Selection of fruits with enhanced health benefits and superior flavor is an important aspect of peach breeding. Understanding the genetic interplay between appearance and flavor chemicals remains a major challenge. We identify the most important volatiles contributing to consumer preferences for peach, thus establishing priorities for improving flavor quality. We quantify volatiles of a peach population consisting of 184 accessions and demonstrate major reductions in the important flavor volatiles linalool and Z-3-hexenyl acetate in red-fleshed accessions. We identify 474 functional gene regulatory networks (GRNs), among which GRN05 plays a crucial role in controlling both red flesh and volatile content through the NAM/ATAF1/2/CUC (NAC) transcription factor PpBL. Overexpressing PpBL results in reduced expression of PpNAC1, a positive regulator for Z-3-hexenyl acetate and linalool synthesis. Additionally, we identify haplotypes for three tandem PpAATs that are significantly correlated with reduced gene expression and ester content. We develop genetic resources for improvement of fruit quality.

Cervical cancer is the fourth most common cancer in females. Genome-wide association studies (GWASs) have proposed cervical cancer susceptibility variants at the HLA locus on chromosome 6p21. To corroborate these findings and investigate their functional impact in cervical tissues and cell lines, we genotyped nine variants from cervical cancer GWASs (rs17190106, rs535777, rs1056429, rs2763979, rs143954678, rs113937848, rs3117027, rs3130214, and rs9477610) in a German hospital-based series of 1122 invasive cervical cancers, 1408 dysplasias, and 1196 healthy controls. rs17190106, rs1056429 and rs143954678/rs113937848 associated with cervical malignancies overall, while rs17190106 and rs535777 associated specifically with invasive cancer (OR = 0.69, 95% CI = 0.55-0.86, p = 0.001) or adenocarcinomas (OR = 1.63, 95%CI = 1.17-2.27, p = 0.004), respectively. We tested these and one previously genotyped GWAS variant, rs9272117, for potential eQTL effects on 36 gene transcripts at the HLA locus in 280 cervical epithelial tissues. The strongest eQTL pairs were rs9272117 and HLA-DRB6 (p = 1.9x10E-5), rs1056429 and HLA-DRB5 (p = 2.5x10E-4), and rs535777 and HLA-DRB1 (p = 2.7x10E-4). We also identified transcripts that were specifically upregulated (DDX39B, HCP5, HLA-B, LTB, NFKBIL1) or downregulated (HLA-C, HLA-DPB2) in HPV+ or HPV16+ samples. In comparison, treating cervical epithelial cells with proinflammatory cytokine γ-IFN led to a dose-dependent induction of HCP5, HLA-B, HLA-C, HLA-DQB1, HLA-DRB1, HLA-DRB6, and repression of HSPA1L. Taken together, these results identify relevant genes from both the MHC class I and II regions that are inflammation-responsive in cervical epithelium and associate with HPV (HCP5, HLA-B, HLA-C) and/or with genomic cervical cancer risk variants (HLA-DRB1, HLA-DRB6). They may thus constitute important contributors to the immune escape of precancerous cells after HPV-infection.

Phantom tooth pain (PTP) is one type of non-odontogenic neuropathic toothache, which rarely occurs after appropriate pulpectomy or tooth extraction. The cause of PTP is unknown. We investigated pain-related genetic factors that are associated with PTP. Four pain-associated genes, including G protein-coupled receptor 158 (GPR158) and phosphoribosyl transferase domain containing 1 (PRTFDC1), are adjacent to each other on the human genome. Some of these four genes or their genomic region may be related to PTP. We statistically analyzed associations between single-nucleotide polymorphisms (SNPs) in the genomic region and PTP in patients with PTP (PTP group), other orofacial pain (OFP group), and healthy control subjects. We then performed a database search of expression quantitative trait loci (eQTLs). For the seven SNPs that were significantly associated with PTP even after Bonferroni correction, we focused on the rs12411980 tag SNP (P = 9.42 × 10-4). Statistical analyses of the PTP group and healthy subject groups (group labels: NOC and TD) revealed that the rate of the GG genotype of the rs12411980 SNP was significantly higher in the PTP group than in the healthy subject groups (PTP group vs. NOC group: P = 2.92 × 10-4, PTP group vs. TD group: P = 5.46 × 10-4; percentage of GG: 30% in PTP group, 12% in NOC group, 11% in TD group). These results suggest that the GG genotype of the rs12411980 SNP is more susceptible to PTP. The rs2765697 SNP that is in strong linkage disequilibrium with the rs12411980 SNP is an eQTL that is associated with higher PRTFDC1 expression in the minor allele homozygotes in the healthy subject groups of the rs2765697 SNP. Thus, PRTFDC1 expression similarly increases in the minor allele homozygotes (GG genotype) in the healthy subject groups of the rs12411980 SNP, which would lead to greater susceptibility to PTP.

