Pub Date : 2024-09-05DOI: 10.1101/2024.09.05.24313137
Meritxell Oliva, Emily King, Reza Hammond, John S. Lee, Bridget Riley-Gillis, Justyna Resztak, Jacob Degner
To better understand COVID-19 pathobiology and to prioritize treatment targets, we sought to identify human genes influencing genetically driven disease risk and severity, and to identify additional organismal-level phenotypes impacted by pleiotropic COVID-19-associated genomic loci. To this end, we performed ancestry-aware, trans-layer, multi-omic analyses by integrating recent COVID-19 Host Genetics Initiative genome-wide association (GWAS) data from six ancestry endpoints - African, Amerindian, South Asian, East Asian, European and meta-ancestry - with quantitative trait loci (QTL) and GWAS endpoints by colocalization analyses. We identified colocalizations for 47 COVID-19 loci with 307 GWAS trait endpoints and observed a highly variable (1-435 endpoint colocalizations) degree of pleiotropy per COVID-19 locus but a high representation of pulmonary traits. For those, directionality of effect mapped to COVID-19 pathological alleles pinpoints to systematic protective effects for COPD, detrimental effects for lung adenocarcinoma, and locus-dependent effects for IPF. Among 64 QTL-COVID-19 colocalized loci, we identified associations with most reported (47/53) and half of unreported (19/38) COVID-19-associated loci, including 9 loci identified in non-European cohorts. We generated colocalization evidence metrics and visualization tools, and integrated pulmonary-specific QTL signal, to aid the identification of putative causal genes and pulmonary cells. For example, among likely causal genes not previously linked to COVID-19, we identified desmoplakin-driven IPF-shared genetic perturbations in alveolar cells. Altogether, we provide insights into COVID-19 biology by identifying molecular and phenotype links to the genetic architecture of COVID-19 risk and severity phenotypes; further characterizing previously reported loci and providing novel insights for uncharacterized loci.
{"title":"Integration of GWAS and multi-omic QTLs identifies uncharacterized COVID-19 gene-biotype and phenotype associations","authors":"Meritxell Oliva, Emily King, Reza Hammond, John S. Lee, Bridget Riley-Gillis, Justyna Resztak, Jacob Degner","doi":"10.1101/2024.09.05.24313137","DOIUrl":"https://doi.org/10.1101/2024.09.05.24313137","url":null,"abstract":"To better understand COVID-19 pathobiology and to prioritize treatment targets, we sought to identify human genes influencing genetically driven disease risk and severity, and to identify additional organismal-level phenotypes impacted by pleiotropic COVID-19-associated genomic loci. To this end, we performed ancestry-aware, trans-layer, multi-omic analyses by integrating recent COVID-19 Host Genetics Initiative genome-wide association (GWAS) data from six ancestry endpoints - African, Amerindian, South Asian, East Asian, European and meta-ancestry - with quantitative trait loci (QTL) and GWAS endpoints by colocalization analyses. We identified colocalizations for 47 COVID-19 loci with 307 GWAS trait endpoints and observed a highly variable (1-435 endpoint colocalizations) degree of pleiotropy per COVID-19 locus but a high representation of pulmonary traits. For those, directionality of effect mapped to COVID-19 pathological alleles pinpoints to systematic protective effects for COPD, detrimental effects for lung adenocarcinoma, and locus-dependent effects for IPF. Among 64 QTL-COVID-19 colocalized loci, we identified associations with most reported (47/53) and half of unreported (19/38) COVID-19-associated loci, including 9 loci identified in non-European cohorts. We generated colocalization evidence metrics and visualization tools, and integrated pulmonary-specific QTL signal, to aid the identification of putative causal genes and pulmonary cells. For example, among likely causal genes not previously linked to COVID-19, we identified desmoplakin-driven IPF-shared genetic perturbations in alveolar cells. Altogether, we provide insights into COVID-19 biology by identifying molecular and phenotype links to the genetic architecture of COVID-19 risk and severity phenotypes; further characterizing previously reported loci and providing novel insights for uncharacterized loci.","PeriodicalId":501375,"journal":{"name":"medRxiv - Genetic and Genomic Medicine","volume":"52 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142192146","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-04DOI: 10.1101/2024.09.04.24313010
Ehsan Khajouei, Valentina Ghisays, Ignazio S. Piras, Kiana L. Martinez, Marcus Naymik, Preston Ngo, Tam C. Tran, Joshua C. Denny, Travis J. Wheeler, Matthew J. Huentelman, Eric M. Reiman, Jason H. Karnes
Background Genetic variation in APOE is associated with altered lipid metabolism, as well as cardiovascular and neurodegenerative disease risk. However, prior studies are largely limited to European ancestry populations and differential risk by sex and ancestry has not been widely evaluated. We utilized a phenome-wide association study (PheWAS) approach to explore APOE- associated phenotypes in the All of Us Research Program.
