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.

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.

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.

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.

A case-control design for testing and estimating epigenetic effects on complex diseases.

Epigenetic modifications may play an important role in the formation and progression of complex diseases through the regulation of gene expression. The systematic identification of epigenetic variants that contribute to human diseases can be made possible using genome-wide association studies (GWAS), although epigenetic effects are currently not included in commonly used case-control designs for GWAS. Here, we show that epigenetic modifications can be integrated into a case-control setting by dissolving the overall genetic effect into its different components, additive, dominant and epigenetic. We describe a general procedure for testing and estimating the significance of each component based on a conventional chi-squared test approach. Simulation studies were performed to investigate the power and false-positive rate of this procedure, providing recommendations for its practical use. The integration of epigenetic variants into GWAS can potentially improve our understanding of how genetic, environmental and stochastic factors interact with epialleles to construct the genetic architecture of complex diseases.

Shape mapping: genetic mapping meets geometric morphometrics.

Knowledge about biological shape has important implications in biology and biomedicine, but the underlying genetic mechanisms for shape variation have not been well studied. Statistical models play a pivotal role in mapping specific quantitative trait loci (QTLs) that contribute to biological shape and its developmental trajectories. We describe and assess a statistical framework for shape gene identification that incorporates shape and image analysis into a mixture-model framework for QTL mapping. Statistical parameters that define genotype-specific differences in biological shape are estimated by implementing statistical and computational algorithms. A state-of-the-art procedure is described to examine the control patterns of specific QTLs on the origin, properties and functions of biological shape. The statistical framework described will help to address many integrative biological and genetic questions and challenges in shape variation faced by the life sciences community.

A statistical model for QTL mapping in polysomic autotetraploids underlying double reduction.

As a group of economically important species, linkage mapping of polysomic autotetraploids, including potato, sugarcane and rose, is difficult to conduct due to their unique meiotic property of double reduction that allows sister chromatids to enter into the same gamete. We describe and assess a statistical model for mapping quantitative trait loci (QTLs) in polysomic autotetraploids. The model incorporates double reduction, built in the mixture model-based framework and implemented with the expectation-maximization algorithm. It allows the simultaneous estimation of QTL positions, QTL effects and the degree of double reduction as well as the assessment of the estimation precision of these parameters. We performed computer simulation to examine the statistical properties of the method and validate its use through analyzing real data in tetraploid switchgrass.

A synthetic framework for modeling the genetic basis of phenotypic plasticity and its costs.

The phenotype of an individual is controlled not only by its genes, but also by the environment in which it grows. A growing body of evidence shows that the extent to which phenotypic changes are driven by the environment, known as phenotypic plasticity, is also under genetic control, but an overall picture of genetic variation for phenotypic plasticity remains elusive. Here, we develop a model for mapping quantitative trait loci (QTLs) that regulate environment-induced plastic response. This model enables geneticists to test whether there exist actual QTLs that determine phenotypic plasticity and, if there are, further test how plasticity QTLs control the costs of plastic response by dissecting the genetic correlation of phenotypic plasticity and trait value. The model was used to analyze real data for grain yield of winter wheat (Triticum aestivum), leading to the detection of pleiotropic QTLs and epistatic QTLs that affect phenotypic plasticity and its cost in this crop.

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.

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.

Global incidence trends of early-onset colorectal cancer and related exposures in early-life: an ecological analysis based on the GBD 2019.

Background: The incidence of early-onset colorectal cancer (EOCRC) is increasing globally. This study aims to describe the temporal trends of incidence and explore related risk exposures in early-life at the country level based on the GBD 2019. Methods: Data on the incidence and attributable risk factors of EOCRC were obtained from the GBD 2019. Temporal trends of age-standardized incidence were evaluated by average annual percentage change (AAPC). Early-life exposures were indicated as summary exposure values (SEV) of selected factors, SDI and GDP per capita in previous decades and at ages 0-4, 5-9, 10-14 and 15-19 years. Weighted linear or non-linear regressions were applied to evaluate the ecological aggregate associations of the exposures with incidences of EOCRC. Results: The global age-standardized incidence of EOCRC increased from 3.05 (3.03, 3.07) to 3.85 (3.83, 3.86) per 100,000 during 1990 and 2019. The incidence was higher in countries with high socioeconomic levels, and increased drastically in countries in East Asia and Caribbean, particularly Jamaica, Saudi Arabia and Vietnam. The GDP per capita, SDI, and SEVs of iron deficiency, alcohol use, high body-mass index, and child growth failure in earlier years were more closely related with the incidences of EOCRC in 2019. Exposures at ages 0-4, 5-9, 10-14 and 15-19 years were also associated with the incidences, particularly for the exposures at ages 15-19 years. Conclusion: The global incidence of EOCRC increased during past three decades. The large variations at regional and national level may be related with the distribution of risk exposures in early life.

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.

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.

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.