Konrad
@konradjk.bsky.social
1.2K followers 44 following 25 posts
Genomicist, computational biologist. Assistant professor @ MGH, HMS. Associate member @ Broad Institute https://klab.is
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konradjk.bsky.social
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
konradjk.bsky.social
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!)
konradjk.bsky.social
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
konradjk.bsky.social
Starter pack of people who create starter packs?
konradjk.bsky.social
You mean “ReNally???”?
konradjk.bsky.social
Heh, it was on our list but somehow never made it into the pre-submission checklist. Will do!
konradjk.bsky.social
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)
konradjk.bsky.social
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!
konradjk.bsky.social
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
konradjk.bsky.social
Welcome new followers (and thanks @michelnivard.bsky.social)! I’m loving the critical mass, and to celebrate, I’ll post some exciting new content (my first time posting here and not on the the other site)
Reposted by Konrad
ksamocha.bsky.social
Recently out on #bioRxiv: our updated approach to identify regional variability in missense mutation intolerance (“constraint”) in protein-coding genes using the gnomAD database.

www.biorxiv.org/content/10.1...

1/10
konradjk.bsky.social
As genomic analyses scale to millions of exomes/genomes, we need a scalable infrastructure to process/QC/handle these data while retaining all the metrics needed for downstream analysis. A new preprint from the Hail team proposes a way to do this! Comments welcome: www.biorxiv.org/content/10.1...
The Scalable Variant Call Representation: Enabling Genetic Analysis Beyond One Million Genomes
bioRxiv - the preprint server for biology, operated by Cold Spring Harbor Laboratory, a research and educational institution
www.biorxiv.org
konradjk.bsky.social
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
konradjk.bsky.social
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
konradjk.bsky.social
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
konradjk.bsky.social
Interestingly, these scores also provide additional insight into genes regulated by these regions, even those underpowered by previous constraint metrics:
konradjk.bsky.social
Gnocchi extends our constraint metrics to the non-coding genome, highlighting for instance, disease-associated non-coding CNVs
konradjk.bsky.social
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
konradjk.bsky.social
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
Hail | Index
hail.is
konradjk.bsky.social
CHARR operates only on homozygous alternate sites and scales very well (“cost per 1M samples” might be my new favorite metric):