Pub Date : 2024-08-23DOI: 10.1101/2024.08.23.24312408
Dirk Taenzler, Frank Hause, Andreas Merkenschlager, Andrea Sinz
Background ADCY5-related dyskinesia is a rare disorder caused by mutations in the ADCY5 gene resulting in abnormal involuntary movements. Currently, there are no standardized guidelines to treat this condition. Objectives The aim of this study is to evaluate the efficacy of theophylline administration in improving symptoms and quality of life in patients with ADCY5-related dyskinesia. Methods A retrospective study was conducted involving 12 patients (aged 2-34 years) with ADCY5-related dyskinesia. Participants completed a questionnaire about theophylline administration, including dosage, improvement of symptoms, adverse effects, and changes in quality of life. Data were analyzed for reported efficacy and side effects. Results Theophylline administration demonstrated substantial efficacy, with 92% (11 out of 12) of patients reporting significant improvements in their movement disorders. The average improvement score was 7.0 (SD 1.9) on a 10-point scale. Notable improvements included reductions in severity and frequency of episodes, improved gait, more independent mobility, psycho-social well-being, and quality of sleep. Adverse effects were reported by 6 patients, including dystonia, speech worsening, headaches, nausea, impaired sleep, and agitation. Conclusions Theophylline shows substantial promise as a treatment option for ADCY5-related dyskinesia, improving various aspects of patients' quality of life and movement disorder symptoms. Further research is needed to optimize dosing, to understand long-term effects, and to explore combinational drug therapies. Despite the small cohort size and the retrospective nature of this study, the results support theophylline administration to decrease dyskinetic movements and enhance overall quality of life in patients.
{"title":"Treatment Efficacy of Theophylline in ADYC5 Dyskinesia: A Retrospective Case Series Study","authors":"Dirk Taenzler, Frank Hause, Andreas Merkenschlager, Andrea Sinz","doi":"10.1101/2024.08.23.24312408","DOIUrl":"https://doi.org/10.1101/2024.08.23.24312408","url":null,"abstract":"Background\u0000ADCY5-related dyskinesia is a rare disorder caused by mutations in the ADCY5 gene resulting in abnormal involuntary movements. Currently, there are no standardized guidelines to treat this condition.\u0000Objectives\u0000The aim of this study is to evaluate the efficacy of theophylline administration in improving symptoms and quality of life in patients with ADCY5-related dyskinesia.\u0000Methods\u0000A retrospective study was conducted involving 12 patients (aged 2-34 years) with ADCY5-related dyskinesia. Participants completed a questionnaire about theophylline administration, including dosage, improvement of symptoms, adverse effects, and changes in quality of life. Data were analyzed for reported efficacy and side effects.\u0000Results\u0000Theophylline administration demonstrated substantial efficacy, with 92% (11 out of 12) of patients reporting significant improvements in their movement disorders. The average improvement score was 7.0 (SD 1.9) on a 10-point scale. Notable improvements included reductions in severity and frequency of episodes, improved gait, more independent mobility, psycho-social well-being, and quality of sleep. Adverse effects were reported by 6 patients, including dystonia, speech worsening, headaches, nausea, impaired sleep, and agitation.\u0000Conclusions\u0000Theophylline shows substantial promise as a treatment option for ADCY5-related dyskinesia, improving various aspects of patients' quality of life and movement disorder symptoms. Further research is needed to optimize dosing, to understand long-term effects, and to explore combinational drug therapies. Despite the small cohort size and the retrospective nature of this study, the results support theophylline administration to decrease dyskinetic movements and enhance overall quality of life in patients.","PeriodicalId":501375,"journal":{"name":"medRxiv - Genetic and Genomic Medicine","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142224770","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 : 2024-08-23DOI: 10.1101/2024.08.23.24312119
Rebecca D Ganetzky, Katelynn D Stanley, Laura E MacMullen, Ibrahim George-sankoh, Jing Wang, Amy Goldstein, Marni J. Falk
Introduction Single large-scale mtDNA deletions (SLSMD) result in Single Large Scale Deletion Syndromes (SLSMDS). SLSMDS presentations have classically been recognized to encompass at least three distinct clinical phenotypes, Pearson Syndrome (PS), Kearns-Sayre Syndrome (KSS), and Chronic Progressive Ophthalmoplegia (CPEO). Methods Facilitated review of electronic medical records, manual charts, and REDCap research databases was performed to complete a retrospective natural history study of 32 SLSMDS participants in a single health system seen between 2002 and 2020. Characteristics evaluated included genetic and clinical laboratory test values, growth parameters, signs and symptoms, demographics, and patient reported outcome measures of fatigue, quality of life, and overall function. Results Detailed cohort characterization highlighted that a recurrent deleted region involving MT-ND5 occurs in 96% of SLSMD subjects regardless of clinical phenotype, which tended to evolve over time. Higher blood heteroplasmy correlated with earlier age of onset. GDF-15 was elevated in all SLSMD subjects. A PS history yielded negative survival prognosis. Furthermore, increased fatigue and decreased quality of life were reported in SLSMD subjects with advancing age. Conclusion Retrospective natural history study of SLSMDS subjects demonstrated the evolution of classically considered PS, KSS, and CPEO clinical presentations within affected individuals, which may inform future clinical trial development.
