Twin and family studies are typically used to elucidate the relative contribution of genetic and environmental variation to phenotypic variation. Here, we apply a quantitative genetic method based on hierarchical clustering, to blood plasma lipidomics data obtained in a healthy cohort consisting of 37 monozygotic and 28 dizygotic twin pairs, and 52 of their biological nontwin siblings. Such data are informative of the concentrations of a wide range of lipids in the studied blood samples. An important advantage of hierarchical clustering is that it can be applied to a high-dimensional 'omics' type data, whereas the use of many other quantitative genetic methods for analysis of such data is hampered by the large number of correlated variables. For this study we combined two lipidomics data sets, originating from two different measurement blocks, which we corrected for block effects by 'quantile equating'. In the analysis of the combined data, average similarities of lipidomics profiles were highest between monozygotic (MZ) cotwins, and became progressively lower between dizygotic (DZ) cotwins, among sex-matched nontwin siblings and among sex-matched unrelated participants, respectively. Our results suggest that (1) shared genetic background, shared environment, and similar age contribute to similarities in blood plasma lipidomics profiles among individuals; and (2) that the power of quantitative genetic analyses is enhanced by quantile equating and combination of data sets obtained in different measurement blocks.