RETROSPECTIVE VARYING COEFFICIENT ASSOCIATION ANALYSIS OF LONGITUDINAL BINARY TRAITS: APPLICATION TO THE IDENTIFICATION OF GENETIC LOCI ASSOCIATED WITH HYPERTENSION.

Many genetic studies contain rich information on longitudinal phenotypes that require powerful analytical tools for optimal analysis. Genetic analysis of longitudinal data that incorporates temporal variation is important for understanding the genetic architecture and biological variation of complex diseases. Most of the existing methods assume that the contribution of genetic variants is constant over time and fail to capture the dynamic pattern of disease progression. However, the relative influence of genetic variants on complex traits fluctuates over time. In this study, we propose a retrospective varying coefficient mixed model association test, RVMMAT, to detect time-varying genetic effect on longitudinal binary traits. We model dynamic genetic effect using smoothing splines, estimate model parameters by maximizing a double penalized quasi-likelihood function, design a joint test using a Cauchy combination method, and evaluate statistical significance via a retrospective approach to achieve robustness to model misspecification. Through simulations we illustrated that the retrospective varying-coefficient test was robust to model misspecification under different ascertainment schemes and gained power over the association methods assuming constant genetic effect. We applied RVMMAT to a genome-wide association analysis of longitudinal measure of hypertension in the Multi-Ethnic Study of Atherosclerosis. Pathway analysis identified two important pathways related to G-protein signaling and DNA damage. Our results demonstrated that RVMMAT could detect biologically relevant loci and pathways in a genome scan and provided insight into the genetic architecture of hypertension.

A qualitative interview study on colorectal cancer screening in China.

Background: The effectiveness of triage screening for colorectal cancer (CRC) is not fully achieved in Chinese populations, mainly due to low compliance to colonoscopy follow-up. This study aimed to collect viewpoints of experts in China on ongoing screening programs and emerging screening tests for CRC, which may help to improve effectiveness of CRC screening in the country. Methods: We conducted 15 semi-structured interviews with experts involving CRC screening in China during October to November of 2020. Interview topics included personal characteristics, work context, opinions on ongoing screening programs, challenges and opportunities in optimization of screening strategies, and prospects for CRC screening in near future. To analyze the data, we used a generic qualitative research approach inspired by grounded theory, including open, axial, and selective coding. Results: This analysis revealed a total of 83 initial categories, 37 subcategories and 10 main categories, which included 4 core categories of current modality for CRC screening, factors influencing screening effectiveness, optimization of CRC screening modality, and prospects for development of CRC screening. The results provide insight into the factors underlying the challenges of the ongoing CRC screening programs in China: the most important concern is the low compliance to colonoscopy, followed by the low specificity of the currently-used initial tests. The experts proposed to use quantitative instead of qualitative fecal immunochemical test (FIT), and optimize risk assessment tools to improve specificity of initial tests. Regarding the emerging screening tests, 9 of 15 experts did not think that the novel techniques are good enough to replace the current tests, but can be used complementarily in opportunistic screening for CRC. Conclusion: The viewpoints of Chinese experts suggested that use quantitative FIT or optimize risk assessment tools may help to identify high-risk individuals of CRC more accurately, improve adherence to colonoscopy, and thus fully achieve the effectiveness of screening.

Brain Registration and Evaluation for Zebrafish (BREEZE)-mapping: A pipeline for whole-brain structural and activity analyses.

Here, we present Brain Registration and Evaluation for Zebrafish (BREEZE)-mapping, a user-friendly pipeline for the registration and analysis of whole-brain images in larval zebrafish. We describe steps for pre-processing, registration, quantification, and visualization of whole-brain phenotypes in zebrafish mutants of genes associated with neurodevelopmental and neuropsychiatric disorders. By utilizing BioImage Suite Web, an open-source software package originally developed for processing human brain imaging data, we provide a highly accessible whole-brain mapping protocol developed for users with general computational proficiency. For complete details on the use and execution of this protocol, please refer to Weinschutz Mendes et al. (2023).1.

iDESC: identifying differential expression in single-cell RNA sequencing data with multiple subjects.

