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

A large-scale GWAS provides insight on diabetes-dependent genetic effects on the glomerular filtration rate, a common metric to monitor kidney health in disease. 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 (n(DM) = 178,691, n(noDM) = 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

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

Multi-ancestry genome-wide association study accounting for gene-psychosocial factor interactions identifies novel loci for blood pressure traits

Publisher Copyright: © 2020 The Author(s)Psychological and social factors are known to influence blood pressure (BP) and risk of hypertension and associated cardiovascular diseases. To identify novel BP loci, we carried out genome-wide association meta-analyses of systolic, diastolic, pulse, and mean arterial BP, taking into account the interaction effects of genetic variants with three psychosocial factors: depressive symptoms, anxiety symptoms, and social support. Analyses were performed using a two-stage design in a sample of up to 128,894 adults from five ancestry groups. In the combined meta-analyses of stages 1 and 2, we identified 59 loci (p value < 5e−8), including nine novel BP loci. The novel associations were observed mostly with pulse pressure, with fewer observed with mean arterial pressure. Five novel loci were identified in African ancestry, and all but one showed patterns of interaction with at least one psychosocial factor. Functional annotation of the novel loci supports a major role for genes implicated in the immune response (PLCL2), synaptic function and neurotransmission (LIN7A and PFIA2), as well as genes previously implicated in neuropsychiatric or stress-related disorders (FSTL5 and CHODL). These findings underscore the importance of considering psychological and social factors in gene discovery for BP, especially in non-European populations.Peer reviewe

Using symptom-based case predictions to identify host genetic factors that contribute to COVID-19 susceptibility

Epidemiological and genetic studies on COVID-19 are currently hindered by inconsistent and limited testing policies to confirm SARS-CoV-2 infection. Recently, it was shown that it is possible to predict COVID-19 cases using cross-sectional self-reported disease-related symptoms. Here, we demonstrate that this COVID-19 prediction model has reasonable and consistent performance across multiple independent cohorts and that our attempt to improve upon this model did not result in improved predictions. Using the existing COVID-19 prediction model, we then conducted a GWAS on the predicted phenotype using a total of 1,865 predicted cases and 29,174 controls. While we did not find any common, large-effect variants that reached genome-wide significance, we do observe suggestive genetic associations at two SNPs (rs11844522, p = 1.9x10-7; rs5798227, p = 2.2x10-7). Explorative analyses furthermore suggest that genetic variants associated with other viral infectious diseases do not overlap with COVID-19 susceptibility and that severity of COVID-19 may have a different genetic architecture compared to COVID-19 susceptibility. This study represents a first effort that uses a symptom-based predicted phenotype as a proxy for COVID-19 in our pursuit of understanding the genetic susceptibility of the disease. We conclude that the inclusion of symptom-based predicted cases could be a useful strategy in a scenario of limited testing, either during the current COVID-19 pandemic or any future viral outbreak.Peer reviewe

Increased genetic contribution to wellbeing during the COVID-19 pandemic

Physical and mental health are determined by an interplay between nature, for example genetics, and nurture, which encompasses experiences and exposures that can be short or long-lasting. The COVID-19 pandemic represents a unique situation in which whole communities were suddenly and simultaneously exposed to both the virus and the societal changes required to combat the virus. We studied 27,537 population-based biobank participants for whom we have genetic data and extensive longitudinal data collected via 19 questionnaires over 10 months, starting in March 2020. This allowed us to explore the interaction between genetics and the impact of the COVID-19 pandemic on individuals' wellbeing over time. We observe that genetics affected many aspects of wellbeing, but also that its impact on several phenotypes changed over time. Over the course of the pandemic, we observed that the genetic predisposition to life satisfaction had an increasing influence on perceived quality of life. We also estimated heritability and the proportion of variance explained by shared environment using variance components methods based on pedigree information and household composition. The results suggest that people's genetic constitution manifested more prominently over time, potentially due to social isolation driven by strict COVID-19 containment measures. Overall, our findings demonstrate that the relative contribution of genetic variation to complex phenotypes is dynamic rather than static

Agreement between Computerized and Human Assessment of Performance on the Ruff Figural Fluency Test

The Ruff Figural Fluency Test (RFFT) is a sensitive test for nonverbal fluency suitable for all age groups. However, assessment of performance on the RFFT is time-consuming and may be affected by interrater differences. Therefore, we developed computer software specifically designed to analyze performance on the RFFT by automated pattern recognition. The aim of this study was to compare assessment by the new software with conventional assessment by human raters. The software was developed using data from the Lifelines Cohort Study and validated in an independent cohort of the Prevention of Renal and Vascular End Stage Disease (PREVEND) study. The total study population included 1,761 persons: 54% men; mean age (SD), 58 (10) years. All RFFT protocols were assessed by the new software and two independent human raters (criterion standard). The mean number of unique designs (SD) was 81 (29) and the median number of perseverative errors (interquartile range) was 9 (4 to 16). The intraclass correlation coefficient (ICC) between the computerized and human assessment was 0.994 (95%CI, 0.988 to 0.996; p<0.001) and 0.991 (95%CI, 0.990 to 0.991; p<0.001) for the number of unique designs and perseverative errors, respectively. The mean difference (SD) between the computerized and human assessment was -1.42 (2.78) and +0.02 (1.94) points for the number of unique designs and perseverative errors, respectively. This was comparable to the agreement between two independent human assessments: ICC, 0.995 (0.994 to 0.995; p<0.001) and 0.985 (0.982 to 0.988; p<0.001), and mean difference (SD), -0.44 (2.98) and +0.56 (2.36) points for the number of unique designs and perseverative errors, respectively. We conclude that the agreement between the computerized and human assessment was very high and comparable to the agreement between two independent human assessments. Therefore, the software is an accurate tool for the assessment of performance on the RFFT

