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19 results on '"Bonazzola, Rodrigo"'

1. Predicting risk of cardiovascular disease using retinal OCT imaging

Author

Maldonado-Garcia, Cynthia, Bonazzola, Rodrigo, Ferrante, Enzo, Julian, Thomas H, Sergouniotis, Panagiotis I, Ravikumara, Nishant, and Frangi, Alejandro F

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

We investigated the potential of optical coherence tomography (OCT) as an additional imaging technique to predict future cardiovascular disease (CVD). We utilised a self-supervised deep learning approach based on Variational Autoencoders (VAE) to learn low-dimensional representations of high-dimensional 3D OCT images and to capture distinct characteristics of different retinal layers within the OCT image. A Random Forest (RF) classifier was subsequently trained using the learned latent features and participant demographic and clinical data, to differentiate between patients at risk of CVD events (MI or stroke) and non-CVD cases. Our predictive model, trained on multimodal data, was assessed based on its ability to correctly identify individuals likely to suffer from a CVD event(MI or stroke), within a 5-year interval after image acquisition. Our self-supervised VAE feature selection and multimodal Random Forest classifier differentiate between patients at risk of future CVD events and the control group with an AUC of 0.75, outperforming the clinically established QRISK3 score (AUC= 0.597). The choroidal layer visible in OCT images was identified as an important predictor of future CVD events using a novel approach to model explanability. Retinal OCT imaging provides a cost-effective and non-invasive alternative to predict the risk of cardiovascular disease and is readily accessible in optometry practices and hospitals., Comment: 18 pages for main manuscript, 7 figures, 2 pages for appendix and preprint for a journal

Published
2024

2. Multi-view Hybrid Graph Convolutional Network for Volume-to-mesh Reconstruction in Cardiovascular MRI

Author

Gaggion, Nicolás, Matheson, Benjamin A., Xia, Yan, Bonazzola, Rodrigo, Ravikumar, Nishant, Taylor, Zeike A., Milone, Diego H., Frangi, Alejandro F., and Ferrante, Enzo

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract

Cardiovascular magnetic resonance imaging is emerging as a crucial tool to examine cardiac morphology and function. Essential to this endeavour are anatomical 3D surface and volumetric meshes derived from CMR images, which facilitate computational anatomy studies, biomarker discovery, and in-silico simulations. However, conventional surface mesh generation methods, such as active shape models and multi-atlas segmentation, are highly time-consuming and require complex processing pipelines to generate simulation-ready 3D meshes. In response, we introduce HybridVNet, a novel architecture for direct image-to-mesh extraction seamlessly integrating standard convolutional neural networks with graph convolutions, which we prove can efficiently handle surface and volumetric meshes by encoding them as graph structures. To further enhance accuracy, we propose a multiview HybridVNet architecture which processes both long axis and short axis CMR, showing that it can increase the performance of cardiac MR mesh generation. Our model combines traditional convolutional networks with variational graph generative models, deep supervision and mesh-specific regularisation. Experiments on a comprehensive dataset from the UK Biobank confirm the potential of HybridVNet to significantly advance cardiac imaging and computational cardiology by efficiently generating high-fidelity and simulation ready meshes from CMR images.

Published
2023

3. Learned Local Attention Maps for Synthesising Vessel Segmentations

Author

Deo, Yash, Bonazzola, Rodrigo, Dou, Haoran, Xia, Yan, Wei, Tianyou, Ravikumar, Nishant, Frangi, Alejandro F., and Lassila, Toni

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract

Magnetic resonance angiography (MRA) is an imaging modality for visualising blood vessels. It is useful for several diagnostic applications and for assessing the risk of adverse events such as haemorrhagic stroke (resulting from the rupture of aneurysms in blood vessels). However, MRAs are not acquired routinely, hence, an approach to synthesise blood vessel segmentations from more routinely acquired MR contrasts such as T1 and T2, would be useful. We present an encoder-decoder model for synthesising segmentations of the main cerebral arteries in the circle of Willis (CoW) from only T2 MRI. We propose a two-phase multi-objective learning approach, which captures both global and local features. It uses learned local attention maps generated by dilating the segmentation labels, which forces the network to only extract information from the T2 MRI relevant to synthesising the CoW. Our synthetic vessel segmentations generated from only T2 MRI achieved a mean Dice score of $0.79 \pm 0.03$ in testing, compared to state-of-the-art segmentation networks such as transformer U-Net ($0.71 \pm 0.04$) and nnU-net($0.68 \pm 0.05$), while using only a fraction of the parameters. The main qualitative difference between our synthetic vessel segmentations and the comparative models was in the sharper resolution of the CoW vessel segments, especially in the posterior circulation.

