Genetic studies of anthropometric traits and methods for analysis of multiple phenotypes
Principal Investigator: Joel Hirschhorn
Approved Research ID: 11898
Approval date: July 31st 2015
Our major ongoing research aim is to understand the genetic basis of anthropometric traits, including anthropometric measures of obesity and of skeletal growth. The health conditions relevant to these traits are obesity and its metabolic complications, such as diabetes, and also abnormalities of skeletal growth such as short stature. We have previously identified hundreds of loci associated with these traits, providing insights into genetic architecture and novel biology. We aim to extend these studies using data from the UK Biobank. We also plan to use these data to extend ongoing efforts to develop methodologic approaches to analyzing multiple phenotypes simultaneously. Our work would combine the UK biobank data with large ongoing efforts in the GIANT consortium to greatly advance our understanding of the genetic basis and biology of obesity, a major risk factor for common illnesses in society, and of the genetic basis and biology of normal skeletal growth. Improved understanding could yield better predictive biomarkers or guide future therapeutic development for obesity, and also potentially diseases of abnormal skeletal growth. Our methodological work and our work on height, a model polygenic trait, would inform genetic studies of many important polygenic diseases. We will perform genetic association analysis (similar to prior work we have helped lead for anthropometric traits). In this approach, we use well-established statistical methods to systematically search for genetic variants that affect measures of obesity (such as body mass index) or of anthropometric traits skeletal growth (such as adult height). We will also use more complex statistical methods designed for analysis of multiple traits simultaneously, and test whether these new methods improve our power to discover these genetic variants, and also whether they can better classify variants and biological pathways by their effect on human traits. We would analyze the full cohort.