Genomic prediction using different estimation methodology, blending and cross-validation techniques for growth traits and visual scores in Hereford and Braford cattle Article Swipe
Gabriel Soares Campos
,
F. A. Reimann
,
Leandro Lunardini Cardoso
,
Carlos Eduardo Ranquetat Ferreira
,
Vinícius Silva Junqueira
,
Patrícia Iana Schmidt
,
José Braccini Neto
,
M. J. I. Yokoo
,
B. P. Sollero
,
Arione Augusti Boligon
,
F. F. Cardoso
·
YOU?
·
· 2018
· Open Access
·
· DOI: https://doi.org/10.1093/jas/sky175
YOU?
·
· 2018
· Open Access
·
· DOI: https://doi.org/10.1093/jas/sky175
The objective of the present study was to evaluate the accuracy and bias of direct and blended genomic predictions using different methods and cross-validation techniques for growth traits (weight and weight gains) and visual scores (conformation, precocity, muscling, and size) obtained at weaning and at yearling in Hereford and Braford breeds. Phenotypic data contained 126,290 animals belonging to the Delta G Connection genetic improvement program, and a set of 3,545 animals genotyped with the 50K chip and 131 sires with the 777K. After quality control, 41,045 markers remained for all animals. An animal model was used to estimate (co)variance components and to predict breeding values, which were later used to calculate the deregressed estimated breeding values (DEBV). Animals with genotype and phenotype for the traits studied were divided into 4 or 5 groups by random and k-means clustering cross-validation strategies. The values of accuracy of the direct genomic values (DGV) were moderate to high magnitude for at weaning and at yearling traits, ranging from 0.19 to 0.45 for the k-means and 0.23 to 0.78 for random clustering among all traits. The greatest gain in relation to the pedigree BLUP (PBLUP) was 9.5% with the BayesB method with both the k-means and the random clustering. Blended genomic value accuracies ranged from 0.19 to 0.56 for k-means and from 0.21 to 0.82 for random clustering. The analyses using the historical pedigree and phenotypes contributed additional information to calculate the GEBV, and in general, the largest gains were for the single-step (ssGBLUP) method in bivariate analyses with a mean increase of 43.00% among all traits measured at weaning and of 46.27% for those evaluated at yearling. The accuracy values for the marker effects estimation methods were lower for k-means clustering, indicating that the training set relationship to the selection candidates is a major factor affecting accuracy of genomic predictions. The gains in accuracy obtained with genomic blending methods, mainly ssGBLUP in bivariate analyses, indicate that genomic predictions should be used as a tool to improve genetic gains in relation to the traditional PBLUP selection.
Related Topics
Concepts
Biology
Best linear unbiased prediction
Statistics
Cluster analysis
Regression
Random effects model
Beef cattle
Estimation
Restricted maximum likelihood
Biotechnology
Animal science
Mathematics
Estimation theory
Selection (genetic algorithm)
Computer science
Artificial intelligence
Management
Economics
Medicine
Internal medicine
Meta-analysis
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1093/jas/sky175
- OA Status
- green
- Cited By
- 17
- References
- 39
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2802246675
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W2802246675Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1093/jas/sky175Digital Object Identifier
- Title
-
Genomic prediction using different estimation methodology, blending and cross-validation techniques for growth traits and visual scores in Hereford and Braford cattleWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2018Year of publication
- Publication date
-
2018-05-06Full publication date if available
- Authors
-
Gabriel Soares Campos, F. A. Reimann, Leandro Lunardini Cardoso, Carlos Eduardo Ranquetat Ferreira, Vinícius Silva Junqueira, Patrícia Iana Schmidt, José Braccini Neto, M. J. I. Yokoo, B. P. Sollero, Arione Augusti Boligon, F. F. CardosoList of authors in order
