The Experts below are selected from a list of 318 Experts worldwide ranked by ideXlab platform
John W. Taylor - One of the best experts on this subject based on the ideXlab platform.
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Population Structure and gene evolution in Saccharomyces cerevisiae.
Fems Yeast Research, 2006Co-Authors: Erlend Aa, Kaare Magne Nielsen, Rachel I. Adams, Jeffrey P Townsend, John W. TaylorAbstract:The fully sequenced genomes of four species within the Saccharomyces sensu stricto complex provide a wealth of information for molecular-evolutionary inference. Yet virtually nothing is known about Population-genetic variation within these species, including the molecular-biological and genetic-model organism S. cerevisiae. Here we investigate the Population-genetic variation and Population Structure of S. cerevisiae by sequencing the four loci CDC19, PHD1, FZF1 and SSU1 in 27 strains. Sequence analysis demonstrates a distinct Population Structure in S. cerevisiae, distinguishing strains collected from a Pennsylvanian oak forest and strains collected from vineyards, perhaps due to ecological rather than geographic factors. The low level of conflict observed between the gene trees estimated for each locus implies moderate recombination in nature. High polymorphism in the gene SSU1 provides evidence of diversifying selection on its protein product, a sulfite exporter, perhaps associated with the use of sulfur-based fungicides in vineyards. FZF1, encoding a transcription factor regulating the expression level of SSU1, displays even greater polymorphism. This, the first multilocus sequence study of Population Structure in natural isolates of S. cerevisiae, is the first study to demonstrate Population Structure within S. cerevisiae, and the first study to detect historical selection on a locus important to the natural history of wine yeast.
Alaaeldin M. Hafez - One of the best experts on this subject based on the ideXlab platform.
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Nonparametric approaches for Population Structure analysis.
Human genomics, 2018Co-Authors: Luluah Al-husain, Alaaeldin M. HafezAbstract:The analysis of Population Structure has many applications in medical and Population genetic research. Such analysis is used to provide clear insight into the underlying genetic Population subStructure and is a crucial prerequisite for any analysis of genetic data. The analysis involves grouping individuals into subPopulations based on shared genetic variations. The most widely used markers to study the variation of DNA sequences between Populations are single nucleotide polymorphisms. Data preprocessing is a necessary step to assess the quality of the data and to determine which markers or individuals can reasonably be included in the analysis. After preprocessing, several methods can be utilized to uncover Population subStructure, which can be categorized into two broad approaches: parametric and nonparametric. Parametric approaches use statistical models to infer Population Structure and assign individuals into subPopulations. However, these approaches suffer from many drawbacks that make them impractical for large datasets. In contrast, nonparametric approaches do not suffer from these drawbacks, making them more viable than parametric approaches for analyzing large datasets. Consequently, nonparametric approaches are increasingly used to reveal Population subStructure. Thus, this paper reviews and discusses the nonparametric approaches that are available for Population Structure analysis along with some implications to resolve challenges.
Erlend Aa - One of the best experts on this subject based on the ideXlab platform.
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Population Structure and gene evolution in Saccharomyces cerevisiae.
Fems Yeast Research, 2006Co-Authors: Erlend Aa, Kaare Magne Nielsen, Rachel I. Adams, Jeffrey P Townsend, John W. TaylorAbstract:The fully sequenced genomes of four species within the Saccharomyces sensu stricto complex provide a wealth of information for molecular-evolutionary inference. Yet virtually nothing is known about Population-genetic variation within these species, including the molecular-biological and genetic-model organism S. cerevisiae. Here we investigate the Population-genetic variation and Population Structure of S. cerevisiae by sequencing the four loci CDC19, PHD1, FZF1 and SSU1 in 27 strains. Sequence analysis demonstrates a distinct Population Structure in S. cerevisiae, distinguishing strains collected from a Pennsylvanian oak forest and strains collected from vineyards, perhaps due to ecological rather than geographic factors. The low level of conflict observed between the gene trees estimated for each locus implies moderate recombination in nature. High polymorphism in the gene SSU1 provides evidence of diversifying selection on its protein product, a sulfite exporter, perhaps associated with the use of sulfur-based fungicides in vineyards. FZF1, encoding a transcription factor regulating the expression level of SSU1, displays even greater polymorphism. This, the first multilocus sequence study of Population Structure in natural isolates of S. cerevisiae, is the first study to demonstrate Population Structure within S. cerevisiae, and the first study to detect historical selection on a locus important to the natural history of wine yeast.
Lior Pachter - One of the best experts on this subject based on the ideXlab platform.
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Expression reflects Population Structure.