{"title":"Phenome-Wide Association of APOE Alleles in the All of Us Research Program","authors":"Ehsan Khajouei, Valentina Ghisays, Ignazio S. Piras, Kiana L. Martinez, Marcus Naymik, Preston Ngo, Tam C. Tran, Joshua C. Denny, Travis J. Wheeler, Matthew J. Huentelman, Eric M. Reiman, Jason H. Karnes","doi":"10.1101/2024.09.04.24313010","DOIUrl":"https://doi.org/10.1101/2024.09.04.24313010","url":null,"abstract":"<strong>Background</strong> Genetic variation in <em>APOE</em> is associated with altered lipid metabolism, as well as cardiovascular and neurodegenerative disease risk. However, prior studies are largely limited to European ancestry populations and differential risk by sex and ancestry has not been widely evaluated. We utilized a phenome-wide association study (PheWAS) approach to explore <em>APOE</em>- associated phenotypes in the <em>All of Us</em> Research Program.","PeriodicalId":501375,"journal":{"name":"medRxiv - Genetic and Genomic Medicine","volume":"61 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142192150","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-04DOI: 10.1101/2024.09.02.24312466
Raymond Noordam, Wenyi Wang, Pavithra Nagarajan, Heming Wang, Michael R Brown, Amy R Bentley, Qin Hui, Aldi T Kraja, John L Morrison, Jeffrey R O'Connel, Songmi Lee, Karen Schwander, Traci M Bartz, Lisa de las Fuentes, Mary F Feitosa, Xiuqing Guo, Xu Hanfei, Sarah E Harris, Zhijie Huang, Mart Kals, Christophe Lefevre, Massimo Mangino, Yuri Milaneschi, Peter van der Most, Natasha L Pacheco, Nicholette D Palmer, Varun Rao, Rainer Rauramaa, Quan Sun, Yasuharu Tabara, Dina Vojinovic, Yujie Wang, Stefan Weiss, Qian Yang, Wei Zhao, Wanyng Zhu, Md Abu Yusuf Ansari, Hugues Aschard, Pramod Anugu, Themistocles L Assimes, John Attia, Laura D Baker, Christie Ballantyne, Lydia Bazzano, Eric Boerwinkle, Brain Cade, Hung-hsin Chen, Wei Chen, Yii-Der Ida Chen, Zekai Chen, Kelly Cho, Illeana De Anda-Duran, Latchezar Dimitrov, Anh Do, Todd Edwards, Tariq Faquih, Aroon Hingorani, Susan P Fisher-Hoch, J. Michael Gaziano, Sina A Gharib, Ayush Giri, Mohsen Ghanbari, Hans Jorgen Grabe, Mariaelisa Graff, C Charles Gu, Jiang He, Sami Heikkinen, James Hixson, Yuk-Lam Ho, Michelle M Hood, Serena C Houghton, Carrie A Karvonen-Gutierrez, Takahisa Kawaguchi, Tuomas O Kilpelainen, Pirjo Komulainen, Henry J Lin, Gregorio V Linchangzo, Annemari I Luik, Jintao Ma, James B Meigs, Joseph B McCormick, Christina Menni, Ilja M Nolte, Jimm M Norris, Lauren E Petty, Hannah G Polikowsky, Laura M Raffield, Stephen S Rich, Renata L Riha, Thomas C Russ, Edward A Ruiz-Narvaez, Colleen M Sitlani, Jennifer A Smith, Harold Snieder, Tamar Sofer, Botong Shen, Jingxian Tang, Kent D Taylor, Maris Tader-Laving, Rima Triatin, Michael Y Tsai, Henry Volzke, Kenneth E Westerman, Rui Xia, Jie Yao, Kristin L Young, Ruiyuan Zhang, Alan B Zonderman, Xiaofeng Zhu, Jennifer E Below, Simon R Cox, Michelle Evans, Myriam Fornage, Ervin R Fox, Nora Franceschini, Sioban D Harlow, Elizabeth Holliday, M Arfan Ikram, Tanika Kelly, Timo A Lakka, Deborah A Lawlor, Changwei Li, Ching-Ti Liu, Reedik Magi, Alisa K Manning, Famihiko Matsuda, Alanna C Morrison, Matthias Nauck, Kari E North, Brenda WJH Penninx, Michael A Province, Bruce M Psaty, Jerome I Rotter, Tim D Spector, Lynne E Wagenknecht, Ko Willems van Dijk, Lifelines Cohort Study, Million Veteran Program, Cashell E Jaquish, Peter WF Wilson, Patricia A Peyser, Patricia B Munroe, Paul S de Vries, W James Gauderman, Yan V Sun, Han Chen, Clint L Miller, Thomas W Winkler, Dabeeru C Rao, Susan Redline, Diana van Heemst
We performed large-scale genome-wide gene-sleep interaction analyses of lipid levels to identify novel genetic variants underpinning the biomolecular pathways of sleep-associated lipid disturbances and to suggest possible druggable targets. We collected data from 55 cohorts with a combined sample size of 732,564 participants (87% European ancestry) with data on lipid traits (high-density lipoprotein [HDL-c] and low-density lipoprotein [LDL-c] cholesterol and triglycerides [TG]). Short (STST) and long (LTST) total sleep time were defined by the extreme 20% of the age- and sex-standardized values within each cohort. Based on cohort-level summary statistics data, we performed meta-analyses for the one-degree of freedom tests of interaction and two-degree of freedom joint tests of the main and interaction effect. In the cross-population meta-analyses, the one-degree of freedom variant-sleep interaction test identified 10 loci (Pint<5.0e-9) not previously observed for lipids. Of interest, the ASPH locus (TG, LTST) is a target for aspartic and succinic acid metabolism previously shown to improve sleep and cardiovascular risk. The two-degree of freedom analyses identified an additional 7 loci that showed evidence for variant-sleep interaction (Pjoint<5.0e-9 in combination with Pint<6.6e-6). Of these, the SLC8A1 locus (TG, STST) has been considered a potential treatment target for reduction of ischemic damage after acute myocardial infarction. Collectively, the 17 (9 with STST; 8 with LTST) loci identified in this large-scale initiative provides evidence into the biomolecular mechanisms underpinning sleep-duration-associated changes in lipid levels. The identified druggable targets may contribute to the development of novel therapies for dyslipidemia in people with sleep disturbances.
