Prioritize Candidate Genes Research Articles

Transcriptional regulatory network reveals key transcription factors for regulating agronomic traits in soybean

BackgroundTranscription factors (TFs) bind regulatory genomic regions to orchestrate spatio-temporal expression of target genes. Global dissection of the cistrome is critical for elucidating transcriptional networks underlying complex agronomic traits in crops.ResultsHere, we generate a comprehensive genome-wide binding map for 148 TFs using DNA affinity purification sequencing in soybean. We find TF binding sites (TFBSs) exhibit elevated chromatin accessibility and contain more rare alleles than other genomic regions. Intriguingly, the methylation variations at TFBSs partially contribute to expression bias among whole genome duplication paralogs. Furthermore, we construct a soybean gene regulatory network (SoyGRN) by integrating TF-target interactions with diverse datasets encompassing gene expression, TFBS motifs, chromatin accessibility, and evolutionarily conserved regulation. SoyGRN comprises 2.44 million genome-wide interactions among 3188 TFs and 51,665 target genes. We successfully identify key TFs governing seed coat color and oil content and prioritize candidate genes within quantitative trait loci associated with various agronomic traits using SoyGRN. To accelerate utilization of SoyGRN, we develop an interactive webserver (www.soytfbase.cn) for soybean community to explore functional TFs involved in trait regulation.ConclusionsOverall, our study unravels intricate landscape of TF-target interactions in soybean and provides a valuable resource for dissecting key regulators for control of agronomic traits to accelerate soybean improvement.

Investigating biological nitrogen fixation via single-cell transcriptomics.

The extensive use of nitrogen fertilizers has detrimental environmental consequences, and it is fundamental for society to explore sustainable alternatives. One promising avenue is engineering root nodule symbiosis, a naturally occurring process in certain plant species within the nitrogen-fixing clade, into non-leguminous crops. Advancements in single-cell transcriptomics provide unprecedented opportunities to dissect the molecular mechanisms underlying root nodule symbiosis at the cellular level. This review summarizes key findings from single-cell studies in Medicago truncatula, Lotus japonicus, and Glycine max. We highlight how these studies address fundamental questions about the development of root nodule symbiosis, including the following findings: Single-cell transcriptomics has revealed a conserved transcriptional program in root hair and cortical cells during rhizobial infection, suggesting a common infection pathway across legume species. Characterization of determinate and indeterminate nodules using single-cell technologies supports the compartmentalization of nitrogen fixation, assimilation, and transport into distinct cell populations. Single-cell transcriptomics data has enabled the identification of novel root nodule symbiosis genes and provided new approaches for prioritizing candidate genes for functional characterization. Trajectory inference and RNA velocity analyses of single-cell transcriptomics data have allowed the reconstruction of cellular lineages and dynamic transcriptional states during root nodule symbiosis.

Proteogenomic analysis of human cerebrospinal fluid identifies neurologically relevant regulation and implicates causal proteins for Alzheimer's disease.

The integration of quantitative trait loci (QTLs) with disease genome-wide association studies (GWASs) has proven successful in prioritizing candidate genes at disease-associated loci. QTL mapping has been focused on multi-tissue expression QTLs or plasma protein QTLs (pQTLs). We generated a cerebrospinal fluid (CSF) pQTL atlas by measuring 6,361 proteins in 3,506 samples. We identified 3,885 associations for 1,883 proteins, including 2,885 new pQTLs, demonstrating unique genetic regulation in CSF. We identified CSF-enriched pleiotropic regions on chromosome (chr)3q28 near OSTN and chr19q13.32 near APOE that were enriched for neuron specificity and neurological development. We integrated our associations with Alzheimer's disease (AD) through proteome-wide association study (PWAS), colocalization and Mendelian randomization and identified 38 putative causal proteins, 15 of which have drugs available. Finally, we developed a proteomics-based AD prediction model that outperforms genetics-based models. These findings will be instrumental to further understand the biology and identify causal and druggable proteins for brain and neurological traits.

Analysis of CNS autoimmunity in genetically diverse mice reveals unique phenotypes and mechanisms.

