This article is part of the supplement: Genetic Analysis Workshop 14: Microsatellite and single-nucleotide polymorphism

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Genetic Analysis Workshop 14: microsatellite and single-nucleotide polymorphism marker loci for genome-wide scans

Joan E Bailey-Wilson1*, Laura Almasy2, Mariza de Andrade3, Julia Bailey4, Heike Bickeböller5, Heather J Cordell6, E Warwick Daw7, Lynn Goldin8, Ellen L Goode9, Courtney Gray-McGuire10, Wayne Hening11, Gail Jarvik12, Brion S Maher13, Nancy Mendell14, Andrew D Paterson15, John Rice16, Glen Satten17, Brian Suarez16, Veronica Vieland18, Marsha Wilcox19, Heping Zhang20, Andreas Ziegler21 and Jean W MacCluer2

Author Affiliations

1 Inherited Disease Research Branch, National Human Genome Research Institute, National Institutes of Health, Baltimore, Maryland 21224, USA

2 Department of Genetics, Southwest Foundation for Biomedical Research, San Antonio, Texas, USA

3 Mayo Clinic, Rochester, Minnesota, USA

4 University of California Los Angeles, Los Angeles, California, USA

5 Universitat Gottingen, Germany

6 Department of Medical Genetics, University of Cambridge, UK

7 University of Texas M.D. Anderson Cancer Center, Houston, Texas, USA

8 National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA

9 Mayo Clinic College of Medicine, Rochester, Minnesota, USA

10 Case Western Reserve University, Cleveland, Ohio, USA

11 Johns Hopkins School of Medicine, Baltimore, Maryland, USA

12 University of Washington, Seattle, Washington, USA

13 Center for Craniofacial and Dental Genetics, University of Pittsburgh, Pittsburgh, Pennsylvania, USA

14 State University of New York at Stony Brook, Stony Brook, New York, USA

15 The Hospital for Sick Children, Toronto, Canada

16 Washington University School of Medicine, St. Louis, Missouri, USA

17 Centers for Disease Control, Atlanta, Georgia, USA

18 University of Iowa, Iowa City, Iowa, USA

19 Boston University, Boston, Massachusetts, USA

20 Yale University, New Haven, Connecticut, USA

21 Universitat de Luebeck, Luebeck, Germany

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BMC Genetics 2005, 6(Suppl 1):S1  doi:10.1186/1471-2156-6-S1-S1


The electronic version of this article is the complete one and can be found online at:


Published:30 December 2005

© 2005 Bailey-Wilson et al; licensee BioMed Central Ltd

This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Preface

This supplement to BMC Genetics contains the proceedings of the Genetic Analysis Workshop 14 (GAW14), which was held September 7–10, 2004, in Noordwijkerhout, The Netherlands. These workshops have been held since 1982 and now are held biennially. They serve as a forum for statisticians, epidemiologists, geneticists, and other scientists interested in these fields to introduce novel statistical methods and to evaluate and compare novel and existing methods. At each GAW, an existing dataset is selected, and a set of simulated data is devised such that statistical questions of wide and current interest may be addressed. These data are made available to scientists worldwide who then report the results of their analyses of these data at the GAW meeting. GAW attendees must submit an analysis of one of these datasets, or be a workshop organizer or a dataset provider. The purpose of these workshops is to allow the comparison of statistical methodologies for genetic epidemiology using the same, well-described datasets. More information about GAW, including details of upcoming workshops, may be found at http://www.gaworkshop.org webcite.

For GAW14, the overarching theme was comparison of microsatellite and single-nucleotide polymorphism (SNP) markers for genome-wide scans and the statistical methods that can best exploit the information provided in such scans for linkage and association studies. Two datasets were available for GAW14 participants to analyze. As is traditional at GAWs, one of these was a simulated dataset and one consisted of data from an actual human study. Attempts were made in the data simulation to mirror many of the characteristics of the real dataset, including a map of microsatellite markers plus a denser map of SNP markers that were available for fine-mapping. Both datasets are discussed briefly below, and more detailed descriptions can be found in Edenberg et al. [1] and Greenberg et al. [2].

