Detection of familial aggregation of a disease is important for studying possible genetic and environmental factors contributing to disease etiology. Accurate quantification of familial aggregation can provide guidance for subsequent, more sophisticated genetic studies. This article presents a statistical model and method for detecting both inter- and intra-class aggregation of a binary trait with family data. The method used here is based on the logistic regression model which incorporates effects of individual covariates while measuring familial aggregation of risk as the odds ratios among classes of relatives. An estimation equation approach is presented where the joint distribution of binary traits among family members need not be fully specified. Data from a genetic epidemiologic study on liver cancer in Shanghai are analyzed for illustration, and reveal strong aggregation of risk even after adjusting for covariates. Effects of non-random sampling and ascertainment bias are also discussed.
Beschreibung
Measuring familial aggregation by using odds-ratio regression models. - PubMed - NCBI
%0 Journal Article
%1 Liang:1991:Genet-Epidemiol:1806405
%A Liang, K Y
%A Beaty, T H
%D 1991
%J Genet Epidemiol
%K CorrelatedData statistics
%N 6
%P 361-370
%R 10.1002/gepi.1370080602
%T Measuring familial aggregation by using odds-ratio regression models
%U https://www.ncbi.nlm.nih.gov/pubmed/1806405
%V 8
%X Detection of familial aggregation of a disease is important for studying possible genetic and environmental factors contributing to disease etiology. Accurate quantification of familial aggregation can provide guidance for subsequent, more sophisticated genetic studies. This article presents a statistical model and method for detecting both inter- and intra-class aggregation of a binary trait with family data. The method used here is based on the logistic regression model which incorporates effects of individual covariates while measuring familial aggregation of risk as the odds ratios among classes of relatives. An estimation equation approach is presented where the joint distribution of binary traits among family members need not be fully specified. Data from a genetic epidemiologic study on liver cancer in Shanghai are analyzed for illustration, and reveal strong aggregation of risk even after adjusting for covariates. Effects of non-random sampling and ascertainment bias are also discussed.
@article{Liang:1991:Genet-Epidemiol:1806405,
abstract = {Detection of familial aggregation of a disease is important for studying possible genetic and environmental factors contributing to disease etiology. Accurate quantification of familial aggregation can provide guidance for subsequent, more sophisticated genetic studies. This article presents a statistical model and method for detecting both inter- and intra-class aggregation of a binary trait with family data. The method used here is based on the logistic regression model which incorporates effects of individual covariates while measuring familial aggregation of risk as the odds ratios among classes of relatives. An estimation equation approach is presented where the joint distribution of binary traits among family members need not be fully specified. Data from a genetic epidemiologic study on liver cancer in Shanghai are analyzed for illustration, and reveal strong aggregation of risk even after adjusting for covariates. Effects of non-random sampling and ascertainment bias are also discussed.},
added-at = {2019-11-20T09:55:11.000+0100},
author = {Liang, K Y and Beaty, T H},
biburl = {https://www.bibsonomy.org/bibtex/26b13498ff47c0f1ce571ac20f26993ee/jkd},
description = {Measuring familial aggregation by using odds-ratio regression models. - PubMed - NCBI},
doi = {10.1002/gepi.1370080602},
interhash = {478a56e2badcf09feb5873ff637661b4},
intrahash = {6b13498ff47c0f1ce571ac20f26993ee},
journal = {Genet Epidemiol},
keywords = {CorrelatedData statistics},
number = 6,
pages = {361-370},
pmid = {1806405},
timestamp = {2019-11-20T09:55:11.000+0100},
title = {Measuring familial aggregation by using odds-ratio regression models},
url = {https://www.ncbi.nlm.nih.gov/pubmed/1806405},
volume = 8,
year = 1991
}