© 1992 Oxford University Press
Human Molecular Genetics, Vol. 1, No. 9
663-666
MINI REVIEW La carte des microsatellites est arrivee! John A.Todd Nuffield Department of Surgery, University of Oxford, John Radcliffe Hospital, Headington, Oxford 0X3 9DU, UK Received November 8, 1992
Weissenbach and colleagues at Genethon in Paris have published a landmark in human genome program (1). They have characterised and genetically mapped 813 polymorphic microsatellites into 23 linkage groups covering about 90% of the 22 autosomes and the X chromosome. This achievement demonstrates the feasibility of organising 'people off the street' to run a semi-automated, well-funded laboratory to systematically generate large amounts of data, and sets the trend for the next ten years of the program. Genome-wide approaches are the most efficient, and, with current technology are feasible—it is just a case now of cranking the handle and collating and disseminating the information. This is not 'more of the same' or factory science, it is simply a means to an end, a necessary reagent if we are to identify the genes that cause common human genetic diseases, and hoist preventative medicine out of the epidemiological folklore of this century into the new genetic-epidemiology era of the next century. These projects are not run by science bureaucrats, but by scientists like Weissenbach, Cohen, Lander and Lathrop who are interested in solving the genetics of common multifactorial or complex diseases, and have been lucky enough to have been given the money to do the job. Bureaucrat-controlled science is often faltering and ineffective because the bureaucrats do not fully understand the final goals of the work, and therefore cannot make the commitment required to support ambitious research projects. Many of us in the field of mammalian genetics are now gorged with the fruits of the mouse and human microsatellites genetic maps, which are accessible though PCR to even the most provincial of laboratories, who often possess the most interesting affected families and clinical material. Nevertheless, we want more. Weissenbach, Weber and many other laboratories are generating many more microsatellites to increase the resolution and informativeness of the human map to the 1—2 cM level. Lander and colleagues are doing the same for the mouse genome (2). Beckmann and Lathrop and colleagues at CEPH (Paris) (3) and Lander and colleagues (4) have also developed several hundred rat microsatellites. Perhaps the most exciting aspect of these genetic maps is that they provide a collection of ordered sequence-tagged sites (STS) with which to assemble the first genome-wide YAC contigs. Weissenbach and colleagues reported 2506 microsatellite STSs (1), which can be used to STS-content map the megabase YAC library produced by Cohen and colleagues (5). These, plus the existing or potential PCR-analysed microsatellite STSs, which include 368, mostly identified in gene sequences (6), 100 from Dracapoli and colleagues (7) and 246 from Weber and colleagues at Marshfield (Release 10) already provide over 30% of the 10,000 STSs required to YAC contig
map the human genome. The same machinery employed to generate and map microsatellites can to used to assemble the physical map. Within 2—3 years scientists currently involved in labour-intensive, inefficient positional cloning projects should be able to pull their favourite YAC contig off the shelf and proceed with the hard part of positional cloning—the identification of the polymorphisms or mutations that cause or predispose to disease. Technology The application of PCR to the detection of size variation at simple tandem repeats of 1 - 5 bps in size was reported originally by three groups (8-10), although genetic polymorphism of simple sequence repeats had been recognised several years before by Tautz and colleagues (11). Their general features and statistics have reviewed recently (12). It is not known precisely what mechanism (s) causes variation at microsatellites but fortunately, the mutation rate is low ranging from 0.1 % to 0.045% in human (1, 13) and 1-0.01% in mice (14), permitting their use as probes for genetic linkage analysis and also for linkage disequilibrium mapping. It has always been a problem scoring dinucleotide microsatellites (mononucleotide microsatellites are generally even worse) because of the artefactual bands generated by the PCR, commonly 2 bp shorter than the most intensely staining or hybridising PCR band. Weissenbach and colleagues have improved this technology by adopting the methods of multiplex sequencing (15) to detect several (up to 16) marker loci on one gel (16). They co-precipitate PCR products in 96-well microtitre plates from several different microsatellite marker loci, electrophorese the products on standard 'sequencing' denaturing urea-acrylamide gels, transfer the DNA to nylon membrane and probe the membrane sequentially with each unique PCR primer (16). In theory, this could provide 768 genotypes per 48 lane gel. The main advantages are that each gel provides a lot of information and that the blots can be probed non-radioactively using commercially-available fluorescent labelling systems. We and others (Nigel Spurr, personal communication) have found this method to work, and is currently the method of choice to analyse large numbers of microsatellites on large numbers of individuals. The other modification which was recommended by Michel Georges (17) and which we now use as standard is the addition of 40% formamide in the gel. Formamide eliminates many of the additional bands, making scoring significantly more simple. Still, some microsatellites are more easily typed than others but this will not be a problem as the density and choice of marker loci increases. It is anticipated that over 4,000 polymorphic should be available within the next 2—3 years. Automated DNA sequencing machines can be used to type
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INTRODUCTION
664 Human Molecular Genetics, Vol. 1, No. 9 most important complementation groups of genes affecting a complex trait. In the presence of locus heterogeneity no recombinant can be trusted and the level of apparent recombination is related directly to the presence of unlinked loci that cause the same effect. Linkage mapping of oligogenic, and perhaps even polygenic disease will require step-wise (25, 30, 31) or simultaneous search (32, 33) procedures to uncover contributing loci. A prerequisite for such studies is therefore a complete linkage map of microsatellites in the disease families. In the future the best defined linkage maps will be in the intensely studied families that are affected with the common diseases. Linkage to a 5—20 cM region per se does not, however help much in the identification of the disease-associated mutations. This requires finer mapping by exploiting linkage disequilibrium which might exist between marker polymorphisms and the disease-causing mutations.
