Pub Date : 2024-07-22DOI: 10.1101/2024.07.18.24310581
Stephanie Efthymiou, Cailyn Leo, Chenghong Deng, Kejia Zhang, Sheng-Jia Lin, Reza Maroofian, Rauan Kaiyrzhanov, Renee Qing Lin, Irem Karagoz, Annarita Scardamaglia, Daniel Owrang, Valentina Turchetti, Friederike Jahnke, Cassidy Petree, Anna V Derrick, Mark I Rees, Javeria Raza Alvi, Tipu Sultan, Chumei Li, Marie-Line Jacquemont, Frederic Tran-Mau-Them, Maria Irene Valenzuela, Rich Sidlow, Grace Yoon, Michelle Morrow, Alexis Carere, Mary O'Connor, Julie Fleischer, Erica H. Gerkes, Chanika Phornphutkul, Bertrand Isidor, Clotilde Rivier-Ringenbach, Christophe Philippe, Semra Hiz Kurul, Didem Soydemir, Bulent Kara, Deniz Sunnetci-Akkoyunlu, Viktoria Bothe, Konrad Platzer, Dagmar Wieczorek, Margarete Koch-Hogrebe, Nils Rahner, Ann-Charlotte Thuresson, Hans Matsson, Carina Frykholm, Sevcan Tug Bozdogan, Atil Bisgin, Nicolas Chatron, Gaetan Lesca, Sara Cabet, Zeynep Tumer, Tina Duelund Hjortshoj, Gitte Ronde, Thorsten Marquardt, Janine Reunert, Erum Afzal, Mina Zamani, Reza Azizimalamiri, Hamid Galehdari, Pardis Nourbakhshd, Niloofar Chamanrou, Seo-Kyung Chung, Mohnish Suri, Paul J Benke, Maha S Zaki, Joseph G Gleeson, Daniel G Calame, Davut Pehlivan, Halil Ibrahim Yilmaz, Alper Gezdirici, Aboulfazl Rad, Iman Sabri Abumansour, Gabriela Oprea, Jai Sidpra, Kshitij Mankad, Barbara Vona, Andrew Fry, Gaurav K Varshney, Henry Houlden, Dragony Fu
The post-transcriptional modification of tRNAs plays a key role in tRNA folding and function to ensure proper levels of protein synthesis during growth and development. Pathogenic variants in tRNA modification enzymes have been implicated in diverse human neurodevelopmental and neurological disorders. However, the molecular basis for many of these disorders remains unknown, thereby limiting our understanding and potential treatment of pathologies linked to tRNA modification. Here, we describe an extensive cohort of 31 individuals from 24 unrelated families with bi-allelic variants in the tRNA methyltransferase 1 (TRMT1) gene who present with a syndromic neurodevelopmental disorder universally characterized by intellectual disability in affected patients. Developmental delay, behavioral abnormalities and facial dysmorphisms represent additional core phenotypes of this syndrome. The variants include novel and ultra-rare TRMT1 variants that segregate with clinical pathology. We found that a subset of variants causes mis-splicing and loss of TRMT1 protein expression. Notably, patient cells with TRMT1 variants exhibit a deficiency in tRNA modifications catalyzed by TRMT1. Molecular analysis of TRMT1 variants reveal distinct regions of the TRMT1 protein required for tRNA modification activity and binding, including a TRMT1 subdomain critical for tRNA interaction. Importantly, depletion of TRMT1 in zebrafish is sufficient to induce developmental and behavioral phenotypes that recapitulate those observed in human patients with pathogenic TRMT1 variants. Altogether, these findings demonstrate that loss of TRMT1-catalyzed tRNA modifications leads to a syndromic form of intellectual disability and elucidate the molecular underpinnings of tRNA modification deficiency caused by pathogenic TRMT1 variants.
{"title":"Biallelic pathogenic variants in TRMT1 disrupt tRNA modification and induce a syndromic neurodevelopmental disorder","authors":"Stephanie Efthymiou, Cailyn Leo, Chenghong Deng, Kejia Zhang, Sheng-Jia Lin, Reza Maroofian, Rauan Kaiyrzhanov, Renee Qing Lin, Irem Karagoz, Annarita Scardamaglia, Daniel Owrang, Valentina Turchetti, Friederike Jahnke, Cassidy Petree, Anna V Derrick, Mark I Rees, Javeria Raza Alvi, Tipu Sultan, Chumei Li, Marie-Line Jacquemont, Frederic Tran-Mau-Them, Maria Irene Valenzuela, Rich Sidlow, Grace Yoon, Michelle Morrow, Alexis Carere, Mary O'Connor, Julie Fleischer, Erica H. Gerkes, Chanika Phornphutkul, Bertrand Isidor, Clotilde Rivier-Ringenbach, Christophe Philippe, Semra Hiz Kurul, Didem Soydemir, Bulent Kara, Deniz Sunnetci-Akkoyunlu, Viktoria Bothe, Konrad Platzer, Dagmar Wieczorek, Margarete Koch-Hogrebe, Nils Rahner, Ann-Charlotte Thuresson, Hans Matsson, Carina Frykholm, Sevcan Tug Bozdogan, Atil Bisgin, Nicolas Chatron, Gaetan Lesca, Sara Cabet, Zeynep Tumer, Tina Duelund Hjortshoj, Gitte Ronde, Thorsten Marquardt, Janine Reunert, Erum Afzal, Mina Zamani, Reza Azizimalamiri, Hamid Galehdari, Pardis Nourbakhshd, Niloofar Chamanrou, Seo-Kyung Chung, Mohnish Suri, Paul J Benke, Maha S Zaki, Joseph G Gleeson, Daniel G Calame, Davut Pehlivan, Halil Ibrahim Yilmaz, Alper Gezdirici, Aboulfazl Rad, Iman Sabri Abumansour, Gabriela Oprea, Jai Sidpra, Kshitij Mankad, Barbara Vona, Andrew Fry, Gaurav K Varshney, Henry Houlden, Dragony Fu","doi":"10.1101/2024.07.18.24310581","DOIUrl":"https://doi.org/10.1101/2024.07.18.24310581","url":null,"abstract":"The post-transcriptional modification of tRNAs plays a key role in tRNA folding and function to ensure proper levels of protein synthesis during growth and development. Pathogenic variants in tRNA modification enzymes have been implicated in diverse human neurodevelopmental and neurological disorders. However, the molecular basis for many of these disorders remains unknown, thereby limiting our understanding and potential treatment of pathologies linked to tRNA modification. Here, we describe an extensive cohort of 31 individuals from 24 unrelated families with bi-allelic variants in the <em>tRNA methyltransferase 1</em> (<em>TRMT1</em>) gene who present with a syndromic neurodevelopmental disorder universally characterized by intellectual disability in affected patients. Developmental delay, behavioral abnormalities and facial dysmorphisms represent additional core phenotypes of this syndrome. The variants include novel and ultra-rare TRMT1 variants that segregate with clinical pathology. We found that a subset of variants causes mis-splicing and loss of TRMT1 protein expression. Notably, patient cells with TRMT1 variants exhibit a deficiency in tRNA modifications catalyzed by TRMT1. Molecular analysis of TRMT1 variants reveal distinct regions of the TRMT1 protein required for tRNA modification activity and binding, including a TRMT1 subdomain critical for tRNA interaction. Importantly, depletion of TRMT1 in zebrafish is sufficient to induce developmental and behavioral phenotypes that recapitulate those observed in human patients with pathogenic TRMT1 variants. Altogether, these findings demonstrate that loss of TRMT1-catalyzed tRNA modifications leads to a syndromic form of intellectual disability and elucidate the molecular underpinnings of tRNA modification deficiency caused by pathogenic TRMT1 variants.","PeriodicalId":501375,"journal":{"name":"medRxiv - Genetic and Genomic Medicine","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141771237","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 : 2024-07-22DOI: 10.1101/2024.07.22.24310816
