Awad et al. Genetics Selection Evolution 2011, 43:8 http://www.gsejournal.org/content/43/1/8 Genotypes were assigned using GENESCAN and GENO- TYPER (Applied Biosystems) software programs. We performed double genotyping of all families and ances- tors using two independent runs. For ambiguous geno- types, the raw data were re-evaluated and animals were re-genotyped if necessary. SNP selection, genotyping and haplotyping SNP genotyping was carried out by Tierzuchtforschung e. V. München using the commercial Illumina Bovine SNP50 Bead chip featuring 54 001 SNP (http://www.- illumina.com/; Illumina, San Diego) that span the bovine genome, excluding Y-chromosome. The genotype calling was performed with the GenCall application, as imple- mented in Illumina Bead chip Genotyping analysis soft- ware. This application computes a Gencall score for each locus, which evaluates the quality of genotypes. We included only animals with confirmed paternity and with a call rate above 0.98. Furthermore, we only used markers with a call rate above 0.90. We excluded all markers pro- ducing more than 1% paternity problems in pairs with confirmed paternity, and also excluded all markers that were non-informative in the Fleckvieh population or with an unknown chromosomal position. This yielded 43 806 informative SNP available for the whole-genome analysis in the Fleckvieh population, of which 1 976 are found on the BTA5. Two hundred and forty of these covered the region most likely containing the QTL in the distal part of BTA5 and were used in the present study. We performed SNP genotyping in two stages. First, 75 animals i.e. the gransires of the nine initial GD families and their ancestors, and also a number of potential GD- family sires and their ancestors, were genotyped with the SNP chip and their haplotypes were reconstructed with the BEAGLE program [28]. These 75 animals con- stitute a complex pedigree (Figure 1) in which it is pos- sible to trace the segregating haplotypes five generations back to some important ancestors of the Fleckvieh population, born in the 1960’s and 1970’s. This pedigree represents almost all of the important bull lines origi- nating from a wide range of dams. Considering this, and the fact that a large proportion of the included bull dams are unrelated (no common grand-parents), these 75 animals provide a good representation of the haplo- type diversity in the breeding Fleckvieh population. Sec- ond, the new families (G10, G11, M02, M10 and M11) containing the target haplotype segment of ancestor A0 were genotyped with microsatellite markers and with the genome-wide SNP chip. These animals and 173 additional Fleckvieh animals not closely related to ancestor A0 (but genotyped with the SNP chip in other projects running in our laboratory) were also haplotyped using the BEAGLE program. Page 5 of 11 Linkage map construction The relative positions of microsatellite markers were re- evaluated by the CRI-MAP program [29]. A physical map was constructed according to the sequence data of all the markers (Table 1) using the basic alignment search tool (BLAST) and the latest cattle genome sequence http://genome.ucsc.edu/cgi-bin/hgGateway. Our genetic data was used to resolve cases where more than one marker order was obtained from published linkage and physical maps. When our genetic data sup- ported a marker order different from that of the public linkage map, but in accordance to the physical map, we modified the relative position (cM) of the markers along with the corresponding sequence. The linkage and phy- sical maps were used as a framework to insert the newly designed marker (LMU0505) with the build option of the CRI-MAP program. The resulting final map (Table 1) was used for all the following analyses. QTL fine mapping LDL mapping by microsatellite markers Joint linkage disequilibrium and linkage (LDL) analysis is a variance component approach and we used linear mixed models to estimate variance components as described previously [24]. Thereby, we used the Markov chain Monte Carlo (MCMC) implemented in the pro- gram LDLRAMS [30-32] (version 1.76) to estimate IBD probabilities in general complex pedigrees [30-32]. To estimate LD-based IBD probabilities, we assumed the number of generations since the base population (muta- tion age) and the past effective population size to be 100, and the initial homozygosity at each microsatellite mar- ker in the base population was set to 0.35. In addition, the program LDLRAMS exploits allele frequencies in the population. To calculate an unbiased estimation of allele frequencies in the Fleckvieh population, we performed allele counting within the complex pedigree. We counted both alleles of all genotyped founder individuals and only the maternal allele of descendents in the pedigree. Two complex pedigrees consisting of 2 089 (MSPED2089) and 1 038 (MSPED1038) animals, respectively, were analysed by LDLRAMS. The MSPED2089pedigree included nine GD families from the previous study (G01 to G09), two additional GD families (G10 and G11), three maternal grandsire families (M02, M10 and M11), some highly related animals and some important ancestors (paternal and maternal grandsires of phenotyped sons and of family sires). The MSPED1038 pedigree included two GD families (G01 and G02) found to be segregating for QTL in the previous study, two additional GD (G10 and G11) families and three MGS families (M02, M10 and M11) sampled according to the results of the haplotype analy- sis. For both LDL analyses, as implemented in the