Alternative Applications of Genotyping Array Data Using Multivariant Methods

Polymorphisms in drug-metabolizing genes and urinary bladder cancer susceptibility and prognosis: Possible impacts and future management

Epidemiological studies have shown the association of genetic variants with risks of occupational and environmentally induced cancers, including bladder (BC). The current review summarizes the effects of variants in genes encoding phase I and II enzymes in well-designed studies to highlight their contribution to BC susceptibility and prognosis. Polymorphisms in genes codifying drug-metabolizing proteins are of particular interest because of their involvement in the metabolism of exogenous genotoxic compounds, such as tobacco and agrochemicals. The prognosis between muscle-invasive and non-muscle-invasive diseases is very different, and it is difficult to predict which will progress worse. Web of Science, PubMed, and Medline were searched to identify studies published between January 1, 2010, and February 2023. We included 73 eligible studies, more than 300 polymorphisms, and 46 genes/loci. The most studied candidate genes/loci of phase I metabolism were CYP1B1, CYP1A1, CYP1A2, CYP3A4, CYP2D6, CYP2A6, CYP3E1, and ALDH2, and those in phase II were GSTM1, GSTT1, NAT2, GSTP1, GSTA1, GSTO1, and UGT1A1. We used the 46 genes to construct a network of proteins and to evaluate their biological functions based on the Reactome and KEGG databases. Lastly, we assessed their expression in different tissues, including normal bladder and BC samples. The drug-metabolizing pathway plays a relevant role in BC, and our review discusses a list of genes that could provide clues for further exploration of susceptibility and prognostic biomarkers.

Application of whole-genome tiling array at Shanghai port, China: An alternative method for SARS-CoV-2 surveillance

The ongoing coronavirus disease 2019 (COVID-19) pandemic, driven by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), highlights the critical role of genomic surveillance in tracking rapidly spreading viruses and their evolving lineages. The emergence of the SARS-CoV-2 tiling array, a comprehensive tool capable of capturing the entire viral genome, has presented a promising avenue for variants. This study introduces the SARS-CoV-2 tiling array as a novel method for port inspection. Using next-generation sequencing as a benchmark, 35 positive samples underwent sequencing through both methodologies, including the Alpha variant (B.1.1.7), Delta variants (AY.120, AY.122, AY.23.1), and Omicron variants (BA.1, BA.2, BA.2.75, BA.4, BA.5, BE.1, BF.7, BN.1, BQ.1, XBB.1) within the sample set. The whole-genome tiling array demonstrated successful identification of various sublineages of SARS-CoV-2. The average sequencing coverage rates were 99.22% (96.82%-99.92%) for the whole-genome tiling array and 98.56% (92.81%-99.59%) for Illumina sequencing, respectively. The match rates of these two methods ranged from 92.81%-99.59%, with an average rate of 98.56%. Among the benefits of the whole-genome tiling array are its cost-effectiveness and equipment simplification, making it particularly suitable for identifying SARS-CoV-2 variants in the front-line inspection department. The aforementioned findings provide valuable insights into the surveillance of COVID-19 and present a pragmatic solution for improving quarantine measures at entry points.

Genomic variants disrupt miRNA-mRNA regulation

Micro RNA (miRNA) and its regulatory effect on messenger RNA (mRNA) gene expression are a major focus in cancer research. Disruption in the normal miRNA-mRNA regulation network can result in serious cascading biological repercussions. In this study, we curated miRNA-related variants from major genomic consortiums and thoroughly evaluated how these variants could exert their effects by cross-validating with independent functional knowledge bases. Nearly all known variants (more than 664 million) categorized by type (germline, somatic, epigenetic) were mapped to the genomic regions involved in miRNA-mRNA binding (miRNA seeds and miRNA-mRNA 3'-UTR binding sequence). Subsets of miRNA-related variants supported by additional functional evidence, such as expression Quantitative Trait Loci (eQTL) and Genome-Wide Association Study (GWAS), were identified and scrutinized. Our results show that variants in miRNA seeds can substantially alter the composition of an miRNA's target mRNA set. Various functional analyses converged to reveal a post-transcriptional complex regulatory network where miRNA, eQTL, and RNA-binding protein intertwined to disseminate the impact of genomic variants. These results may potentially explain how certain variants affect disease/trait risks in genome wide association studies.

