Expression Quantitative Trait Loci Research Articles

KLF2 enhancer variant rs4808485 increases lupus risk by modulating inflammasome machinery and cellular homoeostasis

ObjectiveA recent genome-wide association study linked KLF2 as a novel Asian-specific locus for systemic lupus erythematosus (SLE) susceptibility. However, the underlying causal functional variant(s), cognate target gene(s) and genetic mechanisms...

Associations of lipids and lipid-modifying drug target genes with atrial fibrillation risk based on genomic data

BackgroundThe causal associations of lipids and the drug target genes with atrial fibrillation (AF) risk remain obscure. We aimed to investigate the causal associations using genetic evidence.MethodsMendelian randomization (MR) analyses were conducted using summary-level genome-wide association studies (GWASs) in European and East Asian populations. Lipid profiles (low-density lipoprotein cholesterol, triglyceride, and lipoprotein[a]) and lipid-modifying drug target genes (3-hydroxy-3-methylglutaryl-CoA reductase, proprotein convertase subtilisin/kexin type 9, NPC1-like intracellular cholesterol transporter 1, apolipoprotein C3, angiopoietin-like 3, and lipoprotein[a]) were used as exposures. AF was used as an outcome. The inverse variance weighted method was applied as the primary method. Summary-data-based Mendelian randomization analyses were performed for further validation using expression quantitative trait loci data. Mediation analyses were conducted to explore the indirect effect of coronary heart disease.ResultsIn the European population, MR analyses demonstrated that elevated levels of lipoprotein(a) increased AF risk. Moreover, analyses focusing on drug targets revealed that the genetically proxied target gene LPA, which simulates the effects of drug intervention by reducing lipoprotein(a), exhibited an association with AF risk. This association was validated in independent datasets. There were no consistent and significant associations observed for other traits when analyzed in different datasets. This finding was also corroborated by Summary-data-based Mendelian randomization analyses between LPA and AF. Mediation analyses revealed that coronary heart disease plays a mediating role in this association. However, in the East Asian population, no statistically significant evidence was observed to support these associations.ConclusionsThis study provided genetic evidence that Lp(a) may be a causal factor for AF and that LPA may represent a promising pharmacological target for preventing AF in the European population.

Therapeutic targets for age-related macular degeneration: proteome-wide Mendelian randomization and colocalization analyses.

Currently, effective therapeutic drugs for age-related macular degeneration (AMD) are urgently needed, and it is crucial to explore new treatment targets. The proteome is indispensable for exploring disease targets, so we conducted a Mendelian randomization (MR) of the proteome to identify new targets for AMD and its related subtypes. The plasma protein level data used in this study were obtained from two large-scale studies of protein quantitative trait loci (pQTL), comprising 35,559 and 54,219 samples, respectively. The expression quantitative trait loci (eQTL) data were sourced from eQTLGen and GTEx Version 8. The discovery set for AMD data and subtypes was derived from the FinnGen study, consisting of 9,721 AMD cases and 381,339 controls, 5,239 wet AMD cases and 273,920 controls, and 6,651 dry AMD cases and 272,504 controls. The replication set for AMD data was obtained from the study by Winkler TW et al., comprising 14,034 cases and 91,234 controls. Summary Mendelian randomization (SMR) analysis was employed to assess the association between QTL data and AMD and its subtypes, while colocalization analysis was performed to determine whether they share causal variants. Additionally, chemical exploration and molecular docking were utilized to validate potential drugs targeting the identified proteins. SMR and colocalization analysis jointly identified risk-associated proteins for AMD and its subtypes, including 5 proteins (WARS1, BRD2, IL20RB, TGFB1, TNFRSF10A) associated with AMD, 2 proteins (WARS1, IL20RB) associated with Dry-AMD, and 9 proteins (COL10A1, WARS1, VTN, SDF2, LBP, CD226, TGFB1, TNFRSF10A, CSF2) associated with Wet-AMD. The results revealed potential therapeutic chemicals, and molecular docking indicated a good binding between the chemicals and protein structures. Proteome-wide MR have identified risk-associated proteins for AMD and its subtypes, suggesting that these proteins may serve as potential therapeutic targets worthy of further clinical investigation.