Linkage mapping of multifactorial disease Because families with multiple living affected relatives and parents are rare, and difficult and expensive to collect, genetic markers should be highly polymorphic. Nearly 75% (605) of Weissenbach's microsatellites have heterozygosity values above 0.7, adding to the 205 already mapped on the human genome map published recently (22). To date there are probably approaching 1000 of these highly polymorphic microsatellites available, giving an average resolution of more than 1 per 5 cM. For extracting the most information from valuable families we need a highly informative microsatellite every 1-2 cM. In multiplex families with type 1 diabetes with parents and at least two affected siblings from the UK, marker loci with published PIC values > 0.75 generally make at least 65% of the families fully informative, but this depends on the ethnic origin of the individuals used to estimate the microsatellite allele frequencies. There are noticeable gaps in the microsatellite map , and these may be more frequent in pro-terminal regions of chromosomes (1, 15). It will be worth making special efforts to extract the microsatellites from these regions by isolating YACs using existing probes, including many minisatellites, which tend to be clustered in proterminal regions (23), and gene sequences. The power of an exclusion mapping study depends on the polymorphism of the probes and their density. Linkage studies of complex human traits using marker loci with PIC values less than 0.7 are not considered cost-effective (24). Clearly, within 2—3 years the molecular map will not be limiting. Even now the number of microsatellites is beyond the capacity of averagesized laboratories. The stage is now set for comprehensive linkage mapping of genes that predispose to the common human multifactorial disorders such as diabetes, hypertension, cancer and heart disease. Microsatellites have already contributed to the localisation of new genes for familial Alzheimer's disease (25) and diabetes (26-28). The genes for these diseases, which make the greatest impact on healthcare will be difficult to map because it is likely there will be several different ways of obtaining the same phenotype. A useful comparison is consideration of the number of different mutations in bacteria that can cause disruption of the biosynthesis of an amino acid. Locus heterogeneity is a major confounding factor in the study of these diseases, which will require studies of families from different ethnic groups and constant re-alignment of the clinical characteristics of the disease and its associated subphenotypes (29). Even then, and with a completely adequate microsatellite map it may not be feasible to define all but the
Linkage disequilibrium Linkage disequilibrium or gametic association occurs when alleles at two linked marker loci occur more frequently together than expected based on their individual frequencies in the population. As demonstrated in cystic fibrosis (34) linkage disequilibrium can be used to fine map the disease gene to regions in the order of 100kb, although this is not the case for Huntington's disease (35). Microsatellites, due to their low mutation rate have already scored several successes in the linkage disequilibrium mapping. A microsatellite allele (Z+4) at the glucokinase locus on chromosome 7p is associated with susceptibility to non-insulindependent diabetes mellitus (NIDDM) in American blacks (28). This result formed the basis for the discovery that a rare autosomal dominant form of NIDDM is caused by mutations in the glucokinase gene (26, 36). A tetranucleotide microsatellite allele (Z-16) in the tyrosine hydroxylase gene on chromosome Ilpl5, which is adjacent to the insulin and insulin-like growth factor II genes is associated with susceptibility to insulindependent diabetes mellitus (IDDM) (12). Another tetranucleotide microsatellite allele near the gene encoding myelin basic protein on chromosome 18 is positively associated with susceptibility to multiple sclerosis (37). Microsatellites alleles at the Friedreich ataxia (38), muscular dystrophy (20) and cystic fibrosis (39) loci are in disequilibrium with the disease mutations. The microsatellites in the cystic fibrosis and muscular dystrophy genes will facilitate carrier detection. At a frequency of at least one every 30kb of the human genome (40) (this is based on the frequency of (GT) microsatellites only), microsatellites will be powerful probes in the search for linkage disequilibrium (41), and will increase the power of the candidate gene approach. Animals with multifactorial disease Until recently it was thought that common, multifactorial diseases and other complex traits might be polygenic, with a large number of unmapable, small gene effects acting additively to cause the trait. In a model of type 1 diabetes, the spontaneously diabetic NOD mouse we have been able to show by construction of the first microsatellite linkage map of the mouse genome that autoimmune diabetes is an oligogenic trait, with, so far, nine unlinked loci contributing to immune destruction of the insulinproducing j3-cells (42-44). It is likely therefore that human IDDM is also oligogenic, with genes on chromosome 6p21 (the major histocompatibility complex; MHC) and 1 Ipl5 (insulin gene region) already linked to disease (45—47). If, or rather when the major genetic determinants of diabetes or hypertension or
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microsatellites (18-20), although the cost of the machine is prohibitive, the through-put of the system and its software is not fully developed yet and optimisation of the fiuorescenated primers is required. The advantages are that low numbers of PCR cycles can be used thus reducing artefactual bands and that it may be possible to obtain uniform allele sizes, which would permit accurate assignment of allele names (based on size) and optimise the information obtained for linkage analysis and linkage disequilibrium mapping. Also, it may be possible to take genotype data from the gel electronically and send it directly to the linkage programs. This advance would be significant, particularly in the reduction in the error rate of genotyping. Uniformity in microsatellite allele typing is important and several recommendations have been made recently (21). This problem will have to addressed more seriously as the use of microsatellites in clinical genetics increases.
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Subsequently, Dietrich and colleagues have developed very large numbers of mouse microsatellites producing a linkage map of equivalent density and coverage to the human map (2). Since microsatellites are variant even between inbred strains (55), this map can be used to dissect the numerous traits which have been studied and selected for in mice. It will be now be possible to map genes for complex developmental and behavioural traits. At the risk of overstating the case, we are in the middle of a revolution sparked off by PCR and microsatellites. I only hope that the data bases (GBASE and the Genome Data Base) can cope. ACKNOWLEDGEMENTS I thank the Wellcome Trust for continuing support, including a Wellcome Senior Fellowship in Basic Biomedical Science. My research is also supported by the Medical Research Council, the Juvenile Diabetes Foundation and the British Diabetic Association.
REFERENCES 1. Weissenbach, J., Gyapay, G., Dib, C , Vignal, A., Morissette, J., Millasseau, P., Vaysseix, G., and Lathrop, M. (1992) Nature 359, 794-801. 2. Dietrich, W., Katz, H., Lincoln, S.E., Shin, H.S., Friedman,J., Dracopoli, N., and Lander, E.S. (1992) Genetics 131, 423-447.
