Pub Date : 2024-09-14DOI: 10.1101/2024.09.13.24313582
Aravind Lathika Rajendrakumar, Konstantin Arbeev, Olivia Bagley, Anatoliy I Yashin, Svetlana Ukraintseva
Introduction We investigated the interplay between infections and APOE4 on brain glucose hypometabolism, an early preclinical feature of Alzheimer's Disease (AD) pathology. Methods Multivariate linear regression analysis was performed on 1,509 participants of the Alzheimer's Disease Neuroimaging Initiative (ADNI). The outcomes were the rank-normalized hypometabolic convergence index (HCI) and statistical regions of interest (SROI) for AD and mild cognitive impairment (MCI). Further, the HCI and its change in the presence and absence of APOE4 were evaluated. Results Infections were associated with greater hypometabolism [0.15, 95% CI: 0.03, 0.27, p=0.01], with a more pronounced effect among APOE4 carriers, indicating an interaction effect. A higher HCI (0.44, p=0.01) was observed in APOE4 carriers with multiple infections, compared to (0.11, p=0.08) for those with a single infection, revealing a dose-response relationship. The corresponding estimates for the association of infections with SROI AD and SROI MCI were -0.01 (p=0.02) and -0.01 (p=0.04) respectively. Conclusion Our findings suggest that infections and APOE4 jointly contribute to brain glucose hypometabolism and AD pathology, supporting a "multi-hit" mechanism in AD development.
{"title":"APOE4 and Infectious Diseases Jointly Contribute to Brain Glucose Hypometabolism, a Biomarker of Alzheimers Pathology: New findings from the ADNI","authors":"Aravind Lathika Rajendrakumar, Konstantin Arbeev, Olivia Bagley, Anatoliy I Yashin, Svetlana Ukraintseva","doi":"10.1101/2024.09.13.24313582","DOIUrl":"https://doi.org/10.1101/2024.09.13.24313582","url":null,"abstract":"Introduction\u0000We investigated the interplay between infections and APOE4 on brain glucose hypometabolism, an early preclinical feature of Alzheimer's Disease (AD) pathology.\u0000Methods\u0000Multivariate linear regression analysis was performed on 1,509 participants of the Alzheimer's Disease Neuroimaging Initiative (ADNI). The outcomes were the rank-normalized hypometabolic convergence index (HCI) and statistical regions of interest (SROI) for AD and mild cognitive impairment (MCI). Further, the HCI and its change in the presence and absence of APOE4 were evaluated.\u0000Results\u0000Infections were associated with greater hypometabolism [0.15, 95% CI: 0.03, 0.27, p=0.01], with a more pronounced effect among APOE4 carriers, indicating an interaction effect. A higher HCI (0.44, p=0.01) was observed in APOE4 carriers with multiple infections, compared to (0.11, p=0.08) for those with a single infection, revealing a dose-response relationship. The corresponding estimates for the association of infections with SROI AD and SROI MCI were -0.01 (p=0.02) and -0.01 (p=0.04) respectively.\u0000Conclusion\u0000Our findings suggest that infections and APOE4 jointly contribute to brain glucose hypometabolism and AD pathology, supporting a \"multi-hit\" mechanism in AD development.","PeriodicalId":501375,"journal":{"name":"medRxiv - Genetic and Genomic Medicine","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142257021","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-13DOI: 10.1101/2024.09.12.24313579
Eric J Barnett, Yanli Zhang-James, Jonathan Hess, Stephen J Glatt, Stephen V Faraone
Despite high heritability estimates, complex genetic disorders have proven difficult to predict with genetic data. Genomic research has documented polygenic inheritance, cross-disorder genetic correlations, and enrichment of risk by functional genomic annotation, but the vast potential of that combined knowledge has not yet been leveraged to build optimal risk models. Additional methods are likely required to progress genetic risk models of complex genetic disorders towards clinical utility. We developed a framework that uses annotations providing genomic context alongside genotype data as input to convolutional neural networks to predict disorder risk. We validated models in a matched-pairs type 2 diabetes dataset. A neural network using genotype data (AUC: 0.66) and a convolutional neural network using context-informed genotype data (AUC: 0.65) both significantly outperformed polygenic risk score approaches in classifying type-2 diabetes. Adversarial ancestry tasks eliminated the predictability of ancestry without changing model performance.
