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Computational Methods For Genetics Of Complex Traits
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Book Synopsis Computational Methods for Genetics of Complex Traits by :
Download or read book Computational Methods for Genetics of Complex Traits written by and published by Academic Press. This book was released on 2010-11-10 with total page 211 pages. Available in PDF, EPUB and Kindle. Book excerpt: The field of genetics is rapidly evolving, and new medical breakthroughs are occurring as a result of advances in knowledge gained from genetics reasearch. This thematic volume of Advances in Genetics looks at Computational Methods for Genetics of Complex traits. Explores the latest topics in neural circuits and behavior research in zebrafish, drosophila, C.elegans, and mouse models Includes methods for testing with ethical, legal, and social implications Critically analyzes future prospects
Book Synopsis Computational Methods for Disease Diagnosis and Understanding the Genetics of Complex Traits by : Lisa Gai
Download or read book Computational Methods for Disease Diagnosis and Understanding the Genetics of Complex Traits written by Lisa Gai and published by . This book was released on 2021 with total page 99 pages. Available in PDF, EPUB and Kindle. Book excerpt: An ever increasing wealth of biological data has become available in recent years, and with it, the potential to understand complex traits and extract disease relevant information from these many forms of data through computational methods. Understanding the genetic architecture behind complex traits can help us understand disease risk and adverse drug reactions, and to guide the development of treatment strategies. Many variants identified by genome-wide association studies (GWAS) have been found to affect multiple traits, either directly or through shared pathways. Analyzing multiple traits at once can increase power to detect shared variant effects from publicly available GWAS summary statistics. Use of multiple traits may also improve accuracy when estimating variant effects, which can be used in polygenic scores to stratify individuals by disease risk. This dissertation presents a method, CONFIT, for combining GWAS in multiple traits for variant discovery, and explores a few potential multi-trait methods for estimating polygenic scores. Computational methods can also be used to identify patients already suffering from disease who would benefit from treatment. Towards this end, this dissertation also presents work on deep learning to detect patients with orbital disease from image data with high accuracy and recall.
Book Synopsis Computational Approaches to Understanding the Genetic Architecture of Complex Traits by : Brielin C. Brown
Download or read book Computational Approaches to Understanding the Genetic Architecture of Complex Traits written by Brielin C. Brown and published by . This book was released on 2016 with total page 90 pages. Available in PDF, EPUB and Kindle. Book excerpt: Advances in DNA sequencing technology have resulted in the ability to generate genetic data at costs unimaginable even ten years ago. This has resulted in a tremendous amount of data, with large studies providing genotypes of hundreds of thousands of individuals at millions of genetic locations. This rapid increase in the scale of genetic data necessitates the development of computational methods that can analyze this data rapidly without sacrificing statistical rigor. The low cost of DNA sequencing also provides an opportunity to tailor medical care to an individuals unique genetic signature. However, this type of precision medicine is limited by our understanding of how genetic variation shapes disease. Our understanding of so- called complex diseases is particularly poor, and most identified variants explain only a tiny fraction of the variance in the disease that is expected to be due to genetics. This is further complicated by the fact that most studies of complex disease go directly from genotype to phenotype, ignoring the complex biological processes that take place in between. Herein, we discuss several advances in the field of complex trait genetics. We begin with a review of computational and statistical methods for working with genotype and phenotype data, as well as a discussion of methods for analyzing RNA-seq data in effort to bridge the gap between genotype and phenotype. We then describe our methods for 1) improving power to detect common variants associated with disease, 2) determining the extent to which different world populations share similar disease genetics and 3) identifying genes which show differential expression between the two haplotypes of a single individual. Finally, we discuss opportunities for future investigation in this field.
Book Synopsis Handbook on Analyzing Human Genetic Data by : Shili Lin
Download or read book Handbook on Analyzing Human Genetic Data written by Shili Lin and published by Springer Science & Business Media. This book was released on 2009-10-13 with total page 340 pages. Available in PDF, EPUB and Kindle. Book excerpt: This handbook offers guidance on selections of appropriate computational methods and software packages for specific genetic problems. Coverage strikes a balance between methodological expositions and practical guidelines for software selections. Wherever possible, comparisons among competing methods and software are made to highlight the relative advantages and disadvantage of the approaches.
