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Yang Q, Lu X, Li G, Zhang H, Zhou C, Yin J, Han W, Yang H. Genetic Analysis of Egg Production Traits in Luhua Chickens: Insights from a Multi-Trait Animal Model and a Genome-Wide Association Study. Genes (Basel) 2024; 15:796. [PMID: 38927732 PMCID: PMC11202424 DOI: 10.3390/genes15060796] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2024] [Revised: 06/10/2024] [Accepted: 06/13/2024] [Indexed: 06/28/2024] Open
Abstract
Egg production plays a pivotal role in the economic viability of hens. To analyze the genetic rules of egg production, a total of 3151 Luhua chickens were selected, the egg production traits including egg weight at first laying (Start-EW), egg weight at 43 weeks (EW-43), egg number at 43 weeks (EN-43), and total egg number (EN-All) were recorded. Then, the effects of related factors on egg production traits were explored, using a multi-trait animal model for genetic parameter estimation and a genome-wide association study (GWAS). The results showed that body weight at first egg (BWFE), body weight at 43 weeks (BW-43), age at first egg (AFE), and seasons had significant effects on the egg production traits. Start-EW and EW-43 had moderate heritability of 0.30 and 0.21, while EN-43 and EN-All had low heritability of 0.13 and 0.16, respectively. Start-EW exhibited a robust positive correlation with EW-43, while Start-EW was negatively correlated with EN-43 and EN-All. Furthermore, gene ontology (GO) results indicated that Annexin A2 (ANXA2) and Frizzled family receptor 7 (FZD7) related to EW-43, Cyclin D1 (CCND1) and A2B adenosine receptor (ADORA2B) related to EN-All, and have been found to be mainly involved in metabolism and growth processes, and deserve more attention and further study. This study contributes to accelerating genetic progress in improving low heritability egg production traits in layers, especially in Luhua chickens.
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Affiliation(s)
- Qianwen Yang
- College of Mathematical Science, Yangzhou University, Yangzhou 225009, China;
| | - Xubin Lu
- College of Animal Science and Technology, Yangzhou University, Yangzhou 225009, China; (X.L.); (H.Y.)
| | - Guohui Li
- Jiangsu Institute of Poultry Science, Yangzhou 225611, China; (G.L.); (H.Z.); (C.Z.); (J.Y.)
| | - Huiyong Zhang
- Jiangsu Institute of Poultry Science, Yangzhou 225611, China; (G.L.); (H.Z.); (C.Z.); (J.Y.)
| | - Chenghao Zhou
- Jiangsu Institute of Poultry Science, Yangzhou 225611, China; (G.L.); (H.Z.); (C.Z.); (J.Y.)
| | - Jianmei Yin
- Jiangsu Institute of Poultry Science, Yangzhou 225611, China; (G.L.); (H.Z.); (C.Z.); (J.Y.)
| | - Wei Han
- Jiangsu Institute of Poultry Science, Yangzhou 225611, China; (G.L.); (H.Z.); (C.Z.); (J.Y.)
| | - Haiming Yang
- College of Animal Science and Technology, Yangzhou University, Yangzhou 225009, China; (X.L.); (H.Y.)
