This page has been archived and is no longer updated
Population genetic structure of variable drug response.
Author: J. F. Wilson
Keywords
Keywords for this Article
Add keywords to your Content
Save
|
Cancel
Share
|
Cancel
Revoke
|
Cancel
Rate & Certify
Rate Me...
Rate Me
!
Comment
Save
|
Cancel
Flag Inappropriate
The Content is
Objectionable
Explicit
Offensive
Inaccurate
Comment
Flag Content
|
Cancel
Delete Content
Reason
Delete
|
Cancel
Close
Full Screen
"article nature genetics ? volume 29 ? november 2001 265 Population genetic structure of variable drug response James F. Wilson 1,2 , Michael E. Weale 3,4 , Alice C. Smith 1 , Fiona Gratrix 1 , Benjamin Fletcher 3 , Mark G. Thomas 3 , Neil Bradman 3 & David B. Goldstein 1 Published online: 29 October 2001, DOI: 10.1038/ng761 Geographic patterns of genetic variation, including variation at drug metabolizing enzyme (DME) loci and drug targets, indicate that geographic structuring of inter-individual variation in drug response may occur frequently. This raises two questions: how to represent human population genetic structure in the evaluation of drug safety and efficacy, and how to relate this structure to drug response. We address these by (i) inferring the genetic struc- ture present in a heterogeneous sample and (ii) comparing the distribution of DME variants across the inferred genetic clusters of individuals. We find that commonly used ethnic labels are both insufficient and inaccurate rep- resentations of the inferred genetic clusters, and that drug-metabolizing profiles, defined by the distribution of DME variants, differ significantly among the clusters. We note, however, that the complexity of human demo- graphic history means that there is no obvious natural clustering scheme, nor an obvious appropriate degree of resolution. Our comparison of drug-metabolizing profiles across the inferred clusters establishes a framework for assessing the appropriate level of resolution in relating genetic structure to drug response. 1 Galton Laboratory, Department of Biology, University College London, London, UK. 2 Department of Zoology, University of Oxford, Oxford, UK. 3 The Centre for Genetic Anthropology, Departments of Biology, University College London, London, UK. 4 Genostics Ltd, 28/30 Little Russell Street, London WC1A 2HN, UK. Correspondence should be addressed to D.B.G. (e-mail: d.goldstein@ucl.ac.uk). Introduction Many drugs that show therapeutic potential never reach the mar- ket because of adverse reactions in some individuals, whereas other drugs in common use are effective for only a fraction of the population in which they are prescribed. This variation in drug response depends on many factors, such as sex, age and the envi- ronment, as well as genetic determinants. Since the 1950s, phar- macogenetic studies have systematically identified allelic variants at genes that influence drug response, including those of both drug-metabolizing enzymes (DMEs) 1 and drug targets 2 , such as the cytochrome P450 monooxygenase CYP2D6 (refs. 3,4) and the N-acetyl transferase NAT2 (ref. 5) genes. Detailed functional analysis of variants at genes such as these has clearly shown the importance of genetic variation in drug responses. For example, analysis of NAT2 alleles has identified amino acid?replacement mutations that reduce activity and a noncoding mutation that reduces translation, lowering the concentration of the enzyme 5 . In the case of CYP2D6, common variants include a frameshift leading to a truncated, nonfunctional protein and a splice-site mutation resulting in the absence of the protein 3,4 . These and other examples indicate that genetic tests might predict an indi- vidual?s response to specific drugs, allowing medicines to be tai- lored to specific genetic makeups. Because of the potential commercial and clinical significance of such personalized medi- cines, an understanding of the genetic role of variable drug response is an important goal of biomedical research. In addition to concerns surrounding individual variation in drug response, the geographic distribution of certain variants has highlighted the possible importance of average differences in drug response across populations. Genetic polymorphisms in DMEs, which probably contribute significantly to phenotypic variation in drug response, all vary in frequency among popula- tions 2 , some by as much as twelvefold 1 . For example, the well- known poor-metabolizer phenotype of debrisoquine oxidation is due to variant alleles of CYP2D6. Between 5% and 10% of Euro- peans, but only ?1% of Japanese, have loss-of-function variants at this locus that affect the metabolism of more than 40 drugs, including such commonly used agents as ?