Qiongshi Lu

Statistical geneticist @UWMadison

[Qiongshi Lu @Q_StatGen]

@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📰https://t.co/UjPbAmhrp8
Software🧑‍💻https://t.co/564QCMFIEV

[Jiacheng Miao @Jiacheng_Miao]

We showed that polygenic components of treatment efficacy capture far more variation than individual variants, but it's hard to detect them from treatment samples alone (too small). Need transfer learning from large GWAS to pull it out.
https://t.co/b0fEOam3ND

[Sushmita Roy @sroyyors.bsky.social,genomic.social @sroyyors]

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!
https://t.co/YEB43csUX6

[Qiongshi Lu @Q_StatGen]

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🧑‍💻https://t.co/ksnWMFuRuQ

[Qiongshi Lu @Q_StatGen]

@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📰https://t.co/UjPbAmhrp8
Software🧑‍💻https://t.co/564QCMFIEV

[Qiongshi Lu @Q_StatGen]

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.

[Nature Human Behaviour @NatureHumBehav]

In this Article, the authors introduce PIGEON, a statistical framework for estimating gene-environment interactions for complex traits. @Jiacheng_Miao @Q_StatGen
https://t.co/kWbUZdsduB

[Qiongshi Lu @Q_StatGen]

@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

[Qiongshi Lu @Q_StatGen]

@MariosGeorgakis Appreciate the thread!

[Qiongshi Lu @Q_StatGen]

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!

[Qiongshi Lu @Q_StatGen]

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📰: https://t.co/dLsmZvcjiV
Sumstats⬇️: https://t.co/0LJOblAOmR

[Qiongshi Lu @Q_StatGen]

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

[Abdel Abdellaoui @dr_appie]

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: https://t.co/hV7ZrIok1G