There is an increasing need to develop and apply powerful statistical tests to detect multiple traits-single locus associations, as arising from neuroimaging genetics and other studies. For example, in the Alzheimer's Disease Neuroimaging Initiative (ADNI), in addition to genome-wide single nucleotide polymorphisms (SNPs), thousands of neuroimaging and neuropsychological phenotypes as intermediate phenotypes for Alzheimer's disease, have been collected. Although some classic methods like MANOVA and newly proposed methods may be applied, they have their own limitations. For example, MANOVA cannot be applied to binary and other discrete traits. In addition, the relationships among these methods are not well understood. Importantly, since these tests are not data adaptive, depending on the unknown association patterns among multiple traits and between multiple traits and a locus, these tests may or may not be powerful. In this paper we propose a class of data-adaptive weights and the corresponding weighted tests in the general framework of generalized estimation equations (GEE). A highly adaptive test is proposed to select the most powerful one from this class of the weighted tests so that it can maintain high power across a wide range of situations. Our proposed tests are applicable to various types of traits with or without covariates. Importantly, we also analytically show relationships among some existing and our proposed tests, indicating that many existing tests are special cases of our proposed tests. Extensive simulation studies were conducted to compare and contrast the power properties of various existing and our new methods. Finally, we applied the methods to an ADNI dataset to illustrate the performance of the methods. We conclude with the recommendation for the use of the GEE-based Score test and our proposed adaptive test for their high and complementary performance.

Two new gene-based association analysis methods, called and for GWAS individual-level and summary data, respectively, were recently proposed to integrate GWAS with eQTL data, alleviating two common problems in GWAS by boosting statistical power and facilitating biological interpretation of GWAS discoveries. Based on a novel reformulation of PrediXcan and TWAS, we propose a more powerful gene-based association test to integrate single set or multiple sets of eQTL data with GWAS individual-level data or summary statistics. The proposed test was applied to several GWAS datasets, including two lipid summary association datasets based on [Formula: see text] and [Formula: see text] samples, respectively, and uncovered more known or novel trait-associated genes, showcasing much improved performance of our proposed method. The software implementing the proposed method is freely available as an R package.

The development of combination immunotherapy based on the mediation of regulatory mechanisms of the tumor immune microenvironment (TIME) is promising. However, a deep understanding of tumor immunology must involve the systemic tumor immune environment (STIE) which was merely illustrated previously. Here, we aim to review recent advances in single-cell transcriptomics and spatial transcriptomics for the studies of STIE, TIME, and their interactions, which may reveal heterogeneity in immunotherapy responses as well as the dynamic changes essential for the treatment effect. We review the evidence from preclinical and clinical studies related to TIME, STIE, and their significance on overall survival, through different immunomodulatory pathways, such as metabolic and neuro-immunological pathways. We also evaluate the significance of the STIE, TIME, and their interactions as well as changes after local radiotherapy and systemic immunotherapy or combined immunotherapy. We focus our review on the evidence of lung cancer, hepatocellular carcinoma, and nasopharyngeal carcinoma, aiming to reshape STIE and TIME to enhance immunotherapy efficacy.

A new and powerful approach, called imaging-wide association study (IWAS), is proposed to integrate imaging endophenotypes with GWAS to boost statistical power and enhance biological interpretation for GWAS discoveries. IWAS extends the promising transcriptome-wide association study (TWAS) from using gene expression endophenotypes to using imaging and other endophenotypes with a much wider range of possible applications. As illustration, we use gray-matter volumes of several brain regions of interest (ROIs) drawn from the ADNI-1 structural MRI data as imaging endophenotypes, which are then applied to the individual-level GWAS data of ADNI-GO/2 and a large meta-analyzed GWAS summary statistics dataset (based on about 74000 individuals), uncovering some novel genes significantly associated with Alzheimer’s disease (AD). We also compare the performance of IWAS with TWAS, showing much larger numbers of significant AD-associated genes discovered by IWAS, presumably due to the stronger link between brain atrophy and AD than that between gene expression of normal individuals and the risk for AD. The proposed IWAS is general and can be applied to other imaging endophenotypes, and GWAS individual-level or summary association data.

Most existing genome-wide association analyses are cross-sectional, utilizing only phenotypic data at a single time point, e.g. baseline. On the other hand, longitudinal studies, such as Alzheimer's Disease Neuroimaging Initiative (ADNI), collect phenotypic information at multiple time points. In this article, as a case study, we conducted both longitudinal and cross-sectional analyses of the ADNI data with several brain imaging (not clinical diagnosis) phenotypes, demonstrating the power gains of longitudinal analysis over cross-sectional analysis. Specifically, we scanned genome-wide single nucleotide polymorphisms (SNPs) with 56 brain-wide imaging phenotypes processed by FreeSurfer on 638 subjects. At the genome-wide significance level () or a less stringent level (e.g. ), longitudinal analysis of the phenotypic data from the baseline to month 48 identified more SNP-phenotype associations than cross-sectional analysis of only the baseline data. In particular, at the genome-wide significance level, both SNP rs429358 in gene APOE and SNP rs2075650 in gene TOMM40 were confirmed to be associated with various imaging phenotypes in multiple regions of interests (ROIs) by both analyses, though longitudinal analysis detected more regional phenotypes associated with the two SNPs and indicated another significant SNP rs439401 in gene APOE. In light of the power advantage of longitudinal analysis, we advocate its use in current and future longitudinal neuroimaging studies.

