Large-scale dependent multiple testing via higher-order hidden Markov models.

Taking into account the local dependence structure in large-scale multiple testing is expected to improve both the efficiency of the testing procedure and the interpretability of scientific findings. The hidden Markov model (HMM), as an effective model to describe the sequential dependence, has been successfully applied to large-scale multiple testing with local correlations. However, in many applications, the first-order Markov chain is not flexible enough to capture the complexity of local correlations.

Large-scale selection of highly informative microhaplotypes for ancestry inference and population specific informativeness.

Microhaplotypes (MHs) describe physically close genetic markers that are inherited together and are gaining prominence due to their efficiency in forensic, clinical, and population studies. They excel in kinship analysis, DNA mixture detection, and ancestry inference, offering advantages in precision over individual SNPs and STRs. In this study, a pipeline was developed to efficiently select highly informative MHs from large-scale genomic datasets.

Benchmarking Mendelian randomization methods for causal inference using genome-wide association study summary statistics.

Mendelian randomization (MR), which utilizes genetic variants as instrumental variables (IVs), has gained popularity as a method for causal inference between phenotypes using genetic data. While efforts have been made to relax IV assumptions and develop new methods for causal inference in the presence of invalid IVs due to confounding, the reliability of MR methods in real-world applications remains uncertain. Instead of using simulated datasets, we conducted a benchmark study evaluating 16 two-sample summary-level MR methods using real-world genetic datasets to provide guidelines for the best practices.

How admixed captive breeding populations could be rescued using local ancestry information.

This paper asks the question: can genomic information be used to recover a species that is already on the pathway to extinction due to genetic swamping from a related and more numerous population? We show that a breeding strategy in a captive breeding program can use whole genome sequencing to identify and remove segments of DNA introgressed through hybridisation. The proposed policy uses a generalized measure of kinship or heterozygosity accounting for local ancestry, that is, whether a specific genetic location was inherited from the target of conservation. We then show that optimizing these measures would minimize undesired ancestry while also controlling kinship and/or heterozygosity, in a simulated breeding population.

100 ancient genomes show repeated population turnovers in Neolithic Denmark.

Major migration events in Holocene Eurasia have been characterized genetically at broad regional scales. However, insights into the population dynamics in the contact zones are hampered by a lack of ancient genomic data sampled at high spatiotemporal resolution. Here, to address this, we analysed shotgun-sequenced genomes from 100 skeletons spanning 7,300 years of the Mesolithic period, Neolithic period and Early Bronze Age in Denmark and integrated these with proxies for diet (C and N content), mobility (Sr/Sr ratio) and vegetation cover (pollen).

The selection landscape and genetic legacy of ancient Eurasians.

The Holocene (beginning around 12,000 years ago) encompassed some of the most significant changes in human evolution, with far-reaching consequences for the dietary, physical and mental health of present-day populations. Using a dataset of more than 1,600 imputed ancient genomes, we modelled the selection landscape during the transition from hunting and gathering, to farming and pastoralism across West Eurasia. We identify key selection signals related to metabolism, including that selection at the FADS cluster began earlier than previously reported and that selection near the LCT locus predates the emergence of the lactase persistence allele by thousands of years.

scDMV: a zero-one inflated beta mixture model for DNA methylation variability with scBS-seq data.

Motivation: The utilization of single-cell bisulfite sequencing (scBS-seq) methods allows for precise analysis of DNA methylation patterns at the individual cell level, enabling the identification of rare populations, revealing cell-specific epigenetic changes, and improving differential methylation analysis. Nonetheless, the presence of sparse data and an overabundance of zeros and ones, attributed to limited sequencing depth and coverage, frequently results in reduced precision accuracy during the process of differential methylation detection using scBS-seq. Consequently, there is a pressing demand for an innovative differential methylation analysis approach that effectively tackles these data characteristics and enhances recognition accuracy.

Hemoglobin level is negatively associated with sarcopenia and its components in Chinese aged 60 and above.

Introduction: Sarcopenia and low hemoglobin level are common in older adults. Few studies have evaluated the association between hemoglobin level and sarcopenia and with inconsistent findings. The multifaceted effects of sarcopenia on the human body and the high prevalence of anemia in the Chinese population make it necessary to explore the association between the two.

Elevated alanine transaminase is nonlinearly associated with in-hospital death in ICU-admitted diabetic ketoacidosis patients.

Aims: To investigate the association between alanine transaminase (ALT) and in-hospital death in patients admitted to the intensive care unit for diabetic ketoacidosis (DKA).