Multiple sclerosis (MS) is a complex disease with significant heterogeneity in disease course and progression. Genetic studies have identified numerous loci associated with MS risk, but the genetic basis of disease progression remains elusive. To address this, we leveraged the Collaborative Cross (CC), a genetically diverse mouse strain panel, and experimental autoimmune encephalomyelitis (EAE). The 32 CC strains studied captured a wide spectrum of EAE severity, trajectory, and presentation, including severe-progressive, monophasic, relapsing remitting, and axial rotary-EAE (AR-EAE), accompanied by distinct immunopathology. Sex differences in EAE severity were observed in 6 strains. Quantitative trait locus analysis revealed distinct genetic linkage patterns for different EAE phenotypes, including EAE severity and incidence of AR-EAE. Machine learning-based approaches prioritized candidate genes for loci underlying EAE severity (Abcc4 and Gpc6) and AR-EAE (Yap1 and Dync2h1). This work expands the EAE phenotypic repertoire and identifies potentially novel loci controlling unique EAE phenotypes, supporting the hypothesis that heterogeneity in MS disease course is driven by genetic variation.

Monoallelic loss-of-function variants in GSK3B lead to autism and developmental delay.

De novo variants adjacent to the canonical splicing sites or in the well-defined splicing-related regions are more likely to impair splicing but remain under-investigated in autism spectrum disorder (ASD). By analyzing large, recent ASD genome sequencing cohorts, we find a significant burden of de novo potential splicing-disrupting variants (PSDVs) in 5048 probands compared to 4090 unaffected siblings. We identified 55 genes with recurrent de novo PSDVs that were highly intolerant to variation. Forty-six of these genes have not been strongly implicated in ASD or other neurodevelopmental disorders previously, including GSK3B. Through international, multicenter collaborations, we assembled genotype and phenotype data for 15 individuals with GSK3B variants and identified common phenotypes including developmental delay, ASD, sleeping disturbance, and aggressive behavior. Using available single-cell transcriptomic data, we show that GSK3B is enriched in dorsal progenitors and intermediate forms of excitatory neurons in the developing brain. We showed that Gsk3b knockdown in mouse excitatory neurons interferes with dendrite arborization and spine maturation which could not be rescued by de novo missense variants identified from affected individuals. In summary, our findings suggest that PSDVs may play an important role in the genetic etiology of ASD and allow for the prioritization of new ASD candidate genes. Importantly, we show that genetic variation resulting in GSK3B loss-of-function can lead to a neurodevelopmental disorder with core features of ASD and developmental delay.

Identifying pathogenic variants in rare pediatric neurological diseases using exome sequencing.

Variant annotations are crucial for efficient identification of pathogenic variants. In this study, we retrospectively analyzed the utility of four annotation tools (allele frequency, ClinVar, SpliceAI, and Phenomatcher) in identifying 271 pathogenic single nucleotide and small insertion/deletion variants (SNVs/small indels). Although variant filtering based on allele frequency is essential for narrowing down on candidate variants, we found that 13 de novo pathogenic variants in autosomal dominant or X-linked dominant genes are registered in gnomADv4.0 or 54KJPN, with an allele frequency of less than 0.001%, suggesting that very rare variants in large cohort data can be pathogenic de novo variants. Notably, 38.4% candidate SNVs/small indels are registered in the ClinVar database as pathogenic or likely pathogenic, which highlights the significance of this database. SpliceAI can detect candidate variants affecting RNA splicing, leading to the identification of four variants located 11 to 50bp away from the exon-intron boundary. Prioritization of candidate genes by proband phenotype using the PhenoMatcher module revealed that approximately 95% of the candidate genes had a maximum PhenoMatch score ≥ 0.6, suggesting the utility of phenotype-based variant prioritization. Our results suggest that a combination of multiple annotation tools and appropriate evaluation can improve the diagnosis of rare diseases.

