{"id":128,"date":"2025-10-24T15:46:44","date_gmt":"2025-10-24T15:46:44","guid":{"rendered":"https:\/\/mrivas.su.domains\/gbe\/?page_id=128"},"modified":"2025-11-03T20:55:09","modified_gmt":"2025-11-03T20:55:09","slug":"publications","status":"publish","type":"page","link":"https:\/\/mrivas.su.domains\/gbe\/publications\/","title":{"rendered":"Publications"},"content":{"rendered":"\n
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Spherical harmonic decomposition identifies localized genetic effects for ASCC2 <\/em>inframe deletion.<\/figcaption><\/figure>\n\n\n\n

Take home: we introduce a spherical-harmonics regression framework for PheWAS that embeds phenotypes on the unit sphere to jointly model global genetic effect patterns and detect variant-specific, spatially localized signals via residual analysis and spherical-cap enrichment, enabling interpretable, rotation-invariant mapping of genetic effects across phenomes.<\/p>\n\n\n\n

Rivas, Manuel A. \u201cJoint Spherical-Harmonics Regression for PheWAS: Global Maps, Residual Localization, and Spherical-Cap Enrichment.\u201d bioRxiv, 27 Oct. 2025<\/a><\/p>\n\n\n\n

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Genes associated with epilepsy using the unified model compared to associations obtained from EPI25 study.<\/figcaption><\/figure>\n\n\n\n

Take home: we identify 14 epilepsy genes using a new statistical meta-regression model framework that we present in the paper.<\/p>\n\n\n\n

Aguilar, Oscar; Rivas, Mijail; Rivas, Manuel A., \u201cA Unified Meta-Regression Model Identifies Genes Associated with Epilepsy.\u201d<\/a><\/p>\n\n\n\n

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Performance of the type 1 diabetes genetic risk score (GRS2) across multiple ancestries.<\/figcaption><\/figure>\n\n\n\n

Take home: we develop a genetic risk score for type 1 diabetes that generalizes to multiple ancestries.<\/p>\n\n\n\n

Luckett AM, Oram RA, Deutsch AJ, Ortega HI, Fraser DP, Ashok K, Manning AK, Mercader JM, Rivas MA, Udler MS, Weedon MN, Gloyn AL, Sharp SA. Standardized Measurement of Type 1 Diabetes Polygenic Risk Across Multiancestry Population Cohorts. Diabetes Care. 2025 Jun 1;48(6):e81-e83. doi: 10.2337\/dc25-0142. PMID: 40267362; PMCID: PMC12094190.<\/a><\/p>\n\n\n\n

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Efficient regression for whole genome sequencing studies.<\/figcaption><\/figure>\n\n\n\n

Take home: we introduce a novel data format and regression framework that dramatically accelerates large-scale genomic analyses while minimizing storage and computational costs.<\/p>\n\n\n\n

Rivas MA, Chang C. Efficient storage and regression computation for population-scale genome sequencing studies. Bioinformatics. 2025 Mar 4;41(3):btaf067. doi: 10.1093\/bioinformatics\/btaf067. PMID: 39932865; PMCID: PMC11893150.<\/a><\/p>\n\n\n\n

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Study design for studying the genetics of cardiometabolic disease progression using snpnet-Cox.<\/figcaption><\/figure>\n\n\n\n

Take home: we show that genetic variation influences the progression of cardiometabolic disease<\/p>\n\n\n\n

Justesen JM, Venkataraman G, Tanigawa Y, Li R, Hastie T, Tibshirani R, Knowles JW, Rivas MA. Genetics of cardiometabolic disease progression. medRxiv [Preprint]. 2025 Feb 3:2025.02.01.25321518. doi: 10.1101\/2025.02.01.25321518. PMID: 39974115; PMCID: PMC11838626.<\/a><\/p>\n\n\n\n

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Genetics of transient global amnesia.<\/figcaption><\/figure>\n\n\n\n

Take home: we show that transient global amnesia (TGA) has a genetic component. We identify 9 regions of the genome associated to TGA.<\/p>\n\n\n\n

Rivas, Manuel A., et al. \u201cGenetics of Transient Amnesia Highlights a Vascular Role in Memory.\u201d medRxiv, 20 Aug. 2024, https:\/\/doi.org\/10.1101\/2024.08.18.24312185<\/a><\/p>\n\n\n\n

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Foundation model pipeline for docking small molecules to genetically validated targets.<\/figcaption><\/figure>\n\n\n\n

Take home: we introduce the Smiles2Dock dataset with ~25M protein\u2013ligand scores from docking 1.7M ChEMBL ligands against 15 AlphaFold proteins, plus an initial Transformer baseline.<\/p>\n\n\n\n

Le Menestrel, Thomas, and Manuel A. Rivas. \u201cSmiles2Dock: An Open Large-Scale Multi-Task Dataset for ML-Based Molecular Docking.\u201d arXiv<\/em>, 9 June 2024, arXiv:2406.05738.<\/a><\/p>\n\n\n\n

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Predicting disease risk using multi-omics data across ancestries.<\/figcaption><\/figure>\n\n\n\n

Take home: we demonstrate that pretraining on diverse multi-omics and ancestry data substantially improves disease risk prediction across populations.<\/p>\n\n\n\n

Le Menestrel, Thomas, Erin Craig, Robert Tibshirani, Trevor Hastie, and Manuel A. Rivas. Using Pre-training and Interaction Modeling for Ancestry-Specific Disease Prediction Using Multiomics Data from the UK Biobank. arXiv preprint arXiv:2404.17626, 2024.<\/a><\/p>\n\n\n\n

