Preprint posted on bioRxiv, 2018
Citation: C. DeBoever, M. Aguirre, Y. Tanigawa, C. C. A. Spencer, T. Poterba, M. J. Daly, M. Pirinen, M. A. Rivas, Bayesian model comparison for rare variant association studies of multiple phenotypes. bioRxiv, 257162 (2018). https://doi.org/10.1101/257162
Published in Journal of Plant Research, 2018
Citation: T. Ohta, T. Kawashima, N. O. Shinozaki, A. Dobashi, S. Hiraoka, T. Hoshino, K. Kanno, T. Kataoka, S. Kawashima, M. Matsui, W. Nemoto, S. Nishijima, N. Suganuma, H. Suzuki, Y. Taguchi, Y. Takenaka, Y. Tanigawa, M. Tsuneyoshi, K. Yoshitake, Y. Sato, R. Yamashita, K. Arakawa, W. Iwasaki, Collaborative environmental DNA sampling from petal surfaces of flowering cherry Cerasus × yedoensis ‘Somei-yoshino’ across the Japanese archipelago. J Plant Res. 131, 709–717 (2018). https://doi.org/10.1007/s10265-018-1017-x
Published in Nature Communications, 2018
Using the UK Biobank population cohort, we investigated the genetic effects of Protein-truncating variants (PTVs) and the clinical impacts.
Citation: C. DeBoever, Y. Tanigawa, M. E. Lindholm, G. McInnes, A. Lavertu, E. Ingelsson, C. Chang, E. A. Ashley, C. D. Bustamante, M. J. Daly, M. A. Rivas, Medical relevance of protein-truncating variants across 337,205 individuals in the UK Biobank study. Nat Commun. 9, 1612 (2018). https://doi.org/10.1038/s41467-018-03910-9
Published in Pacific Symposium on Biocomputing, 2018
We propose SNPs2ChIP, a method to infer biological functions of non-coding variants through unsupervised statistical learning methods applied to publicly-available epigenetic datasets.
Citation: S. Anand, L. Kalesinskas, C. Smail, Y. Tanigawa, SNPs2ChIP: Latent Factors of ChIP-seq to infer functions of non-coding SNPs. Pac Symp Biocomput. 2019, 24: 184-195 (WORLD SCIENTIFIC, 2018). https://doi.org/10.1142/9789813279827_0017
Published in Bioinformatics, 2018
We present Global Biobank Engine as a platform to visualize genome- and phenome-wide associations and to perform statistical inference using those association data.
Citation: G. McInnes, Y. Tanigawa, C. DeBoever, A. Lavertu, J. E. Olivieri, M. Aguirre, M. A. Rivas, Global Biobank Engine: enabling genotype-phenotype browsing for biobank summary statistics. Bioinformatics (2019). https://doi.org/10.1093/bioinformatics/bty999
Published in Molecular Psychiatry, 2019
Citation: D. M. Ruderfer, C. G. Walsh, M. W. Aguirre, Y. Tanigawa, J. D. Ribeiro, J. C. Franklin, M. A. Rivas, Significant shared heritability underlies suicide attempt and clinically predicted probability of attempting suicide. Mol Psychiatry. 1 (2019). https://doi.org/10.1038/s41380-018-0326-8
Preprint posted on bioRxiv, 2019
In this project led by Junyang Qian, we developed BASIL, a novel algorithm to fit large-scale L1 penalized (Lasso) regression model using an iterative procedure, and implemented R snpnet package specially designed for genetic data. We demonstrate the ability of this approach in an application to UK Biobank dataset.
Citation: J. Qian, Y. Tanigawa, W. Du, M. Aguirre, R. Tibshirani, M. A. Rivas, T. Hastie, A Fast and Scalable Framework for Large-scale and Ultrahigh-dimensional Sparse Regression with Application to the UK Biobank. bioRxiv, 630079 (2019). https://doi.org/10.1101/630079
Preprint posted on bioRxiv, 2019
We characterized the genetics of 35 biomarkers in UK Biobank. We performed the association and fine-mapping analysis to prioritize the causal variants, constructed the polygenic risk score (PRS) models, and evaluated their medical relevance with causal inference and PRS-PheWAS. We demonstrate a new approach, called multi-PRS, to improve PRS by combining PRSs across traits.