3. Hilbert, P., Lindpaintner, K., Beckmann, J.S., Senkawa, T., Soubrier, F., Dubay, C , Cartwright, P., De Gouyon, B., Julier, C , Takahasi, S., Vincent, M., Ganten, D., Georges, M., and Lathrop, G.M. (1991) Nature 353, 521-529. 4. Jacob, H.J., Undpaintner, K., Lincoln, S.E., Kusumi, K., Bunker, R.K., Mao, Y.-P., Ganten, D., Dzau, V.J., and Lander, E.S. (1991) Cell 67, 213-224. 5. Chumakov, I., Rigault, P., Guillou, S., Ougen, P., Billaul, A., Guasconi, G., Gervy, P., LeGall, I., Soularue, P., Grinas, L., Bougueleret, L., Bellanne-Chantelot, C , Lacroix, B., Barillot, E., Gesnouin, P., Pook, S., Vaysseix, G., Frelat, G., Schmitz, A., Sambucy, J.-L., Bosch, A., Esu'vill, X., Weissenbach, J., Vignal, A., Riethman, H., Cox, D., Patterson, D., Gardiner, K., Hattori, M., Sakaki, Y., Ichikawa, H., Ohlri, M., Le Paslier, D., Heilig, H., Antooarakis, S., and Cohen, D. (1992) Nature 359, 380-387. 6. Williamson, R., Bowcock, A., Kidd, K., Pearson, P., Schmidtke, J., Ceverha, P., Chipperfield, M., Cooper, D.N., Coutelle, C , Hewitt, J., Klinger, K., Langley, K., Beckmann, J., Tolley, M., and Maidak, B. (1991) Cytogenet. Cell Genet. 58, 1190-1832. 7. Hudson, T.J., Engelstein, M., Lee, M.K., Ho, E.C., Rubenfield, M.J., Adams, C.P., Housman, D.E., and Dracopoli, N.C. (1992) Genomics 13, 622-629. 8. Weber, J.L. and May, P.E. (1989) Am. J. Hum. Genet. 44, 388-396. 9. Litt, M. and Luty, J.A. (1989) Am. J. Hum. Genet. 44, 397-401. 10. Smeets, H.J.M., Brunner, H.G., Ropers, H.H., and Wieringa, B. (1989) Human Genetics 83, 245-251. 11. Tautz, D. and Renz, M. (1984) Nucleic Adds Research 12, 4127-4138. 12. Hearne, CM., Ghosh, S., and Todd, J.A. (1992) Trends Genet. 8, 288-294. 13. Kwiatkowski, D.J., Henske, E.P., Weimer, K., Ozelius, L., Gusella, J.F., and Haines, J. (1992) Genomics 12, 229-240. 14. Dallas, J.F. (1992) Mammalian Genome 3, 452-456. 15. Hazan, J., Dubay, C , Pankowiak, M.P., Becuwe, N., and Weissenbach, J. (1992) Genomics 12, 183-189. 16. Vignal, A., Gyapay, G., Hazan, J., Nguyen, S., Dupraz, C , Cheron, N., Becuwe, N., Trenchant, M., and Weissenbach, J. (1992) Methods in Molecular Genetics 1, in press. 17. Moore, S.S., Sargeant, L.L., King, T.J., Mattick, J.S., Georges, M., and Hetzel, D.J.S. (1991) Genomics 10, 654-660. 18. Carrano, A.V., Lamerdin, J., Ashworth, L.K., Watkins, B., Branscomb, E., Slezak, T., Raff, M., De Jong, P.J., Keith, D., McBride, L., Meister, S., and Kronick, M. (1989) Genomics 4, 129-136. 19. Edwards, A., Civitello, A., Hammond, H., and CT, C. (1991) Am. J. Hum. Genet. 49, 746-756. 20. Schwartz, L.S., Tarleton, J., Popovich, B., Seltzer, W.K., and Hoffman, E.P. (1992) Am. J. Hum. Genet. 51, 721-729. 21. Knowles, J.A., Vieland, VJ., and Gilliam, T.C. (1992)/On. J. Hum. Genet. 51, 905-909. 22. NIH/CEPH Collaborative Mapping Group. N.C.M. (1992) Science 258, 67-86. 23. Royle, N.J., Clarkson, R.E., Wong, Z., and Jeffreys, A.J. (1988) Genomics 3, 352-360. 24. Risch, N. (1990) Am. J. Hum. Genet. 46, 242-253. 25. Schcllenberg, G.D., Bird, T.D., Wijsman, E.M., Orr, H.T., Anderson, L., Nemens, E., White, J.A., Bonnycastle, L., Weber, J.L., Alonso, M.E., Potter, H., Heston, L.L., and Martin, G.M. (1992) Science 258, 668-671. 26. Froguel, P., Vaxillaire, M., Sun, F., Velho, G., Zouali, H., Butel, M.O., Lesage, S., Vionnet, N., Clement, K., Fougerousse, F., Tanizawa, Y., Weissenbach, J., Beckmann, J.S., Lathrop, G.M., Passa, P., Permutt, M.A., and Cohen, C. (1992) Nature 356, 162-164. 27. Bell, G.I., Xiang, K.-S., Newman, M.V., Wu, S.-H., Wright, L.G., Fajans, S.S., Spielman, R.S., and Cox, N.J. (1991) Proc. Nad. Acad Sci. USA 88, 1484-1488. 28. Chiu, K.C., Province, M.A., and Permutt, M.A. (1992) Diabetes 41, 843-849. 29. Risch, N. (1992) Science 255, 803-804. 30. Wilson, A.F., Elston, R.C., Sellers, T.A., Bailey-Wilson, J.E., Gersting, J.M., Deen, D.K., Sorant, A.J.M., Tran, L.D., Amos, C.I., and Siervogel, R.M. (1990) Am. J. Med. Genet. 35, 425-432. 31. Kandt, R.S., Haines, J.L., Smith, M., Northrup, H., Gardner, RJ.M., Short, M.P., Dumars, K., Roach, E.S., Steingold, S., Wall, S., Blanton, S.H., Flodman, P., Kwiatkowski, D.J., Jewell, A., Weber, J.L., Roses, A.D., and Pericak-Vance, M.A. (1992) Nature Genetics 2, 3 7 - 4 1 . 32. Lander, E.S. and Botstein, D. (1986) Proc. Nail. Acad. Sci. USA 83, 7353-7357. 33. Hyer, R.N., Julier, C , Bucldey, J.D., Trucco, M., Rotter, J., Spielman, R.S., Bamett, A.H., Bain, S.C., Boitard, C , Deschamps, I., Todd, J.A., Bell, J.I., and Lathrop, G.M. (1991) Am J. Hum. Genet. 48, 243-257.
