| Popis: |
We will perform exploratory data analysis in a longitudinal family-based dataset to detect genome-wide SNP effects on five outcomes (body mass index, communication and motor skills, internalising and externalising behaviour) across early development (e.g. ages: 1.5, 3, and 5 years old). We will perform hypothesis-free scans of the genome (GWA), to detect quantitative trait loci that affect phenotypic variance (vQTL) and mean values (mQTL). Genome-wide summary statistics will be carried forward for secondary analyses. Our primary research question is: do higher vPGS predict greater variability in outcomes? To explore this, we will perform ten-fold leave-one-out GWA-PGS analyses, to provide GWA discovery and PGS target sub-samples (more details are provided below). Discovery-GWA will test for mean and variance effects of SNPs on child outcomes, using both child genotype data and parental genotype data. GWA-summary-statistics for each outcome will be used to compute three PGS in the left-out sample: child-vPGS, child-mPGS and parent-mPGS. To assess whether higher vPGS predict greater variability in outcomes, all three PGS will be modelled simultaneously in a trio-based approach, to estimate the effect of child-vPGS, controlling for mean-variance confounding (child-mPGS) and family level confounding (parent-mPGS). |
