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Preprints

• De Castro AL, Wang J, Bonney-King JG, Morota G, Miller-Cushon EK, and Yu H. AnimalMotionViz: an interactive software tool for tracking and visualizing animal motion patterns using computer vision. bioRxiv. [bioRxiv]
• Suela MM, Azevedo CF, Nascimento ACC, Morota G, Lopes da Silva F, and Nascimento M. Structural equation models to interpret multi-trait genome-wide association studies for morphological and productive traits in Soybean [Glycine max (L.) Merr.]. Research Square. [Research Square]

2025

• Bi Y, Walia H, Obata T, and Morota G. 2025. Genomic prediction of metabolic content in rice grain in response to warmer night conditions. Crop Science. Early view. [DOI] [bioRxiv]

2024

• Zhang Z, Abraham S, Lee A, Li Y, Morota G, Ha DS, and Shin S. 2024. SegIt: Empowering sensor data labeling with enhanced efficiency and security. MLMI 2024: Proceedings of the 2024 7th International Conference on Machine Learning and Machine Intelligence (MLMI). 97-103. Osaka, Japan. August 2-4. [DOI]
• Sabag I, Pnini S, Morota G, and Peleg Z. 2024. Refining flowering date enhances sesame yield independently of day-length. BMC Plant Biology. 24:711. [DOI] [PubMed] [EuropePMC]
• Kravitz A, Liao M, Morota G, Tyler R, Cockrum RR, Manohar BM, Ronald BSM, Collins MT, and Sriranganathan N. 2024. Retrospective single nucleotide polymorphism analysis of host resistance and susceptibility to ovine Johne’s disease using restored FFPE DNA. International Journal of Molecular Sciences. 25:7748. [DOI] [PubMed] [EuropePMC]
• Habimana V, Nguluma AS, Nziku ZC, Ekine-Dzivenu CC, Morota G, Mrode R, and Chenyambuga SW. 2024. Heat stress effects on physiological and milk yield traits of lactating Holstein Friesian crossbreds reared in Tanga region, Tanzania. Animals. 14:1914. [DOI] [PubMed] [EuropePMC] [Preprints]
• Sabag I, Bi, Y, Sahoo MM, Herrmann I, Morota G, and Peleg Z. 2024. Leveraging genomics and temporal high-throughput phenotyping to enhance association mapping and yield prediction in sesame. The Plant Genome. 17:e20481. [DOI] [PubMed] [EuropePMC] [bioRxiv]
• Sandhu J, Irvin L, Chandaran AK, Oguro S, Paul P, Dhatt B, Hussain W, Cunningham SS, Quinones CO, Lorence A, Adviento-Borbe MA, Staswick P, Morota G, and Walia H. 2024. Natural variation in LONELY GUY-like 1 regulates rice grain weight under warmer night conditions. Plant Physiology. 196:164-180. [DOI] [PubMed] [EuropePMC]
• Maurer JJ, Cheng Y, Pedroso A, Thompson KK, Akter S, Kwan T, Morota G, Kinstler S, Porwollik S, McClelland M, Escalante-Semerena JC, and Lee MD. 2024. Peeling back the many layers of competitive exclusion. Frontiers in Microbiology. 15:1342887. [DOI] [PubMed] [EuropePMC]
• Silva CM, Mezzomo, HC, Ribeiro JPO, Signorini VS, Lima GW, Torres Vieira EF, Portes MF, Morota G, Corredo LP, and Nardino M. 2024. Insights on multi-spectral vegetation indices derived from UAV-based high-throughput phenotyping for indirect selection in tropical wheat breeding. Euphytica. 220:35. [DOI] [Research Square]
• Aydin KB, Bi Y, Brito LF, Ulutaş Z, and Morota G. 2024. Review of sheep breeding and genetic research in Türkiye. Frontiers in Genetics. 15:1308113. [DOI] [PubMed] [EuropePMC]
• Wang J, Hu Y, Xiang L, Morota G, Brooks SA, Wickens CL Miller-Cushon EK, and Yu H. 2024. Technical note: ShinyAnimalCV: open-source cloud-based web application for object detection, segmentation, and three-dimensional visualization of animals using computer vision. Journal of Animal Science. 102:1-6. [DOI] [PubMed] [EuropePMC] [arXiv]

