Joint CMU-Pitt Ph.D. Program in Computational Biology Seminar

  • Associate Professor
  • Department of Molecular Biology, Cellular Biology and Biochemistry
  • Brown University

Pathogenic variations often disrupt splicing – implications for clinical genetics and evolutionary models of regulatory signals

The lack of tools to identify causative variants from sequencing data greatly limits the promise of Precision Medicine. Previous studies suggest one-third of disease alleles alter splicing. We discovered that splicing defects cluster in diseases (e.g. haploinsufficient genes). We analyzed 4,964 published disease-causing exonic mutations using a Massively Parallel Splicing Assay (MaPSy) that showed 81% concordance rate with patient tissue splicing. ~10% of exonic mutations altered splicing, mostly by disrupting multiple stages of the spliceosome assembly. Analyzing natural variations in the human population yields remarkable insight into the evolution of the splicing code.  we test 708 de novo exonic variants for defects in splicing and find a striking correspondence between the severity of an exonic variant’s effect on the protein code and severity of splicing defect (synonymous < missense < stop codon). We propose this overlap is explained by ESE arising through purifying selection on the protein coding function of exons. A key feature of any signaling system is a high signal to noise ratio. For ESEs, this means an enrichment in exons relative to introns. Mutational bias effectively becomes a second driver of ESE evolution. Hypermutable motifs are passively depleted from introns thereby reducing noise in the ESE recognition system.

Dr. Will Fairbrother majored in Chemistry at Oberlin College (Oberlin, OH) and received his PhD from Columbia University in 2000. Dr. Fairbrother was a PhRMA Post-doctoral Fellow in Informatics at Massachusetts Institute of Technology (MIT) under mentorship of Christopher Burge and Nobel Laureate Phillip Sharp. Dr Fairbrother is currently a tenured, associate professor in the MCB Department and the Director of Graduate Studies for the Center for Computational Molecular Biology at Brown. His research has focused on precision medicine and RNA genomics. the Fairbrother lab is using high-throughput biochemical screens and computational methods to understand the specificity of RNA processing.  Results from Dr. Fairbrother’s lab suggest 1/3 of all hereditary disease mutations affect the processing of genes. More recently, Dr. Fairbrother and his laboratory have become interested in developing methods for analyzing clinical sequencing experiments (e.g., whole-genome and whole-exome sequencing data). To this end, he is active with the Mendelian Genetics Research Group at Harvard.

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