Qiongshi Lu

@Jiacheng_Miao's PIGEON paper on GxE methodology is now published @NatureHumBehav with a new title. In our view, this paper can reshape the study design for future complex trait GxE work. Paper📰doi.org/10.1038/s41562… Software🧑‍💻github.com/qlu-lab/GSUB

Interested to pursue graduate studies (MS/PhD) at the intersection of biological, biomedical problems and data science? Join us for a virtual open house on Nov 13th to learn more about our Biomedical Data Science program at UW Madison @UWMadison! biostat.wiscweb.wisc.edu/education/pros…

I had the wrong software link earlier in the thread. See below for the correct one. We've done three very different PIGEON GxE applications. Read the paper for more details. All sumstats produced in the paper are publicly available too. Software🧑‍💻github.com/qlu-lab/PIGEON

Based on this advance, we make two predictions: 1) The future success of complex trait GxE research resides in sharing and meta-analyzing GWIS summary statistics. 2) Future GxE method development should focus on techniques that rely only on summary-level data.

@Jiacheng_Miao's PIGEON paper on GxE methodology is now published @NatureHumBehav with a new title. In our view, this paper can reshape the study design for future complex trait GxE work. Paper📰doi.org/10.1038/s41562… Software🧑‍💻github.com/qlu-lab/GSUB

In this Article, the authors introduce PIGEON, a statistical framework for estimating gene-environment interactions for complex traits. @Jiacheng_Miao @Q_StatGen nature.com/articles/s4156…

@zkutalik Thank you! An important feature of this model is that it's supposed to be robust to the choice of traits (attaching a screenshot snippet here and we explained more in the paper). We also tried using any 3 out of the 4 traits to obtain the index GWAS and basically got same results

Really cool and innovative approach! I wonder how much the results/conclusions depend on the choice of the 4 traits. Also, it is very interesting to see how the EDU-BMI rG swaps sign upon conditioning on the partner's choice index.

@MariosGeorgakis Appreciate the thread!

A very interesting read on the genetic architecture of partner choice "index" — or according to economics literature our "attractiveness in the marriage market"🧬 1/5

This is a big project with many more results in the paper. But overall, we are excited about being able to study genetic associations with something we cannot directly measure. Lead author @qinwen_zzz is an undergrad who's doing PhD interviews recently. Snatch him while you can!

The index GWAS also allows us to remove assortative-mating-driven biases in GWAS applications. For example, genetic correlation of education and height reduced to null after conditioning on the index. But rg of education and cognition remained unchanged

Beyond excited to share this new paper with all of you . It's the most fun we've ever had. We figured out how to study a latent index driving partner choice without measuring it directly🥂 @qinwen_zzz Preprint📰: biorxiv.org/content/10.110… Sumstats⬇️: qlu-lab.org/data.html

Our first multi-national GWAS on income is out in @NatureHumBehav 🧬💰 - 162 loci (88 novel) - significant heterogeneity between countries - significant heterogeneity between men and women - unique non-education income effects Open Access Link: nature.com/articles/s4156…

@stphn_drn has a detailed thread to show all our results. He also implemented these new ensemble features in our PUMAS software. Stephen is applying for PhD programs this year. Look out for his application. We want him in Wisconsin too! Thread🧵:x.com/stphn_drn/stat…

One of the applications is ensemble learning. We've explored a vanilla version before through linear regression, and have done some cross-ancestry applications. Here, we realize its full potential and showcase much better ensemble learning models on sumstats

Our new PRS paper is out! We introduce a method to create regularized ensemble PRS on GWAS sumstats. Individual-level data is no longer needed for sophisticated ensemble learning @ZijieZhao1996 @stphn_drn Paper📰:biorxiv.org/content/10.110… Software🧑‍💻:github.com/qlu-lab/PUMAS