Abstract
[Truncated abstract] Genome-wide association studies (GWASs) are a hypothesis free approach to investigating genetic factors that influence health and disease. Whilst they have been relatively successful in uncovering novel genetic variants associated with complex human diseases, it is largely only the ‘low hanging fruit’ that have been described to date, leaving much of the heritability of any given trait unexplained.
Geneticists are beginning to perform more complex analyses to improve our understanding of genetic determinants of disease, including the investigation of how genes play a role in the development of a trait over time in longitudinal studies. Compared to cross-sectional analyses, longitudinal studies are advantageous for investigating genetic associations as they: 1) allow information to be repeated among individuals across various time points; 2) facilitate the detection of genetic variants that influence trajectories rather than simple differences in phenotypes; and 3) allow the detection of genes that are associated with age of onset of a trait. Improving analytic techniques for conducting longitudinal GWASs offers the opportunity to advance our understanding of the aetiology of health and disease.
The core aim of this thesis was to develop an appropriate modelling framework to conduct GWASs of complex traits in longitudinal study designs. Body mass index (BMI) trajectories throughout childhood were chosen for this research for several reasons. Firstly, obesity (defined by high BMI) is a complex disorder with increasing incidence, particularly during the first decades of life, and it is important to gain an understanding into the developmental processes that precedes the obesity diagnosis.
Secondly, obesity is linked to increased risk of many other diseases including type-two diabetes, the metabolic syndrome, mental health disorders, respiratory problems and some cancers. The principles underlying life course epidemiology suggest that the link between these diseases begins in early life. Thirdly, the genetic determinants of BMI remain largely unknown. Finally, BMI trajectories over childhood are difficult to model statistically due to the complexities in the shape of the growth curve and differences between individuals rate of growth within the population.
Geneticists are beginning to perform more complex analyses to improve our understanding of genetic determinants of disease, including the investigation of how genes play a role in the development of a trait over time in longitudinal studies. Compared to cross-sectional analyses, longitudinal studies are advantageous for investigating genetic associations as they: 1) allow information to be repeated among individuals across various time points; 2) facilitate the detection of genetic variants that influence trajectories rather than simple differences in phenotypes; and 3) allow the detection of genes that are associated with age of onset of a trait. Improving analytic techniques for conducting longitudinal GWASs offers the opportunity to advance our understanding of the aetiology of health and disease.
The core aim of this thesis was to develop an appropriate modelling framework to conduct GWASs of complex traits in longitudinal study designs. Body mass index (BMI) trajectories throughout childhood were chosen for this research for several reasons. Firstly, obesity (defined by high BMI) is a complex disorder with increasing incidence, particularly during the first decades of life, and it is important to gain an understanding into the developmental processes that precedes the obesity diagnosis.
Secondly, obesity is linked to increased risk of many other diseases including type-two diabetes, the metabolic syndrome, mental health disorders, respiratory problems and some cancers. The principles underlying life course epidemiology suggest that the link between these diseases begins in early life. Thirdly, the genetic determinants of BMI remain largely unknown. Finally, BMI trajectories over childhood are difficult to model statistically due to the complexities in the shape of the growth curve and differences between individuals rate of growth within the population.
| Original language | English |
|---|---|
| Qualification | Doctor of Philosophy |
| Publication status | Unpublished - 2013 |
Access to Document
- Download PDF
This work is protected by Copyright. You may print or download ONE copy of this document for the purpose of your own non-commercial research or study. Any other use requires permission from the copyright owner. The Copyright Act requires you to attribute any copyright works you quote or paraphrase.