Metabolic Traits and Stroke Risk in Individuals of African Ancestry: Mendelian Randomization Analysis.

BACKGROUND AND PURPOSE: Metabolic traits affect ischemic stroke (IS) risk, but the degree to which this varies across different ethnic ancestries is not known. Our aim was to apply Mendelian randomization to investigate the causal effects of type 2 diabetes (T2D) liability and lipid traits on IS risk in African ancestry individuals, and to compare them to estimates obtained in European ancestry individuals. METHODS: For African ancestry individuals, genetic proxies for T2D liability and circulating lipids were obtained from a meta-analysis of the African Partnership for Chronic Disease Research study, the UK Biobank, and the Million Veteran Program (total N=77 061). Genetic association estimates for IS risk were obtained from the Consortium of Minority Population Genome-Wide Association Studies of Stroke (3734 cases and 18 317 controls). For European ancestry individuals, genetic proxies for the same metabolic traits were obtained from Million Veteran Program (lipids N=297 626, T2D N=148 726 cases, and 965 732 controls), and genetic association estimates for IS risk were obtained from the MEGASTROKE study (34 217 cases and 406 111 controls). Random-effects inverse-variance weighted Mendelian randomization was used as the main method, complemented with sensitivity analyses more robust to pleiotropy. RESULTS: Higher genetically proxied T2D liability, LDL-C (low-density lipoprotein cholesterol), total cholesterol and lower genetically proxied HDL-C (high-density lipoprotein cholesterol) were associated with increased risk of IS in African ancestry individuals (odds ratio per doubling the odds of T2D liability [95% CI], 1.09 [1.07-1.11]; per standard-deviation increase in LDL-C, 1.12 [1.04-1.21]; total cholesterol: 1.23 [1.06-1.43]; HDL-C, 0.93 [0.89-0.99]). There was no evidence for differences in these estimates when performing analyses in European ancestry individuals. CONCLUSIONS: Our analyses support a causal effect of T2D liability and lipid traits on IS risk in African ancestry individuals, with Mendelian randomization estimates similar to those obtained in European ancestry individuals.

Transcription-translation error: In-silico investigation of the structural and functional impact of deleterious single nucleotide polymorphisms in GULP1 gene.

Nonsynonymous single nucleotide polymorphisms (nsSNPs) are one of the most common forms of mutations known to disrupt the product of translation thereby altering the protein structure-function relationship. GULP1 (PTB domain-containing engulfment adaptor protein 1) is an evolutionarily conserved adaptor protein that has been associated with glycated hemoglobin (HbA1c) in Genome-Wide Association Studies (GWAS). In order to understand the role of GULP1 in the etiology of diabetes, it is important to study some functional nsSNPs present within the GULP1 protein. We, therefore, used a SNPinformatics approach to retrieve, classify, and determine the stability effect of some nsSNPs. Y27C, G142D, A144T, and Y149C were jointly predicted by the pathogenic-classifying tools to be disease-causing, however, only G142D, A144T, and Y149C had their structural architecture perturbed as predicted by I-MUTANT and MuPro. Interestingly, G142D and Y149C occur at positions 142 and 149 of GULP1 which coincidentally are found within the binding site of GULP1. Protein-Protein interaction analysis also revealed that GULP1 interacted with 10 proteins such as Cell division cycle 5-like protein (CDC5L), ADP-ribosylation factor 6 (ARF6), Arf-GAP with coiled-coil (ACAP1), and Multiple epidermal growth factor-like domains protein 10 (MEGF10), etc. Taken together, rs1357922096, rs1264999716, and rs128246649 could be used as genetic biomarkers for the diagnosis of diabetes. However, being a computational study, these nsSNPs require experimental validation to explore their metabolic involvement in the pathogenesis of diseases.

Computational and drug target analysis of functional single nucleotide polymorphisms associated with Haemoglobin Subunit Beta (HBB) gene.

There is overwhelming evidence implicating Haemoglobin Subunit Beta (HBB) protein in the onset of beta thalassaemia. In this study for the first time, we used a combined SNP informatics and computer algorithms such as Neural network, Bayesian network, and Support Vector Machine to identify deleterious non-synonymous Single Nucleotide Polymorphisms (nsSNPs) present in the HBB gene. Our findings highlight three major mutation points (R31G, W38S, and Q128P) within the HBB gene sequence that have significant statistical and computational associations with the onset of beta thalassaemia. The dynamic simulation study revealed that R31G, W38S, and Q128P elicited high structural perturbation and instability, however, the wild type protein was considerably stable. Ten compounds with therapeutic potential against HBB were also predicted by structure-based virtual screening. Interestingly, the instability caused by the mutations was reversed upon binding to a ligand. This study has been able to predict potential deleterious mutants that can be further explored in the understanding of the pathological basis of beta thalassaemia and the design of tailored inhibitors.