- Email: [email protected]
Detecting genetic differences
may increase the risk for endometriosis development (3). To the best of our knowledge, our report was the first to describe a striking accumulation of endometriosis in three generations. Our study was designed to eliminate intrinsic drawbacks and draw attention to what we feel is an important observation. Demetrios A. Arvanitis, B.Sc. Department of Virology, Medical School University of Crete Heraklion, Crete, Greece Anastasia G. Goumenou, M.D. Department of Obstetrics and Gynecology University Hospital of Heraklion Heraklion, Crete, Greece Ioannis M. Matalliotakis, Ph.D. Department of Obstetrics and Gynecology University Hospital of Heraklion Heraklion, Crete, Greece Eugenios E. Koumantakis, Ph.D. Department of Obstetrics and Gynecology University Hospital of Heraklion Heraklion, Crete, Greece Demetrios A. Spandidos, D.Sc. Department of Virology, Medical School University of Crete Heraklion, Crete, Greece April 9, 2002
References 1. Arvanitis DA, Goumenou AG, Matalliotakis IM, Koumantakis EE, Spandidos DA. Low-penetrance genes are associated with increased susceptibility to endometriosis. Fertil Steril 2001;76:1202– 6. 2. Thomson G, Esposito MS. The genetics of complex diseases. Trends Cell Biol 1999;9:M17–20. 3. Hadfield RM, Manek S, Weeks DE, Mardon HJ, Barlow DH, Kennedy SH. Linkage and association studies of the relationship between endometriosis and genes encoding the detoxification enzymes GSTM1, GSTT1 and CYP1A1. Mol Hum Reprod 2001;7:1073– 8.
PII S0015-0282(02)03263-6
Detecting genetic differences Editorial Commentary This is a short, but “lightning rod” paper, because setting up an appropriate study design for endometriosis susceptibility genes is a hot-button topic. Arvanitis and colleagues in Crete try to codify a statistical relationship between several DNA markers and the endometriosis phenotype using a single family with four to five affected female members. The study attempts to show an association between certain alleles and endometriosis by comparing with controls the lifetime risks for the disease among first-degree relatives within a single family (1). One has to be impressed by the numbers of affected individuals in the pedigree; however, the identification of susceptibility genes for complex or multiFERTILITY & STERILITY威
factorial diseases is substantially more demanding than identifying single genes disorders. Different allele sharing models that compare cases with a wide variety of controls are beginning to fill the scientific literature. Trying to evaluate the relative merit and validity of each study design or model and the attendant statistics involves a steep learning curve for most of us. The cogent letter from Sun-Wei Guo and colleagues points out that this can be treacherous ground to traverse. Study designs for association studies require significant numbers of cases and/or family collections to be adequately powered for appropriate statistical analysis. Particular attention must be given to sample size when more than one variable is studied simultaneously, and care must be taken to ensure that multiple testing is performed with correction. In addition, it is critical to be sure that individual subgroups retain adequate power to detect significant associations with narrow confidence intervals. Subgroup analysis is a valid route to generate hypotheses, but these hypotheses must be tested in additional patient populations. The spurious association of the follistatin gene with polycystic ovary syndrome is a good example of some of these common problems (2). Studies of these types may require sample sizes of over 1000 individuals to generate robust data. More and more, the literature is teeming with reports of associations that cannot be replicated, or for which corroboration by linkage has been impossible to find. From Dr. Guo’s letter it is clear that study design, especially the selection of controls and scientific strategies, are critical in the investigation of the genetic basis for complex traits. For controls, one must debate the relative merits of general populations versus isolated or founder populations or the use of families versus more conventional case-control comparisons. Statistical corrections for multiple comparisons, population stratification, power of the testing system for the study at hand, and error estimates are critical. Studies that do not take each of these factors into consideration frequently generate type I errors that overrepresent the contribution of a locus or candidate gene to a disease. The tendency of authors to overinterpret results coupled with a positive publication bias further increases the frequency of spurious associations in the published literature. From the early years of positional cloning, one may recall the identification of a marker that seemed to segregate with the manic-depressive psychosis. Everyone was manic— but shortly after the publication of the paper, several family members lacking the “diagnostic marker” developed manic-depressive psychosis. Everyone promptly became depressed. Everyone has a similar depressing tale or anecdote about a favorite gene that fell by the wayside. Criticisms of association studies are invariably directed at the selection and validity of the control group. In the Arvanitis paper, the control group (mean age 26.2 ⫾ SD 6.8 years) came from a Crete hospital and were recruited immediately postpartum on the basis of parity (two to three 443