Human-augmented large language model-driven selection of glutathione peroxidase 4 as a candidate blood transcriptional biomarker for circulating erythroid cells

The identification of optimal candidate genes from large-scale blood transcriptomic data is crucial for developing targeted assays to monitor immune responses. Here, we introduce a novel, optimized large language model (LLM)-based approach for prioritizing candidate biomarkers from blood transcriptional modules. Focusing on module M14.51 from the BloodGen3 repertoire, we implemented a multi-step LLM-driven workflow. Initial high-throughput screening used GPT-4, Claude 3, and Claude 3.5 Sonnet to score and rank the module’s constituent genes across six criteria. Top candidates then underwent high-resolution scoring using Consensus GPT, with concurrent manual fact-checking and, when needed, iterative refinement of the scores based on user feedback. Qualitative assessment of literature-based narratives and analysis of reference transcriptome data further refined the selection process. This novel multi-tiered approach consistently identified Glutathione Peroxidase 4 (GPX4) as the top candidate gene for module M14.51. GPX4’s role in oxidative stress regulation, its potential as a future drug target, and its expression pattern across diverse cell types supported its selection. The incorporation of reference transcriptome data further validated GPX4 as the most suitable candidate for this module. This study presents an advanced LLM-driven workflow with a novel optimized scoring strategy for candidate gene prioritization, incorporating human-in-the-loop augmentation. The approach identified GPX4 as a key gene in the erythroid cell-associated module M14.51, suggesting its potential utility for biomarker discovery and targeted assay development. By combining AI-driven literature analysis with iterative human expert validation, this method leverages the strengths of both artificial and human intelligence, potentially contributing to the development of biologically relevant and clinically informative targeted assays. Further validation studies are needed to confirm the broader applicability of this human-augmented AI approach.

Multiomic analysis of genes related to oil traits in legumes provide insights into lipid metabolism and oil richness in soybean

Soybean (Glycine max) and common bean (Phaseolus vulgaris) diverged approximately 19 million years ago. While these species share a whole-genome duplication (WGD), the Glycine lineage experienced a second, independent WGD. Despite the significance of these WGDs, their impact on gene families related to oil-traits remains poorly understood. Here, we report an in-depth investigation of oil-related gene families in soybean, common bean, and twenty-eight other legume species. We adopted a systematic approach that included a total of 605 RNAseq samples for transcriptome and co-expression analyse s, identification of orthologous groups, gene duplication modes and evolutionary rates, and family expansions and contractions. We curated a list of oil candidate genes and found that 91.5% of the families containing these genes expanded in soybean in comparison to common bean. Notably, we observed an expansion of triacylglycerol (TAG) biosynthesis (∼3:1) and an erosion of TAG degradation (∼1.4:1) families in soybean in comparison to common bean. In addition, TAG degradation genes were two-fold more expressed in common bean than in soybean, suggesting that oil degradation is also important for the sharply contrasting seed oil contents in these species. We found 17 transcription factor hub genes that are likely regulators of lipid metabolism. Finally, we inferred expanded and contracted families and correlated these patterns with oil content found in different legume species. In summary, our results do not only shed light on the evolution of oil metabolism genes in soybean, but also present multifactorial evidence supporting the prioritization of promising candidate genes that, if experimentally validated, could accelerate the development of high-oil soybean varieties.

Genome-Wide Association Study Reveals the Genetic Architecture of Growth and Meat Production Traits in a Chicken F2 Resource Population.

For genomic selection to enhance the efficiency of broiler production, finding SNPs and candidate genes that define the manifestation of main selected traits is essential. We conducted a genome-wide association study (GWAS) for growth and meat productivity traits of roosters from a chicken F2 resource population (n = 152). The population was obtained by crossing two breeds with contrasting phenotypes for performance indicators, i.e., Russian White (slow-growing) and Cornish White (fast-growing). The birds were genotyped using the Illumina Chicken 60K SNP iSelect BeadChip. After LD filtering of the data, 54,188 SNPs were employed for the GWAS analysis that allowed us to reveal significant specific associations for phenotypic traits of interest and economic importance. At the threshold value of p < 9.2 × 10-7, 83 SNPs associated with body weight at the age of 28, 42, and 63 days were identified, as well as 171 SNPs associated with meat qualities (average daily gain, slaughter yield, and dressed carcass weight and its components). Moreover, 34 SNPs were associated with a group of three or more traits, including 15 SNPs significant for a group of growth traits and 5 SNPs for a group of meat productivity indicators. Relevant to these detected SNPs, nine prioritized candidate genes associated with the studied traits were revealed, including WNT2, DEPTOR, PPA2, UNC80, DDX51, PAPPA, SSC4D, PTPRU, and TLK2. The found SNPs and candidate genes can serve as genetic markers for growth and meat performance characteristics in chicken breeding in order to achieve genetic improvement in broiler production.