-blockers, codeine and tricyclic antidepressants. The CYP2D6 ultra-rapid metabolizer alleles also vary in frequency, even within Europe, from ?10% in Northern Spain to 1?2% in Sweden 6 . Polymorphisms in DMEs can lead to acute toxic responses and unwanted drug?drug inter- actions or to therapeutic failure from augmented drug metabo- lism (as in the case of CYP2D6 duplications) 1,7 . These observations show that for some drugs, the tradeoffs between efficacy and adverse drug reaction not only will differ between individuals but also will show differences in average effects across different populations 8 . Genetically structured pop- ulations may be composed of two or more subpopulations with distinct drug-reaction profiles and thus may be better considered separately in some contexts. This raises the questions of the appropriate way to infer human population genetic structure in the context of the evaluation of drug safety and efficacy, and of how to relate this inferred genetic structure to drug response. To address this problem, we have used presumably neutral microsatellite markers to infer genetic clusters for a heteroge- neous population, such as may be used in drug trials large enough to allow detection of both genetic and environmental � 2001 Nature Pub lishing Gr oup http://g enetics.nature . com � 2001 Nature Publishing Group http://genetics.nature.com effects (for instance, Phase III trials). We compared the frequen- cies of functionally significant alleles at DME loci across the inferred clusters as an easily defined surrogate for drug response. Using this approach, we (i) show that there is considerable scope for population-genetic structuring in drug response in diverse metropolitan populations, because of the variation they harbor in DME allele frequency differences among identifiable genetic clusters (ii) establish a framework for determining the appropri- ate level of resolution (that is, the number of inferred clusters that should be used) in relating this population-genetic structuring to drug response and (iii) show that commonly used ethnic labels (such as Black, Caucasian and Asian) are insufficient and inaccu- rate descriptions of human genetic structure. Results We genotyped 16 chromosome 1 microsatellites from the ABI prism panel 1 (an average of 17 cM apart) and 23 X-linked microsatellites (?2 cM apart) 9 in each of eight populations: South African Bantu speakers (46), Amharic- and Oromo-speaking Ethiopians from Shewa and Wollo provinces collected in Addis Ababa (48), Ashkenazi Jews (48), Armenians (48), Norwe- gian speakers from Oslo (47), Chinese from Sichuan in southwestern China (39), Papua New Guineans from Madang (48) and Afro- Caribbeans collected in London (30). Genetic structure We used a model-based clustering method implemented by the program STRUCTURE 10 to assign individuals to subclusters on the basis of these genetic data, ignoring their actual population affiliations. This mimics a scenario in which there is cryptic population structure, or no information as to the ethnic origin of the individuals. Briefly, the model imple- mented in STRUCTURE assumes K clusters, each character- ized by a set of allele frequencies at each locus; the admixture model then estimates the proportion of each individual?s genome having ancestry in each cluster. We estimated Pr(X|K), where X represents the data, using a model allowing admix- ture, for K between 1 and 6. From this and a uniform prior on K between 1 and 6, we estimated Pr(K|X) using Bayes?s theo- rem (Table 1) 10 . Virtually all of the posterior probability den- sity is on K=4. The apportionment of individuals (the average per-individual proportion of ancestry) from each of the eight populations into the four STRUCTURE-defined clusters (Table 2) broadly corresponds to four geographical areas: Western Eurasia, Sub-Saharan Africa, China and New Guinea. Notably, 62% of the Ethiopians fall in the first cluster, which encompasses the majority of the Jews, Norwe- gians and Armenians, indicating that placement of these individu- als in a ?Black? cluster would be an inaccurate reflection of the genetic structure. Only 24% of the Ethiopians are placed in the cluster with the Bantu and most of the Afro-Caribbeans; however, article 266 nature genetics ? volume 29 ? november 2001 Table 1 ? Inferring the number of clusters K ln Pr(X|K) Pr(K|X) 1 ?33680.97 ?0 2 ?32650.80 ?0 3 ?32046.80 ?0 4 ?31943.23 1.000 5 ?31972.33 ?0 6 ?31987.10 ?0 Fig. 1 Allele frequencies at each DME gene in