A high density linkage map of the bovine genome
© Arias et al; licensee BioMed Central Ltd. 2009
Received: 20 December 2007
Accepted: 24 April 2009
Published: 24 April 2009
Recent technological advances have made it possible to efficiently genotype large numbers of single nucleotide polymorphisms (SNPs) in livestock species, allowing the production of high-density linkage maps. Such maps can be used for quality control of other SNPs and for fine mapping of quantitative trait loci (QTL) via linkage disequilibrium (LD).
A high-density bovine linkage map was constructed using three types of markers. The genotypic information was obtained from 294 microsatellites, three milk protein haplotypes and 6769 SNPs. The map was constructed by combining genetic (linkage) and physical information in an iterative mapping process. Markers were mapped to 3,155 unique positions; the 6,924 autosomal markers were mapped to 3,078 unique positions and the 123 non-pseudoautosomal and 19 pseudoautosomal sex chromosome markers were mapped to 62 and 15 unique positions, respectively. The linkage map had a total length of 3,249 cM. For the autosomes the average genetic distance between adjacent markers was 0.449 cM, the genetic distance between unique map positions was 1.01 cM and the average genetic distance (cM) per Mb was 1.25.
There is a high concordance between the order of the SNPs in our linkage map and their physical positions on the most recent bovine genome sequence assembly (Btau 4.0). The linkage maps provide support for fine mapping projects and LD studies in bovine populations. Additionally, the linkage map may help to resolve positions of unassigned portions of the bovine genome.
Advances in technology have dramatically increased the ability to cost-effectively genotype a large number of SNPs in humans and farm animals [1, 2]. The majority of the SNPs have been placed in physical, but not linkage maps. Increasing the resolution of bovine linkage maps will improve estimates of linkage disequilibrium (LD) [3, 4] and increase the success rate of fine mapping quantitative trait loci (QTL) in cattle. The possibility that any particular SNP does not have a functional role is outweighed by its indirect use as a genetic marker associated to a causal variant . In addition, mapped SNPs provide information about LD patterns over the genome and allow the identification of haplotype blocks [4, 6, 7].
Historically a diverse variety of methodologies and procedures have been used to order bovine chromosomal segments [8–16]. A physical map  and several linkage maps have been reported for the bovine genome [16, 18–22]. To date, the linkage map of Snelling et al.  has the highest number of genetic markers positioned. Their linkage map is comprised of 4,585 markers (including 913 SNPs), in 2,475 unique positions covering 3,058 centimorgans (cM) in total. Since Kappes et al.  reported advances in the sequencing of the bovine genome, a 7.1 fold coverage of the genome has been attained and this has generated over 2 million bovine SNPs that are currently in NCBI dbSNP Build 129 . Affymetrix produced a commercial genotyping panel of approximately 10,000 bovine SNPs , 92% of which were derived from this sequencing resource ; the remaining eight percent were derived from Australia's Commonwealth Scientific and Industrial Research Organisation (CSIRO) .
The objective of this work is to present a high-density bovine linkage map (HDBLM) that combines a low-density microsatellite based linkage map (LDM) with SNPs from the Affymetrix GeneChip™ Bovine Mapping 10K SNP kit (hereafter called 10K SNP panel) . Results from the HDBLM could enhance the understanding of the alignment and orientation of contigs and scaffolds in the bovine genome assembly, thus allowing the examination of relationships between physical distances, linkage disequilibrium (LD) and genetic map distance. This would provide a framework to identify causal relationships between genomic variation and animal performance traits.
Genotypes were received from Affymetrix (Santa Clara CA, USA) for 9,713 SNPs with an average call rate of 99.25% for the 10K SNP panel. A total of 1,891 SNPs were removed for the following reasons: departure from Hardy-Weinberg Equilibrium (HWE) (120), more than 50 inheritance inconsistencies (260), having an allele with frequency lower than 5% (1,494), and less than 10 informative meioses (17) (Additional file 1). Genotypes from six animals were used as blind duplicates with an average concordance between of samples of 99.93%. A total of 1,189 SNPs (hereafter called orphan SNPs) were not initially assigned to any one chromosome; 1,053 of these SNPs were subsequently assigned to a single chromosome. There were 955 SNPs from the 10K SNP panel initially incorrectly assigned to a chromosome (hereafter called displaced SNPs), 779 of which we were able to re-assign to a different chromosome. The stringent threshold criteria utilized for the assignment of these SNPs prevented the allocation of some of the 136 orphan SNPs and some of the 176 displaced SNPs. The inability to place to a chromosome some of these orphan and displaced SNPs could have been reduced by lowering the stringency of the threshold criteria used during the assignment. The final marker data set consisted of 7,510 SNPs from the 10K SNP panel in addition to 294 microsatellites, three milk protein haplotypes and two gene-based SNPs.