prior children). Because they were selected for fertility, one might question whether they had an equal opportunity to experience the outcome of interest (endometriosis) compared to the experimental group. The association in this study may be due to the structure of the population studied rather than linkage disequilibrium (LD). For an association study of this type the control group should represent a random sample of the population, be age matched, be devoid of a systematic bias in geographic origin, and be a randomly mating population. In the future, the markers of choice will be large sets of single nucleotide polymorphisms (SNPs). These single nucleotide substitutions are highly abundant, stable, and account for 90% of the sequence variation in the human genome. At the same time the continued cost-efficient development of DNA “chip” technology will allow fine-scale genome screens for associations without specifying in advance a candidate locus. The correspondent, Sun-Wei Guo at the Medical College and Children’s Hospital of Wisconsin, is one of the real experts in the quantitative sciences. He is one of many who are trying to develop statistical models to provide quality control for these types of association studies. The Arvanitis study and many others in the literature to date illustrate that it is possible to perform rapidly an analysis of candidates genes and generate novel insights (1–3). For now, the utility of SNP maps will be most apparent in these types of candidate gene studies. Even though the majority of such candidate-gene studies will have “meaningful” negative results, they will give us complete knowledge of the patterns of variation across selected genes. Ultimately most of the questions being asked by reproductive endocrinologists involve complex disorders (such as endometriosis and polycystic ovary syndrome) and will require
full genome association studies. In contrast to candidategene analysis, the performance of whole genome–scale association studies will require highly dense sets of polymorphic markers (⬎700,000 SNPs) that can be rapidly typed on large numbers of patient samples. In terms of cost per genotype (pennies per genotype), the latter is still beyond the genotyping capacity of most laboratories. However, many new and promising technologies for fully automated highthroughput SNP genotyping are emerging. The ultimate purpose of such studies is to understand diseases such as endometriosis and identify new therapeutic targets for treatment. For those of us who are still in the “slow lane,” it is increasingly clear that the Human Genome Project has given us access to the abundant genetic variation in the human genome. The prudent use of this information—and the technology to exploit it—will help to dissect the genetic and environmental factors implicated in common human disease for many years to come. Paul G. McDonough, M.D. Editor, Letters April 24, 2002
References 1. Arvanitis DA, Goumenou AG, Matalliotakis IM, Koumantakis EE, Spandidos DA. Low-penetrance genes are associated with increased susceptibility to endometriosis. Fertil Steril 2001;76:1202– 6. 2. Urbanek M, Legro RS, Driscoll DA, Azziz R, Ehrmann DA, Norman RJ, et al. Thirty-seven candidate genes for polycystic ovary syndrome: strongest evidence for linkage is with follistatin. Proc Natl Acad Sci USA 1999;96:8573– 8578. 3. Hatfield RM, Manek S, Weeks DE, Mardon HJ, Barlow DH, Kennedy SH. Linkage and association studies of the relationship between endometriosis and genes encoding deteoxification enzymes GSTM1, GSTT1, and CYP1A1. Mol. Human Reprod 2001;7:1073– 8.
PII S0015-0282(02)03264-8
INSTRUCTIONS FOR LETTERS-TO-THE-EDITOR This section of the journal is set aside for critical comments directed to a specific article that has recently been published in the journal. Letters should be brief (400 words), double spaced, and limited to a maximum of 5 citations. The letters and replies should be prepared according to journal format. Illustrative material is accepted only with permission of the Editor. With your correspondence, please include your complete mailing address, telephone and fax numbers, and e-mail address if available. The Editor reserves the right to shorten letters, delete objectionable comments, and make other changes to comply with the style of the journal. Send communications to Paul G. McDonough, M.D., Department of Obstetrics and Gynecology, Medical College of Georgia, 1120 15th Street, Augusta, Georgia 30912 (Telephone: 706-721-3832; FAX: 706-721-0574; 706-737-4302; e-mail: [email protected]).
