TABLE 3.

Effect of population structure on power and precision of regression-based LD mapping of QTL

	No. SNPs included in regression methods
Genotype regression			Haplotype regression
Population structure	1	2	4	2	4
Power to detect QTL (%)
Ne = 100 for 100 generations	70	73	74	74	68
Ne = 500 for 85 and then 100 for 15 generations	39	43	45	49	50
Ns = 30 and Nd = 150 for 100 generations	64	65	67	67	63
Mean absolute error of position (cM) for significant QTL
Ne = 100 for 100 generations	0.88	1.00	1.32	0.97	1.30
Ne = 500 for 85 and then 100 for 15 generations	1.78	1.68	1.62	1.62	1.54
Ns = 30 and Nd = 150 for 100 generations	0.92	1.03	1.34	1.01	1.32
Mean absolute error of position (cM) for all QTL
Ne =100 for 100 generations	1.13	1.18	1.41	1.12	1.38
Ne = 500 for 85 and then 100 for 15 generations	2.12	1.99	1.81	1.86	1.66
Ns = 30 and Nd = 150 for 100 generations	1.23	1.26	1.45	1.20	1.40

Power (detection at 1% regionwise level) and precision for regression-based LD mapping methods are shown under three population structures: (1) N_e =100 for 100 generations, unrelated individuals; (2) N_e = 500 for first 85 generations and N_e = 100 for last 15 generations, unrelated individuals; and (3) 30 sires, each mated to 5 dams (N_s = 30 and N_d = 150) for 100 generations, which provides N_e =100 but related individuals. In the base population, SNPs and a central biallelic QTL were simulated with allele frequency of 0.5 and in linkage equilibrium. The other parameters are sample size in generation 100 = 500, QTL effect = 5% of the phenotypic variance, and marker density = 10 SNPs/11 cM. Results are based on 10,000 replicates.