2023

• Bi Y, Campos LM, Wang J, Yu H, Hanigan MD, and Morota G. 2023. Depth video data-enabled predictions of longitudinal dairy cow body weight using thresholding and Mask R-CNN algorithms. Smart Agricultural Technology. 6:100352. [DOI] [arXiv]
• Habimana V, Nguluma AS, Nziku ZC, Ekine-Dzivenu CC, Morota G, Mrode R, and Chenyambuga SW. 2023. Heat stress effects on milk yield traits and metabolites and mitigation strategies for dairy cattle breeds reared in tropical and sub-tropical countries. Frontiers in Veterinary Science. 10:1121499. [DOI] [PubMed] [EuropePMC]
• Suela MM, Azevedo CF, Nascimento ACC, Momen M, Caixeta ET, Morota G, and Nascimento M. 2023. Genome-wide association study for morphological, physiological, and productive traits in Coffea arabica using structural equation models. Tree Genetics and Genomes. 19:23. [DOI]
• Baba T, Morota G, Kawakami J, Goto Y, Oka T, Masuda Y, Brito LF, Cockrum RR, and Kawahara T. 2023. Longitudinal genome-wide association analysis using a single-step random regression model for height in Japanese Holstein cattle. JDS Communications. 4:363-368. [DOI] [PubMed] [EuropePMC]
• Yassue RM, Galli G, Chen CJ, Fritsche-Neto R, and Morota G. 2023. Genome-wide association analysis of hyperspectral reflectance data to dissect the genetic architecture of growth-related traits in maize under plant growth-promoting bacteria inoculation. Plant Direct. 7:e492. [DOI] [PubMed] [EuropePMC] [bioRxiv]
• Bi Y, Yassue RM, Paul P, Dhatt BK, Sandhu J, Do PT, Walia H, Obata T, and Morota G. 2023. Evaluating metabolic and genomic data for predicting grain traits under high night temperature stress in rice. G3: Genes, Genomes, Genetics. 13.5. [DOI] [PubMed] [EuropePMC] [bioRxiv]
• Sabag I, Bi Y, Peleg Z, and Morota G. 2023. Multi-environment analysis enhances genomic prediction accuracy of agronomic traits in sesame. Frontiers in Genetics. 14:1108416. [DOI] [PubMed] [EuropePMC] [bioRxiv]
• Habimana V, Ekine-Dzivenu C, Nguluma AS, Nziku ZC, Morota G, Chenyambuga SW, and Mrode R. 2023. Genes and models for estimating genetic parameters for heat tolerance in dairy cattle. Frontiers in Genetics. 14:1127175. [DOI] [PubMed] [EuropePMC]
• Wang Z, Yu D, Morota G, Dhakal K, Singer W, Lord N, Huang H, Chen P, Mozzoni L, Li S, and Zhang B. 2023. Genome-wide association analysis of sucrose and alanine contents in edamame bean. Frontiers in Plant Science. 13:1086007. [DOI] [PubMed] [EuropePMC]
• Yassue RM, Galli G, Fritsche-Neto R, and Morota G. 2023. Classification of plant growth-promoting bacteria inoculation status and prediction of growth-related traits in tropical maize using hyperspectral image and genomic data. Crop Science. 63:88-100. [DOI] [bioRxiv]

