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Whole-genome Level (whole-genome + level)

Distribution by Scientific Domains

Life Sciences	100%

Selected Abstracts

Genome-wide single nucleotide polymorphisms reveal population history and adaptive divergence in wild guppies

MOLECULAR ECOLOGY, Issue 5 2010
EVA-MARIA WILLING
Abstract Adaptation of guppies (Poecilia reticulata) to contrasting upland and lowland habitats has been extensively studied with respect to behaviour, morphology and life history traits. Yet population history has not been studied at the whole-genome level. Although single nucleotide polymorphisms (SNPs) are the most abundant form of variation in many genomes and consequently very informative for a genome-wide picture of standing natural variation in populations, genome-wide SNP data are rarely available for wild vertebrates. Here we use genetically mapped SNP markers to comprehensively survey genetic variation within and among naturally occurring guppy populations from a wide geographic range in Trinidad and Venezuela. Results from three different clustering methods, Neighbor-net, principal component analysis (PCA) and Bayesian analysis show that the population substructure agrees with geographic separation and largely with previously hypothesized patterns of historical colonization. Within major drainages (Caroni, Oropouche and Northern), populations are genetically similar, but those in different geographic regions are highly divergent from one another, with some indications of ancient shared polymorphisms. Clear genomic signatures of a previous introduction experiment were seen, and we detected additional potential admixture events. Headwater populations were significantly less heterozygous than downstream populations. Pairwise FST values revealed marked differences in allele frequencies among populations from different regions, and also among populations within the same region. FST outlier methods indicated some regions of the genome as being under directional selection. Overall, this study demonstrates the power of a genome-wide SNP data set to inform for studies on natural variation, adaptation and evolution of wild populations [source]

Microarray-based comparison of genetic differences between strains of Streptomyces turgidiscabies with focus on the pathogenicity island

MOLECULAR PLANT PATHOLOGY, Issue 6 2010
MARJA AITTAMAA
SUMMARY The areas of the pathogenicity island (PAI) designated as ,colonization region' (CR) and ,toxicogenic region' (TR) [Lerat et al. (2009) Mol. Plant Pathol. 10, 579,585] contain genes required for virulence and phytoxin production, respectively, in Streptomyces spp. causing common scab on potatoes. The PAI was tested for genetic variability by microarray analysis in strains of S. turgidiscabies isolated from potatoes in Finland. The data revealed four types of PAI based on divergent CR and TR which occurred in different combinations. Only one PAI type was highly similar to S. scabies (strains 87.22 and ATTC49173). Using probes designed for the predicted genes of S. scabies, two gene clusters in S. scabies appeared to be similar to most strains of S. turgidiscabies and contained PAI genes corresponding to CR and TR. They were located approximately 5 Mb apart in the S. scabies genome, as compared with only 0.3 Mb in S. turgidiscabies Car8. Data from comparative genomic hybridization with probes designed for S. scabies genes and for the PAI of S. turgidiscabies were compared by multilocus cluster analysis, which revealed two strains of S. turgidiscabies that were very closely related at the whole-genome level, but contained distinctly different PAIs. The type strain of S. reticuliscabiei (DSM41804; synonymous to S. turgidiscabies) was clustered with S. turgidiscabies. Taken together, the data indicate wide genetic variability of PAIs among strains of S. turgidiscabies, and demonstrate that PAI is made up of a mosaic of regions which may undergo independent evolution. [source]

A Variable-Sized Sliding-Window Approach for Genetic Association Studies via Principal Component Analysis

ANNALS OF HUMAN GENETICS, Issue 6 2009
Rui Tang
Summary Recently with the rapid improvements in high-throughout genotyping techniques, researchers are facing the very challenging task of analysing large-scale genetic associations, especially at the whole-genome level, without an optimal solution. In this study, we propose a new approach for genetic association analysis that is based on a variable-sized sliding-window framework and employs principal component analysis to find the optimum window size. With the help of the bisection algorithm in window-size searching, our method is more computationally efficient than available approaches. We evaluate the performance of the proposed method by comparing it with two other methods,a single-marker method and a variable-length Markov chain method. We demonstrate that, in most cases, the proposed method out-performs the other two methods. Furthermore, since the proposed method is based on genotype data, it does not require any computationally intensive phasing program to account for uncertain haplotype phase. [source]

Location analysis of DNA-bound proteins at the whole-genome level: untangling transcriptional regulatory networks

BIOESSAYS, Issue 6 2001
Béatrice Nal
In this post-sequencing era, geneticists can focus on functional genomics on a much larger scale than ever before. One goal is the discovery and elucidation of the intricate genetic networks that co-ordinate transcriptional activation in different regulatory circuitries. High-throughput gene expression measurement using DNA arrays has thus become routine strategy. This approach, however, does not directly identify gene loci that belong to the same regulatory group; e.g., those that are bound by a common (set of) transcription factor(s). Working in yeast, two groups have recently published an elegant method that could circumvent this problem, by combining chromatin immunoprecipitation and DNA microarrays.(1,2) The method is likely to provide a powerful tool for the dissection of global regulatory networks in eukaryotic cells. BioEssays 23:473,476, 2001. © 2001 John Wiley & Sons, Inc. [source]