Scientia Agricultura Sinica ›› 2022, Vol. 55 ›› Issue (19): 3854-3861.doi: 10.3864/j.issn.0578-1752.2022.19.014

• ANIMAL SCIENCE·VETERINARY SCIENCE • Previous Articles     Next Articles

Analysis of Indirect Genetic Effects on Body Weight of 42 Day-Old Rugao Yellow Chickens

GUO Jun(),WANG KeHua,HAN Wei,DOU TaoCun,WANG XingGuo,HU YuPing,MA Meng,QU Liang()   

  1. Jiangsu Institute of Poultry Sciences, Yangzhou 225125, Jiangsu
  • Received:2021-12-22 Accepted:2022-03-09 Online:2022-10-01 Published:2022-10-10
  • Contact: Liang QU E-mail:guojun.yz@gmail.com;sqbreeding@126.com

Abstract:

【Background】 In addition to regulating its own phenotype, the genotype of an individual animal also affects the performance of other animals within a social group through resource allocation or behavioral interaction, and this phenomenon is called an indirect genetic effect. In the animal breeding, if the genetic model harbored the indirect genetic effects, it will not only improve the social relationship between individuals, but also obtain more genetic gains. 【Objective】 In this study, Rugao Yellow Chickens raised in group cages were used as the test animals, and the indirect genetic model was used to evaluate body weight data, aiming to provide a flexible model to select the Rugao Yellow Chickens. 【Method】The body weight data was collected from the breeding group of Rugao Yellow Chickens. The fowls were weighed at the age of 42 days-old, and 11 983 raw data were collected. The data cleaning procedure included: i. removing outlier beyond three standard deviations either side of the mean; ii. eliminate the fowl without marker; iii. get rid of unknown sexed fowls; iv. the fowls with less than 4 records within a social group were also excluded. The pedigree data consisted of 12 208 fowls, including 11 735 chickens with body weight records and 473 chickens without records, 10 560 chickens without progeny and 1 648 with progeny, and the progeny of them included 208 male breeders and 1 440 female breeders. With SPSS software packages, ANOVA was used to test the influence of environmental factors on body weight and determine the factors included in the fixed effects. The classic animal model and indirect genetic model were used to analyze the variance components and genetic parameters of Rugao Yellow Chickens, and to test whether there was a dilution effect on the indirect genetic variance. The genetic model included the general fixed effects, fixed regression terms, additive genetic effects, indirect genetic effects, common environmental effects and residuals. In this study, the fixed regression term included cage sizes, and random terms included additive genetic effects, indirect genetic effects and common environmental effects. The initial value of the dilution parameter was set to 0, and it was step increased to 1.0 in increments of 0.1. After evaluating with AIC and BIC standard, the dilution parameter should be set to 0. Accounting for heterogeneous errors did not alter the estimates of genetic parameters and variance components. Therefore, the homogeneous error was assumed. Using WOMBAT software, estimates of variance components and genetic parameters converged for both classic and indirect genetic models (with or without dilution effect). 【Result】The fixed effects included the combination of laying batch-row-sex level. The significant indirect genetic variance for body weight of 42-day-old Rugao Yellow Chickens was found, for the additive heritability was 0.54±0.02, and the total heritable variation was 0.66±0.06. The corporation relationship between individuals presented in the same cage of Rugao Yellow Chickens. The direction of additive genetic and indirect genetic selection was the same, and the genetic correlation coefficient was 0.41. There was no dilution effect in the indirect genetic variance of Rugao Yellow Chickens. The indirect genetic variances were distinguished between sexes, and so on heritability and genetic correlation coefficient. 【Conclusion】Indirect genetic model could be used for the genetic evaluation and selection of body weight during the rearing period. Compared with those classic animal models, the indirect genetic models could achieve an additional genetic gain.

