Genetic Feed Efficiency Traits in Dairy Cattle: A Systematic Meta-Analysis
محورهای موضوعی :M. Hajipour 1 , A. Ehsani 2 , M. Ghaderi-Zefrehei 3 , F. Rafeie 4 , H. Motahari Nezhad 5 , E.N. Esfahani 6
1 - Department of Animal Science, Faculty of Agriculture, Tarbiat Modares University, Tehran, Iran
2 - Department of Animal Science, Faculty of Agriculture, Tarbiat Modares University, Tehran, Iran
3 - Department of Animal Science, Faculty of Agriculture, Yasouj University, Yasouj, Iran
4 - Department of Agricultural Biotechnology, Faculty of Agricultural Sciences, University of Guilan, Rasht, Iran
5 - Department of Knowledge and Information Science, University of Isfahan, Isfahan, Iran
6 - Department of Agriculture, Payam Noor University, Tehran, Iran
کلید واژه: dairy cattle, feed efficiency, heritability, meta-analysis,
چکیده مقاله :
Efficient feed utilization is crucial for sustainable dairy cattle production. This systematic review and meta-analysis pooled heritability estimates for feed intake (FI), feed saved (FS), dry matter intake (DMI), feed conversion ratio (FCR), and residual feed intake (RFI) in dairy cattle and determined the genetic correla-tions between these traits and other production efficiency traits. Data from 68 studies published between 2000 and 2022 were analyzed using random effects meta-analysis and restricted maximum likelihood esti-mation. The pooled heritability estimates were as follows: 0.32 (95% CI: 0.29-0.36, I²=98%, P<0.0001) for DMI, 0.29 (95% CI: 0.10-0.46, I²=99%, P<0.0001) for FS, 0.17 (95% CI: 0.15-0.19, I²=89.3%, P<0.0001) for RFI, and 0.11 (95% CI: 0.08-0.13, I²=0%, P<0.0001) for FCR. Strong positive genetic correlations were observed between DMI and net energy of lactation intake (NELI) (0.90), RFI and average daily feed intake (ADFI) (0.80), and FCR and RFI (0.68). Conversely, negative genetic correlations were found between FCR and average feeding rate (AFR) (-0.38), FCR and average daily gain (ADG) (-0.34), FCR and longis-simus muscle area (LMA) (-0.28), and FCR and hip height (HIPHT) (-0.14). These findings suggest poten-tial indicators for selection indices and genetic improvement strategies, offering valuable insights for en-hancing breeding programs and optimizing feed utilization in dairy cattle. Ultimately, this contributes to more sustainable livestock production and improved food security.
Efficient feed utilization is crucial for sustainable dairy cattle production. This systematic review and meta-analysis pooled heritability estimates for feed intake (FI), feed saved (FS), dry matter intake (DMI), feed conversion ratio (FCR), and residual feed intake (RFI) in dairy cattle and determined the genetic correla-tions between these traits and other production efficiency traits. Data from 68 studies published between 2000 and 2022 were analyzed using random effects meta-analysis and restricted maximum likelihood esti-mation. The pooled heritability estimates were as follows: 0.32 (95% CI: 0.29-0.36, I²=98%, P<0.0001) for DMI, 0.29 (95% CI: 0.10-0.46, I²=99%, P<0.0001) for FS, 0.17 (95% CI: 0.15-0.19, I²=89.3%, P<0.0001) for RFI, and 0.11 (95% CI: 0.08-0.13, I²=0%, P<0.0001) for FCR. Strong positive genetic correlations were observed between DMI and net energy of lactation intake (NELI) (0.90), RFI and average daily feed intake (ADFI) (0.80), and FCR and RFI (0.68). Conversely, negative genetic correlations were found between FCR and average feeding rate (AFR) (-0.38), FCR and average daily gain (ADG) (-0.34), FCR and longis-simus muscle area (LMA) (-0.28), and FCR and hip height (HIPHT) (-0.14). These findings suggest poten-tial indicators for selection indices and genetic improvement strategies, offering valuable insights for en-hancing breeding programs and optimizing feed utilization in dairy cattle. Ultimately, this contributes to more sustainable livestock production and improved food security.