Book Synopsis Computational Methods to Analyze Large-scale Genetic Studies of Complex Human Traits by : Huwenbo Shi
Download or read book Computational Methods to Analyze Large-scale Genetic Studies of Complex Human Traits written by Huwenbo Shi and published by . This book was released on 2018 with total page 163 pages. Available in PDF, EPUB and Kindle. Book excerpt: Large-scale genome-wide association studies (GWAS) have produced a rich resource of genetic data over the past decade, urging the need to develop computational and statistical methods that analyze these data. This dissertation presents four statistical methods that model the correlation structure between genetic variants and its effect on GWAS summary association statistics to help understand the genetic basis of complex human traits and diseases. The first method employs the multivariate Bernoulli distribution to model haplotype data, allowing for higher-order interactions among genetic variants, and shows better accuracy in predicting DNase I hypersensitivity status. The second method partitions heritability into small regions on the genome using GWAS summary statistics data, while accounting for complex correlation structures among genetic variants, and uncovers the genetic architectures of complex human traits and diseases. Extending the second method into pairs of traits, the third method partitions genetic correlation into small genomic regions using GWAS summary statistics data, and provides insights into the shared genetic basis between pairs of traits. Finally, the fourth method dissects population-specific and shared causal genetic variants of complex traits in two continental populations, using GWAS summary statistics data obtained from samples of different ethnicities, and reveals differences in genetic architectures of two continental populations.
Book Synopsis Computational Genetic Approaches for the Dissection of Complex Traits by : Nicholas A. Furlotte
Download or read book Computational Genetic Approaches for the Dissection of Complex Traits written by Nicholas A. Furlotte and published by . This book was released on 2013 with total page 105 pages. Available in PDF, EPUB and Kindle. Book excerpt: Over the past two decades, major technological innovations have transformed the field of genetics allowing researchers to examine the relationship between genetic and phenotypic variation at an unprecedented level of granularity. As a result, genetics has increasingly become a data-driven science, demanding effective statistical procedures and efficient computational methods and necessitating a new interface that some refer to as computational genetics. In this dissertation, I focus on a few problems existing within this interface. First, I introduce a method for calculating gene coexpression in a way that is robust to statistical confounding introduced through expression hetero- geneity. Heterogeneity in experimental conditions causes separate microarrays to be more correlated than expected by chance. This additional correlation between arrays induces correlation between gene expression measurements, in effect causing spuri- ous gene coexpression. By formulating the problem of calculating coexpression in a linear mixed-model framework, I show how it is possible to account for the cor- relation between microarrays and produce coexpression values that are robust to ex- pression heterogeneity. Second, I introduce a meta-analysis technique that allows for genome-wide association studies to be combined across populations that are known to contain population structure. This development was motivated by a specific problem in mouse genetics, the aim of which is to utilize multiple mouse association studies jointly. I show that by combining the studies using meta-analysis, while accounting for population structure, the proposed method achieves increased statistical power and increased association resolution. Next, I will introduce a computational and statistical procedure for performing genome-wide association using longitudinal measurements. I show that by accounting for the genetic and environmental correlation between mea- surements originating from the same individual, it is possible to increase association power. Finally, I will introduce a statistical and computational construct called the matrix-variate linear mixed-model (mvLMM), which is used for multiple phenotype genome-wide association. I show how the application of this method results in increased association power over single trait mapping and leads to a dramatic reduction in computational time over classical multiple phenotype optimization procedures. For example, where a classically-based approach takes hours to perform parameter optimization for moderate sample sizes mvLMM takes minutes. This technique is both a generalization and improvement on the previously proposed longitudinal analysis technique and its innovation has the potential to impact many current problems in the field of computational genetics.