the STRUC- TURE-defined clusters. In all but the last two, black indi- cates wildtype and white, mutant; for CYP2D6, all mutant alleles are pooled as white, and for NAT2 both tested mutant alleles (*5 and *6) are pooled as white. Cytochrome P450 1A2 (CYP1A2) metabolizes several drugs and carcinogens, including the analgesic acetaminophen (Tylenol) and probably antipsychotic drugs 18 . CYP2C19 metabolizes diazepam, barbiturates and antidepressants, and a polymorphic variant is responsible for the classical mephenytoin poor-metabolizer phenotype 19 . The classical debrisoquine poor-metabolizer phenotype is due to a vari- ant of CYP2D6 7 , and NAT2 is responsible for the classical isoniazid polymorphism 5 . NAD(P):quinone oxidoreductase (DIA4) converts quinones to stable hydroxyquinones and bioactivates antitumor quinones and nitrobenzenes 15 . Glutathione-S-transferase M1 (GSTM1) conjugates various electrophilic compounds, including potent environmental carcinogens such as aflatoxin B 1 epoxides 1 . The two NAT2 polymorphisms we genotyped both result in slow acetylator alleles which lead to increased risks of drug toxicity and of certain cancers 1,5 . Of the CYP2D6 alleles we assayed, CYP2D6*1 is wildtype, *3 and *4 have no activity (which can lead to an acute toxic response to some drugs) and *2, *9 and *10 have reduced activ- ity 17,20 . The CYP1A2 variant genotyped leads to increased enzyme inducibility in smokers 21 . We genotyped the major polymorphism in CYP2C19 responsible for the mephenytoin poor-metabolizer trait. After the administration of various drugs, this variant can lead to bone marrow toxicity, fatal blood dyscrasias and other adverse responses 1 . Increased susceptibilities to various cancers are associated with the deletion polymorphism in GSTM1 genotyped here, dramatically so for smokers 1,14 . The mutation in DIA4 leads to a complete absence of the protein and thus loss of protection against the toxic and carcinogenic effects of quinones 15 . Frequencies are shown for groupings corresponding to those shown in Table 2. 34% 66% 31% 69% 40% 60% 41% 59% 53% 47% 26% 74% 91% 9% 78% 22% 47% 53% 47% 53% 83% 17% 63% 37% 89% 11% 61% 39% 69% 31% 55% 45% 54% 46% 67% 33% 73% 27% 75% 25% 81% 19% 47% 53% 30% 70% 58% 42% ABCD CYP1A2 CYP2D6 DIA4 NAT2 GSTM1 CYP2C19 � 2001 Nature Pub lishing Gr oup http://g enetics.nature . com � 2001 Nature Publishing Group http://genetics.nature.com article nature genetics ? volume 29 ? november 2001 267 21% of the Afro-Caribbeans are placed in a cluster with the West Eurasians (presumably reflecting genetic exchange with Euro- peans). Finally, China and New Guinea are placed almost entirely in separate clusters, indicating that the ethnic label ?Asian? is also an inaccurate description of population structure. Consideration of only the X-linked microsatellites for the pur- poses of clustering supports K=3 with a clustering very similar to that for the entire dataset, except that the Chinese and New Guinean clusters are combined into one. When only the chromo- some 1 microsatellites are used, the clustering is essentially the same as for the whole dataset. This discrepancy may be explained by one of two factors: (i) a lack of resolution in the X chromosome microsatellites or (ii) a biological factor such as the different num- ber of X chromosomes and autosomes carried by males and females. To test these hypotheses, we carried out structure runs on the chromosome 1 data using an amount of information equal to that available from the X chromosome (22 alleles). The chromo- some 1 microsatellites continued to support K=4, indicating that a lack of resolution in the X chromosome microsatellites may not have been the explanation. Perhaps, because the X chromosome spends more time in the female germline than does chromosome 1 and because females have a higher migration rate than males 11 , the X-linked loci have less genetic structure. Smaller random sub- sets of the loci support a variety of values for K and do not agree on the clustering scheme (data not shown). This is probably because there are no natural clusters, as there has not been a his- tory of bifurcation in human populations. Our results indicate that a reasonably high number of loci should be used to obtain consistency in clustering; one approach would be to use one marker from each chromosome arm. All of the analyses we pre- sent use the full dataset, resulting in four clusters (Table 2). Drug-metabolizing enzymes Our selection of DMEs includes representatives of both phase I (oxidation or reduction) and phase II (conjugation) drug metab- olism. We included three enzymes of the phase I cytochrome P450 family: CYP1A2, CYP2C19 and CYP2D6. We also included three conjugating or phase II metabolism enzymes: NAT2, NAD(P):quinone oxidoreductase (DIA4) and glutathione-S- transferase M1 (GSTM1). We determined allele frequencies at 11 variants in the genes encoding these six DMEs, all of which are known to be functionally significant (Fig. 1). There are notable differences in the allele frequencies of DME- encoding genes between the genetically identified clusters (Fig. 1) for five of six reported loci. To assess differentiation across clus- ters, we counted allele frequencies in each of the clusters and cal- culated ? 