Skip to main content

Advertisement

Log in

Genetic Associations in Preterm Birth: A Primer of Marker Selection, Study Design, and Data Analysis

  • Review Article
  • Published:
The Journal of the Society for Gynecologic Investigation: JSGI Aims and scope Submit manuscript

Abstract

Spontaneous preterm birth (PTB; delivery before 37 weeks gestation) is a primary risk factor for infant morbidity and mortality. The etiology is unclear, but there is evidence that there is a genetic predisposition to PTB. Armed with the suggestion of genetic risk factors and the failure to identify useful biomarkers, investigators are starting to actively pursue the role of genetic predisposition in PTB. Several studies have been done to date assessing the role of single gene variants. However, positive findings have failed to replicate. We argue that heterogeneity in study designs, definition of phenotype, single-nucleotide polymorphism (SNP) selection, population selection, and sample size makes data interpretation difficult in complex phenotypes such as PTB. In this review, we introduce general concepts of study designs in genetic epidemiology, selection of candidate genes and markers for analysis, and analytical methodologies. We also introduce how the concept of gene-gene interactions (biologic epistatis) and gene-environment interactions may affect the predisposition to PTB.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Lockwood CJ. Biochemical predictors of prematurity. Front Horm Res 2001;27:258–268.

    Article  CAS  PubMed  Google Scholar 

  2. Romero R, Mazor M, Munoz H, Gomez R, Galasso M, Sherer DM. The preterm labor syndrome. Ann N Y Acad Sci 1994;734:414–429.

    Article  CAS  PubMed  Google Scholar 

  3. Goldenberg RL, Goepfert AR, Ramsey PS. Biochemical markers for the prediction of preterm birth. Am J Obstet Gynecol 2005;192:S36–S46.

    Article  CAS  PubMed  Google Scholar 

  4. Mathews TJ, Menacker F, MacDorman MF. Centers for Disease Control and Prevention, National Center for Health Statistics. Infant mortality statistics from the 2002 period: Linked birth/infant death data set. Natl Vital Stat Rep 2004;53:1–29.

    CAS  PubMed  Google Scholar 

  5. Martin JA, Hamilton BE, Sutton PD, Ventura SJ, Menacker F, Munson ML. Births: Final data for 2002. Natl Vital Stat Rep 2003;52:1–113.

    PubMed  Google Scholar 

  6. Clausson B, Lichtenstein P, Cnattingius S. Genetic influence on birth weight and gestational length determined by studies in offspring of twins. BJOG 2000;107:375–381.

    Article  CAS  PubMed  Google Scholar 

  7. Treloar SA, Macones GA, Mitchell LE, Martin NG. Genetic influences on premature parturition in an Australian twin sample. Twin Res 2000;3:80–82.

    Article  CAS  PubMed  Google Scholar 

  8. Ward R. Familial aggregation and genetic epidemiology of blood pressure. In: Brenner JM, ed. Hypertension: pathophysiology, diagnosis and management. New York: Raven, 1990.

    Google Scholar 

  9. Rotimi CN, Cooper RS, Cao G, et al. Maximum-likelihood generalized heritability estimate for blood pressure in Nigerian families. Hypertension 1999;33:874–878.

    Article  CAS  PubMed  Google Scholar 

  10. Bakketeig LS, Hoffman HJ, Harley EE. The tendency to repeat gestational age and birth weight in successive births. Am J Obstet Gynecol 1979;135:1086–1103.

    Article  CAS  PubMed  Google Scholar 

  11. Porter TF, Fraser AM, Hunter CY, Ward RH, Varner MW. The risk of preterm birth across generations. Obstet Gynecol 1997;90:63–67.

    Article  CAS  PubMed  Google Scholar 

  12. Carr-Hill RA, Hall MH. The repetition of spontaneous preterm labour. Br J Obstet Gynaecol 1985;92:921–928.

    Article  CAS  PubMed  Google Scholar 

  13. Winkvist A, Mogren I, Hogberg U. Familial patterns in birth characteristics: Impact on individual and population risks. Int J Epidemiol 1998;27:248–254.

