Whole genome linkage disequilibrium maps in cattle
- Stephanie D McKay1,
- Robert D Schnabel2,
- Brenda M Murdoch1,
- Lakshmi K Matukumalli3, 4,
- Jan Aerts5,
- Wouter Coppieters6,
- Denny Crews1, 7,
- Emmanuel Dias Neto8, 9,
- Clare A Gill10,
- Chuan Gao10,
- Hideyuki Mannen11,
- Paul Stothard1,
- Zhiquan Wang1,
- Curt P Van Tassell3,
- John L Williams12,
- Jeremy F Taylor2 and
- Stephen S Moore1Email author
© McKay et al; licensee BioMed Central Ltd. 2007
Received: 28 March 2007
Accepted: 25 October 2007
Published: 25 October 2007
Bovine whole genome linkage disequilibrium maps were constructed for eight breeds of cattle. These data provide fundamental information concerning bovine genome organization which will allow the design of studies to associate genetic variation with economically important traits and also provides background information concerning the extent of long range linkage disequilibrium in cattle.
Linkage disequilibrium was assessed using r2 among all pairs of syntenic markers within eight breeds of cattle from the Bos taurus and Bos indicus subspecies. Bos taurus breeds included Angus, Charolais, Dutch Black and White Dairy, Holstein, Japanese Black and Limousin while Bos indicus breeds included Brahman and Nelore. Approximately 2670 markers spanning the entire bovine autosomal genome were used to estimate pairwise r2 values. We found that the extent of linkage disequilibrium is no more than 0.5 Mb in these eight breeds of cattle.
Linkage disequilibrium in cattle has previously been reported to extend several tens of centimorgans. Our results, based on a much larger sample of marker loci and across eight breeds of cattle indicate that in cattle linkage disequilibrium persists over much more limited distances. Our findings suggest that 30,000–50,000 loci will be needed to conduct whole genome association studies in cattle.
Linkage disequilibrium (LD) maps are fundamental tools for exploring the genetic basis of economically important traits in cattle. Likewise, comparative LD maps enable us to explore the degree of diversity between breeds of cattle and to detect genomic regions that have been subject to selective sweeps within the different dairy and beef breeds which represent different biological attributes (e.g. Continental European vs. British). The currently available information regarding LD in cattle is primarily based on microsatellite studies performed in dairy cattle. The most extensive of these studies used 284 genome-wide microsatellites in a population of Dutch Black and White Dairy cattle  to show that syntenic LD extended up to several tens of centimorgans (cM). Haplotypes for 581 maternally inherited gametes were used to estimate LD using Lewontin's normalized D'. The results indicated high levels of LD not only between closely linked markers but for markers located as much as 40 cM (~40 Mb) apart. Two subsequent studies examined the extent of LD in cattle although both used fewer animals and microsatellites [2, 3]. Vallejo  selected distantly related animals to quantify the level of genetic diversity in United States Holstein cattle. While only 23 Holstein bulls were genotyped with 54 microsatellite loci that spanned most of the autosomal genome, extensive LD was detected in the United States Holstein population in agreement with the findings of Farnir et al. . Tenesa et al.  genotyped 50 Holstein bulls for 13 microsatellites spanning BTA2 and BTA6, to determine the extent of LD in the United Kingdom Holstein population. The average D' value was 44% with significant LD reported only for distances less than 10.3 cM. Linkage disequilibrium among non syntenic loci was not significant.
More recently Khatkar et al.  scored 220 BTA6 single nucleotide polymorphisms (SNPs) in a sample of 433 Australian dairy bulls and estimated LD between marker pairs using D'. While they found that LD decayed with increasing distance between markers, D' did not reach background until an average distance of 20 Mb separated the markers. They also found that there was extensive variability in the magnitude of D' at any one distance. The rate of decay of LD estimated using SNPs  was much greater than that estimated using microsatellites , which is consistent with the findings of Varilo et al.  that more informative marker systems are able to detect LD over greater physical distances.
Only recently has the extent of LD been examined in beef cattle populations. A sample of 162 half-sib progeny from a Japanese black sire and 406 half-sib Japanese brown cattle were genotyped with 246 and 156 autosomal microsatellite markers, respectively . For syntenic markers, the mean D' was 16.3% for Japanese Brown and 25.1% for Japanese Black. Characteristic of D' as a measure of LD, significant LD was observed for marker pairs separated by as much as 40 cM in both breeds.