2 ; we also tested for differences in allele frequencies using logistic regression. Using both methods, and correcting for multi- ple comparisons, the allele frequency distributions are signifi- cantly different for four of the six loci (significant for NAT2, CYP2C19, DIA4 and CYP2D6). The pattern is particularly striking at CYP2C19, where the frequency of the mutant allele (the mephenytoin polymorphism) in cluster B is more than four times that of cluster A (P<0.0001). We also observed extreme differenti- ation between clusters B and D for DIA4, for which the frequency of the mutant allele (which provides no protection against the toxic effects of quinones) differs by almost five-fold (P<0.0001). This is a notable difference, as clusters B and D would be com- bined as ?Asian? in current drug evaluation using ethnic labels. NAT2 also shows significant differentiation between these two clusters, as well as among the others. We observed strong to mod- est differences in allele frequencies for the other DME genes between at least two pairs of the clusters in each case. To further explore cluster differentiation we counted the number of loci for which there are significant allele frequency differences (using ? 2 ) for each of the pairs of clusters. Without correcting for multiple comparisons, this number varied from 2 (of 6 loci) for B versus D, to 5 (of 6) for B versus C. Given the important differences in drug response determined by these variants, the scope for genetic struc- turing in drug response clearly is high. For some drugs, therefore, the trade-off between therapeutic response and adverse drug reac- tions will differ between the clusters identified here, making this kind of genetic analysis important in checking for such effects in any phase III clinical trial. We compared the predictive value of the genetic clusters to that of commonly used ethnic labels by counting the DME allele fre- quencies in the grouping resulting from those labels: Caucasian 42% 58% 32% 68% 33% 67% 67% 33% 42% 58% 78% 22% 79% 21% 30% 70% 51% 49% 26% 74% 92% 8% 79% 21% 51% 49% 48% 52% 85% 15% 65% 35% 68% 32% 63% 37% ABC CYP1A2 CYP2D6 DIA4 NAT2 GSTM1 CYP2C19 Fig. 2 Allele frequencies at each of the DME variants in the ethnically labeled groups. See Fig. 1 legend for details. A, Bantu, Ethiopian and Afro-Caribbean frequencies; B, those for Norwegians, Ashkenazi Jews and Armenians; C, those for Chinese and New Guineans. Table 2 ? Proportion of membership of each sampled population in STRUCTURE-defined subclusters Population A B C D Bantu 0.04 0.02 0.93 0.02 Ashkenazi 0.96 0.01 0.01 0.02 Ethiopia 0.62 0.08 0.24 0.06 Norway 0.96 0.02 0.01 0.01 Armenia 0.90 0.04 0.02 0.05 China 0.09 0.05 0.01 0.84 Papua New Guinea 0.02 0.95 0.01 0.02 Afro-Caribbean 0.21 0.03 0.73 0.03 � 2001 Nature Pub lishing Gr oup http://g enetics.nature . com � 2001 Nature Publishing Group http://genetics.nature.com article 268 nature genetics ? volume 29 ? november 2001 (Norwegian, Ashkenazi Jew, Armenian), Black (Bantu, Ethiopian, Afro-Caribbean) and Asian (Chinese, New Guinean; Fig. 2). Notably, for DIA4, the large frequency difference between clusters B and D (driven by the differentiation between China and New Guinea) is averaged when both populations are lumped; the mutant allele frequency is thus only one and a half times as high as that in the other two groups. Indeed, the overall differentiation for the ethnic groups is not significant after cor- rection for multiple comparisons. Note that in no case did we observe the reverse in our data: that is, the ethnic labels never show sharp differentiation that is not observed in the clusters. In addition, only in the case of CYP2D6 are the allele frequency dif- ferentials as high as they are for genetically defined clusters. Although there is some DME allele frequency differentiation between ethnically labeled groups, in most cases it is less than that seen for the genetic clusters. To confirm