Identification and characterization of multi-species conserved sequences.
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Comparative sequence analysis has become an essential component of studies aiming to elucidate genome function. The increasing availability of genomic sequences from multiple vertebrates is creating the need for computational methods that can detect highly conserved regions in a robust fashion. Towards that end, we are developing approaches for identifying sequences that are conserved across multiple species; we call these "Multi-species Conserved Sequences" (or MCSs). Here we report two strategies for MCS identification, demonstrating their ability to detect virtually all known actively conserved sequences (specifically, coding sequences) but very little neutrally evolving sequence (specifically, ancestral repeats). Importantly, we find that a substantial fraction of the bases within MCSs (approximately 70%) resides within non-coding regions; thus, the majority of sequences conserved across multiple vertebrate species has no known function. Initial characterization of these MCSs has revealed sequences that correspond to clusters of transcription factor-binding sites, non-coding RNA transcripts, and other candidate functional elements. Finally, the ability to detect MCSs represents a valuable metric for assessing the relative contribution of a species' sequence to identifying genomic regions of interest, and our results indicate that the currently available genome sequences are insufficient for the comprehensive identification of MCSs in the human genome. (+info)
Assessment of families for excess risk of lymphedema of the leg in a lymphatic filariasis-endemic area.
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The influence of host genes on the distribution of lymphedema due to lymphatic filariasis is unknown. To assess this, pedigree and disease information were collected from lymphedema patients in a lymphatic filariasis-endemic area. These patients were female, with an average age of approximately 40 years, who were enrolled between June 1995 and July 1999 in a lymphedema treatment clinic, and from the rural Haitian community served by the clinic. Interviews were conducted between September 1998 and December 1999. Families with multiple lymphedema cases were of similar size, with an average of 15 members, as those families with only a single lymphedema case. We determined whether families observed to have multiple lymphedema cases had a higher prevalence of lymphedema than expected when stratified population estimates and family size were considered. Lymphedema of the leg was excessive in 15 of 43 families with multiple lymphedema cases. The number of families demonstrating excess disease was significantly different than was expected based on population estimates of lymphedema prevalence (P = 0.026). Families with multiple cases of lymphedema were not significantly larger in family size than families with a single lymphedema cases. Twelve of the 15 families had a male with lymphedema, which influenced the interpretation of the results. The significance of these results is discussed. (+info)
Testing the null hypothesis in small area analysis.
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The goal of small area analysis is often to demonstrate that hospital admission rates or procedure rates vary greatly among regions, suggesting the occurrence of unnecessary admissions or procedures in some regions. Recent articles have shown that such variation may be largely due to chance, even if no underlying differences exist among the small areas; thus, it is important to test if the observed variation is larger than expected by chance. In this article we discuss how the appropriate method for testing the null hypothesis depends on the distribution of the number of admissions at the person level. If it is not possible for an individual to have more than one admission for a given procedure, the appropriate test is a simple chi-square test. If multiple admissions are possible, a modified chi-square test can be used to account for the excess variability due to multiple admissions. Failure to make the correct modification to the chi-square test in this latter case can result in spurious results. This underscores the importance of collecting data on multiple admissions in order to estimate the distribution of the number of admissions at the individual-patient level. (+info)
Accounting for historical information in designing experiments: the Bayesian approach.
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Two of the most important statistical problems in human and animal experimentation are the selection of an appropriate number of units to include in a given study and the allocation of these units to the various treatments. Properly addressing these issues allows the units to be used as efficiently as possible, which can contribute to addressing the overall issue of reducing the number of subjects in experimentation. To do so, reliable historical information is of particular importance. In the present paper, we describe the Bayesian approach to determining sample size and allocating units, with particular regard to how the use of historical data can be optimised. The paper focuses on two specific problems: the comparison of normal means and of binomial proportions. (+info)
Incidence of serious adverse events during nocturnal polysomnography.
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OBJECTIVES: The purpose of the study was to verify whether minimal concern is warranted in regard to serious adverse effects in the sleep laboratory. DESIGN: A prospective multicenter study PARTICIPANTS: Three scoring teams for 17 sleep laboratories. METHODS: Reports of adverse events occurring during polysomnography or identified upon scoring a study were collected over an 18-month time period. Incidence of mortality and adverse events were evaluated using a binomial distribution based on the Bernoulli process. RESULTS: Of 16,084 studies, the mortality rate during or 2 weeks after an adverse event, as noted, was 0.006%, and the overall rate of adverse events was 0.35%. CONCLUSIONS: Adverse event rates are low; however, procedures for handling medical emergencies or adverse events during or after polysomnography are prudent, and those studies performed for research should include preparedness for the possibility of adverse events. (+info)
Proper interpretation of non-differential misclassification effects: expectations vs observations.
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BACKGROUND: Many investigators write as if non-differential exposure misclassification inevitably leads to a reduction in the strength of an estimated exposure-disease association. Unfortunately, non-differentiality alone is insufficient to guarantee bias towards the null. Furthermore, because bias refers to the average estimate across study repetitions rather than the result of a single study, bias towards the null is insufficient to guarantee that an observed estimate will be an underestimate. Thus, as noted before, exposure misclassification can spuriously increase the observed strength of an association even when the misclassification process is non-differential and the bias it produced is towards the null. METHODS: We present additional results on this topic, including a simulation study of how often an observed relative risk is an overestimate of the true relative risk when the bias is towards the null. RESULTS: The frequency of overestimation depends on many factors: the value of the true relative risk, exposure prevalence, baseline (unexposed) risk, misclassification rates, and other factors that influence bias and random error. CONCLUSIONS: Non-differentiality of exposure misclassification does not justify claims that the observed estimate must be an underestimate; further conditions must hold to get bias towards the null, and even when they do hold the observed estimate may by chance be an overestimate. (+info)
Multiple-trait restricted maximum likelihood for simulated measures of ovulation rate with underlying multivariate normal distributions.
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A data set that was used to estimate covariance components with REML for an animal model with eight measures of ovulation rate treated as separate traits was used as a template to simulate data sets of eight multivariate normal traits that were then truncated to binomial traits. The model for simulation included eight measures on 610 animals with 1,071 animals in the numerator relationship matrix. Heritabilities were equal for the eight measures, and both genetic and phenotypic correlations among the measures were equal. Ten replications for each combination of heritability (.15, .25, and .35) and genetic correlation (.50, .66, and .90) were simulated on the normal scale. For each replicate, estimates of the eight heritabilities and 28 genetic correlations were obtained by multiple-trait REML. The usual transformation of heritability estimated on the binomial scale overestimated heritability on the normal scale. Genetic correlations on the binomial scale seriously underestimated the correlations on the normal scale. Standard errors of the estimates obtained by replication were somewhat larger than the approximate SE from REMLPK (the multi-trait REML program of K. Meyer). A final set of 10 simulated replications with heritability of .25 and genetic correlation of 1.00 resulted in average estimates of .18 for heritability and of .66 for genetic correlation that agree closely with those from the analysis of measures of ovulation at eight estrous cycles used as a template; averages for heritability of .16 and for genetic correlation of .66 were obtained. (+info)
Real-time kinetics of gene activity in individual bacteria.
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Protein levels have been shown to vary substantially between individual cells in clonal populations. In prokaryotes, the contribution to such fluctuations from the inherent randomness of gene expression has largely been attributed to having just a few transcripts of the corresponding mRNAs. By contrast, eukaryotic studies tend to emphasize chromatin remodeling and burst-like transcription. Here, we study single-cell transcription in Escherichia coli by measuring mRNA levels in individual living cells. The results directly demonstrate transcriptional bursting, similar to that indirectly inferred for eukaryotes. We also measure mRNA partitioning at cell division and correlate mRNA and protein levels in single cells. Partitioning is approximately binomial, and mRNA-protein correlations are weaker earlier in the cell cycle, where cell division has recently randomized the relative concentrations. Our methods further extend protein-based approaches by counting the integer-valued number of transcript with single-molecule resolution. This greatly facilitates kinetic interpretations in terms of the integer-valued random processes that produce the fluctuations. (+info)