{"title":"Recognizing the Evolution of Clinical Syndrome Spectrum Progression in Individuals with Single Large-Scale mitochondrial DNA deletion syndromes (SLSMDS)","authors":"Rebecca D Ganetzky, Katelynn D Stanley, Laura E MacMullen, Ibrahim George-sankoh, Jing Wang, Amy Goldstein, Marni J. Falk","doi":"10.1101/2024.08.23.24312119","DOIUrl":"https://doi.org/10.1101/2024.08.23.24312119","url":null,"abstract":"Introduction\u0000Single large-scale mtDNA deletions (SLSMD) result in Single Large Scale Deletion Syndromes (SLSMDS). SLSMDS presentations have classically been recognized to encompass at least three distinct clinical phenotypes, Pearson Syndrome (PS), Kearns-Sayre Syndrome (KSS), and Chronic Progressive Ophthalmoplegia (CPEO).\u0000Methods\u0000Facilitated review of electronic medical records, manual charts, and REDCap research databases was performed to complete a retrospective natural history study of 32 SLSMDS participants in a single health system seen between 2002 and 2020. Characteristics evaluated included genetic and clinical laboratory test values, growth parameters, signs and symptoms, demographics, and patient reported outcome measures of fatigue, quality of life, and overall function.\u0000Results\u0000Detailed cohort characterization highlighted that a recurrent deleted region involving MT-ND5 occurs in 96% of SLSMD subjects regardless of clinical phenotype, which tended to evolve over time. Higher blood heteroplasmy correlated with earlier age of onset. GDF-15 was elevated in all SLSMD subjects. A PS history yielded negative survival prognosis. Furthermore, increased fatigue and decreased quality of life were reported in SLSMD subjects with advancing age.\u0000Conclusion\u0000Retrospective natural history study of SLSMDS subjects demonstrated the evolution of classically considered PS, KSS, and CPEO clinical presentations within affected individuals, which may inform future clinical trial development.","PeriodicalId":501375,"journal":{"name":"medRxiv - Genetic and Genomic Medicine","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142192213","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 : 2024-08-07DOI: 10.1101/2024.08.06.24311318
Daniella H Hock, Nikeisha J Caruana, Liana N Semcesen, Nicole J Lake, Luke E Formosa, Sumudu SC Amarasekera, Tegan Stait, Simone Tregoning, Leah E Frajman, David RL Robinson, Megan Ball, Boris Reljic, Bryony Ryder, Mathew J Wallis, Anand Vasudevan, Cara Beck, Heidi Peters, Joy Lee, MitoMDT Diagnostic Network for Genomics and Omics, Vasiliki Karlaftis, Chantal Attard, Paul Monagle, Amanda Samarasinghe, Rosie Brown, Weimin Bi, Monkol Lek, Robert McFarland, Robert W Taylor, Michael T Ryan, Zornitza Stark, John Christodoulou, Alison G Compton, David R Thorburn, David A Stroud
Only half of individuals with suspected rare diseases receive a definitive genetic diagnosis following genomic testing. A genetic diagnosis allows access to appropriate patient care and reduces the number of potentially unnecessary interventions and related healthcare costs. Here, we demonstrate that an untargeted quantitative mass-spectrometry approach quantifying >6,000 proteins in primary fibroblasts representing >80% of known mitochondrial disease genes can provide functional evidence for 88% of individuals in a cohort of known primary mitochondrial diseases. We profiled >90 individuals, including 28 with confirmed disease and diagnosed 6 individuals with variants in both nuclear and mitochondrial genes. Lastly, we developed an ultra-rapid proteomics pipeline using minimally invasive peripheral blood mononuclear cells to support upgrade of variant pathogenicity in as little as 54 hours in critically ill infants with suspected mitochondrial disorders. This study supports the integration of a single untargeted proteomics test into routine diagnostic practice for the diagnosis of rare genetic disorders in clinically actionable timelines, offering a paradigm shift for the functional validation of genetic variants.