Fully exploiting SNP arrays: a systematic review on the tools to extract underlying genomic structure

Single nucleotide polymorphisms (SNPs) are the most abundant type of genomic variation and the most accessible to genotype in large cohorts. However, they individually explain a small proportion of phenotypic differences between individuals. Ancestry, collective SNP effects, structural variants, somatic mutations or even differences in historic recombination can potentially explain a high percentage of genomic divergence. These genetic differences can be infrequent or laborious to characterize; however, many of them leave distinctive marks on the SNPs across the genome allowing their study in large population samples. Consequently, several methods have been developed over the last decade to detect and analyze different genomic structures using SNP arrays, to complement genome-wide association studies and determine the contribution of these structures to explain the phenotypic differences between individuals. We present an up-to-date collection of available bioinformatics tools that can be used to extract relevant genomic information from SNP array data including population structure and ancestry; polygenic risk scores; identity-by-descent fragments; linkage disequilibrium; heritability and structural variants such as inversions, copy number variants, genetic mosaicisms and recombination histories. From a systematic review of recently published applications of the methods, we describe the main characteristics of R packages, command-line tools and desktop applications, both free and commercial, to help make the most of a large amount of publicly available SNP data.

GTQC: Automated Genotyping Array Quality Control and Report

Genotyping array is the most economical approach for conducting large-scale genome-wide genetic association studies. Thorough quality control is key to generating high integrity genotyping data and robust results. Quality control of genotyping array is generally a complicated process, as it requires intensive manual labor in implementing the established protocols and curating a comprehensive quality report. There is an urgent need to reduce manual intervention via an automated quality control process. Based on previously established protocols and strategies, we developed an R package GTQC (GenoTyping Quality Control) to automate a majority of the quality control steps for general array genotyping data. GTQC covers a comprehensive spectrum of genotype data quality metrics and produces a detailed HTML report comprising tables and figures. Here, we describe the concepts underpinning GTQC and demonstrate its effectiveness using a real genotyping dataset. R package GTQC streamlines a majority of the quality control steps and produces a detailed HTML report on a plethora of quality control metrics, thus enabling a swift and rigorous data quality inspection prior to downstream GWAS and related analyses. By significantly cutting down on the time on genotyping quality control procedures, GTQC ensures maximum utilization of available resources and minimizes waste and inefficient allocation of manual efforts. GTQC tool can be accessed at https://github.com/slzhao/GTQC.

Detecting SARS-CoV-2 and its variant strains with a full genome tiling array

Coronavirus disease 2019 pandemic is the most damaging pandemic in recent human history. Rapid detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and variant strains is paramount for recovery from this pandemic. Conventional SARS-CoV-2 tests interrogate only limited regions of the whole SARS-CoV-2 genome, which are subjected to low specificity and miss the opportunity of detecting variant strains. In this work, we developed the first SARS-CoV-2 tiling array that captures the entire SARS-CoV-2 genome at single nucleotide resolution and offers the opportunity to detect point mutations. A thorough bioinformatics protocol of two base calling methods has been developed to accompany this array. To demonstrate the effectiveness of the tiling array, we genotyped all genomic positions of eight SARS-CoV-2 samples. Using high-throughput sequencing as the benchmark, we show that the tiling array had a genome-wide accuracy of at least 99.5%. From the tiling array analysis results, we identified the D614G mutation in the spike protein in four of the eight samples, suggesting the widespread distribution of this variant at the early stage of the outbreak in the United States. Two additional nonsynonymous mutations were identified in one sample in the nucleocapsid protein (P13L and S197L), which may complicate future vaccine development. With around $5 per array, supreme accuracy, and an ultrafast bioinformatics protocol, the SARS-CoV-2 tiling array makes an invaluable toolkit for combating current and future pandemics. Our SARS-CoV-2 tilting array is currently utilized by Molecular Vision, a CLIA-certified lab for SARS-CoV-2 diagnosis.

The phenotype of 15 cases with rare 8q24.13-q24.3 deletions-A new syndrome or still an enigma?

Diagnosis of rare copy number variants (CNVs) with scarce literature evidence poses a major challenge for interpretation of the clinical significance of chromosomal microarray analysis (CMA) results, especially in the prenatal setting. Bioinformatic tools can be used to assist in this issue; however, this prediction can be imprecise. Our objective was to describe the phenotype of the rare copy number losses encompassing the 8q24.13-q24.3 locus, and to find common features in terms of genomic coordinates, gene content, and clinical phenotypic characteristics. Appropriate cases were retrieved using local databases of two largest Israeli centers performing CMA analysis. In addition, literature and public databases search was performed. Local database search yielded seven new patients with del (8)(q24.13q24.3) (one of these with an additional copy number variant). Literature and public databases search yielded eight additional patients. The cases showed high phenotypic variability, ranging from asymptomatic adults and fetuses with normal ultrasound to patients with autism/developmental delay (6/11 postnatal cases, 54.5%). No clear association was noted between the specific disease-causing/high-pLI gene content of the described del (8)(q24.13q24.3) to neurodevelopmental disorders, except for a possibly relevant locus encompassing the KCNQ3 gene. We present the challenges in classification of rare variants with limited clinical information. In such cases, genotype-phenotype correlation must be assessed with extra-caution and possibly using additional methods to assist the classification, especially in the prenatal setting.