Differential and shared genetic effects on kidney function between diabetic and non-diabetic individuals

Funding Information: The Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) supported the meta-analysis—Project-ID 387509280—SFB1350 (Subproject C6 to I.M.H.). A.M.H., B.R., and R.T. were supported by VACSR&D MVP grant CX001897. This research is based on data from the Million Veteran Program, Office of Research and Development, Veterans Health Administration, and was supported by VACSR&D MVP grant CX001897 (A.M.H.). This publication does not represent the views of the Department of Veteran Affairs or the United States Government. We conducted this research using the UK Biobank resource under the application number 20272. We thank Paola Bilani for collecting author information. Extended acknowledgements are provided in Supplementary Note for all studies, in Supplementary Note for MVP and in Supplementary Note for LifeLines. Funding Information: GlaxoSmithKline and Merck & Co employed A.Y.C. Janssen Pharmaceuticals and GlaxoSmithKline employed D.M.W. K.B.S., L.M.Y.-A. and M.A.L. are full-time employees of GlaxoSmithKline. M.S. receives funding from Pfizer Inc. for a project not related to this research. J.Ä. reports personal fees from AstraZeneca, Boehringer Ingelheim and Novartis, outside of the submitted work. D.F.G., H.H., K.S., P.S., G.S. and U.T. are employees of deCODE/Amgen Inc. Kevin Ho received support by Fresenius Medical Care North America. M.K. is employed with Synlab Holding Deutschland GmbH. W.K. reports consulting fees from AstraZeneca, Novartis, Pfizer, The Medicines Company, DalCor, Kowa, Amgen, Corvidia, Daiichi-Sankyo, Genentech, Novo Nordisk, Esperion, OMEICOS, LIB Therapeutics, speaker honoraria from Amgen, AstraZeneca, Novartis, Berlin-Chemie, Sanofi, and Bristol-Myers Squibb, and grants and non-financial support from Abbott, Roche Diagnostics, Beckmann, and Singulex, outside the submitted work. C.L. received Grants/ Research Support from Bayer Ag/ Novo Nordisk, Husband works for Vertex. As of January 2020, A.M. is an employee of Genentech, and a holder of Roche stock. W.M. is employed with Synlab Holding Deutschland GmbH. D.O.M.-K. is a partime research physician at Metabolon, Inc. M.A.N. was supported by a consulting contract between Data Tecnica International LLC and the National Institute on Aging (NIA), National Institutes of Health (NIH), Bethesda, MD, USA and consults for a number of small biotech and pharma. M.L.O. received grant support from GlaxoSmithKline during conduct of the study and received support from Novartis, Merck, Amgen, and AstraZeneca. L.S.P. has served on Scientific Advisory Boards for Janssen, and has or had research support from Merck, Pfizer, Eli Lilly, Novo Nordisk, Sanofi, PhaseBio, Roche, Abbvie, Vascular Pharmaceuticals, Janssen, Glaxo SmithKline, and the Cystic Fibrosis Foundation. He is also a cofounder, Officer and Board member and stockholder for a company, Diasyst, Inc., which markets software aimed to help improve diabetes management. A.I.P. and D.F.R. are employees of Merck Sharp Dohme Corp. Bruce.M.P. serves on the steering committee of the Yale Open Data Access Project funded by Johnson & Johnson. P.R. received fees to his institution for research support from AstraZeneca and Novo Nordisk; for steering group participation from AstraZeneca, Gilead, Novo Nordisk, and Bayer; for lectures from Bayer, Eli Lilly and Novo Nordisk; and for advisory boards from Sanofi and