Key words: Rugao Yellow Chickens, heritability, indirect genetic effect, body weight, cooperation behavior

Fig.1

Body weight at 42 day-olds of Rugao Yellow Chickens"

Table 1

The body weight of 42-day-old Rugao Yellow Chickens based on the cage size"

单笼养殖量
Cage size
样本量(只)
Sample size
体重平均值
Mean
(g)
标准差
Standard deviation
(g)
均值的 95% 置信区间 95% CI 极小值
Minimum
(g)
极大值
Maximum
(g)
下限 Low 上限 Upper
4 852 418.24b 68.38 413.64 422.84 228 632
5 6 175 407.07c 65.46 405.43 408.70 217 632
6 1 596 380.93e 50.14 378.47 383.39 243 593
7 1 568 447.33a 55.50 444.58 450.08 252 599
8 1 544 388.45d 45.84 386.16 390.73 247 552
总数 Total 11 735 407.25 63.15 406.11 408.39 217 632

Table 2

Information criterion for indirect genetic model"

稀释参数
Dilution parameter
最大似然值对数
Log maximum likelihood
AIC BIC
d=0.0 -50781.95 101573.90 101610.74
d=0.1 -50781.64 101573.28 101610.13
d=0.2 -50781.32 101572.64 101609.49
d=0.3 -50780.99 101630.84 101667.69
d=0.4 -50780.66 101640.68 101677.53
d=0.5 -50780.33 102135.95 102172.80
d=0.6 -50780.07 101976.20 102013.05
d=0.7 -50779.97 101820.13 101856.98
d=0.8 -50780.10 101771.75 101808.60
d=0.9 -50780.46 101710.10 101746.94
d=1.0 -50780.97 101653.24 101690.09

Table 3

Variance components and parameters estimated on body weight of 42-day-old Rugao Yellow Chickens"

