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Section: Animal Science
Topic: Agricultural sciences, Genetics/genomics

Genetic background of body reserves in laying hens through backfat thickness phenotyping

Bédère, Nicolas1  ; Dupont, Joëlle2 ; Baumard, Yannick3; Staub, Christophe4; Gourichon, David3; Frédéric, Elleboudt5; Le Roy, Pascale1 ; Zerjal, Tatiana6 
1 PEGASE, INRAE, Institut Agro, 35590, Saint Gilles, France
2 CNRS, IFCE, INRAE, Université de Tours, PRC, 37380, Nouzilly, France
3 INRAE, PEAT, 37380, Nouzilly, France
4 INRAE, PAO, 37380, Nouzilly, France
5 INRAE, Université de Tours, CHU de Tours, Plate-forme PIXANIM, Nouzilly, France
6 Université Paris-Saclay, INRAE, AgroParisTech, GABI, 78350, Jouy-en-Josas, France

10.24072/pcjournal.412 - Peer Community Journal, Volume 4 (2024), article no. e41.

Get full text PDF Peer reviewed and recommended by PCI

In this study, we pursued three primary objectives: firstly to test and validate the phenotyping of backfat thickness as an indicator of the overall fatness of laying hens; secondly, to estimate genetic parameters for this trait; thirdly, to study the phenotypic and genetic relationships between this trait and other traits related to production and body composition. To address these questions, hens from two lines under divergent selection for residual feed intake, were phenotyped for body weight, body composition traits (backfat, total fat volume, and blood adipokines levels), and egg number. Linear mixed models enabled to estimate variance components and calculate genetic parameters. The two lines largely differed in body fatness: the efficient line had larger backfat and lower chemerin levels compared to the inefficient line. However, there were no significantly differences between the two lines concerning body weight, total fat volume, other blood adipokines levels (adiponectin, ghrelin, and visfatin), and egg production. The genetic parameter estimation revealed moderate heritability (0.38 and 0.42) for backfat and body weight, high heritability (higher than 0.80) for blood adipokines levels and low heritability (0.24 and 0.27) for egg production and total fat volume. The backfat and total fat volume were genetically highly and positively correlated (0.91). The body weight and total fat volume were also highly positively correlated (0.67). However, backfat and body weight were moderately positively correlated (0.39). The genetic correlation between backfat and egg number was moderate and negative. In conclusion, backfat could provide additional genetic information to that of the body weight as a selection criterion for body reserves. However, its correlation with laying performance should be taken into account to avoid undesired responses to selection

Published online:
DOI: 10.24072/pcjournal.412
Type: Research article
Keywords: Body composition, Body reserves, Backfat thickness, Ultrasonography, CT-scan, Adipokines, Genetic correlations, Heritability, Laying hens
Bédère, Nicolas 1; Dupont, Joëlle 2; Baumard, Yannick 3; Staub, Christophe 4; Gourichon, David 3; Frédéric, Elleboudt 5; Le Roy, Pascale 1; Zerjal, Tatiana 6

1 PEGASE, INRAE, Institut Agro, 35590, Saint Gilles, France
2 CNRS, IFCE, INRAE, Université de Tours, PRC, 37380, Nouzilly, France
3 INRAE, PEAT, 37380, Nouzilly, France
4 INRAE, PAO, 37380, Nouzilly, France
5 INRAE, Université de Tours, CHU de Tours, Plate-forme PIXANIM, Nouzilly, France
6 Université Paris-Saclay, INRAE, AgroParisTech, GABI, 78350, Jouy-en-Josas, France
License: CC-BY 4.0
Copyrights: The authors retain unrestricted copyrights and publishing rights 
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     author = {B\'ed\`ere, Nicolas and Dupont, Jo\"elle and Baumard, Yannick and Staub, Christophe and Gourichon, David and Fr\'ed\'eric, Elleboudt and Le Roy, Pascale and Zerjal, Tatiana},
     title = {Genetic background of body reserves in laying hens through backfat thickness phenotyping},
     journal = {Peer Community Journal},
     eid = {e41},
     publisher = {Peer Community In},
     volume = {4},
     year = {2024},
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Bédère, Nicolas; Dupont, Joëlle; Baumard, Yannick; Staub, Christophe; Gourichon, David; Frédéric, Elleboudt; Le Roy, Pascale; Zerjal, Tatiana. Genetic background of body reserves in laying hens through backfat thickness phenotyping. Peer Community Journal, Volume 4 (2024), article  no. e41. doi : 10.24072/pcjournal.412. https://peercommunityjournal.org/articles/10.24072/pcjournal.412/

Peer reviewed and recommended by PCI : 10.24072/pci.animsci.100233

Conflict of interest of the recommender and peer reviewers:
The recommender in charge of the evaluation of the article and the reviewers declared that they have no conflict of interest (as defined in the code of conduct of PCI) with the authors or with the content of the article.

[1] Arnold, J. W.; Bertrand, J. K.; Benyshek, L. L.; Ludwig, C. Estimates of genetic parameters for live animal ultrasound, actual carcass data, and growth traits in beef cattle, Journal of Animal Science, Volume 69 (1991) no. 3, pp. 985-992 | DOI

[2] Bain, M. M.; Nys, Y.; Dunn, I. Increasing persistency in lay and stabilising egg quality in longer laying cycles. What are the challenges?, British Poultry Science, Volume 57 (2016) no. 3, pp. 330-338 | DOI

[3] Barbe, A.; Mellouk, N.; Ramé, C.; Grandhaye, J.; Staub, C.; Venturi, E.; Cirot, M.; Petit, A.; Anger, K.; Chahnamian, M.; Ganier, P.; Callut, O.; Cailleau-Audouin, E.; Metayer-Coustard, S.; Riva, A.; Froment, P.; Dupont, J. A grape seed extract maternal dietary supplementation in reproductive hens reduces oxidative stress associated to modulation of plasma and tissue adipokines expression and improves viability of offsprings, PLOS ONE, Volume 15 (2020) no. 4 | DOI

[4] Bartlett, M. S. Some Examples of Statistical Methods of Research in Agriculture and Applied Biology, Supplement to the Journal of the Royal Statistical Society, Volume 4 (1937) no. 2 | DOI

[5] Baéza, E.; Le Bihan-Duval, E. Chicken lines divergent for low or high abdominal fat deposition: a relevant model to study the regulation of energy metabolism, Animal, Volume 7 (2013) no. 6, pp. 965-973 | DOI

[6] Becot, L.; Bedere, N.; Burlot, T.; Coton, J.; Le Roy, P. Nest acceptance, clutch, and oviposition traits are promising selection criteria to improve egg production in cage-free system, PLOS ONE, Volume 16 (2021) no. 5 | DOI

[7] Bedere, N.; Berghof, T. V. L.; Peeters, K.; Pinard-van der Laan, M.-H.; Visscher, J.; David, I.; Mulder, H. A. Using egg production longitudinal recording to study the genetic background of resilience in purebred and crossbred laying hens, Genetics Selection Evolution, Volume 54 (2022) no. 1 | DOI

[8] Bordas, A.; Tixier‐Boichard, M.; Merat, P. Direct and correlated responses to divergent selection for residual food intake in Rhode island red laying hens, British Poultry Science, Volume 33 (1992) no. 4, pp. 741-754 | DOI

[9] Bourneuf, E.; Hérault, F.; Chicault, C.; Carré, W.; Assaf, S.; Monnier, A.; Mottier, S.; Lagarrigue, S.; Douaire, M.; Mosser, J.; Diot, C. Microarray analysis of differential gene expression in the liver of lean and fat chickens, Gene, Volume 372 (2006), pp. 162-170 | DOI

[10] Byerly, T. C.; Kessler, J. W.; Gous, R. M.; Thomas, O. P. Feed Requirements for Egg Production, Poultry Science, Volume 59 (1980) no. 11, pp. 2500-2507 | DOI

[11] Cai, W.; Casey, D. S.; Dekkers, J. C. M. Selection response and genetic parameters for residual feed intake in Yorkshire swine, Journal of Animal Science, Volume 86 (2008) no. 2, pp. 287-298 | DOI

[12] Cobo, E.; Molette, C.; Touze, J.-L.; Venturi, E.; Bernadet, M.-D.; Staub, C. Mise en place d’une méthodologie en imagerie pour prédire des caractères de production chez les palmipèdes à foie gras., 2015 (https://www.itavi.asso.fr/publications/mise-en-place-d-une-methodologie-en-imagerie-pour-predire-des-caracteres-de-production-chez-les-palmipedes-a-foie-gras)

