Simone Rubinacci

Statistical Genomics @FIMM_UH @broadinstitute

[Veera Rajagopal  @doctorveera]

A new paper in @Nature from David Reich, @aliakbari23 and colleagues breaks the conventional understanding of recent human evolution. The field believed that strong selection in the recent past (~10,000 years) was rare, with few exceptions like the lactase persistence locus. In this paper, the authors challenge that belief, showing that we weren't looking at the problem right.

Previous studies that looked for evidence of selection using ancient DNA addressed the problem cross-sectionally, asking if allele frequencies differed across populations more than what one would expect based on genetic drift and migration. Most arrived at the conclusion that population structure primarily explained the observed differences. Here, the authors addressed the problem longitudinally, accounting for when ancient individuals lived by explicitly modeling time as a variable in the analysis. It turns out doing it this way dramatically increases power, increasing the number of genome-wide significant selection signals by 20-fold!

Looking at why accounting for the time variable led to such dramatic changes in results, the authors find that previous studies missed so much because selection often happened not on new variants leading to dramatic sweeps (the conventional model: new variant -> selection -> increase in frequency) but on already existing variants driven by transient environmental pressures. Many of these variants underwent reversals, selected up when a pressure existed, then purged when it disappeared or the trade-off cost became dominant. A great example is the TYK2 variant, where an allele boosting immunity was selected for thousands of years because it protected against TB, then got purged as TB endemicity declined and the autoimmune cost took over.

The scale of what they found is striking: hundreds of loci showing strong selection in the past 10,000 years with a median selection coefficient of ~0.86%. This number is pretty big in evolutionary terms, meaning allele frequencies have been shifting by ~1% per generation in a consistent direction. Previous selection scans found a maximum of 20 loci, and this one finds hundreds. That isn't an incremental change. It fundamentally reframes our understanding of how common strong selection has been in recent human history.

Some of the most striking findings come from polygenic selection, where hundreds of small-effect alleles were pushed in the same direction simultaneously. Polygenic scores based on large-scale GWAS of today predict recent negative selection for traits like body fat, waist circumference and schizophrenia, and positive selection for others like cognitive traits. One important caveat is that GWAS phenotypes are measured in industrialized societies today, and how well they capture what was actually being selected in ancient environments is debatable.

For me personally, these findings have direct implications for drug discovery. When using human genetics to find drug targets, we often fixate on the benefit and risk profiles of variants visible today. But we need to be aware that a variant's benefit:harm ratio might be environmentally contingent, and could reverse when the wrong environment manifests. An evolutionary understanding of a variant's association with traits is therefore essential.

The same logic applies, perhaps even more urgently, to embryo selection. Selecting embryos based on polygenic traits is humans making permanent, heritable decisions for their offspring with a narrow view of today's environment. The ancient DNA record now shows that cost-benefit landscapes flip over time. So, an embryo carrying man-made selections is carrying those changes into an unpredictable future environment.

The broader takeaway is that human evolution didn't freeze in the last 10,000 years. We just lacked the tools and datasets to see its movement. The current findings are based on European populations. I am curious to see these analyses extended to other populations too, like South Asian, East Asian and African populations, which might be holding more surprises to blow our minds.

Akbari et al. Nature 2026
https://t.co/3WWjpTiVgA

[AJHG @AJHGNews]

📣New from Kumar et al!
📄MetaGLIMPSE: Meta-imputation of low-coverage sequencing data for modern and ancient genomes

https://t.co/8dkS9LN2zq

[Pradeep Natarajan @pnatarajanmd]

Delighted to collaborate with this exciting preprint led by @MGLevin & Po-Ru Loh, developing a novel LPA haplotype-based prediction model using @AllofUsResearch & validating in multiple biobanks. This substantially improves the identification of individuals with high Lp(a) across genetic ancestries who have been genotyped https://t.co/gFlEOyeJaL @medrxivpreprint

[ChikashiTerao @ChikashiTerao]

My invited manuscript is now online in Nature Reviews Genetics. I highlight a landmark 2018 Nature paper by Po-Ru Loh (Harvard Medical School) that established large-scale detection of mosaic chromosomal alterations from DNA microarray data.

