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Genome-Wide Association Analysis Using R.

Julio Isidro-Sánchez1, Deniz Akdemir2, Gracia Montilla-Bascón2

  • 1Agriculture and Food Science, University College Dublin, Room 1.38, Belfield, Dublin 4, Ireland. j.isidro@ucd.ie.

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|January 30, 2017
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Summary
This summary is machine-generated.

This guide offers practical R code for genome-wide association studies (GWAS) in oats using genotype by sequencing (GBS) markers. It addresses common statistical analysis issues for efficient oat breeding and genetic research.

Keywords:
Bonferroni correctionFalse discovery rateGWASLinkage disequilibriumPopulation structure

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Area of Science:

  • Agricultural Science
  • Genetics
  • Bioinformatics

Background:

  • Genome-wide association studies (GWAS) are crucial for identifying genetic loci associated with traits.
  • Genotype by sequencing (GBS) provides a cost-effective method for high-throughput genotyping.
  • Oats (Avena sativa) are an important global crop requiring genetic improvement.

Purpose of the Study:

  • To provide a practical guide for conducting GWAS in oats using R.
  • To demonstrate the application of the rrBLUP R package for statistical analysis.
  • To address common challenges encountered during GWAS data analysis.

Main Methods:

  • Utilizing the R programming language for statistical analysis.
  • Implementing the rrBLUP package for mixed-model analysis in GWAS.
  • Applying genotype by sequencing (GBS) data for marker analysis.

Main Results:

  • The chapter presents R code for performing GWAS.
  • Practical considerations and potential issues in GWAS analysis are discussed.
  • The rrBLUP package is demonstrated for analyzing oat GBS data.

Conclusions:

  • This chapter serves as a practical resource for researchers conducting oat GWAS.
  • The provided R code and guidelines facilitate efficient genetic analysis.
  • The study aims to enhance oat breeding through accessible GWAS methodologies.