QuBBD: Mathematical models for a molecular genetic understanding of population variation in risk of cardiovascular disease

全基因组关联研究（GWAS）是一种强有力的方法，用于定位基因组中含有与性状变异相关的单核苷酸差异（单核苷酸变异或SNV）的区域。从GWAS鉴定的基因组区域缩小到负责性状变异的个体SNV对于基因组中介于基因之间的区域特别具有挑战性。 GWAS中的第二个挑战是大量的SNV需要高水平的统计严格性，因此，许多生物学相关的SNV被遗漏。 GWAS用于许多人类疾病特征（如冠状动脉疾病或CAD），因此，解决这两个挑战将在生物学和生物医学研究中具有广泛的意义。 该奖项支持启动一个合作研究项目，该项目将通过开发数学模型来解决这两个挑战，该模型整合了来自细胞和群体研究的各种类型的测量和信息，以确定影响性状变异的基因之间的SNV，并提高GWAS的统计能力。 由于CAD的患病率（美国有1500万人），CAD是改善GWAS的一个重要应用。该项目的目标是（1）创建和评估一个综合统计模型，以提高GWAS分析的能力，并发现新的基因-性状关联;（2）创建和评估一个机器学习模型，以识别基因间GWAS区域内的调控变体。 这些模型将整合来自Frachial Heart Study SHARe数据库、ENCODE项目、GTEx项目和CARDIOGRAMplusC 4D的大规模数据集的特征。这些模型的性能将以先前公布的模型为基准。 该项目的重要成果将是：（1）第一个用于综合GWAS分析的分析统计模型，该模型将提供易于解释的显著性评分;（2）定量验证和可解释的机器学习模型，用于组合基因组信息类型以预测调控变体;（3）模型中整合的基因组特征的特征重要性评分;和（4）鉴定新的GWAS基因座，用于CAD风险的群体变异，以及基因座内的调节变体，与它们相关的基因和转录因子（以及最终的基因功能注释）。 这些方法的软件实现将在一个开放源码软件库中共享。 该奖项由美国国立卫生研究院大数据到知识（BD 2K）计划与国家科学基金会数学科学部合作支持。

Genome-wide association studies (GWAS) are a powerful approach for mapping the regions of the genome containing single-nucleotide differences in the population (single-nucleotide variants or SNVs) that are associated with trait variation. Narrowing down from GWAS-identified genomic regions to the individual SNVs that are responsible for trait variation is particularly challenging for regions of the genome that are in-between genes. A second challenge in GWAS is that the large number of SNVs necessitates a high level of statistical stringency, and thus, many biologically relevant SNVs are missed. GWAS is used for many human disease traits (such as coronary artery disease or CAD), and thus, addressing these two challenges would have broad significance in biology and biomedical research. This award supports initiation of a collaborative research project that will address these two challenges by developing mathematical models that integrate a variety of types of measurements and information derived from cells and population studies, in order to pinpoint SNVs in-between genes that affect trait variation, and to improve the statistical power of GWAS. CAD is a high-significance application for improving GWAS because of CAD's prevalence (15 million in the U.S.).The objectives of this project are to (1) create and evaluate an integrative statistical model for improving power for GWAS analysis and for discovering novel gene-trait associations and (2) create and evaluate a machine-learning model for identifying regulatory variants within intergenic GWAS regions. The models would incorporate features from large-scale datasets from the Framingham Heart Study SHARe database, the ENCODE project, the GTEx project, and CARDIOGRAMplusC4D. The models' performance would be benchmarked against previously published models. The project's significant outcomes would be: (1) the first analytic statistical model for integrative GWAS analysis that would provide a readily interpretable significance score; (2) a quantitatively validated and interpretable machine-learning model for combining genomic information types to predict regulatory variants; (3) feature importance scores for the genomic features that are integrated within the model; and (4) identification of new GWAS loci for population variation in CAD risk, and, for regulatory variants within the loci, the genes and transcription factors (and ultimately, the gene functional annotations) with which they are associated. Software implementations of the methods will be shared in an open-source software repository. This award is supported by the National Institutes of Health Big Data to Knowledge (BD2K) Initiative in partnership with the National Science Foundation Division of Mathematical Sciences.