Pleiotropy of Copy Number Variation in Human Genome

Кашеварова, А. А.; Drozdov, G. V.; Fedotov, D. A.; Лебедев, И. Н.

doi:10.1134/s1022795422100040

Cited by 2 publications

(2 citation statements)

References 127 publications

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“…For instance, altering the copy number of a dose-sensitive gene like BHLHA9 can be detrimental to disease pathogenesis, whereas changing the copy number of dose-insensitive genes is unlikely to cause harm [50]. Even within the same chromosomal region, CNVs may be associated with different phenotypes, ranges of severity, and incomplete penetration [51]. In the present study, among 39 detected cases of chromosomal abnormalities, 8 CNV-related chromosomal disorders were known to have complete penetrance: DiGeorge, Klinefelter, Down, Triple-X, Turner, Williams (duplication and deletion), and Prader-Willi/Angelman syndromes.…”

Section: Discussionmentioning

confidence: 99%

Improving CNV Detection Performance in Microarray Data Using a Machine Learning-Based Approach

Goh,

Kwon,

Kim

et al. 2023

Diagnostics

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Copy number variation (CNV) is a primary source of structural variation in the human genome, leading to several disorders. Therefore, analyzing neonatal CNVs is crucial for managing CNV-related chromosomal disabilities. However, genomic waves can hinder accurate CNV analysis. To mitigate the influences of the waves, we adopted a machine learning approach and developed a new method that uses a modified log R ratio instead of the commonly used log R ratio. Validation results using samples with known CNVs demonstrated the superior performance of our method. We analyzed a total of 16,046 Korean newborn samples using the new method and identified CNVs related to 39 genetic disorders were identified in 342 cases. The most frequently detected CNV-related disorder was Joubert syndrome 4. The accuracy of our method was further confirmed by analyzing a subset of the detected results using NGS and comparing them with our results. The utilization of a genome-wide single nucleotide polymorphism array with wave offset was shown to be a powerful method for identifying CNVs in neonatal cases. The accurate screening and the ability to identify various disease susceptibilities offered by our new method could facilitate the identification of CNV-associated chromosomal disease etiologies.

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Section: Discussionmentioning

confidence: 99%

Improving CNV Detection Performance in Microarray Data Using a Machine Learning-Based Approach

Goh,

Kwon,

Kim

et al. 2023

Diagnostics

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“…[1] Many genes in human beings exhibit the phenomenon of pleiotropism. [2] Human genome genetic variation effects on gene expression that leads to phenotypic variation and disease susceptibility. [3] The vitamin-D receptor gene (VDRG) is around 75kb has pleiotropic effects on biological actions of 1,25dihydroxy vitamin -D3 to modulate the expression of genes in human beings.…”

Section: Introductionmentioning

confidence: 99%

Codon usage bias study of the Vitamin D receptor (VDR) gene of Multiple Sclerosis and Diabetes-1 patients

Barik

Turuk

Giri

2023

Preprint

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The codon bias usage study of the 33 Vitamin D Receptor (VDR) genes of the Multiple sclerosis and Diabetes-1 patients were characterized. Various computational tools were used to find out the codon usage pattern of the VDR genes in both group of patients. The base compositions, nucleotide substation rates, codon adaptation index, hydrophobic nature of the amino acids of VDR gene of both group of patients were analysed. The analysis of the synonymous codon usage pattern of the genes would helpful in the heterologous expression of the VDR genes leads to codon optimizations in Multiple Sclerosis and Diabetes-1 patients. The codon usage bias analysis of the VDR gene of the Multiple sclerosis and Diabetes-1 patients through computational approach determined the pattern of VDR gene expression and evolution during the acquiring of the disease of the patients.

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Pleiotropy of Copy Number Variation in Human Genome

Cited by 2 publications

References 127 publications

Improving CNV Detection Performance in Microarray Data Using a Machine Learning-Based Approach

Improving CNV Detection Performance in Microarray Data Using a Machine Learning-Based Approach

Codon usage bias study of the Vitamin D receptor (VDR) gene of Multiple Sclerosis and Diabetes-1 patients

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