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Estimation of genetic and environmental factors for binary traits using family data
Author(s) -
Pawitan Y.,
Reilly M.,
Nilsson E.,
Cnattingius S.,
Lichtenstein P.
Publication year - 2004
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.1603
Subject(s) - inference , computer science , binary data , sample size determination , linear model , generalized linear mixed model , mixed model , variance (accounting) , statistical inference , binary number , sample (material) , statistics , econometrics , data mining , machine learning , mathematics , artificial intelligence , chemistry , arithmetic , accounting , chromatography , business
While the family‐based analysis of genetic and environmental contributions to continuous or Gaussian traits is now straightforward using the linear mixed models approach, the corresponding analysis of complex binary traits is still rather limited. In the latter we usually rely on twin studies or pairs of relatives, but these studies often have limited sample size or have difficulties in dealing with the dependence between the pairs. Direct analysis of extended family data can potentially overcome these limitations. In this paper, we will describe various genetic models that can be analysed using an extended family structure. We use the generalized linear mixed model to deal with the family structure and likelihood‐based methodology for parameter inference. The method is completely general, accommodating arbitrary family structures and incomplete data. We illustrate the methodology in great detail using the Swedish birth registry data on pre‐eclampsia, a hypertensive condition induced by pregnancy. The statistical challenges include the specification of sensible models that contain a relatively large number of variance components compared to standard mixed models. In our illustration the models will account for maternal or foetal genetic effects, environmental effects, or a combination of these and we show how these effects can be readily estimated using family data. Copyright © 2004 John Wiley & Sons, Ltd.