gs://ukb-diverse-pops-public/misc/pairwise/pairwise_correlations_regressed.txt.bgz - it’s coded in the way that our pan-UKB phenotypes were so not sure if it’s super easy to use but that’s pairwise r_p for ~14k phenos

Special thanks to all co-authors that got this here including @masakanai.bsky.social @rahulg603.bsky.social @dalygene.bsky.social @egatkinson.bsky.social and of course @genetisaur.bsky.social for driving this through 5 years of work (after the first GWASes were done!)

Tons of lessons learned around carefully controlling population stratification, using heritability as a QC metric, and probably most importantly, quantifying novelty in a mega-phenotype analysis. Some really cool analyses to find interesting biology e.g. allelic series and ancestry-enriched variants

Starter pack of people who create starter packs?

You mean “ReNally???”?

Heh, it was on our list but somehow never made it into the pre-submission checklist. Will do!

Interesting question. We do have a “gnomAD-new” analysis in there but haven’t broken down by ancestry - i fear a lot is going to be driven by “not yet observed” (which is the same across all ancestries)

It gets a bit more complicated though - these scores have a mix of impacts of variant-to-gene, as well as prioritizing which genes, when disrupted, lead to phenotypes. Perhaps a new method that combines both these insights optimally will outperform them all!

We found that population-focused methods do best for identifying highly impactful variants (de novo’s in individuals with developmental disorders for instance), while the deep learning methods are better at prioritizing inherited variation in biobanks

Paella is good. With LOEUF I assumed the culmination would be an omelette but CHARR is better in a paella, so maybe it’s a multi-course meal

Extended data figure 2b has exclusive exon-only. I think we internally made some with intermediate overlaps and it was an intermediate result as you’d expect

And thanks to Ryan Dhindsa and Slavé Petrovski for the excellent writeup and context around our work. Excited for the times ahead! www.nature.com/articles/d41...

This is all thanks to an amazing production team, browser team, and steering committee @gnomad-project.bsky.social, the 76,156 individuals that provided their genomes, and support from Broad Genomics and Hail

Interestingly, these scores also provide additional insight into genes regulated by these regions, even those underpowered by previous constraint metrics:

Gnocchi extends our constraint metrics to the non-coding genome, highlighting for instance, disease-associated non-coding CNVs

We built a new metric we called gnocchi (genomic non-coding constraint of haploinsufficient variation), building on methods that find depletions of variation (natural selection), which we show can prioritize functional variation

Thanks to Wenhan Lu for driving this effort, Hail (hail.is) for building the scalable infrastructure that enabled this, and @gnomAD-project.bsky.social for the data and support

CHARR operates only on homozygous alternate sites and scales very well (“cost per 1M samples” might be my new favorite metric):