{"title":"Untargeted proteomics enables ultra-rapid variant prioritization in mitochondrial and other rare diseases","authors":"Daniella H Hock, Nikeisha J Caruana, Liana N Semcesen, Nicole J Lake, Luke E Formosa, Sumudu SC Amarasekera, Tegan Stait, Simone Tregoning, Leah E Frajman, David RL Robinson, Megan Ball, Boris Reljic, Bryony Ryder, Mathew J Wallis, Anand Vasudevan, Cara Beck, Heidi Peters, Joy Lee, MitoMDT Diagnostic Network for Genomics and Omics, Vasiliki Karlaftis, Chantal Attard, Paul Monagle, Amanda Samarasinghe, Rosie Brown, Weimin Bi, Monkol Lek, Robert McFarland, Robert W Taylor, Michael T Ryan, Zornitza Stark, John Christodoulou, Alison G Compton, David R Thorburn, David A Stroud","doi":"10.1101/2024.08.06.24311318","DOIUrl":"https://doi.org/10.1101/2024.08.06.24311318","url":null,"abstract":"Only half of individuals with suspected rare diseases receive a definitive genetic diagnosis following genomic testing. A genetic diagnosis allows access to appropriate patient care and reduces the number of potentially unnecessary interventions and related healthcare costs. Here, we demonstrate that an untargeted quantitative mass-spectrometry approach quantifying >6,000 proteins in primary fibroblasts representing >80% of known mitochondrial disease genes can provide functional evidence for 88% of individuals in a cohort of known primary mitochondrial diseases. We profiled >90 individuals, including 28 with confirmed disease and diagnosed 6 individuals with variants in both nuclear and mitochondrial genes. Lastly, we developed an ultra-rapid proteomics pipeline using minimally invasive peripheral blood mononuclear cells to support upgrade of variant pathogenicity in as little as 54 hours in critically ill infants with suspected mitochondrial disorders. This study supports the integration of a single untargeted proteomics test into routine diagnostic practice for the diagnosis of rare genetic disorders in clinically actionable timelines, offering a paradigm shift for the functional validation of genetic variants.","PeriodicalId":501375,"journal":{"name":"medRxiv - Genetic and Genomic Medicine","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141948286","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 : 2024-08-07DOI: 10.1101/2024.08.07.24311600
Xueen Liu, Jiale Zhang
Objectives: Prior observational studies have suggested a potential association between the usual walking pace and migraine. In the present study, we utilized Mendelian randomization (MR) to investigate the presence of causality and elucidate the specific causal relationship between these two variables. Methods: We performed a genome-wide association study on a population of 499,562 individuals of European ancestry, which revealed 34 genetic variants that exhibited a strong association with the usual walking pace. Additionally, we obtained summary statistics for genome-wide association studies on migraine from several sources. To assess the causal estimates, we employed the random effects inverse variance weighted method (IVW) and several other Mendelian randomizations (MR) methods, including MR-Egger, weighted median, Simple mode, Weighted mode, and MR-PRESSO, to confirm the robustness of our results. Results: Our analysis demonstrated a strong causal association between genetically predicted usual walking pace and a decreased risk of migraine, as determined by inverse variance weighted analysis (odds ratio = 0.33; 95% CI = 0.17 to 0.63; P < 0.001). This association was consistently observed across our investigation's various Mendelian randomization (MR) methods. Conclusions: This study supports a potential causal association between increased walking speed and a decreased risk of migraine.
{"title":"Potential Causal Relationship between Faster Walking Pace and Reduced Migraine Risk: A Mendelian Randomization Study","authors":"Xueen Liu, Jiale Zhang","doi":"10.1101/2024.08.07.24311600","DOIUrl":"https://doi.org/10.1101/2024.08.07.24311600","url":null,"abstract":"Objectives: Prior observational studies have suggested a potential association between the usual walking pace and migraine. In the present study, we utilized Mendelian randomization (MR) to investigate the presence of causality and elucidate the specific causal relationship between these two variables.\u0000Methods: We performed a genome-wide association study on a population of 499,562 individuals of European ancestry, which revealed 34 genetic variants that exhibited a strong association with the usual walking pace. Additionally, we obtained summary statistics for genome-wide association studies on migraine from several sources. To assess the causal estimates, we employed the random effects inverse variance weighted method (IVW) and several other Mendelian randomizations (MR) methods, including MR-Egger, weighted median, Simple mode, Weighted mode, and MR-PRESSO, to confirm the robustness of our results.\u0000Results: Our analysis demonstrated a strong causal association between genetically predicted usual walking pace and a decreased risk of migraine, as determined by inverse variance weighted analysis (odds ratio = 0.33; 95% CI = 0.17 to 0.63; P < 0.001). This association was consistently observed across our investigation's various Mendelian randomization (MR) methods.\u0000Conclusions: This study supports a potential causal association between increased walking speed and a decreased risk of migraine.","PeriodicalId":501375,"journal":{"name":"medRxiv - Genetic and Genomic Medicine","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141948285","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 : 2024-08-07DOI: 10.1101/2024.08.06.24311572
Anni Moore, Rasika Venkatesh, Michael Levin, Scott M. Damrauer, Nosheen Reza, Thomas Cappola, Marylyn D Ritchie
Heart failure (HF) is one of the most common, complex, heterogeneous diseases in the world, with over 1-3% of the global population living with the condition. Progression of HF can be tracked via MRI measures of structural and functional changes to the heart, namely left ventricle (LV), including ejection fraction, mass, end-diastolic volume, and LV end-systolic volume. Moreover, while genome-wide association studies (GWAS) have been a useful tool to identify candidate variants involved in HF risk, they lack crucial tissue-specific and mechanistic information which can be gained from incorporating additional data modalities. This study addresses this gap by incorporating transcriptome-wide and proteome-wide association studies (TWAS and PWAS) to gain insights into genetically-regulated changes in gene expression and protein abundance in precursors to HF measured using MRI-derived cardiac measures as well as full-stage all-cause HF. We identified several gene and protein overlaps between LV ejection fraction and end-systolic volume measures. Many of the overlaps identified in MRI-derived measurements through TWAS and PWAS appear to be shared with all-cause HF. We implicate many putative pathways relevant in HF associated with these genes and proteins via gene-set enrichment and protein-protein interaction network approaches. The results of this study (1) highlight the benefit of using multi-omics to better understand genetics and (2) provide novel insights as to how changes in heart structure and function may relate to HF.
