Objective
The obesity rates are rapidly increasing worldwide concomitantly with rising prevalence of chronic diseases, including cardiovascular disease and type 2 diabetes. Individual trait genome-wide association studies of common variants (minor allele frequency, MAF>5%) have highlighted complex genetic relationships between related cardiometabolic phenotypes with an intriguing pattern of associated overlapping DNA sequence variant effects, which does not always follow the epidemiological correlations. Joint analysis of multiple correlated traits: (i) increases power for variant discovery; and (ii) facilitates dissection of the genetic mechanisms underlying multi-phenotype association signals, including evaluation of the evidence for pleiotropy. Multi-phenotype analysis methods for common variants have been proposed, however, with the current focus being in low-frequency and rare variants (MAF<5%/1%), novel method development for identification of such effects is required. The project has three goals:
1) To develop a multi-phenotype analysis method for rare variants and to test it on at least 20000 individuals directly available to me. The availability of high-throughput “omics” data, including sequencing data and serum metabolites, adds further challenges to the methods development, e.g. due to genotype uncertainty and hundreds of correlated traits. I will extend the methodological development to the methods for meta-analysis of rare variants, given the need to combine genetic effects across many individual studies;
2) To create an efficient publicly available software tool for the developed methods;
3) To dissect the genetic architecture behind cardiometabolic phenotypes by conducting a large-scale multi-phenotype meta-analysis of rare variant effects on metabolic traits within international consortia. This timely and highly relevant project will allow me to embark on an independent academic career in the field of statistical genetics where my research interests lie.
Fields of science
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques.
- natural sciencescomputer and information sciencessoftware
- natural sciencesbiological sciencesgeneticsDNA
- medical and health sciencesclinical medicineendocrinologydiabetes
- medical and health sciencesclinical medicinecardiologycardiovascular diseases
- medical and health scienceshealth sciencesnutritionobesity
Topic(s)
Call for proposal
FP7-PEOPLE-2013-IEF
See other projects for this call
Funding Scheme
MC-IEF - Intra-European Fellowships (IEF)Coordinator
SW7 2AZ LONDON
United Kingdom