BACKGROUND: Association testing between molecular phenotypes and genomic variants can help to understand how genotype affects phenotype. RNA sequencing provides access to molecular phenotypes such as gene expression and alternative splicing while DNA sequencing or microarray genotyping are the prevailing options to obtain genomic variants.
RESULTS: We genotype variants for 74 male Braunvieh cattle from both DNA (~ 13-fold coverage) and deep total RNA sequencing from testis, vas deferens, and epididymis tissue (~ 250 million reads per tissue). We show that RNA sequencing can be used to identify approximately 40% of variants (7-10 million) called from DNA sequencing, with over 80% precision. Within highly expressed coding regions, over 92% of expected variants were called with nearly 98% precision. Allele-specific expression and putative post-transcriptional modifications negatively impact variant genotyping accuracy from RNA sequencing and contribute to RNA-DNA differences. Variants called from RNA sequencing detect roughly 75% of eGenes identified using variants called from DNA sequencing, demonstrating a nearly 2-fold enrichment of eQTL variants. We observe a moderate-to-strong correlation in nominal association p-values (Spearman ρ2 ~ 0.6), although only 9% of eGenes have the same top associated variant.
CONCLUSIONS: We find hundreds of thousands of RNA-DNA differences in variants called from RNA and DNA sequencing on the same individuals. We identify several highly significant eQTL when using RNA sequencing variant genotypes which are not found with DNA sequencing variant genotypes, suggesting that using RNA sequencing variant genotypes for association testing results in an increased number of false positives. Our findings demonstrate that caution must be exercised beyond filtering for variant quality or imputation accuracy when analysing or imputing variants called from RNA sequencing.

The secreted phospholipase A2 (sPLA2) isoform, sPLA2-IIA, has been implicated in a variety of diseases and conditions, including bacteremia, cardiovascular disease, COVID-19, sepsis, adult respiratory distress syndrome, and certain cancers. Given its significant role in these conditions, understanding the regulatory mechanisms impacting its levels is crucial. Genome-wide association studies (GWAS) have identified several single nucleotide polymorphisms (SNPs), including rs11573156, that are associated with circulating levels of sPLA2-IIA. The work in the manuscript leveraged 4 publicly available datasets to investigate the mechanism by which rs11573156 influences sPLA2-IIA levels via bioinformatics and modeling analysis. Through genotype-tissue expression (GTEx), 234 expression quantitative trait loci (eQTLs) were identified for the gene that encodes for sPLA2-IIA, PLA2G2A. SNP2TFBS was used to ascertain the binding affinities between transcription factors (TFs) to both the reference and alternative alleles of identified eQTL SNPs. Subsequently, candidate TF-SNP interactions were cross-referenced with the ChIP-seq results in matched tissues from ENCODE. SP1-rs11573156 emerged as the significant TF-SNP pair in the liver. Further analysis revealed that the upregulation of PLA2G2A transcript levels through the rs11573156 variant was likely affected by tissue SP1 protein levels. Using an ordinary differential equation based on Michaelis-Menten kinetic assumptions, we modeled the dependence of PLA2G2A transcription on SP1 protein levels, incorporating the SNP influence. Collectively, our analysis strongly suggests that the difference in the binding dynamics of SP1 to different rs11573156 alleles may underlie the allele-specific PLA2G2A expression in different tissues, a mechanistic model that awaits future direct experimental validation. This mechanism likely contributes to the variation in circulating sPLA2-IIA protein levels in the human population, with implications for a wide range of human diseases.

The genetic control underlying natural variation in lignin content and composition in trees is not fully understood. We performed a systems genetic analysis to uncover the genetic regulation of lignin biosynthesis in a natural \'SwAsp\' population of aspen (Populus tremula) trees. We analyzed gene expression by RNA sequencing (RNA-seq) in differentiating xylem tissues, and lignin content and composition using Pyrolysis-GC-MS in mature wood of 268 trees from 99 genotypes. Abundant variation was observed for lignin content and composition, and genome-wide association study identified proteins in the pentose phosphate pathway and arabinogalactan protein glycosylation among the top-ranked genes that are associated with these traits. Variation in gene expression and the associated genetic polymorphism was revealed through the identification of 312 705 local and 292 003 distant expression quantitative trait loci (eQTL). A co-expression network analysis suggested modularization of lignin biosynthesis and novel functions for the lignin-biosynthetic CINNAMYL ALCOHOL DEHYDROGENASE 2 and CAFFEOYL-CoA O-METHYLTRANSFERASE 3. PHENYLALANINE AMMONIA LYASE 3 was co-expressed with HOMEOBOX PROTEIN 5 (HB5), and the role of HB5 in stimulating lignification was demonstrated in transgenic trees. The systems genetic approach allowed linking natural variation in lignin biosynthesis to trees´ responses to external cues such as mechanical stimulus and nutrient availability.

Most gene expression and alternative splicing quantitative trait loci (eQTL/sQTL) studies have been biased toward European ancestry individuals. Here, we performed eQTL and sQTL analyses using TOPMed whole-genome sequencing-derived genotype data and RNA-sequencing data from stored peripheral blood mononuclear cells in 1,012 African American participants from the Jackson Heart Study (JHS). At a false discovery rate of 5%, we identified 17,630 unique eQTL credible sets covering 16,538 unique genes; and 24,525 unique sQTL credible sets covering 9,605 unique genes, with lead QTL at P < 5e-8. About 24% of independent eQTLs and independent sQTLs with a minor allele frequency > 1% in JHS were rare (minor allele frequency < 0.1%), and therefore unlikely to be detected, in European ancestry individuals. Finally, we created an open database, which is freely available online, allowing fast query and bulk download of our QTL results.

