| Year |
Citation |
Score |
| 2024 |
Kravitz A, Liao M, Morota G, Tyler R, Cockrum R, Manohar BM, Ronald BSM, Collins MT, Sriranganathan N. 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. PMID 39062990 DOI: 10.3390/ijms25147748 |
0.314 |
|
| 2024 |
Sabag I, Bi Y, Sahoo MM, Herrmann I, Morota G, Peleg Z. Leveraging genomics and temporal high-throughput phenotyping to enhance association mapping and yield prediction in sesame. The Plant Genome. e20481. PMID 38926134 DOI: 10.1002/tpg2.20481 |
0.425 |
|
| 2024 |
Aydin KB, Bi Y, Brito LF, Ulutaş Z, Morota G. Review of sheep breeding and genetic research in Türkiye. Frontiers in Genetics. 15: 1308113. PMID 38333619 DOI: 10.3389/fgene.2024.1308113 |
0.39 |
|
| 2023 |
Baba T, Morota G, Kawakami J, Gotoh Y, Oka T, Masuda Y, Brito LF, Cockrum RR, Kawahara T. Longitudinal genome-wide association analysis using a single-step random regression model for height in Japanese Holstein cattle. Jds Communications. 4: 363-368. PMID 37727246 DOI: 10.3168/jdsc.2022-0347 |
0.39 |
|
| 2023 |
Massahiro Yassue R, Galli G, James Chen CP, Fritsche-Neto R, Morota G. 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. PMID 37102161 DOI: 10.1002/pld3.492 |
0.363 |
|
| 2023 |
Sabag I, Bi Y, Peleg Z, Morota G. Multi-environment analysis enhances genomic prediction accuracy of agronomic traits in sesame. Frontiers in Genetics. 14: 1108416. PMID 36992702 DOI: 10.3389/fgene.2023.1108416 |
0.422 |
|
| 2023 |
Wang Z, Yu D, Morota G, Dhakal K, Singer W, Lord N, Huang H, Chen P, Mozzoni L, Li S, Zhang B. Genome-wide association analysis of sucrose and alanine contents in edamame beans. Frontiers in Plant Science. 13: 1086007. PMID 36816489 DOI: 10.3389/fpls.2022.1086007 |
0.318 |
|
| 2022 |
Kadlec R, Indest S, Castro K, Waqar S, Campos LM, Amorim ST, Bi Y, Hanigan MD, Morota G. Automated acquisition of top-view dairy cow depth image data using an RGB-D sensor camera. Translational Animal Science. 6: txac163. PMID 36601061 DOI: 10.1093/tas/txac163 |
0.704 |
|
| 2022 |
Qu J, Morota G, Cheng H. A Bayesian random regression method using mixture priors for genome-enabled analysis of time-series high-throughput phenotyping data. The Plant Genome. e20228. PMID 35904052 DOI: 10.1002/tpg2.20228 |
0.444 |
|
| 2022 |
Morota G, Jarquin D, Campbell MT, Iwata H. Statistical Methods for the Quantitative Genetic Analysis of High-Throughput Phenotyping Data. Methods in Molecular Biology (Clifton, N.J.). 2539: 269-296. PMID 35895210 DOI: 10.1007/978-1-0716-2537-8_21 |
0.361 |
|
| 2022 |
Murphy MD, Fernandes SB, Morota G, Lipka AE. Assessment of two statistical approaches for variance genome-wide association studies in plants. Heredity. PMID 35538221 DOI: 10.1038/s41437-022-00541-1 |
0.349 |
|
| 2021 |
Amorim ST, Tsuyuzaki K, Nikaido I, Morota G. Improved MeSH analysis software tools for farm animals. Animal Genetics. 53: 171-172. PMID 34859479 DOI: 10.1111/age.13159 |
0.69 |
|
| 2021 |
Sabag I, Morota G, Peleg Z. Genome-wide association analysis uncovers the genetic architecture of tradeoff between flowering date and yield components in sesame. Bmc Plant Biology. 21: 549. PMID 34809568 DOI: 10.1186/s12870-021-03328-4 |
