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 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 Statistical Methods for Detecting Expression Quantitative Trait Loci EQTL written by Wei Zhang and published by . This book was released on 2009 with total page 224 pages. Available in PDF, EPUB and Kindle. Book excerpt: We applied the procedure to a data set consisting of gene expression and genotypes for 112 segregants of S. cerevisiae (Brem and Kruglyak 2005). Our method identified modules containing genes mapped to previously reported eQTL hot spots, and dissected these large eQTL hot spots into several modules corresponding to different causal regulators or primary and secondary responses to causal perturbations. In addition, we identified nine modules associated with pairs of eQTLs, of which two have been previously reported, including the mating module (Brem et al. 2005) and the ZAP1 target module (Lee et al. 2006). We demonstrated that one of the novel modules containing many daughter-cell expressed genes is regulated by AMN1 and BPH1 .
Download or read book New Statistical Methods in Bioinformatics written by Rhonda DeCook and published by . This book was released on 2006 with total page 270 pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis focuses on new statistical methods in the area of bioinformatics which uses computers and statistics to solve biological problems. The first study discusses a method for detecting a quantitative trait locus (QTL) when the trait of interest has a zero-inflated Poisson (ZIP) distribution. Though existing methods based on normality may be reasonably applied to some ZIP distributions, the characteristics of other ZIP distributions make such an application inappropriate. We compare our method to an existing non-parametric approach, and we illustrate our method using QTL data collected on two ecotypes of the Arabidopsis thaliana plant where the trait of interest is shoot count. The second study discusses a method to detect differentially expressed genes in an unreplicated multiple-treatment microarray timecourse experiment. In a two-sample setting, differential expression is well defined as non-equal means, but in the present setting, there are numerous expression patterns that may qualify as differential expression, and that may be of interest to the researcher. This method provides the researcher with a list of significant genes, an associated false discovery rate for that list, and a 'best model' choice for every gene. The model choice component is relevant because the alternative hypothesis of differential expression does not dictate one specific alternative expression pattern. In fact, in this type of experiment, there are many possible expression patterns of interest to the researcher. Using simulations, we provide information on the specificity and sensitivity of detection under a variety of true expression patterns using receiver operating characteristic curves. The method is illustrated using an Arabidopsis thaliana microarray experiment with five time points and three treatment groups. The third study discusses a new type of analysis, called eQTL analysis. This analysis brings together the methods of microarray and QTL analyses in order to detect locations on the genome that control gene expression. These controlling loci are called expression QTL, or eQTL. Locating eQTL can help researchers uncover complex networks in biological systems. The method is illustrated using an Arabidopsis thaliana eQTL experiment with 22,787 genes and 288 markers.
Download or read book eQTL Analysis written by Xinghua Mindy Shi and published by Humana. This book was released on 2021-01-02 with total page 252 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume details state-of-art eQTL analysis, where interdisciplinary researchers are provided both theoretical and practical guidance to eQTL analysis and interpretation. Chapters guide readers through methods and tools for eQTL and QTL analysis and the usage of such analysis in various scenarios. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, lists of the necessary materials and reagents, step-by-step, readily reproducible laboratory protocols, and tips on troubleshooting and avoiding known pitfalls. Authoritative and cutting-edge, eQTL Analysis: Methods and Protocols to ensure successful results in the further study of this vital field.
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 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 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 Statistical Considerations of Expression Quantitative Loci eQTL Mapping with Next Generation Sequencing Data written by Kang-Hsien Fan and published by . This book was released on 2017 with total page 192 pages. Available in PDF, EPUB and Kindle. Book excerpt: Expression quantitative trait loci (eQTL) are loci on the genome that contribute to the expression levels of the messenger RNAs in an organism. They link the static genetic information of DNA sequence variation together with the dynamic genetic information of gene expression. Moreover, sequencing has become the dominant technology for genomic research, such as eQTL studies. Because of the precise resolution from genomic sequencing, it produces a tremendous amount of data for either gene expression profiles or the genetic variants in the subjects. Therefore, it requires the extraordinary intense significant threshold for multiple-testing adjustment if enormous numbers of statistical analyses are employed. Some strategies for reducing the total numbers of tests, for example by considering the physical distance between a genetic marker and a gene; or by constructing a co-expressed gene network, are designed to increase the statistical power for trans-eQTL detection. Here we proposed a statistical workflow to increase the trans-eQTL mapping power by both implementing a network-free co-expression method and the blocked weight false discovery rate (FDR) multiple-testing adjustment. On the other hand, RNA-sequence analyses use numbers of aligned short reads count to a gene as the proxy of expression level for such gene. The accuracy of the alignment is questionable when subject's genome has higher polyploidy. For example, lots of plants have more than two copies of chromosomes, as well as many homologous and paralogous genes that share great similarities in nucleotide sequence. The miss-assigned reads cause false positive results and lack of power to detect eQTL while using RNA sequencing data in plants. Thus, we also establish a bioinformatics and statistical framework to map eQTL with RNA sequencing data from polyploid libraries.
Download or read book Statistical Issues and Novel Strategies for Expression Quantitative Trait Loci Mapping written by Shaoyu Li and published by . This book was released on 2011 with total page 157 pages. Available in PDF, EPUB and Kindle. Book excerpt:
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 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.
