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Trait Mapping Approaches Through Association Analysis in Plants

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Plant Genetics and Molecular Biology

Part of the book series: Advances in Biochemical Engineering/Biotechnology ((ABE,volume 164))

Abstract

Previously, association mapping (AM) methodology was used to unravel genetic complications in animal science by measuring the complex traits for candidate and non-candidate genes. Nowadays, this statistical approach is widely used to clarify the complexity in plant breeding program-based genome-wide breeding strategies, marker development, and diversity analysis. This chapter is particularly focused on methodologies with limitations and provides an overview of AM models and software used up to now. Association or linkage disequilibrium mapping has become a very popular method for discovering candidate and non-candidate genes and confirmation of quantitative trait loci (QTL) on various parts of the genome and in marker-assisted selection for breeding. Previously, various QTL investigations were carried out for different plants exclusively by linkage mapping. To help to understand the basics of modern molecular genetic techniques, in this chapter we summarize previous studies done on different crops. AM offers high-resolution power when there is large genotypic diversity and low linkage disequilibrium (LD) for the germplasm being investigated. The benefits of AM, compared with traditional QTL mapping, include a relatively detailed mapping resolution and a far less time-consuming approach since no mapping populations need to be generated. The advancements in genotyping and computational techniques have encouraged the use of AM. AM provides a fascinating approach for genetic investigation of QTLs, due to its resolution and the possibility to study the various genomic areas at the same time without construction of mapping populations. In this chapter we also discuss the advantages and disadvantages of AM, especially in the dicotyledonous crops Fabaceae and Solanaceae, with various genome-size reproductive strategies (clonal vs. sexual), and statistical models. The main objective of this chapter is to highlight the uses of association genetics in major and minor crop species that have trouble being analyzed for dissection of complex traits by identification of the factor responsible for controlling the effect of trait.

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Abbreviations

AM:

Association mapping

CV:

Coefficient variance

EST:

Expressed sequence tags

FDR:

False discovery rate

FWER:

Family-wise error rate

GLM:

General linear model

GS:

Genomic selection

GWAS:

Genome-wide association study

LD:

Linkage disequilibrium

MAS:

Marker-assisted selection

MCA:

Multiple correspondence analysis

MCMC:

Markov chain Monte Carlo

MLM:

Mixed linear model

MLMM:

Multiple locus multiple marker

MTMM:

Multiple trait multiple marker

PCA:

Principal component analysis

QTL:

Quantitative trait locus

SA:

Structure analysis

SLST:

Single locus single trait

SNP:

Single nucleotide polymorphism

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Authors and Affiliations

  1. National Agri-Food Biotechnology Institute (NABI), Mohali, India

    M. Saba Rahim, Himanshu Sharma, Afsana Parveen & Joy K. Roy

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  1. M. Saba Rahim
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  2. Himanshu Sharma
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  3. Afsana Parveen
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  4. Joy K. Roy
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Correspondence to Joy K. Roy .

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Editors and Affiliations

  1. International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad, India

    Rajeev K. Varshney

  2. International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad, India

    Manish K. Pandey

  3. International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad, India

    Annapurna Chitikineni

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Saba Rahim, M., Sharma, H., Parveen, A., Roy, J.K. (2018). Trait Mapping Approaches Through Association Analysis in Plants. In: Varshney, R., Pandey, M., Chitikineni, A. (eds) Plant Genetics and Molecular Biology. Advances in Biochemical Engineering/Biotechnology, vol 164. Springer, Cham. https://doi.org/10.1007/10_2017_50

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