Livestock production both contributes to and is affected by global climate change, and substantial modifications will be required to increase its climate resilience. In this context, reliance on dominant commercial livestock breeds, featuring small effective population sizes, makes current production strategies vulnerable if their production is restricted to environments, which may be too costly to support under future climate scenarios. The adaptability of animal populations to future environments will therefore become important. To help evaluate the role of genetics in climate adaptation, we compared selection strategies in dairy cattle using breeding simulations, where genomic selection was used on two negatively correlated traits for production (assumed to be moderately heritable) and adaptation (assumed to have low heritability). Compared with within-population breeding, genomic introgression produced a more positive genetic change for both production and adaptation traits. Genomic introgression from highly adapted but low production value populations into highly productive but low adaptation populations was most successful when the adaptation trait was given a lower selection weight than the production trait. Genomic introgression from highly productive population to highly adapted population was most successful when the adaptation trait was given a higher selection weight than the production trait. Both these genomic introgression schemes had the lowest risk of inbreeding. Our results suggest that both adaptation and production can potentially be improved simultaneously by genomic introgression.
Access optionsAccess options
Subscribe to Journal
Get full journal access for 1 year
only $37.75 per issue
All prices are NET prices.
VAT will be added later in the checkout.
Rent or Buy article
Get time limited or full article access on ReadCube.
All prices are NET prices.
Åby BA, Meuwissen THE (2014) Selection strategies utilizing genetic resources to adapt livestock to climate change. In: 10th World Congress on Genetics Applied to Livestock Production, Vancouver, Canada, 17–22 August 2014.
Aguilar I, Misztal I, Johnson DL, Legarra A, Tsuruta S, Lawlor TJ (2010) Hot topic: a unified approach to utilize phenotypic, full pedigree, and genomic information for genetic evaluation of Holstein final score. J Dairy Sci 93:743–752
Berman A (2011) Invited review: are adaptations present to support dairy cattle productivity in warm climates? J Dairy Sci 94:2147–2158
Christensen O, Lund MS (2010) Genomic prediction when some animals are not genotyped. Genet Sel Evol 42:2
Das R, Sailo L, Verma N, Bharti P, Saikai J, Imtiwati, Kumar R (2016) Impact of heat stress on health and performance of dairy animals: a review. Vet World 9:260–268
Food and Agriculture Organisation of the United Nations (2015). Coping with climate change - the roles of genetic resources for food and agriculture. Rome.
Gutierrez JP, Cervantes I, Goyache F (2009) Improving the estimation of realized effective population sizes in farm animals. J Anim Breed Genet 126:327–332
Gaspa G, Veerkamp R, Calus MPL, Windig JJ (2015) Assessment of genomic selection for introgression of polledness into Holstein Friesian cattle by simulation. Livest Sci 179:86–95
Hoffmann I (2010) Climate change and the characterization, breeding and conservation of animal genetic resources. Anim Genet 41(Suppl. 1):32–46
Hoffmann I (2013) Adaptation to climate change – exploring the potential of locally adapted breeds. Animal 7:346–362
Kantanen J, Løvendahl P, Strandberg E, Eythorsdottir E, Li M-H, Kettunen-Præbel A, Berg P, Meuwissen T (2015) Utilization of farm animal genetic resources in a changing agro-ecological environment in the Nordic countries. Front Genet 6:52
Leroy G, Baumung R, Boettcher P, Scherf B, Hoffman I (2016) Review: sustainability of crossbreeding in developing countries; definitely not like crossing a meadow. Animal 10:262–273
Li MH, Nogovitsina E, Ivanova Z, Erhardt G, Vilkki J, Popv R, Ammosov I, Kiselyova T, Kantanen J (2005) Genetic contribution of indigenous Yakutian cattle to two hybrid populations, revealed by microsatellite variation. Asian Aust J Anim Sci 18:613–619
Mirkena T, Duguma G, Haile A, Tibbo M, Okeyo AM, Wurzinger M, Sölkner J (2010) Genetics of adaptation in domestic fam animals: a review. Livest Sci 132:1–12
Nardone A, Ronchi B, Lacetera N, Bernabucci U (2006) Climatic effects on productive traits in livestock. Vet Res Commun 30(Suppl. 1):75–81
Negussie E, Brännäng E, Rottmann OJ (1999) Reproductive performance and herd life of dairy cattle at Asela livestock farm, Arsi, Ethiopia. II: crossbreds with 50, 75 and 87.5% European inheritance. J Anim Breed Genet 116:225–234
Niyas PA, Chaidanya K, Shaji S, Sejian V, Bhatta R, Bagtah M, Roa GSLHVP, Kurien EK, Girish V (2015) Apadtatoin of livestock to environmental challenges. J Vet Sci Med Diag 4:3
Ødegård J, Sonesson AK, Yazdi MH, Meuwissen THE (2009a) Introgression of a major QTL from an inferior into a superior population using genomic selection. Genet Sel Evol 41:38
Ødegård J, Yazdi MH, Sonesson AK, Meuwissen THE (2009b) Incorporating desirable genetic characteristics from an inferior into a superior population using genomic selection. Genetics 181:737–745
Phocas F, Belloc C, Bidanel J, Delaby L, Dourmad JY, Dumont B, Ezanno P, Fortun-Lamothe L, Foucras G, Frappat B, González-García E, Hazard D, Larzul C, Lubac S, Mignon-Grasteau S, Moreno CR, Tixier-Boichard M, Brochard M (2016) Review: towards the agroecological management of ruminants, pigs and poultry through the development of sustainable breeding programmes: I-selection goals and criteria. Animal 10:1749–1759
Pitt D, Bruford MW, Barbato M, Orozco-terWengel P, Martínez R, Sevane N (2019) Demography and rapid local adaptation shape Creole cattle genome diversity in the tropics. Evol Apps 12:105–122
Pritchard T, Coffey M, Mrode R, Wall E (2013a) Genetic parameters for production, health, fertility and longevity traits in dairy cows. Animal 7:34–46
Pritchard T, Coffey M, Mrode R, Wall E (2013b) Understanding the genetics of survival in dairy cows. J Dairy Sci 96:3296–3309
Sargolzaei M, Schenkel FS (2009) QMSim: a large-scale genome simulator for livestock. Bioinformatics 25:680–681
Sasaki O (2013) Estimation of genetic parameters for longevity traits in dairy cattle: a review with focus on the characteristics of analytical models. Anim Sci J 84:449–460
Solberg TR, Sonesson AK, Woolliams JA, Meuwissen THE (2008) Genomic selection using different marker types and densities. J Anim Sci 86:2447–2454
Strandén I, Lidauer M (1999) Solving large mixed models using preconditioned conjugate gradient iteration. J Dairy Sci 82:2779–2787
Strandén I, Vuori K (2006). RelaX2: pedigree analysis program. In: 8th World Congress on Genetics Applied to Livestock Production, Belo Horizonte, MG, Brazil, Volume 27.30, 13–18 August 2006.
Strandén I, Garrick DJ (2009) Technical note: derivation of equivalent computing algorithms for genomic predictions and reliabilities of animal merit. J Dairy Sci 92:2971–2975
Visscher PM, Haley CS, Thompson R (1996) Marker-assisted introgression in backcross breeding programs. Genetics 144:1923–1932
This study is part of ClimGen (“Climate Genomics for Farm Animal Adaptation”) project funded by FACCE-JPI ERA-NET Plus on Climate Smart Agriculture.
Conflict of interest
The authors declare that they have no conflict of interest.
Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
The members of Climate Genomics Consortium are listed in Appendix.