Justin T Reese, Leonardo Chimirri, Daniel Danis, J Harry Caufield, Kyran Wissink Wissink, Elena Casiraghi, Giorgio Valentini, Melissa A Haendel, Christopher J Mungall, Peter N Robinson
Large language models (LLM) have shown great promise in supporting differential diagnosis, but 23 available published studies on the diagnostic accuracy evaluated small cohorts (number of cases, 30-422, mean 104) and have evaluated LLM responses subjectively by manual curation (23/23 studies). The performance of LLMs for rare disease diagnosis has not been evaluated systematically. Here, we perform a rigorous and large-scale analysis of the performance of a GPT-4 in prioritizing candidate diagnoses, using the largest-ever cohort of rare disease patients. Our computational study used 5267 computational case reports from previously published data. Each case was formatted as a Global Alliance for Genomics and Health (GA4GH) phenopacket, in which clinical anomalies were represented as Human Phenotype Ontology (HPO) terms. We developed software to generate prompts from each phenopacket. Prompts were sent to Generative Pre-trained Transformer 4 (GPT-4), and the rank of the correct diagnosis, if present in the response, was recorded. The mean reciprocal rank of the correct diagnosis was 0.24 (with the reciprocal of the MRR corresponding to a rank of 4.2), and the correct diagnosis was placed in rank 1 in 19.2% of the cases, in the first 3 ranks in 28.6%, and in the first 10 ranks in 32.5%. Our study is the largest to be reported to date and provides a realistic estimate of the performance of GPT-4 in rare disease medicine.
{"title":"Evaluation of the Diagnostic Accuracy of GPT-4 in Five Thousand Rare Disease Cases","authors":"Justin T Reese, Leonardo Chimirri, Daniel Danis, J Harry Caufield, Kyran Wissink Wissink, Elena Casiraghi, Giorgio Valentini, Melissa A Haendel, Christopher J Mungall, Peter N Robinson","doi":"10.1101/2024.07.22.24310816","DOIUrl":"https://doi.org/10.1101/2024.07.22.24310816","url":null,"abstract":"Large language models (LLM) have shown great promise in supporting differential diagnosis, but 23 available published studies on the diagnostic accuracy evaluated small cohorts (number of cases, 30-422, mean 104) and have evaluated LLM responses subjectively by manual curation (23/23 studies). The performance of LLMs for rare disease diagnosis has not been evaluated systematically. Here, we perform a rigorous and large-scale analysis of the performance of a GPT-4 in prioritizing candidate diagnoses, using the largest-ever cohort of rare disease patients. Our computational study used 5267 computational case reports from previously published data. Each case was formatted as a Global Alliance for Genomics and Health (GA4GH) phenopacket, in which clinical anomalies were represented as Human Phenotype Ontology (HPO) terms. We developed software to generate prompts from each phenopacket. Prompts were sent to Generative Pre-trained Transformer 4 (GPT-4), and the rank of the correct diagnosis, if present in the response, was recorded. The mean reciprocal rank of the correct diagnosis was 0.24 (with the reciprocal of the MRR corresponding to a rank of 4.2), and the correct diagnosis was placed in rank 1 in 19.2% of the cases, in the first 3 ranks in 28.6%, and in the first 10 ranks in 32.5%. Our study is the largest to be reported to date and provides a realistic estimate of the performance of GPT-4 in rare disease medicine.","PeriodicalId":501375,"journal":{"name":"medRxiv - Genetic and Genomic Medicine","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141784956","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 : 2024-07-22DOI: 10.1101/2024.07.22.24310800
Wayne Robert Danter
Background: Xeroderma pigmentosum, complementation group A (XPA), is a rare genetic disorder characterized by marked sensitivity to ultraviolet (UV) radiation, leading to increased risks of skin cancer, accelerated aging, and significant neurologic disorders. XPA prominently impacts DNA repair mechanisms, specifically nucleotide excision repair (NER), which is crucial for correcting UV-induced DNA damage. Methods: This study utilized an advanced aiHumanoids platform to simulate the disease progression in individuals with XPA from birth to age 20 years. The virtual longitudinal study assessed the impacts of moderate and severe XPA under various UV exposure scenarios. The research included 25 age-matched wild-type controls to elucidate the comparative effects of XPA on DNA damage, genomic instability, cancer risk, and neurological outcomes. Results: Using Wilcoxon sign rank p values and Cliffs delta estimates of true effect size, the aiHumanoid simulations revealed significant differences in DNA repair efficiency between XPA affected and control groups, with pronounced deficits in XPA cohorts under UV exposure. Genomic instability and skin cancer risks were consistently elevated across all XPA simulations, particularly under UV stress. Neurological assessments indicated greater susceptibility to disorders in younger XPA subjects, with effects moderating somewhat with age. Conclusion: The aiHumanoid platform provided novel insights into the progression of XPA, highlighting the severe impact of UV exposure on individuals with this condition. These findings advocate for early intervention strategies and underscore the necessity for rigorous protective measures against UV radiation, especially in younger populations. This research contributes to our further understanding of XPA, potentially guiding future therapeutic developments including early stage virtual drug trials and preventive approaches personalized to individual risk profiles.
