Pub Date : 2024-08-31DOI: 10.1101/2024.08.30.24312857
Sara A. Norton, Aaron J. Gorelik, Sarah E. Paul, Emma C. Johnson, David AA Baranger, Jayne L Siudzinski, Zhaolong Adrian Li, Erin Bondy, Hailey Modi, Nicole R. Karcher, Tamara Hershey, Alexander S. Hatoum, Arpana Agrawal, Ryan Bogdan
BACKGROUND C-reactive protein (CRP) is a moderately heritable marker of systemic inflammation that is associated with adverse physical and mental health outcomes. Identifying factors associated with genetic liability to elevated CRP in childhood may inform our understanding of variability in CRP that could be targeted to prevent and/or delay the onset of related health outcomes.
{"title":"A Phenome-Wide Association Study (PheWAS) of Genetic Risk for C-Reactive Protein in Children of European Ancestry: Results From the ABCD Study","authors":"Sara A. Norton, Aaron J. Gorelik, Sarah E. Paul, Emma C. Johnson, David AA Baranger, Jayne L Siudzinski, Zhaolong Adrian Li, Erin Bondy, Hailey Modi, Nicole R. Karcher, Tamara Hershey, Alexander S. Hatoum, Arpana Agrawal, Ryan Bogdan","doi":"10.1101/2024.08.30.24312857","DOIUrl":"https://doi.org/10.1101/2024.08.30.24312857","url":null,"abstract":"<strong>BACKGROUND</strong> C-reactive protein (CRP) is a moderately heritable marker of systemic inflammation that is associated with adverse physical and mental health outcomes. Identifying factors associated with genetic liability to elevated CRP in childhood may inform our understanding of variability in CRP that could be targeted to prevent and/or delay the onset of related health outcomes.","PeriodicalId":501375,"journal":{"name":"medRxiv - Genetic and Genomic Medicine","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142192169","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 : 2024-08-31DOI: 10.1101/2024.08.30.24312836
Anyi Yang, Xingzhong Zhao, Yucheng T. Yang, Xing-Ming Zhao
The integration of expression quantitative trait loci (eQTLs) and genome-wide association study (GWAS) findings to identify causal genes aids in elucidating the biological mechanisms and the discovery of potential drug targets underlying complex traits. This can be achieved by Mendelian randomization (MR), but to date, most MR studies investigating the contribution of genes to brain phenotypes have been conducted on heterogeneous brain tissues and not on specific cell types, thus limiting our knowledge at the cellular level. In this study, we employ a MR framework to infer cell type-specific causal relationships between gene expression and brain-associated complex traits, using eQTL data from eight cell types and large-scale GWASs of 123 imaging-derived phenotypes (IDPs) and 26 brain disorders and behaviors (DBs). Our analysis constructs a cell type-specific causal gene atlas for IDPs and DBs, which include 254 and 217 potential causal cell type-specific eQTL target genes (eGenes) for IDPs and DBs, respectively. The identified results exhibit high cell type specificity, with over 90% of gene-IDP and 80% of gene-DB associations being unique to a single cell type. We highlight shared cell type-specific patterns between IDPs and DBs, characterize the putative causal pathways among cell type-specific causal eGenes, DBs and IDPs, and reveal the spatiotemporal expression patterns of these cell type-specific causal eGenes. We also demonstrate that cell type-specific causal eGenes can characterize the associations between IDPs and DBs. In summary, our study provides novel insights into the genetic foundations at the cellular level that influence brain structures, disorders and behaviors, which reveals important implications for therapeutic targets and brain health management.
