Table 1.

CAUSAL-Imp Achieves Better Statistics in Northern Finland Birth Cohort Data Set

Phenotype	chr	rsID	True statistics	DIST*	CAUSAL-Imp	IMPUTE2
TG	2	rs673548	−5.444	−5.37	−5.38	−4.46
	8	rs10096633	−5.679	−5.63	−5.64	−5.17
	15	rs2624265	4.22	3.55	4.15	3.60
HDL	15	rs1532085	7.13	5.59	7.17	6.47
	16	rs3764261	12.01	8.23	8.28	6.47
	16	rs255049	6.06	5.11	5.61	5.70
	17	rs9891572	4.25	3.99	4.02	4.40
LDL	1	rs646776	−7.70	−7.92	−7.92	−6.96
	2	rs693	6.81	6.27	6.63	5.91
	11	rs102275	−4.51	−4.43	−4.44	−4.54
	11	rs174546	−4.52	−4.43	−4.45	−4.58
	11	rs174556	−4.69	−4.73	−4.75	−4.62
	11	rs1535	−4.43	−4.46	−4.46	−4.45
	19	rs11668477	−5.96	−3.78	−3.78	−5.33
	19	rs157580	−5.161	−2.6	−5.24	−4.20
CRP	12	rs2650000	−7.08	−5.25	−7.36	−6.05
GLU	2	rs560887	−6.97	−6.21	−6.80	−5.69
	7	rs10244051	5.31	4.34	4.67	4.97
	7	rs2191348	5.30	4.33	4.66	4.97
	11	rs1447352	−6.35	−5.08	−5.39	−4.75
	11	rs7121092	−5.50	−4.93	−5.78	−4.60

We run association on the NFBC data set. We consider the SNPs that are reported significant in a previous study (Sabatti et al., 2008). Then, we treat these SNPs as untyped and impute the marginal statistics using CAUSAL-Imp, DIST^*, and IMPUTE2. Our method tends to produce summary statistics closer to the estimated marginal statistics than the two other methods.

TG, triglycerides; HDL, high-density lipoprotein; LDL, low-density lipoprotein; CRP, C-reactive protein; GLU, glucose.

Bold values indicate the best results.