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Ni A, Calus MPL, Bovenhuis H, Yuan J, Wang Y, Sun Y, Chen J. Genetic parameters, reciprocal cross differences, and age-related heterosis of egg-laying performance in chickens. Genet Sel Evol 2023; 55:87. [PMID: 38062365 PMCID: PMC10702067 DOI: 10.1186/s12711-023-00862-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Accepted: 11/28/2023] [Indexed: 12/18/2023] Open
Abstract
BACKGROUND Egg-laying performance is economically important in poultry breeding programs. Crossbreeding between indigenous and elite commercial lines to exploit heterosis has been an upward trend in traditional layer breeding for niche markets. The objective of this study was to analyse the genetic background and to estimate the heterosis of longitudinal egg-laying traits in reciprocal crosses between an indigenous Beijing-You and an elite commercial White Leghorn layer line. Egg weights were measured for the first three eggs, monthly from 28 to 76 weeks of age, and at 86 and 100 weeks of age. Egg quality traits were measured at 32, 54, 72, 86, and 100 weeks of age. Egg production traits were measured from the start of lay until 43, 72, and 100 weeks of age. Heritabilities and phenotypic and genetic correlations were estimated. Heterosis was estimated as the percentage difference of performance of a crossbred from that of the parental average. Reciprocal cross differences were estimated as the difference between the reciprocal crossbreds as a percentage of the parental average. RESULTS Estimates of heritability of egg weights ranged from 0.29 to 0.75. Estimates of genetic correlations between egg weights at different ages ranged from 0.72 to 1.00. Estimates of heritability for cumulative egg numbers until 43, 72, and 100 weeks of age were around 0.15. Estimates of heterosis for egg weight and cumulative egg number increased with age, ranging from 1.0 to 9.0% and from 1.4 to 11.6%, respectively. From 72 to 100 weeks of age, crossbreds produced more eggs per week than the superior parent White Leghorn (3.5 eggs for White Leghorn, 3.8 and 3.9 eggs for crossbreds). Heterosis for eggshell thickness ranged from 2.7 to 6.6% when using Beijing-You as the sire breed. No significant difference between reciprocal crosses was observed for the investigated traits, except for eggshell strength at 54 weeks of age. CONCLUSIONS The heterosis was substantial for egg weight and cumulative egg number, and increased with age, suggesting that non-additive genetic effects are important in crossbreds between the indigenous and elite breeds. Generally, the crossbreds performed similar to or even outperformed the commercial White Leghorns for egg production persistency.
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Affiliation(s)
- Aixin Ni
- State Key Laboratory of Animal Biotech Breeding, Key Laboratory of Animal (Poultry) Genetics Breeding and Reproduction of Ministry of Agriculture and Rural Affairs, Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, 100193, China
- Animal Breeding and Genomics, Wageningen University and Research, P.O. Box 338, 6700 AH, Wageningen, The Netherlands
| | - Mario P L Calus
- Animal Breeding and Genomics, Wageningen University and Research, P.O. Box 338, 6700 AH, Wageningen, The Netherlands
| | - Henk Bovenhuis
- Animal Breeding and Genomics, Wageningen University and Research, P.O. Box 338, 6700 AH, Wageningen, The Netherlands
| | - Jingwei Yuan
- State Key Laboratory of Animal Biotech Breeding, Key Laboratory of Animal (Poultry) Genetics Breeding and Reproduction of Ministry of Agriculture and Rural Affairs, Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, 100193, China
| | - Yuanmei Wang
- State Key Laboratory of Animal Biotech Breeding, Key Laboratory of Animal (Poultry) Genetics Breeding and Reproduction of Ministry of Agriculture and Rural Affairs, Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, 100193, China
| | - Yanyan Sun
- State Key Laboratory of Animal Biotech Breeding, Key Laboratory of Animal (Poultry) Genetics Breeding and Reproduction of Ministry of Agriculture and Rural Affairs, Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, 100193, China.
| | - Jilan Chen
- State Key Laboratory of Animal Biotech Breeding, Key Laboratory of Animal (Poultry) Genetics Breeding and Reproduction of Ministry of Agriculture and Rural Affairs, Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, 100193, China.