BACKGROUND: Single-cell RNA sequencing (scRNA-seq) technology has enabled assessment of transcriptome-wide changes at single-cell resolution. Due to the heterogeneity in environmental exposure and genetic background across subjects, subject effect contributes to the major source of variation in scRNA-seq data with multiple subjects, which severely confounds cell type specific differential expression (DE) analysis. Moreover, dropout events are prevalent in scRNA-seq data, leading to excessive number of zeroes in the data, which further aggravates the challenge in DE analysis. RESULTS: We developed iDESC to detect cell type specific DE genes between two groups of subjects in scRNA-seq data. iDESC uses a zero-inflated negative binomial mixed model to consider both subject effect and dropouts. The prevalence of dropout events (dropout rate) was demonstrated to be dependent on gene expression level, which is modeled by pooling information across genes. Subject effect is modeled as a random effect in the log-mean of the negative binomial component. We evaluated and compared the performance of iDESC with eleven existing DE analysis methods. Using simulated data, we demonstrated that iDESC had well-controlled type I error and higher power compared to the existing methods. Applications of those methods with well-controlled type I error to three real scRNA-seq datasets from the same tissue and disease showed that the results of iDESC achieved the best consistency between datasets and the best disease relevance. CONCLUSIONS: iDESC was able to achieve more accurate and robust DE analysis results by separating subject effect from disease effect with consideration of dropouts to identify DE genes, suggesting the importance of considering subject effect and dropouts in the DE analysis of scRNA-seq data with multiple subjects.

Diagnostic accuracy of risk assessment and fecal immunochemical test in colorectal cancer screening: Results from a population-based program and meta-analysis.

BACKGROUND: Fecal immunochemical test (FIT) is a commonly used initial test for colorectal cancer (CRC) screening. Parallel use of FIT with risk assessment (RA) could improve the detection of non-bleeding lesions, but at the expense of compromising sensitivity. In this study, we evaluated the accuracy of FIT and/or RA in the Shanghai CRC screening program, and systematically reviewed the relevant evaluations worldwide. METHODS: RA and 2-specimen FIT were used in parallel in the Shanghai screening program, followed by a colonoscopy among those with positive results. Sensitivity, specificity, detection rate of CRC, positive predictive value (PPV), and other measures with their 95% confident intervals were calculated for each type of tests and several assumed combined tests. We further searched PubMed, Embase, Web of Science, and Cochrane Library for relevant studies published in English up to January 5, 2022. RESULTS: By the end of 2019, a total of 1,901,360 participants of the screening program completed 3,045,108 tests, with 1,901,360 first-time tests and 1,143,748 subsequent tests. Parallel use of RA and 2-specimen FIT achieved a sensitivity of 0.78 (0.77-0.80), a specificity of 0.78 (0.78-0.78), PPV of 0.89% (0.86-0.92), and a detection rate of 1.99 (1.93-2.05) for CRC per 1000 among participants enrolled in the first screening round, and performed similarly among those who participated for several times. A meta-analysis of 103 published observational studies demonstrated a higher sensitivity [0.76 (0.36, 0.94)] but a much lower specificity [0.59 (0.28, 0.85)] of parallel use of RA and FIT for detecting CRC in average-risk populations than in our subjects. One-specimen FIT, the most commonly used initial test, had a pooled specificity comparable to the Shanghai screening program (0.92 vs. 0.91), but a much higher pooled sensitivity (0.76 vs. 0.57). CONCLUSION: Our results indicate the limitation of FIT only as an initial screening test for CRC in Chinese populations, and highlight the higher sensitivity of parallel use of RA and FIT. Attempts should be made to optimize RA to improve effectiveness of screening in the populations.

High-throughput functional analysis of autism genes in zebrafish identifies convergence in dopaminergic and neuroimmune pathways.

Advancing from gene discovery in autism spectrum disorders (ASDs) to the identification of biologically relevant mechanisms remains a central challenge. Here, we perform parallel in vivo functional analysis of 10 ASD genes at the behavioral, structural, and circuit levels in zebrafish mutants, revealing both unique and overlapping effects of gene loss of function. Whole-brain mapping identifies the forebrain and cerebellum as the most significant contributors to brain size differences, while regions involved in sensory-motor control, particularly dopaminergic regions, are associated with altered baseline brain activity. Finally, we show a global increase in microglia resulting from ASD gene loss of function in select mutants, implicating neuroimmune dysfunction as a key pathway relevant to ASD biology.