Informative meioses for autosomal chromosomes
Description of linkage maps
Mean rec. dist.e (cM)
S. dev. rec. dist.f
Minimum rec. dist.g (cM)
Maximum rec. dist.h (cM)
C. var. rec. dist.i
Average recombination distance per Mb
Linkage map (cM)
Physical map (Mb)a
We were unable to assign 2,946 of the 9,713 SNPs to the linkage map. Of the 7,822 SNPs that passed quality control, 7,510 SNPs were allocated to a confirmed chromosome. Six hundred and fifty two SNPs that had an assigned chromosome were not mapped because a unique map position could not be found and their inclusion based on physical position served to increase the length of linkage map above the defined threshold. There were 91 SNPs with unknown physical position, thus preventing their insertion analysis.
Comparison with Bovine genome Btau 4.0
The linkage map presented in this paper is the most dense map to date for cattle; the relatively high number of informative meioses per available SNP represented in the 10K SNP panel is greater than that reported by Snelling et al.  thus enabling a high degree of marker placement by the mapping software. The number of SNPs that were available from the 10K SNP panel could have been increased further. For example, this could have been accomplished by lowering the allele frequency criterion used to remove any SNP, from 5% to 2%. This would have allowed informative meioses to dictate the placement or rejection of SNPs by the mapping software into the linkage maps. The detection of displaced SNPs was carefully monitored. Some displaced SNPs had formed clusters with small genetic distances between them but the cluster was placed further than the established threshold of 20 cM from either the most-distal or most-proximal marker of any other linkage group. The success rate for identifying these SNPs relied on the information content of each one of the markers. We set more stringent criteria for marker placement than was previously published ; that is, we only accepted clusters with LOD scores above 15 and where at least two microsatellites belonged to the linkage group. Further, we did not allow linkage to any other groups. The subsequent placement of orphan and displaced SNPs in other than the originally assigned linkage maps assured us that the methodology utilised in assigning such markers to a chromosome was appropriate. Our autosomal linkage map of 3097.4 cM is very similar in length with the map presented by Ihara et al. of 3,013.5 cM ; is longer in 16 and shorter in 13 of the bovine autosomal chromosomes, with an average absolute difference of 8.2 cM per chromosome. The biggest difference in length occurred for chromosome 14, where our map was longer by 23.5 cM. The extra length for chromosome 14 is due equally to extra marker coverage and to expansion in the linkage map. For example, our linkage map for chromosome 14 contains additional markers at the proximal and distal regions of the Ihara et al.  linkage map, but the distance between common proximal and distal markers is larger. Of the two markers that mapped proximal to, and the 12 markers mapped distal to common markers with the linkage map of Ihara et al. , only one was placed during mapping round 5: Insertion phase, which utilises physical map data (see methodologies section). The positions of all other markers in these two regions were based on linkage information. The genetic positions of the two proximal markers and the 11 distal markers that were placed by linkage information are in concordance with their physical position, except for a cluster of three SNPs. The order of common microsatellite markers that were assigned to our genetic map as well as several other bovine linkage maps [16, 18–22] are in complete agreement. Likewise, SNPs common to the genetic map presented by Snelling  and our linkage maps are in concordance.
The addition of 6,767 SNPs from the 10K SNP panel to the low-density microsatellite-based maps (LDM) resulted in both expansion and additional coverage of the linkage maps. The expansion was explained by an increase in genetic distance from proximal to distal markers of LDMs. This additional coverage was measured by an increase in genetic distances from the placement of the last SNPs of the 10K SNP panel to the proximal and the distal marker of a LDM. The magnitude of the expansion was of 80.4 cM and the coverage increased by 338.2 cM. The high reliability of the map presented here was made possible because of a high accuracy of genotyping, thorough pre-screening of the genotypic data for inconsistencies (mis-inheritance, departure from HWE, low allele frequency and less than 10 informative meioses), relatively high numbers of informative meioses and the ability to place orphan and displaced SNPs. Hence this map will be useful to monitor the bovine genome assembly. Using the approach applied by Breen et al. , a map resolution of 0.80 cM between autosomal markers could be obtained from an average of 366.9 informative meioses. For our 3,097.4 cM autosomal linkage map the number of markers that could potentially be placed to unique positions is 3,872. Our autosomal linkage map has 3,078 unique marker positions and should be considered as not fully saturated. The average Kosambi distance is lower than that presented by Snelling et al. . However, the coefficient of variation (CV) is greater, indicating that our linkage maps have a higher proportion of marker clusters (Table 2), (Figure 1). The insertion of an otherwise un-mapped SNP by using its physical position is the most probable cause for the increased value in observed CV. An un-mapped SNP that belongs to a scaffold that already includes a mapped SNP(s) is not expected to increase genetic distances because it creates a cluster rather than a singleton.