2022

• Kadlec R, Indest S, Castro K, Waqar S, Campos LM, Amorim ST, Bi Y, Hanigan MD, and Morota G. 2022. Automated acquisition of top-view dairy cow depth image data using an RGB-D sensor camera. Translational Animal Science. 6:1-6. [DOI] [PubMed] [EuropePMC]
• de Novais FJ, Yu H, Cesar ASM, Momen M, Poleti MD, Petry B, Mourao GB, de Almeida Regitano LC, Morota G, and Coutinho LL. 2022. Multi-omic data integration for the study of production, carcass, and meat quality traits in Nellore cattle. Frontiers in Genetics. 13:948240. [DOI] [PubMed] [EuropePMC]
• Chandaran AK, Sandhu J, Irvin L, Paul P, Hussain W, Gao T, Staswick P, Yu H, Morota G, and Walia H. 2022. Rice Chalky Grain 5 regulates natural variation for grain quality under heat stress. Frontiers in Plant Science. 13:1026472. [DOI] [PubMed] [EuropePMC]
• Alghamdi S, Zhao Z, Ha DS, Morota G, and Ha SS. 2022. Improved pig behavior analysis by optimizing window sizes for individual behaviors on acceleration and angular velocity data. Journal of Animal Science. 100:1-9. [DOI] [PubMed] [EuropePMC]
• Chen CJ, Morota G, and Cheng H. 2022. VTag: automatic pipeline to annotate video data for pig phenomics studies. Proceedings, 12th World Congress of Genetics Applied to Livestock Production. 12:545-548. Rotterdam, The Netherlands. July 3-8. [DOI]
• Morota G, Jarquin D, Campbell MT, and Iwata H. 2022. Statistical methods for the quantitative genetic analysis of high-throughput phenotyping data. In High Throughput Plant Phenotyping: Methods and Protocols. Molecular Biology Series, Springer, New York. 2539:269-296. [DOI] [PubMed] [EuropePMC] [arXiv]
• Murphy MD, Fernandes SB, Morota G, and Lipka AE. 2022. Assessment of two statistical approaches for variance genome-wide association studies in plants. Heredity. 129:93–102. [DOI] [bioRxiv] [PubMed] [EuropePMC]
• Notter DR, Heidaritabar M, Burke JM, Shirali M, Murdoch BM, Morgan JLM, Morota G, Sonstegard TS, Becker GM, Spangler GL, MacNeil MD, and Miller JE. 2022. Single nucleotide polymorphism effects on lamb fecal egg count estimated breeding values in progeny-tested Katahdin sires. Frontiers in Genetics. 13:866176. [DOI] [PubMed] [EuropePMC]
• Qu J, Morota G, and Cheng H. 2022. A Bayesian random regression method using mixture priors for genome-enabled analysis of time-series high-throughput phenotyping data. The Plant Genome. 15:e20228. [DOI] [PubMed] [EuropePMC]
• Chen CJ, Morota G, Lee K, Zhang Z, and Cheng H. 2022. VTag: a semi-supervised pipeline for tracking pig activity with a single top-view camera. Journal of Animal Science. 100:1-10. [DOI] [PubMed] [EuropePMC]
• Yassue RM, Galli G, Borsato Junior R, Cheng H, Morota G, and Fritsche-Neto R. 2022. A low-cost greenhouse-based high-throughput phenotyping platform for genetic studies: A case study in maize under inoculation with plant growth-promoting bacteria. The Plant Phenome Journal. 5:e20043. [DOI] [bioRxiv]
• Amorim ST, Tsuyuzaki K, Nikaido I, and Morota G. 2022. Improved MeSH analysis software tools for farm animals. Animal Genetics. 53:171-172. [DOI] [PubMed] [EuropePMC]

