Statistical Methods For Integrating Quantitative Trait Loci Annotation In Post Gwas Analysis

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

Author : Kunling Huang
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (14 download)

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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.

Statistical Methods for Genetic Variants Detection with Epigenomic Information

Author : Maria Constanza Rojo
Publisher :
ISBN 13 :
Total Pages : 158 pages
Book Rating : 4.:/5 (113 download)

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Book Synopsis Statistical Methods for Genetic Variants Detection with Epigenomic Information by : Maria Constanza Rojo

Download or read book Statistical Methods for Genetic Variants Detection with Epigenomic Information written by Maria Constanza Rojo and published by . This book was released on 2019 with total page 158 pages. Available in PDF, EPUB and Kindle. Book excerpt: Genome-wide association studies (GWAS) have successfully identified thousands of genetic variants contributing to disease and other phenotypes. However, significant obstacles hamper our ability to elucidate causal variants, identify genes affected by causal variants, and characterize the mechanisms by which genotypes influence phenotypes. The increasing availability of genome-wide functional annotation data provides unique opportunities to incorporate prior information into the analysis of GWAS to better understand the impact of variants on disease etiology. Regulatory genomic information has been recognized as a potential source that can improve the detection and biological interpretation of single-nucleotide polymorphisms (SNPs) in GWAS. Although there have been many advances in incorporating prior information into the prioritization of trait-associated variants in GWAS, functional annotation data has played a secondary role in the joint analysis of GWAS and molecular (i.e., expression) quantitative trait loci (eQTL) data in assessing evidence of association. Moreover, current methodologies that aim to integrate such annotation information focus mainly on fine-mapping and overlook the importance of its usage in earlier stages of GWAS analysis. Equally important, there is a lack of development in proper statistical frameworks that can perform selection of annotations and SNPs jointly. To address these shortcomings, we develop two statistical models: iFunMed and GRAD. iFunMed is a novel mediation framework to integrate GWAS and eQTL data with the utilization of publicly available functional annotation data. iFunMed extends the scope of standard mediation analysis by incorporating information from multiple genetic variants at a time and leveraging variant-level summary statistics. GRAD integrates high-dimensional auxiliary information into high-dimensional regression. This method allows annotation information to assist the detection of important genetic variants while identifying relevant annotation simultaneously. We provide an upper bound for the estimation error of the SNP effect sizes to gain insights on what factors affect estimation accuracy. For iFunMed, data-driven computational experiments convey how informative annotations improve SNP selection performance while emphasizing the robustness of the model to non-informative annotations. Applications to the Framingham Heart Study data indicate that iFunMed is able to boost the detection of SNPs with mediation effects that can be attributed to regulatory mechanisms. Simulation experiments indicate that GRAD can improve the identification of genetic variants by increasing the average area under the precision-recall curve by up to 60\%. Real data applications to the Framingham Heart Study show that GRAD can select relevant genetic variants while detecting several transcription factors involved in specific phenotypical changes.

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

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.

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:

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.

Phenotypes and Genotypes

Author : Florian Frommlet
Publisher : Springer
ISBN 13 : 9781447153115
Total Pages : 290 pages
Book Rating : 4.1/5 (531 download)

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Book Synopsis Phenotypes and Genotypes by : Florian Frommlet

Download or read book Phenotypes and Genotypes written by Florian Frommlet and published by Springer. This book was released on 2016-01-06 with total page 290 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents the methodology of association mapping in experimental populations and genome-wide association studies (GWAS). The main emphasis is placed on methods based on modifications of the Bayesian information criterion, designed specifically to handle multiple testing problems in large-scale genome scans for trait loci (TL). The book is written at the level of a graduate course for bioinformatics students. The first chapter introduces the major concepts of quantitative trait loci (QTL) mapping. The second chapter discusses the methodology of QTL mapping in experimental populations, with the main emphasis on the related issues of model selection in linear models. The approach is then extended to TL via generalized linear models. Chapter three describes the methods for GWAS and related multiple testing and model selection problems. In both chapters two and three the properties of QTL mapping methods are illustrated with computer simulations and real data analysis.

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.

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.

Quantitative Trait Loci Analysis in Animals

Author : Joel Ira Weller
Publisher : CABI
ISBN 13 : 1845937341
Total Pages : 288 pages
Book Rating : 4.8/5 (459 download)

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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.

Medical Epigenetics

Author : Trygve Tollefsbol
Publisher : Academic Press
ISBN 13 : 0128032405
Total Pages : 944 pages
Book Rating : 4.1/5 (28 download)

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Book Synopsis Medical Epigenetics by : Trygve Tollefsbol

Download or read book Medical Epigenetics written by Trygve Tollefsbol and published by Academic Press. This book was released on 2016-06-21 with total page 944 pages. Available in PDF, EPUB and Kindle. Book excerpt: Medical Epigenetics provides a comprehensive analysis of the importance of epigenetics to health management. The purpose of this book is to fill a current need for a comprehensive volume on the medical aspects of epigenetics with a focus on human systems, epigenetic diseases that affect these systems and modes of treating epigenetic-based disorders and diseases. The intent of this book is to provide a stand-alone comprehensive volume that will cover all human systems relevant to epigenetic maladies and all major aspects of medical epigenetics. The overall goal is to provide the leading book on medical epigenetics that will be useful not only to physicians, nurses, medical students and many others directly involved with health care, but also investigators in life sciences, biotech companies, graduate students and many others who are interested in more applied aspects of epigenetics. Research in the area of translational epigenetics is a cornerstone of this volume. Critical reviews dedicated to the burgeoning role of epigenetics in medical practice Coverage of emerging topics including twin epigenetics as well as epigenetics of gastrointestinal disease, muscle disorders, endocrine disorders, ocular medicine, pediatric diseases, sports medicine, noncoding RNA therapeutics, pain management and regenerative medicine Encompasses a disease-oriented perspective of medical epigenetics as well as diagnostic and prognostic epigenetic approaches to applied medicine

