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Biomedical subjects

D Nettleton

Publications and source records attributed to D Nettleton.

8 recordsLinked to original sources

Selective transcriptional profiling for trait-based eQTL mapping.

There is currently great interest in identifying genetic loci associated with the expression of one or more genes by combining methods for the mapping of quantitative trait loci (QTL) with microarray technology. Because of the high cost of microarrays, it is useful to develop strategies for selecting a subset of individuals for expression profiling. We describe a method of selective transcriptional profiling that uses available information on individual quantitative traits and marker data to choose an optimal set of individuals for measurement with microarrays. We show how to analyse the resulting data to identify genes whose expression is associated with a QTL for a traditional trait or any other locus. Examples are used to illustrate the method and highlight some challenges associated with the analysis of selected data.

Animals↗

Evaluation of linkage disequilibrium measures between multi-allelic markers as predictors of linkage disequilibrium between markers and QTL.

Effectiveness of marker-assisted selection (MAS) and quantitative trait loci (QTL) mapping using population-wide linkage disequilibrium (LD) between markers and QTL depends on the extent of LD and how it declines with distance in a population. Because marker-QTL LD cannot be observed directly, the objective of this study was to evaluate alternative measures of observable LD between multi-allelic markers as predictors of usable LD of multi-allelic markers with presumed biallelic QTL. Observable LD between marker pairs was evaluated using eight existing measures and one new measure. These consisted of two pooled and standardized measures of LD between pairs of alleles at two markers based on Lewontin's LD measure, two pooled measures of squared correlations between alleles, one standardized measure using Hardy-Weinberg heterozygosities, and four measures based on the chi-square statistic for testing for association between alleles at two loci. In simulated populations with a range of LD generated by drift and a range of marker polymorphism, marker-marker LD measured by a standardized chi-square statistic (denoted chi(2')) was found to be the best predictor of useable marker-QTL LD for a group of multi-allelic markers. Estimates of the level and decline of marker-marker LD with distance obtained from chi(2') were linearly and highly correlated with usable LD of those markers with QTL across population structures and marker polymorphism. Corresponding relationships were poorer for the other marker-marker LD measures. Therefore, when LD is generated by drift, chi(2') is recommended to quantify the amount and extent of usable LD in a population for QTL mapping and MAS based on multi-allelic markers.

Alleles↗

Controlling the proportion of false positives in multiple dependent tests.

Genome scan mapping experiments involve multiple tests of significance. Thus, controlling the error rate in such experiments is important. Simple extension of classical concepts results in attempts to control the genomewise error rate (GWER), i.e., the probability of even a single false positive among all tests. This results in very stringent comparisonwise error rates (CWER) and, consequently, low experimental power. We here present an approach based on controlling the proportion of false positives (PFP) among all positive test results. The CWER needed to attain a desired PFP level does not depend on the correlation among the tests or on the number of tests as in other approaches. To estimate the PFP it is necessary to estimate the proportion of true null hypotheses. Here we show how this can be estimated directly from experimental results. The PFP approach is similar to the false discovery rate (FDR) and positive false discovery rate (pFDR) approaches. For a fixed CWER, we have estimated PFP, FDR, pFDR, and GWER through simulation under a variety of models to illustrate practical and philosophical similarities and differences among the methods.

Chromosome Mapping↗

Complementary DNA macroarray analyses of differential gene expression in porcine fetal and postnatal muscle.

To study differential gene expression in porcine skeletal muscle, a porcine complementary DNA (cDNA) macroarray was produced that contained 327 expressed sequence tags (EST) derived from whole embryo and adult skeletal muscle, and differential display PCR products from fetal and postnatal muscle. Total RNA from four muscle samples, 75- and 105-d fetal hind limb muscles, and 1- and 7-wk postnatal semitendinosus muscle was used to make radiolabeled targets for duplicate hybridization to the macroarray membranes in an initial screen for expression. All EST that gave clear signals (n = 238) were then re-arrayed, and hybridization was conducted with additional biological replication of samples in the 75-d and 1-wk ages. Signal intensity for each gene was normalized to signal intensity measured at control spots on each membrane, which consisted of total cDNA from liver, lung, spleen, and skeletal muscle. Both normalized ratio levels and a mixed linear model analyses were used to identify genes differentially expressed among the muscle samples. Results showed 28 genes had differences in expression level greater than twofold between the 75-d fetal and 1-wk muscle RNA samples. All 28 genes were also identified as genes with significantly different (P < 0.01) expression using a mixed linear model analysis. Nineteen of these 28 genes had significant matches (basic local alignment search tool [BLAST] score > 100; P < 0.01) to known genes, two matched genes encoding human hypothetical proteins, and seven had no significant matches to Genbank nonredundant and dbEST (database of expressed sequence tags) entries. These results were confirmed for representative genes with RNA blot analysis of seven developmental time points, including RNA from the same muscle samples tested previously in the macroarray. The RNA blot results confirmed the macroarray results for all selected genes, demonstrating that the macroarray technique used in this study is accurate and reproducible. An unknown muscle clone (M218) with a slightly less than twofold increase in expression from the 75-d to the 1-wk age (1 wk/75 d = 1.94; P = 0.0114) was also shown to differ between these two ages using RNA blot analysis, demonstrating the methods used to identify differentially expressed genes may be conservative. The association between expression patterns of vimentin and desmin was also investigated. Results indicate the switch in intermediate filament protein from vimentin to desmin occurs primarily at the level of transcription and/or RNA processing.

