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

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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.