2021

• Sabag I, Morota G, and Peleg Z. 2021. Genome-wide association analysis uncovers the genetic architecture of tradeoff between flowering date and yield components in sesame. BMC Plant Biology. 21:549. [bioRxiv] [DOI] [PubMed] [EuropePMC]
• Mota LFM, Pegolo S, Baba T, Morota G, Penagaricano F, Bittante G, and Cecchinato A. 2021. Comparison of single-breed and multi-breed training population for infrared predictions of novel phenotypes in Holstein cows. Animals. 11:1993. [DOI] [PubMed] [EuropePMC]
• Silva FF, Morota G, and Rosa GJM. 2021. Editorial: High-throughput phenotyping in the genomic improvement of livestock. Frontiers in Genetics. 12:707343. [DOI] [PubMed] [EuropePMC]
• Mota LFM, Pegolo S, Baba T, Penagaricano F, Morota G, Bittante G, and Cecchinato A. 2021. Evaluating the performance of machine learning methods and variable selection methods for predicting difficult-to-measure traits in Holstein dairy cattle using milk infrared spectral data. Journal of Dairy Science. 104:8107-8121. [DOI] [PubMed] [EuropePMC]
• Baba T, Pegolo S, Mota LFM, Penagaricano F, Bittante G, Cecchinato A, and Morota G. 2021. Integrating genomic and infrared spectral data improves the prediction of milk protein composition in dairy cattle. Genetics Selection Evolution. 53:29. [DOI] [PubMed] [EuropePMC]
• Morota G, Cheng H, Cook D, and Tanaka E. 2021. ASAS-NANP SYMPOSIUM: Prospects for interactive and dynamic graphics in the era of data-rich animal science. Journal of Animal Science. 99:1-17. [DOI] [PubMed] [EuropePMC]
• Gonçalves MTV, Morota G, Almeida Costa PM, Vidigal PMP, Barbosa MHP, and Peternelli LA. 2021. Near-infrared spectroscopy outperforms genomics for predicting sugarcane feedstock quality traits. PLOS ONE. 16(3):e0236853. [bioRxiv] [DOI] [PubMed] [EuropePMC]
• Yu H and Morota G. 2021. GCA: An R package for genetic connectedness analysis using pedigree and genomic data. BMC Genomics. 22:119. [DOI] [PubMed] [EuropePMC] [bioRxiv]
• Yu H, Lee K, and Morota G. 2021. Forecasting dynamic body weight of non-restrained pigs from images using an RGB-D sensor camera. Translational Animal Science. 5:1-9. [DOI] [PubMed] [EuropePMC]
• Pegolo S, Yu H, Morota G, Bisutti V, Rosa GJM, Bittante G, and Cecchinato A. 2021. Structural equation modelling for unravelling the multivariate genomic architecture of milk proteins in dairy cattle. Journal of Dairy Science. 104:5705-5718. [DOI] [PubMed] [EuropePMC]
• Zhu F, Paul P, Hussain W, Wallman K, Dhatt BK, Sandhu J, Irvin L, Morota G, Yu H, and Walia H. 2021. SeedExtractor: an open-source GUI for seed image analysis. Frontiers in Plant Science. 11:581546. [DOI] [PubMed] [EuropePMC] [bioRxiv]
• Momen M, Bhatta M, Hussain W, Yu H, and Morota G. Modeling multiple phenotypes in wheat using data-driven genomic exploratory factor analysis and Bayesian network learning. 2021. Plant Direct. 5:e00304. [DOI] [PubMed] [EuropePMC] [bioRxiv]
• Dhatt B, Paul P, Sandhu J, Hussain W, Irvin L, Zhu F, Adviento-Borbe M, Lorence A, Staswick P, Yu H, Morota G, and Walia H. 2021. Allelic variation in rice Fertilization independent endosperm 1 contributes to grain width under high night temperature stress. New Phytologist. 229:335-350. [DOI] [PubMed] [EuropePMC]

2020

• Wang Z, Chapman D, Morota G, and Cheng H. 2020. A Multiple-trait Bayesian variable selection regression method for integrating phenotypic causal networks in genome-wide association studies. G3: Genes, Genomes, Genetics. 10:4439-4448. [DOI] [PubMed] [EuropePMC] [bioRxiv]
• Amorim ST, Yu H, Momen M, de Albuquerque, LG, Pereira, ASC, Baldi F, and Morota G. 2020. An assessment of genomic connectedness measures in Nellore cattle. Journal of Animal Science. 98:1-12. [DOI] [PubMed] [EuropePMC]
• Campbell MT, Grondin A, Walia H, and Morota G. 2020. Leveraging genome-enabled growth models to study shoot growth responses to water deficit in rice (Oryza sativa). Journal of Experimental Botany. 71:5669-5679. [DOI] [PubMed] [EuropePMC] [bioRxiv]
• Yu H, Morota G, Celestino Jr. EF, Dahlen CR, Wagner SA, Riley DG, and Hulsman Hanna LL. 2020. Deciphering cattle temperament measures derived from a four-platform standing scale using genetic factor analytic modeling. Frontiers in Genetics. 11:599. [DOI] [PubMed] [EuropePMC] [bioRxiv]
• Pegolo S, Momen M, Morota G, Rosa GJM, Gianola G, Bittante G, and Cecchinato A. 2020. Structural equation modeling for investigating multi-trait genetic architecture of udder health in dairy cattle. Scientific Reports. 10:7751. [DOI] [PubMed] [EuropePMC]
• Roudbar MA, Mohammadabadi MR, Mehrgardi AA, Abdollahi-Arpanahi R, Momen M, Morota G, Lopes FB, Gianola D, and Rosa GJM. 2020. Integration of single nucleotide variants and whole-genome DNA methylation profiles for classification of rheumatoid arthritis cases from controls. Heredity. 124:658-674. [DOI] [PubMed] [EuropePMC]
• Hussain W, Campbell MT, Jarquin D, Walia H, and Morota G. 2020. Variance heterogeneity genome-wide mapping for cadmium in bread wheat reveals novel genomic loci and epistatic interactions. The Plant Genome. 13:e20011. [DOI] [PubMed] [EuropePMC] [bioRxiv]
• Baba T, Momen M, Campbell MT, Walia H, and Morota G. 2020. Multi-trait random regression models increase genomic prediction accuracy for a temporal physiological trait derived from high-throughput phenotyping. PLOS ONE. 15(2):e0228118. [DOI] [PubMed] [EuropePMC] [bioRxiv]
• Paul P, Dhatt B, Sandhu J, Hussain W, Irvin L, Morota G, Staswick P, and Walia H. 2020. Divergent phenotypic response of rice accessions to transient heat stress during early seed development. Plant Direct. 4:1–13. [DOI] [PubMed] [EuropePMC]