PSIV-5 Prioritizing candidate genes for seminal traits and residual feed intake in cattle: Integrating QTLome Analysis and RNAseq Datasets

Abstract The QTLome represents the collection of QTLs (quantitative trait loci) documented for each trait within a specific species, detailing the position, effect, and mode of inheritance of each locus. Despite the abundance of known QTLs for productive traits in cattle, only a few genes have been identified as selection markers. This study aimed to prioritize genes within validated QTLs associated with crucial traits in cattle by integrating this information with RNAseq data. Initially, bovine QTLs (n = 161,730) were sourced from the AnimalQTLdb database (version 44), and both graphical and quantitative analyses were conducted using R software. Only annotations within the UMD 3.1 genome were considered, with overlapping QTLs from the same scientific report grouped together. Following quality control measures, the number of QTLs was reduced to 37,146. Validated QTLs were determined by identifying overlapping annotations from various studies or those within a distance of &amp;lt; 1 Mb for the same phenotypic trait, resulting in a total of 3,856 validated QTLs. For gene prioritization, validated QTLs were selected based on semen traits and residual feed intake (RFI). Transcriptomes corresponding to these traits were sourced from NCBI (PRJNA516089 and PRJEB7696). Sequence filtering was performed using the fastp program, and transcript counts were obtained with SALMON software. Sequences were aligned to the Ensembl UMD 3.1 reference genome (version 94), and differential expression analysis was conducted using the R package DESeq2, considering only the group factor (high/low) for each trait (fertility/RFI). Integration of QTLs and transcriptomes utilized the methodology of the cageminer R package, which prioritizes candidate genes within validated QTLs, identifies those grouped in co-expression modules enriched by reference genes, and correlates with the study phenotype (biserial r &amp;gt; 0.2). Reference genes were obtained from Ignatieva et al. (2021 PMID 34290736) and Chen et al. (2021; PMID 33664767). Among the 18 validated QTLs for semen traits, four candidate genes (TSPAN6, QSER1, ADRB2, AKAP4) were identified. Except for ADRB2, these genes exhibited significant differential expression (P &amp;lt; 0.05) in contrasting groups of Holstein cattle. They are associated with semen traits and perform functions such as regulation of spermatogenesis, development, activation, and cell growth. Regarding RFI, 127 validated QTLs yielded 205 prioritized genes, of which 123 showed significant differential expression (P &amp;lt; 0.05) in divergent groups of the Nelore breed. These genes are involved in the immune response and the polymerization and organization of actin filaments, processes that have been reported as biologically relevant to RFI. The implemented strategy was efficient for gene prioritization through the integration of QTLs and RNAseq.

Prioritization of candidate genes for major QTLs governing yield traits employing integrated multi-omics approach in rice (Oryza sativa L.).

Rapidly identifying candidate genes underlying major QTLs is crucial for improving rice (Oryza sativa L.). In this study, we developed a workflow to rapidly prioritize candidate genes underpinning 99 major QTLs governing yield component traits. This workflow integrates multiomics databases, including sequence variation, gene expression, gene ontology, co-expression analysis, and protein-protein interaction. We predicted 206 candidate genes for 99 reported QTLs governing ten economically important yield-contributing traits using this approach. Among these, transcription factors belonging to families of MADS-box, WRKY, helix-loop-helix, TCP, MYB, GRAS, auxin response factor, and nuclear transcription factor Y subunit were promising. Validation of key prioritized candidate genes in contrasting rice genotypes for sequence variation and differential expression identified Leucine-Rich Repeat family protein (LOC_Os03g28270) and cytochrome P450 (LOC_Os02g57290) as candidate genes for the major QTLs GL1 and pl2.1, which govern grain length and panicle length, respectively. In conclusion, this study demonstrates that our workflow can significantly narrow down a large number of annotated genes in a QTL to a very small number of the most probable candidates, achieving approximately a 21-fold reduction. These candidate genes have potential implications for enhancing rice yield.

Genetic Foundations of Nellore Traits: A Gene Prioritization and Functional Analyses of Genome-Wide Association Study Results.

