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Genetically Determined Ventricular Arrhythmias
Published in Andrea Natale, Oussama M. Wazni, Kalyanam Shivkumar, Francis E. Marchlinski, Handbook of Cardiac Electrophysiology, 2020
Houman Khakpour, Jason S. Bradfield
To date, gain of function mutations in 3 potassium channel genes have been associated with SQTS (KCNH2, KCNQ1, KCNJ2).43,44 Mutations in the CACNA1C and CACNB2 genes, which encode the alpha- and beta-subunits of the L-type cardiac calcium channels have been described as well.2 These mutations result in an abnormally rapid repolarization.
The QT interval
Published in Andrew R Houghton, Making Sense of the ECG, 2019
Although congenital long QT syndromes are well recognized, it is only since 2000 that congenital short QT syndrome has been recognized as a clinical entity. The congenital short QT syndromes appear to follow an autosomal dominant pattern of inheritance and mutations affecting the genes KCNH2, KCNQ1 and KCNJ2 (which are linked to potassium channels) and CACNA1C, CACNB2 and CACNA2D1 (which are linked to calcium channels) have so far been identified.
Machine learning, pharmacogenomics, and clinical psychiatry: predicting antidepressant response in patients with major depressive disorder
Published in Expert Review of Clinical Pharmacology, 2022
William V. Bobo, Bailey Van Ommeren, Arjun P. Athreya
Fabbri and colleagues investigated the accuracies of machine learning models that combined genomic, clinical, and sociodemographic factors for predicting response, remission, and treatment-resistance in patients with MDD after 4 weeks of treatment with SSRIs or SNRIs [65]. The study datasets included patients with MDD from pooled European samples and a separate dataset consisting of STAR*D participants for external validation. Genomic predictors included 44 SNPs in or near CACNA1C, CACNB2, ANK3, GRM7, TCF4, ITIH3, SYNE1, and FKBP5 that were chosen by the investigators. Machine learning models included neural networks, recursive partitioning, learning vector quantization, gradient boosted machines (GBMs), and RFs. When combined with clinical and demographic characteristics, the best-performing candidate genes (ANK3, CADNB2, FKBP5, and CACNA1C) for predicting response (≥50% reduction in HAMD-21 or MADRS scores at weeks 4 or 6), remission (score ≤7 on the HAMD-21 or <10 on the MADRS), or treatment resistance (poor response to at least two consecutive antidepressant trials of adequate design) in the European datasets were tested for associations with response and remission in treatment-resistant STAR*D patients. Neural networks and GBMs had the highest predictive accuracies among the models tested in STAR*D (mean Accuracy 73%, Sensitivity 0.83, Specificity 0.56), although predictive performances did not differ greatly across all machine learning algorithms.
Personalizing treatments for patients based on cardiovascular phenotyping
Published in Expert Review of Precision Medicine and Drug Development, 2022
Pharmacogenetic profiling has also identified a SNP related to statin intolerance in the solute carrier organic anion transporter 1B1 (SLCO1B1) gene, which encodes a membrane-bound sodium-independent organic anion transporter that transports statin drugs into the liver and has been associated with myopathy, the main reason why ~50% of patients discontinue the drug [76,77]. The relationship between SNPs in SLCO1B1 and statin discontinuation, however, remains controversial [78]. The noncoding rs4263657 polymorphism and the nonsynonymous rs4149056 polymorphism, which was in nearly complete linkage disequilibrium with rs4363657, have been associated with myopathy related to statin use [77–79]. A real-world population study that compared individuals with confirmed statin-induced myopathy with tolerant individuals who were genotyped found that only the rs4149056 polymorphism was associated with severe myopathy or rhabdomyolysis (OR: 5.15; 95% CI: 3.13–8.45, p = 2.6 × 10−9) [80]. Genetic variants that modify responses to other cardiovascular disease-related drugs have also been identified. These drugs include angiotensin converting enzyme inhibitors (variants in ACE, AGTR1); beta-blockers (variants in ADRB1, ADRB2, GRK5, GRK4); and calcium channel blockers (variants in CACNB2, CACNA1C) (reviewed in [81]).
Association and epistatic analysis of white matter related genes across the continuum schizophrenia and autism spectrum disorders: The joint effect of NRG1-ErbB genes
Published in The World Journal of Biological Psychiatry, 2022
C. Prats, M. Fatjó-Vilas, M. J. Penzol, O. Kebir, L. Pina-Camacho, D. Demontis, B. Crespo-Facorro, V. Peralta, A. González-Pinto, E. Pomarol-Clotet, S. Papiol, M. Parellada, M. O. Krebs, L. Fañanás
According to its function, the selected genes are related to (i) myelin structure (MAG, MBP, PLP1, MOG, CNP, PTEN, AKT1, and FYN), (ii) oligodendrocyte development (QKI), (iii) synaptic plasticity and axonal regeneration (OMG, CDH10, and MAG), (iv) transcription and signalling factors (OLIG2, NRG1, ErbB2, ErbB3, and ErbB4), (v) cell adhesion molecules and receptors (NRXN1, CNTNAP2, and SPON1), and (vi) calcium channels (CACNA1C and CACNB2) and coding for zinc finger binding protein (ZNF804A).