Risk Scoring Systems for Predicting the Presence of Colorectal Neoplasia by Fecal Immunochemical Test Results in Chinese Population.

INTRODUCTION: Adherence to colonoscopy screening for colorectal cancer (CRC) is low in general populations, including those tested positive in the fecal immunochemical test (FIT). Developing tailored risk scoring systems by FIT results may allow for more accurate identification of individuals for colonoscopy. METHODS: Among 807,109 participants who completed the primary tests in the first-round Shanghai CRC screening program, 71,023 attended recommended colonoscopy. Predictors for colorectal neoplasia were used to develop respective scoring systems for FIT-positive or FIT-negative populations using logistic regression and artificial neural network methods. RESULTS: Age, sex, area of residence, history of mucus or bloody stool, and CRC in first-degree relatives were identified as predictors for CRC in FIT-positive subjects, while a history of chronic diarrhea and prior cancer were additionally included for FIT-negative subjects. With an area under the receiver operating characteristic curve of more than 0.800 in predicting CRC, the logistic regression-based systems outperformed the artificial neural network-based ones and had a sensitivity of 68.9%, a specificity of 82.6%, and a detection rate of 0.24% by identifying 17.6% subjects at high risk. We also reported an area under the receiver operating characteristic curve of about 0.660 for the systems predicting CRC and adenoma, with a sensitivity of 57.8%, a specificity of 64.6%, and a detection rate of 6.87% through classifying 38.1% subjects as high-risk individuals. The performance of the scoring systems for CRC was superior to the currently used method in Mainland, China, and comparable with the scoring systems incorporating the FIT results. DISCUSSION: The tailored risk scoring systems may better identify high-risk individuals of colorectal neoplasia and facilitate colonoscopy follow-up. External validation is warranted for widespread use of the scoring systems.

Improved risk scoring systems for colorectal cancer screening in Shanghai, China.

BACKGROUND: An optimal risk-scoring system enables more targeted offers for colonoscopy in colorectal cancer (CRC) screening. This analysis aims to develop and validate scoring systems using parametric and non-parametric methods for average-risk populations. METHODS: Screening data of 807,695 subjects and 2806 detected cases in the first-round CRC screening program in Shanghai were used to develop risk-predictive models and scoring systems using logistic-regression (LR) and artificial-neural-network (ANN) methods. Performance of established scoring systems was evaluated using area under the receiver operating characteristic curve (AUC), calibration, sensitivity, specificity, number of high-risk individuals and potential detection rates of CRC. RESULTS: Age, sex, CRC in first-degree relatives, chronic diarrhoea, mucus or bloody stool, history of any cancer and faecal-immunochemical-test (FIT) results were identified as predictors for the presence of CRC. The AUC of LR-based system was 0.642 when using risk factors only in derivation set, and increased to 0.774 by further incorporating one-sample FIT results, and to 0.808 by including two-sample FIT results, while those for ANN-based systems were 0.639, 0.763 and 0.805, respectively. Better calibrations were observed for the LR-based systems than the ANN-based ones. Compared with the currently used initial tests, parallel use of FIT with LR-based systems resulted in improved specificities, less demands for colonoscopy and higher detection rates of CRC, while parallel use of FIT with ANN-based systems had higher sensitivities; incorporating FIT in the scoring systems further increased specificities, decreased colonoscopy demands and improved detection rates of CRC. CONCLUSIONS: Our results indicate the potentials of LR-based scoring systems incorporating one- or two-sample FIT results for CRC mass screening. External validation is warranted for scaling-up implementation in the Chinese population.

A global view of adherence to colonoscopy follow-up in cascade screening of colorectal cancer.

