%0 Journal Article %1 huang2018estimation %A Huang, Yi-Fei %A Siepel, Adam %D 2018 %I Cold Spring Harbor Laboratory %K DFE annotation distribution_of_fitness_effects machine_learning methods %R 10.1101/441337 %T Estimation of allele-specific fitness effects across human protein-coding sequences and implications for disease %U https://doi.org/10.1101%2F441337 %X A central challenge in human genomics is to understand the cellular, evolutionary, and clinical significance of genetic variants. Here we introduce a unified population-genetic and machine- learning model, called Linear Allele-Specific Selection InferencE (LASSIE), for estimating the fitness effects of all potential single-nucleotide variants, based on polymorphism data and pre- dictive genomic features. We applied LASSIE to 51 high-coverage genome sequences annotated with 33 genomic features, and constructed a map of allele-specific selection coefficients across all protein-coding sequences in the human genome. We show that this map is informative about both human evolution and disease.

@article{huang2018estimation, abstract = {A central challenge in human genomics is to understand the cellular, evolutionary, and clinical significance of genetic variants. Here we introduce a unified population-genetic and machine- learning model, called Linear Allele-Specific Selection InferencE (LASSIE), for estimating the fitness effects of all potential single-nucleotide variants, based on polymorphism data and pre- dictive genomic features. We applied LASSIE to 51 high-coverage genome sequences annotated with 33 genomic features, and constructed a map of allele-specific selection coefficients across all protein-coding sequences in the human genome. We show that this map is informative about both human evolution and disease.}, added-at = {2019-05-15T00:14:07.000+0200}, author = {Huang, Yi-Fei and Siepel, Adam}, biburl = {https://www.bibsonomy.org/bibtex/2eb5873ae83ddd31c68b65726a5360e34/peter.ralph}, doi = {10.1101/441337}, interhash = {03906edbd9c6e2539d3698b0769b1528}, intrahash = {eb5873ae83ddd31c68b65726a5360e34}, keywords = {DFE annotation distribution_of_fitness_effects machine_learning methods}, month = oct, publisher = {Cold Spring Harbor Laboratory}, timestamp = {2019-05-15T00:14:07.000+0200}, title = {Estimation of allele-specific fitness effects across human protein-coding sequences and implications for disease}, url = {https://doi.org/10.1101%2F441337}, year = 2018 }