Longitudinal genomic analyses of automatically-recorded vaginal temperature in lactating sows under heat stress conditions based on random regression models Article Swipe
Hui Wen
,
Jay S Johnson
,
Pedro H F Freitas
,
Jacob M Maskal
,
Leonardo Siqueira Glória
,
André Campêlo Araujo
,
Victor Breno Pedrosa
,
Francesco Tiezzi
,
Christian Maltecca
,
Yijian Huang
,
A. P. Schinckel
,
Luiz F. Brito
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1186/s12711-023-00868-1
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1186/s12711-023-00868-1
Background Automatic and continuous recording of vaginal temperature (T V ) using wearable sensors causes minimal disruptions to animal behavior and can generate data that enable the evaluation of temporal body temperature variation under heat stress (HS) conditions. However, the genetic basis of T V in lactating sows from a longitudinal perspective is still unknown. The objectives of this study were to define statistical models and estimate genetic parameters for T V in lactating sows using random regression models, and identify genomic regions and candidate genes associated with HS indicators derived from automatically-recorded T V . Results Heritability estimates for T V ranged from 0.14 to 0.20 over time (throughout the day and measurement period) and from 0.09 to 0.18 along environmental gradients (EG, − 3.5 to 2.2, which correspond to dew point values from 14.87 to 28.19 ˚C). Repeatability estimates of T V over time and along EG ranged from 0.57 to 0.66 and from 0.54 to 0.77, respectively. T V measured from 12h00 to 16h00 had moderately high estimates of heritability (0.20) and repeatability (0.64), indicating that this period might be the most suitable for recording T V for genetic selection purposes. Significant genotype-by-environment interactions (GxE) were observed and the moderately high estimates of genetic correlations between pairs of extreme EG indicate potential re-ranking of selection candidates across EG. Two important genomic regions on chromosomes 10 (59.370–59.998 Mb) and16 (21.548–21.966 Mb) were identified. These regions harbor the genes CDC123 , CAMK1d , SEC61A2 , and NUDT5 that are associated with immunity, protein transport, and energy metabolism. Across the four time-periods, respectively 12, 13, 16, and 10 associated genomic regions across 14 chromosomes were identified for T V . For the three EG classes, respectively 18, 15, and 14 associated genomic windows were identified for T V , respectively. Each time-period and EG class had uniquely enriched genes with identified specific biological functions, including regulation of the nervous system, metabolism and hormone production. Conclusions T V is a heritable trait with substantial additive genetic variation and represents a promising indicator trait to select pigs for improved heat tolerance. Moderate GxE for T V exist, indicating potential re-ranking of selection candidates across EG. T V is a highly polygenic trait regulated by a complex interplay of physiological, cellular and behavioral mechanisms.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1186/s12711-023-00868-1
- https://gsejournal.biomedcentral.com/counter/pdf/10.1186/s12711-023-00868-1
- OA Status
- gold
- Cited By
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- References
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- Related Works
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- OpenAlex ID
- https://openalex.org/W4390051992
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4390051992Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1186/s12711-023-00868-1Digital Object Identifier
- Title
-
Longitudinal genomic analyses of automatically-recorded vaginal temperature in lactating sows under heat stress conditions based on random regression modelsWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-12-21Full publication date if available
- Authors
-
Hui Wen, Jay S Johnson, Pedro H F Freitas, Jacob M Maskal, Leonardo Siqueira Glória, André Campêlo Araujo, Victor Breno Pedrosa, Francesco Tiezzi, Christian Maltecca, Yijian Huang, A. P. Schinckel, Luiz F. BritoList of authors in order
- Landing page
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https://doi.org/10.1186/s12711-023-00868-1Publisher landing page
- PDF URL
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https://gsejournal.biomedcentral.com/counter/pdf/10.1186/s12711-023-00868-1Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://gsejournal.biomedcentral.com/counter/pdf/10.1186/s12711-023-00868-1Direct OA link when available
- Concepts
-