    Article  CAS  PubMed  Google Scholar 

  14. Hoyert DL, Freedman MA, Strobino DM, Guyer B. Annual summary of vital statistics: 2000. Pediatrics 2001;108:1241–1255.

    Article  CAS  PubMed  Google Scholar 

  15. Guyer B, Hoyert DL, Martin JA, Ventura SJ, MacDorman MF, Strobino DM. Annual summary of vital statistics—1998. Pediatrics 1999;104:1229–1246.

    Article  CAS  PubMed  Google Scholar 

  16. Macones GA, Perry S, Elkousy M, Clothier B, Ural SH, Strauss JF III. A polymorphism in the promoter region of TNF and bacterial vaginosis: Preliminary evidence of gene-environment interaction in the etiology of spontaneous preterm birth. Am J Obstet Gynecol 2004;190:1504–1508.

    Article  CAS  PubMed  Google Scholar 

  17. Annells MF, Hart PH, Mullighan CG, et al. Interleukins-1, -4, -6, -10, tumor necrosis factor, transforming growth factor-beta, FAS, and mannose-binding protein C gene polymorphisms in Australian women: Risk of preterm birth. Am J Obstet Gynecol 2004;191:2056–2067.

    Article  CAS  PubMed  Google Scholar 

  18. Dizon-Townson DS, Major H, Varner M, Ward K. A promoter mutation that increases transcription of the tumor necrosis factor-alpha gene is not associated with preterm delivery. Am J Obstet Gynecol 1997;177:810–813.

    Article  CAS  PubMed  Google Scholar 

  19. Amory JH, Adams KM, Lin MT, Hansen JA, Eschenbach DA, Hitti J. Adverse outcomes after preterm labor are associated with tumor necrosis factor-alpha polymorphism -863, but not -308, in mother-infant pairs. Am J Obstet Gynecol 2004;191:1362–1367.

    Article  CAS  PubMed  Google Scholar 

  20. Menon R, Merial di M, Betran AP, et al. Lack of association between tumor necrosis factor-α promoter polymorphism (-308), TNF concentration, and preterm birth. Am J Obstet Gynecol (in press).

  21. Colhoun HM, McKeigue PM, Davey Smith G. Problems of reporting genetic associations with complex outcomes. Lancet 2003;361:865–872.

    Article  PubMed  Google Scholar 

  22. Pfaff CL, Parra EJ, Bonil la C, et al. Population structure in admixed populations: Effect of admixture dynamics on the pattern of linkage disequilibrium. Am J Hum Genet 2001;68:198–207.

    Article  CAS  PubMed  Google Scholar 

  23. Marchini J, Cutler D, Patterson N, et al. International HapMap Consortium. A comparison of phasing algorithms for trios and unrelated individuals. Am J Hum Genet 2006;78:437–450.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Hutchinson M, Spanaki C, Lebedev S, Plaitakis A. Genetic basis of common diseases: The general theory of Mendelian recessive genetics. Med Hypoth 2005;65:282–286.

    Article  Google Scholar 

  25. Khoury MJ, Thrasher JF, Burke W, Gettig EA, Fridinger F, Jackson R. Challenges in communicating genetics: A public health approach. Genet Med 2000;2:198–202.

    Article  CAS  PubMed  Google Scholar 

  26. Vogel F, Motulsky AG. Human genetics. Problems and approaches. 3rd ed. Berlin: Springer-Verlag, 1996.

    Google Scholar 

  27. Scheuner MT, Yoon PW, Khoury MJ. Contribution of Mendelian disorders to common chronic disease: Opportunities for recognition, intervention, and prevention. Am J Med Genet C Semin Med Genet 2004;125:50–65.

    Article  Google Scholar 

  28. Xiong M, Feghali-Bostwick CA, Arnett FC, Zhou X. A systems biology approach to genetic studies of complex diseases. FEBS Lett 2005;579:5325–5332.

    Article  CAS  PubMed  Google Scholar 

  29. Ranna la B. Finding genes influencing susceptibility to complex diseases in the post-genome era. Am J Pharmacogenomics 2001;1:203–221.

    Article  Google Scholar 

  30. Baron M. The search for complex disease genes: Fault by linkage or fault by association? Mol Psychiatry 2001;6:143–149.

