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Table of Contents
Vol. 69, No. 2, 2010
Issue release date: January 2010
Section title: Original Paper
Hum Hered 2010;69:71–79
(DOI:10.1159/000264445)

Allelic Heterogeneity in Genetic Association Meta-Analysis: An Application to DTNBP1 and Schizophrenia

Maher B.S.a · Reimers M.A.b · Riley B.P.a · Kendler K.S.a
aDepartment of Psychiatry, Virginia Institute for Psychiatric and Behavioral Genetics, and bDepartment of Biostatistics, Virginia Commonwealth University, Richmond, Va., USA
email Corresponding Author

Abstract

Background/Aims: Meta-analysis of genetic association studies is a useful approach when individual investigations do not yield studywise significant results but the evidence across studies is modest and homogeneous. Current meta-analysis methods account for heterogeneity by down-weighting studies as a function of between-study variance. We contend that current approaches may obscure interesting phenomena in genetic association data. However, an appropriate approach to examining heterogeneity across studies is lacking. Methods: We develop a novel approach, based on the EM algorithm, to detect allelic heterogeneity, identify subpopulations and assign studies to those subpopulations. We then apply these methods to the association between DTNBP1 and schizophrenia (Scz), one of the most studied relationships in complex disease genetics. We examined 32 published and unpublished population and family-based association studies containing up to 14 SNPs spanning the DTNBP1 locus. Results: We explored heterogeneity in several ways including meta-regression and approaches aimed at exploring the mixture of heterogeneous studies at a particular SNP. We found significant evidence for a mixture of association distributions at multiple loci. Conclusion: We propose a novel approach that is broadly applicable and may be useful in large scale genetic association meta-analyses to detect significant allelic heterogeneity.

© 2009 S. Karger AG, Basel


  