OBJECTIVE: To overview the colonoscopy adherence in cascade screening of colorectal cancer (CRC) and evaluate potential influence of the initial tests based on an ecological evaluation. METHODS: The performance of the initial screening tests and adherence to subsequent colonoscopy were extracted from relevant studies published up to 16 October 2020. The age-standardised incidence (ASRi) of CRC in populations in the year of screening was derived from the Cancer Statistics. RESULTS: One hundred sixty-six observational studies and 60 experimental studies were identified. Most studies applied cascade screening with faecal occult blood tests (FOBTs) as an initial test. The adherence to colonoscopy varied greatly across populations by continents, gross national income and type of initial tests, with a median (interquartile range) of 79.8% (63.1%-87.8%) in observational studies and 82.1% (66.7%-90.4%) in randomised trials. The adherence was positively correlated with the ASRi of CRC (r = 0.145, p = 0.023) and positive predictive value (PPV) of the initial tests (r = 0.206, p = 0.002) in observational studies and correlated with ASRi of CRC (r = 0.309, p = 0.002) and sensitivity of the initial tests (r = -0.704, p = 0.003) in experimental studies. CONCLUSIONS: Adherence to colonoscopy varies greatly across populations and is related with performance of the initial tests, indicating the importance to select appropriate initial tests.

Adherence to colonoscopy in cascade screening of colorectal cancer: A systematic review and meta-analysis.

BACKGROUND AND AIM: This study aims to systematically evaluate adherence to colonoscopy and related factors in cascade screening of colorectal cancer (CRC) among average-risk populations, which is crucial to achieve the effectiveness of CRC screening. METHODS: We searched PubMed, Embase, Web of Science, and Cochrane Library for studies published in English up to October 16, 2020, and reporting the adherence to colonoscopy following positive results of initial screening tests. A random-effects meta-analysis was applied to estimate pooled adherence and 95% confidence intervals. Subgroup analysis and mixed-effects meta-regression analysis were performed to evaluate heterogeneous factors for adherence level. RESULTS: A total of 245 observational and 97 experimental studies were included and generated a pooled adherence to colonoscopy of 76.6% (95% confidence interval: 74.1-78.9) and 80.4% (95% confidence interval: 77.2-83.1), respectively. The adherence varied substantially by calendar year of screening, continents, CRC incidence, socioeconomic status, recruitment methods, and type of initial screening tests, with the initial tests as the most modifiable heterogeneous factor for adherence across both observational (Q = 162.6, P < 0.001) and experimental studies (Q = 23.2, P < 0.001). The adherence to colonoscopy was at the highest level when using flexible sigmoidoscopy as an initial test, followed by using guaiac fecal occult blood test, quantitative or qualitative fecal immunochemical test, and risk assessment. The pooled estimate of adherence was positively associated with specificity and positive predictive value of initial screening tests, but negatively with sensitivity and positivity rate. CONCLUSIONS: Colonoscopy adherence is at a low level and differs by study-level characteristics of programs and populations. Initial screening tests with high specificity or positive predictive value may be followed by a high adherence to colonoscopy.

G2S3: A gene graph-based imputation method for single-cell RNA sequencing data.

Single-cell RNA sequencing technology provides an opportunity to study gene expression at single-cell resolution. However, prevalent dropout events result in high data sparsity and noise that may obscure downstream analyses in single-cell transcriptomic studies. We propose a new method, G2S3, that imputes dropouts by borrowing information from adjacent genes in a sparse gene graph learned from gene expression profiles across cells. We applied G2S3 and ten existing imputation methods to eight single-cell transcriptomic datasets and compared their performance. Our results demonstrated that G2S3 has superior overall performance in recovering gene expression, identifying cell subtypes, reconstructing cell trajectories, identifying differentially expressed genes, and recovering gene regulatory and correlation relationships. Moreover, G2S3 is computationally efficient for imputation in large-scale single-cell transcriptomic datasets.

Regulation and characterization of tumor-infiltrating immune cells in breast cancer.

