@LD_matrix

Postdoc @DanaFarber and @harvardmed | Biostat Ph.D @penn_state | Statistical genetics, functional genomics, data science

 Boston, MA
lidawangpsu.github.io/Lida/
Joined August 2020
  • Tweets45
  • Following411
  • Followers169
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An ASHG planner that doesn't suck (Claude): sashagusev.github.io/ashg-20…
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New work from Pasi A. Jänne, us, and 23andme; led by Jackie LoPiccolo and Steven Micheletti. Characterizing a highly penetrant germline founder mutation for lung cancer.
Science Magazine
@ScienceMagazineSep 17
An inherited genetic mutation may increase lung cancer risk among people who have never smoked, according to a new Science study. By tracing the ancestry of people carrying the rare EGFR T790M variant, researchers uncovered a shared genetic legacy extending back hundreds of years and concentrated in the southeastern United States. Learn more in this week’s issue: scim.ag/46q1kUC
An inherited mutation in the EGFR gene (EGFR T790M) increases lung cancer risk. In this illustration, lungs merge with a map of the United States, reflecting the geographic enrichment of the EGFR T790M mutation. By tracing the ancestry of people carrying this mutation, researchers uncovered a shared genetic legacy extending back hundreds of years and concentrated in the southeastern United States. Illustration: Oliver Uberti

ALT An inherited mutation in the EGFR gene (EGFR T790M) increases lung cancer risk. In this illustration, lungs merge with a map of the United States, reflecting the geographic enrichment of the EGFR T790M mutation. By tracing the ancestry of people carrying this mutation, researchers uncovered a shared genetic legacy extending back hundreds of years and concentrated in the southeastern United States. Illustration: Oliver Uberti

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Lida Wang retweeted
Yun S. Song@yun_s_songSep 9
We are thrilled to share that our GPN-Star manuscript is now published and freely available: doi.org/10.1038/s41586-026-1… (1/n)

Predicting genome-wide functional constraints with GPN-Star

Nature - GPN-Star, a genomic language model with a phylogeny-aware architecture for whole-genome alignment data, is shown to be a scalable and flexible tool for genetic variant effect prediction...

nature.com
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Lida Wang retweeted
Minhui Chen@chen_cmhAug 26
CIGMA is out in Nature — A linear mixed model to unbiasedly characterize cell-type-specific eQTLs. Work advised by @andywdahl and a lot of help from @xinpei_w nature.com/articles/s41586-0…

Cell-type-specific eQTLs underlie the genetic architecture of complex traits

Nature - Single-cell RNA-sequencing shows that cell-type-specific expression quantitative trait loci drive much of complex trait heritability, highlighting cell-type-specific gene regulation as key...

nature.com
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Survey data document US visa types and burdens of time, cost and mental health among international postdocs nature.com/articles/s41587-0… rdcu.be/fB1tP

The invisible tax of US visa bureaucracy on international scholars

Nature Biotechnology - Survey data document US visa types and burdens of time, cost and mental health among international postdocs.

nature.com
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Lida Wang retweeted
Jian Yang@jyang1981Aug 5
Postdoc and research scientist positions available in my lab: yanglab.westlake.edu.cn/jobs…
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Sarah Urbut
@tigerstatdocJul 15
One of my first consults as a cardiology fellow: a 34-year-old, textbook MI. A day earlier, no risk model would have flagged him for prevention. That paradox has driven my work ever since — our models miss how disease actually evolves, dynamically, on top of a genetic background. @Nature nature.com/articles/s41586-0…
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Noam Brown
@polynoamialMay 28
Source: henrikkarlsson.xyz/p/go
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New work from us pooling together clinical sequencing data to identify large effect common risk variants for rare cancer types. This included a novel HLA locus that interacts with HPV infection to increase Anal Cancer risk.
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A little jukebox that plays the top hits from a desired country and year (ordered by most distinct hits for that country).
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Happy to share our new preprint from @SashaGusevPosts and @nmancuso_ labs! We introduce Mr. PEG, a framework integrating perturbational screens, eQTL, and GWAS data to identify mediating genes for complex traits. (1/n) medrxiv.org/content/10.64898…

Integrating perturbational screens, eQTL, and GWAS data identifies mediating genes for complex...

