Identification of susceptible genes for complex chronic diseases based on disease risk functional SNPs and interaction networks

Wan, Li; Zhu, Lina; Huang, Hao; He, Yuehan; Liu, Jun-Jie; Liu, Weimin; Chen, Lina; He, Weiming

doi:10.1016/j.jbi.2017.09.006

Cited by 8 publications

(1 citation statement)

References 72 publications

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“…UAEDIAB study has identified a list of patient's related measurements including obesity/BMI, hypertension, obstructive sleep apnea, and dyslipidemia that correlated with diabetes. Interestingly, most of these identified phenotypes have previously been associated with genetic variants [13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

An Integrative Phenotype–Genotype Approach Using Phenotypic Characteristics from the UAE National Diabetes Study Identifies HSD17B12 as a Candidate Gene for Obesity and Type 2 Diabetes

Hachim

Aljaibeji

Hamoudi

et al. 2020

Genes

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The United Arab Emirates National Diabetes and Lifestyle Study (UAEDIAB) has identified obesity, hypertension, obstructive sleep apnea, and dyslipidemia as common phenotypic characteristics correlated with diabetes mellitus status. As these phenotypes are usually linked with genetic variants, we hypothesized that these phenotypes share single nucleotide polymorphism (SNP)-clusters that can be used to identify causal genes for diabetes. We explored the National Human Genome Research Institute-European Bioinformatics Institute Catalog of Published Genome-Wide Association Studies (NHGRI-EBI GWAS) to list SNPs with documented association with the UAEDIAB-phenotypes as well as diabetes. The shared chromosomal regions affected by SNPs were identified, intersected, and searched for Enriched Ontology Clustering. The potential SNP-clusters were validated using targeted DNA next-generation sequencing (NGS) in two Emirati diabetic patients. RNA sequencing from human pancreatic islets was used to study the expression of identified genes in diabetic and non-diabetic donors. Eight chromosomal regions containing 46 SNPs were identified in at least four out of the five UAEDIAB-phenotypes. A list of 34 genes was shown to be affected by those SNPs. Targeted NGS from two Emirati patients confirmed that the identified genes have similar SNP-clusters. ASAH1, LRP4, FES, and HSD17B12 genes showed the highest SNPs rate among the identified genes. RNA-seq analysis revealed high expression levels of HSD17B12 in human islets and to be upregulated in type 2 diabetes (T2D) donors. Our integrative phenotype-genotype approach is a novel, simple, and powerful tool to identify clinically relevant potential biomarkers in diabetes. HSD17B12 is a novel candidate gene for pancreatic β-cell function.

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Section: Introductionmentioning

confidence: 99%

An Integrative Phenotype–Genotype Approach Using Phenotypic Characteristics from the UAE National Diabetes Study Identifies HSD17B12 as a Candidate Gene for Obesity and Type 2 Diabetes

Hachim

Aljaibeji

Hamoudi

et al. 2020

Genes

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Flexible Supercapacitor with a Pure DNA Gel Electrolyte

Mitta¹,

Rana

Kim³

et al. 2022

Adv Materials Inter

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of charge storage has been driven by the advancement of batteries and supercapacitors applied for a variety of eco-friendly electronics and vehicles. [1,2] Supercapacitors are representative electrochemical systems that are attractive as advanced energy storage devices due to their higher power density, long cycle life, low cost, and easy fabrication. [3][4][5] Either organic [6] or inorganic [5,7] materials have been used in electrodes and electrolytes, which are major components in the electrochemical energy storage and conversion system. The development of electrolyte materials has been less emphasized than electrodes despite their importance. Due to

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Disease genes prediction by HMM based PU-learning using gene expression profiles

Nikdelfaz

Jalili

2018

Journal of Biomedical Informatics

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Predicting disease candidate genes from human genome is a crucial part of nowadays biomedical research. According to observations, diseases with the same phenotype have the similar biological characteristics and genes associated with these same diseases tend to share common functional properties. Therefore, by applying machine learning methods, new disease genes are predicted based on previous ones. In recent studies, some semi-supervised learning methods, called Positive-Unlabeled Learning (PU-Learning) are used for predicting disease candidate genes. In this study, a novel method is introduced to predict disease candidate genes through gene expression profiles by learning hidden Markov models. In order to evaluate the proposed method, it is applied on a mixed part of 398 disease genes from three disease types and 12001 unlabeled genes. Compared to the other methods in literature, the experimental results indicate a significant improvement in favor of the proposed method.

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Identification of susceptible genes for complex chronic diseases based on disease risk functional SNPs and interaction networks

Cited by 8 publications

References 72 publications

An Integrative Phenotype–Genotype Approach Using Phenotypic Characteristics from the UAE National Diabetes Study Identifies HSD17B12 as a Candidate Gene for Obesity and Type 2 Diabetes

An Integrative Phenotype–Genotype Approach Using Phenotypic Characteristics from the UAE National Diabetes Study Identifies HSD17B12 as a Candidate Gene for Obesity and Type 2 Diabetes

Flexible Supercapacitor with a Pure DNA Gel Electrolyte

Disease genes prediction by HMM based PU-learning using gene expression profiles

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