Kaitlin Samocha

Massive single-cell study by Kanai et al (www.medrxiv.org/content/10.1...):
- Once statistical power is high, constrained genes have more (though weaker) eQTLs.
- Chromatin-QTLs near constrained genes have "normal" effect sizes, colocalize more with disease, but exhibit attenuated peak-gene effects.

Genotype-level quality control substantially reduces error rates in population-scale whole-genome sequencing Population-scale whole-genome sequencing data will contain many individual-level genotype errors, even after allele-level quality control (QC). We establish the need for genotype-level QC using UK Bio...

New paper on everyone’s favourite topic, QC!
We show why you should do genotype-level QC on your WGS data

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

Very real quotes about this paper -
“The most exciting, mind-blowing paper of the year!”
“On a par with Fisher 1918”
“I read it every night. Just so beautiful”

Exploring penetrance of clinically relevant variants in over 800,000 humans from the Genome Aggregation Database - Nature Communications Here the authors provide an explanation for 95% of examined predicted loss of function variants found in disease-associated haploinsufficient genes in the Genome Aggregation Database (gnomAD),…

New study of 800K+ genomes from gnomAD reveals most “pathogenic” variants in healthy people aren’t truly disease-tolerant. They are explained by annotation errors, mosaicism, or compensatory variants. 🧬
A big step for precision medicine!
www.nature.com/articles/s41...

I haven't found it printed but there is a PDF link here: www.ashg.org/wp-content/u...

Improved allele frequencies in gnomAD through local ancestry inference - Nature Communications This study incorporates local ancestry into the Genome Aggregation Database (gnomAD) to improve allele frequency estimates for admixed populations, enhancing variant interpretation and enabling more accurate and equitable genomic research and clinical care.

📃 We’re excited to share our latest work, now published in Nature Communications — a major update to the Genome Aggregation Database (gnomAD) that improves allele frequency resolution for two gnomAD-defined genetic ancestry groups using local ancestry inference (LAI).

Sperm sequencing reveals extensive positive selection in the male germline - Nature A combination of whole-genome NanoSeq with deep whole-exome and targeted NanoSeq is used to accurately characterize mutation rates and genes under positive selection in sperm cells.

Now published! Our paper on:
(1) Accurate sequencing of sperm at scale
(2) Positive selection of spermatogenesis driver mutations across the exome
(3) Offspring disease risks from male reproductive aging
[1/n]
www.nature.com/articles/s41...

Image of an old building in Oxford with the heading 'postdoc opportunities' and the text 'computational approaches to improve rare disease diagnosis and treatment' and 'Big Data Institute, University of Oxford'

📣 We are recruiting! Please share!!

Are you a bioinformatician / computational scientist who wants to apply your skills to understanding regulatory biology and improving rare disease diagnosis and treatment? 🧠 💻 🧬 🩺

We have two roles available 👇

🧵 1/4

Isn't genetics cool???

Within only 145 nucleotides(!) of a non-coding RNA (RNU4-2) - different variants in distinct regions / structures cause three distinct disorders!!! (all discovered within the last 18 months)

🤯🤓🧬❤️

🗣️ Quote of #ESHG2025 (so far)

"Who licks bone !?!" 🦴
- Johannes Krause

Anyone have that on your bingo card?

Well apparently archeologists do, to distinguish bone from stones and it causes problems in DNA sequencing. 🤔

We are just wrapping up day 1 at #ESGH2025 in beautiful Milan. For those who want some extra fun while listening to the great science, you can play bingo.👇

I know multiple of these have already occurred.

Buongiorno Milano! Ready for a great day 1 of #eshg2025?
Packed program of excellent science 8.30am-8.00pm - plus networking event till 9.30pm to meet many friends, colleagues and collaborators! …andiamo @eshg.bsky.social @eshgyoung.bsky.social

Scalable automated reanalysis of genomic data in research and clinical rare disease cohorts Reanalysis of genomic data in rare disease is highly effective in increasing diagnostic yields but remains limited by manual approaches. Automation and optimization for high specificity will be necess...

🤗 Hugely excited to share our work on automating iterative reanalysis in #raredisease, preprint out: www.medrxiv.org/content/10.1... 🤖🧬

github.com/populationge...

A superb collaboration with @dgmacarthur.bsky.social @cassimons.bsky.social @heidirehm.bsky.social @ksamocha.bsky.social and many more!

