- Open Access
Genetic properties of feed efficiency parameters in meat-type chickens
© Aggrey et al; licensee BioMed Central Ltd. 2010
- Received: 22 March 2010
- Accepted: 29 June 2010
- Published: 29 June 2010
Feed cost constitutes about 70% of the cost of raising broilers, but the efficiency of feed utilization has not kept up the growth potential of today's broilers. Improvement in feed efficiency would reduce the amount of feed required for growth, the production cost and the amount of nitrogenous waste. We studied residual feed intake (RFI) and feed conversion ratio (FCR) over two age periods to delineate their genetic inter-relationships.
We used an animal model combined with Gibb sampling to estimate genetic parameters in a pedigreed random mating broiler control population.
Heritability of RFI and FCR was 0.42-0.45. Thus selection on RFI was expected to improve feed efficiency and subsequently reduce feed intake (FI). Whereas the genetic correlation between RFI and body weight gain (BWG) at days 28-35 was moderately positive, it was negligible at days 35-42. Therefore, the timing of selection for RFI will influence the expected response. Selection for improved RFI at days 28-35 will reduce FI, but also increase growth rate. However, selection for improved RFI at days 35-42 will reduce FI without any significant change in growth rate. The nature of the pleiotropic relationship between RFI and FCR may be dependent on age, and consequently the molecular factors that govern RFI and FCR may also depend on stage of development, or on the nature of resource allocation of FI above maintenance directed towards protein accretion and fat deposition. The insignificant genetic correlation between RFI and BWG at days 35-42 demonstrates the independence of RFI on the level of production, thereby making it possible to study the molecular, physiological and nutrient digestibility mechanisms underlying RFI without the confounding effects of growth. The heritability estimate of FCR was 0.49 and 0.41 for days 28-35 and days 35-42, respectively.
Selection for FCR will improve efficiency of feed utilization but because of the genetic dependence of FCR and its components, selection based on FCR will reduce FI and increase growth rate. However, the correlated responses in both FI and BWG cannot be predicted accurately because of the inherent problem of FCR being a ratio trait.
- Genetic Correlation
- Feed Intake
- Body Weight Gain
- Heritability Estimate
- Feed Efficiency
Feed cost constitutes about 70% of the total cost of live production, but the efficiency of feed utilization has not kept up the growth potential of today's broilers. Improvement in feed efficiency will reduce the amount of feed required for growth, the production cost and the amount of nitrogenous waste . Efficiency in feed intake (FI) is more difficult to quantify than growth, and as a result different measures of feed efficiency have been developed, each of which reflects different mathematical and biological aspects of efficiency. In broiler chickens, feed efficiency is usually expressed as the amount of FI per body weight gain (BWG) referred to as feed conversion ratio (FCR). However, Chambers and Lin  have shown that a large proportion of the variation in FI and age constant FCR among broilers are due to body weight (BW) and efficiency of nutrient utilization. Also, variability in maintenance requirement, a major contributing factor to FI, is not accounted for in FCR. Statistically, FCR is a ratio trait and is not normally distributed, with no real mean and variance, and according to Atchley et al.  the non-normality of a ratio trait is increased when the magnitude of coefficient of variation of the denominator is increased. Pearson  has derived formulae to approximate the variance of a ratio and phenotypic correlation between two ratios but the lack of genetic independence of FCR from FI and BWG makes it difficult to improve without direct effect on growth.
Koch et al.  have introduced the concept of residual feed intake (RFI) that accounts for both maintenance requirements and growth. Residual FI represents the amount of FI not accounted for by maintenance BW and BWG. Selection on RFI has been proposed to improve feed efficiency because of its phenotypic independence of maintenance BW and BWG. The phenotypic independence of RFI from its estimating components is the direct result of the distributing properties of the regression procedure .
Kennedy et al.  have shown that genetic variability in RFI is not independent of metabolic BW and BWG. Luiting  have demonstrated that feeding behavior, nutrient digestibility, maintenance requirements, and energy homeostasis and partitioning affect RFI in laying hens. Aggrey et al.  have demonstrated that the proportion of protein energy retained is associated with feed efficiency. Jorgensen et al.  have also shown that variability in apparent metabolizable energy requirements affects feed efficiency in meat-type birds. Therefore, RFI may reflect more the variability in maintenance BW than differences in BWG. Genetic variability in RFI has been investigated in beef cattle [5, 11–14] and pigs [15–17]. To date, there are only a few studies on RFI in broilers with heritabilities ranging from 0.21-0.49 [19, 20]. Estimates on genetic correlation between RFI and BWG have ranged from almost zero [11, 20] to positive values . These differences may be due to sample size or statistical methods of estimation. The true genetic relationship between RFI and its components or lack thereof would allow for an accurate predictive correlated response to selecting for RFI.
