TreeMap: a structured approach to fine mapping of eQTL variants

Graph Visualization: Alternative Models Inspired by Bioinformatics

Currently, the methods and means of human-machine interaction and visualization as its integral part are being increasingly developed. In various fields of scientific knowledge and technology, there is a need to find and select the most effective visualization models for various types of data, as well as to develop automation tools for the process of choosing the best visualization model for a specific case. There are many data visualization tools in various application fields, but at the same time, the main difficulty lies in presenting data of an interconnected (node-link) structure, i.e., networks. Typically, a lot of software means use graphs as the most straightforward and versatile models. To facilitate visual analysis, researchers are developing ways to arrange graph elements to make comparing, searching, and navigating data easier. However, in addition to graphs, there are many other visualization models that are less versatile but have the potential to expand the capabilities of the analyst and provide alternative solutions. In this work, we collected a variety of visualization models, which we call alternative models, to demonstrate how different concepts of information representation can be realized. We believe that adapting these models to improve the means of human-machine interaction will help analysts make significant progress in solving the problems researchers face when working with graphs.

Effect of all-but-one conditional analysis for eQTL isolation in peripheral blood

Expression quantitative trait locus detection has become increasingly important for understanding how noncoding variants contribute to disease susceptibility and complex traits. The major challenges in expression quantitative trait locus fine-mapping and causal variant discovery relate to the impact of linkage disequilibrium on signals due to one or multiple functional variants that lie within a credible set. We perform expression quantitative trait locus fine-mapping using the all-but-one approach, conditioning each signal on all others detected in an interval, on the Consortium for the Architecture of Gene Expression cohorts of microarray-based peripheral blood gene expression in 2,138 European-ancestry human adults. We contrast these results with traditional forward stepwise conditional analysis and a Bayesian localization method. All-but-one conditioning significantly modifies effect-size estimates for 51% of 2,351 expression quantitative trait locus peaks, but only modestly affects credible set size and location. On the other hand, both conditioning approaches result in unexpectedly low overlap with Bayesian credible sets, with just 57% peak concordance and between 50% and 70% SNP sharing, leading us to caution against the assumption that any one localization method is superior to another. We also cross reference our results with ATAC-seq data, cell-type-specific expression quantitative trait locus, and activity-by-contact-enhancers, leading to the proposal of a 5-tier approach to further reduce credible set sizes and prioritize likely causal variants for all known inflammatory bowel disease risk loci active in immune cells.

Bioinformatic approach for the discovery of cis-eQTL signals during fruit ripening of a woody species as grape (Vitis vinifera L.)

Expression quantitative trait loci (eQTLs) are associations between genetic variants, such as Single Nucleotide Polymorphisms (SNPs), and gene expression. eQTLs are an important tool to understand the genetic variance of gene expression of complex phenotypes. eQTLs analyses are common in biomedical models but are scarce in woody crop species such as fruit trees or grapes. In this study, a comprehensive bioinformatic analysis was conducted leveraging with expression data from two different growth stages, around ripening onset, of 10 genotypes of grape (Vitis vinifera L.). A total of 2170 cis-eQTL were identified in 212 gene modulated at ripening onset. The 48% of these DEGs have a known function. Among the annotated protein-coding genes, terpene synthase, auxin-regulatory factors, GRFS, ANK_REP_REGION domain-containing protein, Kinesin motor domain-containing protein and flavonol synthase were noted. This new inventory of cis-eQTLs influencing gene expression during fruit ripening will be an important resource to examine variation for this trait and will help to elucidate the complex genetic architecture underlying this process in grape.

BCL2A1 and CCL18 Are Predictive Biomarkers of Cisplatin Chemotherapy and Immunotherapy in Colon Cancer Patients

Background: Cisplatin enhances the antitumor T cell response, and the combination of PD-L1 blockade produces a synergistic therapeutic effect. However, the clinical correlation between cisplatin and immunotherapy in colon cancer (CC) is unknown.

Methods: Using the “pRRophetic” package, we calculated the IC50 of cisplatin. The correlation between cisplatin IC50, cisplatin resistance–related genes (CCL18 and BCL2A1), and immunotherapy were preliminarily verified in TCGA and further validated in independent cohorts (GSE39582 and GSE17538), cisplatin-resistant CC cell line DLD1, and our own clinical specimens. Classification performance was evaluated using the AUC value of the ROC curve. Scores of immune signatures, autophagy, ferroptosis, and stemness were quantified using the ssGSEA algorithm.

