Genetic Effects on the Correlation Structure of CVD Risk Factors: Exome-Wide Data From a Ghanaian Population

Identification of novel variants for complicating cardiac disease in the scrub typhus infection using whole genome sequencing

BACKGROUND/AIMS

Scrub typhus infection has been known to complicate cardiovascular diseases mainly attributing to high mortality. Genetic susceptibility loci for complicating cardiac diseases such as atrial fibrillation, heart failure, and ischemic heart disease identified by genomic study have been limited in scrub typhus infection. Therefore, we investigated the genetic novel variants predicting complicating cardiac diseases in patients with confirmed scrub typhus infection using whole genome sequencing.

METHODS

We performed a prospective study for eight consecutive patients with scrub typhus infection. During follow-up, six cases were clinically diagnosed with complicating cardiac diseases and two controls without complicating cardiac diseases. The whole genomes of the all patients were sequenced, and the individual sequence variants were compared between accordcase and control patients. Variant genotypes were compared and identified as a single nucleotide polymorphism (SNP) of the different genotype distributions between six cases and two controls.

RESULTS

The GG genotype in SNP (rs4977397) of solute carrier 24 family member 2 (SLC24A2) gene and non-TT genotype in SNP (rs2676750) of adenosine deaminase, RNA specific, B2 (ADARB2) gene were distinctively found in the case patients with complicated cardiac disease, compared with control patents in the scrub typhus infection.

CONCLUSION

We suggest that the SNPs of SLC24A2 and ADARB2 might be genetic surrogate markers for complicating cardiac diseases in the scrub typhus infection. Our study show that early detection based on individual sequence variants might be feasible to predict complicating cardiac diseases in patients with scrub typhus infection, if further studies with more participants confirm these findings.

Risk factors for Keshan disease: a prospective cohort study protocol of gut flora

BACKGROUND

Keshan disease is an endemic cardiomyopathy of undefined causes. Being involved in the unclear pathogenesis of Keshan disease, a clear diagnosis, and effective treatment cannot be initiated. However, the rapid development of gut flora in cardiovascular disease combined with omics and big data platforms may promote the discovery of new diagnostic markers and provide new therapeutic options. This study aims to identify biomarkers for the early diagnosis and further explore new therapeutic targets for Keshan disease.

METHODS

This cohort study consists of two parts. Though the first part includes 300 participants, however, recruiting will be continued for the eligible participants. After rigorous screening, the blood samples, stools, electrocardiograms, and ultrasonic cardiogram data would be collected from participants to elucidate the relationship between gut flora and host. The second part includes a prospective follow-up study for every 6 months within 2 years. Finally, deep mining of big data and rapid machine learning will be employed to analyze the baseline data, experimental data, and clinical data to seek out the new biomarkers to predict the pathogenesis of Keshan disease.

DISCUSSION

Our study will clarify the distribution of gut flora in patients with Keshan disease and the abundance and population changes of gut flora in different stages of the disease. Through the big data platform analyze the relationship between environmental factors, clinical factors, and gut flora, the main factors affecting the occurrence of Keshan disease were identified, and the changed molecular pathways of gut flora were predicted. Finally, the specific gut flora and molecular pathways affecting Keshan disease were identified by metagenomics combined with metabonomic analysis.

TRIAL REGISTRATION

ChiCTR1900026639. Registered on 16 October 2019.

Dissecting in silico Mutation Prediction of Variants in African Genomes: Challenges and Perspectives

Genomic medicine is set to drastically improve clinical care globally due to high throughput technologies which enable speedy in silico detection and analysis of clinically relevant mutations. However, the variability in the in silico prediction methods and categorization of functionally relevant genetic variants can pose specific challenges in some populations. In silico mutation prediction tools could lead to high rates of false positive/negative results, particularly in African genomes that harbor the highest genetic diversity and that are disproportionately underrepresented in public databases and reference panels. These issues are particularly relevant with the recent increase in initiatives, such as the Human Heredity and Health (H3Africa), that are generating huge amounts of genomic sequence data in the absence of policies to guide genomic researchers to return results of variants in so-called actionable genes to research participants. This report (i) provides an inventory of publicly available Whole Exome/Genome data from Africa which could help improve reference panels and explore the frequency of pathogenic variants in actionable genes and related challenges, (ii) reviews available in silico prediction mutation tools and the criteria for categorization of pathogenicity of novel variants, and (iii) proposes recommendations for analyzing pathogenic variants in African genomes for their use in research and clinical practice. In conclusion, this work proposes criteria to define mutation pathogenicity and actionability in human genetic research and clinical practice in Africa and recommends setting up an African expert panel to oversee the proposed criteria.