Mission

We develop quantitative and computational methods and tools to sift through the vast amounts of genomic and other high dimensional data with the goal of making discoveries that can be translated to improve human health.

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We are releasing prediction models trained on GTEx Version 7 data. Download from here. We have updated our processing pipeline, and restricted to individuals of European ancestry to obtain more reliable LD data. This reduces false positive associations in the Summary Version of PrediXcan. Because of this choice, the gain in sample size relative to V6p is modest (ranging from -18 to 89), with whole blood, LCLs and fibroblasts experiencing reduced sample size.

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After two years in the Lab, Scott has decided to join the well paid workforce. Scott is a wonderful colleague to all of us and has made important contributions to our team. Thank you and best of lucks, Scott 🍀🍀🍀

Also thank you, Wenndy, for organizing and buying the present for Scott.

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Keep in mind that significant associations shown here do not imply causality. That said, given that PrediXcan is seeking to test the role of gene expression variation on traits and we and others have shown that significant PrediXcan genes are enriched in causal genes, these results should be useful to delve into the mechanisms underlying gene to phenotype associations. False positives can arise because of several factors LD contamination By computing the probability of LD contamination, we try to reduce false positives due to LD rather than genuine colocalization of trait and expression causal variants.

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PrediXcan and other transcriptome wide association study (TWAS) methods discover and prioritize genes based on a functional mechanism –regulation of gene expression. We agree that we have to temper over-enthusiasm, but Wainberg et al’s paper could represent a backlash to the enthusiasm that the community has for this approach, which we believe is well placed. Below are our responses to some of the statements of the paper. PrediXcan/TWAS associations do not imply causality

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Most GWAS and eQTL studies have been performed in European samples. So how well do models trained in Europeans translate to other populations? Segal et al have shown that predictions of gene expression levels are robust across populations (link) The following figure shows the p-value of the correlation between predicted and observed expression levels in European and African samples from the 1000 Genomes set (GEUVADIS RNA-seq) using model trained in GTEx with majority European individuals.

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Recent Publications

  • Google Scholar List

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  • Survey of the Heritability and Sparse Architecture of Gene Expression Traits across Human Tissues

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  • A gene-based association method for mapping traits using reference transcriptome data

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  • Poly-Omic Prediction of Complex Traits: OmicKriging

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  • On sharing quantitative trait GWAS results in an era of multiple-omics data and the limits of genomic privacy

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  • Mixed effects modeling of proliferation rates in cell-based models: consequence for pharmacogenomics and cancer

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Recent & Upcoming Talks

Software & Resources

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Gene Correlation Across Tissues

Gene correlation across tissues

Web Apps

Access our web applications (PrediXcan, multi tissue PrediXcan)

predixcan

Mechanistically driven gene level association test

summary predixcan

Summary extension of PrediXcan

predictdb

Database of Prediction models to be used with PrediXcan

gene2pheno

Catalog of gene to phenome associations

Omic Kriging

Predicting traits integrating heterogeneous sources of data

Current Members

  • Hae Kyung Im, PhD - Bio

  • Jiamao Zheng, PhD - Bio

  • Alvaro Barbeira, MS - Bio

  • Milton Pividori, PhD - Bio

  • Rodrigo Bonazzola, MS - Bio

Alumni

  • Scott Dickinson, MS - Bio

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