12 research outputs found

    A murine specific expansion of the Rhox cluster involved in embryonic stem cell biology is under natural selection

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    BACKGROUND: The rodent specific reproductive homeobox (Rhox) gene cluster on the X chromosome has been reported to contain twelve homeobox-containing genes, Rhox1-12. RESULTS: We have identified a 40 kb genomic region within the Rhox cluster that is duplicated eight times in tandem resulting in the presence of eight paralogues of Rhox2 and Rhox3 and seven paralogues of Rhox4. Transcripts have been identified for the majority of these paralogues and all but three are predicted to produce full-length proteins with functional potential. We predict that there are a total of thirty-two Rhox genes at this genomic location, making it the most gene-rich homoeobox cluster identified in any species. From the 95% sequence similarity between the eight duplicated genomic regions and the synonymous substitution rate of the Rhox2, 3 and 4 paralogues we predict that the duplications occurred after divergence of mouse and rat and represent the youngest homoeobox cluster identified to date. Molecular evolutionary analysis reveals that this cluster is an actively evolving region with Rhox2 and 4 paralogues under diversifying selection and Rhox3 evolving neutrally. The biological importance of this duplication is emphasised by the identification of an important role for Rhox2 and Rhox4 in regulating the initial stages of embryonic stem (ES) cell differentiation. CONCLUSION: The gene rich Rhox cluster provides the mouse with significant biological novelty that we predict could provide a substrate for speciation. Moreover, this unique cluster may explain species differences in ES cell derivation and maintenance between mouse, rat and human

    A population genetic approach to mapping neurological disorder genes using deep resequencing

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    Deep resequencing of functional regions in human genomes is key to identifying potentially causal rare variants for complex disorders. Here, we present the results from a large-sample resequencing (n  =  285 patients) study of candidate genes coupled with population genetics and statistical methods to identify rare variants associated with Autism Spectrum Disorder and Schizophrenia. Three genes, MAP1A, GRIN2B, and CACNA1F, were consistently identified by different methods as having significant excess of rare missense mutations in either one or both disease cohorts. In a broader context, we also found that the overall site frequency spectrum of variation in these cases is best explained by population models of both selection and complex demography rather than neutral models or models accounting for complex demography alone. Mutations in the three disease-associated genes explained much of the difference in the overall site frequency spectrum among the cases versus controls. This study demonstrates that genes associated with complex disorders can be mapped using resequencing and analytical methods with sample sizes far smaller than those required by genome-wide association studies. Additionally, our findings support the hypothesis that rare mutations account for a proportion of the phenotypic variance of these complex disorders

    A Population Genetic Approach to Mapping Neurological Disorder Genes Using Deep Resequencing

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    Deep resequencing of functional regions in human genomes is key to identifying potentially causal rare variants for complex disorders. Here, we present the results from a large-sample resequencing (n = 285 patients) study of candidate genes coupled with population genetics and statistical methods to identify rare variants associated with Autism Spectrum Disorder and Schizophrenia. Three genes, MAP1A, GRIN2B, and CACNA1F, were consistently identified by different methods as having significant excess of rare missense mutations in either one or both disease cohorts. In a broader context, we also found that the overall site frequency spectrum of variation in these cases is best explained by population models of both selection and complex demography rather than neutral models or models accounting for complex demography alone. Mutations in the three disease-associated genes explained much of the difference in the overall site frequency spectrum among the cases versus controls. This study demonstrates that genes associated with complex disorders can be mapped using resequencing and analytical methods with sample sizes far smaller than those required by genome-wide association studies. Additionally, our findings support the hypothesis that rare mutations account for a proportion of the phenotypic variance of these complex disorders

    Selection shapes malaria genomes and drives divergence between pathogens infecting hominids versus rodents