Methods: A cohort of 2,684 patients was constructed from the eICU Collaborative Research Database. Baseline demographic and clinical characteristics were summarized.

scDLC: a deep learning framework to classify large sample single-cell RNA-seq data.

Background: Using single-cell RNA sequencing (scRNA-seq) data to diagnose disease is an effective technique in medical research. Several statistical methods have been developed for the classification of RNA sequencing (RNA-seq) data, including, for example, Poisson linear discriminant analysis (PLDA), negative binomial linear discriminant analysis (NBLDA), and zero-inflated Poisson logistic discriminant analysis (ZIPLDA). Nevertheless, few existing methods perform well for large sample scRNA-seq data, in particular when the distribution assumption is also violated.

CLARITY: comparing heterogeneous data using dissimilarity.

Integrating datasets from different disciplines is hard because the data are often qualitatively different in meaning, scale and reliability. When two datasets describe the same entities, many scientific questions can be phrased around whether the (dis)similarities between entities are conserved across such different data. Our method, CLARITY, quantifies consistency across datasets, identifies where inconsistencies arise and aids in their interpretation.

The Trend of TIM3 Expression on T Cells in Patients With Nontuberculous Mycobacterial Lung Disease: From Immune Cell Dysfunction to Clinical Severity.

Background: The incidence of nontuberculous mycobacterial lung disease (NTM-LD) is increasing worldwide. Immune exhaustion has been reported in NTM-LD, but T-cell immunoglobulin and mucin domain-containing protein 3 (TIM3), a co-inhibitory receptor on T cells, has been scarcely studied.

Methods: Patients with NTM-LD and healthy controls were prospectively recruited from July 2014 to August 2019 at three tertiary referral centers in Taiwan.

The Dynamic Change of Immune Checkpoints and CD14+ Monocytes in Latent Tuberculosis Infection.

Controlling latent tuberculosis infection (LTBI) is important for preventing tuberculosis (TB). However, the immune regulation of LTBI remains uncertain. Immune checkpoints and CD14+ monocytes are pivotal for immune defense but have been scarcely studied in LTBI.

An efficient Gehan-type estimation for the accelerated failure time model with clustered and censored data.

In medical studies, the collected covariates contain underlying outliers. For clustered/longitudinal data with censored observations, the traditional Gehan-type estimator is robust to outliers in response but sensitive to outliers in the covariate domain, and it also ignores the within-cluster correlations. To take account of within-cluster correlations, varying cluster sizes, and outliers in covariates, we propose weighted Gehan-type estimating functions for parameter estimation in the accelerated failure time model for clustered data.

A scaling-free minimum enclosing ball method to detect differentially expressed genes for RNA-seq data.

Background: Identifying differentially expressed genes between the same or different species is an urgent demand for biological and medical research. For RNA-seq data, systematic technical effects and different sequencing depths are usually encountered when conducting experiments. Normalization is regarded as an essential step in the discovery of biologically important changes in expression.

Category encoding method to select feature genes for the classification of bulk and single-cell RNA-seq data.

Bulk and single-cell RNA-seq (scRNA-seq) data are being used as alternatives to traditional technology in biology and medicine research. These data are used, for example, for the detection of differentially expressed (DE) genes. Several statistical methods have been developed for the classification of bulk and single-cell RNA-seq data.

Model estimation and selection for partial linear varying coefficient EV models with longitudinal data.

In this paper, we consider the estimation and model selection for longitudinal partial linear varying coefficient errors-in-variables (EV) models when the covariates are measured with some additive errors. Bias-corrected penalized quadratic inference functions method is proposed based on quadratic inference functions with two penalty function terms. The proposed method can not only handle the measurement errors of covariates and within-subject correlations but also estimate and select significant non-zero parametric and nonparametric components simultaneously.

Selecting Classification Methods for Small Samples of Next-Generation Sequencing Data.

Next-generation sequencing has emerged as an essential technology for the quantitative analysis of gene expression. In medical research, RNA sequencing (RNA-seq) data are commonly used to identify which type of disease a patient has. Because of the discrete nature of RNA-seq data, the existing statistical methods that have been developed for microarray data cannot be directly applied to RNA-seq data.

Genome-wide cell-free DNA methylation analyses improve accuracy of non-invasive diagnostic imaging for early-stage breast cancer.

Early detection is crucial to improve breast cancer (BC) patients' outcomes and survival. Mammogram and ultrasound adopting the Breast Imaging Reporting and Data System (BI-RADS) categorization are widely used for BC early detection, while suffering high false-positive rate leading to unnecessary biopsy, especially in BI-RADS category-4 patients. Plasma cell-free DNA (cfDNA) carrying on DNA methylation information has emerged as a non-invasive approach for cancer detection.