Simulation of African and non-African low and high coverage whole genome sequence data to assess variant calling approaches.

Current variant calling (VC) approaches have been designed to leverage populations of long-range haplotypes and were benchmarked using populations of European descent, whereas most genetic diversity is found in non-European such as Africa populations. Working with these genetically diverse populations, VC tools may produce false positive and false negative results, which may produce misleading conclusions in prioritization of mutations, clinical relevancy and actionability of genes. The most prominent question is which tool or pipeline has a high rate of sensitivity and precision when analysing African data with either low or high sequence coverage, given the high genetic diversity and heterogeneity of this data. Here, a total of 100 synthetic Whole Genome Sequencing (WGS) samples, mimicking the genetics profile of African and European subjects for different specific coverage levels (high/low), have been generated to assess the performance of nine different VC tools on these contrasting datasets. The performances of these tools were assessed in false positive and false negative call rates by comparing the simulated golden variants to the variants identified by each VC tool. Combining our results on sensitivity and positive predictive value (PPV), VarDict [PPV = 0.999 and Matthews correlation coefficient (MCC) = 0.832] and BCFtools (PPV = 0.999 and MCC = 0.813) perform best when using African population data on high and low coverage data. Overall, current VC tools produce high false positive and false negative rates when analysing African compared with European data. This highlights the need for development of VC approaches with high sensitivity and precision tailored for populations characterized by high genetic variations and low linkage disequilibrium.

Dissecting Generalizability and Actionability of Disease-Associated Genes From 20 Worldwide Ethnolinguistic Cultural Groups.

Findings resulting from whole-genome sequencing (WGS) have markedly increased due to the massive evolvement of sequencing methods and have led to further investigations such as clinical actionability of genes, as documented by the American College of Medical Genetics and Genomics (ACMG). ACMG's actionable genes (ACGs) may not necessarily be clinically actionable across all populations worldwide. It is critical to examine the actionability of these genes in different populations. Here, we have leveraged a combined WES from the African Genome Variation and 1000 Genomes Project to examine the generalizability of ACG and potential actionable genes from four diseases: high-burden malaria, TB, HIV/AIDS, and sickle cell disease. Our results suggest that ethnolinguistic cultural groups from Africa, particularly Bantu and Khoesan, have high genetic diversity, high proportion of derived alleles at low minor allele frequency (0.0-0.1), and the highest proportion of pathogenic variants within HIV, TB, malaria, and sickle cell diseases. In contrast, ethnolinguistic cultural groups from the non-Africa continent, including Latin American, Afro-related, and European-related groups, have a high proportion of pathogenic variants within ACG than most of the ethnolinguistic cultural groups from Africa. Overall, our results show high genetic diversity in the present actionable and known disease-associated genes of four African high-burden diseases, suggesting the limitation of transferability or generalizability of ACG. This supports the use of personalized medicine as beneficial to the worldwide population as well as actionable gene list recommendation to further foster equitable global healthcare. The results point out the bias in the knowledge about the frequency distribution of these phenotypes and genetic variants associated with some diseases, especially in African and African ancestry populations.

A broad survey of DNA sequence data simulation tools.

In silico DNA sequence generation is a powerful technology to evaluate and validate bioinformatics tools, and accordingly more than 35 DNA sequence simulation tools have been developed. With such a diverse array of tools to choose from, an important question is: Which tool should be used for a desired outcome? This question is largely unanswered as documentation for many of these DNA simulation tools is sparse. To address this, we performed a review of DNA sequence simulation tools developed to date and evaluated 20 state-of-art DNA sequence simulation tools on their ability to produce accurate reads based on their implemented sequence error model. We provide a succinct description of each tool and suggest which tool is most appropriate for the given different scenarios. Given the multitude of similar yet non-identical tools, researchers can use this review as a guide to inform their choice of DNA sequence simulation tool. This paves the way towards assessing existing tools in a unified framework, as well as enabling different simulation scenario analysis within the same framework.