Molecular group and correlation guided structural learning for multi-phenotype prediction.

We propose a supervised learning bioinformatics tool, Biological gRoup guIded muLtivariate muLtiple lIneAr regression with peNalizaTion (Brilliant), designed for feature selection and outcome prediction in genomic data with multi-phenotypic responses. Brilliant specifically incorporates genome and/or phenotype grouping structures, as well as phenotype correlation structures, in feature selection, effect estimation, and outcome prediction under a penalized multi-response linear regression model. Extensive simulations demonstrate its superior performance compared to competing methods.

BLEND: Probabilistic Cellular Deconvolution with Automated Reference Selection.

Cellular deconvolution aims to estimate cell type fractions from bulk transcriptomic and other omics data. Most existing deconvolution methods fail to account for the heterogeneity in cell type-specific (CTS) expression across bulk samples, ignore discrepancies between CTS expression in bulk and cell type reference data, and provide no guidance on cell type reference selection or integration. To address these issues, we introduce BLEND, a hierarchical Bayesian method that leverages multiple reference datasets.

scMD facilitates cell type deconvolution using single-cell DNA methylation references.

The proliferation of single-cell RNA-sequencing data has led to the widespread use of cellular deconvolution, aiding the extraction of cell-type-specific information from extensive bulk data. However, those advances have been mostly limited to transcriptomic data. With recent developments in single-cell DNA methylation (scDNAm), there are emerging opportunities for deconvolving bulk DNAm data, particularly for solid tissues like brain that lack cell-type references.

DeepGAMI: deep biologically guided auxiliary learning for multimodal integration and imputation to improve genotype-phenotype prediction.

Background: Genotypes are strongly associated with disease phenotypes, particularly in brain disorders. However, the molecular and cellular mechanisms behind this association remain elusive. With emerging multimodal data for these mechanisms, machine learning methods can be applied for phenotype prediction at different scales, but due to the black-box nature of machine learning, integrating these modalities and interpreting biological mechanisms can be challenging.

scMD: cell type deconvolution using single-cell DNA methylation references.

The proliferation of single-cell RNA sequencing data has led to the widespread use of cellular deconvolution, aiding the extraction of cell type-specific information from extensive bulk data. However, those advances have been mostly limited to transcriptomic data. With recent development in single-cell DNA methylation (scDNAm), new avenues have been opened for deconvolving bulk DNAm data, particularly for solid tissues like the brain that lack cell-type references.

Transcriptional risk scores in Alzheimer's disease: From pathology to cognition.

Introduction: Our previously developed blood-based transcriptional risk scores (TRS) showed associations with diagnosis and neuroimaging biomarkers for Alzheimer's disease (AD). Here, we developed brain-based TRS.

Methods: We integrated AD genome-wide association study summary and expression quantitative trait locus data to prioritize target genes using Mendelian randomization.

Accurate estimation of rare cell type fractions from tissue omics data via hierarchical deconvolution.

Bulk transcriptomics in tissue samples reflects the average expression levels across different cell types and is highly influenced by cellular fractions. As such, it is critical to estimate cellular fractions to both deconfound differential expression analyses and infer cell type-specific differential expression. Since experimentally counting cells is infeasible in most tissues and studies, cellular deconvolution methods have been developed as an alternative.

Spatiotemporal and genetic regulation of A-to-I editing throughout human brain development.

Posttranscriptional RNA modifications by adenosine-to-inosine (A-to-I) editing are abundant in the brain, yet elucidating functional sites remains challenging. To bridge this gap, we investigate spatiotemporal and genetically regulated A-to-I editing sites across prenatal and postnatal stages of human brain development. More than 10,000 spatiotemporally regulated A-to-I sites were identified that occur predominately in 3' UTRs and introns, as well as 37 sites that recode amino acids in protein coding regions with precise changes in editing levels across development.

A study on the association of placental and maternal urinary phthalate metabolites.

Background: Phthalate exposure in pregnancy is typically estimated using maternal urinary phthalate metabolite levels. Our aim was to evaluate the association of urinary and placental tissue phthalates, and to explore the role of maternal and pregnancy characteristics that may bias estimates.

Methods: Fifty pregnancies were selected from the CANDLE Study, recruited from 2006 to 2011 in Tennessee.

Integrative analysis of spatial transcriptome with single-cell transcriptome and single-cell epigenome in mouse lungs after immunization.

Understanding lung immunity requires an unbiased profiling of tissue-resident T cells at their precise anatomical locations within the lung, but such information has not been characterized in the immunized mouse model. In this pilot study, using 10x Genomics Chromium and Visium platform, we performed an integrative analysis of spatial transcriptome with single-cell RNA-seq and single-cell ATAC-seq on lung cells from mice after immunization using a well-established infection model. We built an optimized deconvolution pipeline to accurately decipher specific cell-type compositions by anatomic location.

Schizophrenia-associated differential DNA methylation in brain is distributed across the genome and annotated to MAD1L1, a locus at which DNA methylation and transcription phenotypes share genetic variation with schizophrenia risk.