[13] Diot, M.; Reverchon, M.; Rame, C.; Froment, P.; Brillard, J.-P.; Brière, S.; Levêque, G.; Guillaume, D.; Dupont, J. Expression of adiponectin, chemerin and visfatin in plasma and different tissues during a laying season in turkeys, Reproductive Biology and Endocrinology, Volume 13 (2015) | DOI

[14] El‐Kazzi, M.; Bordas, A.; Gandemer, G.; Minvielle, F. Divergent selection for residual food intake in Rhode Island Red egg‐laying lines: Gross carcase composition, carcase adiposity and lipid contents of tissues, British Poultry Science, Volume 36 (1995) no. 5, pp. 719-728 | DOI

[15] Gabarrou, J.-F.; Geraert, P.; Francois, N.; Guillaumin, S.; Picard, M.; Bordas, A. Energy balance of laying hens selected on residual food consumption, British Poultry Science, Volume 39 (1998) no. 1, pp. 79-89 | DOI

[16] Gabarrou, J.-F.; Geraert, P. A.; Williams, J.; Ruffier, L.; Rideau, N. Glucose–insulin relationships and thyroid status of cockerels selected for high or low residual food consumption, British Journal of Nutrition, Volume 83 (2000) no. 6, pp. 645-651 | DOI

[17] Gabarrou, J.-F.; Géraert, P.-A.; Picard, M.; Bordas, A. Diet-Induced Thermogenesis in Cockerels Is Modulated by Genetic Selection for High or Low Residual Feed Intake, The Journal of Nutrition, Volume 127 (1997) no. 12, pp. 2371-2376 | DOI

[18] Gilbert, H.; Bidanel, J.-P.; Gruand, J.; Caritez, J.-C.; Billon, Y.; Guillouet, P.; Lagant, H.; Noblet, J.; Sellier, P. Genetic parameters for residual feed intake in growing pigs, with emphasis on genetic relationships with carcass and meat quality traits, Journal of Animal Science, Volume 85 (2007) no. 12, pp. 3182-3188 | DOI

[19] Gilbert, H.; Billon, Y.; Brossard, L.; Faure, J.; Gatellier, P.; Gondret, F.; Labussière, E.; Lebret, B.; Lefaucheur, L.; Le Floch, N.; Louveau, I.; Merlot, E.; Meunier-Salaün, M.-C.; Montagne, L.; Mormede, P.; Renaudeau, D.; Riquet, J.; Rogel-Gaillard, C.; Van Milgen, J.; Vincent, A.; Noblet, J. Review: divergent selection for residual feed intake in the growing pig, Animal, Volume 11 (2017) no. 9, pp. 1427-1439 | DOI

[20] Gilmour, A. R.; Gogel, B. J.; Cullis, B. R.; Welham, S. J.; Thompson, R. ASReml User Guide Release 4.2 Functional Specification, 2022 ( https://asreml.kb.vsni.co.uk/wp-content/uploads/sites/3/ASReml-4.2-Functional-Specification.pdf)

[21] Gilmour, A. R.; Thompson, R.; Cullis, B. R. Average Information REML: An Efficient Algorithm for Variance Parameter Estimation in Linear Mixed Models, Biometrics, Volume 51 (1995) no. 4, pp. 1440-1450 | DOI

[22] Grandhaye, J.; Lecompte, F.; Staub, C.; Venturi, E.; Plotton, I.; Cailleau-Audouin, E.; Ganier, P.; Ramé, C.; Brière, S.; Dupont, J.; Froment, P. Assessment of the body development kinetic of broiler breeders by non-invasive imaging tools, Poultry Science, Volume 98 (2019) no. 9, pp. 4140-4152 | DOI

[23] Henderson, C. R. Best linear unbiased estimation and prediction under a selection model, Biometrics, Volume 31 (1975) no. 2, pp. 423-447 | DOI

[24] Kim, S.; Lee, G. H.; Lee, S.; Park, S. H.; Pyo, H. B.; Cho, J. S. Body fat measurement in computed tomography image, Biomedical Sciences Instrumentation, Volume 35 (1999), pp. 303-308

[25] Krzysik-Walker, S. M.; Ocón-Grove, O. M.; Maddineni, S. R.; Hendricks, G. L.; Ramachandran, R. Is visfatin an adipokine or myokine? Evidence for greater visfatin expression in skeletal muscle than visceral fat in chickens, Endocrinology, Volume 149 (2008) no. 4, pp. 1543-1550 | DOI

