Download or read book Statistical Methods for QTL Mapping written by Zehua Chen and published by CRC Press. This book was released on 2016-04-19 with total page 308 pages. Available in PDF, EPUB and Kindle. Book excerpt: While numerous advanced statistical approaches have recently been developed for quantitative trait loci (QTL) mapping, the methods are scattered throughout the literature. Statistical Methods for QTL Mapping brings together many recent statistical techniques that address the data complexity of QTL mapping. After introducing basic genetics topics an
Download or read book A Guide to QTL Mapping with R qtl written by Karl W. Broman and published by Springer. This book was released on 2011-12-02 with total page 400 pages. Available in PDF, EPUB and Kindle. Book excerpt: Comprehensive discussion of QTL mapping concepts and theory Detailed instructions on the use of the R/qtl software, the most featured and flexible software for QTL mapping Two case studies illustrate QTL analysis in its entirety
Download or read book Statistical Genomics written by Ben Hui Liu and published by CRC Press. This book was released on 2017-11-22 with total page 642 pages. Available in PDF, EPUB and Kindle. Book excerpt: Genomics, the mapping of the entire genetic complement of an organism, is the new frontier in biology. This handbook on the statistical issues of genomics covers current methods and the tried-and-true classical approaches.
Download or read book Statistical Genetics of Quantitative Traits written by Rongling Wu and published by Springer Science & Business Media. This book was released on 2007-07-17 with total page 371 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book introduces the basic concepts and methods that are useful in the statistical analysis and modeling of the DNA-based marker and phenotypic data that arise in agriculture, forestry, experimental biology, and other fields. It concentrates on the linkage analysis of markers, map construction and quantitative trait locus (QTL) mapping, and assumes a background in regression analysis and maximum likelihood approaches. The strength of this book lies in the construction of general models and algorithms for linkage analysis, as well as in QTL mapping in any kind of crossed pedigrees initiated with inbred lines of crops.
Download or read book Quantitative Trait Loci written by Nicola J. Camp and published by Springer Science & Business Media. This book was released on 2008-02-03 with total page 362 pages. Available in PDF, EPUB and Kindle. Book excerpt: In Quantitative Trait Loci: Methods and Protocols, a panel of highly experienced statistical geneticists demonstrate in a step-by-step fashion how to successfully analyze quantitative trait data using a variety of methods and software for the detection and fine mapping of quantitative trait loci (QTL). Writing for the nonmathematician, these experts guide the investigator from the design stage of a project onwards, providing detailed explanations of how best to proceed with each specific analysis, to find and use appropriate software, and to interpret results. Worked examples, citations to key papers, and variations in method ease the way to understanding and successful studies. Among the cutting-edge techniques presented are QTDT methods, variance components methods, and the Markov Chain Monte Carlo method for joint linkage and segregation analysis.
Download or read book Quantitative Trait Loci Analysis in Animals written by Joel Ira Weller and published by CABI. This book was released on 2009 with total page 288 pages. Available in PDF, EPUB and Kindle. Book excerpt: Quantitative Trait Loci (QTL) is a topic of major agricultural significance for efficient livestock production. This book covers various statistical methods that have been used or proposed for detection and analysis of QTL and marker-and gene-assisted selection in animal genetics and breeding.
