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Association study designs for complex diseases

Nature Reviews Genetics volume 2, pages 91–99 (2001)Cite this article

Key Points

  • Genetic association analysis is a popular approach for identifying genetic variation that correlates with phenotypic variation, such as susceptibility to complex disease.

  • Association studies have a chequered history. Many published studies cannot be reproduced, or be substantiated by linkage data.

  • Genetic association occurs as a result of linkage disequilibrium (LD). But LD levels vary within the genome and between populations, making it difficult to predict the best sample populations for a particular study.

  • The most popular sampling strategy is the case-control study. Selection of the control population is key to the success of this approach, and small sample sizes or poorly matched controls are sources of error in association studies.

  • Prospective study designs can avoid the errors of case-control studies, but require large sample sizes. Family-based studies are also useful in overcoming errors due to population stratification.

  • Multiple testing of the same population, or population subgroups, is another source of error.

  • With the availability of the human genome sequence, and new methods for genotyping single nucleotide polymorphisms, association studies will become increasingly popular. Applications will include whole-genome screens and regional LD mapping.

  • More rigorous study design, independent replication of data and careful attention to the effects of multiple testing are among the recommendations that will improve the value of association data in the future.

Abstract

Assessing the association between DNA variants and disease has been used widely to identify regions of the genome and candidate genes that contribute to disease. However, there are numerous examples of associations that cannot be replicated, which has led to scepticism about the utility of the approach for common conditions. With the discovery of massive numbers of genetic markers and the development of better tools for genotyping, association studies will inevitably proliferate. Now is the time to consider critically the design of such studies, to avoid the mistakes of the past and to maximize their potential to identify new components of disease.

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References

  1. Mullikin, J. C. et al. An SNP map of human chromosome 22. Nature 407, 516–520 (2000).

    CAS  PubMed  Google Scholar 

  2. Altshuler, D. et al. An SNP map of the human genome generated by reduced representation shotgun sequencing. Nature 407, 513– 516 (2000).

    CAS  PubMed  Google Scholar 

  3. Drews, J. & Ryser, S. The role of innovation in drug development . Nature Biotechnol. 15, 1318– 1319 (1997).

    CAS  Google Scholar 

  4. Terwilliger, J. D. & Weiss, K. M. Linkage disequilibrium mapping of complex disease: fantasy or reality? Curr. Opin. Biotechnol. 9, 578–594 ( 1998).

    CAS  PubMed  Google Scholar 

  5. Gambaro, G., Anglani, F. & D'Angelo, A. Association studies of genetic polymorphisms and complex disease. Lancet 355, 308– 111 (2000).

    CAS  PubMed  Google Scholar 

  6. Weiss, K. M. & Terwilliger, J. D. How many diseases does it take to map a gene with SNPs? Nature Genet. 26, 151–157 (2000).This paper is essential reading for anyone undertaking association studies of common characters. The primary aim is to elucidate the difficulties in identifying genetic loci that contribute to complex traits. The literature cited covers some necessary population genetics material.

    CAS  PubMed  Google Scholar 

  7. Risch, N. J. Searching for genetic determinants in the new millennium. Nature 405, 847–856 ( 2000).An excellent summary of current statistical procedures and their comparative strengths and weaknesses for complex trait mapping. Very useful for comparing linkage and association and for distinguishing familial influences on discrete versus quantitative traits.

    CAS  PubMed  Google Scholar 

  8. Schork, N. J., Cardon, L. R. & Xu, X. The future of genetic epidemiology. Trends Genet. 14, 266–272 ( 1998).

    CAS  PubMed  Google Scholar 

  9. Collins, F. Positional cloning moves from perditional to traditional. Nature Genet. 9, 347–350 ( 1995).

    CAS  PubMed  Google Scholar 

  10. Lander, E. S. & Schork, N. J. Genetic dissection of complex traits. Science 265, 2037– 2048 (1994).

    CAS  PubMed  Google Scholar 

  11. Risch, N. & Merikangas, K. The future of genetic studies of complex human diseases. Science 273, 1516–1517 (1996).

    CAS  PubMed  Google Scholar 

  12. Jorde, L. B. Linkage disequilibrium and the search for complex disease genes. Genome Res. 10, 1435–1444 (2000).

    CAS  PubMed  Google Scholar 

  13. Xiong, M. & Guo, S. W. Fine-scale genetic mapping based on linkage disequilibrium: theory and applications. Am. J. Hum. Genet. 60, 1513–1531 ( 1997).