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heart disease are defined it will be possible to screen for individuals at high risk, and at last begin to unravel the complexities of the environmental factors. The use of mice and rats to dissect complex traits such as diabetes (42), hypertension (3, 4) and epilepsy (48) has led the way in exclusion mapping of the susceptibility genes. The potential physiological effects of chromosomal regions that contained linked susceptibility loci can be tested by the construction of congenic strains. Such strains contain most of the genome of the disease-sensitive strain except for a specific, 'controlled' region, which encodes the diseaseresistant allele at the susceptibility locus. They are constructed by simply backcrossing the resistant strain to the sensitive one and by selecting the diabetes-resistance locus using flanking microsatellite markers to genotype DNA from animals during the breeding programme. The ability to screen the entire genome rapidly using mitrosatellites greatly facilitates and accelerates the development of congenic strains. The contributions of, and interactions between multiple genes can be assessed using such strains, without the need to actually identify the mutant gene. Using this approach it will be possible to fine map susceptibility genes which make only minor contributions to the phenotype. Underpinning the complexity of multifactorial disease in rodents and humans is locus heterogeneity and the presence of multiple biochemical pathways, any one of which could be defective. Genetic dissection of animal models of disease will allow identification of which pathways are important, and these are likely to be important for the pathophysiology of human disease and for the development of future preventative treatments. In addition, there is remarkable homology between the mouse and human genomes (49, 50). Several diseases and traits are caused by mutations in the same gene in both species (51, 52). For example, an IDDM-associated amino acid polymorphism of the mouse ldd-1 autoimmune diabetes locus in the MHC is precisely conserved in the IDDM1 locus in the human MHC (53). By comparative mapping it is possible to predict, in some cases with near certainty the position of the human homologue based on the map location of the mouse gene and vice versa. The utility of mouse genetics to study mammalian development and disease is of course further strengthened by the use of transgenic and gene targeting technology, as recently summarised by Brown and Goldstein (54).
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34. Kercm, B.S., Rommens, J.M., Buchanan, J.A., Marloewicz, D.", Cox, K.T., Chalravorti, A., Buchwald, M., and Tsui, L.C. (1989) Science 245, 1073-1080. 35. Andrew, S., Theilmann, J., Hedrick, A., Mah, D., Weber, B., and Hayden, M.R. (1992) Genomics 13, 301-311. 36. Vionnet, N., Stoffel, M., Takeda, J., Yasuda, K., Bell, G.I., Zouali, H., Lesage, S., Velho, G., Iris, F., Passa, P., Froguel, P., and D., C. (1992) Nanire 356, 721-722. 37. Tienari, P J . , Wikstrom, J., Sajantila, A., Palo, J., and Peltonen, L. (1992) 77K Lancet 340, 987-991. 38. Fujita, R., Hanauer, A., Sirugo, G., Heilig, R., and Mandel, J.-L. (1990) Proc. Nail. Acad. Sd. USA 87, 1796-1800. 39. Monal, N., Nunes, V., Casals, T., and EstiviU, X. (1991) Genomics 10, 692-698. 40. Soilings, R., Ford, A., Nelson, D., Tomey, D., Hildebrand, C , and Moyzis, R. (1991) Genomics 10, 807-815. 41. Sherrington, R., Melmer, G., Dixon, M., Curtis, D., Mankoo, B., Kalsi, G., and Gurling, H. (1991) Am. J. Hum. Genet. 49, 966-971. 42. Todd, J.A., Aitman, T.J., Cornall, RJ., Ghosh, S., Hall, J.R.S., Hearne, C M . , Knight, A.M., Love, J.M., McAleer, M.A., Prins, J.-B., Rodrigues, N., Lalhrop, G.M., Pressey, A., DeLarato, N., Peterson, L.B., and Wicker, L.S. (1991) Nature 351, 542-547. 43. Todd, J.A. and Bain, S.C. (1992) Diabetes 41, 1029-1034. 44. Cornall, RJ., Prins, J.-B:, Todd, J.A., Pressey, A., DeLarato, N.H., Wicker, L.S., and Peterson, L.B. (1991) Nature 353, 262-265. 45. Bell, G.I., Horita, S., and Karam, J.H. (1984) Diabetes 33, 176-83. 46. Julier, C , Hyer, R.N., Davies, J., Merlin, F., Soularu, P., Briant, L., Cathelineau, G., Deschamps, I., Rotter, J.I., Froguel, P., Boitard, C , Bell, J.I., and Lathrop, G.M. (1991) Nature 354, 155-159. 47. Bain, S.C, Prins, J.B., Heame, C M . , Rodrigues, N.R., Rowe, B.R., Pritchard, L.E., Ritchie, R.J., Hall, J.R.S., Undlien, D.E., Rormingen, K.S., Dunger, D.B., Bamett, A.H., and Todd, J.A. (1992) Nature Genetics 2, 212-215. 48. Rise, M.L., Frankel, W.N., Coffin, J.M., and Seyfried, T.N. (1991) Science 253, 669-673. 49. Nadeau, J.H., Davisson, M.T., Doolittle, D.P., Grant, P., Hillyard, A.L., Kosowsky, M.R., and Roderick, T.H. (1992) Mammalian Genome 3, 480-536. 50. Davisson, M.T., Lalley, P.A., Peters, J., Doolittle, D.P., Hillyard, A.L., and Searie, A.G. (1991) Cytogenet Cell Genet 58, 1152-1189. 51. Darling, S.M. and Abbott, C M . (1992) BioEssays 14, 359-367. 52. Todd, J.A. (1992) Gnome News 9, 24-30. 53. Todd, J.A., Bell, J.I., and McDevitt, H.O. (1987) Nature 329, 599-604. 54. Brown, M.S. and Goldstein, J.L. (1992) Cell 71, 187-188. 55. Love, J.M., Knight, A.M., McAleer, A.M., and Todd, J.A. (1990) Nucleic Adds Research 18, 4123-4130.