背景：补体A群色素性大疱症（XPA）是一种罕见的遗传性疾病，其特征是对紫外线（UV）辐射明显敏感，导致患皮肤癌、加速衰老和严重神经系统疾病的风险增加。XPA 严重影响 DNA 修复机制，特别是核苷酸切除修复（NER），而 NER 对纠正紫外线引起的 DNA 损伤至关重要：本研究利用先进的 aiHumanoids 平台模拟 XPA 患者从出生到 20 岁的疾病进展过程。这项虚拟纵向研究评估了中度和重度 XPA 在各种紫外线照射情况下的影响。研究包括 25 个年龄匹配的野生型对照组，以阐明 XPA 对 DNA 损伤、基因组不稳定性、癌症风险和神经系统结果的比较影响：使用 Wilcoxon 符号秩 p 值和真实效应大小的 Cliffs delta 估计值，aiHumanoid 模拟揭示了受 XPA 影响组和对照组之间在 DNA 修复效率方面的显著差异，在紫外线照射下，XPA 组群存在明显缺陷。在所有 XPA 模拟中，基因组不稳定性和皮肤癌风险都持续升高，尤其是在紫外线压力下。神经系统评估表明，年轻的 XPA 受试者更容易出现失调，随着年龄的增长，影响会有所缓和：aiHumanoid平台提供了有关XPA进展的新见解，突出了紫外线照射对这种疾病患者的严重影响。这些发现提倡早期干预策略，并强调了采取严格的紫外线辐射防护措施的必要性，尤其是在年轻人群中。这项研究有助于我们进一步了解 XPA，并有可能指导未来的治疗开发，包括早期虚拟药物试验和针对个体风险特征的个性化预防方法。
{"title":"Age-Dependent Effects of UV Exposure and Xeroderma Pigmentosum Group A on DNA Damage, Repair Mechanisms, Genomic Instability, Cancer Risk, and Neurological Disorders","authors":"Wayne Robert Danter","doi":"10.1101/2024.07.22.24310800","DOIUrl":"https://doi.org/10.1101/2024.07.22.24310800","url":null,"abstract":"Background: Xeroderma pigmentosum, complementation group A (XPA), is a rare genetic disorder characterized by marked sensitivity to ultraviolet (UV) radiation, leading to increased risks of skin cancer, accelerated aging, and significant neurologic disorders. XPA prominently impacts DNA repair mechanisms, specifically nucleotide excision repair (NER), which is crucial for correcting UV-induced DNA damage.\u0000Methods: This study utilized an advanced aiHumanoids platform to simulate the disease progression in individuals with XPA from birth to age 20 years. The virtual longitudinal study assessed the impacts of moderate and severe XPA under various UV exposure scenarios. The research included 25 age-matched wild-type controls to elucidate the comparative effects of XPA on DNA damage, genomic instability, cancer risk, and neurological outcomes.\u0000Results: Using Wilcoxon sign rank p values and Cliffs delta estimates of true effect size, the aiHumanoid simulations revealed significant differences in DNA repair efficiency between XPA affected and control groups, with pronounced deficits in XPA cohorts under UV exposure. Genomic instability and skin cancer risks were consistently elevated across all XPA simulations, particularly under UV stress. Neurological assessments indicated greater susceptibility to disorders in younger XPA subjects, with effects moderating somewhat with age.\u0000Conclusion: The aiHumanoid platform provided novel insights into the progression of XPA, highlighting the severe impact of UV exposure on individuals with this condition. These findings advocate for early intervention strategies and underscore the necessity for rigorous protective measures against UV radiation, especially in younger populations. This research contributes to our further understanding of XPA, potentially guiding future therapeutic developments including early stage virtual drug trials and preventive approaches personalized to individual risk profiles.","PeriodicalId":501375,"journal":{"name":"medRxiv - Genetic and Genomic Medicine","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141784955","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 : 2024-07-22DOI: 10.1101/2024.07.22.24310683
Mark Stevenson, Asha L Bayliss, Victoria J Stokes, Katherine A English, Kreepa G Kooblall, Roman Fischer, Raphael Heilig, Iolanda Vendrell, Maria E W A Albers, Meghan Bartos, Amber Begtrup, Alexia Bourgois, Rebecca Buchert, David J Carey, Deanna A Carere, Amanda Carnevale, Kristl G Claeys, Benjamin Cogne, Gregory Costain, Nicole de Leeuw, Anne-Sophie Denommé-Pichon, Elizabeth J Donner, Eftychia Drogouti, David A Dyment, Balram Gangaram, Tobias B Haack, Jeremy S Haley, Solveig Heide, Ralf A Hussain, Bertrand Isidor, Louise Izatt, Adeline Jacquinet, Jane Juusola, Juliette J Kahle, Boris Keren, Eric W Klee, Evgenia Kokosali, Brendan C Lanpher, Erica L Macke, Elysa J Marco, Kirsty McWalter, Bryce A Mendelsohn, Aubrey Milunshy, Matthew Osmond, Amelie Piton, Angelika Riess, Valentin Ruault, Patrick Rump, Sarah Schuhmann, Amelle L Shillington, Diane T Smelser, Lot Snijders Blok, Frederic Tran Mau-Them, Christos Tsakalidis, Abigail Turnwald, Koen L I Van Gassen, Kristof Van Schil, Georgia Vasileiou, Marissa Vawter-Lee, Marjolaine Willems, Marjolein H Willemsen, Lily C Wong-Kisiel, Antje Wonneberger, Ioannis Zaganas, Genomics England Research Consortium, Fadil M Hannan, Kate E Lines, Rajesh V Thakker
Adaptor-Related Protein Complex 2 Sigma-1 Subunit (AP2S1) encodes AP2σ2, which forms part of the heterotetrameric AP2 complex that is composed of α, β2, μ2, and σ2 subunits and has a pivotal role in clathrin-mediated endocytosis (CME). AP2S1 variants involving the Arg15 residue are associated with familial hypocalciuric hypercalcaemia type 3 (FHH3). Here, we report 5 different AP2S1 variants (AP2σ2: p.Arg10Trp, p.Arg10Gln, p.Lys18Glu, p.Lys18Asn and p.Arg61His) in 26 patients with neurodevelopmental delay, of whom >70% had epilepsy, 50% had brain abnormalities, and none had hypercalcaemia. All 5 variants decreased cell viability, 4 reduced CME transferrin uptake, and 4 disrupted interactions with other AP2 complex subunits, thereby affecting AP2 formation. Furthermore, AP2σ2 p.Arg10Trp had reduced interactions with 44 human proteins including intersectin 1, a component required for clathrin-coated pit formation and synaptic vesicle dynamics in neurones. Thus, our results show that AP2σ2 variants may disrupt CME and be associated with neurodevelopmental disorders.