The Collaborative Study on the Genetics of Alcoholism (COGA) generously donated extensive family data on alcoholism, related phenotypes, pertinent covariates and a set of previously analyzed genome-scan microsatellite marker genotypes for use in GAW14. COGA is a nine-site national collaborative study funded by the National Institute on Alcohol Abuse and Alcoholism (NIAAA) and the National Institute on Drug Abuse (NIDA) with the primary goal of identifying and characterizing genes that affect the susceptibility to develop alcohol dependence and related phenotypes. COGA has been committed to sharing data with the scientific community to expedite progress in understanding alcoholism and related phenotypes. COGA also provided data to GAW11, and has created an archival database of these families, with both phenotypic data and immortalized cell lines; these data are accessible to investigators for further study through NIAAA http://www.niaaa.nih.gov/extramural/projcoga.htm webcite. Genome-wide linkage scans, using microsatellite markers, have been performed on both an initial dataset of 105 multigenerational pedigrees and a replication dataset with 157 multigenerational pedigrees. In a departure from GAW tradition, extensive new SNP genotyping was performed on DNA provided by COGA for the previously genotyped families. Illumina, Affymetrix, and the Center for Inherited Disease Research (CIDR) performed this work and donated these data to GAW14 and to COGA. A subset of 143 informative families, selected from the initial and replication datasets, were genotyped for these SNP markers. A total of 1,350 COGA samples were genotyped by Affymetrix for 11,560 SNPs from their GeneChip Mapping 10 K array and by Illumina for their Linkage III Panel containing 4,763 SNP markers. These data are described in detail in Edenberg et al. [1].

The simulated data were designed to have similarities to the real dataset. It was assumed that a complex trait such as alcoholism might have both genetic and environmental risk factors. It was further assumed that such a complex trait might be defined/measured in a variety of different ways by different investigators, have associated endophenotypes that are common in the general population, and is likely to be not one disease but a heterogeneous collection of clinically similar, but genetically distinct, entities. Disease characteristics and parameters were constant throughout all the simulations, but four different "studies" were simulated used varying ascertainment schemes based on differing assumptions about disease characteristics. One of the studies contained multiplex two and three generation pedigrees with at least four affected members. The simulated disease was a psychiatric condition with many associated behaviors (endophenotypes), almost all of which were genetic in origin. The underlying disease model contained four major genes and two modifier genes. The four major genes interacted with each other to produce three different "phenotypes", which were themselves heterogeneous. The population parameters were calibrated so that the major genes could be discovered by linkage analysis in most datasets. The association evidence was more difficult to calibrate but was designed to find statistically significant association in 50% of datasets. Some linkage disequilibrium between marker loci was simulated around some of the genes and also in areas without disease genes. Data distributed to participants contained about 1,000 SNPs and 400 microsatellite markers. Data obtainable via the internet consisted of a finer 10,000 SNP map, which also contained data on controls. These data are described in detail in Greenberg et al. [2].

In the spring of 2004, the availability of the GAW14 data was announced by e-mail to the more than 2,000 individuals on the GAW mailing list. A total of 129 groups requested GAW14 data. The COGA data were requested by 95 groups and the simulated data by 88 groups; 88 groups requested both datasets. In the summer of 2004, 183 contributed papers were received describing analyses of these datasets. A book and CD containing these contributions plus papers describing the datasets were distributed to workshop participants.

