25 resultados para Genetic Testing


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Finding motifs that can elucidate rules that govern peptide binding to medically important receptors is important for screening targets for drugs and vaccines. This paper focuses on elucidation of peptide binding to I-A(g7) molecule of the non-obese diabetic (NOD) mouse - an animal model for insulin-dependent diabetes mellitus (IDDM). A number of proposed motifs that describe peptide binding to I-A(g7) have been proposed. These motifs results from independent experimental studies carried out on small data sets. Testing with multiple data sets showed that each of the motifs at best describes only a subset of the solution space, and these motifs therefore lack generalization ability. This study focuses on seeking a motif with higher generalization ability so that it can predict binders in all A(g7) data sets with high accuracy. A binding score matrix representing peptide binding motif to A(g7) was derived using genetic algorithm (GA). The evolved score matrix significantly outperformed previously reported

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Rising costs of antimalarial agents are increasing the demand for accurate diagnosis of malaria. Rapid diagnostic tests (RDTs) offer great potential to improve the diagnosis of malaria, particularly in remote areas. Many RDTs are based on the detection of Plasmodium falciparum histidine-rich protein (PfHRP) 2, but reports from field tests have questioned their sensitivity and reliability. We hypothesize that the variability in the results of PfHRP2-based RDTs is related to the variability in the target antigen. We tested this hypothesis by examining the genetic diversity of PfHRP2, which includes numerous amino acid repeats, in 75 P. falciparum lines and isolates originating from 19 countries and testing a subset of parasites by use of 2 PfHRP2-based RDTs. We observed extensive diversity in PfHRP2 sequences, both within and between countries. Logistic regression analysis indicated that 2 types of repeats were predictive of RDT detection sensitivity (87.5% accuracy), with predictions suggesting that only 84% of P. falciparum parasites in the Asia-Pacific region are likely to be detected at densities

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HFE-associated hereditary haemochromatosis is a recessive, iron-overload disorder that affects about one in 200 north Europeans and that can be easily prevented. However, genetic screening for this disease is controversial, and so we assessed whether such screening was suitable for communities. Cheek-brush screening for the Cys282Tyr HFE mutation was offered to individuals in the workplace. Outcomes were assessed by questionnaires before and after testing. 11307 individuals were screened. We recorded no increase in anxiety. in individuals who were homozygous for the Cys282Tyr mutation or non-homozygous. Self-reported tiredness before testing was significantly higher in homozygous participants than in non-homozygous participants (chi(2) test, p=0.029). Of the 47 homozygous individuals identified, 46 have taken steps to treat or prevent iron accumulation. Population genetic screening for HFE-associated hereditary haemochromatosis can be practicable and acceptable.

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We outline and evaluate competing explanations of three relationships that have consistently been found between cannabis use and the use of other illicit drugs, namely, ( 1) that cannabis use typically precedes the use of other illicit drugs; and that ( 2) the earlier cannabis is used, and ( 3) the more regularly it is used, the more likely a young person is to use other illicit drugs. We consider three major competing explanations of these patterns: ( 1) that the relationship is due to the fact that there is a shared illicit market for cannabis and other drugs which makes it more likely that other illicit drugs will be used if cannabis is used; ( 2) that they are explained by the characteristics of those who use cannabis; and ( 3) that they reflect a causal relationship in which the pharmacological effects of cannabis on brain function increase the likelihood of using other illicit drugs. These explanations are evaluated in the light of evidence from longitudinal epidemiological studies, simulation studies, discordant twin studies and animal studies. The available evidence indicates that the association reflects in part but is not wholly explained by: ( 1) the selective recruitment to heavy cannabis use of persons with pre-existing traits ( that may be in part genetic) that predispose to the use of a variety of different drugs; ( 2) the affiliation of cannabis users with drug using peers in settings that provide more opportunities to use other illicit drugs at an earlier age; ( 3) supported by socialisation into an illicit drug subculture with favourable attitudes towards the use of other illicit drugs. Animal studies have raised the possibility that regular cannabis use may have pharmacological effects on brain function that increase the likelihood of using other drugs. We conclude with suggestions for the type of research studies that will enable a decision to be made about the relative contributions that social context, individual characteristics, and drug effects make to the relationship between cannabis use and the use of other drugs.

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Evolutionary change results from selection acting on genetic variation. For migration to be successful, many different aspects of an animal's physiology and behaviour need to function in a co-coordinated way. Changes in one migratory trait are therefore likely to be accompanied by changes in other migratory and life-history traits. At present, we have some knowledge of the pressures that operate at the various stages of migration, but we know very little about the extent of genetic variation in various aspects of the migratory syndrome. As a consequence, our ability to predict which species is capable of what kind of evolutionary change, and at which rate, is limited. Here, we review how our evolutionary understanding of migration may benefit from taking a quantitative-genetic approach and present a framework for studying the causes of phenotypic variation. We review past research, that has mainly studied single migratory traits in captive birds, and discuss how this work could be extended to study genetic variation in the wild and to account for genetic correlations and correlated selection. In the future, reaction-norm approaches may become very important, as they allow the study of genetic and environmental effects on phenotypic expression within a single framework, as well as of their interactions. We advocate making more use of repeated measurements on single individuals to study the causes of among-individual variation in the wild, as they are easier to obtain than data on relatives and can provide valuable information for identifying and selecting traits. This approach will be particularly informative if it involves systematic testing of individuals under different environmental conditions. We propose extending this research agenda by using optimality models to predict levels of variation and covariation among traits and constraints. This may help us to select traits in which we might expect genetic variation, and to identify the most informative environmental axes. We also recommend an expansion of the passerine model, as this model does not apply to birds, like geese, where cultural transmission of spatio-temporal information is an important determinant of migration patterns and their variation.

