Published:
We provide the polygenic risk score models computed for Testosterone described in the sex-specific genetic effects on biomarker manuscript.
Reference: Y. Tanigawa, E. Flynn, and M. A. Rivas, The snpnet polygenic risk score coefficients for Testosterone levels described in 'Sex-specific genetic effects across biomarkers'. figshare. Dataset. (2020). https://doi.org/10.6084/m9.figshare.12793490.v1
Published:
We provide analysis and visualization scripts used in our biomarker manuscript from the Rivas lab.
Resource: The analysis and visualization scripts used in our biomarker manuscript from the Rivas lab https://github.com/rivas-lab/biomarkers
Published:
We provide datasets described in our biomarker manuscript.
Reference: Y. Tanigawa, N. Sinnott-Armstrong, C. Benner, and M. A. Rivas, Datasets described in 'Genetics of 35 blood and urine biomarkers in the UK Biobank'. National Institutes of Health. Collection. (2020). https://doi.org/10.35092/yhjc.c.5043872.v1
Published:
We provide datasets described in our ANGPTL7 paper.
Reference: Y. Tanigawa, and M. A. Rivas, Datasets described in 'Rare protein-altering variants in ANGPTL7 lower intraocular pressure and protect against glaucoma'. National Institutes of Health. Collection. (2020) https://doi.org/10.35092/yhjc.c.4990529.v1
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As a part of the COVID-19 Host Genetics Initiative, we perform the following set of analyses to better understand the genetic basis of COVID-19 susceptibility and severity.
Resource: The Rivas Lab's COVID-19 Host Genetics analysis repository https://github.com/rivas-lab/covid19
Published:
We provide datasets described in our DeGAs paper.
Reference: Y. Tanigawa, and M. A. Rivas, Decomposed matrices used for the analysis described in 'Components of genetic associations across 2,138 phenotypes in the UK Biobank highlight adipocyte biology'. (2019). https://doi.org/10.35092/yhjc.9202247.v1
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As a resource accompanying our recent publication, we provide an interactive web application so that users can test DeGAs (decomposition of genetic associations).
Resource: The DeGAs app in Global Biobank Engine https://gbe.stanford.edu/degas
Published:
We provide an update on Genomic Regions Enrichment of Annotations Tool (version 4) from the Bejerano lab.
Resource: Genomic Regions Enrichment of Annotations Tool (version 4). http://great.stanford.edu/
Published:
We, the Rivas Lab, have aggregated summary statistics from population cohorts, originally from over 330,000 individuals from UK Biobank, and provide a browser and inference engine for the community. As of July 2020, our data now feature over 750,000 individuals across three cohorts: UK Biobank, Million Veterans Program and Biobank Japan.
Resource: Global Biobank Engine https://gbe.stanford.edu/