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Regulation of transcription and translation are mechanisms through which genetic variants affect complex traits. Expression quantitative trait locus (eQTL) studies have been more successful at identifying cis-eQTL (within 1 Mb of the transcription start site) than trans-eQTL. Here, we tested the cis component of gene expression for association with observed plasma protein levels to identify cis- and trans-acting genes that regulate protein levels. We used transcriptome prediction models from 49 Genotype-Tissue Expression (GTEx) Project tissues to predict the cis component of gene expression and tested the predicted expression of every gene in every tissue for association with the observed abundance of 3,622 plasma proteins measured in 3,301 individuals from the INTERVAL study. We tested significant results for replication in 971 individuals from the Trans-omics for Precision Medicine (TOPMed) Multi-Ethnic Study of Atherosclerosis (MESA). We found 1,168 and 1,210 cis- and trans-acting associations that replicated in TOPMed (FDR

Abstract Background Transcriptome-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. Methods We 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. Results We 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. Conclusion We 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.

Transcriptome-wide association studies (TWASs) have investigated the role of genetically regulated transcriptional activity in the etiologies of breast and ovarian cancer. However, methods performed to date have focused on the regulatory effects of risk-associated SNPs thought to act in cis on a nearby target gene. With growing evidence for distal (trans) regulatory effects of variants on gene expression, we performed TWASs of breast and ovarian cancer using a Bayesian genome-wide TWAS method (BGW-TWAS) that considers effects of both cis - and trans -expression quantitative trait loci (eQTLs). We applied BGW-TWAS to whole-genome and RNA sequencing data in breast and ovarian tissues from the Genotype-Tissue Expression project to train expression imputation models. We applied these models to large-scale GWAS summary statistic data from the Breast Cancer and Ovarian Cancer Association Consortia to identify genes associated with risk of overall breast cancer, non-mucinous epithelial ovarian cancer, and 10 cancer subtypes. We identified 101 genes significantly associated with risk with breast cancer phenotypes and 8 with ovarian phenotypes. These loci include established risk genes and several novel candidate risk loci, such as ACAP3 , whose associations are predominantly driven by trans -eQTLs. We replicated several associations using summary statistics from an independent GWAS of these cancer phenotypes. We further used genotype and expression data in normal and tumor breast tissue from the Cancer Genome Atlas to examine the performance of our trained expression imputation models. This work represents an in-depth look into the role of trans eQTLs in the complex molecular mechanisms underlying these diseases. We performed a transcriptome-wide association study of breast and ovarian cancer (along with subtypes) that considered regulatory effects of variants both proximal and distal to a given gene. We identified over 100 genes associated with at least 1 cancer type. We validated many of these genes within independent cancer datasets. [ABSTRACT FROM AUTHOR]

Background: Transcriptome-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. Methods: We 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. Results: We 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. Conclusion: We 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. [ABSTRACT FROM AUTHOR]

Sequence variants modulating gene function or expression affect various heritable traits, including the number of neurons within a population. The present study employed a forward-genetic approach to identify candidate causal genes and their sequence variants controlling the number of one type of retinal neuron, the AII amacrine cell. Data from twenty-six recombinant inbred (RI) strains of mice derived from the parental C57BL/6J (B6/J) and A/J laboratory strains were used to identify genomic loci regulating cell number. Large variation in cell number is present across the RI strains, from a low of ∼57,000 cells to a high of ∼87,000 cells. Quantitative trait locus (QTL) analysis revealed three prospective controlling genomic loci, on Chromosomes (Chrs) 9, 11, and 19, each contributing additive effects that together approach the range of variation observed. Composite interval mapping validated two of these loci, and chromosome substitution strains, in which the A/J genome for Chr 9 or 19 was introgressed on a B6/J genetic background, showed increased numbers of AII amacrine cells as predicted by those two QTL effects. Analysis of the respective genomic loci identified candidate controlling genes defined by their retinal expression, their established biological functions, and by the presence of sequence variants expected to modulate gene function or expression. Two candidate genes, Dtx4 on Chr 19, being a regulator of Notch signaling, and Dixdc1 on Chr 9, a modulator of the WNT-β-catenin signaling pathway, were explored in further detail. Postnatal overexpression of Dtx4 was found to reduce the frequency of amacrine cells, while Dixdc1 knockout retinas contained an excess of AII amacrine cells. Sequence variants in each gene were identified, being the likely sources of variation in gene expression, ultimately contributing to the final number of AII amacrine cells.

Misfolded proteins in the endoplasmic reticulum (ER) elicit the ER stress response, a large transcriptional response driven by 3 well-characterized transcription factors (TFs). This transcriptional response is variable across different genetic backgrounds. One mechanism in which genetic variation can lead to transcriptional variability in the ER stress response is through altered binding and activity of the 3 main TFs: XBP1, ATF6, and ATF4. This work attempts to better understand this mechanism by first creating a computational pipeline to identify potential binding sites throughout the human genome. We utilized GTEx data sets to identify cis-eQTLs that fall within predicted TF binding sites (TFBSs). We also utilized the ClinVar database to compare the number of pathogenic vs benign variants at different positions of the binding motifs. Finally, we performed a cis-eQTL analysis on human cell lines experiencing ER stress to identify ciseQTLs that regulate the variable ER stress response. The majority of these cis-eQTLs are unique to a given condition: control or ER stress. Some of these stress-specific cis-eQTLs fall within putative binding sites of the 3 main ER stress response TFs, providing a potential mechanism by which these cis-eQTLs might be impacting gene expression under ER stress conditions through altered TF binding. This study represents the first cis-eQTL analysis on human samples experiencing ER stress and is a vital step toward identifying the genetic components responsible for the variable ER stress response. [ABSTRACT FROM AUTHOR]

The polymorphism of the rs1815739 locus of the ACTN3 gene and of the rs11227639 cis-eQTL that affects expression of this gene was studied in populations of the northern (Chukchi, Koryaks, Evens, and Evenki) and southern (Buryats, Altaians, and Tuvinians) parts of Siberia. It was shown that the frequencies of the rs1815739-C allele of the ACTN3 gene (corresponding to the 577R amino acid variant) and the rs11227639-A upregulating ACTN3 allele, which increases the expression level of this gene, are significantly higher in the northern part of Siberia than in the southern part. Similarly, in the northerly direction, the frequency of the combination of CC/AA genotypes at these loci increases. The revealed pattern of the geographical distribution of alleles and genotypes at the rs1815739 and rs11227639 loci in Siberian populations may be due to adaptation to cold and the processes of thermoregulation of the body when exposed to cold. It is assumed that an increase in the frequency of the rs1815739-C and rs11227639-A alleles in the aboriginal populations of the northern part of Siberia is associated with an increase in the role of shivering thermogenesis during adaptation to cold exposure and also contributes to an increase in muscle mass in their carriers, which reduces heat loss in the conditions of the North. [ABSTRACT FROM AUTHOR]

Carcass yield traits are of considerable economic importance for farm animals, which act as a major contributor to the world's food supply. Genome-wide association studies (GWASs) have identified many genetic variants associated with carcass yield traits in beef cattle. However, their functions are not effectively illustrated. In this study, we performed an integrative analysis of gene-based GWAS with expression quantitative trait locus (eQTL) analysis to detect candidate genes for carcass yield traits and validate their effects on bovine skeletal muscle satellite cells (BSCs). The gene-based GWAS and cis-eQTL analysis revealed 1780 GWAS and 1538 cis-expression genes. Among them, we identified 153 shared genes that may play important roles in carcass yield traits. Notably, the identified cis-eQTLs of PON3 and PRIM2 were significantly (p < 0.001) enriched in previous GWAS loci for carcass traits. Furthermore, overexpression of PON3 and PRIM2 promoted the BSCs' proliferation, increased the expression of MYOD and downregulated the expression of MYOG, which indicated that these genes may inhibit myogenic differentiation. In contrast, PON3 and PRIM2 were significantly downregulated during the differentiation of BSCs. These findings suggested that PON3 and PRIM2 may promote the proliferation of BSCs and inhibit them in the pre-differentiation stage. Our results further contribute to the understanding of the molecular mechanisms of carcass yield traits in beef cattle. [ABSTRACT FROM AUTHOR]

