Table 1.

Methods used by the participants to the XV^th QTLMAS workshop

First author	Label	Method	Description
Shariati	BayesS_1	2 steps (all SNP)	First step: a GBLUP giving estimation of SNP effects. Groups of size 150, 75 (SPNa) or 50 (SNPb) are made assembling SNP of similar effect. Second step: BayesA with all or a limited (1500 or 450) number of SNP and a unique SNP effect variance per group.
	BayesS_2	2 steps (1500 SNP)
	BayesS_3	2 steps-Bayes (450 SNPa)
	BayesS_4	2 steps-Bayes (450 SNPb)
Ogutu	RR	Ridge regression
	GBLUP_O	GBLUP	Qualified Ridge Regression BLUP by the authors
	LASSO_O	LASSO
	LASSO_ad	Adaptative LASSO	Following Zou [21], data-driven weights are added to the penalty to force LASSO to be consistent
	EN	Elastic net
	EN_ad	Adaptative EN	Mixture of adaptative lasso and EN
Wang	BayesA_W	BayesA
	BayesB_W	BayesB
	BayesCπ_W	BayesCπ
	TABLUP	TABLUP	In the genomic matrix, loci IBD probability estimations are weighted by their effect variance estimated from BayesB [11]
	GBLUP_W	GBLUP
Mucha	AM	Animal model	All models are estimating haplotypes effects. Haplotypes are obtained using the PHASE software [18]. RM1 and RM2 differ by the estimation of the haplotype effect variance
	FM	Fixed effect
	RM1	Random model 1
	RM2	Random model 2
Zeng	GBLUPa_Z	GBLUP1	Additive effect only
	GBLUPd_Z	GBLUP2	Additive and dominance effect
	BayesB _W	BayesB
	BayesCπ_W	BayesCπ
Usai	LASSO_Uc	LASSO-LARS classic	The penalty is describes as ∑|βj|≤t. In the LASSO-LARS classic, the t parameter is the average number of active SNP in 1000 simulations. In strategy 1, the number which occurred more than 5% of the times and in strategy 2, which minimized a selection criteria
	LASSO_Uc1	LASSO-LARS strategy 1
	LASSO_Uc2	LASSO-LARS strategy 2
Schurink	BayesZ	BayesZ	Similar to BayesCπ, with a Bernoulli prior for π