A systems genomics and genetics approach to identify the genetic regulatory network for lignin content in Brassica napus seeds.

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.

Exploring the Interplay between the Hologenome and Complex Traits in Bovine and Porcine Animals Using Genome-Wide Association Analysis.

Genome-wide association studies (GWAS) significantly enhance our ability to identify trait-associated genomic variants by considering the host genome. Moreover, the hologenome refers to the host organism's collective genetic material and its associated microbiome. In this study, we utilized the hologenome framework, called Hologenome-wide association studies (HWAS), to dissect the architecture of complex traits, including milk yield, methane emissions, rumen physiology in cattle, and gut microbial composition in pigs. We employed four statistical models: (1) GWAS, (2) Microbial GWAS (M-GWAS), (3) HWAS-CG (hologenome interaction estimated using COvariance between Random Effects Genome-based restricted maximum likelihood (CORE-GREML)), and (4) HWAS-H (hologenome interaction estimated using the Hadamard product method). We applied Bonferroni correction to interpret the significant associations in the complex traits. The GWAS and M-GWAS detected one and sixteen significant SNPs for milk yield traits, respectively, whereas the HWAS-CG and HWAS-H each identified eight SNPs. Moreover, HWAS-CG revealed four, and the remaining models identified three SNPs each for methane emissions traits. The GWAS and HWAS-CG detected one and three SNPs for rumen physiology traits, respectively. For the pigs' gut microbial composition traits, the GWAS, M-GWAS, HWAS-CG, and HWAS-H identified 14, 16, 13, and 12 SNPs, respectively. We further explored these associations through SNP annotation and by analyzing biological processes and functional pathways. Additionally, we integrated our GWA results with expression quantitative trait locus (eQTL) data using transcriptome-wide association studies (TWAS) and summary-based Mendelian randomization (SMR) methods for a more comprehensive understanding of SNP-trait associations. Our study revealed hologenomic variability in agriculturally important traits, enhancing our understanding of host-microbiome interactions.

Genetic variability of aquaporin expression in maize: from eQTLs to a MITE insertion regulating PIP2; 5 expression.

Plant aquaporins are involved in numerous physiological processes, such as cellular homeostasis, tissue hydraulics, transpiration, and nutrient supply, and are key players of the response to environmental cues. While varying expression patterns of aquaporin genes have been described across organs, developmental stages and stress conditions, the underlying regulation mechanisms remain elusive. Hence, this work aimed to shed light on the expression variability of four plasma membrane intrinsic protein (PIP) genes in maize (Zea mays) leaves, and its genetic causes, through eQTL (expression quantitative trait locus) mapping across a 252-hybrid diversity panel. Significant genetic variability in PIP transcript abundance was observed to different extents depending on the isoforms. The genome-wide association study mapped numerous eQTLs, both local and distant, thus emphasizing the existing natural diversity of PIP gene expression across the studied panel and the potential to reveal regulatory actors and mechanisms. One eQTL associated with PIP2; 5 expression variation was characterized. Genomic sequence comparison and in vivo reporter assay attributed, at least partly, the local eQTL to a transposon-containing polymorphism in the PIP2; 5 promoter. This work paves the way to the molecular understanding of PIP gene regulation and its possible integration into larger networks regulating physiological and stress-adaptation processes.