{"title":"Deciphering causal relationships between cell type-specific genetic factors and brain imaging-derived phenotypes and disorders","authors":"Anyi Yang, Xingzhong Zhao, Yucheng T. Yang, Xing-Ming Zhao","doi":"10.1101/2024.08.30.24312836","DOIUrl":"https://doi.org/10.1101/2024.08.30.24312836","url":null,"abstract":"The integration of expression quantitative trait loci (eQTLs) and genome-wide association study (GWAS) findings to identify causal genes aids in elucidating the biological mechanisms and the discovery of potential drug targets underlying complex traits. This can be achieved by Mendelian randomization (MR), but to date, most MR studies investigating the contribution of genes to brain phenotypes have been conducted on heterogeneous brain tissues and not on specific cell types, thus limiting our knowledge at the cellular level. In this study, we employ a MR framework to infer cell type-specific causal relationships between gene expression and brain-associated complex traits, using eQTL data from eight cell types and large-scale GWASs of 123 imaging-derived phenotypes (IDPs) and 26 brain disorders and behaviors (DBs). Our analysis constructs a cell type-specific causal gene atlas for IDPs and DBs, which include 254 and 217 potential causal cell type-specific eQTL target genes (eGenes) for IDPs and DBs, respectively. The identified results exhibit high cell type specificity, with over 90% of gene-IDP and 80% of gene-DB associations being unique to a single cell type. We highlight shared cell type-specific patterns between IDPs and DBs, characterize the putative causal pathways among cell type-specific causal eGenes, DBs and IDPs, and reveal the spatiotemporal expression patterns of these cell type-specific causal eGenes. We also demonstrate that cell type-specific causal eGenes can characterize the associations between IDPs and DBs. In summary, our study provides novel insights into the genetic foundations at the cellular level that influence brain structures, disorders and behaviors, which reveals important implications for therapeutic targets and brain health management.","PeriodicalId":501375,"journal":{"name":"medRxiv - Genetic and Genomic Medicine","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142192165","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 : 2024-08-31DOI: 10.1101/2024.08.31.24312873
Alagu Sankareswaran, Pooja Kunte, Diane P Fraser, Mobeen Shaik, Michael N Weedon, Richard A Oram, Chittaranjan S Yajnik, Giriraj R Chandak
Objectives Genetic Risk scores (GRS) classify diabetes types, type 1 (T1D) and type 2 (T2D) in Europeans but the power is limited in other ancestries. We explored the performance of T1DGRS and potential reasons for inferior discrimination ability in diabetes-type classification in Indians.
{"title":"Type 1 Diabetes Genetic Risk Score classifies diabetes subtypes in Indians: Impact of HLA diversity on the lower discriminative ability","authors":"Alagu Sankareswaran, Pooja Kunte, Diane P Fraser, Mobeen Shaik, Michael N Weedon, Richard A Oram, Chittaranjan S Yajnik, Giriraj R Chandak","doi":"10.1101/2024.08.31.24312873","DOIUrl":"https://doi.org/10.1101/2024.08.31.24312873","url":null,"abstract":"<strong>Objectives</strong> Genetic Risk scores (GRS) classify diabetes types, type 1 (T1D) and type 2 (T2D) in Europeans but the power is limited in other ancestries. We explored the performance of T1DGRS and potential reasons for inferior discrimination ability in diabetes-type classification in Indians.","PeriodicalId":501375,"journal":{"name":"medRxiv - Genetic and Genomic Medicine","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142192164","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}
Introduction AD is the most complex disorder leading to dementia worldwide. Despite the disease burden among Indians the mutation spectrum in our subcontinent is not well examined.
{"title":"Exome sequencing identifies ABCA7 as an important gene for familial AD cases from Eastern India","authors":"Dipanwita Sadhukhan, Adreesh Mukherjee, Bidisha Bhattacharyya, Smriti Mishra, Tapas Kumar Banerjee, Gautam Das, Uma Sinharoy, Subhra Prakash Hui, Soma Gupta, Atanu Biswas, Arindam Biswas","doi":"10.1101/2024.08.30.24312765","DOIUrl":"https://doi.org/10.1101/2024.08.30.24312765","url":null,"abstract":"<strong>Introduction</strong> AD is the most complex disorder leading to dementia worldwide. Despite the disease burden among Indians the mutation spectrum in our subcontinent is not well examined.","PeriodicalId":501375,"journal":{"name":"medRxiv - Genetic and Genomic Medicine","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142224773","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 : 2024-08-31DOI: 10.1101/2024.08.30.24312868
Tianyuan Lu, Wenmin Zhang, Cassianne Robinson-Cohen, Corinne D. Engelman, Qiongshi Lu, Ian H. de Boer, Lei Sun, Andrew D. Paterson
Background Understanding gene-environment interaction effects influencing vitamin D status may refine nutrition and public health strategies for vitamin D deficiency. Recent methodological advances have enabled the identification of variance quantitative trait loci (vQTLs) where gene-environment interaction effects are enriched.