Alqaisi O., Moraes L.E., Ndambi O.A. and Williams R.B. (2019). Optimal dairy feed input selection under alternative feeds availability and relative prices. Inf. Proc. Agric. 6, 438-453.
Axelsson H.H., Fikse W.F., Kargo M., Sørensen A.C., Johansson K. and Rydhmer L. (2013). Genomic selection using indicator traits to reduce the environmental impact of milk production. J. Dairy Sci. 96, 5306-5314.
Ayalew W., Aliy M. and Negussie E. (2017). Estimation of ge-netic parameters of the productive and reproductive traits in ethiopian Holstein using multi-trait models. Asian-Australian J. Anim. Sci. 30, 1550-1556.
Bastin C., Vandenplas J. and Gengler N. (2014). Improving dairy cow fertility using milk based indicator traits. Pp. 155 in Proc. 10th World Congr. Genet. Appl. Livest. Prod., Vancouver, Canada.
Behdani E., Ghaderi-Zefrehei M., Rafeie F., Bakhtiarizadeh M.R., Roshanfeker H. and Fayazi J. (2019). RNA-Seq bayesian net-work exploration of immune system in bovine. Iranian J. Bio-techol. 17, 1-17.
Benfica L.F., Sakamoto L.S., Magalhães A.F.B., de Oliveira M.H.V., de Albuquerque L.G., Cavalheiro R., Branco R.H., Cyrillo J.N.S.G. and Mercadante M.E.Z. (2020). Genetic asso-ciation among feeding behavior, feed efficiency, and growth traits in growing indicine cattle. J. Anim. Sci. 98, 1-9.
Berry D.P., Coffey M.P., Pryce J.E., de Haas Y., Løvendahl P., Krattenmacher N., Crowley J.J., Wang Z., Spurlock D., Weigel K., Macdonald K. and Veerkamp R.F. (2014). Interna-tional genetic evaluations for feed intake in dairy cattle through the collation of data from multiple sources. J. Dairy Sci. 97, 3894-3905.
Brito L., Schenkel F., Oliveira H.R., Cánovas Á. and Miglior F. (2018). Meta-analysis of heritability estimates for methane emission indicator traits in cattle and sheep. Pp. 11-16 in Proc. World Congr. Genet. Appl. Livest. Prod., Auckland, New Zea-land.
Brito L.F., Oliveira H.R., Houlahan K., Fonseca P.A.S., Lam S., Butty A.M., Seymour D.J., Vargas G., Chud T.C.S., Silva F.F., Baes C.F., Cánovas A., Miglior F. and Schenkel F.S. (2020). Genetic mechanisms underlying feed utilization and implementation of genomic selection for improved feed effi-ciency in dairy cattle. Canadian J. Anim. Sci. 100, 587-604.
Brunes L.C., Baldi F., Lopes F.B., Lôbo R.B., Espigolan R., Costa M.F.O., Stafuzza N.B. and Magnabosco C.U. (2020). Weighted single‐step genome‐wide association study and pathway analyses for feed efficiency traits in nellore cattle. J. Anim. Breed. Genet. 138, 23-44.
Byskov M.V., Fogh A. and Løvendahl P. (2017). Genetic parame-ters of rumination time and feed efficiency traits in primipa-rous Holstein cows under research and commercial conditions. J. Dairy Sci. 100, 9635-9642.
Chandler J., Cumpston M., Li T., Page M.J. and Welch V.J.H.W. (2019). Cochrane Handbook for Systematic Reviews of Inter-ventions. Wiley, Hoboken.
Cheng T., Einarsdottir E., Kere J. and Gerdhem P. (2022). Idio-pathic scoliosis: a systematic review and meta-analysis of heritability. EFORT Open Rev. 7, 414-421.
Coleman J., Berry D.P., Pierce K.M., Brennan A. and Horan B. (2010). Dry matter intake and feed efficiency profiles of 3 genotypes of Holstein-Friesian within pasture-based systems of milk production. J. Dairy Sci. 93, 4318-4331.
Connor E.E. (2015). Improving feed efficiency in dairy produc-tion: challenges and possibilities. Animal. 9, 395-408.
Connor E.E., Hutchison J.L., Norman H.D., Olson K.M., Van Tassell C.P., Leith J.M. and Baldwin R.L. (2013). Use of re-sidual feed intake in Holsteins during early lactation shows potential to improve feed efficiency through genetic selection. J. Anim. Sci. 91, 3978-3988.
Davis S.R., Macdonald K.A., Waghorn G.C. and Spelman R.J. (2014). Residual feed intake of lactating Holstein-Friesian cows predicted from high-density genotypes and phenotyping of growing heifers. J. Dairy Sci. 97, 1436-1445.
Davoodi P. and Ehsani A.R. (2018). Weighted and un-weighted estimation of economic trait’ heritability in native Iranian chickens by meta-analysis method. Pp. 1-7 in Proc. 8th Iranian Anim. Sci. Congr., University of Kurdistan, Sanandaj, Iran
de Ondarza M.B. and Tricarico J.M. (2017). Review: Advantages and limitations of dairy efficiency measures and the effects of nutrition and feeding management interventions. Prof. Anim. Sci. 33, 393-400.
Diaz I.D.P.S., Crews D.H. and Enns R.M. (2014). Cluster and meta‐analyses of genetic parameters for feed intake traits in growing beef cattle. J. Anim. Breed. Genet. 131, 217-226.
Ferreira Júnior R.J., Bonilha S.F.M., Monteiro F.M., Cyrillo J.N.S.G., Branco R.H., Silva J.A.I.V. and Mercadante M.E.Z. (2018). Evidence of negative relationship between female fer-tility and feed efficiency in nellore cattle. J. Anim. Sci. 96, 4035-4044.
Fisher Z. and Tipton E. (2015). Robumeta: An R-package for robust variance estimation in meta-analysis. arXiv. 1503.02220. https://doi.org/10.48550/arXiv.1503.02220.
Freetly H.C., Kuehn L.A., Thallman R.M. and Snelling W.M. (2020). Heritability and genetic correlations of feed intake, body weight gain, residual gain, and residual feed intake of beef cattle as heifers and cows. J. Anim. Sci. 98, 394-406.
Ghavi Hossein-Zadeh N. (2021). A meta-analysis of heritability estimates for milk fatty acids and their genetic relationship with milk production traits in dairy cows using a random-effects model. Livest. Sci. 244, 104388-104397.
Golshani Jourshari M., Shadparvar A., Ghavi Hossein-Zadeh N., Rafeie F., Banabazi M.H. and Johansson A.M. (2023). Ge-nome-wide association study on abdomen depth, head width, hip width, and withers height in native cattle of Guilan (Bos indicus). PLoS One. 18, e0289612.
Harder I., Stamer E., Junge W. and Thaller G. (2020). Estimation of genetic parameters and breeding values for feed intake and energy balance using pedigree relationships or single-step ge-nomic evaluation in Holstein-Friesian cows. J. Dairy Sci. 103,
2498-2513.
Hardie L.C., VandeHaar M.J., Tempelman R.J., Weigel K.A., Armentano L.E., Wiggans G.R., Veerkamp R.F., de Haas Y., Coffey M.P., Connor E.E., Hanigan M.D., Staples Z., Wang Z., Dekkers J.C.M. and Spurlock D.M. (2017). The genetic and biological basis of feed efficiency in mid-lactation Hol-stein dairy cows. J. Dairy Sci. 100, 9061-9075.
Heida M., Schopen G.C.B., te Pas M.F.W., Gredler-Grandl B. and Veerkamp R.F. (2021). Breeding goal traits accounting for feed intake capacity and roughage or concentrate intake sepa-rately. J. Dairy Sci. 104, 8966-8982.
Herd R.M., Archer J.A. and Arthur P.F. (2003). Reducing the cost of beef production through genetic improvement in residual feed intake: opportunity and challenges to application. J. Anim. Sci. 81, 9-17.
Herd R.M. and Arthur P.F. (2009). Physiological basis for residual feed intake. J. Anim. Sci. 87, 64-71.
