Yi Ding

Announcing our much-awaited updated aladynoulli, a fully interpretable Bayesian model that considers predictive and explanatory disease trajectories across the life course for individuals, anchored on underlying PGS. medrxiv.org/content/10.110…

Using complex models to detect genetic interactions (epistasis) can boost power, but how do we quantify their importance and interpret those 'epistasis signals'? In our latest Genome Research paper, we revisit the simplest form—quadratic interactions—and introduce a scalable algorithm for in-depth epistasis quantification. Our findings highlight the unignorable and heterogeneous impact of epistasis in human genetics. Many thanks to Prateek Anand, Aakarsh Anand, Dr. Joel Mefford, and my PI, Prof. @sr_sankararaman! Full text: genome.cshlp.org/content/early/…

🚨Review alert! Admixed populations make up a large portion of global genetic diversity but are often excluded from genomics. Our paper surveys tools & methods to better include these groups in studies, reducing disparities in genomic medicine. 🧬👏 @doubletaotan @egatkinson👇

Human genome has 3⃣0⃣0⃣0⃣0⃣0⃣0⃣0⃣0⃣0⃣ bp, only 1⃣0⃣0⃣0⃣0⃣ are used in the figure! Chromosomes pass down variants as a group to make sure your kid is more related to you. You never know how innovative racists can be when distorting genetics for their evil propaganda. 😡

SMR Portal: an online analysis, visualisation, and database platform for mapping complex disease genes. Powered by Westlake FutureGene. Try it out at: yanglab.westlake.edu.cn/software/smr_p…. See a tutorial video at: yanglab.westlake.edu.cn/software/smr_p…

📢Excited to share our comprehensive review led by @EstherHL5 on gene-environment interactions. Within, you'll find: 🛠️Summary of current methods by underlying hypothesis 🗺️Framework for considerate epidemiological modeling 🗣️Call for greater contextual diversity needed for GxE

@nmancuso_ congrats!!!🥳🥳🥳

Woke up to some very exciting news. I've been promoted to Associate Professor with tenure, here at USC! 🎉 I'm truly grateful for all of the support and mentorship I've had to get to this point, as well as all of the hard work and effort from trainees.

I will work remotely from the Bay Area 🌴🏖️🌅 and visit Boston ☃️⛷️ occasionally. Looking forward to connecting with friends and colleagues in both locations! ☕️(4/4)

I am immensely grateful to my PhD advisor @bpasaniuc and the UCLA community for their exceptional training and support, which cultivated my research interests and skill set, and led me to this incredible opportunity. (3/4)

🥳Excited to announce my new journey as a Postdoc Research Fellow at Gusev Lab today! @SashaGusevPosts (1/4)

Read more about this new *LDpred2* work, now published in @AJHGNews 🥳 authors.elsevier.com/a/1i372geX6LBo

Meet Rosace, a robust deep mutational scanning analysis tool that incorporates positional information and mean-variance shrinkage. Check it out if you are running DMS experiments or handling DMS data! (1/n) biorxiv.org/content/10.110…

@AndrewHaoyu @xihaoli Thank you Prof Zhang! 😎

@xihaoli @yi_ding_ Congratulations Dr. @yi_ding_ !!! Feel so happy for you!!! Oustanding PhD research!

I am thrilled to have reached this milestone! 🎉I am grateful to my advisor and committee members for their incredible mentorship. My heartfelt thanks also go to my colleagues and friends, who have been an integral part of this amazing journey, providing me support at every step!

@xihaoli Thank you Prof Li😎

@yi_ding_ Congratulations, Dr. @yi_ding_ ! Well deserved👏

@spence_jeffrey_ Thank you Jeff!!! 🥰

I'm thrilled to announce that I'll be defending my thesis tomorrow 4-5pm PST! If you're interested in joining me, please send a DM for Zoom link.

Our new multi-ancestry PRS paper is now on Nature Genetics: #Sec2" target="_blank" rel="nofollow noopener">nature.com/articles/s4158…! Thanks for the guidance from @nilanjan10c @XihongLin . Also huge thanks to all the amazing collborators: @JiananZhan @Jin_10ustc @JingningZhang35 and all others 1/X.

Please check our newly accepted #naturecommunications paper: doi.org/10.1038/s41467… We developed FastKAST, a highly-scalable algorithm to identify non-linear genetic effects on complex traits in large datasets. Applying FastKAST on #UKBB, we discovered new epistatic evidence