The observation that chromosome size increases the average recombination rate was consistent with other studies [30, 31]. The average recombination distance of 1.25 cM per Mb was similar to the value of 1.19 reported by Kong et al. in humans  and approximately twice that of the value of 0.63 found in mice (Shifman et al. ). Based on the bovine assembly Btau 4.0 , the total physical length from first proximal to last distal markers of our linkage maps was 2.605 Gbp (Table 3). Snelling et al.  reported a genome size of 3.1 and 2.9 Gbp estimated from the BAC and sequencing bovine genome project, respectively. Using a physical map of 3 Gbp, the average recombination distance would be approximately 1.1 centimorgans per million base pairs. These inconsistencies also introduce uncertainty in calculating chromosome-wise recombination rates. Inconsistencies between the order of markers in the linkage maps and their physical order (Additional file 2) prevented us from further investigating the recombination distance per physical distance within the chromosome.
The 7K-linkage map presented here has substantially improved on the previously incomplete assignment of SNPs from the 10K SNP panel, and has reordered SNPs that had been wrongly assigned. Thus, our linkage map has shown utility for identifying errors in the current sequence assembly of the bovine genome. In addition, the markers and linkage map will be valuable for fine mapping of QTL [33, 34].
The assignment of SNPs to a chromosome from the 10K SNP panel was incomplete and some of their SNPs were wrongly assigned. The assigning and re-assigning of orphan and displaced SNPs to a chromosome and the further placement of these SNPs to unique positions in the linkage during mapping rounds 2–4 was based totally on linkage information. The inclusion of SNPs with up to 50 mis-inheritances in the construction of linkage maps did not have an effect on recombination distances. The markers and linkage map presented in this paper will be useful in the fine mapping of QTL using LD methods [33, 34]. However, a number of marker clusters and gaps remain (Figure 1). Further marker development that is being undertaken in the bovine genomics community will ensure that there is greater uniformity and marker density over the genome, which will be beneficial for applications of genomic selection . In addition, the placement by linkage of SNPs from the 10K SNP panel (mapping rounds 2–4) will be useful in identifying inaccuracies in sequence assembly in the bovine genome assembly and in correcting chromosomal assignment for some SNPs from the 10K SNP panel. Approximately 20% of SNPs from the 10K SNP panel were not acceptable for map construction. The major factor for non-acceptance was an allele with a frequency lower than 5%. This probably reflects the origin of the SNPs coming primarily from the sequence of a Hereford cattle and being validated in different populations to the New Zealand Holstein-Friesian and Jersey cattle breeds. That is, this limitation could be a reflection of the breed origin of the SNP. The use of breed-specific SNPs and the knowledge of the physical position of SNPs are two aspects that should not be overlooked. Structural discrepancies observed between the order of the markers in the linkage map, and their physical position (Additional file 2), could be attributed to spurious information in the bovine assembly Btau 4.0 . In the opinion of the authors, at the present time, the number of informative meioses has more weight in the acceptance of the linkage position of the markers than their physical position.
Using a unique animal resource, 7066 bovine genetic markers were positioned in our linkage map. Approximately 90% (6767 out of 7510) of the SNPs that passed quality control testing from the 10K SNP panel were placed on the linkage map (Additional file 3). The marker positions in the linkage maps are in good agreement with the physical positions obtained using Btau 4.0 of the bovine genome. The information from this linkage map has been used to describe patterns of LD in the bovine genome . Additionally, it will support further genetic analysis of important economic traits in cattle and will help to resolve challenges encountered in the assembly of the bovine genome. The linkage map is not fully saturated, and thus the addition of more markers would be valuable.
An outbred F2 experiment of Holstein-Friesian and Jersey cattle breeds was undertaken in New Zealand to identify QTL and genes affecting dairy production . The experiment consisted of 817 F2 females, 796 F1dams, 6 F1 sires and 60 F0 males (Additional file 4). All sires of F1 dams and F1 sires are represented in the set of 60 F0 sires. There were no matings between individuals that shared a sire.
In total, 1679 animals (male F0, as well as all F1 and F2 animals) from the experiment were genotyped by external laboratories according to standard practices for fluorescent dye-labelled primers, utilising Applied Biosystems 3100 genetic analysers (Australian Genome Research Facility, Melbourne, Australia and GeneMark™, Hamilton, New Zealand) for 294 microsatellites; three milk protein haplotypes: 1) Alpha s1 casein (CSN1S1) formed by A_CAS_41_26 and AS_CAS_192; located at 6517 and 17807 base pairs (bp) of locus X59856 (accession number X59856, AJ812028) respectively, 2) Kappa casein (CSN3) formed by K_CAS_148, located at 5345 bp of locus X14908 (accession number X14908) and 3) Beta casein (CSN2) formed by B_CAS_37, B_CAS_67, B_CAS_106 and B_CAS_122, located at 690, 8101, 8219, and 8267 bp of locus X14711 (accession number X14711) respectively , two gene-based SNPs (The non-conservative K232A substitution in the DGAT1 gene [39, 40] and the F279Y SNP, which is a substitution in the transmembrane domain of the GHR gene ) and the 10K SNP panel. T six F1 sires were screened for approximate 500 microsatellites. Where four out of six sires were heterozygous, the markers were used. The 10K SNP panel was genotyped 12 months later than the other markers.
