Statistical Methods To Understand The Genetic Architecture Of Complex Traits

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Statistical Methods to Understand the Genetic Architecture of Complex Traits

Author : Farhad Hormozdiari
Publisher :
ISBN 13 :
Total Pages : 239 pages
Book Rating : 4.:/5 (17 download)

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Book Synopsis Statistical Methods to Understand the Genetic Architecture of Complex Traits by : Farhad Hormozdiari

Download or read book Statistical Methods to Understand the Genetic Architecture of Complex Traits written by Farhad Hormozdiari and published by . This book was released on 2016 with total page 239 pages. Available in PDF, EPUB and Kindle. Book excerpt: Genome-wide association studies (GWAS) have successfully identified thousands of risk loci for complex traits. Identifying these variants requires annotating all possible variations between any two individuals, followed by detecting the variants that affect the disease status or traits. High-throughput sequencing (HTS) advancements have made it possible to sequence cohort of individuals in an efficient manner both in term of cost and time. However, HTS technologies have raised many computational challenges. I first propose an efficient method to recover dense genotype data by leveraging low sequencing and imputation techniques. Then, I introduce a novel statistical method (CNVeM) to identify Copy-number variations (CNVs) loci using HTS data. CNVeM was the first method that incorporates multi-mapped reads, which are discarded by all existing methods. Unfortunately, among all GWAS variants only a handful of them have been successfully validated to be biologically causal variants. Identifying causal variants can aid us to understand the biological mechanism of traits or diseases. However, detecting the causal variants is challenging due to linkage disequilibrium (LD) and the fact that some loci contain more than one causal variant. In my thesis, I will introduce CAVIAR (CAusal Variants Identification in Associated Regions) that is a new statistical method for fine mapping. The main advantage of CAVIAR is that we predict a set of variants for each locus that will contain all of the true causal variants with a high confidence level (e.g. 95%) even when the locus contains multiple causal variants. Next, I aim to understand the underlying mechanism of GWAS risk loci. A standard approach to uncover the mechanism of GWAS risk loci is to integrate results of GWAS and expression quantitative trait loci (eQTL) studies; we attempt to identify whether or not a significant GWAS variant also influences expression at a nearby gene in a specific tissue. However, detecting the same variant being causal in both GWAS and eQTL is challenging due to complex LD structure. I will introduce eCAVIAR (eQTL and GWAS CAusal Variants Identification in Associated Regions), a statistical method to compute the probability that the same variant is responsible for both the GWAS and eQTL signal, while accounting for complex LD structure. We integrate Glucose and Insulin-related traits meta-analysis with GTEx to detect the target genes and the most relevant tissues. Interestingly, we observe that most loci do not colocalize between GWAS and eQTL. Lastly, I propose an approach called phenotype imputation that allows one to perform GWAS on a phenotype that is difficult to collect. In our approach, we leverage the correlation structure between multiple phenotypes to impute the uncollected phenotype. I demonstrate that we can analytically calculate the statistical power of association test using imputed phenotype, which can be helpful for study design purposes

Computational Approaches to Understanding the Genetic Architecture of Complex Traits

Author : Brielin C. Brown
Publisher :
ISBN 13 :
Total Pages : 90 pages
Book Rating : 4.:/5 (994 download)

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Book Synopsis Computational Approaches to Understanding the Genetic Architecture of Complex Traits by : Brielin C. Brown