Human Molecular Genetics, Vol. 1, No. 9
663-666
MINI REVIEW La carte des microsatellites est arrivee! John A.Todd Nuffield Department of Surgery, University of Oxford, John Radcliffe Hospital, Headington, Oxford 0X3 9DU, UK Received November 8, 1992
Weissenbach and colleagues at Genethon in Paris have published a landmark in human genome program (1). They have characterised and genetically mapped 813 polymorphic microsatellites into 23 linkage groups covering about 90% of the 22 autosomes and the X chromosome. This achievement demonstrates the feasibility of organising 'people off the street' to run a semi-automated, well-funded laboratory to systematically generate large amounts of data, and sets the trend for the next ten years of the program. Genome-wide approaches are the most efficient, and, with current technology are feasible—it is just a case now of cranking the handle and collating and disseminating the information. This is not 'more of the same' or factory science, it is simply a means to an end, a necessary reagent if we are to identify the genes that cause common human genetic diseases, and hoist preventative medicine out of the epidemiological folklore of this century into the new genetic-epidemiology era of the next century. These projects are not run by science bureaucrats, but by scientists like Weissenbach, Cohen, Lander and Lathrop who are interested in solving the genetics of common multifactorial or complex diseases, and have been lucky enough to have been given the money to do the job. Bureaucrat-controlled science is often faltering and ineffective because the bureaucrats do not fully understand the final goals of the work, and therefore cannot make the commitment required to support ambitious research projects. Many of us in the field of mammalian genetics are now gorged with the fruits of the mouse and human microsatellites genetic maps, which are accessible though PCR to even the most provincial of laboratories, who often possess the most interesting affected families and clinical material. Nevertheless, we want more. Weissenbach, Weber and many other laboratories are generating many more microsatellites to increase the resolution and informativeness of the human map to the 1—2 cM level. Lander and colleagues are doing the same for the mouse genome (2). Beckmann and Lathrop and colleagues at CEPH (Paris) (3) and Lander and colleagues (4) have also developed several hundred rat microsatellites. Perhaps the most exciting aspect of these genetic maps is that they provide a collection of ordered sequence-tagged sites (STS) with which to assemble the first genome-wide YAC contigs. Weissenbach and colleagues reported 2506 microsatellite STSs (1), which can be used to STS-content map the megabase YAC library produced by Cohen and colleagues (5). These, plus the existing or potential PCR-analysed microsatellite STSs, which include 368, mostly identified in gene sequences (6), 100 from Dracapoli and colleagues (7) and 246 from Weber and colleagues at Marshfield (Release 10) already provide over 30% of the 10,000 STSs required to YAC contig
map the human genome. The same machinery employed to generate and map microsatellites can to used to assemble the physical map. Within 2—3 years scientists currently involved in labour-intensive, inefficient positional cloning projects should be able to pull their favourite YAC contig off the shelf and proceed with the hard part of positional cloning—the identification of the polymorphisms or mutations that cause or predispose to disease. Technology The application of PCR to the detection of size variation at simple tandem repeats of 1 - 5 bps in size was reported originally by three groups (8-10), although genetic polymorphism of simple sequence repeats had been recognised several years before by Tautz and colleagues (11). Their general features and statistics have reviewed recently (12). It is not known precisely what mechanism (s) causes variation at microsatellites but fortunately, the mutation rate is low ranging from 0.1 % to 0.045% in human (1, 13) and 1-0.01% in mice (14), permitting their use as probes for genetic linkage analysis and also for linkage disequilibrium mapping. It has always been a problem scoring dinucleotide microsatellites (mononucleotide microsatellites are generally even worse) because of the artefactual bands generated by the PCR, commonly 2 bp shorter than the most intensely staining or hybridising PCR band. Weissenbach and colleagues have improved this technology by adopting the methods of multiplex sequencing (15) to detect several (up to 16) marker loci on one gel (16). They co-precipitate PCR products in 96-well microtitre plates from several different microsatellite marker loci, electrophorese the products on standard 'sequencing' denaturing urea-acrylamide gels, transfer the DNA to nylon membrane and probe the membrane sequentially with each unique PCR primer (16). In theory, this could provide 768 genotypes per 48 lane gel. The main advantages are that each gel provides a lot of information and that the blots can be probed non-radioactively using commercially-available fluorescent labelling systems. We and others (Nigel Spurr, personal communication) have found this method to work, and is currently the method of choice to analyse large numbers of microsatellites on large numbers of individuals. The other modification which was recommended by Michel Georges (17) and which we now use as standard is the addition of 40% formamide in the gel. Formamide eliminates many of the additional bands, making scoring significantly more simple. Still, some microsatellites are more easily typed than others but this will not be a problem as the density and choice of marker loci increases. It is anticipated that over 4,000 polymorphic should be available within the next 2—3 years. Automated DNA sequencing machines can be used to type
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INTRODUCTION
664 Human Molecular Genetics, Vol. 1, No. 9 most important complementation groups of genes affecting a complex trait. In the presence of locus heterogeneity no recombinant can be trusted and the level of apparent recombination is related directly to the presence of unlinked loci that cause the same effect. Linkage mapping of oligogenic, and perhaps even polygenic disease will require step-wise (25, 30, 31) or simultaneous search (32, 33) procedures to uncover contributing loci. A prerequisite for such studies is therefore a complete linkage map of microsatellites in the disease families. In the future the best defined linkage maps will be in the intensely studied families that are affected with the common diseases. Linkage to a 5—20 cM region per se does not, however help much in the identification of the disease-associated mutations. This requires finer mapping by exploiting linkage disequilibrium which might exist between marker polymorphisms and the disease-causing mutations.
