Flexible Hierarchical Bayesian Modeling Extensions To Improve Whole Genome Prediction And Genome Wide Association Analyses
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Flexible Hierarchical Bayesian Modeling Extensions To Improve Whole Genome Prediction And Genome Wide Association Analyses
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Author : Chunyu Chen
language : en
Publisher:
Release Date : 2017
Flexible Hierarchical Bayesian Modeling Extensions To Improve Whole Genome Prediction And Genome Wide Association Analyses written by Chunyu Chen and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017 with Electronic dissertations categories.
Cumulated Index Medicus
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Author :
language : en
Publisher:
Release Date : 1999
Cumulated Index Medicus written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1999 with Medicine categories.
Index Medicus
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Author :
language : en
Publisher:
Release Date : 2004
Index Medicus written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2004 with Medicine categories.
Vols. for 1963- include as pt. 2 of the Jan. issue: Medical subject headings.
Hierarchical Extensions Of Bayesian Parametric Models For Whole Genome Prediction
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Author : Wenzhao Yang
language : en
Publisher:
Release Date : 2014
Hierarchical Extensions Of Bayesian Parametric Models For Whole Genome Prediction written by Wenzhao Yang and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014 with Electronic dissertations categories.
A Bayesian Modeling For Paired Data In Genome Wide Association Studies With Application To Breast Cancer
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Author : Yashi Bu
language : en
Publisher:
Release Date : 2020
A Bayesian Modeling For Paired Data In Genome Wide Association Studies With Application To Breast Cancer written by Yashi Bu and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with Antibody diversity categories.
Many complex human diseases are associated with genetic factors. Identifying genetic markers is the key step to account for disease heritability, and develop disease diagnosis, risk prediction, prevention and therapeutic strategies. Genome-wide association study (GWAS) has emerged as a powerful tool to identify genetic variants that are associated with various cancers. The common statistical methodologies in GWAS focus on case-control data where cases and controls are sampled independently from the populations. Despite the success of GWAS in finding a number of genetic variants that are associated with cancers, the power of conventional GWAS is limited. Extensive research has shown that many tumors develop as a consequence of the progressive accumulation of somatic mutations over time. We focus on GWAS data from tumor and paired normal tissues to unravel the genetic association of somatic mutations. To address the limitation that conventional GWAS methods are not applicable to matched-paired data, we propose in this dissertation a framework that incorporates allelic relative risk, frequency and mutation rate to accommodate the structure of paired data. We first apply the penalized maximum likelihood estimation (MLE) to perform single marker analysis based on the framework. Simulation studies are carried out to assess the performance of penalized MLE. To further improve the estimation accuracy and power of single marker analysis, we develop a Bayesian hierarchical model that takes advantage of applying Bayesian shrinkage and making inferences based on the posterior distributions. The hierarchical Bayesian model has the flexibility to take into account the prior knowledge and extend to multiple marker analysis. We find that the single-marker Bayesian model has improved the estimation and power performance in most simulation scenarios. To identify DNA segments and SNP sets, rather than single genetic variants that are associated with the disease, we develop a multiple-SNP Bayesian model which considers SNP sets that are grouped together in a biologically meaningful way, such as genes or pathways. The multiple SNP analysis considers the joint effects of the SNP set, which improves the power to identify SNPs that have moderate marginal effects by themselves. Simulation studies show that the multi-marker Bayesian model has higher power to identify associated SNPs and lower type I error rates. Next, we apply the proposed methods to a breast cancer data set from The Cancer Genome Atlas (TCGA).We compare the most significant genes identified by single marker analysis and multiple marker analysis to external resources on somatic mutations of breast cancer. We find that both methods identify genes associated with breast cancer, and multiple marker analysis provides more consistent results with external resources.
A Bayesian Hierarchical Framework For Pathway Analysis In Genome Wide Association Studies
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Author : Lei Zhang
language : en
Publisher:
Release Date : 2018
A Bayesian Hierarchical Framework For Pathway Analysis In Genome Wide Association Studies written by Lei Zhang and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018 with Bayesian statistical decision theory categories.
