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Marker Combinations (marker + combination)
Selected AbstractsAge-standardisation when target setting and auditing performance of Down syndrome screening programmesPRENATAL DIAGNOSIS, Issue 11 2004Howard Cuckle Abstract Objective To describe and illustrate a method of setting Down syndrome screening targets and auditing performance that allows for differences in the maternal age distribution. Methods A reference population was determined from a Gaussian model of maternal age. Target detection and false-positive rates were determined by standard statistical modelling techniques, except that the reference population rather than an observed population was used. Second-trimester marker parameters were obtained for Down syndrome from a large meta-analysis, and for unaffected pregnancies from the combined results of more than 600 000 screens in five centres. Audited detection and false-positive rates were the weighted average of the rates in five broad age groups corrected for viability bias. Weights were based on the age distributions in the reference population. Results Maternal age was found to approximate reasonably well to a Gaussian distribution with mean 27 years and standard deviation 5.5 years. Depending on marker combination, the target detection rates were 59 to 64% and false-positive rate 4.2 to 5.4% for a 1 in 250 term cut-off; 65 to 68% and 6.1 to 7.3% for 1 in 270 at mid-trimester. Among the five centres, the audited detection rate ranged from 7% below target to 10% above target, with audited false-positive rates better than the target by 0.3 to 1.5%. Conclusion Age-standardisation should help to improve screening quality by allowing for intrinsic differences between programmes, so that valid comparisons can be made. Copyright © 2004 John Wiley & Sons, Ltd. [source] Positive selection for CD90 as a purging option in acute myeloid leukemia stem cell transplants,CYTOMETRY, Issue 1 2008Nicole Feller Abstract Background: Several studies showed the benefit of purging of acute myeloid leukemia (AML) stem cell transplants. We reported previously that purging by positive selection of CD34+ and CD133+ cells resulted in a 3,4 log tumor cell reduction (TCR) in CD34, and/or CD133, AML, but has been shown to be potentially applicable in only about 50% of cases. Similar to CD34 and CD133, CD90 marks the hematopoietic CD34 positive stem cells capable of full hematopoietic recovery after myeloablative chemotherapy, and therefore, in the present study, we explored whether a similar purging approach is possible using CD90. Methods: CD90 expression was established by flowcytometry in diagnosis AML on the clonogenic AML CD34+ blast population by flow cytometry. Positivity was defined as >3% CD90 (CD34+) expression on blasts. For the calculation of the efficacy of TCR by positive selection, AML blasts were recognized by either prelabeling diagnosis blasts with CD45-FITC in spiking model experiments or using expression of leukemia associated marker combinations both in spiking experiments and in real transplants. Results: In 119 patients with AML and myelodysplastic syndrome, we found coexpression of CD34 and CD90 (>3%) in 42 cases (35%). In AML patients 60 years or younger, representing the patients who are eligible for transplantation, only 23% (16/69) of the patients showed CD90 expression. Positive selection for CD90 in transplants containing CD90 negative AML resulted in a 2.8,4 log TCR in the models used. Conclusions: Purging by positive selection using CD90 can potentially be applied effectively in the majority of AML patients 60 years or younger. © 2007 Clinical Cytometry Society [source] BIOMARKER: The validity of the laboratory marker combinations DOVER and QUVER to detect physician's diagnosis of at-risk drinkingADDICTION BIOLOGY, Issue 1 2007Michael Bentele ABSTRACT Especially in situations where it might be favorable for the patient to dissimulate the existing alcohol problem, ,objective' laboratory tests can be helpful. In this study we report validation of the two combinations DOVER (DOctor VERified) and QUVER (QUestionnarie VERified) of the biological markers percent carbohydrate-deficient transferrin (%CDT) and gamma-glutamyl-transferase (,-GT) to detect patients that have been identified by their physicians with at-risk drinking behavior. Fifty-eight general practitioners (GPs) participated at two study sites in South-West Germany. Patients filled in a questionnaire that included the alcohol use disorders identification test (AUDIT) and gave a blood sample. The GP recorded his/her assessment about the presence of an alcohol-related disorder in the patient. Receiver operating characteristics (ROC) analyses of the marker combinations DOVER and QUVER were performed. A total of 2940 patients participated in the study, of which 2496 completed data sets that could be used for further analysis. The area under the curve (AUC) of 79.5% for DOVER and 77.2% (QUVER) are in a higher range than the values for gamma%CDT (75.7%) or ,-GT (72.5%) and %CDT (64.5%) and suggest superiority of the proposed marker combinations. Cross-validation results were almost identical with 76.6% and 73.3% for DOVER and QUVER, respectively. Our analysis demonstrated that the combination of the markers ,-GT and %CDT with the physician's judgement of the condition as reference was superior to the use of single markers. [source] A tool kit for molecular genetics of Kluyveromyces lactis comprising a congenic strain series and a set of versatile vectorsFEMS YEAST RESEARCH, Issue 3 2010Jürgen J. Heinisch Abstract A set of different marker deletions starting with a ura3 derivative of the Kluyveromyces lactis type strain CBS2359 was constructed. After a first cross to obtain a strain with the opposite mating type that also carried a leu2 allele, continuous back-crosses were used to obtain a congenic strain series with different marker combinations, including deletions in KlHIS3, KlADE2 and KlLAC4. Enzymes involved in carbohydrate metabolism were shown to behave very similarly to the original type strain and other K. lactis strains investigated previously. Moreover, a vector series of Saccharomyces cerevisiae genes flanked by loxP sites was constructed to be used as heterologous deletion cassettes in K. lactis, together with two plasmids for expression of Cre-recombinase for marker regeneration. To increase the frequency of homologous recombination, the Klku80 deletion was also introduced into the congenic strain series. A PCR-based method for determination of mating type is provided. [source] Haplotype interaction analysis of unlinked regionsGENETIC EPIDEMIOLOGY, Issue 4 2005Tim Becker Abstract Genetically complex diseases are caused by interacting environmental factors and genes. As a consequence, statistical methods that consider multiple unlinked genomic regions simultaneously are desirable. Such consideration, however, may lead to a vast number of different high-dimensional tests whose appropriate analysis pose a problem. Here, we present a method to analyze case-control studies with multiple SNP data without phase information that considers gene-gene interaction effects while correcting appropriately for multiple testing. In particular, we allow for interactions of haplotypes that belong to different unlinked regions, as haplotype analysis often proves to be more powerful than single marker analysis. In addition, we consider different marker combinations at each unlinked region. The multiple testing issue is settled via the minP approach; the P value of the "best" marker/region configuration is corrected via Monte-Carlo simulations. Thus, we do not explicitly test for a specific pre-defined interaction model, but test for the global hypothesis that none of the considered haplotype interactions shows association with the disease. We carry out a simulation study for case-control data that confirms the validity of our approach. When simulating two-locus disease models, our test proves to be more powerful than association methods that analyze each linked region separately. In addition, when one of the tested regions is not involved in the etiology of the disease, only a small amount of power is lost with interaction analysis as compared to analysis without interaction. We successfully applied our method to a real case-control data set with markers from two genes controlling a common pathway. While classical analysis failed to reach significance, we obtained a significant result even after correction for multiple testing with our proposed haplotype interaction analysis. The method described here has been implemented in FAMHAP. Genet. Epidemiol. 2005. © 2005 Wiley-Liss, Inc. [source] | ||||||||||