{"title":"A Large-Scale Genome-Wide Gene-Sleep Interaction Study in 732,564 Participants Identifies Lipid Loci Explaining Sleep-Associated Lipid Disturbances","authors":"Raymond Noordam, Wenyi Wang, Pavithra Nagarajan, Heming Wang, Michael R Brown, Amy R Bentley, Qin Hui, Aldi T Kraja, John L Morrison, Jeffrey R O'Connel, Songmi Lee, Karen Schwander, Traci M Bartz, Lisa de las Fuentes, Mary F Feitosa, Xiuqing Guo, Xu Hanfei, Sarah E Harris, Zhijie Huang, Mart Kals, Christophe Lefevre, Massimo Mangino, Yuri Milaneschi, Peter van der Most, Natasha L Pacheco, Nicholette D Palmer, Varun Rao, Rainer Rauramaa, Quan Sun, Yasuharu Tabara, Dina Vojinovic, Yujie Wang, Stefan Weiss, Qian Yang, Wei Zhao, Wanyng Zhu, Md Abu Yusuf Ansari, Hugues Aschard, Pramod Anugu, Themistocles L Assimes, John Attia, Laura D Baker, Christie Ballantyne, Lydia Bazzano, Eric Boerwinkle, Brain Cade, Hung-hsin Chen, Wei Chen, Yii-Der Ida Chen, Zekai Chen, Kelly Cho, Illeana De Anda-Duran, Latchezar Dimitrov, Anh Do, Todd Edwards, Tariq Faquih, Aroon Hingorani, Susan P Fisher-Hoch, J. Michael Gaziano, Sina A Gharib, Ayush Giri, Mohsen Ghanbari, Hans Jorgen Grabe, Mariaelisa Graff, C Charles Gu, Jiang He, Sami Heikkinen, James Hixson, Yuk-Lam Ho, Michelle M Hood, Serena C Houghton, Carrie A Karvonen-Gutierrez, Takahisa Kawaguchi, Tuomas O Kilpelainen, Pirjo Komulainen, Henry J Lin, Gregorio V Linchangzo, Annemari I Luik, Jintao Ma, James B Meigs, Joseph B McCormick, Christina Menni, Ilja M Nolte, Jimm M Norris, Lauren E Petty, Hannah G Polikowsky, Laura M Raffield, Stephen S Rich, Renata L Riha, Thomas C Russ, Edward A Ruiz-Narvaez, Colleen M Sitlani, Jennifer A Smith, Harold Snieder, Tamar Sofer, Botong Shen, Jingxian Tang, Kent D Taylor, Maris Tader-Laving, Rima Triatin, Michael Y Tsai, Henry Volzke, Kenneth E Westerman, Rui Xia, Jie Yao, Kristin L Young, Ruiyuan Zhang, Alan B Zonderman, Xiaofeng Zhu, Jennifer E Below, Simon R Cox, Michelle Evans, Myriam Fornage, Ervin R Fox, Nora Franceschini, Sioban D Harlow, Elizabeth Holliday, M Arfan Ikram, Tanika Kelly, Timo A Lakka, Deborah A Lawlor, Changwei Li, Ching-Ti Liu, Reedik Magi, Alisa K Manning, Famihiko Matsuda, Alanna C Morrison, Matthias Nauck, Kari E North, Brenda WJH Penninx, Michael A Province, Bruce M Psaty, Jerome I Rotter, Tim D Spector, Lynne E Wagenknecht, Ko Willems van Dijk, Lifelines Cohort Study, Million Veteran Program, Cashell E Jaquish, Peter WF Wilson, Patricia A Peyser, Patricia B Munroe, Paul S de Vries, W James Gauderman, Yan V Sun, Han Chen, Clint L Miller, Thomas W Winkler, Dabeeru C Rao, Susan Redline, Diana van Heemst","doi":"10.1101/2024.09.02.24312466","DOIUrl":"https://doi.org/10.1101/2024.09.02.24312466","url":null,"abstract":"We performed large-scale genome-wide gene-sleep interaction analyses of lipid levels to identify novel genetic variants underpinning the biomolecular pathways of sleep-associated lipid disturbances and to suggest possible druggable targets. We collected data from 55 cohorts with a combined sample size of 732,564 participants (87% European ancestry) with data on lipid traits (high-density lipoprotein [HDL-c] and low-density lipoprotein [LDL-c] cholesterol and triglycerides [TG]). Short (STST) and long (LTST) total sleep time were defined by the extreme 20% of the age- and sex-standardized values within each cohort. Based on cohort-level summary statistics data, we performed meta-analyses for the one-degree of freedom tests of interaction and two-degree of freedom joint tests of the main and interaction effect. In the cross-population meta-analyses, the one-degree of freedom variant-sleep interaction test identified 10 loci (Pint<5.0e-9) not previously observed for lipids. Of interest, the ASPH locus (TG, LTST) is a target for aspartic and succinic acid metabolism previously shown to improve sleep and cardiovascular risk. The two-degree of freedom analyses identified an additional 7 loci that showed evidence for variant-sleep interaction (Pjoint<5.0e-9 in combination with Pint<6.6e-6). Of these, the SLC8A1 locus (TG, STST) has been considered a potential treatment target for reduction of ischemic damage after acute myocardial infarction. Collectively, the 17 (9 with STST; 8 with LTST) loci identified in this large-scale initiative provides evidence into the biomolecular mechanisms underpinning sleep-duration-associated changes in lipid levels. The identified druggable targets may contribute to the development of novel therapies for dyslipidemia in people with sleep disturbances.","PeriodicalId":501375,"journal":{"name":"medRxiv - Genetic and Genomic Medicine","volume":"44 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142192167","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-04DOI: 10.1101/2024.09.04.24313051
Maria Zanti, Denise G. O’Mahony, Michael T. Parsons, Leila Dorling, Joe Dennis, Nicholas J. Boddicker, Wenan Chen, Chunling Hu, Marc Naven, Kristia Yiangou, Thomas U. Ahearn, Christine B. Ambrosone, Irene L. Andrulis, Antonis C. Antoniou, Paul L. Auer, Caroline Baynes, Clara Bodelon, Natalia V. Bogdanova, Stig E. Bojesen, Manjeet K. Bolla, Kristen D. Brantley, Nicola J. Camp, Archie Campbell, Jose E. Castelao, Melissa H. Cessna, Jenny Chang-Claude, Fei Chen, Georgia Chenevix-Trench, NBCS Collaborators, Don M. Conroy, Kamila Czene, Arcangela De Nicolo, Susan M. Domchek, Thilo Dörk, Alison M. Dunning, A. Heather Eliassen, D. Gareth Evans, Peter A. Fasching, Jonine D. Figueroa, Henrik Flyger, Manuela Gago-Dominguez, Montserrat García-Closas, Gord Glendon, Anna González-Neira, Felix Grassmann, Andreas Hadjisavvas, Christopher A. Haiman, Ute Hamann, Steven N. Hart, Mikael B.A. Hartman, Weang-Kee