Download or read book Computational Approaches to Understanding the Genetic Architecture of Complex Traits written by Brielin C. Brown and published by . This book was released on 2016 with total page 90 pages. Available in PDF, EPUB and Kindle. Book excerpt: Advances in DNA sequencing technology have resulted in the ability to generate genetic data at costs unimaginable even ten years ago. This has resulted in a tremendous amount of data, with large studies providing genotypes of hundreds of thousands of individuals at millions of genetic locations. This rapid increase in the scale of genetic data necessitates the development of computational methods that can analyze this data rapidly without sacrificing statistical rigor. The low cost of DNA sequencing also provides an opportunity to tailor medical care to an individuals unique genetic signature. However, this type of precision medicine is limited by our understanding of how genetic variation shapes disease. Our understanding of so- called complex diseases is particularly poor, and most identified variants explain only a tiny fraction of the variance in the disease that is expected to be due to genetics. This is further complicated by the fact that most studies of complex disease go directly from genotype to phenotype, ignoring the complex biological processes that take place in between. Herein, we discuss several advances in the field of complex trait genetics. We begin with a review of computational and statistical methods for working with genotype and phenotype data, as well as a discussion of methods for analyzing RNA-seq data in effort to bridge the gap between genotype and phenotype. We then describe our methods for 1) improving power to detect common variants associated with disease, 2) determining the extent to which different world populations share similar disease genetics and 3) identifying genes which show differential expression between the two haplotypes of a single individual. Finally, we discuss opportunities for future investigation in this field.

Statistical Methods for Integrative Analysis of Genomic Data

Author : Jingsi Ming
Publisher :
ISBN 13 :
Total Pages : 141 pages
Book Rating : 4.:/5 (16 download)

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Book Synopsis Statistical Methods for Integrative Analysis of Genomic Data by : Jingsi Ming

Download or read book Statistical Methods for Integrative Analysis of Genomic Data written by Jingsi Ming and published by . This book was released on 2018 with total page 141 pages. Available in PDF, EPUB and Kindle. Book excerpt: Thousands of risk variants underlying complex phenotypes (quantitative traits and diseases) have been identified in genome-wide association studies (GWAS). However, there are still several challenges towards deepening our understanding of the genetic architectures of complex phenotypes. First, the majority of GWAS hits are in non-coding region and their biological interpretation is still unclear. Second, most complex traits are suggested to be highly polygenic, i.e., they are affected by a vast number of risk variants with individually small or moderate effects, whereas a large proportion of risk variants with small effects remain unknown. Third, accumulating evidence from GWAS suggests the pervasiveness of pleiotropy, a phenomenon that some genetic variants can be associated with multiple traits, but there is a lack of unified framework which is scalable to reveal relationship among a large number of traits and prioritize genetic variants simultaneously with functional annotations integrated. In this thesis, we propose two statistical methods to address these challenges using integrative analysis of summary statistics from GWASs and functional annotations. In the first part, we propose a latent sparse mixed model (LSMM) to integrate functional annotations with GWAS data. Not only does it increase the statistical power of identifying risk variants, but also offers more biological insights by detecting relevant functional annotations. To allow LSMM scalable to millions of variants and hundreds of functional annotations, we developed an efficient variational expectation-maximization (EM) algorithm for model parameter estimation and statistical inference. We first conducted comprehensive simulation studies to evaluate the performance of LSMM. Then we applied it to analyze 30 GWASs of complex phenotypes integrated with nine genic category annotations and 127 cell-type specific functional annotations from the Roadmap project. The results demonstrate that our method possesses more statistical power than conventional methods, and can help researchers achieve deeper understanding of genetic architecture of these complex phenotypes. In the second part, we propose a latent probit model (LPM) which combines summary statistics from multiple GWASs and functional annotations, to characterize relationship and increase statistical power to identify risk variants. LPM can also perform hypothesis testing for pleiotropy and annotations enrichment. To enable the scalability of LPM as the number of GWASs increases, we developed an efficient parameter-expanded EM (PX-EM) algorithm which can execute parallelly. We first validated the performance of LPM through comprehensive simulations, then applied it to analyze 44 GWASs with nine genic category annotations. The results demonstrate the benefits of LPM and can offer new insights of disease etiology.

Efficient Methods for Understanding the Genetic Architecture of Complex Traits

Author : Yue N/A Wu
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (134 download)

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Book Synopsis Efficient Methods for Understanding the Genetic Architecture of Complex Traits by : Yue N/A Wu

Download or read book Efficient Methods for Understanding the Genetic Architecture of Complex Traits written by Yue N/A Wu and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Understanding the genetic architecture of complex traits is a central goal of modern human genetics.Recent efforts focused on building large-scale biobanks, that collect genetic and trait data on large numbers of individuals, present exciting opportunities for understanding genetic architecture. However, these datasets also pose several statistical and computational challenges. In this dissertation, we consider a series of statistical models that allow us to infer aspects of the genetic architecture of single and multiple traits. Inference in these models is computationally challenging due to the size of the genetic data -- consisting of millions of genetic variants measured across hundreds of thousands of individuals.We propose a series of scalable computational methods that can perform efficient inference in these models and apply these methods to data from the UK Biobank to showcase their utility.

Statistical Methods and Analysis for Genome-wide Association Studies

Author : Lin Li
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (654 download)

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Book Synopsis Statistical Methods and Analysis for Genome-wide Association Studies by : Lin Li

Download or read book Statistical Methods and Analysis for Genome-wide Association Studies written by Lin Li and published by . This book was released on 2010 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Genome-wide association (GWA) studies utilize a large number of genetic variants, usually single nucleotide polymorphisms (SNPs), across the entire genome to identify genetic basis underlying disease susceptibility or phenotypic variation in a trait of interest. A commonly used analysis tool is single marker analysis (SMA), which tests one SNP at a time. Although it has been successful in identifying some causal loci, further enhancements are possible by considering multi-locus methods that investigate a large number of SNPs simultaneously. One difficulty of doing so is high dimensionality, i.e. the large number of SNPs, making it a challenging statistical problem. My first project addresses this problem in case-control GWA studies. Both the logistic and probit models are considered for binary traits, and three-component mixture priors are assumed to model the fact that only a few SNPs have non-negligible effects. To estimate posterior distributions, I propose three Markov chain Monte Carlo techniques. Specifically, an adaptive independence sampler is proposed for the logistic model, and data augmentation methods are developed for both logistic and probit models. Simulations suggest that they nearly always outperform SMA. The second project deals with GWA studies on quantitative traits with the confounding of population structure. A linear mixed model is used to account for cryptic relatedness between individuals in the sample. I propose an algorithm that is based on least angle regression and can efficiently select a small number of SNPs that are likely to be associated with the trait. Simulations show that the proposed algorithm tends to yield higher ranks for causal loci than least angle regression directly applied, and that both outperform SMA. My third project is part of the so-called CanMap project. More than 1,000 domestic dogs from different breeds, wild canids and village dogs were genotyped on a dense SNP array, and my responsibility was to carry out a GWA analysis for the domestic dog on body weight and other morphological traits including height, shapes, etc. The GWA results enrich our understanding of the impact of strong directional selection on the genetic architecture of complex traits known to be under selection.

The Fundamentals of Modern Statistical Genetics

Author : Nan M. Laird
Publisher : Springer Science & Business Media
ISBN 13 : 1441973389
Total Pages : 226 pages
Book Rating : 4.4/5 (419 download)

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Book Synopsis The Fundamentals of Modern Statistical Genetics by : Nan M. Laird

Download or read book The Fundamentals of Modern Statistical Genetics written by Nan M. Laird and published by Springer Science & Business Media. This book was released on 2010-12-13 with total page 226 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book covers the statistical models and methods that are used to understand human genetics, following the historical and recent developments of human genetics. Starting with Mendel’s first experiments to genome-wide association studies, the book describes how genetic information can be incorporated into statistical models to discover disease genes. All commonly used approaches in statistical genetics (e.g. aggregation analysis, segregation, linkage analysis, etc), are used, but the focus of the book is modern approaches to association analysis. Numerous examples illustrate key points throughout the text, both of Mendelian and complex genetic disorders. The intended audience is statisticians, biostatisticians, epidemiologists and quantitatively- oriented geneticists and health scientists wanting to learn about statistical methods for genetic analysis, whether to better analyze genetic data, or to pursue research in methodology. A background in intermediate level statistical methods is required. The authors include few mathematical derivations, and the exercises provide problems for students with a broad range of skill levels. No background in genetics is assumed.