Systematic druggable genome-wide Mendelian randomization identifies therapeutic targets for lung cancer

BackgroundDrug repurposing provides a cost-effective approach to address the need for lung cancer prevention and therapeutics. We aimed to identify actionable druggable targets using Mendelian randomization (MR).MethodsSummary-level data of gene expression quantitative trait loci (eQTLs) were sourced from the eQTLGen resource. We procured genetic associations with lung cancer and its subtypes from the TRICL, ILCCO studies (discovery) and the FinnGen study (replication). We implemented Summary-data-based Mendelian Randomization analysis to identify potential therapeutic targets for lung cancer. Colocalization analysis was further conducted to assess whether the identified signal pairs shared a causal genetic variant.FindingsIn the main analysis dataset, we identified 55 genes that demonstrate a causal relationship with lung cancer and its subtypes. However, in the replication cohort, only three genes were found to have such a causal association with lung cancer and its subtypes, and of these, HYKK (also known as AGPHD1) was consistently present in both the primary analysis dataset and the replication cohort. Following HEIDI tests and colocalization analyses, it was revealed that HYKK (AGPHD1) is associated with an increased risk of squamous cell carcinoma of the lung, with an odds ratio and confidence interval of OR = 1.28,95%CI = 1.24 to 1.33.InterpretationWe have found that the HYKK (AGPHD1) gene is associated with an increased risk of squamous cell carcinoma of the lung, suggesting that this gene may represent a potential therapeutic target for both the prevention and treatment of lung squamous cell carcinoma.

Chronic Decrements in Energy in Women with Breast Cancer are Associated with Cytokine Gene Polymorphisms

ObjectivesDecrements in energy were found in 67% of women who underwent breast cancer surgery. However, no information is available on chronic decrements in energy and associations with inflammation. Purposes were to identify latent classes of patients with distinct average energy profiles from prior to through 12 months after breast cancer surgery; evaluate for differences in demographic and clinical characteristics between the two extreme average energy classes; and evaluate for polymorphisms for cytokine genes associated with membership in the Low energy class. MethodsWomen (n = 397) completed assessments of energy prior to and for 12 months following breast cancer surgery. Growth mixture modeling was used to identify classes of patients with distinct average energy profiles. Eighty-two single nucleotide polymorphisms (SNPs) among 15 cytokine genes were evaluated. ResultsThree distinct energy profiles were identified (ie, Low [27.0%], Moderate [54.4%], Changing [18.6%]). Data from patients in the Low and Moderate energy classes were used in the candidate gene analyses. Five SNPs and one haplotype in six different genes remained significant in logistic regression analyses (ie, interleukin [IL]-1β rs1143623, IL1 receptor 1 rs3917332 IL4 rs2243263, IL6 HapA1 [that consisted of rs1800795, rs2069830, rs2069840, rs1554606, rs2069845, rs2069849, and rs2069861], nuclear factor kappa beta subunit 1 rs170731, tumor necrosis factor rs1799964). For several SNPs for IL6, expression quantitative trait locis were identified in subcutaneous and visceral adipose tissue and thyroid tissue. In addition, skeletal muscle was identified as an expression quantitative trait loci for nuclear factor kappa beta subunit 1. ConclusionsFindings suggest that cytokine genes are involved in the mechanisms that underlie chronic decrements in energy in women following breast cancer surgery. Given the roles of subcutaneous and visceral adipose and thyroid tissues in metabolism and energy balance, the findings related to IL6 suggest that these polymorphisms may have a functional role in the development and maintenance of chronic decrements in energy.

Single-cell and genome-wide Mendelian randomization identifies causative genes for gout

BackgroundGout is a prevalent manifestation of metabolic osteoarthritis induced by elevated blood uric acid levels. The purpose of this study was to investigate the mechanisms of gene expression regulation in gout disease and elucidate its pathogenesis.MethodsThe study integrated gout genome-wide association study (GWAS) data, single-cell transcriptomics (scRNA-seq), expression quantitative trait loci (eQTL), and methylation quantitative trait loci (mQTL) data for analysis, and utilized two-sample Mendelian randomization study to comprehend the causal relationship between proteins and gout.ResultsWe identified 17 association signals for gout at unique genetic loci, including four genes related by protein-protein interaction network (PPI) analysis: TRIM46, THBS3, MTX1, and KRTCAP2. Additionally, we discerned 22 methylation sites in relation to gout. The study also found that genes such as TRIM46, MAP3K11, KRTCAP2, and TM7SF2 could potentially elevate the risk of gout. Through a Mendelian randomization (MR) analysis, we identified three proteins causally associated with gout: ADH1B, BMP1, and HIST1H3A.ConclusionAccording to our findings, gout is linked with the expression and function of particular genes and proteins. These genes and proteins have the potential to function as novel diagnostic and therapeutic targets for gout. These discoveries shed new light on the pathological mechanisms of gout and clear the way for future research on this condition.

