Quantifying functional impact of non-coding variants with multi-task Bayesian neural network.

Most disease-associated single-nucleotide polymorphisms (SNPs) identified by genome-wide association studies (GWASs) reside in noncoding regions. There is increasing evidence that they play regulatory roles through epigenetic mechanisms by altering local chromatin state, 3D chromatin structure, and looping interactions. We and others have identified 42 CLL risk loci from GWASs. At 12 risk loci located outside of gene promoters, we previously demonstrated that functional SNPs are highly enriched in enhancers and super-enhancers that mostly target BCL2 family genes and transcriptional regulators. However, the study was limited by using only public epigenomic data generated in lymphoblastoid cell lines (LCL) and normal immune related cell types. To gain a better understanding of how the functional SNPs alter local chromatin state and modulate target gene expression in CLL, we generated RNA-seq data (n=588) and transposase-accessible chromatin with sequencing (ATAC-seq) data (n=69) in CLL tumor B cells. We performed molecular quantitative trait loci (QTL) analysis with index and correlated SNPs (R2 0.5) at 41 risk loci (excluding the HLA locus). The analyses identified cis expression QTLs (eQTLs), within 1Mb of genes’ transcription start site, at 25 risk loci and chromatin accessibility QTLs (caQTLs, within peak) at 11 risk loci, with both eQTLs and caQTLs at 8 risk loci. We identified a total of 54 eGenes, for which the expression is affected by nearby SNPs within 1Mb, including 40 novel eGenes at 16 CLL risk loci not previously reported. Notably, based on publicly available chromosome interaction data from CLL, LCL, and normal B cells, for over 70% of the novel eGenes, their promoters showed looping interactions with the regions carrying the eQTLs. Further, 13 SNPs were identified as both caQTL and eQTL, with the associated novel eGenes including CD81 at 11p15.5, IPCEF1 at 6q25.2, as well as CXXC1 and MBD1 at 18q21.1. The result suggests that these SNPs play a role in altering chromatin accessibility contributed to gene expression variation. At 11p15.5 risk locus, SNP rs2651823 (A/G) represents a novel caQTL and eQTL. The alternative G allele is associated with increased chromatin accessibility and CD81 expression. Hi-C data from GM12878 and CD20+ B cells supported interaction between the open chromatin region carrying rs2651823 and CD81 promoter. CD81 facilitates the trafficking of CD19 to the surface of activated B cells, and CD81-null mice showed impaired immune response. In a mouse model, CD81 contributes to tumor progression and metastasis in Lewis lung carcinoma. In summary, our molecular QTL analysis revealed that functional SNPs, located in CLL risk loci, modulate chromatin accessibility and expression of downstream target genes that could impact CLL risk. Citation Format: Huihuang Yan, Zhiquan Wang, Mingma Hoel, Cristine Allmer, Chantal McCabe, Daniel O’Brien, Dennis Robinson, James Cerhan, Sameer Parikh, Esteban Braggio, Susan Slager. Molecular QTL analyses identify functional variants at inherited risk loci in chronic lymphocytic leukemia [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 2763.

Advances and reduction of costs in various sequencing technologies allow for a closer look at variations present in the non-coding regions of the human genome. Correlating non-coding variants with large-scale multi-omic data holds the promise not only of a better understanding of likely causal connections between non-coding DNA and expression of traits but also identifying potential disease-modifying medicines. Genome-phenome association studies have created large datasets of DNA variants that are associated with multiple traits or diseases, such as Alzheimer's disease; yet, the functional consequences of variants, in particular of non-coding variants, remain largely unknown. Recent advances in functional genomics and computational approaches have led to the identification of potential roles of DNA variants, such as various quantitative trait locus (xQTL) techniques. Multi-omic assays and analytic approaches toward xQTL have identified links between genetic loci and human transcriptomic, epigenomic, proteomic and metabolomic data. In this review, we first discuss the recent development of xQTL from multi-omic findings. We then highlight multimodal analysis of xQTL and genetic data for identification of risk genes and drug targets using Alzheimer's disease as an example. We finally discuss challenges and future research directions (e.g. artificial intelligence) for annotation of non-coding variants in complex diseases.

