The full spectrum of lipid molecules in human plasma and their potential role in human health and disease are areas of intense interest ( 1-9 ). This interest owes substantially to the sophisticated technologies that make it possible to accurately capture and quantify the human lipidome ( 8, 10 ). Associative evidence gleaned from plasma lipidomic studies promises vital contributions to biomarker researchan important mainstay of the continued efforts for chronic disease prevention. The plasma lipidomic profi le of humans exhibits a wide range of diversity ( 11,12 ) and is associated with several conditions including obesity ( 13, 14 ), hypertension ( 14-16 ), disorders of glucose metabolism ( 13, 17 ), metabolic syndrome (MS) ( 10, 18 ), cardiovascular diseases ( 19 ), cystic fi brosis ( 20 ), nicotine consumption ( 21 ), and response to antilipid treatment ( 22 ).Lipidomic association studies conducted in the context of families have an important consideration that the concentrations of the plasma lipid species might be phenotypically as well as genetically correlated with each other ( 23 ) especially in related individuals. In that case, it is Abstract Plasma lipidome is now increasingly recognized as a potentially important marker of chronic diseases, but the exact extent of its contribution to the interindividual phenotypic variability in family studies is unknown. Here, we used the rich data from the ongoing San Antonio Family Heart Study (SAFHS) and developed a novel statistical approach to quantify the independent and additive value of the plasma lipidome in explaining metabolic syndrome (MS) variability in Mexican American families recruited in the SAFHS. Our analytical approach included two preprocessing steps: principal components analysis of the highresolution plasma lipidomics data and construction of a subject-subject lipidomic similarity matrix. We then used the Sequential Oligogenic Linkage Analysis Routines software to model the complex family relationships, lipidomic similarities, and other important covariates in a variance components framework. Our results suggested that even after accounting for the shared genetic infl uences, indicators of lipemic status (total serum cholesterol, TGs, and HDL cholesterol), and obesity, the plasma lipidome independently explained 22% of variability in the homeostatic model of assessment-insulin resistance trait and 16% to 22% variability in glucose, insulin, and waist circumference. Our results demonstrate that plasma lipidomic studies can additively contribute to an understanding of the interindividual variability in