The aim of the current study was to estimate genetic parameters pertaining to RFI and FCR of a growing broiler control population at two time periods and to ascertain the genetic relationships among the parameters that contribute to feed efficiency.
Population and animal husbandry
where a is the intercept and b1 and b2 are of partial regression coefficients of FI on BW0.75 and BWG, respectively. Residual feed intake values were generated using regression procedure of SAS . Experimental protocols were in accordance with the procedures of the University of Georgia institutional animal care and use committee.
Data editing and analytical algorithm
where Yijk is the record of the kth chicken from the ith hatch and jth sex; Hatchi = fixed effect of hatch (i = 1,...,8); Sexj = fixed effect of sex (j = 1, 2-male/female); ak = random direct additive genetic effect of individual k, and eijk = random residual error. Analyses were performed using the GIBBS2F90 program based on a Markov Chain Monte Carlo approach. We assumed flat priors for systematic and random effects. The marginal posterior distribution of the trait of interest was obtained using Gibbs sampling. A single chain of 250,000-cycles length was generated. A burn-in period of 150,000 iterations was used as well as a 10-cycle lag to reduce autocorrelation among samples. A total of 10,000 samples were kept for post Gibbs analysis using the POSTGIBBSF90 program (with graph)  to compute the posterior means (point estimate for traits), and the 95% highest posterior regions (HPD95%) of heritability and genetic correlations of the traits. Convergence was ascertained by employing the algorithm of Raftery and Lewis . Bivariate analyses were performed to compute genetic correlations between combinations of traits.
Means (SD) and posterior means of heritability (95% highest posterior density region intervals) of feed efficiency parameters in meat-type chickens
Posterior means of genetic correlations (ra) (95% highest regions intervals) of residual feed intake (RFI) and feed conversion ratio (FCR) parameters in meat-type chickens
rawith RFI, 28-35 d
rawith RFI, 35-42 d
rawith FCR, 28-35d
rawith FCR, 35-42 d
-0.13 (-0.13- -0.12)
-0.05 (-0.06- -0.04)
-0.14 (-0.14- -0.14)
The heritability estimates for both FCR and RFI for both periods were higher than the estimate of Van Bebber and Mercer , however, they were within the limits of published data in beef cattle and pigs [11, 12, 16, 17]. Based on the genetic parameter estimates, selection for low RFI will improve feed efficiency with an expected correlated response in reduced FI. This will also favor birds with lower maintenance energy requirements based on the genetic correlation between RFI and MBW. However, there is a genetic dependency between RFI28-35 and BWG28-35. The positive genetic correlation between RFI28-35 and BWG28-35 suggests that fast growing chickens have greater appetite and consume more feed than needed for growth. This dependency does not exist at days 35-42. Therefore, selection at days 35-42 may be more attractive than at days 28-35. Feed efficiency is a compound trait affected by both feed- and growth-related factors, and these factors vary with age. Therefore the genetic relationships among feed efficiency parameters are also expected to vary with age.
In the current study, the genetic interrelationships among the feed efficiency parameters were different at days 28-35 and days 35-42. The lack of genetic correlation between RFI35-42 and BWG35-42 was similar to that reported in cattle and pigs [11, 12, 14, 16, 23]. However, Cai et al.  and Hogue et al.  have also reported a positive genetic correlation between RFI and average daily gain (ADG) in pigs selected for low RFI, which is similar to the genetic correlation between RFI28-35 and BWG28-35 obtained in this study. The change in genetic correlation between RFI and BWG with age could be due to differences in body composition during the two periods when RFI was determined. Jensen et al.  have also obtained genetic correlations between RFI and ADG of 0.32 and -0.24 at two different ages. In pigs, RFI is negatively correlated to dressing percentage and positively correlated with backfat thickness . The body composition of broiler chickens at days 28-35 is different from that of at days 35-42, therefore it is possible that the internal allocation of resources above maintenance into protein accretion and fat deposition among others could contribute towards the different inter-relationships between factors that affect RFI at these two time periods.
Feed efficiency measured over a long period of time is possibly an aggregate efficiency over different developmental processes which can vary from species to species as well as the management practices under which animals are raised. In meat-type birds, feather development, feeding behavior, skeletal growth, tissue accretion and fat deposition are different developmental processes all of which or combinations of which can affect heritability of RFI and also the genetic correlations among RFI parameters.
