Well, you know me, Michel 😎! Haha, I've been planning to set an account for some time and thought that was a good opportunity to share our work here too. Seems like the post is well received. Thanks for sharing it.

10/10 – What now? Well, there are still a few gaps, and I invite anyone to read our comprehensive discussion. In particular, I’d like to point out our LD score regression analysis predicting how much heritability is captured by T2T genome builds but currently missed by hg38.

9/10 – We release 95% credible sets for the 12,000 loci identified in our WGS-based GWAS.

8/10 – We compared WGS-based and imputation-based GWAS and show a few interesting examples of common haplotypes that seem to be missing from existing imputation panels (e.g., TOPMed).

7/10 – We GWASed all 34 traits, identified ~12,000 significant loci across traits and confirm the strong colocalization between rare- and common-variant associations. In fact, rare-variant detection was best predicted by the presence of a common-variant association within 100 kb.

6/10 – Genetic correlations (between traits) were largely consistent between common and rare variants.

5/10 - On average across traits, WGS accounts for 88% of pedigree-based heritability.

4/10 – For 15/34 traits, we show no significant difference between WGS- and family-based estimates of narrow sense heritability (= additive genetic effects) from within the UK Biobank (thus minimizing the effect of differential measurement errors and phenotype definitions).

3/10 – On average across traits, we estimate that ~23% of our estimated WGS-based heritability is due to rare variants (0.01%<frequency<1%). We also show that coding and non-coding genetic variants account for 21% and 79% of rare-variant WGS-based heritability, respectively.

2/10 – We used the latest release of WGS data in the UK Biobank to produce high-precision estimates of heritability (standard error ~1%) for 34 phenotypes (incl. 4 common diseases). Our analyses include both common (frequency >1%) and rare variants (1%>frequency>0.01%).

1/10 - How much of the heritability estimated with family data can we explain using modern genomic technologies? This question has remained open for more than 15 years! Previous studies have used WGS data from TOPMed answer it but sample sizes and sets of traits remained limited.

My last “Thank You” goes to my colleague, friend and mentor Peter Visscher (who doesn’t hang out here) for inspiring me and many others in the field to work on such a fascinating topic.

And now for the science…

First of all, a huge "Thank You" to our collaborators at Illumina and to all UK Biobank participants. Also, thanks to many colleagues in the fields for stimulating conversation around this topic. Working on this piece has been an amazing ride!