Skip to main content

Detecting Familial Aggregation

  • Protocol
  • First Online:

Part of the book series: Methods in Molecular Biology ((MIMB,volume 850))

Abstract

Beyond calculating parameter estimates to characterize the distribution of genetic features of populations (frequencies of mutations in various regions of the genome, allele frequencies, measures of Hardy–Weinberg disequilibrium), genetic epidemiology aims to identify correlations between genetic variants and phenotypic traits, with considerable emphasis placed on finding genetic variants that increase susceptibility to disease and disease-related traits. However, determining correlation alone does not suffice: genetic variants common in an isolated ethnic group with a high burden of a given disease may show relatively high correlation with disease but, as markers of ethnicity, these may not necessarily have any functional role in disease. To establish a causal relationship between genetic variants and disease (or disease-related traits), proper statistical analyses of human data must incorporate epidemiologic approaches to examining sets of families or unrelated individuals with information available on individuals’ disease status or related traits.

Through different analytical approaches, statistical analysis of human data can answer several important questions about the relationship between genes and disease:

  1. 1.

    Does the disease tend to cluster in families more than expected by chance alone?

  2. 2.

    Does the disease appear to follow a particular genetic model of transmission in families?

  3. 3.

    Do variants at a particular genetic marker tend to cosegregate with disease in families?

  4. 4.

    Do specific genetic markers tend to be carried more frequently by those with disease than by those without, in a given population (or across families)?

The first question can be examined using studies of familial aggregation or correlation. An ancillary question: “how much of the susceptibility to disease (or variation in disease-related traits) might be accounted for by genetic factors?” is typically answered by estimating heritability, the proportion of disease susceptibility or trait variation attributable to genetics. The second question can be formally tested using pedigrees for which disease affection status or trait values are available through a modeling approach known as segregation analysis. The third question can be answered with data on pedigrees with affected members and genotype information at markers of interest, using linkage analysis. The fourth question is answerable using genotype information at markers on unrelated affected and unaffected individuals and/or families with affected and unaffected members. All of these questions can also be explored for quantitative (or continuously distributed) traits by examining variation in trait values between family members or between unrelated individuals. While each of these questions and the analytical approaches for answering them is explored extensively in subsequent chapters (heritability in Chapters 9 and 10, segregation in Chapter 12, linkage in Chapters 13–17, and association in Chapters 18–21 and 23), this chapter focuses on statistical methods to answer questions of familial aggregation.

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

Buying options

Protocol
USD   49.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   89.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   119.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Springer Nature is developing a new tool to find and evaluate Protocols. Learn more

References

  1. del Junco D, et al (1984) The familial aggregation of rheumatoid arthritis and its relationship to the HLA-DR4 association. Am J Epidemiol 119: 813–829

    PubMed  Google Scholar 

  2. Nestadt G, et al (2000) A family study of obsessive-compulsive disorder. Arch Gen Psychiatry 57: 358–363

    Article  PubMed  CAS  Google Scholar 

  3. Peretz I, Cummings S, Dube MP (2007) The genetics of congenital amusia (tone deafness): a family-aggregation study. Am J Hum Genet 81: 582–588

    Article  PubMed  CAS  Google Scholar 

  4. Rice TK (2008) Familial resemblance and heritability. Adv Genet 60: 35–49

    Article  PubMed  Google Scholar 

  5. Visscher PM, Hill WG, Wray NR (2008) Heritability in the genomics era--concepts and misconceptions. Nat Rev Genet 9: 255–266

    Article  PubMed  CAS  Google Scholar 

  6. Naldi L, et al (2001) Family history of psoriasis, stressful life events, and recent infectious disease are risk factors for a first episode of acute guttate psoriasis: results of a case-control study. J Am Acad Dermatol 44: 433–438

    Article  PubMed  CAS  Google Scholar 

  7. Mantel N, Haenszel W (1959) Statistical aspects of the analysis of data from retrospective studies of disease. J Natl Cancer Inst 22: 719–748

    PubMed  CAS  Google Scholar 

  8. Ponz de Leon M, et al (1989) Familial aggregation of tumors in the three-year experience of a population-based colorectal cancer registry. Cancer Res 49: 4344–4348