Download or read book Statistical Methods for QTL Mapping and Genomic Prediction of Multiple Traits and Environments written by and published by . This book was released on 2016 with total page 153 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book A Guide to QTL Mapping with R qtl written by Karl W. Broman and published by Springer Science & Business Media. This book was released on 2009-07-21 with total page 401 pages. Available in PDF, EPUB and Kindle. Book excerpt: Comprehensive discussion of QTL mapping concepts and theory Detailed instructions on the use of the R/qtl software, the most featured and flexible software for QTL mapping Two case studies illustrate QTL analysis in its entirety
Download or read book Statistical Methods for Expression Quantitative Trait Loci EQTL Mapping written by Meng Chen and published by . This book was released on 2006 with total page 164 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Regression based Methods to Map Quantitative Trait Loci Underlying Function valued Phenotypes written by and published by . This book was released on 2014 with total page 82 pages. Available in PDF, EPUB and Kindle. Book excerpt: Most statistical methods for QTL mapping focus on a single phenotype. However, multiple phenotypes are commonly measured, and recent technological advances have greatly simplified the automated acquisition of numerous phenotypes, including function-valued phenotypes, such as growth measured over time. While there exist methods for QTL mapping with function-valued phenotypes, they are generally computationally intensive and focus on single-QTL models. This thesis is composed of two main parts. In the first part, I propose some simple approaches for QTL mapping with function-valued traits, including multiple-QTL models using a penalized likelihood approach. The methods are fast and maintain high power and precision. After identifying multiple QTL by these approaches, we can view the function-valued QTL effects to provide a deeper understanding of the underlying processes. However there are two weaknesses. First, the methods do not work well when the curves are not smooth. Second, they do not take account of the correlation structure among time points. In the second part, I suggest a method to overcome those weaknesses, by smoothing the data set to reduce measurement error, and with functional Principal Component Analysis (PCA). We reduce the functional data into a small number of principal components without much loss of information. We can then proceed with QTL mapping on these dimension-reduced data. We consider four subsequent methods for analysis. First, we simply treat these multiple traits as independent. Second, we use multivariate QTL mapping method proposed by \citet{knott2000}, assuming that the transformed multivariate trait data follow a multivariate normal distribution. The third and fourth ideas are to take the average and maximum of LOD scores for each trait, as considered in the first part. I have implemented these methods in an R package, funqtl (github.com/ikwak2/funqtl ).
Download or read book Statistical Analysis for Mapping Linked Quantitative Trait Loci written by Jie Xu and published by . This book was released on 2005 with total page 266 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Mathematical and Statistical Methods for Genetic Analysis written by Kenneth Lange and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 376 pages. Available in PDF, EPUB and Kindle. Book excerpt: Written to equip students in the mathematical siences to understand and model the epidemiological and experimental data encountered in genetics research. This second edition expands the original edition by over 100 pages and includes new material. Sprinkled throughout the chapters are many new problems.
Download or read book Statistical Methods for Molecular Quantitative Trait Locus Analysis written by Heather J. Zhou and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Molecular quantitative trait locus (molecular QTL, henceforth "QTL") analysis investigates the relationship between genetic variants and molecular traits, helping explain findings in genome-wide association studies. This dissertation addresses two major problems in QTL analysis: hidden variable inference problem and eGene identification problem. Estimating and accounting for hidden variables is widely practiced as an important step in QTL analysis for improving the power of QTL identification. However, few benchmark studies have been performed to evaluate the efficacy of the various methods developed for this purpose. In my first project, I benchmark popular hidden variable inference methods including surrogate variable analysis (SVA), probabilistic estimation of expression residuals (PEER), and hidden covariates with prior (HCP) against principal component analysis (PCA)-a well-established dimension reduction and factor discovery method-via 362 synthetic and 110 real data sets. I show that PCA not only underlies the statistical methodology behind the popular methods but is also orders of magnitude faster, better performing, and much easier to interpret and use. To help researchers use PCA in their QTL analysis, I provide an R package PCAForQTL along with a detailed guide, both of which are available at httpss://github.com/heatherjzhou/PCAForQTL. I believe that using PCA rather than SVA, PEER, or HCP will substantially improve and simplify hidden variable inference in QTL mapping as well as increase the transparency and reproducibility of QTL research. A central task in expression quantitative trait locus (eQTL) analysis is to identify cis-eGenes (henceforth "eGenes"), i.e., genes whose expression levels are regulated by at least one local genetic variant. Among the existing eGene identification methods, FastQTL is considered the gold standard but is computationally expensive as it requires thousands of permutations for each gene. Alternative methods such as eigenMT and TreeQTL have lower power than FastQTL. In my second project, I propose ClipperQTL, which reduces the number of permutations needed from thousands to 20 for data sets with large sample sizes (>450) by using the contrastive strategy developed in Clipper; for data sets with smaller sample sizes, it uses the same permutation-based approach as FastQTL. I show that ClipperQTL performs as well as FastQTL and runs about 500 times faster if the contrastive strategy is used and 50 times faster if the conventional permutation-based approach is used. The R package ClipperQTL is available at httpss://github.com/heatherjzhou/ClipperQTL. This project demonstrates the potential of the contrastive strategy developed in Clipper and provides a simpler and more efficient way of identifying eGenes.
