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Well it’s a bit funky. For better or for worse, we did 1% max MAF in UKB (all that was easily accessible) and 0.1% in AoU, which makes things very different esp for the missense/synonymous space. I tend to focus on the pLoF partially for this reason and that the effect directions are consistent

Huge team effort led by @wenhanl.bsky.social with collaborators across the Broad, VUMC, and the All of Us Research Program. This is a first release — looking forward to the next round as All of Us keeps growing and we integrate more biobanks

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allbyall.researchallofus.org

A new browser integrating common and rare variants is now up at allbyall.researchallofus.org — Manhattan, QQ, PheWAS, locus views, TB of summary stats queryable in real time. Full results will soon be accessible from the All of Us Researcher Workbench as a Featured Workspace.

All by All

The All by All browser maps known and novel associations between genotypes and phenotypes using data contributed by All of Us Research Program participants as of July 1, 2022. All by All encompasses a...

All of Us markedly boosts non-European representation. Nearly half of the significant signals are found only by meta-analysis. Effects are also consistent: across 140 burden associations marginally significant in ≥2 similarity groups, we saw zero opposite-direction effects

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New rare-variant findings include pLoF in TIMD4 → elevated triglycerides, TREM1 → umbilical hernia, and TMEM127 → dermatophytosis. For TIMD4, common variants near the locus were known for lipid traits; the rare-variant evidence adds a direction of effect.

Cross-biobank meta-analysis with UK Biobank (up to 786,871 participants, 222 phenotypes) identified 193 pLoF gene–phenotype associations not significant in either cohort alone, 22 with no prior literature evidence.

Systematic common and rare variant association testing in 392,030 whole genomes in All of Us

🧬 New preprint! We present an All by All analysis in All of Us: common and rare variant association testing across 392,030 whole genomes and 3,602 phenotypes. 1.337 trillion tests, ~50k significant signals, all publicly browseable. www.medrxiv.org/content/10.6...

medrxiv.org

Large-scale genome-wide association studies (GWAS) and rare variant association studies (RVAS) from population biobanks provide valuable resources for gene discovery in complex human traits. We presen...

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Special thanks to all @gnomad-project.bsky.social participants, data contributors, and funders, as well as @ksamocha.bsky.social, Jeremy Guez, and Julia Goodrich, as well as all co-authors for their tireless efforts on this

To nominate disease genes, we introduce two discovery scores: ΔPEPPER flags genes where biological features predicts clinical impact beyond whats published, and DisPo highlights genes under strong constraint with limited literature. Together they prioritize hundreds of candidate genes for follow-up

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We train a new model trained on biomedical literature (PEPPER_XGB). Mix LOEUF and PEPPER to make an OMELET, which outperforms either each individual model in identifying disease genes

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We introduce LOEUF-MIS, combining pLoF and top 1% predicted deleterious missense constraint. This captures not just LoF but also gain-of-function and dominant-negative signals.

Precision-recall curves showing LOEUF-MIS outperforming other metrics

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gnomAD v4's 5x sample increase benefits both common and rare disease: more common variants observed across ancestries improve diagnostic filtering, while more rare variants strengthen constraint metrics for disease gene detection

Figure 1a-b, growth of variation with sample size

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Integrating 730,947 exome sequences with clinical literature improves gene discovery

Excited to share our new preprint on gnomAD v4! We present the full analysis of 730,947 exomes — new constraint metrics, improved LoF annotation (LOFTEE-2), LLM-based literature curation, and a unified framework for gene discovery and rare disease diagnosis. www.medrxiv.org/content/10.6...

medrxiv.org

Accurate estimates of allele frequencies aid in genetic discovery, including rare disease diagnosis, common disease investigations, and population genetics. Here, we present the Genome Aggregation Dat...

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Konrad @konradjk.bsky.social · 31/01/2026

The analogy I find myself frequently using here: if everyone smoked (or no one did), the heritability of lung cancer would go up. We think about this a lot in the context of cross-biobank comparisons

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Konrad @konradjk.bsky.social · 08/10/2025

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

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Konrad @konradjk.bsky.social · 18/09/2025

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!)

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Konrad @konradjk.bsky.social · 18/09/2025

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

Konrad @konradjk.bsky.social · 08/11/2024

We’ve put up summary statistics for over 3,000 traits in the All of Us resource, and a shiny new browser alongside it! Explore your favorite gene or phenotype here: allbyall.researchallofus.org #ASHG24

allbyall.researchallofus.org

All by All

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Konrad @konradjk.bsky.social · 08/11/2024

Starter pack of people who create starter packs?

Konrad @konradjk.bsky.social · 07/11/2024

You mean “ReNally???”?

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Konrad @konradjk.bsky.social · 21/09/2024

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

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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!

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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

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Variant scoring performance across selection regimes depends on variant-to-gene and gene-to-disease components

We have a new preprint that we’d love feedback on! We benchmarked a bunch of variant scoring methods to figure out what they were actually doing, and how they performed across selection regimes: www.biorxiv.org/content/10.1...

biorxiv.org

bioRxiv - the preprint server for biology, operated by Cold Spring Harbor Laboratory, a research and educational institution

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Kaitlin Samocha @ksamocha.bsky.social · 20/04/2024

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)

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Reposted by Konrad

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

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Konrad @konradjk.bsky.social · 31/01/2024

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...

biorxiv.org

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

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Konrad @konradjk.bsky.social · 07/12/2023

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

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Konrad @konradjk.bsky.social · 07/12/2023

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

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:

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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

Konrad @konradjk.bsky.social · 28/11/2023

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.is

Hail | Index

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Konrad @konradjk.bsky.social · 28/11/2023

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