Optimal variable identification for accurate detection of causal expression Quantitative Trait Loci with applications in heart-related diseases

Gene expression plays a pivotal role in various diseases, contributing significantly to their mechanisms. Most GWAS risk loci are in non-coding regions, potentially affecting disease risk by altering gene expression in specific tissues. This expression is notably tissue-specific, with genetic variants substantially influencing it. However, accurately detecting the expression Quantitative Trait Loci (eQTL) is challenging due to limited heritability in gene expression, extensive linkage disequilibrium (LD), and multiple causal variants. The single variant association approach in eQTL analysis is limited by its susceptibility to capture the combined effects of multiple variants, and a bias towards common variants, underscoring the need for a more robust method to accurately identify causal eQTL variants. To address this, we developed an algorithm, CausalEQTL, which integrates L0 +L1 penalized regression with an ensemble approach to localize eQTL, thereby enhancing prediction performance precisely. Our results demonstrate that CausalEQTL outperforms traditional models, including LASSO, Elastic Net, Ridge, in terms of power and overall performance. Furthermore, analysis of heart tissue data from the GTEx project revealed that eQTL sites identified by our algorithm provide deeper insights into heart-related tissue eQTL detection. This advancement in eQTL mapping promises to improve our understanding of the genetic basis of tissue-specific gene expression and its implications in disease. The source code and identified causal eQTLs for CausalEQTL are available on GitHub: https://github.com/zhc-moushang/CausalEQTL.

DeepCBA: A deep learning framework for gene expression prediction in maize based on DNA sequences and chromatin interactions

Chromatin interactions create spatial proximity between distal regulatory elements and target genes in the genome, which has an important impact on gene expression, transcriptional regulation, and phenotypic traits. To date, several methods have been developed for predicting gene expression. However, existing methods do not take into consideration the effect of chromatin interactions on target gene expression, thus potentially reducing the accuracy of gene expression prediction and mining of important regulatory elements. In this study, we developed a highly accurate deep learning-based gene expression prediction model (DeepCBA) based on maize chromatin interaction data. Compared with existing models, DeepCBA exhibits higher accuracy in expression classification and expression value prediction. The average Pearson correlation coefficients (PCCs) for predicting gene expression using gene promoter proximal interactions, proximal-distal interactions, and both proximal and distal interactions were 0.818, 0.625, and 0.929, respectively, representing an increase of 0.357, 0.16, and 0.469 over the PCCs obtained with traditional methods that use only gene proximal sequences. Some important motifs were identified through DeepCBA; they were enriched in open chromatin regions and expression quantitative trait loci and showed clear tissue specificity. Importantly, experimental results for the maize flowering-related gene ZmRap2.7 and the tillering-related gene ZmTb1 demonstrated the feasibility of DeepCBA for exploration of regulatory elements that affect gene expression. Moreover, promoter editing and verification of two reported genes (ZmCLE7 and ZmVTE4) demonstrated the utility of DeepCBA for the precise design of gene expression and even for future intelligent breeding. DeepCBA is available at http://www.deepcba.com/ or http://124.220.197.196/.

A meta-analysis GWAS of taxane-induced peripheral neuropathy in patients with breast cancer.