背景 了解影响维生素 D 状态的基因-环境互作效应可完善针对维生素 D 缺乏症的营养和公共卫生策略。近年来方法学的进步使得基因与环境互作效应富集的变异数量性状位点（vQTLs）的鉴定成为可能。
{"title":"Improved characterization of gene-environment interactions for vitamin D through variance quantitative trait loci","authors":"Tianyuan Lu, Wenmin Zhang, Cassianne Robinson-Cohen, Corinne D. Engelman, Qiongshi Lu, Ian H. de Boer, Lei Sun, Andrew D. Paterson","doi":"10.1101/2024.08.30.24312868","DOIUrl":"https://doi.org/10.1101/2024.08.30.24312868","url":null,"abstract":"<strong>Background</strong> Understanding gene-environment interaction effects influencing vitamin D status may refine nutrition and public health strategies for vitamin D deficiency. Recent methodological advances have enabled the identification of variance quantitative trait loci (vQTLs) where gene-environment interaction effects are enriched.","PeriodicalId":501375,"journal":{"name":"medRxiv - Genetic and Genomic Medicine","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142192166","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 : 2024-08-30DOI: 10.1101/2024.08.28.24312668
Gerardo Fabian-Morales, Vianey Ordoñez-Labastida, William J. Rowell, Christine Lambert, Cairbre Fanslow, Alexander Robertson, Juan C. Zenteno
Background Inherited Retinal Dystrophies (IRDs) are visually disabling monogenic diseases with remarkable genetic and phenotypic heterogeneity. Mutations in more than 300 different genes have been identified as disease causing. Genetic diagnosis of IRDs has been greatly improved thanks to the incorporation of Next Generation Sequencing (NGS) approaches. However, the current IRD molecular diagnosis yield using NGS is approximately 60% and negative cases can be explained by variants that are not usually identified by the widely used short reads-NGS such as structural variants (SVs) or by variants located in uncovered, low complexity, repetitive, highly homologous, or GC-rich regions. Long-read genome sequencing (LR-GS) is an emerging technology that produces 10-20 kb reads and is expected to overcome short-read sequencing limitations in the clinical context, thus improving the diagnostic yield in heterogeneous diseases as IRDs.
{"title":"Resolving the diagnostic odyssey in inherited retinal dystrophies through long-read genome sequencing","authors":"Gerardo Fabian-Morales, Vianey Ordoñez-Labastida, William J. Rowell, Christine Lambert, Cairbre Fanslow, Alexander Robertson, Juan C. Zenteno","doi":"10.1101/2024.08.28.24312668","DOIUrl":"https://doi.org/10.1101/2024.08.28.24312668","url":null,"abstract":"<strong>Background</strong> Inherited Retinal Dystrophies (IRDs) are visually disabling monogenic diseases with remarkable genetic and phenotypic heterogeneity. Mutations in more than 300 different genes have been identified as disease causing. Genetic diagnosis of IRDs has been greatly improved thanks to the incorporation of Next Generation Sequencing (NGS) approaches. However, the current IRD molecular diagnosis yield using NGS is approximately 60% and negative cases can be explained by variants that are not usually identified by the widely used short reads-NGS such as structural variants (SVs) or by variants located in uncovered, low complexity, repetitive, highly homologous, or GC-rich regions. Long-read genome sequencing (LR-GS) is an emerging technology that produces 10-20 kb reads and is expected to overcome short-read sequencing limitations in the clinical context, thus improving the diagnostic yield in heterogeneous diseases as IRDs.","PeriodicalId":501375,"journal":{"name":"medRxiv - Genetic and Genomic Medicine","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142192170","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 : 2024-08-30DOI: 10.1101/2024.08.29.24312805
Samuel Mathieu, Louis-Hippolyte Minvielle Moncla, Mewen Briend, Valentine Duclos, Anne Rufiange, Yohan Bossé, Patrick Mathieu