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3
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Yang H, Li Y, Yuan J, Ni A, Ma H, Wang Y, Zong Y, Zhao J, Jin S, Sun Y, Chen J. Research Note: Genetic parameters for egg production and clutch-related traits in indigenous Beijing-You chickens. Poult Sci 2023; 102:102904. [PMID: 37453280 PMCID: PMC10371837 DOI: 10.1016/j.psj.2023.102904] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Revised: 06/12/2023] [Accepted: 06/24/2023] [Indexed: 07/18/2023] Open
Abstract
Egg products from indigenous chickens have growing market shares as consumers are pursuing differentiation in egg consumption. The genetic improvement in egg production performance of those breeds is crucial for increasing the economic profit. This study aimed to estimate genetic parameters for egg production and clutch-related traits in indigenous Beijing-You chickens for understanding the genetic architecture and exploring proper biological traits for selection. Data on traits including age at first egg (AFE), egg number (EN), average clutch length (ACL), maximum clutch length (MCL), number of clutches (NC) and pauses (NP), and average pause length (APL) were collected from 4 generations of purebred Beijing-You chickens based on the 43-wk and 66-wk of individual egg production record. The heritabilities, genetic and phenotypic correlations were analyzed by the DMU software with the restricted maximum likelihood method in a multivariate animal model. The results showed that the AFE of Beijing-You chickens was 174.45 d of age, and its heritability was as high as 0.62. The heritability was 0.26 for EN43 and 0.18 for EN66. The clutch traits including ACL, MCL, NC, and NP were moderate to high heritable (h2 = 0.15-0.39), but APL was very low heritable (h2 = 0.05). Genetic correlations were high between AFE and EN (rG(AFE, EN43) = -0.79, rG(AFE, EN66) = -0.39), whereas low between AFE and ACL (rG(AFE, ACL43) = -0.08, rG(AFE, ACL66) = 0.01) and MCL (rG(AFE, MCL) = -0.07). EN had higher correlations with ACL (rG(EN43, ACL43) = 0.59, rG(EN66, ACL66) = 0.40) than that with MCL (rG(EN43, MCL43) = 0.56, rG(EN66, MCL66) = 0.32). The heritability for ACL43 (h2 = 0.38) was higher than that for MCL43 (h2 = 0.33). ACL43 had a positive correlation with EN66 (rG(ACL43, EN66) = 0.62). These results indicated that the egg production of whole laying period could be improved by early selection for AFE and ACL at the same time in Beijing-You chickens.
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Affiliation(s)
- Hanhan Yang
- State Key Laboratory of Animal Biotech Breeding, Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing 100193, P. R. China
| | - Yunlei Li
- State Key Laboratory of Animal Biotech Breeding, Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing 100193, P. R. China
| | - Jingwei Yuan
- State Key Laboratory of Animal Biotech Breeding, Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing 100193, P. R. China
| | - Aixin Ni
- State Key Laboratory of Animal Biotech Breeding, Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing 100193, P. R. China
| | - Hui Ma
- State Key Laboratory of Animal Biotech Breeding, Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing 100193, P. R. China
| | - Yuanmei Wang
- State Key Laboratory of Animal Biotech Breeding, Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing 100193, P. R. China
| | - Yunhe Zong
- State Key Laboratory of Animal Biotech Breeding, Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing 100193, P. R. China
| | - Jinmeng Zhao
- State Key Laboratory of Animal Biotech Breeding, Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing 100193, P. R. China
| | - Sihua Jin
- College of Animal Science and Technology, Anhui Agricultural University, Hefei 230036, P. R. China
| | - Yanyan Sun
- State Key Laboratory of Animal Biotech Breeding, Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing 100193, P. R. China.
| | - Jilan Chen
- State Key Laboratory of Animal Biotech Breeding, Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing 100193, P. R. China
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4
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van der Klein SAS, Willems OW, Zuidhof MJ. Multiphasic mixed growth models for turkeys. J Anim Sci 2023; 101:skad094. [PMID: 37119008 PMCID: PMC10158525 DOI: 10.1093/jas/skad094] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Accepted: 04/26/2023] [Indexed: 04/30/2023] Open
Abstract
Growth models are important for optimization of feed formulation and breeding programs in turkeys. The objectives of this study were 1) to compare sex and line differences in turkeys in parameter estimates of mono- and di-phasic Gompertz growth models, and 2) to evaluate mono and diphasic mixed Gompertz growth models to determine the variation in parameter estimates in a group of female line turkey toms. A total of 1,056 manually recorded weekly average body weight (BW) observations from male and female turkeys of a male and female line from weeks 1 to 20 were used for objective 1. Daily median values of automatically collected individual BW of female line turkey toms were used for objective 2 and random components associated with individual subject animals related to mature weight and/or timing of maximum gain during each phase were introduced in the Gompertz model. Growth curve shapes were different between male line toms, male line hens, female line toms, and female line hens (P < 0.001). However, inflection points were similar between male and female line toms and between male and female line hens (14.06 vs. 13.72 wk and 11.22 and 10.71 wk, respectively), while mature BW differed between lines by 6.49 and 3.81 kg for toms and hens, respectively. The normalized growth rate constant (growth rate constant corrected for mature weight) was around the same magnitude between male and female line toms (0.0031 vs. 0.0038, respectively), but slightly lower in male line hens compared to female line hens (0.0072 vs. 0.0091, respectively). Diphasic Gompertz models described growth better in all line × sex combinations compared to the monophasic models (P < 0.001) and mixed diphasic Gompertz models showed improved fit over mixed monophasic Gompertz models. The correlation structure of the random components identified that individuals with a higher mature weight had a later inflection point and lower growth rate coefficients. These results provide tools for improved breeding practices and a structure to evaluate the effects of dietary or environmental factors on growth trajectories.