- Landing page
-
https://doi.org/10.1093/jas/sky175Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://www.ncbi.nlm.nih.gov/pmc/articles/6140894Direct OA link when available
- Concepts
-
Biology, Best linear unbiased prediction, Statistics, Cluster analysis, Regression, Random effects model, Beef cattle, Estimation, Restricted maximum likelihood, Biotechnology, Animal science, Mathematics, Estimation theory, Selection (genetic algorithm), Computer science, Artificial intelligence, Management, Economics, Medicine, Internal medicine, Meta-analysisTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
17Total citation count in OpenAlex
- Citations by year (recent)
-
2024: 1, 2023: 1, 2022: 2, 2021: 7, 2020: 2Per-year citation counts (last 5 years)
- References (count)
-
39Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.0.82 | 219 |
| abstract_inverted_index.9.5% | 191 |
| abstract_inverted_index.BLUP | 188 |
| abstract_inverted_index.bias | 12 |
| abstract_inverted_index.both | 197 |
| abstract_inverted_index.chip | 75 |
| abstract_inverted_index.data | 52 |
| abstract_inverted_index.from | 163, 209, 216 |
| abstract_inverted_index.gain | 182 |
| abstract_inverted_index.high | 153 |
| abstract_inverted_index.into | 128 |
| abstract_inverted_index.mean | 255 |
| abstract_inverted_index.that | 288, 321 |
| abstract_inverted_index.tool | 329 |
| abstract_inverted_index.used | 95, 108, 326 |
| abstract_inverted_index.were | 106, 126, 150, 244, 282 |
| abstract_inverted_index.with | 72, 79, 118, 192, 196, 253, 311 |
| abstract_inverted_index.(DGV) | 149 |
| abstract_inverted_index.3,545 | 69 |
| abstract_inverted_index.777K. | 81 |
| abstract_inverted_index.After | 82 |
| abstract_inverted_index.Delta | 59 |
| abstract_inverted_index.GEBV, | 237 |
| abstract_inverted_index.PBLUP | 339 |
| abstract_inverted_index.among | 177, 259 |
| abstract_inverted_index.gains | 243, 307, 333 |
| abstract_inverted_index.later | 107 |
| abstract_inverted_index.lower | 283 |
| abstract_inverted_index.major | 299 |
| abstract_inverted_index.model | 93 |
| abstract_inverted_index.sires | 78 |
| abstract_inverted_index.size) | 39 |
| abstract_inverted_index.study | 5 |
| abstract_inverted_index.those | 269 |
| abstract_inverted_index.using | 19, 225 |
| abstract_inverted_index.value | 206 |
| abstract_inverted_index.which | 105 |
| abstract_inverted_index.41,045 | 85 |
| abstract_inverted_index.43.00% | 258 |
| abstract_inverted_index.46.27% | 267 |
| abstract_inverted_index.BayesB | 194 |
| abstract_inverted_index.animal | 92 |
| abstract_inverted_index.direct | 14, 146 |
| abstract_inverted_index.factor | 300 |
| abstract_inverted_index.gains) | 31 |
| abstract_inverted_index.groups | 132 |
| abstract_inverted_index.growth | 26 |
| abstract_inverted_index.mainly | 315 |
| abstract_inverted_index.marker | 278 |
| abstract_inverted_index.method | 195, 249 |
| abstract_inverted_index.random | 134, 175, 202, 221 |
| abstract_inverted_index.ranged | 208 |
| abstract_inverted_index.scores | 34 |
| abstract_inverted_index.should | 324 |
| abstract_inverted_index.traits | 27, 124, 261 |
| abstract_inverted_index.values | 115, 141, 148, 275 |
| abstract_inverted_index.visual | 33 |
| abstract_inverted_index.weight | 30 |
| abstract_inverted_index.(DEBV). | 116 |
| abstract_inverted_index.(PBLUP) | 189 |
| abstract_inverted_index.(weight | 28 |
| abstract_inverted_index.126,290 | 54 |
| abstract_inverted_index.Animals | 117 |
| abstract_inverted_index.Blended | 204 |
| abstract_inverted_index.Braford | 49 |
| abstract_inverted_index.animals | 55, 70 |
| abstract_inverted_index.blended | 16 |
| abstract_inverted_index.breeds. | 50 |
| abstract_inverted_index.divided | 127 |
| abstract_inverted_index.effects | 279 |
| abstract_inverted_index.genetic | 62, 332 |
| abstract_inverted_index.genomic | 17, 147, 205, 304, 312, 322 |
| abstract_inverted_index.improve | 331 |
| abstract_inverted_index.k-means | 136, 169, 199, 214, 285 |
| abstract_inverted_index.largest | 242 |
