Gains in QTL Detection Using an Ultra-High Density SNP Map Based on Population Sequencing Relative to Traditional RFLP/SSR Markers

Huge efforts have been invested in the last two decades to dissect the genetic bases of complex traits including yields of many crop plants, through quantitative trait locus (QTL) analyses. However, almost all the studies were based on linkage maps constructed using low-throughput molecular markers, e.g. restriction fragment length polymorphisms (RFLPs) and simple sequence repeats (SSRs), thus are mostly of low density and not able to provide precise and complete information about the numbers and locations of the genes or QTLs controlling the traits. In this study, we constructed an ultra-high density genetic map based on high quality single nucleotide polymorphisms (SNPs) from low-coverage sequences of a recombinant inbred line (RIL) population of rice, generated using new sequencing technology. The quality of the map was assessed by validating the positions of several cloned genes including GS3 and GW5/qSW5, two major QTLs for grain length and grain width respectively, and OsC1, a qualitative trait locus for pigmentation. In all the cases the loci could be precisely resolved to the bins where the genes are located, indicating high quality and accuracy of the map. The SNP map was used to perform QTL analysis for yield and three yield-component traits, number of tillers per plant, number of grains per panicle and grain weight, using data from field trials conducted over years, in comparison to QTL mapping based on RFLPs/SSRs. The SNP map detected more QTLs especially for grain weight, with precise map locations, demonstrating advantages in detecting power and resolution relative to the RFLP/SSR map. Thus this study provided an example for ultra-high density map construction using sequencing technology. Moreover, the results obtained are helpful for understanding the genetic bases of the yield traits and for fine mapping and cloning of QTLs

Herbal Medicines for Parkinson's Disease: A Systematic Review of Randomized Controlled Trials

OBJECTIVE: We conducted systematic review to evaluate current evidence of herbal medicines (HMs) for Parkinson's disease (PD). METHODS: Along with hand searches, relevant literatures were located from the electronic databases including CENTRAL, MEDLINE, EMBASE, CINAHL, AMED, PsycInfo, CNKI, 7 Korean Medical Databases and J-East until August, 2010 without language and publication status. Randomized controlled trials (RCTs), quasi-randomized controlled trials and randomized crossover trials, which evaluate HMs for idiopathic PD were selected for this review. Two independent authors extracted data from the relevant literatures and any disagreement was solved by discussion. RESULTS: From the 3432 of relevant literatures, 64 were included. We failed to suggest overall estimates of treatment effects on PD because of the wide heterogeneity of used herbal recipes and study designs in the included studies. When compared with placebo, specific effects were not observed in favor of HMs definitely. Direct comparison with conventional drugs suggested that there was no evidence of better effect for HMs. Many studies compared combination therapy with single active drugs and combination therapy showed significant improvement in PD related outcomes and decrease in the dose of anti-Parkinson's drugs with low adverse events rate. CONCLUSION: Currently, there is no conclusive evidence about the effectiveness and efficacy of HMs on PD. For establishing clinical evidence of HMs on PD, rigorous RCTs with sufficient statistical power should be promoted in future

Multi-ancestry genome-wide association meta-analysis of Parkinson’s disease

\ua9 2023, This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply. Although over 90 independent risk variants have been identified for Parkinson’s disease using genome-wide association studies, most studies have been performed in just one population at a time. Here we performed a large-scale multi-ancestry meta-analysis of Parkinson’s disease with 49,049 cases, 18,785 proxy cases and 2,458,063 controls including individuals of European, East Asian, Latin American and African ancestry. In a meta-analysis, we identified 78 independent genome-wide significant loci, including 12 potentially novel loci (MTF2, PIK3CA, ADD1, SYBU, IRS2, USP8, PIGL, FASN, MYLK2, USP25, EP300 and PPP6R2) and fine-mapped 6 putative causal variants at 6 known PD loci. By combining our results with publicly available eQTL data, we identified 25 putative risk genes in these novel loci whose expression is associated with PD risk. This work lays the groundwork for future efforts aimed at identifying PD loci in non-European populations

Presence of S100A9-positive inflammatory cells in cancer tissues correlates with an early stage cancer and a better prognosis in patients with gastric cancer

BACKGROUND: S100A9 was originally discovered as a factor secreted by inflammatory cells. Recently, S100A9 was found to be associated with several human malignancies. The purpose of this study is to investigate S100A9 expression in gastric cancer and explore its role in cancer progression. METHODS: S100A9 expression in gastric tissue samples from 177 gastric cancer patients was assessed by immunohistochemistry. The expression of its dimerization partner S100A8 and the S100A8/A9 heterodimer were also assessed by the same method. The effect of exogenous S100A9 on motility of gastric cancer cells AGS and BGC-823 was then investigated. RESULTS: S100A9 was specifically expressed by inflammatory cells such as macrophages and neutrophils in human gastric cancer and gastritis tissues. Statistical analysis showed that a high S100A9 cell count (> = 200) per 200x magnification microscopic field in cancer tissues was predictive of early stage gastric cancer. High S100A9-positive cell count was negatively correlated with lymph node metastasis (P = 0.009) and tumor invasion (P = 0.011). S100A9 was identified as an independent prognostic predictor of overall survival of patients with gastric cancer (P = 0.04). Patients with high S100A9 cell count were with favorable prognosis (P = 0.021). Further investigation found that S100A8 distribution in human gastric cancer tissues was similar to S100A9. However, the number of S100A8-positive cells did not positively correlate with patient survival. The inflammatory cells infiltrating cancer were S100A8/A9 negative, while those in gastritis were positive. Furthermore, exogenous S100A9 protein inhibited migration and invasion of gastric cancer cells. CONCLUSIONS: Our results suggested S100A9-positive inflammatory cells in gastric cancer tissues are associated with early stage of gastric cancer and good prognosis