A total of 232 individuals from 14 countries attended GAW14. Attendees included investigators from four continents: Asia, Australia, Europe, and North America. The 183 contributions submitted to GAW14 were organized into 18 presentation groups of 7 to 15 papers each. The papers were grouped based on common methodological themes. Because the datasets were similar in many important aspects, and could be used to explore similar analytical problems, most presentation groups were assigned with no regard to the dataset analyzed. Thus, many of the groups of papers include analyses of both the simulated data and the COGA data. Within each presentation group, a co-author with previous GAW experience was asked to serve as group leader to facilitate group discussion, organize an oral presentation for the group, and take the lead in writing the group summary papers that will be published in Genetic Epidemiology. The 18 presentation groups were organized around common methodological issues: comparisons of SNPs versus microsatellite markers in linkage studies, integration of SNPs and microsatellites in linkage and association studies, linkage mapping methods, quantitative trait mapping, fine mapping, methods for generating haplotypes and identifying haplotype-tagging SNPs, approaches to dealing with linkage disequilibrium, methods for association mapping, methods for case-control analysis and multivariate analyses, applications of these methods to analysis of alcoholism, smoking and related traits in the COGA data, methods for data mining, methods for dealing with genetic heterogeneity, methods for detecting gene × gene interaction, approaches to dealing with genotyping errors, pedigree errors and missing data, and methods to test for parent of origin, genomic imprinting, mitochondrial and X-linked effects in genome scans. Each presentation group met individually during the workshop and members of most groups communicated beforehand to begin comparing and contrasting the approaches taken and the results obtained by group members. At GAW14, many groups used part of their group meeting time to allow individual investigators to present their work. These group meetings were mostly attended by group participants but were open to all GAW14 attendees. From this process, each group developed an oral presentation, summarizing and synthesizing the work of the individual papers, which was delivered to the full workshop audience during the general sessions. Individual contributions were also presented in the form of 59 posters displayed during four poster sessions.

The 163 papers included in this supplement to BMC Genetics are a subset of the 183 contributions presented at GAW14. All of these papers have been reviewed for scientific merit. The proceedings begin with two papers describing the two datasets, followed by the 163 individual GAW14 contributions organized by presentation group and alphabetically by first author within each group. In addition to the individual papers in this volume, each presentation group has a summary in a forthcoming supplement to the journal Genetic Epidemiology in which the present manuscripts are compared and contrasted and the important themes and results from each group are described. Novel methods for linkage and association analyses using SNPs and microsatellites were developed, compared to existing methods, and applied to the GAW data, resulting in many interesting conclusions concerning appropriate approaches to the analysis of these sorts of data and also concerning areas of methodological development that are currently needed in this field.

Acknowledgements

Many people contributed to the success of Genetic Analysis Workshop 14 by selecting workshop topics, providing real and simulated data, preparing and distributing data and participant contributions, communicating with participants, organizing the meeting, chairing sessions, reviewing manuscripts, and editing the GAW14 proceedings.

The Genetic Analysis Workshops would not exist without the generosity of the investigators who provide data for analysis by workshop participants. We are grateful to the investigators from the Collaborative Study on the Genetics of Alcoholism, Affymetrix, Illumina, and the Center for Inherited Disease Research, who provided data to GAW14: Howard J. Edenberg, Laura J. Bierut, Tony Hinrichs, Kevin Jones, Bernice Porjesz, John P. Rice, Jay A. Tischfield, and Henri Begleiter for the COGA investigators; Paul Boyce, Kimberly F. Doheny, James Pettengill, Elizabeth W. Pugh, and Ya-Yu Tsai for CIDR; Manqiu Cao, Simon Cawley, Richard Chiles, Mark Kelleher, Giulia C. Kennedy, Guoying Liu, Gregory Marcus, and Chun Zhang for Affymetrix, Inc.; Mark Hansen, Celeste McBride, Sarah Shaw Murray, Arnold Oliphant, Todd Rubano, Stu Shannon, and Rhoberta Steeke for Illumina, Inc. The Collaborative Study on the Genetics of Alcoholism is supported by the NIH grant U10AA08403 from the National Institute on Alcohol Abuse and Alcoholism (NIAAA) and the National Institute on Drug Abuse (NIDA). In memory of Theodore Reich, M.D., Co-Principal Investigator of COGA since its inception and one of the founders of modern psychiatric genetics, we acknowledge his immeasurable and fundamental scientific contributions to COGA and the field. CIDR genotyping services were provided by the Center for Inherited Disease Research (CIDR). CIDR is fully funded through a federal contract from the National Institutes of Health to The Johns Hopkins University, contract number N01-HG-65403.