this, we fitted logis- tic regression models to the allele data using membership in the genetic clusters as the explanatory variables, and tested for the increase in goodness of fit obtained by adding the ethnic labels as explanatory variables. We then compared this to the increase in goodness of fit obtained by adding the genetic cluster informa- tion to the ethnic group information. Of those DME loci (NAT2, CYP2C19, DIA4 and CYP2D6) that showed significant differenti- ation in either the clusters or the ethnic groups, in three of four cases, adding genetic cluster information to ethnic labels was more significant than adding ethnic labels to genetic clusters. For CYP2D6, the opposite was true. Multilocus interactions Undesirable drug reactions or interactions, as well as environ- mental sensitivities, may also be due to the existence of variants at two (or more) loci. An example of this may be the case of the increased susceptibility to colorectal cancer in individuals with a rapid/rapid metabolizer phenotype at CYP1A2 and NAT2, espe- cially for those who prefer well-cooked meat 12 . It is important to consider not only differences in allele frequency between the inferred clusters but also differences in frequency for multilocus genotypes. There are large frequency differentials between the clusters we have identified for multilocus genotypes, which may give rise to phenotypic combinations like this; in fact, the fre- quency of the combination CYP1A2-A/A, NAT2*4/? observed in cluster B (47%) is more than twice that seen in clusters A (19%) or C (22%; P<0.01 for overall differentiation). When such inter- actions are important, they may be apparent in the genetic analy- sis described here, from the distribution of drug response across inferred clusters. Discussion By carrying out the clustering analysis with the number of clus- ters set to different values, we can compare the extent of differen- tiation among the clusters to assess the appropriate level of resolution. In the context of a Phase III trial, the appropriate benchmark would reflect the amount of the total variation in drug response explained by the genetic clusters. A surrogate test would be to carry out exact tests of differentiation 13 on relevant functional polymorphisms, stopping when an increase in the number of clusters does not appreciably increase the degree of differentiation. The clustering properties of STRUCTURE, how- ever, can be unstable across different values of K, which compli- cates the implementation of such an analysis. It is well known that there are inter-ethnic differences in DME allele frequencies and thus in drug response. Our focus here, how- ever, has been to assess the scope for average difference in drug response across genetically inferred clusters. Not only can these clusters be derived in the absence of knowledge about ethnicity (or geographic origin), but they are also more informative than commonly used ethnic labels. Because of the potential clinical sig- nificance of average differences in drug response, we conclude that it is not only feasible but a clinical priority to assess genetic struc- ture as a routine part of drug evaluation. When the most important genes influencing response to a par- ticular drug or group of drugs have been identified, it should be possible to personalize medicine on the basis of an individual?s genotype, assuming that routine individual genotyping is com- mercially and technically feasible. Short of such detailed knowl- edge, however, it is important to assess whether drugs work similarly in different genetic subgroups. The appropriate level of clustering may be evaluated empirically by assessing the amount of variation in response explained by the inferred clusters. In addition, we have shown that the common ethnic labels currently available to regulatory authorities show a poor correspondence with genetically inferred clusters. Analysis of population structure in biomedical research Our implementation of STRUCTURE is primarily meant to show that familiar ethnic labels are not accurate guides to genetic structure. We have not attempted to provide a definitive descrip- tion of human population structure. The results of STRUCTURE can, in fact, be quite difficult to interpret. Notably, statistical dif- ficulties may arise when assessing convergence, and the assess- ment of the appropriate value of K is currently not rigorous 10 . These and other issues can lead to anomalous outcomes; for example, an implausible value of K may be supported where one of the clusters is more or less empty. In addition, results may vary for