Scharfe, C. | bioRxiv Science

bioRxiv2019

Massively parallel discovery of human-specific substitutions that alter neurodevelopmental enhancer activity

Genetic changes that altered the function of gene regulatory elements have been implicated in the evolution of the human brain. However, identifying the particular changes that modified regulatory activity during neurodevelopment remains challenging. Here we used massively parallel enhancer assays in human neural stem cells to measure the impact of 32,776 human-specific substitutions on enhancer activity in 1,363 Human Accelerated Regions (HARs) and 3,027 Human Gain Enhancers (HGEs), which include enhancers with novel activities in humans. We found that 31.9% of active HARs and 36.4% of active HGEs exhibited differential activity between human and chimpanzee. This enabled us to isolate the effects of 401 human-specific substitutions from other types of genetic variation in HARs and HGEs. Substitutions acted in both an additive and non-additive manner to alter enhancer activity. Human-specific substitutions altered predicted binding sites for a specific set of human transcription factors (TFs) that were a subset of TF binding sites associated with enhancer activity in our assay. Substitutions within HARs, which are overall highly constrained compared to HGEs, showed smaller effects on enhancer activity, suggesting that the impact of human-specific substitutions may be buffered in enhancers with constrained ancestral functions. Our findings yield insight into the mechanisms by which human-specific genetic changes impact enhancer function and provide a rich set of candidates for experimental studies of regulatory evolution in humans.

genomics↗

bioRxiv2017

DEVELOPING GENE-SPECIFIC META-PREDICTOR OF VARIANT PATHOGENICITY

Rapid, accurate, and inexpensive genome sequencing promises to transform medical care. However, a critical hurdle to enabling personalized genomic medicine is predicting the functional impact of novel genomic variation. Various methods of missense variants pathogenicity prediction have been developed by now. Here we present a new strategy for developing a pathogenicity predictor of improved accuracy by applying and training a supervised machine learning model in a gene-specific manner. Our meta-predictor combines outputs of various existing predictors, supplements them with an extended set of stability and structural features of the protein, as well as its physicochemical properties, and adds information about allele frequency from various datasets. We used such a supervised gene-specific meta-predictor approach to train the model on the CFTR gene, and to predict pathogenicity of about 1,000 variants of unknown significance that we collected from various publicly available and internal resources. Our CFTR-specific meta-predictor based on the Random Forest model performs better than other machine learning algorithms that we tested, and also outperforms other available tools, such as CADD, MutPred, SIFT, and PolyPhen-2. Our predicted pathogenicity probability correlates well with clinical measures of Cystic Fibrosis patients and experimental functional measures of mutated CFTR proteins. Training the model on one gene, in contrast to taking a genome wide approach, allows taking into account structural features specific for a particular protein, thus increasing the overall accuracy of the predictor. Collecting data from several separate resources, on the other hand, allows to accumulate allele frequency information, estimated as the most important feature by our approach, for a larger set of variants. Finally, our predictor will be hosted on the ClinGen Consortium database to make it available to CF researchers and to serve as a feasibility pilot study for other Mendelian diseases.

bioinformatics↗