A Platform for Large-Scale Discovery in Common Disease

项目总结 本补充资料将继续资助我们的常见病基因组学中心(UM1HG008853) 华盛顿大学麦克唐纳基因组研究所，题为《共同的大规模发现平台》 疾病。“在补充期间，我们将继续我们的多种族病例对照全基因组测序 (WGS)研究的重点是定位新的疾病基因和变异，这些基因和变异是潜在的风险和预防早期- 起病的冠心病(EOCAD)。与EOCAD案例一起，我们的目标是选择表型较深的控件 尽可能研究定量心脏代谢危险因素的遗传基础。之后 完成WGS后，我们将组装一个联合调用集，其中包括我们中心的所有EOCAD案例和控件 以实现基因分型和疾病结果之间的关联测试。初步分析的基因类型将 包括检测常见(个体)和罕见(负担)单核苷酸变异(SNV)、插入/缺失 跨编码和非编码空间的变体(Indels)和结构变体(Sv)。在二次分析中，我们 将测试与定量心脏代谢风险因素特征的关联，并将利用不同的模式 混合体，以定位先前绘制的疾病和性状相关基因的潜在因果变异。超越 疾病协会研究，我们将继续与联盟成员合作创建基因组 将由科学界使用的资源，如聚合站点频率、推算 资源，以及开源分析方法。

PROJECT SUMMARY This supplement will continue to fund our Center for Common Disease Genomics (UM1HG008853) at the McDonnell Genome Institute at Washington University entitled, “A platform for large-scale discovery in common disease.” In the supplemental period we will continue our multi-ethnic case-control whole genome sequencing (WGS) study focused on mapping novel disease genes and variants underlying risk and protection from early- onset coronary artery disease (EOCAD). Along with EOCAD cases, we aim to select deeply phenotyped controls whenever possible in order to study the genetic basis of quantitative cardiometabolic risk factors. After completing the WGS, we will assemble a joint callset that includes all EOCAD cases and controls from our center to enable association testing between genotypes and disease outcomes. Genotypes for the primary analysis will include testing common (individual) and rare (burden) single nucleotide variants (SNVs), insertion/deletion variants (indels), and structural variants (SVs) across coding and non-coding space. In secondary analyses, we will test for association with quantitative cardiometabolic risk factor traits and will leverage differential patterns of admixture to map causal variants underlying previously mapped disease and trait associated loci. Beyond disease association studies, we will continue to collaborate with consortium members to create genomic resources that will be used by the scientific community such as aggregated site frequencies, imputation resources, and open source analysis methods.