BMC Genomics 2009, 10:1 http://www.biomedcentral.com/1471-2164/10/1 Genotype cleaning Identification and elimination of genotyping errors is a critically important task in genome mapping projects (see [53]). Several strategies have been applied to identify and eliminate any latent genotyping errors in this study. Ini- tially, all individual genotypes were checked manually before export to Microsoft Excel worksheets. In addition, genotypes were analysed for Mendelian inheritance in the known pedigree using the "PREPARE" option in CRIMAP program version 2.4 [99] and incompatible genotypes were blanked. These blanks were either refilled with the correct genotypes after rechecking within Fragment Pro- filer or after running new PCRs where necessary. If these 'refill's did not work out, genotypes were scored as miss- ing. In some cases, genotypes that were consistent with the Mendelian segregation, but highly improbable due to low relative allele frequencies in the pedigree, were also eliminated. Moreover, after preliminary alignment, the CHROMPIC option of the CRIMAP was used to identify unlikely double crossovers. Data contributing to double crossovers were re-examined and, if suspect, regenerated for second analysis. The overall genotype call rate was 95.5% and when possible, missing data (mostly due to missing DNA samples) were inferred from the genotypes of parents/offspring. Linkage analysis and microsatellite characterization Genetic linkage maps were constructed using the program CRIMAP version 2.4 [99]. In the analyses, autosomal and Z-linked loci were evaluated separately. First, the TWOPOINT option of CRIMAP was used to obtain an estimated recombination fraction and a logarithm of the odds (LOD) score for every pair of markers. A widely used two-point LOD score threshold of three was set as a crite- rion for significant linkage and was used for configuration of a linkage group. Then linked markers were ordered within group following a procedure similar to that employed in [6]. For multipoint analysis of larger linkage groups we used the option BUILD to select loci to be used as a framework for the continuing ordering of them, beginning with the most informative pair of markers and positioning additional markers one at a time in order of decreasing informativeness; the order of selected markers, also called framework loci, was supported by a LOD score of three or higher. The remaining markers were added to the framework map by lowering the LOD threshold value to 2.0 or 1.0 and were represented as accessory markers in their most likely or best position. Finally, the options FLIPS n and FIXED were used to evaluate the statistical support of the proposed order and the distance between markers, respectively. With the FLIPS n (n = 3 – 6) option, it is ensured that the odds in favour of the final order of each set of three to six markers were at least 1000:1 over alternative orders. Sex-average and sex-specific linkage genetic distances were estimated for each linkage group. Relative mapping distance was further tested within group using the FIXED option. All map distances are expressed as Kosambi centiMorgans (cM). The measures of microsatellite variability, including the observed number of alleles (AE), observed heterozygosity (HO), Nei's [100] unbiased estimates of expected hetero- zygosity (HE) and polymorphic information content (PIC), were estimated with the Excel Microsatellite Toolkit 3.1 [101]. Comparative analysis Comparisons of clone sequences of the microsatellites in Siberian jay with the chicken genome sequence http:// www.ncbi.nlm.nih.gov/genome/guide/chicken/ were conducted using MEGABLAST http:www.ncbi.nlm.nih.go v/genome/seq/BlastGen/BlastGen.cgi?taxid=9031. The orthologous zebra finch sequence was identified by per- forming another cross-species MEGABLAST search in NCBI's zebra finch genome resource http:// www.ncbi.nlm.nih.gov/projects/genome/seq/BlastGen/ BlastGen.cgi?taxid=59729. Further, the homologous zebra finch sequences identified above (including their flanks) were matched against the chicken genome assem- bly using the same method as the BLAST analysis between the microsatellites in Siberian jay and the chicken genome sequence. The searches were performed following a proce- dure as detailed in [7,12,13]. At the Expect value E = 1e-5, initial searches were performed under the default setting and later searches were under a less stringent set of param- eters (the so called 'the relaxed setting' in [12]). A locus was assigned to a specific location in the chicken genome if (i) it provided a unique hit at E ≤ 1e-10, or (ii) it provided multiple hits at E ≤ 1e-10 and within the hits the best hit had an E value at least 10 decimal places lower than the next best hit (see [12]). All the graphic maps were gener- ated using MapChart version 2.2 [102]. Authors' contributions SJ designed the study, conducted the laboratory work and prepared the data. MHL inspected the data, performed the data analysis and wrote the manuscript. JM planned and coordinated the whole study, and contributed to the man- uscript writing. All authors read and approved the final manuscript. Page 14 of 17 (page number not for citation purposes)