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Affiliation(s)
| | | | - Martin J Zuidhof
- Department of Agricultural, Food and Nutritional Science, University of Alberta, Edmonton, AB, CanadaT6G 2P5
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5
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Genetic parameters of feather corticosterone and fault bars and correlations with production traits in turkeys (Meleagris gallopavo). Sci Rep 2023; 13:38. [PMID: 36593340 PMCID: PMC9807576 DOI: 10.1038/s41598-022-26734-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2022] [Accepted: 12/19/2022] [Indexed: 01/03/2023] Open
Abstract
Robustness can refer to an animal's ability to overcome perturbations. Intense selection for production traits in livestock has resulted in reduced robustness which has negative implications for livability as well as production. There is increasing emphasis on improving robustness through poultry breeding, which may involve identifying novel phenotypes that could be used in selection strategies. The hypothalamic-pituitary-adrenal (HPA) axis and associated hormones (e.g., corticosterone) participate in many metabolic processes that are related to robustness. Corticosterone can be measured non-invasively in feathers (FCORT) and reflects the average HPA axis activity over the feather growing period, however measurement is expensive and time consuming. Fault bars are visible feather deformities that may be related to HPA axis activity and may be a more feasible indicator trait. In this study, we estimated variance components for FCORT and fault bars in a population of purebred turkeys as well as their genetic and partial phenotypic correlations with other economically relevant traits including growth and efficiency, carcass yield, and meat quality. The estimated heritability for FCORT was 0.21 ± 0.07 and for the fault bar traits (presence, incidence, severity, and index) estimates ranged from 0.09 to 0.24. The genetic correlation of FCORT with breast weight, breast meat yield, fillet weight, and ultimate pH were estimated at -0.34 ± 0.21, -0.45 ± 0.23, -0.33 ± 0.24, and 0.32 ± 0.24, respectively. The phenotypic correlations of FCORT with breast weight, breast meat yield, fillet weight, drum weight, and walking ability were -0.16, -0.23, -0.18, 0.17, and 0.21, respectively. Some fault bar traits showed similar genetic correlations with breast weight, breast meat yield, and walking ability but the magnitude was lower than those with FCORT. While the dataset is limited and results should be interpreted with caution, this study indicates that selection for traits related to HPA axis activity is possible in domestic turkeys. Further research should focus on investigating the association of these traits with other robustness-related traits and how to potentially implement these traits in turkey breeding.