Published
2023

4. Unsupervised ensemble-based phenotyping helps enhance the discoverability of genes related to heart morphology

Author

Bonazzola, Rodrigo, Ferrante, Enzo, Ravikumar, Nishant, Xia, Yan, Keavney, Bernard, Plein, Sven, Syeda-Mahmood, Tanveer, and Frangi, Alejandro F

Subjects
Quantitative Biology - Genomics, Computer Science - Machine Learning
Abstract

Recent genome-wide association studies (GWAS) have been successful in identifying associations between genetic variants and simple cardiac parameters derived from cardiac magnetic resonance (CMR) images. However, the emergence of big databases including genetic data linked to CMR, facilitates investigation of more nuanced patterns of shape variability. Here, we propose a new framework for gene discovery entitled Unsupervised Phenotype Ensembles (UPE). UPE builds a redundant yet highly expressive representation by pooling a set of phenotypes learned in an unsupervised manner, using deep learning models trained with different hyperparameters. These phenotypes are then analyzed via (GWAS), retaining only highly confident and stable associations across the ensemble. We apply our approach to the UK Biobank database to extract left-ventricular (LV) geometric features from image-derived three-dimensional meshes. We demonstrate that our approach greatly improves the discoverability of genes influencing LV shape, identifying 11 loci with study-wide significance and 8 with suggestive significance. We argue that our approach would enable more extensive discovery of gene associations with image-derived phenotypes for other organs or image modalities., Comment: 14 pages of main text, 22 pages of supplemental information

Published
2023

5. SU(4) Kondo entanglement in double quantum dot devices

Author

Bonazzola, Rodrigo, Andrade, J. A., Facio, Jorge I., García, D. J., and Cornaglia, Pablo S.

Subjects
Condensed Matter - Mesoscale and Nanoscale Physics
Abstract

We analyze, from a quantum information theory perspective, the possibility of realizing a SU(4) entangled Kondo regime in semiconductor double quantum dot devices. We focus our analysis on the ground state properties and consider the general experimental situation where the coupling parameters of the two quantum dots differ. We model each quantum dot with an Anderson type Hamiltonian including an interdot Coulomb repulsion and tunnel couplings for each quantum dot to independent fermionic baths. We find that the spin and pseudospin entanglements can be made equal, and the SU(4) symmetry recovered, if the gate voltages are chosen in such a way that the average charge occupancies of the two quantum dots are equal, and the double occupancy on the double quantum dot is suppressed. We present density matrix renormalization group numerical results for the spin and pseudospin entanglement entropies, and analytical results for a simplified model that captures the main physics of the problem., Comment: 9 pages, 7 figures, Phys. Rev. B

Published
2017
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6. Exploiting the GTEx resources to decipher the mechanisms at GWAS loci

Author

Barbeira, Alvaro N., Bonazzola, Rodrigo, Gamazon, Eric R., Liang, Yanyu, Park, YoSon, Kim-Hellmuth, Sarah, Wang, Gao, Jiang, Zhuoxun, Zhou, Dan, Hormozdiari, Farhad, Liu, Boxiang, Rao, Abhiram, Hamel, Andrew R., Pividori, Milton D., Aguet, François, Bastarache, Lisa, Jordan, Daniel M., Verbanck, Marie, Do, Ron, Stephens, Matthew, Ardlie, Kristin, McCarthy, Mark, Montgomery, Stephen B., Segrè, Ayellet V., Brown, Christopher D., Lappalainen, Tuuli, Wen, Xiaoquan, and Im, Hae Kyung

Published
2021
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7. Additional file 10 of Exploiting the GTEx resources to decipher the mechanisms at GWAS loci

Author

Barbeira, Alvaro N., Bonazzola, Rodrigo, Gamazon, Eric R., Yanyu Liang, YoSon Park, Kim-Hellmuth, Sarah, Wang, Gao, Zhuoxun Jiang, Zhou, Dan, Hormozdiari, Farhad, Boxiang Liu, Abhiram Rao, Hamel, Andrew R., Pividori, Milton D., Aguet, François, Bastarache, Lisa, Jordan, Daniel M., Verbanck, Marie, Do, Ron, Stephens, Matthew, Ardlie, Kristin, McCarthy, Mark, Montgomery, Stephen B., Ayellet V. Segrè, Brown, Christopher D., Lappalainen, Tuuli, Xiaoquan Wen, and Im, Hae Kyung