模型
Model
加性遗传方差
Direct genetic variance
间接遗传方差
Indirect genetic variance
共同环境方差
Common environmental variance
残差
Residual variance
表型方差
Phenotypic variance
总遗传效应
值方差
Total heritable variance
狭义遗传力
Narrow heritability
总遗传力
Total heritability
加性-间接遗传相关系数
Correlation between direct and social genetic effects
经典模型
Classic model
1455.05±87.01 230.19±18.20 1228.43±48.59 2713.66±52.96 0.54±0.02
稀释参数 Dilution parameter
d=0.0 1475.26±88.48 3.44±2.07 184.51±25.79 1046.00±49.18 2721.28±53.73 1809.08±188.18 0.54±0.02 0.66±0.06 0.41
d=0.2 1475.15±88.52 7.53±3.02 181.40±26.37 1045.96±49.29 2727.62±53.76 1825.82±172.57 0.54±0.02 0.67±0.06 0.38
d=0.4 1474.30±88.41 15.77±4.32 179.35±26.50 1045.35±49.37 2737.92±53.70 1831.06±153.41 0.54±0.02 0.67±0.05 0.35
d=0.6 1473.27±88.25 32.77±6.19 177.49±26.33 1044.51±49.41 2755.11±53.61 1835.55±135.03 0.53±0.02 0.67±0.04 0.32
d=0.8 1471.53±88.05 66.73±8.80 176.60±25.96 1043.05±49.44 2781.36±53.50 1830.00±119.57 0.53±0.02 0.66±0.03 0.28
d=1.0 1469.71±87.87 133.03±12.40 176.04±25.55 1041.38±49.44 2820.16±53.40 1821.05±108.20 0.52±0.02 0.65±0.03 0.25
按性别分组 Grouped by sex
公鸡
Rooster
1210.07±132.11 10.13±6.55 147.46±50.99 1472.03±93.41 2867.21±75.84 1459.21±266.79 0.42±0.04 0.51±0.09 0.13
母鸡 Hen 1267.38±91.13 0.27±2.42 176.47±29.97 920.74±53.59 2365.73±56.00 1426.16±185.81 0.54±0.03 0.60±0.07 0.90
[1] BIJMA P, MUIR W M, VAN ARENDONK J A M. Multilevel selection 1: Quantitative genetics of inheritance and response to selection. Genetics, 2007, 175(1): 277-288. doi: 10.1534/genetics.106.062711.
doi: 10.1534/genetics.106.062711 pmid: 17110494
[2] MUIR W, SCHINCKEL A. Incorporation of competitive effects in breeding programs to improve productivity and animal well being//7th World Congress on Genetics Applied to Livestock Production. Montpellier, France, 2002: 14-07.
[3] MUIR W M, BIJMA P, SCHINCKEL A. Multilevel selection with kin and non-kin groups, experimental results with Japanese quail (coturnix japonica). Evolution, 2013, 67(6): 1598-1606. doi: 10.1111/evo.12062.
doi: 10.1111/evo.12062 pmid: 23730755
[4] HERRERA-CÁCERES W, SÁNCHEZ J P. Selection for feed efficiency using the social effects animal model in growing Duroc pigs: Evaluation by simulation. Genetics, Selection, Evolution: GSE, 2020, 52(1): 53. doi: 10.1186/s12711-020-00572-4.
doi: 10.1186/s12711-020-00572-4
[5] 国家畜禽遗传资源委员会组. 中国畜禽遗传资源志-家禽志. 北京: 中国农业出版社, 2011.
National Livestock and Poultry Genetic Resources Committee. Animal Genetic Resources in China. Beijing: Chinese Agriculture Press, 2011. (in Chinese)
[6] SCHMIDT-NIELSEN K S. Scaling:Why is Animal Size So Important? New York: Cambridge University Press, 1984.
[7] THIRUVENKADAN A K, PRABAKARAN R, PANNEERSELVAM S. Broiler breeding strategies over the decades: An overview. World’s Poultry Science Journal, 2011, 67(2): 309-336. doi: 10.1017/S0043933911000328.
doi: 10.1017/S0043933911000328
[8] MUIR W M, AGGREY S E. Poultry Genetics, Breeding and Biotechnology. Wallingford: CABI, 2003. doi: 10.1079/9780851996608.0000.
doi: 10.1079/9780851996608.0000
[9] 党李苹, 周雯馨, 刘瑞芳, 白云, 王哲鹏. 略阳乌鸡体重和产蛋数性状遗传参数估计. 中国农业科学, 2020, 53(17): 3620-3628. doi: 10.3864/j.issn.0578-1752.2020.17.018.
doi: 10.3864/j.issn.0578-1752.2020.17.018
DANG L P, ZHOU W X, LIU R F, BAI Y, WANG Z P. Estimation of genetic parameters of body weight and egg number traits of Lueyang black-boned chicken. Scientia Agricultura Sinica, 2020, 53(17): 3620-3628. doi: 10.3864/j.issn.0578-1752.2020.17.018. (in Chinese)
doi: 10.3864/j.issn.0578-1752.2020.17.018