Higgins J.P., Thompson S.G., Deeks J.J. and Altman D.G. (2003). Measuring inconsistency in meta-analyses. BMJ. 327, 557-560.
Higgins J.P.T. and Thompson S.G. (2002). Quantifying heteroge-neity in a meta‐analysis. Stat. Med. 21, 1539-1558.
Houlahan K., Schenkel F.S., Hailemariam D., Lassen J., Kargo M., Cole J.B., Connor E.E., Wegmann S., Junior O., Miglior F., Fleming A., Chud T.C.S. and Baes C.F. (2021). Effects of incorporating dry matter intake and residual feed intake into a selection index for dairy cattle using deterministic modeling. Animals. 11, 1157-1168.
Hu Z.L., Park C.A. and Reecy J.M. (2022). Bringing the animal qtldb and corrdb into the future: meeting new challenges and providing updated services. Nucleic Acid Res. 50, 956-961.
Islam M.S., Jensen J., Løvendahl P., Karlskov-Mortensen P. and Shirali M. (2020). Bayesian estimation of genetic variance and response to selection on linear or ratio traits of feed efficiency in dairy cattle. J. Dairy Sci. 103, 9150-9166.
Jiang W., Mooney M.H. and Shirali M. (2024). Unveiling the genetic landscape of feed efficiency in Holstein dairy cows: Insights into heritability, genetic markers, and pathways via meta-analysis. J. Anim. Sci. 102, 40-49.
Khanal P., Gaddis K.L.P., Vandehaar M.J., Weigel K.L., White H.M., Peñagaricano F., Koltes J.E., Santos J.E.P., Baldwin R.L., Burchard J.F., Dürr J.W. and Tempelman R.J. (2022). Multiple-trait random regression modeling of feed efficiency in us Holsteins. J. Dairy Sci. 105, 5954-5971.
Li B., Berglund B., Fikse W.F., Lassen J., Lidauer M.H., Mänty-saari P. and Løvendahl P. (2017). Neglect of lactation stage leads to naive assessment of residual feed intake in dairy cat-tle. J. Dairy Sci. 100, 9076-9084.
Li B., Fikse W.F., Løvendahl P., Lassen J., Lidauer M.H., Mänty-saari P. and Berglund B. (2018). Genetic heterogeneity of feed intake, energy-corrected milk, and body weight across lacta-tion in primiparous Holstein, nordic red, and jersey cows. J. Dairy Sci. 101, 10011-10021.
Li B., VanRaden P.M., Guduk E., O’Connell J.R., Null D.J., Con-nor E.E., VandeHaar M.J., Tempelman R.J., Weigel K.L. and Cole J.B. (2020). Genomic prediction of residual feed intake in Us Holstein dairy cattle. J. Dairy Sci. 103, 2477-2486.
Lu Y., Vandehaar M.J., Spurlock D.M., Weigel K.A., Armentano L.E., Connor E.E., Coffey M., Veerkamp R.F., de Haas Y., Staples C.R., Wang Z., Hanigan M.D. and Tempelman R.J. (2018). Genome-wide association analyses based on a multi-ple-trait approach for modeling feed efficiency. J. Dairy Sci. 101, 3140-3154.
Manafiazar G., Goonewardene L., Miglior F., Crews D.H., Basarab J.A., Okine E. and Wang Z. (2016). Genetic and phe-notypic correlations among feed efficiency, production and se-lected conformation traits in dairy cows. Animal. 10, 381-389.
Manzanilla Pech C.I.V., Veerkamp R.F., Calus M.P.L., Zom R., van Knegsel A., Pryce J.E. and de Haas Y. (2014). Genetic pa-rameters across lactation for feed intake, fat- and protein-corrected milk, and liveweight in first-parity Holstein cattle. J. Dairy Sci. 97, 5851-5862.
Martin P., Ducrocq V., Gordo D.G.M. and Friggens N.C. (2021). A new method to estimate residual feed intake in dairy cattle using time series data. Animal. 15, 100101-100111.