PLoS genetics, 2018Co-Authors: Brielin C. Brown, Nicolas Bray, Lior PachterAbstract:Population Structure in genotype data has been extensively studied, and is revealed by looking at the principal components of the genotype matrix. However, no similar analysis of Population Structure in gene expression data has been conducted, in part because a naive principal components analysis of the gene expression matrix does not cluster by Population. We identify a linear projection that reveals Population Structure in gene expression data. Our approach relies on the coupling of the principal components of genotype to the principal components of gene expression via canonical correlation analysis. Our method is able to determine the significance of the variance in the canonical correlation projection explained by each gene. We identify 3,571 significant genes, only 837 of which had been previously reported to have an associated eQTL in the GEUVADIS results. We show that our projections are not primarily driven by differences in allele frequency at known cis-eQTLs and that similar projections can be recovered using only several hundred randomly selected genes and SNPs. Finally, we present preliminary work on the consequences for eQTL analysis. We observe that using our projection co-ordinates as covariates results in the discovery of slightly fewer genes with eQTLs, but that these genes replicate in GTEx matched tissue at a slightly higher rate.
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Expression reflects Population Structure
2018Co-Authors: Brielin C. Brown, Nicolas Bray, Lior PachterAbstract:Population Structure in genotype data has been extensively studied, and is revealed by looking at the principal components of the genotype matrix. However, no similar analysis of Population Structure in gene expression data has been conducted, in part because a naive principal components analysis of the gene expression matrix does not cluster by Population. We identify a linear projection that reveals Population Structure in gene expression data. Our approach relies on the coupling of the principal components of genotype to the principal components of gene expression via canonical correlation analysis. Futhermore, we analyze the variance of each gene within the projection matrix to determine which genes significantly influence the projection. We identify thousands of significant genes, and show that a number of the top genes have been implicated in diseases that disproportionately impact African Americans.
Urmila Kulkarni-kale - One of the best experts on this subject based on the ideXlab platform.
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Population Structure and Evolution of Rhinoviruses
2016Co-Authors: Vaishali P. Waman, Mohan M. Kale, Urang S. Kolekar, Urmila Kulkarni-kaleAbstract:Rhinoviruses, formerly known as Human rhinoviruses, are the most common cause of air-borne upper respiratory tract infections in humans. Rhinoviruses belong to the family Picornaviridae and are divided into three species namely, Rhinovirus A,-B and-C, which are antigenically diverse. Genetic recombination is found to be one of the important causes for diversification of Rhinovirus species. Although emerging lineages within Rhinoviruses have been reported, their Population Structure has not been studied yet. The availability of complete genome sequences facilitates study of Population Structure, genetic diversity and underlying evolutionary forces, such as mutation, recombination and selection pressure. Analysis of complete genomes of Rhinoviruses using a model-based Population genetics approach provided a strong evidence for existence of seven genetically distinct subPopulations. As a result of diversification, Rhinovirus A and-C Populations are divided into four and two subPopulations, respectively. Genetically, the Rhinovirus B Population was found to be homogeneous. Intra-species recombination was observed to be prominent in Rhinovirus A and-C species. Significant evidence of episodic positive selection was obtained for several sites within coding sequences of structural and non-structural proteins. This corroborates well with known phenotypic properties such as antigenicity of structural proteins. Episodic positive selection appears to be responsible for emergence of new lineages especially in Rhinovirus A. In summary, the Rhinovirus Population is an ensemble of seven distinct lineages. In case of Rhinovirus A, intra-species recombination an
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Population Structure and Evolution of Rhinoviruses
PloS one, 2014Co-Authors: Vaishali P. Waman, Pandurang Kolekar, Mohan M. Kale, Urmila Kulkarni-kaleAbstract:Rhinoviruses, formerly known as Human rhinoviruses, are the most common cause of air-borne upper respiratory tract infections in humans. Rhinoviruses belong to the family Picornaviridae and are divided into three species namely, Rhinovirus A, -B and -C, which are antigenically diverse. Genetic recombination is found to be one of the important causes for diversification of Rhinovirus species. Although emerging lineages within Rhinoviruses have been reported, their Population Structure has not been studied yet. The availability of complete genome sequences facilitates study of Population Structure, genetic diversity and underlying evolutionary forces, such as mutation, recombination and selection pressure. Analysis of complete genomes of Rhinoviruses using a model-based Population genetics approach provided a strong evidence for existence of seven genetically distinct subPopulations. As a result of diversification, Rhinovirus A and -C Populations are divided into four and two subPopulations, respectively. Genetically, the Rhinovirus B Population was found to be homogeneous. Intra-species recombination was observed to be prominent in Rhinovirus A and -C species. Significant evidence of episodic positive selection was obtained for several sites within coding sequences of structural and non-structural proteins. This corroborates well with known phenotypic properties such as antigenicity of structural proteins. Episodic positive selection appears to be responsible for emergence of new lineages especially in Rhinovirus A. In summary, the Rhinovirus Population is an ensemble of seven distinct lineages. In case of Rhinovirus A, intra-species recombination and episodic positive selection contribute to its further diversification. In case of Rhinovirus C, intra- and inter-species recombinations are responsible for observed diversity. Population genetics approach was further useful to analyze phylogenetic tree topologies pertaining to recombinant strains, especially when trees are derived using complete genomes. Understanding of Population Structure serves as a foundation for designing new vaccines and drugs as well as to explain emergence of drug resistance amongst subPopulations.