Download or read book Deciphering the Genetic Basis for Complex Trait Variation written by Scott A. Funkhouser and published by . This book was released on 2019 with total page 119 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

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.

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

Download or read book Next Steps for Functional Genomics written by National Academies of Sciences, Engineering, and Medicine and published by National Academies Press. This book was released on 2020-12-18 with total page 201 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Methods and Models for the Analysis of Genetic Variation Across Species Using Large scale Genomic Data written by Tanya Ngoc Phung and published by . This book was released on 2018 with total page 213 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Genetic Analysis of Complex Disease written by Jonathan L. Haines and published by John Wiley & Sons. This book was released on 2007-02-26 with total page 507 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Genome Mapping and Genomics in Human and Non Human Primates written by Ravindranath Duggirala and published by Springer. This book was released on 2015-03-25 with total page 305 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

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.

Download or read book Dissecting Pathway level Complex Traits in Yeast written by Alexander Kern 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 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.

Download or read book Socio Genetics written by and published by Academic Press. This book was released on 2009-11-12 with total page 123 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Download or read book Methods for the Quantitative Characterization of the Genetic Basis of Human Complex Traits written by Kathryn Burch and published by . This book was released on 2021 with total page 128 pages. Available in PDF, EPUB and Kindle. Book excerpt: A major finding from the last decade of genome-wide association studies (GWAS) is that variant-phenotype associations are significantly enriched in noncoding regulatory regions of the genome. This result suggests that GWAS associations localize variants that modulate phenotype via gene regulation as opposed to alterations in protein structure/function. However, for most complex traits, most aspects of genetic architecture-the number of causal variants/genes for a trait and the degree to which causal effect sizes are coupled with genomic features such as minor allele frequency (MAF) and linkage disequilibrium (LD)-remain actively debated. In this dissertation, I introduce three new methods to explore and quantitatively characterize complex-trait genetic architecture. First, I derive an unbiased estimator of genome-wide SNP-heritability under a very general random effects model that makes minimal assumptions on the underlying (unknown) genetic architecture of the trait. Second, I introduce a method for estimating the number of causal variants that are shared between two ancestral populations for a given trait, and I discuss the implications of the method and real-data results for improving polygenic risk prediction in ethnic minority populations. Third, I propose methods for partitioning the heritability of individual genes by MAF to identify disease-relevant genes, with the hypothesis that some disease-relevant genes may have relatively large heritability contributions from rare and low-frequency variants while still having low total gene-level heritability.

Download or read book An Evidence Framework for Genetic Testing written by National Academies of Sciences, Engineering, and Medicine and published by National Academies Press. This book was released on 2017-04-21 with total page 149 pages. Available in PDF, EPUB and Kindle. Book excerpt: Advances in genetics and genomics are transforming medical practice, resulting in a dramatic growth of genetic testing in the health care system. The rapid development of new technologies, however, has also brought challenges, including the need for rigorous evaluation of the validity and utility of genetic tests, questions regarding the best ways to incorporate them into medical practice, and how to weigh their cost against potential short- and long-term benefits. As the availability of genetic tests increases so do concerns about the achievement of meaningful improvements in clinical outcomes, costs of testing, and the potential for accentuating medical care inequality. Given the rapid pace in the development of genetic tests and new testing technologies, An Evidence Framework for Genetic Testing seeks to advance the development of an adequate evidence base for genetic tests to improve patient care and treatment. Additionally, this report recommends a framework for decision-making regarding the use of genetic tests in clinical care.

Download or read book Dissecting Genetic Basis of Complex Traits by Haplotype based Association Studies and Integrated Information from Multiple Data Sources written by Yixuan Chen and published by . This book was released on 2010 with total page 151 pages. Available in PDF, EPUB and Kindle. Book excerpt: Characterization of genetic variation and dissection of genetic architectures of complex diseases is critical in understanding their intrinsic mechanisms. Haplotype methods have shown improved power and more consistent results comparing to single-locus based approaches. We propose a new haplotype-based association method for family data. Our approach (termed F_HapMiner) first infers diplotype pairs of each individual in each pedigree assuming no recombination within a family. A phenotype score is then defined for each founder haplotype. Finally, F_HapMiner applies a clustering algorithm on founder haplotypes based on their similarities and identifies haplotype clusters that show significant associations with diseases/traits. Comparisons with single-locus and haplotype-based Transmission Disequilibrium Test (TDT) methods demonstrate that our approach consistently outperforms the TDT-based approaches regardless of disease models, local Linkage Disequilibrium (LD) structures or allele/haplotype frequencies. Traditional linkage analysis and association study may result in hundreds of candidate genes. We propose an expandable framework for gene prioritization that can integrate multiple heterogenous data sources by taking advantage of a unified graphic representation. Gene-gene relationships and gene-disease relationships are then defined based on the overall topology of each network using the diffusion kernel measure. These relationship measures are in turn normalized to derive an overall measure across all networks, which is utilized to rank all candidate genes. Based on the informativeness of available data sources with respect to each specific disease, we also propose an adaptive threshold score to select a small subset of candidate genes for further validation studies. We performed large scale cross-validation analysis using three data sources based on protein interactions, gene expressions and pathway information. Results have shown that our approach consistently outperforms other two state-of-the-art programs. A web tool has been implemented to assist scientists in their genetic studies. Researchers commonly rely on simulated data to evaluate their approaches for detecting high-order interactions in disease gene mapping. A publicly available simulation program is of great interests. We have developed a computer program gs to quickly generate a large number of samples based on real data. Two approaches have been implemented to generate dense SNP haplotype/genotype data that share similar local LD patterns as those in human populations. The first approach takes haplotype pairs from samples as inputs, and the second approach takes patterns of haplotype block structures as inputs. The improved version of gs provides great functionalities and flexibilities to simulate various interaction models. Data generated can serve as a common ground to compare different approaches in detecting interactions.