Quantifying the extent of LD in the bovine genome is a necessary first step for determining the number of markers that will be sufficient for QTL mapping by linkage disequilibrium. The previous studies which used microsatellite markers were either too narrowly focused on particular chromosomes, or were of insufficient resolution to precisely estimate genome-wide LD and almost certainly were unable to precisely estimate short-range LD. The high density and low inherent rates of mutation of SNPs relative to microsatellites within mammalian genomes allows for the identification of ancestral haplotype blocks and the estimation of identity by descent probabilities which are crucial for haplotype-based association studies . In this study, we estimated LD in 8 breeds of cattle utilizing 2670 single nucleotide polymorphism (SNP) markers that were derived from the bovine genome sequence and were aligned to the Btau_3.1 genome sequence assembly.
Results and Discussion
General LD Findings
It has been suggested that when large differences exist between marker allele frequencies, due to the presence of a rare allele, these two measures of LD are divergent . D' estimates historical recombination through allelic association whereas r2 measures the squared correlation coefficient between locus allele frequencies and is strongly influenced by the order in which the mutations arose (genealogy) and not necessarily the physical distance between loci . In the context of QTL mapping, r2 is the preferred measure of LD, because it quantifies the amount of information that can be inferred about one (perhaps nonobservable quantitative trait or disease) locus from another [22, 23], and can therefore be used to estimate the number of loci needed for association studies [23, 24]. For this reason we have used r2 as the primary measure of LD in this study.
Variation in average r2 values between breeds is evident in Figures 4 and 6. Considering the similarity between the Holstein and Dutch Black and White Dairy breeds, we expected comparable average r2 values between these breeds (Figure 4). In fact the extent of LD is quite similar within all of the Bos taurus and within the Bos indicus breeds, however, the Bos indicus appear to have substantially lower levels of LD at short inter-marker distances than do the Bos taurus. This could be the result of ascertainment bias as the SNPs used in this study were detected because they were common SNPs within Bos taurus and their average minor allele frequency was much lower in Bos indicus . An alternative hypothesis is that the lower levels of LD at short inter-marker distances could also reflect historically larger effective population sizes , which seems particularly appropriate for the Nelore. On the other hand, long range LD in Brahman appears to be greater than for the Nelore and other Bos taurus breeds which suggests a smaller current effective population size which is consistent with the relatively recent formation of the breed as an admixture between extant Bos taurus and several imported Bos indicus breeds imported into the U.S. between 1854 and 1926 .
r2 by Chromosome
BTA 7, 12 and 21
We have two theories as to why lower than average LD may exist on BTA 7, 12 and 21. First, because cattle have been selected for production traits for at least 50 generations, there is the possibility that selection on QTL distributed throughout the genome has generated different patterns of LD on individual chromosomes. However, compared to chromosomes of similar size, there does not appear to be fewer QTLs on BTA7, 12 and 21 [28, 29] and selection should have resulted in similar patterns of LD on these chromosomes as on all others. Second, it is possible that chromosomes 7, 12 and 21 have higher than average rates of recombination than do the other autosomes. A comparison between the physical  and genetic  maps of BTA7, 12 and 21 as well as chromosomes of similar physical size, indicates that the physical to genetic size relationship is similar for BTA7, 12 and 21 and other chromosomes of similar physical size. However, regions of increased recombination have been detected on human chromosomes 14 and 15 , which are partially orthologous to BTA21. A complete exploration of these chromosomes in order to study aspects of genome organization that potentially affect recombination rate will require additional markers and animals.
While we included loci in this analysis for which order was consistent between the Btau_3.1 assembly and our radiation hybrid map , the genome coordinates for each locus were obtained from the Btau_3.1 assembly. This assembly spans only 2.43 Gb and while an additional 319 Mb of sequence exists as contigs which are unassigned to chromosomes, we expect the final sequence to be much closer to 2.8 Gb. The unassembled contigs are likely to be biased towards centromeric and telomeric sequences and duplications which are difficult to assemble, but some are no doubt interstitial to chromosomes. The fact that these are unassembled would likely cause a systematic bias towards the underestimation of the physical distance between loci. There also appear to be a significant number or problems with the ordering and orientation of scaffolds within the assembly and these errors are likely to produce random effects on the estimation of distance between syntenic loci. Thus overall, we suspect that the incomplete nature of the assembly results in about a 10% underestimate of the distance between loci. This has only a minor affect on our conclusions and the extent of LD available for association analysis still does not significantly exceed 500 kb. At a physical distance of 100 kb separating flanking SNP loci, the average r2 is 0.15–0.2 and the average r2 between these markers and a QTL located at mid-interval is about 0.3 (Figure. 4). This would appear to be the lowest desirable resolution for whole genome association mapping in bovine and assuming a 2.87 Gb genome, it would require 28,700 fully informative SNPs to saturate the genome at an average resolution of 100 kb. Since the number of validated bovine SNPs is currently insufficient to achieve an even spacing and because many SNPs are likely to have low minor allele frequencies leading to their being uninformative in many populations, we believe that 50,000 SNPs will be the minimum required for whole genome association studies in cattle. Furthermore, the extent of LD on BTA 7, 12 and 21 appears to be much lower than for the autosomal genome as a whole and suggests that SNP density may need to be enhanced on these chromosomes. The construction of a high resolution LD map of the bovine genome will provide further insight into the effects of selection and evolutionary forces upon the genomes of breeds which have been selected for different agricultural purposes.