{"title":"Connecting intermediate phenotypes to disease using multi-omics in heart failure","authors":"Anni Moore, Rasika Venkatesh, Michael Levin, Scott M. Damrauer, Nosheen Reza, Thomas Cappola, Marylyn D Ritchie","doi":"10.1101/2024.08.06.24311572","DOIUrl":"https://doi.org/10.1101/2024.08.06.24311572","url":null,"abstract":"Heart failure (HF) is one of the most common, complex, heterogeneous diseases in the world, with over 1-3% of the global population living with the condition. Progression of HF can be tracked via MRI measures of structural and functional changes to the heart, namely left ventricle (LV), including ejection fraction, mass, end-diastolic volume, and LV end-systolic volume. Moreover, while genome-wide association studies (GWAS) have been a useful tool to identify candidate variants involved in HF risk, they lack crucial tissue-specific and mechanistic information which can be gained from incorporating additional data modalities. This study addresses this gap by incorporating transcriptome-wide and proteome-wide association studies (TWAS and PWAS) to gain insights into genetically-regulated changes in gene expression and protein abundance in precursors to HF measured using MRI-derived cardiac measures as well as full-stage all-cause HF. We identified several gene and protein overlaps between LV ejection fraction and end-systolic volume measures. Many of the overlaps identified in MRI-derived measurements through TWAS and PWAS appear to be shared with all-cause HF. We implicate many putative pathways relevant in HF associated with these genes and proteins via gene-set enrichment and protein-protein interaction network approaches. The results of this study (1) highlight the benefit of using multi-omics to better understand genetics and (2) provide novel insights as to how changes in heart structure and function may relate to HF.","PeriodicalId":501375,"journal":{"name":"medRxiv - Genetic and Genomic Medicine","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141948287","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 : 2024-08-06DOI: 10.1101/2024.08.05.24310862
Stanley T. Crooke, Tracy A Cole, Jeffrey B Carroll, Joseph G Gleeson, Laurence Mignon, Julie Douville, Wendy Chung, Jennifer Bain, Elizabeth M Berry-Kravis, Nelson Leung, Scott Demarest, Emily McCourt, Andy Watt, Berit Powers, Cedrik Ngongang
Recent advances in ″omics″ technologies allow for the identification of an increasing number of individuals with diseases caused by nano-rare mutations. These difficult-to-diagnose individuals are uniquely disadvantaged and pose significant challenges to healthcare systems and society. Despite having diseases caused by actionable single gene mutations, in many cases, there is no commercial path for treatments for such small patient populations. Since antisense oligonucleotide (ASO) technology has proven to be suited to address the needs of a portion of these patients, the n-Lorem Foundation is establishing an industrialized approach that couples detailed genotypic and phenotypic data to the immediate potential for ASO therapy. In this manuscript we have leveraged our experience in assessing the causality of nano-rare genetic variants and associated proximal molecular pathological events to attempt a correlation between detailed genetic data with patient specific phenotypic observations in 173 nano-rare individuals from diverse age groups evaluated for experimental ASO therapy. We found that the time required to achieve a molecular diagnosis varies from 1 month to 36 years, with the mean and median times from symptom onset to diagnosis estimated to be 4.32 years and 2 years, respectively. Amongst submitted cases there is a significant bias toward neurological diseases, with diverse genes and functional families involved and a marked preponderance of mutations in ion channel genes. The variability in phenotypic expression associated with nano-rare variants in genes such as GNAO1, H3F3A, GBE1, UBTF, or PACS1 clearly supports previous observations that phenotypes associated with same variants in the same gene can vary. We also observe that different, but functionally equivalent variants can result in both similar (e.g., TARDBP) and different phenotypes (e.g., GNAO1). Despite the relatively small size of the patient population investigated, this first compilation of its kind allows a variety of insights into the genotype and phenotype relationships in nano-rare conditions. Moreover, we show that our unique patient population presents a remarkable opportunity to apply ″modern omics″ approaches to begin to understand the various homeostatic, compensatory, and secondary effects of these genetic variants on the networks that result in expression of their unique phenotypes. To provide a more detailed description of the processes involved to provide a personalized antisense medicine, we have included nonclinical and clincal data on four exemplary patients who display disease in three different organs, the CNS, the eye and the kidney and are treated with ASOs of different designs. In contrast to traditional drug development, each patient presents unique genomic, ASO design, clinical treatment and management and evaluation challenges.
