You can also find my articles and recent preprints on my Google Scholar profile.
Preprint posted on bioRxiv, 2020
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: 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 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
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 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 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, 2019
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: 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
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: Y. Tanigawa*, E. S. Dyer*, G. Bejerano, WhichTF is dominant in your open chromatin data? bioRxiv, 730200 (2019). https://doi.org/10.1101/730200
Preprint posted on bioRxiv, 2019
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: J. Qian, W. Du, Y. Tanigawa, M. Aguirre, R. Tibshirani, M. A. Rivas, T. Hastie, A Fast and Flexible Algorithm for Solving the Lasso in Large-scale and Ultrahigh-dimensional Problems. bioRxiv, 630079 (2019). https://doi.org/10.1101/630079
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 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
Published in PLOS Genetics, 2020
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 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
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
Published in Bioinformatics, 2018
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 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 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 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
Talk at Stanford University, Department of Biomedical Data Science, Stanford, CA
Talk at Osaka University, Department of Statistical Genetics, Osaka, Japan