2019

• Atagi Y, Morota G, Onogi A, Osawa T, Yasumori T, Adachi K, Yamaguchi S, Aihara M, Goto H, Togashi K, and Iwata H. 2019. Consideration of heat stress in multiple lactation test–day models for dairy production traits. Interbull Bulletin. 55:81-87. [PDF]
• Momen M, Campbell MT, Walia H, and Morota G. 2019. Utilizing trait networks and structural equation models as tools to interpret multi-trait genome-wide association studies. Plant Methods. 15:107. [DOI] [PubMed] [EuropePMC] [bioRxiv]
• Momen M, Campbell MT, Walia H, and Morota G. 2019. Predicting longitudinal traits derived from high-throughput phenomics in contrasting environments using genomic Legendre polynomials and B-splines. G3: Genes, Genomes, Genetics. 9:3369-3380. [DOI] [PubMed] [EuropePMC] [bioRxiv]
• Yu H, Campbell MT, Zhang Q, Walia H, and Morota G. 2019. Genomic Bayesian confirmatory factor analysis and Bayesian network to characterize a wide spectrum of rice phenotypes. G3: Genes, Genomes, Genetics. 9:1975-1986. [DOI] [PubMed] [EuropePMC] [bioRxiv]
• Campbell MT, Walia H, and Morota G. 2019. Leveraging breeding values obtained from random regression models for genetic inference of longitudinal traits. The Plant Genome. 12:180075. [DOI] [PubMed] [EuropePMC] [bioRxiv]

2018

• Hussain W, Campbell MT, Walia H, and Morota G. 2018. ShinyAIM: Shiny-based application of interactive Manhattan plots for longitudinal genome-wide association studies. Plant Direct. 2:1–4. [DOI] [PubMed] [EuropePMC] [bioRxiv]
• Momen M, Mehrgardi AA, Roudbar MA, Kranis A, Pinto RM, Valente BD, Morota G, Rosa GJM, and Gianola D. 2018. Including phenotypic causal networks in genome-wide association studies using mixed effects structural equation models. Frontiers in Genetics. 9:455. [DOI] [PubMed] [EuropePMC] [bioRxiv]
• Momen M and Morota G. 2018. Quantifying genomic connectedness and prediction accuracy from additive and non-additive gene actions. Genetics Selection Evolution. 50:45. [DOI] [PubMed] [EuropePMC]
• Yu H, Spangler ML, Lewis RM, and Morota G. 2018. Do stronger measures of genomic connectedness enhance prediction accuracies across management units? Journal of Animal Science. 96:4490-4500. [DOI] [PubMed] [EuropePMC]
• Momen M, Mehrgardi AA, Sheikhy ASA, Kranis A, Tusell L, Morota G, Rosa GJM, and Gianola D. 2018. Predictive ability of genome-assisted statistical models under various forms of gene action. Scientific Reports. 8:12309. [DOI] [PubMed] [EuropePMC]
• Campbell MT, Walia H, and Morota G. 2018. Utilizing random regression models for genomic prediction of a longitudinal trait derived from high-throughput phenotyping. Plant Direct. 2:1-11. [DOI] [PubMed] [EuropePMC] [bioRxiv]
• Alvarenga AB, Rovadoscki GA, Petrini J, Coutinho LL, Morota G, Spangler ML, Pinto LFB, Carvalho GGP, and Mourao GB. 2018. Linkage disequilibrium in Brazilian Santa Ines breed, Ovis aries. Scientific Reports. 8:8851. [DOI] [PubMed] [EuropePMC]
• Rovadoscki GA, Pertille SFN, Alvarenga AB, Cesar ASM, Pertille F, Petrini J, Franzo V, Soares WVB, Morota G, Spangler ML, Pinto LFB, de Carvalho GGP, Lanna DPD, Coutinho LL, and Mourao GB. 2018. Estimates of genomic heritability and genome-wide association study for fatty acids profile in Santa Ines sheep. BMC Genomics. 19:375. [DOI] [PubMed] [EuropePMC]
• Yu H, Spangler ML, Lewis RM, and Morota G. 2018. Stronger measures of genomic connectedness enhance prediction accuracies across management units. Proceedings, 11th World Congress of Genetics Applied to Livestock Production. 11:406. Auckland, New Zealand. February 11-16. [WCGALP] [PDF]
• Abdollahi-Arpanahi R, Morota G, and Penagaricano F. 2018. Predicting bull fertility using biologically informed genomic models. Proceedings, 11th World Congress of Genetics Applied to Livestock Production. 11:683. Auckland, New Zealand. February 11-16. [WCGALP]
• Mamani GC, Santana BF, Oliveira Junior GA, Mattos E, Ventura RV, Eler JP, Morota G, and Ferraz JBS. 2018. Effect of inbreeding in productive traits in Nellore cattle. Proceedings, 11th World Congress of Genetics Applied to Livestock Production. 11:855. Auckland, New Zealand. February 11-16. [WCGALP]
• Morota G, Ventura RV, Silva FF, Koyama M, and Fernando SC. 2018. BIG DATA ANALYTICS AND PRECISION ANIMAL AGRICULTURE SYMPOSIUM: Machine learning and data mining advance predictive big data analysis in precision animal agriculture. Journal of Animal Science. 96:1540–1550. [DOI] [PubMed] [EuropePMC]
• He J, Xu J, Wu XL, Bauck S, Lee J, Morota G, Kachman SD, and Spangler ML. 2018. Comparing strategies for selection of low-density SNPs for imputation-mediated genomic prediction in U.S. Holsteins. Genetica. 146:137-149. [DOI] [PubMed] [EuropePMC]

