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Background: Adherence-to-colonoscopy in cascade screening of colorectal cancer (CRC) is crucial to the effectiveness of screening, but it has not been systematically evaluated. We aimed to evaluate the colonoscopy adherence among average-risk populations globally. Methods: We searched Pubmed, Embase, Web of Science, and Cochrane Library for observational and experimental studies published in English up to December 31, 2018 and reporting the colonoscopy adherence following positive initial screening. We calculated pooled adherence with its 95% confidence intervals (CIs) using a random-effects meta-analysis. Subgroup and mixed-effects meta-regression analysis were performed to evaluate heterogeneous factors for adherence. Findings: Of 9,772 articles identified, 191 (313 studies) were included in the systematic review. The pooled adherence-to-colonoscopy was 81·6% (95% CI: 79·6-83·4), with the estimates varying substantially by continents, CRC incidence, GDP per capita, recruitment method, screening round and type of initial test. Type of initial test was the most modifiable heterogeneous factor for adherence across studies (Q=53·00, P

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.

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.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.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.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.

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.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.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.Adherence to colonoscopy varies greatly across populations and is related with performance of the initial tests, indicating the importance to select appropriate initial tests.

Single-cell RNA sequencing (scRNA-seq) enables assessment of transcriptome-wide changes at single-cell resolution. However, dominant subject effect in scRNA-seq datasets with multiple subjects severely confounds cell-type-specific differential expression (DE) analysis. We developed iDESC to separate subject effect from disease effect with consideration of dropouts to identify DE genes. iDESC was shown to have well-controlled type I error and high power compared to existing methods and obtained the best consistency between datasets and disease relevance in two scRNA-seq datasets from same disease, suggesting the importance of considering subject effect and dropouts in the DE analysis of scRNA-seq data with multiple subjects.

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.

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., Author summary Single-cell RNA sequencing (scRNA-seq) measures the expression profiles of individual cells. However, dropouts lead to an excessive number of zeros or close to zero values in the data, which may obscure downstream analyses. In this study, we developed G2S3, an imputation method that recovers gene expression in scRNA-seq data by borrowing information from adjacent genes in a gene graph learned by graph signal processing. G2S3 was shown to have superior performance in improving data quality. Moreover, G2S3 is computationally efficient in large-scale scRNA-seq data imputation.

Background: Adherence to colonoscopy in colorectal cancer (CRC) screening is at a low level and varies greatly across populations. No study has examined the influencing factors of colonoscopy adherence at a population level, and this remains to be an unaddressed knowledge gap. We therefore aimed to analyze the correlation between performance of initial CRC screening tests and subsequent adherence to colonoscopy base on an ecological evaluation. Methods: The sensitivity, specificity, and positive predictive value (PPV) of initial CRC screening tests and subsequent adherence to colonoscopy in different populations were extracted from relevant studies published in English up to December 31, 2018. The age-standardized incidences (ASR) of CRC across populations in the year of screening were obtained from the Cancer Statistics data. Locally weighted regression smoothing was used to examine the relationship between adherence to colonoscopy and ASR of CRC. Cut-off points of adherence on ASR were identified by Joinpoint Regression and used in Regression Discontinuity as potential thresholds. Spearman’s correlation analysis was used to calculate correlation coefficients. Findings: We identified 192 eligible articles. The median and interquartile range of adherence to colonoscopy was 82·5% (71·8%-90·5%). The adherence was significantly correlated with ASR of CRC in the study populations (r=0·187, p

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.

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.

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.

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.

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.

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.

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.

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.

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.