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Predicting disease risk using multi-omics data.<\/figcaption><\/figure>\n\n\n\n

Take home: we present methods to improve disease risk prediction and survival modeling in the UK Biobank with multi-omics data, with the greatest gains seen for diseases that have both genetic and metabolic components.<\/p>\n\n\n\n

Aguilar, Oscar, Cheng Chang, Elsa Bismuth, and Manuel A. Rivas. \u201cIntegrative Machine Learning Approaches for Predicting Disease Risk Using Multi-Omics Data from the UK Biobank.\u201dbioRxiv, 20 Apr. 2024, doi:10.1101\/2024.04.16.589819.<\/a><\/p>\n\n\n\n

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Pre-training and the lasso. Conceptual framework.<\/figcaption><\/figure>\n\n\n\n

Take home: we present a multi-class framework that borrows features across classes and some that are specific.<\/p>\n\n\n\n

Craig, Erin, et al. \u201cPretraining and the Lasso.\u201d arXiv<\/em>, 30 Oct. 2024, https:\/\/arxiv.org\/abs\/2401.12911.<\/a><\/p>\n\n\n\n

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Tree based prediction of phenotypes.<\/figcaption><\/figure>\n\n\n\n

Take home: we systematically evaluate a suite of tree-based and linear machine learning methods\u2014including gradient boosting, random forests, and SNPnet\u2014for genotype-to-phenotype prediction using UK Biobank data, optimizing hyperparameters through multi-objective tuning to balance predictive accuracy and computational efficiency.<\/p>\n\n\n\n

Melendez, Alex, Cayetana L\u00f3pez, David Bonet, Gerard Sant, Daniel Mas Montserrat, Jordi Abante, Manuel A. Rivas, Ferran Marqu\u00e8s, and Alexander G. Ioannidis. \u201cAssessing Tree-Based Phenotype Prediction on the UK Biobank.\u201d2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)<\/em>, IEEE, 2023, doi:10.1109\/BIBM58861.2023.10385960.<\/a><\/p>\n\n\n\n

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The TRB variant rs7458379 influences TRBV4-2 expression, highlighting coordinated genetic effects on T-cell receptor composition in narcolepsy.<\/figcaption><\/figure>\n\n\n\n

Take home: We show that narcolepsy type 1 is an autoimmune disorder driven by specific HLA and T-cell receptor variants that shape T-cell responses to influenza infection or vaccination, leading to targeted destruction of hypocretin-producing neurons.<\/p>\n\n\n\n

Ollila, Hanna M., et al. \u201cNarcolepsy Risk Loci Outline Role of T Cell Autoimmunity and Infectious Triggers in Narcolepsy.\u201d Nature Communications, vol. 14, article no. 2709, 2023, https:\/\/doi.org\/10.1038\/s41467-023-36120-z.<\/a><\/p>\n\n\n\n

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ALDH2*2<\/em> is ssociated with coronary artery disease (CAD) and induces endothelial dysfunction.<\/figcaption><\/figure>\n\n\n\n

Take home: We show that SGLT2 inhibitors such as empagliflozin can reverse endothelial dysfunction caused by the common ALDH2*2 alcohol flushing variant, suggesting a potential preventive therapy for coronary artery disease in affected individuals.<\/p>\n\n\n\n

Guo, Hongchao, et al. \u201cSGLT2 Inhibitor Ameliorates Endothelial Dysfunction Associated with the Common ALDH2 Alcohol Flushing Variant.\u201d Science Translational Medicine<\/em>, vol. 15, no. 680, 25 Jan. 2023, eabp9952. American Association for the Advancement of Science. <\/a><\/p>\n\n\n\n

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Study designs where common controls could be used.<\/figcaption><\/figure>\n\n\n\n

Take home: we show that using well-matched, rigorously harmonized common control datasets can greatly expand the power of sequencing studies, but only when careful attention is paid to ancestry, technical, and design biases that otherwise risk confounding results.<\/p>\n\n\n\n

Wojcik GL, Murphy J, Edelson JL, Gignoux CR, Ioannidis AG, Manning A, Rivas MA, Buyske S, Hendricks AE. Opportunities and challenges for the use of common controls in sequencing studies. Nat Rev Genet. 2022 Nov;23(11):665-679. doi: 10.1038\/s41576-022-00487-4. Epub 2022 May 17. PMID: 35581355; PMCID: PMC9765323.<\/a><\/p>\n\n\n\n

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Genes associated to inflammatory bowel diseases.<\/figcaption><\/figure>\n\n\n\n

Take home: We identify multiple rare coding variants associated with Crohn\u2019s disease, notably implicating <\/strong>ATG4C, an autophagy-related cysteine peptidase whose loss-of-function variants significantly increase disease risk, highlighting defective autophagy as a causal mechanism in intestinal inflammation.<\/p>\n\n\n\n

Sazonovs, Aleksejs, et al. \u201cLarge-Scale Sequencing Identifies Multiple Genes and Rare Variants Associated with Crohn\u2019s Disease Susceptibility.\u201d Nature Genetics<\/em>, vol. 54, no. 9, 2022, pp. 1275\u20131283.<\/a><\/p>\n\n\n\n

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Evolution of COVID19 at Stanford University including ancestry.<\/figcaption><\/figure>\n\n\n\n

Take home: We show that integrating viral, host genomic, and clinical data from diverse populations reveals ancestry-specific genetic and biological factors underlying COVID-19 severity.<\/p>\n\n\n\n

Parikh, Victoria N., et al. \u201cDeconvoluting Complex Correlates of COVID-19 Severity with a Multi-Omic Pandemic Tracking Strategy.\u201d Nature Communications<\/em>, vol. 13, article no. 5107, 2022<\/a><\/p>\n\n\n\n