[Hilary Martin @hilsomartin]

I have an opening for a staff scientist or bioinformatician in my group at the Sanger Institute (closing date 24 March). Current projects focus on disentangling rare and common variant contributions to rare neurodevelopmental conditions and to neurodev and perinatal traits. 1/2

[nature @Nature]

Nature research paper: Insights into DNA repeat expansions among 900,000 biobank participants

https://t.co/ewwECkep4i

[Robin Hofmeister @Rbn_Hfmstr]

🚨 Our parent-of-origin study is out in @Nature ! 🧬
Maternal and paternal alleles can have distinct — even opposite — effects on human traits, revealing a hidden layer of genetic architecture that standard GWAS miss.
🔗https://t.co/OWW6vGxGYI

Highlights below!

[bioRxiv Genomics @biorxiv_genomic]

MetaGLIMPSE: Meta Imputation of Low Coverage Sequencing Data for Modern and Ancient Genomes https://t.co/UuR45Hkcfz #biorxiv_genomic

[Simone Rubinacci @simrubk]

@andganna @SebastianMayWi1 Amazing talk, @SebastianMayWi1! Well done!

[Sinan Shi @ShiSinan]

Our paper on the GEL haplotype reference panel and the latest whole-genome imputed #ukbiobank data (21008) is out #NatureGenetics, implying rare non-coding mutations are not as disruptive as coding variants. https://t.co/dqye31dfDJ

[Simone Rubinacci @simrubk]

@leo_speidel @RIKEN_JP Congrats Leo!

[Simone Rubinacci @simrubk]

@Aoxing2 @broadinstitute @FinnGen_FI @FIMM_UH @theNCI @Cambridge_Uni Congrats Aoxing! Super exciting work!

[Simone Rubinacci @simrubk]

@andganna Absolutely my pleasure! Thrilled to be part of such an amazing group of PIs! 🚀

[Simone Rubinacci @simrubk]

@ShaiCarmi Only approved researchers within a specific UK Biobank project can see what is uploaded on the RAP within that project (in practice PIs create as many RAP projects as they want, managing access for their team). In that sense it should pretty similar security as a local cluster.

[Olivier Delaneau @ODelaneau]

Happy to announce the release of 200k UK Biobank WGS phased with SHAPEIT5, together with cutting-edge imputation pipelines. Relevant links:
- Processing: https://t.co/XgfgiL51a9
- Access: https://t.co/iOQD2LQhbl
- Pipelines: https://t.co/XICoC661zc
More details to come!

[Simone Rubinacci @simrubk]

@ODelaneau Join the conversation on GitHub to give us feedback and comments.

https://t.co/QBTB7Bypg7

[Simone Rubinacci @simrubk]

@ODelaneau 🧬 The SNP Array Pipeline.

Covers the entire process from reference panel conversion to pre-phasing with SHAPEIT5 and imputation with IMPUTE5 – all packed into a single applet on the RAP!

[Simone Rubinacci @simrubk]

@ODelaneau 🔬 The Low-Coverage Pipeline.

Empowers imputation of your low-coverage BAM/CRAM files using GLIMPSE2!

Stay tuned for some updates on GLIMPSE2, coming your way very soon.

[Simone Rubinacci @simrubk]

@ODelaneau Our efforts have focused on improving the accuracy of imputation, specifically for rare variants while also addressing the computational challenges of making lcWGS scalable to the newest generation of reference panels. 🧬✨

[Simone Rubinacci @simrubk]

@ODelaneau GLIMPSE2 uses a Gibbs sampler algorithm and incorporates features like sparse reference panel representation, sparse positional Burrows–Wheeler transform matching, and sparse HMM computations. These optimizations enable efficient imputation from large reference panels.