{"title":"Using Genomic Context Informed Genotype Data and Within‐model Ancestry Adjustment to Classify Type 2 Diabetes","authors":"Eric J Barnett, Yanli Zhang-James, Jonathan Hess, Stephen J Glatt, Stephen V Faraone","doi":"10.1101/2024.09.12.24313579","DOIUrl":"https://doi.org/10.1101/2024.09.12.24313579","url":null,"abstract":"Despite high heritability estimates, complex genetic disorders have proven difficult to predict with genetic data. Genomic research has documented polygenic inheritance, cross-disorder genetic correlations, and enrichment of risk by functional genomic annotation, but the vast potential of that combined knowledge has not yet been leveraged to build optimal risk models. Additional methods are likely required to progress genetic risk models of complex genetic disorders towards clinical utility. We developed a framework that uses annotations providing genomic context alongside genotype data as input to convolutional neural networks to predict disorder risk. We validated models in a matched-pairs type 2 diabetes dataset. A neural network using genotype data (AUC: 0.66) and a convolutional neural network using context-informed genotype data (AUC: 0.65) both significantly outperformed polygenic risk score approaches in classifying type-2 diabetes. Adversarial ancestry tasks eliminated the predictability of ancestry without changing model performance.","PeriodicalId":501375,"journal":{"name":"medRxiv - Genetic and Genomic Medicine","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142257020","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-13DOI: 10.1101/2024.09.12.24313572
Kathryn E Werwath, Rebecca B Lawn, Madeleine T Salem, Tayden Li, Brittany L Mitchell, Hanyang Shen, Scott D Gordon, Benson Kung, Ciera Stafford, Mytilee Vemuri, Andrew Ratanatharathorn, Joeri Meijsen, Aladdin H Shadyab, Charles Kooperberg, Karestan C Koenen, Carolyn J Crandall, Nicholas G Martin, Laramie E Duncan
Background: Most women experience hot flashes (hot flushes) during the menopause transition. Menopausal hot flashes typically persist for years. For a sizeable minority of women, hot flashes are severe and substantially impairing. It is worthwhile to further investigate the genetic underpinnings of hot flashes. Method: We conducted the largest trans-ancestry genome-wide association study (GWAS) of hot flashes available to date (N=149,560). We used self-assessment of hot flashes in the Nurses' Health Study, Nurses' Health Study II, Women's Health Initiative, and Queensland Institute of Medical Research samples (total n=42,489). In one sample (UK Biobank, n=107,071) direct assessment of hot flashes was not available, so menopausal hormone therapy was used as a proxy variable. We estimated the heritability of hot flashes and genetic correlations with psychiatric phenotypes using linkage disequilibrium score regression (LDSR). Results: In component analyses and our trans-ancestry meta-analysis, the top locus was on chromosome 4 in the neurokinin 3 receptor gene (TACR3, position 104,556,732, trans-ancestry p=7.2x10-41). A second novel locus was identified (LINC02428, p=3.5x10-8). Gene results implicated TACR3, GRID1, NUDT4, and PHF21B. Using the hot flash GWAS meta-analysis (n=42,489; i.e., no proxy variable), SNP heritability was estimated: h2liab=.08 (h2SNP=.04, se=.02). Genetic correlations were statistically significant between hot flashes and posttraumatic stress disorder (PTSD, rg=0.25, p=0.01), schizophrenia (rg=0.17, p=0.02), and depression (rg=0.21, p=0.01). Discussion: These genomic findings are consistent with independent, robust basic science research which led to a novel treatment for hot flashes, namely, neurokinin 3 receptor antagonists. This new class of hot flash drugs blocks the receptor (neurokinin 3 receptor) coded for by the top locus for hot flashes (TACR3). This GWAS of hot flashes provides an uncommonly clear example of how GWAS findings can point to potent treatment targets for complex brain phenotypes. We also found that the proxy variable (menopausal hormone therapy) pointed to the same target (TACR3), and that exclusively intronic and intergenic variants signaled this target.