12089 Background: Taxane-induced peripheral neuropathy (TIPN) is a common dose-limiting toxicity in the medical management of breast cancer patients. Despite the clinical efficacy of taxane treatment, there is significant heterogeneity in the development of neuropathy and its severity, and no clear genetic etiologies have yet been identified. We sought to discover genetic risk variants in two large electronic health record linked DNA biobanks. Methods: A meta-analysis genome wide association study (GWAS) was performed with participants in the All of Us Research Program and BioVU biobanks. All participants of the study population were of female sex at birth and had a breast cancer diagnosis. Cases were defined as taxane-exposed participants who received their first neuropathy diagnosis code within one year after first taxane exposure. Controls were defined as taxane-exposed participants without a neuropathy diagnosis code and without gabapentin initiation after taxane exposure. GWAS was performed with PLINK version 2.0 using age at taxane initiation, number of taxane cycles received, and the first 10 principal components as covariates. Expression quantitative trait loci (eQTL) analysis was performed with the GTEx 8.0 and EyeGEx databases. Results: The All of Us cohort had 196 cases and 254 controls. The BioVU cohort had 244 cases and 528 controls. In our meta-analysis GWAS, rs3065465 (chr11:69138693; closest gene by distance is LOC338694) was associated with TIPN (OR=2.14, P= 8.27E-09) and was similar across different genetic ancestries (Table). The eQTL analysis of rs3065465 shows significant association with mRNA expression of TPCN2(P= 6.44E-06), a cation channel involved in neuronal differentiation and intracellular calcium homeostasis. Conclusions: rs3065465 represents a potential novel genetic marker for TIPN risk in breast cancer patients.[Table: see text]

TWAS facilitates gene-scale trait genetic dissection through gene expression, structural variations, and alternative splicing in soybean

A genome-wide association study (GWAS) identifies trait-associated loci, but identifying the causal genes can be a bottleneck, due in part to slow decay of linkage disequilibrium (LD). A transcriptome-wide association study (TWAS) addresses this issue by identifying gene expression–phenotype associations or integrating gene expression quantitative trait loci with GWAS results. Here, we used self-pollinated soybean (Glycine max [L.] Merr.) as a model to evaluate the application of TWAS to the genetic dissection of traits in plant species with slow LD decay. We generated RNA sequencing data for a soybean diversity panel and identified the genetic expression regulation of 29 286 soybean genes. Different TWAS solutions were less affected by LD and were robust to the source of expression, identifing known genes related to traits from different tissues and developmental stages. The novel pod-color gene L2 was identified via TWAS and functionally validated by genome editing. By introducing a new exon proportion feature, we significantly improved the detection of expression variations that resulted from structural variations and alternative splicing. As a result, the genes identified through our TWAS approach exhibited a diverse range of causal variations, including SNPs, insertions or deletions, gene fusion, copy number variations, and alternative splicing. Using this approach, we identified genes associated with flowering time, including both previously known genes and novel genes that had not previously been linked to this trait, providing insights complementary to those from GWAS. In summary, this study supports the application of TWAS for candidate gene identification in species with low rates of LD decay.

Bayesian genome-wide TWAS with reference transcriptomic data of brain and blood tissues identified 141 risk genes for Alzheimer’s disease dementia

BackgroundTranscriptome-wide association study (TWAS) is an influential tool for identifying genes associated with complex diseases whose genetic effects are likely mediated through transcriptome. TWAS utilizes reference genetic and transcriptomic data to estimate effect sizes of genetic variants on gene expression (i.e., effect sizes of a broad sense of expression quantitative trait loci, eQTL). These estimated effect sizes are employed as variant weights in gene-based association tests, facilitating the mapping of risk genes with genome-wide association study (GWAS) data. However, most existing TWAS of Alzheimer's disease (AD) dementia are limited to studying only cis-eQTL proximal to the test gene. To overcome this limitation, we applied the Bayesian Genome-wide TWAS (BGW-TWAS) method to leveraging both cis- and trans- eQTL of brain and blood tissues, in order to enhance mapping risk genes for AD dementia.MethodsWe first applied BGW-TWAS to the Genotype-Tissue Expression (GTEx) V8 dataset to estimate cis- and trans- eQTL effect sizes of the prefrontal cortex, cortex, and whole blood tissues. Estimated eQTL effect sizes were integrated with the summary data of the most recent GWAS of AD dementia to obtain BGW-TWAS (i.e., gene-based association test) p-values of AD dementia per gene per tissue type. Then we used the aggregated Cauchy association test to combine TWAS p-values across three tissues to obtain omnibus TWAS p-values per gene.ResultsWe identified 85 significant genes in prefrontal cortex, 82 in cortex, and 76 in whole blood that were significantly associated with AD dementia. By combining BGW-TWAS p-values across these three tissues, we obtained 141 significant risk genes including 34 genes primarily due to trans-eQTL and 35 mapped risk genes in GWAS Catalog. With these 141 significant risk genes, we detected functional clusters comprised of both known mapped GWAS risk genes of AD in GWAS Catalog and our identified TWAS risk genes by protein-protein interaction network analysis, as well as several enriched phenotypes related to AD.ConclusionWe applied BGW-TWAS and aggregated Cauchy test methods to integrate both cis- and trans- eQTL data of brain and blood tissues with GWAS summary data, identifying 141 TWAS risk genes of AD dementia. These identified risk genes provide novel insights into the underlying biological mechanisms of AD dementia and potential gene targets for therapeutics development.

Integrated genetic analysis identifies a protective effect of MSRA against renal cell carcinoma

Purpose: Deficiencies in methionine sulfoxide reductase (MSR) enzymes, which function as a defense system against oxidative stress, have been linked to age-related diseases, including cancer and neurodegenerative diseases. This study aimed to assess the association between genetic variants in MSR genes and susceptibility to renal cell carcinoma (RCC). Materials and methods: We systematically evaluated the effects of 89 common MSR gene polymorphisms on the risk of developing RCC in a cohort of 630 patients and controls. Furthermore, publicly available gene expression datasets were used to analyze gene expression and patient prognosis. Results: After adjusting for covariates and multiple testing corrections, MSRA rs56198596 and rs11782000 showed significant associations with the risk of RCC. Analysis of expression quantitative trait loci indicated that the risk alleles of these 2 variants tended to correlate with reduced MSRA expression. Pooled analyses of 19 kidney cancer gene expression datasets revealed that RCC exhibited lower MSRA expression than did normal tissues (P < 0.001) and that higher MSRA expression was associated with improved patient prognosis (P < 0.001). Conclusion: These findings suggest that MSRA gene variants may affect the risk of RCC, highlighting the potential protective role of MSRA and its contribution to a favorable RCC prognosis.

Potentially functional genetic variants in interferon regulatory factor family genes are associated with colorectal cancer survival.

Interferon regulatory factor (IRF) family genes play a critical role in colorectal cancer (CRC) development and impact patient survival. This study evaluated the influence of functional single nucleotide polymorphisms (SNPs) in IRF genes on CRC survival, including functional predictions and experimental validations. Multivariate Cox regression analysis identified three linked SNPs as significant survival predictors, with the rs141112353 T/T genotype in the 3'UTR region of IRF6 significantly associated with decreased survival (HR = 1.60, P = 6E-04). Expression quantitative trait loci (eQTL) analysis indicated that the rs141112353 TA > T alteration reduced IRF6 expression. Dual luciferase assays showed lower activity for the T allele in the presence of hsa-miR-548ap-3p. Data from The Cancer Genome Atlas (TCGA) and other databases confirmed lower IRF6 levels in CRC tissues, correlating with worse survival and inversely with M2 macrophage infiltration. In vitro, IRF6 overexpression inhibited CRC cell proliferation and M2 macrophage polarization by downregulating MIF expression. These findings suggest that the IRF6 rs141112353 TA > T variant significantly affects CRC survival, potentially by enhancing miR-548-ap-3p binding affinity.

Characterizing the Relationship between Expression Quantitative Trait Loci (eQTLs), DNA Methylation Quantitative Trait Loci (mQTLs), and Breast Cancer Risk Variants.