Age Factors↗

Mining MEDLINE: abstracts, sentences, or phrases?

A growing body of works address automated mining of biochemical knowledge from digital repositories of scientific literature, such as MEDLINE. Some of these works use abstracts as the unit of text from which to extract facts. Others use sentences for this purpose, while still others use phrases. Here we compare abstracts, sentences, and phrases in MEDLINE using the standard information retrieval performance measures of recall, precision, and effectiveness, for the task of mining interactions among biochemical terms based on term co-occurrence. Results show statistically significant differences that can impact the choice of text unit.

Abstracting and Indexing↗

Processing and representation of meta-data for sleep apnea diagnosis with an artificial intelligence approach.

In this article, we revise and try to resolve some of the problems inherent in questionnaire screening of sleep apnea cases and apnea diagnosis based on attributes which are relevant and reliable. We present a way of learning information about the relevance of the data, comparing this with the definition of the information by the medical expert. We generate a predictive data model using a data aggregation operator which takes relevance and reliability information about the data into account to produce a diagnosis for each case. We also introduce a grade of membership for each question response which allows the patient to indicate a level of confidence or doubt in their own judgement. The method is tested with data collected from patients in a Sleep Clinic using questionnaires specially designed for the study. Other artificial intelligence predictive modeling algorithms are also tested on the same data and their predictive accuracy compared to that of the aggregation operator.

Adult↗

Accounting for variability in the use of permutation testing to detect quantitative trait loci.

Locating quantitative trait loci (QTL), or genomic regions associated with known molecular markers, is of increasing interest in a wide variety of applications ranging from human genetics to agricultural genetics. The hope of locating QTL (or genes) affecting a quantitative trait is that it will lead to characterization and possible manipulations of these genes. However, the complexity of both statistical and genetic issues surrounding the location of these regions calls into question the asymptotic statistical results supplying the distribution of the test statistics employed. Coupled with the power of current-day computing, permutation theory was reintroduced for the purpose of estimating the distribution of any test statistic used to test for the location of QTL. Permutation techniques have offered an attractive alternative to significance measures based on asymptotic theory. The ideas of permutation testing are extended in this application to include confidence intervals for the thresholds and p-values estimated in permutation testing procedures. The confidence intervals developed account for the Monte Carlo error associated with practical applications of permutation testing and lead to an effective method of determining an efficient permutation sample size.

Biometry↗

Swine as a model of skin inflammation. Phospholipase A2-induced inflammation.

A predictive animal model of skin inflammation is needed for the development of potential therapeutic agents. The existing models of inflammation rely on animals whose skin physiology or biochemistry differs significantly from human. The objective of this investigation was to evaluate the swine as a potential model of inflammation, because its skin has been recognized to exhibit morphologic and functional similarities to human skin. In the swine, an inflammatory response was produced following intradermal injection of snake venom phospholipase A2 (PLA2). This response was characterized by transient erythema (2-3 h) and microscopic changes of cell infiltration, epidermal hyperplasia, and dermal damage, which were apparent two days after PLA2 and peaked by day 7. In general, these microscopic changes persisted up to 21 days. Treatment with the antiinflammatory steroid, betamethasone dipropionate (Diprolene), gave a significant reduction of the inflammatory responses. Heat-inactivated PLA2, ovalbumin, or saline did not provoke this reaction, although PLA2 inactivated by bromophenacyl bromide alkylation did produce an inflammatory response. The alkylated PLA2 was also able to provoke an inflammatory response in the mouse paw edema assay. These results demonstrate that PLA2 can stimulate an inflammatory response in the swine skin, but that phospholipid hydrolytic activity is not required.

Administration, Topical↗