Genomic and Metabolomic Profiling of Finnish Familial Dyslipidemia Families

描述（由申请人提供）：本提案是利用代谢组学分析和先进的基因组学技术，重新调查92个芬兰家系，确定了两种形式的复杂遗传性血脂异常：家族性合并高脂血症（FCHL）和低血清高密度脂蛋白胆固醇（HDL-C）。这些家族在代谢测量方面进行了广泛的表型分析，虽然连锁分析在几个染色体位置产生了FCHL和HDL-C的强烈发现，但由于缺乏足够强大的表型和基因型表征技术，鉴定因果变异受到限制。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测量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和罕见疾病相关的变异提供了特殊的优势，并为进一步验证几个芬兰人群队列的发现提供了机会。在这个项目中，我们将获得这些谱系中约1400名成员的代谢组学谱。通过对最具遗传信息性的家庭成员（约300个个体）进行全基因组测序（WGS），并结合整个家系的全基因组SNP基因分型，我们将建立一个在这些家系中分离的变异的综合目录。通过连锁和关联分析建立的表型-基因型相关性，以及检测可能有害变异的生物信息学分析，将使我们能够确定特定的变异，这些变异是我们将获得的原始和扩展的代谢表型的候选变异。通过对所有可用谱系成员（估计约900人）的血液样本进行RNA测序获得的基因表达数据将为代谢表型的候选变异优先排序提供额外的证据，并可能提示遗传变异和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.