Genomic and Metabolomic Profiling of Finnish Familial Dyslipidemia Families

描述(由申请人提供)：这项建议是利用代谢组学和先进的基因组学技术重新调查92个芬兰家系，这些家系被确定为两种形式的复杂可遗传血脂异常：家族性混合性高脂血症(FCHL)和低血清高密度脂蛋白胆固醇(HDLC)水平。这些家系在代谢指标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和高密度脂蛋白-C的显著结果，但由于缺乏足够强大的表型和基因分型技术，因果变异的鉴定一直受到限制。我们现在建议通过获得新的表型来重新分析这些家族，这些表型被假设为比以前使用的复合脂类测量更准确地反映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的疾病相关变异提供了特殊的优势，并为进一步验证在几个芬兰人口队列中的发现提供了机会。在这个项目中，我们将获得大约1400个这些家系成员的代谢组谱。通过结合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)和整个家系的全基因组SNP基因分型，我们将建立一个分离在这些家系中的变异的全面目录。通过连锁分析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，将使我们能够识别哪些特定变异是对我们将获得的原始和扩展的代谢表型集有贡献的候选变量。通过对所有可用的家系成员(估计约为900人)的血液样本进行RNA测序获得的基因表达数据将为优先考虑代谢表型的候选变异提供另一种形式的证据，并可能表明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.