Abstract

Additional file 10 Review history

Published
2021
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8. Population-scale tissue transcriptomics maps long non-coding RNAs to complex disease

Author

de Goede, Olivia M., primary, Nachun, Daniel C., additional, Ferraro, Nicole M., additional, Gloudemans, Michael J., additional, Rao, Abhiram S., additional, Smail, Craig, additional, Eulalio, Tiffany Y., additional, Aguet, François, additional, Ng, Bernard, additional, Xu, Jishu, additional, Barbeira, Alvaro N., additional, Castel, Stephane E., additional, Kim-Hellmuth, Sarah, additional, Park, YoSon, additional, Scott, Alexandra J., additional, Strober, Benjamin J., additional, Brown, Christopher D., additional, Wen, Xiaoquan, additional, Hall, Ira M., additional, Battle, Alexis, additional, Lappalainen, Tuuli, additional, Im, Hae Kyung, additional, Ardlie, Kristin G., additional, Mostafavi, Sara, additional, Quertermous, Thomas, additional, Kirkegaard, Karla, additional, Montgomery, Stephen B., additional, Anand, Shankara, additional, Gabriel, Stacey, additional, Getz, Gad A., additional, Graubert, Aaron, additional, Hadley, Kane, additional, Handsaker, Robert E., additional, Huang, Katherine H., additional, Li, Xiao, additional, MacArthur, Daniel G., additional, Meier, Samuel R., additional, Nedzel, Jared L., additional, Nguyen, Duyen T., additional, Segrè, Ayellet V., additional, Todres, Ellen, additional, Balliu, Brunilda, additional, Bonazzola, Rodrigo, additional, Brown, Andrew, additional, Conrad, Donald F., additional, Cotter, Daniel J., additional, Cox, Nancy, additional, Das, Sayantan, additional, Dermitzakis, Emmanouil T., additional, Einson, Jonah, additional, Engelhardt, Barbara E., additional, Eskin, Eleazar, additional, Flynn, Elise D., additional, Fresard, Laure, additional, Gamazon, Eric R., additional, Garrido-Martín, Diego, additional, Gay, Nicole R., additional, Guigó, Roderic, additional, Hamel, Andrew R., additional, He, Yuan, additional, Hoffman, Paul J., additional, Hormozdiari, Farhad, additional, Hou, Lei, additional, Jo, Brian, additional, Kasela, Silva, additional, Kashin, Seva, additional, Kellis, Manolis, additional, Kwong, Alan, additional, Li, Xin, additional, Liang, Yanyu, additional, Mangul, Serghei, additional, Mohammadi, Pejman, additional, Muñoz-Aguirre, Manuel, additional, Nobel, Andrew B., additional, Oliva, Meritxell, additional, Park, Yongjin, additional, Parsana, Princy, additional, Reverter, Ferran, additional, Rouhana, John M., additional, Sabatti, Chiara, additional, Saha, Ashis, additional, Stephens, Matthew, additional, Stranger, Barbara E., additional, Teran, Nicole A., additional, Viñuela, Ana, additional, Wang, Gao, additional, Wright, Fred, additional, Wucher, Valentin, additional, Zou, Yuxin, additional, Ferreira, Pedro G., additional, Li, Gen, additional, Melé, Marta, additional, Yeger-Lotem, Esti, additional, Bradbury, Debra, additional, Krubit, Tanya, additional, McLean, Jeffrey A., additional, Qi, Liqun, additional, Robinson, Karna, additional, Roche, Nancy V., additional, Smith, Anna M., additional, Tabor, David E., additional, Undale, Anita, additional, Bridge, Jason, additional, Brigham, Lori E., additional, Foster, Barbara A., additional, Gillard, Bryan M., additional, Hasz, Richard, additional, Hunter, Marcus, additional, Johns, Christopher, additional, Johnson, Mark, additional, Karasik, Ellen, additional, Kopen, Gene, additional, Leinweber, William F., additional, McDonald, Alisa, additional, Moser, Michael T., additional, Myer, Kevin, additional, Ramsey, Kimberley D., additional, Roe, Brian, additional, Shad, Saboor, additional, Thomas, Jeffrey A., additional, Walters, Gary, additional, Washington, Michael, additional, Wheeler, Joseph, additional, Jewell, Scott D., additional, Rohrer, Daniel C., additional, Valley, Dana R., additional, Davis, David A., additional, Mash, Deborah C., additional, Barcus, Mary E., additional, Branton, Philip A., additional, Sobin, Leslie, additional, Barker, Laura K., additional, Gardiner, Heather M., additional, Mosavel, Maghboeba, additional, Siminoff, Laura A., additional, Flicek, Paul, additional, Haeussler, Maximilian, additional, Juettemann, Thomas, additional, Kent, W. James, additional, Lee, Christopher M., additional, Powell, Conner C., additional, Rosenbloom, Kate R., additional, Ruffier, Magali, additional, Sheppard, Dan, additional, Taylor, Kieron, additional, Trevanion, Stephen J., additional, Zerbino, Daniel R., additional, Abell, Nathan S., additional, Akey, Joshua, additional, Chen, Lin, additional, Demanelis, Kathryn, additional, Doherty, Jennifer A., additional, Feinberg, Andrew P., additional, Hansen, Kasper D., additional, Hickey, Peter F., additional, Jasmine, Farzana, additional, Jiang, Lihua, additional, Kaul, Rajinder, additional, Kibriya, Muhammad G., additional, Li, Jin Billy, additional, Li, Qin, additional, Lin, Shin, additional, Linder, Sandra E., additional, Pierce, Brandon L., additional, Rizzardi, Lindsay F., additional, Skol, Andrew D., additional, Smith, Kevin S., additional, Snyder, Michael, additional, Stamatoyannopoulos, John, additional, Tang, Hua, additional, Wang, Meng, additional, Carithers, Latarsha J., additional, Guan, Ping, additional, Koester, Susan E., additional, Little, A. Roger, additional, Moore, Helen M., additional, Nierras, Concepcion R., additional, Rao, Abhi K., additional, Vaught, Jimmie B., additional, and Volpi, Simona, additional