{"title":"Adaptor protein 2 sigma subunit (AP2S1) variants associated with neurodevelopmental disorders","authors":"Mark Stevenson, Asha L Bayliss, Victoria J Stokes, Katherine A English, Kreepa G Kooblall, Roman Fischer, Raphael Heilig, Iolanda Vendrell, Maria E W A Albers, Meghan Bartos, Amber Begtrup, Alexia Bourgois, Rebecca Buchert, David J Carey, Deanna A Carere, Amanda Carnevale, Kristl G Claeys, Benjamin Cogne, Gregory Costain, Nicole de Leeuw, Anne-Sophie Denommé-Pichon, Elizabeth J Donner, Eftychia Drogouti, David A Dyment, Balram Gangaram, Tobias B Haack, Jeremy S Haley, Solveig Heide, Ralf A Hussain, Bertrand Isidor, Louise Izatt, Adeline Jacquinet, Jane Juusola, Juliette J Kahle, Boris Keren, Eric W Klee, Evgenia Kokosali, Brendan C Lanpher, Erica L Macke, Elysa J Marco, Kirsty McWalter, Bryce A Mendelsohn, Aubrey Milunshy, Matthew Osmond, Amelie Piton, Angelika Riess, Valentin Ruault, Patrick Rump, Sarah Schuhmann, Amelle L Shillington, Diane T Smelser, Lot Snijders Blok, Frederic Tran Mau-Them, Christos Tsakalidis, Abigail Turnwald, Koen L I Van Gassen, Kristof Van Schil, Georgia Vasileiou, Marissa Vawter-Lee, Marjolaine Willems, Marjolein H Willemsen, Lily C Wong-Kisiel, Antje Wonneberger, Ioannis Zaganas, Genomics England Research Consortium, Fadil M Hannan, Kate E Lines, Rajesh V Thakker","doi":"10.1101/2024.07.22.24310683","DOIUrl":"https://doi.org/10.1101/2024.07.22.24310683","url":null,"abstract":"Adaptor-Related Protein Complex 2 Sigma-1 Subunit (AP2S1) encodes AP2σ2, which forms part of the heterotetrameric AP2 complex that is composed of α, β2, μ2, and σ2 subunits and has a pivotal role in clathrin-mediated endocytosis (CME). AP2S1 variants involving the Arg15 residue are associated with familial hypocalciuric hypercalcaemia type 3 (FHH3). Here, we report 5 different AP2S1 variants (AP2σ2: p.Arg10Trp, p.Arg10Gln, p.Lys18Glu, p.Lys18Asn and p.Arg61His) in 26 patients with neurodevelopmental delay, of whom >70% had epilepsy, 50% had brain abnormalities, and none had hypercalcaemia. All 5 variants decreased cell viability, 4 reduced CME transferrin uptake, and 4 disrupted interactions with other AP2 complex subunits, thereby affecting AP2 formation. Furthermore, AP2σ2 p.Arg10Trp had reduced interactions with 44 human proteins including intersectin 1, a component required for clathrin-coated pit formation and synaptic vesicle dynamics in neurones. Thus, our results show that AP2σ2 variants may disrupt CME and be associated with neurodevelopmental disorders.","PeriodicalId":501375,"journal":{"name":"medRxiv - Genetic and Genomic Medicine","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141771236","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 : 2024-07-21DOI: 10.1101/2024.07.18.24310513
He Fang, Stephen M Eacker, Yu Wu, Whitney Neufeld-Kaiser, Mercy Laurino, Sioban Keel, Marshall S. Horwitz, Yajuan Liu
Purpose: Complex chromosomal rearrangements (CCRs) are rare structural variants involving three or more chromosomal break- points. Most de novo reported CCRs pose challenges for diagnosis and management. They often require karyotyping, fluorescence in situ hybridization (FISH), and chromosomal microarray analysis (CMA) for clinical diagnosis because of the limitations of each method. Here we report an inherited exceptionally complex CCR involving 4 chromosomes and 11 breakpoints in a family with multisystem anomalies. Methods: We evaluated the CCRs using karyotyping, FISH, CMA, and two emerging genomic technologies: high-throughput chromosome conformation capture sequencing (Hi-C; aka genomic proximity mapping, GPM) and optical genome mapping (OGM). We also performed functional studies using transcriptome and methylome analyses. Results: The proband, who had intellectual disability and immune deficiency, shared CCRs with her unaffected mother involving chromosomes 1, 7, and 11 by karyotyping. However, CMA revealed a duplication and three deletions in the proband in contrast to her mother's balanced genome. Hi-C (GPM) and OGM detected the CCRs and copy number alterations but also uncovered additional breakpoints at high resolution, including an insertion in 4p and two cryptic rearrangements at 7p. Transcriptome and methylome analyses identified likely biological pathways associated with the proband's phenotypes. Conclusion: Combining cytogenetic and genomic methods provided comprehensive characterization and defined the breakpoints at high resolution in both proband and mother. This underscores the value of novel cytogenetic and genomic techniques in deciphering complex genome rearrangements and the significance of integrative genomic analysis and functional characterization in understanding clinical phenotypes.