    Article  CAS  PubMed  Google Scholar 

  31. Esplin MS, Varner MW. Genetic factors in preterm birth—The future. BJOG 2005;112:97–102.

    Article  CAS  PubMed  Google Scholar 

  32. Varner MW, Esplin MS. Current understanding of genetic factors in preterm birth. BJOG 2005;112:28–31.

    Article  CAS  PubMed  Google Scholar 

  33. Cordell HJ, Clayton DG. Genetic association studies. Lancet 2005;366:1121–1131.

    Article  PubMed  Google Scholar 

  34. Wang WY, Barratt BJ, Clayton DG, Todd JA. Genome-wide association studies: Theoretical and practical concerns. Nat Rev Genet 2005;6:109–118.

    Article  CAS  PubMed  Google Scholar 

  35. Hauser ER, Watanabe RM, Duren WL, Bass MP, Langefeld CD, Boehnke M. Ordered subset analysis in genetic linkage mapping of complex traits. Genet Epidemiol 2004;27:53–63.

    Article  PubMed  Google Scholar 

  36. Schaid DJ, Sommer SS. Genotype relative risks: Methods for design and analysis of candidate-gene association studies. Am J Hum Genet 1993;53:1114–1126.

    CAS  PubMed  PubMed Central  Google Scholar 

  37. Daly AK, Day CP. Candidate gene case-control association studies: Advantages and potential pitfalls. Br J Clin Pharmacol 2001;52:489–499.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Risch NJ. Searching for genetic determinants in the new millenium. Nature 2000;405:847–856.

    Article  CAS  PubMed  Google Scholar 

  39. Risch N, Merikangas K. The future of genetic studies of complex human diseases. Science 1996;273:1516–1517.

    Article  CAS  PubMed  Google Scholar 

  40. Hirschhorn JN. Genetic and genomic approaches to studying stature and pubertal timing. Pediatr Endocrinol Rev 2005;2:351–354.

    PubMed  Google Scholar 

  41. Ardlie KG, Kruglyak L, Seielstad M. Patterns of linkage disequilibrium in the human genome. Nat Rev Genet 2002;3:299–309.

    Article  CAS  PubMed  Google Scholar 

  42. Reich DE, Cargill M, Bolk S, et al. Linkage disequilibrium in the human genome. Nature 2001;411:199–204.

    Article  CAS  PubMed  Google Scholar 

  43. Weiss KM, Clark AG. Linkage disequilibrium and the mapping of complex human traits. Trends Genet 2002;18:19–24.

    Article  CAS  PubMed  Google Scholar 

  44. Ke X, Hunt S, Tapper W, et al. The impact of SNP density on fine-scale patterns of linkage disequilibrium. Hum Mol Genet 2004;13:577–588.

    Article  CAS  PubMed  Google Scholar 

  45. Kruglyak L. Prospects for whole-genome linkage disequilibrium mapping of common disease genes. Nat Genet 1999;22:139–144.

    Article  CAS  PubMed  Google Scholar 

  46. Johnson GC, Esposito L, Barratt BJ, et al. Haplotype tagging for the identification of common disease genes. Nat Genet 2001;29:233–237.

    Article  CAS  PubMed  Google Scholar 

  47. Bonnen PE, Wang PJ, Kimmel M, Chakraborty R, Nelson DL. Haplotype and linkage disequilibrium architecture for human cancer-associated genes. Genome Res 2002;12:1846–1853.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Zhang K, Qin ZS, Liu JS, Chen T, Waterman MS, Sun F. Haplotype block partitioning and tag SNP selection using genotype data and their applications to association studies. Genome Res 2004;14:908–916.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Zhang K, Calabrese P, Nordborg M, Sun F. Haplotype block structure and its applications to association studies: Power and study designs. Am J Hum Genet 2002;71:1386–1394.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Weale ME, Depondt C, Macdonald SJ, et al. Selection and evaluation of tagging SNPs in the neuronal-sodium-channel gene SCN1A: Implications for linkage-disequilibrium gene mapping. Am J Hum Genet 2003;73:551–565.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Stram DO, Haiman CA, Hirschhorn JN, et al. Choosing haplotype-tagging SNPS based on unphased genotype data using a preliminary sample of unrelated subjects with an example from the Multiethnic Cohort Study. Hum Hered 2003;55:27–36.

