Computational framework for identifiable and phase-consistent allele-specific expression quantification

单倍型推断和等位基因特异性转录表达定量是遗传学和基因组学中的两个基本问题。单倍型推断将遗传变异的母本和父本等位基因沿着两条二倍体染色体进行比对，而等位基因特异性表达定量从mRNA序列读取中获得母本和父本来源的转录本的表达水平。这两个问题是相互耦合的，因为一个问题会影响另一个问题的准确性：准确的等位基因特异性表达定量需要准确的单倍型来映射RNA-SEQ读数，而单倍型推断的准确性可以通过等位基因特异性RNA-SEQ读数来提高。虽然已有的工作分别考虑了这两个问题，但本项目开发了一个计算框架，在一个统计框架内联合解决这两个基本问题，以提高推断的单倍型和等位基因特异性表达量化的准确性。这项研究中将要开发的计算方法将推动生物学研究的各个方面，这些方面需要准确的等位基因特异性表达估计和单倍型，包括定位等位基因特异的eQTL，检测印记基因，输入未分型的变异，寻找自然选择的特征，以及检测重组事件。研究结果将用于少数族裔服务机构的外展活动，以招收研究生。该项目开发了一个计算框架，用于从RNA-SEQ和基因数据中获得准确的等位基因特定表达测量和单倍型。现有的两个框架，一个用于转录表达量化，另一个用于单倍型推理(例如Beagle)，被合并到一个框架中，同时保持了原始框架的计算效率。这两个现有框架中的每一个都进行了修改，以解决关于等位基因特异阅读的两个以前未解决的挑战：对于RNA-Seq量化，该项目开发了一种严格的数学方法，以获得基因水平、转录集水平或单个转录水平的可识别的等位基因特异表达估计，而对于单倍型推断，该项目将Beagle的模型与这些研究人员的RNA序列量化方法结合起来，以联合估计彼此一致的可识别的等位基因特异表达水平和单倍型。计算方法是以等位基因特有的eQTL定位为基准，使用来自人类三个组和具有已知单倍型的LG/SM杂交小鼠的基因类型和RNA-SEQ读数。这项研究的结果可在http://www.cs.cmu.edu/~sssykim.This网站上获得，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智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Haplotype inference and allele-specific transcript expression quantification are two fundamentalproblems in genetics and genomics. Haplotype inference aligns maternal and paternal alleles ofgenetic variants along two diploid chromosomes, whereas allele-specific expressionquantification obtains the expression levels of transcripts of maternal and paternal origins fromRNA-seq reads. These two problems are coupled in that one can affect the accuracy of theother: accurate allele-specific expression quantification requires accurate haplotypes to mapRNA-seq reads to and the accuracy of haplotype inference can be enhanced by allele-specificRNA-seq reads. While existing works have considered these two problems separately, this projectdevelops a computational framework to address these two fundamental problems jointly in asingle statistical framework to enhance the accuracy of both inferred haplotypes andallele-specific expression quantification. The computational methods to be developed in thisresearch will advance various aspects of biological research that require accurate allele-specificexpression estimates and haplotypes, including mapping allele-specific eQTLs, detectingimprinted genes, imputing untyped variants, finding signatures of natural selection, anddetecting recombination events. The outcome of the research will be used in outreach activitiesin minority serving institutions to recruit graduate students.The project develops a computational framework for obtaining accurate allele-specificexpression measurements and haplotypes from RNA-seq and genotype data. Two existingframeworks, one for transcript expression quantification and the other for haplotype inference(e.g., Beagle), are combined into a single framework, while keeping the computational efficiencyof the original frameworks. Each of these two existing frameworks is modified to address twopreviously-unmet challenges regarding allele-specific reads: for the RNA-seq quantification, theproject develops a mathematically rigorous approach to obtaining identifiable allele-specificexpression estimates at gene level, at transcript-set level, or at individual transcript level,whereas for haplotype inference, the project couples the model in Beagle with RNA-seqquantification methods of these investigators to jointly estimate identifiable allele-specific expression levels andhaplotypes that are consistent with each other. The computational methods are benchmarkedon allele-specific eQTL mapping, using genotypes and RNA-seq reads from human trios andLG/SM intercross mice with known haplotypes. The outcome of the research is available athttp://www.cs.cmu.edu/~sssykim.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.