    CAS  PubMed  PubMed Central  Google Scholar 

  14. Freimer, N. B. et al. Genetic mapping using haplotype, association and linkage methods suggests a locus for severe bipolar disorder (BPI) at 18q22-q23. Nature Genet. 12, 436–441 (1996).

    CAS  PubMed  Google Scholar 

  15. Hastbacka, J. et al. Linkage disequilibrium mapping in isolated founder populations: diastrophic dysplasia in Finland. Nature Genet. 2, 204–211 (1992).This is becoming a classic paper on using disequilibrium/haplotype data to identify disease loci. The trait studied does not reflect the common disease framework of current widespread interest, but the procedures used offer a useful model from which to start.

    CAS  PubMed  Google Scholar 

  16. Collins, A., Lonjou, C. & Morton, N. E. Genetic epidemiology of single-nucleotide polymorphisms . Proc. Natl Acad. Sci. USA 96, 15173– 15177 (1999).One of a series of key papers by these authors who compare disequilibrium measures, evaluate real data patterns to infer genome-wide marker spacing requirements, and combine population genetics principles with those of disease-gene mapping to characterize allelic association.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Eaves, I. A. et al. The genetically isolated populations of finland and sardinia may not be a panacea for linkage disequilibrium mapping of common disease genes. Nature Genet. 25, 320– 323 (2000).

    CAS  PubMed  Google Scholar 

  18. Taillon-Miller, P. et al. Juxtaposed regions of extensive and minimal linkage disequilibrium in human Xq25 and Xq28. Nature Genet. 25, 324–328 (2000).

    CAS  PubMed  Google Scholar 

  19. Nickerson, D. A. et al. DNA sequence diversity in a 9.7-kb region of the human lipoprotein lipase gene. Nature Genet. 19, 233– 240 (1998).

    CAS  PubMed  Google Scholar 

  20. Clark, A. G. et al. Haplotype structure and population genetic inferences from nucleotide-sequence variation in human lipoprotein lipase. Am. J. Hum. Genet. 63, 595–612 (1998).

    CAS  PubMed  PubMed Central  Google Scholar 

  21. Cargill, M. et al. Characterization of single-nucleotide polymorphisms in coding regions of human genes. Nature Genet. 22, 231–238 (1999).

    CAS  PubMed  Google Scholar 

  22. Halushka, M. K. et al. Patterns of single-nucleotide polymorphisms in candidate genes for blood-pressure homeostasis. Nature Genet. 22, 239–247 (1999).

    CAS  PubMed  Google Scholar 

  23. Templeton, A. R. et al. Recombinational and mutational hotspots within the human lipoprotein lipase gene. Am. J. Hum. Genet. 66, 69– 83 (2000).

    CAS  PubMed  PubMed Central  Google Scholar 

  24. Ott, J. Predicting the range of linkage disequilibrium. Proc. Natl Acad. Sci. USA 97, 2–3 (2000 ).

    CAS  PubMed  PubMed Central  Google Scholar 

  25. Chapman, N. H. & Thompson, E. A. Linkage disequilibrium mapping: the role of population history, size, and structure. Adv. Genet. 42, 413–437 (2001).

    CAS  PubMed  Google Scholar 

  26. Fisher, R. A. The rhesus factor: a study in scientific method. Am. Sci. 35, 95–103 (1947).

    CAS  PubMed  Google Scholar 

  27. Tiwari, J. L. & Terasaki, P. I. HLA and Disease Associations (Springer, New York, 1985).

    Google Scholar 

  28. Lander, E. S. Array of hope. Nature Genet. 21, 3– 4 (1999).

    CAS  PubMed  Google Scholar 

  29. Risch, N. & Teng, J. Design and analysis of linkage disequilibrium studies for complex human diseases. Am. J. Hum. Genet. 61, 1707 (1997).

    Google Scholar 

  30. Risch, N. & Teng, J. The relative power of family-based and case–control designs for linkage disequilibrium studies of complex human diseases I. DNA pooling. Genome Res. 8, 1273–1288 (1998).

    CAS  PubMed  Google Scholar 

  31. Teng, J. & Risch, N. The relative power of family-based and case–control designs for linkage disequilibrium studies of complex human diseases. II. Individual genotyping. Genome Res. 9, 234–241 (1999).