DNA was collected from 70 Angus (USA), 20 Canadian Angus, 40 Charolais (Canada), 40 Brahman (USA), 97 Dutch Black and White Dairy cattle (Belgium), 48 Holstein (USA), 65 Japanese Black (Japan), 43 Limousin (USA) and 97 Nelore (Brazil) cattle. In order to phase the chromosomes using linkage information, we selected small families where members within the families were closely related but the families themselves were not closely related. Family structure and the number of individuals per family varied between the breeds but the general family structure consisted of a male grandparent, male parent and three or more progeny (Additional file 6). This three generation family structure allowed for the efficient estimation of marker phase relationships in the progeny and also produced the most likely phase relationships in each of the parents/grandparents.
Marker Selection and Genotyping
Sequence information for SNPs was obtained from public databases [33, 34]. Loci included in this study met the following criteria; minor allele frequency (MAF) ≥ 0.05 in Angus based on previous screens (data not shown) and concordant order determined by radiation hybrid (RH) mapping  and genomic sequence location. Oligonucleotides were designed, synthesized and assembled into oligo pooled assays (OPA) by Illumina Inc. (San Diego, CA). Genotyping was performed using the manufacturer's protocol for the Illumina® BeadStation 500G [35, 36]).
Locus locations within the bovine genome sequence assembly
Chromosomal coordinates for each SNP were obtained by aligning approximately 250 bp flanking each SNP by BLAST to the latest release of the bovine genome sequence assembly, Btau_3.1. These physical coordinates were compared to the linkage and RH maps of McKay et al. Thirty four markers were excluded from the analysis because their assignment in the sequence assembly was to a chromosome that differed to their linkage or RH map assignment or because they had no chromosomal assignment in Btau_3.1. Marker information can be found in Additional file 7.
Haplotypes and LD Analysis
GENOPROB V2.0 [8, 9] was used to assess genotype score quality and produce whole chromosome phased haplotypes based on the pedigree and physical map locations of the loci. Briefly, GENOPROB uses an allelic peeling algorithm to estimate both the probability that a genotype is correct, denoted as pGmx, and the probability that the order (phase) of the alleles are correct, denoted as oGmx. Only genotypes with a pGmx ≥ 0.95 were used for LD analysis but no restriction was placed on order probability, oGmx. This produced a set of whole chromosome haplotypes comprised of accurately scored genotypes that were in the most likely phase configuration. LD was assessed by generating r2 values using GOLD  independently for the maternally- and paternally-inherited haplotypes. LD data presented here is based only on the maternally inherited haplotypes which avoids the overrepresentation of paternally inherited haplotypes within the primarily male pedigrees.
The authors thank Michel Georges for intellectual contributions. This work was supported through Grant Number 2003A245R awarded to S.S. Moore by the Alberta Agriculture Research Institute. Taylor and Schnabel were supported by the National Research Initiative of the USDA Cooperative State Research, Education and Extension Service (CSREES) grants #2005-35205-15448; #2006-35616-16697 and #2006-35205-16701. J. Aerts was supported by the Biotechnology and Biological Sciences Research Council (BBSRC) grant BBS/B13454 "Bovine genome annotation and analysis."