    PubMed  CAS  Google Scholar 

  9. Hopper JL, Hannah MC, Mathews JD (1984) Genetic Analysis Workshop II: pedigree analysis of a binary trait without assuming an underlying liability. Genet Epidemiol 1: 183–188

    Article  PubMed  CAS  Google Scholar 

  10. Connolly MA, Liang KY (1988) Conditional logistic regression models for correlated binary data. Biometrika 75: 501–506

    Article  Google Scholar 

  11. Hopper JL, Derrick PL (1986) A log-linear model for binary pedigree data. Genet Epidemiol Suppl 1: 73–82

    Article  PubMed  CAS  Google Scholar 

  12. Khoury MJ, Beaty TH, Cohen BH (1993) Fundamentals of genetic epidemiology. Monographs in epidemiology and biostatics v. 19. Oxford University Press: New York

    Google Scholar 

  13. Liang KY, Beaty TH (1991) Measuring familial aggregation by using odds-ratio regression models. Genet Epidemiol 8: 361–370

    Article  PubMed  Google Scholar 

  14. Bishop YMM, Fienberg SE, Holland PW (1975) Discrete multivariate analysis : theory and practice. MIT Press: Cambridge, MA

    Google Scholar 

  15. Cohen BH (1980) Chronic obstructive pulmonary disease: a challenge in genetic epidemiology. Am J Epidemiol 112: 274–288

    PubMed  CAS  Google Scholar 

  16. Schwartz AG, Boehnke M, Moll PP (1988) Family risk index as a measure of familial heterogeneity of cancer risk. A population-based study in metropolitan Detroit. Am J Epidemiol 128: 524–535

    PubMed  CAS  Google Scholar 

  17. Breslow NE, Day NE (1980) Statistical methods in cancer research. IARC scientific publications. International Agency for Research on Cancer: Lyon

    Google Scholar 

  18. Beaty TH, et al (1997) Testing for interaction between maternal smoking and TGFA genotype among oral cleft cases born in Maryland 1992–1996. Cleft Palate Craniofac J 34: 447–454

    Article  PubMed  CAS  Google Scholar 

  19. Seibold MA, Schwartz DA (2010) The Lung: The Natural Boundary Between Nature And Nurture. Annu Rev Physiol 73: 457–478

    Article  Google Scholar 

  20. Garantziotis S, Schwartz DA (2010) Ecogenomics of respiratory diseases of public health significance. Annu Rev Public Health. 31: 37–51.

    Article  PubMed  Google Scholar 

  21. Demeo DL, et al (2007) Determinants of airflow obstruction in severe alpha-1-antitrypsin deficiency. Thorax 62: 806–813

    Article  PubMed  Google Scholar 

  22. Pare G, et al (2010) On the use of variance per genotype as a tool to identify quantitative trait interaction effects: a report from the Women’s Genome Health Study. PLoS Genet 6: e1000981

    Article  PubMed  Google Scholar 

  23. Murcray CE, Lewinger JP, Gauderman WJ (2009) Gene-environment interaction in genome-wide association studies. Am J Epidemiol 169: 219–226.

    Article  PubMed  Google Scholar 

  24. Hwang SJ, et al (1994) Minimum sample size estimation to detect gene-environment interaction in case-control designs. Am J Epidemiol 140: 1029–1037

    PubMed  CAS  Google Scholar 

  25. Foppa I, Spiegelman D (1997) Power and sample size calculations for case-control studies of gene-environment interactions with a polytomous exposure variable. Am J Epidemiol 146: 596–604

    PubMed  CAS  Google Scholar 

  26. Garcia-Closas M, Lubin JH (1999) Power and sample size calculations in case-control studies of gene-environment interactions: comments on different approaches. Am J Epidemiol 149: 689–692

    PubMed  CAS  Google Scholar 

  27. Sturmer T, Brenner H (2000) Potential gain in efficiency and power to detect gene-environment interactions by matching in case-control studies. Genet Epidemiol 18: 63–80