{"title":"Trans-Ancestry GWAS of Hot Flashes Reveals Potent Treatment Target and Overlap with Psychiatric Disorders","authors":"Kathryn E Werwath, Rebecca B Lawn, Madeleine T Salem, Tayden Li, Brittany L Mitchell, Hanyang Shen, Scott D Gordon, Benson Kung, Ciera Stafford, Mytilee Vemuri, Andrew Ratanatharathorn, Joeri Meijsen, Aladdin H Shadyab, Charles Kooperberg, Karestan C Koenen, Carolyn J Crandall, Nicholas G Martin, Laramie E Duncan","doi":"10.1101/2024.09.12.24313572","DOIUrl":"https://doi.org/10.1101/2024.09.12.24313572","url":null,"abstract":"Background: Most women experience hot flashes (hot flushes) during the menopause transition. Menopausal hot flashes typically persist for years. For a sizeable minority of women, hot flashes are severe and substantially impairing. It is worthwhile to further investigate the genetic underpinnings of hot flashes. Method: We conducted the largest trans-ancestry genome-wide association study (GWAS) of hot flashes available to date (N=149,560). We used self-assessment of hot flashes in the Nurses' Health Study, Nurses' Health Study II, Women's Health Initiative, and Queensland Institute of Medical Research samples (total n=42,489). In one sample (UK Biobank, n=107,071) direct assessment of hot flashes was not available, so menopausal hormone therapy was used as a proxy variable. We estimated the heritability of hot flashes and genetic correlations with psychiatric phenotypes using linkage disequilibrium score regression (LDSR). Results: In component analyses and our trans-ancestry meta-analysis, the top locus was on chromosome 4 in the neurokinin 3 receptor gene (TACR3, position 104,556,732, trans-ancestry p=7.2x10-41). A second novel locus was identified (LINC02428, p=3.5x10-8). Gene results implicated TACR3, GRID1, NUDT4, and PHF21B. Using the hot flash GWAS meta-analysis (n=42,489; i.e., no proxy variable), SNP heritability was estimated: h2liab=.08 (h2SNP=.04, se=.02). Genetic correlations were statistically significant between hot flashes and posttraumatic stress disorder (PTSD, rg=0.25, p=0.01), schizophrenia (rg=0.17, p=0.02), and depression (rg=0.21, p=0.01). Discussion: These genomic findings are consistent with independent, robust basic science research which led to a novel treatment for hot flashes, namely, neurokinin 3 receptor antagonists. This new class of hot flash drugs blocks the receptor (neurokinin 3 receptor) coded for by the top locus for hot flashes (TACR3). This GWAS of hot flashes provides an uncommonly clear example of how GWAS findings can point to potent treatment targets for complex brain phenotypes. We also found that the proxy variable (menopausal hormone therapy) pointed to the same target (TACR3), and that exclusively intronic and intergenic variants signaled this target.","PeriodicalId":501375,"journal":{"name":"medRxiv - Genetic and Genomic Medicine","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142257023","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-13DOI: 10.1101/2024.09.12.24313573
John Paliakkara, Stacy Ellenberg, Andrew Ursino, Abigail Smith, James Evans, Joseph Strayhorn, Stephen V Faraone, Yanli Zhang-James
Intermittent Explosive Disorder (IED) is characterized by repeated inability to control aggressive impulses. Although the etiology and neurobiology of impulsive anger and impulse control disorders have been reviewed, no systematic review on these aspects has been published for IED specifically. We conducted a systematic search in seven electronic databases for publications about IED, screened by two authors, and retained twenty-four studies for the review. Our findings highlight a multifactorial etiology and neurobiology of IED, emphasizing the role of the amygdala and orbitofrontal cortex in emotional regulation and impulse control, and supporting interventions that target serotonergic signaling. Research also shows that childhood trauma and adverse family environment may significantly contribute to the development of IED. Yet, genetic studies focusing on IED were largely lacking, despite many examining the genetics underlying aggression as a general trait or other related disorders. Future research using consistently defined IED as a phenotype is required to better understand the etiology and underlying mechanisms and assist in informing the development of more effective interventions for IED.
{"title":"A Systematic Review of the Etiology and Neurobiology of Intermittent Explosive Disorder","authors":"John Paliakkara, Stacy Ellenberg, Andrew Ursino, Abigail Smith, James Evans, Joseph Strayhorn, Stephen V Faraone, Yanli Zhang-James","doi":"10.1101/2024.09.12.24313573","DOIUrl":"https://doi.org/10.1101/2024.09.12.24313573","url":null,"abstract":"Intermittent Explosive Disorder (IED) is characterized by repeated inability to control aggressive impulses. Although the etiology and neurobiology of impulsive anger and impulse control disorders have been reviewed, no systematic review on these aspects has been published for IED specifically. We conducted a systematic search in seven electronic databases for publications about IED, screened by two authors, and retained twenty-four studies for the review. Our findings highlight a multifactorial etiology and neurobiology of IED, emphasizing the role of the amygdala and orbitofrontal cortex in emotional regulation and impulse control, and supporting interventions that target serotonergic signaling. Research also shows that childhood trauma and adverse family environment may significantly contribute to the development of IED. Yet, genetic studies focusing on IED were largely lacking, despite many examining the genetics underlying aggression as a general trait or other related disorders. Future research using consistently defined IED as a phenotype is required to better understand the etiology and underlying mechanisms and assist in informing the development of more effective interventions for IED.","PeriodicalId":501375,"journal":{"name":"medRxiv - Genetic and Genomic Medicine","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142257022","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-12DOI: 10.1101/2024.09.10.24313340
Anne E Justice, Melissa A Kelly, Gary Bellus, Joshua D Green, Raza Zaidi, Taylor Kerrins, Navya Josyula, Teresa Romeo Luperchio, Beth A Kozel, Marc S Williams
Variation in the elastin gene (ELN) may contribute to connective tissue disease beyond the known disease associations of Supravalvar Aortic Stenosis and Cutis Laxa. Exome data from MyCode Community Health Initiative participants were analyzed for ELN rare variants (mean allele frequency <1%, not currently annotated as benign). Participants with variants of interest underwent phenotyping by dual chart review using a standardized abstraction tool. Additionally, all rare variants that met inclusion criteria were collapsed intoan ELN gene burden score to perform a Phenome-wide Association Study (PheWAS). Two hundred and ninety-six eligible participants with relevant ELN variants were identified from 184,293 MyCode participants. One hundred and three of 254 living participants (41%) met phenotypic criteria, most commonly aortic hypoplasia, arterial dilation, aneurysm, and dissection, and connective tissue abnormalities. ELN variation was significantly (P <2.8x10-5) associated with "arterial dissection" in the PheWAS and two connective tissue Phecodes approached significance. Variation in ELN is associated with connective tissue pathology beyond classic phenotypes.
{"title":"Phenotypic Findings Associated with Variation in Elastin","authors":"Anne E Justice, Melissa A Kelly, Gary Bellus, Joshua D Green, Raza Zaidi, Taylor Kerrins, Navya Josyula, Teresa Romeo Luperchio, Beth A Kozel, Marc S Williams","doi":"10.1101/2024.09.10.24313340","DOIUrl":"https://doi.org/10.1101/2024.09.10.24313340","url":null,"abstract":"Variation in the elastin gene (ELN) may contribute to connective tissue disease beyond the known disease\u0000associations of Supravalvar Aortic Stenosis and Cutis Laxa. Exome data from MyCode Community Health\u0000Initiative participants were analyzed for ELN rare variants (mean allele frequency <1%, not currently\u0000annotated as benign). Participants with variants of interest underwent phenotyping by dual chart review using a standardized abstraction tool. Additionally, all rare variants that met inclusion criteria were collapsed intoan ELN gene burden score to perform a Phenome-wide Association Study (PheWAS). Two hundred and\u0000ninety-six eligible participants with relevant ELN variants were identified from 184,293 MyCode participants. One hundred and three of 254 living participants (41%) met phenotypic criteria, most commonly aortic hypoplasia, arterial dilation, aneurysm, and dissection, and connective tissue abnormalities. ELN variation was significantly (P <2.8x10-5) associated with \"arterial dissection\" in the PheWAS and two connective tissue Phecodes approached significance. Variation in ELN is associated with connective tissue pathology beyond classic phenotypes.","PeriodicalId":501375,"journal":{"name":"medRxiv - Genetic and Genomic Medicine","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142192112","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-12DOI: 10.1101/2024.09.11.24313458
Jakob German, Mattia Cordioli, Sarah Urbut, Veronica Tozzo, Kadri Arumae, Roelof A.J. Smit, Jiwoo Lee, Josephine Li, Adrian Janucik, Yi Ding, Akintunde Akinkuolie, Henrike Heyne, Andrea Eoli, Chadi Saad, Yasser Al-Sarraj, Rania Abdel-latif, Alexandra Barry, Zhe Wang, Estonian Biobank research team, Pradeep Natarajan, Samuli Ripatti, Anthony Philippakis, Bogdan Pasaniuc, Lukasz Szczerbinski, Adam Kretowski, Hamdi Mbarek, Ruth J.F. Loos, Uku Vainik, Andrea Ganna
Obesity is a significant public health concern. GLP-1 receptor agonists (GLP1-RA), predominantly in use as a type 2 diabetes treatment, are a promising pharmacological approach for weight loss, while bariatric surgery (BS) remains a durable, but invasive, intervention. Despite observed heterogeneity in weight loss effects, the genetic effects on weight loss from GLP1-RA and BS have not been extensively explored in large sample sizes, and most studies have focused on differences in race and ethnicity, rather than genetic ancestry. We studied whether genetic factors, previously shown to affect body weight, impact weight loss due to GLP1-RA therapy or BS in 10,960 individuals from 9 multi-ancestry biobank studies in 6 countries. The average weight change between 6 and 12 months from therapy initiation was -3.93% for GLP1-RA users, with marginal differences across genetic ancestries. For BS patients the weight change between 6 and 48 months from the operation was -21.17%. There were no significant associations between weight loss due to GLP1-RA and polygenic scores for BMI or type 2 diabetes or specific missense variants in the GLP1R, PCSK1 and APOE genes, after multiple-testing correction. However, a higher polygenic score for BMI was significantly linked to lower weight loss after BS (+0.7% for 1 standard deviation change in the polygenic score, P = 1.24x10-4). In contrast, higher weight at baseline was associated with greater weight loss. Our findings suggest that existing polygenic scores related to weight and type 2 diabetes and missense variants in the drug target gene do not have a large impact on GLP1-RA effectiveness. Our results also confirm the effectiveness of these treatments across all major continental ancestry groups considered.
