@article {91, title = {Assessing Digital Phenotyping to Enhance Genetic Studies of Human Diseases.}, journal = {Am J Hum Genet}, volume = {106}, year = {2020}, month = {2020 05 07}, pages = {611-622}, abstract = {

Population-scale biobanks that combine genetic data and high-dimensional phenotyping for a large number of participants provide an exciting opportunity to perform genome-wide association studies (GWAS) to identify genetic variants associated with diverse quantitative traits and diseases. A major challenge for GWAS in population biobanks is ascertaining disease cases from heterogeneous data sources such as hospital records, digital questionnaire responses, or interviews. In this study, we use genetic parameters, including genetic correlation, to evaluate whether GWAS performed using cases in the UK Biobank ascertained from hospital records, questionnaire responses, and family history of disease implicate similar disease genetics across a range of effect sizes. We find that hospital record and questionnaire GWAS largely identify similar genetic effects for many complex phenotypes and that combining together both phenotyping methods improves power to detect genetic associations. We also show that family history GWAS using cases ascertained on family history of disease agrees with combined hospital record and questionnaire GWAS and that family history GWAS has better power to detect genetic associations for some phenotypes. Overall, this work demonstrates that digital phenotyping and unstructured phenotype data can be combined with structured data such as hospital records to identify cases for GWAS in biobanks and improve the ability of such studies to identify genetic associations.

}, keywords = {Asthma, Databases, Factual, Disease, Female, Genetics, Medical, Genome-Wide Association Study, Genotype, Humans, Male, Neoplasms, Phenotype, United Kingdom}, issn = {1537-6605}, doi = {10.1016/j.ajhg.2020.03.007}, author = {DeBoever, Christopher and Tanigawa, Yosuke and Aguirre, Matthew and McInnes, Greg and Lavertu, Adam and Rivas, Manuel A} } @article {81, title = {Identification of cancer driver genes based on nucleotide context.}, journal = {Nat Genet}, volume = {52}, year = {2020}, month = {2020 02}, pages = {208-218}, abstract = {

Cancer genomes contain large numbers of somatic mutations but few of these mutations drive tumor development. Current approaches either identify driver genes on the basis of mutational recurrence or approximate the functional consequences of nonsynonymous mutations by using bioinformatic scores. Passenger mutations are enriched in characteristic nucleotide contexts, whereas driver mutations occur in functional positions, which are not necessarily surrounded by a particular nucleotide context. We observed that mutations in contexts that deviate from the characteristic contexts around passenger mutations provide a signal in favor of driver genes. We therefore developed a method that combines this feature with the signals traditionally used for driver-gene identification. We applied our method to whole-exome sequencing data from 11,873 tumor-normal pairs and identified 460 driver genes that clustered into 21 cancer-related pathways. Our study provides a resource of driver genes across 28 tumor types with additional driver genes identified according to mutations in unusual nucleotide contexts.

}, keywords = {Cluster Analysis, Computational Biology, Humans, Mutation, Neoplasms, Nucleotides, Proteins, Whole Exome Sequencing}, issn = {1546-1718}, doi = {10.1038/s41588-019-0572-y}, author = {Dietlein, Felix and Weghorn, Donate and Taylor-Weiner, Amaro and Richters, Andr{\'e} and Reardon, Brendan and Liu, David and Lander, Eric S and Van Allen, Eliezer M and Sunyaev, Shamil R} } @article {42, title = {Error-prone bypass of DNA lesions during lagging-strand replication is a common source of germline and cancer mutations.}, journal = {Nat Genet}, volume = {51}, year = {2019}, month = {2019 Jan}, pages = {36-41}, abstract = {

Studies in experimental systems have identified a multitude of mutational mechanisms including DNA replication infidelity and DNA damage followed by inefficient repair or replicative bypass. However, the relative contributions of these mechanisms to human germline mutation remain unknown. Here, we show that error-prone damage bypass on the lagging strand plays a major role in human mutagenesis. Transcription-coupled DNA repair removes lesions on the transcribed strand; lesions on the non-transcribed strand are preferentially converted into mutations. In human polymorphism we detect a striking similarity between mutation types predominant on the non-transcribed strand and on the strand lagging during replication. Moreover, damage-induced mutations in cancers accumulate asymmetrically with respect to the direction of replication, suggesting that DNA lesions are resolved asymmetrically. We experimentally demonstrate that replication delay greatly attenuates the mutagenic effect of ultraviolet irradiation, confirming that replication converts DNA damage into mutations. We estimate that at least 10\% of human mutations arise due to DNA damage.

}, keywords = {Cells, Cultured, DNA, DNA Damage, DNA Repair, DNA Replication, Germ-Line Mutation, Humans, Mutagenesis, Neoplasms, Polymorphism, Single Nucleotide, Transcription, Genetic}, issn = {1546-1718}, doi = {10.1038/s41588-018-0285-7}, author = {Seplyarskiy, Vladimir B and Akkuratov, Evgeny E and Akkuratova, Natalia and Andrianova, Maria A and Nikolaev, Sergey I and Bazykin, Georgii A and Adameyko, Igor and Sunyaev, Shamil R} }