Heritability, Repeatability, Biology, Regression, Selection (genetic algorithm), Statistics, Regression analysis, Heat stress, Animal science, Genetics, Mathematics, Computer science, Machine learningTop concepts (fields/topics) attached by OpenAlex
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-
9Total citation count in OpenAlex
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2025: 4, 2024: 5Per-year citation counts (last 5 years)
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105Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.can | 22 |
| abstract_inverted_index.day | 112 |
| abstract_inverted_index.dew | 132 |
| abstract_inverted_index.for | 70, 100, 187, 191, 277, 296, 346, 352 |
| abstract_inverted_index.had | 168, 306 |
| abstract_inverted_index.the | 27, 40, 111, 184, 202, 239, 260, 282, 318 |
| abstract_inverted_index.− | 125 |
| abstract_inverted_index.(EG, | 124 |
| abstract_inverted_index.(HS) | 37 |
| abstract_inverted_index.0.09 | 118 |
| abstract_inverted_index.0.14 | 105 |
| abstract_inverted_index.0.18 | 120 |
| abstract_inverted_index.0.20 | 107 |
| abstract_inverted_index.0.54 | 157 |
| abstract_inverted_index.0.57 | 152 |
| abstract_inverted_index.0.66 | 154 |
| abstract_inverted_index.2.2, | 128 |
| abstract_inverted_index.Each | 301 |
| abstract_inverted_index.body | 31 |
| abstract_inverted_index.data | 24 |
| abstract_inverted_index.four | 261 |
| abstract_inverted_index.from | 49, 92, 104, 117, 135, 151, 156, 164 |
| abstract_inverted_index.heat | 35, 348 |
| abstract_inverted_index.high | 170, 204 |
| abstract_inverted_index.most | 185 |
| abstract_inverted_index.over | 108, 145 |
| abstract_inverted_index.pigs | 345 |
| abstract_inverted_index.sows | 48, 75 |
| abstract_inverted_index.that | 25, 179, 249 |
| abstract_inverted_index.this | 59, 180 |
| abstract_inverted_index.time | 109, 146 |
| abstract_inverted_index.were | 61, 199, 234, 275, 294 |
| abstract_inverted_index.with | 88, 252, 310, 332 |
| abstract_inverted_index.(GxE) | 198 |
| abstract_inverted_index.0.77, | 159 |
| abstract_inverted_index.12h00 | 165 |
| abstract_inverted_index.14.87 | 136 |
| abstract_inverted_index.16h00 | 167 |
| abstract_inverted_index.28.19 | 138 |
| abstract_inverted_index.NUDT5 | 248 |
| abstract_inverted_index.These | 236 |
| abstract_inverted_index.along | 121, 148 |
| abstract_inverted_index.and16 | 231 |
| abstract_inverted_index.basis | 42 |
| abstract_inverted_index.class | 305 |
| abstract_inverted_index.genes | 86, 240, 309 |
| abstract_inverted_index.might | 182 |
| abstract_inverted_index.pairs | 210 |
| abstract_inverted_index.point | 133 |
| abstract_inverted_index.still | 54 |
| abstract_inverted_index.study | 60 |
| abstract_inverted_index.three | 283 |
| abstract_inverted_index.trait | 331, 342, 370 |
| abstract_inverted_index.under | 34 |
| abstract_inverted_index.using | 12, 76 |
| abstract_inverted_index.which | 129 |
| abstract_inverted_index.˚C). | 139 |
| abstract_inverted_index.(0.20) | 174 |
| abstract_inverted_index.Across | 259 |
| abstract_inverted_index.CAMK1d | 243 |
| abstract_inverted_index.CDC123 | 241 |
| abstract_inverted_index.across | 220, 272, 362 |
| abstract_inverted_index.animal | 19 |
| abstract_inverted_index.causes | 15 |
| abstract_inverted_index.define | 63 |
| abstract_inverted_index.enable | 26 |
| abstract_inverted_index.energy | 257 |
| abstract_inverted_index.exist, | 355 |
| abstract_inverted_index.harbor | 238 |
| abstract_inverted_index.highly | 368 |
| abstract_inverted_index.models | 65 |
| abstract_inverted_index.period | 181 |
| abstract_inverted_index.random | 77 |
| abstract_inverted_index.ranged | 103, 150 |
| abstract_inverted_index.select | 344 |
| abstract_inverted_index.stress | 36 |
| abstract_inverted_index.values | 134 |
| abstract_inverted_index.(0.64), | 177 |
| abstract_inverted_index.Results | 97 |
| abstract_inverted_index.SEC61A2 | 245 |
| abstract_inverted_index.between | 209 |
| abstract_inverted_index.complex | 374 |
| abstract_inverted_index.derived | 91 |
| abstract_inverted_index.extreme | 212 |
| abstract_inverted_index.genetic | 41, 68, 192, 207, 335 |
| abstract_inverted_index.genomic | 82, 224, 270, 292 |
| abstract_inverted_index.hormone | 323 |
| abstract_inverted_index.minimal | 16 |
| abstract_inverted_index.models, | 79 |
| abstract_inverted_index.nervous | 319 |
| abstract_inverted_index.period) | 115 |
| abstract_inverted_index.protein | 254 |
| abstract_inverted_index.regions | 83, 225, 237, 271 |
| abstract_inverted_index.sensors | 14 |
| abstract_inverted_index.system, | 320 |
| abstract_inverted_index.vaginal | 7 |