[10] 郭军, 曲亮, 窦套存, 王星果, 沈曼曼, 胡玉萍, 王克华. 应用随机回归模型估计蛋鸡体重遗传参数. 中国农业科学, 2020, 53(11): 2297-2304. doi: 10.3864/j.issn.0578-1752.2020.11.015.
doi: 10.3864/j.issn.0578-1752.2020.11.015
GUO J, QU L, DOU T C, WANG X G, SHEN M M, HU Y P, WANG K H. Using random regression models to estimate genetic parameters on body weights in layers. Scientia Agricultura Sinica, 2020, 53(11): 2297-2304. doi: 10.3864/j.issn.0578-1752.2020.11.015. (in Chinese)
doi: 10.3864/j.issn.0578-1752.2020.11.015
[11] ASK B, CHRISTENSEN O F, HEIDARITABAR M, MADSEN P, NIELSEN H M. The predictive ability of indirect genetic models is reduced when culled animals are omitted from the data. Genetics, Selection, Evolution: GSE, 2020, 52(1): 8. doi: 10.1186/s12711-020-0527-x.
doi: 10.1186/s12711-020-0527-x pmid: 32041518
[12] PILES M, DAVID I, RAMON J, CANARIO L, RAFEL O, PASCUAL M, RAGAB M, SÁNCHEZ J P. Interaction of direct and social genetic effects with feeding regime in growing rabbits. Genetics, Selection, Evolution: GSE, 2017, 49(1): 58. doi: 10.1186/s12711-017-0333-2.
doi: 10.1186/s12711-017-0333-2
[13] DAVID I, SÁNCHEZ J P, PILES M. Longitudinal analysis of direct and indirect effects on average daily gain in rabbits using a structured antedependence model. Genetics, Selection, Evolution: GSE, 2018, 50(1): 25. doi: 10.1186/s12711-018-0395-9.
doi: 10.1186/s12711-018-0395-9 pmid: 29747574
[14] CHU T T, HENRYON M, JENSEN J, ASK B, CHRISTENSEN O F. Statistical model and testing designs to increase response to selection with constrained inbreeding in genomic breeding programs for pigs affected by social genetic effects. Genetics, Selection, Evolution: GSE, 2021, 53(1): 1. doi: 10.1186/s12711-020-00598-8.
doi: 10.1186/s12711-020-00598-8 pmid: 33397289
[15] ELLEN E D, VISSCHER J, VAN ARENDONK J A M, BIJMA P. Survival of laying hens: Genetic parameters for direct and associative effects in three purebred layer lines. Poultry Science, 2008, 87(2): 233-239. doi: 10.3382/ps.2007-00374.
doi: 10.3382/ps.2007-00374 pmid: 18212365
[16] WALSH B, LYNCH M. Evolution and Selection of Quantitative Traits. Oxford: Oxford University Press, 2018. doi: 10.1093/oso/9780198830870.001.0001.
doi: 10.1093/oso/9780198830870.001.0001
[17] HEIDARITABAR M, BIJMA P, JANSS L, BORTOLUZZI C, NIELSEN H M, MADSEN P, ASK B, CHRISTENSEN O F. Models with indirect genetic effects depending on group sizes: A simulation study assessing the precision of the estimates of the dilution parameter. Genetics, Selection, Evolution: GSE, 2019, 51(1): 24. doi: 10.1186/s12711-019-0466-6.
doi: 10.1186/s12711-019-0466-6 pmid: 31146682
[18] VILLANUEVA R A M, CHEN Z J. ggplot2: Elegant Graphics for Data Analysis. 2nd ed. Measurement: Interdisciplinary Research and Perspectives, 2019, 17(3): 160-167. doi: 10.1080/15366367.2019.1565254.
doi: 10.1080/15366367.2019.1565254
[19] MEYER K. WOMBAT: A tool for mixed model analyses in quantitative genetics by restricted maximum likelihood (REML). Journal of Zhejiang University Science B, 2007, 8(11): 815-821. doi: 10.1631/jzus.2007.B0815.
doi: 10.1631/jzus.2007.B0815
[20] BIJMA P. The quantitative genetics of indirect genetic effects: A selective review of modelling issues. Heredity, 2014, 112 (1): 61-69. doi: 10.1038/hdy.2013.15.
doi: 10.1038/hdy.2013.15 pmid: 23512010
[21] ELLEN E D, RODENBURG T B, ALBERS G A A, BOLHUIS J E, CAMERLINK I, DUIJVESTEIJN N, KNOL E F, MUIR W M, PEETERS K, REIMERT I, SELL-KUBIAK E, VAN ARENDONK J A M, VISSCHER J, BIJMA P. The prospects of selection for social genetic effects to improve welfare and productivity in livestock. Frontiers in Genetics, 2014, 5: 377. doi: 10.3389/fgene.2014.00377.