Key Words

  • Genetic association
  • Heterogeneity
  • Meta-analysis

References

  1. Chanock SJ, Manolio T, Boehnke M, Boerwinkle E, Hunter DJ, Thomas G, Hirschhorn JN, Abecasis G, Altshuler D, Bailey-Wilson JE, Brooks LD, Cardon LR, Daly M, Donnelly P, Fraumeni JF Jr, Freimer NB, Gerhard DS, Gunter C, Guttmacher AE, Guyer MS, Harris EL, Hoh J, Hoover R, Kong CA, Merikangas KR, Morton CC, Palmer LJ, Phimister EG, Rice JP, Roberts J, Rotimi C, Tucker MA, Vogan KJ, Wacholder S, Wijsman EM, Winn DM, Collins FS: Replicating genotype-phenotype associations. Nature 2007;447:655–660.
  2. Moonesinghe R, Khoury MJ, Janssens AC: Most published research findings are false-but a little replication goes a long way. PLoS Med 2007;4:e28.
  3. Zaykin DV, Zhivotovsky LA: Ranks of genuine associations in whole-genome scans. Genetics 2005;171:813–823.
  4. Munafo MR, Flint J: Meta-analysis of genetic association studies. Trends Genet 2004;20:439–444.
  5. Moonesinghe R, Khoury MJ, Liu T, Ioannidis JP: Required sample size and nonreplicability thresholds for heterogeneous genetic associations. Proc Natl Acad Sci USA 2008;105:617–622.
  6. Zintzaras E, Ioannidis JP: Heterogeneity testing in meta-analysis of genome searches. Genet Epidemiol 2005;28:123–137.
  7. Ioannidis JP, Patsopoulos NA, Evangelou E: Heterogeneity in meta-analyses of genome-wide association investigations. PLoS One 2007;2:e841.
  8. Ioannidis JP: Non-replication and inconsistency in the genome-wide association setting. Hum Hered 2007;64:203–213.
  9. Ioannidis JP, Trikalinos TA: Early extreme contradictory estimates may appear in published research: the Proteus phenomenon in molecular genetics research and randomized trials. J Clin Epidemiol 2005;58:543–549.
  10. Lin PI, Vance JM, Pericak-Vance MA, Martin ER: No gene is an island: the flip-flop phenomenon. Am J Hum Genet 2007;80:531–538.
  11. Zaykin DV, Shibata K: Genetic flip-flop without an accompanying change in linkage disequilibrium. Am J Hum Genet 2008;82:794–796.
  12. Gruber JD, Genissel A, Macdonald SJ, Long AD: How repeatable are associations between polymorphisms in achaete-scute and bristle number variation in Drosophila? Genetics 2007;175:1987–1997.
  13. Lewis CM, Levinson DF, Wise LH, Delisi LE, Straub RE, Hovatta I, Williams NM, Schwab SG, Pulver AE, Faraone SV, Brzustowicz LM, Kaufmann CA, Garver DL, Gurling HM, Lindholm E, Coon H, Moises HW, Byerley W, Shaw SH, Mesen A, Sherrington R, O’Neill FA, Walsh D, Kendler KS, Ekelund J, Paunio T, Lonnqvist J, Peltonen L, O’Donovan MC, Owen MJ, Wildenauer DB, Maier W, Nestadt G, Blouin JL, Antonarakis SE, Mowry BJ, Silverman JM, Crowe RR, Cloninger CR, Tsuang MT, Malaspina D, Harkavy-Friedman JM, Svrakic DM, Bassett AS, Holcomb J, Kalsi G, McQuillin A, Brynjolfson J, Sigmundsson T, Petursson H, Jazin E, Zoega T, Helgason T: Genome scan meta-analysis of schizophrenia and bipolar disorder, part II: Schizophrenia. Am J Hum Genet 2003;73:34–48.
  14. Straub RE, MacLean CJ, Walsh D, Kendler KS: Support for schizophrenia vulnerability loci on chromosomes 6p and 8p from Irish families. Cold Spring Harb Symp Quant Biol 1996;61:823–833.
  15. Sun J, Kuo PH, Riley BP, Kendler KS, Zhao Z: Candidate genes for schizophrenia: A survey of association studies and gene ranking. Am J Med Genet B Neuropsychiatr Genet 2008.
  16. Mutsuddi M, Morris DW, Waggoner SG, Daly MJ, Scolnick EM, Sklar P: Analysis of high-resolution HapMap of DTNBP1 (Dysbindin) suggests no consistency between reported common variant associations and schizophrenia. Am J Hum Genet 2006;79:903–909.
  17. Sawcer S, Jones HB, Judge D, Visser F, Compston A, Goodfellow PN, Clayton D: Empirical genomewide significance levels established by whole genome simulations. Genet Epidemiol 1997;14:223–229.
  18. Abecasis GR, Cherny SS, Cookson WO, Cardon LR: Merlin–rapid analysis of dense genetic maps using sparse gene flow trees. Nat Genet 2002;30:97–101.
  19. Spielman RS, McGinnis RE, Ewens WJ: Transmission test for linkage disequilibrium: the insulin gene region and insulin-dependent diabetes mellitus (IDDM). Am J Hum Genet 1993;52:506–516.
  20. Falk CT, Rubinstein P: Haplotype relative risks: an easy reliable way to construct a proper control sample for risk calculations. Ann Hum Genet 1987;51:227–233.