The GAW14 simulated dataset was generated in a collaborative effort among David A. Greenberg, Junying Zhang, Dvora Shmulewitz, Lisa J. Strug, Regina Zimmerman, Veena Singh, and Sudhir Marathe. We are grateful to all of these people for their efforts in the difficult task of designing and creating a simulated dataset that mimicked many of the features of the COGA data, thus offereing participants the opportunity to address issues such as power and false-positive rates. The creation of the simulated dataset was supported in part by NIH grants DK31775, NS27941, and MH65213.

At GAW14, contributions were organized into groups, each focused on a single topic. Group leaders had the difficult task of generating discussion among strangers via e-mail, and organizing presentations that summarized all of the contributions to their group. Their efforts deserve special recognition. We are grateful to the following individuals who led the group discussions and preparations of the presentations (in group numerical order): Marsha Wilcox, Elizabeth W. Pugh, Heping Zhang, E. Warwick Daw, Heather Cordell, Heike Bickeböller, Shelley Bull, Joan E. Bailey-Wilson, Gail Jarvik, Lorena Havill and Tom Dyer, John Witte, Nancy Mendell, John Rice, Adrienne Cupples, Veronica Vieland, Mary Marazita and Brion S. Maher, Brian Suarez, and Konstantin Strauch. Ample time was scheduled for discussion, with 10 discussion periods led by E. Warwick Daw, Heather Cordell, Heike Bickeböller, Veronica Vieland, Konstantin Strauch, Nancy Mendell, John Rice, Adrienne Cupples, Brion Maher, and Brian Suarez. We thank these discussion leaders for their efforts in stimulating lively interactions among participants.

Many scientific reviewers provided useful comments and criticisms of the papers in this volume: Goncalo Abecasis, Laurent Abel, Andrew Allen, Christopher I. Amos, Allison Ashley-Koch, Melissa Austin, Larry Atwood, Michael Badzioch, Agnes Baffoe-Bonnie, M. Michael Barmada, Terri Beaty, Lars Beckmann, Laura Bierut, Timothy Bishop, Michael Boehnke, Stefan Bohringer, George Bonney, Ingrid Borecki, Catherine Bourgain, Alfonso Buil, Shelley Bull, Rita Cantor, Gary Chase, Wei-Min Chen, Françoise Clerget-Darpoux, Anthony Comuzzie, P. Michael Conneally, Nancy Cox, Rob Culverhouse, Adrienne Cupples, Stefan Czerwinski, Gerarda Darlington, Yan Ding, Priya Duggal, Robert C. Elston, Mike Epstein, Carol Etzel, Dani Fallin, Tatiana Foroud, Saurabh Ghosh, Rodney Go, Katrina Goddard, Lynn Goldin, Alisa Goldstein, Ellen L. Goode, Derek Gordon, Harald Göring, Chao-Yu Guo, Jonathan Haines, Robert L. Hanson, Sandra Hasstedt, Beth Hauser, Lorena Havill, Geoffrey Hayes, Simon Heath, Anthony Hinrichs, Jeanine J. Houwing-Duistermaat, Sudha Iyengar, Kevin Jacobs, Andre Kleensang, Alison Klein, Jack Kent, Inke König, Carl Langefeld, Suzanne M. Leal, Juan Pablo Lewinger, Chun Li, Jennifer Lin, Jing-Ping Lin, Shili Lin, James D. Malley, Eden Martin, Lisa Martin, Rasika Mathias, Chantal Merrette, Nandita Mukhopadhyay, Bertram Müller-Myhsok, Rosalind Neuman, Kari North, Dennis O'Rourke, Grier Page, V. Shane Pankratz, Andrew Paterson, Margaret Pericak-Vance, Elizabeth Pugh, Dajun Qian, Treva Rice, Nancy Saccone, André Scherag, Sanjay Shete, Kim Siegmund, Susan Slager, M. Anne Spence, Catherine Stein, Lei Sun, Bradford Towne, Ya-Yu Tsai, Jung-Ying Tzeng, Diane Warren, Bruce Weir, Alexander F. Wilson, John Witte, Ellen Wjisman, Yin Yao, Robert Yu, Gang Zheng, and Xiaoyun Zhong. We are grateful for their contributions.