Linkage mapping of multifactorial disease Because families with multiple living affected relatives and parents are rare, and difficult and expensive to collect, genetic markers should be highly polymorphic. Nearly 75% (605) of Weissenbach's microsatellites have heterozygosity values above 0.7, adding to the 205 already mapped on the human genome map published recently (22). To date there are probably approaching 1000 of these highly polymorphic microsatellites available, giving an average resolution of more than 1 per 5 cM. For extracting the most information from valuable families we need a highly informative microsatellite every 1-2 cM. In multiplex families with type 1 diabetes with parents and at least two affected siblings from the UK, marker loci with published PIC values > 0.75 generally make at least 65% of the families fully informative, but this depends on the ethnic origin of the individuals used to estimate the microsatellite allele frequencies. There are noticeable gaps in the microsatellite map , and these may be more frequent in pro-terminal regions of chromosomes (1, 15). It will be worth making special efforts to extract the microsatellites from these regions by isolating YACs using existing probes, including many minisatellites, which tend to be clustered in proterminal regions (23), and gene sequences. The power of an exclusion mapping study depends on the polymorphism of the probes and their density. Linkage studies of complex human traits using marker loci with PIC values less than 0.7 are not considered cost-effective (24). Clearly, within 2—3 years the molecular map will not be limiting. Even now the number of microsatellites is beyond the capacity of averagesized laboratories. The stage is now set for comprehensive linkage mapping of genes that predispose to the common human multifactorial disorders such as diabetes, hypertension, cancer and heart disease. Microsatellites have already contributed to the localisation of new genes for familial Alzheimer's disease (25) and diabetes (26-28). The genes for these diseases, which make the greatest impact on healthcare will be difficult to map because it is likely there will be several different ways of obtaining the same phenotype. A useful comparison is consideration of the number of different mutations in bacteria that can cause disruption of the biosynthesis of an amino acid. Locus heterogeneity is a major confounding factor in the study of these diseases, which will require studies of families from different ethnic groups and constant re-alignment of the clinical characteristics of the disease and its associated subphenotypes (29). Even then, and with a completely adequate microsatellite map it may not be feasible to define all but the
Linkage disequilibrium Linkage disequilibrium or gametic association occurs when alleles at two linked marker loci occur more frequently together than expected based on their individual frequencies in the population. As demonstrated in cystic fibrosis (34) linkage disequilibrium can be used to fine map the disease gene to regions in the order of 100kb, although this is not the case for Huntington's disease (35). Microsatellites, due to their low mutation rate have already scored several successes in the linkage disequilibrium mapping. A microsatellite allele (Z+4) at the glucokinase locus on chromosome 7p is associated with susceptibility to non-insulindependent diabetes mellitus (NIDDM) in American blacks (28). This result formed the basis for the discovery that a rare autosomal dominant form of NIDDM is caused by mutations in the glucokinase gene (26, 36). A tetranucleotide microsatellite allele (Z-16) in the tyrosine hydroxylase gene on chromosome Ilpl5, which is adjacent to the insulin and insulin-like growth factor II genes is associated with susceptibility to insulindependent diabetes mellitus (IDDM) (12). Another tetranucleotide microsatellite allele near the gene encoding myelin basic protein on chromosome 18 is positively associated with susceptibility to multiple sclerosis (37). Microsatellites alleles at the Friedreich ataxia (38), muscular dystrophy (20) and cystic fibrosis (39) loci are in disequilibrium with the disease mutations. The microsatellites in the cystic fibrosis and muscular dystrophy genes will facilitate carrier detection. At a frequency of at least one every 30kb of the human genome (40) (this is based on the frequency of (GT) microsatellites only), microsatellites will be powerful probes in the search for linkage disequilibrium (41), and will increase the power of the candidate gene approach. Animals with multifactorial disease Until recently it was thought that common, multifactorial diseases and other complex traits might be polygenic, with a large number of unmapable, small gene effects acting additively to cause the trait. In a model of type 1 diabetes, the spontaneously diabetic NOD mouse we have been able to show by construction of the first microsatellite linkage map of the mouse genome that autoimmune diabetes is an oligogenic trait, with, so far, nine unlinked loci contributing to immune destruction of the insulinproducing j3-cells (42-44). It is likely therefore that human IDDM is also oligogenic, with genes on chromosome 6p21 (the major histocompatibility complex; MHC) and 1 Ipl5 (insulin gene region) already linked to disease (45—47). If, or rather when the major genetic determinants of diabetes or hypertension or
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microsatellites (18-20), although the cost of the machine is prohibitive, the through-put of the system and its software is not fully developed yet and optimisation of the fiuorescenated primers is required. The advantages are that low numbers of PCR cycles can be used thus reducing artefactual bands and that it may be possible to obtain uniform allele sizes, which would permit accurate assignment of allele names (based on size) and optimise the information obtained for linkage analysis and linkage disequilibrium mapping. Also, it may be possible to take genotype data from the gel electronically and send it directly to the linkage programs. This advance would be significant, particularly in the reduction in the error rate of genotyping. Uniformity in microsatellite allele typing is important and several recommendations have been made recently (21). This problem will have to addressed more seriously as the use of microsatellites in clinical genetics increases.
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Subsequently, Dietrich and colleagues have developed very large numbers of mouse microsatellites producing a linkage map of equivalent density and coverage to the human map (2). Since microsatellites are variant even between inbred strains (55), this map can be used to dissect the numerous traits which have been studied and selected for in mice. It will be now be possible to map genes for complex developmental and behavioural traits. At the risk of overstating the case, we are in the middle of a revolution sparked off by PCR and microsatellites. I only hope that the data bases (GBASE and the Genome Data Base) can cope. ACKNOWLEDGEMENTS I thank the Wellcome Trust for continuing support, including a Wellcome Senior Fellowship in Basic Biomedical Science. My research is also supported by the Medical Research Council, the Juvenile Diabetes Foundation and the British Diabetic Association.
REFERENCES 1. Weissenbach, J., Gyapay, G., Dib, C , Vignal, A., Morissette, J., Millasseau, P., Vaysseix, G., and Lathrop, M. (1992) Nature 359, 794-801. 2. Dietrich, W., Katz, H., Lincoln, S.E., Shin, H.S., Friedman,J., Dracopoli, N., and Lander, E.S. (1992) Genetics 131, 423-447.