Ho, James M. Hodge, Reiner Hoppe, Sacha J. Howell, kConFab Investigators, Anna Jakubowska, Elza K. Khusnutdinova, Yon-Dschun Ko, Peter Kraft, Vessela N. Kristensen, James V. Lacey, Jingmei Li, Geok Hoon Lim, Sara Lindström, Artitaya Lophatananon, Craig Luccarini, Arto Mannermaa, Maria Elena Martinez, Dimitrios Mavroudis, Roger L. Milne, Kenneth Muir, Katherine L. Nathanson, Rocio Nuñez-Torres, Nadia Obi, Janet E. Olson, Julie R. Palmer, Mihalis I. Panayiotidis, Alpa V. Patel, Paul D.P. Pharoah, Eric C. Polley, Muhammad U. Rashid, Kathryn J. Ruddy, Emmanouil Saloustros, Elinor J. Sawyer, Marjanka K. Schmidt, Melissa C. Southey, Veronique Kiak-Mien Tan, Soo Hwang Teo, Lauren R. Teras, Diana Torres, Amy Trentham-Dietz, Thérèse Truong, Celine M. Vachon, Qin Wang, Jeffrey N. Weitzel, Siddhartha Yadav, Song Yao, Gary R. Zirpoli, Melissa S. Cline, Peter Devilee, Sean V. Tavtigian, David E. Goldgar, Fergus J. Couch, Douglas F. Easton, Amanda B. Spurdle, Kyriaki Michailidou
Clinical genetic testing identifies variants causal for hereditary cancer, information that is used for risk assessment and clinical management. Unfortunately, some variants identified are of uncertain clinical significance (VUS), complicating patient management. Case-control data is one evidence type used to classify VUS, and previous findings indicate that case-control likelihood ratios (LRs) outperform odds ratios for variant classification. As an initiative of the Evidence-based Network for the Interpretation of Germline Mutant Alleles (ENIGMA) Analytical Working Group we analyzed germline sequencing data of BRCA1 and BRCA2 from 96,691 female breast cancer cases and 303,925 unaffected controls from three studies: the BRIDGES study of the Breast Cancer Association Consortium, the Cancer Risk Estimates Related to Susceptibility consortium, and the UK Biobank. We observed 11,227 BRCA1 and BRCA2 variants, with 6,921 being coding, covering 23.4% of BRCA1 and BRCA2 VUS in ClinVar and 19.2% of ClinVar curated (likely) benign or pathogenic variants. Case-control LR evidence was highly consistent with ClinVar assertions for (likely) benign or pathogenic variants; exhibiting 99.1% sensitivity and 95.4% specificity for BRCA1 and 92.2% sensitivity and 86.6% specificity for BRCA2. This approach provides case-control evidence for 785 unclassified variants, that can serve as a valuable element for clinical classification.
{"title":"Analysis of more than 400,000 women provides case-control evidence for BRCA1 and BRCA2 variant classification","authors":"Maria Zanti, Denise G. O’Mahony, Michael T. Parsons, Leila Dorling, Joe Dennis, Nicholas J. Boddicker, Wenan Chen, Chunling Hu, Marc Naven, Kristia Yiangou, Thomas U. Ahearn, Christine B. Ambrosone, Irene L. Andrulis, Antonis C. Antoniou, Paul L. Auer, Caroline Baynes, Clara Bodelon, Natalia V. Bogdanova, Stig E. Bojesen, Manjeet K. Bolla, Kristen D. Brantley, Nicola J. Camp, Archie Campbell, Jose E. Castelao, Melissa H. Cessna, Jenny Chang-Claude, Fei Chen, Georgia Chenevix-Trench, NBCS Collaborators, Don M. Conroy, Kamila Czene, Arcangela De Nicolo, Susan M. Domchek, Thilo Dörk, Alison M. Dunning, A. Heather Eliassen, D. Gareth Evans, Peter A. Fasching, Jonine D. Figueroa, Henrik Flyger, Manuela Gago-Dominguez, Montserrat García-Closas, Gord Glendon, Anna González-Neira, Felix Grassmann, Andreas Hadjisavvas, Christopher A. Haiman, Ute Hamann, Steven N. Hart, Mikael B.A. Hartman, Weang-Kee Ho, James M. Hodge, Reiner Hoppe, Sacha J. Howell, kConFab Investigators, Anna Jakubowska, Elza K. Khusnutdinova, Yon-Dschun Ko, Peter Kraft, Vessela N. Kristensen, James V. Lacey, Jingmei Li, Geok Hoon Lim, Sara Lindström, Artitaya Lophatananon, Craig Luccarini, Arto Mannermaa, Maria Elena Martinez, Dimitrios Mavroudis, Roger L. Milne, Kenneth Muir, Katherine L. Nathanson, Rocio Nuñez-Torres, Nadia Obi, Janet E. Olson, Julie R. Palmer, Mihalis I. Panayiotidis, Alpa V. Patel, Paul D.P. Pharoah, Eric C. Polley, Muhammad U. Rashid, Kathryn J. Ruddy, Emmanouil Saloustros, Elinor J. Sawyer, Marjanka K. Schmidt, Melissa C. Southey, Veronique Kiak-Mien Tan, Soo Hwang Teo, Lauren R. Teras, Diana Torres, Amy Trentham-Dietz, Thérèse Truong, Celine M. Vachon, Qin Wang, Jeffrey N. Weitzel, Siddhartha Yadav, Song Yao, Gary R. Zirpoli, Melissa S. Cline, Peter Devilee, Sean V. Tavtigian, David E. Goldgar, Fergus J. Couch, Douglas F. Easton, Amanda B. Spurdle, Kyriaki Michailidou","doi":"10.1101/2024.09.04.24313051","DOIUrl":"https://doi.org/10.1101/2024.09.04.24313051","url":null,"abstract":"Clinical genetic testing identifies variants causal for hereditary cancer, information that is used for risk assessment and clinical management. Unfortunately, some variants identified are of uncertain clinical significance (VUS), complicating patient management. Case-control data is one evidence type used to classify VUS, and previous findings indicate that case-control likelihood ratios (LRs) outperform odds ratios for variant classification. As an initiative of the Evidence-based Network for the Interpretation of Germline Mutant Alleles (ENIGMA) Analytical Working Group we analyzed germline sequencing data of <em>BRCA1</em> and <em>BRCA2</em> from 96,691 female breast cancer cases and 303,925 unaffected controls from three studies: the BRIDGES study of the Breast Cancer Association Consortium, the Cancer Risk Estimates Related to Susceptibility consortium, and the UK Biobank. We observed 11,227 <em>BRCA1</em> and <em>BRCA2</em> variants, with 6,921 being coding, covering 23.4% of <em>BRCA1</em> and <em>BRCA2</em> VUS in ClinVar and 19.2% of ClinVar curated (likely) benign or pathogenic variants. Case-control LR evidence was highly consistent with ClinVar assertions for (likely) benign or pathogenic variants; exhibiting 99.1% sensitivity and 95.4% specificity for <em>BRCA1</em> and 92.2% sensitivity and 86.6% specificity for <em>BRCA2</em>. This approach provides case-control evidence for 785 unclassified variants, that can serve as a valuable element for clinical classification.","PeriodicalId":501375,"journal":{"name":"medRxiv - Genetic and Genomic Medicine","volume":"47 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142192148","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-04DOI: 10.1101/2024.09.03.24313012