The main goal of this study was to pinpoint functional candidate genes associated with multiple economically important traits in Nellore cattle. After quality control, 1830 genomic regions sourced from 52 scientific peer-reviewed publications were used in this study. From these, a total of 8569 positional candidate genes were annotated for reproduction, 11,195 for carcass, 5239 for growth, and 3483 for morphological traits, and used in an over-representation analysis. The significant genes (adjusted p-values < 0.05) identified in the over-representation analysis underwent prioritization analyses, and enrichment analysis of the prioritized over-represented candidate genes was performed. The prioritized candidate genes were GFRA4, RFWD3, SERTAD2, KIZ, REM2, and ANKRD34B for reproduction; RFWD3, TMEM120A, MIEF2, FOXRED2, DUSP29, CARHSP1, OBI1, JOSD1, NOP58, and LOXL1-AS1 for the carcass; ANKRD34B and JOSD1 for growth traits; and no genes were prioritized for morphological traits. The functional analysis pinpointed the following genes: KIZ (plays a crucial role in spindle organization, which is essential in forming a robust mitotic centrosome), DUSP29 (involved in muscle cell differentiation), and JOSD1 (involved in protein deubiquitination, thereby improving growth). The enrichment of the functional candidate genes identified in this study highlights that these genes play an important role in the expression of reproduction, carcass, and growth traits in Nellore cattle.

A graph theoretical approach to experimental prioritization in genome-scale investigations

The goal of systems biology is to gain a network level understanding of how gene interactions influence biological states, and ultimately inform upon human disease. Given the scale and scope of systems biology studies, resource constraints often limit researchers when validating genome-wide phenomena and potentially lead to an incomplete understanding of the underlying mechanisms. Further, prioritization strategies are often biased towards known entities (e.g. previously studied genes/proteins with commercially available reagents), and other technical issues that limit experimental breadth. Here, heterogeneous biological information is modeled as an association graph to which a high-performance minimum dominating set solver is applied to maximize coverage across the graph, and thus increase the breadth of experimentation. First, we tested our model on retrieval of existing gene functional annotations and demonstrated that minimum dominating set returns more diverse terms when compared to other computational methods. Next, we utilized our heterogenous network and minimum dominating set solver to assist in the process of identifying understudied genes to be interrogated by the International Mouse Phenotyping Consortium. Using an unbiased algorithmic strategy, poorly studied genes are prioritized from the remaining thousands of genes yet to be characterized. This method is tunable and extensible with the potential to incorporate additional user-defined prioritizing information. The minimum dominating set approach can be applied to any biological network in order to identify a tractable subset of features to test experimentally or to assist in prioritizing candidate genes associated with human disease.

Super enhancer acquisition drives expression of oncogenic PPP1R15B that regulates protein homeostasis in multiple myeloma

Multiple myeloma is a hematological malignancy arising from immunoglobulin-secreting plasma cells. It remains poorly understood how chromatin rewiring of regulatory elements contributes to tumorigenesis and therapy resistance in myeloma. Here we generate a high-resolution contact map of myeloma-associated super-enhancers by integrating H3K27ac ChIP-seq and HiChIP from myeloma cell lines, patient-derived myeloma cells and normal plasma cells. Our comprehensive transcriptomic and phenomic analyses prioritize candidate genes with biological and clinical implications in myeloma. We show that myeloma cells frequently acquire SE that transcriptionally activate an oncogene PPP1R15B, which encodes a regulatory subunit of the holophosphatase complex that dephosphorylates translation initiation factor eIF2α. Epigenetic silencing or knockdown of PPP1R15B activates pro-apoptotic eIF2α-ATF4-CHOP pathway, while inhibiting protein synthesis and immunoglobulin production. Pharmacological inhibition of PPP1R15B using Raphin1 potentiates the anti-myeloma effect of bortezomib. Our study reveals that myeloma cells are vulnerable to perturbation of PPP1R15B-dependent protein homeostasis, highlighting a promising therapeutic strategy.

