Abstract
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.
Highlights
Genomewide association studies GWASs have successfully identified many genetic variants consistently associated with human diseases or other traits
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
Our concern here is on correlations among individuals, which are “the central piece of information” 1 in detection and characterization of gene-trait association. Consideration of these correlations has traditionally limited to family data whose critical role in genetic epidemiological study ranges from familial aggregation, segregation, linkage to association 2, and special attention is required in the analysis compared to unrelated individuals from a population
Summary
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. We describe mixed models as a coherent theoretical framework to accommodate correlations for various types of outcomes in relation to many sources of variations. Mixed models will play an important role in practical analysis of data on both families and unrelated individuals when whole genome information is available
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