DNA methylation (DNAm), the addition of a methyl group to a cytosine in DNA, plays an important role in the regulation of gene expression. Single-nucleotide polymorphisms (SNPs) associated with schizophrenia (SZ) by genome-wide association studies (GWAS) often influence local DNAm levels. Thus, DNAm alterations, acting through effects on gene expression, represent one potential mechanism by which SZ-associated SNPs confer risk.

Robust and accurate estimation of cellular fraction from tissue omics data via ensemble deconvolution.

Motivation: Tissue-level omics data such as transcriptomics and epigenomics are an average across diverse cell types. To extract cell-type-specific (CTS) signals, dozens of cellular deconvolution methods have been proposed to infer cell-type fractions from tissue-level data. However, these methods produce vastly different results under various real data settings.

Small nucleolar RNAs in plasma extracellular vesicles and their discriminatory power as diagnostic biomarkers of Alzheimer's disease.

The clinical diagnosis of Alzheimer's disease, at its early stage, remains a difficult task. Advanced imaging technologies and laboratory assays to detect Aβ peptides Aβ42 and Aβ40, total and phosphorylated tau in CSF provide a set of biomarkers of developing AD brain pathology and facilitate the diagnostic process. The search for biofluid biomarkers, other than in CSF, and the development of biomarker assays have accelerated significantly and now represent the fastest-growing field in AD research.

Transcriptional Alterations in Dorsolateral Prefrontal Cortex and Nucleus Accumbens Implicate Neuroinflammation and Synaptic Remodeling in Opioid Use Disorder.

Background: Prevalence rates of opioid use disorder (OUD) have increased dramatically, accompanied by a surge of overdose deaths. While opioid dependence has been extensively studied in preclinical models, an understanding of the biological alterations that occur in the brains of people who chronically use opioids and who are diagnosed with OUD remains limited. To address this limitation, RNA sequencing was conducted on the dorsolateral prefrontal cortex and nucleus accumbens, regions heavily implicated in OUD, from postmortem brains in subjects with OUD.

Brief Report: Health Expenditures for Children with Autism and Family Financial Well-Being in China.

Little is known on the financial well-being of families raising children with autism spectrum disorders (ASD). Family financial well-being has important impacts on the development of children with ASD. The study uses a 2019 survey collected from Chinese families raising a child with ASD (N = 3064) to examine their financial well-being and its association with health expenditures for children.

Identification of cell-type-specific marker genes from co-expression patterns in tissue samples.

Motivation: Marker genes, defined as genes that are expressed primarily in a single-cell type, can be identified from the single-cell transcriptome; however, such data are not always available for the many uses of marker genes, such as deconvolution of bulk tissue. Marker genes for a cell type, however, are highly correlated in bulk data, because their expression levels depend primarily on the proportion of that cell type in the samples. Therefore, when many tissue samples are analyzed, it is possible to identify these marker genes from the correlation pattern.

Bayesian estimation of cell type-specific gene expression with prior derived from single-cell data.

When assessed over a large number of samples, bulk RNA sequencing provides reliable data for gene expression at the tissue level. Single-cell RNA sequencing (scRNA-seq) deepens those analyses by evaluating gene expression at the cellular level. Both data types lend insights into disease etiology.

Transcriptome alterations are enriched for synapse-associated genes in the striatum of subjects with obsessive-compulsive disorder.

Obsessive-compulsive disorder (OCD) is a chronic and severe psychiatric disorder for which effective treatment options are limited. Structural and functional neuroimaging studies have consistently implicated the orbitofrontal cortex (OFC) and striatum in the pathophysiology of the disorder. Recent genetic evidence points to involvement of components of the excitatory synapse in the etiology of OCD.

CCmed: cross-condition mediation analysis for identifying replicable trans-associations mediated by cis-gene expression.

Motivation: Trans-acting expression quantitative trait loci (eQTLs) collectively explain a substantial proportion of expression variation, yet are challenging to detect and replicate since their effects are often individually weak. A large proportion of genetic effects on distal genes are mediated through cis-gene expression. Cis-association (between SNP and cis-gene) and gene-gene correlation conditional on SNP genotype could establish trans-association (between SNP and trans-gene).

ESCO: single cell expression simulation incorporating gene co-expression.

Motivation: Gene-gene co-expression networks (GCN) are of biological interest for the useful information they provide for understanding gene-gene interactions. The advent of single cell RNA-sequencing allows us to examine more subtle gene co-expression occurring within a cell type. Many imputation and denoising methods have been developed to deal with the technical challenges observed in single cell data; meanwhile, several simulators have been developed for benchmarking and assessing these methods.

De novo missense variants disrupting protein-protein interactions affect risk for autism through gene co-expression and protein networks in neuronal cell types.

Background: Whole-exome sequencing studies have been useful for identifying genes that, when mutated, affect risk for autism spectrum disorder (ASD). Nonetheless, the association signal primarily arises from de novo protein-truncating variants, as opposed to the more common missense variants. Despite their commonness in humans, determining which missense variants affect phenotypes and how remains a challenge.