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Background Bullous pemphigoid (BP) and atopic dermatitis (AD) are currently thought to be tightly related, yet studies of the mechanisms of co‐morbidities are lacking. Methods We obtained GWAS data for BP (N = 376,274) and AD (N = 796,661) from the Finnish Genetic Research Program dataset and the UK Biobank, separately. Then, the following four analyses were performed: (1) cross‐trait linkage disequilibrium score regression (LDSC) to assess the genetic correlation between BP and AD, (2) cross‐phenotype association analysis (CPASSOC) to identify multiple effector loci shared by BP and AD, (3) transcriptome‐wide association study (TWAS) to determine whether their cross‐organizational expression patterns share genes with a common biological mechanism of relevance, and (4) bidirectional Mendelian randomization (MR) analysis to assess bidirectional causal effects of BP and AD. Results We found a positive genetic association between BP and AD (rg = 0.5476, p = 0.0495) as well as identified four pleiotropic loci and 59 common genes affecting BP and AD. Bidirectional MR analysis suggested that BP promotes the risk of AD. Conclusions We revealed a genetic link between BP and AD, which is associated with biological pleiotropy and causality. Awareness of the association between BP and AD helps dermatologists manage patients with these illnesses. We revealed a genetic link between atopic dermatitis and bullous pemphigoid, which is associated with biological pleiotropy and causality. Awareness of the association between BP and AD helps dermatologists manage patients with these illnesses.

Background The relationship between germline genetic variation and breast cancer survival is largely unknown, especially in understudied minority populations who often have poorer survival. Genome-wide association studies (GWAS) have interrogated breast cancer survival but often are underpowered due to subtype heterogeneity and clinical covariates and detect loci in non-coding regions that are difficult to interpret. Transcriptome-wide association studies (TWAS) show increased power in detecting functionally relevant loci by leveraging expression quantitative trait loci (eQTLs) from external reference panels in relevant tissues. However, ancestry- or race-specific reference panels may be needed to draw correct inference in ancestrally diverse cohorts. Such panels for breast cancer are lacking. Results We provide a framework for TWAS for breast cancer in diverse populations, using data from the Carolina Breast Cancer Study (CBCS), a population-based cohort that oversampled black women. We perform eQTL analysis for 406 breast cancer-related genes to train race-stratified predictive models of tumor expression from germline genotypes. Using these models, we impute expression in independent data from CBCS and TCGA, accounting for sampling variability in assessing performance. These models are not applicable across race, and their predictive performance varies across tumor subtype. Within CBCS ( N  = 3,828), at a false discovery-adjusted significance of 0.10 and stratifying for race, we identify associations in black women near AURKA , CAPN13 , PIK3CA , and SERPINB5 via TWAS that are underpowered in GWAS. Conclusions We show that carefully implemented and thoroughly validated TWAS is an efficient approach for understanding the genetics underpinning breast cancer outcomes in diverse populations.

RIMap-RISC is a web-accessible database for transcriptome-wide modeling of human microRNA (miRNA) targeting. It computes plausible transcript-miRNA interactions and records their position, duplex secondary structure, free energy, site classification, dissociation constant, target accessibility, and evolutionary conservation. RIMap-RISC supports transcript-wide queries and allows users to explore and export interaction data through an interactive interface or RESTful API programmatic access. Unlike existing tools, RIMap-RISC integrates duplex-structure prediction within a biophysical framework modeling the bipartite architecture of RISC, accommodating a bridge between seed and supplementary pairing. A novel, unambiguous, miRNA-centric nomenclature for interaction types is also introduced.

Soybean (Glycine max (L.) Merr.) is among the most valuable crops based on its nutritious seed protein and oil. Protein quality, evaluated as the ratio of glycinin (11S) to β-conglycinin (7S), can play a role in food and feed quality. To help uncover the underlying differences between high and low protein soybean varieties, we performed differential expression analysis on high and low total protein soybean varieties and high and low 11S soybean varieties grown in four locations across Eastern and Western Canada over three years (2018–2020). Simultaneously, ten individual differential expression datasets for high vs. low total protein soybeans and ten individual differential expression datasets for high vs. low 11S soybeans were assessed, for a total of 20 datasets. The top 15 most upregulated and the 15 most downregulated genes were extracted from each differential expression dataset and cross-examination was conducted to create shortlists of the most consistently differentially expressed genes. Shortlisted genes were assessed for gene ontology to gain a global appreciation of the commonly differentially expressed genes. Genes with roles in the lipid metabolic pathway and carbohydrate metabolic pathway were differentially expressed in high total protein and high 11S soybeans in comparison to their low total protein and low 11S counterparts. Expression differences were consistent between East and West locations with the exception of one, Glyma.03G054100. These data are important for uncovering the genes and biological pathways responsible for the difference in seed protein between high and low total protein or 11S cultivars.

Potato microtuber (MT) development through in vitro techniques are ideal propagules for producing high quality potato plants. MT formation is influenced by several factors, i.e., photoperiod, sucrose, hormones, and osmotic stress. We have previously developed a protocol of MT induction in medium with sucrose (8% w/v), gelrite (6g/L), and 2iP as cytokinin under darkness. To understand the molecular mechanisms involved, we performed a transcriptome-wide analysis. Here we show that 1715 up- and 1624 down-regulated genes were involved in this biological process. Through the protein–protein interaction (PPI) network analyses performed in the STRING database (v11.5), we found 299 genes tightly associated in 14 clusters. Two major clusters of up-regulated proteins fundamental for life growth and development were found: 29 ribosomal proteins (RPs) interacting with 6 PEBP family members and 117 cell cycle (CC) proteins. The PPI network of up-regulated transcription factors (TFs) revealed that at least six TFs–MYB43, TSF, bZIP27, bZIP43, HAT4 and WOX9–may be involved during MTs development. The PPI network of down-regulated genes revealed a cluster of 83 proteins involved in light and photosynthesis, 110 in response to hormone, 74 in hormone mediate signaling pathway and 22 related to aging.

