CAREER: Scalable algorithms for regularized and non-linear genetic models of gene expression

DNA突变对基因的工作方式有着深远的影响，但目前还不清楚哪些突变会影响哪些基因。目前，由于在分析基因组学数据方面的挑战，我们的知识有限，例如欧洲研究参与者的过度代表性和不能充分捕获数据的简单统计模型所产生的偏差。该项目跨越三个主要科学目标克服了这些挑战，其中创新的统计模型将DNA突变映射到目标基因，以及两个教育目标，其中科学培训和多样性同时培养。首先，研究人员将提高未被充分研究的个体群体（如少数民族群体）的突变定位到目标基因的保真度。其次，研究人员将开发一种新的方法，通过考虑基因如何在全基因组网络中相互作用来将突变与基因联系起来，从而提示许多未表征突变的功能影响。第三，研究人员将利用反映单细胞基因组分析数据自然分布的可扩展模型来描述突变发挥作用的特定细胞。这项研究通过引入新的、稳健的统计模型，将突变与目标基因联系起来，推动了生物信息学和人类遗传学领域的发展。该项目还增强了生物医学发现的公平性和多样性，同时增强了研究环境的多样性。针对后者，研究人员针对资源匮乏社区的高中生发起了为期数周的校园研究计划，并为本科生和研究生提供遗传学培训课程，提供工业界和学术界都梦寐以求的定量跨学科技能。该奖项将产生广泛的数据集、开源统计模型和基因组学工具、高影响力出版物和课程材料，从而吸引并推动科学界参与和推动相关研究。该项目侧重于开发新的遗传模型，以了解特定的遗传变异如何影响基因表达。这些模型克服了目前在描述遗传变异功能方面的局限性，遗传变异的后续目标通常是在人类表型（如身高和癌症风险）的调节中暗示靶基因。现有算法的挑战包括由于有限的样本量而导致的统计问题（特别是对于未充分研究的少数群体），限制从全基因组分析中获得知识的多重假设负担，以及模型规范错误，特别是对日益流行的新数据类型，如单细胞基因组学。研究人员通过三个主要目标来解决这些挑战。首先，研究人员通过联合建模全球不同数据集的遗传关联，将遗传变异与未充分研究的少数民族人群的基因表达变化联系起来。其次，研究人员开发了一种综合方法，利用基因调控网络的先验知识和先进的机器学习算法，将全基因组遗传变异映射到基因表达的变化，以减少多次测试的负担。第三，研究人员设计了一种新的统计模型，以高分辨率表征基因表达调控的细胞类型特异性；该模型利用了单细胞数据的自然分布，解决了最先进方法的模型错误，并通过对数百万个供体的单细胞测量进行建模来降低测量噪声。该奖项支持开源基因组软件和描述基因变异功能的数据存储库的生成，同时也为资源不足的高中生和有动力的本科生和研究生创造教育和培训机会。共生研究和教育交织在一种关系中，这种关系有望增强研究环境的多样性，以及研究群体的多样性。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

DNA mutations have a profound effect on how genes work, but it’s still not well understood which mutations affect which genes. Currently, our knowledge is limited due to challenges in analyzing genomics data, such as bias arising from an overrepresentation of European study participants and simplistic statistical models that do not sufficiently capture the data. This project overcomes these challenges across three main scientific goals, in which innovative statistical models map DNA mutations to their target genes, and two educational goals, in which scientific training and diversity are simultaneously cultivated. First, the investigators will improve the fidelity of mapping mutations to target genes for groups of individuals that are not well-studied, such as minority populations. Second, the investigators will develop a new method to connect mutations to genes by considering how genes interact with each other in genome-wide networks, suggesting functional effects for many uncharacterized mutations. Third, the investigators will characterize the specific cells in which mutations exert their effects using scalable models that reflect the natural distribution of data from single cell genomic assays. This research advances the fields of bioinformatics and human genetics by introducing new, robust statistical models that link mutations to their target genes. This project also enhances equity and diversity in biomedical discoveries, while simultaneously enhancing diversity within research environments. Toward the latter, the investigators initiate a multi-week on-campus research program for high school students from under-resourced communities, as well as genetics training courses for undergraduate and graduate students, supplying quantitative interdisciplinary skills coveted by industry and academia alike. This award will generate extensive datasets, open-source statistical models and genomics tools, high-impact publications, and course materials, thereby engaging and fueling the scientific community to partake and propel related research. This project focuses on developing new genetic models to understand how specific genetic variations influence gene expression. These models overcome current limitations in characterizing the function of genetic variation, which often has the subsequent goal of implicating target genes in the regulation of human phenotypes such as height and cancer risk. Challenges of existing algorithms include statistical issues due to finite sample sizes (especially for understudied minority populations), multiple hypothesis burdens restricting the knowledge gained from genome-wide analysis, and model misspecification especially for new datatypes of growing popularity, such as single cell genomics. The investigators address these challenges across three main objectives. First, the investigators link genetic variation to changes in gene expression in understudied minority populations, by jointly modeling genetic associations across globally diverse datasets. Second, the investigators develop a comprehensive approach to map genome-wide genetic variants to changes in gene expression using a priori knowledge of gene regulatory networks and advanced machine learning algorithms to reduce the burden of multiple testing. Third, the investigators design a new statistical model to characterize the cell-type-specificity of gene expression regulation at high resolution; this model leverages the natural distribution of single cell data, resolving model misspecification of state-of-the-art methods and reduces measurement noise by modeling millions of single cell measurements across donors. This award supports the generation of open-source genomics software and data repositories characterizing the function of genetic variants, while also creating educational and training opportunities for under-resourced high school students and motivated undergraduate and graduate students. The symbiotic research and educational intertwine in a relationship that is expected to enhance both the diversity in research environments, as well as the diversity in research cohorts.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.