SNP Quality Control
Before undertaking construction of high-density bovine linkage maps, SNPs from the 10K SNP panel were screened for segregation distortion by HWE  and mis-inheritance. A SNP showing any of the following criteria: departure from HWE (P-value less than 0.001), more than 50 records of mis-inheritance (inheritance had previously been confirmed from the microsatellites), an allele with a frequency lower than 5% in the F0 and F1 populations, or less than 10 informative meioses, was deleted from further analysis. The remaining SNPs that passed quality control testing for map construction each had at least one case of mis-inheritance.
The linkage mapping utilized 1679 individuals from the F2 design described by Spelman et al. . All informative meioses for the autosomal maps are male and thus the maps are male-specific. The same is true of the pseudoautosomal part of the sex chromosome.
The non-pseudoautosomal part of the sex chromosome was constructed differently; it utilized maternally-derived genotypes (F1 dam) and was therefore a female-specific map. The F2 daughters' genotypes were comprised of maternally-derived alleles as well as paternally- (F1 sire) inherited haplotypes. The maternally-inherited alleles were derived by subtracting the maternally-inherited haplotypes from the progeny genotypes as follows. Because recombination is not possible for the haploid sex chromosome in males, these maternally-inherited haplotypes represented entire (non-pseudoautosomal) chromosomes. This in turn enabled the maternally-inherited haplotype to be determined in the F2. As for their F2 daughters, the F1 dams' chromosome-long haplotypes were known. This is because their sires (the F0 maternal grandsires) were genotyped. Therefore the F1 dams' phases were known, increasing the ability to observe recombination events amongst their F2 offspring. Our linkage map is based on a two-generation pedigree and it could be further enhanced using a three-generation pedigree. The number of animals involved in the pedigree structure, number of markers involved in map construction and limitations in hardware capability limited the use of a three-generation pedigree.
Construction Low-density microsatellite based linkage map (LDM)
There were five rounds of mapping. The first one used limited marker data (294 microsatellites, three milk protein haplotypes and two gene-based SNPs) and hence resulted in a low-density microsatellite-based linkage map. Subsequent rounds incorporated SNPs from the 10K SNP panel and enabled the construction of high-density linkage maps.
Mapping round 1
Construction of High-Density Bovine Linkage Maps
The 10K SNP panel did not have complete assignment of SNPs to a specific chromosome. Of the 7822 SNPs available from the 10K SNP panel, 1189 (orphan SNPs) were not initially assigned to a chromosome. Using the mapping information from mapping round 1, CRI-MAP V.2.4 (TWOPOINT option) [43, 44], 1053 of these orphan SNPs were assigned to a chromosome. The criteria were: a likelihood of odds (LOD) threshold greater than 15 with at least two microsatellites belonging to the same linkage group and no other significant linkage to an alternative chromosome. In addition to CRI-MAP V.2.4 [43, 44], the expert system software package MultiMap  was used to create the high-density bovine linkage map.
The MultiMap  parameter flip was evaluated by using different values. The optimum values for the flip parameter for these types of dense linkage maps are above three. When parameter flip values over three were used for the bovine chromosome 29 with 144 markers, it was found to be time-consuming, (from four-fold to 196-fold for flips 4 to flips 6, respectively) or halted when the parameter flip was set to seven. Our ability to support the final placement of markers in linkage maps with the use of a value higher than three for the parameter flips was prevented by the constraints of our computer hardware.
Mapping round 2
For each bovine chromosome, three low-density linkage maps were constructed: 1) low-density microsatellite linkage map (LD1) (Figure 3(2b)), 2) low-density SNP linkage map (LD2) (Figure 3(2c)), and 3) low-density microsatellite-SNP linkage map (LD3) (Figure 3(2d)). This mapping round was undertaken to map 7686 SNPs that had been physically assigned to a chromosome. MultiMap  constructs comprehensive maps by using framework maps that can either be built by the program or supplied by the user. For LD1, the LDM from mapping round 1 was used as the framework. No framework map was used for LD2. For LD3, the map constructed by CRI-MAP V. 2.4 – Build option [43, 44] (2a) was used as the framework. To enter a linkage map, the position for the SNPs had to exceed a LOD score of three with the Flips Option set to three. After all qualifying SNPs were mapped; the LOD score for SNP acceptance was lowered to two, thus allowing additional markers to be positioned. LD1 maps will always have all makers from LDM, plus additional SNPs from the 10K SNP panel.