  21. Terwilliger JD, Ott J: A haplotype-based ‘haplotype relative risk’ approach to detecting allelic associations. Hum Hered 1992;42:337–346.
  22. Schaid DJ, Sommer SS: Comparison of statistics for candidate-gene association studies using cases and parents. Am J Hum Genet 1994;55:402–409.
  23. Kazeem GR, Farrall M: Integrating case-control and TDT studies. Ann Hum Genet 2005;69:329–335.
  24. DerSimonian R, Laird N: Meta-analysis in clinical trials. Control Clin Trials 1986;7:177–188.
  25. Berlin JA, Laird NM, Sacks HS, Chalmers TC: A comparison of statistical methods for combining event rates from clinical trials. Stat Med 1989;8:141–151.
  26. Woolf B: On estimating the relation between blood group and disease. Ann Hum Genet 1955;19:251–253.
  27. Woolf B: The log likelihood ratio test (the G-test); methods and tables for tests of heterogeneity in contingency tables. Ann Hum Genet 1957;21:397–409.
  28. Devlin B, Risch N: A comparison of linkage disequilibrium measures for fine-scale mapping. Genomics 1995;29:311–322.
  29. Straub RE, Jiang Y, MacLean CJ, Ma Y, Webb BT, Myakishev MV, Harris-Kerr C, Wormley B, Sadek H, Kadambi B, Cesare AJ, Gibberman A, Wang X, O’Neill FA, Walsh D, Kendler KS: Genetic variation in the 6p22.3 gene DTNBP1, the human ortholog of the mouse dysbindin gene, is associated with schizophrenia. Am J Hum Genet 2002;71:337–348.
  30. Ferreira MA, O’Donovan MC, Meng YA, Jones IR, Ruderfer DM, Jones L, Fan J, Kirov G, Perlis RH, Green EK, Smoller JW, Grozeva D, Stone J, Nikolov I, Chambert K, Hamshere ML, Nimgaonkar VL, Moskvina V, Thase ME, Caesar S, Sachs GS, Franklin J, Gordon-Smith K, Ardlie KG, Gabriel SB, Fraser C, Blumenstiel B, Defelice M, Breen G, Gill M, Morris DW, Elkin A, Muir WJ, McGhee KA, Williamson R, Macintyre DJ, Maclean AW, St Clair D, Robinson M, Van Beck M, Pereira AC, Kandaswamy R, McQuillin A, Collier DA, Bass NJ, Young AH, Lawrence J, Ferrier IN, Anjorin A, Farmer A, Curtis D, Scolnick EM, McGuffin P, Daly MJ, Corvin AP, Holmans PA, Blackwood DH, Gurling HM, Owen MJ, Purcell SM, Sklar P, Craddock N: Collaborative genome-wide association analysis supports a role for ANK3 and CACNA1C in bipolar disorder. Nat Genet 2008;40:1042–1044.
  31. O’Donovan MC, Craddock N, Norton N, Williams H, Peirce T, Moskvina V, Nikolov I, Hamshere M, Carroll L, Georgieva L, Dwyer S, Holmans P, Marchini JL, Spencer CC, Howie B, Leung HT, Hartmann AM, Moller HJ, Morris DW, Shi Y, Feng G, Hoffmann P, Propping P, Vasilescu C, Maier W, Rietschel M, Zammit S, Schumacher J, Quinn EM, Schulze TG, Williams NM, Giegling I, Iwata N, Ikeda M, Darvasi A, Shifman S, He L, Duan J, Sanders AR, Levinson DF, Gejman PV, Gejman PV, Sanders AR, Duan J, Levinson DF, Buccola NG, Mowry BJ, Freedman R, Amin F, Black DW, Silverman JM, Byerley WF, Cloninger CR, Cichon S, Nothen MM, Gill M, Corvin A, Rujescu D, Kirov G, Owen MJ: Identification of loci associated with schizophrenia by genome-wide association and follow-up. Nat Genet 2008;40:1053–1055.
  32. Patsopoulos NA, Evangelou E, Ioannidis JP: Sensitivity of between-study heterogeneity in meta-analysis: proposed metrics and empirical evaluation. Int J Epidemiol 2008;37:1148–1157.
  33. Slager SL, Huang J, Vieland VJ: Effect of allelic heterogeneity on the power of the transmission disequilibrium test. Genet Epidemiol 2000;18:143–156.
  34. Neale BM, Sham PC: The future of association studies: gene-based analysis and replication. Am J Hum Genet 2004;75:353–362.
  35. Fisher RA: Statistical Methods for Research Workers. Oliver and Boyd, London, 1932.
  36. Gornick MC, Addington AM, Sporn A, Gogtay N, Greenstein D, Lenane M, Gochman P, Ordonez A, Balkissoon R, Vakkalanka R, Weinberger DR, Rapoport JL, Straub RE:Dysbindin (DTNBP1, 6p22.3) is associated with childhood-onset psychosis and endophenotypes measured by the Premorbid Adjustment Scale (PAS). J Autism Dev Disord 2005;35:831–838.
  37. Turunen JA, Peltonen JO, Pietiläinen OP, Hennah W, Loukola A, Paunio T, Silander K, Ekelund J, Varilo T, Partonen T, Lönnqvist J, Peltonen L: The role of DTNBP1, NRG1, and AKT1 in the genetics of schizophrenia in Finland. Schizophr Res 2007;91:27–36.
  38. Duan J, Martinez M, Sanders AR, Hou C, Burrell GJ, Krasner AJ, Schwartz DB, Gejman PV: DTNBP1 (Dystrobrevin binding protein 1) and schizophrenia: association evidence in the 3′ end of the gene. Hum Hered 2007;64:97–106.

