GWAS have been enormously successful in uncovering novel genetic variants associated with a range of complex human diseases, but the majority have used cross-sectional data and relatively simplistic statistical tests. Longitudinal studies are advantageous for investigating genetic associations as they: 1) facilitate the detection of genetic variants that influence change in a trait over time; and 2) allow the detection of genes that are associated with the age of onset of a trait. Improving analytic techniques for conducting longitudinal GWAS offers the opportunity to advance our understanding of the aetiology of health and disease.

To test statistical methods for modelling trajectories, we have selected two growth traits: height and body mass index (BMI). These traits were selected as most cohorts within EGG (The Early Growth Genetic consortium) have data available for analysis, therefore maximizing our sample size. Height was chosen as its trajectory has a relatively simple (linear) shape, whereas BMI is more complex but is of interest to a large number of groups within the consortium. Ideally, the methods we define will be transferable to other traits of interest across the consortium.

Childhood obesity is associated with poor mental and physical health and is one of the strongest predictors of adult obesity. BMI, the most commonly used quantitative measure of adiposity, follows a well-characterised trajectory throughout childhood: a rapid increase soon after birth until approximately 9 months, the adiposity peak, followed by a gradual decline until around 4–6 years of age, the adiposity rebound, followed by an increase again until the end of puberty. Although the community has identified a large number of genetic variants associated with adult BMI, relatively little is known about the genetic determinants of BMI throughout infancy and childhood, or the rate of growth across early life.

Distinct height growth patterns, particularly during puberty, have also been linked with adverse health outcomes such as poor cardio-metabolic health. Analysis in the EGG consortium using SITAR growth curve analysis has shown that velocity of height growth throughout puberty is genetically correlated with adult health, including glycemic traits, metabolites, bone density measures, lung function and lung cancer. By investigating height growth across childhood, rather than just puberty, we will be able to investigate whether these genetic correlations are more generally related to early life growth.