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Related Concept Videos

Genome-wide Association Studies-GWAS01:11

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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Single Nucleotide Polymorphisms-SNPs01:05

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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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Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay EMSA and DNA-affinity Precipitation Assay DAPA
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Performing Genome-Wide Association Studies Using rMVP.

Xiaolei Liu1,2, Lilin Yin1, Haohao Zhang3

  • 1Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction, Ministry of Education & College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, China.

Methods in Molecular Biology (Clifton, N.J.)
|May 31, 2022
PubMed
Summary
This summary is machine-generated.

Genome-wide association studies (GWAS) identify genetic links to traits. This work introduces rMVP, an R package to efficiently perform GWAS on large datasets, improving accuracy and reducing errors.

Keywords:
FarmCPUGLMGWASMLMVisualizationrMVP

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Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization
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Area of Science:

  • Genetics
  • Bioinformatics
  • Statistical Genomics

Background:

  • Genome-wide association studies (GWAS) are crucial for understanding the genetic architecture of complex traits.
  • Increasing data size in GWAS presents computational challenges, necessitating efficient analytical tools.
  • Accurate identification of genetic variants requires advanced statistical models to minimize false positives and negatives.

Purpose of the Study:

  • To present an R package, rMVP, designed for efficient and accurate genome-wide association studies.
  • To provide a comprehensive workflow for handling large-scale genomic data in GWAS.
  • To facilitate the application of sophisticated statistical models for enhanced GWAS analysis.

Main Methods:

  • Utilizing the rMVP R package for GWAS data processing and analysis.
  • Implementing data preparation and population structure evaluation within rMVP.
  • Performing association tests using diverse statistical models and generating high-quality visualizations.

Main Results:

  • Demonstration of rMVP's capability to handle large datasets efficiently.
  • Successful application of advanced statistical models for robust GWAS results.
  • Generation of high-quality visualizations for intuitive interpretation of GWAS findings.

Conclusions:

  • The rMVP package offers an efficient solution for performing genome-wide association studies with big data.
  • rMVP enables the use of advanced statistical methods to improve the reliability of GWAS results.
  • This tool facilitates comprehensive GWAS analysis, from data preparation to result visualization.