Whole-Genome Sequencing and Genomic Variant Analysis of Kazakh Individuals

Cluster Analysis in Diabetes Research: A Systematic Review Enhanced by a Cross-Sectional Study

Background: Diabetes mellitus is a heterogeneous metabolic disorder that poses substantial challenges in the management of patients with diabetes. Emerging research underscores the potential of unsupervised cluster analysis as a promising methodological approach for unraveling the complex heterogeneity of diabetes mellitus. This systematic review evaluated the effectiveness of unsupervised cluster analysis in identifying diabetes phenotypes, elucidating the risks of diabetes-related complications, and distinguishing treatment responses. Methods: We searched MEDLINE Complete, PubMed, and Web of Science and reviewed forty-one relevant studies. Additionally, we conducted a cross-sectional study using K-means cluster analysis of real-world clinical data from 558 patients with diabetes. Results: A key finding was the consistent reproducibility of the five clusters across diverse populations, encompassing various patient origins and ethnic backgrounds. MOD and MARD were the most prevalent clusters, while SAID was the least prevalent. Subgroup analysis stratified by ethnic group indicated a higher prevalence of SIDD among individuals of Asian descent than among other ethnic groups. These clusters shared similar phenotypic traits and risk profiles for complications, with some variations in their distribution and key clinical variables. Notably, the SIRD subtype was associated with a wide spectrum of kidney-related clinical presentations. Alternative clustering techniques may reveal additional clinically relevant diabetes subtypes. Our cross-sectional study identified five subgroups, each with distinct profiles of glycemic control, lipid metabolism, blood pressure, and renal function. Conclusions: Overall, the results suggest that unsupervised cluster analysis holds promise for revealing clinically meaningful subgroups with distinct characteristics, complication risks, and treatment responses that may remain undetected using conventional approaches.

Bioinformatics characterization of variants of uncertain significance in pediatric sensorineural hearing loss

Introduction

Rapid advancements in Next Generation Sequencing (NGS) and bioinformatics tools have allowed physicians to obtain genetic testing results in a more rapid, cost-effective, and comprehensive manner than ever before. Around 50% of pediatric sensorineural hearing loss (SNHL) cases are due to a genetic etiology, thus physicians regularly utilize targeted sequencing panels that identify variants in genes related to SNHL. These panels allow for early detection of pathogenic variants which allows physicians to provide anticipatory guidance to families. Molecular testing does not always reveal a clear etiology due to the presence of multigenic variants with varying classifications, including the presence of Variants of Uncertain Significance (VUS). This study aims to perform a preliminary bioinformatics characterization of patients with variants associated with Type II Usher Syndrome in the presence of other multigenic variants. We also provide an interpretation algorithm for physicians reviewing molecular results with medical geneticists.

Methods

Review of records for multigenic and/or VUS results identified several potential subjects of interest. For the purposes of this study, two ADGRV1 compound heterozygotes met inclusion criteria. Sequencing, data processing, and variant calling (the process by which variants are identified from sequence data) was performed at Invitae (San Francisco CA). The preliminary analysis followed the recommendations outlined by the American College of Medical Genetics and Association for Molecular Pathology (ACMG-AMP) in 2015 and 2019. The present study utilizes computational analysis, predictive data, and population data as well as clinical information from chart review and publicly available information in the ClinVar database.

Results

Two subjects were identified as compound heterozygotes for variants in the gene ADGRV1. Subject 1's variants were predicted as deleterious, while Subject 2's variants were predicted as non-deleterious. These results were based on known information of the variants from ClinVar, multiple lines of computational data, population databases, as well as the clinical presentation.

Discussion

Early molecular diagnosis through NGS is ideal, as families are then able to access a wide range of resources that will ultimately support the child as their condition progresses. We recommend that physicians build strong relationships with medical geneticists and carefully review their interpretation before making recommendations to families, particularly when addressing the VUS. Reclassification efforts of VUS are supported by studies like ours that provide evidence of pathogenic or benign effects of variants.