Book Synopsis Efficient Methods for Understanding the Genetic Architecture of Complex Traits by : Yue N/A Wu
Download or read book Efficient Methods for Understanding the Genetic Architecture of Complex Traits written by Yue N/A Wu and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Understanding the genetic architecture of complex traits is a central goal of modern human genetics.Recent efforts focused on building large-scale biobanks, that collect genetic and trait data on large numbers of individuals, present exciting opportunities for understanding genetic architecture. However, these datasets also pose several statistical and computational challenges. In this dissertation, we consider a series of statistical models that allow us to infer aspects of the genetic architecture of single and multiple traits. Inference in these models is computationally challenging due to the size of the genetic data -- consisting of millions of genetic variants measured across hundreds of thousands of individuals.We propose a series of scalable computational methods that can perform efficient inference in these models and apply these methods to data from the UK Biobank to showcase their utility.
Book Synopsis Computational Genetics and Genomics by : Gary Peltz
Download or read book Computational Genetics and Genomics written by Gary Peltz and published by Springer Science & Business Media. This book was released on 2007-11-05 with total page 309 pages. Available in PDF, EPUB and Kindle. Book excerpt: Ultimately, the quality of the tools available for genetic analysis and experimental disease models will be assessed on the basis of whether they provide new information that generates novel treatments for human disease. In addition, the time frame in which genetic discoveries impact clinical practice is also an important dimension of how society assesses the results of the significant public financial investment in genetic research. Because of the investment and the increased expectation that new tre- ments will be found for common diseases, allowing decades to pass before basic discoveries are made and translated into new therapies is no longer acceptable. Computational Genetics and Genomics: Tools for Understanding Disease provides an overview and assessment of currently available and developing tools for genetic analysis. It is hoped that these new tools can be used to identify the genetic basis for susceptibility to disease. Although this very broad topic is addressed in many other books and journal articles, Computational Genetics and Genomics: Tools for Understanding Disease focuses on methods used for analyzing mouse genetic models of biomedically - portant traits. This volume aims to demonstrate that commonly used inbred mouse strains can be used to model virtually all human disea- related traits. Importantly, recently developed computational tools will enable the genetic basis for differences in disease-related traits to be rapidly identified using these inbred mouse strains. On average, a decade is required to carry out the development process required to demonstrate that a new disease treatment is beneficial.
Book Synopsis Genetic Dissection of Complex Traits by : D.C. Rao
Download or read book Genetic Dissection of Complex Traits written by D.C. Rao and published by Academic Press. This book was released on 2008-04-23 with total page 788 pages. Available in PDF, EPUB and Kindle. Book excerpt: The field of genetics is rapidly evolving and new medical breakthroughs are occuring as a result of advances in knowledge of genetics. This series continually publishes important reviews of the broadest interest to geneticists and their colleagues in affiliated disciplines. Five sections on the latest advances in complex traits Methods for testing with ethical, legal, and social implications Hot topics include discussions on systems biology approach to drug discovery; using comparative genomics for detecting human disease genes; computationally intensive challenges, and more
Book Synopsis Computational Genetic Approaches for Understanding the Genetic Basis of Complex Traits by : Eun Yong Kang
Download or read book Computational Genetic Approaches for Understanding the Genetic Basis of Complex Traits written by Eun Yong Kang and published by . This book was released on 2013 with total page 273 pages. Available in PDF, EPUB and Kindle. Book excerpt: Recent advances in genotyping and sequencing technology have enabled researchers to collect an enormous amount of high-dimensional genotype data. These large scale genomic data provide unprecedented opportunity for researchers to study and analyze the genetic factors of human complex traits. One of the major challenges in analyzing these high-throughput genomic data is requirements for effective and efficient computational methodologies. In this thesis, I introduce several methodologies for analyzing these genomic data which facilitates our understanding of the genetic basis of complex human traits. First, I introduce a method for inferring biological networks from high-throughput data containing both genetic variation information and gene