{"title":"Association between plausible genetic factors and weight loss from GLP1-RA and bariatric surgery: a multi-ancestry study in 10 960 individuals from 9 biobanks","authors":"Jakob German, Mattia Cordioli, Sarah Urbut, Veronica Tozzo, Kadri Arumae, Roelof A.J. Smit, Jiwoo Lee, Josephine Li, Adrian Janucik, Yi Ding, Akintunde Akinkuolie, Henrike Heyne, Andrea Eoli, Chadi Saad, Yasser Al-Sarraj, Rania Abdel-latif, Alexandra Barry, Zhe Wang, Estonian Biobank research team, Pradeep Natarajan, Samuli Ripatti, Anthony Philippakis, Bogdan Pasaniuc, Lukasz Szczerbinski, Adam Kretowski, Hamdi Mbarek, Ruth J.F. Loos, Uku Vainik, Andrea Ganna","doi":"10.1101/2024.09.11.24313458","DOIUrl":"https://doi.org/10.1101/2024.09.11.24313458","url":null,"abstract":"Obesity is a significant public health concern. GLP-1 receptor agonists (GLP1-RA), predominantly in use as a type 2 diabetes treatment, are a promising pharmacological approach for weight loss, while bariatric surgery (BS) remains a durable, but invasive, intervention. Despite observed heterogeneity in weight loss effects, the genetic effects on weight loss from GLP1-RA and BS have not been extensively explored in large sample sizes, and most studies have focused on differences in race and ethnicity, rather than genetic ancestry. We studied whether genetic factors, previously shown to affect body weight, impact weight loss due to GLP1-RA therapy or BS in 10,960 individuals from 9 multi-ancestry biobank studies in 6 countries. The average weight change between 6 and 12 months from therapy initiation was -3.93% for GLP1-RA users, with marginal differences across genetic ancestries. For BS patients the weight change between 6 and 48 months from the operation was -21.17%. There were no significant associations between weight loss due to GLP1-RA and polygenic scores for BMI or type 2 diabetes or specific missense variants in the GLP1R, PCSK1 and APOE genes, after multiple-testing correction. However, a higher polygenic score for BMI was significantly linked to lower weight loss after BS (+0.7% for 1 standard deviation change in the polygenic score, P = 1.24x10-4). In contrast, higher weight at baseline was associated with greater weight loss. Our findings suggest that existing polygenic scores related to weight and type 2 diabetes and missense variants in the drug target gene do not have a large impact on GLP1-RA effectiveness. Our results also confirm the effectiveness of these treatments across all major continental ancestry groups considered.","PeriodicalId":501375,"journal":{"name":"medRxiv - Genetic and Genomic Medicine","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142224754","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-12DOI: 10.1101/2024.09.11.24313439
Julian Daniel Sunday Willett, Mohammad Waqas, Younjung Choi, Tiffany Ngai, Kristina Mullin, Rudolph E Tanzi, Dmitry Prokopenko
Alzheimer's disease (AD) is the most prevalent form of dementia. While many AD-associated genetic determinants have been previously identified, few studies have analyzed individuals of non-European ancestry. Here, we describe a multi-ancestry genome-wide association study of clinically-diagnosed AD and AD-by-proxy using whole genome sequencing data from NIAGADS, NIMH, UKB, and All of Us (AoU) consisting of 49,149 cases (12,074 clinically-diagnosed and 37,075 AD-by-proxy) and 383,225 controls. Nearly half of NIAGADS and AoU participants are of non-European ancestry. For clinically-diagnosed AD , we identified 14 new loci - five common (FBN2/SCL27A6, AC090115.1, DYM, KCNG1/AL121785.1, TIAM1) and nine rare (VWA5B1, RNU6-755P/LMX1A, MOB1A, MORC1-AS1, LINC00989, PDE4D, RNU2-49P/CDO1, NEO1, and SLC35G3/AC022916.1). Meta-analysis of UKB and AoU AD-by-proxy cases yielded two new rare loci (RPL23/LASP1 and CEBPA/ AC008738.6) which were also nominally significant in NIAGADS. In summary, we provide evidence for 16 novel AD loci and advocate for more studies using WGS-based GWAS of diverse cohorts.