biological reasons, such as when markers are affected differ- entially by forces acting on the genome, such as gene flow. Detailed analysis of STRUCTURE output and other clustering schemes, using a standard battery of markers in a global sample of human populations, will be needed to arrive at a canonical clustering scheme for use in biomedical research. Such an evalua- tion would need to be geographically exhaustive and to include a sufficient number of markers throughout the genome to ensure that the resulting clustering scheme is robust; consistent results should be obtained with different marker and sample sets. Methods Microsatellite markers and structure inference. All subjects were unrelat- ed males. We genotyped 9 the following X-linked microsatellites: DXS984, 996, 1036, 1053, 1062, 1203, 1204, 1205, 1206, 1211, 1212, 1220, 1223, 7103, 8014, 8061, 8068, 8073, 8085, 8086, 8087 and 8099. We genotyped the fol- lowing chromosome 1 microsatellites: D1S196, 206, 213, 249, 255, 450, 484, 2667, 2726, 2785, 2797, 2800, 2836, 2842, 2878 and 2890. The chromosome 1 markers form part of the ABI Prism linkage mapping panel 1 and were amplified according to the manufacturer?s instructions. We assigned indi- viduals into clusters using the admixture model in the program STRUC- TURE 10 , with no correlation in allele frequencies among populations and a burn-in time of at least 1 million steps, followed by another 1 million steps of the Markov Chain for data collection. We carried out multiple runs for each set of conditions to be sure that the chain had converged; in total, we carried out more than 500 runs. DME genotyping. We sequenced the intronic C734A transversion in CYP1A2 and two SNPs in NAT2: C481T, defining allele *5 (in complete allelic associa- tion with Ile113Thr) and G590A (giving Arg197Gln), defining allele *6. We classified all other alleles as *4, and combined the two mutant allele frequen- cies for the purpose of binary analysis. We genotyped the deletion allele of glutathione-S-transferase M1 (GSTM1) using GSTM4 amplification as an internal control 14 . We genotyped the C191T transition (giving Pro187Ser) in DIA4 (ref. 15) and the G117A transition (leading to a truncated protein) in CYP2C19 (ref. 16) using polymerase chain reaction?restriction fragment length polymorphism (PCR?RFLP). We labeled GSTM1 and RFLP ampli- cons fluorescently and determined sizes on an ABI 3100 automated sequencer � 2001 Nature Pub lishing Gr oup http://g enetics.nature . com � 2001 Nature Publishing Group http://genetics.nature.com (Applied Biosystems). We typed CYP2D6 SNPs by gene-specific PCR, fol- lowed by nested multiplex reamplification?RFLP detection of the following ?key? mutations 17 : C100T (Pro34Ser; alleles *10 and *4), G1846A (splicing defect; allele *4), A2549del (frameshift; allele *3), 2613?2615AGAdel (Lys281del; allele *9) and C2850T (Arg296Cys; allele *2). All other chromo- somes were denoted *1 (thus, this category includes some non-wildtype alle- les). For the binary analyses, we considered CYP2D6*1 as having normal activity and all other alleles as having reduced activity. We labeled CYP2D6 amplicons using fluorescent primers and sized them on an ABI 377 automat- ed sequencer (Applied Biosystems; genotyping details available from B.F.). In the case of GSTM1, the assay does not allow differentiation between homozy- gous and heterozygous presence of the nondeletion allele. For this case, we carried out calculations on genotype frequencies and homozygous deletion versus homozygous or heterozygous for the nondeletion allele. We estimated the accuracy of our genotyping by retesting a number of samples from each population. Error rates varied from 0 to 7% for the DME SNPs and from 0 to 5% for the microsatellites. DME differentiation across clusters. We calculated DME allele frequen- cies in the clusters by distributing an individual?s genotype among the clus- ters, according to the proportion of ancestry that the individual had in each cluster, as determined by STRUCTURE output. When individuals were placed in the cluster in which they had the most ancestry, the results changed very little (data not shown). To meet the assumption of a multino- mial distribution, we evaluated ? 2 tables after placing individuals in the clusters in which they had most ancestry. Acknowledgments D.B.G. is a Royal Society/Wolfson Research Merit Award holder. Received 30 July; accepted 4 October 2001. 1. Weber, W.W. Pharmacogenetics (Oxford University Press, Oxford, 1997). 2. Evans, W.E. & Relling, M.V. Pharmacogenomics: translating functional genomics into rational therapeutics. Science 286, 487?491 (1999). 