Introducing GWAS Signals, a Weekly Digest of Human Genetics Literature

GWAS Signals is a sister publication to GWAS Stories, powered by an AI-driven workflow

People who have followed my writing for long have known that I read a lot. I read far more papers than I could ever write about. As years go by, as things change in my personal and professional life, I am finding it difficult to find time to write. I believe that it's not that I don't have time. It's that I lack the skills to find time when multiple things compete for my poor attention. Amidst these struggles I somehow once in a while sit and make an attempt to write about the fascinating scientific discoveries that I read last week, last month, or many months ago, sometimes without even a memory of the time of my encounter with the story. Such half-written stories lie half-baked in my drafts. So, you get it: what comes out in GWAS Stories are the ones that managed to get past all these barriers.

With the rapidly advancing AI field, I'd be lying if I said I haven't been using it to read, write and think about science. I use AI tools more than you'd guess. Among the many things I've been experimenting with using AI is this new newsletter, GWAS Signals. It's honestly a mere sneak peek into the papers I look at, for the readers who already follow the ones I write about. It's not a flood of everything that crosses my screen, but the carved-out fraction that stood out, the papers that felt worth a few seconds to glance at and see what they are about.

Like most of you, I keep marvelling at how radically the way we consume knowledge is changing. In the early days, my source for new research papers was mostly Twitter. The little bird told me about all the exciting papers that scientists shared online with real enthusiasm, the work they had just published or preprinted or were about to present at a conference. Once in a while I would check the home pages of Nature, Nature Genetics and a few others. Then came the exodus, when Twitter became X and researchers left in droves. I stayed, but I soon realized that scrolling X was no longer keeping me current with my field. So I went back to building my own knowledge source: RSS feeds for my favourite journals, preprints, subjects and PubMed searches. And then AI arrived. With a little bit of context, I found it could triage my feed remarkably well, and not just triage but summarize the key findings. Now I spend my energy brushing through those findings instead of hunting for the papers, and it helps with my FOMO that comes from knowing the field moves faster than I can read.

So here is how it actually runs now. I still choose the journals and topics I want to follow. AI agents do the rest: they ingest the feeds, apply my interest filter, and present a curated list of papers on a dashboard that I try to visit every day. I give my feedback on the curation, marking each paper as one of the following: love, like, good to know, or skip. The system takes that feedback, adjusts what it presents, and gets a little better at curation each cycle. Once a week it picks around eight papers, often around a shared theme, and drafts them into a newsletter. I do not write that prose by hand, though I keep nudging it towards more clarity and brevity.

I should be honest about why I do this. This whole ritual is mainly for my own reading. It is an attempt to stay current with a field that moves faster than I can, and I would want to keep doing it even if no one else read a word of it. But putting it out in the open adds a small discipline. Knowing a few of you are on the other side of it makes me a little more faithful to the ritual than I would be on my own. The ritual itself might not remain the same; it will evolve over time based on how the technologies underneath the workflow evolve, but the effort to have a ritual to read the literature will stay. And I'll try to make it useful for you too.

One caveat to note. These write-ups are drafted by AI, and AI can hallucinate. I read the draft, highlight key sentences, and catch errors before I publish. Yet, hallucinations could escape my attention and bleed into the draft. So, treat GWAS Signals as a pointer, not a verdict. When a summary makes you lean in, when a finding catches you enough that you want to lean on it, refer to the original paper. The signal is meant to send you to the source, not to stand in for it.

You can find GWAS Signals at gwassignals.substack.com, or just hit subscribe below.