Yuchang Wu

Biostatistician @ University of Wisconsin-Madison.
Scientist @ Qiongshi Lu's group.
子曰：“学而不思则罔，思而不学则殆。”

[Yuchang Wu @yy_stat]

@AlexTISYoung Claude is really good at software engineering. Tried both paid version of Gemini (edu version) and ChatGPT. I think Gemini is better for scientific research. But it needs very precise prompting, so I usually first ask it to optimize my prompt, then answer its own prompt.

[Yuchang Wu @yy_stat]

@SashaGusevPosts edu email account can get an edu version of Gemini, which should have less hallucinations.

[Yuchang Wu @yy_stat]

@Jiacheng_Miao very cool!

[Yuchang Wu @yy_stat]

@dougthespeed We first copy each chromosome in plink format to active environment, extract HM3 SNPs, and then merge all the HM3 SNPs from each chromosome. The copying process is slow (~1h for one larger chr?), but doable. You can run the job in background. Hail is always painful to me.

[Yuchang Wu @yy_stat]

@AlexTISYoung sorry to hear this. Hope you'll have a speedy and full recovery!

[Yuchang Wu @yy_stat]

@SashaGusevPosts good question. I don't know since I'm not closely following on this topic.

[Yuchang Wu @yy_stat]

update: The filter function on data browser does not give accurate results: some categories and some variants meet the criteria are missing. Guess we have to use VAT for more serious work.

[Yuchang Wu @yy_stat]

Wow! Need a LoF variant list in AllofUs. Filter the giant variant annotation table? Hours of waiting! Ended up copying and pasting data browser results to DeepSeek and in 10 seconds I got a clean tab-separated file! Cool!

[Yuchang Wu @yy_stat]

@SashaGusevPosts Does the environment correlate with genetics? Otherwise, I don't have the intuition why GWAS could be problematic.

[Stephen Dorn @stphn_drn]

My first co-author paper is now preprinted! With
@ZijieZhao1996 and @Q_StatGen
we introduce PUMAS-ensemble, a regularized ensemble PRS requiring only GWAS summary statistics and LD reference data as input.
https://t.co/nmjR8ftPjg

[Qiongshi Lu @Q_StatGen]

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📰:https://t.co/fUZD6TXzdj
Software🧑‍💻:https://t.co/YIYhPAIDpY

[bioRxiv Genetics @biorxiv_genetic]

One score to rule them all: regularized ensemble polygenic risk prediction with GWAS summary statistics https://t.co/jWjLMjdFxK #biorxiv_genetic

[Yuchang Wu @yy_stat]

@geyu_zhou yes; I was too excited to see the job done and totally forgot to copy the results to bucket

[Yuchang Wu @yy_stat]

pain in using cloud computing: took 1 day 10 hours and 44 minutes to finish some analysis using AllofUs data, and I accidentally deleted the "environment" and all the results are gone forever ... 😭😭😭

[Yuchang Wu @yy_stat]

@dajiangliu81 @AllofUsResearch @AllofUsCEO email them and they're usually very responsive. hope this could you resolved without any problems!

[Jiacheng Miao @Jiacheng_Miao]

#ASHG24 Interested in how to reliably use AI/ML to accelerate genetic discovery?

Stop by poster 4151 this afternoon to chat!

[Qiongshi Lu @Q_StatGen]

Four presentations from us this year, covering very diverse topics. Stop by and say hi🍷#ASHG24 #ASHG2024
1) Genetic effect heterogeneity @yy_stat
2) Genetics of partner choice @S5b6bY284u9qqoa
3) Perturb-seq/GWAS data integration @stphn_drn
4) ML-assisted GWAS @Jiacheng_Miao

[Yuchang Wu @yy_stat]

While we didn't explore it in this paper, another tool (POP-GWAS) from our lab (https://t.co/yQdolmx6W6) may offer a better solution when dealing with phenotypes with biases. --- also check it out!

[Qiongshi Lu @Q_StatGen]

POP-GWAS led by @Jiacheng_Miao is published today in @NatureGenet. This work showcases modern data science leveraging the power of machine learning while maintaining statistical validity. It also provides a GWAS solution when the outcome is ML-predicted https://t.co/XBrZ7MBpcd

[Yuchang Wu @yy_stat]

Thrilled to see this published on Nature Genetics! We found weird results using AD GWAX in another project and discovered that the bias is actually a very serious issue in the AD field. This underscores the importance of data quality. Larger is not always better!

[Qiongshi Lu @Q_StatGen]

Our paper on misleading biases in AD GWAS-by-proxy is published @NatureGenet. We identify the source of biases and explore strategies to reduce them @yy_stat @SunZhongxuan
Paper📰: https://t.co/Xdq51wVBw6
Software🧑‍💻: https://t.co/564QCMFIEV
Sumstats⬇️: https://t.co/0LJOblAgxj