Splicing Quantitative Trait Loci Research Articles

Abstract 4118735: Interpretable deep learning translation of GWAS findings for drug repurposing in Atrial Fibrillation

Introduction: Translating human genetic findings, such as genome-wide association studies (GWAS) to pathobiology and the discovery of therapeutic target remains a major challenge for Atrial Fibrillation (AF). We previously published a network topology-based deep learning framework to identify disease-associated genes (NETTAG). Hypothesis: By using a deep learning framework, we can efficiently identify AF risk genes, druggable targets, and candidates of repurposable drugs. Aims: To identify potential AF related genes and repurposable drug candidates using our deep learning framework. Methods: First, we collected the reported quantitative trait loci (QTLs) for AF from human heart tissues. Then, we identified the overlaps between the QTLs and the previously reported 150 AF GWAS loci in the latest meta-analysis. We previously built a comprehensive human protein-protein interactome using 18 publicly available databases, containing 351,444 unique PPIs and 17,706 proteins. Using the human protein-protein interactome and the overlaps between AF GWAS hits and QTLs, we prioritized genes and defined the genes with the top 1 % predicted score as AF risk genes (afRGs) using the NETTAG. Then, we assembled drugs from the Drugbank database relating 2,938 FDA-approved drugs or clinically investigated molecules. Using network proximity approaches to evaluate the closest distance between afRGs and a drug’s targets within the human protein-protein interactome, we computationally predicted drugs for AF using Z scores <-2.0. Results: We first collected the overlaps between AF GWAS hits and QTLs, which constituted 27 expression and 12 splicing QTLs. Via NETTAG, we identified 176 afRGs. Among the 176 predicted afRGs, 12 proteins (gene products of afRGs) have been identified as known drug targets with FDA-approved medicines. In total, 1,275 targets have been widely investigated as therapeutic targets for treating AF. Using the closest-based network proximity approach, we computationally identified 49 candidate drugs. These included drugs both reportedly potentially treating AF, such as Pioglitazone (Z=2.29), Telmisartan (Z=-2.52), Sildenafil (Z=-2.86), and those have never been reported before, such as Balsalazide (Z=-2.32). Conclusion: Using a deep learning methodology that utilized GWAS and QTL findings, we identified risk genes and repurposing drug candidates for AF.

Network medicine informed multiomics integration identifies drug targets and repurposable medicines for Amyotrophic Lateral Sclerosis

Amyotrophic Lateral Sclerosis (ALS) is a devastating, immensely complex neurodegenerative disease by lack of effective treatments. We developed a network medicine methodology via integrating human brain multi-omics data to prioritize drug targets and repurposable treatments for ALS. We leveraged non-coding ALS loci effects from genome-wide associated studies (GWAS) on human brain expression quantitative trait loci (QTL) (eQTL), protein QTL (pQTL), splicing QTL (sQTL), methylation QTL (meQTL), and histone acetylation QTL (haQTL). Using a network-based deep learning framework, we identified 105 putative ALS-associated genes enriched in known ALS pathobiological pathways. Applying network proximity analysis of predicted ALS-associated genes and drug-target networks under the human protein-protein interactome (PPI) model, we identified potential repurposable drugs (i.e., Diazoxide and Gefitinib) for ALS. Subsequent validation established preclinical evidence for top-prioritized drugs. In summary, we presented a network-based multi-omics framework to identify drug targets and repurposable treatments for ALS and other neurodegenerative disease if broadly applied.