Studies on autism spectrum disorder (ASD) have amassed substantial evidence for the role of genetics in the disease’s phenotypic manifestation. A large number of coding and non-coding variants with low penetrance likely act in a combinatorial manner to explain the variable forms of ASD. However, many of these combined interactions, both additive and epistatic, remain undefined. Coalitional game theory (CGT) is an approach that seeks to identify players (individual genetic variants or genes) who tend to improve the performance—association to a disease phenotype of interest—of any coalition (subset of co-occurring genetic variants) they join. This method has been previously applied to boost biologically informative signal from gene expression data and exome sequencing data but remains to be explored in the context of cooperativity among non-coding genomic regions. We describe our extension of previous work, highlighting non-coding chromosomal regions relevant to ASD using CGT on alteration data of 4595 fully sequenced genomes from 756 multiplex families. Genomes were encoded into binary matrices for three types of non-coding regions previously implicated in ASD and separated into ASD (case) and unaffected (control) samples. A player metric, the Shapley value, enabled determination of individual variant contributions in both sets of cohorts. A total of 30 non-coding positions were found to have significantly elevated player scores and likely represent significant contributors to the genetic coordination underlying ASD. Cross-study analyses revealed that a subset of mutated non-coding regions (all of which are in human accelerated regions (HARs)) and related genes are involved in biological pathways or behavioral outcomes known to be affected in autism, suggesting the importance of single nucleotide polymorphisms (SNPs) within HARs in ASD. These findings support the use of CGT in identifying hidden yet influential non-coding players from large-scale genomic data, to better understand the precise underpinnings of complex neurodevelopmental disorders such as autism.

It is estimated that the impact of related genes on the risk of Alzheimer’s disease (AD) is nearly 70%. Identifying candidate causal genes can help treatment and diagnosis. The maturity of sequencing technology and the reduction of cost make genome-wide association study (GWAS) become an important means to find disease-related mutation sites. Because of linkage disequilibrium (LD), neither the gene regulated by SNP nor the specific SNP can be determined. Because GWAS is affected by sample size and interaction, we introduced empirical Bayes (EB) to make a meta-analysis of GWAS to greatly eliminate the bias caused by sample and the interaction of SNP. In addition, most SNPs are in the noncoding region, so it is not clear how they relate to phenotype. In this paper, expression quantitative trait locus (eQTL) studies and methylation quantitative trait locus (mQTL) studies are combined with GWAS to find the genes associated with Alzheimer disease in expression levels by pleiotropy. Summary data-based Mendelian randomization (SMR) is introduced to integrate GWAS and eQTL/mQTL data. Finally, we prioritized 274 significant SNPs, which belong to 20 genes by eQTL analysis and 379 significant SNPs, which belong to seven known genes by mQTL. Among them, 93 SNPs and 2 genes are overlapped. Finally, we did 10 case studies to prove the effectiveness of our method.

A list of genes have been identified to carry mutations causing familial ALS such as SOD1, TARDBP, C9orf72. But for sporadic ALS, which is 90% of all ALS cases, the underlying genetic variants are still largely unknown. There are multiple genome-wide association study (GWAS) for sporadic ALS, but usually a large number nominated SNP can hardly be replicated in larger cohort analysis. Also majority of GWAS SNP lie within noncoding region of genome, imposing a huge challenge to study their biological role in ALS pathology. With the rapid development of next-generation sequencing technology, we are able to sequence exome and whole-genome of a large number of ALS patients to search for novel genetic variants and their potential biological function. Here by analyzing exam data, we discovered two novel or extremely rare missense mutations of DPP6 from a Mestizo Mexican ALS family. We showed the two mutations could exert loss-of-function effect by affecting electrophysiological properties of Potassium channels as well as the membrane localization of DPP6. To our knowledge this is the first report of DPP6 nonsynonymous mutations in familial ALS patients. In addition, by analyzing whole-genome data, we discovered strong linkage disequilibrium between SNP rs12608932, a repeatedly significant ALS GWAS signal, and one polymorphic TGGA tetra-nucleotide tandem repeat, which is further flanked by large TGGA repetitive sequences. We also demonstrated rs12608932 risk allele is associated with reduced UNC13A expression level in human cerebellum and UNC13A knockout could lead to shorter survival in SOD1-G93A ALS mice. Thus the TGGA repeat might be the real underlying genetic variation that confer risk to sporadic ALS.

