brach G, Friedrich A, Hou J, Schacherer J. Diallel panel reveals a significant impact of low-frequency genetic variants on gene expression variation in yeast. Mol Syst Biol 2024; 20:362-373. [PMID: 38355920 PMCID: PMC10987670 DOI: 10.1038/s44320-024-00021-0] [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: 09/15/2023] [Revised: 01/29/2024] [Accepted: 01/30/2024] [Indexed: 02/16/2024] Open
Abstract
Unraveling the genetic sources of gene expression variation is essential to better understand the origins of phenotypic diversity in natural populations. Genome-wide association studies identified thousands of variants involved in gene expression variation, however, variants detected only explain part of the heritability. In fact, variants such as low-frequency and structural variants (SVs) are poorly captured in association studies. To assess the impact of these variants on gene expression variation, we explored a half-diallel panel composed of 323 hybrids originated from pairwise crosses of 26 natural Saccharomyces cerevisiae isolates. Using short- and long-read sequencing strategies, we established an exhaustive catalog of single nucleotide polymorphisms (SNPs) and SVs for this panel. Combining this dataset with the transcriptomes of all hybrids, we comprehensively mapped SNPs and SVs associated with gene expression variation. While SVs impact gene expression variation, SNPs exhibit a higher effect size with an overrepresentation of low-frequency variants compared to common ones. These results reinforce the importance of dissecting the heritability of complex traits with a comprehensive catalog of genetic variants at the population level.
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Affiliation(s)
- Andreas Tsouris
- Université de Strasbourg, CNRS, GMGM UMR 7156, Strasbourg, France
| | - Gauthier Brach
- Université de Strasbourg, CNRS, GMGM UMR 7156, Strasbourg, France
| | - Anne Friedrich
- Université de Strasbourg, CNRS, GMGM UMR 7156, Strasbourg, France
| | - Jing Hou
- Université de Strasbourg, CNRS, GMGM UMR 7156, Strasbourg, France.
| | - Joseph Schacherer
- Université de Strasbourg, CNRS, GMGM UMR 7156, Strasbourg, France.
- Institut Universitaire de France (IUF), Paris, France.
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Curman P, Jebril W, Larsson H, Bachar-Wikstrom E, Cederlöf M, Wikstrom JD. Darier disease is associated with neurodegenerative disorders and epilepsy. Sci Rep 2024; 14:7109. [PMID: 38531956 DOI: 10.1038/s41598-024-57779-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Accepted: 03/21/2024] [Indexed: 03/28/2024] Open
Abstract
Darier disease (DD) is a rare monogenetic skin disorder with limited data on its potential association with neurological disorders. This study aimed to investigate the association between DD and neurological disorders, specifically Parkinson's disease, dementias, and epilepsy. Using Swedish national registers in a period spanning between 1977 and 2013, 935 individuals with DD were compared with up to 100 comparison individuals each, randomly selected from the general population based on birth year, sex, and county of residence at the time of the first diagnosis of DD. Individuals with DD had increased risks of being diagnosed with Parkinson's disease (RR 2.1, CI 1.1; 4.4), vascular dementia (RR 2.1, CI 1.0; 4.2), and epilepsy, (RR 2.5, CI 1.8; 3.5). No association of DD with other dementias were detected. This study demonstrates a new association between DD and neurodegenerative disorders and epilepsy, underlining the need for increased awareness, interdisciplinary collaboration, and further research to understand the underlying mechanisms. Early identification and management of neurological complications in DD patients could improve treatment strategies and patient outcomes. The findings also highlight the role of SERCA2 in the pathophysiology of neurological disorders, offering new targets for future research and potentials for novel treatments.
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Affiliation(s)
- Philip Curman
- Dermatology and Venereology Division, Department of Medicine (Solna), Karolinska Institutet, Stockholm, Sweden
- Dermato-Venereology Clinic, Karolinska University Hospital, Eugeniavägen 3, 17164, Stockholm, Sweden
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - William Jebril
- Dermatology and Venereology Division, Department of Medicine (Solna), Karolinska Institutet, Stockholm, Sweden
- Dermato-Venereology Clinic, Karolinska University Hospital, Eugeniavägen 3, 17164, Stockholm, Sweden
| | - Henrik Larsson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- School of Medical Sciences, Faculty of Medicine and Health, Örebro University, Örebro, Sweden
| | - Etty Bachar-Wikstrom
- Dermatology and Venereology Division, Department of Medicine (Solna), Karolinska Institutet, Stockholm, Sweden
| | - Martin Cederlöf
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- School of Medical Sciences, Faculty of Medicine and Health, Örebro University, Örebro, Sweden
| | - Jakob D Wikstrom
- Dermatology and Venereology Division, Department of Medicine (Solna), Karolinska Institutet, Stockholm, Sweden.
- Dermato-Venereology Clinic, Karolinska University Hospital, Eugeniavägen 3, 17164, Stockholm, Sweden.
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27
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Carré C, Carluer JB, Chaux C, Estoup-Streiff C, Roche N, Hosy E, Mas A, Krouk G. Next-Gen GWAS: full 2D epistatic interaction maps retrieve part of missing heritability and improve phenotypic prediction. Genome Biol 2024; 25:76. [PMID: 38523316 PMCID: PMC10962106 DOI: 10.1186/s13059-024-03202-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Accepted: 02/19/2024] [Indexed: 03/26/2024] Open
Abstract
The problem of missing heritability requires the consideration of genetic interactions among different loci, called epistasis. Current GWAS statistical models require years to assess the entire combinatorial epistatic space for a single phenotype. We propose Next-Gen GWAS (NGG) that evaluates over 60 billion single nucleotide polymorphism combinatorial first-order interactions within hours. We apply NGG to Arabidopsis thaliana providing two-dimensional epistatic maps at gene resolution. We demonstrate on several phenotypes that a large proportion of the missing heritability can be retrieved, that it indeed lies in epistatic interactions, and that it can be used to improve phenotype prediction.
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Affiliation(s)
- Clément Carré
- BionomeeX, Montpellier, France.
- IMAG, Univ. Montpellier, CNRS, Montpellier, France.
- IPSiM, Univ. Montpellier, CNRS, INRAE, Montpellier, France.
| | - Jean Baptiste Carluer
- IMAG, Univ. Montpellier, CNRS, Montpellier, France
- IPSiM, Univ. Montpellier, CNRS, INRAE, Montpellier, France
| | | | | | | | - Eric Hosy
- Interdisciplinary Institute for Neuroscience, University of Bordeaux, CNRS, Bordeaux, France
| | - André Mas
- BionomeeX, Montpellier, France.
- IMAG, Univ. Montpellier, CNRS, Montpellier, France.
| | - Gabriel Krouk
- BionomeeX, Montpellier, France.
- IPSiM, Univ. Montpellier, CNRS, INRAE, Montpellier, France.
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28
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Oh EY, Han KM, Kim A, Kang Y, Tae WS, Han MR, Ham BJ. Integration of whole-exome sequencing and structural neuroimaging analysis in major depressive disorder: a joint study. Transl Psychiatry 2024; 14:141. [PMID: 38461185 PMCID: PMC10924915 DOI: 10.1038/s41398-024-02849-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Revised: 02/07/2024] [Accepted: 02/22/2024] [Indexed: 03/11/2024] Open
Abstract
Major depressive disorder (MDD) is a common mental illness worldwide and is triggered by an intricate interplay between environmental and genetic factors. Although there are several studies on common variants in MDD, studies on rare variants are relatively limited. In addition, few studies have examined the genetic contributions to neurostructural alterations in MDD using whole-exome sequencing (WES). We performed WES in 367 patients with MDD and 161 healthy controls (HCs) to detect germline and copy number variations in the Korean population. Gene-based rare variants were analyzed to investigate the association between the genes and individuals, followed by neuroimaging-genetic analysis to explore the neural mechanisms underlying the genetic impact in 234 patients with MDD and 135 HCs using diffusion tensor imaging data. We identified 40 MDD-related genes and observed 95 recurrent regions of copy number variations. We also discovered a novel gene, FRMPD3, carrying rare variants that influence MDD. In addition, the single nucleotide polymorphism rs771995197 in the MUC6 gene was significantly associated with the integrity of widespread white matter tracts. Moreover, we identified 918 rare exonic missense variants in genes associated with MDD susceptibility. We postulate that rare variants of FRMPD3 may contribute significantly to MDD, with a mild penetration effect.
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Affiliation(s)
- Eun-Young Oh
- Division of Life Sciences, College of Life Sciences and Bioengineering, Incheon National University, Incheon, Republic of Korea
| | - Kyu-Man Han
- Department of Psychiatry, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Republic of Korea
- Brain Convergence Research Center, Korea University College of Medicine, Seoul, Republic of Korea
| | - Aram Kim
- Department of Biomedical Sciences, Korea University College of Medicine, Seoul, Republic of Korea
| | - Youbin Kang
- Department of Biomedical Sciences, Korea University College of Medicine, Seoul, Republic of Korea
| | - Woo-Suk Tae
- Brain Convergence Research Center, Korea University College of Medicine, Seoul, Republic of Korea
| | - Mi-Ryung Han
- Division of Life Sciences, College of Life Sciences and Bioengineering, Incheon National University, Incheon, Republic of Korea.
| | - Byung-Joo Ham
- Department of Psychiatry, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Republic of Korea.
- Brain Convergence Research Center, Korea University College of Medicine, Seoul, Republic of Korea.
