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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Predictdb Here you can find transcriptome prediction models for the PrediXcan family of methods: S-PrediXcan, MultiXcan and S-MultiXcan. .db files are prediction models, usable by all methods. .txt.gz files are compilations of LD reference for summary-based methods (S- prefix). S-PrediXcan is meant to use the single-tissue LD reference files (“covariances”) appropriate to each model. S-MultiXcan uses single-tissue prediction models and a cross-tissue LD reference. GTEx v8 models on eQTL and sQTL We have produced different families of prediction models for sQTL and eQTL, using several prediction strategies, on GTEx v8 release data.

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We have recently published a new set of prediction models trained on GTEx v8 data (as part of efforts detailed in this preprint. We have overhauled the model construction, incorporating posterior inclusion probabilities and global patterns of tissue sharing, while also benefiting from larger sample sizes. We cover both expression and alternate splicing mechanisms. We are very excited about these new models and the potential for new discoveries. However, these models require additional GWAS preprocessing in some public GWAS studies, which we describe here.

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In this report I calculate the lower bounds of p-values when using very rare variants, for which minor allele counts are in the single digits. This report was prepared back in 2012 for the T2D-GENES consortium that had just generated 10K whole exome sequenced data.

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We have opened direct access to the gene2pheno database, where we are hosting the PrediXcan results of close to 3000 phenotypes (from public GWAS meta analysis results and UKBiobank results from Ben Neale/HAIL team). Below are R functions that will allow you access and query the database. These results are based on GTEx V6p models and details of the analysis can be found in our preprint link to preprint in press now in Nature Communications.

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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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Recent Publications

  • Exploring the phenotypic consequences of tissue specific gene expression variation inferred from GWAS summary statistics

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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

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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 - ORCID

  • Alvaro Barbeira, MS - Bio

  • Milton Pividori, PhD - Bio - ORCID

  • Yanyu Liang (GGSB Grad Student) - Bio

  • Padma Sheila Rajagopal, MD - Bio

  • Josh Jiang (Zhuoxun) - Bio

  • Bayle Smith-Salzberg - Bio

  • Owen Melia - Bio

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