Hypertension or elevated blood pressure (BP) is a worldwide clinical challenge and the leading primary risk factor for kidney dysfunctions, heart failure, and cerebrovascular disease. The kidney is a central regulator of BP by maintaining sodium-water balance. Multiple genome-wide association studies revealed that BP is a heritable quantitative trait, modulated by several genetic, epigenetic, and environmental factors. The SNPs identified in genome-wide association studies predominantly (>95%) reside within noncoding genomic regions, making it difficult to understand how they regulate BP. Given the central role of the kidney in regulating BP, we hypothesized that chromatin-accessible regions in renal tissue would be enriched for BP-associated single nucleotide polymorphisms. We manually dissected 2 important kidney segments that maintain the sodium-water balance: proximal tubules and medullary thick ascending limbs from the human and rat kidneys. To delineate their chromatin and transcriptomic profiles, we performed the assay for transposase-accessible chromatin and RNA sequencing, respectively. The chromatin accessibility maps revealed the shared and unique cis-regulatory elements that modulate the chromatin accessibility in proximal tubule and medullary thick ascending limbs of humans and rats. We developed a visualization tool to compare the cross-species epigenomic maps to identify potential regulatory targets for hypertension pathogenesis. We also identified a significant enrichment of BP-associated single nucleotide polymorphisms (1064 for human proximal tubule and 1172 for human medullary thick ascending limbs) within accessible chromatin regions of both segments, including rs1173771 and rs1421811 at the NPR3 locus and rs1800470 at the TGFb1 locus. Collectively, this study lays a foundation for interrogating how intergenic single nucleotide polymorphisms may regulate polygenic traits such as BP.

BackgroundWhite striping (WS) is an emerging muscular defect occurring on breast and thigh muscles of broiler chickens. It is characterized by the presence of white striations parallel to the muscle fibers and has significant consequences for meat quality. The etiology of WS remains poorly understood, even if previous studies demonstrated that the defect prevalence is related to broiler growth and muscle development. Moreover, recent studies showed moderate to high heritability values of WS, which emphasized the role of genetics in the expression of the muscle defect. The aim of this study was to identify the first quantitative trait loci (QTLs) for WS as well as breast muscle yield (BMY) and meat quality traits using a genome-wide association study (GWAS). We took advantage of two divergent lines of chickens selected for meat quality through Pectoralis major ultimate pH (pHu) and which exhibit the muscular defect. An expression QTL (eQTL) detection was further performed for some candidate genes, either suggested by GWAS analysis or based on their biological function.ResultsForty-two single nucleotide polymorphisms (SNPs) associated with WS and other meat quality traits were identified. They defined 18 QTL regions located on 13 chromosomes. These results supported a polygenic inheritance of the studied traits and highlighted a few pleiotropic regions. A set of 16 positional and/or functional candidate genes was designed for further eQTL detection. A total of 132 SNPs were associated with molecular phenotypes and defined 21 eQTL regions located on 16 chromosomes. Interestingly, several co-localizations between QTL and eQTL regions were observed which could suggest causative genes and gene networks involved in the variability of meat quality traits and BMY.ConclusionsThe QTL mapping carried out in the current study for WS did not support the existence of a major gene, but rather suggested a polygenic inheritance of the defect and of other studied meat quality traits. We identified several candidate genes involved in muscle metabolism and structure and in muscular dystrophies. The eQTL analyses showed that they were part of molecular networks associated with WS and meat quality phenotypes and suggested a few putative causative genes.

The relationship between germline genetic variation and breast cancer survival is largely unknown, especially in understudied minority populations. Traditional genome-wide association studies (GWAS) have interrogated survival associations in cohorts of predominantly European ancestries but are often underpowered due to subtype heterogeneity in breast cancer and a wide range of clinical covariates. Furthermore, these analyses tend to detect loci in non-coding regions, which require follow-up functional studies to interpret. Recent work in transcriptome-wide association studies (TWAS) has shown increased power in detecting functionally-relevant, trait-associated loci by leveraging information from expression quantitative trait loci (eQTLs) in external reference panels in relevant tissues. However, race-specific reference panels for TWAS may be needed to draw correct inference in large, ethnically-heterogeneous cohorts, and such panels for breast cancer are lacking. In this work, we provide a framework for TWAS for breast cancer in diverse populations, using data from the Carolina Breast Cancer Study (CBCS), a population-based cohort that oversampled for women self-identifying as African American. Using Nanostring expression data from CBCS, we perform an eQTL analysis for 417 breast cancer-related genes to train race-stratified predictive models of tumor expression from germline genotypes. We use these models to impute expression in held-out samples from the CBCS and in The Cancer Genome Atlas, using a permutation method to assess predictive performance accounting for sampling variability. We find that these race-stratified expression models are not always applicable across race, depending on the imputation cohort. Furthermore, their predictive performance varies by breast cancer subtypes. Lastly, we conduct a small-scale TWAS for breast cancer mortality in CBCS (N = 3,828), controlling for covariates used in previous GWAS for breast cancer survival. At an false discovery rate-adjusted P-value less than 0.1, adjusting for self-reported race, we find hazardous associations near CAPN13 (2p23.1), VAV3 (1p13.3), and BLK (8p23.1) and a protective association near SERPINB5 (18q21.33) in TWAS that are underpowered in GWAS. This approach shows increased power for detection of survival-associated genomic loci, demonstrating the relative strength of TWAS over GWAS. A carefully implemented TWAS is an efficient alternative to GWAS for understanding the genetic architecture underpinning breast cancer outcomes in diverse human populations and across biologically distinct tumor types. Citation Format: Arjun Bhattacharya, Montserrat García-Closas, Andrew F. Olshan, Charles M. Perou, Melissa A. Troester, Michael I Love. A framework for transcriptome-wide association studies in breast cancer in diverse study populations [abstract]. In: Proceedings of the Twelfth AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2019 Sep 20-23; San Francisco, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2020;29(6 Suppl_2):Abstract nr B065.

