This study proposes the comprehensive index of biomarker (CIB), based on the consistency of a biomarker in case control (Youden index, J) and cohort studies (Crc), to evaluate biomarker efficacy. CIB was calculated as the mean of J and Crc. Analysis of the effect of sensitivity and specificity on CIB and ROC analysis of CIB were performed in simulated and actual datasets. J and CIB had similar values for high-probability events (say probability was 0.50), but there was a significant difference between J and CIB for low-probability events (say probability was 0.05). Therefore, as the subjects considered for diagnosis are usually symptomatic, the occurrence of a disease can be assumed to be a high-probability event. In contrast, as the subjects considered in screening for a disease are usually healthy and asymptomatic, the occurrence of a disease is assumed to be a low-probability event. Although J is the common index used to evaluate the diagnostic effectiveness, unfortunately, the J value is significantly larger than CIB value in a low-probability event, showing overestimation for screening purpose. CIB could have more potential than J for determining the screening efficacy of a biomarker. The efficacy of a biomarker could differ for diagnostic, screening, predictive, and prognostic purposes, and it would be better to evaluate the efficacy of biomarkers for specific systems or contexts.

Need for discriminating between diagnostic and screening efficacy to estimate a biomarker based on case control and cohort studies.

Objective The purpose of this study were to establish a model of quantitative evaluation that uses the population mean as a baseline of variations and describe variations derived from different types and systems using new concepts. Methods The observed datasets, including measurement data and relative data, were transformed to 0–1.0 using the population mean. Datasets derived from different types (same category of dataset, different categories of datasets, and datasets with the same baseline) were transformed using different methods. The ‘middle compared index’ (MCI) was used to describe the change in magnitude as follows: [a/(a+b)+(1−b)/(2−a−b)−1]1.7, where ‘a’ represents the number after the magnitude change and ‘b’ represents the number before the magnitude change. Actual data were used to observe the MCI’s ability to evaluate variations quantitatively. Results When the value before the magnitude change was equal to that after the magnitude change, the MCI was equal to 0; when the value before the magnitude change was equal to 0 and that after the magnitude change was equal to 1, the MCI was equal to 1. This implies the MCI is valid. When the value before the magnitude change was 0 and that after the magnitude change was 0.5, or when the value before the magnitude change was 0.5 and that after the magnitude change was 1.0, each MCI was approximately equal to 0.5. The values derived from the absolute, ratio, and MCI methods were different, indicating that the MCI is an independent index. Conclusion The MCI perfectly performs as an evaluation model using the population mean as the baseline, and it may be more a reasonable index than the ratio or absolute methods. The MCI increases our understanding of quantitative variations in evaluation measures of association using new concepts.

Quantitative evaluations of variations using the population mean as a baseline for bioinformatics interpretation

Modern sequencing technologies produce a single consensus sequence without distinguishing between homologous chromosomes. Haplotype phasing solves this limitation by identifying alleles on the maternal and paternal chromosomes. This information is critical for understanding gene expression models in genetic disease research. Furthermore, the haplotype phasing of three homologous chromosomes in trisomy cells is more complicated than that in disomy cells. In this study, we attempted the accurate and complete haplotype phasing of chromosome 21 in trisomy 21 cells. To separate homologs, we established three corrected disomy cell lines (ΔPaternal chromosome, ΔMaternal chromosome 1, and ΔMaternal chromosome 2) from trisomy 21 induced pluripotent stem cells by eliminating one chromosome 21 utilizing the Cre-loxP system. These cells were then whole-genome sequenced by a next-generation sequencer. By simply comparing the base information of the whole-genome sequence data at the same position between each corrected disomy cell line, we determined the base on the eliminated chromosome and performed phasing. We phased 51,596 single nucleotide polymorphisms (SNPs) on chromosome 21, randomly selected seven SNPs spanning the entire length of the chromosome, and confirmed that there was no contradiction by direct sequencing. 

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Experimental method for haplotype phasing across the entire length of chromosome 21 in trisomy 21 cells using a chromosome elimination technique

Biomarkers are used for clinical diagnostic purposes, but existing indexes exhibit limitations in terms of the resolving power of biomarkers. This paper proposes a new index, the magnitude-standardized index (MSI), to describe the quantitative variations and resolving powers of different biomarkers. In MSI analysis models, variation scales for ratios and differences are considered simultaneously, and a higher MSI value implies a stronger risk or effect for a biological factor. We explain the rationale for the MSI via hybrid and geometric methods and verify its efficacy through simulation experiments. Our results indicate that the MSI is superior to the Youden index and odds ratio for describing resolving power. When two biomarkers with similar Youden index values, odds ratios, or MSI values but different positive test rates (or cardinal numbers) were combined, all three index values increased; however, only the MSI value remained relatively stable. For a very small cardinal number, such as that of a single nucleotide polymorphism, the MSI value is at most half of the maximum value (0.5), allowing comparisons between MSI values for biomarkers with different cardinal numbers. The MSI can thus provide a better quantifiable evaluation of the resolving power of biomarkers with different cardinal numbers.

Quantificational evaluation of the resolving power of qualitative biomarkers with different cardinal numbers based on a magnitude-standardized index.

Objective Successful allogeneic hematopoietic stem cell transplantation (HSCT) relies on human leukocyte antigen (HLA) matching. However, whether HLA-matching between donors and recipients increases recipients’ risk of genetic disease remains unclear. Methods We investigated whether HLA-matched donor cells used for HSCT have similar microsatellite DNA polymorphisms to HSCT recipients at 19 randomly selected loci including CSF1PO, D12S391, D13S317, D16S539, D18S51, D19S433, D21S11, D2S1338, D3S1358, D5S818, D6S1043, D7S820, D8S1179, FGA, PentaD, PentaE, TH01, TPOX, and VWA. We analyzed allele matching at each short tandem repeat (STR) loci in HLA-matched and mismatched (control) groups using binary outcomes and a quantitative numerical method. Results The frequencies were similar between the HLA-matched group and the mismatched group for D6S1043. However, the allele matching rate was higher in the HLA-matched group than that in the mismatched group at 14 of the 19 STR loci. Overall, a significant increase in the rate of STR matching was observed in the HLA-matched group compared to the mismatched group (P = .004). Conclusion It would be interesting to know if the HLA matched pairs came more often in question than their mismatched counterparts as candidates for fully HLA-matched unrelated HSCT. The actual risk for HSCT donors developing these diseases needs further evaluation.

Jiang Chun-ling

Papers

Quantificational evaluation of the resolving power of qualitative biomarkers with different cardinal numbers based on a magnitude-standardized index.

Exploring the Risks of Genetic Similarity Between Donor and Recipient in Human Leukocyte Antigen-Matched Transplantation.