0.369 |
|
| 2021 |
Silva FF, Morota G, Rosa GJM. Editorial: High-Throughput Phenotyping in the Genomic Improvement of Livestock. Frontiers in Genetics. 12: 707343. PMID 34220969 DOI: 10.3389/fgene.2021.707343 |
0.303 |
|
| 2021 |
Baba T, Pegolo S, Mota LFM, Peñagaricano F, Bittante G, Cecchinato A, Morota G. Integrating genomic and infrared spectral data improves the prediction of milk protein composition in dairy cattle. Genetics, Selection, Evolution : Gse. 53: 29. PMID 33726672 DOI: 10.1186/s12711-021-00620-7 |
0.347 |
|
| 2021 |
Gonçalves MTV, Morota G, Costa PMA, Vidigal PMP, Barbosa MHP, Peternelli LA. Near-infrared spectroscopy outperforms genomics for predicting sugarcane feedstock quality traits. Plos One. 16: e0236853. PMID 33661948 DOI: 10.1371/journal.pone.0236853 |
0.37 |
|
| 2021 |
Yu H, Morota G. GCA: an R package for genetic connectedness analysis using pedigree and genomic data. Bmc Genomics. 22: 119. PMID 33588757 DOI: 10.1186/s12864-021-07414-7 |
0.402 |
|
| 2021 |
Momen M, Bhatta M, Hussain W, Yu H, Morota G. Modeling multiple phenotypes in wheat using data-driven genomic exploratory factor analysis and Bayesian network learning. Plant Direct. 5: e00304. PMID 33532691 DOI: 10.1002/pld3.304 |
0.352 |
|
| 2020 |
Wang Z, Chapman D, Morota G, Cheng H. A Multiple-Trait Bayesian Variable Selection Regression Method for Integrating Phenotypic Causal Networks in Genome-Wide Association Studies. G3 (Bethesda, Md.). PMID 33020191 DOI: 10.1534/g3.120.401618 |
0.423 |
|
| 2020 |
Hussain W, Campbell MT, Jarquin D, Walia H, Morota G. Variance heterogeneity genome-wide mapping for cadmium in bread wheat reveals novel genomic loci and epistatic interactions. The Plant Genome. 13: e20011. PMID 33016629 DOI: 10.1002/tpg2.20011 |
0.321 |
|
| 2020 |
Amorim ST, Yu H, Momen M, de Albuquerque LG, Pereira ASC, Baldi F, Morota G. An assessment of genomic connectedness measures in Nellore cattle. Journal of Animal Science. PMID 32877515 DOI: 10.1093/jas/skaa289 |
0.722 |
|
| 2020 |
Yu H, Morota G, Celestino EF, Dahlen CR, Wagner SA, Riley DG, Hulsman Hanna LL. Deciphering Cattle Temperament Measures Derived From a Four-Platform Standing Scale Using Genetic Factor Analytic Modeling. Frontiers in Genetics. 11: 599. PMID 32595702 DOI: 10.3389/Fgene.2020.00599 |
0.416 |
|
| 2020 |
Pegolo S, Momen M, Morota G, Rosa GJM, Gianola D, Bittante G, Cecchinato A. Structural equation modeling for investigating multi-trait genetic architecture of udder health in dairy cattle. Scientific Reports. 10: 7751. PMID 32385377 DOI: 10.1038/S41598-020-64575-3 |
0.532 |
|
| 2020 |
Amiri Roudbar M, Mohammadabadi MR, Ayatollahi Mehrgardi A, Abdollahi-Arpanahi R, Momen M, Morota G, Brito Lopes F, Gianola D, Rosa GJM. Integration of single nucleotide variants and whole-genome DNA methylation profiles for classification of rheumatoid arthritis cases from controls. Heredity. PMID 32127659 DOI: 10.1038/S41437-020-0301-4 |
0.552 |
|
| 2020 |
Baba T, Momen M, Campbell MT, Walia H, Morota G. Multi-trait random regression models increase genomic prediction accuracy for a temporal physiological trait derived from high-throughput phenotyping. Plos One. 15: e0228118. PMID 32012182 DOI: 10.1371/journal.pone.0228118 |