AnimalGWASAtlas: Annotation and prioritization of GWAS loci and quantitative trait loci for animal complex traits.

Background:Genome-wide association studies have identified many genetic loci that are robustly associated with coronary artery disease (CAD). However, the underlying biological mechanisms are still unknown for most of these loci, hindering the progress to medical translation. Evidence suggests that the genetic influence on CAD susceptibility may act partly through vascular smooth muscle cells (VSMCs).Methods:We undertook genotyping, RNA sequencing, and cell behavior assays on a large bank of VSMCs (n>1499). Expression quantitative trait locus and splicing quantitative trait locus analyses were performed to identify genes with an expression that was influenced by CAD-associated variants. To identify candidate causal genes for CAD, we ascertained colocalizations of VSMC expression quantitative trait locus signals with CAD association signals by performing causal variants identification in associated regions analysis and the summary data–based mendelian randomization test. Druggability analysis was then performed on the candidate causal genes. CAD risk variants were tested for associations with VSMC proliferation, migration, and apoptosis. Collective effects of multiple CAD-associated variants on VSMC behavior were estimated by polygenic scores.Results:Approximately 60% of the known CAD-associated variants showed statistically significant expression quantitative trait locus or splicing quantitative trait locus effects in VSMCs. Colocalization analyses identified 84 genes with expression quantitative trait locus signals that significantly colocalized with CAD association signals, identifying them as candidate causal genes. Druggability analysis indicated that 38 of the candidate causal genes were druggable, and 13 had evidence of drug-gene interactions. Of the CAD-associated variants tested, 139 showed suggestive associations with VSMC proliferation, migration, or apoptosis. A polygenic score model explained up to 5.94% of variation in several VSMC behavior parameters, consistent with polygenic influences on VSMC behavior.Conclusions:This comprehensive analysis shows that a large percentage of CAD loci can modulate gene expression in VSMCs and influence VSMC behavior. Several candidate causal genes identified are likely to be druggable and thus represent potential therapeutic targets.

High‐throughput genotyping in estimating genetic resources and detecting pathogens in aquaculture

Aflatoxin (AF) contamination, caused by Aspergillus flavus, compromises the food safety and marketability of commodities, such as maize, cotton, peanuts, and tree nuts. Multigenic inheritance of AF resistance impedes conventional introgression of resistance traits into high-yielding commercial maize varieties. Several AF resistance-associated quantitative trait loci (QTLs) and markers have been reported from multiple biparental mapping and genome-wide association studies (GWAS) in maize. However, QTLs with large confidence intervals (CI) explaining inconsistent phenotypic variance limit their use in marker-assisted selection. Meta-analysis of published QTLs can identify significant meta-QTLs (MQTLs) with a narrower CI for reliable identification of genes and linked markers for AF resistance. Using 276 out of 356 reported QTLs controlling resistance to A. flavus infection and AF contamination in maize, we identified 58 MQTLs on all 10 chromosomes with a 66.5% reduction in the average CI. Similarly, a meta-analysis of maize genes differentially expressed in response to (a)biotic stresses from the to-date published literature identified 591 genes putatively responding to only A. flavus infection, of which 14 were significantly differentially expressed (-1.0 ≤ Log2Fc ≥ 1.0; p ≤ 0.05). Eight MQTLs were validated by their colocalization with 14A. flavus resistance-associated SNPs identified from GWAS in maize. A total of 15 genes were physically close between the MQTL intervals and SNPs. Assessment of 12 MQTL-linked SSR markers identified three markers that could discriminate 14 and eight cultivars with resistance and susceptible responses, respectively. A comprehensive meta-analysis of QTLs and differentially expressed genes led to the identification of genes and makers for their potential application in marker-assisted breeding of A. flavus-resistant maize varieties.