3. Gough, A.C. et al. Identification of the primary gene defect at the cytochrome P450 CYP2D locus. Nature 347, 773?776 (1990). 4. Kagimoto, M., Heim, M., Kagimoto, K., Zeugin, T. & Meyer, U.A. Multiple mutations of the human cytochrome P450IID6 gene (CYP2D6) in poor metabolizers of debrisoquine. Study of the functional significance of individual mutations by expression of chimeric genes. J. Biol. Chem. 265, 17209?17214 (1990). 5. Blum, M., Demierre, A., Grant, D.M., Heim, M. & Meyer, U.A. Molecular mechanism of slow acetylation of drugs and carcinogens in humans. Proc. Natl Acad. Sci. USA 88, 5237?5241 (1991). 6. Bernal, M.L. et al. Ten percent of North Spanish individuals carry duplicated or triplicated CYP2D6 genes associated with ultrarapid metabolism of debrisoquine. Pharmacogenetics 9, 657?660 (1999). 7. Meyer, U.A. & Zanger, U.M. Molecular mechanisms of genetic polymorphisms of drug metabolism. Annu. Rev. Pharmacol. Toxicol. 37, 269?296 (1997). 8. International Conference on Harmonisation. Ethnic Factors in the Acceptability of Foreign Clinical Data. (International Conference on Harmonisation, 1998). 9. Wilson, J.F. & Goldstein, D.B. Consistent long-range linkage disequilibrium generated by admixture in a Bantu?Semitic hybrid population. Am. J. Hum. Genet. 67, 926?935 (2000). 10. Pritchard, J.K., Stephens, M. & Donnelly, P. Inference of population structure using multilocus genotype data. Genetics 155, 945?959 (2000). 11. Seielstad, M.T., Minch, E. & Cavalli-Sforza, L.L. Genetic evidence for a higher female migration rate in humans. Nature Genet. 20, 278?280 (1998). 12. Kohlmeier, L., DeMarini, D. & Piegorsch, W. Gene-nutrient interactions in nutritional epidemiology. in Design Concepts in Nutritional Epidemiology (eds. Margetts, B. & Nelson, M.) 312?337 (Oxford University Press, Oxford, 1997). 13. Raymond, M. & Rousset, F. An exact test for population differentiation. Evolution 49, 1280?1283 (1995). 14. Krajinovic, M., Labuda, D., Richer, C., Karimi, S. & Sinnett, D. Susceptibility to childhood acute lymphoblastic leukemia: influence of CYP1A1, CYP2D6, GSTM1, and GSTT1 genetic polymorphisms. Blood 93, 1496?1501 (1999). 15. Gaedigk, A. et al. NAD(P)H:quinone oxidoreductase: polymorphisms and allele frequencies in Caucasian, Chinese and Canadian Native Indian and Inuit populations. Pharmacogenetics 8, 305?313 (1998). 16. Goldstein, J.A. & Blaisdell, J. Genetic tests which identify the principal defects in CYP2C19 responsible for the polymorphism in mephenytoin metabolism. Methods Enzymol. 272, 210?218 (1996). 17. Gaedigk, A. et al. Optimization of cytochrome P4502D6 (CYP2D6) phenotype assignment using a genotyping algorithm based on allele frequency data. Pharmacogenetics 9, 669?682 (1999). 18. Basile, V.S. et al. A functional polymorphism of the cytochrome P450 1A2 (CYP1A2) gene: association with tardive dyskinesia in schizophrenia. Mol. Psychiatry 5, 410?417 (2000). 19. Ferguson, R.J. et al. A new genetic defect in human CYP2C19: mutation of the initiation codon is responsible for poor metabolism of S-mephenytoin. J. Pharmacol. Exp. Ther. 284, 356?361 (1998). 20. Daly, A.K. et al. Nomenclature for human CYP2D6 alleles. Pharmacogenetics 6, 193?201 (1996). 21. Sachse, C., Brockmoller, J., Bauer, S. & Roots, I. Functional significance of a C?A polymorphism in intron 1 of the cytochrome P450 CYP1A2 gene tested with caffeine. Br. J. Clin. Pharmacol. 47, 445?449 (1999). article nature genetics ? volume 29 ? november 2001 269 � 2001 Nature Pub lishing Gr oup http://g enetics.nature . com � 2001 Nature Publishing Group http://genetics.nature.com "
Add Content to Group
|
Bookmark
|
Keywords
|
Flag Inappropriate
share
Close
Digg
Facebook
MySpace
Google+
Comments
Close
Please Post Your Comment
*
The Comment you have entered exceeds the maximum length.
Submit
|
Cancel
*
Required
Comments
Please Post Your Comment
No comments yet.
Save Note
Note
View
Public
Private
Friends & Groups
Friends
Groups
Save
|
Cancel
|
Delete
Please provide your notes.
Next
|
Prev
|
Close
|
Edit
|
Delete
Genetics
Gene Inheritance and Transmission
Gene Expression and Regulation
Nucleic Acid Structure and Function
Chromosomes and Cytogenetics
Evolutionary Genetics
Population and Quantitative Genetics
Genomics
Genes and Disease
Genetics and Society
Cell Biology
Cell Origins and Metabolism
Proteins and Gene Expression
Subcellular Compartments
Cell Communication
Cell Cycle and Cell Division
Scientific Communication
Career Planning
Loading ...
Scitable Chat
Register
|
Sign In
Visual Browse
Close
Comments
CloseComments
Please Post Your Comment