Mucha S., Tortereau F., Doeschl-Wilson A., Rupp R. and Coning-ton J. (2022). Animal board invited review: Meta-analysis of genetic parameters for resilience and efficiency traits in goats and sheep. Animal. 16, 100456-100466.
Mulder H.A., Veerkamp R.F., Ducro B.J., van Arendonk J.A.M. and Bijma P. (2006). Optimization of dairy cattle breeding programs for different environments with genotype by envi-ronment interaction. J. Dairy Sci. 89, 1740-1752.
Oliveira H., Schenkel F., Richardson C.M.R., Miglior F. and Brito L. (2019). PSVIII-19 meta-analysis of genetic parameter esti-mates for feed efficiency traits in dairy cattle. J. Anim. Sci. 97, 271-272.
Oliveira Junior G.A., Schenkel F.S., Alcantara L., Houlahan K., Lynch C. and Baes C.F. (2021). Estimated genetic parameters for all genetically evaluated traits in Canadian Holsteins. J. Dairy Sci. 104, 9002-9015.
Omer E.A.M., Hinrichs D., Addo S. and Roessler R. (2022). De-velopment of a breeding program for improving the milk yield performance of butana cattle under smallholder production conditions using a stochastic simulation approach. J. Dairy Sci. 105, 5261-5270.
Patience J.F., Rossoni-Serão M.C. and Gutiérrez N.A. (2015). A review of feed efficiency in swine: Biology and application. J. Anim. Sci. Biotech. 6, 33-41.
Pryce J.E., Gonzalez-Recio O., Nieuwhof G., Wales W.J., Coffey M.P., Hayes B.J. and Goddard M.E. (2015). Hot Topic: Defi-nition and implementation of a breeding value for feed effi-ciency in dairy cows. J. Dairy Sci. 98, 7340-7350.
Quintana D.S. (2015). From pre-registration to publication: a non-technical primer for conducting a meta-analysis to synthesize correlational data. Front. Psychol. 6, 1549-1558.
Richardson C.M., Amer P.R., Hely F.S., van den Berg I. and Pryce J.E. (2021). Estimating methane coefficients to predict the environmental impact of traits in the australian dairy breeding program. J. Dairy Sci. 104, 10979-10990.
Schultz, N.E. and Weigel K.A. (2019). Inclusion of herdmate data improves genomic prediction for milk-production and feed-efficiency traits within north american dairy herds. J. Dairy Sci. 102, 11081-11091.
Schweer K.R., Kachman S.D., Kuehn L.A., Freetly H.C., Pollak J.E. and Spangler M.L. (2018). Genome-wide association study for feed efficiency traits using snp and haplotype mod-els. J. Anim. Sci. 96, 2086-2098.
Shetty N., Løvendahl P., Lund M.S. and Buitenhuis A.J. (2017). Prediction and validation of residual feed intake and dry mat-ter intake in danish lactating dairy cows using mid-infrared spectroscopy of milk. J. Dairy Sci. 100, 253-264.
Spurlock D.M., Dekkers J.C.M., Fernando R., Koltes D.A. and Wolc A. (2012). Genetic parameters for energy balance, feed efficiency, and related traits in Holstein cattle. J. Dairy Sci. 95, 5393-5402.
Tarekegn G.M., Karlsson J., Kronqvist C., Berglund B., Holtenius K. and Strandberg E. (2021). Genetic parameters of forage dry matter intake and milk produced from forage in swedish red and Holstein dairy cows. J. Dairy Sci. 104, 4424-4440.
Tempelman R.J., Spurlock D.M., Coffey M., Veerkamp R.F., Armentano L.E., Weigel K.A., de Haas Y., Staples C.R., Con-nor E.E., Lu Y. and VandeHaar M.J. (2015). Heterogeneity in genetic and nongenetic variation and energy sink relationships for residual feed intake across research stations and countries. J. Dairy Sci. 98, 2013-2026.
Tesema Z., Alemayehu K., Getachew T., Kebede D., Deribe B., Taye M., Tilahun M., Lakew M., Kefale A., Belayneh N., Zegeye A. and Yizengaw L. (2020). Estimation of genetic pa-rameters for growth traits and kleiber ratios in boer x central highland goat. Trop. Anim. Health Prod. 52, 3195-3205.
