Many traits, such as body height and body weight, undergo a developmental change. Traditional genetic analyses of these traits are based on the association between the genotype and phenotype measured at individual times. But this approach does not consider the dynamic feature of the traits and, therefore, limits the scope of its inference about genetic control. A natural way is to connect the genotype with the dynamic trajectories of a trait, enabling geneticists and clinicians to study the temporal pattern of genetic control.

Such a dynamic model has been developed in our group at Penn State University. This model 1 integrates mathematical aspects of trait formation and progression with genetic variation within the framework of genome-wide association studies (GWAS). It thus shows substantial biological relevance of the results and, meanwhile, possesses significant statistical merits because of parsimonious modeling of the time-dependent genetic effects and longitudinal covariance structure. We have performed extensive simulation studies to test and validate the usefulness of this new model. Although favorable statistical properties have been detected, we have a keen interest in applying this model to a large-scale longitudinal data set. By reading your article published in PLoS Medicine 2 and looking at your webpage, we are very confident that our model matches your data extremely well. If we get your data, we will commit our time to analyze and interpret them, and write papers for publication jointly.

Our specific data needs are:

(1) genotype data for all the SNPs you may have in cohort studies.

(2) phenotypic data measured at multiple time points. In your case, body height and weight from birth to ages 11 years were measured. We are also interested in other longitudinal measures, such as blood pressure, but we will request these data later.

Our plan: Once we get these data, we will analyze them in a couple of weeks and report the results to you at various stages. Meanwhile, we will be drafting a manuscript together with your group and publish it jointly in a top journal. In particular, we will be collaborating closely throughout this project with Nicholas Timpson from the Department of Social Medicine, University of Bristol.

As to the authorship, the following is our suggestion: If the results are from reanalyzing your data with our new model, we suggest that we are the first author and you are coauthors. A leader from each group (Penn State and Bristol) can be jointly a corresponding author. If your data are new and we simply help you analyze, you should be the first and corresponding authors, but we request the coauthorship.