Journal of Probability and Statistics
Volume 2012 (2012), Article ID 485174, 16 pages
Review Article

Mixed Modeling with Whole Genome Data

MRC Epidemiology Unit & Institute of Metabolic Science, Addrenbrooke's Hospital, Box 285, Hills Road, Cambridge CB2 0QQ, UK

Received 2 March 2012; Accepted 20 April 2012

Academic Editor: Yongzhao Shao

Copyright © 2012 Jing Hua Zhao and Jian'an Luan. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Objective. We consider the need for a modeling framework for related individuals and various sources of variations. The relationships could either be among relatives in families or among unrelated individuals in a general population with cryptic relatedness; both could be refined or derived with whole genome data. As with variations they can include oliogogenes, polygenes, single nucleotide polymorphism (SNP), and covariates. Methods. We describe mixed models as a coherent theoretical framework to accommodate correlations for various types of outcomes in relation to many sources of variations. The framework also extends to consortium meta-analysis involving both population-based and family-based studies. Results. Through examples we show that the framework can be furnished with general statistical packages whose great advantage lies in simplicity and exibility to study both genetic and environmental effects. Areas which require further work are also indicated. Conclusion. Mixed models will play an important role in practical analysis of data on both families and unrelated individuals when whole genome information is available.