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29
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Liu Y, Ren H, Zhang Y, Deng W, Ma X, Zhao L, Li X, Sham P, Wang Q, Li T. Temporal changes in brain morphology related to inflammation and schizophrenia: an omnigenic Mendelian randomization study. Psychol Med 2024:1-9. [PMID: 38445386 DOI: 10.1017/s003329172400014x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/07/2024]
Abstract
BACKGROUND Over the past several decades, more research focuses have been made on the inflammation/immune hypothesis of schizophrenia. Building upon synaptic plasticity hypothesis, inflammation may contribute the underlying pathophysiology of schizophrenia. Yet, pinpointing the specific inflammatory agents responsible for schizophrenia remains a complex challenge, mainly due to medication and metabolic status. Multiple lines of evidence point to a wide-spread genetic association across genome underlying the phenotypic variations of schizophrenia. METHOD We collected the latest genome-wide association analysis (GWAS) summary data of schizophrenia, cytokines, and longitudinal change of brain. We utilized the omnigenic model which takes into account all genomic SNPs included in the GWAS of trait, instead of traditional Mendelian randomization (MR) methods. We conducted two round MR to investigate the inflammatory triggers of schizophrenia and the resulting longitudinal changes in the brain. RESULTS We identified seven inflammation markers linked to schizophrenia onset, which all passed the Bonferroni correction for multiple comparisons (bNGF, GROA(CXCL1), IL-8, M-CSF, MCP-3 (CCL7), TNF-β, CRP). Moreover, CRP were found to significantly influence the linear rate of brain morphology changes, predominantly in the white matter of the cerebrum and cerebellum. CONCLUSION With an omnigenic approach, our study sheds light on the immune pathology of schizophrenia. Although these findings need confirmation from future studies employing different methodologies, our work provides substantial evidence that pervasive, low-level neuroinflammation may play a pivotal role in schizophrenia, potentially leading to notable longitudinal changes in brain morphology.
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Affiliation(s)
- Yunjia Liu
- Mental Health Center and Psychiatric Laboratory, The State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu, Sichuan, China
- Nanhu Brain-computer Interface Institute, Hangzhou 311100, China
| | - Hongyan Ren
- Mental Health Center and Psychiatric Laboratory, The State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu, Sichuan, China
- Nanhu Brain-computer Interface Institute, Hangzhou 311100, China
| | - Yamin Zhang
- Affiliated Mental Health Center & Hangzhou Seventh People's Hospital and School of Brain Science and Brain Medicine, Zhejiang University School of Medicine, Hangzhou, 310058, China
- Lingang Laboratory, Shanghai 200031, China
- Liangzhu Laboratory, MOE Frontier Science Center for Brain Science and Brain-machine Integration, State Key Laboratory of Brain-machine Intelligence, Zhejiang University, 1369 West Wenyi Road, Hangzhou 311121, China
| | - Wei Deng
- Affiliated Mental Health Center & Hangzhou Seventh People's Hospital and School of Brain Science and Brain Medicine, Zhejiang University School of Medicine, Hangzhou, 310058, China
- Lingang Laboratory, Shanghai 200031, China
- Liangzhu Laboratory, MOE Frontier Science Center for Brain Science and Brain-machine Integration, State Key Laboratory of Brain-machine Intelligence, Zhejiang University, 1369 West Wenyi Road, Hangzhou 311121, China
| | - Xiaohong Ma
- Mental Health Center and Psychiatric Laboratory, The State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu, Sichuan, China
| | - Liansheng Zhao
- Mental Health Center and Psychiatric Laboratory, The State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu, Sichuan, China
| | - Xiaojing Li
- Affiliated Mental Health Center & Hangzhou Seventh People's Hospital and School of Brain Science and Brain Medicine, Zhejiang University School of Medicine, Hangzhou, 310058, China
- Lingang Laboratory, Shanghai 200031, China
- Liangzhu Laboratory, MOE Frontier Science Center for Brain Science and Brain-machine Integration, State Key Laboratory of Brain-machine Intelligence, Zhejiang University, 1369 West Wenyi Road, Hangzhou 311121, China
| | - Pak Sham
- State Key Laboratory of Brain and Cognitive Sciences, Centre for Genomic Sciences, and Department of Psychiatry, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
| | - Qiang Wang
- Mental Health Center and Psychiatric Laboratory, The State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu, Sichuan, China
| | - Tao Li
- Nanhu Brain-computer Interface Institute, Hangzhou 311100, China
- Affiliated Mental Health Center & Hangzhou Seventh People's Hospital and School of Brain Science and Brain Medicine, Zhejiang University School of Medicine, Hangzhou, 310058, China
- Lingang Laboratory, Shanghai 200031, China
- Liangzhu Laboratory, MOE Frontier Science Center for Brain Science and Brain-machine Integration, State Key Laboratory of Brain-machine Intelligence, Zhejiang University, 1369 West Wenyi Road, Hangzhou 311121, China
- NHC and CAMS Key Laboratory of Medical Neurobiology, Zhejiang University, Hangzhou 310058, China
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30
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Woerner J, Sriram V, Nam Y, Verma A, Kim D. Uncovering genetic associations in the human diseasome using an endophenotype-augmented disease network. Bioinformatics 2024; 40:btae126. [PMID: 38527901 PMCID: PMC10963079 DOI: 10.1093/bioinformatics/btae126] [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: 09/18/2023] [Revised: 01/17/2024] [Indexed: 03/27/2024] Open
Abstract
MOTIVATION Many diseases, particularly cardiometabolic disorders, exhibit complex multimorbidities with one another. An intuitive way to model the connections between phenotypes is with a disease-disease network (DDN), where nodes represent diseases and edges represent associations, such as shared single-nucleotide polymorphisms (SNPs), between pairs of diseases. To gain further genetic understanding of molecular contributors to disease associations, we propose a novel version of the shared-SNP DDN (ssDDN), denoted as ssDDN+, which includes connections between diseases derived from genetic correlations with intermediate endophenotypes. We hypothesize that a ssDDN+ can provide complementary information to the disease connections in a ssDDN, yielding insight into the role of clinical laboratory measurements in disease interactions. RESULTS Using PheWAS summary statistics from the UK Biobank, we constructed a ssDDN+ revealing hundreds of genetic correlations between diseases and quantitative traits. Our augmented network uncovers genetic associations across different disease categories, connects relevant cardiometabolic diseases, and highlights specific biomarkers that are associated with cross-phenotype associations. Out of the 31 clinical measurements under consideration, HDL-C connects the greatest number of diseases and is strongly associated with both type 2 diabetes and heart failure. Triglycerides, another blood lipid with known genetic causes in non-mendelian diseases, also adds a substantial number of edges to the ssDDN. This work demonstrates how association with clinical biomarkers can better explain the shared genetics between cardiometabolic disorders. Our study can facilitate future network-based investigations of cross-phenotype associations involving pleiotropy and genetic heterogeneity, potentially uncovering sources of missing heritability in multimorbidities. AVAILABILITY AND IMPLEMENTATION The generated ssDDN+ can be explored at https://hdpm.biomedinfolab.com/ddn/biomarkerDDN.
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Affiliation(s)
- Jakob Woerner
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, United States
- Genomics and Computational Biology Graduate Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, United States
| | - Vivek Sriram
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, United States
- Genomics and Computational Biology Graduate Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, United States
| | - Yonghyun Nam
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, United States
| | - Anurag Verma
- Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, United States
- Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, PA 19104, United States
| | - Dokyoon Kim
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, United States
- Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, PA 19104, United States
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31
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Spörlein C, Kristen C, Schmidt R. The intergenerational transmission of risk and trust attitudes: Replicating and extending "Dohmen, Falk, Huffman and Sunde 2012" using genetically informed twin data. SOCIAL SCIENCE RESEARCH 2024; 119:102982. [PMID: 38609303 DOI: 10.1016/j.ssresearch.2024.102982] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/10/2023] [Revised: 01/11/2024] [Accepted: 01/20/2024] [Indexed: 04/14/2024]
Abstract
This replication revisits an influential contribution on the intergenerational transmission of risk and trust attitudes, which, based on data from the German Socioeconomic Panel (GSOEP), reveals a positive correlation between parents' and children's attitudes. The authors of the original study argue that socialization in the family is important in the transmission process. The replication is motivated by mounting evidence indicating that within-family transmission has a considerable genetic component, which calls into question socialization as the main transmission pathway. To consider genetic transmission in addition to social transmission, the replication relies on the German twin family panel TwinLife. The findings reveal that, first, most of the variation in children's risk and social trust attitudes is attributable to differences in the non-shared environment, followed by genetic differences, whereas differences in the shared family environment - the main candidate for social transmission - do not matter. Second, correlations between parents' and children's attitudes essentially involve genetic similarity. Third, family conditions do not moderate these relationships. Thus, the findings do not support the socialization assumption.
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Affiliation(s)
- Christoph Spörlein
- Department of Sociology, Heinrich-Heine-University, Düsseldorf, Universitätsstraße 1, 40225, Düsseldorf, Germany.
| | - Cornelia Kristen
- Chair of Sociology, Area Social Stratification, University of Bamberg, Feldkirchenstraße 21, 96052, Bamberg, Germany.
| | - Regine Schmidt
- Chair of Sociology, Area Social Stratification, University of Bamberg, Feldkirchenstraße 21, 96052, Bamberg, Germany.
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32
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Gnanapragasam N, Prasanth VV, Sundaram KT, Kumar A, Pahi B, Gurjar A, Venkateshwarlu C, Kalia S, Kumar A, Dixit S, Kohli A, Singh UM, Singh VK, Sinha P. Extreme trait GWAS (Et-GWAS): Unraveling rare variants in the 3,000 rice genome. Life Sci Alliance 2024; 7:e202302352. [PMID: 38148113 PMCID: PMC10751245 DOI: 10.26508/lsa.202302352] [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: 09/01/2023] [Revised: 12/12/2023] [Accepted: 12/13/2023] [Indexed: 12/28/2023] Open
Abstract
Identifying high-impact, rare genetic variants associated with specific traits is crucial for crop improvement. The 3,010 rice genome (3K RG) dataset offers a valuable resource for discovering genomic regions with potential applications in crop breeding. We used Extreme Trait GWAS (Et-GWAS), employing bulk pooling and allele frequency measurement to efficiently extract rare variants from the 3K RG. This innovative approach facilitates the detection of associations between genetic variants and target traits, concentrating and quantifying rare alleles. In our study, on grain yield under drought stress, Et-GWAS successfully identified five key genes (OsPP2C11, OsK5.2, OsIRO2, OsPEX1, and OsPWA1) known for enhancing yield under drought. In addition, we examined the overlap of our results with previously reported qDTY-QTLs and observed that OsUCH1 and OsUCH2 genes were located within qDTY2.2 We compared Et-GWAS with conventional GWAS, finding it effectively capturing most candidate genes associated with the target trait. Validation with resistant starch showed similar results. To enhance user-friendliness, we developed a GUI for Et-GWAS; https://et-gwas.shinyapps.io/Et-GWAS/.
