Using a novel approach for a two-locus model that provides a greatly increased power to detect multiple quantitative trait loci (QTLs) in simulated data, we identified in a sample of 961 female sib-pairs, three genome-wide significant QTLs for apolipoprotein A1 on chromosomes 8p21.1-q13.1 (LOD score 3.71), 9q21.32-33.1 (LOD score 3.28) and 10p15.1-p13 (LOD score 5.51), two for lipoprotein (a) on chromosomes 6q25.2-q27 (LOD score 10.18) and 21q21.1-q21.3 (LOD score 4.57) and two for triglycerides on chromosomes 4q28.3-32.1 (LOD score 3.71) and 5q23.1-q32 (LOD score 3.60). The two-locus ordered-subset analysis has led to the confirmation of known and likely identification of novel regions linked to serum lipid levels that would have otherwise been missed and deserves wider application in linkage analyses of quantitative traits. Given the relative lack of power for the sample sizes commonly used in human genetics linkage studies, minor QTL effects often go undetected and those that are detected will be upwardly biased. We show through simulation that the discrepancy between the real and estimated QTL-effects is often likely to generate an unpredictable source of false-negative errors, using multi-locus models, reducing the power to detect multiple QTLs through oligogenic linkage analysis. The successful simultaneous modelling of the identified QTLs in a multi-locus context helps to eliminate false positives and increases the power to detect linkages, adding compelling evidence that they are likely to be reliable QTLs for these lipid traits.