Analysis and Simulation Methods for Artificial Selection Experiments in the Investigation of the Genetic Basis of Complex Traits PDF Download
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Author: Darren Kessner
Publisher:
ISBN:
Category :
Languages : en
Pages : 151
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Book Description
One of the fundamental goals of research in modern genetics is to determine the genetic basis for complex traits. One experimental approach to this problem is artificial selection, where individual organisms are selected each generation for extreme values of the trait under study. In these experiments, the investigator identifies putative trait loci based on genetic differentiation in evolved populations. Recently, researchers have combined artificial selection with genome- wide pooled massively parallel sequencing to identify quantitative trait loci. In this dissertation, I present analysis and simulation methods applicable to pooled sequencing and artificial selection experiments. In Chapter 1, I provide some background on artificial selection, massively parallel sequencing, and the use of simulations in population genetics. In Chapter 2, I present an expectation-maximization (EM) algorithm for estimating haplotype frequencies in a pooled sample directly from mapped sequence reads, in the case where the possible haplotypes are known. This method is relevant to the analysis of pooled sequencing data from selection experiments, in addition to the calculation of proportions of different species within a metagenomics sample. The method outperforms existing methods based on single-site allele frequencies, as well as simple approaches using sequence read data. I have implemented the method in a freely available open-source software tool called harp (Haplotype Analysis of Reads in Pools). In Appendix A, I present additional analyses to show that the method improves estimates of relative abundances and community diversity at higher taxon levels. In Chapter 3, I present a new forward-in-time simulator forqs (Forward-in-time simulation of Recombination, Quantitative traits, and Selection). forqs was designed to investigate haplotype patterns resulting from scenarios where substantial evolutionary change has taken place in a small number of generations due to recombination and/or selection on polygenic quantitative traits. The simulator uses a memory-efficient representation of chromosomes that allows the simulation of whole genomes. In addition, forqs explicitly models quantitative traits, and its modular design gives the user great flexibility in specifying trait architectures, selection and demography. In Chapter 4, I present a new analysis of the power of artificial selection experiments to detect and localize quantitative trait loci (QTLs), using the forqs simulator from Chapter 3. I show that modeling loci with constant selection coefficients does not fully capture the dynamics of QTLs under artificial selection. I also show that a substantial portion of the genetic variance of the trait (50-100%) can be explained by detected QTLs in as little as 20 generations of selection, depending on the trait architecture and experimental design. Furthermore, I show that the power to detect and localize QTLs depends crucially on the opportunity for recombination during the experiment. Finally, I show that an increase in power is obtained by leveraging founder haplotype information to obtain allele frequency estimates (using the harp method from Chapter 2).
Author: Darren Kessner
Publisher:
ISBN:
Category :
Languages : en
Pages : 151
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Book Description
One of the fundamental goals of research in modern genetics is to determine the genetic basis for complex traits. One experimental approach to this problem is artificial selection, where individual organisms are selected each generation for extreme values of the trait under study. In these experiments, the investigator identifies putative trait loci based on genetic differentiation in evolved populations. Recently, researchers have combined artificial selection with genome- wide pooled massively parallel sequencing to identify quantitative trait loci. In this dissertation, I present analysis and simulation methods applicable to pooled sequencing and artificial selection experiments. In Chapter 1, I provide some background on artificial selection, massively parallel sequencing, and the use of simulations in population genetics. In Chapter 2, I present an expectation-maximization (EM) algorithm for estimating haplotype frequencies in a pooled sample directly from mapped sequence reads, in the case where the possible haplotypes are known. This method is relevant to the analysis of pooled sequencing data from selection experiments, in addition to the calculation of proportions of different species within a metagenomics sample. The method outperforms existing methods based on single-site allele frequencies, as well as simple approaches using sequence read data. I have implemented the method in a freely available open-source software tool called harp (Haplotype Analysis of Reads in Pools). In Appendix A, I present additional analyses to show that the method improves estimates of relative abundances and community diversity at higher taxon levels. In Chapter 3, I present a new forward-in-time simulator forqs (Forward-in-time simulation of Recombination, Quantitative traits, and Selection). forqs was designed to investigate haplotype patterns resulting from scenarios where substantial evolutionary change has taken place in a small number of generations due to recombination and/or selection on polygenic quantitative traits. The simulator uses a memory-efficient representation of chromosomes that allows the simulation of whole genomes. In addition, forqs explicitly models quantitative traits, and its modular design gives the user great flexibility in specifying trait architectures, selection and demography. In Chapter 4, I present a new analysis of the power of artificial selection experiments to detect and localize quantitative trait loci (QTLs), using the forqs simulator from Chapter 3. I show that modeling loci with constant selection coefficients does not fully capture the dynamics of QTLs under artificial selection. I also show that a substantial portion of the genetic variance of the trait (50-100%) can be explained by detected QTLs in as little as 20 generations of selection, depending on the trait architecture and experimental design. Furthermore, I show that the power to detect and localize QTLs depends crucially on the opportunity for recombination during the experiment. Finally, I show that an increase in power is obtained by leveraging founder haplotype information to obtain allele frequency estimates (using the harp method from Chapter 2).
Author: Isadore Michael Lerner
Publisher:
ISBN:
Category : Animal breeding
Languages : en
Pages : 346
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Book Description
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: Kathryn Burch
Publisher:
ISBN:
Category :
Languages : en
Pages : 128
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Book Description
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.
