{"title":"Prediction of Phenotype from DNA Variants","authors":"M. Goddard, T. Meuwissen, H. Daetwyler","doi":"10.1002/9781119487845.ch28","DOIUrl":"https://doi.org/10.1002/9781119487845.ch28","url":null,"abstract":"","PeriodicalId":216924,"journal":{"name":"Handbook of Statistical Genomics","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133711108","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Ethical Issues in Statistical Genetics","authors":"S. Wallace, R. Ashcroft","doi":"10.1002/9781119487845.CH19","DOIUrl":"https://doi.org/10.1002/9781119487845.CH19","url":null,"abstract":"","PeriodicalId":216924,"journal":{"name":"Handbook of Statistical Genomics","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121471053","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Sequence Covariation Analysis in Biological Polymers","authors":"W. Taylor, Shaun M. Kandathil, David T. Jones","doi":"10.1002/9781119487845.ch11","DOIUrl":"https://doi.org/10.1002/9781119487845.ch11","url":null,"abstract":"","PeriodicalId":216924,"journal":{"name":"Handbook of Statistical Genomics","volume":"105 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133381622","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Statistical Methods for Plant Breeding","authors":"I. Mackay, H. Piepho, A. Garcia","doi":"10.1002/9781119487845.ch17","DOIUrl":"https://doi.org/10.1002/9781119487845.ch17","url":null,"abstract":"","PeriodicalId":216924,"journal":{"name":"Handbook of Statistical Genomics","volume":"84 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124882608","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Statistical Methods to Detect Archaic Admixture and Identify Introgressed Sequences","authors":"Liming Li, J. Akey","doi":"10.1002/9781119487845.ch9","DOIUrl":"https://doi.org/10.1002/9781119487845.ch9","url":null,"abstract":"","PeriodicalId":216924,"journal":{"name":"Handbook of Statistical Genomics","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132204870","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Coalescent Theory","authors":"M. Nordborg","doi":"10.1002/9780470061619.ch25","DOIUrl":"https://doi.org/10.1002/9780470061619.ch25","url":null,"abstract":"The coalescent process is a powerful modeling tool for population genetics. The allelic states of all homologous gene copies in a population are determined by the genealogical and mutational history of these copies. The coalescent approach is based on the realization that the genealogy is usually easier to model backward in time, and that selectively neutral mutations can then be superimposed afterwards. A wide range of biological phenomena can be modeled using this approach. Whereas almost all of classical population genetics considers the future of a population given a starting point, the coalescent considers the present, while taking the past into account. This allows the calculation of probabilities of sample configurations under the stationary distribution of various population genetic models, and makes full likelihood analysis of polymorphism data possible. It also leads to extremely efficient computer algorithms for generating simulated data from such distributions, data which can then be compared with observations as a form of exploratory","PeriodicalId":216924,"journal":{"name":"Handbook of Statistical Genomics","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130437990","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Statistical Methods in Metabolomics","authors":"T. Ebbels, M. Iorio, D. Stephens","doi":"10.1002/9781119487845.ch34","DOIUrl":"https://doi.org/10.1002/9781119487845.ch34","url":null,"abstract":"","PeriodicalId":216924,"journal":{"name":"Handbook of Statistical Genomics","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114476179","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Mathematical Models in Population Genetics","authors":"N. Barton, A. Etheridge","doi":"10.1002/9781119487845.ch4","DOIUrl":"https://doi.org/10.1002/9781119487845.ch4","url":null,"abstract":"","PeriodicalId":216924,"journal":{"name":"Handbook of Statistical Genomics","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120946641","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Probabilistic Models for the Study of Protein Evolution","authors":"J. Thorne, N. Goldman","doi":"10.1002/9780470061619.CH14","DOIUrl":"https://doi.org/10.1002/9780470061619.CH14","url":null,"abstract":"Model choice is one of the central statistical issues in the study of protein evolution. Models are indispensable tools for characterizing the process of protein evolution, many aspects of which are not easily amenable to direct experimentation. In these cases, only probabilistic models can assess the fit between assumptions and data. Ideally, a probabilistic model of protein evolution would provide a good statistical fit to the data and would simultaneously be parameterized in a manner that facilitates biological insight. In addition, probabilistic models of protein sequence evolution can provide the foundation for likelihood-based methods of phylogeny reconstruction and protein structure prediction. In this chapter, models of protein evolution are reviewed. The strengths and limitations of these models are emphasized. \u0000 \u0000 \u0000Keywords: \u0000 \u0000amino acid replacement; \u0000evolutionary model; \u0000phylogeny; \u0000probabilistic model; \u0000protein evolution; \u0000protein structure","PeriodicalId":216924,"journal":{"name":"Handbook of Statistical Genomics","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132806940","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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