Oral Presentation 9th GeneMappers Conference 2012

Deep sequencing identifies coding variants influencing lipidomic profiles  (#24)

Claire Bellis 1 , Peter J Meikle 2 , Jacqui M Weir 2 , Jeremy B Jowett 2 , Satish Kumar 1 , Marcio Almedia 1 , Juan M Peralta 1 , Ellen E Quillen 1 , Michael C Mahaney 1 , Thomas D Dyer 1 , Laura Almasy 1 , John Blangero 1 , Joanne E Curran 1
  1. Genetics, Texas Biomedical Research Institute, San Antonio, Texas, USA
  2. Baker IDI Heart and Diabetes Institute, Melbourne, VIC, Australia

The lipidome encompasses the complete universe of fundamental lipid species. It represents a potential gold mine of clinically relevant phenotypes that may be better predictors of disease risk than those that are commonly studied. Additionally, the biologically simpler nature of such lipid species presents the hypothesis that determinants may reside closer to the causal action of genes than more complex integrated lipid measures, such as total cholesterol level, making them valuable phenotypes for finding genes involved in lipid metabolism. Implementation of a targeted capture method involving liquid chromatography electrospray ionization-tandem mass spectrometry provided precise identification and quantification of 356 lipid measures in 1202 Mexican American individuals from large pedigrees in the San Antonio Family Heart Study (SAFHS). Genome-wide association analyses revealed several significant QTL localizations for these canonical lipid phenotypes, however by harnessing the power of whole genome sequence (WGS) data the chance of identifying a functional variant influencing lipid metabolism is significantly increased. We currently have whole genome sequence available for ~950 Mexican American individuals, and will be increasing this to 2,000 in the coming months. In this study, we focus on the potential effects of obligately functional protein altering variants.  Analysis of the WGS data identified 44,985 such non-synonymous coding variants, of which 11,484 are predicted to be highly deleterious (based on PolyPhen prediction, having a score > 0.7). From the SAFHS, 596 individuals have both WGS data and complete lipidomic profiles. Preliminary variant-specific and gene-specific burden association results have identified several rare coding variants apparently influencing lipid species including a highly deleterious rare variant in the GPIHBP1 gene previously known to be involved in severe hypertriglyceridemia. In this gene, an S144F mutation is highly associated with multiple forms of lysophosphatidylcholine. These results illustrate the utility of WGS analysis for direct identification of functional targets influencing lipid measures.