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    BACKGROUND: Malaria kills more people worldwide than all inherited human genetic disorders combined. To characterize how the parasites causing this disease adapt to different host environments, we compared the evolutionary genomics of two distinct groups of malaria pathogens in order to identify critical properties associated with infection of different hosts: those parasites infecting hominids (Plasmodium falciparum and P. reichenowi) versus parasites infecting rodent hosts (P. yoelii yoelii, P. berghei, and P. chabaudi). Adaptation by the parasite to its host is likely highly critical to the evolution of these species. RESULTS: Our comparative analysis suggests that patterns of molecular evolution in the hominid parasite lineage are generally similar to those of the rodent lineage but distinct in several aspects. The most rapidly evolving genes in both lineages are those involved in host-parasite interactions as well as those that show the lowest expression levels. However, we found that, similar to their respective mammal host lineages, parasite genomes infecting hominids are generally less constrained, evolving at faster rates, and accumulating more deleterious mutations than those infecting murids, which may reflect an historical lower effective size of the hominid lineage and relaxed host-driven selective pressures. CONCLUSION: Our study highlights for the first time the differences in trends and rates of evolution in Plasmodium lineages infecting different hosts and emphasizes the potential importance of the variation in effective size between lineages to explain variation in selective constraints among genomes

    Selection shapes malaria genomes and drives divergence between pathogens infecting hominids versus rodents-1

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    that are only expressed at one particular stage. Blue squares: hominid lineage; Red squares: rodent lineage.<p><b>Copyright information:</b></p><p>Taken from "Selection shapes malaria genomes and drives divergence between pathogens infecting hominids versus rodents"</p><p>http://www.biomedcentral.com/1471-2148/8/223</p><p>BMC Evolutionary Biology 2008;8():223-223.</p><p>Published online 30 Jul 2008</p><p>PMCID:PMC2529309.</p><p></p

    Selection shapes malaria genomes and drives divergence between pathogens infecting hominids versus rodents-0

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    Ds to the hominid lineage; the second corresponds to the rodent one.<p><b>Copyright information:</b></p><p>Taken from "Selection shapes malaria genomes and drives divergence between pathogens infecting hominids versus rodents"</p><p>http://www.biomedcentral.com/1471-2148/8/223</p><p>BMC Evolutionary Biology 2008;8():223-223.</p><p>Published online 30 Jul 2008</p><p>PMCID:PMC2529309.</p><p></p

    Genome-wide variation and identification of vaccine targets in the Plasmodium falciparum genome.

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    One goal in sequencing the Plasmodium falciparum genome, the agent of the most lethal form of malaria, is to discover vaccine and drug targets. However, identifying those targets in a genome in which approximately 60% of genes have unknown functions is an enormous challenge. Because the majority of known malaria antigens and drug-resistant genes are highly polymorphic and under various selective pressures, genome-wide analysis for signatures of selection may lead to discovery of new vaccine and drug candidates. Here we surveyed 3,539 P. falciparum genes ( approximately 65% of the predicted genes) for polymorphisms and identified various highly polymorphic loci and genes, some of which encode new antigens that we confirmed using human immune sera. Our collections of genome-wide SNPs ( approximately 65% nonsynonymous) and polymorphic microsatellites and indels provide a high-resolution map (one marker per approximately 4 kb) for mapping parasite traits and studying parasite populations. In addition, we report new antigens, providing urgently needed vaccine candidates for disease control

    ARTICLE Direct Measure of the De Novo Mutation Rate in Autism and Schizophrenia Cohorts

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    The role of de novo mutations (DNMs) in common diseases remains largely unknown. Nonetheless, the rate of de novo deleterious mutations and the strength of selection against de novo mutations are critical to understanding the genetic architecture of a disease. Discovery of high-impact DNMs requires substantial high-resolution interrogation of partial or complete genomes of families via resequencing. We hypothesized that deleterious DNMs may play a role in cases of autism spectrum disorders (ASD) and schizophrenia (SCZ), two etiologically heterogeneous disorders with significantly reduced reproductive fitness. We present a direct measure of the de novo mutation rate (m) and selective constraints from DNMs estimated from a deep resequencing data set generated from a large cohort of ASD and SCZ cases (n ¼ 285) and population control individuals (n ¼ 285) with available parental DNA. A survey of~430 Mb of DNA from 401 synapse-expressed genes across all cases and 25 Mb of DNA in controls found 28 candidate DNMs, 13 of which were cell line artifacts. Our calculated direct neutral mutation rate (1.36 3 10 À8 ) is similar to previous indirect estimates, but we observed a significant excess of potentially deleterious DNMs in ASD and SCZ individuals. Our results emphasize the importance of DNMs as genetic mechanisms in ASD and SCZ and the limitations of using DNA from archived cell lines to identify functional variants
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