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Sparse reduced rank regression for large-scale regression problems. <\/figcaption><\/figure>\n\n\n\n

Take home: We present a method for performing sparse reduced rank regression for large-scale and ultrahigh-dimensional problems with multiple responses.<\/p>\n\n\n\n

Qian, Junyang, Yosuke Tanigawa, Ruilin Li, Robert Tibshirani, Manuel A. Rivas, and Trevor Hastie. \u201cLarge-Scale Multivariate Sparse Regression with Applications to UK Biobank.\u201d The Annals of Applied Statistics<\/em>, vol. 16, no. 3, 2022, pp. 1891\u20131918.<\/a><\/p>\n\n\n\n

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Analyzing outliers improves polygenic risk prediction.<\/figcaption><\/figure>\n\n\n\n

Take home: we show that rare genetic variants linked to extreme gene expression improve prediction of complex traits like obesity when integrated into risk models, revealing that incorporating expression outlier\u2013associated variants enhances the accuracy and clinical utility of genetic risk prediction.<\/p>\n\n\n\n

Smail, Craig, et al. \u201cIntegration of Rare Expression Outlier-Associated Variants Improves Polygenic Risk Prediction.\u201d<\/em> American Journal of Human Genetics<\/em>, vol. 109, no. 6, 2022, pp. 1055-1064.<\/a><\/p>\n\n\n\n

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Genetic mapping of features from cardiac MRI.<\/figcaption><\/figure>\n\n\n\n

Take home: we show that genetic variation strongly influences ascending aortic diameter, and a polygenic score derived from these variants can predict risk for thoracic aortic aneurysm across diverse populations, emphasizing the key causal role of blood pressure in aortic disease.<\/p>\n\n\n\n

Tcheandjieu, Catherine, et al. \u201cHigh Heritability of Ascending Aortic Diameter and Trans-Ancestry Prediction of Thoracic Aortic Disease.\u201d Nature Genetics<\/em>, vol. 54, no. 6, 2022, pp. 772\u2013782.<\/a><\/p>\n\n\n\n

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Genistein modulates cannabinoid receptor (CB1) signaling to counteract \u0394\u2079-THC\u2013induced inflammation and oxidative stress, reducing atherosclerosis risk through cAMP-PKA\u2013NF-\u03baB pathway regulation.<\/figcaption><\/figure>\n\n\n\n

Take home: we show that marijuana\u2019s active compound \u0394\u2079-THC promotes vascular inflammation and atherosclerosis through CB1 receptor activation, and the soybean isoflavone genistein acts as a CB1 antagonist that blocks these effects.<\/p>\n\n\n\n

Wei, T. T., et al. \u201cCannabinoid Receptor 1 Antagonist Genistein Attenuates Marijuana-Induced Vascular Inflammation.\u201d Cell<\/em>, vol. 0, 29 Apr. 2022, S0092-8674(22)00443-3.<\/a><\/p>\n\n\n\n

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Modeling time-to-event to develop asthma from birth.<\/figcaption><\/figure>\n\n\n\n

Take home: we present a computational method for fitting ultrahigh-dimensional time-to-event data.<\/p>\n\n\n\n

Li R, Chang C, Justesen JM, Tanigawa Y, Qian J, Hastie T, Rivas MA, Tibshirani R. Fast Lasso method for large-scale and ultrahigh-dimensional Cox model with applications to UK Biobank. Biostatistics. 2022 Apr 13;23(2):522-540. doi: 10.1093\/biostatistics\/kxaa038.<\/a><\/p>\n\n\n\n

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Atlas of polygenic risk scores trained in UK Biobank.<\/figcaption><\/figure>\n\n\n\n

Take home: we present an atlas of polygenic risk scores across 813 traits where genes have significant predictive power. <\/p>\n\n\n\n

Tanigawa Y, Qian J, Venkataraman G, Justesen JM, Li R, Tibshirani R, Hastie T, Rivas MA. Significant sparse polygenic risk scores across 813 traits in UK Biobank. PLoS Genet. 2022 Mar 24;18(3):e1010105. <\/a><\/p>\n\n\n\n

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Genetics of COVID19.<\/figcaption><\/figure>\n\n\n\n

Take home: we map the genetics of COVID19 via an international collaboration.<\/p>\n\n\n\n

COVID-19 Host Genetics Initiative. \u201cMapping the Human Genetic Architecture of COVID-19.\u201d Nature<\/em>, vol. 600, 2021, pp. 472\u2013477.<\/a><\/p>\n\n\n\n

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Bayesian Multiple Rare variant and Phenotypes (MRP) statistical framework.<\/figcaption><\/figure>\n\n\n\n

Take home: we present Bayesian methods for analyzing rare variants in exome and genome sequencing studies.<\/p>\n\n\n\n

Venkataraman GR, DeBoever C, Tanigawa Y, Aguirre M, Ioannidis AG, Mostafavi H, Spencer CCA, Poterba T, Bustamante CD, Daly MJ, Pirinen M, Rivas MA. Bayesian model comparison for rare-variant association studies. Am J Hum Genet. 2021 Dec 2;108(12):2354-2367. doi: 10.1016\/j.ajhg.2021.11.005. Epub 2021 Nov 24. PMID: 34822764; PMCID: PMC8715195.<\/a><\/p>\n\n\n\n

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Improved predictive power when combining multiple responses when modeling time-to-event data.<\/figcaption><\/figure>\n\n\n\n

Take home: we develop mrCox tool for modeling multiple time-to-event response with ultrahigh-dimensional data. <\/p>\n\n\n\n