doi: 10.3389/fgene.2014.00377 pmid: 25426136
[22] CHEN C Y, MISZTAL I, TSURUTA S, HERRING W O, HOLL J, CULBERTSON M. Influence of heritable social status on daily gain and feeding pattern in pigs. Journal of Animal Breeding and Genetics, 2010, 127(2): 107-112. doi: 10.1111/j.1439-0388.2009.00828.x.
doi: 10.1111/j.1439-0388.2009.00828.x pmid: 20433518
[23] BESTMAN M, RUIS M, HEIJMANS J, MIDDELKOOP K V. 马闯, 马海艳译. 蛋鸡的信号: 禽业养殖管理实用指南. 北京: 中国农业科学技术出版社, 2014.
BESTMAN M, RUIS M, HEIJMANS J, MIDDELKOOP K V. Layer Signals:A Practical Guide for Poultry-Oriented Management. Beijing: China Agricultural Science and Technology Press, 2014. (in Chinese)
[24] NIELSEN H M, ASK B, MADSEN P. Social genetic effects for growth in pigs differ between boars and gilts. Genetics, Selection, Evolution: GSE, 2018, 50(1): 4. doi: 10.1186/s12711-018-0375-0.
doi: 10.1186/s12711-018-0375-0 pmid: 29390956
[25] 仲伟鹏, 罗坤, 孟宪红, 陈宝龙, 隋娟, 孔杰, 曹宝祥, 邢群, 栾生. 限制投喂环境下中国对虾体重的间接遗传效应分析. 中国水产科学, 2018, 25(6): 1245-1251. doi: 10.3724/SP.J.1118.2018.18015.
doi: 10.3724/SP.J.1118.2018.18015
ZHONG W P, LUO K, MENG X H, CHEN B L, SUI J, KONG J, CAO B X, XING Q, LUAN S. An analysis of the indirect genetic effect on the body weight of Fenneropenaeus chinensis under restrictive feeding conditions. Journal of Fishery Sciences of China, 2018, 25(6): 1245-1251. doi: 10.3724/SP.J.1118.2018.18015. (in Chinese)
doi: 10.3724/SP.J.1118.2018.18015
[26] BRICHETTE I, REYERO M I, GARCı́A C. A genetic analysis of intraspecific competition for growth in mussel cultures. Aquaculture, 2001, 192(2/3/4): 155-169. doi: 10.1016/S0044-8486(00)00439-7.
doi: 10.1016/S0044-8486(00)00439-7
[27] ALEMU S W, BERG P, JANSS L, BIJMA P. Estimation of indirect genetic effects in group-housed mink (Neovison vison) should account for systematic interactions either due to kin or sex. Journal of Animal Breeding and Genetics, 2016, 133(1): 43-50. doi: 10.1111/jbg.12163.
doi: 10.1111/jbg.12163 pmid: 25900536
[28] NÄTT D, AGNVALL B, JENSEN P. Large sex differences in chicken behavior and brain gene expression coincide with few differences in promoter DNA-methylation. PLoS ONE, 2014, 9(4): e96376. doi: 10.1371/journal.pone.0096376.
doi: 10.1371/journal.pone.0096376
[29] MOROI S, NISHIMURA K, IMAI N, KUNISHIGE K, SATO S, GOTO T. Rapid behavioral assay using handling test provides breed and sex differences in tameness of chickens. Brain and Behavior, 2019, 9(10): e01394. doi: 10.1002/brb3.1394.
doi: 10.1002/brb3.1394
[30] CANARIO A, BIJMA P. Pig growth is affected by social genetic effects and social litter effects that depend on group size// Proceedings of the 9th World Congress on Genetic Applied to Livestock Production (WCGALP). 1-6 August 2010, Leipzig, Germany, 2010: 87-87.
[31] NIELSEN H, ASK B, CHRISTENSEN O, JANSS L, HEIDARITABAR M, MADSEN P. Social genetic effects for growth in Landrace pigs with varying group sizes//Proceedings of the 11th World Congress on Genetics Applied to Livestock Production. 2018: 11-16.
[32] POULSEN B G, ASK B, NIELSEN H M, OSTERSEN T, CHRISTENSEN O F. Prediction of genetic merit for growth rate in pigs using animal models with indirect genetic effects and genomic information. Genetics Selection Evolution, 2020, 52: 58. doi: 10.1186/s12711-020-00578-y.
doi: 10.1186/s12711-020-00578-y