Genomic study in opioid-treated cancer patients identifies variants associated with nausea-vomiting

ContextOpioids are the mainstay therapy for patients affected by cancer pain. However, about 10-20% of patients do not benefit from the received analgesic treatment or experience side effects. Genetic variability might account for the variation in individual responses to opioids, both in terms of efficacy and toxicity. ObjectivesThe aim of this genome-wide association study (GWAS) was to identify genetic markers of opioid toxicity, in terms of nausea-vomiting. MethodsCancer patients receiving morphine, oxycodone, buprenorphine, and fentanyl were recruited from different European countries. Data about toxicity (nausea-vomiting score, NVS) and other relevant clinical information were collected, as well as genotyping data. Regression analysis between genotypes of 2,052 patients and NVS was performed, using appropriate covariates, with REGENIE software. ResultsWe found 65 variants associated with NVS (P-value < 1.0×10−5). Of note, 14 intronic variants on chromosome 2 were in NPAS2 gene, encoding a circadian transcription factor reported to play a role in another opioid side effect, the alteration of sleep. Some of these variants were previously identified as splicing quantitative trait loci of the NPAS2 gene. ConclusionsThis is the first GWAS, performed in more than two thousand individually genotyped patients treated with opioids for cancer pain, that investigated the genetic bases of opioid-induced nausea-vomiting. Although further studies are needed to confirm our findings and to characterize the functional role of the identified variants, our results emphasize the importance of performing large pharmacogenomic studies to identify germline variants associated with opioid response, with the ultimate goal of tailoring cancer pain therapies.

Profiling genetically driven alternative splicing across the Indonesian archipelago

One of the regulatory mechanisms influencing the functional capacity of genes is alternative splicing (AS). Previous studies exploring the splicing landscape of human tissues have shown that AS has contributed to human biology, especially in disease progression and the immune response. Nonetheless, this phenomenon remains poorly characterized across human populations, and it is unclear how genetic and environmental variation contribute to AS. Here, we examine a set of 115 Indonesian samples from three traditional island populations spanning the genetic ancestry cline that characterizes Island Southeast Asia. We conduct a global AS analysis between islands to ascertain the degree of functionally significant AS events and their consequences. Using an event-based statistical model, we detected over 1,500 significant differential AS events across all comparisons. Additionally, we identify over 6,000 genetic variants associated with changes in splicing (splicing quantitative trait loci [sQTLs]), some of which are driven by Papuan-like genetic ancestry, and only show partial overlap with other publicly available sQTL datasets derived from other populations. Computational predictions of RNA binding activity reveal that a fraction of these sQTLs directly modulate the binding propensity of proteins involved in the splicing regulation of immune genes. Overall, these results contribute toward elucidating the role of genetic variation in shaping gene regulation in one of the most diverse regions in the world.

Abstract C051: Impact of population pharmacogenomics on cisplatin-induced neurotoxicities