| abstract_inverted_index.markers | 86 |
| abstract_inverted_index.methods | 21, 281 |
| abstract_inverted_index.predict | 102 |
| abstract_inverted_index.present | 4 |
| abstract_inverted_index.quality | 83 |
| abstract_inverted_index.ranging | 162 |
| abstract_inverted_index.ssGBLUP | 316 |
| abstract_inverted_index.studied | 125 |
| abstract_inverted_index.traits, | 161 |
| abstract_inverted_index.traits. | 179 |
| abstract_inverted_index.values, | 104 |
| abstract_inverted_index.weaning | 42, 157, 264 |
| abstract_inverted_index.Hereford | 47 |
| abstract_inverted_index.accuracy | 10, 143, 274, 302, 309 |
| abstract_inverted_index.analyses | 224, 252 |
| abstract_inverted_index.animals. | 90 |
| abstract_inverted_index.blending | 313 |
| abstract_inverted_index.breeding | 103, 114 |
| abstract_inverted_index.control, | 84 |
| abstract_inverted_index.estimate | 97 |
| abstract_inverted_index.evaluate | 8 |
| abstract_inverted_index.general, | 240 |
| abstract_inverted_index.genotype | 119 |
| abstract_inverted_index.greatest | 181 |
| abstract_inverted_index.increase | 256 |
| abstract_inverted_index.indicate | 320 |
| abstract_inverted_index.measured | 262 |
| abstract_inverted_index.methods, | 314 |
| abstract_inverted_index.moderate | 151 |
| abstract_inverted_index.obtained | 40, 310 |
| abstract_inverted_index.pedigree | 187, 228 |
| abstract_inverted_index.program, | 64 |
| abstract_inverted_index.relation | 184, 335 |
| abstract_inverted_index.remained | 87 |
| abstract_inverted_index.training | 290 |
| abstract_inverted_index.yearling | 45, 160 |
| abstract_inverted_index.(ssGBLUP) | 248 |
| abstract_inverted_index.affecting | 301 |
| abstract_inverted_index.analyses, | 319 |
| abstract_inverted_index.belonging | 56 |
| abstract_inverted_index.bivariate | 251, 318 |
| abstract_inverted_index.calculate | 110, 235 |
| abstract_inverted_index.contained | 53 |
| abstract_inverted_index.different | 20 |
| abstract_inverted_index.estimated | 113 |
| abstract_inverted_index.evaluated | 270 |
| abstract_inverted_index.genotyped | 71 |
| abstract_inverted_index.magnitude | 154 |
| abstract_inverted_index.muscling, | 37 |
| abstract_inverted_index.objective | 1 |
| abstract_inverted_index.phenotype | 121 |
| abstract_inverted_index.selection | 295 |
| abstract_inverted_index.yearling. | 272 |
| abstract_inverted_index.Connection | 61 |
| abstract_inverted_index.Phenotypic | 51 |
| abstract_inverted_index.accuracies | 207 |
| abstract_inverted_index.additional | 232 |
| abstract_inverted_index.candidates | 296 |
| abstract_inverted_index.clustering | 137, 176 |
| abstract_inverted_index.components | 99 |
| abstract_inverted_index.estimation | 280 |
| abstract_inverted_index.historical | 227 |
| abstract_inverted_index.indicating | 287 |
| abstract_inverted_index.phenotypes | 230 |
| abstract_inverted_index.precocity, | 36 |
| abstract_inverted_index.selection. | 340 |
| abstract_inverted_index.techniques | 24 |
| abstract_inverted_index.clustering, | 286 |
| abstract_inverted_index.clustering. | 203, 222 |
| abstract_inverted_index.contributed | 231 |
| abstract_inverted_index.deregressed | 112 |
| abstract_inverted_index.improvement | 63 |
| abstract_inverted_index.information | 233 |
| abstract_inverted_index.predictions | 18, 323 |
| abstract_inverted_index.single-step | 247 |
| abstract_inverted_index.strategies. | 139 |
| abstract_inverted_index.traditional | 338 |
| abstract_inverted_index.(co)variance | 98 |
| abstract_inverted_index.predictions. | 305 |
| abstract_inverted_index.relationship | 292 |
| abstract_inverted_index.(conformation, | 35 |
| abstract_inverted_index.cross-validation | 23, 138 |
| cited_by_percentile_year.max | 98 |
| cited_by_percentile_year.min | 89 |
| corresponding_author_ids | https://openalex.org/A5044730955 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 11 |
| corresponding_institution_ids | https://openalex.org/I169248161 |
| citation_normalized_percentile.value | 0.88230447 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |