Articles
Production of a high-resolution genetic map in carrot from multi-parental populations
Article number
1393_23
Pages
175 – 184
Language
English
Abstract
In plant breeding, high-resolution genetic maps are must-have tools for precise quantitative trait locus (QTL) mapping, GWAS fine mapping or MABC linkage-drag reduction.
High-density genetic maps also allow assessing collinearity with physical maps and thereof mutually improve genetic and physical marker positions.
So far, a gap existed for species with low-resolution maps despite the importance of high-density linkage maps.
Traditionally, to build a high-quality genetic map, several bi-parental mapping populations were first created to build separate maps that subsequently were joined together.
However, this approach was tedious, with a lack of user-friendly reliable methods, and did not simultaneously account for all informative meiotic events.
To overcome this, Zheng et al. (2019) recently proposed an algorithm to build consensus genetic maps at once (magicMap pipeline) using multi-parental populations (MPPs). Their method can virtually accommodate any type of mapping population (F2, RIL, BC, MAGIC, NAM, cross-pollinated and even (un)connected mapping populations pooled together so long as the pedigree is known). The bigger size of MPPs should theoretically increase power to detect QTL and their mapping resolution leading to a more refined understanding of the genetic architecture of complex traits.
This novel approach that better exploits genetic diversity can also map markers that are monomorphic within a biparental cross but that become polymorphic across pooled populations.
Here we built a carrot consensus genetic map using a MAGIC population created with 8 genetically diverse inbred lines.
The analysis showed that the Daucus carota genome is complex, with a high degree of segregation distortion.
This makes the use of traditional genetic methods a challenge necessitating stringent thresholds to filter raw data into a proper input data set for genetic map construction.
This innovative method really brings added value for carrot breeding purposes and future genetic analyses.
High-density genetic maps also allow assessing collinearity with physical maps and thereof mutually improve genetic and physical marker positions.
So far, a gap existed for species with low-resolution maps despite the importance of high-density linkage maps.
Traditionally, to build a high-quality genetic map, several bi-parental mapping populations were first created to build separate maps that subsequently were joined together.
However, this approach was tedious, with a lack of user-friendly reliable methods, and did not simultaneously account for all informative meiotic events.
To overcome this, Zheng et al. (2019) recently proposed an algorithm to build consensus genetic maps at once (magicMap pipeline) using multi-parental populations (MPPs). Their method can virtually accommodate any type of mapping population (F2, RIL, BC, MAGIC, NAM, cross-pollinated and even (un)connected mapping populations pooled together so long as the pedigree is known). The bigger size of MPPs should theoretically increase power to detect QTL and their mapping resolution leading to a more refined understanding of the genetic architecture of complex traits.
This novel approach that better exploits genetic diversity can also map markers that are monomorphic within a biparental cross but that become polymorphic across pooled populations.
Here we built a carrot consensus genetic map using a MAGIC population created with 8 genetically diverse inbred lines.
The analysis showed that the Daucus carota genome is complex, with a high degree of segregation distortion.
This makes the use of traditional genetic methods a challenge necessitating stringent thresholds to filter raw data into a proper input data set for genetic map construction.
This innovative method really brings added value for carrot breeding purposes and future genetic analyses.
Authors
L. Aqbouch, J. Osorio y Fortea, S. Arnoux
Keywords
carrot, high resolution genetic map, mapping populations, multi-parental populations, MAGIC population
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