To assess the association of a polygenic risk score (PRS) for functional genetic variants with the risk of developing breast cancer. Summary data-based Mendelian randomization (SMR) and heterogeneity in dependent instruments (HEIDI) were used to identify breast cancer risk variants associated with gene expression and DNA methylation levels. A new SMR-based PRS was computed from the identified variants (functional PRS) and compared to an established 313-variant breast cancer PRS (GWAS PRS). The two scores were evaluated in 3560 breast cancer cases and 3383 non-cancer controls and also in a prospective study (n = 10,213) comprising 418 cases. We identified 149 variants showing pleiotropic association with breast cancer risk (eQTLHEIDI > 0.05 = 9, mQTLHEIDI > 0.05 = 165). The discriminatory ability of the functional PRS (AUCcontinuous [95% CI]: 0.540 [0.526 to 0.553]) was found to be lower than that of the GWAS PRS (AUCcontinuous [95% CI]: 0.609 [0.596 to 0.622]). Even when utilizing 457 distinct variants from both the functional and GWAS PRS, the combined discriminatory performance remained below that of the GWAS PRS (AUCcontinuous, combined [95% CI]: 0.561 [0.548 to 0.575]). A binary high/low-risk classification based on the 80th centile PRS in controls revealed a 6% increase in cases using the GWAS PRS compared to the functional PRS. The functional PRS identified an additional 12% of high-risk cases but also led to a 13% increase in high-risk classification among controls. Similar findings were observed in the SCHS prospective cohort, where the GWAS PRS outperformed the functional PRS, and the highest-performing PRS, a combined model, did not significantly improve over the GWAS PRS. While this study identified potentially functional variants associated with breast cancer risk, their inclusion did not substantially enhance the predictive accuracy of the GWAS PRS.

KnockoffHybrid: A knockoff framework for hybrid analysis of trio and population designs in genome-wide association studies

Both trio and population designs are popular study designs for identifying risk genetic variants in genome-wide association studies (GWASs). The trio design, as a family-based design, is robust to confounding due to population structure, whereas the population design is often more powerful due to larger sample sizes. Here, we propose KnockoffHybrid, a knockoff-based statistical method for hybrid analysis of both the trio and population designs. KnockoffHybrid provides a unified framework that brings together the advantages of both designs and produces powerful hybrid analysis while controlling the false discovery rate (FDR) in the presence of linkage disequilibrium and population structure. Furthermore, KnockoffHybrid has the flexibility to leverage different types of summary statistics for hybrid analyses, including expression quantitative trait loci (eQTL) and GWAS summary statistics. We demonstrate in simulations that KnockoffHybrid offers power gains over non-hybrid methods for the trio and population designs with the same number of cases while controlling the FDR with complex correlation among variants and population structure among subjects. In hybrid analyses of three trio cohorts for autism spectrum disorders (ASDs) from the Autism Speaks MSSNG, Autism Sequencing Consortium, and Autism Genome Project with GWAS summary statistics from the iPSYCH project and eQTL summary statistics from the MetaBrain project, KnockoffHybrid outperforms conventional methods by replicating several known risk genes for ASDs and identifying additional associations with variants in other genes, including the PRAME family genes involved in axon guidance and which may act as common targets for human speech/language evolution and related disorders.

Multi-omics and multi-stages integration identified a novel variant associated with silicosis risk.