Unraveling the molecular pathogenesis of Type 2 Diabetes and its impact on female infertility: A bioinformatics and systems biology approach

Type 2 diabetes mellitus (T2D) has been linked with female infertility (FI). Nevertheless, our understanding of the molecular hallmarks and underlying mechanisms remains elusive. This research article aimed to find the hub genes, pathways, transcription factors, and miRNA involved. For this study, softwares like cytoscape, string, Enrichr, FFL loop, etc., were utilized. This research article employed differentially expressed genes (DEGs) to identify multiple biological targets to understand the association between T2D and female infertility (FI). Between T2D and FI, we found 3869 differentially expressed genes. We have also analyzed different pathways like thyroid hormone signaling pathways, AGE-RAGE signaling pathways in diabetic complications and ubiquitin-mediated proteolysis through pathway analysis. Moreover, hub genes MED17, PRKCG, THRA, FOXO1, NCOA2, PLCG2, COL1A1, CXCL8, PRPF19, ANAPC5, UBE2I, XIAP and KEAP1 have been identified. Additionally, these hub genes were subjected to identify the miRNA-mRNA regulation network specific to T2D-associated female infertility. In the FFL study (Feed Forward Loop), transcription factor (SP1, NFKB1, RELA and FOX01), miRNA (has-mir-7-5p, has-let-7a-5p, hsa-mir-16-5p, hsa-mir-155-5p, has-mir-122-5p, has-let-7b-5p, has-mir-124-3p, has-mir-34a-5p, has-mir-130a-3p, has-let-7i-5p, and hsa-mir-27a-3p) and six genes (XIAP, THRA, NCOA2, MED17, FOXO1, and COL1A1) among the thirteen key genes were recognized as regulator and inhibitor. Our analysis reveals that these genes can serve as a significant biomarker for female infertility linked with Type 2 Diabetes, through the prioritization of candidate genes. This study gives us insight into the molecular and cellular mechanism of T2D-associated FI. This finding helps in developing novel therapeutic approaches and will improve efficacy and reduce side effects of the treatment. This research requires further experimental investigation of the principal targets.

Integrating GWAS with a gene co-expression network better prioritizes candidate genes associated with root metaxylem phenes in maize.

Root metaxylems are phenotypically diverse structures whose function is particularly important under drought stress. Significant research has dissected the genetic machinery underlying metaxylem phenotypes in dicots, but that of monocots are relatively underexplored. In maize (Zea mays), a robust pipeline integrated a genome-wide association study (GWAS) of root metaxylem phenes under well-watered and water-stress conditions with a gene co-expression network to prioritize the strongest gene candidates. We identified 244 candidate genes by GWAS, of which 103 reside in gene co-expression modules most relevant to xylem development. Several candidate genes may be involved in biosynthetic processes related to the cell wall, hormone signaling, oxidative stress responses, and drought responses. Of those, six gene candidates were detected in multiple root metaxylem phenes in both well-watered and water-stress conditions. We posit that candidate genes that are more essential to network function based on gene co-expression (i.e., hubs or bottlenecks) should be prioritized and classify 33 essential genes for further investigation. Our study demonstrates a new strategy for identifying promising gene candidates and presents several gene candidates that may enhance our understanding of vascular development and responses to drought in cereals.

Burden re-analysis of neurodevelopmental disorder cohorts for prioritization of candidate genes.

This study aimed to uncover novel genes associated with neurodevelopmental disorders (NDD) by leveraging recent large-scale de novo burden analysis studies to enhance a virtual gene panel used in a diagnostic setting. We re-analyzed historical trio-exome sequencing data from 745 individuals with NDD according to the most recent diagnostic standards, resulting in a cohort of 567 unsolved individuals. Next, we designed a virtual gene panel containing candidate genes from three large de novo burden analysis studies in NDD and prioritized candidate genes by stringent filtering for ultra-rare de novo variants with high pathogenicity scores. Our analysis revealed an increased burden of de novo variants in our selected candidate genes within the unsolved NDD cohort and identified qualifying de novo variants in seven candidate genes: RIF1, CAMK2D, RAB11FIP4, AGO3, PCBP2, LEO1, and VCP. Clinical data were collected from six new individuals with de novo or inherited LEO1 variants and three new individuals with de novo PCBP2 variants. Our findings add additional evidence for LEO1 as a risk gene for autism and intellectual disability. Furthermore, we prioritize PCBP2 as a candidate gene for NDD associated with motor and language delay. In summary, by leveraging de novo burden analysis studies, employing a stringent variant filtering pipeline, and engaging in targeted patient recruitment, our study contributes to the identification of novel genes implicated in NDDs.

