Multiparent intercross populations in analysis of quantitative traits

Abstract

Most traits of interest to medical, agricultural and animal scientists show continuous variation and complex mode of inheritance. DNA-based markers are being deployed to analyse such complex traits, that are known as quantitative trait loci (QTL). In conventional QTL analysis, F2, backcross populations, recombinant inbred lines, backcross inbred lines and double haploids from biparental crosses are commonly used. Introgression lines and near isogenic lines are also being used for QTL analysis. However, such populations have major limitations like predominantly relying on the recombination events taking place in the F1 generation and mapping of only the allelic pairs present in the two parents. The second generation mapping resources like association mapping, nested association mapping and multiparent intercross populations potentially address the major limitations of available mapping resources. The potential of multiparent intercross populations in gene mapping has been discussed here. In such populations both linkage and association analysis can be conductted without encountering the limitations of structured populations. In such populations, larger genetic variation in the germplasm is accessed and various allelic and cytoplasmic interactions are assessed. For all practical purposes, across crop species, use of eight founders and a fixed population of 1000 individuals are most appropriate. Limitations with multiparent intercross populations are that they require longer time and more resource to be generated and they are likely to show extensive segregation for developmental traits, limiting their use in the analysis of complex traits. However, multiparent intercross population resources are likely to bring a paradigm shift towards QTL analysis in plant species.

Genome-Wide Association Mapping Combined with Reverse Genetics Identifies New Effectors of Low Water Potential-Induced Proline Accumulation in Arabidopsis

“…However, most QTL studies identify intervals encompassing hundreds to thousands of genes. QTL studies typically use two or at most several parental accessions (Rakshit et al, 2012) and, therefore, evaluate only a small fraction of the natural variation in a species. In contrast, GWAS generally combines phenotype and single-nucleotide polymorphism (SNP) data from 100 or more accessions to identify loci with allele frequency correlations to phenotypic variation (Atwell et al, 2010;Fournier-Level et al, 2011) or environment .…”

A Random-Model Approach to QTL Mapping in Multiparent Advanced Generation Intercross (MAGIC) Populations

“…Simulation studies showed that this method can increase statistical power and reduce type I error compared with composite interval mapping (CIM) and multiparent whole-genome average interval mapping (MPWGAIM). We demonstrated the method using a public Arabidopsis thaliana MAGIC population and a mouse MAGIC population.KEYWORDS best linear unbiased prediction; empirical Bayes; mixed model; polygene; restricted maximum likelihood; multiparental populations; Multiparent Advanced Generation Inter-Cross (MAGIC); MPP T HERE is an urgent need to develop and study multiparent advanced generation intercross (MAGIC) populations (Rakshit et al 2012). Along with nested association mapping populations (Yu et al 2008), the MAGIC population is called a second-generation mapping resource (Rakshit et al 2012).…”

“…T HERE is an urgent need to develop and study multiparent advanced generation intercross (MAGIC) populations (Rakshit et al 2012). Along with nested association mapping populations (Yu et al 2008), the MAGIC population is called a second-generation mapping resource (Rakshit et al 2012).…”

“…Along with nested association mapping populations (Yu et al 2008), the MAGIC population is called a second-generation mapping resource (Rakshit et al 2012). Using MAGIC populations to perform QTL mapping was first proposed for mice by Threadgill et al (2002).…”

“…As a result, they are also considered genetic reference populations whose particular genome arrangement can be replicated indefinitely. MAGIC populations in plants undoubtedly will become more popular in the future of plant genetics and breeding (Varshney and Dubey 2009;Rakshit et al 2012;Huang et al 2015), which calls attention to the need for improvements in statistical methods to analyze and interpret data derived from these populations. A recent call for papers on QTL mapping in MAGIC populations by GENETICS and G3 (http://www.genetics.org/) further indicates the urgent need for new technologies in MAGIC population QTL mapping.…”