Download or read book Understanding the Cellular Heterogeneity in Fetal like and Adult Tissues to Study Cell type specific Functional Genetic Variation written by Margaret Kathleen Rose Donovan and published by . This book was released on 2019 with total page 122 pages. Available in PDF, EPUB and Kindle. Book excerpt: Genome-wide association studies (GWAS) have suggested that the underlying genetic basis of complex traits and disease is driven by large numbers of non-coding variants with modest effects that likely act by modifying gene regulation. Towards understanding the regulatory impact of genetic variation, expression quantitative trait loci (eQTL) analyses have been performed across dozens of human tissues to link the influence of genetic variants on gene expression levels. While these eQTL studies have provided important biological insights, they are still limited by not considering the contexts in which these variants function, including the stage of development and cell type. Specifically, others have shown increased disease risk in adulthood has links to fetal origins, suggesting that characterizing gene expression in fetal-like cells could identify genetic variants that are associated with adult traits, but function primarily or solely during development. Additionally, as eQTL studies are typically performed across bulk tissues, unaccounted for cellular heterogeneity present in bulk gene expression measurements can affect genotype-gene expression associations. Thus, it is important to identify regulatory variants that alter gene expression in both primitive and adult cell types and to characterize cellular heterogeneity across tissues to comprehensively understand the genetic basis of complex traits and disease. Here, I present two studies, which utilize gene expression data from fetal-like and adult tissues to characterize cellular heterogeneity at distinct stages of human development. I have examined the cellular heterogeneity in fetal-like induced pluripotent stem cell (iPSC)-derived cardiovascular progenitor cells (CVPCs) using single cell (sc)RNA-seq data to identify cell populations that emerge as a result of the cardiac differentiation. Further, I deconvoluted 180 iPSC-CVPCs and identified factors innate to iPSCs that impacted cardiac fate. Next, I showed that mouse scRNA-seq can be used as an alternative to human scRNA-seq for the deconvolution of adult GTEx bulk tissues and considering cell composition eQTL studies powered the discovery of novel eQTLs, some of which were cell-type-associated and colocalized with GWAS disease loci.