expression profiles from genetically distinct strains of an organism. For this problem, I use causal inference techniques to infer the presence or absence of causal relationships between yeast gene expressions in the framework of graphical causal models. In particular, I utilize prior biological knowledge that genetic variations affect gene expressions, but not vice versa, which allow us to direct the subsequent edges between two gene expression levels. The prediction of a presence of causal relationship as well as the absence of causal relationship between gene expressions can facilitate distinguishing between direct and indirect effects of variation on gene expression levels. I demonstrate the utility of our approach by applying it to data set containing 112 yeast strains and the proposed method identifies the known "regulatory hotspot" in yeast. Second, I introduce efficient pairwise identity by descent (IBD) association mapping method, which utilizes importance sampling to improve efficiency and enables approximation of extremely small p-values. Two individuals are IBD at a locus if they have identical alleles inherited from a common ancestor. One popular approach to find the association between IBD status and disease phenotype is the pairwise method where one compares the IBD rate of case/case pairs to the background IBD rate to detect excessive IBD sharing between cases. One challenge of the pairwise method is computational efficiency. In the pairwise method, one uses permutation to approximate p-values because it is difficult to analytically obtain the asymptotic distribution of the statistic. Since the p-value threshold for genome-wide association studies (GWAS) is necessarily low due to multiple testing, one must perform a large number of permutations which can be computationally demanding. I present Fast-Pairwise to overcome the computational challenges of the traditional pairwise method by utilizing importance sampling to improve efficiency and enable approximation of extremely small p-values. Using the WTCCC type 1 diabetes data, I show that Fast-Pairwise can successfully pinpoint a gene known to be associated to the disease within the MHC region. Finally, I introduce a novel meta analytic approach to identify gene-by-environment interactions by aggregating the multiple studies with varying environmental conditions. Identifying environmentally specific genetic effects is a key challenge in understanding the structure of complex traits. Model organisms play a crucial role in the identification of such gene-by-environment interactions, as a result of the unique ability to observe genetically similar individuals across multiple distinct environments. Many model organism studies examine the same traits but, under varying environmental conditions. These studies when examined in aggregate provide an opportunity to identify genomic loci exhibiting environmentally-dependent effects. In this project, I jointly analyze multiple studies with varying environmental conditions using a meta-analytic approach based on a random effects model to identify loci involved in gene-by-environment interactions. Our approach is motivated by the observation that methods for discovering gene-by-environment interactions are closely related to random effects models for meta-analysis. We show that interactions can be interpreted as heterogeneity and can be detected without utilizing the traditional uni- or multi-variate approaches for discovery of gene-by-environment interactions. I apply our new method to combine 17 mouse studies containing in aggregate 4,965 distinct animals. We identify 26 significant loci involved in High-density lipoprotein (HDL) cholesterol, many of which show significant evidence of involvement in gene-by-environment interactions.
Book Synopsis Using Large-scale Genomics Data to Understand the Genetic Basis of Complex Traits by : Ruowang Li
Download or read book Using Large-scale Genomics Data to Understand the Genetic Basis of Complex Traits written by Ruowang Li and published by . This book was released on 2016 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: With the arrival of big data in genetics in the past decade, the field has experienced drastic changes. One game-changing breakthrough in genetics was the invention of genotyping and sequencing technology that allows researchers to examining single nucleotide polymorphisms (SNPs) across the entire genome. The other major breakthrough was the identification of haplotypes of common alleles in major human populations, which permitted the design of genotyping assays that effectively cover entire human genomes at a resolution appropriate for genetic mapping. Together, these technology breakthroughs have permitted researchers to carry out Genome Wide Association Studies (GWAS) on a wide range of traits including, for example, height and disease status. With GWAS, causal SNPs have been identified for some Mendelian traits, but for more complex genetic traits, the genetic heritability explained by the associated SNPs are low. In addition, high-throughput technologies to generate other types of -omics data