Detecting familial aggregation

Adam C. Naj, Yo Son Park, Terri H. Beaty

Research output: Chapter in Book/Report/Conference proceedingChapter

3 Scopus citations


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. Does the disease tend to cluster in families more than expected by chance alone? 2. Does the disease appear to follow a particular genetic model of transmission in families? 3. Do variants at a particular genetic marker tend to cosegregate with disease in families? 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.

Original languageEnglish (US)
Title of host publicationStatistical Human Genetics
Subtitle of host publicationMethods and Protocols
EditorsRobert Elston, Shuying Sun, Jaya Satagopan
Number of pages32
StatePublished - 2012

Publication series

NameMethods in Molecular Biology
ISSN (Print)1064-3745


  • Cochran-Mantel-Haenszel
  • Conditional logistic regression
  • Familial case-control
  • Familial gene-environment interaction
  • Familial recurrence risk
  • Familial relative risk
  • Family history
  • Family history score
  • Generalized estimating equations
  • Logistic regression
  • Odds ratio
  • Standardized incidence ratio
  • Standardized mortality ratio

ASJC Scopus subject areas

  • Molecular Biology
  • Genetics


Dive into the research topics of 'Detecting familial aggregation'. Together they form a unique fingerprint.

Cite this