Understanding the Genetic Architecture of Complex Traits Through Meta-analysis

Author : Kodi Taraszka
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (139 download)

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Book Synopsis Understanding the Genetic Architecture of Complex Traits Through Meta-analysis by : Kodi Taraszka

Download or read book Understanding the Genetic Architecture of Complex Traits Through Meta-analysis written by Kodi Taraszka and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Exploring how genetic architecture shapes complex traits and diseases is a central premise of human genetics. Over the years, genome-wide association studies (GWAS) have enabled the discovery of numerous genetic variants associated with a variety of complex traits. In addition to the large array of traits analyzed, GWAS in diverse ancestral populations have also seen a significant increase in sample sizes. These efforts led to tens of thousands of publicly available GWAS summary statistics whose known correlation structure could be leveraged for further discovery. In this dissertation, I present two novel methods for the meta-analysis of GWAS summary statistics as well as conduct a pan-cancer meta-analysis of somatic variant burden. For one method, I present a likelihood ratio test for the joint analysis of genetically correlated traits and provide a per trait interpretation framework of the omnibus association. For the other method, I present a Bayesian framework that improves fine mapping of significant associations for one trait by leveraging the complementary information from distinct ancestral backgrounds. In addition to these methods, I analyzed how clinical and polygenic germline features influence somatic variant burden within and across cancer types.

Statistical Methods for Genome-wide Detection of QTL Hotspots Toward Understanding the Complex Genetic Architecture of Quantitative Traits Using Public Databases with Application to Rice

Download Statistical Methods for Genome-wide Detection of QTL Hotspots Toward Understanding the Complex Genetic Architecture of Quantitative Traits Using Public Databases with Application to Rice PDF Online Free

Author : 楊滿霞
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (113 download)

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Book Synopsis Statistical Methods for Genome-wide Detection of QTL Hotspots Toward Understanding the Complex Genetic Architecture of Quantitative Traits Using Public Databases with Application to Rice by : 楊滿霞

Download or read book Statistical Methods for Genome-wide Detection of QTL Hotspots Toward Understanding the Complex Genetic Architecture of Quantitative Traits Using Public Databases with Application to Rice written by 楊滿霞 and published by . This book was released on 2018 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Advances in Statistical Methods for the Genetic Dissection of Complex Traits in Plants

Author : Yuan-Ming Zhang
Publisher : Frontiers Media SA
ISBN 13 : 2832543693
Total Pages : 278 pages
Book Rating : 4.8/5 (325 download)

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Book Synopsis Advances in Statistical Methods for the Genetic Dissection of Complex Traits in Plants by : Yuan-Ming Zhang

Download or read book Advances in Statistical Methods for the Genetic Dissection of Complex Traits in Plants written by Yuan-Ming Zhang and published by Frontiers Media SA. This book was released on 2024-01-26 with total page 278 pages. Available in PDF, EPUB and Kindle. Book excerpt: Genome-wide association studies (GWAS) have been widely used in the genetic dissection of complex traits. However, there are still limits in current GWAS statistics. For example, (1) almost all the existing methods do not estimate additive and dominance effects in quantitative trait nucleotide (QTN) detection; (2) the methods for detecting QTN-by-environment interaction (QEI) are not straightforward and do not estimate additive and dominance effects as well as additive-by-environment and dominance-by-environment interaction effects, leading to unreliable results; and (3) no or too simple polygenic background controls have been employed in QTN-by-QTN interaction (QQI) detection. As a result, few studies of QEI and QQI for complex traits have been reported based on multiple-environment experiments. Recently, new statistical tools, including 3VmrMLM, have been developed to address these needs in GWAS. In 3VmrMLM, all the trait-associated effects, including QTN, QEI and QQI related effects, are compressed into a single effect-related vector, while all the polygenic backgrounds are compressed into a single polygenic effect matrix. These compressed parameters can be accurately and efficiently estimated through a unified mixed model analysis. To further validate these new GWAS methods, particularly 3VmrMLM, they should be rigorously tested in real data of various plants and a wide range of other species.