Download or read book Handbook of Statistical Genomics written by David J. Balding and published by John Wiley & Sons. This book was released on 2019-07-09 with total page 1828 pages. Available in PDF, EPUB and Kindle. Book excerpt: A timely update of a highly popular handbook on statistical genomics This new, two-volume edition of a classic text provides a thorough introduction to statistical genomics, a vital resource for advanced graduate students, early-career researchers and new entrants to the field. It introduces new and updated information on developments that have occurred since the 3rd edition. Widely regarded as the reference work in the field, it features new chapters focusing on statistical aspects of data generated by new sequencing technologies, including sequence-based functional assays. It expands on previous coverage of the many processes between genotype and phenotype, including gene expression and epigenetics, as well as metabolomics. It also examines population genetics and evolutionary models and inference, with new chapters on the multi-species coalescent, admixture and ancient DNA, as well as genetic association studies including causal analyses and variant interpretation. The Handbook of Statistical Genomics focuses on explaining the main ideas, analysis methods and algorithms, citing key recent and historic literature for further details and references. It also includes a glossary of terms, acronyms and abbreviations, and features extensive cross-referencing between chapters, tying the different areas together. With heavy use of up-to-date examples and references to web-based resources, this continues to be a must-have reference in a vital area of research. Provides much-needed, timely coverage of new developments in this expanding area of study Numerous, brand new chapters, for example covering bacterial genomics, microbiome and metagenomics Detailed coverage of application areas, with chapters on plant breeding, conservation and forensic genetics Extensive coverage of human genetic epidemiology, including ethical aspects Edited by one of the leading experts in the field along with rising stars as his co-editors Chapter authors are world-renowned experts in the field, and newly emerging leaders. The Handbook of Statistical Genomics is an excellent introductory text for advanced graduate students and early-career researchers involved in statistical genetics.
Download or read book A Novel Framework for Expression Quantitative Trait Loci Mapping written by Ni Ai and published by . This book was released on 2017-01-26 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation, "A Novel Framework for Expression Quantitative Trait Loci Mapping" by Ni, Ai, 艾妮, was obtained from The University of Hong Kong (Pokfulam, Hong Kong) and is being sold pursuant to Creative Commons: Attribution 3.0 Hong Kong License. The content of this dissertation has not been altered in any way. We have altered the formatting in order to facilitate the ease of printing and reading of the dissertation. All rights not granted by the above license are retained by the author. DOI: 10.5353/th_b4715214 Subjects: Quantitative genetics Gene expression - Statistical methods
Download or read book Genome Wide Association Studies written by Krishnarao Appasani and published by Cambridge University Press. This book was released on 2016-01-14 with total page 449 pages. Available in PDF, EPUB and Kindle. Book excerpt: Experts from academia and industry highlight the potential of genome-wide association studies from basic science to clinical and biotechnological/pharmaceutical applications.
Download or read book Statistical Methods for the Analysis of Expression Quantitative Traits written by Chun Ye and published by . This book was released on 2009 with total page 169 pages. Available in PDF, EPUB and Kindle. Book excerpt: Advances in high throughput molecular profiling technologies to quantify gene expressions and identify genetic variations have ushered in a new era of biology. For the first time, we have the ability to globally survey naturally occurring genetic perturbations to identify those variants that alter gene expressions. Yet to fully realize the potential of the new biology these datasets promise, one must go beyond the standard expression quantitative trait (eQTL) mapping study in order to understand the relationships between gene expressions and genetic variations in the contexts of biological pathways, tissue types and environments. This thesis introduces novel statistical methods that extend the standard eQTL analysis to identify eQTLs in a biological context that are both statistically robust and biologically relevant. First, I address the problem of discovering eQTLs across multiple tissues using a procedure that captures similarities between tissues. I introduce both a general statistical framework for performing multiple tests in multiple dimensions and provide a few strategies for deriving a practical procedure that improves the over all power to detect cross tissue and tissue specific eQTLs. I show in simulation and replication studies that the performance of our method is better than standard likelihood ratio based approaches. I also show across four tissues the ability of this method to identify functionally consistent cross tissue and tissue specific eQTLs. Second, I present an application of mixed models to adjust for unmodeled heterogeneous sample structure resulting from experimental and technical biases in eQTL analysis. After showing that many previous eQTL studies suffer from spurious linkage and association signals that do not duplicate between replicates, I show that our approach performs significantly better by recovering eQTLs that are more concordant between biological replicates and increasing the number of cis eQTLs identified. Third, I present an approach for integrating eQTL analysis with chIP-Chip data to understand the effects of genetic variations on the dynamics of transcription regulation. I developed new statistical tests based on a network component analysis framework where the possible transcription regulatory networks are constrained to known relationships between transcription factors and their targets inferred from chIP-Chip data. Using yeast expression and genotyping data, I show that global trans linkage patterns can often be explained by regulatory eQTLs perturbing these transcription regulatory networks to induce large-scale differential expression. Fourth, I present a novel multivariate-based approach for interpreting differential expression and association studies in the context of annotated gene sets. I devised a new aggregate statistic that introduces the notion of a "tightly regulated" gene set that complements the notion of a differentially expressed gene set. This is motivated by the idea that sets of genes whose expression levels are coordinately differentially expressed with respect to a genetic variation or an experimental condition are more suggestive of real biological pathways. I identified several interesting gene sets in the context of an eQTL study conducted in murine hematopoietic stem cells. Finally, I present a novel model selection method for identifying the presence and absence of causal relationships between genes leveraging expression and variation data. We applied our method to identify "causal regulators" in a yeast eQTL study where global linkage patterns have been observed and attributed to "master regulators" that control the gene expression of a large number of genes. In addition to confirming several previously suggested regulators, we also identified several novel ones.
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:
