Awad et al. Genetics Selection Evolution 2011, 43:8 http://www.gsejournal.org/content/43/1/8 MCMC approach of the program LDLRAMS, we used an initial burn-in of 500 iterations followed by 2 500 itera- tions, with parameter estimates collected for each itera- tion. To avoid entrapment in a local maximum, we performed two independent sampling procedures (i.e. two LDLRAMS runs with different random number seeds). LDL mapping by SNPs Here we used three complex pedigrees for LDL map- ping by SNPs. The first pedigree, SNPPED723, was based on all progeny-tested Fleckvieh animals geno- typed with the SNP chip, and consisted of 325 geno- typed and phenotyped sons, and 16 genotyped and 382 non genotyped ancestors. The second pedigree, SNPPED421, was based on progeny-tested animals that could be traced back to ancestor A0, and consisted of 175 genotyped and phenotyped sons, eight genotyped and 238 non genotyped ancestors. The third pedigree, SNPPED308, was based on animals not related to ancestor A0 according to the known pedigree, and consisted of 144 genotyped and phenotyped animals, 12 genotyped and 152 non genotyped ancestors. These pedigrees were analysed with LDLRAMS using a dense map of 240 SNPs covering the region from 112.650 to 124.780 Mb on BTA5, i.e. a region larger than the 97% confidence interval as determined by 1-LOD support interval [24]. Due to computing constraints, the total marker set was divided into five overlapping sets of 80 SNP each. Since IBD estimates are most accurate in the middle of an investigated marker set, we present log-likelihood ratio (LRT) values only for the internal 40 marker intervals within these windows (that is, excluding the most proximal and most distal 20 markers). We used the model described above, setting the initial homozygosity at each SNP in the base population to 0.75 and using an initial burn-in of 500 iterations followed by 2 500 iterations. The parameter estimates were collected after each iteration. Two independent MCMC sampling procedures (i.e. two LDLRAMS runs with different random number seeds) indicated convergence to a global maximum. Multiple-QTL analysis using linkage disequilibrium and linkage (LDL) analysis method We used the analysis method of Olsen et al. [33], i.e. the same model as for single-QTL analysis, but includ- ing a random QTL effect of a specified marker bracket. That is, the bracket that showed the highest LRT in the single-QTL analysis was included as a random effect in the QTL model in turn, and the analysis was repeated. These analyses searched for an additional QTL, given that the QTL in the specified marker bracket is accounted for, and is similar to the fitting of cofactors [34]. Page 6 of 11 Estimation of model parameters and test statistics The variance components and the logarithm of the likeli- hood (L) of a model containing a QTL as well as residual polygenic effects at position p (logLp) were estimated by AIREML [32,35], which is an integral part of the LDLRAMS and LDL programs. The likelihood of a model without QTL effect (logL0) was calculated on the basis of a polygenic model. The log-likelihood ratio (LRT) was calculated as double difference in logL between models with and without a QTL, i.e. LRT = -2 (logL0-logLp). The LRT test statistic is distributed approximately as chi- square with 1 degree of freedom [36]. The confidence interval (CI) for the QTL position was determined as 1-LOD support interval, which was constructed as the interval surrounding the QTL peak where the LRT exceeds LRTmax - 2 × ln (10), where LRTmax is the maxi- mum LRT-value for the tested QTL [37]. Results Genotypes and linkage map construction Genotypes for 40 microsatellite markers were available to build the BTA5 genetic map. In most of the LDL analyses, only the 21 most distal markers (Table 1) cov- ering the 97% confidence interval were considered. When we controlled if the genotype and haplotype data were plausible, the most distal marker (MNB71), which was genotyped in previous projects [24], showed exten- sive double recombinations with the 12 markers added in the present project. To reduce possible mapping errors, we excluded this marker from all subsequent analyses. Using the build option of the CRI-MAP pro- gram, we re-estimated the marker distances and order. The following changes with respect to the public USDA linkage map were made: (i) according to the phy- sical map (i.e. bp position of release Btau_4.0) and con- firmed by applying the build option of the CRI-MAP program to our own data, the positions of markers BM49 and BM733 are inverted (Table 1); (ii) markers DIK2035 and DIK5277 are both at the same position (120.85 cM) on the USDA linkage map but, according to our genotypes and the physical map results, they are separated, placing DIK2035 (120.38 cM) upstream of DIK5277 (120.82 cM); (iii) the new marker developed in this study (LMU0505) is highly informative for linkage analysis and its relative position between DIK5106 and ETH152 was estimated by applying the build option of the CRI-MAP program. The positions of both flanking markers DIK5106 and ETH152 also changed (Table 1). Haplotype analysis in a complex pedigree Using the algorithm implemented into the program BEAGLE, we haplotyped the 75 animals of the complex pedigree in Figure 1 with 1 976 SNP on BTA5 that are informative in the Fleckvieh population. Thus