The low-density linkage maps (LD1–LD3) comprise a mix of common markers (microsatellites as well as SNPs) and differ from each other only in SNPs from the 10K SNP panel. The three separate low-density maps (LD1, LD2 and LD3) were integrated into one linkage map termed ARTIFICIAL LINKAGE MAP – I (ALMI). The integration procedure was performed observing the following rules: markers that appeared in more than one of the three linkage maps were anchored; markers that occurred only in one of the low-density linkage maps were integrated into the ALMI, retaining their original order with respect to other markers within their own low-density linkage map. The resulting ALMI had a greater number of markers than the individual low-density linkage maps (LD1–LD3). There were no inconsistencies in SNP order among the three different low-density maps. In some cases, the integration of a marker was difficult due to the ambiguous positions where it could be placed. However, this had no impact on the linkage map because MultiMap  was able to resolve the order in the subsequent runs during mapping round 3.
Mapping round 3
SNPs not mapped during mapping round 2 were brought into the linkage map using an iterative procedure with the AMLI used as the framework map. To enter the map, the position for the SNPs had to exceed a LOD score of two with the Flips Option set to two. The proposed placements suggested by MultiMap  for the remaining unmapped SNPs were tested and the SNP was placed if the Kosambi distance was equal or less than 0.5 centimorgans (cM) to the nearest marker. In some instances, a subsection of 20 SNPs in the region of a possible location was created as a framework map; MultiMap  was then able to place such SNPs. This methodology was continued until: a)- no further SNPs were placed into a unique position, b)- Proposed alternative placements suggested by MultiMap  numbered greater than three, or c)- a SNP was placed at both ends of a chromosome. During this mapping phase, several SNPs initially assigned to a specific chromosome were placed more than 20 centimorgans (cM) from either the most-distal or most-proximal marker. These SNPs (955 displaced SNPs) were removed from the linkage group as the linkage information indicated that they had been physically assigned to the wrong chromosome. A total of 779 of these SNPs were successfully assigned to a new chromosome using the previously described method in assigning an orphan SNP to a chromosome.
Mapping round 4
This round consisted of mapping the 779 re-assigned SNPs, followed by one further round of mapping for all SNPs from the 10K SNP panel that had not been placed during mapping round 3. The mapping criteria were same as in mapping round 3.
Mapping round 5: Insertion phase
The remaining unmapped SNPs from the 10K SNP panel after mapping round 4 were inserted into the linkage map at a position where they were neighbouring the SNP with the closest physical position. Initially, the physical positions for SNPs were obtained from the bovine assembly Btau_3.1 . The final physical positions used in the insertion phase were from the bovine assembly Btau_4.0 . The insertion of SNPs was done from proximal to distal orientation. No attempt was made to study consequences of a SNP insertion in the opposite direction. A SNP was retained in the linkage map if its insertion increased the length of the linkage map by less than 0.5 cM, or the Kosambi distance with the nearest markers was equal or less than 0.5 cM.
Recombination distance per physical distance
Recombination distances and marker physical positions (obtained from bovine genome assembly (Btau 4.0) ) were used to estimate recombination distances per physical distances. Pearson correlations were calculated between marker order and their physical positions.
The authors gratefully acknowledge the early pre-publication access under the Fort Lauderdale conventions to the draft bovine genome sequence provided by the Baylor College of Medicine Human Genome Sequencing Center and the Bovine Genome Sequencing Project Consortium. The authors thank Dr. John McEwan for providing suggestions in clarifying content and pointing specific ways to improve the manuscript, Mark Walker from GeneMark, LIC, Hamilton, New Zealand for technical support and information on microsatellites, milk protein haplotypes and gene-based SNPs genotypes, Dr. Anne Winkelman and Vivienne Bennett for editing the manuscript and anonymous reviewers who provided tremendous help by providing accurate, important, and constructive suggestions in making this manuscript more comprehensible.
