Pedigree genotyping: a new pedigree-based approach of QTL identification and allele mining

To date, molecular markers are available for many economically important traits. Unfortunately, lack of knowledge of the allelic variation of the related genes hampers their full exploitation in commercial breeding programs. These markers have usually been identified in one single cross. Consequently, only one or two favourable alleles of the related QTL are identified and exploitable for marker-assisted breeding (MAB), whereas a breeding program may include several alleles. Selection for just these alleles means that many favourable genotypes are ignored, which decreases efficiency and leads to genetic erosion. A new approach, called Pedigree Genotyping, allows the identification and exploitation of most alleles present in an ongoing breeding program. This is achieved by including breeding material itself in QTL detection, thus covering multiple generations and linking many crosses through their common ancestors in the pedigree. The principle of Identity by Descent (IBD) is utilised to express the identity of an allele of a modern selection in terms of alleles of founding cultivars. These founder alleles are used as factors in statistical analyses. Co-dominant markers like SSR (microsatellite) markers are essential in this approach since they are able to connect cultivars, breeding selections and progenies at the molecular marker level by monitoring specific chromosomal segments along family trees. Additional advantages of the use of breeding genetic material are (1) a major reduction in experimental costs since plant material is already available and phenotyped by default (2) continuity over generations within breeding programs with regard to marker research (3) the testing of QTL-alleles against a wide range of genetic backgrounds, making results generally applicable, (4) intra- as well as inter-QTL interactions can be explored. Fruit firmness in apple will be used as an example to illustrate the principles of this powerful approach to detect QTLs and estimate their allelic variation