About the Project
In two recent ground-breaking studies (Horikoshi et al. 2016 Nature; Warrington et al. in press Nature Genetics), we showed that the inverse correlation between birthweight and cardiometabolic disease in particular may in fact be predominantly mediated by genetic rather than environmental factors. However, maternal and offspring genotypes are correlated, meaning that dissecting the genetic and environmental contributions to this relationship is fraught with interpretational difficulties, including the possibility that any genetic effects may be mediated through the mother’s (rather than the offspring’s) genotype operating on the intrauterine environment.
Recently we have developed several new statistical methods to partition genetic effects at loci into maternal and fetal genetic components of variation (Warrington et al. 2018 Int J Epidemiol). The use of novel statistical methods and large datasets represents an exciting opportunity to extend our understanding of the biology underlying DOHAD, and will help identify genes that underpin key pathways important for the development of disease.
The aim of this PhD is to dissect the maternal and fetal contributions to the relationship between offspring birthweight and future risk of disease. The successful candidate will perform analyses on a number of large datasets including (but limited to) the UK Biobank Study and those in the Early Growth Genetics (EGG) consortium. The UK Biobank is a large population based cohort of 500,000 individuals who have been genome-wide genotyped and have self-reported their birthweight, birthweight of their offspring, and doctor diagnosed diseases ( e.g. hypertension, type 2 diabetes, myocardial infarction, and many others of relevance). The EGG consortium includes more than 35 international pregnancy and birth cohorts with genetic data, including studies with data on mother/offspring pairs. The successful candidate will gain experience across a wide range of advanced statistical genetics methodologies including Mendelian randomization (a way of using genetic variants to investigate putatively causal relationships), genome-wide association analysis (GWAS), and genetic restricted maximum likelihood (G-REML) analysis of genome-wide data which can be used to partition variation in phenotypes into genetic and environmental sources of variation. It is also expected that the candidate will assist in the development of new statistical genetics and causal modelling methods.
Horikoshi et al. (2016). Genome-wide associations for birth weight and correlations with adult disease. Nature 538(7624), 248-52.
Warrington NM, Freathy RM, Neale MC, Evans DM (2018). Using structural equation modelling to jointly estimate maternal and fetal effects on birthweight in the UK Biobank. Int J Epidemiol, 47(4), 1229-1241. doi: 10.1093/ije/dyy015.
Warrington et al. (2019). Maternal and fetal genetic effects on birth weight and their relevance to cardio-metabolic risk factors. Nat Genet, 51(5), 804-814.
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