{"title":"Genetic and functional characterization of inherited complex chromosomal rearrangements in a family with multisystem anomalies","authors":"He Fang, Stephen M Eacker, Yu Wu, Whitney Neufeld-Kaiser, Mercy Laurino, Sioban Keel, Marshall S. Horwitz, Yajuan Liu","doi":"10.1101/2024.07.18.24310513","DOIUrl":"https://doi.org/10.1101/2024.07.18.24310513","url":null,"abstract":"Purpose: Complex chromosomal rearrangements (CCRs) are rare structural variants involving three or more chromosomal break- points. Most de novo reported CCRs pose challenges for diagnosis and management. They often require karyotyping, fluorescence in situ hybridization (FISH), and chromosomal microarray analysis (CMA) for clinical diagnosis because of the limitations of each method. Here we report an inherited exceptionally complex CCR involving 4 chromosomes and 11 breakpoints in a family with multisystem anomalies.\u0000Methods: We evaluated the CCRs using karyotyping, FISH, CMA, and two emerging genomic technologies: high-throughput chromosome conformation capture sequencing (Hi-C; aka genomic proximity mapping, GPM) and optical genome mapping (OGM). We also performed functional studies using transcriptome and methylome analyses.\u0000Results: The proband, who had intellectual disability and immune deficiency, shared CCRs with her unaffected mother involving chromosomes 1, 7, and 11 by karyotyping. However, CMA revealed a duplication and three deletions in the proband in contrast to her mother's balanced genome. Hi-C (GPM) and OGM detected the CCRs and copy number alterations but also uncovered additional breakpoints at high resolution, including an insertion in 4p and two cryptic rearrangements at 7p. Transcriptome and methylome analyses identified likely biological pathways associated with the proband's phenotypes.\u0000Conclusion: Combining cytogenetic and genomic methods provided comprehensive characterization and defined the breakpoints at high resolution in both proband and mother. This underscores the value of novel cytogenetic and genomic techniques in deciphering complex genome rearrangements and the significance of integrative genomic analysis and functional characterization in understanding clinical phenotypes.","PeriodicalId":501375,"journal":{"name":"medRxiv - Genetic and Genomic Medicine","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141744705","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 : 2024-07-19DOI: 10.1101/2024.07.18.24310649
Khanh Bao Trang, Alessandra Chesi, Sylvanus Toikumo, James A Pippin, Matthew C Pahl, Joan M O'Brien, Laufey T Amundadottir, Kevin M Brown, Wenli Yang, Jaclyn Welles, Dominic Santoleri, Paul M Titchenell, Patrick Seale, Babette S Zemel, Yadav Wagley, Kurt D Hankenson, Klaus H Kaestner, Stewart A Anderson, Andrew D Wells, Henry R Kranzler, Rachel L Kember, Struan FA Grant
Recent genome-wide association studies (GWAS) have revealed shared genetic components among alcohol, opioid, tobacco and cannabis use disorders. However, the extent of the underlying shared causal variants and effector genes, along with their cellular context, remain unclear. We leveraged our existing 3D genomic datasets comprising high-resolution promoter-focused Capture-C/Hi-C, ATAC-seq and RNA-seq across >50 diverse human cell types to focus on genomic regions that coincide with GWAS loci. Using stratified LD regression, we determined the proportion of genome-wide SNP heritability attributable to the features assayed across our cell types by integrating recent GWAS summary statistics for the relevant traits: alcohol use disorder (AUD), tobacco use disorder (TUD), opioid use disorder (OUD) and cannabis use disorder (CanUD). Statistically significant enrichments (P<0.05) were observed in 14 specific cell types, with heritability reaching 9.2-fold for iPSC-derived cortical neurons and neural progenitors, confirming that they are crucial cell types for further functional exploration. Additionally, several pancreatic cell types, notably pancreatic beta cells, showed enrichment for TUD, with heritability enrichments up to 4.8-fold, suggesting genomic overlap with metabolic processes. Further investigation revealed significant positive genetic correlations between T2D with both TUD and CanUD (FDR<0.05) and a significant negative genetic correlation with AUD. Interestingly, after partitioning the heritability for each cell type's cis-regulatory elements, the correlation between T2D and TUD for pancreatic beta cells was greater (r=0.2) than the global genetic correlation value. Our study provides new genomic insights into substance use disorders and implicates cell types where functional follow-up studies could reveal causal variant-gene mechanisms underpinning these disorders.
