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Biomedical subjects

Taane G Clark

Publications and source records attributed to Taane G Clark.

11 recordsLinked to original sources
PubMed2026

Plasmodium knowlesi can adapt to infect Duffy-negative erythrocytes.

Plasmodium knowlesi, a zoonotic malaria species, has become a significant public health concern in Southeast Asia. In regions such as Malaysia and southern Thailand, P knowlesi incidence has risen, even as other human malaria parasites are nearing elimination. Similar to its close relative Plasmodium vivax, P knowlesi relies on the Duffy antigen receptor for chemokine (DARC) as a key receptor for erythrocyte invasion. Only Duffy-positive individuals are thought to be susceptible to clinical infection. Here, we demonstrate that P knowlesi possesses greater invasion plasticity than previously recognized. This parasite can bypass the need for DARC, as shown by its in vitro adaptation to invade and replicate within Duffy-negative (Fy-) erythrocytes. This adaptation is stable and independent of DARC binding, enabling the adapted parasite line to be maintained in Fy- erythrocytes and to resist inhibition by α-DARC antibodies. Genomic analysis identified a genomic recombination event between the parasite's dbpα and dbpγ genes, resulting in a new chimeric gene dbpαγ. Using CRISPR-Cas9 targeted reversion, we could demonstrate that dbpαγ is essential for invasion of Fy- erythrocytes. These findings shed new light on the invasion plasticity of P knowlesi, with implications for the parasite's potential spread beyond Southeast Asia and for understanding the complex host-cell specificity and atypical invasion pathways seen in P vivax.

Plasmodium knowlesi↗
PubMed2026

Malaria-GENOMAP: a web-based tool for exploring genomic variation of malaria parasites.

MOTIVATION: Malaria, caused by Plasmodium parasites, imposes a significant public health burden. While Plasmodium falciparum remains the primary target of elimination strategies due to its high mortality rate, lesser-known species such as P. malariae, P. vivax, and P. knowlesi continue to contribute to substantial human morbidity. Genomic approaches, including whole-genome sequencing, offer powerful tools for understanding the biology, transmission, and emerging drug resistance of these neglected Plasmodium species. However, there is an urgent need for informatic tools to summarize and visualize the high-dimensional and complex genomic data generated. RESULTS: We developed Malaria-GENOMAP, a user-friendly web-based tool, which integrates genomic variant data, such as allele frequencies, with geographical maps and chromosome-wide to gene views for in-depth exploration. The tool includes variation from P. knowlesi (n = 139), P. malariae (n = 158), P. ovale curtisi (n = 36), P. ovale wallikeri (n = 47), P. simium (n = 38), and P. vivax (n = 1359). It enables the investigation of population structure, geographic associations of mutations, and putative drug resistance markers, offering valuable insights for malaria control efforts. AVAILABILITY AND IMPLEMENTATION: Malaria-GENOMAP is available online at https://genomics.lshtm.ac.uk/malaria-genomaps.

Internet↗
PubMed2006

Bayesian logistic regression using a perfect phylogeny.

Haplotype data capture the genetic variation among individuals in a population and among populations. An understanding of this variation and the ancestral history of haplotypes is important in genetic association studies of complex disease. We introduce a method for detecting associations between disease and haplotypes in a candidate gene region or candidate block with little or no recombination. A perfect phylogeny demonstrates the evolutionary relationship between single-nucleotide polymorphisms (SNPs) in the haplotype blocks. Our approach extends the logic regression technique of Ruczinski and others (2003) to a Bayesian framework, and constrains the model space to that of a perfect phylogeny. Environmental factors, as well as their interactions with SNPs, may be incorporated into the regression framework. We demonstrate our method on simulated data from a coalescent model, as well as data from a candidate gene study of sarcoidosis.

Bayes Theorem↗
PubMed2006

Estimating the number of coding mutations in genotypic- and phenotypic-driven N-ethyl-N-nitrosourea (ENU) screens.

N-ethyl-N-nitrosourea (ENU) is a widely used mutagen in genotypic and phenotypic screens aimed at elucidating gene function. The high rate at which ENU induces point mutations raises the possibility that an observed phenotype may be to the result of another unidentified linked mutation. This article presents methods for estimating the probability of additional linked coding mutations (1) in a given region of DNA using both Poisson and Bayesian models and in (2) an F(1) animal exposed to ENU that has undergone b number of backcrosses. Applying these methods to the mouse data set of Quwailid et al., we estimate that the probability that a confounding mutation is linked to a cloned mutation when the candidate region is 5 Mb is very slim (p < 0.002). Where mutants are identified by genotypic methods, we show that backcrossing in the absence of marker-assisted selection is an inefficient means of eliminating linked confounding mutations.

Animals↗
PubMed2006

Does smoking influence survival in cancer patients through effects on respiratory and vascular disease?

