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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
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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:
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.
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.
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
Book Synopsis A Guide to QTL Mapping with R/qtl by : Karl W. Broman
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
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:
Book Synopsis Quantitative Trait Loci Analysis in Animals by : Joel Ira Weller
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.
Book Synopsis The Applications of New Multi-Locus GWAS Methodologies in the Genetic Dissection of Complex Traits by : Yuan-Ming Zhang
Download or read book The Applications of New Multi-Locus GWAS Methodologies in the Genetic Dissection of Complex Traits written by Yuan-Ming Zhang and published by Frontiers Media SA. This book was released on 2019-06-19 with total page 236 pages. Available in PDF, EPUB and Kindle. Book excerpt: Genome-Wide Association Studies (GWAS) are widely used in the genetic dissection of complex traits. Most existing methods are based on single-marker association in genome-wide scans with population structure and polygenic background controls. To control the false positive rate, the Bonferroni correction for multiple tests is frequently adopted. This stringent correction results in the exclusion of important loci, especially for GWAS in crop genetics. To address this issue, multi-locus GWAS methodologies have been recommended, i.e., FASTmrEMMA, ISIS EM-BLASSO, mrMLM, FASTmrMLM, pLARmEB, pKWmEB and FarmCPU. In this Research Topic, our purpose is to clarify some important issues in the application of multi-locus GWAS methods. Here we discuss the following subjects: First, we discuss the advantages of new multi-locus GWAS methods over the widely-used single-locus GWAS methods in the genetic dissection of complex traits, metabolites and gene expression levels. Secondly, large experiment error in the field measurement of phenotypic values for complex traits in crop genetics results in relatively large P-values in GWAS, indicating the existence of small number of significantly associated SNPs. To solve this issue, a less stringent P-value critical value is often adopted, i.e., 0.001, 0.0001 and 1/m (m is the number of markers). Although lowering the stringency with which an association is made could identify more hits, confidence in these hits would significantly drop. In this Research Topic we propose a new threshold of significant QTN (LOD=3.0 or P-value=2.0e-4) in multi-locus GWAS to balance high power and low false positive rate. Thirdly, heritability missing in GWAS is a common phenomenon, and a series of scientists have explained the reasons why the heritability is missing. In this Research Topic, we also add one additional reason and propose the joint use of several GWAS methodologies to capture more QTNs. Thus, overall estimated heritability would be increased. Finally, we discuss how to select and use these multi-locus GWAS methods.
Book Synopsis Statistical Methods for Integrating Quantitative Trait Loci Annotation in Post-gwas Analysis by : Kunling Huang
Download or read book Statistical Methods for Integrating Quantitative Trait Loci Annotation in Post-gwas Analysis written by Kunling Huang 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.
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.
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.
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.
Book Synopsis Statistical Methods for Molecular Quantitative Trait Locus Analysis by : Heather J. Zhou
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.
Book Synopsis Quantitative Trait Loci (QTL) by : Scott A. Rifkin
Download or read book Quantitative Trait Loci (QTL) written by Scott A. Rifkin and published by Humana Press. This book was released on 2012-05-09 with total page 0 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.
Book Synopsis Modeling the Genetic Architecture of Complex Traits by : Han Hao
Download or read book Modeling the Genetic Architecture of Complex Traits written by Han Hao and published by . This book was released on 2016 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Quantitative trait loci (QTL) mapping is the process of detecting genetic variants that regulate complex traits. Among all complex traits with continuous values, function-valued traits that undergo developmental processes are commonly seen in growth analysis, shape analysis, network analysis, and clinical trials. Functional mapping is a statistical tool for mapping QTLs involved with function-valued phenotypic traits. The utility of functional mapping can be displayed when the phenotypic traits represent developmental processes and can be modeled by either parametric or nonparametric curves.In this dissertation, we focus on the application of functional mapping and the development of computational frameworks in different types of function-valued traits. In Chapter 2, we deal with the situation where the trait values follow a parametric trend. A Richard curve is used to model rice height growth, and QTLs are examined for their association with the onset, offset and duration of the developmentaol process using a Gaussian mixture model. In Chapter 3, the trait values do not have a parametric form. The contour of mei leaves is modeled using Fourier basis expansion, and QTLs are examined for their association with shape development using a high-dimensional analysis of variance (HANOVA) approach. In Chapter 4 and Chapter 5, we study the interactions between two function-valued traits. The growth of two interacting traits is modeled with ordinary differential equation (ODE) systems, and QTLs are examined for their association with the type and intensity of such interactions using a generalized profiling approach.
Book Synopsis Statistical Methods for Expression Quantitative Trait Loci (EQTL) Mapping by : Meng Chen
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:
Book Synopsis Plant Resistance to Viruses by : David Evered
Download or read book Plant Resistance to Viruses written by David Evered and published by John Wiley & Sons. This book was released on 2008-04-30 with total page 226 pages. Available in PDF, EPUB and Kindle. Book excerpt: Concern about the environmental consequences of the widespread use of pesticides has increased, and evidence of pesticide-resistant virus vectors have continued to emerge. This volume presents a timely survey of the mechanisms of plant resistance and examines current developments in breeding for resistance, with particular emphasis on advances in genetic engineering which allow for the incorporation of viral genetic material into plants. Discusses the mechanisms of innate resistance in strains of tobacco, tomato, and cowpea; various aspects of induced resistance, including the characterization and roles of the pathogenesis-related proteins; antiviral substances and their comparison with interferon; and cross-protection between plant virus strains. Also presents several papers which evaluate the status of genetic engineering as it relates to breeding resistant plants. Among these are discussions of the potential use of plant viruses as gene vectors, gene coding for viral coat protein, satellite RNA, and antisense RNA, and practical issues such as the durability of resistant crop plants in the field.