Results: Based on respective medians of three CC cohorts, patients were divided into high- and low-IC50 groups. Compared with the high IC50 group, the low-IC50 group had significantly higher tumor microenvironment (TME) scores and lower tumor purity. Most co-signaling molecules were upregulated in low IC50 group. CC patients with good immunotherapy efficacy (MSI, dMMR, and more TMB) were more attributable to the low-IC50 group. Among seven shared differentially expressed cisplatin resistance–related genes, CCL18 and BCL2A1 had the best predictive efficacy of the above immunotherapy biomarkers. For wet experimental verification, compared with cisplatin-resistant DLD1, similar to PD-L1, CCL18 and BCL2A1 were significantly upregulated in wild-type DLD1. In our own CC tissues, the mRNA expression of CCL18, BCL2A1, and PD-L1 in dMMR were significantly increased. The high group of CCL18 or BCL2A1 had a higher proportion of MSI, dMMR, and more TMB. IC50, CCL18, BCL2A1, and PD-L1 were closely related to scores of immune-related pathways, immune signatures, autophagy, ferroptosis, and stemness. The microRNA shared by BCL2A1 and PD-L1, hsa-miR-137, were significantly associated with CCL18, BCL2A1, and PD-L1, and downregulated in low-IC50 group. The activity of the TOLL-like receptor signaling pathway affected the sensitivity of CC patients to cisplatin and immunotherapy. For subtype analysis, immune C2, immune C6, HM-indel, HM-SNV, C18, and C20 were equally sensitive to cisplatin chemotherapy and immunotherapy.

Conclusions: CC patients sensitive to cisplatin chemotherapy were also sensitive to immunotherapy. CCL18 and BCL2A1 were novel biomarkers for cisplatin and immunotherapy.

Identification of a Methylation-Regulating Genes Prognostic Signature to Predict the Prognosis and Aid Immunotherapy of Clear Cell Renal Cell Carcinoma

Methylation is one of the most extensive modifications of biological macromolecules and affects cell-fate determination, development, aging, and cancer. Several methylation modifications, including 5-methylcytosine and N6-methyladenosine, play an essential role in many cancers. However, little is known about the relationship between methylation and the prognosis of clear cell renal cell carcinoma (ccRCC). Here, we established a methylation-regulating genes prognostic signature (MRGPS) to predict the prognoses of ccRCC patients. We obtained ccRCC samples from The Cancer Genome Atlas and identified methylation-regulatingd genes (MRGs) from the Gene Set Enrichment Analysis database. We also determined differentially expressed genes (DEGs) and performed cluster analysis to identify candidate genes. Subsequently, we established and validated an MRGPS to predict the overall survival of ccRCC patients. This was also verified in 15 ccRCC samples collected from the Fujian Provincial Hospital via quantitative real-time transcription (qRT-PCR). While 95 MRGs were differentially expressed (DEGs1) between tumor and normal tissues, 17 MRGs were differentially expressed (DEGs2) between cluster 1 and 2. Notably, 13 genes common among DEGs1 and DEGs2 were identified as hub genes. In fact, we established three genes (NOP2, NSUN6, and TET2) to be an MRGPS based on their multivariate Cox regression analysis coefficients (p < 0.05). A receiver operating characteristic curve analysis confirmed this MRGPS to have a good prognostic performance. Moreover, the MRGPS was associated with characteristics of the tumor immune microenvironment and responses to inhibitor checkpoint inhibitors. Data from “IMvigor 210” demonstrated that patients with a low MRGPS would benefit more from atelozumab (p < 0.05). Furthermore, a multivariate analysis revealed that MRGPS was an independent risk factor associated with ccRCC prognosis (p < 0.05). Notably, a nomogram constructed by combining with clinical characteristics (age, grade, stage, and MRGPS risk score) to predict the overall survival of a ccRCC patient had a favorable predictive value. Eventually, our qRT-PCR results showed that tumor tissues had higher NOP2 and NSUN6 expression levels and lower TET2 expression than normal tissues of ccRCC samples. While the proposed MRGPS comprising NOP2, NSUN6, and TET2 can be an alternative prognostic biomarker for ccRCC patients, it is a promising index for personalized ICI treatments against ccRCC.

Methods for statistical fine-mapping and their applications to auto-immune diseases

Although genome-wide association studies (GWAS) have identified thousands of loci in the human genome that are associated with different traits, understanding the biological mechanisms underlying the association signals identified in GWAS remains challenging. Statistical fine-mapping is a method aiming to refine GWAS signals by evaluating which variant(s) are truly causal to the phenotype. Here, we review the types of statistical fine-mapping methods that have been widely used to date, with a focus on recently developed functionally informed fine-mapping (FIFM) methods that utilize functional annotations. We then systematically review the applications of statistical fine-mapping in autoimmune disease studies to highlight the value of statistical fine-mapping in biological contexts.