The genome-wide association studies (GWAS) aim to identify genetic variants, typically single nucleotide polymorphisms (SNPs), associated with a disease/trait. A commonly used analytic strategy in GWAS is to test for association with one single SNP at a time. However, such a strategy lacks power to detect associations that are caused by joint effects of multiple variants, each with a modest effect of its own. Pathway analysis jointly tests the combined effects of all SNPs in all genes belonging to a molecular pathway. This analysis is usually more powerful than single-SNP analyses for detecting joint effects of variants in a pathway. Moreover, due to biological functionality of pathways, a significant result lends itself more easily to interpretation. In this dissertation, we develop a Bayesian hierarchical model that fully models the natural three-level hierarchy inherent in pathway structure, namely SNP—gene—pathway, unlike most other methods that use ad hoc ways of combining such information. We model the effects at each level conditional on the effects of the levels preceding them within the generalized linear model framework. This joint modeling allows detection of not only the associated pathways but also testing for association with genes and SNPs within significant pathways and significant genes in a hierarchical manner, which can be useful for follow-up studies. To deal with the high dimensionality of such a unified model, we regularize the regression coefficients through an appropriate choice of priors. We fit the model using a combination of Iteratively Weighted Least Squares and Expectation-Maximization algorithms to estimate the posterior modes and their standard errors. The inference is carried out in a hierarchical manner from pathways to genes to SNPs. Hierarchical false discovery rate (FDR) is used for multiplicity adjustment of the entire inference procedure. We also explore the utility of effective number of parameters proposed in the Bayesian literature in our context of multiplicity adjustment using the hierarchical FDR. To study the proposed approach, we conduct simulations with samples generated under realistic linkage disequilibrium patterns obtained from the HapMap project. We find that our method has higher power than some standard approaches in several settings for identifying pathways that have multiple modest-sized variants. Moreover, it can also pinpoint associated genes once a pathway is implicated, a feature unavailable in other methods. We also find that the use of the effective number of parameters can boost the power to detect associated genes and helps in distinguishing them from the null genes. We apply the proposed method to two GWAS datasets on breast and renal cancer.
Probabilistic Graphical Models For Genetics Genomics And Postgenomics
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Author : Christine Sinoquet
language : en
Publisher: OUP Oxford
Release Date : 2014-09-18
Probabilistic Graphical Models For Genetics Genomics And Postgenomics written by Christine Sinoquet and has been published by OUP Oxford this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-09-18 with Science categories.
Nowadays bioinformaticians and geneticists are faced with myriad high-throughput data usually presenting the characteristics of uncertainty, high dimensionality and large complexity. These data will only allow insights into this wealth of so-called 'omics' data if represented by flexible and scalable models, prior to any further analysis. At the interface between statistics and machine learning, probabilistic graphical models (PGMs) represent a powerful formalism to discover complex networks of relations. These models are also amenable to incorporating a priori biological information. Network reconstruction from gene expression data represents perhaps the most emblematic area of research where PGMs have been successfully applied. However these models have also created renewed interest in genetics in the broad sense, in particular regarding association genetics, causality discovery, prediction of outcomes, detection of copy number variations, and epigenetics. This book provides an overview of the applications of PGMs to genetics, genomics and postgenomics to meet this increased interest. A salient feature of bioinformatics, interdisciplinarity, reaches its limit when an intricate cooperation between domain specialists is requested. Currently, few people are specialists in the design of advanced methods using probabilistic graphical models for postgenomics or genetics. This book deciphers such models so that their perceived difficulty no longer hinders their use and focuses on fifteen illustrations showing the mechanisms behind the models. Probabilistic Graphical Models for Genetics, Genomics and Postgenomics covers six main themes: (1) Gene network inference (2) Causality discovery (3) Association genetics (4) Epigenetics (5) Detection of copy number variations (6) Prediction of outcomes from high-dimensional genomic data. Written by leading international experts, this is a collection of the most advanced work at the crossroads of probabilistic graphical models and genetics, genomics, and postgenomics. The self-contained chapters provide an enlightened account of the pros and cons of applying these powerful techniques.
Sparse Model Building From Genome Wide Variation With Graphical Models
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Author : Benjamin Alexander Logsdon
language : en
Publisher:
Release Date : 2011
Sparse Model Building From Genome Wide Variation With Graphical Models written by Benjamin Alexander Logsdon and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011 with categories.