Assessing the association between candidate single-nucleotide polymorphisms (SNPs) identified by multi-omics approaches and susceptibility to silicosis. RNA-seq analysis was performed to screen the differentially expressed mRNAs in the fibrotic lung tissues of mice exposed to silica particles. Following this, we integrated the SNPs located in the above human homologenes with the silicosis-related genome-wide association study (GWAS) data to select the candidate SNPs. Then, expression quantitative trait locus (eQTL)-SNPs were identified by the GTEx database. Next, we validated the associations between the functional eQTL-SNPs and silicosis susceptibility by additional case-control study. And the contribution of the identified SNP and its host gene in the fibrosis process was further validated by functional experiments. A total of 12 eQTL-SNPs were identified in the screening stage. The results of the validation stage suggested that the variant T allele of rs419540 located in IL12RB1 significantly increased the risk of developing silicosis [additive model: odds ratio (OR) = 1.78, 95% confidence interval (CI) 1.11-2.85, P = 0.017]. Furthermore, the combination of GWAS and the results of validation stage also indicated that the variant T allele of rs419540 in IL12RB1 was associated with increased silicosis risk (additive model: OR = 2.07, 95% CI 1.38-3.12, P < 0.001). Additionally, after knockdown or overexpression of IL12RB1, the levels of pro-inflammatory factors, such as IL-12, IFN-γ, and other pro-inflammatory factors, were correspondingly decreased or increased. The novel eQTL-SNP, rs419540, might increase the risk of silicosis by modulating the expression levels of IL12RB1.

An antisense lncRNA mediated mechanism controls desmoplakin gene expression

Abstract Funding Acknowledgements Type of funding sources: Public Institution(s). Main funding source(s): Department of Innovation, Research and University of the Autonomous Province of Bolzano-South Tyrol (Italy). Background Desmosomal genes are established as definitive genes for Arrhythmogenic Cardiomyopathy (ACM). The desmoplakin encoding gene, DSP, is amongst the most frequently mutated genes in ACM patients. While rare genetic variants within desmosomal genes can cause ACM, common variants might also affect cardiac conduction traits in the general population. Purpose 1) To explore the association between common genetic variants in ACM desmosomal genes (DSC2, DSG2, PKP2, JUP, DSP) and cardiac conduction traits. 2) To investigate the effect of a lncRNA, identified from downstream analyses, on DSP gene expression. Methods P-wave, PR, QRS and QT intervals were measured using standard 12-lead electrocardiograms (ECG) in 4295 participants to the Cooperative Health Research in South Tyrol (CHRIS) study (data release 2). Samples were genotyped using the Illumina HumanOmniExpressExome array and imputed on the 1000 Genome Phase 1 panel. We conducted a genetic association study on 3458 variants within the linkage disequilibrium blocks that encompass desmosomal genes, assuming an additive genetic model, at 1.9x10-4 statistical significance. Mendelian Randomization (MR) analyses integrating ECG traits and Genotype-Tissue Expression (GTEx) database transcription levels were performed. A specific GapmeR was used to downregulate the antisense RP3-512B11.3 lncRNA expression in human induced pluripotent stem cell derived cardiomyocytes (hiPSC-CMs). hiPSC-CMs lysates were analyzed by digital droplet PCR and Wes™ immunoassay system. Results The rs2744389 variant in DSP gene is associated with a shorter QRS interval (-1.10 ms per effect allele copy; p=3.5x10-6). In the GTEx database, rs2744389 is identified as an expression quantitative trait locus for the antisense lncRNA. MR results support a causal effect of the lncRNA on QRS. To assess the hypothesis that the lncRNA regulates DSP gene expression, we identified a Gapmer able to downregulate the lncRNA expression by approximately 60% in hiPSC-CMs. The lncRNA downregulation led to a significant increase of DSP expression, both at mRNA and protein level, compared to hiPSC-CMs treated with a negative control Gapmer (DSP/RPP30: 16.39±3.32 vs 11.84±1.79; p=0.0313), (Desmoplakin/total protein: 2.44±0.29 vs 1.66±0.11; p=0.0156). Conclusions Starting with the identification of an association between a common genetic variant in DSP gene and a significantly shorter QRS interval in a general population sample, we further unrevealed a new mechanism regulating DSP expression through an antisense lncRNA. While requiring further evidence, these findings might pave the way to a potentially novel therapeutic approach for DSP mutations carriers characterized by desmoplakin protein deficit, as for example patients affected by ACM, but also by dilated cardiomyopathy, cardiocutaneous diseases such as the Carvajal syndrome, and some cancer conditions.