such as gene expression, DNA methylation, and protein levels data have also emerged recently. How to best utilize the SNP data and other multi-omics data to understand genetic traits is one of the most important questions in the field today. With the increasing prevalence of multi-omics data, new types of analysis schemes and tools are needed to handle the additional complexity of the data. In particular, two areas of method development are in great need. First, statistical methods employed by GWAS do not consider the potential interacting relationships among genetic loci. Thus, methods that can explore the joint effect between multiple genetic loci or genetic factors could unveil new associations. Second, different types of omics data may give distinctive representations of the overall biological system. By combining multi-omics data, we could potentially aggregate non-overlapping information from each individual data types. Thus, the focus of this dissertation is on developing and improving computational methods that can jointly model multiple types of genomics data. First, an evaluation of an existing method, grammatical evolution neural network, was conducted to identify the optimal algorithm settings for the detection of genetic associations. It was found that under certain algorithm settings, the neural networks have been restricted to one-layer simple network. Using a parameter sweep approach, the analysis identified optimal settings that allow for building more flexible network structures. Then, the algorithm was applied to integrate multi-omics data to model drug-induced cytotoxicity for a number of cancer drugs. By combining different types of omics data including SNPs, gene expression and methylation levels, we were able to model a higher portion of the observed variability than any individual data type alone. However, one drawback of the existing neural network approach is the limited interpretability. To this end, a new algorithm based on Bayesian Networks was created. One novelty of the approach is the ability to independently fit a distinct Bayesian Network for each categories of a phenotype. This allows for identifying category specific interactions as well as common interactions across different categories. Analysis using simulated SNP data has shown that the Bayesian Network approach outperformed the Neural Network approach in many settings, particularly in situation where the data contains multiple interacting loci. When applied to a type 2 diabetes dataset, the algorithm was able to identify distinctive interaction patterns between cases and controls. Ultimately, the goal of this dissertation has been to fully take advantage of the newly available data to understand the genetic basis of complex traits.
Book Synopsis Information-sharing Models for Computational Genetics by : Matthew Douglas Edwards
Download or read book Information-sharing Models for Computational Genetics written by Matthew Douglas Edwards and published by . This book was released on 2016 with total page 105 pages. Available in PDF, EPUB and Kindle. Book excerpt: Modern genetics has been transformed by a dramatic explosion of data. As sample sizes and the number of measured data types grow, the need for computational methods tailored to deal with these noisy and complex datasets increases. In this thesis, we develop and apply integrated computational and biological approaches for two genetic problems. First, we build a statistical model for genetic mapping using pooled sequencing, a powerful and efficient technique for rapidly unraveling the genetic basis of complex traits. Our approach explicitly models the pooling process and genetic parameters underlying the noisy observed data, and we use it to calculate accurate intervals that contain the targeted regions of interest. We show that our model outperforms simpler alternatives that do not use all available marker data in a principled way. We apply this model to study several phenotypes in yeast, including the genetic basis of the surprising phenomenon of strain-specific essential genes. We demonstrate the complex genetic basis of many of these strain-specific viability phenotypes and uncover the influence of an inherited virus in modifying their effects. Second, we design a statistical model that uses additional functional information describing large sets of genetic variants in order to predict which variants are likely to cause phenotypic changes. Our technique is able to learn complicated relationships between candidate features and can accommodate the additional noise introduced by training on groups of candidate variants, instead of single labeled variants. We apply this model to a large genetic mapping study in yeast by collecting multiple genome-wide functional measurements. By using our model, we demonstrate the importance of several molecular phenotypes in predicting genetic impact. The common themes in this thesis are the development of computational models that accurately reflect the underlying biological processes and the integration of carefully controlled biological experiments to test and utilize our new models.