Patients with cancers caused by smoking may die because they continue to smoke even after diagnosis of a cancer caused by smoking. We investigated differences in cause-specific mortality between patients diagnosed with smoking-related and non-smoking-related cancers. The causes of death were classified as smoking-related cancer, non-smoking-related cancer, respiratory or vascular disease, and all other causes. We studied all 220 089 people diagnosed with cancer in Scotland between 1986 and 1996, aged between 20 and 85 years, with last follow-up on 31 December 1999. There was a moderate excess risk of dying from respiratory and vascular causes in those with smoking-related cancers, which did not fall with time since diagnosis, consistent with continued smoking by these patients. Mortality among cancer patients might fall if more assistance in stopping smoking was provided for patients who have smoking-related cancers.

Adult↗
PubMed2005

Neuroticism mediates the association of the serotonin transporter gene with lifetime major depression.

BACKGROUND AND OBJECTIVES: An association between a polymorphism in the serotonin transporter gene (5HTT-LPR) and the personality trait of neuroticism has been reported. We sought to address the question of whether trait neuroticism mediates the putative association between this polymorphism and lifetime major depression in adults drawn from the general population. METHODS: Two hundred and fifty-one participants completed the Eysenck Personality Questionnaire and an adapted version of the depression section of the Structured Clinical Interview for DSM-III-R diagnosis, modified for implementation by a self-report questionnaire. A path method was applied to assess the mediator effect of neuroticism on the association between 5HTT-LPR genotype and lifetime major depression. RESULTS: 5HTT-LPR genotype was significantly associated with both neuroticism (p=0.02) and lifetime major depression (p=0.04), and neuroticism with lifetime major depression (p<0.001). Neuroticism accounted for 42.3% of the effect of 5HTT-LPR genotype on lifetime major depression, indicating possible mediation (p<0.001). CONCLUSIONS: These results suggest that neuroticism mediates the association between 5HTT-LPR genotype and lifetime major depression, consistent with models of the aetiology of depression which suggest that anxiety-related personality traits represent a substantial risk factor for affective disorder.

Adult↗
PubMed2005

Finding associations in dense genetic maps: a genetic algorithm approach.

Large-scale association studies hold promise for discovering the genetic basis of common human disease. These studies will consist of a large number of individuals, as well as large number of genetic markers, such as single nucleotide polymorphisms (SNPs). The potential size of the data and the resulting model space require the development of efficient methodology to unravel associations between phenotypes and SNPs in dense genetic maps. Our approach uses a genetic algorithm (GA) to construct logic trees consisting of Boolean expressions involving strings or blocks of SNPs. These blocks or nodes of the logic trees consist of SNPs in high linkage disequilibrium (LD), that is, SNPs that are highly correlated with each other due to evolutionary processes. At each generation of our GA, a population of logic tree models is modified using selection, cross-over and mutation moves. Logic trees are selected for the next generation using a fitness function based on the marginal likelihood in a Bayesian regression frame-work. Mutation and cross-over moves use LD measures to pro pose changes to the trees, and facilitate the movement through the model space. We demonstrate our method and the flexibility of logic tree structure with variable nodal lengths on simulated data from a coalescent model, as well as data from a candidate gene study of quantitative genetic variation.

Algorithms↗
PubMed2004

Assessing publication bias in genetic association studies: evidence from a recent meta-analysis.

Publication bias may exist when nonsignificant findings remain unpublished, thereby artificially inflating the apparent magnitude of an effect. This concern is not new, but it is particularly current in relation to genetic association studies. Data from a recent meta-analysis of association studies of personality were used to assess the potential of different graphical and statistical methods for assessing evidence of publication bias. The results suggest that no single method is sufficient for assessing evidence of publication bias, and that such methods may also offer insight into potential sources of heterogeneity, which may in turn guide the design of future studies.

Humans↗
PubMed2003

Developing a prognostic model in the presence of missing data: an ovarian cancer case study.

When developing prognostic models in medicine, covariate data are often missing and the standard response is to exclude those individuals whose data are incomplete from the analyses. This practice leads to a reduction in the statistical power, and may lead to biased results. We wished to develop a prognostic model for overall survival from 1,189 primary cases (842 deaths) of epithelial ovarian cancer. A complete case analysis restricted the sample size to 518 (380 deaths). After applying a multiple imputation (MI) framework we included three real values for each one imputed, and constructed a model composed of more statistically significant prognostic factors and with increased predictive ability. Missing values can be imputed in cases where the reason for the data being missing is known, particularly where it can be explained by available data. This will increase the power of an analysis and may produce models that are more statistically reliable and applicable within clinical practice.

Adolescent↗
PubMed2002

Quantification of the completeness of follow-up.

Completeness of follow-up is important, especially in clinical trials, since unequal follow-up in the treatment groups can bias the analysis of results. In survival studies, information on participants who do not complete the study is often omitted because their data can be included up to the time at which they were lost to follow-up. We propose a simple measure of completeness that is the ratio of the total observed person-time and the potential person-time of follow-up in a study. Our measure is easy to calculate, can be illustrated pictorially, and can be used to identify subgroups with especially poor follow-up.

Clinical Trials as Topic↗