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Advances in statistical methods to handle large data sets for GWAS in crop breeding

Code: 9781838798345
Boby Mathew, University of Bonn, Germany; Mikko J. Sillanpää, University of Oulu, Finland; and Jens Léon, University of Bonn, Germany

Chapter synopsis: One of the most important statistical methods of handling large data sets for genome-wide association mapping (GWAS) is quantitative trait loci (QTL) analysis. Two approaches to QTL analysis are linkage analysis (LA) and linkage disequilibrium (LD) mapping. Even though association and linkage mapping are viewed as fundamentally different approaches, both methods try to make use of recombination events. This chapter discusses some of the main challenges for GWAS studies with large data sets. This chapter describes both single-locus and multilocus association models, before going on to discuss high dimensional data space in GWAS, the significance threshold for association, and dimensionality reduction methods. Finally, the chapter looks ahead to future trends in this field.

DOI: 10.19103/AS.2019.0051.20
Table of contents 1 Introduction 2 Single-locus association model 3 Multilocus association model 4 High-dimensional data in GWAS 5 Significance threshold for association 6 Dimensionality reduction methods 7 Conclusion and future trends 8 Where to look for further information 9 References

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