Author: Tim Bernard Bigdeli
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
Etiological models of complex disease are elusive[46, 33, 9], as are consistently replicable findings for major genetic susceptibility loci[54, 14, 15, 24]. Commonly-cited explanations invoke low-frequency genomic variation[41], allelic heterogeneity at susceptibility loci[33, 30], variable etiological trajectories[18, 17], and epistatic effects between multiple loci; these represent among the most methodologically-challenging issues in molecular genetic studies of complex traits. The response has been con- sistently reactionary -- hypotheses regarding the relative contributions of known functional elements, or emphasizing a greater role of rare variation[46, 33] have undergone periodic revision, driving increasingly collaborative efforts to ascertain greater numbers of participants and which assay a rapidly-expanding catalogue of human genetic variation. Major deep-sequencing initiatives, such as the 1,000 Genomes Project, are currently identifying human polymorphic sites at frequencies previously unassailable and, not ten years after publication of the first major genome-wide association findings, re-sequencing has already begun to displace GWAS as the standard for genetic analysis of complex traits. With studies of complex disease primed for an unprecedented survey of human genetic variation, it is essential that human geneticists address several prominent, problematic aspects of this research. Realizations regarding the boundaries of human traits previously considered to be effectively disparate in presentation[44, 39, 35, 27, 25, 12, 4, 13], as well as profound insight into the extent of human genetic diversity[23, 22] are not without consequence. Whereas the resolution of fine-mapping studies have undergone persistent refinement, recent polygenic findings suggest a less discriminant basis of genetic liability, raising the question of what a given, unitary association finding actually represents. Furthermore, realistic expectations regarding the pattern of findings for a particular genetic factor between or even within populations remain unclear. Of interest herein are methodologies which exploit the finite extent of genomic variability within human populations to distinguish single-point and cumulative group differences in liability to complex traits, the range of allele frequencies for which common association tests are appropriate, and the relevant dimensionality of common genetic variation within ethnically-concordant but differentially ascertained populations. Using high-density SNP genotype data, we consider both hypothesis-driven and agnostic (genome-wide) approaches to association analysis, and address specific issues pertaining to empirical significance and the statistical properties of commonly- applied tests. Lastly, we demonstrate a novel perspective of genome-wide genetic "background" through exhaustive evaluation of fundamental, stochastic genetic processes in a sample of matched affected and unaffected siblings selected from high- density schizophrenia families.
Author: Theodore Garland
Publisher: Univ of California Press
ISBN: 0520261801
Category : Nature
Languages : en
Pages : 752
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Book Description
This volume summarizes studies in experimental evolution, outlining current techniques and applications, and presenting the field's range of research.
Author: Bruce Walsh
Publisher: Oxford University Press
ISBN: 0192566644
Category : Science
Languages : en
Pages : 1504
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Book Description
Quantitative traits-be they morphological or physiological characters, aspects of behavior, or genome-level features such as the amount of RNA or protein expression for a specific gene-usually show considerable variation within and among populations. Quantitative genetics, also referred to as the genetics of complex traits, is the study of such characters and is based on mathematical models of evolution in which many genes influence the trait and in which non-genetic factors may also be important. Evolution and Selection of Quantitative Traits presents a holistic treatment of the subject, showing the interplay between theory and data with extensive discussions on statistical issues relating to the estimation of the biologically relevant parameters for these models. Quantitative genetics is viewed as the bridge between complex mathematical models of trait evolution and real-world data, and the authors have clearly framed their treatment as such. This is the second volume in a planned trilogy that summarizes the modern field of quantitative genetics, informed by empirical observations from wide-ranging fields (agriculture, evolution, ecology, and human biology) as well as population genetics, statistical theory, mathematical modeling, genetics, and genomics. Whilst volume 1 (1998) dealt with the genetics of such traits, the main focus of volume 2 is on their evolution, with a special emphasis on detecting selection (ranging from the use of genomic and historical data through to ecological field data) and examining its consequences.
Author: Michael Lynch
Publisher: Sinauer Associates Incorporated
ISBN: 9780878934812
Category : Science
Languages : en
Pages : 980
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Book Description
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.
Author: Michael R. Barnes
Publisher: John Wiley & Sons
ISBN: 047086219X
Category : Science
Languages : en
Pages : 432
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Book Description
This timely book illustrates the value of bioinformatics, not simply as a set of tools but rather as a science increasingly essential to navigate and manage the host of information generated by genomics and the availability of completely sequenced genomes. Bioinformatics can be used at all stages of genetics research: to improve study design, to assist in candidate gene identification, to aid data interpretation and management and to shed light on the molecular pathology of disease-causing mutations. Written specifically for geneticists, this book explains the relevance of bioinformatics showing how it may be used to enhance genetic data mining and markedly improve genetic analysis.
Author: Yunbi Xu
Publisher: CABI
ISBN: 1845936248
Category : Science
Languages : en
Pages : 756
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Book Description
Recent advances in plant genomics and molecular biology have revolutionized our understanding of plant genetics, providing new opportunities for more efficient and controllable plant breeding. Successful techniques require a solid understanding of the underlying molecular biology as well as experience in applied plant breeding. Bridging the gap between developments in biotechnology and its applications in plant improvement, Molecular Plant Breeding provides an integrative overview of issues from basic theories to their applications to crop improvement including molecular marker technology, gene mapping, genetic transformation, quantitative genetics, and breeding methodology.
