MarigortaLab

"Best way to put it, nobody fucked with Ernst Mayr" 😅 Bob Trivers died... skeptic.com/article/robert…

"Human Genetics: The Hidden Text of Genome-wide Associations" A classic one from 2007, with lots of great ideas inside, even if, weirdly, it was not cited much...🤨 cell.com/fulltext/S0960…

The math on this project should mass-humble every AI lab on the planet. 1 cubic millimeter. One-millionth of a human brain. Harvard and Google spent 10 years mapping it. The imaging alone took 326 days. They sliced the tissue into 5,000 wafers each 30 nanometers thick, ran them through a $6 million electron microscope, then needed Google’s ML models to stitch the 3D reconstruction because no human team could process the output. The result: 57,000 cells, 150 million synapses, 230 millimeters of blood vessels, compressed into 1.4 petabytes of raw data. For context, 1.4 petabytes is roughly 1.4 million gigabytes. From a speck smaller than a grain of rice. Now scale that. The full human brain is one million times larger. Mapping the whole thing at this resolution would produce approximately 1.4 zettabytes of data. That’s roughly equal to all the data generated on Earth in a single year. The storage alone would cost an estimated $50 billion and require a 140-acre data center, which would make it the largest on the planet. And they found things textbooks don’t contain. One neuron had over 5,000 connection points. Some axons had coiled themselves into tight whorls for completely unknown reasons. Pairs of cell clusters grew in mirror images of each other. Jeff Lichtman, the Harvard lead, said there’s “a chasm between what we already know and what we need to know.” This is why the next step isn’t a human brain. It’s a mouse hippocampus, 10 cubic millimeters, over the next five years. Because even a mouse brain is 1,000x larger than what they just mapped, and the full mouse connectome is the proof of concept before anyone attempts the human one. We’re building AI systems that loosely mimic neural networks while still unable to fully read the wiring diagram of a single cubic millimeter of the thing we’re trying to imitate. The original is 1.4 petabytes per millionth of its volume. Every AI model on Earth fits in a fraction of that. The brain runs on 20 watts and fits in your skull. The data center required to merely describe one-millionth of it would span 140 acres.

✅ The UK Government has today granted access to half a million UK Biobank participants' coded GP data for health research. This change will dramatically increase the power of UK Biobank's dataset to advance the diagnosis, treatment and management of conditions handled by GPs.

Class is often overlooked as a driver of observed career and wealth disparities. This paper is a pretty incredible study into the role of class in the career progression of academia. Class has a substantial impact at nearly every stage of the academic pipeline. Important work. On a personal note, this tracks with my own experience as a first-gen student. In grad school, I was shocked at the number of peers who had academic parents. They arrived on the first day knowing about the journal publishing system, the importance of conferences for networking, etc.

Barbara McClintock's discoveries were so far beyond the understanding of the time that other scientists ignored her work for more than a decade. But she persisted, trusting herself and her evidence. She was awarded the Nobel Prize "for her discovery of mobile genetic elements." Watch the very moment she received the Nobel Prize in Physiology or Medicine in 1983.

🔴 New call! Ikerbasque Research Fellows 2026 ‼️15 positions for promising young postdoctoral researchers 📃The applicants must have their PhD completed between 01/01/2015 and 31/12/2023. 👩‍🔬Applications from women are especially welcomed. 📌More info at calls.ikerbasque.net

"A way to identify the biological basis of GWAS associations" nature.com/articles/d4158… on Ota's "Causal modelling of gene effects from regulators to programs to traits" nature.com/articles/s4158… #20yearsOfGWASblooodyhell

#bioGUNE_EmpoweringWomen | 🚺 Meet Beatriz Mateos This #WomenInBiosciences episode features her computational genomics work on IBD in @MarigortaLab #CICbioGUNE @brta_eus, uncovering how genetic risk variants shape disease 🎥 youtu.be/f0R9fCuNn3U

Including data from 1,047 patients across 19 inflammatory diseases, a new atlas presents a comprehensive model of inflammation in circulating immune cells. nature.com/articles/s4159…

One world, law-bound, vast. Mind wakes, names the hidden order. No plan, yet things align...

Great read on G×E history 👇 Fisher's "statistical GxE" is obv good for prediction within populations. But Hogben's "developmental GxE" seems on point to get mechanisms across contexts. They felt like fighting. By now, it feels like different questions... pubmed.ncbi.nlm.nih.gov/19244846/

How well does TWAS estimate a gene’s direction of effect on a trait? We think of this as an important stress-test for the accuracy of TWAS. In a new pre-print with @PGerlach98341, Jeff Spence, and @jkpritch, we find that TWAS gets the sign wrong around 20-30% of the time! 1/n

@Ikerbasque report on Basque Science in 2025 ➡️ Only region with >2% GDP ➡️ Closer to Catalonia in ERC grants ➡️ Less papers overall, but lead in top IF ➡️ Top in papers with industry (but weak in patents) Obv. data biased towards Spain, would love benchmark with top Euro regions

monthly stack overflow questions over time. 3710 questions last month, just slightly under the 3749 from the first month of it being public. human software engineering had a good run, and now we've come full circle.

Overview of some of top 2025 papers in IBD. Nice read. charlielees.substack.com/p/atomic-ibd-2…

The missing heritability question is now (mostly) answered open.substack.com/pub/theinfinit…

1/2 I am excited to share that I have joined @GTRecerca as a Ramon y Cajal Fellow, where I will establish an independent research line within the @GCATcohort Lab Unit, led by Dr. Rafa de Cid.

Modern GWAS can identify 1000s of significant hits but it can be hard to turn this into biological insight. I'm excited to share our new work combining genetic associations and Perturb-seq to build interpretable causal graphs, out today in @Nature: