Table 1.

Summary of genomic selection studies in developing countries.

References	Breed	Traits studied	Chip size	Population size	Reference population	Size of validation set	Models	Response variable	Accuracy of prediction	Regression coefficients (response variable on predictions)
Boddhireddy et al. (2014)	Nellore cattle	22 traits composed of reproductive, productive, visual body conformation scores traits	Illumina BovineHD BovineSNP50 (50 K) Chip	2,241	1793	448 (5-fold cross validation)	BayesC	EBVs	0.34–0.58	0.40–0.87
Boison et al. (2017)	Gyr (Bos indicus) dairy cattle	Milk, fat and protein yields and Age at first calving	Illumina BovineHD (Bulls) BovineSNP50 (50 K) chip (cows)	464 bulls 1688 cows	264–281 bulls only or plus 1177–1597 cows	115–152 Bulls	GBLUP	dEBVs	0.46–0.56 (only bulls reference) 0.47–0.62 (bull+cow reference)	0.73–0.97 (Bulls only or Bulls plus cows in reference)
Brown et al. (2016)	East Africa Crossbred cattle	Milk yield	Illumina Bovine HD	1038 cows	565–835 cows	178–448 cows	GBLUP and BayesC	Corrected phenotypes	0.32–0.41 (GBLUP) and 0.28–0.35 (BayesC)
Cardoso et al. (2014)	Bradford and Hereford cattle	Tick resistance	Illumina 50 K (cows) and Illumina HD (sires)	113 sires 3545 cows	2765	691 (5-fold cross- validation)	GBLUP, BayesB and ssGBLUP	dEBV	0.38–0.48 vaidation set by K–means clutsering 0.40–0.60 (validation set by random clustering	0.29–1.46 (K-M) 0.25–0.83 (Random)
Costa et al. (2014)	Nellore Cattle	Heifer rebreeding Age at first calving, and Early pregnancy occurrence	Illumina HD	2,056 females	1,853	185 (10-fold cross)	GBLUP, BayesCπ and IBLASSO	dEBV and Corrected Phenotypes	0.29–0.54 (GBLUP) 0.34–0.57 (BayesCπ) and 0.37–0.58 (IBLASSO)	0.84–0.88 (GBLUP) 0.89–1.14 (BayesCπ)) and 0.81–0.87 (IBLASSO)
Fernandes Júnior et al. (2016)	Nellore Cattle	REA, BFT and HCW	Illumina HD	1756 Nellore steers	1405	351 (5-fold cross-validation)	Bayesian ridge regression (BRR), BayesC (BC) and Bayesian Lasso (BL)	Corrected phenotypes and EBVs	0.21–0.46 (BRR) 0.23–0.46 (BC) 0.22–0.47 (BL)	0.40–0.99 (BRR) 0.37–0.93 (BC) 0.39–1.02 (BL)
Neves et al. (2014)	Nellore Cattle	15 Economically important traits	Illumina HD	691 Bulls	307–494	115–187	GBLUP, BayesC, BLASSO	dEBV	0.17–0.72 (GBLUP) 0.20–0.69 (BayesC) 0.19–0.74 (BLASSO)	0.75–1.24 (GBLUP) 1.01–2.35 (BayesC) 0.91–2.17 (BLASSO)
Silva et al. (2016)	Nellore beef cattle	RFI, FCR, ADG and DMI	Illumina HD	788 Cows	424–617	91–337	ssGBLUP, GBLUP and BayesCπ	Corrected phenotypes and EBVs	0.23–0.48 (GBLUP) 0.23–0.48 (BayesCπ) 0.30–0.49 (SSGBLUP)	0.78–0.90 (GBLUP) 0.05–3.10 (BayesCπ) 0.75–1.16 (ssGBLUP)
Terakado et al. (2014)	Nellore Cattle	Weight Gain Birth to weaning (GBW) and weaning to Yearling (BWY)	Illumina HD	1,658 females and 1,002 males	2118 (GBW) 988 (GWY)	531 (GBW) 246 (GWY)	GBLUP	dEBV	0.21–0.48 (GBW) 0.21–0.28 (GWY)	0.38 (GBW) 0.25 (GWY)

EBVs, Estimated breeding values; dEBV, de-regressed breeding values; IBLASSO, Improved Bayesian LASSO.