The effect of immunosuppression blockade therapies depends on the infiltration of effector T cells and other immune cells in tumor. However, it is unclear how molecular pathways regulate the infiltration of immune cells, as well as how interactions between tumor-infiltrating immune cells and T cell activation affect breast cancer patient survival. CIBERSORT was used to estimate the relative abundance of 22 immune cell types. The association between mRNAs and immune cell abundance were assessed by Spearman correlation analysis. Enriched pathways were identified using MetaCore pathway analysis. The interactions between the T cell activation status and the abundance of tumor-infiltrating immune cells were evaluated using Kaplan-Meier survival and multivariate Cox regression models in a publicly available dataset of 1081 breast cancer patients. The role of tumor-infiltrating B cells in antitumor immunity, immune response of T cell subsets, and breakdown of CD4+ T cell peripheral tolerance were positively associated with M1 macrophage and CD8+ T cell but negatively associated with M2 macrophage. Abundant plasma cell was associated with prolonged survival (HR = 0.46, 95% CI: 0.32-0.67), and abundant M2 macrophage was associated with shortened survival (HR = 1.78, 95% CI: 1.23-2.60). There exists a significant interaction between the T cell activation status and the resting DC abundance level (p = 0.025). Molecular pathways associated with tumor-infiltrating immune cells provide future directions for developing cancer immunotherapies to control immune cell infiltration, and further influence T cell activation and patient survival in breast cancer.

Retrospective Association Analysis of Longitudinal Binary Traits Identifies Important Loci and Pathways in Cocaine Use.

Longitudinal phenotypes have been increasingly available in genome-wide association studies (GWAS) and electronic health record-based studies for identification of genetic variants that influence complex traits over time. For longitudinal binary data, there remain significant challenges in gene mapping, including misspecification of the model for phenotype distribution due to ascertainment. Here, we propose L-BRAT (Longitudinal Binary-trait Retrospective Association Test), a retrospective, generalized estimating equation-based method for genetic association analysis of longitudinal binary outcomes. We also develop RGMMAT, a retrospective, generalized linear mixed model-based association test. Both tests are retrospective score approaches in which genotypes are treated as random conditional on phenotype and covariates. They allow both static and time-varying covariates to be included in the analysis. Through simulations, we illustrated that retrospective association tests are robust to ascertainment and other types of phenotype model misspecification, and gain power over previous association methods. We applied L-BRAT and RGMMAT to a genome-wide association analysis of repeated measures of cocaine use in a longitudinal cohort. Pathway analysis implicated association with opioid signaling and axonal guidance signaling pathways. Lastly, we replicated important pathways in an independent cocaine dependence case-control GWAS. Our results illustrate that L-BRAT is able to detect important loci and pathways in a genome scan and to provide insights into genetic architecture of cocaine use.

Colorectal Cancer Screening Modalities in Chinese Population: Practice and Lessons in Pudong New Area of Shanghai, China.

Background: Parallel test of risk stratification and two-sample qualitative fecal immunochemical tests (FITs) are used to screen colorectal cancer (CRC) in Shanghai, China. This study was designed to identify an optimal initial screening modality based on available data. Methods: A total of 538,278 eligible residents participated in the program during the period of January 2013 to June 2017. Incident CRC was collected through program reporting system and by record linkage with the Shanghai Cancer Registry up to December 2017. Logistic regression model was applied to identify significant factors to calculate risk score for CRC. Cutoff points of risk score were determined based on Youden index and defined specificity. Sensitivity, specificity, and positive predictive values (PPVs) were computed to evaluate validity of assumed screening modalities. Results: A total of 446 CRC were screen-detected, and 777 interval or missed cases were identified through record linkage. The risk score system had an optimal cutoff point of 19 and performed better in detecting CRC and predicting long-term CRC risk than did the risk stratification. When using a cutoff point of 24, parallel test of risk score, and FIT were expected to avoid 56 interval CRCs with minimal decrease in PPV and increase in colonoscopy. However, the observed detection rates were much lower than those expected due to low compliance to colonoscopy. Conclusions: Risk score is superior to risk stratification used in the program, particularly when combined with FIT. Compliance to colonoscopy should be improved to guarantee the effectiveness of CRC screening in the population.

Exploratory Study to Identify Radiomics Classifiers for Lung Cancer Histology.