- Dearlove AM: High throughput genotyping technologies. Brief Funct Genomic Proteomic. 2002, 1 (2): 139-150. 10.1093/bfgp/1.2.139.View ArticlePubMedGoogle Scholar
- Syvänen AC: Toward genome-wide SNP genotyping. Nat Genet. 2005, 37 (6s): s5-s10. 10.1038/ng1558.View ArticlePubMedGoogle Scholar
- McKay SD, Schnabel SD, Murdoch BM, Matukumalli LK, Aerts J, Coppieters W, Crews D, Dias Neto E, Gill CA, Gao C, Mannen H, Stothard P, Wang Z, Van Tassel CP, Williams JL, Taylor JF, Moore SS: Whole genome linkage disequilibrium maps in cattle. BMC Genetics. 2007, 8 (2): 74-10.1186/1471-2156-8-74.PubMed CentralView ArticlePubMedGoogle Scholar
- Khatkar MS, Zenger KR, Hobbs M, Hawken RJ, Cavanagh JAL, Barris W, McClintock AE, McClintock S, Thomson PC, Tier B, Nicholas FW, Raadsma HW: A primary assembly of a bovine haplotype block map based on a 15,036-single-nucleotide polymorphism panel genotyped in Holstein-Friesian cattle. Genetics. 2007, 176 (2): 763-772. 10.1534/genetics.106.069369.PubMed CentralView ArticlePubMedGoogle Scholar
- Orr N, Bekker V, Chanock S: Genetic association studies: marking them well. J Infect Dis. 2006, 194 (11): 1475-1477. 10.1086/508552.View ArticlePubMedGoogle Scholar
- Womack JE: Advances in livestock genomics: opening the barn door. Genome Res. 2005, 15 (12): 1699-1705. 10.1101/gr.3809105.View ArticlePubMedGoogle Scholar
- Spelman RJ, Coppieters W: Linkage disequilibrium in the New Zealand Jersey population. Proceedings of the 8th World Congress on Genetics Applied to Livestock Production: 13–18 August 2006, Belo Horizonte, Minas Gerais, Brazil. 2006, 22-21.Google Scholar
- Gustavsson I, Hageltorn M, Zech L: Recognition of cattle chromosomes by the Q- and G-banding techniques. Hereditas. 1976, 82 (2): 157-166. 10.1111/j.1601-5223.1976.tb01552.x.View ArticlePubMedGoogle Scholar
- Di Berardino D, Iannuzzi L: Detailed description of R-banded bovine chromosomes. J Hered. 1982, 73 (6): 434-438.PubMedGoogle Scholar
- Womack JE: The goals and status of the bovine gene map. J Dairy Sci. 1993, 76 (4): 1199-1203.View ArticlePubMedGoogle Scholar
- Band MR, Larson JH, Rebeiz M, Green CA, Heyen DW, Donovan J, Windish R, Steining C, Mahyuddin P, Womack JE, Lewin HA: An ordered comparative map of the cattle and human genomes. Genome Res. 2000, 10 (9): 1359-1368. 10.1101/gr.145900.PubMed CentralView ArticlePubMedGoogle Scholar
- Everts-van der Wind A, Kata SR, Band MR, Rebeiz M, Larkin DM, Everts RE, Green CA, Liu L, Natarajan S, Goldammer T, Lee JH, McKay S, Womack JE, Lewin HA: A 1463 gene cattle-human comparative map with anchor points defined by human genome sequence coordinates. Genome Res. 2004, 14 (7): 1424-1437. 10.1101/gr.2554404.PubMed CentralView ArticlePubMedGoogle Scholar
- Larkin DM, Everts-van der Wind A, Rebeiz M, Schweitzer PA, Bachman S, Green C, Wright CL, Campos EJ, Benson LD, Edwards J, Liu L, Osoegawa K, Womack JE, de Jong PJ, Lewin HA: A cattle-human comparative map built with cattle BAC-ends and human genome sequence. Genome Res. 2003, 13 (8): 1966-1972.PubMed CentralPubMedGoogle Scholar
- Everts-van der Wind A, Larkin DM, Green CA, Elliott JS, Olmstead CA, Chiu R, Schein JE, Marra MA, Womack JE, Lewin HA: A high-resolution whole-genome cattle-human comparative map reveals details of mammalian chromosome evolution. Proc Natl Acad Sci USA. 2005, 102 (51): 18526-18531. 10.1073/pnas.0509285102.View ArticlePubMedGoogle Scholar
- McKay SD, Schnabel SD, Murdoch BM, Aerts J, Gill CA, Gao C, Li C, Matukumalli LK, Stothard P, Wand Z, Van Tassel CP, Williams JL, Taylor JF, Moore SS: Construction of bovine whole-genome radiation hybrid and linkage maps using high-throughput genotyping. Animal Genetics. 2007, 38 (2): 120-125. 10.1111/j.1365-2052.2006.01564.x.PubMed CentralView ArticlePubMedGoogle Scholar
- Snelling WM, Casas E, Stone RT, Keele JW, Harhay GP, Bennett GL, Smith TPL: Linkage mapping bovine EST-based SNP. BMC Genomics. 2005, 6 (1): 74-10.1186/1471-2164-6-74.PubMed CentralView ArticlePubMedGoogle Scholar