Li R, Tanigawa Y, Justesen JM, Taylor J, Hastie T, Tibshirani R, Rivas MA. Survival analysis on rare events using group-regularized multi-response Cox regression. Bioinformatics. 2021 Dec 7;37(23):4437-4443. doi: 10.1093\/bioinformatics\/btab095. PMID: 33560296; PMCID: PMC8652035.<\/a><\/p>\n\n\n\n

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Speeding up penalized regression for ultrahigh-dimensional problems in population-scale biobanks by focusing on X\"\beta\" speedup.<\/figcaption><\/figure>\n\n\n\n

Take home: We present optimization algorithms for ultrahigh-dimensional problems. <\/p>\n\n\n\n

Li R, Chang C, Tanigawa Y, Narasimhan B, Hastie T, Tibshirani R, Rivas MA. Fast numerical optimization for genome sequencing data in population biobanks. Bioinformatics. 2021 Nov 18;37(22):4148-4155. doi: 10.1093\/bioinformatics\/btab452. PMID: 34146108; PMCID: PMC9206591.<\/a><\/p>\n\n\n\n

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Association between sleep features and psychiatric diagnoses.<\/figcaption><\/figure>\n\n\n\n

Take home: we show that accelerometer derived sleep measures are associated to psychiatric diagnoses.<\/p>\n\n\n\n

Wainberg M, Jones SE, Beaupre LM, Hill SL, Felsky D, Rivas MA, Lim ASP, Ollila HM, Tripathy SJ. Association of accelerometer-derived sleep measures with lifetime psychiatric diagnoses: A cross-sectional study of 89,205 participants from the UK Biobank. PLoS Med. 2021 Oct 12;18(10):e1003782. doi: 10.1371\/journal.pmed.1003782. PMID: 34637446; PMCID: PMC8509859.<\/a><\/p>\n\n\n\n

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Decomposition of genetic associations across multiple population biobanks: Biobank Japan and UK Biobank.<\/figcaption><\/figure>\n\n\n\n

Take home: we show that our approach DeGAS (Decomposotion of Genetic Association Studies) can be applied to summary statistics from multiple biobanks to get disease insights.<\/p>\n\n\n\n

Sakaue S, Kanai M, Tanigawa Y, …, Rivas MA, Daly MJ, Matsuda K, Okada Y. A cross-population atlas of genetic associations for 220 human phenotypes. Nat Genet. 2021 Oct;53(10):1415-1424. doi: 10.1038\/s41588-021-00931-x. Epub 2021 Sep 30. PMID: 34594039; PMCID: PMC12208603.<\/a><\/p>\n\n\n\n

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Effects of APOC3 genetic variant on triglyceride levels in European and Indian populations.<\/figcaption><\/figure>\n\n\n\n

Take home: we show that APOC3 genetic variants affect triglyceride levels in Indian and European populations.<\/p>\n\n\n\n

Goyal S, Tanigawa Y, …, Rivas MA, Aston CE, Sanghera DK. APOC3 genetic variation, serum triglycerides, and risk of coronary artery disease in Asian Indians, Europeans, and other ethnic groups. Lipids Health Dis. 2021 Sep 21;20(1):113. doi: 10.1186\/s12944-021-01531-8. PMID: 34548093; PMCID: PMC8456544.<\/a><\/p>\n\n\n\n

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Bayesian clustering of genetic variants across their lipid profile. <\/figcaption><\/figure>\n\n\n\n

Take home: we present a new Bayesian mixture model for clustering rare variant genetic effects solely based on summary statistics from univariate regression. <\/p>\n\n\n\n

Venkataraman, Guhan Ram, Yosuke Tanigawa, Matti Pirinen, and Manuel A. Rivas. \u201cBayesian Mixture Model for Clustering Rare-Variant Effects in Human Genetic Studies.\u201d bioRxiv<\/em>, 6 Aug. 2021<\/a><\/p>\n\n\n\n

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Genetic risk scores for diseases based on a combination of traits.<\/figcaption><\/figure>\n\n\n\n

Take home: we present a framework that models polygenic risk as a combination of latent genetic components, enabling interpretable dissection of individual disease risk into biologically meaningful subtypes without sacrificing predictive accuracy.<\/p>\n\n\n\n

Aguirre M, Tanigawa Y, Venkataraman GR, Tibshirani R, Hastie T, Rivas MA. Polygenic risk modeling with latent trait-related genetic components. Eur J Hum Genet. 2021 Jul;29(7):1071-1081. doi: 10.1038\/s41431-021-00813-0. Epub 2021 Feb 8. PMID: 33558700; PMCID: PMC8298449.<\/a><\/p>\n\n\n\n

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Transcriptome changes due to exercise.<\/figcaption><\/figure>\n\n\n\n

Take home:we show that transcriptional response to exercise follows distinct, time-dependent trajectories across muscle and blood, identifies SMAD3 as a central regulator, and uncovers age- and sex-specific molecular adaptations that shape how humans respond to physical activity.<\/p>\n\n\n\n

Amar, David, et al. \u201cTime Trajectories in the Transcriptomic Response to Exercise: A Meta-Analysis.\u201d Nature Communications<\/em>, vol. 12, no. 3471, 2021<\/a><\/p>\n\n\n\n

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C-index for PRS and clinical risk factors for atrial fibrillation.<\/figcaption><\/figure>\n\n\n\n

Take home: we show that a polygenic risk score (PRS) with the traditional CHA\u2082DS\u2082-VASc clinical score modestly but significantly improves prediction of ischemic stroke risk among patients with atrial fibrillation.<\/p>\n\n\n\n

