A diverse ancestrally-matched reference panel increases genotype imputation accuracy in a underrepresented population.

Variant imputation, a common practice in genome-wide association studies, relies on reference panels to infer unobserved genotypes. Multiple public reference panels are currently available with variations in size, sequencing depth, and represented populations. Currently, limited data exist regarding the performance of public reference panels when used in an imputation of populations underrepresented in the reference panel. Here, we compare the performance of various public reference panels: 1000 Genomes Project, Haplotype Reference Consortium, GenomeAsia 100 K, and the recent Trans-Omics for Precision Medicine (TOPMed) program, when used in an imputation of samples from the Thai population. Genotype yields were assessed, and imputation accuracies were examined by comparison with high-depth whole genome sequencing data of the same sample. We found that imputation using the TOPMed panel yielded the largest number of variants (~ 271 million). Despite being the smallest in size, GenomeAsia 100 K achieved the best imputation accuracy with a median genotype concordance rate of 0.97. For rare variants, GenomeAsia 100 K also offered the best accuracy, although rare variants were less accurately imputable than common variants (30.3% reduction in concordance rates). The high accuracy observed when using GenomeAsia 100 K is likely attributable to the diverse representation of populations genetically similar to the study cohort emphasizing the benefits of sequencing populations classically underrepresented in human genomics.

Epidemiology and risk factors of sudden cardiac arrest.

PURPOSE OF REVIEW: Sudden cardiac arrest (SCA) remains a major health burden around the globe, most often occurring in the community (out-of-hospital cardiac arrest [OHCA]). SCA accounts for 15-20% of all natural deaths in adults in the USA and Western Europe, and up to 50% of all cardiovascular deaths. To reduce this burden, more knowledge is needed about its key facets such as its incidence in various geographies, its risk factors, and the populations that may be at risk. RECENT FINDINGS: SCA results from a complex interaction of inherited and acquired causes, specific to each individual. Resolving this complexity, and designing personalized prevention and treatment, requires an integrated approach in which big datasets that contain all relevant factors are collected, and a multimodal analysis. Such datasets derive from multiple data sources, including all players in the chain-of-care for OHCA. This recognition has led to recently started large-scale collaborative efforts in Europe. SUMMARY: Our insights into the causes of SCA are steadily increasing thanks to the creation of big datasets dedicated to SCA research. These insights may be used to earlier recognize of individuals at risk, the design of personalized methods for prevention, and more effective resuscitation strategies for OHCA.

Discovery of predictors of sudden cardiac arrest in diabetes: rationale and outline of the RESCUED (REcognition of Sudden Cardiac arrest vUlnErability in Diabetes) project.

INTRODUCTION: Early recognition of individuals with increased risk of sudden cardiac arrest (SCA) remains challenging. SCA research so far has used data from cardiologist care, but missed most SCA victims, since they were only in general practitioner (GP) care prior to SCA. Studying individuals with type 2 diabetes (T2D) in GP care may help solve this problem, as they have increased risk for SCA, and rich clinical datasets, since they regularly visit their GP for check-up measurements. This information can be further enriched with extensive genetic and metabolic information. AIM: To describe the study protocol of the REcognition of Sudden Cardiac arrest vUlnErability in Diabetes (RESCUED) project, which aims at identifying clinical, genetic and metabolic factors contributing to SCA risk in individuals with T2D, and to develop a prognostic model for the risk of SCA. METHODS: The RESCUED project combines data from dedicated SCA and T2D cohorts, and GP data, from the same region in the Netherlands. Clinical data, genetic data (common and rare variant analysis) and metabolic data (metabolomics) will be analysed (using classical analysis techniques and machine learning methods) and combined into a prognostic model for risk of SCA. CONCLUSION: The RESCUED project is designed to increase our ability at early recognition of elevated SCA risk through an innovative strategy of focusing on GP data and a multidimensional methodology including clinical, genetic and metabolic analyses.