ObjectiveIt has been forecasted that the rabbiteye blueberry could inhibit osteoporosis. However, the inhibition and prevention of osteoporosis via rabbiteye blueberry are still elusive. This study was aim to evaluate the anti-osteoporosis effects of rabbiteye blueberry in ovariectomized rats.MethodsThirty rats were randomly divided into three groups of ten rats each as follows: sham-operated group (SG), ovariectomized model control group (OMG), and ovariectomized rabbiteye blueberry treatment group (OBG). The blood mineral levels, the alkaline phosphatase (ALP) activity, and osteoprotegerin (OPG) level were determined. The expression analyses of type I collagen, integrin-β1, and focal adhesion kinase (FAK) were performed. Besides, the bone mineral density (BMD) and bone histomorphometry (BH) were measured.ResultsThe ALP activity in SG and OBG was significantly lower than that in OMG. For the OPG level, the significant increase of OPG level in OBG was indicated compared with the other groups. The mRNA expression levels of type I collagen, integrin-β1, and FAK in OMG were significantly lower than those in other groups. The BMD in OMG were all significantly lower than those in SG and OBG. For BH, blueberry significantly improved the trabecular bone volume fraction, trabecular thickness, mean trabecular bone number, and bone formation rate, and decreased the trabecular separation, the percent of bone resorption perimeter, and mean osteoclast number in OBG compared with OMG.ConclusionsThe rabbiteye blueberries had an effective inhibition in bone resorption, bone loss, and reduction of bone strength of ovariectomized rats and could improve the BMD, osteogenic activity, and trabecular bone structure.

This study aims to identify prognostic factors in nasopharyngeal carcinoma (NPC) to improve the current 8th edition TNM classification. A systematic review of the literature reported between 2013 and 2019 in PubMed, Embase, and Scopus was conducted. Studies were included if (1) original clinical studies, (2) ≥50 NPC patients, and (3) analyses on the association between prognostic factors and overall survival. The data elements of eligible studies were abstracted and analyzed. A level of evidence was synthesized for each suggested change to the TNM staging and prognostic factors. Of 5,595 studies screened, 108 studies (44 studies on anatomical criteria and 64 on non-anatomical factors) were selected. Proposed changes/factors with strong evidence included the upstaging paranasal sinus to T4, defining parotid lymph node as N3, upstaging N-category based on presence of lymph node necrosis, as well as the incorporation of non-TNM factors including EBV-DNA level, primary gross tumor volume (GTV), nodal GTV, neutrophil-lymphocyte ratio, lactate dehydrogenase, C-reactive protein/albumin ratio, platelet count, SUVmax of the primary tumor, and total lesion glycolysis. This systematic review provides a useful summary of suggestions and prognostic factors that potentially improve the current staging system. Further validation studies are warranted to confirm their significance.

Background: This study aimed to investigate radiation-induced lymphopenia and its potential risk factors in patients with breast cancer receiving adjuvant radiotherapy.Methods: Breast cancer patients received adjuvant radiotherapy (RT) at our hospital with peripheral lymphocyte counts (PLC) at pre-and immediately after RT (post-RT) were eligible. The primary endpoints were any grade of lymphopenia post-RT and nadir-PLC/pre-PLC <0.8. Patient characteristics, tumor factors, and treatment factors were collected for risk assessment. Data are presented as mean and 95% confidence interval (CI) unless otherwise specified. Matched analysis was used to compare the statistical significance between different RT techniques.Results: A total of 735 consecutive patients met the study criteria. The mean PLC was 1.58×10 9 /L before and 0.99×10 9 /L post-RT (P<0.001). At the end of RT, 60.5% of patients had lymphopenia. Univariate and multivariable logistic analyses showed that RT technique involving RapidArc, mean lung dose, and chemotherapy were significant risk factors (P<0.05) for lymphopenia. RT technique was the only significant risk factor (P<0.05) for nadir-PLC/pre-PLC <0.8. Patients treated with RapidArc had a significantly greater reduction of PLC along with greater V5 of the lungs, even after matching mean lung dose and radiated volume.Conclusions: Lymphopenia is common in patients with breast cancer after adjuvant RT. RT technique is the only significant factor for lymphopenia and nadir-PLC/pre-PLC <0.8, suggesting the significance of RT technique choice to minimize lymphopenia and improve treatment outcomes.