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(searched for: doi:10.1038/s41467-020-15823-7)
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Haran Yogasundaram, Waleed Alhumaid, Tara Dzwiniel, Susan Christian,
Published: 1 January 2021
Canadian Journal of Cardiology; doi:10.1016/j.cjca.2021.01.016

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Sean J. Jurgens, Seung Hoan Choi, Valerie N. Morrill, , , Jennifer L. Halford, Lu-Chen Weng, Victor Nauffal, Carolina Roselli, , et al.
Published: 29 November 2020
Abstract:
BackgroundMany human diseases are known to have a genetic contribution. While genome-wide studies have identified many disease-associated loci, it remains challenging to elucidate causal genes. In contrast, exome sequencing provides an opportunity to identify new disease genes and large-effect variants of clinical relevance. We therefore sought to determine the contribution of rare genetic variation in a curated set of human diseases and traits using a unique resource of 200,000 individuals with exome sequencing data from the UK Biobank.Methods and ResultsWe included 199,832 participants with a mean age of 68 at follow-up. Exome-wide gene-based tests were performed for 64 diseases and 23 quantitative traits using a mixed-effects model, testing rare loss-of-function and damaging missense variants. We identified 51 known and 23 novel associations with 26 diseases and traits at a false-discovery-rate of 1%. There was a striking risk associated with many Mendelian disease genes including: MYPBC3 with over a 100-fold increased odds of hypertrophic cardiomyopathy, PKD1 with a greater than 25-fold increased odds of chronic kidney disease, and BRCA2, BRCA1, ATM and PALB2 with 3 to 10-fold increased odds of breast cancer. Notable novel findings included an association between GIGYF1 and type 2 diabetes (OR 5.6, P=5.35×10−8), elevated blood glucose, and lower insulin-like-growth-factor-1 levels. Rare variants in CCAR2 were also associated with diabetes risk (OR 13, P=8.5×10−8), while COL9A3 was associated with cataract (OR 3.4, P=6.7×10−8). Notable associations for blood lipids and hypercholesterolemia included NR1H3, RRBP1, GIGYF1, SCGN, APH1A, PDE3B and ANGPTL8. A number of novel genes were associated with height, including DTL, PIEZO1, SCUBE3, PAPPA and ADAMTS6, while BSN was associated with body-mass-index. We further assessed putatively pathogenic variants in known Mendelian cardiovascular disease genes and found that between 1.3 and 2.3% of the population carried likely pathogenic variants in known cardiomyopathy, arrhythmia or hypercholesterolemia genes.ConclusionsLarge-scale population sequencing identifies known and novel genes harboring high-impact variation for human traits and diseases. A number of novel findings, including GIGYF1,represent interesting potential therapeutic targets. Exome sequencing at scale can identify a meaningful proportion of the population that carries a pathogenic variant underlying cardiovascular disease.
Current Cardiology Reports, Volume 22, pp 1-10; doi:10.1007/s11886-020-01385-z

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, , Anne Richmond, , Sarah E Graham, Ailin Falkmo Hansen, Brooke N Wolford, , Jonathon Lefaive, Humaira Rasheed, et al.
Published: 5 September 2020
Abstract:
Circulating cardiac troponin proteins are associated with structural heart disease and predict incident cardiovascular disease in the general population. However, the genetic contribution to cardiac troponin I (cTnI) concentrations and its causal effect on cardiovascular phenotypes is unclear. We combine data from the Trøndelag Health Study and the Generation Scotland Scottish Family Health Study and perform a genome-wide association study of highsensitivity cTnI concentrations with 48 115 individuals. We identified 12 genetic loci (8 novel) associated with cTnI concentrations. Associated protein-altering variants highlighted putative functional genes: CAND2, HABP2, ANO5, APOH, FHOD3, TNFAIP2, KLKB1 and LMAN1. Using two-sample Mendelian randomization we confirmed the non-causal role of cTnI in acute myocardial infarction, but could not rule out a causal role for cTnI in heart failure. Using genetically informed methods for causal inference of cTnI helps inform the role and value of measuring cTnI in the general population.
Jawan W. Abdulrahim, Lydia Coulter Kwee, Fawaz Alenezi, Albert Y. Sun, Aris Baras, Teminioluwa A. Ajayi, Ricardo Henao, Christopher L. Holley, Robert W. McGarrah, James P. Daubert, et al.
Journal of the American College of Cardiology, Volume 76, pp 797-808; doi:10.1016/j.jacc.2020.06.037

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, Paul J. Fadel, , Irving H. Zucker
American Journal of Physiology-Heart and Circulatory Physiology, Volume 319; doi:10.1152/ajpheart.00524.2020

Yi Liu, , , , Elena Sorokin, Nick Van Bruggen, ,
Published: 15 July 2020
bioRxiv; doi:10.1101/2020.07.14.187070

Abstract:
Cardiometabolic diseases are an increasing global health burden. While well established socioeconomic, environmental, behavioural, and genetic risk factors have been identified, our understanding of the drivers and mechanisms underlying these complex diseases remains incomplete. A better understanding is required to develop more effective therapeutic interventions. Magnetic resonance imaging (MRI) has been used to assess organ health in a number of studies, but large-scale population-based studies are still in their infancy. Using 38,683 abdominal MRI scans in the UK Biobank, we used deep learning to systematically quantify parameters from individual organs (liver, pancreas, spleen, kidneys, lungs and adipose depots), and demonstrate that image derived phenotypes (volume, fat and iron content) reflect organ health and disease. We show that these traits have a substantial heritable component (8%-44%), and identify 93 independent genome-wide significant associations, including 3 associations with liver fat and one with liver iron that have not previously been reported, and 73 in traits that have not previously been studied. Overall our work demonstrates the utility of deep learning to systematically quantify health parameters from high-throughput MRI across a range of organs and tissues of the abdomen, and to generate new insights into the genetic architecture of complex traits.
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