Book Synopsis Molecular and Computational Approaches to Identification of Genes Underlying Complex Traits by : Martin L. Jirout
Download or read book Molecular and Computational Approaches to Identification of Genes Underlying Complex Traits written by Martin L. Jirout and published by . This book was released on 2008 with total page 236 pages. Available in PDF, EPUB and Kindle. Book excerpt: Understanding the genetic architecture of complex traits is of great interest to the biomedical community. HXB/BXH recombinant inbred (RI) strains, derived from the spontaneously hypertensive rat (SHR) and normotensive Brown Norway (BN. Lx), are an important genomic resource for complex trait analysis by means of genetic linkage mapping. The power and accuracy of quantitative trait locus (QTL) analysis critically depends on the quality of the genetic map. To maximize the potential of the HXB/BXH RI strains for complex trait mapping, the latest available genotype information was used to construct a new genetic linkage map. Further, gene expression profiling and biochemical phenotyping in the adrenal glands of the HXB/BXH rats was performed to address the possible link between the dysregulated catecholamine biosynthesis in the SHR and the development of hypertension. Expression levels and enzyme activities of the two main catecholamine biosynthetic enzymes, Dbh and Pnmt, were found to be regulated from their genic regions (i.e., in cis). Pnmt re-sequencing revealed promoter polymorphisms, which resulted in a decreased response of the transfected SHR promoter to glucocorticoid stimulation. Dbh activity was negatively correlated with systolic blood pressure in RI strains, and Pnmt activity was negatively correlated with heart rate. These heritable changes in enzyme expression suggest primary genetic mechanisms for regulation of catecholamine action and blood pressure control in the SHR. In a separate analysis, genetic determinants of gene expression in the adrenal gland were explored. The adrenal transcriptome assayed via microarrays was subjected to expression quantitative trait locus (eQTL) mapping. Significant clustering of trans-eQTLs was observed, implying that groups of genes are jointly regulated from a single locus. A novel multivariate distance-matrix regression analysis (MDMR) method was applied to identify cis-eQTL genes whose expression profiles strongly correlate with those of the trans-eQTL cluster genes. The resulting genes, Rbm16 and Prp4b, are involved in pre-mRNA processing and as such present leading candidates for further studies aimed at better understanding of the quantitative genetics of gene expression. In conclusion, an important genomic resource was enhanced and then utilized to identify genetic loci controlling key aspects of catecholamine physiology, and differences in global gene expression.
Book Synopsis Genetics and Analysis of Quantitative Traits by : Michael Lynch
Download or read book Genetics and Analysis of Quantitative Traits written by Michael Lynch and published by Sinauer Associates Incorporated. This book was released on 1998-01 with total page 980 pages. Available in PDF, EPUB and Kindle. Book excerpt: Professors Lynch and Walsh bring together the diverse array of theoretical and empirical applications of quantitative genetics in a work that is comprehensive and accessible to anyone with a rudimentary understanding of statistics and genetics.
Book Synopsis Chambre de Commerce Belgo-Danoise by :
Download or read book Chambre de Commerce Belgo-Danoise written by and published by . This book was released on 1961 with total page 731 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Practical Applications of Computational Biology & Bioinformatics, 15th International Conference (PACBB 2021) by : Miguel Rocha
Download or read book Practical Applications of Computational Biology & Bioinformatics, 15th International Conference (PACBB 2021) written by Miguel Rocha and published by Springer Nature. This book was released on 2021-08-27 with total page 188 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book features novel research papers spanning many different subfields in bioinformatics and computational biology, presenting the latest research on the practical applications to promote fruitful interactions between young researchers in different areas related to the field. Clearly, biology is increasingly becoming a science of information, requiring tools from the computational sciences. To address these challenges, we have seen the emergence of a new generation of interdisciplinary scientists with a strong background in the biological and computational sciences. PACBB'21 expects to contribute to this effort by encouraging a successful collaboration of researchers in different areas related to bioinformatics. The PACBB'21 technical program included 17 papers covering many different subfields in bioinformatics and computational biology. Therefore, this conference, held in Salamanca (Spain), definitely promotes the collaboration of scientists from different research groups and with different backgrounds (computer scientists, mathematicians, biologists) to reach breakthrough solutions for these challenges.
Book Synopsis Systems Genetics by : Florian Markowetz
Download or read book Systems Genetics written by Florian Markowetz and published by Cambridge University Press. This book was released on 2015-07-02 with total page 287 pages. Available in PDF, EPUB and Kindle. Book excerpt: Whereas genetic studies have traditionally focused on explaining heritance of single traits and their phenotypes, recent technological advances have made it possible to comprehensively dissect the genetic architecture of complex traits and quantify how genes interact to shape phenotypes. This exciting new area has been termed systems genetics and is born out of a synthesis of multiple fields, integrating a range of approaches and exploiting our increased ability to obtain quantitative and detailed measurements on a broad spectrum of phenotypes. Gathering the contributions of leading scientists, both computational and experimental, this book shows how experimental perturbations can help us to understand the link between genotype and phenotype. A snapshot of current research activity and state-of-the-art approaches to systems genetics are provided, including work from model organisms such as Saccharomyces cerevisiae and Drosophila melanogaster, as well as from human studies.