最近的全基因组关联研究（GWAS）揭示了酒精、阿片类药物、烟草和大麻使用障碍之间的共同遗传因素。然而，潜在的共享因果变异和效应基因的范围及其细胞背景仍不清楚。我们利用现有的三维基因组数据集（包括高分辨率启动子Capture-C/Hi-C、ATAC-seq和RNA-seq），横跨50种不同的人类细胞类型，重点研究与GWAS位点相吻合的基因组区域。我们使用分层 LD 回归法，通过整合相关性状（酒精使用障碍 (AUD)、烟草使用障碍 (TUD)、阿片类药物使用障碍 (OUD) 和大麻使用障碍 (CanUD)）的近期 GWAS 统计摘要，确定了全基因组 SNP 遗传性可归因于细胞类型检测特征的比例。在 14 种特定细胞类型中观察到了统计学意义上的显着富集（P<0.05），iPSC 衍生的皮质神经元和神经祖细胞的遗传率达到 9.2 倍，证实它们是进一步功能探索的关键细胞类型。此外，几种胰腺细胞类型，特别是胰腺β细胞，显示出TUD的富集，遗传性富集高达4.8倍，表明基因组与代谢过程重叠。进一步研究发现，T2D 与 TUD 和 CanUD 之间存在明显的正遗传相关性（FDR<0.05），而与 AUD 之间存在明显的负遗传相关性。有趣的是，在对每种细胞类型的顺式调节元件的遗传率进行分区后，胰腺β细胞的 T2D 与 TUD 之间的相关性（r=0.2）大于整体遗传相关值。我们的研究为药物使用障碍提供了新的基因组学见解，并涉及到细胞类型，对这些细胞类型的功能跟踪研究可能会揭示这些障碍的因果变异基因机制。
{"title":"Shared and unique 3D genomic features of substance use disorders across multiple cell types","authors":"Khanh Bao Trang, Alessandra Chesi, Sylvanus Toikumo, James A Pippin, Matthew C Pahl, Joan M O'Brien, Laufey T Amundadottir, Kevin M Brown, Wenli Yang, Jaclyn Welles, Dominic Santoleri, Paul M Titchenell, Patrick Seale, Babette S Zemel, Yadav Wagley, Kurt D Hankenson, Klaus H Kaestner, Stewart A Anderson, Andrew D Wells, Henry R Kranzler, Rachel L Kember, Struan FA Grant","doi":"10.1101/2024.07.18.24310649","DOIUrl":"https://doi.org/10.1101/2024.07.18.24310649","url":null,"abstract":"Recent genome-wide association studies (GWAS) have revealed shared genetic components among alcohol, opioid, tobacco and cannabis use disorders. However, the extent of the underlying shared causal variants and effector genes, along with their cellular context, remain unclear. We leveraged our existing 3D genomic datasets comprising high-resolution promoter-focused Capture-C/Hi-C, ATAC-seq and RNA-seq across >50 diverse human cell types to focus on genomic regions that coincide with GWAS loci. Using stratified LD regression, we determined the proportion of genome-wide SNP heritability attributable to the features assayed across our cell types by integrating recent GWAS summary statistics for the relevant traits: alcohol use disorder (AUD), tobacco use disorder (TUD), opioid use disorder (OUD) and cannabis use disorder (CanUD). Statistically significant enrichments (P<0.05) were observed in 14 specific cell types, with heritability reaching 9.2-fold for iPSC-derived cortical neurons and neural progenitors, confirming that they are crucial cell types for further functional exploration. Additionally, several pancreatic cell types, notably pancreatic beta cells, showed enrichment for TUD, with heritability enrichments up to 4.8-fold, suggesting genomic overlap with metabolic processes. Further investigation revealed significant positive genetic correlations between T2D with both TUD and CanUD (FDR<0.05) and a significant negative genetic correlation with AUD. Interestingly, after partitioning the heritability for each cell type's cis-regulatory elements, the correlation between T2D and TUD for pancreatic beta cells was greater (r=0.2) than the global genetic correlation value. Our study provides new genomic insights into substance use disorders and implicates cell types where functional follow-up studies could reveal causal variant-gene mechanisms underpinning these disorders.","PeriodicalId":501375,"journal":{"name":"medRxiv - Genetic and Genomic Medicine","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141746372","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 : 2024-07-19DOI: 10.1101/2024.07.18.24310562
Griet De Clercq, Lies Vantomme, Barbara Dewaele, Bert Callewaert, Olivier Vanakker, Sandra Janssens, Bart Loeys, Mojca Strazisar, Wouter De Coster, Joris R Vermeesch, Annelies Dheedene, Björn Menten
Structural variants (SVs) are important contributors to human disease. Their characterization remains however difficult due to their size and association with repetitive regions. Long-read sequencing (LRS) and optical genome mapping (OGM) can aid as their molecules span multiple kilobases and capture SVs in full. In this study, we selected six individuals who presented with unresolved SVs. We applied LRS onto all individuals and OGM to a subset of three complex cases. LRS detected and fully resolved the interrogated SV in all samples. This enabled a precise molecular diagnosis in two individuals. Overall, LRS identified 100% of the junctions at single-basepair level, providing valuable insights into their formation mechanisms without need for additional data sources. Application of OGM added straightforward variant phasing, aiding in the unravelment of complex rearrangements. These results highlight the potential of LRS and OGM as follow-up molecular tests for complete SV characterization. We show that they can assess clinically relevant structural variation at unprecedented resolution. Additionally, they detect (complex) cryptic rearrangements missed by conventional methods. This ultimately leads to an increased diagnostic yield, emphasizing their added benefit in a diagnostic setting. To aid their rapid adoption, we provide detailed laboratory and bioinformatics workflows in this manuscript.
{"title":"Full characterization of unresolved structural variation through long-read sequencing and optical genome mapping","authors":"Griet De Clercq, Lies Vantomme, Barbara Dewaele, Bert Callewaert, Olivier Vanakker, Sandra Janssens, Bart Loeys, Mojca Strazisar, Wouter De Coster, Joris R Vermeesch, Annelies Dheedene, Björn Menten","doi":"10.1101/2024.07.18.24310562","DOIUrl":"https://doi.org/10.1101/2024.07.18.24310562","url":null,"abstract":"Structural variants (SVs) are important contributors to human disease. Their characterization remains however difficult due to their size and association with repetitive regions. Long-read sequencing (LRS) and optical genome mapping (OGM) can aid as their molecules span multiple kilobases and capture SVs in full. In this study, we selected six individuals who presented with unresolved SVs. We applied LRS onto all individuals and OGM to a subset of three complex cases. LRS detected and fully resolved the interrogated SV in all samples. This enabled a precise molecular diagnosis in two individuals. Overall, LRS identified 100% of the junctions at single-basepair level, providing valuable insights into their formation mechanisms without need for additional data sources. Application of OGM added straightforward variant phasing, aiding in the unravelment of complex rearrangements. These results highlight the potential of LRS and OGM as follow-up molecular tests for complete SV characterization. We show that they can assess clinically relevant structural variation at unprecedented resolution. Additionally, they detect (complex) cryptic rearrangements missed by conventional methods. This ultimately leads to an increased diagnostic yield, emphasizing their added benefit in a diagnostic setting. To aid their rapid adoption, we provide detailed laboratory and bioinformatics workflows in this manuscript.","PeriodicalId":501375,"journal":{"name":"medRxiv - Genetic and Genomic Medicine","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141746375","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 : 2024-07-19DOI: 10.1101/2024.07.18.24310651
Sophie Breunig, Younga Heather Lee, Elizabeth W Karlson, Arjun Krishnan, Jeremy M Lawrence, Lukas Schaffer, Andrew David Grotzinger
Importance: Autoimmune and autoinflammatory diseases have been linked to psychiatric disorders in the phenotypic and genetic literature. However, a comprehensive model that investigates the association between a broad range of psychiatric disorders and immune-mediated disease in a multivariate framework is lacking. Objective: This study aims to establish a factor structure based on the genetic correlations of immune-mediated diseases and investigate their genetic relationships with clusters of psychiatric disorders. Design, Setting, and Participants: We utilized Genomic Structural Equation Modeling (Genomic SEM) to establish a factor structure of 11 immune-mediated diseases. Genetic correlations between these immune factors were examined with five established factors across 13 psychiatric disorders representing compulsive, schizophrenia/bipolar, neurodevelopmental, internalizing, and substance use disorders. We included GWAS summary statistics of individuals of European ancestry with sample sizes from 1,223 cases for Addison's disease to 170,756 cases for major depressive disorder. Main Outcomes and Measures: Genetic correlations between psychiatric and immune-mediated disease factors and traits to determine genetic overlap. We develop and validate a new heterogeneity metric, QFactor, that quantifies the degree to which factor correlations are driven by more specific pairwise associations. We also estimate residual genetic correlations between pairs of psychiatric disorders and immune-mediated diseases. Results: A four-factor model of immune-mediated diseases fit the data well and described a continuum from autoimmune to autoinflammatory diseases. The four factors reflected autoimmune, celiac, mixed pattern, and autoinflammatory diseases. Analyses revealed seven significant factor correlations between the immune and psychiatric factors, including autoimmune and mixed pattern diseases with the internalizing and substance use factors, and autoinflammatory diseases with the compulsive, schizophrenia/bipolar, and internalizing factors. Additionally, we find evidence of divergence in associations within factors as indicated by QFactor. This is further supported by 14 significant residual genetic correlations between individual psychiatric disorders and immune-mediated diseases. Conclusion and Relevance: Our results revealed genetic links between clusters of immune-mediated diseases and psychiatric disorders. Current analyses indicate that previously described relationships between specific psychiatric disorders and immune-mediated diseases often capture broader pathways of risk sharing indexed by our genomic factors, yet are more specific than a general association across all psychiatric disorders and immune-mediated diseases.