Abstract Background: Studies have shown that taxane-treated breast cancer patients with African (AFR) ancestry experience worse peripheral sensory neuropathy (PSN), while African American vincristine-treated leukemia patients have less PSN than Europeans (EUR). We hypothesized that ancestral disparities may exist for cisplatin-induced neurotoxicities and may relate to differences in allele frequency and resultant gene expression of SNPs associated with these toxicities. Methods: In our Platinum Study of 1680 genotyped testicular cancer survivors (TCS), multidimensional scaling scores were anchored by the 1000 Genomes population to classify geographic ancestry. PSN (EORTC-CIPN20) and other toxicities plus clinical and lifestyle variables were examined for associations between geographic ancestry and phenotypes using logistic regression and Wilcoxon rank sum test. Genotyped SNPs with Fst &amp;gt; 0.25 in 1000 Genomes were identified and evaluated with GTEx data to find expression or splicing quantitative trait loci in nerve/brain tissue or for cisplatin-associated candidate genes, across all tissues. LDmatrix was used to find linkage disequilibrium. If a block of SNPs had R2 &amp;gt; 0.75, one independent SNP was kept for association analysis. Logistic regression with age at questionnaire completion and 10 genetic principal components as covariates was used to calculate the association between genotype and toxicity. Results: In an analysis of patients who received 400-450 mg/m2 of cisplatin including EUR (n=681), Asian (ASN) axis (n=44) and AFR (n=13), the odds ratio (OR) for any neuropathy vs. none in the AFR vs. EUR was 7.8 (P=0.049) and in the AFR vs. ASN axis (including East Asian 1000 Genomes populations) was 10.0 (P=0.034). TCS with AFR ancestry had significantly more vertigo. There were 19,874 SNPs for analysis from the genome-wide approach and 118 SNPs from the candidate gene approach. While associations between genotype and toxicity did not reach Bonferroni threshold, some SNPs had p-values in the 10-5 range. For PSN, one SNP had increased frequency of a risk allele in the AFR population while the other had increased frequency of a protective allele, while for vertigo, four SNPs had increased frequency of risk alleles in the AFR population. Of interest was rs34904346, in which the TT vs. AA genotype had an OR of 1.9 (p=2x10-5) for any vs. no PSN, with the T allele having 90% frequency in AFR ancestry patients versus ∼60% in other populations. This SNP and three other independent SNPs associated with PSN incidence (p∼10-3) are eQTLs for RNF24 in nerve tissue, which interacts with membrane proteins that regulate intracellular calcium levels. Conclusions: We show preliminary evidence for the role of differing risk allele frequencies across populations in explaining disparities in cisplatin-induced PSN and vertigo, likely in combination with structural and environmental factors. Genotyping risk alleles could customize dosing, treatment, and post-treatment monitoring to benefit all cisplatin-treated patients. Citation Format: Swetha Nakshatri, Paul C. Dinh, Darren R. Feldman, Robert J. Hamilton, Chunkit Fung, David J. Vaughn, Christian Kollmannsberger, Robert Huddart, Lawrence H. Einhorn, Nancy J. Cox, Lois B. Travis, Eileen Dolan. Impact of population pharmacogenomics on cisplatin-induced neurotoxicities [abstract]. In: Proceedings of the 17th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2024 Sep 21-24; Los Angeles, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2024;33(9 Suppl):Abstract nr C051.

Gene expression and splicing QTL analysis of blood cells in African American participants from the Jackson Heart Study.

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

A Deep Dive into Statistical Modeling of RNA Splicing QTLs Reveals New Variants that Explain Neurodegenerative Disease.

Genome-wide association studies (GWAS) have identified thousands of putative disease causing variants with unknown regulatory effects. Efforts to connect these variants with splicing quantitative trait loci (sQTLs) have provided functional insights, yet sQTLs reported by existing methods cannot explain many GWAS signals. We show current sQTL modeling approaches can be improved by considering alternative splicing representation, model calibration, and covariate integration. We then introduce MAJIQTL, a new pipeline for sQTL discovery. MAJIQTL includes two new statistical methods: a weighted multiple testing approach for sGene discovery and a model for sQTL effect size inference to improve variant prioritization. By applying MAJIQTL to GTEx, we find significantly more sGenes harboring sQTLs with functional significance. Notably, our analysis implicates the novel variant rs582283 in Alzheimer's disease. Using antisense oligonucleotides, we validate this variant's effect by blocking the implicated YBX3 binding site, leading to exon skipping in the gene MS4A3.

Colocalised Genetic Associations Reveal Alternative Splicing Variants as Candidate Causal Links for Breast Cancer Risk in 10 Loci.

Genome-wide association studies (GWASs) have revealed numerous loci associated with breast cancer risk, yet the precise causal variants, their impact on molecular mechanisms, and the affected genes often remain elusive. We hypothesised that specific variants exert their influence by affecting cis-regulatory alternative splice elements. An analysis of splicing quantitative trait loci (sQTL) in healthy breast tissue from female individuals identified multiple variants linked to alterations in splicing ratios. Through colocalisation analysis, we pinpointed 43 variants within twelve genes that serve as candidate causal links between sQTL and GWAS findings. In silico splice analysis highlighted a potential mechanism for three genes-FDPS, SGCE, and MRPL11-where variants in proximity to or on the splice site modulate usage, resulting in alternative splice transcripts. Further in vitro/vivo studies are imperative to fully understand how these identified changes contribute to breast oncogenesis. Moreover, investigating their potential as biomarkers for breast cancer risk could enhance screening strategies and early detection methods for breast cancer.

SexAnnoDB, a knowledgebase of sex-specific regulations from multi-omics data of human cancers

BackgroundSexual differences across molecular levels profoundly impact cancer biology and outcomes. Patient gender significantly influences drug responses, with divergent reactions between men and women to the same drugs. Despite databases on sex differences in human tissues, understanding regulations of sex disparities in cancer is limited. These resources lack detailed mechanistic studies on sex-biased molecules.MethodsIn this study, we conducted a comprehensive examination of molecular distinctions and regulatory networks across 27 cancer types, delving into sex-biased effects. Our analyses encompassed sex-biased competitive endogenous RNA networks, regulatory networks involving sex-biased RNA binding protein-exon skipping events, sex-biased transcription factor-gene regulatory networks, as well as sex-biased expression quantitative trait loci, sex-biased expression quantitative trait methylation, sex-biased splicing quantitative trait loci, and the identification of sex-biased cancer therapeutic drug target genes. All findings from these analyses are accessible on SexAnnoDB (https://ccsm.uth.edu/SexAnnoDB/).ResultsFrom these analyses, we defined 126 cancer therapeutic target sex-associated genes. Among them, 9 genes showed sex-biased at both the mRNA and protein levels. Specifically, S100A9 was the target of five drugs, of which calcium has been approved by the FDA for the treatment of colon and rectal cancers. Transcription factor (TF)-gene regulatory network analysis suggested that four TFs in the SARC male group targeted S100A9 and upregulated the expression of S100A9 in these patients. Promoter region methylation status was only associated with S100A9 expression in KIRP female patients. Hypermethylation inhibited S100A9 expression and was responsible for the downregulation of S100A9 in these female patients.ConclusionsComprehensive network and association analyses indicated that the sex differences at the transcriptome level were partially the result of corresponding sex-biased epigenetic and genetic molecules. Overall, SexAnnoDB offers a discipline-specific search platform that could potentially assist basic experimental researchers or physicians in developing personalized treatment plans.

Long-read proteogenomics to connect disease-associated sQTLs to the protein isoform effectors of disease

A major fraction of loci identified by genome-wide association studies (GWASs) mediate alternative splicing, but mechanistic interpretation is hindered by the technical limitations of short-read RNA sequencing (RNA-seq), which cannot directly link splicing events to full-length protein isoforms. Long-read RNA-seq represents a powerful tool to characterize transcript isoforms, and recently, infer protein isoform existence. Here, we present an approach that integrates information from GWASs, splicing quantitative trait loci (sQTLs), and PacBio long-read RNA-seq in a disease-relevant model to infer the effects of sQTLs on the ultimate protein isoform products they encode. We demonstrate the utility of our approach using bone mineral density (BMD) GWAS data. We identified 1,863 sQTLs from the Genotype-Tissue Expression (GTEx) project in 732 protein-coding genes that colocalized with BMD associations (H4PP ≥ 0.75). We generated PacBio Iso-Seq data (N= ∼22 million full-length reads) on human osteoblasts, identifying 68,326 protein-coding isoforms, of which 17,375 (25%) were unannotated. By casting the sQTLs onto protein isoforms, we connected 809 sQTLs to 2,029 protein isoforms from 441 genes expressed in osteoblasts. Overall, we found that 74 sQTLs influenced isoforms likely impacted by nonsense-mediated decay and 190 that potentially resulted in the expression of unannotated protein isoforms. Finally, we functionally validated colocalizing sQTLs in TPM2, in which siRNA-mediated knockdown in osteoblasts showed two TPM2 isoforms with opposing effects on mineralization but exhibited no effect upon knockdown of the entire gene. Our approach should be to generalize across diverse clinical traits and to provide insights into protein isoform activities modulated by GWAS loci.

Joint models reveal genetic architecture of pubertal stage transitions and their association with BMI in admixed Chilean population.

Early or late pubertal onset can lead to disease in adulthood, including cancer, obesity, type 2 diabetes, metabolic disorders, bone fractures, and psychopathologies. Thus, knowing the age at which puberty is attained is crucial as it can serve as a risk factor for future diseases. Pubertal development is divided into five stages of sexual maturation in boys and girls according to the standardized Tanner scale. We performed genome-wide association studies (GWAS) on the "Growth and Obesity Chilean Cohort Study" cohort composed of admixed children with mainly European and Native American ancestry. Using joint models that integrate time-to-event data with longitudinal trajectories of body mass index (BMI), we identified genetic variants associated with phenotypic transitions between pairs of Tanner stages. We identified $42$ novel significant associations, most of them in boys. The GWAS on Tanner $3\rightarrow 4$ transition in boys captured an association peak around the growth-related genes LARS2 and LIMD1 genes, the former of which causes ovarian dysfunction when mutated. The associated variants are expression and splicing Quantitative Trait Loci regulating gene expression and alternative splicing in multiple tissues. Further, higher individual Native American genetic ancestry proportions predicted a significantly earlier puberty onset in boys but not in girls. Finally, the joint models identified a longitudinal BMI parameter significantly associated with several Tanner stages' transitions, confirming the association of BMI with pubertal timing.

Reference-informed prediction of alternative splicing and splicing-altering mutations from sequences.

Alternative splicing plays a crucial role in protein diversity and gene expression regulation in higher eukaryotes, and mutations causing dysregulated splicing underlie a range of genetic diseases. Computational prediction of alternative splicing from genomic sequences not only provides insight into gene-regulatory mechanisms but also helps identify disease-causing mutations and drug targets. However, the current methods for the quantitative prediction of splice site usage still have limited accuracy. Here, we present DeltaSplice, a deep neural network model optimized to learn the impact of mutations on quantitative changes in alternative splicing from the comparative analysis of homologous genes. The model architecture enables DeltaSplice to perform "reference-informed prediction" by incorporating the known splice site usage of a reference gene sequence to improve its prediction on splicing-altering mutations. We benchmarked DeltaSplice and several other state-of-the-art methods on various prediction tasks, including evolutionary sequence divergence on lineage-specific splicing and splicing-altering mutations in human populations and neurodevelopmental disorders, and demonstrated that DeltaSplice outperformed consistently. DeltaSplice predicted ∼15% of splicing quantitative trait loci (sQTLs) in the human brain as causal splicing-altering variants. It also predicted splicing-altering de novo mutations outside the splice sites in a subset of patients affected by autism and other neurodevelopmental disorders (NDDs), including 19 genes with recurrent splicing-altering mutations. Integration of splicing-altering mutations with other types of de novo mutation burdens allowed the prediction of eight novel NDD-risk genes. Our work expanded the capacity of in silico splicing models with potential applications in genetic diagnosis and the development of splicing-based precision medicine.

Nucleotide excision repair gene polymorphisms and hepatoblastoma susceptibility in Eastern Chinese children: A five-center case-control study.

