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Comparing Copy Number Variations and SNPs

Comparing Copy Number Variations and SNPs

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%...

Single Nucleotide Polymorphisms-SNPs

Single Nucleotide Polymorphisms-SNPs

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,...

Genome-wide Association Studies-GWAS

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Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
GWAS does not require the identification of the target gene involved in...

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Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

Cis-SNPs Set Testing and PrediXcan Analysis for Gene Expression Data using Linear Mixed Models.

Ping Zeng^1,2, Ting Wang³, Shuiping Huang⁴

¹Xuzhou Medical University, Department of Epidemiology and Biostatistics, Xuzhou, 221004, China. zpstat@xzhmu.edu.cn.

Scientific Reports

|November 12, 2017

Summary

This summary is machine-generated.

Identifying expression quantitative trait loci (eQTL) genes is crucial for understanding complex diseases. This study reveals gene expression

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Area of Science:

Genetics
Genomics
Molecular Biology

Background:

Understanding the functional mechanisms of single nucleotide polymorphisms (SNPs) associated with complex diseases remains a challenge.
Expression quantitative trait loci (eQTL) studies highlight the role of regulatory variants in SNP function.
Identifying eGenes and analyzing cis-SNP effect sizes can elucidate disease genetic architecture and molecular underpinnings.

Purpose of the Study:

To identify expression quantitative trait loci (eQTL) genes (eGenes) using linear mixed models (LMM) and likelihood ratio tests (LRT) in the Geuvadis dataset.
To investigate the association of identified eGenes with complex diseases using PrediXcan analysis in the WTCCC dataset.
To explore the role of gene expression as an intermediate mechanism linking genetic variants to complex diseases.

Main Methods:

Applied linear mixed models (LMM) to gene expression data for eGene detection.
Utilized likelihood ratio tests (LRT) for statistical significance testing of eGenes.
Performed PrediXcan analysis with LMM-estimated weights to associate genes with seven complex diseases (T1D, CD, RA, T2D) in WTCCC data.

Main Results:

Approximately 11% of genes were identified as eGenes in the Geuvadis dataset.
Enrichment of eGenes was observed in linkage disequilibrium (LD) blocks, notably the MHC region.
Significant associations were found between genes and T1D (64), CD (5), RA (21), and T2D (1), with many T1D and RA genes located in the MHC region.

Conclusions:

Gene expression plays a significant intermediate role in the association between variants and complex diseases.
The findings underscore the importance of eQTL analysis in dissecting the genetic basis of complex diseases.
The MHC region is highlighted as a key area for regulatory variants influencing complex disease susceptibility.