Published
2021
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9. Analysis pipeline for 'The impact of sex on gene expression across human tissues'

Author

Oliva, Meritxell, Muñoz-Aguirre, Manuel, Kim-Hellmuth, Sarah, Wucher, Valentin, Gewirtz, Ariel D.H., Cotter, Daniel J., Parsana, Princy, Kasela, Silva, Balliu, Brunilda, Viñuela, Ana, Castel, Stephane E., Mohammadi, Pejman, Aguet, François, Zou, Yuxin, Khramtsova, Ekaterina A., Skol, Andrew D., Garrido-Martín, Diego, Reverter, Ferran, Brown, Andrew, Evans, Patrick, Gamazon, Eric R., Payne, Anthony, Bonazzola, Rodrigo, Barbeira, Alvaro N., Hamel, Andrew R., Martinez-Perez, Angel, Soria, José Manuel, GTEx Consortium, Pierce, Brandon L., Stephens, Matthew, Eskin, Eleazar, Dermitzakis, Emmanouil T., Segrè, Ayellet V., Im, Hae Kyung, Engelhardt, Barbara E., Ardlie, Kristin G., Montgomery, Stephen B., Battle, Alexis J., Lappalainen, Tuuli, Guigó, Roderic, and Stranger, Barbara E.

Subjects
gene expression
Abstract

Sex-biased gene expression analysis pipeline for the research publication "The impact of sex on gene expressionacross human tissues". Details on how to set up and runthe pipeline are available on the README.md file.

Published
2020
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10. Additional file 3 of sn-spMF: matrix factorization informs tissue-specific genetic regulation of gene expression

Author

He, Yuan, Chhetri, Surya B., Arvanitis, Marios, Srinivasan, Kaushik, Aguet, François, Ardlie, Kristin G., Barbeira, Alvaro N., Bonazzola, Rodrigo, Im, Hae Kyung, Brown, Christopher D., and Battle, Alexis

Subjects
MathematicsofComputing_GENERAL, Data_FILES, TheoryofComputation_GENERAL
Abstract

Additional file 3 GTEx Consortium Information.