2017

• Morota G. ShinyGPAS: 2017. Interactive genomic prediction accuracy simulator based on deterministic formulas. Genetics Selection Evolution. 49:91. [DOI] [PubMed] [EuropePMC] [bioRxiv]
• Abdollahi-Arpanahi R, Morota G, and Penagaricano F. 2017. Predicting bull fertility using genomic data and biological information. Journal of Dairy Science. 100:9656-9666. [DOI] [PubMed] [EuropePMC]
• Yu H, Spangler ML, Lewis RM, and Morota G. 2017. Genomic relatedness strengthens genetic connectedness across management units. G3: Genes, Genomes, Genetics. 10:3543-3556. [DOI] [PubMed] [EuropePMC] [bioRxiv]
• Beissinger TM and Morota G. 2017. Medical subject heading (MeSH) annotations illuminate maize genetics and evolution. Plant Methods. 13:8. [DOI] [PubMed] [EuropePMC] [bioRxiv]

2016

• Morota G, Beissinger TM, and Penagaricano F. 2016. MeSH-informed enrichment analysis and MeSH-guided semantic similarity among functional terms and gene products in chicken. G3: Genes, Genomes, Genetics. 6:2447-2453. [DOI] [PubMed] [EuropePMC] [bioRxiv]
• Abdollahi-Arpanahi R, Morota G, Valente BD, Kranis A, Rosa GJM, and Gianola D. 2016. Differential contribution of genomic regions to marked genetic variation and prediction of quantitative traits in broiler chickens. Genetics Selection Evolution. 48:10. [DOI] [PubMed] [EuropePMC]

2015

• Hu Y, Morota G, Rosa GJM, and Gianola D. 2015. Prediction of plant height in Arabidopsis thaliana from DNA methylation data. Genetics. 201:779-793. [DOI] [PubMed] [EuropePMC]
• Valente BD, Morota G, Penagaricano F, Gianola D, Weigel KA, and Rosa GJM. 2015. The causal meaning of genomic predictors and how it affects construction and comparison of genome-enabled selection models. Genetics. 200:483-494. [DOI] [PubMed] [EuropePMC] [bioRxiv] [arXiv]
• Morota G, Penagaricano F, Petersen JL, Ciobanu DC, 2015. Tsuyuzaki K, and Nikaido I. An application of MeSH enrichment analysis in livestock. Animal Genetics. 46:381-387. [DOI] [PubMed] [EuropePMC]
• Abdollahi-Arpanahi R, Morota G, Valente BD, Kranis A, Rosa GJM, and Gianola D. 2015. Assessment of bagging GBLUP for whole-genome prediction of broiler chicken traits. Journal of Animal Breeding and Genetics. 132:218-228. [DOI] [PubMed] [EuropePMC]
• Tsuyuzaki K, Morota G, Ishii M, Nakazato T, Miyazaki S, and Nikaido I. 2015. MeSH ORA framework: R/Bioconductor packages to support MeSH over-representation analysis. BMC Bioinformatics. 16:45. [DOI] [PubMed] [EuropePMC]