O\u2019Sullivan, Jack W., et al. \u201cCombining Clinical and Polygenic Risk Improves Stroke Prediction Among Individuals With Atrial Fibrillation.\u201d Circulation: Genomic and Precision Medicine<\/em>, vol. 14, no. 3, 24 May 2021<\/a><\/p>\n\n\n\n

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Computation time of our proposed algorithm versus state-of-the art D-Shapley algorithm. <\/figcaption><\/figure>\n\n\n\n

Take home: we derive the first analytic formulas for distributional Shapley values (DShapley)\u2014quantifying each data point\u2019s contribution to model performance\u2014and introduces algorithms that make their computation orders of magnitude faster while offering new theoretical insight into how data characteristics affect value.<\/p>\n\n\n\n

Kwon, Yongchan, Manuel A. Rivas, and James Zou. “Efficient Computation and Analysis of Distributional Shapley Values.” Proceedings of the 24th International Conference on Artificial Intelligence and Statistics (AISTATS 2021)<\/em>, vol. 130, PMLR, 2021, pp. 631\u2013639.<\/a><\/p>\n\n\n\n

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ADCY5 genetic variant affects biological processes from the molecular to the organismal level \u2014 influencing both glucose metabolism and bone density.<\/figcaption><\/figure>\n\n\n\n

Take home: we find a single noncoding variant, rs56371916 in ADCY5<\/em>, that links bone and glucose metabolism by altering SREBP1-mediated regulation of lipid oxidation in adipocytes and osteoblasts, revealing a shared genetic mechanism underlying type 2 diabetes and bone mineral density.<\/p>\n\n\n\n

Sinnott-Armstrong, Nasa, et al. \u201cA Regulatory Variant at 3q21.1 Confers an Increased Pleiotropic Risk for Hyperglycemia and Altered Bone Mineral Density.\u201d Cell Metabolism<\/em>, vol. 33, no. 3, 2021, pp. 615\u2013628.e13.<\/a><\/p>\n\n\n\n

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GWAS of serum urate identifies key urate transporters and kidney-specific regulatory regions driving urate metabolism.<\/figcaption><\/figure>\n\n\n\n

Take home: we show that even well-understood molecular traits like urate, IGF-1, and testosterone are controlled by a few core biological pathways but also influenced by thousands of small-effect variants across the genome, revealing a highly polygenic genetic architecture.<\/p>\n\n\n\n

Sinnott-Armstrong, Nasa, Sahin Naqvi, Manuel Rivas, and Jonathan K. Pritchard. \u201cGWAS of Three Molecular Traits Highlights Core Genes and Pathways alongside a Highly Polygenic Background.\u201d eLife<\/em>, vol. 10, 2021, article e58615<\/a><\/p>\n\n\n\n

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Association between genetic variant and microbial metabolic pathway in inflammatory bowel disease.<\/figcaption><\/figure>\n\n\n\n

Take home: we show that host genetic variants influence the composition and metabolic functions of the gut microbiota, highlighting key gene\u2013microbiota interactions that shape inflammatory bowel disease pathogenesis.<\/p>\n\n\n\n

Hu S et al.Whole exome sequencing analyses reveal gene\u2013microbiota interactions in the context of IBD.<\/em> Gut. 2021;70(2):285\u2013296.<\/a><\/p>\n\n\n\n

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Coding variant associations to 35 biomarkers.<\/figcaption><\/figure>\n\n\n\n

Take home: we conduct a comprehensive study of 35 blood and urine biomarkers in the UK Biobank that reveals thousands of genetic loci underlying biomarker variation, clarifies causal links to common diseases, and shows that combining biomarker-based polygenic scores improves disease risk prediction beyond single-trait models.<\/p>\n\n\n\n

Sinnott-Armstrong, Nasa, et al. \u201cGenetics of 35 Blood and Urine Biomarkers in the UK Biobank.\u201d Nature Genetics<\/em>, vol. 53, no. 2, 2021, pp. 185\u2013194<\/a><\/p>\n\n\n\n

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Graphical causal inference models for population biobanks. <\/figcaption><\/figure>\n\n\n\n

Take home: we develop cGAUGE, a causal graphical inference model for population biobank datasets.<\/p>\n\n\n\n

Amar, David, Nasa Sinnott-Armstrong, Euan A. Ashley, and Manuel A. Rivas. \u201cGraphical Analysis for Phenome-Wide Causal Discovery in Genotyped Population-Scale Biobanks.<\/em>\u201d Nature Communications<\/em>, vol. 12, no. 350, 13 Jan. 2021<\/a><\/p>\n\n\n\n

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Sex effects mixture model identifies genetic architecture for testosterone levels.<\/figcaption><\/figure>\n\n\n\n
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Sex specific testosterone polygenic risk score. <\/figcaption><\/figure>\n\n\n\n

Take home: we develop a Bayesian mixture model to model the genetics of testosterone levels in men and females. We also train sex-specific testosterone polygenic risk scores. <\/p>\n\n\n\n

Flynn E, Tanigawa Y, Rodriguez F, Altman RB, Sinnott-Armstrong N, Rivas MA. Sex-specific genetic effects across biomarkers. Eur J Hum Genet. 2021 Jan;29(1):154-163. doi: 10.1038\/s41431-020-00712-w. Epub 2020 Sep 1. PMID: 32873964; PMCID: PMC7794464.<\/a><\/p>\n\n\n\n

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Cardiac imaging genetics – aortic valve area.<\/figcaption><\/figure>\n\n\n\n

Take home: we develop deep learning models to measure aortic valve area and map the genetics of it.<\/p>\n\n\n\n