    CAS  PubMed  Google Scholar 

  32. Keavney, B. Genetic association studies in complex diseases. J. Hum. Hypertens. 14, 361–367 ( 2000).

    CAS  PubMed  Google Scholar 

  33. Keavney, B. et al. Large-scale test of hypothesised associations between the angiotensin-converting-enzyme insertion/deletion polymorphism and myocardial infarction in about 5000 cases and 6000 controls. International Studies of Infarct Survival (ISIS) Collaborators. Lancet 355, 434–442 (2000).The need for association studies to involve thousands of patients is clearly shown by comparing the results of a number of typical, small studies with that of a large-scale, well-controlled design. Reference 33 offers a similar example for non-insulin-dependent diabetes mellitus.

    CAS  PubMed  Google Scholar 

  34. Altshuler, D. et al. The common PPARgamma Pro12Ala polymorphism is associated with decreased risk of type 2 diabetes. Nature Genet. 26 , 76–80 (2000).

    CAS  PubMed  Google Scholar 

  35. Cambien, F. et al. Deletion polymorphism in the gene for angiotensin-converting enzyme is a potent risk factor for myocardial infarction. Nature 359, 641–644 ( 1992).

    CAS  PubMed  Google Scholar 

  36. Arnheim, N., Strange, C. & Erlich, H. Use of pooled DNA samples to detect linkage disequilibrium of polymorphic restriction fragments and human disease: studies of the HLA class II loci. Proc. Natl Acad. Sci. USA 82, 6970–6974 (1985).

    CAS  PubMed  PubMed Central  Google Scholar 

  37. Barcellos, L. F. et al. Association mapping of disease loci, by use of a pooled DNA genomic screen. Am. J. Hum. Genet. 61, 734 –747 (1997).

    CAS  PubMed  PubMed Central  Google Scholar 

  38. Daniels, J. et al. A simple method for analyzing microsatellite allele image patterns generated from DNA pools and its application to allelic association studies. Am. J. Hum. Genet. 62, 1189– 1197 (1998).

    CAS  PubMed  PubMed Central  Google Scholar 

  39. Shaw, S. H., Carrasquillo, M. M., Kashuk, C., Puffenberger, E. G. & Chakravarti, A. Allele frequency distributions in pooled DNA samples: applications to mapping complex disease genes. Genome Res. 8, 111– 123 (1998).

    CAS  PubMed  Google Scholar 

  40. Kirov, G., Williams, N., Sham, P., Craddock, N. & Owen, M. J. Pooled genotyping of microsatellite markers in parent-offspring trios. Genome Res. 10, 105– 115 (2000).

    CAS  PubMed  PubMed Central  Google Scholar 

  41. Falk, C. T. & Rubinstein, P. Haplotype relative risks: an easy reliable way to construct a proper control sample for risk calculations . Ann. Hum. Genet. 51, 227– 233 (1987).

    CAS  PubMed  Google Scholar 

  42. Spielman, R. S., McGinnis, R. E. & Ewens, W. J. Transmission test for linkage disequilibrium: the insulin gene region and insulin-dependent diabetes mellitus. Am. J. Hum. Genet. 52, 506–516 (1993).The TDT test and its immediate predecessors changed the way human genetic studies were conducted throughout the past decade. This is the original paper describing the method.

    CAS  PubMed  PubMed Central  Google Scholar 

  43. Spielman, R. S., McGinnis, R. E. & Ewens, W. J. The transmission/disequilibrium test detects cosegregation and linkage. Am. J. Hum. Genet. 54, 559– 560 (1994).

    CAS  PubMed  PubMed Central  Google Scholar 

  44. Spielman, R. S. & Ewens, W. J. The TDT and other family-based tests for linkage disequilibrium and association. Am. J. Hum. Genet. 59, 983–989 (1996).

    CAS  PubMed  PubMed Central  Google Scholar 

  45. Sham, P. C. & Curtis, D. An extended transmission/disequilibrium test (TDT) for multiallelic marker loci. Ann. Hum. Genet. 59, 323–326 (1995).

    CAS  PubMed  Google Scholar 

  46. Spielman, R. S. & Ewens, W. J. A sibship test for linkage in the presence of association: The sib transmission/disequilibrium test. Am. J. Hum. Genet. 62, 450– 458 (1998).

    CAS  PubMed  PubMed Central  Google Scholar 

  47. Curtis, D. Use of siblings as controls in case–control association studies. Ann. Hum. Genet. 61, 319–333 (1997).

    CAS  PubMed  Google Scholar 

  48. Martin, E. R., Kaplan, N. L. & Weir, B. S. Tests for linkage and association in nuclear families . Am. J. Hum. Genet. 61, 439– 448 (1997).