In this paper, we present PRIME-BSPre, a template-based genome-wide method for predicting protein-RNA binding sites that incorporates the RNA sequence and secondary structure as well as the tertiary structure of corresponding RBPs. We are pioneers in introducing low Shannon entropy algorithm in PRIME-BSPre to describe the binding preferences of RBPs on RNA motifs. The LS-PEAK derived from LS-Scores in PRIME-BSPre is utilized to optimize the alignments screening. PRIME-BSPre has been successfully benchmarked on the human genome, demonstrating its excellent prediction performance on independent RBP datasets and its robustness across different cell lines.

Non-linear Kuramoto model has been used to study synchronised or sync behaviour in numerous fields; however, its application in biology is scarce. Here, the basic model has been introduced and examples where large-scale small-world or scale-free networks are crucial for spontaneous sync have been provide even for low coupling strength. This information was next checked for relevance in living systems where it is now well known that biological networks are scale-free. A recent transcriptome-wide data analysis of a Saccharomyces cerevisiae biofilm showed that low- to middle-expressed genes are key for scale invariance in biology. Together, the current data indicate that a biological network connectivity structure with low coupling strength, or expression levels, is sufficient for sync behaviour. For biofilm regulation, it may, therefore, be necessary to investigate large-scale low-expression genes rather than small-scale high-expression genes.

Psoriasis and insomnia have co-morbidities, however, their common genetic basis is still unclear. We analyzed psoriasis and insomnia with summary statistics from genome-wide association studies. We first quantified overall genetic correlations, then ascertained multiple effector loci and expression-trait associations, and lastly, we analyzed the causal effects between psoriasis and insomnia. A prevalent genetic link between psoriasis and insomnia was found, four pleiotropic loci affecting psoriasis and insomnia were identified, and 154 genes were shared, indicating a genetic link between psoriasis and insomnia. Yet, there is no causal relationship between psoriasis and insomnia by two-sample Mendelian randomization. We discovered a genetic connection between insomnia and psoriasis driven by biological pleiotropy and unrelated to causation. Cross-trait analysis indicates a common genetic basis for psoriasis and insomnia. The results of this study highlight the importance of sleep management in the pathogenesis of psoriasis.

Background Dementia with Lewy bodies (DLB) is the second most common type of degenerative dementia in older patients. As with other multifactorial diseases, the pathogenesis results from interactions of environmental and genetic factors. The genetic basis of DLB is not yet fully understood. Recent genomic analyses of DLB in Caucasian cohorts identified genetic susceptibility loci for DLB, but the comprehensive genomic analysis in Asians was still not performed. Methods We conducted a genome-wide association study (GWAS) in Japanese subjects (211 DLB cases and 6113 controls) to clarify the genetic architecture of DLB pathogenesis. Results We identified the East Asian–specific DHTKD1 locus (rs138587229) on chromosome 10 with genome-wide significance (GWS; P  = 3.27 × 10 –8 ) and the ICOS / PARD3B locus on chromosome 2 with suggestive significance ( P  = 3.95 × 10 –7 ) as novel DLB genetic risk loci. We also confirmed the APOE locus (rs429358, P  < 5.0 × 10 –8 ), a known risk locus for DLB and Alzheimer’s disease in Caucasians. The DHTKD1 locus was associated with the gene expression of SEC61A2 and showed a causal relationship with cholinesterase levels. In a trans-ethnic meta-analysis that included Japanese, UK Biobank, and other Caucasian GWAS, we confirmed the risk for DLB at APOE and SNCA loci with GWS. Transcriptome-wide association analysis identified ZNF155 and ZNF284 in the brain cortex and GPRIN3 in the substantia nigra as putative causal genes for DLB. Conclusions This is the first GWAS for DLB in East Asians, and our findings provide new biological and clinical insights into the pathogenesis of DLB.

Main conclusion Identification and characterization of 5,446 mlncRNAs from Salvia miltiorrhiza showed that the majority of identified mlncRNAs were stress responsive, providing a framework for elucidating mlncRNA functions in S. miltiorrhiza. mRNA-like noncoding RNAs (mlncRNAs) are transcribed by RNA polymerase II and are polyadenylated, capped and spliced. They play important roles in plant development and defense responses. However, there is no information available for mlncRNAs in Salvia miltiorrhiza Bunge, the first Chinese medicinal material entering the international market. To perform a transcriptome-wide identification of S. miltiorrhiza mlncRNAs, we assembled over 8 million RNA-seq reads from GenBank database and 5,624 ESTs from PlantGDB into 44422 unigenes. Using a computational identification pipeline, we identified 5446 S. miltiorrhiza mlncRNA candidates from the assembled unigenes. Of the 5446 mlncRNAs, 2 are primary transcripts of conserved miRNAs, and 2030 can be grouped into 470 families with at least two members in a family. Quantitative real-time PCR analysis of mlncRNAs with at least 900 nt showed that the majority were differentially expressed in roots, stems, leaves and flowers and responsive to methyl jasmonate (MeJA) treatment in S. miltiorrhiza. Analysis of published RNA-seq data showed that a total of 3,044 mlncRNAs were expressed in hairy roots of S. miltiorrhiza and the expression of 1,904 of the 3,044 mlncRNAs was altered by yeast extract and Ag⁺ treatment. The results indicate that the majority of mlncRNAs are involved in plant response to stress. This study provides a framework for understanding the roles of mlncRNAs in S. miltiorrhiza.