Nucleotide excision repair (NER) plays a vital role in maintaining genome stability, and the effect of NER gene polymorphisms on hepatoblastoma susceptibility is still under investigation. This study aimed to evaluate the relationship between NER gene polymorphisms and the risk of hepatoblastoma in Eastern Chinese Han children. In this five-center case-control study, we enrolled 966 subjects from East China (193 hepatoblastoma patients and 773 healthy controls). The TaqMan method was used to genotype 19 single nucleotide polymorphisms (SNPs) in NER pathway genes, including ERCC1, XPA, XPC, XPD, XPF, and XPG. Then, multivariate logistic regression analysis was performed, and odds ratios (ORs) and 95% confidence intervals (95% CIs) were utilized to assess the strength of associations. Three SNPs were related to hepatoblastoma risk. XPC rs2229090 and XPD rs3810366 significantly contributed to hepatoblastoma risk according to the dominant model (adjusted OR=1.49, 95% CI=1.07-2.08, P=0.019; adjusted OR=1.66, 95% CI=1.12-2.45, P=0.012, respectively). However, XPD rs238406 conferred a significantly decreased risk of hepatoblastoma under the dominant model (adjusted OR=0.68, 95% CI=0.49-0.95; P=0.024). Stratified analysis demonstrated that these significant associations were more prominent in certain subgroups. Moreover, there was evidence of functional implications of these significant SNPs suggested by online expression quantitative trait loci (eQTLs) and splicing quantitative trait loci (sQTLs) analysis. In summary, NER pathway gene polymorphisms (XPC rs2229090, XPD rs3810366, and XPD rs238406) are significantly associated with hepatoblastoma risk, and further research is required to verify these findings.

Population-level exploration of alternative splicing and its unique role in controlling agronomic traits of rice.

Alternative splicing (AS) plays crucial roles in regulating various biological processes in plants. However, the genetic mechanisms underlying AS and its role in controlling important agronomic traits in rice (Oryza sativa) remain poorly understood. In this study, we explored AS in rice leaves and panicles using the rice minicore collection. Our analysis revealed a high level of transcript isoform diversity, with approximately one-fifth of the potential isoforms acting as major transcripts in both tissues. Regarding the genetic mechanism of AS, we found that the splicing of 833 genes in the leaf and 1,230 genes in the panicle was affected by cis-genetic variation. Twenty-one percent of these AS events could only be explained by large structural variations. Approximately 77.5% of genes with significant splicing quantitative trait loci (sGenes) exhibited tissue-specific regulation, and AS can cause 26.9% (leaf) and 23.6% (panicle) of sGenes to have altered, lost, or gained functional domains. Additionally, through splicing-phenotype association analysis, we identified phosphate-starvation-induced RING-type E3 ligase (OsPIE1; LOC_Os01g72480), whose splicing ratio was significantly associated with plant height. In summary, this study provides an understanding of AS in rice and its contribution to the regulation of important agronomic traits.

Cross-ancestry atlas of gene, isoform, and splicing regulation in the developing human brain.

Neuropsychiatric genome-wide association studies (GWASs), including those for autism spectrum disorder and schizophrenia, show strong enrichment for regulatory elements in the developing brain. However, prioritizing risk genes and mechanisms is challenging without a unified regulatory atlas. Across 672 diverse developing human brains, we identified 15,752 genes harboring gene, isoform, and/or splicing quantitative trait loci, mapping 3739 to cellular contexts. Gene expression heritability drops during development, likely reflecting both increasing cellular heterogeneity and the intrinsic properties of neuronal maturation. Isoform-level regulation, particularly in the second trimester, mediated the largest proportion of GWAS heritability. Through colocalization, we prioritized mechanisms for about 60% of GWAS loci across five disorders, exceeding adult brain findings. Finally, we contextualized results within gene and isoform coexpression networks, revealing the comprehensive landscape of transcriptome regulation in development and disease.

A multi-tissue, splicing-based joint transcriptome-wide association study identifies susceptibility genes for breast cancer