    CAS  PubMed  PubMed Central  Google Scholar 

  49. Allison, D. B. Transmission-disequilibrium tests for quantitative traits. Am. J. Hum. Genet. 60, 676–690 (1997).

    CAS  PubMed  PubMed Central  Google Scholar 

  50. Rabinowitz, D. A transmission disequilibrium test for quantitative trait loci. Hum. Hered. 47, 342–350 (1997).

    CAS  PubMed  Google Scholar 

  51. Abecasis, G. R., Cardon, L. R. & Cookson, W. O. A general test of association for quantitative traits in nuclear families. Am. J. Hum. Genet. 66, 279–292 (2000).

    CAS  PubMed  Google Scholar 

  52. Martin, E. R., Monks, S. A., Warren, L. L. & Kaplan, N. L. A test for linkage and association in general pedigrees: The pedigree disequilibrium test. Am. J. Hum. Genet. 67, 146– 154 (2000).

    CAS  PubMed  PubMed Central  Google Scholar 

  53. Pritchard, L. E. et al. Analysis of the CD3 gene region and type 1 diabetes: application of fluorescence-based technology to linkage disequilibrium mapping. Hum. Mol. Genet. 4, 197–202 (1995).

    CAS  PubMed  Google Scholar 

  54. Bennett, S. T. & Todd, J. A. Human type 1 diabetes and the insulin gene: Principles of mapping polygenes. Annu. Rev. Genet. 30, 343–370 ( 1996).

    CAS  PubMed  Google Scholar 

  55. Bennett, S. T. et al. Insulin VNTR allele-specific effect in type 1 diabetes depends on identity of untransmitted paternal allele. The IMDIAB Group. Nature Genet. 17, 350–352 (1997).

    CAS  PubMed  Google Scholar 

  56. Merriman, T. R. et al. Transmission of haplotypes of microsatellite markers rather than single marker alleles in the mapping of a putative type 1 diabetes susceptibility gene (IDDM6). Hum. Mol. Genet. 7, 517– 524 (1998).

    CAS  PubMed  Google Scholar 

  57. Eaves, I. A. et al. Transmission ratio distortion at the INS-IGF2 VNTR. Nature Genet. 22, 324–325 (1999).

    CAS  PubMed  Google Scholar 

  58. Lernmark, A. & Ott, J. Sometimes it's hot, sometimes it's not . Nature Genet. 19, 213– 214 (1998).

    CAS  PubMed  Google Scholar 

  59. Goring, H. H. & Terwilliger, J. D. Linkage analysis in the presence of errors IV: joint pseudomarker analysis of linkage and/or linkage disequilibrium on a mixture of pedigrees and singletons when the mode of inheritance cannot be accurately specified. Am. J. Hum. Genet. 66, 1310–1327 (2000).

    CAS  PubMed  PubMed Central  Google Scholar 

  60. Morton, N.E. & Collins, A. Tests and estimates of allelic association in complex inheritance. Proc. Natl Acad. Sci. USA 95 , 11389–93 (1998).

    CAS  PubMed  PubMed Central  Google Scholar 

  61. Riordan, J. R. et al. Identification of the cystic fibrosis gene: cloning and characterization of complementary DNA. Science 245, 1066– 1073 (1989).

    CAS  PubMed  Google Scholar 

  62. Rommens, J. M. et al. Identification of the cystic fibrosis gene: chromosome walking and jumping. Science 245, 1059– 1065 (1989).

    CAS  PubMed  Google Scholar 

  63. Kerem, B. et al. Identification of the cystic fibrosis gene: genetic analysis . Science 245, 1073–1080 (1989).

    CAS  PubMed  Google Scholar 

  64. Huntington's Disease Collaborative Research Group. A novel gene containing a trinucleotide repeat that is expanded and unstable on Huntington's disease chromosomes. Cell 72, 971–983 (1993).

  65. Martin, E. R. et al. SNPing away at complex diseases: analysis of single-nucleotide polymorphisms around APOE in Alzheimer disease. Am. J. Hum. Genet. 67, 383–394 ( 2000).

    CAS  PubMed  PubMed Central  Google Scholar 

  66. Martin, E. R. et al. Analysis of association at single nucleotide polymorphisms in the APOE region. Genomics 63, 7– 12 (2000).

    CAS  PubMed  Google Scholar 

  67. Horikawa, Y. et al. Genetic variation in the gene encoding calpain-10 is associated with type 2 diabetes mellitus. Nature Genet. 26, 163–175 (2000).