Statistical Methods for Integrating Quantitative Trait Loci Annotation in Post-gwas Analysis

Author : Kunling Huang (Ph.D.)
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (139 download)

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Book Synopsis Statistical Methods for Integrating Quantitative Trait Loci Annotation in Post-gwas Analysis by : Kunling Huang (Ph.D.)

Download or read book Statistical Methods for Integrating Quantitative Trait Loci Annotation in Post-gwas Analysis written by Kunling Huang (Ph.D.) and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Recent advances in large-scale genome-wide association studies (GWAS) have highlighted the involvement of common genetic variants in complex traits and diseases. Data integration efforts linking GWAS signals with functional annotation data have provided insights into the genetic architecture of numerous human complex traits. For example, expression quantitative trait loci (eQTL) studies in relevant biological tissues provide gene candidates for complex diseases, which can be tested as therapeutic targets. Integrating multi-omics annotation data with GWAS association data brings in orthogonal information and improves the understanding of complex trait etiology. In this dissertation, we present two approaches to link genomic annotations to genotype-phenotype associations identified through GWAS. The two approaches both associate complex traits with genetically imputed molecular traits (i.e., gene expression levels and metabolite levels), and identify regulatory and metabolic machineries underlying a variety of complex traits.We start with integrating eQTLs with autism spectrum disorder (ASD) in parent-offspring trios by quantifying the transmission disequilibrium of genetically regulated gene expression from parents to offspring and performing transcriptome-wide association studies (TWAS). We identify transcription factor POU3F2 in our analysis. POU3F2 mainly expresses in developmental brain and the gene targets regulated by POU3F2 are enriched for known risk genes for ASD and loss-of-function de novo mutations in ASD probands. TWAS suggests that ASD genes affected by very rare mutations may be regulated by an unlinked transcription factor affected by common genetic variations. Next, we extend our TWAS framework to study the regulatory roles of metabolite quantitative trait loci (mQTL). We introduce metabolome-wide association study (MWAS), which integrates metabolomics data with genetics data. We benchmarked and optimized genetic prediction models for a total of 703 metabolites from cerebrospinal fluid, plasma, and urine, and performed a biobank-wide association scan between imputed metabolite levels and 530 complex traits in UK Biobank. We found a total of 1,311 significant metabolite-trait associations after performing Bonferroni correction across all tested associations. The significant MWAS results explain the difference in human body fat mass and body fat-free mass. In summary, we perform joint analysis on eQTL/mQTL data and complex trait GWAS to identify genes or metabolites relevant to complex traits. Our approaches improve our understanding of the phenotypic outcomes of non-coding genetic variations and may contribute to novel biomarker discovery, clinical diagnosis improvement, and therapeutics development.

Integrative Statistical Methods to Understand the Genetic Basis of Complex Trait

Author : Gleb Kichaev
Publisher :
ISBN 13 :
Total Pages : 166 pages
Book Rating : 4.:/5 (15 download)

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Book Synopsis Integrative Statistical Methods to Understand the Genetic Basis of Complex Trait by : Gleb Kichaev

Download or read book Integrative Statistical Methods to Understand the Genetic Basis of Complex Trait written by Gleb Kichaev and published by . This book was released on 2018 with total page 166 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Genome-wide Association study (GWAS) is one of the primary tools for understanding the genetic basis of complex traits. In this dissertation I introduce enhanced statistical methods to do integrative GWAS analysis with functional genomic data. First, I describe an integrative fine-mapping framework to prioritize causal variants at known GWAS risk loci. Next, I expand upon this framework to exploit genetic heterogeniety across human populations to improve statistical efficiency. I then consider a new inference strategy to reduce the computational burden of the methodology. Finally, I propose a new approach for GWAS discovery that leverages functional genomic data through polygenic modeling.