Boehringer Ingelheim outside of this work. V.S. has received a modest honorarium from Sanofi for consulting. He also has ongoing research collaboration with Bayer Ltd. (all outside of the present study). L.W. received institutional grants from GlaxoSmithKline, AstraZeneca, BMS, Boehringer-Ingelheim, Pfizer, MSD and Roche Diagnostics. H.W. has received grant support paid to the institution and fees for serving on Steering Committees of the ODYSSEY trial from Sanofi and Regeneron Pharmaceuticals, the ISCHEMIA and the MINT studies from the National Institutes of Health, the STRENGTH trial from Omthera Pharmaceuticals, the HEART-FID study from American Regent, the DAL-GENE study from DalCor Pharma UK Inc., the AEGIS-II study from CSL Behring, the SCORED and SOLOIST-WHF from Sanofi Aventis Australia Pty. Ltd., and the CLEAR OUTCOMES study from Esperion Therapeutics. M.P. is partly funded by the study FinnGen ( www.finngen.fi ), which is jointly funded by a Finnish Governmental agency Business Finland and thirteen international pharmaceutical companies: Abbvie, AstraZeneca, Biogen, Boehringer Ingelheim, Bristol-Myers Squibb, Genentech, a member of the Roche Group, GlaxoSmithKline (GSK), Janssen, Maze Therapeutics, MSD (the tradename of Merck & Co., Inc, Kenilworth, NJ USA), Novartis, Pfizer and Sanofi. C.C.K. is an Editorial Board Member for Communications Biology, but was not involved in the editorial review of, nor the decision to publish this article. The remaining authors declare no competing interests. Funding Information: The Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) supported the meta-analysis—Project-ID 387509280—SFB1350 (Subproject C6 to I.M.H.). A.M.H., B.R., and R.T. were supported by VACSR&D MVP grant CX001897. This research is based on data from the Million Veteran Program, Office of Research and Development, Veterans Health Administration, and was supported by VACSR&D MVP grant CX001897 (A.M.H.). This publication does not represent the views of the Department of Veteran Affairs or the United States Government. We conducted this research using the UK Biobank resource under the application number 20272. We thank Paola Bilani for collecting author information. Extended acknowledgements are provided in Supplementary Note 4 for all studies, in Supplementary Note 5 for MVP and in Supplementary Note 6 for LifeLines. Publisher Copyright: © 2022, The Author(s).Reduced glomerular filtration rate (GFR) can progress to kidney failure. Risk factors include genetics and diabetes mellitus (DM), but little is known about their interaction. We conducted genome-wide association meta-analyses for estimated GFR based on serum creatinine (eGFR), separately for individuals with or without DM (nDM = 178,691, nnoDM = 1,296,113). Our genome-wide searches identified (i) seven eGFR loci with significant DM/noDM-difference, (ii) four additional novel loci with suggestive difference and (iii) 28 further novel loci (including CUBN) by allowing for potential difference. GWAS on eGFR among DM individuals identified 2 known and 27 potentially responsible loci for diabetic kidney disease. Gene prioritization highlighted 18 genes that may inform reno-protective drug development. We highlight the existence of DM-only and noDM-only effects, which can inform about the target group, if respective genes are advanced as drug targets. Largely shared effects suggest that most drug interventions to alter eGFR should be effective in DM and noDM.Peer reviewe