    CAS  PubMed  Google Scholar 

  68. Roses, A. D. Pharmacogenetics and the practice of medicine. Nature 405, 857–865 (2000).

    CAS  PubMed  Google Scholar 

  69. Keavney, B. et al. Measured haplotype analysis of the angiotensin-I converting enzyme gene. Hum. Mol. Genet. 7, 1745– 1751 (1998).The ACE locus and ACE phenotype is a model quantitative system. Despite the unusually clear haplotype relationships in this gene and population, the study clearly demonstrates the difficulty in distinguishing which specific variants are responsible for phenotypic variability.

    CAS  PubMed  Google Scholar 

  70. Moffatt, M. F., Traherne, J. A., Abecasis, G. R. & Cookson, W. O. Single nucleotide polymorphism and linkage disequilibrium within the TCR alpha/delta locus. Hum. Mol. Genet. 9, 1011– 1019 (2000).

    CAS  PubMed  Google Scholar 

  71. Abecasis, G. R. et al. Patterns of linkage disequilibrium from three genomic regions . Am. J. Hum. Genet. 68, 191– 197 (2001).

    CAS  PubMed  Google Scholar 

  72. Farrall, M. et al. Fine-mapping of an ancestral recombination breakpoint in DCP1 . Nature Genet. 23, 270– 271 (1999).

    CAS  PubMed  Google Scholar 

  73. Abecasis, G. R., Cookson, W. O. & Cardon, L. R. Pedigree tests of transmission disequilibrium. Eur. J. Hum. Genet. 8, 545–551 (2000).

    CAS  PubMed  Google Scholar 

  74. Todd, J. A. et al. Identification of susceptibility loci for insulin-dependent diabetes mellitus by trans-racial gene mapping. Nature 338, 587–589 (1989).

    CAS  PubMed  Google Scholar 

  75. Mijovic, C. H., Barnett, A. H. & Todd, J. A. Genetics of diabetes. Trans-racial gene mapping studies . Baillieres Clin. Endocrinol. Metab. 5, 321–340 (1991).

    CAS  PubMed  Google Scholar 

  76. Cardon, L. R. & Watkins, H. Waiting for the working draft from the human genome project: A huge achievement, but not of immediate medical use. Br. Med. J. 320, 1221– 1222 (2000).

    Google Scholar 

  77. Kruglyak, L. Prospects for whole-genome linkage disequilibrium mapping of common disease genes. Nature Genet. 22, 139– 144 (1999).Mathematical population genetics modelling is used to simulate background levels of linkage disequilibrium in the genome, indicating that very fine-scale maps are required for disease gene association mapping. Although hotly contested and not always supported by empirical reports, this paper clearly outlines the issues and importance of disequilibrium levels in the genome.

    CAS  PubMed  Google Scholar 

  78. Collins, A. & Morton, N. E. Mapping a disease locus by allelic association. Proc. Natl Acad. Sci. USA 95, 1741–1745 (1998).

    CAS  PubMed  PubMed Central  Google Scholar 

  79. Cox, N. J. et al. Loci on chromosomes 2 (NIDDM1) and 15 interact to increase susceptibility to diabetes in Mexican Americans. Nature Genet. 21, 213–215 ( 1999).

    CAS  PubMed  Google Scholar 

  80. Risch, N. Evolving methods in genetic epidemiology. 2. Genetic linkage from an epidemiologic perspective. Epidemiol. Rev. 19, 24– 32 (1997).

    CAS  PubMed  Google Scholar 

  81. Potter, J. D. At the interfaces of epidemiology, genetics and genomics. Nature Rev. Genet. 2, 142–147 ( 2001).

    CAS  PubMed  Google Scholar 

  82. Khoury, M. J., Beaty, T. H. & Cohen, B. H. Fundamentals of Genetic Epidemiology (Oxford Univ. Press, Oxford, 1993).

    Google Scholar 

  83. Huttley, G. A., Smith, M. W., Carrington, M. & O'Brien, S. J. A scan for linkage disequilibrium across the human genome. Genetics 152, 1711–1722 ( 1999).

    CAS  PubMed  PubMed Central  Google Scholar 

  84. Goddard, K. A., Hopkins, P. J., Hall, J. M. & Witte, J. S. Linkage disequilibrium and allele-frequency distributions for 114 single-nucleotide polymorphisms in five populations. Am. J. Hum. Genet. 66, 216–234 (2000).