{"title":"Genotypic and phenotypic analysis of 173 patients with extremely rare pathogenic mutations who applied for experimental antisense oligonucleotide treatment","authors":"Stanley T. Crooke, Tracy A Cole, Jeffrey B Carroll, Joseph G Gleeson, Laurence Mignon, Julie Douville, Wendy Chung, Jennifer Bain, Elizabeth M Berry-Kravis, Nelson Leung, Scott Demarest, Emily McCourt, Andy Watt, Berit Powers, Cedrik Ngongang","doi":"10.1101/2024.08.05.24310862","DOIUrl":"https://doi.org/10.1101/2024.08.05.24310862","url":null,"abstract":"Recent advances in ″omics″ technologies allow for the identification of an increasing number of individuals with diseases caused by nano-rare mutations. These difficult-to-diagnose individuals are uniquely disadvantaged and pose significant challenges to healthcare systems and society. Despite having diseases caused by actionable single gene mutations, in many cases, there is no commercial path for treatments for such small patient populations. Since antisense oligonucleotide (ASO) technology has proven to be suited to address the needs of a portion of these patients, the n-Lorem Foundation is establishing an industrialized approach that couples detailed genotypic and phenotypic data to the immediate potential for ASO therapy. In this manuscript we have leveraged our experience in assessing the causality of nano-rare genetic variants and associated proximal molecular pathological events to attempt a correlation between detailed genetic data with patient specific phenotypic observations in 173 nano-rare individuals from diverse age groups evaluated for experimental ASO therapy. We found that the time required to achieve a molecular diagnosis varies from 1 month to 36 years, with the mean and median times from symptom onset to diagnosis estimated to be 4.32 years and 2 years, respectively. Amongst submitted cases there is a significant bias toward neurological diseases, with diverse genes and functional families involved and a marked preponderance of mutations in ion channel genes. The variability in phenotypic expression associated with nano-rare variants in genes such as GNAO1, H3F3A, GBE1, UBTF, or PACS1 clearly supports previous observations that phenotypes associated with same variants in the same gene can vary. We also observe that different, but functionally equivalent variants can result in both similar (e.g., TARDBP) and different phenotypes (e.g., GNAO1). Despite the relatively small size of the patient population investigated, this first compilation of its kind allows a variety of insights into the genotype and phenotype relationships in nano-rare conditions. Moreover, we show that our unique patient population presents a remarkable opportunity to apply ″modern omics″ approaches to begin to understand the various homeostatic, compensatory, and secondary effects of these genetic variants on the networks that result in expression of their unique phenotypes.\u0000To provide a more detailed description of the processes involved to provide a personalized antisense medicine, we have included nonclinical and clincal data on four exemplary patients who display disease in three different organs, the CNS, the eye and the kidney and are treated with ASOs of different designs. In contrast to traditional drug development, each patient presents unique genomic, ASO design, clinical treatment and management and evaluation challenges.","PeriodicalId":501375,"journal":{"name":"medRxiv - Genetic and Genomic Medicine","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141948292","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 : 2024-08-05DOI: 10.1101/2024.08.02.24311422
Eric Czech, Rafal Wojdyla, Daniel Himmelstein, Daniel Frank, Nick Miller, Jack Milwid, Adam Kolom, Jeff Hammerbacher
Choosing which drug targets to pursue for a given disease is one of the most impactful decisions made in the global development of new medicines. This study examines the extent to which the outcomes of clinical trials can be predicted based on a small set of longitudinal (temporally labeled) evidence and properties of drug targets and diseases. We demonstrate a novel statistical learning framework for identifying the top 2% of target-disease pairs that are as much as 4-5x more likely to advance beyond phase 2 trials. This framework is 1.5-2x more effective than an Open Targets composite score based on the same set of evidence. It is also 2x more effective than a common measure for genetic support that has been observed previously, as well as in this study, to confer a 2x higher likelihood of success. Utilizing a subset of our biomedical evidence base, non-negative linear models resulting from this framework can produce simple weighting schemes across various types of human, animal, and cell model genomic, transcriptomic, proteomic, and clinical evidence to identify previously undeveloped target-disease pairs poised for clinical success. In this study we further explore: i) how longitudinal treatment of evidence relates to leakage and reverse causality in biomedical research and how temporalized evidence can mitigate common forms of potential biases and inflation ii) the relative impact of different types of features on our predictions; and iii) an analysis of the space of currently undeveloped, tractable targets predicted with these methods to have the highest likelihood of clinical success. To ease reproduction and deployment, no data is used outside of Open Targets and the described methods require no expert knowledge, and can support expansion of lines of evidence to further improve performance.