Download or read book Quantitative Trait Loci QTL written by Scott A. Rifkin and published by Humana Press. This book was released on 2016-08-23 with total page 331 pages. Available in PDF, EPUB and Kindle. Book excerpt: Over the last two decades advances in genotyping technology, and the development of quantitative genetic analytical techniques have made it possible to dissect complex traits and link quantitative variation in traits to allelic variation on chromosomes or quantitative trait loci (QTLs). In Quantitative Trait Loci (QTLs):Methods and Protocols, expert researchers in the field detail methods and techniques that focus on specific components of the entire process of quantitative train loci experiments. These include methods and techniques for the mapping populations, identifying quantitative trait loci, extending the power of quantitative trait locus analysis, and case studies. Written in the highly successful Methods in Molecular BiologyTM series format, the chapters include the kind of detailed description and implementation advice that is crucial for getting optimal results in the laboratory. Thorough and intuitive, Quantitative Trait Loci (QTLs):Methods and Protocols aids scientists in the further study of the links between phenotypic and genotypic variation in fields from medicine to agriculture, from molecular biology to evolution to ecology.
Download or read book Genome Wide Association Studies and Genomic Prediction written by Cedric Gondro and published by Humana Press. This book was released on 2013-06-12 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: With the detailed genomic information that is now becoming available, we have a plethora of data that allows researchers to address questions in a variety of areas. Genome-wide association studies (GWAS) have become a vital approach to identify candidate regions associated with complex diseases in human medicine, production traits in agriculture, and variation in wild populations. Genomic prediction goes a step further, attempting to predict phenotypic variation in these traits from genomic information. Genome-Wide Association Studies and Genomic Prediction pulls together expert contributions to address this important area of study. The volume begins with a section covering the phenotypes of interest as well as design issues for GWAS, then moves on to discuss efficient computational methods to store and handle large datasets, quality control measures, phasing, haplotype inference, and imputation. Later chapters deal with statistical approaches to data analysis where the experimental objective is either to confirm the biology by identifying genomic regions associated to a trait or to use the data to make genomic predictions about a future phenotypic outcome (e.g. predict onset of disease). As part of the Methods in Molecular Biology series, chapters provide helpful, real-world implementation advice.
Download or read book Statistical Methods Computing and Resources for Genome Wide Association Studies written by Riyan Cheng and published by Frontiers Media SA. This book was released on 2021-08-24 with total page 148 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Principles of Statistical Genomics written by Shizhong Xu and published by Springer Science & Business Media. This book was released on 2012-09-10 with total page 428 pages. Available in PDF, EPUB and Kindle. Book excerpt: Statistical genomics is a rapidly developing field, with more and more people involved in this area. However, a lack of synthetic reference books and textbooks in statistical genomics has become a major hurdle on the development of the field. Although many books have been published recently in bioinformatics, most of them emphasize DNA sequence analysis under a deterministic approach. Principles of Statistical Genomics synthesizes the state-of-the-art statistical methodologies (stochastic approaches) applied to genome study. It facilitates understanding of the statistical models and methods behind the major bioinformatics software packages, which will help researchers choose the optimal algorithm to analyze their data and better interpret the results of their analyses. Understanding existing statistical models and algorithms assists researchers to develop improved statistical methods to extract maximum information from their data. Resourceful and easy to use, Principles of Statistical Genomics is a comprehensive reference for researchers and graduate students studying statistical genomics.
