Progress and challenges in pedigree-based QTL analysis utilizing high density marker data on related full sib families: A case study on fruit firmness in apple

Presentation Type: 
oral
Abstract: 

The power to identify, map and quantify QTL underlying complex traits may substantially increase when considering multiple segregating progenies simultaneously. The relatedness among these progenies can be fully exploited when the pedigree is known and the ancestors are available for genotyping. Previously, we successfully used the FlexQTL software to map QTL in such a Pedigree Based Analysis (PBA) approach to a large pedigreed population in apple with only 87 SSR markers (Bink et al. 2014). The current availability of high-throughput SNP genotyping infrastructures allows marker genotyping at much higher densities, which will contribute to higher mapping resolution and more accurate QTL characterization but also poses challenges to the QTL mapping software. Within the framework of the projects SCRI-RosBREED (#2009-51181-05808) and EU-FP7 FruitBreedomics (#FP7- 265582), we have improved and extended functionality of the FlexQTL software to better handle large numbers of SNP markers with regard to computation time, the phasing of the lowly-informative SNPs in complex pedigrees (e.g., inbreeding loops). Furthermore visualization tools were added to inspect recombination events. Furthermore, highly informative multi-allelic haplotypes can now be built comprising consecutive low informative SNPs, which will reduce computational effort without losing information from the high density marker data. The progress and challenges on utilizing high density SNP data in Rosaceae crops will be highlighted via a case study, i.e., applying the new version of FlexQTL software to fruit firmness trait on a large 20K genotyped apple population as available from the FruitBreedomics project.

Reference: Bink MCAM et al (2014). Theor Appl Genet: 1-18.(doi: 10.1007/s00122-014-2281-3).

Keywords: 
Bayesian analysis
VisualFlexQTL software
identity by descent (IBD)
genomic breeding values
genotype probabilities
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