C\u00f3rdova-Palomera, Aldo, et al. \u201cCardiac Imaging of Aortic Valve Area from 34,287 UK Biobank Participants Reveals Novel Genetic Associations and Shared Genetic Comorbidity with Multiple Disease Phenotypes.\u201d Circulation: Genomic and Precision Medicine, vol. 13, no. 6, Oct. 2020, e003014. American Heart Association<\/a><\/p>\n\n\n\n

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PheWAS of 26 Mendelian genes.<\/figcaption><\/figure>\n\n\n\n

Take home: we study the phenome-wide association of 26 Mendelian genes in UK Biobank. <\/p>\n\n\n\n

Tcheandjieu, Catherine, et al. \u201cA Phenome-Wide Association Study of 26 Mendelian Genes Reveals Phenotypic Expressivity of Common and Rare Variants within the General Population.<\/em>\u201d PLOS Genetics<\/em>, vol. 16, no. 11, Nov. 2020, e1008802.<\/a><\/p>\n\n\n\n

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snpnet fitting penalized regression to ultrahigh-dimensional problems in population biobanks.<\/figcaption><\/figure>\n\n\n\n

Take home: we introduce BASIL, a fast and scalable algorithm for applying lasso and elastic-net regression to massive, high-dimensional datasets like the UK Biobank, enabling efficient genome-wide prediction and feature selection across millions of genetic variants.<\/p>\n\n\n\n

Qian, Junyang, et al. \u201cA Fast and Scalable Framework for Large-Scale and Ultrahigh-Dimensional Sparse Regression with Application to the UK Biobank.<\/em>\u201d PLOS Genetics<\/em>, vol. 16, no. 10, Oct. 2020, e1009141.<\/a><\/p>\n\n\n\n

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Genetic correlation of predicted risk of suicide and attempted suicide along with other phenotypes.<\/figcaption><\/figure>\n\n\n\n

Take home: we show that common genetic variation contributes meaningfully to suicide attempt risk, and clinically predicted suicide risk from electronic health records shares a significant genetic basis with self-reported suicide attempts.<\/p>\n\n\n\n

Ruderfer, Douglas M., et al. \u201cSignificant Shared Heritability Underlies Suicide Attempt and Clinically Predicted Probability of Attempting Suicide.\u201d Molecular Psychiatry<\/em>, vol. 25, no. 10, 2020, pp. 2422\u20132430.<\/a><\/p>\n\n\n\n

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Tagging clinical notes with ICD codes using deep learning algorithm. <\/figcaption><\/figure>\n\n\n\n

Take home: we present FastTag, a deep learning model that can accurately and efficiently classify unstructured human and veterinary medical narratives into top-level disease categories, reducing the need for manual coding and enabling cross-domain applications.<\/p>\n\n\n\n

Venkataraman, Guhan Ram, et al. \u201cFasTag: Automatic Text Classification of Unstructured Medical Narratives.\u201d PLOS One<\/em>, vol. 15, no. 6, 2020, e0234647. PLOS<\/em><\/a><\/p>\n\n\n\n

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Assessing digital phenotyping for human genetic studies.<\/figcaption><\/figure>\n\n\n\n

Take home: we present MultiVariate Polygenic Mixture Model (MVPMM) to assess use of digital phenotypes in human genetic studies.<\/p>\n\n\n\n

DeBoever, Christopher, et al. \u201cAssessing Digital Phenotyping to Enhance Genetic Studies of Human Diseases.\u201d The American Journal of Human Genetics<\/em>, vol. 106, no. 5, 2020, pp. 611\u2013622.<\/a><\/p>\n\n\n\n

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Protective loss-of-function variants in ANGPTL7 against glaucoma.<\/figcaption><\/figure>\n\n\n\n

Take home: we discover<\/strong><\/em> mutations in ANGPTL7 <\/em>that protect against glaucoma and lower intraocular pressure.<\/p>\n\n\n\n

Tanigawa, Yosuke, et al. \u201cRare Protein-Altering Variants in ANGPTL7<\/em> Lower Intraocular Pressure and Protect against Glaucoma.\u201d PLOS Genetics<\/em>, vol. 16, no. 5, 2020, e1008682.<\/a><\/p>\n\n\n\n

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Type 2 diabetes prevalence increases with higher body mass index (BMI), and this relationship is consistent across individuals with and without a family history of diabetes.<\/figcaption><\/figure>\n\n\n\n

Take home: we show that lower body mass index causally reduces the risk of type 2 diabetes across all levels of genetic risk, family history, and baseline BMI, meaning weight loss benefits everyone\u2014not just those at high risk.<\/p>\n\n\n\n

Wainberg, Michael, et al. \u201cHomogeneity in the Association of Body Mass Index with Type 2 Diabetes across the UK Biobank: A Mendelian Randomization Study.\u201d PLOS Medicine<\/em>, vol. 16, no. 12, 2019<\/a><\/p>\n\n\n\n

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Rare and common variants associated to inflammatory bowel diseases.<\/figcaption><\/figure>\n\n\n\n

Take home: we review rare and common genetic variants associated to inflammatory bowel diseases. <\/p>\n\n\n\n

Guhan R Venkataraman, Manuel A Rivas, Rare and common variant discovery in complex disease: the IBD case study, Human Molecular Genetics<\/em>, Volume 28, Issue R2, 15 October 2019, Pages R162\u2013R169<\/a><\/p>\n\n\n\n

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Decomposition of genetic association results reveals biological and biomarker components of disease.<\/figcaption><\/figure>\n\n\n\n