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Affiliation(s)
| | | | | | - Ajay Kumar
- International Rice Research Institute, South Asia Hub, Patancheru, India
| | - Bandana Pahi
- International Rice Research Institute, South Asia Hub, Patancheru, India
| | - Anoop Gurjar
- International Rice Research Institute, South-Asia Regional Centre, Varanasi, India
| | | | - Sanjay Kalia
- Department of Biotechnology, CGO Complex, New Delhi, India
| | - Arvind Kumar
- International Rice Research Institute, South-Asia Regional Centre, Varanasi, India
| | - Shalabh Dixit
- International Rice Research Institute, Los Banos, Philippines
| | - Ajay Kohli
- International Rice Research Institute, Los Banos, Philippines
| | - Uma Maheshwer Singh
- International Rice Research Institute, South-Asia Regional Centre, Varanasi, India
| | - Vikas Kumar Singh
- International Rice Research Institute, South Asia Hub, Patancheru, India
| | - Pallavi Sinha
- International Rice Research Institute, South Asia Hub, Patancheru, India
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Zhang Q, Liu J, Liu H, Ao L, Xi Y, Chen D. Genome-wide epistasis analysis reveals gene-gene interaction network on an intermediate endophenotype P-tau/Aβ 42 ratio in ADNI cohort. Sci Rep 2024; 14:3984. [PMID: 38368488 PMCID: PMC10874417 DOI: 10.1038/s41598-024-54541-8] [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: 10/22/2023] [Accepted: 02/14/2024] [Indexed: 02/19/2024] Open
Abstract
Alzheimer's disease (AD) is a progressive neurodegenerative disorder and the most common cause of dementia in the elderly worldwide. The exact etiology of AD, particularly its genetic mechanisms, remains incompletely understood. Traditional genome-wide association studies (GWAS), which primarily focus on single-nucleotide polymorphisms (SNPs) with main effects, provide limited explanations for the "missing heritability" of AD, while there is growing evidence supporting the important role of epistasis. In this study, we performed a genome-wide SNP-SNP interaction detection using a linear regression model and employed multiple GPUs for parallel computing, significantly enhancing the speed of whole-genome analysis. The cerebrospinal fluid (CSF) phosphorylated tau (P-tau)/amyloid-[Formula: see text] (A[Formula: see text]) ratio was used as a quantitative trait (QT) to enhance statistical power. Age, gender, and clinical diagnosis were included as covariates to control for potential non-genetic factors influencing AD. We identified 961 pairs of statistically significant SNP-SNP interactions, explaining a high-level variance of P-tau/A[Formula: see text] level, all of which exhibited marginal main effects. Additionally, we replicated 432 previously reported AD-related genes and found 11 gene-gene interaction pairs overlapping with the protein-protein interaction (PPI) network. Our findings may contribute to partially explain the "missing heritability" of AD. The identified subnetwork may be associated with synaptic dysfunction, Wnt signaling pathway, oligodendrocytes, inflammation, hippocampus, and neuronal cells.
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Affiliation(s)
- Qiushi Zhang
- School of Computer Science, Northeast Electric Power University, 169 Changchun Street, Jilin, 132012, China
| | - Junfeng Liu
- School of Computer Science, Northeast Electric Power University, 169 Changchun Street, Jilin, 132012, China
| | - Hongwei Liu
- College of Intelligent Systems Science and Engineering, Harbin Engineering University, 145 Nantong Street, Harbin, China
| | - Lang Ao
- School of Computer Science, Northeast Electric Power University, 169 Changchun Street, Jilin, 132012, China
| | - Yang Xi
- School of Computer Science, Northeast Electric Power University, 169 Changchun Street, Jilin, 132012, China
| | - Dandan Chen
- School of Automation Engineering, Northeast Electric Power University, 169 Changchun Street, Jilin, 132012, China.
- College of Intelligent Systems Science and Engineering, Harbin Engineering University, 145 Nantong Street, Harbin, China.
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34
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Zhang L, Yuan X, Li X, Zhang X, Mao Y, Hu S, Andreassen OA, Wang Y, Song X. Gut microbial diversity moderates polygenic risk of schizophrenia. Front Psychiatry 2024; 15:1275719. [PMID: 38362027 PMCID: PMC10868137 DOI: 10.3389/fpsyt.2024.1275719] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Accepted: 01/04/2024] [Indexed: 02/17/2024] Open
Abstract
Background Schizophrenia (SCZ) is a heritable disorder with a polygenic architecture, and the gut microbiota seems to be involved in its development and outcome. In this study, we investigate the interplay between genetic risk and gut microbial markers. Methods We included 159 first-episode, drug-naïve SCZ patients and 86 healthy controls. The microbial composition of feces was characterized using the 16S rRNA sequencing platform, and five microbial α-diversity indices were estimated [Shannon, Simpson, Chao1, the Abundance-based Eoverage Estimator (ACE), and a phylogenetic diversity-based estimate (PD)]. Polygenic risk scores (PRS) for SCZ were constructed using data from large-scale genome-wide association studies. Effects of microbial α-diversity, microbial abundance, and PRS on SCZ were evaluated via generalized linear models. Results We confirmed that PRS was associated with SCZ (OR = 2.08, p = 1.22×10-5) and that scores on the Shannon (OR = 0.29, p = 1.15×10-8) and Simpson (OR = 0.29, p = 1.25×10-8) indices were inversely associated with SCZ risk. We found significant interactions (p < 0.05) between PRS and α-diversity indices (Shannon, Simpson, and PD), with the effects of PRS being larger in those exhibiting higher diversity compared to those with lower diversity. Moreover, the PRS effects were larger in individuals with a high abundance of the genera Romboutsia, Streptococcus, and Anaerostipes than in those with low abundance (p < 0.05). All three of these genera showed protective effects against SCZ. Conclusion The current findings suggest an interplay between the gut microbiota and polygenic risk of SCZ that warrants replication in independent samples. Experimental studies are needed to determine the underpinning mechanisms.
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Affiliation(s)
- Liyuan Zhang
- Department of Psychiatry, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Henan International Joint Laboratory of Biological Psychiatry, Zhengzhou University, Zhengzhou, China
- Henan Psychiatric Transformation Research Key Laboratory, Zhengzhou University, Zhengzhou, China
| | - Xiuxia Yuan
- Department of Psychiatry, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Henan International Joint Laboratory of Biological Psychiatry, Zhengzhou University, Zhengzhou, China
- Henan Psychiatric Transformation Research Key Laboratory, Zhengzhou University, Zhengzhou, China
| | - Xue Li
- Department of Psychiatry, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Henan International Joint Laboratory of Biological Psychiatry, Zhengzhou University, Zhengzhou, China
- Henan Psychiatric Transformation Research Key Laboratory, Zhengzhou University, Zhengzhou, China
| | - Xiaoyun Zhang
- Department of Psychiatry, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Henan International Joint Laboratory of Biological Psychiatry, Zhengzhou University, Zhengzhou, China
- Henan Psychiatric Transformation Research Key Laboratory, Zhengzhou University, Zhengzhou, China
| | - Yiqiao Mao
- School of Information Engineering, Zhengzhou University, Zhengzhou, China
| | - Shaohua Hu
- Department of Psychiatry, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Ole A. Andreassen
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, Oslo University Hospital, University of Oslo, Oslo, Norway
| | - Yunpeng Wang
- Centre for Lifespan Changes in Brain and Cognition (LCBC), Department of Psychology, University of Oslo, Oslo, Norway
| | - Xueqin Song
- Department of Psychiatry, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Henan International Joint Laboratory of Biological Psychiatry, Zhengzhou University, Zhengzhou, China
- Henan Psychiatric Transformation Research Key Laboratory, Zhengzhou University, Zhengzhou, China
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35
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So HC, Xue X, Ma Z, Sham PC. SumVg: Total Heritability Explained by All Variants in Genome-Wide Association Studies Based on Summary Statistics with Standard Error Estimates. Int J Mol Sci 2024; 25:1347. [PMID: 38279346 PMCID: PMC10816209 DOI: 10.3390/ijms25021347] [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: 10/13/2023] [Revised: 01/15/2024] [Accepted: 01/16/2024] [Indexed: 01/28/2024] Open
Abstract
Genome-wide association studies (GWAS) are commonly employed to study the genetic basis of complex traits/diseases, and a key question is how much heritability could be explained by all single nucleotide polymorphisms (SNPs) in GWAS. One widely used approach that relies on summary statistics only is linkage disequilibrium score regression (LDSC); however, this approach requires certain assumptions about the effects of SNPs (e.g., all SNPs contribute to heritability and each SNP contributes equal variance). More flexible modeling methods may be useful. We previously developed an approach recovering the "true" effect sizes from a set of observed z-statistics with an empirical Bayes approach, using only summary statistics. However, methods for standard error (SE) estimation are not available yet, limiting the interpretation of our results and the applicability of the approach. In this study, we developed several resampling-based approaches to estimate the SE of SNP-based heritability, including two jackknife and three parametric bootstrap methods. The resampling procedures are performed at the SNP level as it is most common to estimate heritability from GWAS summary statistics alone. Simulations showed that the delete-d-jackknife and parametric bootstrap approaches provide good estimates of the SE. In particular, the parametric bootstrap approaches yield the lowest root-mean-squared-error (RMSE) of the true SE. We also explored various methods for constructing confidence intervals (CIs). In addition, we applied our method to estimate the SNP-based heritability of 12 immune-related traits (levels of cytokines and growth factors) to shed light on their genetic architecture. We also implemented the methods to compute the sum of heritability explained and the corresponding SE in an R package SumVg. In conclusion, SumVg may provide a useful alternative tool for calculating SNP heritability and estimating SE/CI, which does not rely on distributional assumptions of SNP effects.
