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Author: Scott A. Funkhouser
Publisher:
ISBN: 9781088389997
Category : Electronic dissertations
Languages : en
Pages : 119
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Book Description
Within any population, complex trait variation can be attributed to an impressive number of genetic factors. Identification of such factors has been made possible, in part, by large biomedical datasets comprised of genotypes and phenotypes for hundreds of thousands of individuals. Furthermore, understanding the biological mechanisms through which genetic variation creates complex trait variation has been facilitated by high-throughput sequencing technology, used to quantify molecular, intermediate phenotypes. Despite such datasets being widely available, we lack understanding of the full spectrum of genetic effects, including gene-by-sex (GxS) interactions. We also have yet to uncover various molecular phenotypes that may "link" genetic variation to complex trait variation. To address these gaps in knowledge, the following chapters will 1) develop and utilize statistical methodology for mapping GxS interactions among human traits, and 2) utilize a pig model to characterize RNA editing-a relatively understudied form of transcriptional regulation- and evaluate its potential to link genetic variation with complex trait variation.Growing evidence from genome-wide parameter estimates suggest males and females from human populations possess differing genetic architectures. Despite this, mapping GxS interactions remains challenging, suggesting that the magnitude of a typical GxS interaction is exceedingly small. We have developed a local Bayesian regression (LBR) approach to estimate sex-specific single nucleotide polymorphism (SNP) marker effects after fully accounting for local linkage-disequilibrium (LD) patterns. This provided means to infer GxS interactions either at the SNP level, or by aggregating multiple sex-specific SNP effects to make inferences at the level of small, LD-based regions. In simulations, LBR provided greater power and resolution to detect GxS interactions than the traditional approach to genome-wide association (GWA), single-marker regression (SMR).When using LBR to analyze human traits from the UK Biobank (N ∼ 250,000) including height, BMI, bone-mineral density, and waist-to-hip ratio, we find evidence of novel GxS interactions where sex-specific effects explain a very small proportion of phenotypic variance (R2
Author: Scott A. Funkhouser
Publisher:
ISBN: 9781088389997
Category : Electronic dissertations
Languages : en
Pages : 119
Get Book Here
Book Description
Within any population, complex trait variation can be attributed to an impressive number of genetic factors. Identification of such factors has been made possible, in part, by large biomedical datasets comprised of genotypes and phenotypes for hundreds of thousands of individuals. Furthermore, understanding the biological mechanisms through which genetic variation creates complex trait variation has been facilitated by high-throughput sequencing technology, used to quantify molecular, intermediate phenotypes. Despite such datasets being widely available, we lack understanding of the full spectrum of genetic effects, including gene-by-sex (GxS) interactions. We also have yet to uncover various molecular phenotypes that may "link" genetic variation to complex trait variation. To address these gaps in knowledge, the following chapters will 1) develop and utilize statistical methodology for mapping GxS interactions among human traits, and 2) utilize a pig model to characterize RNA editing-a relatively understudied form of transcriptional regulation- and evaluate its potential to link genetic variation with complex trait variation.Growing evidence from genome-wide parameter estimates suggest males and females from human populations possess differing genetic architectures. Despite this, mapping GxS interactions remains challenging, suggesting that the magnitude of a typical GxS interaction is exceedingly small. We have developed a local Bayesian regression (LBR) approach to estimate sex-specific single nucleotide polymorphism (SNP) marker effects after fully accounting for local linkage-disequilibrium (LD) patterns. This provided means to infer GxS interactions either at the SNP level, or by aggregating multiple sex-specific SNP effects to make inferences at the level of small, LD-based regions. In simulations, LBR provided greater power and resolution to detect GxS interactions than the traditional approach to genome-wide association (GWA), single-marker regression (SMR).When using LBR to analyze human traits from the UK Biobank (N ∼ 250,000) including height, BMI, bone-mineral density, and waist-to-hip ratio, we find evidence of novel GxS interactions where sex-specific effects explain a very small proportion of phenotypic variance (R2
Author: Eun Yong Kang
Publisher:
ISBN:
Category :
Languages : en
Pages : 273
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Book Description
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.
Author: D.C. Rao
Publisher: Academic Press
ISBN: 0080569110
Category : Medical
Languages : en
Pages : 788
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Book Description
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
Author: National Academies of Sciences, Engineering, and Medicine
Publisher: National Academies Press
ISBN: 0309676738
Category : Science
Languages : en
Pages : 201
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Book Description
One of the holy grails in biology is the ability to predict functional characteristics from an organism's genetic sequence. Despite decades of research since the first sequencing of an organism in 1995, scientists still do not understand exactly how the information in genes is converted into an organism's phenotype, its physical characteristics. Functional genomics attempts to make use of the vast wealth of data from "-omics" screens and projects to describe gene and protein functions and interactions. A February 2020 workshop was held to determine research needs to advance the field of functional genomics over the next 10-20 years. Speakers and participants discussed goals, strategies, and technical needs to allow functional genomics to contribute to the advancement of basic knowledge and its applications that would benefit society. This publication summarizes the presentations and discussions from the workshop.