  

Author Contacts

Dr. Brion Maher
Department of Psychiatry
Virginia Commonwealth University
Richmond, VA 23298-0126 (USA)
Tel. +1 804 828 8928, Fax +1 804 828 1471, E-Mail bsmaher@vcu.edu

  

Article Information

Received: March 30, 2009
Accepted after revision: June 24, 2009
Published online: December 4, 2009
Number of Print Pages : 9
Number of Figures : 1, Number of Tables : 1, Number of References : 38
Additional supplementary material is available online - Number of Parts : 1

  

Publication Details

Human Heredity (International Journal of Human and Medical Genetics)

Vol. 69, No. 2, Year 2010 (Cover Date: January 2010)

Journal Editor: Devoto M. (Philadelphia, Pa.)
ISSN: 0001-5652 (Print), eISSN: 1423-0062 (Online)

For additional information: http://www.karger.com/HHE


Copyright / Drug Dosage / Disclaimer

Copyright: All rights reserved. No part of this publication may be translated into other languages, reproduced or utilized in any form or by any means, electronic or mechanical, including photocopying, recording, microcopying, or by any information storage and retrieval system, without permission in writing from the publisher or, in the case of photocopying, direct payment of a specified fee to the Copyright Clearance Center.
Drug Dosage: The authors and the publisher have exerted every effort to ensure that drug selection and dosage set forth in this text are in accord with current recommendations and practice at the time of publication. However, in view of ongoing research, changes in goverment regulations, and the constant flow of information relating to drug therapy and drug reactions, the reader is urged to check the package insert for each drug for any changes in indications and dosage and for added warnings and precautions. This is particularly important when the recommended agent is a new and/or infrequently employed drug.
Disclaimer: The statements, opinions and data contained in this publication are solely those of the individual authors and contributors and not of the publishers and the editor(s). The appearance of advertisements or/and product references in the publication is not a warranty, endorsement, or approval of the products or services advertised or of their effectiveness, quality or safety. The publisher and the editor(s) disclaim responsibility for any injury to persons or property resulting from any ideas, methods, instructions or products referred to in the content or advertisements.

Abstract

Background/Aims: Meta-analysis of genetic association studies is a useful approach when individual investigations do not yield studywise significant results but the evidence across studies is modest and homogeneous. Current meta-analysis methods account for heterogeneity by down-weighting studies as a function of between-study variance. We contend that current approaches may obscure interesting phenomena in genetic association data. However, an appropriate approach to examining heterogeneity across studies is lacking. Methods: We develop a novel approach, based on the EM algorithm, to detect allelic heterogeneity, identify subpopulations and assign studies to those subpopulations. We then apply these methods to the association between DTNBP1 and schizophrenia (Scz), one of the most studied relationships in complex disease genetics. We examined 32 published and unpublished population and family-based association studies containing up to 14 SNPs spanning the DTNBP1 locus. Results: We explored heterogeneity in several ways including meta-regression and approaches aimed at exploring the mixture of heterogeneous studies at a particular SNP. We found significant evidence for a mixture of association distributions at multiple loci. Conclusion: We propose a novel approach that is broadly applicable and may be useful in large scale genetic association meta-analyses to detect significant allelic heterogeneity.

© 2009 S. Karger AG, Basel


  

Author Contacts

Dr. Brion Maher
Department of Psychiatry
Virginia Commonwealth University
Richmond, VA 23298-0126 (USA)
Tel. +1 804 828 8928, Fax +1 804 828 1471, E-Mail bsmaher@vcu.edu

  

Article Information

Received: March 30, 2009
Accepted after revision: June 24, 2009
Published online: December 4, 2009
Number of Print Pages : 9
Number of Figures : 1, Number of Tables : 1, Number of References : 38
Additional supplementary material is available online - Number of Parts : 1

  

Publication Details

Human Heredity (International Journal of Human and Medical Genetics)

Vol. 69, No. 2, Year 2010 (Cover Date: January 2010)

Journal Editor: Devoto M. (Philadelphia, Pa.)
ISSN: 0001-5652 (Print), eISSN: 1423-0062 (Online)