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Affiliation(s)
- Hon-Cheong So
- School of Biomedical Sciences, The Chinese University of Hong Kong, Shatin, Hong Kong, China; (X.X.); (Z.M.)
- KIZ-CUHK Joint Laboratory of Bioresources and Molecular Research of Common Diseases, Kunming Institute of Zoology and The Chinese University of Hong Kong, Shatin, Hong Kong, China
- Department of Psychiatry, The Chinese University of Hong Kong, Shatin, Hong Kong, China
- CUHK Shenzhen Research Institute, Shenzhen 518057, China
- Margaret K. L. Cheung Research Centre for Management of Parkinsonism, The Chinese University of Hong Kong, Shatin, Hong Kong, China
- Hong Kong Branch of the Chinese Academy of Sciences Center for Excellence in Animal Evolution and Genetics, The Chinese University of Hong Kong, Shatin, Hong Kong, China
- Brain and Mind Institute, The Chinese University of Hong Kong, Shatin, Hong Kong, China
| | - Xiao Xue
- School of Biomedical Sciences, The Chinese University of Hong Kong, Shatin, Hong Kong, China; (X.X.); (Z.M.)
| | - Zhijie Ma
- School of Biomedical Sciences, The Chinese University of Hong Kong, Shatin, Hong Kong, China; (X.X.); (Z.M.)
| | - Pak-Chung Sham
- Department of Psychiatry, The University of Hong Kong, Pokfulam, Hong Kong, China;
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36
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Pereira V, Kuzmin E. Trans-regulatory variant network contributes to missing heritability. CELL GENOMICS 2024; 4:100470. [PMID: 38216282 PMCID: PMC10794830 DOI: 10.1016/j.xgen.2023.100470] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Revised: 11/30/2023] [Accepted: 11/30/2023] [Indexed: 01/14/2024]
Abstract
In a recent Cell Genomics article, Tsouris et al.1 analyze the transcriptomes of a large diallel panel of hybrids from Saccharomyces cerevisiae natural isolates to study cis- and trans-regulatory changes underlying gene expression variation. Vanessa Pereira and Elena Kuzmin discuss the authors' findings and the wider context in missing heritability research in this preview.
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Affiliation(s)
- Vanessa Pereira
- Department of Biology, Concordia University, Montreal, Canada; Centre for Applied Synthetic Biology, Centre for Structural and Functional Genomics, Concordia University, Montreal, Canada
| | - Elena Kuzmin
- Department of Biology, Concordia University, Montreal, Canada; Centre for Applied Synthetic Biology, Centre for Structural and Functional Genomics, Concordia University, Montreal, Canada; Department of Human Genetics, Rosalind & Morris Goodman Cancer Institute, McGill University, Montreal, Canada.
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37
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Brown KL, Ramlall V, Zietz M, Gisladottir U, Tatonetti NP. Estimating the heritability of SARS-CoV-2 susceptibility and COVID-19 severity. Nat Commun 2024; 15:367. [PMID: 38191623 PMCID: PMC10774300 DOI: 10.1038/s41467-023-44250-7] [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: 08/01/2022] [Accepted: 12/05/2023] [Indexed: 01/10/2024] Open
Abstract
SARS-CoV-2 has infected over 340 million people, prompting therapeutic research. While genetic studies can highlight potential drug targets, understanding the heritability of SARS-CoV-2 susceptibility and COVID-19 severity can contextualize their results. To date, loci from meta-analyses explain 1.2% and 5.8% of variation in susceptibility and severity respectively. Here we estimate the importance of shared environment and additive genetic variation to SARS-CoV-2 susceptibility and COVID-19 severity using pedigree data, PCR results, and hospitalization information. The relative importance of genetics and shared environment for susceptibility shifted during the study, with heritability ranging from 33% (95% CI: 20%-46%) to 70% (95% CI: 63%-74%). Heritability was greater for days hospitalized with COVID-19 (41%, 95% CI: 33%-57%) compared to shared environment (33%, 95% CI: 24%-38%). While our estimates suggest these genetic architectures are not fully understood, the shift in susceptibility estimates highlights the challenge of estimation during a pandemic, given environmental fluctuations and vaccine introduction.
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Affiliation(s)
| | - Vijendra Ramlall
- Department of Biomedical Informatics, Columbia University, New York, NY, USA
- Department of Physiology & Cellular Biophysics, Columbia University, New York, NY, USA
| | - Michael Zietz
- Department of Biomedical Informatics, Columbia University, New York, NY, USA
| | - Undina Gisladottir
- Department of Biomedical Informatics, Columbia University, New York, NY, USA
| | - Nicholas P Tatonetti
- Department of Biomedical Informatics, Columbia University, New York, NY, USA.
- Department of Computational Biomedicine, Cedars-Sinai Medical Center, West Hollywood, CA, USA.
- Cedars-Sinai Cancer, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
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38
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Periyasamy S, Youssef P, John S, Thara R, Mowry BJ. Genetic interactions of schizophrenia using gene-based statistical epistasis exclusively identify nervous system-related pathways and key hub genes. Front Genet 2024; 14:1301150. [PMID: 38259618 PMCID: PMC10800577 DOI: 10.3389/fgene.2023.1301150] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2023] [Accepted: 12/12/2023] [Indexed: 01/24/2024] Open
Abstract
Background: The relationship between genotype and phenotype is governed by numerous genetic interactions (GIs), and the mapping of GI networks is of interest for two main reasons: 1) By modelling biological robustness, GIs provide a powerful opportunity to infer compensatory biological mechanisms via the identification of functional relationships between genes, which is of interest for biological discovery and translational research. Biological systems have evolved to compensate for genetic (i.e., variations and mutations) and environmental (i.e., drug efficacy) perturbations by exploiting compensatory relationships between genes, pathways and biological processes; 2) GI facilitates the identification of the direction (alleviating or aggravating interactions) and magnitude of epistatic interactions that influence the phenotypic outcome. The generation of GIs for human diseases is impossible using experimental biology approaches such as systematic deletion analysis. Moreover, the generation of disease-specific GIs has never been undertaken in humans. Methods: We used our Indian schizophrenia case-control (case-816, controls-900) genetic dataset to implement the workflow. Standard GWAS sample quality control procedure was followed. We used the imputed genetic data to increase the SNP coverage to analyse epistatic interactions across the genome comprehensively. Using the odds ratio (OR), we identified the GIs that increase or decrease the risk of a disease phenotype. The SNP-based epistatic results were transformed into gene-based epistatic results. Results: We have developed a novel approach by conducting gene-based statistical epistatic analysis using an Indian schizophrenia case-control genetic dataset and transforming these results to infer GIs that increase the risk of schizophrenia. There were ∼9.5 million GIs with a p-value ≤ 1 × 10-5. Approximately 4.8 million GIs showed an increased risk (OR > 1.0), while ∼4.75 million GIs had a decreased risk (OR <1.0) for schizophrenia. Conclusion: Unlike model organisms, this approach is specifically viable in humans due to the availability of abundant disease-specific genome-wide genotype datasets. The study exclusively identified brain/nervous system-related processes, affirming the findings. This computational approach fills a critical gap by generating practically non-existent heritable disease-specific human GIs from human genetic data. These novel datasets can train innovative deep-learning models, potentially surpassing the limitations of conventional GWAS.
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Affiliation(s)
- Sathish Periyasamy
- Queensland Brain Institute, The University of Queensland, Brisbane, QLD, Australia
- Queensland Centre for Mental Health Research, The Park Centre for Mental Health, Wacol, QLD, Australia
| | - Pierre Youssef
- Queensland Brain Institute, The University of Queensland, Brisbane, QLD, Australia
| | - Sujit John
- Schizophrenia Research Foundation, Chennai, Tamil Nadu, India
| | | | - Bryan J. Mowry
- Queensland Brain Institute, The University of Queensland, Brisbane, QLD, Australia
- Queensland Centre for Mental Health Research, The Park Centre for Mental Health, Wacol, QLD, Australia
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39
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Drago A. Genetic signatures of suicide attempt behavior: insights and applications. Expert Rev Proteomics 2024; 21:41-53. [PMID: 38315076 DOI: 10.1080/14789450.2024.2314143] [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: 09/21/2023] [Accepted: 01/22/2024] [Indexed: 02/07/2024]
Abstract
INTRODUCTION Every year about 800,000 complete suicide events occur. The identification of biologic markers to identify subjects at risk would be helpful in targeting specific support treatments. AREA COVERED A narrative review defines the meta-analytic level of current evidence about the biologic markers of suicide behavior (SB). The meta-analytic evidence gathered so far indicates that the hypothesis-driven research largely failed to identify the biologic markers of suicide. The most consistent and replicated result was reported for: 1) 5-HTR2A T102C, associated with SB in patients with schizophrenia (OR = 1.73 (1.11-2.69)) and 2) BDNF Val66Met (rs6265), with the Met-Val + Val-Val carriers found to be at risk for suicide in the Caucasian population (OR: 1.96 (1.58-2.43)), while Val-Val vs. Val-Met + Met carriers found to be at risk for suicide in the Asian populations (OR: 1.36 (1.04-1.78)). GWAS-based meta-analyses indicate some positive replicated findings regarding the DRD2, Neuroligin gene, estrogen-related genes, and genes involved in gene expression. EXPERT OPINION Most consistent results were obtained when analyzing sub-samples of patients. Some promising results come from the implementation of the polygenic risk score. There is no current consensus about an implementable biomarker for SB.