Author: Tanya Ngoc Phung
Publisher:
ISBN:
Category :
Languages : en
Pages : 213
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Book Description
Understanding how different evolutionary processes shape genetic variation within and between species is an important question in population genetics. The advent of next generation sequencing has allowed for many theories and hypotheses to be tested explicitly with data. However, questions such as what evolutionary processes affect neutral divergence (DNA differences between species) or genetic variation in different regions of the genome (such as on autosomes versus sex chromosomes) or how many genetic variants contribute to complex traits are still outstanding. In this dissertation, I utilized different large-scale genomic datasets and developed statistical methods to determine the role of natural selection on genetic variation between species, sex-biased evolutionary processes on shaping patterns of genetic variation on the X chromosome and autosomes, and how population history, mutation, and natural selection interact to control complex traits. First, I used genome-wide divergence data between multiple pairs of species ranging in divergence time to show that natural selection has reduced divergence at neutral sites that are linked to those under direct selection. To determine explicitly whether and to what extent linked selection and/or mutagenic recombination could account for the pattern of neutral divergence across the genome, I developed a statistical method and applied it to human-chimp neutral divergence dataset. I showed that a model including both linked selection and mutagenic recombination resulted in the best fit to the empirical data. However, the signal of mutagenic recombination could be coming from biased gene conversion. Comparing genetic diversity between the X chromosome and the autosomes could provide insights into whether and how sex-biased processes have affected genetic variation between different genomic regions. For example, X/A diversity ratio greater than neutral expectation could be due to more X chromosomes than expected and could be a result of mating practices such as polygamy where there are more reproducing females than males. I next utilized whole-genome sequences from dogs and wolves and found that X/A diversity is lower than neutral expectation in both dogs and wolves in ancient time-scales, arguing for evolutionary processes resulting in more males reproducing compared to females. However, within breed dogs, patterns of population differentiation suggest that there have been more reproducing females, highlighting effects from breeding practices such as popular sire effect where one male can father many offspring with multiple females. In medical genetics, a complete understanding of the genetic architecture is essential to unravel the genetic basis of complex traits. While genome wide association studies (GWAS) have discovered thousands of trait-associated variants and thus have furthered our understanding of the genetic architecture, key parameters such as the number of causal variants and the mutational target size are still under-studied. Further, the role of natural selection in shaping the genetic architecture is still not entirely understood. In the last chapter, I developed a computational method called InGeAr to infer the mutational target size and explore the role of natural selection on affecting the variant's effect on the trait. I found that the mutational target size differs from trait to trait and can be large, up to tens of megabases. In addition, purifying selection is coupled with the variant's effect on the trait. I discussed how these results support the omnigenic model of complex traits. In summary, in this dissertation, I utilized different types of large genomic dataset, from genome-wide divergence data to whole genome sequence data to GWAS data to develop models and statistical methods to study how different evolutionary processes have shaped patterns of genetic variation across the genome.
Author: Jonathan L. Haines
Publisher: John Wiley & Sons
ISBN: 0471781134
Category : Science
Languages : en
Pages : 507
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Book Description
Second Edition features the latest tools for uncovering thegenetic basis of human disease The Second Edition of this landmark publication bringstogether a team of leading experts in the field to thoroughlyupdate the publication. Readers will discover the tremendousadvances made in human genetics in the seven years that haveelapsed since the First Edition. Once again, the editorshave assembled a comprehensive introduction to the strategies,designs, and methods of analysis for the discovery of genes incommon and genetically complex traits. The growing social, legal,and ethical issues surrounding the field are thoroughly examined aswell. Rather than focusing on technical details or particularmethodologies, the editors take a broader approach that emphasizesconcepts and experimental design. Readers familiar with theFirst Edition will find new and cutting-edge materialincorporated into the text: Updated presentations of bioinformatics, multiple comparisons,sample size requirements, parametric linkage analysis, case-controland family-based approaches, and genomic screening New methods for analysis of gene-gene and gene-environmentinteractions A completely rewritten and updated chapter on determininggenetic components of disease New chapters covering molecular genomic approaches such asmicroarray and SAGE analyses using single nucleotide polymorphism(SNP) and cDNA expression data, as well as quantitative trait loci(QTL) mapping The editors, two of the world's leading genetic epidemiologists,have ensured that each chapter adheres to a consistent and highstandard. Each one includes all-new discussion questions andpractical examples. Chapter summaries highlight key points, and alist of references for each chapter opens the door to furtherinvestigation of specific topics. Molecular biologists, human geneticists, geneticepidemiologists, and clinical and pharmaceutical researchers willfind the Second Edition a helpful guide to understanding thegenetic basis of human disease, with its new tools for detectingrisk factors and discovering treatment strategies.