2014

• Morota G and Gianola D. 2014. Kernel-based whole-genome prediction of complex traits: a review. Frontiers in Genetics. 5:363. [DOI] [PubMed] [EuropePMC]
• Gianola D, Morota G, and Crossa J. 2014. Genome-enabled Prediction of Complex Traits with Kernel Methods: What Have We Learned? Proceedings, 10th World Congress of Genetics Applied to Livestock Production. Vancouver, BC, Canada. August 17-22. [WCGALP] [PDF]
• Valente BD, Morota G, Rosa GJM, Gianola D, and Weigel KA. 2014. Causal meaning of genomic predictors: Implication on genome-enabled selection modeling. Proceedings, 10th World Congress of Genetics Applied to Livestock Production. Vancouver, BC, Canada. August 17-22. [WCGALP] [PDF]
• Morota G, Boddhireddy P, Vukasinovic N, Gianola D, and DeNise S. 2014. Kernel-based variance components estimation and whole-genome prediction of pre-corrected phenotypes and progeny tests for dairy cow health traits. Frontiers in Genetics. 5:56. [DOI] [PubMed] [EuropePMC]
• Morota G, Abdollahi-Arpanahi R, Kranis A, and Gianola D. 2014. Genome-enabled prediction of broiler traits in chickens using genomic annotation. BMC Genomics. 15:109. [DOI] [PubMed] [EuropePMC]
• Abdollahi-Arpanahi R, Pakdel A, Nejati-Javaremi A, Moradi-Shahrbabak M, Morota G, Valente BD, Kranis A, Rosa GJM, and Gianola D. 2014. Dissection of additive genetic variability for quantitative traits in chickens using SNP markers. Journal of Animal Breeding and Genetics. 131:183-193. [DOI] [PubMed] [EuropePMC]
• Abdollahi-Arpanahi R, Nejati-Javaremi A, Pakdel A, Moradi-Shahrbabak M, Morota G, Valente BD, Kranis A, Rosa GJM, and Gianola D. 2014. Effect of allele frequencies, effect sizes and number of markers on prediction of quantitative traits in chickens. Journal of Animal Breeding and Genetics. 131:123-133. [DOI] [PubMed] [EuropePMC]

2013

• Morota G, Koyama M, Rosa GJM, Weigel KA, and Gianola D. 2013. Predicting complex traits using a diffusion kernel on genetic markers with an application to dairy cattle and wheat data. Genetics Selection Evolution. 45:17. [DOI] [PubMed] [EuropePMC]
• Morota G and Gianola D. 2013. Evaluation of linkage disequilibrium in wheat with an L1 regularized sparse Markov network. Theoretical and Applied Genetics. 126:1991-2002. [DOI] [PubMed] [EuropePMC]

2012

• Morota G, Valente BD, Rosa GJM, Weigel KA, and Gianola D. 2012. An assessment of linkage disequilibrium in Holstein cattle using a Bayesian network. Journal of Animal Breeding and Genetics. 129:474-487. [DOI] [PubMed] [EuropePMC] [Genomic Selection Virtual Issue]

2011

• Bueno Filho* JS, Morota G*, Tran Q, Maenner MJ, Vera-Cala LM , Engelman CD, and Meyers KJ. 2011. Analysis of human mini-exome sequencing data from Genetic Analysis Workshop 17 using a Bayesian hierarchical mixture model. BMC Proceedings, 5(Suppl 9):S93. *equal contribution. [DOI] [PubMed] [EuropePMC]

bioRxived manuscripts

• Vidigal PMP, Momen M, Costa PMA, Barbosa MHP, Morota G, and Peternelli LA. Regional genomic heritability mapping for agronomic traits in sugarcane. bioRxiv. [bioRxiv]
• Campbell MT, Yu H, Momen M, and Morota G. Examining the relationships between phenotypic plasticity and local environments with genomic structural equation models. bioRxiv. [bioRxiv]