[1] DONG MingMing,ZHAO FanFan,GE JianJun,ZHAO JunLiang,WANG Dan,XU Lei,ZHANG MengHua,ZHONG LiWei,HUANG XiXia,WANG YaChun. Heritability Estimation and Correlation Analysis of Longevity and Milk Yield of Holstein Cattle in Xinjiang Region [J]. Scientia Agricultura Sinica, 2022, 55(21): 4294-4303.
[2] LIU YouChun,LIU WeiSheng,WANG XingDong,YANG YanMin,WEI Xin,SUN Bin,ZHANG Duo,YANG YuChun,LIU Cheng,LI TianZhong. Screening and Inheritance of Fruit Storage-Related Traits Based on Reciprocal Cross of Southern×Northern High Bush Blueberry (Vaccinium Linn) [J]. Scientia Agricultura Sinica, 2020, 53(19): 4045-4056.
[3] DANG LiPing,ZHOU WenXin,LIU RuiFang,BAI Yun,WANG ZhePeng. Estimation of Genetic Parameters of Body Weight and Egg Number Traits of Lueyang Black-Boned Chicken [J]. Scientia Agricultura Sinica, 2020, 53(17): 3620-3628.
[4] GUO Jun,QU Liang,DOU TaoCun,WANG XingGuo,SHEN ManMan,HU YuPing,WANG KeHua. Using Random Regression Models to Estimate Genetic Parameters on Body Weights in Layers [J]. Scientia Agricultura Sinica, 2020, 53(11): 2297-2304.
[5] ZHAO Yong,ZHAO PeiFang,HU Xin,ZHAO Jun,ZAN FengGang,YAO Li,ZHAO LiPing,YANG Kun,QIN Wei,XIA HongMing,LIU JiaYong. Evaluation of 317 Sugarcane Germplasm Based on Agronomic Traits Rating Data [J]. Scientia Agricultura Sinica, 2019, 52(4): 602-615.
[6] PAN Chen-1, HU Yan-2, BAO Man-Zhu-1, AI Ye-1, HE Yan-Hong-1. Analysis of Genetic Effects of the Cross Combinations of Tagetes patula [J]. Scientia Agricultura Sinica, 2014, 47(12): 2395-2404.
[7] HU Yan, XU Wen-Juan, LIU Hong-Xiang, SONG Wei-Tao, SONG Chi, TAO Zhi-Yun, DAN Yan-Ju, LI Hui-Fang. The Profiles of Related Genes mRNA Expression in Duck Hypothalamus-Pituitary Growth Axis During Early Development [J]. Scientia Agricultura Sinica, 2013, 46(17): 3712-3720.
[8] LIU Jun-Feng, WU Chen, KONG Xiang-Feng, YANG Huan-Sheng, GENG Mei-Mei, YIN Yu-Long, HE Ruo-Gang. Analysis of Chemical Composition in Amniotic Fluid and Allantonic Fluid of Fetus with Different Body Weights in Huanjiang Mini-Pigs [J]. Scientia Agricultura Sinica, 2011, 44(19): 4066-4071.
[9] LIU Xiao-Gang, LI Da-Biao, HOU Xian-Zhi, KAO Gui-Lan, WANG Hai-Rong, YANG Jin-Li, ZHANG Chong-Zhi, XIA Wei. Effect of Undernutrition and Compensation on the Growth of Small Intestinal Mucosa in Lambs [J]. Scientia Agricultura Sinica, 2011, 44(17): 3613-3621.
[10] GAN Zhi-cai,SHANG Lun-xue,LIU Yong,YU Yong-xiong
. Correlation Analysis of Agronomic Characters and Heritability of Saponins Content in Medicago sativa L.
[J]. Scientia Agricultura Sinica, 2010, 43(2): 259-265 .
[11] ZHANG Min-zhao,ZONG Yu,WANG Xue-ying,CAI Xue,ZHANG Zhi-yong
. Study on the Death-Feigning Behavior of the Harmful Mollusk, Cathaica fasciola (Draparnaud 1801)
[J]. Scientia Agricultura Sinica, 2009, 42(11): 3914-3921 .
[12]

.

Study on the Potential of Duck Hepatitis Virus (DHV-Ⅰ) to Stimulate the Body Weight Gain and Body Length Gain and the Effects of Silymarin on Them in Younger Duck

[J]. Scientia Agricultura Sinica, 2009, 42(1): 304-311 .
[13] . The Studies on the Genetic Difference of Nitrogen Utilization Efficiency in Winter Wheat Varieties [J]. Scientia Agricultura Sinica, 2007, 40(3): 472-477 .
[14] . Comparative study on Analysis and Fitting of Tibetan Chicken Growth Curve [J]. Scientia Agricultura Sinica, 2006, 39(10): 2159- .
[15] ,,,,,,. The Correlation Analysis of Microsatellite DNA Marks for Some Production Performances in Meat Sheep [J]. Scientia Agricultura Sinica, 2006, 39(10): 2095-2100 .
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!