3. Hilbert, P., Lindpaintner, K., Beckmann, J.S., Senkawa, T., Soubrier, F., Dubay, C , Cartwright, P., De Gouyon, B., Julier, C , Takahasi, S., Vincent, M., Ganten, D., Georges, M., and Lathrop, G.M. (1991) Nature 353, 521-529. 4. Jacob, H.J., Undpaintner, K., Lincoln, S.E., Kusumi, K., Bunker, R.K., Mao, Y.-P., Ganten, D., Dzau, V.J., and Lander, E.S. (1991) Cell 67, 213-224. 5. Chumakov, I., Rigault, P., Guillou, S., Ougen, P., Billaul, A., Guasconi, G., Gervy, P., LeGall, I., Soularue, P., Grinas, L., Bougueleret, L., Bellanne-Chantelot, C , Lacroix, B., Barillot, E., Gesnouin, P., Pook, S., Vaysseix, G., Frelat, G., Schmitz, A., Sambucy, J.-L., Bosch, A., Esu'vill, X., Weissenbach, J., Vignal, A., Riethman, H., Cox, D., Patterson, D., Gardiner, K., Hattori, M., Sakaki, Y., Ichikawa, H., Ohlri, M., Le Paslier, D., Heilig, H., Antooarakis, S., and Cohen, D. (1992) Nature 359, 380-387. 6. Williamson, R., Bowcock, A., Kidd, K., Pearson, P., Schmidtke, J., Ceverha, P., Chipperfield, M., Cooper, D.N., Coutelle, C , Hewitt, J., Klinger, K., Langley, K., Beckmann, J., Tolley, M., and Maidak, B. (1991) Cytogenet. Cell Genet. 58, 1190-1832. 7. Hudson, T.J., Engelstein, M., Lee, M.K., Ho, E.C., Rubenfield, M.J., Adams, C.P., Housman, D.E., and Dracopoli, N.C. (1992) Genomics 13, 622-629. 8. Weber, J.L. and May, P.E. (1989) Am. J. Hum. Genet. 44, 388-396. 9. Litt, M. and Luty, J.A. (1989) Am. J. Hum. Genet. 44, 397-401. 10. Smeets, H.J.M., Brunner, H.G., Ropers, H.H., and Wieringa, B. (1989) Human Genetics 83, 245-251. 11. Tautz, D. and Renz, M. (1984) Nucleic Adds Research 12, 4127-4138. 12. Hearne, CM., Ghosh, S., and Todd, J.A. (1992) Trends Genet. 8, 288-294. 13. Kwiatkowski, D.J., Henske, E.P., Weimer, K., Ozelius, L., Gusella, J.F., and Haines, J. (1992) Genomics 12, 229-240. 14. Dallas, J.F. (1992) Mammalian Genome 3, 452-456. 15. Hazan, J., Dubay, C , Pankowiak, M.P., Becuwe, N., and Weissenbach, J. (1992) Genomics 12, 183-189. 16. Vignal, A., Gyapay, G., Hazan, J., Nguyen, S., Dupraz, C , Cheron, N., Becuwe, N., Trenchant, M., and Weissenbach, J. (1992) Methods in Molecular Genetics 1, in press. 17. Moore, S.S., Sargeant, L.L., King, T.J., Mattick, J.S., Georges, M., and Hetzel, D.J.S. (1991) Genomics 10, 654-660. 18. Carrano, A.V., Lamerdin, J., Ashworth, L.K., Watkins, B., Branscomb, E., Slezak, T., Raff, M., De Jong, P.J., Keith, D., McBride, L., Meister, S., and Kronick, M. (1989) Genomics 4, 129-136. 19. Edwards, A., Civitello, A., Hammond, H., and CT, C. (1991) Am. J. Hum. Genet. 49, 746-756. 20. Schwartz, L.S., Tarleton, J., Popovich, B., Seltzer, W.K., and Hoffman, E.P. (1992) Am. J. Hum. Genet. 51, 721-729. 21. Knowles, J.A., Vieland, VJ., and Gilliam, T.C. (1992)/On. J. Hum. Genet. 51, 905-909. 22. NIH/CEPH Collaborative Mapping Group. N.C.M. (1992) Science 258, 67-86. 23. Royle, N.J., Clarkson, R.E., Wong, Z., and Jeffreys, A.J. (1988) Genomics 3, 352-360. 24. Risch, N. (1990) Am. J. Hum. Genet. 46, 242-253. 25. Schcllenberg, G.D., Bird, T.D., Wijsman, E.M., Orr, H.T., Anderson, L., Nemens, E., White, J.A., Bonnycastle, L., Weber, J.L., Alonso, M.E., Potter, H., Heston, L.L., and Martin, G.M. (1992) Science 258, 668-671. 26. Froguel, P., Vaxillaire, M., Sun, F., Velho, G., Zouali, H., Butel, M.O., Lesage, S., Vionnet, N., Clement, K., Fougerousse, F., Tanizawa, Y., Weissenbach, J., Beckmann, J.S., Lathrop, G.M., Passa, P., Permutt, M.A., and Cohen, C. (1992) Nature 356, 162-164. 27. Bell, G.I., Xiang, K.-S., Newman, M.V., Wu, S.-H., Wright, L.G., Fajans, S.S., Spielman, R.S., and Cox, N.J. (1991) Proc. Nad. Acad Sci. USA 88, 1484-1488. 28. Chiu, K.C., Province, M.A., and Permutt, M.A. (1992) Diabetes 41, 843-849. 29. Risch, N. (1992) Science 255, 803-804. 30. Wilson, A.F., Elston, R.C., Sellers, T.A., Bailey-Wilson, J.E., Gersting, J.M., Deen, D.K., Sorant, A.J.M., Tran, L.D., Amos, C.I., and Siervogel, R.M. (1990) Am. J. Med. Genet. 35, 425-432. 31. Kandt, R.S., Haines, J.L., Smith, M., Northrup, H., Gardner, RJ.M., Short, M.P., Dumars, K., Roach, E.S., Steingold, S., Wall, S., Blanton, S.H., Flodman, P., Kwiatkowski, D.J., Jewell, A., Weber, J.L., Roses, A.D., and Pericak-Vance, M.A. (1992) Nature Genetics 2, 3 7 - 4 1 . 32. Lander, E.S. and Botstein, D. (1986) Proc. Nail. Acad. Sci. USA 83, 7353-7357. 33. Hyer, R.N., Julier, C , Bucldey, J.D., Trucco, M., Rotter, J., Spielman, R.S., Bamett, A.H., Bain, S.C., Boitard, C , Deschamps, I., Todd, J.A., Bell, J.I., and Lathrop, G.M. (1991) Am J. Hum. Genet. 48, 243-257.