Published
2020
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11. Additional file 10 of Impact of admixture and ancestry on eQTL analysis and GWAS colocalization in GTEx

Author

Gay, Nicole R., Gloudemans, Michael, Antonio, Margaret L., Abell, Nathan S., Balliu, Brunilda, YoSon Park, Martin, Alicia R., Musharoff, Shaila, Abhiram S. Rao, Aguet, François, Barbeira, Alvaro N., Bonazzola, Rodrigo, Hormozdiari, Farhad, Ardlie, Kristin G., Brown, Christopher D., Im, Hae Kyung, Lappalainen, Tuuli, Xiaoquan Wen, and Montgomery, Stephen B.

Abstract

Additional file 10. Review History.

Published
2020
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14. The GTEx Consortium atlas of genetic regulatory effects across human tissues

Author

Aguet, François, Barbeira, Alvaro N, Bonazzola, Rodrigo, Brown, Andrew, Castel, Stephane E, Jo, Brian, Kasela, Silva, Kim-Hellmuth, Sarah, Liang, Yanyu, Oliva, Meritxell, Parsana, Princy E, Flynn, Elise, Fresard, Laure, Gaamzon, Eric R, Hamel, Andrew R, He, Yuan, Hormozdiari, Farhad, Mohammadi, Pejman, Muñoz-Aguirre, Manuel, Park, YoSon, Saha, Ashis, Segrć, Ayellet V, Strober, Benjamin J, Wen, Xiaoquan, Wucher, Valentin, Das, Sayantan, Garrido-Martín, Diego, Gay, Nicole R, Handsaker, Robert E, Hoffman, Paul J, Kashin, Seva, Kwong, Alan, Li, Xiao, MacArthur, Daniel, Rouhana, John M, Stephens, Matthew, Todres, Ellen, Viñuela, Ana, Wang, Gao, Zou, Yuxin, Brown, Christopher D, Cox, Nancy, Dermitzakis, Emmanouil, Engelhardt, Barbara E, Getz, Gad, Guigo, Roderic, Montgomery, Stephen B, Stranger, Barbara E, Im, Hae Kyung, Battle, Alexis, Ardlie, Kristin G, Lappalainen, Tuuli, and Universitat Politècnica de Catalunya. Doctorat en Estadística i Investigació Operativa

Subjects
Enginyeria química::Biotecnologia [Àrees temàtiques de la UPC], 0303 health sciences, 03 medical and health sciences, 0302 clinical medicine, Genotype-Tissue Expression, Atlas (topology), Genetics, Computational biology, Biology, Genètica, 030217 neurology & neurosurgery, 030304 developmental biology
Abstract

The Genotype-Tissue Expression (GTEx) project was established to characterize genetic effects on the transcriptome across human tissues, and to link these regulatory mechanisms to trait and disease associations. Here, we present analyses of the v8 data, based on 17,382 RNA-sequencing samples from 54 tissues of 948 post-mortem donors. We comprehensively characterize genetic associations for gene expression and splicing incisandtrans, showing that regulatory associations are found for almost all genes, and describe the underlying molecular mechanisms and their contribution to allelic heterogeneity and pleiotropy of complex traits. Leveraging the large diversity of tissues, we provide insights into the tissue-specificity of genetic effects, and show that cell type composition is a key factor in understanding gene regulatory mechanisms in human tissues.