Statistical Methods for Expression Quantitative Trait Loci (EQTL) Mapping

Author : Meng Chen
Publisher :
ISBN 13 :
Total Pages : 164 pages
Book Rating : 4.:/5 (89 download)

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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:

Statistical Methods for Gene Selection and Genetic Association Studies

Author :
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (138 download)

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Book Synopsis Statistical Methods for Gene Selection and Genetic Association Studies by :

Download or read book Statistical Methods for Gene Selection and Genetic Association Studies written by and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Abstract : This dissertation includes five Chapters. A brief description of each chapter is organized as follows. In Chapter One, we propose a signed bipartite genotype and phenotype network (GPN) by linking phenotypes and genotypes based on the statistical associations. It provides a new insight to investigate the genetic architecture among multiple correlated phenotypes and explore where phenotypes might be related at a higher level of cellular and organismal organization. We show that multiple phenotypes association studies by considering the proposed network are improved by incorporating the genetic information into the phenotype clustering. In Chapter Two, we first illustrate the proposed GPN to GWAS summary statistics. Then, we assess contributions to constructing a well-defined GPN with a clear representation of genetic associations by comparing the network properties with a random network, including connectivity, centrality, and community structure. The network topology annotations based on the sparse representations of GPN can be used to understand the disease heritability for the highly correlated phenotypes. In applications of phenome-wide association studies, the proposed GPN can identify more significant pairs of genetic variant and phenotype categories. In Chapter Three, a powerful and computationally efficient gene-based association test is proposed, aggregating information from different gene-based association tests and also incorporating expression quantitative trait locus information. We show that the proposed method controls the type I error rates very well and has higher power in the simulation studies and can identify more significant genes in the real data analyses. In Chapter Four, we develop six statistical selection methods based on the penalized regression for inferring target genes of a transcription factor (TF). In this study, the proposed selection methods combine statistics, machine learning , and convex optimization approach, which have great efficacy in identifying the true target genes. The methods will fill the gap of lacking the appropriate methods for predicting target genes of a TF, and are instrumental for validating experimental results yielding from ChIP-seq and DAP-seq, and conversely, selection and annotation of TFs based on their target genes. In Chapter Five, we propose a gene selection approach by capturing gene-level signals in network-based regression into case-control association studies with DNA sequence data or DNA methylation data, inspired by the popular gene-based association tests using a weighted combination of genetic variants to capture the combined effect of individual genetic variants within a gene. We show that the proposed gene selection approach have higher true positive rates than using traditional dimension reduction techniques in the simulation studies and select potentially rheumatoid arthritis related genes that are missed by existing methods.

Efficient and Robust Statistical Methodologies for Quantitative Trait Loci Analysis

Author : Fei Zou
Publisher :
ISBN 13 :
Total Pages : 110 pages
Book Rating : 4.:/5 (89 download)

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Book Synopsis Efficient and Robust Statistical Methodologies for Quantitative Trait Loci Analysis by : Fei Zou

Download or read book Efficient and Robust Statistical Methodologies for Quantitative Trait Loci Analysis written by Fei Zou and published by . This book was released on 2001 with total page 110 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Statistical Methods for Molecular Quantitative Trait Locus Analysis

Author : Heather J. Zhou
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (139 download)

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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.

Handbook of Statistical Genomics

Author : David J. Balding
Publisher : John Wiley & Sons
ISBN 13 : 1119429226
Total Pages : 1224 pages
Book Rating : 4.1/5 (194 download)

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Book Synopsis Handbook of Statistical Genomics by : David J. Balding

Download or read book Handbook of Statistical Genomics written by David J. Balding and published by John Wiley & Sons. This book was released on 2019-07-02 with total page 1224 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.

Statistical Methods for Genomics and Genetics Data Analysis

Author : Ziyue Wang
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (123 download)

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Book Synopsis Statistical Methods for Genomics and Genetics Data Analysis by : Ziyue Wang

Download or read book Statistical Methods for Genomics and Genetics Data Analysis written by Ziyue Wang and published by . This book was released on 2020 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Over the past decades, genome research has led to major technological advances in sequencing, genotyping, and phenotyping. Identifying the genetic basis of disease as well as the relationship and function of genes becomes a central problem in a number of biological endeavors, as it is essential for understanding disease mechanism. In this dissertation, I develop, implement, evaluate and apply statistical and computational methods for analysis of various types of genomic data. A unifying theme in my work is developing methods to address problems that arise in the labs of my collaborators, focusing on the right balance between computational simplicity and impact. The first method I developed focuses on integrating genetic information across mouse and human genome to uncover important disease-related genetic signals. Namely, I developed the cross-species-integration (CSI) pipeline with two modules: an iterative mapping procedure to narrow down QTL regions of interest and a concordant test to improve functional inference in GWAS. The second method focuses on developing a gene association network model to learn relationships among genes. Specifically, I developed scNBN, a Negative Binomial based graphical model combining a proper neighborhood selection algorithm for recovering gene association networks using scRNA-seq data. The final part introduces the R packages that implement above methods which are beneficial for both statisticians and scientists who are interested in performing these analyses.