Daniel E. Weeks

I was sad to learn that my postdoctoral mentor, David Botstein, died yesterday. I started with David as a postdoc in 1998, and he had a profound effect on both my life and scientific career. He was a giant in the field of genetics, making seminal contributions in both yeast and human genetics. 1/

An important #rstats post here. (Perhaps a better title: Why tidymodels might not be so tidy)

I don't eschew this kind of discussion (|> vs %>%, ...) as R Flame Wars. In fact, it's important to continue to hone the language tools we use to express & implement our ideas/models/graphs.

Interbreeding between Neanderthals and modern humans was strongly sex biased Sex biases in admixture and other demographic processes are recurrent features throughout human evolution. For admixture between Neanderthals and anatomically modern humans (AMHs), sex bias has been p...

Many living people carry fragments of Neanderthal DNA, remnants of ancient interbreeding events, with uneven distribution across chromosomes. New work by @sarahtishkoff.bsky.social lab suggests patterns are most consistent with Neanderthal contribution to human populations being highly male biased.🧪

Specifically, the idea of source credibility just doesn't apply straightforwardly to LLM interactions. When I get info from another person I can ask: how does the content relate to their expertise? Their lived experience? Their interests and biases?

8/

What's your favourite plot type?
Extra points if it's a cool science type plot.

I really like Violin plots with swam overlays.

Happy to share our latest paper, the work of @clarajiang.bsky.social who trained brain-based predictors of depression on the UK Biobank (N=7,500 curated cases and matched controls) using AI/deeplearning as well as efficient statistical learning (BLUP).

See thread below for a summary of findings

What even is truth? Seems like it should be the purview of philosophers and poets — clearly not people (me) designing snakemake workflows.

I can't be the only one who is made /uncomfortable/ by the way that razor clams burrow into sand, right?

The final moments of this video by Kate Crump on the Oregon coastline are sort of the crowning glory of horror. 😬

But how does this even work?
Let's talk about bivalve mobility (if we must).

Retraction notice: “the authors acknowledged that their analyses include coding errors. The central error arose from the inverse hyperbolic sine being calculated based on y+1, … left over from an earlier version of the study in which a log (y+1) approach was used instead of the hyperbolic sine.”

@wytamma.bsky.social 's WASM tools have transformed my experience of teaching Python to first year undergraduate biologists this year. Since last year, I've been teaching ~400 undergrads how to code (functions, lists, dictionaries, loops) over (one hour intro lecture +) two 2-hour practicals. 1/n

Leveraging a Genetic Proxy to Investigate the Effects of Lifelong Cardiac Sodium Channel Blockade | Circulation BACKGROUND: Atrial fibrillation and other cardiac arrhythmias pose a major public health burden, but prevention remains difficult. We investigated a genetic variant that we found to act like a natural...

Check out our new article in #Circulation on an astonishing genetic variant in the ion channel #SCN5A that enables us to learn about #cardiac-arrhythmia and their treatment with sodium channel blockers.
@finngen.bsky.social
www.ahajournals.org/doi/10.1161/...
Congrats to @jwanner.bsky.social

Screening, sorting, and the feedback cycles that imperil peer review The process of peer review is vital to contemporary science, but is also under enormous strain. This study uses mathematical models to dissect the threats to the long-term viability of peer review, su...

1. Kevin Gross and I have a new paper out today PLOS Biology.

We used economic models based around screening games and the market for unpaid labor to highlight a meltdown cycle threatening peer review.

Flow diagram. At the center is the key cycle: Authors reduce self-screening --> Reviewer pool becomes depleted --> Accuracy of review declines --> [back to] Authors reduce self-screening. Outside arrows lead to this central cycle. Greater emphasis on elite journals --> authors reduce self-screening More journals create more opportunities for fresh review --> reviewer pool becomes depleted Common pool dilemma reduces desk rejection rates --> reviewer pool becomes depleted. There is an additional cycle: Accuracy of review declines --> Authors learn less from reviews --> Authors reduce self-screening

7. We show that this setup leads to a pernicious positive feedback loop. When submissions increase, more peer reviewers are required. Either current reviewers have to spread their effort thinner, or less qualified reviewers must be recruited. Either way, review accuracy drops.

I saw this when it was a preprint? They have a unique version of the estimand-estimator-estimates workflow, will be useful for may biologists, not just ecologists. We need more case studies for ecologists to emulate and for norms to shift so "regression and storytelling" becomes unpublishable.

#rstats Here's a useful guide to creating publication-ready #ggplot figures to journal specifications, which is often quite fiddly.

jaquent.github.io/2026/02/crea...

Good advice from the 'bind_cols' documentation:

"Where possible prefer using a join to combine multiple data frames. bind_cols() binds the rows in order in which they appear so it is easy to create meaningless results without realising it."

#RStats #DefensiveProgramming

yes i wrote a stats textbook shut up shut up shut up. statistics is a discipline in which everybody has impostor syndrome and if you don't then honestly you're probably lying to yourself

#rstats

The Australian and New Zealand Journal of Statistics will be running a special issue commemorating the 25th anniversary of the official release of R. (The Univ. of Auckland was the birthplace of R.) They invited various people to contribute articles, including me. 🧵 1/

Yes, you still have to have enough domain knowledge and coding skill to be able to read, evaluate, test, and rigorously check the generated code, since the generated code could easily be incorrect or non-robust to edge cases.

Partitioned polygenic scores show mechanistic heterogeneity in type 2 diabetes and hypertension comorbidity - PubMed Type 2 diabetes and hypertension are common health conditions that often occur together, suggesting shared biological mechanisms. To explore this relationship, we analyse large-scale multiomic data to uncover genetic factors underlying type 2 diabetes and blood pressure comorbidity. We curate 1304 i …

The doi link didn’t work for me so here’s the PubMed link

pubmed.ncbi.nlm.nih.gov/41663376/

Last term I tried an experiment: I walked into my Tech and Design Ethics class, admitted that I had *no idea* what to do about ChatGPT - so I would let them figure it out.

As in: their first project was to decide and write the ChatGPT policy for the class.

Here's what happened:

Brilliant and beautiful!

“In 2025, sponsored research by faculty generated more than $4.8 billion. Compared to that sum, donors contribute a pittance: even the profligate Mr. Bloomberg doesn’t come close. And yet for some reason it’s the donors rather than the faculty who sit on the board and make the big decisions.“

Graphic cover slide titled “Day in the Life: Brooke Wolford, Statistical Geneticist.” Subheading notes she is a faculty member at the Norwegian University of Science and Technology in Trondheim, Norway. Clean, professional layout introducing the series.

Brooke describes starting her day with coffee in bed and checking emails during her 40-minute commute to campus in Trondheim. She mentions living in Norway and beginning work around 8:30 a.m.

Brooke shares that faculty in Norway are required to complete pedagogical training. She attends a session focused on improving teaching practices and supporting student learning.

A day in the life of Brooke Wolford, a statistical geneticist at NTNU (Norway). It's proof that science is both technical and deeply human at the same time. 🧬☕️🐕

Really important point here from Elsje. It is crucial to include information about genetic/familial links when considering causes of disorders but lets not hype them as the ultimate solution.

Sometimes, package like modelling packages initially work with tibbles, but then other functions (simple toy-example: `coef()`) do not. Then you start searching *your* code for issues, until you realise it's the tibble that cannot be processed in the *others* code.

American professors, who wants to come to Oxford for a year or two? Get in quick - deadline soon.
www.balliol.ox.ac.uk/vacancy-geor...

Tables in publications are often the worst. Even worse is when cells are merged and data is formatted with lines, fonts, and next to each other, as this is only for visualizing data, not for data use. I hate them. If you're using XLSX files as a supplement, give them a useful format!

The 3 hardest things to learn as a scientist:
1. Trust the data.. especially when it’s not what you expected,
2. Trust the data.. allowing it to change your direction,
3. Trust the data.. but not too much: test with new data at every turn.

Yes, they can hallucinate papers that don't exist, discuss results that seem to be imaginary, and can be confusing and inconsistent. But talking to tenured professors may still be helpful