{"title":"Examining the Genetic Links between Clusters of Immune-mediated Diseases and Psychiatric Disorders","authors":"Sophie Breunig, Younga Heather Lee, Elizabeth W Karlson, Arjun Krishnan, Jeremy M Lawrence, Lukas Schaffer, Andrew David Grotzinger","doi":"10.1101/2024.07.18.24310651","DOIUrl":"https://doi.org/10.1101/2024.07.18.24310651","url":null,"abstract":"Importance:\u0000Autoimmune and autoinflammatory diseases have been linked to psychiatric disorders in the phenotypic and genetic literature. However, a comprehensive model that investigates the association between a broad range of psychiatric disorders and immune-mediated disease in a multivariate framework is lacking. Objective:\u0000This study aims to establish a factor structure based on the genetic correlations of immune-mediated diseases and investigate their genetic relationships with clusters of psychiatric disorders. Design, Setting, and Participants: We utilized Genomic Structural Equation Modeling (Genomic SEM) to establish a factor structure of 11 immune-mediated diseases. Genetic correlations between these immune factors were examined with five established factors across 13 psychiatric disorders representing compulsive, schizophrenia/bipolar, neurodevelopmental, internalizing, and substance use disorders. We included GWAS summary statistics of individuals of European ancestry with sample sizes from 1,223 cases for Addison's disease to 170,756 cases for major depressive disorder. Main Outcomes and Measures:\u0000Genetic correlations between psychiatric and immune-mediated disease factors and traits to determine genetic overlap. We develop and validate a new heterogeneity metric, QFactor, that quantifies the degree to which factor correlations are driven by more specific pairwise associations. We also estimate residual genetic correlations between pairs of psychiatric disorders and immune-mediated diseases. Results:\u0000A four-factor model of immune-mediated diseases fit the data well and described a continuum from autoimmune to autoinflammatory diseases. The four factors reflected autoimmune, celiac, mixed pattern, and autoinflammatory diseases. Analyses revealed seven significant factor correlations between the immune and psychiatric factors, including autoimmune and mixed pattern diseases with the internalizing and substance use factors, and autoinflammatory diseases with the compulsive, schizophrenia/bipolar, and internalizing factors. Additionally, we find evidence of divergence in associations within factors as indicated by QFactor. This is further supported by 14 significant residual genetic correlations between individual psychiatric disorders and immune-mediated diseases. Conclusion and Relevance:\u0000Our results revealed genetic links between clusters of immune-mediated diseases and psychiatric disorders. Current analyses indicate that previously described relationships between specific psychiatric disorders and immune-mediated diseases often capture broader pathways of risk sharing indexed by our genomic factors, yet are more specific than a general association across all psychiatric disorders and immune-mediated diseases.","PeriodicalId":501375,"journal":{"name":"medRxiv - Genetic and Genomic Medicine","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141744799","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 : 2024-07-19DOI: 10.1101/2024.07.18.24310667
Yang Wu, Zhili Zheng, Loic Thibaut, Michael E. Goddard, Naomi R. Wray, Peter M. Visscher, Jian Zeng
Fine-mapping refines genotype-phenotype association signals to identify causal variants underlying complex traits. However, current methods typically focus on individual genomic segments without considering the global genetic architecture. Here, we demonstrate the advantages of performing genome-wide fine-mapping (GWFM) and develop methods to facilitate GWFM. In simulations and real data analyses, GWFM outperforms current methods in error control, mapping power and precision, replication rate, and trans-ancestry phenotype prediction. For 48 well-powered traits in the UK Biobank, we identify causal variants that collectively explain 17% of the SNP-based heritability, and predict that fine-mapping 50% of that would require 2 million samples on average. We pinpoint a known causal variant, as proof-of-principle, at FTO for body mass index, unveil a hidden secondary variant with evolutionary conservation, and identify new missense causal variants for schizophrenia and Crohn disease. Overall, we analyse 600 complex traits with 13 million SNPs, highlighting the efficacy of GWFM with functional annotations.
