{"doi":"10.32942/x2qg67","title":"Mutation bias and the predictability of evolution","abstract":"<jats:p>Predicting evolutionary outcomes is an important research goal in a diversity of contexts. The focus of evolutionary forecasting is usually on adaptive processes, and efforts to improve prediction typically focus on selection. However, adaptive processes often rely on new mutations, which can be strongly influenced by predictable biases in mutation. Here we provide an overview of existing theory and evidence for such mutation-biased adaptation and consider the implications of these results for the problem of prediction, in regard to topics such as the evolution of infectious diseases,  resistance to biochemical agents, as well as cancer and other kinds of somatic evolution.  We argue that empirical knowledge of mutational biases is likely to improve in the near future, and that this knowledge is readily applicable to the challenges of short-term prediction.</jats:p>","journal":null,"year":null,"id":639790,"datarank":0.3139392844291078,"base_score":1.6094379124341003,"endowment":1.6094379124341003,"self_citation_contribution":0.24141568686511508,"citation_network_contribution":0.07252359756399268,"self_endowment_contribution":0.24141568686511508,"citer_contribution":0.07252359756399268,"corpus_percentile":null,"corpus_rank":null,"citation_count":4,"citer_count":3,"citers_with_citation_signal":2,"citers_with_endowment":2,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1662537,"name":"Bryan Gitschlag","orcid":null,"position":1,"is_corresponding":false},{"id":1662538,"name":"Hana Rozhonova","orcid":null,"position":2,"is_corresponding":false},{"id":552098,"name":"Arlin Stoltzfus","orcid":"0000-0002-0963-1357","position":3,"is_corresponding":false},{"id":1662539,"name":"David McCandlish","orcid":null,"position":4,"is_corresponding":false},{"id":1662540,"name":"Joshua Payne","orcid":null,"position":5,"is_corresponding":false},{"id":989507,"name":"Alejandro Cano","orcid":"0000-0003-4285-9182","position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"Mutation bias and the predictability of evolution","abstract":"<jats:p>Predicting evolutionary outcomes is an important research goal in a diversity of contexts. The focus of evolutionary forecasting is usually on adaptive processes, and efforts to improve prediction typically focus on selection. However, adaptive processes often rely on new mutations, which can be strongly influenced by predictable biases in mutation. Here we provide an overview of existing theory and evidence for such mutation-biased adaptation and consider the implications of these results for the problem of prediction, in regard to topics such as the evolution of infectious diseases,  resistance to biochemical agents, as well as cancer and other kinds of somatic evolution.  We argue that empirical knowledge of mutational biases is likely to improve in the near future, and that this knowledge is readily applicable to the challenges of short-term prediction.</jats:p>","is_dataset_classified":null,"base_score":1.6094379124341003,"endowment":1.6094379124341003,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"19767382","pmcid":null,"openalex_id":"https://openalex.org/W4310662668","authors":[],"funders":[{"funder_name":"Swiss National Science Foundation","grant_id":"192541","title":"The deformability of empirical genotype-phenotype landscapes"}],"total_grants":1,"fwci":null,"citation_percentile":null,"influential_citations":0,"citation_trend":[{"year":2022,"count":1},{"year":2023,"count":3}],"oa_status":"gold","license":"cc-by-nc-sa","oa_locations":[{"url":"https://ecoevorxiv.org/repository/object/4756/download/9489/","host_type":""},{"url":"https://ecoevorxiv.org/repository/object/4756/download/9489/","host_type":""},{"url":"https://doi.org/10.32942/x2qg67","host_type":""},{"url":"http://hdl.handle.net/20.500.11850/607285","host_type":"repository"},{"url":"https://doi.org/10.3929/ethz-b-000607285","host_type":"repository"},{"url":"https://doi.org/10.1098/rstb.2022.0055","host_type":""},{"url":"https://dx.doi.org/10.3929/ethz-b-000607285","host_type":""},{"url":"https://pubmed.ncbi.nlm.nih.gov/37004719","host_type":""},{"url":"http://dx.doi.org/10.1098/rstb.2022.0055","host_type":""}],"fields_of_study":["Evolution and Genetic Dynamics","Genetic diversity and population structure","Genetic Associations and Epidemiology","0301 basic medicine","0303 health sciences","03 medical and health sciences"],"mesh_terms":[],"keywords":["Predictability","Mutation","Adaptation (eye)","Selection (genetic algorithm)","Focus (optics)","Empirical evidence","Adaptive evolution","Computer science","Biology","Artificial intelligence","Genetics","Epistemology","Mathematics","Gene","Population genetics","Acclimatization","Articles","Biological Evolution","Adaptation, Physiological","Adaptation; Mutation; Prediction; Theory; Population genetics","Evolution, Molecular","Bias","Theory","Adaptation","Prediction"],"sdg_mappings":[{"sdg_number":3,"sdg_label":"3. 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