- Farnir F, Coppieters W, Arranz JJ, Berzi P, Cambisano N, Grisart B, Karim L, Marcq F, Moreau L, Mni M, Nezer C, Simon P, Vanmanshoven P, Wagenaar D, Georges M: Extensive genome-wide linkage disequilibrium in cattle. Genome Res. 2000, 10 (2): 220-227. 10.1101/gr.10.2.220.View ArticlePubMedGoogle Scholar
- Vallejo RL, Li YL, Rogers GW, Ashwell MS: Genetic diversity and background linkage disequilibrium in the North American Holstein cattle population. J Dairy Sci. 2003, 86 (12): 4137-4147.View ArticlePubMedGoogle Scholar
- Tenesa A, Knott SA, Ward D, Smith D, Williams JL, Visscher PM: Estimation of linkage disequilibrium in a sample of the United Kingdom dairy cattle population using unphased genotypes. J Anim Sci. 2003, 81 (3): 617-623.PubMedGoogle Scholar
- Khatkar MS, Collins A, Cavanagh JA, Hawken RJ, Hobbs M, Zenger KR, Barris W, McClintock AE, Thomson PC, Nicholas FW, Raadsma HW: A first-generation metric linkage disequilibrium map of bovine chromosome 6. Genetics. 2006, 174 (1): 79-85. 10.1534/genetics.106.060418.PubMed CentralView ArticlePubMedGoogle Scholar
- Varilo T, Paunio T, Parker A, Perola M, Meyer J, Terwilliger JD, Peltonen L: The interval of linkage disequilibrium (LD) detected with microsatellite and SNP markers in chromosomes of Finnish populations with different histories. Hum Mol Genet. 2003, 12 (1): 51-59. 10.1093/hmg/ddg005.View ArticlePubMedGoogle Scholar
- Odani M, Narita A, Watanabe T, Yokouchi K, Sugimoto Y, Fujita T, Oguni T, Matsumoto M, Sasaki Y: Genome-wide linkage disequilibrium in two Japanese beef cattle breeds. Anim Genet. 2006, 37 (2): 139-144. 10.1111/j.1365-2052.2005.01400.x.View ArticlePubMedGoogle Scholar
- Vignal A, Milan D, SanCristobal M, Eggen A: A review on SNP and other types of molecular markers and their use in animal genetics. Genet Sel Evol. 2002, 34 (3): 275-305. 10.1051/gse:2002009.PubMed CentralView ArticlePubMedGoogle Scholar
- Thallman RM, Bennett GL, Keele JW, Kappes SM: Efficient computation of genotype probabilities for loci with many alleles: I. Allelic peeling. J Anim Sci. 2001, 79 (1): 26-33.PubMedGoogle Scholar
- Thallman RM, Bennett GL, Keele JW, Kappes SM: Efficient computation of genotype probabilities for loci with many alleles: II. Iterative method for large, complex pedigrees. J Anim Sci. 2001, 79 (1): 34-44.PubMedGoogle Scholar
- McKay SD, Schnabel RD, Murdoch BM, Aerts J, Gill CA, Gao C, Li C, Matukumalli LK, Stothard P, Wang Z, Van Tassell CP, Williams JL, Taylor JF, Moore SS: Construction of bovine whole-genome radiation hybrid and linkage maps using high-throughput genotyping. Anim Genet. 2007, 38 (2): 120-125. 10.1111/j.1365-2052.2006.01564.x.PubMed CentralView ArticlePubMedGoogle Scholar
- Baylor College of Medicine- Bovine Genome Project Website: http://www.hgsc.bcm.tmc.edu/projects/bovine/.