阿尔茨海默病（AD）是最普遍的痴呆症。虽然以前已经发现了许多与阿兹海默症相关的遗传决定因素，但很少有研究对非欧洲血统的个体进行分析。在此，我们利用来自 NIAGADS、NIMH、UKB 和 All of Us (AoU) 的全基因组测序数据，对 49149 例病例（12074 例临床诊断病例和 37075 例代理 AD 病例）和 383225 例对照进行了一项多血统全基因组关联研究。近一半的 NIAGADS 和 AoU 参与者为非欧洲血统。对于临床诊断的 AD，我们发现了 14 个新位点--5 个常见位点（FBN2/SCL27A6、AC090115.1、DYM、KCNG1/AL121785.1、TIAM1）和 9 个罕见位点（VWA5B1、RNU6-755P/LMX1A、MOB1A、MORC1-AS1、LINC00989、PDE4D、RNU2-49P/CDO1、NEO1 和 SLC35G3/AC022916.1）。对 UKB 和 AoU AD-by-proxy 病例进行的元分析发现了两个新的罕见基因位点（RPL23/LASP1 和 CEBPA/AC008738.6），这两个位点在 NIAGADS 中也具有名义意义。总之，我们为 16 个新的 AD 基因位点提供了证据，并提倡使用基于 WGS 的 GWAS 对不同队列进行更多研究。
{"title":"Identification of 16 novel Alzheimer's disease susceptibility loci using multi-ancestry meta-analyses of clinical Alzheimer's disease and AD-by-proxy cases from four whole genome sequencing datasets","authors":"Julian Daniel Sunday Willett, Mohammad Waqas, Younjung Choi, Tiffany Ngai, Kristina Mullin, Rudolph E Tanzi, Dmitry Prokopenko","doi":"10.1101/2024.09.11.24313439","DOIUrl":"https://doi.org/10.1101/2024.09.11.24313439","url":null,"abstract":"Alzheimer's disease (AD) is the most prevalent form of dementia. While many AD-associated genetic determinants have been previously identified, few studies have analyzed individuals of non-European ancestry. Here, we describe a multi-ancestry genome-wide association study of clinically-diagnosed AD and AD-by-proxy using whole genome sequencing data from NIAGADS, NIMH, UKB, and All of Us (AoU) consisting of 49,149 cases (12,074 clinically-diagnosed and 37,075 AD-by-proxy) and 383,225 controls. Nearly half of NIAGADS and AoU participants are of non-European ancestry. For clinically-diagnosed AD , we identified 14 new loci - five common (FBN2/SCL27A6, AC090115.1, DYM, KCNG1/AL121785.1, TIAM1) and nine rare (VWA5B1, RNU6-755P/LMX1A, MOB1A, MORC1-AS1, LINC00989, PDE4D, RNU2-49P/CDO1, NEO1, and SLC35G3/AC022916.1). Meta-analysis of UKB and AoU AD-by-proxy cases yielded two new rare loci (RPL23/LASP1 and CEBPA/ AC008738.6) which were also nominally significant in NIAGADS. In summary, we provide evidence for 16 novel AD loci and advocate for more studies using WGS-based GWAS of diverse cohorts.","PeriodicalId":501375,"journal":{"name":"medRxiv - Genetic and Genomic Medicine","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142192114","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}
Objective: Self reported data can be a valuable resource for understanding health outcomes, behaviors, disease prevalence, and risk factors, yet underutilized in epidemiological research. While observational studies have linked sleep traits with diabetes, evidence using self reported diabetes data for causal connection is lacking. Methods: We performed a two sample Mendelian randomization (MR) analysis using Inverse Variance Weighting (IVW), IVW with multiplicative random effects (IVW MRE), Maximum Likelihood (ML), MR-Egger regression, and Weighted Median models, with genetic variants linked to five sleep traits (sleep duration, insomnia, snoring, daytime dozing, and chronotype) and self reported diabetes from the UK Biobank dataset. The study utilized MR Egger and MR PRESSO regression to evaluate pleiotropy and outliers, IVW Q statistics to detect heterogeneity, the MR Steiger test to assess directionality, and leave one out sensitivity analysis to ensure the reliability. Results: ML provided positive causal associations between genetically predicted insomnia (p = 0.002, OR = 1.021, 95% CI: 1.008 to 1.035) and daytime dozing (p = 0.014, OR = 1.029, 95% CI: 1.006 to 1.052) with diabetes, while IVW and IVW-MRE analysis showed a trend towards significance. Snoring showed mixed evidence, while genetically predicted sleep duration was marginally associated with diabetes (p = 0.053, OR = 0.992, 95% CI: 0.984 to 1.000) with the weighted median method, indicating a potential small protective effect. No causal association was found between chronotype and diabetes. Conclusion: This exploratory MR study provides evidence for the effect of insomnia, daytime dozing, sleep duration and snoring on diabetes risk. These findings underscore the importance of considering self reported health outcomes in epidemiological research.