Genome-Wide Association Study of Smoking Trajectory and Meta-Analysis of Smoking Status in 842,000 Individuals

Here we report a large genome-wide association study (GWAS) for longitudinal smoking phenotypes in 286,118 individuals from the Million Veteran Program (MVP) where we identified 18 loci for smoking trajectory of current versus never in European Americans, one locus in African Americans, and one in Hispanic Americans. Functional annotations prioritized several dozen genes where significant loci co-localized with either expression quantitative trait loci or chromatin interactions. The smoking trajectories were genetically correlated with 209 complex traits, for 33 of which smoking was either a causal or a consequential factor. We also performed European-ancestry meta-analyses for smoking status in the MVP and GWAS & Sequencing Consortium of Alcohol and Nicotine use (GSCAN) (Ntotal = 842,717) and identified 99 loci for smoking initiation and 13 loci for smoking cessation. Overall, this large GWAS of longitudinal smoking phenotype in multiple populations, combined with a meta-GWAS for smoking status, adds new insights into the genetic vulnerability for smoking behavior

Stroke genetics informs drug discovery and risk prediction across ancestries

© 2022. The Author(s).Previous genome-wide association studies (GWASs) of stroke - the second leading cause of death worldwide - were conducted predominantly in populations of European ancestry1,2. Here, in cross-ancestry GWAS meta-analyses of 110,182 patients who have had a stroke (five ancestries, 33% non-European) and 1,503,898 control individuals, we identify association signals for stroke and its subtypes at 89 (61 new) independent loci: 60 in primary inverse-variance-weighted analyses and 29 in secondary meta-regression and multitrait analyses. On the basis of internal cross-ancestry validation and an independent follow-up in 89,084 additional cases of stroke (30% non-European) and 1,013,843 control individuals, 87% of the primary stroke risk loci and 60% of the secondary stroke risk loci were replicated (P < 0.05). Effect sizes were highly correlated across ancestries. Cross-ancestry fine-mapping, in silico mutagenesis analysis3, and transcriptome-wide and proteome-wide association analyses revealed putative causal genes (such as SH3PXD2A and FURIN) and variants (such as at GRK5 and NOS3). Using a three-pronged approach4, we provide genetic evidence for putative drug effects, highlighting F11, KLKB1, PROC, GP1BA, LAMC2 and VCAM1 as possible targets, with drugs already under investigation for stroke for F11 and PROC. A polygenic score integrating cross-ancestry and ancestry-specific stroke GWASs with vascular-risk factor GWASs (integrative polygenic scores) strongly predicted ischaemic stroke in populations of European, East Asian and African ancestry5. Stroke genetic risk scores were predictive of ischaemic stroke independent of clinical risk factors in 52,600 clinical-trial participants with cardiometabolic disease. Our results provide insights to inform biology, reveal potential drug targets and derive genetic risk prediction tools across ancestries.Peer reviewe

A saturated map of common genetic variants associated with human height

Common single-nucleotide polymorphisms (SNPs) are predicted to collectively explain 40–50% of phenotypic variation in human height, but identifying the specific variants and associated regions requires huge sample sizes1. Here, using data from a genome-wide association study of 5.4 million individuals of diverse ancestries, we show that 12,111 independent SNPs that are significantly associated with height account for nearly all of the common SNP-based heritability. These SNPs are clustered within 7,209 non-overlapping genomic segments with a mean size of around 90 kb, covering about 21% of the genome. The density of independent associations varies across the genome and the regions of increased density are enriched for biologically relevant genes. In out-of-sample estimation and prediction, the 12,111 SNPs (or all SNPs in the HapMap 3 panel2) account for 40% (45%) of phenotypic variance in populations of European ancestry but only around 10–20% (14–24%) in populations of other ancestries. Effect sizes, associated regions and gene prioritization are similar across ancestries, indicating that reduced prediction accuracy is likely to be explained by linkage disequilibrium and differences in allele frequency within associated regions. Finally, we show that the relevant biological pathways are detectable with smaller sample sizes than are needed to implicate causal genes and variants. Overall, this study provides a comprehensive map of specific genomic regions that contain the vast majority of common height-associated variants. Although this map is saturated for populations of European ancestry, further research is needed to achieve equivalent saturation in other ancestries.publishedVersionPeer reviewe

Dissecting the Shared Genetic Architecture of Suicide Attempt, Psychiatric Disorders, and Known Risk Factors

BACKGROUND: Suicide is a leading cause of death worldwide, and nonfatal suicide attempts, which occur far more frequently, are a major source of disability and social and economic burden. Both have substantial genetic etiology, which is partially shared and partially distinct from that of related psychiatric disorders. METHODS: We conducted a genome-wide association study (GWAS) of 29,782 suicide attempt (SA) cases and 519,961 controls in the International Suicide Genetics Consortium (ISGC). The GWAS of SA was conditioned on psychiatric disorders using GWAS summary statistics via multitrait-based conditional and joint analysis, to remove genetic effects on SA mediated by psychiatric disorders. We investigated the shared and divergent genetic architectures of SA, psychiatric disorders, and other known risk factors. RESULTS: Two loci reached genome-wide significance for SA: the major histocompatibility complex and an intergenic locus on chromosome 7, the latter of which remained associated with SA after conditioning on psychiatric disorders and replicated in an independent cohort from the Million Veteran Program. This locus has been implicated in risk-taking behavior, smoking, and insomnia. SA showed strong genetic correlation with psychiatric disorders, particularly major depression, and also with smoking, pain, risk-taking behavior, sleep disturbances, lower educational attainment, reproductive traits, lower socioeconomic status, and poorer general health. After conditioning on psychiatric disorders, the genetic correlations between SA and psychiatric disorders decreased, whereas those with nonpsychiatric traits remained largely unchanged. CONCLUSIONS: Our results identify a risk locus that contributes more strongly to SA than other phenotypes and suggest a shared underlying biology between SA and known risk factors that is not mediated by psychiatric disorders

Genetic drivers of heterogeneity in type 2 diabetes pathophysiology.