Most genetic variants identified through genome-wide association studies (GWAS) are suspected to be regulatory in nature, but only a small fraction colocalize with expression quantitative trait loci (eQTLs, variants associated with expression of a gene). Therefore, it is hypothesized but largely untested that integration of disease GWAS with context-specific eQTLs will reveal the underlying genes driving disease associations. We used colocalization and transcriptomic analyses to identify shared genetic variants and likely causal genes associated with critically ill COVID-19 and idiopathic pulmonary fibrosis. We first identified five genome-wide significant variants associated with both diseases. Four of the variants did not demonstrate clear colocalization between GWAS and healthy lung eQTL signals. Instead, two of the four variants colocalized only in cell-type and disease-specific eQTL datasets. These analyses pointed to higher ATP11A expression from the C allele of rs12585036, in monocytes and in lung tissue from primarily smokers, which increased risk of IPF and decreased risk of critically ill COVID-19. We also found lower DPP9 expression (and higher methylation at a specific CpG) from the G allele of rs12610495, acting in fibroblasts and in IPF lungs, and increased risk of IPF and critically ill COVID-19. We further found differential expression of the identified causal genes in diseased lungs when compared to non-diseased lungs, specifically in epithelial and immune cell types. These findings highlight the power of integrating GWAS, context-specific eQTLs, and transcriptomics of diseased tissue to harness human genetic variation to identify causal genes and where they function during multiple diseases.

UNASSIGNED: Osteoarthritis (OA) entails a prevalent chronic ailment, marked by the widespread involvement of entire joints. Prolonged low-grade synovial inflammation serves as the key instigator for a cascade of pathological alterations in the joints.
UNASSIGNED: The study seeks to explore potential therapeutic targets for OA and investigate the associated mechanistic pathways.
UNASSIGNED: Summary-level data for OA were downloaded from the genome-wide association studies (GWAS) database, expression quantitative trait loci (eQTL) data were acquired from the eQTLGen consortium, and synovial chip data for OA were obtained from the GEO database. Following the integration of data and subsequent Mendelian randomization analysis, differential analysis, and weighted gene co-expression network analysis (WGCNA) analysis, core genes that exhibit a significant causal relationship with OA traits were pinpointed. Subsequently, by employing three machine learning algorithms, additional identification of gene targets for the complexity of OA was achieved. Additionally, corresponding ROC curves and nomogram models were established for the assessment of clinical prognosis in patients. Finally, western blotting analysis and ELISA methodology were employed for the initial validation of marker genes and their linked pathways.
UNASSIGNED: Twenty-two core genes with a significant causal relationship to OA traits were obtained. Through the application of distinct machine learning algorithms, MAT2A and RBM6 emerged as diagnostic marker genes. ROC curves and nomogram models were utilized for evaluating both the effectiveness of the two identified marker genes associated with OA in diagnosis. MAT2A governs the synthesis of SAM within synovial cells, thereby thwarting synovial fibrosis induced by the TGF-β1-activated Smad3/4 signaling pathway.
UNASSIGNED: The first evidence that MAT2A and RBM6 serve as robust diagnostic for OA is presented in this study. MAT2A, through its involvement in regulating the synthesis of SAM, inhibits the activation of the TGF-β1-induced Smad3/4 signaling pathway, thereby effectively averting the possibility of synovial fibrosis. Concurrently, the development of a prognostic risk model facilitates early OA diagnosis, functional recovery evaluation, and offers direction for further therapy.

Seed quality traits of oilseed rape, Brassica napus (B. napus), exhibit quantitative inheritance determined by its genetic makeup and the environment via the mediation of a complex genetic architecture of hundreds to thousands of genes. Thus, instead of single gene analysis, network-based systems genomics and genetics approaches that combine genotype, phenotype, and molecular phenotypes offer a promising alternative to uncover this complex genetic architecture. In the current study, systems genetics approaches were used to explore the genetic regulation of lignin traits in B. napus seeds. Four QTL (qLignin_A09_1, qLignin_A09_2, qLignin_A09_3, and qLignin_C08) distributed on two chromosomes were identified for lignin content. The qLignin_A09_2 and qLignin_C08 loci were homologous QTL from the A and C subgenomes, respectively. Genome-wide gene regulatory network analysis identified eighty-three subnetworks (or modules); and three modules with 910 genes in total, were associated with lignin content, which was confirmed by network QTL analysis. eQTL (expression quantitative trait loci) analysis revealed four cis-eQTL genes including lignin and flavonoid pathway genes, cinnamoyl-CoA-reductase (CCR1), and TRANSPARENT TESTA genes TT4, TT6, TT8, as causal genes. The findings validated the power of systems genetics to identify causal regulatory networks and genes underlying complex traits. Moreover, this information may enable the research community to explore new breeding strategies, such as network selection or gene engineering, to rewire networks to develop climate resilience crops with better seed quality.