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heart disease are defined it will be possible to screen for individuals at high risk, and at last begin to unravel the complexities of the environmental factors. The use of mice and rats to dissect complex traits such as diabetes (42), hypertension (3, 4) and epilepsy (48) has led the way in exclusion mapping of the susceptibility genes. The potential physiological effects of chromosomal regions that contained linked susceptibility loci can be tested by the construction of congenic strains. Such strains contain most of the genome of the disease-sensitive strain except for a specific, 'controlled' region, which encodes the diseaseresistant allele at the susceptibility locus. They are constructed by simply backcrossing the resistant strain to the sensitive one and by selecting the diabetes-resistance locus using flanking microsatellite markers to genotype DNA from animals during the breeding programme. The ability to screen the entire genome rapidly using mitrosatellites greatly facilitates and accelerates the development of congenic strains. The contributions of, and interactions between multiple genes can be assessed using such strains, without the need to actually identify the mutant gene. Using this approach it will be possible to fine map susceptibility genes which make only minor contributions to the phenotype. Underpinning the complexity of multifactorial disease in rodents and humans is locus heterogeneity and the presence of multiple biochemical pathways, any one of which could be defective. Genetic dissection of animal models of disease will allow identification of which pathways are important, and these are likely to be important for the pathophysiology of human disease and for the development of future preventative treatments. In addition, there is remarkable homology between the mouse and human genomes (49, 50). Several diseases and traits are caused by mutations in the same gene in both species (51, 52). For example, an IDDM-associated amino acid polymorphism of the mouse ldd-1 autoimmune diabetes locus in the MHC is precisely conserved in the IDDM1 locus in the human MHC (53). By comparative mapping it is possible to predict, in some cases with near certainty the position of the human homologue based on the map location of the mouse gene and vice versa. The utility of mouse genetics to study mammalian development and disease is of course further strengthened by the use of transgenic and gene targeting technology, as recently summarised by Brown and Goldstein (54).
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34. Kercm, B.S., Rommens, J.M., Buchanan, J.A., Marloewicz, D.", Cox, K.T., Chalravorti, A., Buchwald, M., and Tsui, L.C. (1989) Science 245, 1073-1080. 35. Andrew, S., Theilmann, J., Hedrick, A., Mah, D., Weber, B., and Hayden, M.R. (1992) Genomics 13, 301-311. 36. Vionnet, N., Stoffel, M., Takeda, J., Yasuda, K., Bell, G.I., Zouali, H., Lesage, S., Velho, G., Iris, F., Passa, P., Froguel, P., and D., C. (1992) Nanire 356, 721-722. 37. Tienari, P J . , Wikstrom, J., Sajantila, A., Palo, J., and Peltonen, L. (1992) 77K Lancet 340, 987-991. 38. Fujita, R., Hanauer, A., Sirugo, G., Heilig, R., and Mandel, J.-L. (1990) Proc. Nail. Acad. Sd. USA 87, 1796-1800. 39. Monal, N., Nunes, V., Casals, T., and EstiviU, X. (1991) Genomics 10, 692-698. 40. Soilings, R., Ford, A., Nelson, D., Tomey, D., Hildebrand, C , and Moyzis, R. (1991) Genomics 10, 807-815. 41. Sherrington, R., Melmer, G., Dixon, M., Curtis, D., Mankoo, B., Kalsi, G., and Gurling, H. (1991) Am. J. Hum. Genet. 49, 966-971. 42. Todd, J.A., Aitman, T.J., Cornall, RJ., Ghosh, S., Hall, J.R.S., Hearne, C M . , Knight, A.M., Love, J.M., McAleer, M.A., Prins, J.-B., Rodrigues, N., Lalhrop, G.M., Pressey, A., DeLarato, N., Peterson, L.B., and Wicker, L.S. (1991) Nature 351, 542-547. 43. Todd, J.A. and Bain, S.C. (1992) Diabetes 41, 1029-1034. 44. Cornall, RJ., Prins, J.-B:, Todd, J.A., Pressey, A., DeLarato, N.H., Wicker, L.S., and Peterson, L.B. (1991) Nature 353, 262-265. 45. Bell, G.I., Horita, S., and Karam, J.H. (1984) Diabetes 33, 176-83. 46. Julier, C , Hyer, R.N., Davies, J., Merlin, F., Soularu, P., Briant, L., Cathelineau, G., Deschamps, I., Rotter, J.I., Froguel, P., Boitard, C , Bell, J.I., and Lathrop, G.M. (1991) Nature 354, 155-159. 47. Bain, S.C, Prins, J.B., Heame, C M . , Rodrigues, N.R., Rowe, B.R., Pritchard, L.E., Ritchie, R.J., Hall, J.R.S., Undlien, D.E., Rormingen, K.S., Dunger, D.B., Bamett, A.H., and Todd, J.A. (1992) Nature Genetics 2, 212-215. 48. Rise, M.L., Frankel, W.N., Coffin, J.M., and Seyfried, T.N. (1991) Science 253, 669-673. 49. Nadeau, J.H., Davisson, M.T., Doolittle, D.P., Grant, P., Hillyard, A.L., Kosowsky, M.R., and Roderick, T.H. (1992) Mammalian Genome 3, 480-536. 50. Davisson, M.T., Lalley, P.A., Peters, J., Doolittle, D.P., Hillyard, A.L., and Searie, A.G. (1991) Cytogenet Cell Genet 58, 1152-1189. 51. Darling, S.M. and Abbott, C M . (1992) BioEssays 14, 359-367. 52. Todd, J.A. (1992) Gnome News 9, 24-30. 53. Todd, J.A., Bell, J.I., and McDevitt, H.O. (1987) Nature 329, 599-604. 54. Brown, M.S. and Goldstein, J.L. (1992) Cell 71, 187-188. 55. Love, J.M., Knight, A.M., McAleer, A.M., and Todd, J.A. (1990) Nucleic Adds Research 18, 4123-4130.
