Association mapping - concept and its application in vegetable crop improvement
Authors: Manoj Kumar. Nalla*, Kranthi Tirlangi**, Rakesh Sharma V*, Gururaj B. Matapathi* and Pradeep K. Jatav*.
*Division of Vegetable science, ICAR- Indian Agriculture Research Institute, New Delhi-12
** Division of Fruit science, Dr.YSR Horticulture University, V.R. gudem-534101
Corresponding author: email@example.com
Many horticulturally important traits such as productivity and quality, tolerance to environmental stresses, and some of forms of disease resistance are controlled by polygene. Identification of these complex traits and utilization in a crop improvement further requires mapping them in a genome of crop species using molecular markers.
The level of the genetic diversity is pivotal for world food security and survival of human civilization on earth.However, many agriculturally important variations such as productivity and quality, tolerance to environmental stresses, and some of forms of disease resistance are controlled by polygenes. These complex traits are referred to as quantitative trait loci (QTLs), and it is challenging to identify QTLs based on only traditional phenotypic evaluation. Identification of QTLs of agronomic importance and its utilization in a crop improvement further requires mapping of these QTLs in a genome of crop species using molecular markers. This was the major breakthrough and accomplishment in many crops in "genomics era" since the end of the 20th century, and now extended to flourish in the 21st century.
Association mapping identifies QTLs by examining the marker-trait associations that can be attributed to the strength of linkage disequilibrium between markers and traits across a set of diverse germplasm. Advantages of Association Mapping over classical QTL mapping are High resolution of mapping (<10 cM), Historic mutations and recombination's are considered, time required is less, Exploitation of Genetic Diversity, Development of Mapping population is not required and more than two alleles per locus can be studied simultaneously
Procedure of association mapping:
(1) Selection of a group of individuals from a natural population or germplasm collection with wide coverage of genetic diversity;
(2) Recording or measuring the phenotypic characteristics (yield, quality, tolerance, or resistance) of selected population groups, preferably, in different environments and multiple replication/ trial design;
(3) Genotyping a mapping population individuals with available molecular markers;
(4) Quantification of the extent of LD of a chosen population genome using a molecular marker data;
(5) Assessment of the population structure (the level of genetic differentiation among groups within a sampled population individuals) and kinship (coefficient of relatedness between pairs of each individuals within a sample); and
(6) based on information gained through quantification of LD and population structure, correlation of phenotypic and genotypic/haplotypic data with the application of an appropriate statistical approach that reveals "marker tags" positioned within close proximity of targeted trait of interest. Consequently, a specific gene(s) controlling a QTL of interest can be cloned using the marker tags and annotated for an exact biological function.
It is important to gain knowledge of the patterns of LD for genomic regions of the "target" organisms and the specificity of the extent of LD among different populations or groups to design and conduct unbiased association mapping. There are two terms used in population genetics, linkage equilibrium (LE), and linkage disequilibrium (LD) to describe linkage relationships (co-occurrence) of alleles at different loci in a population. LE is a random association of alleles at different loci and equals the product of allele frequencies within haplotypes. In contrast, LD is a non-random association of alleles at different loci, describing the condition with non equal (increased or reduced) frequency of the haplotypes in a population at random combination of alleles at different loci.
Association Analysis Softwares:
A. Population structure Software: STRUCTURE (http://pritch.bsd.uchicago.edu) is an excellent program to estimate population structure (Pritchard et al., 2000). If the samples are not randomly mated, it is critical that population structure be included in the association analysis.
B. Linkage Disequilibrium Software: Arlequin (http://lgb.unige.ch/arlequin) can handle a wide range of markers and sequences. It can also calculate LD from genotypic data...
C. Association software:
1. SAS (http://www.sas.com) is a general-purpose statistical software package and can carry out a wide range of statistics useful for association analysis.
2. STRAT (http://pritch.bsd.uchicago.edu) can be used for testing association of binary traits across structured populations.
3. TASSEL (http://www.maizegenetics.net) can perform ANOVA and logistic regression association tests that control for population structure.
Applications of association mapping:
• High resolution mapping
• Marker-trait associations
• Genetic diversity studies & Understanding evolution
• More precise QTL mapping
• Marker assisted selection
• Cost effective powerful gene tagging method
• Best approach in perennial horticultural crops
• Map based cloning of genes for difficult traits
About Author / Additional Info:
My self pursuing Ph.D in vegetable science in Indian agricultural research Institute.