Citation: N. Sinnott-Armstrong*, Y. Tanigawa*, D. Amar, N. J. Mars, M. Aguirre, G. R. Venkataraman, M. Wainberg, H. M. Ollila, J. P. Pirruccello, J. Qian, A. Shcherbina, FinnGen, F. Rodriguez, T. L. Assimes, V. Agarwala, R. Tibshirani, T. Hastie, S. Ripatti, J. K. Pritchard, M. J. Daly, M. A. Rivas, Genetics of 38 blood and urine biomarkers in the UK Biobank. bioRxiv, 660506 (2019). https://doi.org/10.1101/660506
Preprint posted on bioRxiv, 2019
Citation: Y. Tanigawa, M. Wainberg, J. Karjalainen, T. Kiiskinen, S. Lemmelä, J. A. Turunen, A. Palotie, FinnGen, M. J. Daly, M. A. Rivas, Rare protein-altering variants in ANGPTL7 lower intraocular pressure and protect against glaucoma. bioRxiv, 677443 (2019). https://doi.org/10.1101/677443
Preprint posted on bioRxiv, 2019
To identify functionally important transcription factors (TFs), we developed WhichTF.
Citation: Y. Tanigawa*, E. S. Dyer*, G. Bejerano, WhichTF is dominant in your open chromatin data? bioRxiv, 730200 (2019). https://doi.org/10.1101/730200
Published in Nature Communications, 2019
While many pleiotropic genetic loci have been identified, how they contribute to phenotypes across traits and diseases is unclear. Here, the authors propose decomposition of genetic associations (DeGAs), which uses singular value decomposition, to characterize the underlying latent structure of genetic associations of 2,138 phenotypes.
Citation: Y. Tanigawa*, J. Li*, J. M. Justesen, H. Horn, M. Aguirre, C. DeBoever, C. Chang, B. Narasimhan, K. Lage, T. Hastie, C. Y. Park, G. Bejerano, E. Ingelsson, M. A. Rivas, Components of genetic associations across 2,138 phenotypes in the UK Biobank highlight adipocyte biology. Nat Commun. 10, 1-14 (2019). https://doi.org/10.1038/s41467-019-11953-9
Preprint posted on bioRxiv, 2019
Citation: Y. Tanigawa, M. A. Rivas, Reported CCR5-∆32 deviation from Hardy-Weinberg equilibrium is explained by poor genotyping of rs62625034. bioRxiv, 791517 (2019). https://doi.org/10.1101/791517
Preprint posted on bioRxiv, 2019
Citation: M. Aguirre, Y. Tanigawa, G. Venkataraman, R. J. Tibshirani, T. Hastie, M. A. Rivas, Polygenic risk modeling with latent trait-related genetic components. bioRxiv, 808675 (2019). https://doi.org/10.1101/808675
Preprint posted on bioRxiv, 2019
In this study led by Emily Flynn, we discovered a surprising sex-specificity in the genetics of testosterone. Yosuke performed polygenic risk score (PRS) analysis and demonstrated that PRS models trained for each sex show improvements in predictive accuracy.
Citation: E. Flynn, Y. Tanigawa, F. Rodriguez, R. B. Altman, N. Sinnott-Armstrong, M. A. Rivas, Sex-specific genetic effects across biomarkers. bioRxiv, 837021 (2019). https://doi.org/10.1101/837021
Preprint posted on bioRxiv, 2019
Citation: C. DeBoever, A. J. Venkatakrishnan, J. M. Paggi, F. M. Heydenreich, S.-A. Laurin, M. Masureel, Y. Tanigawa, G. Venkataraman, M. Bouvier, R. Dror, M. A. Rivas, Medical relevance of common protein-altering variants in GPCR genes across 337,205 individuals in the UK Biobank study. bioRxiv, 2019.12.13.876250 (2019). https://doi.org/10.1101/2019.12.13.876250
Preprint posted on bioRxiv, 2020
Citation: R. Li, C. Chang, J. M. Justesen, Y. Tanigawa, J. Qian, T. Hastie, M. A. Rivas, R. J. Tibshirani, Fast Lasso method for Large-scale and Ultrahigh-dimensional Cox Model with applications to UK Biobank. bioRxiv, 2020.01.20.913194 (2020). https://doi.org/10.1101/2020.01.20.913194
Preprint posted on Preprints.org, 2020
Citation: Y. Tanigawa, M. Rivas, Initial Review and Analysis of COVID-19 Host Genetics and Associated Phenotypes (2020). https://doi.org/10.20944/preprints202003.0356.v1
Preprint posted on medRxiv, 2020
Citation: A. Cordova-Palomera, C. Tcheandjieu, J. Fries, P. Varma, V. Chen, M. Fiterau, K. Xiao, H. Tejeda, B. Keavney, H. Cordell, Y. Tanigawa, G. Venkataraman, M. Rivas, C. Re, E. Ashley, J. R. Priest, Cardiac imaging of aortic valve area from 26,142 UK Biobank participants reveal novel genetic associations and shared genetic comorbidity with multiple disease phenotypes. medRxiv, 2020.04.09.20060012 (2020). https://doi.org/10.1101/2020.04.09.20060012
Published in PLOS Genetics, 2020
From the analysis of more than 500,000 individuals in population cohorts, we identified rare protein-altering variants in ANGPTL7 that reduces the risk of glaucoma.