0.422 |
|
| 2019 |
Momen M, Campbell MT, Walia H, Morota G. Utilizing trait networks and structural equation models as tools to interpret multi-trait genome-wide association studies. Plant Methods. 15: 107. PMID 31548847 DOI: 10.1186/S13007-019-0493-X |
0.439 |
|
| 2019 |
Momen M, Campbell MT, Walia H, Morota G. Predicting Longitudinal Traits Derived from High-Throughput Phenomics in Contrasting Environments Using Genomic Legendre polynomials and B-splines. G3 (Bethesda, Md.). PMID 31427454 DOI: 10.1534/G3.119.400346 |
0.402 |
|
| 2019 |
Campbell M, Momen M, Walia H, Morota G. Leveraging Breeding Values Obtained from Random Regression Models for Genetic Inference of Longitudinal Traits. The Plant Genome. 12. PMID 31290928 DOI: 10.3835/plantgenome2018.10.0075 |
0.417 |
|
| 2019 |
Yu H, Campbell MT, Zhang Q, Walia H, Morota G. Genomic Bayesian Confirmatory Factor Analysis and Bayesian Network to Characterize a Wide Spectrum of Rice Phenotypes. G3 (Bethesda, Md.). PMID 30992319 DOI: 10.1534/G3.119.400154 |
0.463 |
|
| 2018 |
Campbell M, Walia H, Morota G. Utilizing random regression models for genomic prediction of a longitudinal trait derived from high-throughput phenotyping. Plant Direct. 2: e00080. PMID 31245746 DOI: 10.1002/pld3.80 |
0.395 |
|
| 2018 |
Momen M, Ayatollahi Mehrgardi A, Amiri Roudbar M, Kranis A, Mercuri Pinto R, Valente BD, Morota G, Rosa GJM, Gianola D. Including Phenotypic Causal Networks in Genome-Wide Association Studies Using Mixed Effects Structural Equation Models. Frontiers in Genetics. 9: 455. PMID 30356716 DOI: 10.3389/Fgene.2018.00455 |
0.599 |
|
| 2018 |
Momen M, Morota G. Quantifying genomic connectedness and prediction accuracy from additive and non-additive gene actions. Genetics, Selection, Evolution : Gse. 50: 45. PMID 30223766 DOI: 10.1186/s12711-018-0415-9 |
0.443 |
|
| 2018 |
Yu H, Spangler ML, Lewis RM, Morota G. Do stronger measures of genomic connectedness enhance prediction accuracies across management units? Journal of Animal Science. PMID 30165381 DOI: 10.1093/Jas/Sky316 |
0.477 |
|
| 2018 |
Momen M, Mehrgardi AA, Sheikhi A, Kranis A, Tusell L, Morota G, Rosa GJM, Gianola D. Predictive ability of genome-assisted statistical models under various forms of gene action. Scientific Reports. 8: 12309. PMID 30120288 DOI: 10.1038/S41598-018-30089-2 |
0.579 |
|
| 2018 |
Alvarenga AB, Rovadoscki GA, Petrini J, Coutinho LL, Morota G, Spangler ML, Pinto LFB, Carvalho GGP, Mourão GB. Linkage disequilibrium in Brazilian Santa Inês breed, Ovis aries. Scientific Reports. 8: 8851. PMID 29892085 DOI: 10.1038/S41598-018-27259-7 |
0.399 |
|
| 2018 |
Rovadoscki GA, Pertile SFN, Alvarenga AB, Cesar ASM, Pértille F, Petrini J, Franzo V, Soares WVB, Morota G, Spangler ML, Pinto LFB, Carvalho GGP, Lanna DPD, Coutinho LL, Mourão GB. Estimates of genomic heritability and genome-wide association study for fatty acids profile in Santa Inês sheep. Bmc Genomics. 19: 375. PMID 29783944 DOI: 10.1186/S12864-018-4777-8 |
0.412 |
|
| 2018 |