- Snelling WM, Chiu R, Schein JE, Hobbs M, Abbey CA, Adelson DL, Aerts J, Bennett GL, Bosdet IE, Boussaha M, Brauning R, Caetano AR, Costa MM, Crawford AM, Dalyrmple BP, Eggen A, Everts-van der Wind A, Floriot S, Gautier M, Gill CA, Green RD, Holt R, Jann O, Jones SJM, Kappes SM, Keele JW, de Jong PJ, Larkin DM, Lewin HA, McEwan JC, McKay S, Marra MA, Mathewson CA, Matukumalli LK, Moore SS, Murdoch B, Nicholas FW, Osoegawa K, Roy A, Salih H, Schibler L, Schnabel RD, Silveri L, Skow LC, Smith TPL, Sonstegard TS, Taylor JF, Tellam R, Van Tassell CP, Williams JL, Womack JE, Wye NH, Yang G, Zhao S, International Bovine BAC Mapping Consortium: A physical map of the bovine genome. Genome Biol. 2007, 8 (8): R165-10.1186/gb-2007-8-8-r165.PubMed CentralView ArticlePubMedGoogle Scholar
- Bishop MD, Kappes SM, Keele JW, Stone RT, Sunden SLF, Hawkins GA, Toldo SS, Fries R, Grosz MD, Yoo J, Beattie CW: A genetic linkage map for cattle. Genetics. 1994, 136 (2): 619-639.PubMed CentralPubMedGoogle Scholar
- Barendse W, Armitage SM, Kossarek LM, Shalom A, Kirkpatrick BW, Ryan AM, Clayton D, Li L, Neibergs HL, Zhang N, Grosse WM, Weiss J, Creighton P, McCarthy F, Ron M, Teale AJ, Fries R, McGraw RA, Moore SS, Georges M, Soller M, Womack JE, Hetzel DJS: A genetic linkage map of the bovine genome. Nat Genet. 1994, 6 (3): 227-235. 10.1038/ng0394-227.View ArticlePubMedGoogle Scholar
- Barendse W, Vaiman D, Kemp SJ, Sugimoto Y, Armitage SM, Williams JL, Sun HS, Eggen A, Agaba M, Aleyasin SA, Band M, Bishop MD, Buitkamp J, Byrne K, Collins F, Cooper L, Coppettiers W, Denys B, Drinkwater RD, Easterday K, Elduque C, Ennis S, Erhardt G, Ferretti L, Flavin N, Gao Q, Georges M, Gurung R, Harlizius B, Hawkins G, Hetzel J, Hirano T, Hulme D, Jorgensen C, Kessler M, Kirkpatrick BW, Konfortov B, Kostia S, Kuhn C, Lenstra JA, Leveziel H, Lewin HA, Leyhe B, Lil L, Martin Burriel I, McGraw RA, Miller JR, Moody DE, Moore SS, Nakane S, Nijman IJ, Olsaker I, Pomp D, Rando A, Ron M, Shalom A, Teale AJ, Thieven U, Urquhart BGD, Vage D-I, Weghe Van de A, Varvio S, Velmala R, Vilkki J, Weikard R, Woodside C, Womack JE, Zanotti M, Zaragoza P: A medium-density genetic linkage map of the bovine genome. Mamm Genome. 1997, 8 (1): 21-28. 10.1007/s003359900340.View ArticlePubMedGoogle Scholar
- Kappes SM, Keele JW, Stone RT, McGraw RA, Sonstegard TS, Smith TP, Lopez-Corrales NL, Beattie CW: A second-generation linkage map of the bovine genome. Genome Res. 1997, 7: 235-249. 10.1101/gr.7.3.235.View ArticlePubMedGoogle Scholar
- Ihara N, Takasuga A, Mizoshita K, Takeda H, Sugimoto M, Mizoguchi Y, Hirano T, Itoh T, Watanabe T, Reed KM, Snelling WM, Kappes SM, Beattie CW, Bennett GL, Sugimoto Y: A comprehensive genetic map of the cattle genome based on 3802 microsatellites. Genome Res. 2004, 14 (10a): 1987-1998. 10.1101/gr.2741704.PubMed CentralView ArticlePubMedGoogle Scholar
- Kappes SM, Green RD, Van Tassell CP: Sequencing the bovine genome and developing a haplotype map: approaches and opportunities. Proceedings of the 8th World Congress on Genetics Applied to Livestock Production: 13–18 August 2006, Belo Horizonte, Minas Gerais, Brazil. 2006, 22-01.Google Scholar
- NCBI dbSNP Build 129 Bos Taurus. [http://www.ncbi.nlm.nih.gov/sites/entrez?db=snp&cmd=Search&dopt=DocSum&term=txid9913%5BOrganism%3Anoexp%5D]
- Website, Affymetrix. [https://www.affymetrix.com/analysis/netaffx/index.affx]
- Website, BcoM-BGP. [http://www.hgsc.bcm.tmc.edu/projects/bovine/]
- Website, CSIRO, Livestock Genomics. [http://www.livestockgenomics.csiro.au/]
- Breen M, Deakin L, Macdonald B, Miller S, Sibson R, Tarttelin E, Avner P, Bourgade F, Guenet JL, Montagutelli X, Poirier C, Simon D, Tailor D, Bishop M, Kelly M, Rysavy F, Rastan S, Norris D, Shepherd D, Abbott C, Pilz A, Hodge S, Jackson I, Boyd Y, Blair H, Maslen G, Todd JA, Reed PW, Stoye J, Ashworth A, McCarthy L, Cox R, Schalkwyk L, Lehrach H, Klose J, Gangadharan U, Brown S, The European Backcross Collaborative Group: Towards high resolution maps of the mouse and human genomes-a facility for ordering markers to 0.1 cM resolution. Hum Mol Genet. 1994, 3 (4): 621-627. 10.1093/hmg/3.4.621.View ArticleGoogle Scholar
