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Elucidating genome structure evolution by analysis of isoapostatic gene clusters using statistics of variance of gene distances.

ABSTRACT: Identifying genomic regions that descended from a common ancestor is important for understanding the function and evolution of genomes. In related genomes, clusters of homologous gene pairs serve as evidence for candidate homologous regions, which make up genomic core. Previous studies on the structural organization of bacterial genomes revealed that basic backbone of genomic core is interrupted by genomic islands. Here, we applied statistics using variance of distances as a measure to classify conserved genes within a set of genomes according to their "isoapostatic" relationship, which keeps nearly identical distances of genes. The results of variance statistics analysis of cyanobacterial genomes including Prochlorococcus, Synechococcus, and Anabaena indicated that the conserved genes are classified into several groups called "virtual linkage groups (VLGs)" according to their positional conservation of orthologs over the genomes analyzed. The VLGs were used to define mosaic domain structure of the genomic core. The current model of mosaic genomic domains can explain global evolution of the genomic core of cyanobacteria. It also visualizes islands of lateral gene transfer. The stability and the robustness of the variance statistics are discussed. This method will also be useful in deciphering the structural organization of genomes in other groups of bacteria.

SUBMITTER: Sasaki NV

PROVIDER: S-EPMC2839351 | biostudies-literature | 2009 Dec

REPOSITORIES: biostudies-literature

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Elucidating genome structure evolution by analysis of isoapostatic gene clusters using statistics of variance of gene distances.

Sasaki Naobumi V NV Sato Naoki N

Genome biology and evolution 20091218

Identifying genomic regions that descended from a common ancestor is important for understanding the function and evolution of genomes. In related genomes, clusters of homologous gene pairs serve as evidence for candidate homologous regions, which make up genomic core. Previous studies on the structural organization of bacterial genomes revealed that basic backbone of genomic core is interrupted by genomic islands. Here, we applied statistics using variance of distances as a measure to classify ...[more]

PMID: 20333218

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Project description:Linear mixed models (LMMs) are among the most commonly used tools for genetic association studies. However, the standard method for estimating variance components in LMMs-the restricted maximum likelihood estimation method (REML)-suffers from several important drawbacks: REML requires individual-level genotypes and phenotypes from all samples in the study, is computationally slow, and produces downward-biased estimates in case control studies. To remedy these drawbacks, we present an alternative framework for variance component estimation, which we refer to as MQS. MQS is based on the method of moments (MoM) and the minimal norm quadratic unbiased estimation (MINQUE) criterion, and brings two seemingly unrelated methods-the renowned Haseman-Elston (HE) regression and the recent LD score regression (LDSC)-into the same unified statistical framework. With this new framework, we provide an alternative but mathematically equivalent form of HE that allows for the use of summary statistics. We provide an exact estimation form of LDSC to yield unbiased and statistically more efficient estimates. A key feature of our method is its ability to pair marginal z-scores computed using all samples with SNP correlation information computed using a small random subset of individuals (or individuals from a proper reference panel), while capable of producing estimates that can be almost as accurate as if both quantities are computed using the full data. As a result, our method produces unbiased and statistically efficient estimates, and makes use of summary statistics, while it is computationally efficient for large data sets. Using simulations and applications to 37 phenotypes from 8 real data sets, we illustrate the benefits of our method for estimating and partitioning SNP heritability in population studies as well as for heritability estimation in family studies. Our method is implemented in the GEMMA software package, freely available at www.xzlab.org/software.html.

Dataset Information

Elucidating genome structure evolution by analysis of isoapostatic gene clusters using statistics of variance of gene distances.

Publications

Elucidating genome structure evolution by analysis of isoapostatic gene clusters using statistics of variance of gene distances.

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