Estimation of disease risk under bivariate models of multifactorial inheritance

Steven O. Moldin, John P. Rice, Paul Van Eerdewegh, Irving I. Gottesman, L. Erlenmeyer‐Kimling, Neil J. Risch

Research output: Contribution to journalArticlepeer-review

8 Scopus citations

Abstract

Adjunct consideration of both qualitative (affection status) and quantitative (correlated liability indicator) information to define a bivariate phenotype can increase considerably the accuracy and efficiency of disease risk estimation. A general approach for calculating morbid risks to offspring on the basis of parental affection status and an offspring quantitative trait is presented. We also describe two different bivariate models of multifactorial inheritance, as implemented in the computer programs POINTER and YPOINT, and make explicit their assumptions/constraints when estimating the within‐person and parent–offspring correlations necessary for calculation of morbid risks. We use psychometric family data on schizophrenia from the New York High‐Risk Project to estimate these correlations and illustrate our methods. Our results show that even when a trait is only moderately correlated with liability, incorporation of quantitative trait information can lead to resolution of a range of risk to offspring that is not possible through reliance on parental affection status alone. Bivariate models provide a useful methodology for incorporating quantitative indicators of liability in the investigation of genetically complex diseases.

Original languageEnglish (US)
Pages (from-to)371-386
Number of pages16
JournalGenetic epidemiology
Volume7
Issue number5
DOIs
StatePublished - 1990

Keywords

  • Minnesota Multiphasic Personality Inventory (MMPI)
  • bivariate phenotype
  • indicators (correlates) of liability
  • longitudinal high‐risk research
  • multivariate normal distribution
  • schizophrenia
  • segregation analysis

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