Matthew Osmond, E. Magda Price, Orion J. Buske, Mackenzie Frew, Madeline Couse, Taila Hartley, Conor Klamann, Hannah G. B. H. Le, Jenny Xu, Delvin So, Anjali Jain, Kevin Lu, Kevin Mo, Hannah Wyllie, Erika Wall, Hannah G. Driver, Warren A. Cheung, Ana S.A. Cohen, Emily G. Farrow, Isabelle Thiffault, Care4Rare Canada Consortium, Andrei L. Turinsky, Tomi Pastinen, Michael Brudno, Kym M. Boycott
Background Genomic matchmaking - the process of identifying multiple individuals with overlapping phenotypes and rare variants in the same gene - is an important tool facilitating gene discoveries for unsolved rare genetic disease (RGD) patients. Current approaches are two-sided, meaning both patients being matched must have the same candidate gene flagged. This limits the number of unsolved RGD patients eligible for matchmaking. A one-sided approach to matchmaking, in which a gene of interest is queried directly in the genome-wide sequencing data of RGD patients, would make matchmaking possible for previously undiscoverable individuals. However, platforms and workflows for this approach have not been well established.
{"title":"One-Sided Matching Portal (OSMP): a tool to facilitate rare disease patient matchmaking","authors":"Matthew Osmond, E. Magda Price, Orion J. Buske, Mackenzie Frew, Madeline Couse, Taila Hartley, Conor Klamann, Hannah G. B. H. Le, Jenny Xu, Delvin So, Anjali Jain, Kevin Lu, Kevin Mo, Hannah Wyllie, Erika Wall, Hannah G. Driver, Warren A. Cheung, Ana S.A. Cohen, Emily G. Farrow, Isabelle Thiffault, Care4Rare Canada Consortium, Andrei L. Turinsky, Tomi Pastinen, Michael Brudno, Kym M. Boycott","doi":"10.1101/2024.09.03.24313012","DOIUrl":"https://doi.org/10.1101/2024.09.03.24313012","url":null,"abstract":"<strong>Background</strong> Genomic matchmaking - the process of identifying multiple individuals with overlapping phenotypes and rare variants in the same gene - is an important tool facilitating gene discoveries for unsolved rare genetic disease (RGD) patients. Current approaches are two-sided, meaning both patients being matched must have the same candidate gene flagged. This limits the number of unsolved RGD patients eligible for matchmaking. A one-sided approach to matchmaking, in which a gene of interest is queried directly in the genome-wide sequencing data of RGD patients, would make matchmaking possible for previously undiscoverable individuals. However, platforms and workflows for this approach have not been well established.","PeriodicalId":501375,"journal":{"name":"medRxiv - Genetic and Genomic Medicine","volume":"6 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142192151","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-04DOI: 10.1101/2024.09.04.24313061
Daniel S. Malawsky, Mahmoud Koko, Petr Danacek, Wei Huang, Olivia Wootton, Qinqin Huang, Emma E. Wade, Sarah J. Lindsay, Rosalind Arden, Matthew E. Hurles, Hilary C. Martin
Common and rare genetic variants that impact adult cognitive performance also contribute to risk of rare neurodevelopmental conditions involving cognitive deficits in children. However, their influence on cognitive performance across early life remains poorly understood. Here, we investigate the contribution of common genome-wide and rare exonic variation to cognitive performance across childhood and adolescence primarily using the Avon Longitudinal Study of Parents and Children (n=6,495 unrelated children). We show that the effect of common variants associated with educational attainment and cognitive performance increases as children age. Conversely, the negative effect of deleterious rare variants attenuates with age. Using trio analyses, we show that these age-related trends are driven by direct genetic effects on the individual who carries these variants. We further find that the increasing effects of common variants are stronger in individuals at the upper end of the phenotype distribution, whereas the attenuating effects of rare variants are stronger in those at the lower end. Concordant results were observed in the Millenium Cohort Study (5,920 children) and UK Biobank (101,232 adults). The effects of common and rare genetic variation on childhood cognitive performance are broadly comparable in magnitude to those of other factors such as parental educational attainment, maternal illness and preterm birth. The effects of maternal illness and preterm birth on childhood cognitive performance also attenuate with age, whereas the effect of parental educational attainment does not. Furthermore, we show that the relative contribution of these various factors differ depending on whether one considers their contribution to phenotypic variance across the entire population or to the risk of poor outcomes. Our findings may help explain the apparent incomplete penetrance of rare damaging variants associated with neurodevelopmental conditions. More generally, they also show the importance of studying dynamic genetic influences across the life course and their differential effects across the phenotype distribution.
