Analysis of common complex traits and genome-wide sequence data
A new wave of increasingly detailed genetic information is being produced by next generation sequencing technologies. This opens up new possibilities in understanding the contribution of genetic variation to many traits and diseases. This project focuses on the analysis of a comprehensive spectrum of genetic variation, evidence for association with complex phenotypes and following up on the activity of the UK10K sequencing project (www.uk10k.org). Work will employ genome-wide whole sequence data from the Avon Longitudinal Study of Parents and Children (ALSPAC, www.bris.ac.uk/alspac) along with data from TwinsUK to examine the nature of the genomic architecture of complex traits (across a frequency and variation type spectrum), to explore potentially de novo association signals and to follow up confirmed signals using multi-omic data under collection and using genotype based recall analyses.
Aims & Objectives
Genome-wide sequence data available on ALSPAC will be used to perform analysis of the genetic contribution to common complex traits at a new level of detail. With existing genetic data (common genetic variation across the genome) and further collections at the level of the phenome, methylome and transcriptome, the potential application of these data is broad and will allow for a number of hypotheses and possible methodological developments to be explored.
This proposal aims to explore the potential in analysing the spectrum of genetic variation existing in low read depth genome-wide sequence data alongside a rich phenotypic resource. Work will use these data and imputed data from the entire ALSPAC cohort to assess the impact of intermediate frequency genetic variants on specific phenotypes of interest and to assess the potential for this type of genetic data in the assessment of rare exosmic coding variation. There is further potential to develop the use of these data along lines of population genetic inference, the analysis of coding variation and functional changes in the pursuit of recall experiments or Mendelian randomization exercises. In addition, the parallel analysis of data from numerous sources including the methylome, the transcriptome and the environment will be possible.
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