    Article  PubMed  CAS  Google Scholar 

  28. Gauderman, WJ (2002) Sample size requirements for association studies of gene-gene interaction. Am J Epidemiol 155: 478–484

    Article  PubMed  Google Scholar 

  29. Claus EB, Risch NJ, Thompson WD (1990) Age at onset as an indicator of familial risk of breast cancer. Am J Epidemiol 131: 961–972

    PubMed  CAS  Google Scholar 

  30. Mettlin C, et al (1990) The association of age and familial risk in a case-control study of breast cancer. Am J Epidemiol 131: 973–983

    PubMed  CAS  Google Scholar 

  31. Pulver AE, Liang KY (1991) Estimating effects of proband characteristics on familial risk: II. The association between age at onset and familial risk in the Maryland schizophrenia sample. Genet Epidemiol 8: 339–350

    Article  PubMed  CAS  Google Scholar 

  32. Liang KY, Zeger SL (1986) Longitudinal data analysis using generalized linear models. Biometrika 73: 13–22

    Article  Google Scholar 

  33. Liang KY (1987) Extended Mantel-Haenszel estimating procedure for multivariate logistic regression models. Biometrics 43: 289–299

    Article  PubMed  CAS  Google Scholar 

  34. Liang KY, Pulver AE (1996) Analysis of case-control/family sampling design. Genet Epidemiol 13: 253–270

    Article  PubMed  CAS  Google Scholar 

  35. Hsu L, Zhao LP (1996) Assessing familial aggregation of age at onset, by using estimating equations, with application to breast cancer. Am J Hum Genet 58: 1057–1071

    PubMed  CAS  Google Scholar 

  36. Li H, Yang P, Schwartz AG (1998) Analysis of age of onset data from case-control family studies. Biometrics 54: 1030–1039

    Article  PubMed  CAS  Google Scholar 

  37. Liang KY (1991) Estimating effects of probands’ characteristics on familial risk: I. Adjustment for censoring and correlated ages at onset. Genet Epidemiol 8: 329–338

    Article  PubMed  CAS  Google Scholar 

  38. Liang KY, Zeger SL (1993) Regression analysis for correlated data. Annu Rev Public Health 14: 43–68

    Article  PubMed  CAS  Google Scholar 

  39. Maestri NE, et al (1988) Assessing familial aggregation of congenital cardiovascular malformations in case-control studies. Genet Epidemiol 5: 343–354

    Article  PubMed  CAS  Google Scholar 

  40. Khoury MJ, Beaty TH, Liang KY (1988) Can familial aggregation of disease be explained by familial aggregation of environmental risk factors? Am J Epidemiol 127: 674–683

    PubMed  CAS  Google Scholar 

  41. Thomas DC (2004) Statistical methods in genetic epidemiology. Oxford University Press: New York

    Google Scholar 

  42. Bratt O, et al (2010) Effects of prostate-specific antigen testing on familial prostate cancer risk estimates. J Natl Cancer Inst 102: 1336–1343

    Article  PubMed  CAS  Google Scholar 

  43. Beaty TH, et al (1997) Analyzing sibship correlations in birth weight using large sibships from Norway. Genet Epidemiol, 14: 423–433

    Article  PubMed  CAS  Google Scholar 

  44. Wedderburn RWM. (1974) Quasi-likelihood functions, generalized linear models, and the Gauss—Newton method. Biometrika 61: 439–447

    Google Scholar 

  45. Hanrahan LP, et al (1990) SMRFIT: a Statistical Analysis System (SAS) program for standardized mortality ratio analyses and Poisson regression model fits in community disease cluster investigations. Am J Epidemiol, 1990. 132(Suppl): S116-122

    PubMed  CAS  Google Scholar 

  46. Tokuhata GK, Lilienfeld AM (1963) Familial aggregation of lung cancer in humans. J Natl Cancer Inst 30: 289–312

    PubMed  CAS  Google Scholar 

  47. Feinleib M, et al (1977) The NHLBI twin study of cardiovascular disease risk factors: methodology and summary of results. Am J Epidemiol 106: 284–285

