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Gibson, Greg

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End date: 2007 × Start date: 2007 × search.filters.applied.f.isOrgUnitOfPublicationTitle: College of Sciences × search.filters.applied.f.resourcesubtype: Article ×

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Impact of Population Structure on Genetic Diversity of a Potential Vaccine Target in the Canine Hookworm (Ancylostoma caninum)

2007-08 , Moser, Jennifer M. , Carbone, Ignazio , Arasu, Prema , Gibson, Greg

Ancylostoma caninum is a globally distributed canine parasitic nematode. To test whether positive selection, population structure, or both affect genetic variation at the candidate vaccine target Ancylostoma secreted protein 1 (asp-1), we have quantified the genetic variation in A. caninum at asp-1 and a mitochondrial gene, cytochrome oxidase subunit 1 (cox-1), using the statistical population analysis tools found in the SNAP Workbench. The mitochondrial gene cox-1 exhibits moderate diversity within 2 North American samples, comparable to the level of variation observed in other parasitic nematodes. The protein coding portion for the C-terminal half of asp-1 shows similar levels of genetic variation in a Wake County, North Carolina, sample as cox-1. Standard tests of neutrality provide little formal evidence for selection acting on this locus, but haplotype networks for 2 of the exon regions have significantly different topologies, consistent with different evolutionary forces shaping variation at either end of a 1.3-kilobase stretch of sequence. Evidence for gene flow among geographically distinct samples suggests that the mobility of hosts of A. caninum is an important contributing factor to the population structure of the parasite.

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Mixture modeling of transcript abundance classes in natural populations

2007 , Hsieh, Wen-Ping , Passador-Gurgel, Gisele , Stone, Eric A. , Gibson, Greg

Background. Populations diverge in genotype and phenotype under the influence of such evolutionary processes as genetic drift, mutation accumulation, and natural selection. Because genotype maps onto phenotype by way of transcription, it is of interest to evaluate how these evolutionary factors influence the structure of variation at the level of transcription. Here, we explore the distributions of cis-acting and trans-acting factors and their relative contributions to expression of transcripts that exhibit two or more classes of abundance among individuals within populations. Results. Expression profiling using cDNA microarrays was conducted in Drosophila melanogaster adult female heads for 58 nearly isogenic lines from a North Carolina population and 50 from a California population. Using a mixture modeling approach, transcripts were identified that exhibit more than one mode of transcript abundance across the samples. Power studies indicate that sample sizes of 50 individuals will generally be sufficient to detect divergent transcript abundance classes. The distribution of transcript abundance classes is skewed toward low frequency minor classes, which is reminiscent of the typical skew in genotype frequencies. Similar results are observed in reported data on gene expression in human lymphoblast cell lines, in which analysis of association with linked polymorphisms implies that cis-acting single nucleotide polymorphisms make only a modest contribution to bimodal distributions of transcript abundance. Conclusion. Population surveys of gene expression may complement genetical genomics as a general approach to quantifying sources of transcriptional variation. Differential expression of transcripts among individuals is due to a complex interplay of cis-acting and trans-acting factors.

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Genetic Variation for Cardiac Dysfunction in Drosophila

2007-07-11 , Ocorr, Karen A. , Crawley, Timothy , Gibson, Greg , Bodmer, Rolf

Background Common diseases may be attributed to combinations of variant alleles, but there are few model systems where the interactions among such variants can be studied in controlled genetic crosses. While association studies are designed to detect common polymorphisms of moderate effect, new approaches are required to characterize the impact on disease of interactions among rare alleles. Methodology/Principal Findings We show that wild populations of Drosophila melanogaster harbor rare polymorphisms of major effect (RAME) that predispose flies to a specific disease phenotype, age-dependent cardiac dysfunction. A screen of fifty inbred wild-type lines revealed a continuous spectrum of pacing-induced heart failure that generally increases in frequency with age. High-speed video analysis of the inbred lines with high rates of inducible heart failure indicates specific defects in cardiac function, including arrhythmias and contractile disorders (‘cardiomyopathies’). A combination of bulked segregant analysis and single feature polymorphism (SFP) detection localizes one of the cardiac susceptibility loci to the 97C interval on the fly genome. Conclusions/Significance Wild-type Drosophila, like humans, are predisposed to cardiac dysfunction. Identification of factors associated with these naturally occurring cardiac traits promises to provide important insights into the epidemiology of cardiac disease.

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Simultaneous clustering of gene expression data with clinical chemistry and pathological evaluations reveals phenotypic prototypes

2007-02-23 , Bushel, Pierre R. , Wolfinger, Russell D. , Gibson, Greg

Background: Commonly employed clustering methods for analysis of gene expression data do not directly incorporate phenotypic data about the samples. Furthermore, clustering of samples with known phenotypes is typically performed in an informal fashion. The inability of clustering algorithms to incorporate biological data in the grouping process can limit proper interpretation of the data and its underlying biology. Results: We present a more formal approach, the modk-prototypes algorithm, for clustering biological samples based on simultaneously considering microarray gene expression data and classes of known phenotypic variables such as clinical chemistry evaluations and histopathologic observations. The strategy involves constructing an objective function with the sum of the squared Euclidean distances for numeric microarray and clinical chemistry data and simple matching for histopathology categorical values in order to measure dissimilarity of the samples. Separate weighting terms are used for microarray, clinical chemistry and histopathology measurements to control the influence of each data domain on the clustering of the samples. The dynamic validity index for numeric data was modified with a category utility measure for determining the number of clusters in the data sets. A cluster's prototype, formed from the mean of the values for numeric features and the mode of the categorical values of all the samples in the group, is representative of the phenotype of the cluster members. The approach is shown to work well with a simulated mixed data set and two real data examples containing numeric and categorical data types. One from a heart disease study and another from acetaminophen (an analgesic) exposure in rat liver that causes centrilobular necrosis. Conclusion: The modk-prototypes algorithm partitioned the simulated data into clusters with samples in their respective class group and the heart disease samples into two groups (sick and buff denoting samples having pain type representative of angina and non-angina respectively) with an accuracy of 79%. This is on par with, or better than, the assignment accuracy of the heart disease samples by several well-known and successful clustering algorithms. Following modk-prototypes clustering of the acetaminophen-exposed samples, informative genes from the cluster prototypes were identified that are descriptive of, and phenotypically anchored to, levels of necrosis of the centrilobular region of the rat liver. The biological processes cell growth and/or maintenance, amine metabolism, and stress response were shown to discern between no and moderate levels of acetaminophen-induced centrilobular necrosis. The use of well-known and traditional measurements directly in the clustering provides some guarantee that the resulting clusters will be meaningfully interpretable.

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