Background:The association between smoking dependence and the risk of developing Barrett’s esophagus remains unclear. This study aimed to investigate whether a causal relationship exists between smoking dependence and Barrett’s esophagus, using Mendelian randomization analysis.Methods:Two-sample Mendelian randomization analysis was conducted to evaluate the impact of smoking and Barrett’s esophagus. Additionally, we applied summary data-based Mendelian randomization techniques to combine information from genome-wide association studies (GWAS) with expression quantitative trait locus and methylation quantitative trait locus.Results:Multivariable Mendelian randomization showed an association between smoking per day (odds ratio = 1.2, 95% confidence interval: 1.038–1.38, p = 0.014) or current smoking (odds ratio = 2.41, 95% confidence interval: 1.06–5.5, p = 0.037) and Barrett’s esophagus. Inverse variance-weighted methods of bidirectional Mendelian randomization also revealed that smoking per day was significantly associated with elevated risks of Barrett’s esophagus (odds ratio = 1.34, 95% confidence interval: 1.092–1.649, p = 0.005), while Barrett’s esophagus was also a susceptibility factor for smoking per day (odds ratio = 1.05, 95% confidence interval: 1.017–1.087, p = 0.003). By incorporating consistent summary data-based Mendelian randomization associations between DNA methylation and smoke/Barrett’s esophagus, gene expression and smoke/Barrett’s esophagus, and DNA methylation and gene expression, we identified that the genetic variant-cg00935895-RBM43 (ENSG00000184898)-smoke/Barrett’s esophagus axis exerted an effect on smoke/Barrett’s esophagus by altering the DNA methylation level, which regulated the expression level of RBM43.Conclusions:Our study provides evidence of a bidirectional causal association between smoking and Barrett’s esophagus from a genetic perspective, which sheds new light on the potential role of RBM43 as a mediator in facilitating the impact of smoking and Barrett’s esophagus.

Only one-third of immune-associated genome-wide association study (GWAS) loci colocalize with expression quantitative trait loci (eQTLs), leaving most mechanisms unresolved. To address this, we created a unified single-cell chromatin accessibility (scATAC) map of ∼280,000 peripheral immune cells from 48 individuals, including 20 COVID-19 patients. Topic modeling of scATAC data identified continuous cell states and revealed disease-relevant cellular contexts. We identified 37,390 chromatin accessibility QTLs (caQTLs) at 10% false discovery rate and observed extensive sharing of caQTLs, with <20% confined to a single context. Notably, caQTLs explained ∼50% more GWAS loci compared to eQTLs, nominating putative causal genes for some unexplained loci. Yet most GWAS-colocalizing caQTLs lacked eQTL support, limiting causal inference from chromatin data alone. Thus, while caQTLs can improve GWAS interpretation, robust mechanistic insights require integration with gene expression and other functional evidence. Our work underscores that cellular context is critical for regulatory variant interpretation and emphasizes the need to map genetic effects in disease-relevant cell states.

The study of the genetic basis underlying the host response to Mycobacterium avium subsp. paratuberculosis (MAP) is usually performed using genome-wide association studies (GWAS), which assess the individual association between genotyped markers, typically single-nucleotide polymorphisms (SNPs), and phenotypic traits of interest (quantitative or qualitative). However, most SNPs identified through GWAS are located in non-coding regions, making it challenging to determine their functional relevance and to link them to target genes. To date, only a limited number of cis-expression quantitative trait loci (cis-eQTLs) with effects on gene expression and susceptibility or resistance to bovine paratuberculosis (PTB) have been characterized. Cis-QTLs can influence mRNA expression by altering the level, timing, or localization of gene expression, thereby potentially contributing to variability in PTB susceptibility or resistance. This review synthesizes recent efforts to uncover the genetic architecture of resistance or susceptibility to MAP infection by integrating transcriptomic and genomic data, with a particular focus on the identification and functional interpretation of cis-eQTLs. Furthermore, we discuss the potential practical applications of validated cis-eQTLs in genomic selection programs, genetic screening assays, and CRISPR-based genome editing approaches.