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Objective: The aim of this study was to quantitatively summarize the evidence for VDR FokI gene polymorphism and osteoporosis risk in postmenopausal women. Materials and methods:Electronic search at PubMed, EMBASE, Weipu database, and Wanfang database was conducted to select studies. Case-control studies containing available genotype frequencies of F/f were chosen, and Odds ratio (OR) with 95% confidence interval (CI) was used to assess the strength of this revelance. Results:The case-control studies including 2199 osteoporosis cases and 2231 controls were identified. Overall meta-analysis indicated that individuals with the homozygous ff genotype had increased risk of osteoporosis(Recessive model: OR=1.551, 95% CI: 1.035~2.325,p=0.034).In the stratified analysis, individuals with the ff genotype in the Recessive model had increased risk of osteoporosis in Asian subjects(OR=2.644, 95% CI: 1.583~4.419,p= 0.000),but not in Caucasian subjects(OR= 1.288, 95%CI: 0.783~2.118, p = 0.318) and Mixed subjects (OR= 0.885, 95%CI: 0.686~1.141, p =0.346). A symmetric funnel plot, the Begg-test (P=0.094) suggested that lack of publication bias. The studies conducted in each of the defined number of osteoporosis-had no effect of the FokI polymorphism on osteoporosis except for the ff versus Ff+FF genotype comparison for osteoporosis subgroup. Conclusion:In conclusion, our meta-analysis suggests that VDR Fok I genotype is associated with increased risk of osteoporosis in Asian but not in caucasian. To draw comprehensive and true conclusions, further prospective studies with larger numbers of participants worldwide are needed to examine associations between VDR Fok I polymorphism and osteoporosis.

The advancement of Third-Generation Sequencing (TGS) techniques has significantly increased the length of sequencing to several kilobases, thereby facilitating the identification of alternative splicing (AS) events and isoform expressions. Recently, numerous computational methods for isoform detection using long-read sequencing data have been developed. However, there is lack of prior comparative studies that systemically evaluates the performance of these software tools, implemented with different algorithms, under various simulations that encompass potential influencing factors. In this study, we conducted a benchmarking analysis of eleven methods implemented in eight computational tools capable of identifying isoform structures from TGS RNA sequencing data. We evaluated their performances using simulated data, which represented diverse sequencing platforms generated by an in-house simulator, as well as experimental data. Our comprehensive results demonstrate the guided mode of StringTie2 and Bambu achieved the best performance in sensitivity and precision, respectively. This study provides valuable guidance for future research on AS analysis and the ongoing improvement of tools for isoform detection using TGS data.