- Kosambi DD: The estimation of map distance from recombination values. Ann Eugen. 1944, 12 (3): 172-175.Google Scholar
- Shifman S, Bell JT, Copley RR, Taylor MS, Williams RW, Mott R, Flint J: A high-resolution single nucleotide polymorphism genetic map of the mouse genome. PloS Biol. 2006, 4 (12): e395-10.1371/journal.pbio.0040395.PubMed CentralView ArticlePubMedGoogle Scholar
- Kong A, Gudbjartsson DF, Sainz J, Jonsdottir GM, Gudjonsson SA, Richardsson B, Sigurdardottir S, Barnard J, Hallbeck B, Masson G, Shlien A, Palsson ST, Frigge ML, Thorgeirsson TE, Gulcher JR, Stefansson JR: A high-resolution recombination map of the human genome. Nat Genet. 2002, 31 (3): 241-247.PubMedGoogle Scholar
- Bovine Genome Sequence Assembly. [ftp://ftp.hgsc.bcm.tmc.edu/pub/data/Btaurus/fasta/Btau20070913-freeze/]
- Meuwissen THE, Goddard ME: Fine mapping of quantitative trait loci using linkage disequilibria with closely linked marker loci. Genetics. 2000, 155 (1): 421-430.PubMed CentralPubMedGoogle Scholar
- Meuwissen THE, Karlsen A, Lein S, Olsaker I, Goddard ME: Fine mapping of a quantitative trait locus for twinning rate using combined and linkage disequilibrium mapping. Genetics. 2002, 161 (1): 373-379.PubMed CentralPubMedGoogle Scholar
- Meuwissen THE, Hayes BJ, Goddard ME: Prediction of total genetic value using genome-wide dense marker maps. Genetics. 2001, 157 (4): 1819-1829.PubMed CentralPubMedGoogle Scholar
- de Roos APW, Hayes BJ, Spelman RJ, Goddard ME: Linkage disequilibrium and persistence of phase in Holstein Friesian, Jersey and Angus cattle. Genetics. 2008, 179: 1503-12. 10.1534/genetics.107.084301.PubMed CentralView ArticlePubMedGoogle Scholar
- Spelman RJ, Miller FM, Hooper JD, Thielen M, Garrick DJ: Experimental design for QTL trial involving New Zealand Friesian and Jersey breeds. Proc Assoc Assoc Advmt Breed Genet. 2001, 14: 393-396.Google Scholar
- NCBI web site. [http://www.ncbi.nlm.nih.gov]
- Grisart B, Coppieters W, Farnir F, Karim L, Ford C, Berzi P, Cambisano N, Mni M, Reid S, Simon P, Spelman R, Georges M, Snell R: Positional candidate cloning of a QTL in dairy cattle: identification of a missense mutation in the bovine DGAT1 gene with major effect on milk yield and composition. Genome Res. 2002, 12 (2): 222-231. 10.1101/gr.224202.View ArticlePubMedGoogle Scholar
- Grisart B, Farnir F, Karim L, Ford C, Cambisano N, Kim J-J, Kvasz A, Mni M, Simon P, Frere J-M, Coppieters W, Georges M: Genetic and functional confirmation of the causality of the DGAT1 K232A quantitative trait nucleotide in affecting milk yield and composition. Proc Natl Acad Sci USA. 2004, 101 (8): 2398-2403. 10.1073/pnas.0308518100.PubMed CentralView ArticlePubMedGoogle Scholar
- Blott S, Kim JJ, Moisio S, Schmidt-Küntzel A, Cornet A, Berzi P, Cambisano N, Ford C, Grisart B, Johnson D, Karim L, Simon P, Snell R, Spelman R, Wong J, Vilkki J, Georges M, Farnir F, Coppieters W: Molecular dissection of a quantitative trait locus: a phenylalanine-to-Tyrosine substitution in the transmembrane domain of the bovine growth hormone receptor is associated with a major effect on milk yield and composition. Genetics. 2003, 163 (1): 253-266.PubMed CentralPubMedGoogle Scholar
- Wigginton JE, Cutler DJ, Abecasis GR: A note on exact test of Hardy-Weinberg equilibrium. Am J of Hum Genet. 2005, 76 (5): 887-893. 10.1086/429864.View ArticleGoogle Scholar
- Green P, Falls K, Crooks S: Documentation for Cri-MAP, Version 2.4. Washington University School of Medicine, St Louis, Missouri, MO, USA. 1990Google Scholar
- CRI-MAP Documentation. [http://linkage.rockefeller.edu/soft/crimap/]
- Matise TC, Perlin M, Chakravarti A: Automated construction of genetic linkage maps using an expert system (MultiMap): a human genome linkage map. Nat Genet. 1994, 6 (4): 384-390. 10.1038/ng0494-384.View ArticlePubMedGoogle Scholar
- Bovine Genome Sequence Assembly. [ftp://ftp.hgsc.bcm.tmc.edu/pub/data/Btaurus/fasta/Btau20060815-freeze/]
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.