Toward mapping the biology of the genome

OUP accepted manuscript

Alternative splicing is frequently involved in the diversification of protein function and can also be modulated for therapeutic purposes. Here we develop a predictive model, called Exon ByPASS (predicting Exon skipping Based on Protein amino acid SequenceS), to assess the criticality of exon inclusion based solely on information contained in the amino acid sequence upstream and downstream of the exon junctions. By focusing on protein sequence, Exon ByPASS predicts exon skipping independent of tissue and species in the absence of any intronic information. We validate model predictions using transcriptomic and proteomic data and show that the model can capture exon skipping in different tissues and species. Additionally, we reveal potential therapeutic opportunities by predicting synthetically skippable exons and neo-junctions arising in cancer cells.

Detectable clonal mosaicism in the human genome

Human genetic mosaicism is the presence of two or more cellular populations with distinct genotypes in an individual who developed from a single fertilized ovum. While initially observed across a spectrum of rare genetic disorders, detailed assessment of data from genome-wide association studies now reveal that detectable clonal mosaicism involving large structural alterations (>2 Mb) can also be seen in populations of apparently healthy individuals. The first generation of descriptive studies has generated new interest in understanding the molecular basis of the affected genomic regions, percent of the cellular subpopulation involved, and developmental timing of the underlying mutational event, which could reveal new insights into the initiation, clonal expansion, and phenotypic manifestations of mosaic events. Early evidence indicates detectable clonal mosaicism increases in frequency with age and could preferentially occur in males. The observed pattern of recurrent events affecting specific chromosomal regions indicates some regions are more susceptible to these events, which could reflect inter-individual differences in genomic stability. Moreover, it is also plausible that the presence of large structural events could be associated with cancer risk. The characterization of detectable genetic mosaicism reveals that there could be important dynamic changes in the human genome associated with the aging process, which could be associated with risk for common disorders, such as cancer, cardiovascular disease, diabetes, and neurological disorders.

Predicting cell types and genetic variations contributing to disease by combining GWAS and epigenetic data

Genome-wide association studies (GWASs) identify single nucleotide polymorphisms (SNPs) that are enriched in individuals suffering from a given disease. Most disease-associated SNPs fall into non-coding regions, so that it is not straightforward to infer phenotype or function; moreover, many SNPs are in tight genetic linkage, so that a SNP identified as associated with a particular disease may not itself be causal, but rather signify the presence of a linked SNP that is functionally relevant to disease pathogenesis. Here, we present an analysis method that takes advantage of the recent rapid accumulation of epigenomics data to address these problems for some SNPs. Using asthma as a prototypic example; we show that non-coding disease-associated SNPs are enriched in genomic regions that function as regulators of transcription, such as enhancers and promoters. Identifying enhancers based on the presence of the histone modification marks such as H3K4me1 in different cell types, we show that the location of enhancers is highly cell-type specific. We use these findings to predict which SNPs are likely to be directly contributing to disease based on their presence in regulatory regions, and in which cell types their effect is expected to be detectable. Moreover, we can also predict which cell types contribute to a disease based on overlap of the disease-associated SNPs with the locations of enhancers present in a given cell type. Finally, we suggest that it will be possible to re-analyze GWAS studies with much higher power by limiting the SNPs considered to those in coding or regulatory regions of cell types relevant to a given disease.