Single nucleotide polymorphisms (SNPs) and the search for obesity-related genes

Miranda, Débora Marques de; Romano-Silva, Marco Aurélio; Marco, Luiz

doi:10.1590/s0004-27302008000400001

Arq Bras Endocrinol Metab

2008

DOI: 10.1590/s0004-27302008000400001

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Single nucleotide polymorphisms (SNPs) and the search for obesity-related genes

Débora Marques de Miranda

Marco Aurélio Romano-Silva²,

Luiz Marco³

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2021

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“…Moreover, SNP expression levels are powerful indicators that can be used to distinguish genes across tissues. Many studies have associated variants such as SNPs with disease progression, e.g., cardiovascular risks [4], obesity [5], hypoxia (a condition characterized by a limited oxygen supply) [6,7], and coronary artery disease [8]. Furthermore, most existing work has focused on developing disease prediction models based on SNPs associated with a single disease only, for example, breast cancer [9], inflammatory bowel disease [10], and obesity [11].…”

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A novel feature extraction method based on highly expressed SNPs for tissue-specific gene prediction

Dhaliwal

Wagner

2021

J Big Data

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Background Gene expression provides a means for an organism to produce gene products necessary for the organism to live. Variation in the significant gene expression levels can distinguish the gene and the tissue in which the gene is expressed. Tissue-specific gene expression, often determined by single nucleotide polymorphisms (SNPs), provides potential molecular markers or therapeutic targets for disease progression. Therefore, SNPs are good candidates for identifying disease progression. The current bioinformatics literature uses gene network modeling to summarize complex interactions between transcription factors, genes, and gene products. Here, our focus is on the SNPs’ impact on tissue-specific gene expression levels. To the best of our knowledge, we are not aware of any studies that distinguish tissue-specific genes using SNP expression levels. Method We propose a novel feature extraction method based on highly expressed SNPs using k-mers as features. We also propose optimal k-mer and feature sizes used in our approach. Determining the optimal sizes is still an open research question as it depends on the dataset and purpose of the analysis. Therefore, we evaluate our algorithm’s performance on a range of k-mer and feature sizes using a multinomial naive Bayes (MNB) classifier on genes in the 49 human tissues from the Genotype-Tissue Expression (GTEx) portal. Conclusions Our approach achieves practical performance results with k-mers of size 3. Based on the purpose of the analysis and the number of tissue-specific genes under study, feature sizes [7, 8, 9] and [8, 9, 10] are typically optimal for the machine learning model.

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confidence: 99%

A novel feature extraction method based on highly expressed SNPs for tissue-specific gene prediction

Dhaliwal

Wagner

2021

J Big Data

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Single nucleotide polymorphisms (SNPs) and the search for obesity-related genes

Cited by 1 publication

References 4 publications

A novel feature extraction method based on highly expressed SNPs for tissue-specific gene prediction

A novel feature extraction method based on highly expressed SNPs for tissue-specific gene prediction

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