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Related Concept Videos

Single Nucleotide Polymorphisms-SNPs01:05

Single Nucleotide Polymorphisms-SNPs

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A single nucleotide polymorphism or SNP is a single nucleotide variation at a specific genomic position in a large population. It is the most prevalent type of sequence variation found in the human genome. Point mutations that occur in more than 1% of the population qualify as SNPs. These are present once every 1000 nucleotides on an average in the human genome. Replacement of a purine with another purine (A/G) or a pyrimidine with another pyrimidine (C/T) is known as a transition. In contrast,...
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Comparing Copy Number Variations and SNPs02:26

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Sequencing of the human genome has opened up several best-kept secrets of the genome. Scientists have identified thousands of genome variations that exist within a population. These variations can be a single nucleotide or a larger chromosomal variation.
Copy number variations or CNVs are the structural variations that cover more than 1kb of DNA sequence. The single nucleotide polymorphism (SNP), on the other hand, is a single nucleotide change or a point mutation that is found in more than 1%...
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Regulation of Expression at Multiple Steps01:23

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The gene expression in cells is regulated at different stages: (i) transcription, (ii) RNA processing, (iii) RNA localization, and (iv) translation. Transcriptional regulation is mediated by regulatory proteins such as transcription factors, activators, or repressors—these control gene expression by initiating or inhibiting the transcription of genes. Once a precursor or pre-mRNA is produced, it undergoes post-transcriptional modification, including 5' capping, splicing, and the...
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Cis-regulatory sequences are short fragments of non-coding DNA that are present on the same chromosomes as the genes that they regulate. These fragments serve as binding sites for transcriptional regulators, proteins that are responsible for controlling gene transcription and differential gene expression across cell types in eukaryotes. Cis-regulatory sequences can be close to the gene of interest or thousands of bases away in the DNA sequence; however, those sequences that are further away are...
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Cis-regulatory Sequences02:02

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What is Gene Expression?01:36

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A gene is a stretch of DNA that serves as the blueprint for functional RNAs and proteins. Since DNA is comprised  of nucleotides and proteins are comprised of amino acids, a mediator is required to convert the information encoded in DNA into proteins. This mediator is the messenger RNA (mRNA). mRNA copies the blueprint from DNA by a process called transcription. In eukaryotes, transcription occurs in the nucleus by complementary base-pairing with the DNA template. The mRNA is then...
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Related Experiment Video

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Identification of Alternative Splicing and Polyadenylation in RNA-seq Data
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Prediction of gene expression with cis-SNPs using mixed models and regularization methods.

Ping Zeng1,2, Xiang Zhou3, Shuiping Huang4

  • 1Department of Epidemiology and Biostatistics, Xuzhou Medical University, 209 Tongshan Rd, Xuzhou, Jiangsu, 221004, China. zpstat@xzhmu.edu.cn.

BMC Genomics
|May 12, 2017
PubMed
Summary

Predicting gene expression from single nucleotide polymorphisms (SNPs) is possible. Bayesian sparse linear mixed models (BSLMM) offer robust performance across various scenarios for gene expression prediction.

Keywords:
Bayesian sparse linear mixed modelCis-SNPsElastic netGene expressionLassoLinear mixed modelPrediction model

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Area of Science:

  • Genetics
  • Bioinformatics
  • Computational Biology

Background:

  • Human gene expression is heritable, enabling prediction from single nucleotide polymorphisms (SNPs).
  • Predicting gene expression aids in understanding SNP function and the molecular basis of diseases.

Purpose of the Study:

  • Compare prediction methods for gene expression using cis-single nucleotide polymorphisms (cis-SNPs).
  • Evaluate model performance under diverse genetic architectures and real-world data.

Main Methods:

  • Compared linear mixed models (LMM), sparse models (Lasso, elastic net/ENET), and Bayesian sparse linear mixed models (BSLMM).
  • Evaluated methods via simulations and application to Geuvadis gene expression data.

Main Results:

  • All tested methods (Lasso, ENET, LMM, BSLMM) performed best under their specific assumptions.
  • Bayesian sparse linear mixed models (BSLMM) showed robust performance across scenarios.
  • Highly predictive genes (R² ≥ 0.30) suggested sparse genetic architectures, with Lasso, ENET, and BSLMM outperforming LMM.
  • Predictive genes were enriched in independent linkage disequilibrium (LD) blocks.

Conclusions:

  • Gene expression is predictable from cis-SNPs using established models.
  • Predictive genes are enriched in independent LD blocks, offering insights into SNP function.
  • Gene expression prediction aids functional interpretation of SNPs identified in Genome-Wide Association Studies (GWAS).