444
Letters to the Editor
Vol. 78, No. 2, August 2002
References 1. Arvanitis DA, Goumenou AG, Matalliotakis IM, Koumantakis EE, Spandidos DA. Low-penetrance genes are associated with increased susceptibility to endometriosis. Fertil Steril 2001;76:1202– 6. 2. Thomson G, Esposito MS. The genetics of complex diseases. Trends Cell Biol 1999;9:M17–20. 3. Hadfield RM, Manek S, Weeks DE, Mardon HJ, Barlow DH, Kennedy SH. Linkage and association studies of the relationship between endometriosis and genes encoding the detoxification enzymes GSTM1, GSTT1 and CYP1A1. Mol Hum Reprod 2001;7:1073– 8.
PII S0015-0282(02)03263-6
Detecting genetic differences Editorial Commentary This is a short, but “lightning rod” paper, because setting up an appropriate study design for endometriosis susceptibility genes is a hot-button topic. Arvanitis and colleagues in Crete try to codify a statistical relationship between several DNA markers and the endometriosis phenotype using a single family with four to five affected female members. The study attempts to show an association between certain alleles and endometriosis by comparing with controls the lifetime risks for the disease among first-degree relatives within a single family (1). One has to be impressed by the numbers of affected individuals in the pedigree; however, the identification of susceptibility genes for complex or multiFERTILITY & STERILITY威
factorial diseases is substantially more demanding than identifying single genes disorders. Different allele sharing models that compare cases with a wide variety of controls are beginning to fill the scientific literature. Trying to evaluate the relative merit and validity of each study design or model and the attendant statistics involves a steep learning curve for most of us. The cogent letter from Sun-Wei Guo and colleagues points out that this can be treacherous ground to traverse. Study designs for association studies require significant numbers of cases and/or family collections to be adequately powered for appropriate statistical analysis. Particular attention must be given to sample size when more than one variable is studied simultaneously, and care must be taken to ensure that multiple testing is performed with correction. In addition, it is critical to be sure that individual subgroups retain adequate power to detect significant associations with narrow confidence intervals. Subgroup analysis is a valid route to generate hypotheses, but these hypotheses must be tested in additional patient populations. The spurious association of the follistatin gene with polycystic ovary syndrome is a good example of some of these common problems (2). Studies of these types may require sample sizes of over 1000 individuals to generate robust data. More and more, the literature is teeming with reports of associations that cannot be replicated, or for which corroboration by linkage has been impossible to find. From Dr. Guo’s letter it is clear that study design, especially the selection of controls and scientific strategies, are critical in the investigation of the genetic basis for complex traits. For controls, one must debate the relative merits of general populations versus isolated or founder populations or the use of families versus more conventional case-control comparisons. Statistical corrections for multiple comparisons, population stratification, power of the testing system for the study at hand, and error estimates are critical. Studies that do not take each of these factors into consideration frequently generate type I errors that overrepresent the contribution of a locus or candidate gene to a disease. The tendency of authors to overinterpret results coupled with a positive publication bias further increases the frequency of spurious associations in the published literature. From the early years of positional cloning, one may recall the identification of a marker that seemed to segregate with the manic-depressive psychosis. Everyone was manic— but shortly after the publication of the paper, several family members lacking the “diagnostic marker” developed manic-depressive psychosis. Everyone promptly became depressed. Everyone has a similar depressing tale or anecdote about a favorite gene that fell by the wayside. Criticisms of association studies are invariably directed at the selection and validity of the control group. In the Arvanitis paper, the control group (mean age 26.2 ⫾ SD 6.8 years) came from a Crete hospital and were recruited immediately postpartum on the basis of parity (two to three 443
