Using the longest significance run to estimate region-specific p-values in genetic association mapping studies

BMC Bioinformatics

Table 3 Comparison of power and false discovery rate among the procedures of Bonferroni correction and two LSR methods.

Marker number	Relative risk	Allele frequency	Power			False discovery rate			Length*
			Bonferroni	LSR₁	LSR₂	Bonferroni	LSR₁	LSR₂	LSR₁	LSR₂
50	2.0	0.075	0.15	0.31	0.44	0.14	0	0.005	5.95	7.765
	2.0	0.1	0.285	0.57	0.66	0.07	0	0	7.74	9.66
	2.5	0.075	0.53	0.855	0.895	0.06	0.005	0.005	10.22	12.05
	2.5	0.1	0.68	0.935	0.98	0.03	0	0	11.23	13.215
	3.0	0.075	0.83	0.965	0.99	0.04	0.005	0	11.9	13.9
	3.0	0.1	0.93	0.995	1	0.03	0	0	12.6	14.6
100	2.0	0.075	0.065	0.33	0.5	0.28	0.03	0.04	5.91	7.51
	2.0	0.1	0.185	0.55	0.635	0.18	0.02	0.055	7.28	9.055
	2.5	0.075	0.47	0.835	0.91	0.11	0.015	0.04	9.82	11.755
	2.5	0.1	0.605	0.945	0.98	0.05	0.005	0.005	11.33	13.475
	3.0	0.075	0.78	0.985	1	0.02	0.005	0	12.2	14.1
	3.0	0.1	0.91	1	1	0.03	0	0	12.8	14.6

Simulation was based on 200 replications with sample size 1000, and SNP data were generated under additive disease model with 15 markers in linkage disequilibrium. Screening criteria for LSR:α₁ = 0.10, and level of significance for all methods:α₂ = 0.05

ISSN: 1471-2105