NV-ng0205.indd N E W S A N D V I E W S 1 1 8 VOLUME 37 | NUMBER 2 | FEBRUARY 2005 | NATURE GENETICS to phosphorylate itself and its downstream targets, and this could be part of its mecha- nism for inhibiting the activation of DNA- repair complexes both at telomeres and at double-strand breaks. The demonstration by Bradshaw et al. that TRF2 is rapidly recruited to generic double-strand breaks will initiate a mutually productive period of interaction between the fields of DNA repair and telo- mere biology, as the roles for telomeric fac- tors in the choreography of repair come into the spotlight. 1. Bradshaw, P.S., Stavropoulos, D.J. & Meyn, M.S. Nat. Genet. 37, 193–197 (2005). 2. de Lange, T. Nat. Rev. Mol. Cell. Biol. 5, 323–329 (2004). 3. Griffith, J.D. et al. Cell 97, 503–514 (1999). 4. Stansel, R.M., de Lange, T. & Griffith, J.D. EMBO J. 20, 5532–5540 (2001). 5. Hardy, C.F., Sussel, L. & Shore, D. Genes Dev. 6, 801–814 (1992). 6. Silverman, J., Takai, H., Buonomo, S.B., Eisenhaber, F. & de Lange, T. Genes Dev. 18, 2108–2119 (2004). 7. Xu, L. & Blackburn, E.H. J. Cell. Biol. 167, 819–830 (2004). 8. van Steensel, B., Smogorzewska, A. & de Lange, T. Cell 92, 401–413 (1998). 9. Wang, R.C., Smogorzewska, A. & de Lange, T. Cell 119, 355–368 (2004). 10. Gommers-Ampt, J., Lutgerink, J. & Borst, P. Nucleic Acids Res. 19, 1745–1751 (1991). 11. Steinert, S., Shay, J.W. & Wright, W.E. Mol. Cell. Biol. 24, 4571–4580 (2004). 12. Karlseder, J. et al. PLoS Biol. 2, E240 (2004) The beauty of admixture Ariel Darvasi & Sagiv Shifman Admixture mapping is an old concept that has only now been applied with markers across the entire genome. Such a study scanning an African American population identified two chromosomal regions affecting susceptibility to hypertension. Anecdotally, children of parents of mixed ethnicities are exotically beautiful. More sci- entifically established is the merit of admixed populations for gene mapping purposes. The potential value of admixed populations was suggested more than half a century ago1. Substantial theoretical and practical aspects have been developed since then (reviewed by McKeigue2). A genome scan to identify genes affecting a complex trait is now pre- sented for the first time to our knowledge by Xiaofeng Zhu and colleagues on page 177 of this issue3. The admixed population The concept behind admixture mapping is simple (Fig. 1). In essence, admixture map- ping is most similar to linkage analysis in experimental crosses with inbred strains, with specific similarity to advanced inter- cross lines4. An advanced intercross line is a population derived from two inbred strains that were randomly intercrossed for several generations. An advanced intercross line constitutes the ideal admixed population: all variations can be identified in one of the two progenitors, the mean ancestral composition is 50% for each progenitor, allele frequencies in the progenitor populations are either 1 or 0, and random mating is followed after a sin- gle generation of intercrossing the progeni- tors. In a human admixed population, these ideal conditions will never be met, resulting in decreased power for mapping purposes. Except for gene effect, which has a strong influence on power, the parameter that mostly affects power, specifically in admixture map- ping, is the extent of difference in allele fre- quency between the ancestral populations5. Ariel Darvasi is in The Life Sciences Institute, The Hebrew University, Jerusalem 91904, Israel. Sagiv Shifman is in the Wellcome Trust Centre for Human Genetics, Oxford OX3 7BN, UK. e- mail: arield@cc.huji.ac.il, sagiv@well.ox.ac.uk Case Control Population 1 Population 2 Disease gene location Figure 1 Schematic of one chromosome pair from each of several individuals in an admixed population. A group of cases (for a given disease) and a group of controls are separately presented at the bottom left and the bottom right, respectively. For one of the control individuals (arrow), a schematic presentation of all its ancestors in the last four generations is shown in the upper part of the figure. Admixture mapping can be ideally applied if population 1 (blue) and population 2 (red) carry a different allele at the disease locus (dashed line). Whole-genome scanning under the admixture mapping strategy consists of scanning the genome and identifying the regions with an excess of ‘red’ ancestry in the cases versus the controls, assuming that the ‘red’ population carries the predisposition allele. The size of the blocks from different ancestors will depend on the number of generations since the populations were mixed. © 2 0 0 5 N a tu re P u b li s h in g G ro u p h tt p :/ /w w w .n a tu re .c o m /n a tu re g e n e ti c s N E W S A N D V I E W S NATURE GENETICS | VOLUME 37 | NUMBER 2 | FEBRUARY 2005 1 1 9 For example, in the extreme case where the allele affecting a disease has the same fre- quency in both ancestral populations, admix- ture mapping cannot be efficiently applied. In contrast, the power of admixture mapping will be only mildly affected by the percentage contributed by each population to the admix- ture, as long as that proportion is between 20% and 80% (ref. 5). Genome scan for hypertension In an effort to identify chromosomal regions affecting hypertension, Zhu et al.3 carried out a genome scan with 269 microsatellite mark- ers and a total of 737 cases (hypertensives) and 573 controls (normotensives). Cases and controls were selected from the African American population. African Americans are an admixed population with ∼75% African ancestry and ∼25% European ancestry6 and are thus appropriate for admixture mapping. All individuals were sampled from three net- works (GenNet, GENOA and HyperGEN) in geographically distinct locations participat- ing in the Family Blood Pressure Program. Zhu et al.3 initially explored hyperten- sive cases only, independently in the three networks, and found an excess of African ancestry in more than one network on chro- mosomes 4, 6 and 21. In particular, two markers around 6q24 showed an excess of African ancestry in all three populations. To validate the significance of these results, they compared the excess of African ances- try found in the cases with that found in controls. The excess of African ancestry was shifted upwards in cases relative to controls. The entire shift can be attributed to two chro- mosomal regions at 6q24 and 21q21 where the excess of African ancestry was signifi- cant in cases but not in controls. Therefore, these findings suggest that the chromosomal regions 6q24 and 21q21 contain genes affect- ing predisposition to hypertension. Support for the chromosome 6q24 findings can be drawn from previous linkage studies that found evidence for linkage between this chromosomal region and hypertension or related traits7,8. The large size of this chromosomal region (37 cM, including all markers with Z score >2.5) may suggest that more than one gene affecting hypertension is present. This is not unexpected, as cis-acting linked genes will behave as a single gene with a larger effect (the combined effect of the two genes) in an admixture mapping experiment, hence having greater power of being picked up in a genome scan. The 21q21 region needs further replication to establish its validity, as this region has not previously been suggested to be associated with hypertension. A complementary approach Two main approaches have been used to search for genes affecting complex traits: linkage analysis and association analysis9. Linkage analysis has two key disadvantages: relatively low statistical power for detecting modest effects10, and low mapping resolution, which prevents gene identification even after a region has been detected9. Association anal- ysis also has two key disadvantages. Because this approach is based on linkage disequilib- rium or on testing the potential functional polymorphisms, the number of polymor- phisms that need to be scanned in the entire genome is painfully high (>100,000)11. The second disadvantage is the diminishing power that occurs with high genetic heterogene- ity12. Admixture mapping is a strategy that falls between linkage analysis and association analysis in many respects (Table 1). Although admixture mapping has a sub- stantially lower mapping resolution than association analysis, as long as genotyping costs are a limiting factor, admixture map- ping will be a good approach for the initial genome scan. Admixture mapping is particu- larly appropriate for traits for which there is a large difference in the phenotypic prevalence in the ancestral populations of the admix- ture. Nevertheless, admixture mapping is not limited to those traits and will still work if the allele frequencies of the disease locus are different in the ancestors of the admixed population. This is more likely to occur when the disease prevalence varies in the ancestral populations. Given the advantages of admixture map- ping, it is notable that this experiment has only now been done. One reason for this might be the notion (which might be cor- rect) that more markers are required for an adequate whole-genome scan with admix- ture mapping5 than were used in the current experiment. In addition, admixture mapping is efficient only if the allele frequencies of the markers are substantially different in the ancestral populations. In that respect, it now seems that microsatellite panels might be more informative than originally thought13. Consequently, a standard panel of markers, normally used in linkage experiments, suc- cessfully served Zhu et al.3 in their admix- ture mapping study. A word of caution is appropriate, though. The unexpected success might be due to the specific constellations particular to the current experiment, includ- ing chance. Therefore, the study of Zhu et al.3, which applied admixture mapping to hypertension and concluded with success- ful and robust results, will still require some replications in other traits and with other samples before its generality can be estab- lished. The current results, however, are undoubtedly promising enough to encour- age the scientific community to carry out these essential replications. 1. Rife, D.C. Am. J. Hum. Genet. 6, 26–33 (1954). 2. McKeigue, P.M. Am. J. Hum. Genet. 76, 1–7 (2005). 3. Zhu, X. et al. Nat. Genet. 37, 177–181 (2005). 4. Darvasi, A. & Soller, M. Genetics 141, 1199–1207 (1995). 5. Patterson, N. et al. Am. J. Hum. Genet. 74, 979– 1000 (2004). 6. Destro-Bisol, G. et al. Hum. Genet. 104, 149–157 (1999). 7. Krushkal, J. et al. Circulation 99, 1407–1410 (1999). 8. Arya, R. et al. Diabetes 51, 841–847 (2002). 9. Lander, E.S. & Schork, N.J. Science 265, 2037– 2048 (1994). 10. Risch, N. & Merikangas, K. Science 273, 1516–1517 (1996). 11. Risch, N.J. Nature 405, 847–856 (2000). 12. Weiss, K.M. & Terwilliger, J.D. Nat Genet. 26, 151– 157 (2000). 13. Tang, H. et al. Am. J. Hum. Genet. (in the press). Table 1 Main characteristics of mapping strategies Linkage analysis Admixture mapping Association analysis Statistical power Low High* High Number of SNPs required for whole genome scan Low Low High Sensitivity to genetic heterogeneity Low Moderate High Mapping resolution Poor Intermediate Good *Power diminishes to zero with equal allele frequencies in the ancestral population. © 2 0 0 5 N a tu re P u b li s h in g G ro u p h tt p :/ /w w w .n a tu re .c o m /n a tu re g e n e ti c s