Download or read book Sparse Model Learning for Identifying Nucleotide Motifs and Inferring Genotype and Phenotype Associations written by Indika Priyantha Kuruppu Appuhamilage and published by . This book was released on 2016 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Variation in gene expression is an important mechanism underlying phenotypic variation in morphological, physiological and behavioral traits as well as disease susceptibility. A connection between DNA variants and gene expression levels not only provides more understanding of the biological network, but also enhances the mapping of these quantitative traits. Thus, an understanding of the mechanism of gene expression and the genotype/phenotype relationship is of paramount importance to both scientific research and social economics. The primary functionality of the gene expression process is to convert information stored in genes into gene products such as RNAs or proteins. The fundamental of this complex process is controlled by a class of proteins known as transcription factors (TFs) that bind to special locations of the DNA double helix. These special binding sites, known as transcription factor binding sites (TFBSs), are generally short motifs of 6-20 base pairs. Furthermore, the discovery of new TFBSs will contribute to the establishment of gene regulation networks, diagnosis of genetic diseases and new drug design. On the other hand, the genotype/phenotype relationship is mainly explained by multiple quantitative trait loci (QTLs), epistatic effects and environmental factors. A QTL is a section of DNA that correlates with variation in a phenotype. The QTL typically is linked to, or contains, the genes that control that phenotype interactions among QTLs or between genes, and environmental factors contribute substantially to variation in complex traits. During the last two decades the use of QTLs has proven to be effective for increasing food production, resistance to diseases and pests, tolerance to heat, cold and draught, and to improve nutrient content in animal and plant breeding. Therefore, the objective of this dissertation is to develop sparse models for such high dimensional data, develop accurate sparse variable selection and estimation algorithms for the models and design statistical methods for robust hypothesis tests for the TFBSs identification and QTL mapping problems. Although the sparse model learning works presented in this thesis are used in the context of TFBSs identification or QTL mapping problems, the algorithms are equally applicable to a broad range of problems, such as whole-genome QTL mapping and pathway-based genome-wide association study (GWAS), etc. The widely used computational methods for identifying TFBSs based on the position weight matrix (PWM) assume that the nucleotides at different positions of the TFBSs are independent. However, several experimental results demonstrate the dependencies among different positions. Recently, Bayesian networks (BN) and variable order Bayesian networks (VOBN) were proposed to model such dependencies and thereby improve the accuracy of predicting TFBSs. However, BN and VOBN model the dependencies in a directional manner, which may hinder their capability of completely capturing complex dependencies. To this end, we develop a Markov random field (MRF) based model for TFBSs capable of capturing complex unidirectional relationships among motifs. To capture the large extent of dependencies in a sparse model without causing overfitting, we develop a feature selection method that carefully chooses only the most relevant features of the model. An exhaustive simulation study affirmed that our MRF-based method outperforms other state-of-the-art methods based on VOBN. To further reduce the computational complexity of our algorithm, we introduce a novel pairwise MRF model to the TFBSs, and develop a fast algorithm to learn the model parameters. Specifically, we adopt an optimization method that employs the log determinant relaxation approach to evaluate the partition function in the MRF, which dramatically reduces the computational complexity of the algorithm. For the genotype/phenotype association problem, we develop a novel empirical Bayesian least absolute shrinkage and selection operator (EBlasso) algorithm with normal and exponential (NE) and normal, exponential and gamma (NEG) hierarchical prior distributions. Both of these algorithms employ a novel proximal gradient approach to simultaneously estimate model parameters that leads to extremely fast convergence. Furthermore, we develop a novel proximal gradient hybrid model capable of detecting more QTLs than its vanilla flavor, but still maintaining a lower false positive rate. Having both covariance and posterior modes estimated, they also provide a statistical testing method that considers as much information as possible without increasing the degrees of freedom (DF). Extensive simulation studies are carried out to evaluate the performance of the proposed methods, and real datasets are analyzed for validation. Both simulation and real data analyses suggest that the new methods are fast and accurate genotype-phenotype association methods that can easily handle high dimensional data, including possible main and interaction effects with orders of magnitude faster than existing state-of-the-art methods. Specifically, with the EBlasso-NEG, our new algorithm could easily handle more than [10]^5 possible effects within few seconds running on an average personal computer. Given the fundamental importance of gene expression and genotype/phenotype associations in understanding the genetic basis of complex biological system, the MRF, pairwise-MRF, EBlasso-NE, EBlasso-NEG and EBlasso-NEG hybrid algorithms and software packages developed in this dissertation achieve the effectiveness, robustness and efficiency needed for successful application to biology. With the advancement of high-throughput molecular technologies in generating information at genetic, epigenetic, transcriptional and posttranscriptional levels, the methods developed here have broad applications to infer TFBSs and different types of genotype and phenotypes associations.

Download or read book Adaptive Genetic Variation in the Wild written by Timothy A. Mousseau and published by Oxford University Press. This book was released on 2000-01-13 with total page 276 pages. Available in PDF, EPUB and Kindle. Book excerpt: Two of the great mysteries of biology yet to be explored concern the distribution and abundance of genetic variation in natural populations and the genetic architecture of complex traits. These are tied together by their relationship to natural selection and evolutionary history, and some of the keys to disclosing these secrets lie in the study of wild organisms in their natural environments. This book, featuring a superb selection of papers from leading authors, summarizes the state of current understanding about the extent of genetic variation within wild populations and the ways to monitor such variation. It proposes the idea that a fundamental objective of evolutionary ecology is necessary to predict organism, population, community, and ecosystem response to environmental change. In fact, the overall theme of the papers centers around the expression of genetic variation and how it is shaped by the action of natural selection in the natural environment. Patterns of adaptation in the past and the genetic basis of traits likely to be under selection in a dynamically changing environment is discussed along with a wide variety of techniques to test for genetic variation and its consequences, ranging from classical demography to the use of molecular markers. This book is perfect for professionals and graduate students in genetics, biology, ecology, conservation biology, and evolution.

Download or read book Mapping Genotype to Phenotype with High throughput Empirical Approaches written by Katherine R. Lawrence and published by . This book was released on 2021 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Understanding how genetic variation gives rise to phenotypic variation is a central goal of biology. The structure of this genotype-phenotype map, or landscape, underlies the dynamics of populations adapting under natural selection, and quantitative understanding will be required to predict and engineer outcomes in evolving organisms like viral pathogens, cancer cells, or microbial communities. Characterizing the landscape structure remains largely an empirical question, and observing general patterns requires high-throughput, high-powered experiments that systematically probe landscapes in different biological contexts.