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Published January 18, 2025 | Version v1
Dataset Open

Results for "Efficient count-based models improve power and robustness for large-scale single-cell eQTL mapping"

Authors/Creators

  • 1. ROR icon University of Southern California

Description

Results and datasets for jaxQTL paper: Efficient count-based models improve power and robustness for large-scale single-cell eQTL mapping. We are compiling results for cis-SNP-gene pairs summary statistics and will be available online shortly.

Files

Files (701.2 MB)

Name Size Download all
md5:913d4edba47aa5203097c0db5468d6e5
99.9 MB Download
md5:db62afd77a6795a8e6aa17d9598eebc7
601.3 MB Download

Additional details

Funding

National Institutes of Health
P01CA196569
National Institutes of Health
Characterizing the evolutionary architecture of complex disease within and across diverse populations R01HG012133
National Institutes of Health
An integrative multi-omics approach to characterize prostate cancer risk in diverse populations R01CA258808
National Institutes of Health
Characterizing genetic signatures of natural selection to understand human diseases R35GM147789
National Institutes of Health
Haplotype-aware models of gene and isoform expression with application to genetic studies of disease in diverse populations R01GM140287
National Institutes of Health
From common to rare variant functional architectures of human diseases R00HG010160

Software

Repository URL
https://github.com/mancusolab/jaxqtl_analysis
Development Status
Active