Statistical Methods, Computing, and Resources for Genome-Wide Association Studies

Author : Riyan Cheng
Publisher : Frontiers Media SA
ISBN 13 : 2889712125
Total Pages : 148 pages
Book Rating : 4.8/5 (897 download)

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Book Synopsis Statistical Methods, Computing, and Resources for Genome-Wide Association Studies by : Riyan Cheng

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:

Methods and Models for the Analysis of Genetic Variation Across Species Using Large-scale Genomic Data

Author : Tanya Ngoc Phung
Publisher :
ISBN 13 :
Total Pages : 213 pages
Book Rating : 4.:/5 (18 download)

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Book Synopsis Methods and Models for the Analysis of Genetic Variation Across Species Using Large-scale Genomic Data by : Tanya Ngoc Phung

Download or read book Methods and Models for the Analysis of Genetic Variation Across Species Using Large-scale Genomic Data written by Tanya Ngoc Phung and published by . This book was released on 2018 with total page 213 pages. Available in PDF, EPUB and Kindle. Book excerpt: Understanding how different evolutionary processes shape genetic variation within and between species is an important question in population genetics. The advent of next generation sequencing has allowed for many theories and hypotheses to be tested explicitly with data. However, questions such as what evolutionary processes affect neutral divergence (DNA differences between species) or genetic variation in different regions of the genome (such as on autosomes versus sex chromosomes) or how many genetic variants contribute to complex traits are still outstanding. In this dissertation, I utilized different large-scale genomic datasets and developed statistical methods to determine the role of natural selection on genetic variation between species, sex-biased evolutionary processes on shaping patterns of genetic variation on the X chromosome and autosomes, and how population history, mutation, and natural selection interact to control complex traits. First, I used genome-wide divergence data between multiple pairs of species ranging in divergence time to show that natural selection has reduced divergence at neutral sites that are linked to those under direct selection. To determine explicitly whether and to what extent linked selection and/or mutagenic recombination could account for the pattern of neutral divergence across the genome, I developed a statistical method and applied it to human-chimp neutral divergence dataset. I showed that a model including both linked selection and mutagenic recombination resulted in the best fit to the empirical data. However, the signal of mutagenic recombination could be coming from biased gene conversion. Comparing genetic diversity between the X chromosome and the autosomes could provide insights into whether and how sex-biased processes have affected genetic variation between different genomic regions. For example, X/A diversity ratio greater than neutral expectation could be due to more X chromosomes than expected and could be a result of mating practices such as polygamy where there are more reproducing females than males. I next utilized whole-genome sequences from dogs and wolves and found that X/A diversity is lower than neutral expectation in both dogs and wolves in ancient time-scales, arguing for evolutionary processes resulting in more males reproducing compared to females. However, within breed dogs, patterns of population differentiation suggest that there have been more reproducing females, highlighting effects from breeding practices such as popular sire effect where one male can father many offspring with multiple females. In medical genetics, a complete understanding of the genetic architecture is essential to unravel the genetic basis of complex traits. While genome wide association studies (GWAS) have discovered thousands of trait-associated variants and thus have furthered our understanding of the genetic architecture, key parameters such as the number of causal variants and the mutational target size are still under-studied. Further, the role of natural selection in shaping the genetic architecture is still not entirely understood. In the last chapter, I developed a computational method called InGeAr to infer the mutational target size and explore the role of natural selection on affecting the variant's effect on the trait. I found that the mutational target size differs from trait to trait and can be large, up to tens of megabases. In addition, purifying selection is coupled with the variant's effect on the trait. I discussed how these results support the omnigenic model of complex traits. In summary, in this dissertation, I utilized different types of large genomic dataset, from genome-wide divergence data to whole genome sequence data to GWAS data to develop models and statistical methods to study how different evolutionary processes have shaped patterns of genetic variation across the genome.