- Schaeffer SW, Miller EL: Estimates of linkage disequilibrium and the recombination parameter determined from segregating nucleotide sites in the alcohol dehydrogenase region of Drosophila pseudoobscura. Genetics. 1993, 135 (2): 541-552.PubMed CentralPubMedGoogle Scholar
- Du FX, Clutter AC, Lohuis MM: Characterizing linkage disequilibrium in pig populations. Int J Biol Sci. 2007, 3 (3): 166-178.PubMed CentralView ArticlePubMedGoogle Scholar
- Lindblad-Toh K, Wade CM, Mikkelsen TS, Karlsson EK, Jaffe DB, Kamal M, Clamp M, Chang JL, Kulbokas EJ, Zody MC, Mauceli E, Xie X, Breen M, Wayne RK, Ostrander EA, Ponting CP, Galibert F, Smith DR, DeJong PJ, Kirkness E, Alvarez P, Biagi T, Brockman W, Butler J, Chin CW, Cook A, Cuff J, Daly MJ, DeCaprio D, Gnerre S, Grabherr M, Kellis M, Kleber M, Bardeleben C, Goodstadt L, Heger A, Hitte C, Kim L, Koepfli KP, Parker HG, Pollinger JP, Searle SM, Sutter NB, Thomas R, Webber C, Baldwin J, Abebe A, Abouelleil A, Aftuck L, Ait-Zahra M, Aldredge T, Allen N, An P, Anderson S, Antoine C, Arachchi H, Aslam A, Ayotte L, Bachantsang P, Barry A, Bayul T, Benamara M, Berlin A, Bessette D, Blitshteyn B, Bloom T, Blye J, Boguslavskiy L, Bonnet C, Boukhgalter B, Brown A, Cahill P, Calixte N, Camarata J, Cheshatsang Y, Chu J, Citroen M, Collymore A, Cooke P, Dawoe T, Daza R, Decktor K, DeGray S, Dhargay N, Dooley K, Dooley K, Dorje P, Dorjee K, Dorris L, Duffey N, Dupes A, Egbiremolen O, Elong R, Falk J, Farina A, Faro S, Ferguson D, Ferreira P, Fisher S, FitzGerald M, Foley K, Foley C, Franke A, Friedrich D, Gage D, Garber M, Gearin G, Giannoukos G, Goode T, Goyette A, Graham J, Grandbois E, Gyaltsen K, Hafez N, Hagopian D, Hagos B, Hall J, Healy C, Hegarty R, Honan T, Horn A, Houde N, Hughes L, Hunnicutt L, Husby M, Jester B, Jones C, Kamat A, Kanga B, Kells C, Khazanovich D, Kieu AC, Kisner P, Kumar M, Lance K, Landers T, Lara M, Lee W, Leger JP, Lennon N, Leuper L, LeVine S, Liu J, Liu X, Lokyitsang Y, Lokyitsang T, Lui A, Macdonald J, Major J, Marabella R, Maru K, Matthews C, McDonough S, Mehta T, Meldrim J, Melnikov A, Meneus L, Mihalev A, Mihova T, Miller K, Mittelman R, Mlenga V, Mulrain L, Munson G, Navidi A, Naylor J, Nguyen T, Nguyen N, Nguyen C, Nguyen T, Nicol R, Norbu N, Norbu C, Novod N, Nyima T, Olandt P, O'Neill B, O'Neill K, Osman S, Oyono L, Patti C, Perrin D, Phunkhang P, Pierre F, Priest M, Rachupka A, Raghuraman S, Rameau R, Ray V, Raymond C, Rege F, Rise C, Rogers J, Rogov P, Sahalie J, Settipalli S, Sharpe T, Shea T, Sheehan M, Sherpa N, Shi J, Shih D, Sloan J, Smith C, Sparrow T, Stalker J, Stange-Thomann N, Stavropoulos S, Stone C, Stone S, Sykes S, Tchuinga P, Tenzing P, Tesfaye S, Thoulutsang D, Thoulutsang Y, Topham K, Topping I, Tsamla T, Vassiliev H, Venkataraman V, Vo A, Wangchuk T, Wangdi T, Weiand M, Wilkinson J, Wilson A, Yadav S, Yang S, Yang X, Young G, Yu Q, Zainoun J, Zembek L, Zimmer A, Lander ES: Genome sequence, comparative analysis and haplotype structure of the domestic dog. Nature. 2005, 438 (7069): 803-819. 10.1038/nature04338.View ArticlePubMedGoogle Scholar
- Hinds DA, Stuve LL, Nilsen GB, Halperin E, Eskin E, Ballinger DG, Frazer KA, Cox DR: Whole-genome patterns of common DNA variation in three human populations. Science. 2005, 307 (5712): 1072-1079. 10.1126/science.1105436.View ArticlePubMedGoogle Scholar
- Schnabel RD, Kim JJ, Ashwell MS, Sonstegard TS, Van Tassell CP, Connor EE, Taylor JF: Fine-mapping milk production quantitative trait loci on BTA6: analysis of the bovine osteopontin gene. Proc Natl Acad Sci U S A. 2005, 102 (19): 6896-6901. 10.1073/pnas.0502398102.PubMed CentralView ArticlePubMedGoogle Scholar
- Ardlie KG, Kruglyak L, Seielstad M: Patterns of linkage disequilibrium in the human genome. Nat Rev Genet. 2002, 3 (4): 299-309. 10.1038/nrg777.View ArticlePubMedGoogle Scholar