BACKGROUND: Radiomics can quantify tumor phenotypic characteristics non-invasively by applying feature algorithms to medical imaging data. In this study of lung cancer patients, we investigated the association between radiomic features and the tumor histologic subtypes (adenocarcinoma and squamous cell carcinoma). Furthermore, in order to predict histologic subtypes, we employed machine-learning methods and independently evaluated their prediction performance. METHODS: Two independent radiomic cohorts with a combined size of 350 patients were included in our analysis. A total of 440 radiomic features were extracted from the segmented tumor volumes of pretreatment CT images. These radiomic features quantify tumor phenotypic characteristics on medical images using tumor shape and size, intensity statistics, and texture. Univariate analysis was performed to assess each feature's association with the histological subtypes. In our multivariate analysis, we investigated 24 feature selection methods and 3 classification methods for histology prediction. Multivariate models were trained on the training cohort and their performance was evaluated on the independent validation cohort using the area under ROC curve (AUC). Histology was determined from surgical specimen. RESULTS: In our univariate analysis, we observed that fifty-three radiomic features were significantly associated with tumor histology. In multivariate analysis, feature selection methods ReliefF and its variants showed higher prediction accuracy as compared to other methods. We found that Naive Baye's classifier outperforms other classifiers and achieved the highest AUC (0.72; p-value = 2.3 × 10(-7)) with five features: Stats_min, Wavelet_HLL_rlgl_lowGrayLevelRunEmphasis, Wavelet_HHL_stats_median, Wavelet_HLL_stats_skewness, and Wavelet_HLH_glcm_clusShade. CONCLUSION: Histological subtypes can influence the choice of a treatment/therapy for lung cancer patients. We observed that radiomic features show significant association with the lung tumor histology. Moreover, radiomics-based multivariate classifiers were independently validated for the prediction of histological subtypes. Despite achieving lower than optimal prediction accuracy (AUC 0.72), our analysis highlights the impressive potential of non-invasive and cost-effective radiomics for precision medicine. Further research in this direction could lead us to optimal performance and therefore to clinical applicability, which could enhance the efficiency and efficacy of cancer care.

A model for computing genes governing marital dissolution through sentimental dynamics.

Adverse sentimental relationships that cause marital dissolution may involve a genetic component composed of genes from a couple, which interact with cultural, sociological, psychological and economic factors. However, the identification of these genes is very challenging. Here, we address this challenge by developing a computational model that can identify specific genes that impact on sentimental relationships of couples. The model was derived by implementing the second law of thermodynamics that quantifies sentimental relationships within a dynamic gene identification framework, called systems mapping. The model is equipped with a capacity to characterize and test the pattern of how genes from a couple interact with each other to determine the dynamic behavior of their marital relationships. The testing procedure is based on comparing genotypic differences in mathematical parameters of sentimental dynamics described by a group of ordinary differential equations (ODE). The model allows the test of individual parameters or a combination of parameters, addressing specific details related to martial relationships. The model may find its implications for designing an optimal effort policy and therapy to maintain a harmonic family in light of genetic blueprints of individual couples.

Systems mapping of genes controlling chemotherapeutic drug efficiency for cancer stem cells.

Cancer can be controlled effectively by using chemotherapeutic drugs to inhibit cancer stem cells, but there is considerable inter-patient variability regarding how these cells respond to drug intervention. Here, we describe a statistical framework for mapping genes that control tumor responses to chemotherapeutic drugs as well as the efficacy of treatments in arresting tumor growth. The framework integrates the mathematical aspects of the cancer stem cell hypothesis into genetic association studies, equipped with a capacity to quantify the magnitude and pattern of genetic effects on the kinetic decline of cancer stem cells in response to therapy. By quantifying how specific genes and their interactions govern drug response, the model provides essential information to tailor personalized drugs for individual patients.

Modeling phenotypic plasticity in growth trajectories: a statistical framework.

Phenotypic plasticity, that is multiple phenotypes produced by a single genotype in response to environmental change, has been thought to play an important role in evolution and speciation. Historically, knowledge about phenotypic plasticity has resulted from the analysis of static traits measured at a single time point. New insight into the adaptive nature of plasticity can be gained by an understanding of how organisms alter their developmental processes in a range of environments. Recent advances in statistical modeling of functional data and developmental genetics allow us to construct a dynamic framework of plastic response in developmental form and pattern. Under this framework, development, genetics, and evolution can be synthesized through statistical bridges to better address how evolution results from phenotypic variation in the process of development via genetic alterations.