Linear Models in Statistics

Author : Alvin C. Rencher
Publisher : John Wiley & Sons
ISBN 13 : 0470192607
Total Pages : 690 pages
Book Rating : 4.4/5 (71 download)

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Book Synopsis Linear Models in Statistics by : Alvin C. Rencher

Download or read book Linear Models in Statistics written by Alvin C. Rencher and published by John Wiley & Sons. This book was released on 2008-01-07 with total page 690 pages. Available in PDF, EPUB and Kindle. Book excerpt: The essential introduction to the theory and application of linear models—now in a valuable new edition Since most advanced statistical tools are generalizations of the linear model, it is neces-sary to first master the linear model in order to move forward to more advanced concepts. The linear model remains the main tool of the applied statistician and is central to the training of any statistician regardless of whether the focus is applied or theoretical. This completely revised and updated new edition successfully develops the basic theory of linear models for regression, analysis of variance, analysis of covariance, and linear mixed models. Recent advances in the methodology related to linear mixed models, generalized linear models, and the Bayesian linear model are also addressed. Linear Models in Statistics, Second Edition includes full coverage of advanced topics, such as mixed and generalized linear models, Bayesian linear models, two-way models with empty cells, geometry of least squares, vector-matrix calculus, simultaneous inference, and logistic and nonlinear regression. Algebraic, geometrical, frequentist, and Bayesian approaches to both the inference of linear models and the analysis of variance are also illustrated. Through the expansion of relevant material and the inclusion of the latest technological developments in the field, this book provides readers with the theoretical foundation to correctly interpret computer software output as well as effectively use, customize, and understand linear models. This modern Second Edition features: New chapters on Bayesian linear models as well as random and mixed linear models Expanded discussion of two-way models with empty cells Additional sections on the geometry of least squares Updated coverage of simultaneous inference The book is complemented with easy-to-read proofs, real data sets, and an extensive bibliography. A thorough review of the requisite matrix algebra has been addedfor transitional purposes, and numerous theoretical and applied problems have been incorporated with selected answers provided at the end of the book. A related Web site includes additional data sets and SAS® code for all numerical examples. Linear Model in Statistics, Second Edition is a must-have book for courses in statistics, biostatistics, and mathematics at the upper-undergraduate and graduate levels. It is also an invaluable reference for researchers who need to gain a better understanding of regression and analysis of variance.

Statistical Genetics of Quantitative Traits

Author : Rongling Wu
Publisher : Springer Science & Business Media
ISBN 13 : 038768154X
Total Pages : 371 pages
Book Rating : 4.3/5 (876 download)

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Book Synopsis Statistical Genetics of Quantitative Traits by : Rongling Wu

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.

Quantitative Trait Loci

Author : Nicola J. Camp
Publisher : Springer Science & Business Media
ISBN 13 : 1592591760
Total Pages : 362 pages
Book Rating : 4.5/5 (925 download)

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Book Synopsis Quantitative Trait Loci by : Nicola J. Camp

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.

Introduction to Statistical Methods in Modern Genetics

Author : M.C. Yang
Publisher : CRC Press
ISBN 13 : 9789056991340
Total Pages : 264 pages
Book Rating : 4.9/5 (913 download)

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Book Synopsis Introduction to Statistical Methods in Modern Genetics by : M.C. Yang

Download or read book Introduction to Statistical Methods in Modern Genetics written by M.C. Yang and published by CRC Press. This book was released on 2000-02-23 with total page 264 pages. Available in PDF, EPUB and Kindle. Book excerpt: Although the basic statistical theory behind modern genetics is not very difficult, most statistical genetics papers are not easy to read for beginners in the field, and formulae quickly become very tedious to fit a particular area of application. Introduction to Statistical Methods in Modern Genetics distinguishes between the necessary and unnecessary complexity in a presentation designed for graduate-level statistics students. The author keeps derivations simple, but does so without losing the mathematical details. He also provides the required background in modern genetics for those looking forward to entering this arena. Along with some of the statistical tools important in genetics applications, students will learn: How a gene is found How scientists have separated the genetic and environmental aspects of a person's intelligence How genetics are used in agriculture to improve crops and domestic animals What a DNA fingerprint is and why there are controversies about it Although the author assumes students have a foundation in basic statistics, an appendix provides the necessary background beyond the elementary, including multinomial distributions, inference on frequency tables, and discriminant analysis. With clear explanations, a multitude of figures, and exercise sets in each chapter, this text forms an outstanding entrée into the rapidly expanding world of genetic data analysis.