TABLE 2.

Comparison of regression-based LD mapping methods with identity-by-descent (IBD) methods when the QTL explains 2% of the phenotypic variance

No. SNPs included in model	Marker density (no. SNPs in 11-cM region)
	6			10			20
		Geno	Haplo	IBD	Geno	Haplo	IBD	Geno	Haplo	IBD
	Power to detect QTL (%)
1	26	—	18	31	—	21	34	—	22
2	25	23	25	28	27	30	31	28	34
4	24	15	28	28	18	32	30	19	31
6	—	—	27	—	—	34	—	—	32
8	—	—	—	—	—	32	—	—	—
	Mean absolute error of position (cM) for significant QTL
1	1.13	—	1.26	0.93	—	1.16	0.85	—	1.03
2	1.33	1.31	1.27	1.10	1.13	1.06	0.96	0.94	0.95
4	1.39	1.36	1.23	1.42	1.48	1.06	1.15	1.25	0.99
6	—	—	1.36	—	—	1.10	—	—	0.96
8	—	—	—	—	—	1.20	—	—	—
	Mean absolute error of position (cM) for all QTL
1	1.71	—	1.67	1.41	—	1.45	1.28	—	1.28
2	1.71	1.69	1.64	1.51	1.55	1.44	1.37	1.41	1.25
4	1.41	1.38	1.54	1.64	1.66	1.38	1.50	1.64	1.27
6	—	—	1.69	—	—	1.41	—	—	1.25
8	—	—	—	—	—	1.50	—	—	—

Power (detection at 1% regionwise level) and precision are shown for each LD mapping method: (1) Geno, regression on genotypes at 1, 2, or 4 adjacent SNPs; (2) Haplo, regression on assumed known haplotypes of 2 or 4 adjacent SNPs; and (3) IBD, identity-by-descent methods using single SNP genotype or assumed known haplotypes of 2, 4, 6, or 8 adjacent SNPs. In the base population, SNPs were simulated with allele frequency of 0.5 and in linkage equilibrium, and a QTL was simulated with unique alleles at the center of the 11-cM region. The other parameters are N_e = 100, number of generations since mutation = 100, and sample size in generation 100 = 500. Results are based on 10,000 replicates.