Number of STR repeats as a potential new quantitative genetic marker for complex diseases, illustrated by schizophrenia

Forensic STR Loci and Schizophrenia: An Exploration of Implications for Forensic Applications and Genetic Privacy

BACKGROUND/OBJECTIVES

Short tandem repeat (STR) loci are widely used in forensic genetics for identification and kinship analysis. Traditionally, these loci were selected to avoid medical associations, but recent studies suggest that loci such as TH01 and D16S539 may be linked to psychiatric conditions like schizophrenia. This study explores these potential associations and considers the privacy implications related to disease susceptibility.

METHODS

We analyzed 19 STR loci, including CODIS core loci and additional loci like Penta D and Penta E. Statistical analyses were conducted on a dataset of schizophrenia patients and matched control individuals to assess the relationship between STR polymorphisms and schizophrenia risk.

RESULTS

No significant associations were found between the 19 analyzed loci and schizophrenia in this dataset. While initial analyses revealed minor allele frequency differences at the D3S1358, D13S317, and TPOX loci between the schizophrenia and control groups, these differences did not retain statistical significance following Bonferroni correction (corrected p < 0.0026 for all loci).

CONCLUSIONS

Although no significant associations were found between STR loci and schizophrenia, this study highlights the importance of considering the potential for forensic DNA data to reveal health-related information. As forensic DNA databases continue to expand, there is a growing need to reassess ethical and legal guidelines to ensure the protection of individual privacy. Future research should continue exploring these genetic associations with larger, more diverse samples to further understand their implications.

A new pathway for considering trigger factors based on parallel-serial connection models and displaying the relationships of causal factors in low-probability events

BACKGROUND

To determine the effect size of observed factors considering trigger factors based on parallel-serial models and to explore how multiple factors can be related to the result of complex events for low-probability events with binary outcomes.

METHODS

A low-probability event with a true binary outcome can be explained by a trigger factor. The models were based on the parallel-serial connection of switches; causal factors, including trigger factors, were simplified as switches. Effect size values of an observed factor for an outcome were calculated as SAR = (Pe-Pn)/(Pe + Pn), where Pe and Pn represent percentages in the exposed and nonexposed groups, respectively, and SAR represents standardized absolute risk. The influence of trigger factors is eliminated by SAR. Actual data were collected to obtain a deeper understanding of the system.

RESULTS

SAR values of < 0.25, 0.25-0.50, and > 0.50 indicate low, medium, and high effect sizes, respectively. The system of data visualization based on the parallel-serial connection model revealed that at least 7 predictors with SAR > 0.50, including a trigger factor, were needed to predict schizophrenia. The SAR of the HLADQB1*03 gene was 0.22 for schizophrenia.

CONCLUSIONS

It is likely that the trigger factors and observed factors had a cumulative effect, as indicated by the parallel-serial connection model for binary outcomes. SAR may allow better evaluation of the effect size of a factor in complex events by eliminating the influence of trigger factors. The efficiency and efficacy of observational research could be increased if we are able to clarify how multiple factors can be related to a result in a pragmatic manner.

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

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.

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

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.

Genetic risk analysis for an individual according to the theory of programmed onset, illustrated by lung and liver cancers

OBJECTIVE

To investigate cancer association in the genome and the genetic risk of death from major cancers according to the theory of programmed onset for an individual.

METHODS

Alleles of 15 randomly selected short tandem repeat (STR) loci, including D6S1043, D12S391, CSF1PO, D7S820, D2S1338, D3S1358, vWA, FGA, D8S1179, D21S11, D18S51, D5S818, D13S317, D16S539 and D19S433, were determined in 50 patients with lung cancer and 50 patients with liver cancer. The onset age of patients with and without the alleles was compared with Cox regression. Frequencies of significant alleles from Cox regression between the cancer group and control population were analysed through logistic regression for cross-validation. The death probability in an individual carrying two or one of two cancer-related alleles or not carrying two cancer-related alleles was calculated with outcomes of case-control studies translated into the results of the cohort studies.

RESULTS

It was confirmed that D18S51-20 was a lung cancer-related allele and that D21S11-30.2 and D6S1043-18 were liver cancer-related alleles. Probabilities of death from lung or liver cancers ranged from 0.115 to 0.395, respectively, for those who carry and/or do not carry D18S51-20, D21S11-30.2, D6S1043-18.

CONCLUSIONS

A more efficient method could be devised for genetic risk analysis according to the theory of programmed onset. The analysis of the CODIS-STR loci (STR loci listed in the US combined DNA indexing system) as genetic markers may provide an efficient and reliable approach to estimate an individual's genetic predisposition.

A Case-Control Study of the Association between Polymorphisms in the Fibrinogen Alpha Chain Gene and Schizophrenia

Our previous studies using the mass spectrum analysis provided evidence that fibrinopeptide A (FPA) could be a potential biomarker for schizophrenia diagnosis. We sought further to demonstrate that variants in the fibrinogen alpha chain gene (FGA) coded FPA might confer vulnerability to schizophrenia. 1,145 patients with schizophrenia and 1,016 healthy volunteers from the Han population in Northeast China were recruited. The association of three tag single nucleotide polymorphisms (SNPs) (rs2070011 in the 5'UTR, rs2070016 in intron 4, and rs2070022 in the 3'UTR) in FGA and schizophrenia was examined using a case-control study design. Genotypic distributions of these three SNPs were not found to be significantly different between cases and controls (rs2070011: χ² = 1.28, P = 0.528; rs2070016: χ² = 4.11, P = 0.128; rs2070022: χ² = 1.23, P = 0.541). There were also no significant differences in SNP allelic frequencies between cases and controls (all P > 0.05). Additionally, the frequency of haplotypes consisting of alleles of these three SNPs was not significantly different between cases and healthy control subjects (global χ² = 9.27, P = 0.159). Our study did not show a significant association of FGA SNPs with schizophrenia. Future studies may need to test more FGA SNPs in a larger sample to identify those SNPs with a minor or moderate effect on schizophrenia.