For additional information: http://www.karger.com/HHE


Article / Publication Details

First-Page Preview
Abstract of Original Paper

Received: 3/30/2009
Accepted: 6/24/2009
Published online: 12/4/2009
Issue release date: January 2010

Number of Print Pages: 9
Number of Figures: 1
Number of Tables: 1

ISSN: 0001-5652 (Print)
eISSN: 1423-0062 (Online)

For additional information: http://www.karger.com/HHE


Copyright / Drug Dosage

Copyright: All rights reserved. No part of this publication may be translated into other languages, reproduced or utilized in any form or by any means, electronic or mechanical, including photocopying, recording, microcopying, or by any information storage and retrieval system, without permission in writing from the publisher or, in the case of photocopying, direct payment of a specified fee to the Copyright Clearance Center.
Drug Dosage: The authors and the publisher have exerted every effort to ensure that drug selection and dosage set forth in this text are in accord with current recommendations and practice at the time of publication. However, in view of ongoing research, changes in goverment regulations, and the constant flow of information relating to drug therapy and drug reactions, the reader is urged to check the package insert for each drug for any changes in indications and dosage and for added warnings and precautions. This is particularly important when the recommended agent is a new and/or infrequently employed drug.
Disclaimer: The statements, opinions and data contained in this publication are solely those of the individual authors and contributors and not of the publishers and the editor(s). The appearance of advertisements or/and product references in the publication is not a warranty, endorsement, or approval of the products or services advertised or of their effectiveness, quality or safety. The publisher and the editor(s) disclaim responsibility for any injury to persons or property resulting from any ideas, methods, instructions or products referred to in the content or advertisements.