In the literature on broilers while data on RFI is scant, information on FCR is abundant possibly due to its ease of computation and to the producers' direct association of cost and profits to quantities of feed. The heritability estimate of FCR was 0.49 and 0.41 for days 28-35 and days 35-42, respectively. FCR is a ratio trait that is not normally distributed  and is subject to skewness and kurtosis as a result of the changes in BWG (denominator) coefficient of variation and subsequently affect SD, covariances and correlations . Selection for FCR will improve efficiency of feed utilization but because of the genetic dependence of FCR and its components, selection for reduced FCR will reduce FI and increase growth rate. Increases in both FI and BWG cannot be predicted accurately because of the inherent problem of FCR being a ratio trait. Lin  has developed a linear index based on the components of FCR. Gunsett , Famula  and Campo and Rodriguez  have shown that the linear index is more efficient than direct selection on the ratio. However, Gunsett  has also pointed out that the advantage of the linear index decreases as the correlation between the two component traits increases or as the heritability of both components moves towards equality.
The genetic correlation between RFI and FCR was 0.31 at days 28-35 compared to 0.84 at days 35-42. This suggests that the nature of the pleiotropic relationship between RFI and FCR may be dependent on age, and consequently the molecular, physiological and nutritional factors that govern RFI and FCR may also depend on time of development, or on the nature of resource allocation of FI above maintenance designated for protein accretion and fat deposition. The lack of genetic correlation between RFI and BWG at days 35-42 provides the independence of RFI on the level of production, thereby making it possible to study the molecular, physiological and nutrient digestibility mechanisms underlying RFI without the confounding effects of growth.
Estimating genetic properties of RFI provides the genetic parameters that are needed in combination with economic values in the selection criteria in order to ascertain the economic benefits of selecting for feed efficiency.
This work was supported by USDA NRI grant 2009-35205-05208 and Georgia Food Industry Partnership grant 10.26KR696-110. We appreciate the support of Poultry Research Center of University of Georgia, and the numerous volunteers who assisted in the data collection. We also thank Ignacy Misztal and Shogo Tsuruta for the use of their Fortran programs.
- Zhang W, Aggrey SE: Genetic variability in feed utilization efficiency of meat-type birds. World's Poult Sci J. 2003, 59: 328-339. 10.1079/WPS20030020.View ArticleGoogle Scholar
- Chambers JR, Lin CY: Age-constant versus weight-constant feed consumption and efficiency in broiler chickens. Poult Sci. 1988, 67: 565-676.View ArticlePubMedGoogle Scholar
- Atchley WR, Gaskins CT, Anderson D: Statistical properties of ratios. I. Empirical results. Syst Zool. 1976, 25: 137-148. 10.2307/2412740.View ArticleGoogle Scholar
- Pearson K: Mathematical contributions to the theory of evolution - on a form of spurious correlation which may arise when indices are used in the measurements of organs. Proc Royal Soc Lond. 1897, 60: 489-498. 10.1098/rspl.1896.0076.View ArticleGoogle Scholar
- Koch RM, Swiger LA, Chambers D, Gregory KE: Efficiency of feed use in beef cattle. J Anim Sci. 1963, 22: 486-494.Google Scholar
- Netter J, Wasserman W, Kutner MH: Applied linear statistical models. 2004, New York: McGraw-Hill, 5Google Scholar
- Kennedy BW, van de Werf JHJ, Meuwissen THE: Genetics and statistical properties of residual feed intake. J Anim Sci. 1993, 71: 3239-3250.PubMedGoogle Scholar
- Luiting P: Genetic variation in energy partitioning of laying hens: cause of genetic variation in residual feed consumption. World's Poult Sci J. 1990, 46: 133-152. 10.1079/WPS19900017.View ArticleGoogle Scholar
- Aggrey SE, Sanglikar AP, Karnuah AB, McMurtry JP: Molecular basis of meat-type birds. Proceedings of the 23rd World's Poultry Congress: 30 June-4 July 2008; Brisbane. 2008, CD Rom. Wpc08Final00035;Google Scholar
