Estimating effects of latent and measured genotypes in multilevel models

Multilevel modelling is a data analysis technique for analysing linear mode ls in samples with a hierarchical or clustered structure. Clustered data ar e often present in genetic research where family members may either be requ ired or serve a methodological purpose to study hereditary factors. These s amples imply a natural hierarchy because genetically related individuals ar e grouped within families. We first demonstrate the use of multilevel model ling to study latent genetic and environmental components of variance in ex tended families where subjects may be related as twins, full siblings, half siblings, or cousins. Next, measured genotypes are included to estimate lo cus effects. Because the model accounts for the clustering of observations by estimating a random intercept at the family level, it tests for genotype effects on the phenotype within families so that possible population strat ification effects cannot cause false positive results. Several extensions a re discussed such as testing for genotype-environment interactions, analysi ng different types of response scales, or tailoring the model to other samp le structures. To illustrate the approach we used birth weight data of 5562 children from 3643 fathers from 3186 mothers in 2873 extended families to which simulated genotypes of a hypothetical locus were added.