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Affiliation(s)
- Antonio Drago
- Department of Clinical Medicine, Aalborg University Hospital, Aalborg, Denmark
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40
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Kachuri L, Chatterjee N, Hirbo J, Schaid DJ, Martin I, Kullo IJ, Kenny EE, Pasaniuc B, Witte JS, Ge T. Principles and methods for transferring polygenic risk scores across global populations. Nat Rev Genet 2024; 25:8-25. [PMID: 37620596 PMCID: PMC10961971 DOI: 10.1038/s41576-023-00637-2] [Citation(s) in RCA: 45] [Impact Index Per Article: 45.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/11/2023] [Indexed: 08/26/2023]
Abstract
Polygenic risk scores (PRSs) summarize the genetic predisposition of a complex human trait or disease and may become a valuable tool for advancing precision medicine. However, PRSs that are developed in populations of predominantly European genetic ancestries can increase health disparities due to poor predictive performance in individuals of diverse and complex genetic ancestries. We describe genetic and modifiable risk factors that limit the transferability of PRSs across populations and review the strengths and weaknesses of existing PRS construction methods for diverse ancestries. Developing PRSs that benefit global populations in research and clinical settings provides an opportunity for innovation and is essential for health equity.
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Affiliation(s)
- Linda Kachuri
- Department of Epidemiology and Population Health, Stanford University School of Medicine, Stanford, CA, USA
- Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA
| | - Nilanjan Chatterjee
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Jibril Hirbo
- Department of Medicine Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Daniel J Schaid
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
| | - Iman Martin
- Division of Genomic Medicine, National Human Genome Research Institute, Bethesda, MD, USA
| | - Iftikhar J Kullo
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA
| | - Eimear E Kenny
- Institute for Genomic Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Bogdan Pasaniuc
- Department of Computational 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 Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - John S Witte
- Department of Epidemiology and Population Health, Stanford University School of Medicine, Stanford, CA, USA.
- Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA.
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA.
- Department of Genetics, Stanford University, Stanford, CA, USA.
| | - Tian Ge
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA.
- Center for Precision Psychiatry, Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
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Hoffmann M, Poschenrieder JM, Incudini M, Baier S, Fitz A, Maier A, Hartung M, Hoffmann C, Trummer N, Adamowicz K, Picciani M, Scheibling E, Harl MV, Lesch I, Frey H, Kayser S, Wissenberg P, Schwartz L, Hafner L, Acharya A, Hackl L, Grabert G, Lee SG, Cho G, Cloward M, Jankowski J, Lee HK, Tsoy O, Wenke N, Pedersen AG, Bønnelykke K, Mandarino A, Melograna F, Schulz L, Climente-González H, Wilhelm M, Iapichino L, Wienbrandt L, Ellinghaus D, Van Steen K, Grossi M, Furth PA, Hennighausen L, Di Pierro A, Baumbach J, Kacprowski T, List M, Blumenthal DB. Network medicine-based epistasis detection in complex diseases: ready for quantum computing. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.11.07.23298205. [PMID: 38076997 PMCID: PMC10705612 DOI: 10.1101/2023.11.07.23298205] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
Most heritable diseases are polygenic. To comprehend the underlying genetic architecture, it is crucial to discover the clinically relevant epistatic interactions (EIs) between genomic single nucleotide polymorphisms (SNPs)1-3. Existing statistical computational methods for EI detection are mostly limited to pairs of SNPs due to the combinatorial explosion of higher-order EIs. With NeEDL (network-based epistasis detection via local search), we leverage network medicine to inform the selection of EIs that are an order of magnitude more statistically significant compared to existing tools and consist, on average, of five SNPs. We further show that this computationally demanding task can be substantially accelerated once quantum computing hardware becomes available. We apply NeEDL to eight different diseases and discover genes (affected by EIs of SNPs) that are partly known to affect the disease, additionally, these results are reproducible across independent cohorts. EIs for these eight diseases can be interactively explored in the Epistasis Disease Atlas (https://epistasis-disease-atlas.com). In summary, NeEDL is the first application that demonstrates the potential of seamlessly integrated quantum computing techniques to accelerate biomedical research. Our network medicine approach detects higher-order EIs with unprecedented statistical and biological evidence, yielding unique insights into polygenic diseases and providing a basis for the development of improved risk scores and combination therapies.
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Affiliation(s)
- Markus Hoffmann
- Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, Germany
- Institute for Advanced Study (Lichtenbergstrasse 2 a, D-85748 Garching, Germany), Technical University of Munich, Germany
- National Institute of Diabetes, Digestive, and Kidney Diseases, Bethesda, MD 20892, United States of America
| | - Julian M. Poschenrieder
- Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, Germany
- Institute for Computational Systems Biology, University of Hamburg, Germany
| | - Massimiliano Incudini
- Dipartimento di Informatica, Universit’a di Verona, Strada le Grazie 15 - 34137, Verona, Italy
| | - Sylvie Baier
- Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, Germany
| | - Amelie Fitz
- Department of Health Technology, Section for Bioinformatics, Technical University of Denmark, DTU, 2800 Kgs. Lyngby, Denmark
- Copenhagen Prospective Studies on Asthma in Childhood (COPSAC), Herlev and Gentofte Hospital, University of Copenhagen, Copenhagen, Denmark
| | - Andreas Maier
- Institute for Computational Systems Biology, University of Hamburg, Germany
| | - Michael Hartung
- Institute for Computational Systems Biology, University of Hamburg, Germany
| | - Christian Hoffmann
- Institute for Computational Systems Biology, University of Hamburg, Germany
| | - Nico Trummer
- Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, Germany
| | - Klaudia Adamowicz
- Institute for Computational Systems Biology, University of Hamburg, Germany
| | - Mario Picciani
- Computational Mass Spectrometry, Technical University of Munich, Freising, Germany
| | - Evelyn Scheibling
- Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, Germany
| | - Maximilian V. Harl
- Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, Germany
| | - Ingmar Lesch
- Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, Germany
| | - Hunor Frey
- Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, Germany
| | - Simon Kayser
- Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, Germany
| | - Paul Wissenberg
- Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, Germany
| | - Leon Schwartz
- Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, Germany
| | - Leon Hafner
- Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, Germany
- Institute for Advanced Study (Lichtenbergstrasse 2 a, D-85748 Garching, Germany), Technical University of Munich, Germany
| | - Aakriti Acharya
- Division Data Science in Biomedicine, Peter L. Reichertz Institute for Medical Informatics, Technische Universität Braunschweig and Hannover Medical School, Rebenring 56, 38106 Braunschweig, Germany
- Braunschweig Integrated Centre of Systems Biology (BRICS), Technische Universität Braunschweig, Rebenring 56, 38106 Braunschweig, Braunschweig, Germany
| | - Lena Hackl
- Institute for Computational Systems Biology, University of Hamburg, Germany
| | - Gordon Grabert
- Division Data Science in Biomedicine, Peter L. Reichertz Institute for Medical Informatics, Technische Universität Braunschweig and Hannover Medical School, Rebenring 56, 38106 Braunschweig, Germany
- Braunschweig Integrated Centre of Systems Biology (BRICS), Technische Universität Braunschweig, Rebenring 56, 38106 Braunschweig, Braunschweig, Germany
| | - Sung-Gwon Lee
- National Institute of Diabetes, Digestive, and Kidney Diseases, Bethesda, MD 20892, United States of America
- School of Biological Sciences and Technology, Chonnam National University, Gwangju, Korea
| | - Gyuhyeok Cho
- Department of Chemistry, Gwangju Institute of Science and Technology, Gwangju, Korea
| | - Matthew Cloward
- Department of Biology, Brigham Young University, Provo, UT, USA
| | - Jakub Jankowski
- National Institute of Diabetes, Digestive, and Kidney Diseases, Bethesda, MD 20892, United States of America
| | - Hye Kyung Lee
- National Institute of Diabetes, Digestive, and Kidney Diseases, Bethesda, MD 20892, United States of America
| | - Olga Tsoy
- Institute for Computational Systems Biology, University of Hamburg, Germany
| | - Nina Wenke
- Institute for Computational Systems Biology, University of Hamburg, Germany
| | - Anders Gorm Pedersen
- Department of Health Technology, Section for Bioinformatics, Technical University of Denmark, DTU, 2800 Kgs. Lyngby, Denmark
| | - Klaus Bønnelykke
- Copenhagen Prospective Studies on Asthma in Childhood (COPSAC), Herlev and Gentofte Hospital, University of Copenhagen, Copenhagen, Denmark
| | - Antonio Mandarino
- International Centre for Theory of Quantum Technologies, University of Gdańsk, 80-309 Gdańsk, Poland
| | - Federico Melograna
- BIO3 - Systems Genetics; GIGA-R Medical Genomics, University of Liège, Liège, Belgium
- BIO3 - Systems Medicine; Department of Human Genetics, KU Leuven, Leuven, Belgium
| | - Laura Schulz
- Leibniz Supercomputing Centre of the Bavarian Academy of Sciences and Humanities (LRZ), Garching b. München, Germany
| | | | - Mathias Wilhelm
- Computational Mass Spectrometry, Technical University of Munich, Freising, Germany
| | - Luigi Iapichino
- Leibniz Supercomputing Centre of the Bavarian Academy of Sciences and Humanities (LRZ), Garching b. München, Germany
| | - Lars Wienbrandt
