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Detecting Familial Aggregation

  • Adam C. Naj
  • Terri H. Beaty
Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 1666)

Abstract

In addition to characterizing the distribution of genetic features of populations (mutation and allele frequencies; measures of Hardy–Weinberg equilibrium), genetic epidemiology and statistical genetics aim to explore and define the role of genomic variation in risk of disease or variation in traits of interest. To facilitate this kind of exploration, genetic epidemiology and statistical genetics address a series of questions:
  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.

    Does variation at a particular genomic position tend to cosegregate with disease in families?

     
  4. 4.

    Do specific genetic variants tend to be carried more frequently by those with disease than by those without these variants 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 variance in a trait or in risk to a disease 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 genomic markers in pedigrees with affected members informative for linkage, where meiotic cross-over events are estimated or assessed. The fourth question is answerable using genotype data on genomic markers on unrelated affected and unaffected individuals and/or families with affected members 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  8 and  9; segregation in Chapter  12; linkage in Chapters  13 17; and association in Chapters  18 20), this chapter focuses on statistical methods to address questions of familial aggregation of qualitative phenotypes (e.g., disease status) or quantitative phenotypes.

While studies exploring genotype–phenotype correlations are arguably the most important and common type of statistical genetic study performed, these studies are performed under the assumption that genetic contributors at least partially explain risk of a disease or a trait of interest. This may not always be the case, especially with diseases or traits known to be strongly influenced by environmental factors. For this reason, before any of the last three questions described above can be answered, it is important to ask first whether the disease clusters among family members more than unrelated persons, as this constitutes evidence of a possible heritable contribution to disease, justifying the pursuit of studies answering the other questions. In this chapter, the underlying principles of familial aggregation studies are addressed to provide an understanding and set of analytical tools to help answer the question if diseases or traits of interest are likely to be heritable and therefore justify subsequent statistical genetic studies to identify specific genetic causes.

Key words

Family history Family history score Familial case–control Cochran–Mantel–Haenzel (CMH) Odds ratio Logistic regression Conditional logistic regression Familial gene–environment interaction Generalized estimating equations (GEE) Familial relative risk Familial recurrence risk Standardized incidence ratio (SIR) Standardized mortality ratio (SMR) 

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Copyright information

© Springer Science+Business Media LLC 2017

Authors and Affiliations

  1. 1.Department of Biostatistics, Epidemiology, and InformaticsUniversity of Pennsylvania Perelman School of MedicinePhiladelphiaUSA
  2. 2.Department of Pathology and Laboratory MedicineUniversity of Pennsylvania Perelman School of MedicinePhiladelphiaUSA
  3. 3.Center for Clinical Epidemiology and BiostatisticsUniversity of Pennsylvania Perelman School of MedicinePhiladelphiaUSA
  4. 4.Department of EpidemiologyJohns Hopkins University Bloomberg School of Public HealthBaltimoreUSA

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