Background: F(2) resource populations have been used extensively to map QTL segregating between pig breeds. A limitation associated with the use of these resource populations for fine mapping of QTL is the reduced number of founding individuals and recombinations of founding haplotypes occurring in the population. These limitations, however, become advantageous when attempting to impute unobserved genotypes using within family segregation information. A trade-off would be to re-type F(2) populations using high density SNP panels for founding individuals and low density panels (tagSNP) in F(2) individuals followed by imputation. Subsequently a combined meta-analysis of several populations would provide adequate power and resolution for QTL mapping, and could be achieved at relatively low cost. Such a strategy allows the wealth of phenotypic information that has previously been obtained on experimental resource populations to be further mined for QTL identification. In this study we used experimental and simulated high density genotypes (HD-60K) from an F(2) cross to estimate imputation accuracy under several genotyping scenarios.
Results: Selection of tagSNP using physical distance or linkage disequilibrium information produced similar imputation accuracies. In particular, tagSNP sets averaging 1 SNP every 2.1 Mb (1,200 SNP genome-wide) yielded imputation accuracies (IA) close to 0.97. If instead of using custom panels, the commercially available 9K chip is used in the F(2), IA reaches 0.99. In order to attain such high imputation accuracy the F(0) and F(1) generations should be genotyped at high density. Alternatively, when only the F(0) is genotyped at HD, while F(1) and F(2) are genotyped with a 9K panel, IA drops to 0.90.
Conclusions: Combining 60K and 9K panels with imputation in F(2) populations is an appealing strategy to re-genotype existing populations at a fraction of the cost.