Thread by @mikeweale

1/9 Hot on the heels of our CAD paper (ahajournals.org/doi/10.1161/CI…) comes our clinical validation across multiple ethnicities and ancestries of a new integrated risk tool for atherosclerotic cardiovascular disease (ASCVD) (shiny post-proof format: ajconline.org/article/S0002-…).

2/9 ASCVD includes both coronary artery disease (CAD) and ischaemic stroke (IS). We meta-analysed 10 GWAS datasets across multiple traits, ethnicities and ancestries to derive our polygenic risk score (PRS) for ASCVD.

3/9 We then trained an appropriate effect size for the PRS using additional cross-ethnicity / cross-ancestry individual-level data (4 different cohorts), and combined the PRS with the US standard-of-care (SoC) risk tool to form a new ASCVD-IRT integrated risk tool.

4/9 We validated our ASCVD-IRT using 3 additional independent prospective cohorts. We demonstrated superior accuracy over the SoC tool in both Black and White ethnicities, and both African and European ancestries (NRI = Net Reclassification Improvement).

5/9 As with our previous paper (ahajournals.org/doi/10.1161/CI…), we found that the PRS had the biggest benefit in younger middle-aged men (40-54yo) (overall NRI = 10.3%, 95% CI 5.7 – 15.0; Black ARIC cohort NRI = 8.5%, 95% CI 0.4 – 16.7).

6/9 We demonstrated superior performance in large-n ethnicities (White, Black, South Asian). What about other ethnicities? Can we generalise? 3 arguments in favour…

7/9 (1) PRS performance is strong in multiple ethnicities (see 3/9); (2) correlation between PRS and SoC risk is low, suggesting independent effects; (3) previous work suggests that ‘PRS attenuation’ is greatest in African ancestries, so other groups should be intermediate.

8/9 In summary, adding genetics to clinical risk tools improves their accuracy, across multiple ethnicities and ancestries. As before, genetics is especially helpful in younger middle-aged men, which provides an opportunity for beneficial early intervention.

9/9 Thanks to all our many cohort and GWAS participants and investigators, without whom this study would not have been possible! Special thanks to @EastLondonGenes for additional GWAS data. Co-authors: @fa7gen @euanashley @hannah_wand @VincentPlagnol @genemodeller @Genomicsplc...