- Jorgensen H, Sorensen P, Egum BO: Protein and energy metabolism in broiler chickens selected for either body weight gain or food efficiency. Brit Poult Sci. 1990, 31: 517-524. 10.1080/00071669008417283.View ArticleGoogle Scholar
- Arthur PF, Archer JA, Johnston DJ, Herd RM, Richardson EC, Parnell PF: Genetic and phenotypic variance and covariance components for feed intake, feed efficiency and other postweaning traits in Angus cattle. J Anim Sci. 2001, 79: 2805-2811.PubMedGoogle Scholar
- Arthur PF, Renand G, Krauss D: Genetic and phenotypic relationships among different measures of growth and efficiency in young Charolais bulls. Livest Prod Sci. 2001, 68: 131-139. 10.1016/S0301-6226(00)00243-8.View ArticleGoogle Scholar
- Schenkel FS, Miller SP, Wilton JW: Genetic parameters and breed differences for feed efficiency, growth and body composition traits of young beef bulls. Can J Anim Sci. 2004, 84: 177-184.View ArticleGoogle Scholar
- Van der Westhuizen RR, van der Westhuizen J, Schoeman SJ: Genetic relationship between feed efficiency and profitability traits in beef cattle. South African J Anim Sci. 2004, 34: 50-52.View ArticleGoogle Scholar
- Mrode RA, Kennedy BW: Genetic variation in measures of food efficiency in pigs and their genetic relationships with growth rate and backfat. Anim Prod. 1993, 56: 225-232. 10.1017/S0003356100021309.View ArticleGoogle Scholar
- Gilbert H, Bidanel JP, Gruand J, Caritez JC, 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. J Anim Sci. 2007, 85: 3182-3188. 10.2527/jas.2006-590.View ArticlePubMedGoogle Scholar
- Cai W, Casey DS, Dekkers JCM: Selection response and genetic parameters for residual feed intake in Yorkshire swine. J Anim Sci. 2008, 86: 287-298. 10.2527/jas.2007-0396.View ArticlePubMedGoogle Scholar
- SAS Institute: SAS User's Guide. 1998, Cary: SAS Institute, Version 8.12Google Scholar
- Van Bebber J, Mercer JT: Selection for efficiency of broilers. A comparison of properties of residual feed intake and feed conversion ratio. Proceedings of the 5th World Congress On Genetics Applied to Livestock Production: 7-12 August 1994; Guelph. 1994, 53-56.Google Scholar
- Pakdel A, van Arendonk JAM, Vereijken AL, Bovenhuis H: Genetic parameters of ascites-related traits in broilers: correlations with feed efficiency and carcass traits. Br Poult Sci. 2005, 46: 43-53. 10.1080/00071660400023805.View ArticlePubMedGoogle Scholar
- Misztal I, Tsuruta S, Strabel T, Auvray B, Druet T, Lee DH: BLUP90 and related programs (BGF90). Proceedings of the 7th World Congress on Genetics Applied to Livestock Production: 19-23 August 2002; Tours. 2002, 743-744.Google Scholar
- Raftery AE, Lewis S: How many iterations in the Gibbs sampler?. Bayesian Statistics. Edited by: Bernando JM, Berger JO, Dawid AP, Smith AFM. 1992, New York: Oxford Univ Press, 4: 763-773.Google Scholar
- Herd RM, Bishop SC: Genetic variation in residual feed intake and its association with other production traits in British Hereford cattle. Livest Prod Sci. 2000, 63: 111-119. 10.1016/S0301-6226(99)00122-0.View ArticleGoogle Scholar
- Hoque MA, Kadowaki H, Shibata T, Oikawa T, Suzuki K: Genetic parameters for measures of residual feed intake and growth traits in seven generations of Duroc pigs. Livest Prod Sci. 2009, 121: 45-49. 10.1016/j.livsci.2008.05.016.View ArticleGoogle Scholar
- Jensen J, Mao IL, Anderson BB, Madsen P: Phenotypic and genetic relationships between residual energy intake and growth, feed intake, and carcass trials of young bulls. J Anim Sci. 1992, 70: 386-395.PubMedGoogle Scholar
- Fieller EC: The distribution of the index in a normal bivariate population. Biometrika. 1932, 24: 428-440.View ArticleGoogle Scholar
- Lin CY: Relative efficiency of selection methods for improvement of feed efficiency. J Dairy Sci. 1980, 63: 491-494. 10.3168/jds.S0022-0302(80)82960-2.View ArticleGoogle Scholar
- Gunsett FC: Linear index selection to improve traits defined as ratios. J Anim Sci. 1984, 59: 1185-1193.Google Scholar
- Famula TR: The equivalence of two linear methods for the improvement of traits expressed as ratios. Theor Appl Genet. 1990, 79: 853-856.PubMedGoogle Scholar
- Campo JL, Rodriguez M: Relative efficiency of selection methods to improve a ratio of two traits in Tribolium. Theor Appl Genet. 1990, 80: 343-348. 10.1007/BF00210070.View ArticlePubMedGoogle Scholar
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.