Splicing-based transcriptome-wide association studies (splicing-TWASs) of breast cancer have the potential to identify susceptibility genes. However, existing splicing-TWASs test the association of individual excised introns in breast tissue only and thus have limited power to detect susceptibility genes. In this study, we performed a multi-tissue joint splicing-TWAS that integrated splicing-TWAS signals of multiple excised introns in each gene across 11 tissues that are potentially relevant to breast cancer risk. We utilized summary statistics from a meta-analysis that combined genome-wide association study (GWAS) results of 424,650 women of European ancestry. Splicing-level prediction models were trained in GTEx (v.8) data. We identified 240 genes by the multi-tissue joint splicing-TWAS at the Bonferroni-corrected significance level; in the tissue-specific splicing-TWAS that combined TWAS signals of excised introns in genes in breast tissue only, we identified nine additional significant genes. Of these 249 genes, 88 genes in 62 loci have not been reported by previous TWASs, and 17 genes in seven loci are at least 1 Mb away from published GWAS index variants. By comparing the results of our splicing-TWASs with previous gene-expression-based TWASs that used the same summary statistics and expression prediction models trained in the same reference panel, we found that 110 genes in 70 loci that are identified only by the splicing-TWASs. Our results showed that for many genes, expression quantitative trait loci (eQTL) did not show a significant impact on breast cancer risk, whereas splicing quantitative trait loci (sQTL) showed a strong impact through intron excision events.

Identification and characterization of whole blood gene expression and splicing quantitative trait loci during early to mid-lactation of dairy cattle

BackgroundCharacterization of regulatory variants (e.g., gene expression quantitative trait loci, eQTL; gene splicing QTL, sQTL) is crucial for biologically interpreting molecular mechanisms underlying loci associated with complex traits. However, regulatory variants in dairy cattle, particularly in specific biological contexts (e.g., distinct lactation stages), remain largely unknown. In this study, we explored regulatory variants in whole blood samples collected during early to mid-lactation (22–150 days after calving) of 101 Holstein cows and analyzed them to decipher the regulatory mechanisms underlying complex traits in dairy cattle.ResultsWe identified 14,303 genes and 227,705 intron clusters expressed in the white blood cells of 101 cattle. The average heritability of gene expression and intron excision ratio explained by cis-SNPs is 0.28 ± 0.13 and 0.25 ± 0.13, respectively. We identified 23,485 SNP-gene expression pairs and 18,166 SNP-intron cluster pairs in dairy cattle during early to mid-lactation. Compared with the 2,380,457 cis-eQTLs reported to be present in blood in the Cattle Genotype-Tissue Expression atlas (CattleGTEx), only 6,114 cis-eQTLs (P < 0.05) were detected in the present study. By conducting colocalization analysis between cis-e/sQTL and the results of genome-wide association studies (GWAS) from four traits, we identified a cis-e/sQTL (rs109421300) of the DGAT1 gene that might be a key marker in early to mid-lactation for milk yield, fat yield, protein yield, and somatic cell score (PP4 > 0.6). Finally, transcriptome-wide association studies (TWAS) revealed certain genes (e.g., FAM83H and TBC1D17) whose expression in white blood cells was significantly (P < 0.05) associated with complex traits.ConclusionsThis study investigated the genetic regulation of gene expression and alternative splicing in dairy cows during early to mid-lactation and provided new insights into the regulatory mechanisms underlying complex traits of economic importance.

Pangenome-genotyped structural variation improves molecular phenotype mapping in cattle.

Expression and splicing quantitative trait loci (e/sQTL) are large contributors to phenotypic variability. Achieving sufficient statistical power for e/sQTL mapping requires large cohorts with both genotypes and molecular phenotypes, and so, the genomic variation is often called from short-read alignments, which are unable to comprehensively resolve structural variation. Here we build a pangenome from 16 HiFi haplotype-resolved cattle assemblies to identify small and structural variation and genotype them with PanGenie in 307 short-read samples. We find high (>90%) concordance of PanGenie-genotyped and DeepVariant-called small variation and confidently genotype close to 21 million small and 43,000 structural variants in the larger population. We validate 85% of these structural variants (with MAF > 0.1) directly with a subset of 25 short-read samples that also have medium coverage HiFi reads. We then conduct e/sQTL mapping with this comprehensive variant set in a subset of 117 cattle that have testis transcriptome data, and find 92 structural variants as causal candidates for eQTL and 73 for sQTL. We find that roughly half of the top associated structural variants affecting expression or splicing are transposable elements, such as SV-eQTL for STN1 and MYH7 and SV-sQTL for CEP89 and ASAH2 Extensive linkage disequilibrium between small and structural variation results in only 28 additional eQTL and 17 sQTL discovered when including SVs, although many top associated SVs are compelling candidates.