{"title":"Clinical Advancement Forecasting","authors":"Eric Czech, Rafal Wojdyla, Daniel Himmelstein, Daniel Frank, Nick Miller, Jack Milwid, Adam Kolom, Jeff Hammerbacher","doi":"10.1101/2024.08.02.24311422","DOIUrl":"https://doi.org/10.1101/2024.08.02.24311422","url":null,"abstract":"Choosing which drug targets to pursue for a given disease is one of the most impactful decisions made in the global development of new medicines. This study examines the extent to which the outcomes of clinical trials can be predicted based on a small set of longitudinal (temporally labeled) evidence and properties of drug targets and diseases. We demonstrate a novel statistical learning framework for identifying the top 2% of target-disease pairs that are as much as 4-5x more likely to advance beyond phase 2 trials. This framework is 1.5-2x more effective than an Open Targets composite score based on the same set of evidence. It is also 2x more effective than a common measure for genetic support that has been observed previously, as well as in this study, to confer a 2x higher likelihood of success. Utilizing a subset of our biomedical evidence base, non-negative linear models resulting from this framework can produce simple weighting schemes across various types of human, animal, and cell model genomic, transcriptomic, proteomic, and clinical evidence to identify previously undeveloped target-disease pairs poised for clinical success. In this study we further explore: i) how longitudinal treatment of evidence relates to leakage and reverse causality in biomedical research and how temporalized evidence can mitigate common forms of potential biases and inflation ii) the relative impact of different types of features on our predictions; and iii) an analysis of the space of currently undeveloped, tractable targets predicted with these methods to have the highest likelihood of clinical success. To ease reproduction and deployment, no data is used outside of Open Targets and the described methods require no expert knowledge, and can support expansion of lines of evidence to further improve performance.","PeriodicalId":501375,"journal":{"name":"medRxiv - Genetic and Genomic Medicine","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141948289","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 : 2024-08-03DOI: 10.1101/2024.08.01.24311329
Sarah W Zaranek, Alexander Wait Zaranek, Peter Amstutz, Jingxuan Bao, Jiong Chen, Tom Clegg, Hannah Craft, Taeho Jo, Brian Lee, Kwangsik Nho, Sophia I Thomopoulos, Christos Davatzikos, Li Shen, Heng Huang, Paul M Thompson, Andrew J Saykin, The Alzheimer's Disease Neuroimaging Initiative as a consortium author for the AI4AD Initiative
Currently, the ability to analyze large-scale whole genome sequence (WGS) data is limited due to both the size of the data and the inability of many existing tools to scale. To address this challenge, we use data "tiling" to efficiently partition whole genome sequences into smaller segments resulting in a simple numeric matrix of small integers. This lossless representation is particularly suitable for machine learning (ML) models. As an example of the benefits of tiling, we showcase results from tiled data as part of the Artificial Intelligence for Alzheimer's Disease (AI4AD) consortium. AI4AD is a coordinated initiative to develop transformative AI approaches for high throughput analysis of next generation sequencing and related imaging, AD biomarker, and cognitive data. The collective effort integrates imaging, genomic, biomarker, and cognitive data to address fundamental barriers in AD prevention and drug discovery. One of the project's initial aims is to discover new genetic signatures in WGS data that can be used to understand AD risk and progression in conjunction with imaging, biomarker and cognitive data. We tiled and analyzed 15,000+ genomes from the Alzheimer's Disease Sequencing Project (ADSP) and the Alzheimer's Disease Neuroimaging Initiative (ADNI). We tile 11,762 genomes, a subset of the release which does not include family-based datasets (AD Cases: 4,983, age range: 50-90 years , mean age: 73.8 years). We illustrate the use of tiled data in ML classification methods to predict phenotypes. Specifically, we identify and prioritize tile variants/genetic variants that are possible genetic signatures for AD. The model shows added predictive value from variants of genes previously found to be associated with AD risk, age of onset, neurofibrillary tangle measurements, and other AD-related traits--including the APOE variant (rs429358).
目前，分析大规模全基因组序列（WGS）数据的能力受到限制，原因在于数据的大小和许多现有工具无法扩展。为了应对这一挑战，我们利用数据 "平铺 "技术将全基因组序列有效地分割成更小的片段，形成一个简单的小整数数字矩阵。这种无损表示法特别适合机器学习（ML）模型。作为平铺好处的一个例子，我们展示了平铺数据的结果，这是阿尔茨海默病人工智能（AI4AD）联盟的一部分。AI4AD 是一项协调行动，旨在为下一代测序和相关成像、AD 生物标记和认知数据的高通量分析开发变革性人工智能方法。这一集体努力整合了成像、基因组、生物标志物和认知数据，以解决注意力缺失症预防和药物发现方面的基本障碍。该项目的初步目标之一是在 WGS 数据中发现新的遗传特征，这些特征可与成像、生物标记和认知数据相结合，用于了解注意力缺失症的风险和进展。我们对来自阿尔茨海默病测序项目（ADSP）和阿尔茨海默病神经影像计划（ADNI）的15000多个基因组进行了平铺和分析。我们平铺了 11,762 个基因组，这是此次发布的一个子集，其中不包括基于家庭的数据集（AD 病例：4,983 例，年龄范围：50-90 岁，平均年龄：73.8 岁）。我们说明了如何在预测表型的 ML 分类方法中使用平铺数据。具体来说，我们识别并优先处理了可能是 AD 遗传特征的瓦片变异/遗传变异。该模型显示了先前发现的与 AD 风险、发病年龄、神经纤维缠结测量和其他 AD 相关特征（包括 APOE 变体 (rs429358)）相关的基因变异所带来的预测价值。