    CAS  PubMed  PubMed Central  Google Scholar 

  85. Majewski, J. & Ott, J. GT repeats are associated with recombination on human chromosome 22. Genome Res. 10, 1108–1114 (2000).

    CAS  PubMed  PubMed Central  Google Scholar 

  86. Abbott, A. Manhattan versus Reykjavik. Nature 406, 340–342 (2000).

    CAS  PubMed  Google Scholar 

  87. Borecki, I. B. & Suarez, B. K. Linkage and association: basic concepts. Adv. Genet. 42, 45–66 (2001).

    CAS  PubMed  Google Scholar 

  88. Slatkin, M. Linkage disequilibrium in growing and stable populations. Genetics 137, 331–336 ( 1994).

    CAS  PubMed  PubMed Central  Google Scholar 

  89. Hartl, D. L. & Clark, A. G. Principles of Population Genetics (Sinauer Associates, Sunderland, MA, 1997).

    Google Scholar 

  90. Pritchard, J. K. & Rosenberg, N. A. Use of unlinked genetic markers to detect population stratification in association studies . Am. J. Hum. Genet. 65, 220– 228 (1999).This paper describes the use of unlinked genetic markers to detect population stratification, with minimal mathematical complexity. The key issues of marker spacing and informativeness are evaluated in detail. Reference 94 should be read in follow-up of this paper to see how stratification can be accounted for when it is present.

    CAS  PubMed  PubMed Central  Google Scholar 

  91. Devlin, B. & Roeder, K. Genomic control for association studies . Biometrics 55, 997–1004 (1999).

    CAS  PubMed  Google Scholar 

  92. Pritchard, J. K., Stephens, M. & Donnelly, P. Inference of population structure using multilocus genotype data. Genetics 155, 945– 959 (2000).

    CAS  PubMed  PubMed Central  Google Scholar 

  93. Pritchard, J. K., Stephens, M., Rosenberg, N. A. & Donnelly, P. Association mapping in structured populations. Am. J. Hum. Genet. 67, 170–181 ( 2000).

    CAS  PubMed  PubMed Central  Google Scholar 

  94. Bacanu, S. A., Devlin, B. & Roeder, K. The power of genomic control. Am. J. Hum. Genet. 66, 1933–1944 ( 2000).

    CAS  PubMed  PubMed Central  Google Scholar 

  95. Witte, J. S., Elston, R. C. & Schork, N. J. Genetic dissection of complex traits. Nature Genet. 12, 355–358 (1996).

    CAS  PubMed  Google Scholar 

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Acknowledgements

This work was supported by the Wellcome Trust and in part by a grant from the NIH (to L.R.C.). We wish to thank Dr Joe Terwilliger for critical review of this manuscript.

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  1. Nuffield Department of Clinical Medicine, University of Oxford, Headington, OX3 9DU, Oxford, UK

    Lon R. Cardon & John I. Bell

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HLA

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FURTHER INFORMATION

TDT and statistical genetics software

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Glossary

POWER

The probability of correctly rejecting the null hypothesis when it is truly false. For association studies, the power can be considered as the probability of correctly detecting a genuine association.

GENETIC DRIFT

The random fluctuation in allele frequencies as genes are transmitted from one generation to the next.

POPULATION ADMIXTURE

A population in which multiple subgroups are included. Admixture often refers to intermarriage/reproduction from different groups of individuals, but most simply is used to denote a population of subgroups having different allele frequencies (see population stratification).

PROSPECTIVE COHORT

Longitudinal study of individuals initially assessed for exposure to certain risk factors and then followed over time to evaluate the progression towards specific outcomes (often disease).

LOCUS HETEROGENEITY

The appearance of phenotypically similar characteristics resulting from mutations at different genetic loci. Differences in effect size or in replication between studies and samples are often ascribed to different loci leading to the same disease.

POPULATION STRATIFICATION

The presence of multiple subgroups with different allele frequencies within a population. The different underlying allele frequencies in sampled subgroups might be independent of the disease within each group, and they can lead to erroneous conclusions of linkage disequilibrium or disease relevance.

TYPE I ERROR

The probability of rejecting the null hypothesis when it is true. For association studies, Type I errors are manifest as false-positive reports of phenotype–genotype correlation.

RISK RATIO

A measure of association effect reflecting the probability of disease in people with a particular allele or genotype versus the probability of disease in those who do not have the particular genotype.

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Cardon, L., Bell, J. Association study designs for complex diseases. Nat Rev Genet 2, 91–99 (2001). https://doi.org/10.1038/35052543

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