Published
2019

15. A Quantitative Proteome Map of the Human Body

Author

Jiang, Lihua, primary, Wang, Meng, additional, Lin, Shin, additional, Jian, Ruiqi, additional, Li, Xiao, additional, Chan, Joanne, additional, Dong, Guanlan, additional, Fang, Huaying, additional, Robinson, Aaron E., additional, Snyder, Michael P., additional, Aguet, François, additional, Anand, Shankara, additional, Ardlie, Kristin G., additional, Gabriel, Stacey, additional, Getz, Gad, additional, Graubert, Aaron, additional, Hadley, Kane, additional, Handsaker, Robert E., additional, Huang, Katherine H., additional, Kashin, Seva, additional, MacArthur, Daniel G., additional, Meier, Samuel R., additional, Nedzel, Jared L., additional, Nguyen, Duyen Y., additional, Segrè, Ayellet V., additional, Todres, Ellen, additional, Balliu, Brunilda, additional, Barbeira, Alvaro N., additional, Battle, Alexis, additional, Bonazzola, Rodrigo, additional, Brown, Andrew, additional, Brown, Christopher D., additional, Castel, Stephane E., additional, Conrad, Don, additional, Cotter, Daniel J., additional, Cox, Nancy, additional, Das, Sayantan, additional, de Goede, Olivia M., additional, Dermitzakis, Emmanouil T., additional, Engelhardt, Barbara E., additional, Eskin, Eleazar, additional, Eulalio, Tiffany Y., additional, Ferraro, Nicole M., additional, Flynn, Elise, additional, Fresard, Laure, additional, Gamazon, Eric R., additional, Garrido-Martín, Diego, additional, Gay, Nicole R., additional, Guigó, Roderic, additional, Hamel, Andrew R., additional, He, Yuan, additional, Hoffman, Paul J., additional, Hormozdiari, Farhad, additional, Hou, Lei, additional, Im, Hae Kyung, additional, Jo, Brian, additional, Kasela, Silva, additional, Kellis, Manolis, additional, Kim-Hellmuth, Sarah, additional, Kwong, Alan, additional, Lappalainen, Tuuli, additional, Li, Xin, additional, Liang, Yanyu, additional, Mangul, Serghei, additional, Mohammadi, Pejman, additional, Montgomery, Stephen B., additional, Muñoz-Aguirre, Manuel, additional, Nachun, Daniel C., additional, Nobel, Andrew B., additional, Oliva, Meritxell, additional, Park, YoSon, additional, Park, Yongjin, additional, Parsana, Princy, additional, Reverter, Ferran, additional, Rouhana, John M., additional, Sabatti, Chiara, additional, Saha, Ashis, additional, Skol, Andrew D., additional, Stephens, Matthew, additional, Stranger, Barbara E., additional, Strober, Benjamin J., additional, Teran, Nicole A., additional, Viñuela, Ana, additional, Wang, Gao, additional, Wen, Xiaoquan, additional, Wright, Fred, additional, Wucher, Valentin, additional, Zou, Yuxin, additional, Ferreira, Pedro G., additional, Li, Gen, additional, Melé, Marta, additional, Yeger-Lotem, Esti, additional, Barcus, Mary E., additional, Bradbury, Debra, additional, Krubit, Tanya, additional, McLean, Jeffrey A., additional, Qi, Liqun, additional, Robinson, Karna, additional, Roche, Nancy V., additional, Smith, Anna M., additional, Sobin, Leslie, additional, Tabor, David E., additional, Undale, Anita, additional, Bridge, Jason, additional, Brigham, Lori E., additional, Foster, Barbara A., additional, Gillard, Bryan M., additional, Hasz, Richard, additional, Hunter, Marcus, additional, Johns, Christopher, additional, Johnson, Mark, additional, Karasik, Ellen, additional, Kopen, Gene, additional, Leinweber, William F., additional, McDonald, Alisa, additional, Moser, Michael T., additional, Myer, Kevin, additional, Ramsey, Kimberley D., additional, Roe, Brian, additional, Shad, Saboor, additional, Thomas, Jeffrey A., additional, Walters, Gary, additional, Washington, Michael, additional, Wheeler, Joseph, additional, Jewell, Scott D., additional, Rohrer, Daniel C., additional, Valley, Dana R., additional, Davis, David A., additional, Mash, Deborah C., additional, Branton, Philip A., additional, Barker, Laura K., additional, Gardiner, Heather M., additional, Mosavel, Maghboeba, additional, Siminoff, Laura A., additional, Flicek, Paul, additional, Haeussler, Maximilian, additional, Juettemann, Thomas, additional, Kent, W. James, additional, Lee, Christopher M., additional, Powell, Conner C., additional, Rosenbloom, Kate R., additional, Ruffier, Magali, additional, Sheppard, Dan, additional, Taylor, Kieron, additional, Trevanion, Stephen J., additional, Zerbino, Daniel R., additional, Abell, Nathan S., additional, Akey, Joshua, additional, Chen, Lin, additional, Demanelis, Kathryn, additional, Doherty, Jennifer A., additional, Feinberg, Andrew P., additional, Hansen, Kasper D., additional, Hickey, Peter F., additional, Jasmine, Farzana, additional, Kaul, Rajinder, additional, Kibriya, Muhammad G., additional, Li, Jin Billy, additional, Li, Qin, additional, Linder, Sandra E., additional, Pierce, Brandon L., additional, Rizzardi, Lindsay F., additional, Smith, Kevin S., additional, Stamatoyannopoulos, John, additional, Tang, Hua, additional, Carithers, Latarsha J., additional, Guan, Ping, additional, Koester, Susan E., additional, Little, A. Roger, additional, Moore, Helen M., additional, Nierras, Concepcion R., additional, Rao, Abhi K., additional, Vaught, Jimmie B., additional, and Volpi, Simona, additional

Published
2020
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17. Impact of admixture and ancestry on eQTL analysis and GWAS colocalization in GTEx.