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6
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Durosaro SO, Iyasere OS, Ilori BM, Oguntade DO, Oyeniran VJ, Oghate EB, Fasola HO, Ozoje MO. Genetic parameters of fear-related behaviours in Nigerian indigenous turkey poults: A pilot study. J Vet Behav 2023. [DOI: 10.1016/j.jveb.2023.01.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
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7
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Gao G, Chen P, Zhou C, Zhao X, Zhang K, Wu R, Zhang C, Wang Y, Xie Y, Wang Q. Genome-wide association study for reproduction-related traits in Chinese domestic goose. Br Poult Sci 2022; 63:754-760. [PMID: 35775663 DOI: 10.1080/00071668.2022.2096402] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
1. This study measured six reproduction traits in a Sichuan white goose population (209 individuals), including fertility, qualified egg rate, plasma concentrations of progesterone (P), follicle-stimulating hormone (FSH), prolactin (PRL) and oestrogen (E2).2. Whole-genome resequencing data from the same goose population (209 individuals) were used in a genome-wide association study (GWAS) utilising a mixed linear model to investigate the genes and genetic markers associated with reproduction traits. The frequency of the selected SNPs and haplotypes were determined using the Matrix-Assisted Laser Desorption Ionisation Time-Of-Flight Mass Spectrometry (MALDI-TOF MS) method.3. In total, 42 SNPs significantly associated with these traits were identified. A haplotype block was constructed based on five SNPs that were significantly associated with qualified egg rate, with individuals having the haplotype CCTTAAGGAA having the lowest qualified egg rate.4. In conclusion, these results provided potential markers for marker-assisted selection to improve goose reproductive performance and a basis for elucidating the genetics of goose reproduction.
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Affiliation(s)
- G Gao
- Department of Poultry Science, Chongqing Academy of Animal Science, Chongqing, P. R. China.,Institute of Animal Genetics and Breeding, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu, Sichuan, P. R. China.,Chongqing Engineering Research Center of Goose Genetic Improvement, Chongqing, P. R. China
| | - P Chen
- Animal Husbandry and Veterinary Station, Sucheng District Suqian, Jiangsu, P. R. China
| | - C Zhou
- Department of Poultry Science, Chongqing Academy of Animal Science, Chongqing, P. R. China.,Chongqing Engineering Research Center of Goose Genetic Improvement, Chongqing, P. R. China
| | - X Zhao
- Department of Poultry Science, Chongqing Academy of Animal Science, Chongqing, P. R. China.,Chongqing Engineering Research Center of Goose Genetic Improvement, Chongqing, P. R. China
| | - K Zhang
- Department of Poultry Science, Chongqing Academy of Animal Science, Chongqing, P. R. China.,Chongqing Engineering Research Center of Goose Genetic Improvement, Chongqing, P. R. China
| | - R Wu
- Department of Poultry Science, Chongqing Academy of Animal Science, Chongqing, P. R. China.,Chongqing Engineering Research Center of Goose Genetic Improvement, Chongqing, P. R. China
| | - C Zhang
- Department of Poultry Science, Chongqing Academy of Animal Science, Chongqing, P. R. China.,Chongqing Engineering Research Center of Goose Genetic Improvement, Chongqing, P. R. China
| | - Y Wang
- Department of Poultry Science, Chongqing Academy of Animal Science, Chongqing, P. R. China.,Chongqing Engineering Research Center of Goose Genetic Improvement, Chongqing, P. R. China
| | - Y Xie
- Department of Poultry Science, Chongqing Academy of Animal Science, Chongqing, P. R. China.,Chongqing Engineering Research Center of Goose Genetic Improvement, Chongqing, P. R. China
| | - Q Wang
- Department of Poultry Science, Chongqing Academy of Animal Science, Chongqing, P. R. China.,Chongqing Engineering Research Center of Goose Genetic Improvement, Chongqing, P. R. China
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8
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Investigating inbreeding in the turkey (Meleagris gallopavo) genome. Poult Sci 2021; 100:101366. [PMID: 34525446 PMCID: PMC8445901 DOI: 10.1016/j.psj.2021.101366] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2021] [Revised: 06/02/2021] [Accepted: 06/24/2021] [Indexed: 02/06/2023] Open
Abstract
