Handbook of Statistical Genomics最新文献

{"title":"Variant Interpretation and Genomic Medicine","authors":"K. Carss, D. Goldstein, V. Aggarwal, S. Petrovski","doi":"10.1002/9781119487845.ch27","DOIUrl":"https://doi.org/10.1002/9781119487845.ch27","url":null,"abstract":"","PeriodicalId":216924,"journal":{"name":"Handbook of Statistical Genomics","volume":"82 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":"127499738","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":"Bayesian Methods for Gene Expression Analysis","authors":"A. Lewin, L. Bottolo, S. Richardson","doi":"10.1002/9781119487845.ch30","DOIUrl":"https://doi.org/10.1002/9781119487845.ch30","url":null,"abstract":"","PeriodicalId":216924,"journal":{"name":"Handbook of Statistical Genomics","volume":"68 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":"120960750","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":"Disease Risk Models","authors":"A. Meisner, N. Chatterjee","doi":"10.1002/9781119487845.ch29","DOIUrl":"https://doi.org/10.1002/9781119487845.ch29","url":null,"abstract":"","PeriodicalId":216924,"journal":{"name":"Handbook of Statistical Genomics","volume":"58 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":"126834565","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":"Modelling Non‐homogeneous Dynamic Bayesian Networks with Piecewise Linear Regression Models","authors":"M. Grzegorczyk, D. Husmeier","doi":"10.1002/9781119487845.ch32","DOIUrl":"https://doi.org/10.1002/9781119487845.ch32","url":null,"abstract":"In statistical genomics and systems biology non-homogeneous dynamic Bayesian networks (NH-DBNs) have become an important tool for learning regulatory networks and signalling pathways from post-genomic data, such as gene expression time series. This chapter gives an overview of various state-of-the-art NH-DBN models with a variety of features. All NH-DBNs, presented here, have in common that they are Bayesian models that combine linear regression with multiple changepoint processes. The NH-DBN models can be used for learning the network structures of time-varying regulatory processes from data, where the regulatory interactions are subject to temporal change. We conclude this chapter with an illustration of the methodology on two applications, related to morphogenesis in Drosophila and synthetic biology in yeast.","PeriodicalId":216924,"journal":{"name":"Handbook of Statistical Genomics","volume":"63 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":"132568537","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":"Detecting Natural Selection","authors":"A. Stern, R. Nielsen","doi":"10.1002/9781119487845.CH14","DOIUrl":"https://doi.org/10.1002/9781119487845.CH14","url":null,"abstract":"","PeriodicalId":216924,"journal":{"name":"Handbook of Statistical Genomics","volume":"13 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":"121223501","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":"Population Structure, Demography and Recent Admixture","authors":"G. Hellenthal","doi":"10.1002/9781119487845.ch8","DOIUrl":"https://doi.org/10.1002/9781119487845.ch8","url":null,"abstract":"","PeriodicalId":216924,"journal":{"name":"Handbook of Statistical Genomics","volume":"66 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":"126228276","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":"Inferring Causal Associations between Genes and Disease via the Mapping of Expression Quantitative Trait Loci","authors":"S. Sieberts, E. Schadt","doi":"10.1002/9781119487845.CH25","DOIUrl":"https://doi.org/10.1002/9781119487845.CH25","url":null,"abstract":"","PeriodicalId":216924,"journal":{"name":"Handbook of Statistical Genomics","volume":"12 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":"123636500","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 Modeling and Inference in Genetics","authors":"Daniel Wegmann, C. Leuenberger","doi":"10.1002/9781119487845.ch1","DOIUrl":"https://doi.org/10.1002/9781119487845.ch1","url":null,"abstract":"Given the long mathematical history and tradition in genetics, and particularly in population genetics, it is not surprising that model-based statistical inference has always been an integral part of statistical genetics, and vice versa. Since the big data revolution due to novel sequencing technologies, statistical genetics has further relied heavily on numerical methods for inference. In this chapter we give a brief overview over the foundations of statistical inference, including both the frequentist and Bayesian schools, and introduce analytical and numerical methods commonly applied in statistical genetics. A particular focus is put on recent approximate techniques that now play an important role in several fields of statistical genetics. We conclude by applying several of the algorithms introduced to hidden Markov models, which have been used very successfully to model processes along chromosomes. Throughout we strive for the impossible task of making the material accessible to readers with limited statistical background, while hoping that it will also constitute a worthy refresher for more advanced readers. Readers who already have a solid statistical background may safely skip the first introductory part and jump directly to Section 1.2.3. 1.1 Statistical Models and Inference Statistical inference offers a formal approach to characterizing a random phenomenon using observations, either by providing a description of a past phenomenon, or by giving some predictions about future phenomena of a similar nature. This is typically done by estimating a vector of parameters θ from on a vector of observations or data , using the formal framework and laws of probability. The interpretation of probabilities is a somewhat contentious issue, with multiple competing interpretations. Specifically, probabilities can be seen as the frequencies with which specific events occur in a repeatable experiment (the frequentist interpretation; Lehmann and Casella, 2006), or as reflecting the uncertainty or degree of belief about the state of a random variable (the Bayesian interpretation; Robert, 2007). In frequentist statistics, only  is thus considered as a random variable, while in Bayesian statistics both and θ are considered random variables. The goal of this chapter is not, however, to enter any debate about the validity of the two competing schools of thought. Instead, our aim is to introduce the most commonly used inference methods of both schools. Indeed, most researchers in statistical genetics, including ourselves, choose their approaches pragmatically based on computational considerations rather than Handbook of Statistical Genomics, Fourth Edition, Volume 1. Edited by David J. Balding, Ida Moltke and John Marioni. © 2019 John Wiley & Sons Ltd. Published 2019 by John Wiley & Sons Ltd. CO PY RI GH TE D M AT ER IA L JWST943-c01 JWST943-Balding May 28, 2019 16:48 Printer Name: Trim: 254mm × 178mm 2 D. Wegmann and C. Leuenberger strong philosophical grou","PeriodicalId":216924,"journal":{"name":"Handbook of Statistical Genomics","volume":"37 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":"124778336","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":"Replication and Meta‐analysis of Genome‐Wide Association Studies","authors":"F. Dudbridge, P. Newcombe","doi":"10.1002/9781119487845.ch22","DOIUrl":"https://doi.org/10.1002/9781119487845.ch22","url":null,"abstract":"","PeriodicalId":216924,"journal":{"name":"Handbook of Statistical Genomics","volume":"26 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":"134143534","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":"Improving Genetic Association Analysis through Integration of Functional Annotations of the Human Genome","authors":"Q. Lu, Hongyu Zhao","doi":"10.1002/9781119487845.ch24","DOIUrl":"https://doi.org/10.1002/9781119487845.ch24","url":null,"abstract":"","PeriodicalId":216924,"journal":{"name":"Handbook of Statistical Genomics","volume":"443 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":"133366126","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}