- Institute of Clinical Molecular Biology, Christian Albrechts University of Kiel, Kiel, Germany
| | - David Ellinghaus
- Institute of Clinical Molecular Biology, Christian Albrechts University of Kiel, Kiel, Germany
| | - Kristel Van Steen
- BIO3 - Systems Genetics; GIGA-R Medical Genomics, University of Liège, Liège, Belgium
- BIO3 - Systems Medicine; Department of Human Genetics, KU Leuven, Leuven, Belgium
| | - Michele Grossi
- European Organization for Nuclear Research (CERN), Geneva 1211, Switzerland
| | - Priscilla A. Furth
- National Institute of Diabetes, Digestive, and Kidney Diseases, Bethesda, MD 20892, United States of America
- Departments of Oncology & Medicine, Georgetown University, Washington, DC, USA
| | - Lothar Hennighausen
- Institute for Advanced Study (Lichtenbergstrasse 2 a, D-85748 Garching, Germany), Technical University of Munich, Germany
- National Institute of Diabetes, Digestive, and Kidney Diseases, Bethesda, MD 20892, United States of America
| | - Alessandra Di Pierro
- Dipartimento di Informatica, Universit’a di Verona, Strada le Grazie 15 - 34137, Verona, Italy
| | - Jan Baumbach
- Institute for Computational Systems Biology, University of Hamburg, Germany
- Computational BioMedicine Lab, University of Southern Denmark, Denmark
| | - Tim Kacprowski
- Division Data Science in Biomedicine, Peter L. Reichertz Institute for Medical Informatics, Technische Universität Braunschweig and Hannover Medical School, Rebenring 56, 38106 Braunschweig, Germany
- Braunschweig Integrated Centre of Systems Biology (BRICS), Technische Universität Braunschweig, Rebenring 56, 38106 Braunschweig, Braunschweig, Germany
| | - Markus List
- Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, Germany
| | - David B. Blumenthal
- Department Artificial Intelligence in Biomedical Engineering (AIBE), Friedrich-Alexander University Erlangen-Nürnberg (FAU), Erlangen, Germany
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42
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Tang D, Freudenberg J, Dahl A. Factorizing polygenic epistasis improves prediction and uncovers biological pathways in complex traits. Am J Hum Genet 2023; 110:1875-1887. [PMID: 37922884 PMCID: PMC10645564 DOI: 10.1016/j.ajhg.2023.10.002] [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: 06/05/2023] [Revised: 10/04/2023] [Accepted: 10/05/2023] [Indexed: 11/07/2023] Open
Abstract
Epistasis is central in many domains of biology, but it has not yet been proven useful for understanding the etiology of complex traits. This is partly because complex-trait epistasis involves polygenic interactions that are poorly captured in current models. To address this gap, we developed a model called Epistasis Factor Analysis (EFA). EFA assumes that polygenic epistasis can be factorized into interactions between a few epistasis factors (EFs), which represent latent polygenic components of the observed complex trait. The statistical goals of EFA are to improve polygenic prediction and to increase power to detect epistasis, while the biological goal is to unravel genetic effects into more-homogeneous units. We mathematically characterize EFA and use simulations to show that EFA outperforms current epistasis models when its assumptions approximately hold. Applied to predicting yeast growth rates, EFA outperforms the additive model for several traits with large epistasis heritability and uniformly outperforms the standard epistasis model. We replicate these prediction improvements in a second dataset. We then apply EFA to four previously characterized traits in the UK Biobank and find statistically significant epistasis in all four, including two that are robust to scale transformation. Moreover, we find that the inferred EFs partly recover pre-defined biological pathways for two of the traits. Our results demonstrate that more realistic models can identify biologically and statistically meaningful epistasis in complex traits, indicating that epistasis has potential for precision medicine and characterizing the biology underlying GWAS results.
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Affiliation(s)
- David Tang
- Section of Genetic Medicine, University of Chicago, Chicago, IL, USA; Program in Bioinformatics and Integrative Genomics, Harvard Medical School, Boston, MA, USA.
| | - Jerome Freudenberg
- Section of Genetic Medicine, University of Chicago, Chicago, IL, USA; Bioinformatics Interdepartmental Program, University of California Los Angeles, Los Angeles, CA, USA
| | - Andy Dahl
- Section of Genetic Medicine, University of Chicago, Chicago, IL, USA.
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43
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Dutta A, McDonald BA, Croll D. Combined reference-free and multi-reference based GWAS uncover cryptic variation underlying rapid adaptation in a fungal plant pathogen. PLoS Pathog 2023; 19:e1011801. [PMID: 37972199 PMCID: PMC10688896 DOI: 10.1371/journal.ppat.1011801] [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: 05/10/2023] [Revised: 11/30/2023] [Accepted: 11/06/2023] [Indexed: 11/19/2023] Open
Abstract
Microbial pathogens often harbor substantial functional diversity driven by structural genetic variation. Rapid adaptation from such standing variation threatens global food security and human health. Genome-wide association studies (GWAS) provide a powerful approach to identify genetic variants underlying recent pathogen adaptation. However, the reliance on single reference genomes and single nucleotide polymorphisms (SNPs) obscures the true extent of adaptive genetic variation. Here, we show quantitatively how a combination of multiple reference genomes and reference-free approaches captures substantially more relevant genetic variation compared to single reference mapping. We performed reference-genome based association mapping across 19 reference-quality genomes covering the diversity of the species. We contrasted the results with a reference-free (i.e., k-mer) approach using raw whole-genome sequencing data in a panel of 145 strains collected across the global distribution range of the fungal wheat pathogen Zymoseptoria tritici. We mapped the genetic architecture of 49 life history traits including virulence, reproduction and growth in multiple stressful environments. The inclusion of additional reference genome SNP datasets provides a nearly linear increase in additional loci mapped through GWAS. Variants detected through the k-mer approach explained a higher proportion of phenotypic variation than a reference genome-based approach and revealed functionally confirmed loci that classic GWAS approaches failed to map. The power of GWAS in microbial pathogens can be significantly enhanced by comprehensively capturing structural genetic variation. Our approach is generalizable to a large number of species and will uncover novel mechanisms driving rapid adaptation of pathogens.
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Affiliation(s)
- Anik Dutta
- Plant Pathology, Institute of Integrative Biology, ETH Zurich, Zurich, Switzerland
| | - Bruce A. McDonald
- Plant Pathology, Institute of Integrative Biology, ETH Zurich, Zurich, Switzerland
| | - Daniel Croll
- Laboratory of Evolutionary Genetics, Institute of Biology, University of Neuchâtel, Neuchâtel, Switzerland
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44
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Zhang Y, Choi KW, Delaney SW, Ge T, Pingault JB, Tiemeier H. Shared Genetic Risk in the Association of Screen Time With Psychiatric Problems in Children. JAMA Netw Open 2023; 6:e2341502. [PMID: 37930702 PMCID: PMC10628728 DOI: 10.1001/jamanetworkopen.2023.41502] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Accepted: 09/21/2023] [Indexed: 11/07/2023] Open
Abstract
Importance Children's exposure to screen time has been associated with poor mental health outcomes, yet the role of genetic factors remains largely unknown. Objective To assess the extent of genetic confounding in the associations between screen time and attention problems or internalizing problems in preadolescent children. Design, Setting, and Participants This cohort study analyzed data obtained between 2016 and 2019 from the Adolescent Brain Cognitive Development Study at 21 sites in the US. The sample included children aged 9 to 11 years of genetically assigned European ancestry with self-reported screen time. Data were analyzed between November 2021 and September 2023. Exposure Child-reported daily screen time (in hours) was ascertained from questionnaires completed by the children at baseline. Main Outcomes and Measures Child psychiatric problems, specifically attention and internalizing problems, were measured with the parent-completed Achenbach Child Behavior Checklist at the 1-year follow-up. Genetic sensitivity analyses model (Gsens) was used, which incorporated polygenic risk scores (PRSs) of both exposure and outcomes as well as either single-nucleotide variant (SNV; formerly single-nucleotide polymorphism)-based heritability or twin-based heritability to estimate genetic confounding. Results The 4262 children in the sample included 2269 males (53.2%) with a mean (SD) age of 9.9 (0.6) years. Child screen time was associated with attention problems (β = 0.10 SD; 95% CI, 0.07-0.13 SD) and internalizing problems (β = 0.03 SD; 95% CI, 0.003-0.06 SD). The television time PRS was associated with child screen time (β = 0.18 SD; 95% CI, 0.14-0.23 SD), the attention-deficit/hyperactivity disorder PRS was associated with attention problems (β = 0.13 SD; 95% CI, 0.10-0.16 SD), and the depression PRS was associated with internalizing problems (β = 0.10 SD; 95% CI, 0.07-0.13 SD). These PRSs were associated with cross-traits, suggesting genetic confounding. Estimates using PRSs and SNV-based heritability showed that genetic confounding accounted for most of the association between child screen time and attention problems and for 42.7% of the association between child screen time and internalizing problems. When PRSs and twin-based heritability estimates were used, genetic confounding fully explained both associations. Conclusions and Relevance Results of this study suggest that genetic confounding may explain a substantial part of the associations between child screen time and psychiatric problems. Genetic confounding should be considered in sociobehavioral studies of modifiable factors for youth mental health.