影响成人认知能力的常见和罕见基因变异也会导致儿童出现认知障碍的罕见神经发育疾病的风险。然而，人们对它们对生命早期认知能力的影响仍然知之甚少。在此，我们主要利用雅芳父母与子女纵向研究（Avon Longitudinal Study of Parents and Children，n=6,495 名无血缘关系的儿童）来研究常见的全基因组变异和罕见的外显子变异对儿童和青少年认知能力的影响。我们的研究表明，随着儿童年龄的增长，与教育程度和认知能力相关的常见变异的影响也在增加。相反，有害稀有变异体的负面影响会随着年龄的增长而减弱。通过三元分析，我们发现这些与年龄相关的趋势是由携带这些变异体的个体所受到的直接遗传效应驱动的。我们进一步发现，在表型分布的高端个体中，常见变异体的增加效应更强，而在低端个体中，罕见变异体的减弱效应更强。在千年队列研究（5,920 名儿童）和英国生物库（101,232 名成人）中观察到了一致的结果。常见和罕见基因变异对儿童认知能力的影响在程度上与父母教育程度、母亲疾病和早产等其他因素的影响大致相当。孕产妇疾病和早产对儿童认知能力的影响也会随着年龄的增长而减弱，而父母受教育程度的影响则不会。此外，我们还发现，这些不同因素的相对作用也不尽相同，这取决于我们考虑的是它们对整个人群表型变异的作用，还是对不良后果风险的作用。我们的发现可能有助于解释与神经发育状况相关的罕见损伤性变异的明显不完全渗透性。更广泛地说，这些发现还表明了研究整个生命过程中的动态遗传影响及其对表型分布的不同影响的重要性。
{"title":"The differential effects of common and rare genetic variants on cognitive performance across development","authors":"Daniel S. Malawsky, Mahmoud Koko, Petr Danacek, Wei Huang, Olivia Wootton, Qinqin Huang, Emma E. Wade, Sarah J. Lindsay, Rosalind Arden, Matthew E. Hurles, Hilary C. Martin","doi":"10.1101/2024.09.04.24313061","DOIUrl":"https://doi.org/10.1101/2024.09.04.24313061","url":null,"abstract":"Common and rare genetic variants that impact adult cognitive performance also contribute to risk of rare neurodevelopmental conditions involving cognitive deficits in children. However, their influence on cognitive performance across early life remains poorly understood. Here, we investigate the contribution of common genome-wide and rare exonic variation to cognitive performance across childhood and adolescence primarily using the Avon Longitudinal Study of Parents and Children (n=6,495 unrelated children). We show that the effect of common variants associated with educational attainment and cognitive performance increases as children age. Conversely, the negative effect of deleterious rare variants attenuates with age. Using trio analyses, we show that these age-related trends are driven by direct genetic effects on the individual who carries these variants. We further find that the increasing effects of common variants are stronger in individuals at the upper end of the phenotype distribution, whereas the attenuating effects of rare variants are stronger in those at the lower end. Concordant results were observed in the Millenium Cohort Study (5,920 children) and UK Biobank (101,232 adults). The effects of common and rare genetic variation on childhood cognitive performance are broadly comparable in magnitude to those of other factors such as parental educational attainment, maternal illness and preterm birth. The effects of maternal illness and preterm birth on childhood cognitive performance also attenuate with age, whereas the effect of parental educational attainment does not. Furthermore, we show that the relative contribution of these various factors differ depending on whether one considers their contribution to phenotypic variance across the entire population or to the risk of poor outcomes. Our findings may help explain the apparent incomplete penetrance of rare damaging variants associated with neurodevelopmental conditions. More generally, they also show the importance of studying dynamic genetic influences across the life course and their differential effects across the phenotype distribution.","PeriodicalId":501375,"journal":{"name":"medRxiv - Genetic and Genomic Medicine","volume":"75 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142192147","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-04DOI: 10.1101/2024.09.03.24312863
Daniel Greene, Koenraad De Wispelaere, Jon Lees, Andrea Katrinecz, Sonia Pascoal, Emma Hales, Marta Codina-Solà, Irene Valenzuela, Eduardo F. Tizzano, Giles Atton, Deirdre Donnelly, Nicola Foulds, Joanna Jarvis, Shane McKee, Michael O’Donoghue, Mohnish Suri, Pradeep Vasudevan, Kathy Stirrups, Natasha P. Morgan, Kathleen Freson, Andrew D. Mumford, Ernest Turro
The major spliceosome comprises the five snRNAs U1, U2, U4, U5 and U6. We recently showed that mutations in RNU4-2, which encodes U4 snRNA, cause one of the most prevalent monogenic neurodevelopmental disorders. Here, we report that recurrent germline mutations in RNU2-2P, a 191bp gene encoding U2 snRNA, are responsible for a related disorder. By genetic association, we implicated recurrent de novo single nucleotide mutations at nucleotide positions 4 and 35 of RNU2-2P among nine cases. We replicated this finding in six additional cases, bringing the total to 15. The disorder is characterized by intellectual disability, neurodevelopmental delay, autistic behavior, microcephaly, hypotonia, epilepsy and hyperventilation. All cases display a severe and complex seizure phenotype. Our findings cement the role of major spliceosomal snRNAs in the etiologies of neurodevelopmental disorders.
