Mining the genome for lipid genes.

Mining of the genome for lipid genes has since the early 1970s helped to shape our understanding of how triglycerides are packaged (in chylomicrons), repackaged (in very low density lipoproteins; VLDL), and hydrolyzed, and also how remnant and low-density lipoproteins (LDL) are cleared from the circulation. Gene discoveries have also provided insights into high-density lipoprotein (HDL) biogenesis and remodeling. Interestingly, at least half of these key molecular genetic studies were initiated with the benefit of prior knowledge of relevant proteins. In addition, multiple important findings originated from studies in mouse, and from other types of non-genetic approaches. Although it appears by now that the main lipid pathways have been uncovered, and that only modulators or adaptor proteins such as those encoded by LDLRAP1, APOA5, ANGPLT3/4, and PCSK9 are currently being discovered, genome wide association studies (GWAS) in particular have implicated many new loci based on statistical analyses; these may prove to have equally large impacts on lipoprotein traits as gene products that are already known. On the other hand, since 2004 - and particularly since 2010 when massively parallel sequencing has become de rigeur - no major new insights into genes governing lipid metabolism have been reported. This is probably because the etiologies of true Mendelian lipid disorders with overt clinical complications have been largely resolved. In the meantime, it has become clear that proving the importance of new candidate genes is challenging. This could be due to very low frequencies of large impact variants in the population. It must further be emphasized that functional genetic studies, while necessary, are often difficult to accomplish, making it hazardous to upgrade a variant that is simply associated to being definitively causative. Also, it is clear that applying a monogenic approach to dissect complex lipid traits that are mostly of polygenic origin is the wrong way to proceed. The hope is that large-scale data acquisition combined with sophisticated computerized analyses will help to prioritize and select the most promising candidate genes for future research. We suggest that at this point in time, investment in sequence technology driven candidate gene discovery could be recalibrated by refocusing efforts on direct functional analysis of the genes that have already been discovered. This article is part of a Special Issue entitled: From Genome to Function.