Genomic and Metabolomic Profiling of Finnish Familial Dyslipidemia Families

University Of California Los Angeles, Los Angeles CA

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Abstract

DESCRIPTION (provided by applicant): This proposal is to re-investigate, using metabolomic profiling and advanced genomics technologies, 92 Finnish pedigrees that were ascertained for two forms of complex heritable dyslipidemia: familial combined hyperlipidemia (FCHL) and low serum levels of high density lipoprotein cholesterol (HDL-C). These families were extensively phenotyped for metabolic measures and while linkage analyses yielded strong findings for FCHL and HDL-C in several chromosomal locations, identification of causal variants had been limited by the lack of sufficiently powerful technologies for both phenotypic and genotypic characterization. We now propose to re-analyze these families by obtaining new phenotypes hypothesized to more accurately reflect the biological underpinnings of dyslipidemias than the previously used composite lipid measures. The unique population structure of Finland provides special advantages for discovery of low frequency and rare disease- related variants in these families and opportunities for further validation of findings in several Finnish population cohorts In this project we will obtain metabolomic profiles on about 1400 members of these pedigrees. By combining whole genome sequencing (WGS) of the most genetically informative family members (about 300 individuals) with genome wide SNP genotyping of the entire pedigrees, we will establish a comprehensive catalog of variants segregating in these pedigrees. Phenotype-genotype correlations established by linkage and association analyses, along with bioinformatic analyses that detect likely deleterious variants, will enable us to identify the specific variants hat are candidates for contributing to the original and expanded set of metabolic phenotypes that we will obtain. Gene expression data to be obtained by RNA sequencing of blood samples from all available pedigree members (estimated to be about 900 individuals) will provide an additional form of evidence to prioritize candidate variants for metabolic phenotypes and may suggest relationships between genetic variation and gene function.