Take home: we show that breaking down genetic associations across thousands of traits using the DeGAs method (SVD of summary statistic matrix) reveals key biological components\u2014particularly those linked to adipocyte biology\u2014that help explain how genetic variation contributes to complex diseases like obesity and heart disease.<\/p>\n\n\n\n

Tanigawa, Yosuke, et al. \u201cComponents of Genetic Associations across 2,138 Phenotypes in the UK Biobank Highlight Adipocyte Biology.\u201d Nature Communications<\/em>, vol. 10, no. 4064, 2019<\/a><\/p>\n\n\n\n

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Phenome-wide association map of copy number variants. <\/figcaption><\/figure>\n\n\n\n

Take home: we present a phenome-wide association map of copy number variants detected in the UK population biobank (UK Biobank). <\/p>\n\n\n\n

Aguirre, Matthew, et al. \u201cPhenome-wide Burden of Copy-Number Variation in the UK Biobank.\u201d The American Journal of Human Genetics<\/em>, vol. 105, no. 3, 2019, pp. 373\u2013383.<\/a><\/p>\n\n\n\n

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Global Biobank Engine for visualizing and browsing genetic association results across population biobanks.<\/figcaption><\/figure>\n\n\n\n

Take home: we present Global Biobank Engine, a software for supporting genetic association results from population biobanks. <\/p>\n\n\n\n

McInnes, Gregory, et al. \u201cGlobal Biobank Engine: Enabling Genotype-Phenotype Browsing for Biobank Summary Statistics.\u201d Bioinformatics<\/em>, vol. 35, no. 14, July 2019, pp. 2495\u20132497. Oxford University Press <\/a><\/p>\n\n\n\n

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TWAS has multiple associations, but susceptible to input data selection.<\/figcaption><\/figure>\n\n\n\n

Take home: we show that there are inherent challenges in transcriptome wide association studies that makes it challenging to identify causal genes by using that approach. <\/p>\n\n\n\n

Wainberg, Michael, et al. \u201cOpportunities and Challenges for Transcriptome-Wide Association Studies.\u201d Nature Genetics<\/em>, vol. 51, no. 4, 2019, pp. 592\u2013599<\/a><\/p>\n\n\n\n

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NUDT15 genetic variants associated with thiopurine induced myelosuppression in patients with IBD.<\/figcaption><\/figure>\n\n\n\n

Take home: we identify genetic variants in NUDT15 that associated with thiopurine induced myelosuppression in patients with inflammatory bowel diseases.<\/p>\n\n\n\n

Walker, Michael A., et al. \u201cAssociation of Genetic Variants in NUDT15<\/em> with Thiopurine-Induced Myelosuppression in Patients with Inflammatory Bowel Disease.\u201d JAMA<\/em>, vol. 321, no. 8, 2019, pp. 773\u2013785.<\/a><\/p>\n\n\n\n

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Differences in Crohn’s disease genetic risk in Ashkenazi vesus non-Ashkenazi Jewish European population.<\/figcaption><\/figure>\n\n\n\n

Take home: we characterize a founder-effect\u2013driven enrichment of rare and common genetic variants in the Ashkenazi Jewish population contributes significantly to their increased risk of Crohn\u2019s disease and certain rare Mendelian disorders.<\/p>\n\n\n\n

Rivas, Manuel A., et al. \u201cInsights into the Genetic Epidemiology of Crohn\u2019s and Rare Diseases in the Ashkenazi Jewish Population.\u201d PLOS Genetics<\/em>, vol. 14, no. 5, 24 May 2018, e1007329.<\/a><\/p>\n\n\n\n

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Study design of protein-truncating variant phenome-wide association study.<\/figcaption><\/figure>\n\n\n\n

Take home: we conduct a large-scale analysis of protein-truncating variants in over 337,000 UK Biobank participants reveals their significant medical relevance, identifying both protective and risk associations across diverse diseases and underscoring their value for understanding disease mechanisms and therapeutic targeting.<\/p>\n\n\n\n

DeBoever, Christopher, et al. \u201cMedical Relevance of Protein-Truncating Variants across 337,205 Individuals in the UK Biobank Study.\u201d Nature Communications<\/em>, vol. 9, no. 1612, 2018<\/a><\/p>\n\n\n\n

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X Chromosome inactivation study design.<\/figcaption><\/figure>\n\n\n\n

Take home: we reveal that X chromosome inactivation in humans is incomplete and variable across genes, individuals, tissues, and cells, contributing to sex differences in gene expression and potentially influencing health and disease.<\/p>\n\n\n\n

Tukiainen, Taru, et al. \u201cLandscape of X Chromosome Inactivation across Human Tissues.\u201d Nature<\/em>, vol. 550, no. 7675, 2017, pp. 244\u2013248.<\/a><\/p>\n\n\n\n

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biMM: bivariate Mixture Model for estimating heritability and genetic covariances in large population cohorts. <\/figcaption><\/figure>\n\n\n\n

Take home: we develop an efficient method for estimating heritability and genetic covariances in large-scale population cohorts. <\/p>\n\n\n\n

Pirinen, Matti, et al. \u201cbiMM: Efficient Estimation of Genetic Variances and Covariances for Cohorts with High-Dimensional Phenotype Measurements.\u201d Bioinformatics<\/em>, vol. 33, no. 15, 2017, pp. 2405\u20132407.<\/a><\/p>\n\n\n\n

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Mosaic mutation distribution across different solid cancer types. <\/figcaption><\/figure>\n\n\n\n

Take home: we identify associations between mosaic mutations and solid cancers. <\/p>\n\n\n\n

Artomov, Mykyta, et al. \u201cMosaic Mutations in Blood DNA Sequence Are Associated with Solid Tumor Cancers.\u201d npj Genomic Medicine<\/em>, vol. 2, no. 22, 2017<\/a><\/p>\n\n\n\n