Type 2 diabetes (T2D) is a heterogeneous disease that develops through diverse pathophysiological processes1,2 and molecular mechanisms that are often specific to cell type3,4. Here, to characterize the genetic contribution to these processes across ancestry groups, we aggregate genome-wide association study data from 2,535,601 individuals (39.7% not of European ancestry), including 428,452 cases of T2D. We identify 1,289 independent association signals at genome-wide significance (P < 5 × 10-8) that map to 611 loci, of which 145 loci are, to our knowledge, previously unreported. We define eight non-overlapping clusters of T2D signals that are characterized by distinct profiles of cardiometabolic trait associations. These clusters are differentially enriched for cell-type-specific regions of open chromatin, including pancreatic islets, adipocytes, endothelial cells and enteroendocrine cells. We build cluster-specific partitioned polygenic scores5 in a further 279,552 individuals of diverse ancestry, including 30,288 cases of T2D, and test their association with T2D-related vascular outcomes. Cluster-specific partitioned polygenic scores are associated with coronary artery disease, peripheral artery disease and end-stage diabetic nephropathy across ancestry groups, highlighting the importance of obesity-related processes in the development of vascular outcomes. Our findings show the value of integrating multi-ancestry genome-wide association study data with single-cell epigenomics to disentangle the aetiological heterogeneity that drives the development and progression of T2D. This might offer a route to optimize global access to genetically informed diabetes care

A saturated map of common genetic variants associated with human height.

Common single-nucleotide polymorphisms (SNPs) are predicted to collectively explain 40-50% of phenotypic variation in human height, but identifying the specific variants and associated regions requires huge sample sizes1. Here, using data from a genome-wide association study of 5.4 million individuals of diverse ancestries, we show that 12,111 independent SNPs that are significantly associated with height account for nearly all of the common SNP-based heritability. These SNPs are clustered within 7,209 non-overlapping genomic segments with a mean size of around 90 kb, covering about 21% of the genome. The density of independent associations varies across the genome and the regions of increased density are enriched for biologically relevant genes. In out-of-sample estimation and prediction, the 12,111 SNPs (or all SNPs in the HapMap 3 panel2) account for 40% (45%) of phenotypic variance in populations of European ancestry but only around 10-20% (14-24%) in populations of other ancestries. Effect sizes, associated regions and gene prioritization are similar across ancestries, indicating that reduced prediction accuracy is likely to be explained by linkage disequilibrium and differences in allele frequency within associated regions. Finally, we show that the relevant biological pathways are detectable with smaller sample sizes than are needed to implicate causal genes and variants. Overall, this study provides a comprehensive map of specific genomic regions that contain the vast majority of common height-associated variants. Although this map is saturated for populations of European ancestry, further research is needed to achieve equivalent saturation in other ancestries

Genetic drivers of heterogeneity in type 2 diabetes pathophysiology

Type 2 diabetes (T2D) is a heterogeneous disease that develops through diverse pathophysiological processes1,2 and molecular mechanisms that are often specific to cell type3,4. Here, to characterize the genetic contribution to these processes across ancestry groups, we aggregate genome-wide association study data from 2,535,601 individuals (39.7% not of European ancestry), including 428,452 cases of T2D. We identify 1,289 independent association signals at genome-wide significance (P < 5 × 10-8) that map to 611 loci, of which 145 loci are, to our knowledge, previously unreported. We define eight non-overlapping clusters of T2D signals that are characterized by distinct profiles of cardiometabolic trait associations. These clusters are differentially enriched for cell-type-specific regions of open chromatin, including pancreatic islets, adipocytes, endothelial cells and enteroendocrine cells. We build cluster-specific partitioned polygenic scores5 in a further 279,552 individuals of diverse ancestry, including 30,288 cases of T2D, and test their association with T2D-related vascular outcomes. Cluster-specific partitioned polygenic scores are associated with coronary artery disease, peripheral artery disease and end-stage diabetic nephropathy across ancestry groups, highlighting the importance of obesity-related processes in the development of vascular outcomes. Our findings show the value of integrating multi-ancestry genome-wide association study data with single-cell epigenomics to disentangle the aetiological heterogeneity that drives the development and progression of T2D. This might offer a route to optimize global access to genetically informed diabetes care.Peer reviewe