Morota G, Ventura RV, Silva FF, Koyama M, Fernando SC. Machine learning and data mining advance predictive big data analysis in precision animal agriculture. Journal of Animal Science. PMID 29385611 DOI: 10.1093/Jas/Sky014 |
0.339 |
|
| 2017 |
Morota G. ShinyGPAS: interactive genomic prediction accuracy simulator based on deterministic formulas. Genetics, Selection, Evolution : Gse. 49: 91. PMID 29262775 DOI: 10.1186/S12711-017-0368-4 |
0.404 |
|
| 2017 |
He J, Xu J, Wu XL, Bauck S, Lee J, Morota G, Kachman SD, Spangler ML. Comparing strategies for selection of low-density SNPs for imputation-mediated genomic prediction in U. S. Holsteins. Genetica. PMID 29243001 DOI: 10.1007/S10709-017-0004-9 |
0.441 |
|
| 2017 |
Abdollahi-Arpanahi R, Morota G, Peñagaricano F. Predicting bull fertility using genomic data and biological information. Journal of Dairy Science. PMID 28987577 DOI: 10.3168/Jds.2017-13288 |
0.516 |
|
| 2017 |
Yu H, Spangler ML, Lewis RM, Morota G. Genomic Relatedness Strengthens Genetic Connectedness Across Management Units. G3 (Bethesda, Md.). PMID 28860185 DOI: 10.1534/G3.117.300151 |
0.465 |
|
| 2017 |
Beissinger TM, Morota G. Medical Subject Heading (MeSH) annotations illuminate maize genetics and evolution. Plant Methods. 13: 8. PMID 28250803 DOI: 10.1186/S13007-017-0159-5 |
0.702 |
|
| 2017 |
Yu H, Spangler ML, Lewis RM, Morota G. 189 Genomic relatedness strengthens genetic connectedness across management units Journal of Animal Science. 95: 93-94. DOI: 10.2527/Asasann.2017.189 |
0.41 |
|
| 2016 |
Morota G, Beissinger TM, Peñagaricano F. MeSH-Informed Enrichment Analysis and MeSH-Guided Semantic Similarity among Functional Terms and Gene Products in Chicken. G3 (Bethesda, Md.). PMID 27261003 DOI: 10.1534/G3.116.031096 |
0.68 |
|
| 2016 |
Abdollahi-Arpanahi R, Morota G, Valente BD, Kranis A, Rosa GJ, Gianola D. Differential contribution of genomic regions to marked genetic variation and prediction of quantitative traits in broiler chickens. Genetics, Selection, Evolution : Gse. 48: 10. PMID 26842494 DOI: 10.1186/S12711-016-0187-Z |
0.623 |
|
| 2016 |
He J, Wu XL, Bauck S, Xu JQ, Lee J, Morota G, Kachman SD, Spangler ML. P1028 Comparing 2 strategies for selecting low density SNPs for imputation-mediated, multiple-trait genomic prediction in a U.S. Holstein population Journal of Animal Science. 94: 28-29. DOI: 10.2527/Jas2016.94Supplement428A |
0.389 |
|
| 2015 |
Hu Y, Morota G, Rosa GJ, Gianola D. Prediction of Plant Height in Arabidopsis thaliana Using DNA Methylation Data. Genetics. PMID 26253546 DOI: 10.1534/Genetics.115.177204 |
0.588 |
|
| 2015 |
Morota G, Peñagaricano F, Petersen JL, Ciobanu DC, Tsuyuzaki K, Nikaido I. An application of MeSH enrichment analysis in livestock. Animal Genetics. PMID 26036323 DOI: 10.1111/Age.12307 |
0.485 |
|
| 2015 |
Valente BD, Morota G, Peñagaricano F, Gianola D, Weigel K, Rosa GJ. The Causal Meaning of Genomic Predictors and How It Affects Construction and Comparison of Genome-Enabled Selection Models. Genetics. 200: 483-94. PMID 25908318 DOI: 10.1534/Genetics.114.169490 |
0.637 |
|
| 2015 |