Vanessa Olmo has had major responsibility for all aspects of workshop organization since GAW7, in 1991. She continues to have primary responsibility for workshop logistics, including interaction with participants, organizers, editors, and publisher; data distribution; local organization; maintenance of the GAW web site and mailing list; and preparation of the proceedings. The Genetic Analysis Workshops would not succeed without her dedication and hard work. We also thank Selina Flores, who helped with data distribution, communications with participants, and preparation of the pre-GAW volume; Richard Polich, Tom Dyer, Laura Almasy, Linda Freeman-Shade and Gerry Vest all worked on preparing the data. April Hopstetter, Manager of Technical Publications and Printing at the Southwest Foundation for Biomedical Research, assisted with editing of the GAW14 proceedings, while Maria Messenger and Malinda Mann typeset the articles. Rene Sandoval and Rudy Sandoval were responsible for putting together the final pre-GAW volume.

We are grateful to Cornelia van Duijn and the local organizing committee, Martina von Stein, Marjolijn Kasi, Sytske Flore, Liu Fan, Mark Sie, and Jan Knoop, for devoting countless hours to the planning and organization of a very successful GAW14 in the Netherlands. Numerous organizations provided funding for scholarships to postdoctoral fellows and graduate students to help defray their expenses in attending GAW14: NIAAA, CIDR (NHGRI), Chemgenex Pharmaceuticals, and an anonymous donor. We are grateful for their generosity.

Long-term planning for the Genetic Analysis Workshops is the responsibility of the Genetic Analysis Workshop Advisory Committee. Its members are Laura Almasy, Chris Amos, Joan Bailey-Wilson, Heike Bickeböller, Françoise Clerget-Darpoux, Heather Cordell, Lynn Goldin, Jean MacCluer (chairman), Brian Suarez, Duncan Thomas, and John Witte.

The National Institute of General Medical Sciences has provided continuous funding for the Genetic Analysis Workshops since 1982, through grant R01 GM31575 to Jean MacCluer. We are particularly grateful to Irene Eckstrand of NIGMS for her enthusiasm and interest in the GAWs during the past 24 years. The Genetic Analysis Workshop would not be possible without the support of Dr. Eckstrand and NIGMS.

Finally, The Genetic Analysis Workshops could not have enjoyed continuous success without the ongoing, enthusiastic support of the GAW participants.

References

  1. Edenberg HJ, Bierut LJ, Boyce P, Cao M, Cawley S, Chiles R, Doheny KF, Hansen M, Hinrichs T, Jones K, Kelleher M, Kennedy GC, Liu G, Marcus G, McBride C, Murray SS, Oliphant A, Pettengill J, Porjesz B, Pugh EW, Rice JP, Rubano T, Shannon S, Steeke R, Tischfield JA, Tsai YY, Zhang C, Begleiter H: Description of the data from the Collaborative Study on the Genetics of Alcoholism (COGA) and single-nucleotide polymorphism genotyping for Genetic Analysis Workshop 14.

    BMC Genet 2005, 6(Suppl 1):S2. PubMed Abstract | BioMed Central Full Text | PubMed Central Full Text OpenURL

  2. Greenberg DA, Zhang J, Shmulewitz D, Strug LJ, Zimmerman R, Singh V, Marathe S: Construction of the model for the Genetic Analysis Workshop 14 simulated data: genotype-phenotype relationships, gene interaction, linkage, association, disequilibrium, and ascertainment effects for a complex phenotype.

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