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Testing for simultaneous vicariance across comparative phylogeographic data sets is a notoriously difficult problem hindered by mutational variance, the coalescent variance, and variability across pairs of sister taxa in parameters that affect genetic divergence. We simulate vicariance to characterize the behaviour of several commonly used summary statistics across a range of divergence times, and to characterize this behaviour in comparative phylogeographic datasets having multiple taxon-pairs. We found Tajima's D to be relatively uncorrelated with other summary statistics across divergence times, and using simple hypothesis testing of simultaneous vicariance given variable population sizes, we counter-intuitively found that the variance across taxon pairs in Nei and Li's net nucleotide divergence (pi(net)), a common measure of population divergence, is often inferior to using the variance in Tajima's D across taxon pairs as a test statistic to distinguish ancient simultaneous vicariance from variable vicariance histories. The opposite and more intuitive pattern is found for testing more recent simultaneous vicariance, and overall we found that depending on the timing of vicariance, one of these two test statistics can achieve high statistical power for rejecting simultaneous vicariance, given a reasonable number of intron loci (> 5 loci, 400 bp) and a range of conditions. These results suggest that components of these two composite summary statistics should be used in future simulation-based methods which can simultaneously use a pool of summary statistics to test comparative the phylogeographic hypotheses we consider here.

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Nucleic acid amplification tests (NAATs) for the detection of Neisseria gonorrhoeae became available in the early 1990s. Although offering several advantages over traditional detection methods, N. gonorrhoeae NAATs do have some limitations. These include cost, risk of carryover contamination, inhibition, and inability to provide antibiotic resistance data. In addition, there are sequence-related limitations that are unique to N. gonorrhoeae NAATs. In particular, false-positive results are a major consideration. These primarily stem from the frequent horizontal genetic exchange occurring within the Neisseria genus, leading to commensal Neisseria species acquiring N. gonorrhoeae genes. Furthermore, some N. gonorrhoeae subtypes may lack specific sequences targeted by a particular NAAT. Therefore, NAAT false-negative results because of sequence variation may occur in some gonococcal populations. Overall, the N. gonorrhoeae species continues to present a considerable challenge for molecular diagnostics. The need to evaluate N. gonorrhoeae NAATs before their use in any new patient population and to educate physicians on the limitations of these tests is emphasized in this review.

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Background Considerable evidence from twin and adoption studies indicates that genetic and shared environmental factors play a significant role in the initiation of smoking behavior. Although twin and adoption designs are powerful to detect genetic and environmental influences, they do not provide information on the processes of assortative mating and parent–offspring transmission and their contribution to the variability explained by genetic and/or environmental factors. Methods We examined the role of genetic and environmental factors for smoking initiation using an extended kinship design. This design allows the simultaneous testing of additive and non-additive genetic, shared and individual-specific environmental factors, as well as sex differences in the expression of genes and environment in the presence of assortative mating and combined genetic and cultural transmission. A dichotomous lifetime smoking measure was obtained from twins and relatives in the Virginia 30,000 sample. Results Results demonstrate that both genetic and environmental factors play a significant role in the liability to smoking initiation. Major influences on individual differences appeared to be additive genetic and unique environmental effects, with smaller contributions from assortative mating, shared sibling environment, twin environment, cultural transmission and resulting genotype–environment covariance. The finding of negative cultural transmission without dominance led us to investigate more closely two possible mechanisms for the lower parent–offspring correlations compared to the sibling and DZ twin correlations in subsets of the data: (i) age × gene interaction, and (ii) social homogamy. Neither mechanism provided a significantly better explanation of the data, although age regression was significant. Conclusions This study showed significant heritability, partly due to assortment, and significant effects of primarily non-parental shared environment on smoking initiation.

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The MFG test is a family-based association test that detects genetic effects contributing to disease in offspring, including offspring allelic effects, maternal allelic effects and MFG incompatibility effects. Like many other family-based association tests, it assumes that the offspring survival and the offspring-parent genotypes are conditionally independent provided the offspring is affected. However, when the putative disease-increasing locus can affect another competing phenotype, for example, offspring viability, the conditional independence assumption fails and these tests could lead to incorrect conclusions regarding the role of the gene in disease. We propose the v-MFG test to adjust for the genetic effects on one phenotype, e.g., viability, when testing the effects of that locus on another phenotype, e.g., disease. Using genotype data from nuclear families containing parents and at least one affected offspring, the v-MFG test models the distribution of family genotypes conditional on offspring phenotypes. It simultaneously estimates genetic effects on two phenotypes, viability and disease. Simulations show that the v-MFG test produces accurate genetic effect estimates on disease as well as on viability under several different scenarios. It generates accurate type-I error rates and provides adequate power with moderate sample sizes to detect genetic effects on disease risk when viability is reduced. We demonstrate the v-MFG test with HLA-DRB1 data from study participants with rheumatoid arthritis (RA) and their parents, we show that the v-MFG test successfully detects an MFG incompatibility effect on RA while simultaneously adjusting for a possible viability loss.

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In this paper, we address some issue related to evaluating and testing evolutionary algorithms. A landscape generator based on Gaussian functions is proposed for generating a variety of continuous landscapes as fitness functions. Through some initial experiments, we illustrate the usefulness of this landscape generator in testing evolutionary algorithms.