High throughput sequencing and expression characterization have lead to an explosion of phenotypic and genotypic molecular data underlying both experimental studies and outbred populations. We develop a novel class of algorithms to reconstruct sparse models among these molecular phenotypes (e.g. expression products) and genotypes (e.g. single nucleotide polymorphisms), via both a Bayesian hierarchical model, when the sample size is much smaller than the model dimension (i.e. p n) and the well characterized adaptive lasso algo- rithm. Specifically, we propose novel approaches to the problems of increasing power to detect additional loci in genome-wide association studies using our variational algorithm, efficiently learning directed cyclic graphs from expression and genotype data using the adaptive lasso, and constructing genomewide undirected graphs among genotype, expression and downstream phenotype data using an extension of the variational feature selection algorithm. The Bayesian hierarchical model is derived for a parametric multiple regression model with a mixture prior of a point mass and normal distribution for each regression coefficient, and appropriate priors for the set of hyperparameters. When combined with a probabilistic consistency bound on the model dimension, this approach leads to very sparse solutions without the need for cross validation. We use a variational Bayes approximate inference approach in our algorithm, where we impose a complete factorization across all parameters for the approximate posterior distribution, and then minimize the KullbackLeibler divergence between the approximate and true posterior distributions. Since the prior distribution is non-convex, we restart the algorithm many times to find multiple posterior modes, and combine information across all discovered modes in an approximate Bayesian model averaging framework, to reduce the variance of the posterior probability estimates. We perform analysis of three major publicly available data-sets: the HapMap 2 genotype and expression data collected on immortalized lymphoblastoid cell lines, the genome-wide gene expression and genetic marker data collected for a yeast intercross, and genomewide gene expression, genetic marker, and downstream phenotypes related to weight in a mouse F2 intercross. Based on both simulations and data analysis we show that our algorithms can outperform other state of the art model selection procedures when including thousands to hundreds of thousands of genotypes and expression traits, in terms of aggressively controlling false discovery rate, and generating rich simultaneous statistical models.
Gene Environment Interaction And Extension To Empirical Hierarchical Bayes Models In Genome Wide Association Studies
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Author : Elena Viktorova
language : en
Publisher:
Release Date : 2014
Gene Environment Interaction And Extension To Empirical Hierarchical Bayes Models In Genome Wide Association Studies written by Elena Viktorova and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014 with categories.
There are over 100,000 human diseases of which only around 10,000 are known to be monogenic, resulting from modification in a single gene. Many multifactorial diseases, such as cancer and lung cancer in particular, are outcomes of the interplay between genetic and environmental factors. It is well known that smoking is the major environmental risk factor in lung cancer. In recent years, great progress in genotyping technology and cost control has enabled researchers to perform large-scale association studies, involving thousands of individuals genotyped on millions of markers. To date, geno ...
Bayesian Analysis Of Gene Expression Data
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Author : Bani K. Mallick
language : en
Publisher: John Wiley & Sons
Release Date : 2009-07-20
Bayesian Analysis Of Gene Expression Data written by Bani K. Mallick and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-07-20 with Mathematics categories.
The field of high-throughput genetic experimentation is evolving rapidly, with the advent of new technologies and new venues for data mining. Bayesian methods play a role central to the future of data and knowledge integration in the field of Bioinformatics. This book is devoted exclusively to Bayesian methods of analysis for applications to high-throughput gene expression data, exploring the relevant methods that are changing Bioinformatics. Case studies, illustrating Bayesian analyses of public gene expression data, provide the backdrop for students to develop analytical skills, while the more experienced readers will find the review of advanced methods challenging and attainable. This book: Introduces the fundamentals in Bayesian methods of analysis for applications to high-throughput gene expression data. Provides an extensive review of Bayesian analysis and advanced topics for Bioinformatics, including examples that extensively detail the necessary applications. Accompanied by website featuring datasets, exercises and solutions. Bayesian Analysis of Gene Expression Data offers a unique introduction to both Bayesian analysis and gene expression, aimed at graduate students in Statistics, Biomedical Engineers, Computer Scientists, Biostatisticians, Statistical Geneticists, Computational Biologists, applied Mathematicians and Medical consultants working in genomics. Bioinformatics researchers from many fields will find much value in this book.