    Article  PubMed  Google Scholar 

  52. Chapman JM, Cooper JD, Todd JA, Clayton DG. Detecting disease associations due to linkage disequilibrium using haplotype tags: A class of tests and the determinants of statistical power. Hum Hered 2003;56:18–31.

    Article  PubMed  Google Scholar 

  53. Lowe CE, Cooper JD, Chapman JM, et al. Cost-effective analysis of candidate genes using htSNPs: A staged approach. Genes Immun 2004;5:301–305.

    Article  CAS  PubMed  Google Scholar 

  54. Byng MC, Whittaker JC, Cuthbert AP, Mathew CG, Lewis CM. SNP subset selection for genetic association studies. Ann Hum Genet 2003;67:543–556.

    Article  CAS  PubMed  Google Scholar 

  55. Lewontin RC. The interaction of selection and linkage. II. Optimum models. Genetics 1964;50:757–782.

    CAS  PubMed  PubMed Central  Google Scholar 

  56. Morton NE, Zhang W, Taillon-Miller P, Ennis S, Kwok PY, Collins A. The optimal measure of allelic association. Proc Natl Acad Sci U S A 2001;98:5217–5221.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Gabriel SB, Schaffner SF, Nguyen H, et al. The structure of haplotype blocks in the human genome. Science 2002;296:2225–2229.

    Article  CAS  PubMed  Google Scholar 

  58. Jor de LB. Linkage disequilibrium and the search for complex disease genes. Genome Res 2000;10:1435–1444.

    Article  Google Scholar 

  59. Coffey CS, Hebert PR, Krumholz HM, Morgan TM, Williams SM, Moore JH. Reporting of model validation procedures in human studies of genetic interactions. Nutrition 2004;20:69–73.

    Article  CAS  PubMed  Google Scholar 

  60. Williams SM, Haines JL, Moore JH. The use of animal models in the study of complex disease: All else is never equal or why do so many human studies fail to replicate animal findings? Bioessays 2004;26:170–179.

    Article  CAS  PubMed  Google Scholar 

  61. Khoury MJ, Beaty TH, Cohen BH, eds. Fundamentals of epidemiology. New York: Oxford University Press, 1993:82–123.

    Google Scholar 

  62. Heinonen OP, Slone D, Monson RR, Hook EB, Shapiro S. Cardiovascular birth defects and antenatal exposure to female sex hormones. N Engl J Med 1977;296:67–70.

    Article  CAS  PubMed  Google Scholar 

  63. Adams MM, Finley S, Hansen H, et al. Utilization of prenatal genetic diagnosis in women 35 years of age and older in the United States, 1677 to 1978. Am J Obstet Gynecol 1981;139:673–677.

    Article  CAS  PubMed  Google Scholar 

  64. Weinberg CR, Umbach DM. A hybrid design for studying genetic influences on risk of diseases with onset early in life. Am J Hum Genet 2005;77:627–636.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Zheng G, Tian X. Robust trend tests for genetic association using matched case-control design. Stat Med 2006;25:3160–3173.

    Article  PubMed  Google Scholar 

  66. Rothman KJ. Modern epidemiology. Boston: Little Brown, 1986.

    Google Scholar 

  67. Freedman ML, Reich D, Penney KL, et al. Assessing the impact of population stratification on genetic association studies. Nat Genet 2004;36:388–393.

    Article  CAS  PubMed  Google Scholar 

  68. Pritchard JK, Donnelly P. Case-control studies of association in structured or admixed populations. Theor Popul Biol 2001;60:227–237.

    Article  CAS  PubMed  Google Scholar 

  69. Pritchard JK, et al. Association mapping in structured populations. Am J Hum Genet 2000;1:170–181.

    Article  Google Scholar 

  70. Devlin B, Roeder K. Genomic control for association studies. Biometrics 1999;55:997–1004.

    Article  CAS  PubMed  Google Scholar 

  71. Pritchard JK, Stephens M, Donnelly P. Inference of population structure using multilocus genotype data. Genetics 2000;2:945–959.