The detrimental effects of increased homozygosity due to inbreeding have prompted the development of methods to reduce inbreeding. The detection of runs of homozygosity (ROH), or contiguous stretches of homozygous marker genotypes, can be used to describe and quantify the level of inbreeding in an individual. The estimation of inbreeding coefficients can be calculated based on pedigree information, ROH, or the genomic relationship matrix. The aim of this study was to detect and describe ROH in the turkey genome and compare estimates of pedigree-based inbreeding coefficients (FPED) with genomic-based inbreeding coefficients estimated from ROH (FROH) and the genomic relationship matrix (FGRM). A total of 2,616,890 pedigree records were available. Of these records, 6,371 genotyped animals from three purebred turkey (Meleagris gallopavo) lines between 2013 and 2019 were available, and these were obtained using a dense single nucleotide polymorphism array (56,452 SNPs). The overall mean length of detected ROH was 2.87 ± 0.29 Mb with a mean number of 84.87 ± 8.79 ROH per animal. Short ROH with lengths of 1 to 2 Mb long were the most abundant throughout the genome. Mean ROH coverage differed greatly between chromosomes and lines. Considering inbreeding coefficient means across all lines, genomic derived inbreeding coefficients (FROH = 0.27; FGRM = 0.32) were higher than coefficients estimated from pedigree records (FPED = 0.14). Correlations between FROH and FPED, FROH and FGRM, and FPED and FGRM ranged between 0.19 to 0.31, 0.68 to 0.73, and 0.17 to 0.30, respectively. Additionally, correlations between FROH from different lengths and FPED substantially increased with ROH length from -0.06 to 0.33. Results of the current research, including the distribution of ROH throughout the genome and ROH-derived inbreeding estimates, can provide a more comprehensive description of inbreeding in the turkey genome. This knowledge can be used to evaluate genetic diversity, a requirement for genetic improvement, and develop methods to minimize inbreeding in turkey breeding programs.
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Abdalla EA, Wood BJ, Baes CF. Accuracy of breeding values for production traits in turkeys (Meleagris gallopavo) using recursive models with or without genomics. Genet Sel Evol 2021; 53:16. [PMID: 33593272 PMCID: PMC7885440 DOI: 10.1186/s12711-021-00611-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2020] [Accepted: 02/01/2021] [Indexed: 11/10/2022] Open
Abstract
Background Knowledge about potential functional relationships among traits of interest offers a unique opportunity to understand causal mechanisms and to optimize breeding goals, management practices, and prediction accuracy. In this study, we inferred the phenotypic causal networks among five traits in a turkey population and assessed the effect of the use of such causal structures on the accuracy of predictions of breeding values. Methods Phenotypic data on feed conversion ratio, residual feed intake, body weight, breast meat yield, and walking score in addition to genotype data from a commercial breeding population were used. Causal links between the traits were detected using the inductive causation algorithm based on the joint distribution of genetic effects obtained from a standard Bayesian multiple trait model. Then, a structural equation model was implemented to infer the magnitude of causal structure coefficients among the phenotypes. Accuracies of predictions of breeding values derived using pedigree- and blending-based multiple trait models were compared to those obtained with the pedigree- and blending-based structural equation models. Results In contrast to the two unconditioned traits (i.e., feed conversion ratio and breast meat yield) in the causal structures, the three conditioned traits (i.e., residual feed intake, body weight, and walking score) showed noticeable changes in estimates of genetic and residual variances between the structural equation model and the multiple trait model. The analysis revealed interesting functional associations and indirect genetic effects. For example, the structural coefficient for the path from body weight to walking score indicated that a 1-unit genetic improvement in body weight is expected to result in a 0.27-unit decline in walking score. Both structural equation models outperformed their counterpart multiple trait models for the conditioned traits. Applying the causal structures led to an increase in accuracy of estimated breeding values of approximately 7, 6, and 20% for residual feed intake, body weight, and walking score, respectively, and different rankings of selection candidates for the conditioned traits. Conclusions Our results suggest that structural equation models can improve genetic selection decisions and increase the prediction accuracy of breeding values of selection candidates. The identified causal relationships between the studied traits should be carefully considered in future turkey breeding programs.