- Ke X, Hunt S, Tapper W, Lawrence R, Stavrides G, Ghori J, Whittaker P, Collins A, Morris AP, Bentley D, Cardon LR, Deloukas P: The impact of SNP density on fine-scale patterns of linkage disequilibrium. Hum Mol Genet. 2004, 13 (6): 577-588. 10.1093/hmg/ddh060.View ArticlePubMedGoogle Scholar
- Khatkar MS, Zenger KR, Hobbs M, Hawken RJ, Cavanagh JA, 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
- Boyles AL, Scott WK, Martin ER, Schmidt S, Li YJ, Ashley-Koch A, Bass MP, Schmidt M, Pericak-Vance MA, Speer MC, Hauser ER: Linkage disequilibrium inflates type I error rates in multipoint linkage analysis when parental genotypes are missing. Hum Hered. 2005, 59 (4): 220-227. 10.1159/000087122.PubMed CentralView ArticlePubMedGoogle Scholar
- Daly MJ, Rioux JD, Schaffner SF, Hudson TJ, Lander ES: High-resolution haplotype structure in the human genome. Nat Genet. 2001, 29 (2): 229-232. 10.1038/ng1001-229.View ArticlePubMedGoogle Scholar
- Zhao H, Nettleton D, Soller M, Dekkers JC: Evaluation of linkage disequilibrium measures between multi-allelic markers as predictors of linkage disequilibrium between markers and QTL. Genet Res. 2005, 86 (1): 77-87. 10.1017/S001667230500769X.View ArticlePubMedGoogle Scholar
- Pritchard JK, Przeworski M: Linkage disequilibrium in humans: models and data. Am J Hum Genet. 2001, 69 (1): 1-14. 10.1086/321275.PubMed CentralView ArticlePubMedGoogle Scholar
- Kruglyak L: Prospects for whole-genome linkage disequilibrium mapping of common disease genes. Nat Genet. 1999, 22 (2): 139-144. 10.1038/9642.View ArticlePubMedGoogle Scholar
- Weiss KM, Clark AG: Linkage disequilibrium and the mapping of complex human traits. Trends Genet. 2002, 18 (1): 19-24. 10.1016/S0168-9525(01)02550-1.View ArticlePubMedGoogle Scholar
- Tenesa A, Navarro P, Hayes BJ, Duffy DL, Clarke GM, Goddard ME, Visscher PM: Recent human effective population size estimated from linkage disequilibrium. Genome Res. 2007, 17 (4): 520-526. 10.1101/gr.6023607.PubMed CentralView ArticlePubMedGoogle Scholar
- Sanders JO: History and development of Zebu cattle in the United States. J Anim Sci. 1980, 50: 1188-1200.Google Scholar
- Hu ZL, Reecy JM: Animal QTLdb: beyond a repository : A public platform for QTL comparisons and integration with diverse types of structural genomic information. Mamm Genome. 2007, 18 (1): 1-4. 10.1007/s00335-006-0105-8.View ArticlePubMedGoogle Scholar
- Polineni P, Aragonda P, Xavier SR, Furuta R, Adelson DL: The bovine QTL viewer: a web accessible database of bovine Quantitative Trait Loci. BMC Bioinformatics. 2006, 7: 283-10.1186/1471-2105-7-283.PubMed CentralView ArticlePubMedGoogle Scholar
- Website, NCBI, Mapview: . [http://www.ncbi.nlm.nih.gov/mapview/map_search.cgi?taxid=9913]
- Website, USDA, MARC: . [http://www.marc.usda.gov/genome/genome.html]
- Yu A, Zhao C, Fan Y, Jang W, Mungall AJ, Deloukas P, Olsen A, Doggett NA, Ghebranious N, Broman KW, Weber JL: Comparison of human genetic and sequence-based physical maps. Nature. 2001, 409 (6822): 951-953. 10.1038/35057185.View ArticlePubMedGoogle Scholar
- Website, NCBI, dbSNP: . [http://www.ncbi.nlm.nih.gov/projects/SNP/]
- Website, Medicine BC, FTP: . [ftp://ftp.hgsc.bcm.tmc.edu/pub/data/Btaurus/snp]
- Oliphant A, Barker DL, Stuelpnagel JR, Chee MS: BeadArray technology: enabling an accurate, cost-effective approach to high-throughput genotyping. Biotechniques. 2002, Suppl: 56-8, 60-1..PubMedGoogle Scholar
- Website, Illumina: . [http://www.illumina.com]
- Abecasis GR, Cookson WO: GOLD--graphical overview of linkage disequilibrium. Bioinformatics. 2000, 16 (2): 182-183. 10.1093/bioinformatics/16.2.182.View ArticlePubMedGoogle Scholar
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