Tsuyuzaki K, Morota G, Ishii M, Nakazato T, Miyazaki S, Nikaido I. MeSH ORA framework: R/Bioconductor packages to support MeSH over-representation analysis. Bmc Bioinformatics. 16: 45. PMID 25887539 DOI: 10.1186/S12859-015-0453-Z |
0.399 |
|
| 2015 |
Abdollahi-Arpanahi R, Morota G, Valente BD, Kranis A, Rosa GJ, Gianola D. Assessment of bagging GBLUP for whole-genome prediction of broiler chicken traits. Journal of Animal Breeding and Genetics = Zeitschrift FüR TierzüChtung Und ZüChtungsbiologie. 132: 218-28. PMID 25727456 DOI: 10.1111/Jbg.12131 |
0.598 |
|
| 2014 |
Morota G, Gianola D. Kernel-based whole-genome prediction of complex traits: a review. Frontiers in Genetics. 5: 363. PMID 25360145 DOI: 10.3389/Fgene.2014.00363 |
0.657 |
|
| 2014 |
Morota G, Boddhireddy P, Vukasinovic N, Gianola D, Denise S. Kernel-based variance component estimation and whole-genome prediction of pre-corrected phenotypes and progeny tests for dairy cow health traits. Frontiers in Genetics. 5: 56. PMID 24715901 DOI: 10.3389/Fgene.2014.00056 |
0.641 |
|
| 2014 |
Morota G, Abdollahi-Arpanahi R, Kranis A, Gianola D. Genome-enabled prediction of quantitative traits in chickens using genomic annotation. Bmc Genomics. 15: 109. PMID 24502227 DOI: 10.1186/1471-2164-15-109 |
0.625 |
|
| 2014 |
Abdollahi-Arpanahi R, Pakdel A, Nejati-Javaremi A, Moradi Shahrbabak M, Morota G, Valente BD, Kranis A, Rosa GJ, Gianola D. Dissection of additive genetic variability for quantitative traits in chickens using SNP markers. Journal of Animal Breeding and Genetics = Zeitschrift FüR TierzüChtung Und ZüChtungsbiologie. 131: 183-93. PMID 24460953 DOI: 10.1111/Jbg.12079 |
0.581 |
|
| 2014 |
Abdollahi-Arpanahi R, Nejati-Javaremi A, Pakdel A, Moradi-Shahrbabak M, Morota G, Valente BD, Kranis A, Rosa GJ, Gianola D. Effect of allele frequencies, effect sizes and number of markers on prediction of quantitative traits in chickens. Journal of Animal Breeding and Genetics = Zeitschrift FüR TierzüChtung Und ZüChtungsbiologie. 131: 123-33. PMID 24397350 DOI: 10.1111/Jbg.12075 |
0.581 |
|
| 2013 |
Morota G, Koyama M, Rosa GJ, Weigel KA, Gianola D. Predicting complex traits using a diffusion kernel on genetic markers with an application to dairy cattle and wheat data. Genetics, Selection, Evolution : Gse. 45: 17. PMID 23763755 DOI: 10.1186/1297-9686-45-17 |
0.606 |
|
| 2013 |
Morota G, Gianola D. Evaluation of linkage disequilibrium in wheat with an L1-regularized sparse Markov network. Tag. Theoretical and Applied Genetics. Theoretische Und Angewandte Genetik. 126: 1991-2002. PMID 23661079 DOI: 10.1007/S00122-013-2112-Y |
0.521 |
|
| 2012 |
Morota G, Valente BD, Rosa GJ, Weigel KA, Gianola D. An assessment of linkage disequilibrium in Holstein cattle using a Bayesian network. Journal of Animal Breeding and Genetics = Zeitschrift FüR TierzüChtung Und ZüChtungsbiologie. 129: 474-87. PMID 23148973 DOI: 10.1111/Jbg.12002 |
0.534 |
|
| 2011 |
Bueno Filho JS, Morota G, Tran Q, Maenner MJ, Vera-Cala LM, Engelman CD, Meyers KJ. Analysis of human mini-exome sequencing data from Genetic Analysis Workshop 17 using a Bayesian hierarchical mixture model. Bmc Proceedings. 5: S93. PMID 22373180 DOI: 10.1186/1753-6561-5-S9-S93 |
0.456 |
|
| Show low-probability matches. |