References

  1. Chanock SJ, Manolio T, Boehnke M, Boerwinkle E, Hunter DJ, Thomas G, Hirschhorn JN, Abecasis G, Altshuler D, Bailey-Wilson JE, Brooks LD, Cardon LR, Daly M, Donnelly P, Fraumeni JF Jr, Freimer NB, Gerhard DS, Gunter C, Guttmacher AE, Guyer MS, Harris EL, Hoh J, Hoover R, Kong CA, Merikangas KR, Morton CC, Palmer LJ, Phimister EG, Rice JP, Roberts J, Rotimi C, Tucker MA, Vogan KJ, Wacholder S, Wijsman EM, Winn DM, Collins FS: Replicating genotype-phenotype associations. Nature 2007;447:655–660.
  2. Moonesinghe R, Khoury MJ, Janssens AC: Most published research findings are false-but a little replication goes a long way. PLoS Med 2007;4:e28.
  3. Zaykin DV, Zhivotovsky LA: Ranks of genuine associations in whole-genome scans. Genetics 2005;171:813–823.
  4. Munafo MR, Flint J: Meta-analysis of genetic association studies. Trends Genet 2004;20:439–444.
  5. Moonesinghe R, Khoury MJ, Liu T, Ioannidis JP: Required sample size and nonreplicability thresholds for heterogeneous genetic associations. Proc Natl Acad Sci USA 2008;105:617–622.
  6. Zintzaras E, Ioannidis JP: Heterogeneity testing in meta-analysis of genome searches. Genet Epidemiol 2005;28:123–137.
  7. Ioannidis JP, Patsopoulos NA, Evangelou E: Heterogeneity in meta-analyses of genome-wide association investigations. PLoS One 2007;2:e841.
  8. Ioannidis JP: Non-replication and inconsistency in the genome-wide association setting. Hum Hered 2007;64:203–213.
  9. Ioannidis JP, Trikalinos TA: Early extreme contradictory estimates may appear in published research: the Proteus phenomenon in molecular genetics research and randomized trials. J Clin Epidemiol 2005;58:543–549.
  10. Lin PI, Vance JM, Pericak-Vance MA, Martin ER: No gene is an island: the flip-flop phenomenon. Am J Hum Genet 2007;80:531–538.
  11. Zaykin DV, Shibata K: Genetic flip-flop without an accompanying change in linkage disequilibrium. Am J Hum Genet 2008;82:794–796.
  12. Gruber JD, Genissel A, Macdonald SJ, Long AD: How repeatable are associations between polymorphisms in achaete-scute and bristle number variation in Drosophila? Genetics 2007;175:1987–1997.
  13. Lewis CM, Levinson DF, Wise LH, Delisi LE, Straub RE, Hovatta I, Williams NM, Schwab SG, Pulver AE, Faraone SV, Brzustowicz LM, Kaufmann CA, Garver DL, Gurling HM, Lindholm E, Coon H, Moises HW, Byerley W, Shaw SH, Mesen A, Sherrington R, O’Neill FA, Walsh D, Kendler KS, Ekelund J, Paunio T, Lonnqvist J, Peltonen L, O’Donovan MC, Owen MJ, Wildenauer DB, Maier W, Nestadt G, Blouin JL, Antonarakis SE, Mowry BJ, Silverman JM, Crowe RR, Cloninger CR, Tsuang MT, Malaspina D, Harkavy-Friedman JM, Svrakic DM, Bassett AS, Holcomb J, Kalsi G, McQuillin A, Brynjolfson J, Sigmundsson T, Petursson H, Jazin E, Zoega T, Helgason T: Genome scan meta-analysis of schizophrenia and bipolar disorder, part II: Schizophrenia. Am J Hum Genet 2003;73:34–48.
  14. Straub RE, MacLean CJ, Walsh D, Kendler KS: Support for schizophrenia vulnerability loci on chromosomes 6p and 8p from Irish families. Cold Spring Harb Symp Quant Biol 1996;61:823–833.
  15. Sun J, Kuo PH, Riley BP, Kendler KS, Zhao Z: Candidate genes for schizophrenia: A survey of association studies and gene ranking. Am J Med Genet B Neuropsychiatr Genet 2008.
  16. Mutsuddi M, Morris DW, Waggoner SG, Daly MJ, Scolnick EM, Sklar P: Analysis of high-resolution HapMap of DTNBP1 (Dysbindin) suggests no consistency between reported common variant associations and schizophrenia. Am J Hum Genet 2006;79:903–909.
  17. Sawcer S, Jones HB, Judge D, Visser F, Compston A, Goodfellow PN, Clayton D: Empirical genomewide significance levels established by whole genome simulations. Genet Epidemiol 1997;14:223–229.
  18. Abecasis GR, Cherny SS, Cookson WO, Cardon LR: Merlin–rapid analysis of dense genetic maps using sparse gene flow trees. Nat Genet 2002;30:97–101.
  19. Spielman RS, McGinnis RE, Ewens WJ: Transmission test for linkage disequilibrium: the insulin gene region and insulin-dependent diabetes mellitus (IDDM). Am J Hum Genet 1993;52:506–516.
  20. Falk CT, Rubinstein P: Haplotype relative risks: an easy reliable way to construct a proper control sample for risk calculations. Ann Hum Genet 1987;51:227–233.
  21. Terwilliger JD, Ott J: A haplotype-based ‘haplotype relative risk’ approach to detecting allelic associations. Hum Hered 1992;42:337–346.
