| 1. |
- Roselli, Carolina, et al.
(författare)
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Multi-ethnic genome-wide association study for atrial fibrillation
- 2018
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Ingår i: Nature Genetics. - : Springer Science and Business Media LLC. - 1061-4036 .- 1546-1718. ; 50:9, s. 1225-1233
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Tidskriftsartikel (refereegranskat)abstract
- Atrial fibrillation (AF) affects more than 33 million individuals worldwide(1) and has a complex heritability(2). We conducted the largest meta-analysis of genome-wide association studies (GWAS) for AF to date, consisting of more than half a million individuals, including 65,446 with AF. In total, we identified 97 loci significantly associated with AF, including 67 that were novel in a combined-ancestry analysis, and 3 that were novel in a European-specific analysis. We sought to identify AF-associated genes at the GWAS loci by performing RNA-sequencing and expression quantitative trait locus analyses in 101 left atrial samples, the most relevant tissue for AF. We also performed transcriptome-wide analyses that identified 57 AF-associated genes, 42 of which overlap with GWAS loci. The identified loci implicate genes enriched within cardiac developmental, electrophysiological, contractile and structural pathways. These results extend our understanding of the biological pathways underlying AF and may facilitate the development of therapeutics for AF.
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| 2. |
- Aragam, Krishna G., et al.
(författare)
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Phenotypic Refinement of Heart Failure in a National Biobank Facilitates Genetic Discovery
- 2019
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Ingår i: Circulation. - 0009-7322. ; 139:4, s. 489-501
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Tidskriftsartikel (refereegranskat)abstract
- Heart failure (HF) is a morbid and heritable disorder for which the biological mechanisms are incompletely understood. We therefore examined genetic associations with HF in a large national biobank, and assessed whether refined phenotypic classification would facilitate genetic discovery. Methods: We defined all-cause HF among 488 010 participants from the UK Biobank and performed a genome-wide association analysis. We refined the HF phenotype by classifying individuals with left ventricular dysfunction and without coronary artery disease as having nonischemic cardiomyopathy (NICM), and repeated a genetic association analysis. We then pursued replication of lead HF and NICM variants in independent cohorts, and performed adjusted association analyses to assess whether identified genetic associations were mediated through clinical HF risk factors. In addition, we tested rare, loss-of-function mutations in 24 known dilated cardiomyopathy genes for association with HF and NICM. Finally, we examined associations between lead variants and left ventricular structure and function among individuals without HF using cardiac magnetic resonance imaging (n=4158) and echocardiographic data (n=30 201). Results: We identified 7382 participants with all-cause HF in the UK Biobank. Genome-wide association analysis of all-cause HF identified several suggestive loci (P<1×10 -6 ), the majority linked to upstream HF risk factors, ie, coronary artery disease (CDKN2B-AS1 and MAP3K7CL) and atrial fibrillation (PITX2). Refining the HF phenotype yielded a subset of 2038 NICM cases. In contrast to all-cause HF, genetic analysis of NICM revealed suggestive loci that have been implicated in dilated cardiomyopathy (BAG3, CLCNKA-ZBTB17). Dilated cardiomyopathy signals arising from our NICM analysis replicated in independent cohorts, persisted after HF risk factor adjustment, and were associated with indices of left ventricular dysfunction in individuals without clinical HF. In addition, analyses of loss-of-function variants implicated BAG3 as a disease susceptibility gene for NICM (loss-of-function variant carrier frequency=0.01%; odds ratio,12.03; P=3.62×10 -5 ). Conclusions: We found several distinct genetic mechanisms of all-cause HF in a national biobank that reflect well-known HF risk factors. Phenotypic refinement to a NICM subtype appeared to facilitate the discovery of genetic signals that act independently of clinical HF risk facto rs and that are associated with subclinical left ventricular dysfunction.
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| 3. |
- Hindy, George, et al.
