Genomic and Metabolomic Profiling of Finnish Familial Dyslipidemia Families

描述（由申请人提供）：本提案旨在使用代谢组学分析和先进的基因组学技术重新研究92个芬兰家系，这些家系被确定为两种形式的复杂遗传性血脂异常：家族性混合型高脂血症（FCHL）和低血清高密度脂蛋白胆固醇（HDL-C）水平。这些家庭广泛的表型代谢措施，虽然连锁分析产生了强有力的发现FCHL和HDL-C在几个染色体位置，鉴定因果变异已有限的缺乏足够强大的技术，表型和基因型表征。我们现在建议通过获得新的表型来重新分析这些家族，这些表型假设比以前使用的复合脂质措施更准确地反映了血脂异常的生物学基础。芬兰独特的人口结构为在这些家族中发现低频率和罕见疾病相关的变异提供了特殊的优势，并为在几个芬兰人口队列中进一步验证发现提供了机会。在本项目中，我们将获得这些家系中约1400名成员的代谢组学谱。通过将遗传信息量最大的家族成员（约300个个体）的全基因组测序（WGS）与整个谱系的全基因组SNP基因分型相结合，我们将建立这些谱系中分离的变体的综合目录。通过连锁和关联分析建立的表型-基因型相关性，沿着检测可能有害变体的生物信息学分析，将使我们能够鉴定特定变体，这些特定变体是有助于我们将获得的原始和扩展代谢表型集的候选者。通过对所有可用谱系成员（估计约900个个体）的血液样本进行RNA测序获得的基因表达数据将提供额外形式的证据，以优先考虑代谢表型的候选变体，并可能表明遗传变异与基因功能之间的关系。

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.