{"title":"Investigating the Causal Relationship Between Sleep-Related Traits and Self-Reported Diabetes: A Mendelian Randomization Study","authors":"Nismabi Adimaveettil Nisamudheen, Dinesh Velayutham, Puthen Veettil Jithesh","doi":"10.1101/2024.09.09.24313314","DOIUrl":"https://doi.org/10.1101/2024.09.09.24313314","url":null,"abstract":"Objective: Self reported data can be a valuable resource for understanding health outcomes, behaviors, disease prevalence, and risk factors, yet underutilized in epidemiological research. While observational studies have linked sleep traits with diabetes, evidence using self reported diabetes data for causal connection is lacking.\u0000Methods: We performed a two sample Mendelian randomization (MR) analysis using Inverse Variance Weighting (IVW), IVW with multiplicative random effects (IVW MRE), Maximum Likelihood (ML), MR-Egger regression, and Weighted Median models, with genetic variants linked to five sleep traits (sleep duration, insomnia, snoring, daytime dozing, and chronotype) and self reported diabetes from the UK Biobank dataset. The study utilized MR Egger and MR PRESSO regression to evaluate pleiotropy and outliers, IVW Q statistics to detect heterogeneity, the MR Steiger test to assess directionality, and leave one out sensitivity analysis to ensure the reliability.\u0000Results: ML provided positive causal associations between genetically predicted insomnia (p = 0.002, OR = 1.021, 95% CI: 1.008 to 1.035) and daytime dozing (p = 0.014, OR = 1.029, 95% CI: 1.006 to 1.052) with diabetes, while IVW and IVW-MRE analysis showed a trend towards significance. Snoring showed mixed evidence, while genetically predicted sleep duration was marginally associated with diabetes (p = 0.053, OR = 0.992, 95% CI: 0.984 to 1.000) with the weighted median method, indicating a potential small protective effect. No causal association was found between chronotype and diabetes.\u0000Conclusion: This exploratory MR study provides evidence for the effect of insomnia, daytime dozing, sleep duration and snoring on diabetes risk. These findings underscore the importance of considering self reported health outcomes in epidemiological research.","PeriodicalId":501375,"journal":{"name":"medRxiv - Genetic and Genomic Medicine","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142224772","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-10DOI: 10.1101/2024.09.09.24313018
Kenneth Westerman, Chirag J Patel, James B Meigs, Daniel I Chasman, Alisa K Manning
Discovery and translation of gene-environment interactions (GxEs) influencing clinical outcomes is limited by low statistical power and poor mechanistic understanding. Molecular omics data may help address these limitations, but their incorporation into GxE testing requires principled analytic approaches. We focused on genetic modification of the established mechanistic link between dietary long-chain omega-3 fatty acid (dN3FA) intake, plasma N3FA (pN3FA), and chronic inflammation as measured by high sensitivity CRP (hsCRP). We considered an approach that decomposes the overall genetic effect modification into components upstream and downstream of a molecular mediator to increase the potential to discover gene-N3FA interactions. Simulations demonstrated improved power of the upstream and downstream tests compared to the standard approach when the molecular mediator for many biologically plausible scenarios. The approach was applied in the UK Biobank (N = 188,700) with regression models that used measures of dN3FA (based on fish and fish oil intake), pN3FA (% of total fatty acids measured by nuclear magnetic resonance), and hsCRP. Mediation analysis showed that pN3FA fully mediated the dN3FA-hsCRP main effect relationship. Next, we separately tested modification of the dN3FA-hsCRP ("standard"), dN3FA-pN3FA ("upstream"), and pN3FA-hsCRP ("downstream") associations. The known FADS1-3 locus variant rs174535 reached p = 1.6x10-12 in the upstream discovery analysis, with no signal in the downstream analysis (p = 0.94). It would not have been prioritized based on a naive analysis with dN3FA exposure and hsCRP outcome (p = 0.097), indicating the value of the decomposition approach. Gene-level enrichment testing of the genome-wide results further prioritized two genes from the downstream analysis, CBLL1 and MICA, with links to immune cell counts and function. In summary, a molecular mediator-focused interaction testing approach enhanced statistical power to identify GxEs while homing in on relevant sub-components of the dN3FA-hsCRP pathway.