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Affiliation(s)
- Emhimad A Abdalla
- Centre for Genetic Improvement of Livestock, University of Guelph, Guelph, ON, Canada.
| | - Benjamin J Wood
- Centre for Genetic Improvement of Livestock, University of Guelph, Guelph, ON, Canada.,School of Veterinary Science, University of Queensland, Gatton Campus, Gatton, QLD, Australia.,Hybrid Turkeys, C-650 Riverbend Drive, Suite C, Kitchener, Canada
| | - Christine F Baes
- Centre for Genetic Improvement of Livestock, University of Guelph, Guelph, ON, Canada.,Institute of Genetics, Vetsuisse Faculty, University of Bern, Bern, Switzerland
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10
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Abdalla EA, Id‐Lahoucine S, Cánovas A, Casellas J, Schenkel FS, Wood BJ, Baes CF. Discovering lethal alleles across the turkey genome using a transmission ratio distortion approach. Anim Genet 2020; 51:876-889. [PMID: 33006154 PMCID: PMC7702127 DOI: 10.1111/age.13003] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/28/2020] [Indexed: 12/23/2022]
Abstract
Deviation from Mendelian inheritance expectations (transmission ratio distortion, TRD) has been observed in several species, including the mouse and humans. In this study, TRD was characterized in the turkey genome using both allelic (specific- and unspecific-parent TRD) and genotypic (additive- and dominance-TRD) parameterizations within a Bayesian framework. In this study, we evaluated TRD for 23 243 genotyped Turkeys across 56 393 autosomal SNPs. The analyses included 500 sires, 2013 dams and 11 047 offspring (trios). Three different haplotype sliding windows of 4, 10 and 20 SNPs were used across the autosomal chromosomes. Based on the genotypic parameterizations, 14 haplotypes showed additive and dominance TRD effects highlighting regions with a recessive TRD pattern. In contrast, the allelic model uncovered 12 haplotype alleles with the allelic TRD pattern which showed an underrepresentation of heterozygous offspring in addition to the absence of homozygous animals. For regions with the allelic pattern, only one particular region showed a parent-specific TRD where the penetrance was high via the dam, but low via the sire. The gene set analysis uncovered several gene ontology functional terms, Reactome pathways and several Medical Subject Headings that showed significant enrichment of genes associated with TRD. Many of these gene ontology functional terms (e.g. mitotic spindle assembly checkpoint, DRM complex and Aneuploidy), Reactome pathways (e.g. Mismatch repair) and Medical Subject Headings (e.g. Adenosine monophosphate) are known to be related to fertility, embryo development and lethality. The results of this study revealed potential novel candidate lethal haplotypes, functional terms and pathways that may enhance breeding programs in Turkeys through reducing mortality and improving reproduction rate.
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Affiliation(s)
- E. A. Abdalla
- Centre for Genetic Improvement of Livestock, Department of Animal BiosciencesUniversity of GuelphGuelphONN1G 2W1Canada
| | - S. Id‐Lahoucine
- Centre for Genetic Improvement of Livestock, Department of Animal BiosciencesUniversity of GuelphGuelphONN1G 2W1Canada
| | - A. Cánovas
- Centre for Genetic Improvement of Livestock, Department of Animal BiosciencesUniversity of GuelphGuelphONN1G 2W1Canada
| | - J. Casellas
- Departament de Ciència Animal i dels AlimentsUniversitat Autònoma de BarcelonaBellaterra08193Spain
| | - F. S. Schenkel
- Centre for Genetic Improvement of Livestock, Department of Animal BiosciencesUniversity of GuelphGuelphONN1G 2W1Canada
| | - B. J. Wood
- Centre for Genetic Improvement of Livestock, Department of Animal BiosciencesUniversity of GuelphGuelphONN1G 2W1Canada
- Hybrid TurkeysC‐650 Riverbend Drive, Suite CKitchenerONN2K 3S2Canada
- School of Veterinary ScienceUniversity of QueenslandGattonQld4343Australia
| | - C. F. Baes
- Centre for Genetic Improvement of Livestock, Department of Animal BiosciencesUniversity of GuelphGuelphONN1G 2W1Canada
- Institute of Genetics, Vetsuisse FacultyUniversity of BernBern3001Switzerland
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