Summary The ever-growing genetic cohorts lead to an increase in scale of molecular Quantitative Trait Loci (QTL) studies, creating opportunities for more extensive two samples Mendelian randomization (MR) investigations aiming to identify causal relationships between molecular traits and diseases. This increase led to the identification of multiple causal candidates and potential drug targets over time. However, the increase in scale of such studies and higher dimension multi-omic data come with computational challenges. We present “LArge SCAle MOLecular Mendelian Randomization with Julia” (LaScaMolMR.jl), an open-sourced integrated Julia package optimized for Omic-wide Mendelian Randomization (OWMR) Studies. This versatile package eliminates the two-language problem and implements fast algorithms for instrumental variable selection approaches with both cis and trans instruments and performs the most popular regression estimators for MR studies with molecular exposures. It reduces the compute time via meta-programming allowing easy deployment of multi-threaded approach and the internalization of linkage disequilibrium investigation of potential instrumental variables. Via its integrated approach and high-computational performance, LaScaMolMR.jl allows users who have minimal programming experience to perform large scale OWMR studies.
摘要 遗传队列的不断扩大导致分子数量性状位点（QTL）研究规模的扩大，为旨在确定分子性状与疾病之间因果关系的更广泛的双样本孟德尔随机化（MR）研究创造了机会。随着时间的推移，这种增加导致了多种因果关系候选者和潜在药物靶点的确定。然而，随着此类研究规模的扩大和多原子数据维度的提高，计算方面也面临着挑战。我们提出了 "LArge SCAle MOLecular Mendelian Randomization with Julia"（LaScaMolMR.jl），这是一个开源的集成 Julia 软件包，专为全基因组孟德尔随机化（OWMR）研究而优化。这个多功能软件包消除了双语言问题，实现了顺式和反式工具变量选择方法的快速算法，并为分子暴露的 MR 研究执行了最常用的回归估计器。它通过元编程减少了计算时间，从而可以轻松部署多线程方法，并将潜在工具变量的联系不平衡调查内部化。LaScaMolMR.jl 通过其集成方法和高计算性能，可让只有极少编程经验的用户进行大规模 OWMR 研究。
{"title":"Efficient molecular mendelian randomization screens with LaScaMolMR.jl","authors":"Samuel Mathieu, Louis-Hippolyte Minvielle Moncla, Mewen Briend, Valentine Duclos, Anne Rufiange, Yohan Bossé, Patrick Mathieu","doi":"10.1101/2024.08.29.24312805","DOIUrl":"https://doi.org/10.1101/2024.08.29.24312805","url":null,"abstract":"<strong>Summary</strong> The ever-growing genetic cohorts lead to an increase in scale of molecular Quantitative Trait Loci (QTL) studies, creating opportunities for more extensive two samples Mendelian randomization (MR) investigations aiming to identify causal relationships between molecular traits and diseases. This increase led to the identification of multiple causal candidates and potential drug targets over time. However, the increase in scale of such studies and higher dimension multi-omic data come with computational challenges. We present “LArge SCAle MOLecular Mendelian Randomization with Julia” (LaScaMolMR.jl), an open-sourced integrated Julia package optimized for Omic-wide Mendelian Randomization (OWMR) Studies. This versatile package eliminates the two-language problem and implements fast algorithms for instrumental variable selection approaches with both cis and trans instruments and performs the most popular regression estimators for MR studies with molecular exposures. It reduces the compute time via meta-programming allowing easy deployment of multi-threaded approach and the internalization of linkage disequilibrium investigation of potential instrumental variables. Via its integrated approach and high-computational performance, LaScaMolMR.jl allows users who have minimal programming experience to perform large scale OWMR studies.","PeriodicalId":501375,"journal":{"name":"medRxiv - Genetic and Genomic Medicine","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142192178","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}