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Affiliation(s)
- Yingzhe Zhang
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
| | - Karmel W. Choi
- Center for Precision Psychiatry, Department of Psychiatry, Massachusetts General Hospital, Boston
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston
| | - Scott W. Delaney
- Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
| | - Tian Ge
- Center for Precision Psychiatry, Department of Psychiatry, Massachusetts General Hospital, Boston
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston
| | - Jean-Baptiste Pingault
- Department of Clinical, Educational and Health Psychology, University College London, London, United Kingdom
- Social, Genetic, and Developmental Psychiatry Centre, King’s College London, London, United Kingdom
| | - Henning Tiemeier
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
- Department of Social and Behavioral Sciences, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
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45
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Urbut SM, Koyama S, Hornsby W, Bhukar R, Kheterpal S, Truong B, Selvaraj MS, Neale B, O’Donnell CJ, Peloso GM, Natarajan P. Bayesian multivariate genetic analysis improves translational insights. iScience 2023; 26:107854. [PMID: 37766997 PMCID: PMC10520309 DOI: 10.1016/j.isci.2023.107854] [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: 01/10/2023] [Revised: 05/15/2023] [Accepted: 09/05/2023] [Indexed: 09/29/2023] Open
Abstract
While lipid traits are known essential mediators of cardiovascular disease, few approaches have taken advantage of their shared genetic effects. We apply a Bayesian multivariate size estimator, mash, to GWAS of four lipid traits in the Million Veterans Program (MVP) and provide posterior mean and local false sign rates for all effects. These estimates borrow information across traits to improve effect size accuracy. We show that controlling local false sign rates accurately and powerfully identifies replicable genetic associations and that multivariate control furthers the ability to explain complex diseases. Our application yields high concordance between independent datasets, more accurately prioritizes causal genes, and significantly improves polygenic prediction beyond state-of-the-art methods by up to 59% for lipid traits. The use of Bayesian multivariate genetic shrinkage has yet to be applied to human quantitative trait GWAS results, and we present a staged approach to prediction on a polygenic scale.
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Affiliation(s)
- Sarah M. Urbut
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA 02114, USA
- Program in Medical and Population Genetics, Broad Institute, Cambridge, MA 02142, USA
| | - Satoshi Koyama
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA 02114, USA
- Program in Medical and Population Genetics, Broad Institute, Cambridge, MA 02142, USA
- Department of Medicine Harvard Medical School, Boston, MA 02115, USA
| | - Whitney Hornsby
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA 02114, USA
- Program in Medical and Population Genetics, Broad Institute, Cambridge, MA 02142, USA
- Department of Medicine Harvard Medical School, Boston, MA 02115, USA
| | - Rohan Bhukar
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA 02114, USA
- Program in Medical and Population Genetics, Broad Institute, Cambridge, MA 02142, USA
- Department of Medicine Harvard Medical School, Boston, MA 02115, USA
| | - Sumeet Kheterpal
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA 02114, USA
- Program in Medical and Population Genetics, Broad Institute, Cambridge, MA 02142, USA
| | - Buu Truong
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA 02114, USA
- Program in Medical and Population Genetics, Broad Institute, Cambridge, MA 02142, USA
- Department of Medicine Harvard Medical School, Boston, MA 02115, USA
| | - Margaret S. Selvaraj
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA 02114, USA
- Program in Medical and Population Genetics, Broad Institute, Cambridge, MA 02142, USA
- Department of Medicine Harvard Medical School, Boston, MA 02115, USA
| | - Benjamin Neale
- Program in Medical and Population Genetics, Broad Institute, Cambridge, MA 02142, USA
- Department of Medicine Harvard Medical School, Boston, MA 02115, USA
- Analytic Translational and Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Christopher J. O’Donnell
- Department of Medicine Harvard Medical School, Boston, MA 02115, USA
- VA Boston Department of Veterans Affairs, Boston, MA 02130, USA
| | - Gina M. Peloso
- Department of Biostatistics, Boston University School of Public Health, Boston, MA 02218, USA
| | - Pradeep Natarajan
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA 02114, USA
- Program in Medical and Population Genetics, Broad Institute, Cambridge, MA 02142, USA
- Department of Medicine Harvard Medical School, Boston, MA 02115, USA
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46
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Verplaetse N, Passemiers A, Arany A, Moreau Y, Raimondi D. Large sample size and nonlinear sparse models outline epistatic effects in inflammatory bowel disease. Genome Biol 2023; 24:224. [PMID: 37798735 PMCID: PMC10552306 DOI: 10.1186/s13059-023-03064-y] [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: 03/21/2023] [Accepted: 09/20/2023] [Indexed: 10/07/2023] Open
Abstract
BACKGROUND Despite clear evidence of nonlinear interactions in the molecular architecture of polygenic diseases, linear models have so far appeared optimal in genotype-to-phenotype modeling. A key bottleneck for such modeling is that genetic data intrinsically suffers from underdetermination ([Formula: see text]). Millions of variants are present in each individual while the collection of large, homogeneous cohorts is hindered by phenotype incidence, sequencing cost, and batch effects. RESULTS We demonstrate that when we provide enough training data and control the complexity of nonlinear models, a neural network outperforms additive approaches in whole exome sequencing-based inflammatory bowel disease case-control prediction. To do so, we propose a biologically meaningful sparsified neural network architecture, providing empirical evidence for positive and negative epistatic effects present in the inflammatory bowel disease pathogenesis. CONCLUSIONS In this paper, we show that underdetermination is likely a major driver for the apparent optimality of additive modeling in clinical genetics today.
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Affiliation(s)
- Nora Verplaetse
- Department of of Electrical Engineering, Katholieke Universiteit Leuven, Leuven, Belgium.
| | - Antoine Passemiers
- Department of of Electrical Engineering, Katholieke Universiteit Leuven, Leuven, Belgium
| | - Adam Arany
- Department of of Electrical Engineering, Katholieke Universiteit Leuven, Leuven, Belgium
| | - Yves Moreau
- Department of of Electrical Engineering, Katholieke Universiteit Leuven, Leuven, Belgium
| | - Daniele Raimondi
- Department of of Electrical Engineering, Katholieke Universiteit Leuven, Leuven, Belgium.
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Zhu X, Yang Y, Lorincz-Comi N, Li G, Bentley A, de Vries PS, Brown M, Morrison AC, Rotimi C, James Gauderman W, Rao DC, Aschard H. A new Approach to Identify Gene-Environment Interactions and Reveal New Biological Insight in Complex traits. RESEARCH SQUARE 2023:rs.3.rs-3338723. [PMID: 37886448 PMCID: PMC10602131 DOI: 10.21203/rs.3.rs-3338723/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/28/2023]
Abstract
There is a long-standing debate about the magnitude of the contribution of gene-environment interactions to phenotypic variations of complex traits owing to the low statistical power and few reported interactions to date. To address this issue, the CHARGE Gene-Lifestyle Interactions Working Group has been spearheading efforts to investigate G × E in large and diverse samples through meta-analysis. Here, we present a powerful new approach to screen for interactions across the genome, an approach that shares substantial similarity to the Mendelian randomization framework. We identified and confirmed 5 loci (6 independent signals) interacting with either cigarette smoking or alcohol consumption for serum lipids, and empirically demonstrated that interaction and mediation are the major contributors to genetic effect size heterogeneity across populations. The estimated lower bound of the interaction and environmentally mediated contribution ranges from 1.76% to 14.05% of SNP heritability of serum lipids in Cross-Population data. Our study improves the understanding of the genetic architecture and environmental contributions to complex traits.
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Affiliation(s)
- Xiaofeng Zhu
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, Ohio, USA
| | - Yihe Yang
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, Ohio, USA
| | - Noah Lorincz-Comi
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, Ohio, USA
| | - Gen Li
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, Ohio, USA
| | - Amy Bentley
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Paul S de Vries
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, Texas, USA
| | - Michael Brown
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, Texas, USA
| | - Alanna C Morrison
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, Texas, USA
| | - Charles Rotimi
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - W. James Gauderman
- Biostatistics, Department of Preventive Medicine, University of Southern California, Los Angeles, CA, USA
| | - DC Rao
- Division of Biostatistics, Washington University School of Medicine, St. Louis, MO, USA
| | - Hugues Aschard
- Institut Pasteur, Université Paris Cité, Department of Computational Biology, F-75015 Paris, France
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48
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Riehl JFL, Cole CT, Morrow CJ, Barker HL, Bernhardsson C, Rubert‐Nason K, Ingvarsson PK, Lindroth RL. Genomic and transcriptomic analyses reveal polygenic architecture for ecologically important traits in aspen ( Populus tremuloides Michx.). Ecol Evol 2023; 13:e10541. [PMID: 37780087 PMCID: PMC10534199 DOI: 10.1002/ece3.10541] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Revised: 08/30/2023] [Accepted: 09/04/2023] [Indexed: 10/03/2023] Open
Abstract
Intraspecific genetic variation in foundation species such as aspen (Populus tremuloides Michx.) shapes their impact on forest structure and function. Identifying genes underlying ecologically important traits is key to understanding that impact. Previous studies, using single-locus genome-wide association (GWA) analyses to identify candidate genes, have identified fewer genes than anticipated for highly heritable quantitative traits. Mounting evidence suggests that polygenic control of quantitative traits is largely responsible for this "missing heritability" phenomenon. Our research characterized the genetic architecture of 30 ecologically important traits using a common garden of aspen through genomic and transcriptomic analyses. A multilocus association model revealed that most traits displayed a highly polygenic architecture, with most variation explained by loci with small effects (likely below the detection levels of single-locus GWA methods). Consistent with a polygenic architecture, our single-locus GWA analyses found only 38 significant SNPs in 22 genes across 15 traits. Next, we used differential expression analysis on a subset of aspen genets with divergent concentrations of salicinoid phenolic glycosides (key defense traits). This complementary method to traditional GWA discovered 1243 differentially expressed genes for a polygenic trait. Soft clustering analysis revealed three gene clusters (241 candidate genes) involved in secondary metabolite biosynthesis and regulation. Our work reveals that ecologically important traits governing higher-order community- and ecosystem-level attributes of a foundation forest tree species have complex underlying genetic structures and will require methods beyond traditional GWA analyses to unravel.