{"title":"Mutations in the U2 snRNA gene RNU2-2P cause a severe neurodevelopmental disorder with prominent epilepsy","authors":"Daniel Greene, Koenraad De Wispelaere, Jon Lees, Andrea Katrinecz, Sonia Pascoal, Emma Hales, Marta Codina-Solà, Irene Valenzuela, Eduardo F. Tizzano, Giles Atton, Deirdre Donnelly, Nicola Foulds, Joanna Jarvis, Shane McKee, Michael O’Donoghue, Mohnish Suri, Pradeep Vasudevan, Kathy Stirrups, Natasha P. Morgan, Kathleen Freson, Andrew D. Mumford, Ernest Turro","doi":"10.1101/2024.09.03.24312863","DOIUrl":"https://doi.org/10.1101/2024.09.03.24312863","url":null,"abstract":"The major spliceosome comprises the five snRNAs U1, U2, U4, U5 and U6. We recently showed that mutations in <em>RNU4-</em>2, which encodes U4 snRNA, cause one of the most prevalent monogenic neurodevelopmental disorders. Here, we report that recurrent germline mutations in <em>RNU2-2P</em>, a 191bp gene encoding U2 snRNA, are responsible for a related disorder. By genetic association, we implicated recurrent <em>de novo</em> single nucleotide mutations at nucleotide positions 4 and 35 of <em>RNU2-2P</em> among nine cases. We replicated this finding in six additional cases, bringing the total to 15. The disorder is characterized by intellectual disability, neurodevelopmental delay, autistic behavior, microcephaly, hypotonia, epilepsy and hyperventilation. All cases display a severe and complex seizure phenotype. Our findings cement the role of major spliceosomal snRNAs in the etiologies of neurodevelopmental disorders.","PeriodicalId":501375,"journal":{"name":"medRxiv - Genetic and Genomic Medicine","volume":"312 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142224769","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-04DOI: 10.1101/2024.09.04.24313052
Kenneth Chi-Yin Wong, Perry Bok-Man Leung, Benedict Ka-Wa Lee, Zoe Zi-Yu Zheng, Emily Man-Wah Tsang, Meng-Hui Liu, Kelly Wing-Kwan Lee, Shi-Tao Rao, Pak-Chung Sham, Simon Sai-Yu Lui, Hon-Cheong So
Second-generation antipsychotics (SGAs) are widely used to treat schizophrenia (SCZ), but they often induce metabolic side effects, including dyslipidemia and obesity, posing significant clinical challenges. While genetic factors are believed to contribute to the variability of these side effects, pharmacogenetic studies remain limited. This study aimed to identify genetic variants associated with SGA-induced lipid and BMI changes in a Chinese SCZ cohort using genome-wide association studies (GWASs). A naturalistic longitudinal cohort of Chinese SCZ patients receiving SGAs was followed for up to 18.7 years. We analyzed the patients’ genotypes (N=669), lipid profiles and BMI, utilizing 19 316 prescription records and 3 917 to 7 596 metabolic measurements per outcome. Linear mixed models were used to estimate the random effects of SGAs on lipid profiles and BMI changes for each patient. GWAS and gene set analyses were conducted with false discovery rate (FDR) correction. Two genome-wide significant SNPs were identified under an additive genetic model: rs6532055 in ABCG2 (olanzapine-induced LDL changes) and rs2644520 near SORCS1 (aripiprazole-induced triglyceride changes). Three additional SNPs achieved genome-wide significance under non-additive models: rs115843863 near UPP2 (clozapine-induced HDL changes), rs2514895 near KIRREL3 (paliperidone-induced LDL changes), and rs188405603 in SLC2A9 (quetiapine-induced triglyceride changes). Gene-based analysis revealed six genome-wide significant (p<2.73e-06, Bonferroni correction) genes: ABCG2, APOA5, ZPR1, GCNT4, MAST2, and CRTAC1. Four gene sets were significantly associated with SGA-induced metabolic side effects. This pharmacogenetic GWAS identified several genetic variants associated with metabolic side effects of seven SGAs, potentially informing personalized treatment strategies to minimize metabolic risk in SCZ patients.
{"title":"Pharmacogenetic Study of Antipsychotic-Induced Lipid and BMI Changes in Chinese Schizophrenia Patients: A Genome-Wide Association Study","authors":"Kenneth Chi-Yin Wong, Perry Bok-Man Leung, Benedict Ka-Wa Lee, Zoe Zi-Yu Zheng, Emily Man-Wah Tsang, Meng-Hui Liu, Kelly Wing-Kwan Lee, Shi-Tao Rao, Pak-Chung Sham, Simon Sai-Yu Lui, Hon-Cheong So","doi":"10.1101/2024.09.04.24313052","DOIUrl":"https://doi.org/10.1101/2024.09.04.24313052","url":null,"abstract":"Second-generation antipsychotics (SGAs) are widely used to treat schizophrenia (SCZ), but they often induce metabolic side effects, including dyslipidemia and obesity, posing significant clinical challenges. While genetic factors are believed to contribute to the variability of these side effects, pharmacogenetic studies remain limited. This study aimed to identify genetic variants associated with SGA-induced lipid and BMI changes in a Chinese SCZ cohort using genome-wide association studies (GWASs). A naturalistic longitudinal cohort of Chinese SCZ patients receiving SGAs was followed for up to 18.7 years. We analyzed the patients’ genotypes (<em>N</em>=669), lipid profiles and BMI, utilizing 19 316 prescription records and 3 917 to 7 596 metabolic measurements per outcome. Linear mixed models were used to estimate the random effects of SGAs on lipid profiles and BMI changes for each patient. GWAS and gene set analyses were conducted with false discovery rate (FDR) correction. Two genome-wide significant SNPs were identified under an additive genetic model: rs6532055 in <em>ABCG2</em> (olanzapine-induced LDL changes) and rs2644520 near <em>SORCS1</em> (aripiprazole-induced triglyceride changes). Three additional SNPs achieved genome-wide significance under non-additive models: rs115843863 near <em>UPP2</em> (clozapine-induced HDL changes), rs2514895 near <em>KIRREL3</em> (paliperidone-induced LDL changes), and rs188405603 in <em>SLC2A9</em> (quetiapine-induced triglyceride changes). Gene-based analysis revealed six genome-wide significant (p<2.73e-06, Bonferroni correction) genes: <em>ABCG2</em>, <em>APOA5</em>, <em>ZPR1</em>, <em>GCNT4</em>, <em>MAST2</em>, and <em>CRTAC1</em>. Four gene sets were significantly associated with SGA-induced metabolic side effects. This pharmacogenetic GWAS identified several genetic variants associated with metabolic side effects of seven SGAs, potentially informing personalized treatment strategies to minimize metabolic risk in SCZ patients.","PeriodicalId":501375,"journal":{"name":"medRxiv - Genetic and Genomic Medicine","volume":"2 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142192149","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-03DOI: 10.1101/2024.09.01.24312906