cfDNA consists of degraded DNA fragments released into body fluids. Its genetic and pathological information makes it useful for prenatal testing and early tumor detection. However, the mechanisms behind cfDNA biology are largely unknown. In this study, for the first time, we conducted a GWAS study to explore the genetic basis of cfDNA features, termed cfGWAS, in 28,016 pregnant women. We identified 84 significant loci, including well-known cfDNA-related genes DFFB and DNASE1L3, and numerous novel genes potentially involved in cfDNA biology, including PANX1 and DNASE1L1. The findings were further verified through independent GWAS and experimental validation in knockout mice and cell lines. Subsequent analyses revealed strong causal relationships of hematological indicators on cfDNA features. In summary, we presented the first cfGWAS, revealing the genetic basis of cfDNA biology from genome-wide scale. Novel knowledge uncovered by this study keep the promise to revolutionize liquid biopsy technology and potential new drug targeted for certain disease. Given exist of the millions cfDNA whole-genome-sequencing data generated from clinical testing, the potential of this paradigm is enormous.
{"title":"cfGWAS reveal genetic basis of cell-free DNA features","authors":"Huanhuan Zhu, Yan Zhang, Shuang Zeng, Linxuan Li, Rijing Ou, Xinyi Zhang, Yu Lin, Ying Lin, Chuang Xu, Lin Wang, Guodan Zeng, Jingyu Zeng, Lingguo Li, Yongjian Jia, Yu Wang, Fei Luo, Meng Yang, Yuxuan Hu, Xiameizi Li, Han Xiao, Xun Xu, Jian Wang, Aifen Zhou, Haiqiang Zhang, Xin Jin","doi":"10.1101/2024.08.28.24312755","DOIUrl":"https://doi.org/10.1101/2024.08.28.24312755","url":null,"abstract":"cfDNA consists of degraded DNA fragments released into body fluids. Its genetic and pathological information makes it useful for prenatal testing and early tumor detection. However, the mechanisms behind cfDNA biology are largely unknown. In this study, for the first time, we conducted a GWAS study to explore the genetic basis of cfDNA features, termed cfGWAS, in 28,016 pregnant women. We identified 84 significant loci, including well-known cfDNA-related genes DFFB and DNASE1L3, and numerous novel genes potentially involved in cfDNA biology, including PANX1 and DNASE1L1. The findings were further verified through independent GWAS and experimental validation in knockout mice and cell lines. Subsequent analyses revealed strong causal relationships of hematological indicators on cfDNA features. In summary, we presented the first cfGWAS, revealing the genetic basis of cfDNA biology from genome-wide scale. Novel knowledge uncovered by this study keep the promise to revolutionize liquid biopsy technology and potential new drug targeted for certain disease. Given exist of the millions cfDNA whole-genome-sequencing data generated from clinical testing, the potential of this paradigm is enormous.","PeriodicalId":501375,"journal":{"name":"medRxiv - Genetic and Genomic Medicine","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142192176","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}
Founder events influence recessive diseases in highly endogamous populations. Several Indian populations have experienced significant founder events and maintained strict endogamy. Genomic studies in Indian populations often lack in addressing clinical implications of these phenomena. We performed whole-exome sequencing of 281 individuals from four South Indian groups to evaluate population-specific disease causing mutations associated with founder events. Our study revealed a high inbreeding rate of 59% across the groups. We identified ∼29.2% of the variants to be exclusive to a single population and uncovered 1,284 novel exonic variants, underscoring the genetic underrepresentation of Indian populations. Among these, 23 predicted as deleterious were found in heterozygous state, suggesting they may be pathogenic in a homozygous state and are common in the endogamous groups. Approximately 40-68% of the identified pathogenic variants showed significantly higher occurrence rates. Pharmacogenomic analysis revealed distinct allele frequencies in CYP450 and non-CYP450 gene variants, highlighting heterogeneous drug responses and associated risks. We report a high prevalence of ankylosing spondylitis in Reddys, linked to HLA-B*27:04 allele and strong founder effect. Our findings emphasize the need for expanded genomic research in understudied Indian populations to elucidate disease risk and medical profiles, eventually aiming towards precision medicine and mitigating disease burden.