{"title":"Discovering Genetic Signatures Associated with Alzheimer's Disease in Tiled Whole Genome Sequence Data: Results from the Artificial Intelligence for Alzheimer's Disease (AI4AD) Consortium","authors":"Sarah W Zaranek, Alexander Wait Zaranek, Peter Amstutz, Jingxuan Bao, Jiong Chen, Tom Clegg, Hannah Craft, Taeho Jo, Brian Lee, Kwangsik Nho, Sophia I Thomopoulos, Christos Davatzikos, Li Shen, Heng Huang, Paul M Thompson, Andrew J Saykin, The Alzheimer's Disease Neuroimaging Initiative as a consortium author for the AI4AD Initiative","doi":"10.1101/2024.08.01.24311329","DOIUrl":"https://doi.org/10.1101/2024.08.01.24311329","url":null,"abstract":"Currently, the ability to analyze large-scale whole genome sequence (WGS) data is limited due to both the size of the data and the inability of many existing tools to scale. To address this challenge, we use data \"tiling\" to efficiently partition whole genome sequences into smaller segments resulting in a simple numeric matrix of small integers. This lossless representation is particularly suitable for machine learning (ML) models. As an example of the benefits of tiling, we showcase results from tiled data as part of the Artificial Intelligence for Alzheimer's Disease (AI4AD) consortium. AI4AD is a coordinated initiative to develop transformative AI approaches for high throughput analysis of next generation sequencing and related imaging, AD biomarker, and cognitive data. The collective effort integrates imaging, genomic, biomarker, and cognitive data to address fundamental barriers in AD prevention and drug discovery. One of the project's initial aims is to discover new genetic signatures in WGS data that can be used to understand AD risk and progression in conjunction with imaging, biomarker and cognitive data. We tiled and analyzed 15,000+ genomes from the Alzheimer's Disease Sequencing Project (ADSP) and the Alzheimer's Disease Neuroimaging Initiative (ADNI). We tile 11,762 genomes, a subset of the release which does not include family-based datasets (AD Cases: 4,983, age range: 50-90 years , mean age: 73.8 years). We illustrate the use of tiled data in ML classification methods to predict phenotypes. Specifically, we identify and prioritize tile variants/genetic variants that are possible genetic signatures for AD. The model shows added predictive value from variants of genes previously found to be associated with AD risk, age of onset, neurofibrillary tangle measurements, and other AD-related traits--including the APOE variant (rs429358).","PeriodicalId":501375,"journal":{"name":"medRxiv - Genetic and Genomic Medicine","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141948291","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 : 2024-08-03DOI: 10.1101/2024.08.01.24311343
Rosario Carmona, Javier Perez-Florido, Gema Roldan, Carlos Loucera, Virginia Aquino, Noemi Toro-Barrios, Jose L Fernandez-Rueda, Gerrit Bostelmann, Daniel Lopez-Lopez, Francisco M Ortuno, Beatriz Morte, CSVS Crowdsourcing Group, Maria Pena-Chilet, Joaquin Dopazo
The escalating adoption of Next Generation Sequencing (NGS) in clinical diagnostics reveals genetic variations, termed secondary findings (SFs), with health implications beyond primary diagnoses. The Collaborative Spanish Variant Server (CSVS), a crowdsourced database, contains genomic data from more than 2100 unrelated Spanish individuals. Following the American College of Medical genetics (ACMG) guidelines, CSVS was analyzed, identifying pathogenic or likely pathogenic variants in 78 actionable genes (ACMG list v3.1) to ascertain SF prevalence in the Spanish population. Among 1129 samples, 60 reportable SFs were found in 5% of individuals, impacting 32 ACMG-listed genes, notably associated with cardiovascular disease (59.4%), cancer (25%), inborn errors of metabolism (6.3%), and other miscellaneous phenotypes (9.4%). The study emphasizes utilizing dynamic population databases for periodic SF assessment, aligning with evolving ACMG recommendations. These findings illuminate the prevalence of significant genetic variants, enriching understanding of secondary findings in the Spanish population.
{"title":"Unveiling the Landscape of Reportable Genetic Secondary Findings in the Spanish Population: A Comprehensive Analysis Using the Collaborative Spanish Variant Server Database","authors":"Rosario Carmona, Javier Perez-Florido, Gema Roldan, Carlos Loucera, Virginia Aquino, Noemi Toro-Barrios, Jose L Fernandez-Rueda, Gerrit Bostelmann, Daniel Lopez-Lopez, Francisco M Ortuno, Beatriz Morte, CSVS Crowdsourcing Group, Maria Pena-Chilet, Joaquin Dopazo","doi":"10.1101/2024.08.01.24311343","DOIUrl":"https://doi.org/10.1101/2024.08.01.24311343","url":null,"abstract":"The escalating adoption of Next Generation Sequencing (NGS) in clinical diagnostics reveals genetic variations, termed secondary findings (SFs), with health implications beyond primary diagnoses. The Collaborative Spanish Variant Server (CSVS), a crowdsourced database, contains genomic data from more than 2100 unrelated Spanish individuals. Following the American College of Medical genetics (ACMG) guidelines, CSVS was analyzed, identifying pathogenic or likely pathogenic variants in 78 actionable genes (ACMG list v3.1) to ascertain SF prevalence in the Spanish population. Among 1129 samples, 60 reportable SFs were found in 5% of individuals, impacting 32 ACMG-listed genes, notably associated with cardiovascular disease (59.4%), cancer (25%), inborn errors of metabolism (6.3%), and other miscellaneous phenotypes (9.4%). The study emphasizes utilizing dynamic population databases for periodic SF assessment, aligning with evolving ACMG recommendations. These findings illuminate the prevalence of significant genetic variants, enriching understanding of secondary findings in the Spanish population.","PeriodicalId":501375,"journal":{"name":"medRxiv - Genetic and Genomic Medicine","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141948288","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 : 2024-08-02DOI: 10.1101/2024.08.01.24311295