  22. Schaid DJ, Sommer SS: Comparison of statistics for candidate-gene association studies using cases and parents. Am J Hum Genet 1994;55:402–409.
  23. Kazeem GR, Farrall M: Integrating case-control and TDT studies. Ann Hum Genet 2005;69:329–335.
  24. DerSimonian R, Laird N: Meta-analysis in clinical trials. Control Clin Trials 1986;7:177–188.
  25. Berlin JA, Laird NM, Sacks HS, Chalmers TC: A comparison of statistical methods for combining event rates from clinical trials. Stat Med 1989;8:141–151.
  26. Woolf B: On estimating the relation between blood group and disease. Ann Hum Genet 1955;19:251–253.
  27. Woolf B: The log likelihood ratio test (the G-test); methods and tables for tests of heterogeneity in contingency tables. Ann Hum Genet 1957;21:397–409.
  28. Devlin B, Risch N: A comparison of linkage disequilibrium measures for fine-scale mapping. Genomics 1995;29:311–322.
  29. Straub RE, Jiang Y, MacLean CJ, Ma Y, Webb BT, Myakishev MV, Harris-Kerr C, Wormley B, Sadek H, Kadambi B, Cesare AJ, Gibberman A, Wang X, O’Neill FA, Walsh D, Kendler KS: Genetic variation in the 6p22.3 gene DTNBP1, the human ortholog of the mouse dysbindin gene, is associated with schizophrenia. Am J Hum Genet 2002;71:337–348.
  30. Ferreira MA, O’Donovan MC, Meng YA, Jones IR, Ruderfer DM, Jones L, Fan J, Kirov G, Perlis RH, Green EK, Smoller JW, Grozeva D, Stone J, Nikolov I, Chambert K, Hamshere ML, Nimgaonkar VL, Moskvina V, Thase ME, Caesar S, Sachs GS, Franklin J, Gordon-Smith K, Ardlie KG, Gabriel SB, Fraser C, Blumenstiel B, Defelice M, Breen G, Gill M, Morris DW, Elkin A, Muir WJ, McGhee KA, Williamson R, Macintyre DJ, Maclean AW, St Clair D, Robinson M, Van Beck M, Pereira AC, Kandaswamy R, McQuillin A, Collier DA, Bass NJ, Young AH, Lawrence J, Ferrier IN, Anjorin A, Farmer A, Curtis D, Scolnick EM, McGuffin P, Daly MJ, Corvin AP, Holmans PA, Blackwood DH, Gurling HM, Owen MJ, Purcell SM, Sklar P, Craddock N: Collaborative genome-wide association analysis supports a role for ANK3 and CACNA1C in bipolar disorder. Nat Genet 2008;40:1042–1044.
  31. O’Donovan MC, Craddock N, Norton N, Williams H, Peirce T, Moskvina V, Nikolov I, Hamshere M, Carroll L, Georgieva L, Dwyer S, Holmans P, Marchini JL, Spencer CC, Howie B, Leung HT, Hartmann AM, Moller HJ, Morris DW, Shi Y, Feng G, Hoffmann P, Propping P, Vasilescu C, Maier W, Rietschel M, Zammit S, Schumacher J, Quinn EM, Schulze TG, Williams NM, Giegling I, Iwata N, Ikeda M, Darvasi A, Shifman S, He L, Duan J, Sanders AR, Levinson DF, Gejman PV, Gejman PV, Sanders AR, Duan J, Levinson DF, Buccola NG, Mowry BJ, Freedman R, Amin F, Black DW, Silverman JM, Byerley WF, Cloninger CR, Cichon S, Nothen MM, Gill M, Corvin A, Rujescu D, Kirov G, Owen MJ: Identification of loci associated with schizophrenia by genome-wide association and follow-up. Nat Genet 2008;40:1053–1055.
  32. Patsopoulos NA, Evangelou E, Ioannidis JP: Sensitivity of between-study heterogeneity in meta-analysis: proposed metrics and empirical evaluation. Int J Epidemiol 2008;37:1148–1157.
  33. Slager SL, Huang J, Vieland VJ: Effect of allelic heterogeneity on the power of the transmission disequilibrium test. Genet Epidemiol 2000;18:143–156.
  34. Neale BM, Sham PC: The future of association studies: gene-based analysis and replication. Am J Hum Genet 2004;75:353–362.
  35. Fisher RA: Statistical Methods for Research Workers. Oliver and Boyd, London, 1932.
  36. Gornick MC, Addington AM, Sporn A, Gogtay N, Greenstein D, Lenane M, Gochman P, Ordonez A, Balkissoon R, Vakkalanka R, Weinberger DR, Rapoport JL, Straub RE:Dysbindin (DTNBP1, 6p22.3) is associated with childhood-onset psychosis and endophenotypes measured by the Premorbid Adjustment Scale (PAS). J Autism Dev Disord 2005;35:831–838.
  37. Turunen JA, Peltonen JO, Pietiläinen OP, Hennah W, Loukola A, Paunio T, Silander K, Ekelund J, Varilo T, Partonen T, Lönnqvist J, Peltonen L: The role of DTNBP1, NRG1, and AKT1 in the genetics of schizophrenia in Finland. Schizophr Res 2007;91:27–36.
  38. Duan J, Martinez M, Sanders AR, Hou C, Burrell GJ, Krasner AJ, Schwartz DB, Gejman PV: DTNBP1 (Dystrobrevin binding protein 1) and schizophrenia: association evidence in the 3′ end of the gene. Hum Hered 2007;64:97–106.