Niels Mørch, Andrés Barrena Calderón, Timo Lehmann Kvamme, Julie Grinderslev Donskov, Blanka Zana, Simon Durand, Jovana Bjekic, Maro G Machizawa, Makiko Yamada, Filip Ottosson, Jonas Bybjerg-Grauholm, Madeleine Ernst, Anders Dupont Børglum, Kristian Sandberg, Per Qvist
Background: Mental health encompasses emotional, psychological, and social dimensions, extending beyond the mere absence of illness. Shaped by a complex interplay of hereditary factors and life experiences, mental health can deteriorate into clinical conditions necessitating intervention. However, the ambiguity between pathological and non-pathological states, along with overlapping clinical profiles, challenges traditional diagnostic procedures, highlighting the need for a dimensional approach in stratified psychiatry. Methods: We analyzed comprehensive phenotypic data from ~300 young Danish participants, including psychometric assessments, brain imaging, genetics, and circulatory OMICs markers. Using a novel psychometry-based archetyping approach, we employed soft-clustering analyses to stratify participants based on distinct cognitive, emotional, and behavioral patterns, while exploring their genetic and neurobiological underpinnings. Results: Five psychometric archetypes were identified, representing a continuum of mental health traits. One archetype, characterized by high neuroticism, emotional dysregulation, and elevated stress and depression scores, was firmly associated with self-reported mental health diagnoses, psychiatric comorbidities, and family history of mental illness. Genetic predisposition to mental health conditions, reflected in polygenic scores (PGSs), accounted for up to 9% of the variance in archetypes, with significant contributions from neuroimaging-related PGSs. The overlaps between broader genetic profiles and archetypes further confirmed their biological foundations. Neuroimaging data linked the risk-associated archetype to both regional and global brain volumetric changes, while metabolomic analysis identified differentiating metabolites related to mood regulation and neuroinflammation. Conclusions: This study demonstrates the feasibility of data-driven stratification of the general population into distinct risk groups defined by multimodal mental health signatures. This stratification offers a robust framework for understanding mental health variation and holds significant potential for advancing early screening and targeted intervention strategies in the young population.
{"title":"Identification of multimodal mental health signatures in the young population using deep phenotyping","authors":"Niels Mørch, Andrés Barrena Calderón, Timo Lehmann Kvamme, Julie Grinderslev Donskov, Blanka Zana, Simon Durand, Jovana Bjekic, Maro G Machizawa, Makiko Yamada, Filip Ottosson, Jonas Bybjerg-Grauholm, Madeleine Ernst, Anders Dupont Børglum, Kristian Sandberg, Per Qvist","doi":"10.1101/2024.09.01.24312906","DOIUrl":"https://doi.org/10.1101/2024.09.01.24312906","url":null,"abstract":"<strong>Background:</strong> Mental health encompasses emotional, psychological, and social dimensions, extending beyond the mere absence of illness. Shaped by a complex interplay of hereditary factors and life experiences, mental health can deteriorate into clinical conditions necessitating intervention. However, the ambiguity between pathological and non-pathological states, along with overlapping clinical profiles, challenges traditional diagnostic procedures, highlighting the need for a dimensional approach in stratified psychiatry.\u0000<strong>Methods:</strong> We analyzed comprehensive phenotypic data from ~300 young Danish participants, including psychometric assessments, brain imaging, genetics, and circulatory OMICs markers. Using a novel psychometry-based archetyping approach, we employed soft-clustering analyses to stratify participants based on distinct cognitive, emotional, and behavioral patterns, while exploring their genetic and neurobiological underpinnings.\u0000<strong>Results:</strong> Five psychometric archetypes were identified, representing a continuum of mental health traits. One archetype, characterized by high neuroticism, emotional dysregulation, and elevated stress and depression scores, was firmly associated with self-reported mental health diagnoses, psychiatric comorbidities, and family history of mental illness. Genetic predisposition to mental health conditions, reflected in polygenic scores (PGSs), accounted for up to 9% of the variance in archetypes, with significant contributions from neuroimaging-related PGSs. The overlaps between broader genetic profiles and archetypes further confirmed their biological foundations. Neuroimaging data linked the risk-associated archetype to both regional and global brain volumetric changes, while metabolomic analysis identified differentiating metabolites related to mood regulation and neuroinflammation.\u0000<strong>Conclusions:</strong> This study demonstrates the feasibility of data-driven stratification of the general population into distinct risk groups defined by multimodal mental health signatures. This stratification offers a robust framework for understanding mental health variation and holds significant potential for advancing early screening and targeted intervention strategies in the young population.","PeriodicalId":501375,"journal":{"name":"medRxiv - Genetic and Genomic Medicine","volume":"396 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142192152","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Objectives Autoimmune diseases (ADs) play a significant and intricate role in the onset of cardiovascular diseases (CVDs). Our study aimed to elucidate the shared genetic etiology between Ads and CVDs.
{"title":"Genetic analyses identify shared genetic components related to autoimmune and cardiovascular diseases","authors":"Jun Qiao, Minjing Chang, Miaoran Chen, Yuhui Zhao, Jiawei Hao, Pengwei Zhang, Ruixin Zhou, Liuyang Cai, Feng Liu, Xiaoping Fan, Siim Pauklin, Rongjun Zou, Zhixiu Li, Yuliang Feng","doi":"10.1101/2024.09.01.24310190","DOIUrl":"https://doi.org/10.1101/2024.09.01.24310190","url":null,"abstract":"<strong>Objectives</strong> Autoimmune diseases (ADs) play a significant and intricate role in the onset of cardiovascular diseases (CVDs). Our study aimed to elucidate the shared genetic etiology between Ads and CVDs.","PeriodicalId":501375,"journal":{"name":"medRxiv - Genetic and Genomic Medicine","volume":"6 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142192153","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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