This paper was highlighted as Editors’ Choice in Science.
Citation: Y. Tanigawa, M. Wainberg, J. Karjalainen, T. Kiiskinen, G. Venkataraman, S. Lemmelä, J. A. Turunen, R. R. Graham, A. S. Havulinna, M. Perola, A. Palotie, FinnGen, M. J. Daly, M. A. Rivas, Rare protein-altering variants in ANGPTL7 lower intraocular pressure and protect against glaucoma. PLOS Genetics. 16, e1008682 (2020). https://doi.org/10.1371/journal.pgen.1008682
Published in The American Journal of Human Genetics, 2020
Citation: C. DeBoever, Y. Tanigawa, M. Aguirre, G. McInnes, A. Lavertu, M. A. Rivas, Assessing Digital Phenotyping to Enhance Genetic Studies of Human Diseases. The American Journal of Human Genetics. 106, 611-622 (2020). https://doi.org/10.1016/j.ajhg.2020.03.007
Preprint posted on bioRxiv, 2020
Citation: G. R. Venkataraman, J. E. Olivieri, C. DeBoever, Y. Tanigawa, J. M. Justesen, M. A. Rivas, Pervasive additive and non-additive effects within the HLA region contribute to disease risk in the UK Biobank. bioRxiv, 2020.05.28.119669 (2020). https://doi.org/10.1101/2020.05.28.119669
Preprint posted on bioRxiv, 2020
In this study led by Junyang Qian, we present a method to fit sparse multi-variate and multi-response regression model. When demonstrate the application to the UK Biobank biomarker traits, where we investigated the latent structure of regression coefficients using biplot representation.
Citation: J. Qian, Y. Tanigawa, R. Li, R. Tibshirani, M. A. Rivas, T. Hastie, Large-Scale Sparse Regression for Multiple Responses with Applications to UK Biobank. bioRxiv, 2020.05.30.125252 (2020). https://doi.org/10.1101/2020.05.30.125252
Preprint posted on bioRxiv, 2020
Citation: R. Li, Y. Tanigawa, J. M. Justesen, J. Taylor, T. Hastie, R. Tibshirani, M. A. Rivas, Fast Sparse-Group Lasso Method for Multi-response Cox Model with Applications to UK Biobank. bioRxiv, 2020.06.21.163675 (2020). https://doi.org/10.1101/2020.06.21.163675
Preprint posted on medRxiv, 2020
In this pre-print, we describe a new method to generate host and pathogen genomic data.
Citation: J. E. Gorzynski*, H. N. D. Jong*, D. Amar, C. R. Hughes, A. Ioannidis, R. Bierman, D. Liu, Y. Tanigawa, A. Kistler, J. Kamm, J. Kim, L. Cappello, N. F. Neff, S. Rubinacci, O. Delaneua, M. J. Shoura, K. Seo, A. Kirillova, A. Raja, S. Sutton, C. Huang, M. K. Sahoo, K. C. Mallempati, G. Montero-Martin, K. Osoegawa, N. Watson, N. Hammond, R. Joshi, M. Fernandez-Vina, J. W. Christle, M. T. Wheeler, P. Febbo, K. Farh, G. Schroth, F. Desouza, J. Palacios, J. Salzman, B. A. Pinsky, M. A. Rivas, C. D. Bustamante, E. A. Ashley, V. N. Parikh, High-throughput SARS-CoV-2 and host genome sequencing from single nasopharyngeal swabs, medRxiv, 2020.07.27.20163147 (2020). https://doi.org/10.1101/2020.07.27.20163147
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/
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:
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 datasets described in our DeGAs paper.
Citation: 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
Published:
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 ANGPTL7 paper.
Citation: 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
Published:
We provide datasets described in our biomarker manuscript.
Citation: 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 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:
I was one of the invited guest speakers at Statistical genetics summer school at Osaka University.
Published:
I gave a research-in-progress talk in our department seminar.
Graduate course, Stanford University, Department of Biomedical Data Science, 2018
I was a teaching assistant for a graduate-level class, BIODS215: Topics in Biomedical Data Science, Large-scale inference. I taught a section on “Data visualization”, hosted office hours, maintained the class website, and graded the assignments.
Graduate course, Stanford University, Department of Biomedical Data Science, 2019
I was a teaching assistant for a graduate-level class, BIOMEDIN 210: Modeling Biomedical Systems: Ontology, Terminology, Problem Solving (CS 270).
Graduate course, Stanford University, Department of Biomedical Data Science, 2020
I was a teaching assistant for a graduate-level class, BIODS215: Topics in Biomedical Data Science, Large-scale inference. I taught a section on “Reproducible large-scale inference”, hosted office hours, maintained the class website, and graded the assignments.