1. Bernabucci U, Biffani S, Buggiotti L, Vitali A,
Laceter اa N, and Nardone A (2014) The effects
of heat stress in Italian Holstein dairy cattle.
Journal of Dairy Science, 97(1): 471-486.
2. Bohlouli M, Alijani S, Naderi S, Yin T, and
König S (2019) Prediction accuracies and genetic
parameters for test-day traits from genomic and
pedigree-based random regression models with or
without heat stress interactions. Journal of Dairy
Science, 102(1): 488-502.
3. Forneris NS, Steibel J, Legarra A, Vitezica Z,
Bates R, Ernst C, Basso A and Cantet R (2016)
A comparison of methods to estimate genomic
relationships using pedigree and markers in
livestock populations. Journal of Animal
Breeding and Genetics, 133: 452-462.
4. Hammami H, Rekik B and Gengler N (2009)
Genotype by environment interaction in dairy
/Interactions entre génotype et environnement
chez les bovins laitiers. Biotechnologie,
Agronomie, Société et Environnement,13(1): 155.
5. Hayes BJ, Bowman PJ, Chamberlain AJ, Savin
K, Van Tassell CP, Sonstegard T and Goddard
ME (2009) A validated genome wide association
study to breed cattle adapted to an environment
altered by climate change. PLoS One, 4:e6676.
6. Hijmans RJ, Williams E, Vennes C and Hijman
MRJ (2016) Package‘geosphere’. Accessed Nov.
19, 2017. https: / / cran .r-project .org/ web/
packages/ geosphere/ index .html.
7. Kelly CF and Bond TE (1971) Bioclimatic
Factors and Their Measurement: A Guide to
Environmental Research on Animals. National
Academy Press, Washington, DC.
8. Lillehammer M, Árnyasi M, Lien S, Olsen HG,
Sehested E, Ødegård J and Meuwissen THE
(2007b) A genome scan for quantitative trait
locus by environment interactions for production
traits. Journal of Dairy Science, 90: 3482-3489.
9. Meuwissen THE, Hayes B and Goddard ME
(2001) Prediction of total genetic value using
genome-wide dense marker maps. Genetics,
157: 1819-1829.
10. Misztal I, Tsuruta S, Strabel T, Auvray B,
Druet T and Lee DH (2002) BLUPF90 and
related programs. Communication no. 28-07.
In: Proceedings of the 7th World Congress for
the Genetic Applied Livestock Production,
Montpellier, France.
11. Misztal I, Legarra A and Aguilar I (2014)
Using recursion to compute the inverse of the
genomic relationship matrix. Journal of Dairy
Science, 97(6): 3943-3952.
12. Nejati-Javaremi A, Smith C and Gibson JP
(1997) Effect of Total Allelic Relationship on
Accuracy of Evaluation and Response to
Selection. Journal of Animal Science, 75(June):
1738-1745.
13. Nishiura A, Sasaki O, Aihara M, Takeda H and
Satoh M (2015) Genetic analysis of fat-toprotein
ratio, milk yield and somatic cell score
of Holstein cows in Japan in the first three
lactations by using a random regression model.
Journal of Animal Science, 86: 961-969.
14. Oliveira HR, Brito LF, Silva FF, Lourenco
DAL, Jamrozik J and Schenkel FS (2019)
Genomic prediction of lactation curves for
milk, fat, protein and somatic cell score in
Holstein cattle. Journal of Dairy Science,
102(1): 452-463.
15. Pérez-Cabal MA, Vazquez AI, Gianola D,
Rosa GJM and Weigel KA (2012) Accuracy of
genome-enabled prediction in a dairy cattle
population using different cross-validation
layouts. Frontiers in Genetics, 3: 27.
16. Purcell S, Neale B, Todd-Brown K, Thomas L,
Ferreira MAR, Bender D, Maller J, Sklar P, de
Bakker PIW, Daly MJ and Sham PC (2007)
PLINK: A tool set for whole-genome association
and population-based linkage analyses. American
Journal of Human Genetics, 81: 559-575.
17. Santana M, Bignardi A, Pereira R, Stefani G
and Faro LEl (2017) Genetics of heat tolerance
for milk yield and quality in Holsteins. Animal,
11: 4-14.
18. Saatchi M, McClure MC, McKay SD, Rolf
MM, Kim J, Decker JE, Taxis TM, Chapple
RH, Ramey HR, Northcutt SL, Bauck S,
Woodward B, Dekkers JCM, Fernando RL,
Schnabel RD, Garrick DJ and Taylor JF (2011)
Accuracies of genomic breeding values in
American Angus beef cattle using K-means
clustering for cross-validation. Genetics
Selection Evolution, 43: 40.
19. Sánchez JP, Misztal I, Aguilar I, Zumbach B
and Rekaya R (2009) Genetic determination of
the onset of heat stress on daily milk
production in the US Holstein cattle. Journal
of Dairy Science, 92(8): 4035-4045.
20. Sargolzaei M, Chesnais JP and Schenkel FS
(2011) FImpute-An efficient imputation
algorithm for dairy cattle populations. Journal
of Dairy Science, 94(1): 421.
21. Tiezzi F, de los Campos G, Parker Gaddis K
and Maltecca C (2017) Genotype by
environment (climate) interaction improves
genomic prediction for production traits in US
Holstein cattle. Journal of Dairy Science, 100.
22. VanRaden PM and Sullivan PG (2010)
International genomic evaluation methods for
dairy cattle. Genetics Selection Evolution, 42(1):
7.
23. Wiggans GR, Sonstegard TS, VanRaden PM,
Matukumalli LK, Schnabel RD, Taylor JF,
Schenkel FS and Van Tassell CP (2009)
Selection of single nucleotide polymorphisms
and quality of genotypes used in genomic
evaluation of dairy cattle in the United States
and Canada. Journal of Dairy Science, 92:
3431-3436.
24. Yao C, De Los Campos G, VandeHaar M,
Spurlock D, Armentano L, Coffey M, De Haas
Y, Veerkamp R, Staples C and Connor E (2017)
Use of genotype×environment interaction
modelto accommodate genetic heterogeneity for
residual feed intake, dry matter intake, net energy
in milk, and metabolic body weight in dairy
cattle. Journal of Dairy Science, 00: 2007-2016.
25. Yin T and Konig S (2016) Genomics for
phenotype prediction and management
purposes. Animal Frontiers, 6: 65-72