[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123605-en":3,"doc-seo-123605-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},123605,1099513958762,"Logic","https://ap-avatar.wpscdn.com/avatar/1000023916a998db790?x-image-process=image/resize,m_fixed,w_180,h_180&k=1784791008015729253",8,"Research & Report","Inferring the heritability of bacterial traits in the era of machine learning","Quantification of heritability is a core objective in genetics, enabling assessment of how additive genetic variation contributes to phenotypic variability. Traditional quantitative-genetics tools have been extended by population genomics, where large samples motivate machine learning approaches for inferring heritability. This work systematically reviews recent ML advances and applies them to bacterial genomes, focusing on antibiotic resistance and virulence. A heritability model reflecting realistic genome-wide linkage disequilibrium for a frequently recombining pathogen evaluates diverse inference methods, including GCTA. Benchmarking and real-data analyses show highly variable performance and support tailoring methods to the target organism’s genetic architecture.","arXiv :2208 .04797v2 [ stat .AP] 16 Jan 2023  \nInferring the heritability of bacterial traits in the  \nera of machine learning  \nT. Tien Mai (1),∗ , John A Lees (5),(6) , Rebecca A Gladstone (2) and Jukka Corander (2),(3),(4)  \n(1) Department of Mathematical Sciences, Norwegian University of Science and Technology, Trondheim, Norway.  \n(2) Department of Biostatistics, University of Oslo, Oslo, Norway.  \n(3) Department of Mathematics and Statistics, University of Helsinki, Finland.  \n(4) Pathogens and Microbes, Wellcome Sanger Institute, Hinxton, UK.  \n(5) European Molecular Biology Laboratory, European Bioinformatics Institute EMBL-EBI, Hinxton, UK.  \n(6) MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College, UK.  \nAbstract  \nQuanti􀀌cation of heritability is a fundamental desideratum in genetics, which allows an assessment of the contribution of additive genetic variation to the variability of a trait of interest. The traditional computational approaches for assessing the heritability of a trait have been developed in the 􀀌eld of quantitative genetics. However, the rise of modern population genomics with large sample sizes has led to the development of several new machine learning based approaches to inferring heritability. In this paper, we systematically summarize recent advances in machine learning which can be used to infer heritability. We focus on an application of these methods to bacterial genomes, where heritability plays a key role in understanding phenotypes such as antibiotic resistance and virulence, which are particularly important due to the rising frequency of antimicrobial resistance. By designing a heritability model incorporating realistic patterns of genome-wide linkage disequilibrium for a frequently recombining bacterial pathogen, we test the performance of a wide spectrum of di􀀋erent inference methods, including also GCTA. In addition to the synthetic data benchmark, we present a comparison of the methods for antibiotic resistance traits for multiple bacterial pathogens. Insights from the benchmarking and real data analyses indicate a highly variable performance of the di􀀋erent methods and suggest that heritability inference would likely bene􀀌t from tailoring of the methods to the speci􀀌c genetic architecture of the target organism.  \nKeywords: Antimicrobial resistance; Heritability; Machine learning; Linear model.  \n∗ Corresponding [author.](author. the.t.mai@ntnu.no)[ the.t.mai@ntnu.no](author. the.t.mai@ntnu.no)  \n1 Introduction  \nHeritability is a fundamental quantity in genetic applications [Falconer, 1960, Lynch and Walsh, 1998]  \nwhich speci􀀌es the contribution of additive genetic factors to the variation of a phenotype. In the narrow-sense, heritability is de􀀌ned as the proportion of the variance of a phenotype explained by the additive genetic factors. Heritability can be used to compare the relative importance between genes and environment to the variability of traits, within and across populations. Together with GWAS (genome-wide association studies), the primary tool for discovering the genetic basis of a phenotype of interest, heritability has been playing as a more and more critical role in exploring the genetic architecture of complex traits.  \nCurrent investigations of heritability in the quantitative genetics literature have fo  \ncused on using the linear mixed-e􀀋ect model framework [Speed et al., 2012, Bulik-Sullivan et al., 2015,  \nYang et al., 2010, Golan et al., 2014, Zhou, 2017, Bonnet, 2016 , Speed et al., 2017] . In this framework, the e􀀋ect sizes of genetic markers, usually SNPs (single nucleotide polymorphisms), are assumed to be independent and identically distributed random variables, and often the normal Gaussian distribution is used for computational reasons. The genomic restricted maximum likelihood (GREML) and method of moments are the most widely used methods for heritability inference in this model, and some co","cbCaiivEMEa7TbBg","https://ap.wps.com/l/cbCaiivEMEa7TbBg","pdf",588973,1,21,"English","en",105,"# Abstract\n# Introduction\n## Heritability in genetics and narrow-sense definition\n## Linear mixed-effects models and common tools\n## Machine-learning approaches for heritability inference","[{\"question\":\"What does heritability quantify in genetics applications?\",\"answer\":\"Heritability measures the contribution of additive genetic factors to the variance of a phenotype. In the narrow-sense, it is the proportion of phenotypic variance explained by additive genetic factors.\"},{\"question\":\"Which traditional methods are commonly used for heritability inference in linear mixed-effects models?\",\"answer\":\"The paper highlights GREML and method-of-moments approaches within linear mixed-effects models, with popular software including GCTA, LDSC, and LDAK.\"},{\"question\":\"How does the paper evaluate machine learning methods for heritability in bacteria?\",\"answer\":\"It uses a heritability model that incorporates realistic genome-wide linkage disequilibrium for a frequently recombining bacterial pathogen, testing a broad set of inference methods. It also compares methods for antibiotic resistance traits across multiple bacterial pathogens.\"}]","Inferring the heritability of bacterial traits in the era of machine learning | PDF",1785817596,53,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"inferring-the-heritability-of-bacterial-traits-in-the-era-of-machine-learning","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/inferring-the-heritability-of-bacterial-traits-in-the-era-of-machine-learning/123605/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What does heritability quantify in genetics applications?","Question",{"text":75,"@type":76},"Heritability measures the contribution of additive genetic factors to the variance of a phenotype. In the narrow-sense, it is the proportion of phenotypic variance explained by additive genetic factors.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which traditional methods are commonly used for heritability inference in linear mixed-effects models?",{"text":80,"@type":76},"The paper highlights GREML and method-of-moments approaches within linear mixed-effects models, with popular software including GCTA, LDSC, and LDAK.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the paper evaluate machine learning methods for heritability in bacteria?",{"text":84,"@type":76},"It uses a heritability model that incorporates realistic genome-wide linkage disequilibrium for a frequently recombining bacterial pathogen, testing a broad set of inference methods. It also compares methods for antibiotic resistance traits across multiple bacterial pathogens.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]