    Google Scholar 

  72. Thorsen P, Schendel DE, Deshpan de AD, Vogel I, Dudley DJ, Olsen J. Identification of biological/biochemical marker(s) for preterm delivery. Paediatr Perinat Epidemiol 2001;15:90–103.

    Article  PubMed  Google Scholar 

  73. Moore JH, Williams SM. Traversing the conceptual divide between biological and statistical epistasis: Systems biology and a more modern synthesis. Bioessays 2005;27:637–646.

    Article  CAS  PubMed  Google Scholar 

  74. Moore JH. The ubiquitous nature of epistasis in determining susceptibility to common human diseases. Hum Hered 2003;56:73–82.

    Article  PubMed  Google Scholar 

  75. Wolf JF. Gene interactions from maternal effects. Evolution Int J Org Evolution 2000;54:1882–1898.

    Article  CAS  Google Scholar 

  76. Cheverud JM, Routman EJ. Epistasis and its contribution to genetic variance components. Genetics 1995;139:1455–1461.

    CAS  PubMed  PubMed Central  Google Scholar 

  77. Tong AH, Lesage G, Bader GD, et al. Global mapping of the yeast genetic interaction network. Science 2004;303:808–813.

    Article  CAS  PubMed  Google Scholar 

  78. Menon R, Velez DV, Simhan H, et al. Multilocus interactions at maternal tumor necrosis factor-alpha, tumor necrosis factor receptors, interleukin-6 and interleukin-6 receptor genes predict spontaneous preterm labor in European-American women. Am J Obstet Gynecol 2006;194:1616–1624.

    Article  CAS  PubMed  Google Scholar 

  79. Lee JE, Lee SJ, Namkoong SE, et al. Gene-gene and gene-environmental interactions of p53, p21, and IRF-1 polymorphisms in Korean women with cervix cancer. Int J Gynecol Cancer 2004;14:118–125.

    CAS  PubMed  Google Scholar 

  80. Talmud PJ, Stephens JW. Lipoprotein lipase gene variants and the effect of environmental factors on cardiovascular disease risk. Diabetes Obes Metab 2004;6:1–7.

    Article  CAS  PubMed  Google Scholar 

  81. Romero R, Chaiworapongsa T, Kuivaniemi H, Tromp G. Bacterial vaginosis, the inflammatory response and the risk of preterm birth: A role for genetic epidemiology in the prevention of preterm birth. Am J Obstet Gynecol 2004;190:1509–1519.

    Article  PubMed  Google Scholar 

  82. Newton-Cheh C, Hirschhorn JN. Genetic association studies of complex traits: Design and analysis issues. Mutat Res 2005;573:54–69.

    Article  CAS  PubMed  Google Scholar 

  83. Suh Y, Vijg J. SNP discovery in associating genetic variation with human disease phenotypes. Mutat Res 2005;573:41–53.

    Article  CAS  PubMed  Google Scholar 

  84. Hirschhorn JN. Genetic approaches to studying common diseases and complex traits. Pediatr Res 2005;57:74R–77R.

    Article  PubMed  Google Scholar 

  85. Hirschhorn JN, Lohmueller K, Byrne E, Hirschhorn K. A comprehensive review of genetic association studies. Genet Med 2002;4:45–61.

    Article  CAS  PubMed  Google Scholar 

  86. Cordell HJ, Clayton DG. Genetic association studies. Lancet 2005;366:1121–1131.

    Article  PubMed  Google Scholar 

  87. Belmont JW, Leal SM. Complex phenotypes and complex genetics: An introduction to genetic studies of complex traits. Curr Atheroscler Rep 2005;7:180–187.

    Article  CAS  PubMed  Google Scholar 

  88. Hirschhorn JN, Daly MJ. Genome-wide association studies for common diseases and complex traits. Nat Rev Genet 2005;6:95–108.