Vallimont J.E., Dechow C.D., Daubert J.M., Dekleva M.W., Blum J.W., Barlieb C.M., Liu W., Varga G.A., Heinrichs A.J. and Baumrucker C.R. (2010). Genetic parameters of feed intake, production, body weight, body condition score, and selected type traits of Holstein cows in commercial tie-stall barns. J. Dairy Sci. 93, 4892-4901.
Vallimont J.E., Dechow C.D., Daubert J.M., Dekleva M.W., Blum J.W., Barlieb C.M., Liu W., Varga G.A., Heinrichs A.J. and Baumrucker C.R. (2011). Heritability of gross feed efficiency and associations with yield, intake, residual intake, body weight, and body condition score in 11 commercial Pennsyl-vania tie stalls. J. Dairy Sci. 94, 2108-2113.
VandeHaar M.J., Armentano L.E., Weigel K., Spurlock D.M., Tempelman R.J. and Veerkamp R. (2016). Harnessing the ge-netics of the modern dairy cow to continue improvements in feed efficiency. J. Dairy Sci. 99, 4941-4954.
Van Middelaar C.E., Dijkstra J., Berentsen P.B.M. and De Boer I.J.M. (2014). Cost-effectiveness of feeding strategies to re-duce greenhouse gas emissions from dairy farming. J. Dairy Sci. 97, 2427-2439.
Veerkamp R.F. (1998). Selection for economic efficiency of dairy cattle using information on live weight and feed intake: A re-view. J. Dairy Sci. 81, 1109-1119.
Veerkamp R.F., Coffey M.P., Berry D.P., de Haas Y., Strandberg E., Bovenhuis H., Calus M.P.L. and Wall E. (2012). Genome-wide associations for feed utilisation complex in primiparous Holstein–Friesian dairy cows from experimental research herds in four european countries. Animal. 6, 1738-1749.
Viechtbauer W. (2010). Conducting meta-analyses in R with the Metafor package. J. Stat. Soft. 36, 1-48.
Waghorn G.C., Macdonald K.A., Williams Y., Davis S.R. and Spelman R.J. (2012). Measuring residual feed intake in dairy heifers fed an alfalfa (Medicago sativa) cube diet. J. Dairy Sci. 95, 1462-1471.
Willems Y.E., Boesen N., Li J., Finkenauer C. and Bartels M. (2019). The heritability of self-control: a meta-analysis. Neu-rosci. Biobehav. Rev. 100, 324-334.
Williams Y.J., Pryce J.E., Grainger C., Wales W.J., Linden N., Porker M. and Hayes B.J. (2011). Variation in residual feed intake in Holstein-Friesian dairy heifers in southern Australia. J. Dairy Sci. 94, 4715-4725.
Yao C., de los Campos G., VandeHaar M.J., Spurlock D.M., Ar-mentano L.E., Coffey M., de Haas Y., Veerkamp R.F., Staples C.R., Connor E.E., Wang Z., Hanigan M.D., Tempelman R.J. and Weigel K.A. (2017). Use of genotype × environment in-teraction model to accommodate genetic heterogeneity for re-sidual feed intake, dry matter intake, net energy in milk, and metabolic body weight in dairy cattle. J. Dairy Sci. 100, 2007-2016.
Zamani P., Miraei-Ashtiani S.R. and Mohammadi H. (2008). Ge-netic parameters of residual energy intake and its correlations with other traits in Holstein dairy cattle. Turkish J. Vet. Anim. Sci. 32, 255-261.
Zavadilová L., Kašná E., Krupová Z. and Klímová A. (2021). Health traits in current dairy cattle breeding: A review. Czech J. Anim. Sci. 66, 235-250.
Zhang F., Wang Y., Mukiibi R., Chen L., Vinsky M., Plastow G., Basarab J., Stothard P. and Li C. (2020). Genetic architecture of quantitative traits in beef cattle revealed by genome wide association studies of imputed whole genome sequence vari-ants: i: feed efficiency and component traits. BMC Genomics. 21, 36-45.