    PubMed  CAS  Google Scholar 

  48. Khoury MJ, Erickson JD, James LM (1982) Etiologic heterogeneity of neural tube defects: clues from epidemiology. Am J Epidemiol 115: 538–548

    PubMed  CAS  Google Scholar 

  49. Khoury MJ, Erickson JD, James LM (1982) Etiologic heterogeneity of neural tube defects. II. Clues from family studies. Am J Hum Genet 34: 980–987

    PubMed  CAS  Google Scholar 

  50. ten Kate LP, et al (1982) Familial aggregation of coronary heart disease and its relation to known genetic risk factors. Am J Cardiol 50: 945–953

    Article  PubMed  Google Scholar 

  51. Sattin RW, et al (1985) Family history and the risk of breast cancer. JAMA 253: 1908–1913

    Article  PubMed  CAS  Google Scholar 

  52. Nielsen HE, et al (1987) Risk factors and sib correlation in physiological neonatal jaundice. Acta Paediatr Scand 76: 504–511

    Article  PubMed  CAS  Google Scholar 

  53. Beaty TH, et al (1988) Effect of maternal and infant covariates on sibship correlation in birth weight. Genet Epidemiol 5: 241–253

    Article  PubMed  CAS  Google Scholar 

  54. Linet MS, et al (1989) Familial cancer history and chronic lymphocytic leukemia. A case-control study. Am J Epidemiol 130: 655–664

    PubMed  CAS  Google Scholar 

  55. Lin JP, et al.(1998) Familial clustering of rheumatoid arthritis with other autoimmune diseases. Hum Genet 103: 475–482

    Article  PubMed  CAS  Google Scholar 

  56. Criswell LA, et al (2005) Analysis of families in the multiple autoimmune disease genetics consortium (MADGC) collection: the PTPN22 620W allele associates with multiple autoimmune phenotypes. Am J Hum Genet 76: 561–571

    Article  PubMed  CAS  Google Scholar 

  57. Beaty TH, et al (2006) Analysis of candidate genes on chromosome 2 in oral cleft case-parent trios from three populations. Hum Genet 120: 501–518

    Article  PubMed  CAS  Google Scholar 

  58. Raynor LA, et al (2009) Familial aggregation of age-related hearing loss in an epidemiological study of older adults. Am J Audiol 18: 114–118

    Article  PubMed  Google Scholar 

  59. Krogh C, et al (2010) Familial aggregation and heritability of pyloric stenosis. JAMA 303: 2393–2399

    Article  PubMed  CAS  Google Scholar 

  60. Xiong L, et al (2010) Family study of restless legs syndrome in Quebec, Canada: clinical characterization of 671 familial cases. Arch Neurol 67: 617–622

    Article  PubMed  Google Scholar 

  61. Saito YA, et al (2010) Familial aggregation of irritable bowel syndrome: a family case-control study. Am J Gastroenterol 105: 833–841

    Article  PubMed  Google Scholar 

  62. Lichtenstein P, et al (2010) The genetics of autism spectrum disorders and related neuropsychiatric disorders in childhood. Am J Psychiatry 167: 1357–1363

    Article  PubMed  Google Scholar 

  63. Petersen L, Andersen PK, Sorensen TI (2010) Genetic influences on incidence and case-fatality of infectious disease. PLoS One 5: e10603

    Article  PubMed  Google Scholar 

  64. Liang KY, Beaty TH. (2000) Statistical designs for familial aggregation. Stat Methods Med Res 9: 543–562

    Article  PubMed  CAS  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Adam C. Naj .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2012 Springer Science+Business Media, LLC

About this protocol

Cite this protocol

Naj, A.C., Park, Y.S., Beaty, T.H. (2012). Detecting Familial Aggregation. In: Elston, R., Satagopan, J., Sun, S. (eds) Statistical Human Genetics. Methods in Molecular Biology, vol 850. Humana Press. https://doi.org/10.1007/978-1-61779-555-8_8

Download citation

  • DOI: https://doi.org/10.1007/978-1-61779-555-8_8

  • Published:

  • Publisher Name: Humana Press

  • Print ISBN: 978-1-61779-554-1

  • Online ISBN: 978-1-61779-555-8

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics