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publications

Collaborative environmental DNA sampling from petal surfaces of flowering cherry Cerasus × yedoensis ‘Somei-yoshino’ across the Japanese archipelago

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

Medical relevance of protein-truncating variants across 337,205 individuals in the UK Biobank study

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

SNPs2ChIP: Latent Factors of ChIP-seq to infer functions of non-coding SNPs

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

Global Biobank Engine: enabling genotype-phenotype browsing for biobank summary statistics

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

[Preprint] A Fast and Scalable Framework for Large-scale and Ultrahigh-dimensional Sparse Regression with Application to the UK Biobank

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] Genetics of 38 blood and urine biomarkers in the UK Biobank

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

Components of genetic associations across 2,138 phenotypes in the UK Biobank highlight adipocyte biology

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] Sex-specific genetic effects across biomarkers

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] Medical relevance of common protein-altering variants in GPCR genes across 337,205 individuals in the UK Biobank study

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] Cardiac imaging of aortic valve area from 26,142 UK Biobank participants reveal novel genetic associations and shared genetic comorbidity with multiple disease phenotypes

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

Rare protein-altering variants in ANGPTL7 lower intraocular pressure and protect against glaucoma

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

[Preprint] Large-Scale Sparse Regression for Multiple Responses with Applications to UK Biobank

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] High-throughput SARS-CoV-2 and host genome sequencing from single nasopharyngeal swabs

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

resources

Global Biobank Engine

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/

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