Hannah Currant, Christopher Arthofer, Teresa Ferreira, Gwenaelle Douaud, Barney Hill, Samvida S Venkatesh, Nikolas A Baya, Duncan S Palmer, Saskia Reibe, Anje Moltke-Prehn, Tune H Pers, Andreas Bartsch, Jesper Andersson, Margaret F Lippincott, Yee-Ming Chan, Stephanie B Seminara, Thomas E Nichols, Christoffer Nellaker, Stephen M Smith, Soren Brunak, Frederik J Lange, Cecilia M Lindgren
The hypothalamus, pituitary gland and olfactory bulbs are neuro-anatomical structures key to the regulation of the endocrine system. Variation in their anatomy can affect the function of the reproductive system. To investigate this relationship, we extracted four largely unexplored phenotypes from 34,834 individuals within UK Biobank by quantifying the volume of the hypothalamus, pituitary gland and olfactory bulbs using multi-modal magnetic resonance imaging. Genome-wide association studies of these phenotypes identified 66 independent common genetic associations with endocrine-related neurological volumes (P < 5 × 10−8), five of which had a prior association to testosterone levels, representing enrichment of testosterone-associated SNPs over random chance (P-value = 9.89 × 10−12). Exome-wide rare variant burden analysis identified STAB1 as being significantly associated with hypothalamus volume (P = 3.78 × 10−7), with known associations to brain iron levels. Common variants associated with hypothalamic grey matter volume were also found to be associated with iron metabolism, in which testosterone plays a key role. These results provide initial evidence of common and rare genetic effects on both anatomical variation in neuroendocrine structures and their function in hormone production and regulation. Variants associated with pituitary gland volume were enriched for gene expression specific to theca cells, responsible for testosterone production in ovaries, suggesting shared underlying genetic variation affecting both neurological and gonadal endocrine tissues. Cell-type expression enrichment analysis across hypothalamic cell types identified tanycytes to be associated (P = 1.69 × 10−3) with olfactory bulb volume associated genetic variants, a cell type involved in release of gonadotropin-releasing hormone into the bloodstream. Voxel-wise analysis highlighted associations between the variants associated with pituitary gland volume and regions of the brain involved in the drainage of hormones into the bloodstream. Together, our results suggest a shared role of genetics impacting both the anatomy and function of neuroendocrine structures within the reproductive system in their production and release of reproductive hormones.
{"title":"Genome-wide analysis identifies 66 variants underlying anatomical variation in human neuroendocrine structures and reveals links to testosterone","authors":"Hannah Currant, Christopher Arthofer, Teresa Ferreira, Gwenaelle Douaud, Barney Hill, Samvida S Venkatesh, Nikolas A Baya, Duncan S Palmer, Saskia Reibe, Anje Moltke-Prehn, Tune H Pers, Andreas Bartsch, Jesper Andersson, Margaret F Lippincott, Yee-Ming Chan, Stephanie B Seminara, Thomas E Nichols, Christoffer Nellaker, Stephen M Smith, Soren Brunak, Frederik J Lange, Cecilia M Lindgren","doi":"10.1101/2024.08.01.24311295","DOIUrl":"https://doi.org/10.1101/2024.08.01.24311295","url":null,"abstract":"The hypothalamus, pituitary gland and olfactory bulbs are neuro-anatomical structures key to the regulation of the endocrine system. Variation in their anatomy can affect the function of the reproductive system. To investigate this relationship, we extracted four largely unexplored phenotypes from 34,834 individuals within UK Biobank by quantifying the volume of the hypothalamus, pituitary gland and olfactory bulbs using multi-modal magnetic resonance imaging. Genome-wide association studies of these phenotypes identified 66 independent common genetic associations with endocrine-related neurological volumes (<em>P</em> < 5 × 10<sup>−8</sup>), five of which had a prior association to testosterone levels, representing enrichment of testosterone-associated SNPs over random chance (<em>P</em>-value = 9.89 × 10<sup>−12</sup>). Exome-wide rare variant burden analysis identified <em>STAB1</em> as being significantly associated with hypothalamus volume (<em>P</em> = 3.78 × 10<sup>−7</sup>), with known associations to brain iron levels. Common variants associated with hypothalamic grey matter volume were also found to be associated with iron metabolism, in which testosterone plays a key role. These results provide initial evidence of common and rare genetic effects on both anatomical variation in neuroendocrine structures and their function in hormone production and regulation. Variants associated with pituitary gland volume were enriched for gene expression specific to theca cells, responsible for testosterone production in ovaries, suggesting shared underlying genetic variation affecting both neurological and gonadal endocrine tissues. Cell-type expression enrichment analysis across hypothalamic cell types identified tanycytes to be associated (<em>P</em> = 1.69 × 10<sup>−3</sup>) with olfactory bulb volume associated genetic variants, a cell type involved in release of gonadotropin-releasing hormone into the bloodstream. Voxel-wise analysis highlighted associations between the variants associated with pituitary gland volume and regions of the brain involved in the drainage of hormones into the bloodstream. Together, our results suggest a shared role of genetics impacting both the anatomy and function of neuroendocrine structures within the reproductive system in their production and release of reproductive hormones.","PeriodicalId":501375,"journal":{"name":"medRxiv - Genetic and Genomic Medicine","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141883820","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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