Comparison of quantiferon-TB gold with tuberculin skin test for detecting latent tuberculosis infection prior to liver transplantation.

Screening for latent tuberculosis infection (LTBI) is recommended prior to organ transplantation. The Quantiferon-TB Gold assay (QFT-G) may be more accurate than the tuberculin skin test (TST) in the detection of LTBI. We prospectively compared the results of QFT-G to TST in patients with chronic liver disease awaiting transplantation. Patients were screened for LTBI with both the QFT-G test and a TST. Concordance between test results and predictors of a discordant result were determined. Of the 153 evaluable patients, 37 (24.2%) had a positive TST and 34 (22.2%) had a positive QFT-G. Overall agreement between tests was 85.1% (kappa= 0.60, p < 0.0001). Discordant test results were seen in 12 TST positive/QFT-G negative patients and in 9 TST negative/QFT-G positive patients. Prior BCG vaccination was not associated with discordant test results. Twelve patients (7.8%), all with a negative TST, had an indeterminate result of the QFT-G and this was more likely in patients with a low lymphocyte count (p = 0.01) and a high MELD score (p = 0.001). In patients awaiting liver transplantation, both the TST and QFT-G were comparable for the diagnosis of LTBI with reasonable concordance between tests. Indeterminate QFT-G result was more likely in those with more advanced liver disease.

Mapping Bias Overestimates Reference Allele Frequencies at the HLA Genes in the 1000 Genomes Project Phase I Data.

Next-generation sequencing (NGS) technologies have become the standard for data generation in studies of population genomics, as the 1000 Genomes Project (1000G). However, these techniques are known to be problematic when applied to highly polymorphic genomic regions, such as the human leukocyte antigen (HLA) genes. Because accurate genotype calls and allele frequency estimations are crucial to population genomics analyses, it is important to assess the reliability of NGS data. Here, we evaluate the reliability of genotype calls and allele frequency estimates of the single-nucleotide polymorphisms (SNPs) reported by 1000G (phase I) at five HLA genes (HLA-A, -B, -C, -DRB1, and -DQB1). We take advantage of the availability of HLA Sanger sequencing of 930 of the 1092 1000G samples and use this as a gold standard to benchmark the 1000G data. We document that 18.6% of SNP genotype calls in HLA genes are incorrect and that allele frequencies are estimated with an error greater than ±0.1 at approximately 25% of the SNPs in HLA genes. We found a bias toward overestimation of reference allele frequency for the 1000G data, indicating mapping bias is an important cause of error in frequency estimation in this dataset. We provide a list of sites that have poor allele frequency estimates and discuss the outcomes of including those sites in different kinds of analyses. Because the HLA region is the most polymorphic in the human genome, our results provide insights into the challenges of using of NGS data at other genomic regions of high diversity.