| abstract_inverted_index.windows | 293 |
| abstract_inverted_index.Abstract | 0 |
| abstract_inverted_index.However, | 39 |
| abstract_inverted_index.Moderate | 350 |
| abstract_inverted_index.additive | 334 |
| abstract_inverted_index.behavior | 20 |
| abstract_inverted_index.cellular | 378 |
| abstract_inverted_index.classes, | 285 |
| abstract_inverted_index.enriched | 308 |
| abstract_inverted_index.estimate | 67 |
| abstract_inverted_index.generate | 23 |
| abstract_inverted_index.identify | 81 |
| abstract_inverted_index.improved | 347 |
| abstract_inverted_index.indicate | 214 |
| abstract_inverted_index.measured | 163 |
| abstract_inverted_index.observed | 200 |
| abstract_inverted_index.specific | 312 |
| abstract_inverted_index.suitable | 186 |
| abstract_inverted_index.temporal | 30 |
| abstract_inverted_index.uniquely | 307 |
| abstract_inverted_index.unknown. | 55 |
| abstract_inverted_index.wearable | 13 |
| abstract_inverted_index.Automatic | 2 |
| abstract_inverted_index.candidate | 85 |
| abstract_inverted_index.estimates | 99, 141, 171, 205 |
| abstract_inverted_index.gradients | 123 |
| abstract_inverted_index.heritable | 330 |
| abstract_inverted_index.immunity, | 253 |
| abstract_inverted_index.important | 223 |
| abstract_inverted_index.including | 315 |
| abstract_inverted_index.indicator | 341 |
| abstract_inverted_index.interplay | 375 |
| abstract_inverted_index.lactating | 47, 74 |
| abstract_inverted_index.polygenic | 369 |
| abstract_inverted_index.potential | 215, 357 |
| abstract_inverted_index.promising | 340 |
| abstract_inverted_index.purposes. | 194 |
| abstract_inverted_index.recording | 5, 188 |
| abstract_inverted_index.regulated | 371 |
| abstract_inverted_index.selection | 193, 218, 360 |
| abstract_inverted_index.variation | 33, 336 |
| abstract_inverted_index.Background | 1 |
| abstract_inverted_index.associated | 87, 251, 269, 291 |
| abstract_inverted_index.behavioral | 380 |
| abstract_inverted_index.biological | 313 |
| abstract_inverted_index.candidates | 219, 361 |
| abstract_inverted_index.continuous | 4 |
| abstract_inverted_index.correspond | 130 |
| abstract_inverted_index.evaluation | 28 |
| abstract_inverted_index.functions, | 314 |
| abstract_inverted_index.identified | 276, 295, 311 |
| abstract_inverted_index.indicating | 178, 356 |
| abstract_inverted_index.indicators | 90 |
| abstract_inverted_index.metabolism | 321 |
| abstract_inverted_index.moderately | 169, 203 |
| abstract_inverted_index.objectives | 57 |
| abstract_inverted_index.parameters | 69 |
| abstract_inverted_index.re-ranking | 216, 358 |
| abstract_inverted_index.regression | 78 |
| abstract_inverted_index.regulation | 316 |
| abstract_inverted_index.represents | 338 |
| abstract_inverted_index.tolerance. | 349 |
| abstract_inverted_index.transport, | 255 |
| abstract_inverted_index.(throughout | 110 |
| abstract_inverted_index.Conclusions | 325 |
| abstract_inverted_index.Significant | 195 |
| abstract_inverted_index.chromosomes | 227, 274 |
| abstract_inverted_index.conditions. | 38 |
| abstract_inverted_index.disruptions | 17 |
| abstract_inverted_index.identified. | 235 |
| abstract_inverted_index.measurement | 114 |
| abstract_inverted_index.mechanisms. | 381 |
| abstract_inverted_index.metabolism. | 258 |
| abstract_inverted_index.perspective | 52 |
| abstract_inverted_index.production. | 324 |
| abstract_inverted_index.statistical | 64 |
| abstract_inverted_index.substantial | 333 |
| abstract_inverted_index.temperature | 8, 32 |
| abstract_inverted_index.time-period | 302 |
| abstract_inverted_index.Heritability | 98 |
| abstract_inverted_index.correlations | 208 |
| abstract_inverted_index.heritability | 173 |
| abstract_inverted_index.interactions | 197 |
| abstract_inverted_index.longitudinal | 51 |
| abstract_inverted_index.respectively | 263, 286 |
| abstract_inverted_index.Repeatability | 140 |
| abstract_inverted_index.environmental | 122 |
| abstract_inverted_index.repeatability | 176 |
| abstract_inverted_index.respectively. | 160, 300 |
| abstract_inverted_index.time-periods, | 262 |
| abstract_inverted_index.physiological, | 377 |
| abstract_inverted_index.(21.548–21.966 | 232 |
| abstract_inverted_index.(59.370–59.998 | 229 |
| abstract_inverted_index.automatically-recorded | 93 |
| abstract_inverted_index.genotype-by-environment | 196 |
| cited_by_percentile_year.max | 98 |
| cited_by_percentile_year.min | 97 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 12 |
| citation_normalized_percentile.value | 0.93592608 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |