Reading papers detracts from time that could be spent actually analyzing data, and yet keeping up with new research is necessary to analyze data effectively. I'm still struggling to find a good balance, especially when I feel like I barely have enough time to read my email...

Congratulations again on receiving the Williams Award for the best presentation by a student at IGES!! I really enjoyed meeting you and learning about your cool work!

We can't wait to see you in Cologne this week for #IGES2025!! See below for some important reminders 👇

The use of dichotomized labels for traits that are actually continuous has bothered me ever since I started working on complex trait genetics. Sasha's blog post provides an excellent explanation of why this matters in the context of polygenic risk and embryo selection.

Yesterday I successfully defended my dissertation! It feels surreal to finally reach the end of my PhD journey at @umnsph.bsky.social. Thank you to my adviser Wei Pan and to my committee members @elock.bsky.social, @aazaidi.bsky.social, and Thierry Chekouo for your invaluable mentorship and support!

Excited to share my first PhD paper in the @sbmontgom.bsky.social lab with @tamigj.bsky.social (www.biorxiv.org/content/10.1...)! Standard QTL methods treat each gene independently. But what if a single variant regulates multiple nearby genes at once - what we call “allelic proxitropy”? 🧵 ⬇️

I had exactly the same thought while reading your preprint yesterday! I think that combining our two methods is a very promising idea. I have never initiated a collaboration outside my own lab before, but I would love to pursue this direction together if you are interested!

Very cool work! I'm glad to see others also advocating for moving beyond the marginal, single-gene paradigm. I did a related project for my PhD, extending TWAS to account for genetically regulated co-expression (doi.org/10.1101/2024...). Maybe we can connect once I move to Stanford this fall!

Join us for our next virtual journal club! Jasper Hof will present on his paper "Fast and accurate recurrent event analysis for genome-wide association studies".

📅 June 16, 2025
⏰ 11 am (EDT), 8 am (PDT), 5 pm (CET)
📍 Online

Learn more and register: www.geneticepi.org/index.php?op...

I had a great time at STATGEN 2025 last week! Thank you to everyone who came to my talk and to the organizers for putting on this conference.

This year's IGES Annual Meeting and Education Workshop will be held Aug. 31 - Sept. 2 in the beautiful city of Cologne, Germany. Join us for an exciting lineup of keynote presentations, platform talks, posters, and hands-on tutorials at #IGES2025!

Learn more: www.geneticepi.org/2025-annual-...

Follow @geneticepi.bsky.social to stay up to date on news and events from IGES! This will be the society's primary social media presence going forward, and we will be sharing a lot of cool and useful content. Full disclosure: I manage the IGES account.

Skimming papers that use vastly different terminology or notation is not easy either, but I hope you are right that conventions in these fields are starting to converge!

I am a young person, and I can confirm that I have wondered about this many times. But what is the solution? I've tried searching econometrics journals for papers on IV regression, but I did not get very far because the terminology and notation are completely different from what I'm familiar with.

A reviewer described my COWAS method as "a significant breakthrough in the study of gene-gene (protein-protein) interaction." Before that, the very same paper was desk rejected by four other journals. The moral of this story is obvious and left as an exercise to the reader.

Importantly, type I error was not inflated because imputed data was only used for training, not inference. Overall, we demonstrated that GWAS-based trait imputation can be used to increase sample sizes and thereby boost power for nonlinear TWAS/PWAS methods that require individual-level data. (6/6)

We found that DeLIVR trained on imputed AD status was able to identify disease-relevant genes that were missed if the models were instead trained on observed data, and the same story held true when identifying putative risk proteins. We also showed that LS-imputation has the best performance. (5/6)

We considered the nonlinear TWAS/PWAS methods DeLIVR (based on deep neural networks) and TWAS-LQ (based on a parametric polynomial model). We focused on Alzheimer's disease (AD) and imputed AD status from family history or from GWAS summary statistics using LS-imputation, PRS-CS, and LDpred2. (4/6)

But linear TWAS/PWAS methods have one major advantage: linear stage 2 models can be estimated using GWAS summary statistics instead of individual-level data. So what if we use GWAS summary statistics to create imputed individual-level datasets and then train nonlinear TWAS/PWAS models on them? (3/6)

Transcriptome- and proteome-wide association studies (TWAS/PWAS) traditionally rely on linear models and can only detect linear effects. Recently our group and others developed IV regression methods based on flexible models, which can identify genes with nonlinear effects on complex traits. (2/6)

I'm excited to share our latest work, now out in #PLOSGenetics! If you feel limited by linear models in TWAS or PWAS but don't think you can switch to nonlinear methods due to a lack of individual-level phenotype data, this paper is for you.

🔗 journals.plos.org/plosgenetics...

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