{"title":"Decomposed interaction testing improves detection of genetic modifiers of the relationship of dietary omega-3 fatty acid intake and its plasma biomarkers with hsCRP in the UK Biobank","authors":"Kenneth Westerman, Chirag J Patel, James B Meigs, Daniel I Chasman, Alisa K Manning","doi":"10.1101/2024.09.09.24313018","DOIUrl":"https://doi.org/10.1101/2024.09.09.24313018","url":null,"abstract":"Discovery and translation of gene-environment interactions (GxEs) influencing clinical outcomes is limited by low statistical power and poor mechanistic understanding. Molecular omics data may help address these limitations, but their incorporation into GxE testing requires principled analytic approaches. We focused on genetic modification of the established mechanistic link between dietary long-chain omega-3 fatty acid (dN3FA) intake, plasma N3FA (pN3FA), and chronic inflammation as measured by high sensitivity CRP (hsCRP). We considered an approach that decomposes the overall genetic effect modification into components upstream and downstream of a molecular mediator to increase the potential to discover gene-N3FA interactions. Simulations demonstrated improved power of the upstream and downstream tests compared to the standard approach when the molecular mediator for many biologically plausible scenarios. The approach was applied in the UK Biobank (N = 188,700) with regression models that used measures of dN3FA (based on fish and fish oil intake), pN3FA (% of total fatty acids measured by nuclear magnetic resonance), and hsCRP. Mediation analysis showed that pN3FA fully mediated the dN3FA-hsCRP main effect relationship. Next, we separately tested modification of the dN3FA-hsCRP (\"standard\"), dN3FA-pN3FA (\"upstream\"), and pN3FA-hsCRP (\"downstream\") associations. The known FADS1-3 locus variant rs174535 reached p = 1.6x10-12 in the upstream discovery analysis, with no signal in the downstream analysis (p = 0.94). It would not have been prioritized based on a naive analysis with dN3FA exposure and hsCRP outcome (p = 0.097), indicating the value of the decomposition approach. Gene-level enrichment testing of the genome-wide results further prioritized two genes from the downstream analysis, CBLL1 and MICA, with links to immune cell counts and function. In summary, a molecular mediator-focused interaction testing approach enhanced statistical power to identify GxEs while homing in on relevant sub-components of the dN3FA-hsCRP pathway.","PeriodicalId":501375,"journal":{"name":"medRxiv - Genetic and Genomic Medicine","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142224755","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-10DOI: 10.1101/2024.09.09.24312995
Christina Papangelou, Konstantinos Kyriakidis, Pantelis Natsiavas, Ioanna Chouvarda, Andigoni Malousi
Machine learning and genomic medicine are the mainstays of research in delivering personalized healthcare services for disease diagnosis, risk stratification, tailored treatment, and prediction of adverse effects. However, potential prediction errors in healthcare services can have life-threatening impact, raising reasonable skepticism about whether these applications are beneficial in real-world clinical practices. Conformal prediction is a versatile method that mitigates the risks of singleton predictions by estimating the uncertainty of a predictive model. In this study, we investigate potential applications of conformalized models in genomic medicine and discuss the challenges towards bridging genomic medicine applications with clinical practice. We also demonstrate the impact of a binary transductive model and a regression-based inductive model in predicting drug response and the performance of a multi-class inductive predictor in addressing distribution shifts in molecular subtyping. Additionally, we employed a regression-based inductive predictor to estimate the resistance of cancer cell lines to the anticancer drug afatinib. The main conclusion is that as machine learning and genomic medicine are increasingly infiltrating healthcare services, conformal prediction has the potential to overcome the safety limitations of current methods and could be effectively integrated into uncertainty-informed applications within clinical environments.
{"title":"Reliable machine learning models in genomic medicine using conformal prediction","authors":"Christina Papangelou, Konstantinos Kyriakidis, Pantelis Natsiavas, Ioanna Chouvarda, Andigoni Malousi","doi":"10.1101/2024.09.09.24312995","DOIUrl":"https://doi.org/10.1101/2024.09.09.24312995","url":null,"abstract":"Machine learning and genomic medicine are the mainstays of research in delivering personalized healthcare services for disease diagnosis, risk stratification, tailored treatment, and prediction of adverse effects. However, potential prediction errors in healthcare services can have life-threatening impact, raising reasonable skepticism about whether these applications are beneficial in real-world clinical practices. Conformal prediction is a versatile method that mitigates the risks of singleton predictions by estimating the uncertainty of a predictive model. In this study, we investigate potential applications of conformalized models in genomic medicine and discuss the challenges towards bridging genomic medicine applications with clinical practice. We also demonstrate the impact of a binary transductive model and a regression-based inductive model in predicting drug response and the performance of a multi-class inductive predictor in addressing distribution shifts in molecular subtyping. Additionally, we employed a regression-based inductive predictor to estimate the resistance of cancer cell lines to the anticancer drug afatinib. The main conclusion is that as machine learning and genomic medicine are increasingly infiltrating healthcare services, conformal prediction has the potential to overcome the safety limitations of current methods and could be effectively integrated into uncertainty-informed applications within clinical environments.","PeriodicalId":501375,"journal":{"name":"medRxiv - Genetic and Genomic Medicine","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142192115","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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