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Affiliation(s)
| | | | - Clay J. Morrow
- Department of Forest and Wildlife EcologyUniversity of Wisconsin‐MadisonMadisonWisconsinUSA
| | - Hilary L. Barker
- Department of EntomologyUniversity of Wisconsin‐MadisonMadisonWisconsinUSA
- Present address:
Office of Student SuccessWisconsin Technical College SystemMadisonWisconsinUSA
| | - Carolina Bernhardsson
- Department of Ecology and Environmental ScienceUmeå UniversityUmeåSweden
- Present address:
Department of Organismal Biology, Center for Evolutionary BiologyUppsala UniversityUppsalaSweden
| | - Kennedy Rubert‐Nason
- Department of EntomologyUniversity of Wisconsin‐MadisonMadisonWisconsinUSA
- Present address:
Division of Natural SciencesUniversity of Maine at Fort KentFort KentMaineUSA
| | - Pär K. Ingvarsson
- Department of Plant BiologySwedish University of Agricultural Sciences, Uppsala BioCenterUppsalaSweden
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49
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Arnatkeviciute A, Lemire M, Morrison C, Mooney M, Ryabinin P, Roslin NM, Nikolas M, Coxon J, Tiego J, Hawi Z, Fornito A, Henrik W, Martinot JL, Martinot MLP, Artiges E, Garavan H, Nigg J, Friedman NP, Burton C, Schachar R, Crosbie J, Bellgrove MA. Trans-ancestry meta-analysis of genome wide association studies of inhibitory control. Mol Psychiatry 2023; 28:4175-4184. [PMID: 37500827 PMCID: PMC10827666 DOI: 10.1038/s41380-023-02187-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Revised: 07/01/2023] [Accepted: 07/10/2023] [Indexed: 07/29/2023]
Abstract
Deficits in effective executive function, including inhibitory control are associated with risk for a number of psychiatric disorders and significantly impact everyday functioning. These complex traits have been proposed to serve as endophenotypes, however, their genetic architecture is not yet well understood. To identify the common genetic variation associated with inhibitory control in the general population we performed the first trans-ancestry genome wide association study (GWAS) combining data across 8 sites and four ancestries (N = 14,877) using cognitive traits derived from the stop-signal task, namely - go reaction time (GoRT), go reaction time variability (GoRT SD) and stop signal reaction time (SSRT). Although we did not identify genome wide significant associations for any of the three traits, GoRT SD and SSRT demonstrated significant and similar SNP heritability of 8.2%, indicative of an influence of genetic factors. Power analyses demonstrated that the number of common causal variants contributing to the heritability of these phenotypes is relatively high and larger sample sizes are necessary to robustly identify associations. In Europeans, the polygenic risk for ADHD was significantly associated with GoRT SD and the polygenic risk for schizophrenia was associated with GoRT, while in East Asians polygenic risk for schizophrenia was associated with SSRT. These results support the potential of executive function measures as endophenotypes of neuropsychiatric disorders. Together these findings provide the first evidence indicating the influence of common genetic variation in the genetic architecture of inhibitory control quantified using objective behavioural traits derived from the stop-signal task.
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Affiliation(s)
- Aurina Arnatkeviciute
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Melbourne, VIC, Australia
| | - Mathieu Lemire
- Department of Psychiatry, The Hospital for Sick Children, Toronto, ON, Canada
| | - Claire Morrison
- Department of Psychology and Neuroscience, University of Colorado-Boulder, Boulder, CO, USA
- Institute for Behavioural Genetics, University of Colorado Boulder, Boulder, CO, USA
| | - Michael Mooney
- Department of Medical Informatics & Clinical Epidemiology, Oregon Health & Science University, Portland, OR, USA
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR, USA
| | - Peter Ryabinin
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR, USA
| | - Nicole M Roslin
- Department of Psychiatry, The Hospital for Sick Children, Toronto, ON, Canada
| | - Molly Nikolas
- Department of Psychological and Brain Sciences, University of Iowa, Iowa City, IA, 52242, USA
| | - James Coxon
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Melbourne, VIC, Australia
| | - Jeggan Tiego
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Melbourne, VIC, Australia
| | - Ziarih Hawi
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Melbourne, VIC, Australia
| | - Alex Fornito
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Melbourne, VIC, Australia
| | - Walter Henrik
- Department of Psychiatry and Psychotherapy, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu, Berlin, Germany
| | - Jean-Luc Martinot
- Institut National de la Santé et de la Recherche Médicale, INSERM U1299 "Developmental trajectories & psychiatry" Université Paris-Saclay, Ecole Normale supérieure Paris-Saclay, CNRS, Centre Borelli, Gif-sur-Yvette, France
| | - Marie-Laure Paillère Martinot
- Institut National de la Santé et de la Recherche Médicale, INSERM U1299 "Developmental trajectories & psychiatry" Université Paris-Saclay, Ecole Normale supérieure Paris-Saclay, CNRS, Centre Borelli, Gif-sur-Yvette, France
- AP-HP, Sorbonne Université, Department of Child and Adolescent Psychiatry, Pitié-Salpêtrière Hospital, Paris, France
| | - Eric Artiges
- Institut National de la Santé et de la Recherche Médicale, INSERM U1299 "Developmental trajectories & psychiatry" Université Paris-Saclay, Ecole Normale supérieure Paris-Saclay, CNRS, Centre Borelli, Gif-sur-Yvette, France
- Etablissement Public de Santé (EPS) Barthélemy Durand, 91700, Sainte-Geneviève-des-Bois, France
| | - Hugh Garavan
- Departments of Psychiatry and Psychology, University of Vermont, 05405, Burlington, VT, USA
| | - Joel Nigg
- Division of Psychology, Department of Psychiatry, Oregon Health & Science University, Portland, OR, USA
| | - Naomi P Friedman
- Department of Psychology and Neuroscience, University of Colorado-Boulder, Boulder, CO, USA
- Institute for Behavioural Genetics, University of Colorado Boulder, Boulder, CO, USA
| | - Christie Burton
- Department of Psychiatry, The Hospital for Sick Children, Toronto, ON, Canada
| | - Russell Schachar
- Department of Psychiatry, The Hospital for Sick Children, Toronto, ON, Canada
| | - Jennifer Crosbie
- Department of Psychiatry, The Hospital for Sick Children, Toronto, ON, Canada
| | - Mark A Bellgrove
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Melbourne, VIC, Australia.
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Managò F, Scheggia D, Pontillo M, Mereu M, Mastrogiacomo R, Udayan G, Valentini P, Tata MC, Weinberger DR, Weickert CS, Pompa PP, De Luca MA, Vicari S, Papaleo F. Dopaminergic signalling and behavioural alterations by Comt-Dtnbp1 genetic interaction and their clinical relevance. Br J Pharmacol 2023; 180:2514-2531. [PMID: 37218669 DOI: 10.1111/bph.16147] [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: 06/13/2022] [Revised: 04/26/2023] [Accepted: 05/02/2023] [Indexed: 05/24/2023] Open
Abstract
BACKGROUND AND PURPOSE Cognitive and motor functions are modulated by dopaminergic signalling, which is shaped by several genetic factors. The biological effects of single genetic variants might differ depending on epistatic interactions that can be functionally multi-directional and non-linear. EXPERIMENTAL APPROACH We performed behavioural and neurochemical assessments in genetically modified mice and behavioural assessments and genetic screening in human patients with 22q11.2 deletion syndrome (22q11.2DS). KEY RESULTS Here, we confirm a genetic interaction between the Comt (catechol-O-methyltransferase, human orthologue: COMT) and Dtnbp1 (dystrobrevin binding protein 1, alias dysbindin, human orthologue: DTNBP1) genes that modulate cortical and striatal dopaminergic signalling in a manner not predictable by the effects of each single gene. In mice, Comt-by-Dtnbp1 concomitant reduction leads to a hypoactive mesocortical and a hyperactive mesostriatal dopamine pathway, associated with specific cognitive abnormalities. Like mice, in subjects with the 22q11.2DS (characterized by COMT hemideletion and dopamine alterations), COMT-by-DTNBP1 concomitant reduction was associated with analogous cognitive disturbances. We then developed an easy and inexpensive colourimetric kit for the genetic screening of common COMT and DTNBP1 functional genetic variants for clinical application. CONCLUSIONS AND IMPLICATIONS These findings illustrate an epistatic interaction of two dopamine-related genes and their functional effects, supporting the need to address genetic interaction mechanisms at the base of complex behavioural traits.
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Affiliation(s)
- Francesca Managò
- Genetics of Cognition Laboratory, Neuroscience Area, Istituto Italiano di Tecnologia, Genoa, Italy
| | - Diego Scheggia
- Genetics of Cognition Laboratory, Neuroscience Area, Istituto Italiano di Tecnologia, Genoa, Italy
- Department of Pharmacological and Biomolecular Sciences, University of Milan, Milan, Italy
| | - Maria Pontillo
- Department of Neuroscience, Bambino Gesù Children's Hospital, Rome, Italy
| | - Maddalena Mereu
- Genetics of Cognition Laboratory, Neuroscience Area, Istituto Italiano di Tecnologia, Genoa, Italy
| | - Rosa Mastrogiacomo
- Genetics of Cognition Laboratory, Neuroscience Area, Istituto Italiano di Tecnologia, Genoa, Italy
| | - Gayatri Udayan
- Nanobiointeractions and Nanodiagnostics, Istituto Italiano di Tecnologia, Genoa, Italy
- Department of Engineering for Innovation, University of Salento, Lecce, Italy
| | - Paola Valentini
- Nanobiointeractions and Nanodiagnostics, Istituto Italiano di Tecnologia, Genoa, Italy
| | | | - Daniel R Weinberger
- Lieber Institute for Brain Development, Johns Hopkins University Medical Campus, Baltimore, Maryland, USA
| | - Cynthia S Weickert
- Schizophrenia Research Laboratory, Neuroscience Research Australia, Sydney, Australia
| | - Pier Paolo Pompa
- Nanobiointeractions and Nanodiagnostics, Istituto Italiano di Tecnologia, Genoa, Italy
| | - Maria A De Luca
- Department of Biomedical Sciences, University of Cagliari, Cagliari, Italy
| | - Stefano Vicari
- Department of Neuroscience, Bambino Gesù Children's Hospital, Rome, Italy
| | - Francesco Papaleo
- Genetics of Cognition Laboratory, Neuroscience Area, Istituto Italiano di Tecnologia, Genoa, Italy
- Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy
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