Prevalence of sudden arrhythmic death syndrome-related genetic mutations in an Asian cohort of whole genome sequence

EP Europace ◽  
2020 ◽  
Vol 22 (8) ◽  
pp. 1287-1297
Author(s):  
Pang-Shuo Huang ◽  
Chia-Shan Hsieh ◽  
Sheng-Nan Chang ◽  
Jien-Jiun Chen ◽  
Fu-Chun Chiu ◽  
...  

Abstract Aims Recently, the spectrum of background mutation in the genes implicated in sudden arrhythmic death syndrome (SADS), has been elucidated in the Caucasian populations. However, this information is largely unknown in the Asian populations. Methods and results We assessed the background rare variants (minor allele frequency < 0.01) of major SADS genes in whole genome sequence data of 1514 healthy Taiwanese subjects from the Taiwan Biobank. We found up to 45% of healthy subjects have a rare variant in at least one of the major SADS genes. Around 3.44% of healthy subjects had multiple mutations in one or multiple genes. The background mutation rates in long QT syndrome, catecholaminergic polymorphic ventricular tachycardia, and arrhythmogenic right ventricular cardiomyopathy genes were similar, but those in Brugada syndrome (BrS) (SCN5A) and hypertrophic cardiomyopathy (HCM) genes (MYBPC3, MYH7, and TNNT2) were higher, compared to those reported in the Caucasian populations. Furthermore, the rate of incidental pathogenic variant was highest in MYBPC3 gene. Finally, the number of variant was proportional to the exon length of the gene (R2 = 0.486, P = 0.0056) but not related to its functional or evolutionary importance (degree of evolutionary conservation) (R2 = 0.0008, P = 0.9218), suggesting that the mutation was random. The ratio of variant number over exon nucleotide length was highest in MYBPC3, MYH7, and TNNT2 genes. Conclusion Unique features of background SADS gene mutation in the Asian populations include higher prevalence of incidental variant in HCM, BrS, and long QT 3 (SCN5A) genes. HCM genes have the highest variant number per exon length.

2010 ◽  
Vol 36 (4) ◽  
pp. 688-694
Author(s):  
Yi-Jun WANG ◽  
Yan-Ping LÜ ◽  
Qin XIE ◽  
De-Xiang DENG ◽  
Yun-Long BIAN

2014 ◽  
Vol 40 (12) ◽  
pp. 2059
Author(s):  
Lin-Yi QIAO ◽  
Xin LI ◽  
Zhi-Jian CHANG ◽  
Xiao-Jun ZHANG ◽  
Hai-Xian ZHAN ◽  
...  

IDCases ◽  
2020 ◽  
pp. e01034
Author(s):  
Charlie Tan ◽  
Fang-I Lu ◽  
Patryk Aftanas ◽  
Kara Tsang ◽  
Samira Mubareka ◽  
...  

Author(s):  
Amnon Koren ◽  
Dashiell J Massey ◽  
Alexa N Bracci

Abstract Motivation Genomic DNA replicates according to a reproducible spatiotemporal program, with some loci replicating early in S phase while others replicate late. Despite being a central cellular process, DNA replication timing studies have been limited in scale due to technical challenges. Results We present TIGER (Timing Inferred from Genome Replication), a computational approach for extracting DNA replication timing information from whole genome sequence data obtained from proliferating cell samples. The presence of replicating cells in a biological specimen leads to non-uniform representation of genomic DNA that depends on the timing of replication of different genomic loci. Replication dynamics can hence be observed in genome sequence data by analyzing DNA copy number along chromosomes while accounting for other sources of sequence coverage variation. TIGER is applicable to any species with a contiguous genome assembly and rivals the quality of experimental measurements of DNA replication timing. It provides a straightforward approach for measuring replication timing and can readily be applied at scale. Availability and Implementation TIGER is available at https://github.com/TheKorenLab/TIGER. Supplementary information Supplementary data are available at Bioinformatics online


2021 ◽  
Vol 53 (1) ◽  
Author(s):  
Theo Meuwissen ◽  
Irene van den Berg ◽  
Mike Goddard

Abstract Background Whole-genome sequence (WGS) data are increasingly available on large numbers of individuals in animal and plant breeding and in human genetics through second-generation resequencing technologies, 1000 genomes projects, and large-scale genotype imputation from lower marker densities. Here, we present a computationally fast implementation of a variable selection genomic prediction method, that could handle WGS data on more than 35,000 individuals, test its accuracy for across-breed predictions and assess its quantitative trait locus (QTL) mapping precision. Methods The Monte Carlo Markov chain (MCMC) variable selection model (Bayes GC) fits simultaneously a genomic best linear unbiased prediction (GBLUP) term, i.e. a polygenic effect whose correlations are described by a genomic relationship matrix (G), and a Bayes C term, i.e. a set of single nucleotide polymorphisms (SNPs) with large effects selected by the model. Computational speed is improved by a Metropolis–Hastings sampling that directs computations to the SNPs, which are, a priori, most likely to be included into the model. Speed is also improved by running many relatively short MCMC chains. Memory requirements are reduced by storing the genotype matrix in binary form. The model was tested on a WGS dataset containing Holstein, Jersey and Australian Red cattle. The data contained 4,809,520 genotypes on 35,549 individuals together with their milk, fat and protein yields, and fat and protein percentage traits. Results The prediction accuracies of the Jersey individuals improved by 1.5% when using across-breed GBLUP compared to within-breed predictions. Using WGS instead of 600 k SNP-chip data yielded on average a 3% accuracy improvement for Australian Red cows. QTL were fine-mapped by locating the SNP with the highest posterior probability of being included in the model. Various QTL known from the literature were rediscovered, and a new SNP affecting milk production was discovered on chromosome 20 at 34.501126 Mb. Due to the high mapping precision, it was clear that many of the discovered QTL were the same across the five dairy traits. Conclusions Across-breed Bayes GC genomic prediction improved prediction accuracies compared to GBLUP. The combination of across-breed WGS data and Bayesian genomic prediction proved remarkably effective for the fine-mapping of QTL.


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