[26] Larbier, M.; Leclercq, B. L'œuf et l'alimentation des poules pondeuses, Nutrition et alimentation des volailles (Du labo au terrain), INRA, Paris, 1992, pp. 195-226

[27] Le Roy, P.; Elsen, J. M. Simple test statistics for major gene detection: a numerical comparison, Theoretical and Applied Genetics, Volume 83 (1992) no. 5, pp. 635-644 | DOI

[28] Lerch, S.; De La Torre, A.; Huau, C.; Monziols, M.; Xavier, C.; Louis, L.; Le Cozler, Y.; Faverdin, P.; Lamberton, P.; Chery, I.; Heimo, D.; Loncke, C.; Schmidely, P.; Pires, J. A. Estimation of dairy goat body composition: A direct calibration and comparison of eight methods, Methods, Volume 186 (2021), pp. 68-78 | DOI

[29] Liu, Z.; Yang, N.; Yan, Y.; Li, G.; Liu, A.; Wu, G.; Sun, C. Genome-wide association analysis of egg production performance in chickens across the whole laying period, BMC Genetics, Volume 20 (2019) no. 1 | DOI

[30] Lotfi, E.; Zerehdaran, S.; Ahani Azari, M. Genetic evaluation of carcass composition and fat deposition in Japanese quail, Poultry Science, Volume 90 (2011) no. 10, pp. 2202-2208 | DOI

[31] Luiting, P. Genetic variation of energy partitioning in laying hens: causes of variation in residual feed consumption, World's Poultry Science Journal, Volume 46 (1990) no. 2, pp. 133-152 | DOI

[32] Mellouk, N.; Ramé, C.; Barbe, A.; Grandhaye, J.; Froment, P.; Dupont, J. Chicken Is a Useful Model to Investigate the Role of Adipokines in Metabolic and Reproductive Diseases, International Journal of Endocrinology, Volume 2018 (2018), pp. 1-19 | DOI

[33] Mellouk, N.; Ramé, C.; Marchand, M.; Staub, C.; Touzé, J.-L.; Venturi, É.; Mercerand, F.; Travel, A.; Chartrin, P.; Lecompte, F.; Ma, L.; Froment, P.; Dupont, J. Effect of different levels of feed restriction and fish oil fatty acid supplementation on fat deposition by using different techniques, plasma levels and mRNA expression of several adipokines in broiler breeder hens, PLOS ONE, Volume 13 (2018) no. 1 | DOI

[34] Moreira, G. C. M.; Boschiero, C.; Cesar, A. S. M.; Reecy, J. M.; Godoy, T. F.; Pértille, F.; Ledur, M. C.; Moura, A. S. A. M. T.; Garrick, D. J.; Coutinho, L. L. Integration of genome wide association studies and whole genome sequencing provides novel insights into fat deposition in chicken, Scientific Reports, Volume 8 (2018) no. 1 | DOI

[35] Murugesan, S.; Nidamanuri, A. L. Role of leptin and ghrelin in regulation of physiological functions of chicken, World's Poultry Science Journal, Volume 78 (2022) no. 4, pp. 1021-1036 | DOI

[36] Nkrumah, J. D.; Basarab, J. A.; Wang, Z.; Li, C.; Price, M. A.; Okine, E. K.; Crews, J.; Moore, S. S. Genetic and phenotypic relationships of feed intake and measures of efficiency with growth and carcass merit of beef cattle, Journal of Animal Science, Volume 85 (2007) no. 10, pp. 2711-2720 | DOI

[37] Nunes, B. d. N.; Ramos, S. B.; Savegnago, R. P.; Ledur, M. C.; Nones, K.; Klein, C. H.; Munari, D. P. Genetic parameters for body weight, carcass chemical composition and yield in a broiler-layer cross developed for QTL mapping, Genetics and Molecular Biology, Volume 34 (2011), pp. 429-434 | DOI

[38] Pop, M.; Mărușteri, M. Fat Hounsfield Unit Reference Interval Derived through an Indirect Method, Diagnostics, Volume 13 (2023) no. 11 | DOI

[39] Preisinger, R. Innovative layer genetics to handle global challenges in egg production, British Poultry Science, Volume 59 (2018) no. 1, pp. 1-6 | DOI

[40] R Core Team R: A Language and Environment for Statistical Computing, 2023 (https://www.R-project.org)

