Fig. 2: Common variant influences on circulating retinol.

a Manhattan plot of the meta-analysis of common variants shared between the INTERVAL and METSIM cohorts (Stouffer’s sample size weighted meta-analysis). Variant-wise -log₁₀ P-values for association are plotted, with the dotted red line denoting genome-wide significance. The closest genic transcription start site is labelled for each lead SNP. Constituent GWAS performed using multiple-linear regression. b Manhattan plot, as above, for the larger sample size meta-analysis that includes the ATBC and PLCO cohorts, but with fewer variants available for meta-analysis. Constituent GWAS performed using multiple-linear regression. c Estimates of SNP heritability of retinol (h²), with the error bars denoting the standard errors of the estimates. The first two panels denote estimates using the BLD-LDAK model and the LDAK-thin model, respectively, both using LD tagging files from the Great British ancestry participants in the UK Biobank. The last panel estimates heritability using the LDSR model with LD from the 1000 genomes European participants. Estimates were for the METSIM + INTERVAL meta-analyses (Stouffer’s and IVW), as well as the larger meta-analysis including ATBC/PLCO. d Empirical Bayes’ estimation of non-null effects on retinol genome-wide, stratified by bins of ascendingly sorted LD score by magnitude. The LD score bins were different for each panel – 1000 bins, 1000 genomes European LD scores (top left); 5000 bins, 1000 genomes European LD scores (top right); 1000 bins, UKBB white British LD scores (bottom left); 5000 bins, white British LD scores (bottom right). Each point represents the proportion of non-null effect sizes for that bin, with the trendline estimated using a generalised additive model for the relationship between ascending LD score bin and the proportion of non-null effects.