    Article  CAS  PubMed  Google Scholar 

  89. Hardy GH. Mendalian proportions in a mixed population. Science 1908;28:49–50.

    Article  CAS  PubMed  Google Scholar 

  90. Hartl DL, Clark AG. Principles of population genetics. 3rd ed. Sunderland, MA: Sinauer Associates; 1997.

    Google Scholar 

  91. Nielsen DM, Ehm MG, Weir BS. Detecting marker-disease association by testing for Hardy-Weinberg disequilibrium at a marker locus. Am J Hum Genet 1998;63:1531–1540.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  92. Yong Zou G, Donner A. The merits of testing Hardy-Weinberg equilibrium in the analysis of unmatched case-control data: A cautionary note. Ann Hum Genet 2006;70:923–933.

    Article  Google Scholar 

  93. Rousset F, Raymond M. Testing heterozygote excess and deficiency. Genetics 1995;140:1413–1419.

    CAS  PubMed  PubMed Central  Google Scholar 

  94. Hosmer DW, Lemeshow S. Applied logistic regression. 2nd ed. New York: Wiley; 2000.

    Book  Google Scholar 

  95. Ott J, Hoh J. Set association analysis of SNP case-control and microarray data. J Comput Biol 2003;10:569–574.

    Article  CAS  PubMed  Google Scholar 

  96. Cook NR, Zee RY, Ridker PM. Tree and spline based association analysis of gene-gene interaction models for ischemic stroke. Stat Med 2004;23:1439–1453.

    Article  PubMed  Google Scholar 

  97. Nelson MR, Kardia SL, Ferrell RE, Sing CF. A combinatorial partitioning method to identify multilocus genotypic partitions that predict quantitative trait variation. Genome Res 2001;11:458–470.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  98. Moore J, Lamb J, Brown N, Vaughan D. A comparison of combinatorial partitioning and linear regression for the detection of epistatic effects of the ACE I/D and PAI-1 4G/5G polymorphisms on plasma PAI-1 levels. Clin Genet 2002;62:74–79.

    Article  CAS  PubMed  Google Scholar 

  99. Culverhouse R, Klein T, Shannon W. Detecting epistatic interactions contributing to quantitative traits. Genet Epidemiol 2004;27:141–152.

    Article  PubMed  Google Scholar 

  100. Hahn LW, Moore JH. Ideal discrimination of discrete clinical endpoints using multilocus genotypes. In Silico Biol 2004;4:183–194.

    CAS  PubMed  Google Scholar 

  101. Moore JH. Computational analysis of gene-gene interactions using multifactor dimensionality reduction. Expert Rev Mol Diagn 2004;4:795–803.

    Article  CAS  PubMed  Google Scholar 

  102. Ritchie MD, Hahn LW, Moore JH. Power of multifactor demensionality reduction for detecting gene-gene interactions in the presence of genotyping error, missing data, phenocopy, and genetic heterogeneity. Genet Epidemiol 2003;24:150–157.

    Article  PubMed  Google Scholar 

  103. Hahn LW, Ritchie MD, Moore JH. Multifactor dimensionality reduction software for detecting gene-gene and gene-environment interactions. Bioinformatics 2003;12;19:376–376.

    Article  CAS  Google Scholar 

  104. Cho YM, Ritchie MD, Moore JH, et al. Multifactor-dimensionality reduction shows a two-locus interaction associated with type 2 diabetes mellitus. Diabetologia 2004;47:549–554.

    Article  CAS  PubMed  Google Scholar 

  105. Coffey CS, Hebert PR, Ritchie MD, et al. An application of conditional logistic regression and multifactor dimensionality reduction for detecting gene-gene interactions or risk of myocardial infarction: The importance of model validation. BMC Bioinformatics 2004;5:49–49.

    Article  PubMed  PubMed Central  Google Scholar 

  106. Williams SM, Ritchie MD, Phillips JA 3rd, et al. Multilocus analysis of hypertension: A hierarchical approach. Hum Hered 2004;57:28–38.

    Article  PubMed  Google Scholar 

  107. Moore JH, Williams SM. New strategies for identifying gene-gene interactions in hypertension. Ann Med 2002;34:88–95.

    Article  CAS  PubMed  Google Scholar 

  108. Millstein J, Conti DV, Gilliland FD, Gauderman WJ. A testing framework for identifying susceptibility genes in the presence of epistasis. Am J Hum Genet 2006;78:15–27.