[41] Rafat, S. A. Towards a better optimization of the genetic improvement of chicken breeds: Introduction of simple phenotypic traits related to body composition for easy measurement in the selection programs of laying hens. , Peer Community in Animal Science (2023) | DOI

[42] Resnyk, C. W.; Carré, W.; Wang, X.; Porter, T. E.; Simon, J.; Le Bihan-Duval, E.; Duclos, M. J.; Aggrey, S. E.; Cogburn, L. A. Transcriptional analysis of abdominal fat in chickens divergently selected on bodyweight at two ages reveals novel mechanisms controlling adiposity: validating visceral adipose tissue as a dynamic endocrine and metabolic organ, BMC Genomics, Volume 18 (2017) no. 1 | DOI

[43] Rowland, K.; Ashwell, C. M.; Persia, M. E.; Rothschild, M. F.; Schmidt, C.; Lamont, S. J. Genetic analysis of production, physiological, and egg quality traits in heat-challenged commercial white egg-laying hens using 600k SNP array data, Genetics Selection Evolution, Volume 51 (2019) no. 1 | DOI

[44] Schenkel, F. S.; Miller, S. P.; Wilton, J. W. Genetic parameters and breed differences for feed efficiency, growth, and body composition traits of young beef bulls, Canadian Journal of Animal Science, Volume 84 (2004) no. 2, pp. 177-185 | DOI

[45] Staub, C.; Venturi, E.; Cirot, M.; Léonard, L.; Barrière, P.; Blard, T.; Gaudé, Y.; Gascogne, T.; Yvon, J. M.; Lecompte, F.; Ramé, C.; Reigner, F.; Dupont, J. Ultrasonographic measures of body fatness and their relationship with plasma levels and adipose tissue expression of four adipokines in Welsh pony mares, Domestic Animal Endocrinology, Volume 69 (2019), pp. 75-83 | DOI

[46] Suzuki, K.; Irie, M.; Kadowaki, H.; Shibata, T.; Kumagai, M.; Nishida, A. Genetic parameter estimates of meat quality traits in Duroc pigs selected for average daily gain, longissimus muscle area, backfat thickness, and intramuscular fat content, Journal of Animal Science, Volume 83 (2005) no. 9, pp. 2058-2065 | DOI

[47] Swennen, Q.; Verhulst, P.; Collin, A.; Bordas, A.; Verbeke, K.; Vansant, G.; Decuypere, E.; Buyse, J. Further Investigations on the Role of Diet-Induced Thermogenesis in the Regulation of Feed Intake in Chickens: Comparison of Adult Cockerels of Lines Selected for High or Low Residual Feed Intake, Poultry Science, Volume 86 (2007) no. 9, pp. 1960-1971 | DOI

[48] Tixier-boichard, M.; Boichard, D.; Groeneveld, E.; Bordas, A. Restricted Maximum Likelihood Estimates of Genetic Parameters of Adult Male and Female Rhode Island Red Chickens Divergently Selected for Residual Feed Consumption, Poultry Science, Volume 74 (1995) no. 8, pp. 1245-1252 | DOI

[49] Wolc, A.; Arango, J.; Settar, P.; O’Sullivan, N.; Dekkers, J. Evaluation of egg production in layers using random regression models, Poultry Science, Volume 90 (2011) no. 1, pp. 30-34 | DOI

[50] Wolc, A.; Lisowski, M.; Hill, W.; White, I. Genetic heterogeneity of variance in production traits of laying hens, British Poultry Science, Volume 52 (2011) no. 5, pp. 537-540 | DOI

[51] Wolc, A.; White, I.; Avendano, S.; Hill, W. Genetic variability in residual variation of body weight and conformation scores in broiler chickens, Poultry Science, Volume 88 (2009) no. 6, pp. 1156-1161 | DOI

[52] Xavier, C.; Le Cozler, Y.; Depuille, L.; Caillot, A.; Lebreton, A.; Allain, C.; Delouard, J. M.; Delattre, L.; Luginbuhl, T.; Faverdin, P.; Fischer, A. The use of 3-dimensional imaging of Holstein cows to estimate body weight and monitor the composition of body weight change throughout lactation, Journal of Dairy Science, Volume 105 (2022) no. 5, pp. 4508-4519 | DOI

[53] Yoo, B. H.; Sheldon, B. L.; Podger, R. N. Genetic parameters for Oviposition time and interval in a white leghorn population of recent commercial origin, British Poultry Science, Volume 29 (1988) no. 3, pp. 627-637 | DOI

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