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RNF186 protein-truncating variant mislocalizes protein.<\/figcaption><\/figure>\n\n\n\n

Take home: we identify a protein truncating variant in RNF186, p.R179X, that protects against ulcerative colitis.<\/p>\n\n\n\n

Rivas, Manuel A., et al. \u201cA Protein-Truncating R179X Variant in RNF186<\/em> Confers Protection against Ulcerative Colitis.\u201d Nature Communications<\/em>, vol. 7, Article no. 12342, 2016<\/a><\/p>\n\n\n\n

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Type 2 diabetes genetic architecture.<\/figcaption><\/figure>\n\n\n\n

Take home: using whole genome sequencing we dissect the genetic architecture of type 2 diabetes. <\/p>\n\n\n\n

Fuchsberger, Christian, et al. \u201cThe Genetic Architecture of Type 2 Diabetes.\u201d Nature<\/em>, vol. 536, no. 7614, 2016, pp. 41\u201347.<\/a><\/p>\n\n\n\n

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Bayesian model summary of allele-specific expression data.<\/figcaption><\/figure>\n\n\n\n

Take home: we develop Bayesian models to summarize allele-specific expression data with an emphasis on protein-truncating variants.<\/p>\n\n\n\n

Pirinen, Matti, et al. \u201cAssessing Allele-Specific Expression across Multiple Tissues from RNA-Seq Read Data.\u201d Bioinformatics<\/em>, vol. 31, no. 15, 2015, pp. 2497\u20132504.<\/a><\/p>\n\n\n\n

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Comprehensive transcriptome analysis of protein-truncating variants in GTEx and Geuvadis.
<\/figcaption><\/figure>\n\n\n\n

Take home: we present a survey of the effect of protein-truncating variants on the human transcriptome across multiple tissues. We present a model for assessing the effects of variants proximal to splice junctions. <\/p>\n\n\n\n

Rivas, Manuel A., et al. \u201cEffect of Predicted Protein-Truncating Genetic Variants on the Human Transcriptome.\u201d Science<\/em>, vol. 348, no. 6235, 2015, pp. 666\u2013669.<\/a><\/p>\n\n\n\n

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CARD9 IVS11+1G>C protective variant against Crohn’s disease and ulcerative colitis.<\/figcaption><\/figure>\n\n\n\n

Take home: we present a rare exon sequencing study in inflammatory bowel diseases, identify risk and protective mutations in NOD2, CARD9. We develop new methods and software, referred to as Syzygy, for pooled resequencing studies incorporating new error models.<\/p>\n\n\n\n

Rivas, Manuel A., et al. \u201cDeep Resequencing of GWAS Loci Identifies Independent Rare Variants Associated with Inflammatory Bowel Disease.\u201d Nature Genetics<\/em>, vol. 43, no. 11, 2011, pp. 1066\u20131073.<\/a><\/p>\n\n\n\n

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Genome Analysis Toolkit (GATK) for variant calling from genome sequencing data.<\/figcaption><\/figure>\n\n\n\n

Take home: we present the Genome Analysis Toolkit (GATK) software for variant calling from genome sequencing data.<\/p>\n\n\n\n

DePristo MA, et al. A framework for variation discovery and genotyping using next-generation DNA sequencing data. Nat Genet. 2011 May;43(5):491-8. doi: 10.1038\/ng.806. Epub 2011 Apr 10.<\/a> <\/p>\n\n\n\n

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Drug response correlated with growth rate and baseline ATP levels of the cell line.<\/figcaption><\/figure>\n\n\n\n

Take home: we show that genetic variation significantly influences cellular responses to drugs and gene expression levels in vitro, establishing lymphoblastoid cell lines as a powerful model for linking genotype to cellular phenotype.<\/p>\n<\/div>\n\n\n\n

Choy E, Yelensky R, .., Rivas M, .., Daly MJ, Altshuler D. Genetic analysis of human traits in vitro: drug response and gene expression in lymphoblastoid cell lines. PLoS Genet. 2008 Nov;4(11):e1000287. doi: 10.1371\/journal.pgen.1000287. Epub 2008 Nov 28. PMID: 19043577; PMCID: PMC2583954.<\/a><\/p>\n<\/div>\n\n\n\n

<\/div>\n\n\n\n

<\/p>\n","protected":false},"excerpt":{"rendered":"

Take home: we introduce a spherical-harmonics regression framework for PheWAS that embeds phenotypes on the unit sphere to jointly model global genetic effect patterns and detect variant-specific, spatially localized signals via residual analysis and spherical-cap enrichment, enabling interpretable, rotation-invariant mapping of genetic effects across phenomes. Rivas, Manuel A. \u201cJoint Spherical-Harmonics Regression for PheWAS: Global Maps, […]<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-128","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/mrivas.su.domains\/gbe\/wp-json\/wp\/v2\/pages\/128","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/mrivas.su.domains\/gbe\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/mrivas.su.domains\/gbe\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/mrivas.su.domains\/gbe\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/mrivas.su.domains\/gbe\/wp-json\/wp\/v2\/comments?post=128"}],"version-history":[{"count":125,"href":"https:\/\/mrivas.su.domains\/gbe\/wp-json\/wp\/v2\/pages\/128\/revisions"}],"predecessor-version":[{"id":626,"href":"https:\/\/mrivas.su.domains\/gbe\/wp-json\/wp\/v2\/pages\/128\/revisions\/626"}],"wp:attachment":[{"href":"https:\/\/mrivas.su.domains\/gbe\/wp-json\/wp\/v2\/media?parent=128"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}