Identification of a novel QTL for lean meat percentage using imputed genotypes.

Lean meat percentage is a critical production trait in pig breeding systems with direct implications for the sustainability of the industry. In this study, we conducted a genome-wide association study for lean meat percentage using a cohort of 850 Duroc × (Landrace × Yorkshire) crossbred pigs and we identified QTL on SSC3 and SSC18. Based on the predicted effect of imputed variants and using the PigGTEx database of molecular QTL, we prioritized candidate genes and SNPs located within the QTL regions, which may be involved in the regulation of porcine leanness. Our results indicate that a nonsense mutation in ZC3HAV1L on SSC18 has a direct effect on lean meat percentage.

Genetic regulation of human brain proteome reveals proteins implicated in psychiatric disorders.

Psychiatric disorders are highly heritable yet polygenic, potentially involving hundreds of risk genes. Genome-wide association studies have identified hundreds of genomic susceptibility loci with susceptibility to psychiatric disorders; however, the contribution of these loci to the underlying psychopathology and etiology remains elusive. Here we generated deep human brain proteomics data by quantifying 11,608 proteins across 268 subjects using 11-plex tandem mass tag coupled with two-dimensional liquid chromatography-tandem mass spectrometry. Our analysis revealed 788 cis-acting protein quantitative trait loci associated with the expression of 883 proteins at a genome-wide false discovery rate <5%. In contrast to expression at the transcript level and complex diseases that are found to be mainly influenced by noncoding variants, we found protein expression level tends to be regulated by non-synonymous variants. We also provided evidence of 76 shared regulatory signals between gene expression and protein abundance. Mediation analysis revealed that for most (88%) of the colocalized genes, the expression levels of their corresponding proteins are regulated by cis-pQTLs via gene transcription. Using summary data-based Mendelian randomization analysis, we identified 4 proteins and 19 genes that are causally associated with schizophrenia. We further integrated multiple omics data with network analysis to prioritize candidate genes for schizophrenia risk loci. Collectively, our findings underscore the potential of proteome-wide linkage analysis in gaining mechanistic insights into the pathogenesis of psychiatric disorders.

Unraveling the genetic architecture of blood unfolded p-53 among non-demented elderlies: novel candidate genes for early Alzheimer's disease

BackgroundAlzheimer's disease (AD) is a heritable neurodegenerative disease whose long asymptomatic phase makes the early diagnosis of it pivotal. Blood U-p53 has recently emerged as a superior predictive biomarker for AD in the early stages. We hypothesized that genetic variants associated with blood U-p53 could reveal novel loci and pathways involved in the early stages of AD.ResultsWe performed a blood U-p53 Genome-wide association study (GWAS) on 484 healthy and mild cognitively impaired subjects from the ADNI cohort using 612,843 Single nucleotide polymorphisms (SNPs). We performed a pathway analysis and prioritized candidate genes using an AD single-cell gene program. We fine-mapped the intergenic SNPs by leveraging a cell-type-specific enhancer-to-gene linking strategy using a brain single-cell multimodal dataset. We validated the candidate genes in an independent brain single-cell RNA-seq and the ADNI blood transcriptome datasets. The rs279686 between AASS and FEZF1 genes was the most significant SNP (p-value = 4.82 × 10–7). Suggestive pathways were related to the immune and nervous systems. Twenty-three candidate genes were prioritized at 27 suggestive loci. Fine-mapping of 5 intergenic loci yielded nine cell-specific candidate genes. Finally, 15 genes were validated in the independent single-cell RNA-seq dataset, and five were validated in the ADNI blood transcriptome dataset.ConclusionsWe underlined the importance of performing a GWAS on an early-stage biomarker of AD and leveraging functional omics datasets for pinpointing causal genes in AD. Our study prioritized nine genes (SORCS1, KIF5C, TMEFF2, TMEM63C, HLA-E, ATAT1, TUBB, ARID1B, and RUNX1) strongly implicated in the early stages of AD.