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1.  Comparison of the serum fibrin-fibrinogen degradation products with cytokeratin 19 fragment as biomarkers in patients with lung cancer 
Biomedical Reports  2014;2(5):737-742.
Lung cancer is one of the main causes of cancer-related mortality. The identification of early diagnostic biomarkers improved outcomes for lung cancer patients. Serum fibrin-fibrinogen degradation products (FDP) levels are elevated in numerous malignancies due to hemostatic alterations. The serum FDP levels were compared to the levels of cytokeratin 19 fragment antigen (CYFRA 21-1), another well-established biomarker. The serum samples from 193 lung cancer patients, 84 healthy controls and 106 patients with benign respiratory diseases were obtained. The serum FDP level was measured using the DR-70 immunoassay and the CYFRA 21-1 level was measured by electrochemiluminescence using the Roche Analytics E170. Receiver operating characteristics curves were used to assess the predictive sensitivity and specificity. The mean serum FDP level in lung cancer patients (35.01±229.02 μg/ml) was significantly higher compared to the 190 non-cancerous subjects (0.60±0.75 μg/ml; P=0.039). The mean serum CYFRA 21-1 level in lung cancer patients (4.50±6.67 ng/ml) was also significantly higher compared to the non-cancerous subjects (1.40±0.83 ng/ml; P<0.05). FDP exhibited clinical sensitivity and specificity of 86 and 75%, respectively, at an optimal cut-off at 0.67 μg/ml. CYFRA 21-1 exhibited clinical sensitivity and specificity of 77 and 74%, respectively, at a cut-off of 1.65 ng/ml. The serum FDP area under the curve (0.87) was slightly higher compared to CYFRA 21-1 (0.83). Therefore, it is apparent that serum FDP is comparable to CYFRA 21-1 as a lung cancer biomarker and can be used for clinical practice.
doi:10.3892/br.2014.315
PMCID: PMC4106540  PMID: 25054020
lung cancer; fibrin-fibrinogen degradation products; DR-70 immunoassay; cytokeratin 19 fragment; biomarker
2.  Principal components ancestry adjustment for Genetic Analysis Workshop 17 data 
BMC Proceedings  2011;5(Suppl 9):S66.
Statistical tests on rare variant data may well have type I error rates that differ from their nominal levels. Here, we use the Genetic Analysis Workshop 17 data to estimate type I error rates and powers of three models for identifying rare variants associated with a phenotype: (1) by using the number of minor alleles, age, and smoking status as predictor variables; (2) by using the number of minor alleles, age, smoking status, and the identity of the population of the subject as predictor variables; and (3) by using the number of minor alleles, age, smoking status, and ancestry adjustment using 10 principal component scores. We studied both quantitative phenotype and a dichotomized phenotype. The model with principal component adjustment has type I error rates that are closer to the nominal level of significance of 0.05 for single-nucleotide polymorphisms (SNPs) in noncausal genes for the selected phenotype than the model directly adjusting for population. The principal component adjustment model type I error rates are also closer to the nominal level of 0.05 for noncausal SNPs located in causal genes for the phenotype. The power for causal SNPs with the principal component adjustment model is comparable to the power of the other methods. The power using the underlying quantitative phenotype is greater than the power using the dichotomized phenotype.
doi:10.1186/1753-6561-5-S9-S66
PMCID: PMC3287905  PMID: 22373457
3.  Assessing the impact of global versus local ancestry in association studies 
BMC Proceedings  2009;3(Suppl 7):S107.
Background
To account for population stratification in association studies, principal-components analysis is often performed on single-nucleotide polymorphisms (SNPs) across the genome. Here, we use Framingham Heart Study (FHS) Genetic Analysis Workshop 16 data to compare the performance of local ancestry adjustment for population stratification based on principal components (PCs) estimated from SNPs in a local chromosomal region with global ancestry adjustment based on PCs estimated from genome-wide SNPs.
Methods
Standardized height residuals from unrelated adults from the FHS Offspring Cohort were averaged from longitudinal data. PCs of SNP genotype data were calculated to represent individual's ancestry either 1) globally using all SNPs across the genome or 2) locally using SNPs in adjacent 20-Mbp regions within each chromosome. We assessed the extent to which there were differences in association studies of height depending on whether PCs for global, local, or both global and local ancestry were included as covariates.
Results
The correlations between local and global PCs were low (r < 0.12), suggesting variability between local and global ancestry estimates. Genome-wide association tests without any ancestry adjustment demonstrated an inflated type I error rate that decreased with adjustment for local ancestry, global ancestry, or both. A known spurious association was replicated for SNPs within the lactase gene, and this false-positive association was abolished by adjustment with local or global ancestry PCs.
Conclusion
Population stratification is a potential source of bias in this seemingly homogenous FHS population. However, local and global PCs derived from SNPs appear to provide adequate information about ancestry.
PMCID: PMC2795878  PMID: 20017971

Results 1-3 (3)