Human Developmental Cell Atlas (HDCA) expression data is now displayed. Expression is displayed in 12 sections of a 6-7 post-conception week human embryo, alongside a sagittal view which displays the region of the embryo represented by each section @mhaniffa.bsky.social

My son Charlie — and the breakthrough that changed our lives James Coney and his wife, Sarah, struggled not knowing why their 12-year-old was born with a severe learning disability. In their darkest moments, they blamed themselves. Then, out of the blue, came a...

A few weeks ago, I had an incredibly emotional call with James Coney, a writer for the Sunday Times whose son Charlie was in the @genomicsengland.bsky.social 100k project and was recently diagnosed with ReNU syndrome. This beautiful article tells their story ❤️ www.thetimes.com/article/0bcc...

Yes, I've done this once before for a paper (listing in the system only first and last authors). It is easy to get those handful of people to approve additional authors in the future if the journal requires it.

Not sure if recommended, but it saved time.

Genomics of Rare Disease (hybrid conference) Dates: 9-11 April 2025 Location: Hinxton Hall Conference Centre, Wellcome Genome Campus, UK and online In-person registration deadline: 11 March 2025 Virtual registration deadline: 1 April 2025

Join leading experts working in #RareDisease research at our #GRD25 conference.

📅 Dates: 9-11 April 2025
💭 Share insights in person

Explore the latest #genomics advances accelerating improvements in clinical care for rare disorders, globally.

⏰Secure your place by 11 March: bit.ly/3BpAe44

Projects like this can’t be completed without many others: thanks to Mark Daly for continued mentorship across the years; critical support and work from @anneotation.bsky.social, @konradjk.bsky.social, Predrag Radivojac; the Hail team; and everyone associated with @gnomad-project.bsky.social.

10/10

If this work feels familiar, it is because it is building off older work from our team originally released for ExAC.

I view this iteration as more of a franchise reboot instead of a sequel – we have new leads, but similar themes.

9/10

As with all gnomAD-led projects, we’ve already shared the data and code. Regions are displayed for v2 on the gnomAD browser, the code can be seen on Github (github.com/broadinstitu...), and MPC scores are available for download.

8/10

There is still much more to learn: using 125k exomes, our median region size is ~450bp and we see a relationship between transcript length and the number of regions we can identify due to statistical power.

7/10

Finally, missense constraint information was incorporated into a deleteriousness metric named MPC (Missense deleteriousness Prediction by Constraint), which separates case from control de novo missense variants well with similar performance to ML models like AlphaMissense.

6/10

In collaboration with Predrag Radivojac and team, we demonstrated that coding bases with < 20% of their expected missense variation achieve moderate support for pathogenicity (PM1) following ACMG/AMP guidelines that can be used for clinical classification.

5/10

Missense depleted regions show an enrichment of (1) de novo missense variants in neurodevelopmental disorder cases compared to controls, (2) partitioned common variant heritability for >260 independent traits from the UK Biobank, and (3) ClinVar pathogenic (P/LP) variants.

4/10

Why try to find subgenic regions that are specifically missense constrained? Splitting up genes reveals patterns of negative and neutral selection that are obscured when looking gene-wide, including highlighting regions that have a large number of known pathogenic variants.

3/10

Co-led by the fabulous Katherine Chao and Lily Wang, we used gnomAD v2 and a recursive search to identify ~28% of canonical transcripts that were split into multiple missense constraint regions (measured by variable missense depletion in gnomAD).

2/10

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

Some updated guidance on our gnomAD v4 constraint scores: gnomad.broadinstitute.org/news/2024-03...

The @gnomad-project.bsky.social team is hard at work on v4.1 and improvements across the board, so expect more updates.

Thanks to Katherine Chao for spearheading this blogpost.

A freely accessible version of the paper can be found here: rdcu.be/dsVXx

Our data are freely available on the gnomAD browser (gnomad.broadinstitute.org).

A huge thank you to Mike Guo, Laurent Francioli, Sarah Stenton, and Julia Goodrich for their hard work on this. This, of course, wouldn't be possible without everyone involved with @gnomad-project.bsky.social.

We also counted the number of genes that appeared to have compound heterozygous (in trans) deleterious variants and didn’t find that many. After manual curation, we only found seven genes with high-confidence compound heterozygous loss-of-function variants with some caveats.