Integrating genetic regulation and single-cell expression with GWAS prioritizes causal genes and cell types for glaucoma

Primary open-angle glaucoma (POAG), characterized by retinal ganglion cell death, is a leading cause of irreversible blindness worldwide. However, its molecular and cellular causes are not well understood. Elevated intraocular pressure (IOP) is a major risk factor, but many patients have normal IOP. Colocalization and Mendelian randomization analysis of >240 POAG and IOP genome-wide association study (GWAS) loci and overlapping expression and splicing quantitative trait loci (e/sQTLs) in 49 GTEx tissues and retina prioritizes causal genes for 60% of loci. These genes are enriched in pathways implicated in extracellular matrix organization, cell adhesion, and vascular development. Analysis of single-nucleus RNA-seq of glaucoma-relevant eye tissues reveals that the POAG and IOP colocalizing genes and genome-wide associations are enriched in specific cell types in the aqueous outflow pathways, retina, optic nerve head, peripapillary sclera, and choroid. This study nominates IOP-dependent and independent regulatory mechanisms, genes, and cell types that may contribute to POAG pathogenesis.

Integration of Alzheimer’s Disease GWAS with Molecular QTLs Reveals Prioritized Risk Genes and Disease‐associated Molecular Alterations

AbstractBackgroundDespite the success of genome‐wide association studies (GWAS) to identify genetic variants associated with complex diseases, as the majority of these variants reside in non‐coding genome, one important task is to link these variants to disease‐associated molecular alterations in risk genes. To this end, we developed a systematic gene prioritization methodology that integrated our recent Alzheimer’s disease (AD) GWAS meta‐analysis with molecular quantitative trait loci (QTL) in disease‐relevant tissues and identified highly likely candidate risk genes and molecular alterations within the majority of 42 novel loci identified. In this subsequent study, we updated our pipeline with new molecular QTL catalogues and analyzed the complete landscape of AD risk.MethodWe performed GWAS‐QTL integration in AD‐relevant tissues and cells by querying the lead variants in each locus in the molecular QTL catalogues, (ii) investigating genetic colocalization between GWAS and molecular QTL signals using coloc, and (iii) conducting transcriptome, methylome, and proteome‐wide association studies (TWAS/MWAS/PWAS) using the EADB stage I meta‐analysis GWAS on 85,934 cases and 401,577 controls. The updated gene prioritization pipeline included new analyses based on new brain protein expression QTL (pQTL), cell‐type‐specific expression QTL (ct‐eQTL), expression QTL (eQTL), histone acetylation QTL (haQTL), and methylation QTL (mQTL) catalogues. Moreover, we used Nanopore RNA sequencing to further investigate risk‐associated splicing events revealed by splicing QTL (sQTL) integration.ResultOur brain ct‐eQTL coloc results showed that while the most risk‐associated gene expression alterations are specific to microglia, the proportion of astrocyte‐specific eQTL colocalizations were doubled compared to the previous studies. These included EGFR, a previously prioritized gene due to bulk brain eQTL coloc hits, whose downregulation was regulated by the protective signal specifically in astrocytes. Importantly, in a previously unresolved locus we could prioritize HS3ST5 through astrocyte‐specific eQTLs as well. Furthermore, pQTL coloc and PWAS prioritized SNX1, a previously prioritized gene at a lower confidence together with neighboring FAM96A gene, by implicating its genetic downregulation with increased AD risk.ConclusionOur updated pipeline systematically prioritized AD risk genes and disease‐associated molecular alterations in the majority of risk loci, and contributed to better interpretation of AD GWAS and understanding of genetically‐associated molecular mechanisms underlying AD risk.