{"title":"Endogamy and high prevalence of deleterious mutations in India: evidence from strong founder events","authors":"Pratheusa Machha, Amirtha Gopalan, Yamini Elangovan, Sarath Chandra Mouli Veeravalli, Divya Tej Sowpati, Kumarasamy Thangaraj","doi":"10.1101/2024.08.21.24312342","DOIUrl":"https://doi.org/10.1101/2024.08.21.24312342","url":null,"abstract":"Founder events influence recessive diseases in highly endogamous populations. Several Indian populations have experienced significant founder events and maintained strict endogamy. Genomic studies in Indian populations often lack in addressing clinical implications of these phenomena. We performed whole-exome sequencing of 281 individuals from four South Indian groups to evaluate population-specific disease causing mutations associated with founder events. Our study revealed a high inbreeding rate of 59% across the groups. We identified ∼29.2% of the variants to be exclusive to a single population and uncovered 1,284 novel exonic variants, underscoring the genetic underrepresentation of Indian populations. Among these, 23 predicted as deleterious were found in heterozygous state, suggesting they may be pathogenic in a homozygous state and are common in the endogamous groups. Approximately 40-68% of the identified pathogenic variants showed significantly higher occurrence rates. Pharmacogenomic analysis revealed distinct allele frequencies in CYP450 and non-CYP450 gene variants, highlighting heterogeneous drug responses and associated risks. We report a high prevalence of ankylosing spondylitis in Reddys, linked to <em>HLA-B*27:04</em> allele and strong founder effect. Our findings emphasize the need for expanded genomic research in understudied Indian populations to elucidate disease risk and medical profiles, eventually aiming towards precision medicine and mitigating disease burden.","PeriodicalId":501375,"journal":{"name":"medRxiv - Genetic and Genomic Medicine","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142192173","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 : 2024-08-29DOI: 10.1101/2024.08.29.24312459
Satu Strausz, Martin Broberg, Samuel E Jones, Jukka Koskela, Tuomo Kiiskinen, FinnGen, Aarno Palotie, Tuula Palotie, Adel Bachour, Richa Saxena, Samuli Ripatti, Erik Abner, Hanna M. Ollila
Background Sleep apnea is a common sleep disorder affecting at least ten percent of the population. It is caused by lack of breathing during sleep, typically mediated by obstruction of airways or less frequently by misdirected central signals for breathing. The primary risk factor is a high body mass index (BMI), causing airway obstruction. However, understanding risk factors for sleep apnea in non-obese (BMI < 30) individuals requires further exploration.
{"title":"Regulatory variation at serotonin receptor 1F (HTR1F) modulates arousals and risk for sleep apnea","authors":"Satu Strausz, Martin Broberg, Samuel E Jones, Jukka Koskela, Tuomo Kiiskinen, FinnGen, Aarno Palotie, Tuula Palotie, Adel Bachour, Richa Saxena, Samuli Ripatti, Erik Abner, Hanna M. Ollila","doi":"10.1101/2024.08.29.24312459","DOIUrl":"https://doi.org/10.1101/2024.08.29.24312459","url":null,"abstract":"<strong>Background</strong> Sleep apnea is a common sleep disorder affecting at least ten percent of the population. It is caused by lack of breathing during sleep, typically mediated by obstruction of airways or less frequently by misdirected central signals for breathing. The primary risk factor is a high body mass index (BMI), causing airway obstruction. However, understanding risk factors for sleep apnea in non-obese (BMI < 30) individuals requires further exploration.","PeriodicalId":501375,"journal":{"name":"medRxiv - Genetic and Genomic Medicine","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142192175","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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