    Article  CAS  PubMed  Google Scholar 

  109. Motsinger AA, Lee SL, Mellick G, Ritchie MD. GPNN: Power studies and applications of a neural network method for detecting gene-gene interactions in studies of human disease. BMC Bioinformatics 2006;7:39–39.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  110. Hoh J, Ott J. Mathematical multi-locus approaches to localizing complex human trait genes. Nat Rev Genet 2003;4:701–709.

    Article  CAS  PubMed  Google Scholar 

  111. Concato J, Feinstein AR, Holford TR. The risk of determining risk with multivariable models. Ann Intern Med 1993;118:201–210.

    Article  CAS  PubMed  Google Scholar 

  112. Peduzzi P, Concato J, Kemper E, Holford TR, Feinstein AR. A simulation study of the number of events per variable in logistic regression analysis. J Clin Epidemiol 1996;49:1373–1379.

    Article  CAS  PubMed  Google Scholar 

  113. Bellman RE. Adaptive control processes: A guided tour. Princeton, NJ: Princeton University Press, 1961.

    Book  Google Scholar 

  114. Thornton-Wells TA, Moore JH, Haines JL. Genetics, statistics and human disease: Analytical retooling for complexity. Trends Genet 2004;20:640–647.

    Article  CAS  PubMed  Google Scholar 

  115. Millstein J, Conti DV, Gilliland FD, Gauderman WJ. A testing framework for identifying susceptibility genes in the presence of epistasis. Am J Hum Genet 2006;78:15–27.

    Article  CAS  PubMed  Google Scholar 

  116. Wan Y, Cohen J, Guerra R. A permutation test for the robust sib-pair linkage method. Ann Hum Genet 1997;7618:79–87.

    Google Scholar 

  117. Ritchie MD, Hahn LW, Moore JH. Power of multifactor dimensionality reduction for detecting gene-gene interactions in the presence of genotyping error, missing data, phenocopy, and genetic heterogeneity. Genet Epidemiol 2003;24:150–157.

    Article  PubMed  Google Scholar 

  118. Sanada H, Yatabe J, Midorikawa S, et al. Single-nucleotide polymorphisms for diagnosis of salt-sensitive hypertension. Clin Chem 2006;52:352–360.

    Article  CAS  PubMed  Google Scholar 

  119. Morgan TM, Coffey CS, Krumholz HM. Overestimation of genetic risks owing to small sample sizes in cardiovascular studies. Clin Genet 2003;64:7–17.

    Article  CAS  PubMed  Google Scholar 

  120. Weinberg CR, Wilcox AJ, Lie RT. A log-linear approach to case-parent-triad data: Assessing effects of disease genes that act either directly or through maternal effects and that may be subject to parental imprinting. Am J Hum Genet 1998;62:969–978.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  121. Weinberg CR, Wilcox AJ. Re: “Distinguishing the effects of maternal and offspring genes through studies of ‘case-parent triads’” and “a new method for estimating the risk ratio in studies using case-parental control design.” Am J Epidemiol 1999;150:428–429.

    Article  CAS  PubMed  Google Scholar 

  122. Wilcox AJ, Weinberg CR, Lie RT. Distinguishing the effects of maternal and offspring genes through studies of “case-parent triads.” Am J Epidemiol 1998;148:893–901.

    Article  CAS  PubMed  Google Scholar 

  123. Devlin B, Roeder K. Genomic control for association studies. Biometrics 1999;55:997–1004.

    Article  CAS  PubMed  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Ramkumar Menon MS.

Additional information

The authors would like to thank Jake McCauley for his cntical reading of the manuscript and his constructive comments. We also thank Thrasher Research Funds, UT.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Menon, R., Fortunato, S.J., Thorsen, P. et al. Genetic Associations in Preterm Birth: A Primer of Marker Selection, Study Design, and Data Analysis. Reprod. Sci. 13, 531–541 (2006). https://doi.org/10.1016/j.jsgi.2006.09.006

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1016/j.jsgi.2006.09.006

Key words

Navigation