prior children). Because they were selected for fertility, one might question whether they had an equal opportunity to experience the outcome of interest (endometriosis) compared to the experimental group. The association in this study may be due to the structure of the population studied rather than linkage disequilibrium (LD). For an association study of this type the control group should represent a random sample of the population, be age matched, be devoid of a systematic bias in geographic origin, and be a randomly mating population. In the future, the markers of choice will be large sets of single nucleotide polymorphisms (SNPs). These single nucleotide substitutions are highly abundant, stable, and account for 90% of the sequence variation in the human genome. At the same time the continued cost-efficient development of DNA “chip” technology will allow fine-scale genome screens for associations without specifying in advance a candidate locus. The correspondent, Sun-Wei Guo at the Medical College and Children’s Hospital of Wisconsin, is one of the real experts in the quantitative sciences. He is one of many who are trying to develop statistical models to provide quality control for these types of association studies. The Arvanitis study and many others in the literature to date illustrate that it is possible to perform rapidly an analysis of candidates genes and generate novel insights (1–3). For now, the utility of SNP maps will be most apparent in these types of candidate gene studies. Even though the majority of such candidate-gene studies will have “meaningful” negative results, they will give us complete knowledge of the patterns of variation across selected genes. Ultimately most of the questions being asked by reproductive endocrinologists involve complex disorders (such as endometriosis and polycystic ovary syndrome) and will require
full genome association studies. In contrast to candidategene analysis, the performance of whole genome–scale association studies will require highly dense sets of polymorphic markers (⬎700,000 SNPs) that can be rapidly typed on large numbers of patient samples. In terms of cost per genotype (pennies per genotype), the latter is still beyond the genotyping capacity of most laboratories. However, many new and promising technologies for fully automated highthroughput SNP genotyping are emerging. The ultimate purpose of such studies is to understand diseases such as endometriosis and identify new therapeutic targets for treatment. For those of us who are still in the “slow lane,” it is increasingly clear that the Human Genome Project has given us access to the abundant genetic variation in the human genome. The prudent use of this information—and the technology to exploit it—will help to dissect the genetic and environmental factors implicated in common human disease for many years to come. Paul G. McDonough, M.D. Editor, Letters April 24, 2002
References 1. Arvanitis DA, Goumenou AG, Matalliotakis IM, Koumantakis EE, Spandidos DA. Low-penetrance genes are associated with increased susceptibility to endometriosis. Fertil Steril 2001;76:1202– 6. 2. Urbanek M, Legro RS, Driscoll DA, Azziz R, Ehrmann DA, Norman RJ, et al. Thirty-seven candidate genes for polycystic ovary syndrome: strongest evidence for linkage is with follistatin. Proc Natl Acad Sci USA 1999;96:8573– 8578. 3. Hatfield RM, Manek S, Weeks DE, Mardon HJ, Barlow DH, Kennedy SH. Linkage and association studies of the relationship between endometriosis and genes encoding deteoxification enzymes GSTM1, GSTT1, and CYP1A1. Mol. Human Reprod 2001;7:1073– 8.
PII S0015-0282(02)03264-8
INSTRUCTIONS FOR LETTERS-TO-THE-EDITOR This section of the journal is set aside for critical comments directed to a specific article that has recently been published in the journal. Letters should be brief (400 words), double spaced, and limited to a maximum of 5 citations. The letters and replies should be prepared according to journal format. Illustrative material is accepted only with permission of the Editor. With your correspondence, please include your complete mailing address, telephone and fax numbers, and e-mail address if available. The Editor reserves the right to shorten letters, delete objectionable comments, and make other changes to comply with the style of the journal. Send communications to Paul G. McDonough, M.D., Department of Obstetrics and Gynecology, Medical College of Georgia, 1120 15th Street, Augusta, Georgia 30912 (Telephone: 706-721-3832; FAX: 706-721-0574; 706-737-4302; e-mail: [email protected]).
444
Letters to the Editor
Vol. 78, No. 2, August 2002