Yuchang Wu
@yy_stat
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Biostatistician @ University of Wisconsin-Madison. Scientist @ Qiongshi Lu's group. 子曰：“学而不思则罔，思而不学则殆。”
Wisconsin, USA
Joined April 2019
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.
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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!
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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
biorxiv.org
Ensemble learning has been increasingly popular for boosting the predictive power of polygenic risk scores (PRS), with almost every recent multi-ancestry PRS approach employing ensemble learning as a...
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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
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One score to rule them all: regularized ensemble polygenic risk prediction with GWAS summary statistics https://t.co/jWjLMjdFxK
#biorxiv_genetic
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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 ... 😭😭😭
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#ASHG24 Interested in how to reliably use AI/ML to accelerate genetic discovery? Stop by poster 4151 this afternoon to chat!
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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
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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!
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
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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!
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
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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
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At Stanford for the IGSS conference. Interesting talks, lovely weather, and beautiful campus! I think I like this place.
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New preprint from my group led by @yy_stat @SunZhongxuan. Almost every recent Alzheimer's GWAS combines clinical AD diagnosis and proxy phenotypes based on parental health survey. We report major limitations in the current practices of GWAS-by-proxy (GWAX) https://t.co/lS7Zju5rXy
biorxiv.org
Almost every recent Alzheimer’s disease (AD) genome-wide association study (GWAS) has performed meta-analysis to combine studies with clinical diagnosis of AD with studies that use proxy phenotypes...
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You'll want to check out @Jiacheng_Miao's new paper if your work involves gene-environment interaction. This is our best GxE work so far. We introduce a statistical framework named PIGEON to reimagine how GxE studies could and should unfold.
biorxiv.org
In this study, we introduce PIGEON—a novel statistical framework for quantifying and estimating polygenic gene-environment interaction (GxE) using a variance component analytical approach. Based on...
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Preprinted in time for #ASHG2022! Happy to share @ZijieZhao1996's new work. We present PUMA-CUBS which advances PRS benchmarking & optimization. We can now fine-tune any PRS models and even do ensemble learning to combine models using GWAS sumstats alone!
biorxiv.org
We introduce an innovative statistical framework to optimize and benchmark polygenic risk score (PRS) models using summary statistics of genome-wide association studies. This framework builds upon...
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many genetic studies on autism pretend that E does not exist. In this poster, we proposed a unique study design to quantify the contribution of maternal genotype to child's ASD risk in addition to child's genotype. Come and let's discuss more details if you're interested.
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#ASHG2022 poster alert (Thursday afternoon; Board No.3383): It's well-known that autism has strong environmental contributions (e.g. autism susceptible genes are preferentially expressed in developing brain thus intrauterine environment from the mother is critical), however,
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Excited to finally throw our hat in the ring. Here is our new method (named X-Wing) for cross-ancestry PRS. A key takeaway is that we now do EVERYTHING with sumstats alone which is a major methodological advance. Work is led by @Jiacheng_Miao & Hanmin.
biorxiv.org
Polygenic risk scores (PRS) calculated from genome-wide association studies (GWAS) of Europeans are known to have substantially reduced predictive accuracy in non-European populations, limiting its...
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Excited to see our work published! I enjoyed a lot in this project and enjoyed even more (should I say this? 😃) from two very helpful and constructive reviewer comments! Thanks to all my coauthors; this is an amazing team work!
Our GWAS on infant mortality rate is published in @PNASNews. We use area-level IMR to index early-life levels of hardship conditions including WWII. Doing a GWAS on it revealed (surprisingly clean) signals of very recent selection on beneficial variants
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