{"title":"Genome-wide fine-mapping improves identification of causal variants","authors":"Yang Wu, Zhili Zheng, Loic Thibaut, Michael E. Goddard, Naomi R. Wray, Peter M. Visscher, Jian Zeng","doi":"10.1101/2024.07.18.24310667","DOIUrl":"https://doi.org/10.1101/2024.07.18.24310667","url":null,"abstract":"Fine-mapping refines genotype-phenotype association signals to identify causal variants underlying complex traits. However, current methods typically focus on individual genomic segments without considering the global genetic architecture. Here, we demonstrate the advantages of performing genome-wide fine-mapping (GWFM) and develop methods to facilitate GWFM. In simulations and real data analyses, GWFM outperforms current methods in error control, mapping power and precision, replication rate, and trans-ancestry phenotype prediction. For 48 well-powered traits in the UK Biobank, we identify causal variants that collectively explain 17% of the SNP-based heritability, and predict that fine-mapping 50% of that would require 2 million samples on average. We pinpoint a known causal variant, as proof-of-principle, at FTO for body mass index, unveil a hidden secondary variant with evolutionary conservation, and identify new missense causal variants for schizophrenia and Crohn disease. Overall, we analyse 600 complex traits with 13 million SNPs, highlighting the efficacy of GWFM with functional annotations.","PeriodicalId":501375,"journal":{"name":"medRxiv - Genetic and Genomic Medicine","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141744708","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}
Background Identifying causative variants is crucial for the diagnosis of rare genetic diseases. Over the past two decades, the application of genome sequencing technologies in the field has significantly improved diagnostic outcomes. However, the complexity of data analysis and interpretation continues to limit the efficiency and accuracy of these applications. Various genotype and phenotype-driven filtering and prioritization strategies are used to generate a candidate list of variants for expert curation, with the final report variants determined through knowledge-intensive and labor-intensive expert review. Despite these efforts, the current methods fall short of meeting the growing demand for accurate and efficient diagnosis of rare disease. Recent developments in large language models (LLMs) suggest that LLMs possess the potential to augment or even supplant human labor in this context. Methods In this study, we have developed Genetic Transformer (GeneT), an innovative large language model (LLM) driven approach to accelerate identification of candidate causative variants for rare genetic disease. A comprehensive evaluation was conducted between the fine-tuned large language models and four phenotype-driven methods, including Xrare, Exomiser, PhenIX and PHIVE, alongside six pre-trained LLMs (Qwen1.5-0.5B, Qwen1.5-1.8B, Qwen1.5-4B, Mistral-7B, Meta-Llama-3-8B, Meta-Llama-3-70B). This evaluation focused on performance and hallucinations. Results Genetic Transformer (GeneT) as an innovative LLM-driven approach demonstrated outstanding performance on identification of candidate causative variants, identified the average number of candidate causative variants reduced from an average of 418 to 8, achieving recall rate of 99% in synthetic datasets. Application in real-world clinical setting demonstrated the potential for a 20-fold increase in processing speed, reducing the time required to analyze each sample from approximately 60 minutes to around 3 minutes. Concurrently, the recall rate has improved from 94.36% to 97.85%. An online analysis platform iGeneT was developed to integrate GeneT into the workflow of rare genetic disease analysis. Conclusion Our study represents the inaugural application of fine-tuned LLMs for identifying candidate causative variants, introducing GeneT as an innovative LLM-driven approach, demonstrating its superiority in both simulated data and real-world clinical setting. The study is unique in that it represents a paradigm shift in addressing the complexity of variant filtering and prioritization of whole exome or genome sequencing data, effectively resolving the challenge akin to finding a needle in a haystack.
{"title":"Genetic Transformer: An Innovative Large Language Model Driven Approach for Rapid and Accurate Identification of Causative Variants in Rare Genetic Diseases","authors":"Lungang Liang, Yulan Chen, Taifu Wang, Dan Jiang, Jishuo Jin, Yanmeng Pang, Qin Na, Qiang Liu, Xiaosen Jiang, Wentao Dai, Meifang Tang, Yutao Du, Dirong Peng, Xin Jin, Lijian Zhao","doi":"10.1101/2024.07.18.24310666","DOIUrl":"https://doi.org/10.1101/2024.07.18.24310666","url":null,"abstract":"<strong>Background</strong>\u0000Identifying causative variants is crucial for the diagnosis of rare genetic diseases. Over the past two decades, the application of genome sequencing technologies in the field has significantly improved diagnostic outcomes. However, the complexity of data analysis and interpretation continues to limit the efficiency and accuracy of these applications. Various genotype and phenotype-driven filtering and prioritization strategies are used to generate a candidate list of variants for expert curation, with the final report variants determined through knowledge-intensive and labor-intensive expert review. Despite these efforts, the current methods fall short of meeting the growing demand for accurate and efficient diagnosis of rare disease. Recent developments in large language models (LLMs) suggest that LLMs possess the potential to augment or even supplant human labor in this context.\u0000<strong>Methods</strong>\u0000In this study, we have developed Genetic Transformer (GeneT), an innovative large language model (LLM) driven approach to accelerate identification of candidate causative variants for rare genetic disease. A comprehensive evaluation was conducted between the fine-tuned large language models and four phenotype-driven methods, including Xrare, Exomiser, PhenIX and PHIVE, alongside six pre-trained LLMs (Qwen1.5-0.5B, Qwen1.5-1.8B, Qwen1.5-4B, Mistral-7B, Meta-Llama-3-8B, Meta-Llama-3-70B). This evaluation focused on performance and hallucinations. <strong>Results</strong>\u0000Genetic Transformer (GeneT) as an innovative LLM-driven approach demonstrated outstanding performance on identification of candidate causative variants, identified the average number of candidate causative variants reduced from an average of 418 to 8, achieving recall rate of 99% in synthetic datasets. Application in real-world clinical setting demonstrated the potential for a 20-fold increase in processing speed, reducing the time required to analyze each sample from approximately 60 minutes to around 3 minutes. Concurrently, the recall rate has improved from 94.36% to 97.85%. An online analysis platform iGeneT was developed to integrate GeneT into the workflow of rare genetic disease analysis.\u0000<strong>Conclusion</strong>\u0000Our study represents the inaugural application of fine-tuned LLMs for identifying candidate causative variants, introducing GeneT as an innovative LLM-driven approach, demonstrating its superiority in both simulated data and real-world clinical setting. The study is unique in that it represents a paradigm shift in addressing the complexity of variant filtering and prioritization of whole exome or genome sequencing data, effectively resolving the challenge akin to finding a needle in a haystack.","PeriodicalId":501375,"journal":{"name":"medRxiv - Genetic and Genomic Medicine","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141744801","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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