Most current GWAS-eQTL approaches prioritize genes whose mediating effects on complex traits act through cis -regulation, while trans -acting genes remain largely underexplored. Recent perturbational...

medrxiv.org
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Excited to share our new work of regulatory effects in the brain using sc-RNASeq data. Importantly, we argue that besides analyzing single cell eQTLs, it is important to look beyond cell types and better dissect shared and distinct effects between cell types. nature.com/articles/s41467-0…
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How do GWAS and rare variant burden tests rank gene signals? In new work @Nature with Jeff Spence, @jkpritch, and our wonderful coauthors we find the key factors are what we call Specificity, Length, and Luck! 🧬🧪🧵 nature.com/articles/s41586-0…

Specificity, length and luck drive gene rankings in association studies

Nature - Genetic association tests prioritize candidate genes based on different criteria.

nature.com
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The proportion of epistatic heritability that is estimated as additive by quantitative genetic models. Epistasis deviates more from additivity for lower frequency causal alleles, but on average >80% of biological GxG will just look like statistical G.
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Check out our scPrediXcan paper cell.com/cell-genomics/fullt… Led by talented @Charles_Zhou12, supervised by @MengjieChen6 and me, with thanks to many contributors scPrediXcan integrates deep learning and single cell expression data into a powerful cell type specific TWAS framework.

scPrediXcan integrates deep learning methods and single-cell data into a cell-type-specific...

Zhou et al. introduce scPrediXcan, a novel transcriptome-wide association study framework that integrates the deep learning-based model ctPred for cell-type-specific expression prediction. Applied to...

cell.com
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I'm excited to share a wonderful collaboration with @LD_matrix: JOBS integrates bulk and single-cell eQTLs to boost eQTL discovery. Integrated with GWAS, it identified more disease-linked loci for 14 immune disorders & fueled drug-repurposing. #immunology #drugdiscovery #genomics
Dajiang Liu@dajiangliu8127 Mar 2025
I am excited to share our new work that generates a single cell eQTL atlas for immune cell types. The work integrates bulk eQTLs with sc-eQTLs, which boosts the power for identifying sc-eQTLs for up to 4 folds. As such, we have the power equivalent to sc-RNASeq of 4K individuals.
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I am thrilled to share one of the most important projects from my PhD! We developed the method JOBS (JOint model of Bk-eQTLs as a weighted sum of Sc-eQTLs) and applied it to blood bulk (eQTLGen) and sc-eQTL (OneK1K). 1/3
Dajiang Liu@dajiangliu8127 Mar 2025
I am excited to share our new work that generates a single cell eQTL atlas for immune cell types. The work integrates bulk eQTLs with sc-eQTLs, which boosts the power for identifying sc-eQTLs for up to 4 folds. As such, we have the power equivalent to sc-RNASeq of 4K individuals.
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Additionally, we designed a drug repurposing and validation pipeline, identifying novel drug candidates for autoimmune diseases. Check it out! 🚀 3/3
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Our method significantly boosts the power to identify sc-eQTLs—equivalent to expanding the sample size by 4 times. This enhancement benefits all downstream analyses, leading to more colocalized loci and more cell-type-specific TWAS genes. 2/3
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Lida Wang retweeted
Xin Ma@XinMa1318 Mar 2025
🧠🔥"Put on your 3D glasses...oh wait, you don't need them!"😝 Brain Metabolome just got an upgrade-why look at slices when you can see the whole picture in 3D? Check out our latest work in @NatMetabolism .🚀🔬 🔗doi.org/10.1038/s42255-025-0…
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