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 sizes(1). 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 panel(2)) 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. A large genome-wide association study of more than 5 million individuals reveals that 12,111 single-nucleotide polymorphisms account for nearly all the heritability of height attributable to common genetic variants.Peer reviewe

Discovery of rare variants associated with blood pressure regulation through meta-analysis of 1.3 million individuals

Genetic studies of blood pressure (BP) to date have mainly analyzed common variants (minor allele frequency > 0.05). In a meta-analysis of up to ~1.3 million participants, we discovered 106 new BP-associated genomic regions and 87 rare (minor allele frequency ≤ 0.01) variant BP associations (P < 5 × 10−8), of which 32 were in new BP-associated loci and 55 were independent BP-associated single-nucleotide variants within known BP-associated regions. Average effects of rare variants (44% coding) were ~8 times larger than common variant effects and indicate potential candidate causal genes at new and known loci (for example, GATA5 and PLCB3). BP-associated variants (including rare and common) were enriched in regions of active chromatin in fetal tissues, potentially linking fetal development with BP regulation in later life. Multivariable Mendelian randomization suggested possible inverse effects of elevated systolic and diastolic BP on large artery stroke. Our study demonstrates the utility of rare-variant analyses for identifying candidate genes and the results highlight potential therapeutic targets. © 2020, The Author(s), under exclusive licence to Springer Nature America, Inc. There are 286 authors of this articles not all are listed in this record

A genome-wide association study of rheumatoid arthritis without antibodies against citrullinated peptides

Introduction. Rheumatoid arthritis (RA) patients can be classified based on presence or absence of anticitrullinated peptide antibodies (ACPA) in their serum. This heterogeneity among patients may reflect important biological differences underlying the disease process. To date, the majority of genetic studies have focused on the ACPA-positive group. Therefore, our goal was to analyse the genetic risk factors that contribute to ACPA-negative RA. Methods. We performed a large-scale genome-wide association study (GWAS) in three Caucasian European cohorts comprising 1148 ACPA-negative RA patients and 6008 controls. All patients were screened using the Illumina Human Cyto-12 chip, and controls were genotyped using different genome-wide platforms. Population-independent analyses were carried out by means of logistic regression. Meta-analysis with previously published data was performed as follow-up for selected signals (reaching a total of 1922 ACPA-negative RA patients and 7087 controls). Imputation of classical HLA alleles, aminoacid residues and single nucleotide polymorphisms was undertaken. Results. The combined analysis of the studied cohorts resulted in identification of a peak of association in the HLA-region and several suggestive non-HLA associations. Meta-analysis with previous reports confirmed the association of the HLA region with this subset and an observed association in the CLYBL locus remained suggestive. The imputation and deep interrogation of the HLA region led to identification of a two aminoacid model (HLA-B at position 9 and HLA-DRB1 at position 11) that accounted for the observed genome-wide associations in this region. Conclusions. Our study shed light on the influence of the HLA region in ACPA-negative RA and identified a suggestive risk locus for this condition

KCND3 potassium channel gene variant confers susceptibility to electrocardiographic early repolarization pattern.

BACKGROUNDThe presence of an early repolarization pattern (ERP) on the surface ECG is associated with risk of ventricular fibrillation and sudden cardiac death. Family studies have shown that ERP is a highly heritable trait, but molecular genetic determinants are unknown.METHODSTo identify genetic susceptibility loci for ERP, we performed a GWAS and meta-analysis in 2,181 cases and 23,641 controls of European ancestry.RESULTSWe identified a genome-wide significant (P < 5 × 10-8) locus in the potassium voltage-gated channel subfamily D member 3 (KCND3) gene that was successfully replicated in additional 1,124 cases and 12,510 controls. A subsequent joint meta-analysis of the discovery and replication cohorts identified rs1545300 as the lead SNP at the KCND3 locus (OR 0.82 per minor T allele, P = 7.7 × 10-12) but did not reveal additional loci. Colocalization analyses indicate causal effects of KCND3 gene expression levels on ERP in both cardiac left ventricle and tibial artery.CONCLUSIONSIn this study, we identified for the first time to our knowledge a genome-wide significant association of a genetic variant with ERP. Our findings of a locus in the KCND3 gene provide insights not only into the genetic determinants but also into the pathophysiological mechanism of ERP, discovering a promising candidate for functional studies.FUNDINGThis project was funded by the German Center for Cardiovascular Research (DZHK Shared Expertise SE081 - STATS). For detailed funding information per study, see the Supplemental Acknowledgments