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Investigation of Sudden Cardiac Death
Published in Mary N. Sheppard, Practical Cardiovascular Pathology, 2022
In recent years, with rapid development in next generation sequencing technology, the frequent identification of rare variants in both healthy and affected individuals has been found and determining this variant pathogenicity is challenging. Other genes implicated in BrS include calcium handling genes and desmosomal gene PKP2 but are rare. Current guidelines recommend SCN5A testing which is useful for predictive testing of family members but does not affect clinical management. Compared with genotype negative individuals, pore region mutations in SCN5A are associated with increased risk of cardiac arrest. Given the relatively modest contribution of monogenic SCN5A towards BrS phenotype, polygenic inheritance with environmental and epigenetic factors are important.
Biochemical Markers in Ophthalmology
Published in Ching-Yu Cheng, Tien Yin Wong, Ophthalmic Epidemiology, 2022
Abdus Samad Ansari, Pirro G. Hysi
The genetic architecture of common traits and disorders is more complex. These traits and disorders tend to have a more elaborate genetic architecture, where a large number of common variants located over the entire genome, each with individually small effects, tend to collectively explain the majority of the phenotypic heritability [15]. Although phenotypically powerful rare variants may contribute to common disorders [16], their contribution at the general population level is often modest.
Including Genetic Variables in NTCP Models Where Are We? Where Are We Going?
Published in Tiziana Rancati, Claudio Fiorino, Modelling Radiotherapy Side Effects, 2019
Sarah L. Kerns, Suhong Yu, Catharine M. L. West
Because of the different types of genetic variation and different effects each have on disease susceptibility, risk prediction or stratification models will require multiple SNPs and/or rare variants to achieve clinically actionable sensitivity and specificity. Data simulation studies suggest that a high area under the receiver-operating characteristic (ROC) curve (AUC) can be achieved for a binary disease or outcome when tens to hundreds of SNPs are combined in a predictive model (Janssens et al. 2006). As expected, the number of SNPs needed depends on both the allele frequencies and effect sizes of the SNPs. While rare variants tend to increase risk for disease more so than common SNPs, they are, by definition, rare, and so few individuals in a population will be carriers. In contrast, many individuals will be carriers of multiple common risk SNPs, but each of these SNPs likely only increases disease susceptibility slightly. Thus, it is the combination of multiple genetic variants that is needed to most accurately predict risk or stratify individuals into risk groups. Polygenic scores have been developed for numerous complex diseases and phenotypes (see [Seibert 2018] for examples), and have shown promise for stratification of individuals into high and low risk groups.
Human height: a model common complex trait
Published in Annals of Human Biology, 2023
Mitchell Conery, Struan F. A. Grant
Rare and structural variant associations could also implicate additional regions of the genome, but it seems similarly doubtful that the proportion would grow considerably. Regarding rare variants, Yengo et al. found suggestive evidence that variants with minor allele frequency between 0.1% and 1% concentrate in the same 21% of the genome as the common variants. This result is consistent with those obtained by recent rare variant analyses for height. ∼70% of the 83 low-frequency coding variants identified in a 2017 study (Marouli et al. 2017) lie within loci identified by Yengo et al. and a later analysis of 492 traits showed strong colocalisation of rare and common variants (Backman et al. 2021). Comprehensive results for structural variation are more limited than for rare variants, but we similarly note that 80% of the height-related copy number variants in a recent cataloguing effort (Hujoel et al. 2022) overlapped the loci identified by Yengo et al.
Autoimmune disorders associated with common variable immunodeficiency: prediction, diagnosis, and treatment
Published in Expert Review of Clinical Immunology, 2022
Niloufar Yazdanpanah, Nima Rezaei
With the application of next-generation sequencing (NGS) techniques, initially in research activities and then in clinical settings for diagnostic purposes, our knowledge of the genetic aspects of IEI increased drastically. To understand the genetic component of rare diseases, different common and rare genetic variants are recognized, with either autosomal recessive (AR) or autosomal dominant (AD) inheritance patterns. Nevertheless, categorization of rare variants according to the disease manifestations and clinical findings remained challenging. On the other hand, common variants only partially contribute to disease susceptibility and do not cause the disease phenotype in all identified cases. Therefore, the application of this information in designing new treatment strategies and clinical decision-making remained to be studied.
Polygenic risk scores for predicting outcomes and treatment response in psychiatry: hope or hype?
Published in International Review of Psychiatry, 2022
Laura Fusar-Poli, Bart P. F. Rutten, Jim van Os, Eugenio Aguglia, Sinan Guloksuz
One of the reasons is that the standard PRS approach is based on common genetic variants only. However, rare variants and rare structural changes (e.g. CNVs, deletions, insertions) may also confer risk for mental disorders (Singh et al., 2022) and contribute to the variability in treatment response (Ruderfer et al., 2016). Rare variants are more difficult to genotype and analyze, as genome sequencing rather than genome-wide genotyping is needed. To overcome this limit, next-generation approaches like whole-genome sequencing (WGS) and whole-exome sequencing (WES) have been proposed. WGS consists of the sequencing of the entire genome to identify both polymorphism (i.e. responsible for interindividual phenotypic variability) and pathogenic variants. The identification of these variants in affected individuals can be crucial, although their interpretation is not easy (Dewey et al., 2014). WES is the sequencing of the protein-coding regions of genes and targets approximately 3% of the whole genome. However, it allows identifying variations in the protein-coding region of any gene, rather than in only a select few genes, contrary to PRSs. Because most known mutations that cause disease occur in exons, WES may represent an efficient method to identify possible disease-causing mutations (Suwinski et al., 2019). However, the implementation of these alternative approaches requires larger datasets than currently available SNPs-based GWASs.