Author: Ravindranath Duggirala
Publisher: Springer
ISBN: 3662463067
Category : Science
Languages : en
Pages : 305
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Book Description
This book provides an introduction to the latest gene mapping techniques and their applications in biomedical research and evolutionary biology. It especially highlights the advances made in large-scale genomic sequencing. Results of studies that illustrate how the new approaches have improved our understanding of the genetic basis of complex phenotypes including multifactorial diseases (e.g., cardiovascular disease, type 2 diabetes, and obesity), anatomic characteristics (e.g., the craniofacial complex), and neurological and behavioral phenotypes (e.g., human brain structure and nonhuman primate behavior) are presented. Topics covered include linkage and association methods, gene expression, copy number variation, next-generation sequencing, comparative genomics, population structure, and a discussion of the Human Genome Project. Further included are discussions of the use of statistical genetic and genetic epidemiologic techniques to decipher the genetic architecture of normal and disease-related complex phenotypes using data from both humans and non-human primates.
Author: Gary Peltz
Publisher: Springer Science & Business Media
ISBN: 1592599303
Category : Medical
Languages : en
Pages : 309
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Book Description
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.
Author: Alexander Kern
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
Understanding the genetic and evolutionary basis of complex traits is an outstanding question in genetics. In contrast to Mendelian traits which are usually affected by one or a few genes, complex traits are affected by variation in multiple genes, often hundreds to thousands. This complexity makes it difficult to determine which biological processes and genetic variants affect these traits. Furthermore, identifying natural selection affecting these traits can be difficult without knowledge of which variants are relevant. One method for identifying the genetic basis of complex traits is quantitative trait locus (QTL) mapping using offspring from genetic crosses of laboratory organisms. In Chapter 2, S. cerevisiae offspring from two parental strains between which polygenic selection on gene expression in the ergosterol pathway was identified are examined to see the effect of this selection on metabolite levels, a more downstream endophenotype. While metabolite QTL and expression QTL overlapped well, the selection on gene expression did not lead to the expected changes in metabolite levels. A new test was developed to identify selection on the metabolite levels, and while there was significant evidence of natural selection affecting metabolite levels, it was clear that the selection on gene expression did not predict the selection on metabolite levels, suggesting the need for studying pathways at multiple levels of endophenotypes to understand selection on complex traits. An additional complexity in understanding complex traits is the effects of environment. Not only can the environment in which an organism lives directly affect a complex trait, but some genetic variants will have different effects on fitness or other traits based on the environment. These effects are known as gene-by-environment interactions (GxE). In chapter 3, we develop an improved method for precisely measuring the effects of natural genetic variants in yeast using precision editing and growth competitions. We then use this technique to identify natural variants in S. cerevisiae which have GxE interactions, first within QTL regions and then within the ergosterol biosynthesis pathway. Together, these two chapters advance our understanding of complex traits at the pathway-level, first by looking between levels of endophenotypes, and then looking at complexity imparted by the environment through GxE interactions.
Author:
Publisher: Academic Press
ISBN: 008095393X
Category : Science
Languages : en
Pages : 123
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Book Description
Socio-Genetics seeks to understand both the genetic and environmental contributions to individual variations in behavior. Behaviors, like all complex traits, involve multiple genes, a reality that complicates the search for genetic contributions. As with much other research in genetics, studies of genes and behavior require analysis of families and populations for comparison of those who have the trait in question with those who do not. The result commonly is a statement of "heritability," a statistical construct that estimates the amount of variation in a population that is attributable to genetic factors. The explanatory power of heritability figures is limited, however, applying only to the population studied and only to the environment in place at the time the study was conducted. If the population or the environment changes, the heritability most likely will change as well. Focused on the genetics of complex traits in a variety of organisms—honeybees, mice, and nematodes—this volume discusses environmental influence on genetic programs and evolutionary genetics. Such research is proving important in furthering our understanding of the genetic basis of such diseases as obesity, schizophrenia, multiple sclerosis, and autism, to name a few. Most recent research findings on gene-environment interaction and complex behavior, allows researchers to make predictions about the genetic mechanisms that underlie some basic behaviors—eating, for example—leading to new and novel treatments for some genetically based abnormal behaviors Reviews environmental programming of phenotypic diversity in female reproductive strategies, providing important insight into fertility and in developing therapeutic strategies to treat infertility