Identifying intragenic functional modules of genomic variations associated with cancer phenotypes by learning representation of association networks.

BackgroundGenome-wide Association Studies (GWAS) aims to uncover the link between genomic variation and phenotype. They have been actively applied in cancer biology to investigate associations between variations and cancer phenotypes, such as susceptibility to certain types of cancer and predisposed responsiveness to specific treatments. Since GWAS primarily focuses on finding associations between individual genomic variations and cancer phenotypes, there are limitations in understanding the mechanisms by which cancer phenotypes are cooperatively affected by more than one genomic variation.ResultsThis paper proposes a network representation learning approach to learn associations among genomic variations using a prostate cancer cohort. The learned associations are encoded into representations that can be used to identify functional modules of genomic variations within genes associated with early- and late-onset prostate cancer. The proposed method was applied to a prostate cancer cohort provided by the Veterans Administration's Million Veteran Program to identify candidates for functional modules associated with early-onset prostate cancer. The cohort included 33,159 prostate cancer patients, 3181 early-onset patients, and 29,978 late-onset patients. The reproducibility of the proposed approach clearly showed that the proposed approach can improve the model performance in terms of robustness.ConclusionsTo our knowledge, this is the first attempt to use a network representation learning approach to learn associations among genomic variations within genes. Associations learned in this way can lead to an understanding of the underlying mechanisms of how genomic variations cooperatively affect each cancer phenotype. This method can reveal unknown knowledge in the field of cancer biology and can be utilized to design more advanced cancer-targeted therapies

Characterization of Million Veteran Program (MVP) enrollees with Comprehensive Traumatic Brain Injury Evaluation (CTBIE) data: An analysis of neurobehavioral symptoms

The purpose of this study was to examine neurobehavioral symptom reporting in a large sample of military veterans (N&nbsp;=&nbsp;12,144) who completed the Comprehensive Traumatic Brain Injury Evaluation (CTBIE) and enrolled in the VA's Million Veteran Program (MVP). The CTBIE is a clinician-administered interview that assesses for historical, deployment-related traumatic brain injury (TBI) and evaluates symptoms using the Neurobehavioral Symptom Inventory (NSI). Clinicians completing the CTBIE made clinical determinations about participants' (1) TBI diagnostic status (i.e., CTBIE+&nbsp;or CTBIE-) and (2) current symptom etiology (i.e., Symptom Resolution, TBI, Behavioral Health, Comorbid TBI&nbsp;+&nbsp;Behavioral Health [Comorbid], or Other). We evaluated the association of TBI diagnostic status and symptom etiology group with neurobehavioral symptoms. Results showed a significant association between TBI diagnostic status and all NSI variables, with CTBIE+&nbsp;veterans endorsing greater symptoms than CTBIE- veterans. There was also a significant association between symptom etiology group and all NSI variables; specifically, the Comorbid and Behavioral Health groups generally endorsed significantly greater symptoms compared to the other groups. Follow-up analyses showed that relative to the Symptom Resolution group, the Comorbid and Behavioral Health groups had increased odds of severe/very severe cognitive and affective symptoms, whereas the TBI and Other groups did not. Finally, presence of psychiatric symptoms, pain, post-traumatic amnesia, loss of consciousness, and blast exposure significantly predicted Comorbid symptom etiology group membership. Findings from this large epidemiologic MVP study have relevant clinical implications and further highlight the importance of prioritizing integrated behavioral health interventions for this vulnerable population