Author

Gay, Nicole R., Gloudemans, Michael, Antonio, Margaret L., Abell, Nathan S., Balliu, Brunilda, Park, YoSon, Martin, Alicia R., Musharoff, Shaila, Rao, Abhiram S., Aguet, François, Barbeira, Alvaro N., Bonazzola, Rodrigo, Hormozdiari, Farhad, Ardlie, Kristin G., Brown, Christopher D., Im, Hae Kyung, Lappalainen, Tuuli, Wen, Xiaoquan, and Montgomery, Stephen B.

Published
2020
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18. Exploring the phenotypic consequences of tissue specific gene expression variation inferred from GWAS summary statistics

Author

Barbeira, Alvaro N., Dickinson, Scott P., Torres, Jason M., Zheng, Jiamao, Torstenson, Eric S., Wheeler, Heather E., Shah, Kaanan P., Bonazzola, Rodrigo, Garcia, Tzintzuni, Edwards, Todd, Nicolae, Dan L., Cox, Nancy J., and Im, Hae Kyung

Subjects
0303 health sciences, education.field_of_study, business.industry, Computer science, Concordance, 030305 genetics & heredity, Aggregate (data warehouse), Population, Genome-wide association study, Genomics, Computational biology, Expression (mathematics), 03 medical and health sciences, Software, Component (UML), Gene expression, business, education, Gene, 030304 developmental biology
Abstract

To gain biological insight into the discoveries made by GWAS and meta-analysis studies, effective integration of functional data generated by large-scale efforts such as the GTEx Project is needed. PrediXcan is a gene-level approach that addresses this need by estimating the genetically determined component of gene expression. These predicted expression traits can then be tested for association with phenotype in order to test for mediating role of gene expression levels. Furthermore, due to the polygenic nature of many complex traits, efforts to aggregate multiple GWAS studies and conduct meta-analyses have successfully increased our ability to identify variants of small effect sizes. To take advantage of the results generated by these efforts and to avoid the problems associated with accessing and handling individual-level data (e.g. consent limitations, large computational/storage costs) we have developed an extension of PrediXcan. The new method, MetaXcan, infers the results of PrediXcan using only summary statistics from large-scale GWAS or meta-analyses. Here we show that the concordance between PrediXcan and MetaXcan is excellent when the right reference population is used (R2 > 0.95) and robust to population mismatches (R2 > 0.85). We provide open source local and web-based software for easy implementation through (https://github.com/hakyimlab/MetaXcan)

Published
2016
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19. Exploring the phenotypic consequences of tissue specific gene expression variation inferred from GWAS summary statistics.

Author

Barbeira, Alvaro N., Dickinson, Scott P., Bonazzola, Rodrigo, Jiamao Zheng, Wheeler, Heather E., Torres, Jason M., Torstenson, Eric S., Shah, Kaanan P., Garcia, Tzintzuni, Edwards, Todd L., Stahl, Eli A., Huckins, Laura M., Nicolae, Dan L., Cox, Nancy J., and Hae Kyung Im

Subjects
GENE expression, HUMAN phenotype, GENE mapping, TISSUES, STATISTICS
Abstract

Scalable, integrative methods to understand mechanisms that link genetic variants with phenotypes are needed. Here we derive a mathematical expression to compute PrediXcan (a gene mapping approach) results using summary data (S-PrediXcan) and show its accuracy and general robustness to misspecified reference sets. We apply this framework to 44 GTEx tissues and 100+ phenotypes from GWAS and meta-analysis studies, creating a growing public catalog of associations that seeks to capture the effects of gene expression variation on human phenotypes. Replication in an independent cohort is shown. Most of the associations are tissue specific, suggesting context specificity of the trait etiology. Colocalized significant associations in unexpected tissues underscore the need for an agnostic scanning of multiple contexts to improve our ability to detect causal regulatory mechanisms. Monogenic disease genes are enriched among significant associations for related traits, suggesting that smaller alterations of these genes may cause a spectrum of milder phenotypes. [ABSTRACT FROM AUTHOR]

Published
2018
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