(författare)
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Cardiometabolic Polygenic Risk Scores and Osteoarthritis Outcomes : A Mendelian Randomization Study Using Data From the Malmö Diet and Cancer Study and the UK Biobank
- 2019
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Ingår i: Arthritis and Rheumatology. - : Wiley. - 2326-5191 .- 2326-5205. ; 71:6, s. 925-934
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Tidskriftsartikel (refereegranskat)abstract
- Objective: To investigate the causal role of cardiometabolic risk factors in osteoarthritis (OA) using associated genetic variants. Methods: We studied 27,691 adults from the Malmö Diet and Cancer Study (MDCS) and replicated novel findings among 376,435 adults from the UK Biobank. Trait-specific polygenic risk scores for low-density lipoprotein (LDL) and high-density lipoprotein (HDL) cholesterol levels, triglyceride levels, body mass index (BMI), fasting plasma glucose (FPG) levels, and systolic blood pressure (BP) were used to test the associations of genetically predicted elevations in each trait with incident OA diagnosis (n = 3,559), OA joint replacement (n = 2,780), or both (total OA; n = 4,226) in Mendelian randomization (MR) analyses in the MDCS, and with self-reported and/or hospital-diagnosed OA (n = 65,213) in the UK Biobank. Multivariable MR, MR-Egger, and weighted median MR were used to adjust for potential pleiotropic biases. Results: In the MDCS, genetically predicted elevation in LDL cholesterol level was associated with a lower risk of OA diagnosis (odds ratio [OR] 0.83 [95% confidence interval (95% CI) 0.73–0.95] per 1SD increase) and total OA (OR 0.87 [95% CI 0.78–0.98]), which was supported by multivariable MR for OA diagnosis (OR 0.84 [95% CI 0.75–0.95]) and total OA (0.87 [95% CI 0.78–0.97]), and by conventional 2-sample MR for OA diagnosis (OR 0.86 [95% CI 0.75–0.98]). MR-Egger indicated no pleiotropic bias. Genetically predicted elevation in BMI was associated with an increased risk of OA diagnosis (OR 1.65 [95% CI 1.14–2.41]), while MR-Egger indicated pleiotropic bias and a larger association with OA diagnosis (OR 3.25 [1.26–8.39]), OA joint replacement (OR 3.81 [95% CI 1.39–10.4]), and total OA (OR 3.41 [95% CI 1.43–8.15]). No associations were observed between genetically predicted HDL cholesterol level, triglyceride level, FPG level, or systolic BP and OA outcomes. The associations with LDL cholesterol levels were replicated in the UK Biobank (OR 0.95 [95% CI 0.93–0.98]). Conclusion: Our MR study provides evidence of a causal role of lower LDL cholesterol level and higher BMI in OA.
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| 4. |
- Hindy, George, et al.
(författare)
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Genome-Wide Polygenic Score, Clinical Risk Factors, and Long-Term Trajectories of Coronary Artery Disease
- 2020
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Ingår i: Arteriosclerosis, Thrombosis, and Vascular Biology. - 1524-4636. ; 40:11, s. 2738-2746
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Tidskriftsartikel (refereegranskat)abstract
- OBJECTIVE: To determine the relationship of a genome-wide polygenic score for coronary artery disease (GPSCAD) with lifetime trajectories of CAD risk, directly compare its predictive capacity to traditional risk factors, and assess its interplay with the Pooled Cohort Equations (PCE) clinical risk estimator. Approach and Results: We studied GPSCAD in 28 556 middle-aged participants of the Malmö Diet and Cancer Study, of whom 4122 (14.4%) developed CAD over a median follow-up of 21.3 years. A pronounced gradient in lifetime risk of CAD was observed-16% for those in the lowest GPSCAD decile to 48% in the highest. We evaluated the discriminative capacity of the GPSCAD-as assessed by change in the C-statistic from a baseline model including age and sex-among 5685 individuals with PCE risk estimates available. The increment for the GPSCAD (+0.045, P<0.001) was higher than for any of 11 traditional risk factors (range +0.007 to +0.032). Minimal correlation was observed between GPSCAD and 10-year risk defined by the PCE (r=0.03), and addition of GPSCAD improved the C-statistic of the PCE model by 0.026. A significant gradient in lifetime risk was observed for the GPSCAD, even among individuals within a given PCE clinical risk stratum. We replicated key findings-noting strikingly consistent results-in 325 003 participants of the UK Biobank. CONCLUSIONS: GPSCAD-a risk estimator available from birth-stratifies individuals into varying trajectories of clinical risk for CAD. Implementation of GPSCAD may enable identification of high-risk individuals early in life, decades in advance of manifest risk factors or disease.
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