Background: Short tandem repeats (STRs) were recently found to have significant impacts on gene expression and diseases in humans, but their roles on gene expression and complex traits in pigs remain unexplored. This study investigates the effects of STRs on gene expression in liver tissues based on the whole-genome sequences and RNA-Seq data of a discovery cohort of 260 F6 individuals and a validation population of 296 F7 individuals from a heterogeneous population generated from crosses among eight pig breeds. Results: We identified 5203 and 5868 significantly expression STRs (eSTRs, FDR < 1%) in the F6 and F7 populations, respectively, most of which could be reciprocally validated (π1 = 0.92). The eSTRs explained 27.5% of the cis-heritability of gene expression traits on average. We further identified 235 and 298 fine-mapped STRs through the Bayesian fine-mapping approach in the F6 and F7 pigs, respectively, which were significantly enriched in intron, ATAC peak, compartment A and H3K4me3 regions. We identified 20 fine-mapped STRs located in 100 kb windows upstream and downstream of published complex trait-associated SNPs, which colocalized with epigenetic markers such as H3K27ac and ATAC peaks. These included eSTR of the CLPB, PGLS, PSMD6 and DHDH genes, which are linked with genome-wide association study (GWAS) SNPs for blood-related traits, leg conformation, growth-related traits, and meat quality traits, respectively. Conclusions: This study provides insights into the effects of STRs on gene expression traits. The identified eSTRs are valuable resources for prioritizing causal STRs for complex traits in pigs. [ABSTRACT FROM AUTHOR]

The availability of high-coverage genomes of our extinct relatives, the Neanderthals and Denisovans, and the emergence of large, tissue-specific databases of modern human genetic variation, offer the possibility of probing the effects of modern-derived alleles in specific tissues, such as the brain, and its specific regions. While previous research has explored the effects of introgressed variants in gene expression, the effects of Homo sapiens-specific gene expression variability are still understudied. Here we identify derived, Homo sapiens-specific high-frequency (≥90%) alleles that are associated with differential gene expression across 15 brain structures derived from the GTEx database. We show that regulation by these derived variants targets regions under positive selection more often than expected by chance, and that high-frequency derived alleles lie in functional categories related to transcriptional regulation. Our results highlight the role of these variants in gene regulation in specific regions like the cerebellum and pituitary. [ABSTRACT FROM AUTHOR]

Abstract Background A genome-wide association study (GWAS) correlates variation in the genotype with variation in the phenotype across a cohort, but the causal gene mediating that impact is often unclear. When the phenotype is protein abundance, a reasonable hypothesis is that the gene encoding that protein is the causal gene. However, as variants impacting protein levels can occur thousands or even millions of base pairs from the gene encoding the protein, it is unclear at what distance this simple hypothesis breaks down. Results By making the simple assumption that cis-pQTLs should be distance dependent while trans-pQTLs are distance independent, we arrive at a simple and empirical distance cutoff separating cis- and trans-pQTLs. Analyzing a recent large-scale pQTL study (Pietzner in Science 374:eabj1541, 2021) we arrive at an estimated distance cutoff of 944 kilobasepairs (95% confidence interval: 767–1,161) separating the cis and trans regimes. Conclusions We demonstrate that this simple model can be applied to other molecular GWAS traits. Since much of biology is built on molecular traits like protein, transcript and metabolite abundance, we posit that the mathematical models for cis and trans distance distributions derived here will also apply to more complex phenotypes and traits.

Abstract The availability of high-coverage genomes of our extinct relatives, the Neanderthals and Denisovans, and the emergence of large, tissue-specific databases of modern human genetic variation, offer the possibility of probing the effects of modern-derived alleles in specific tissues, such as the brain, and its specific regions. While previous research has explored the effects of introgressed variants in gene expression, the effects of Homo sapiens-specific gene expression variability are still understudied. Here we identify derived, Homo sapiens-specific high-frequency (≥90%) alleles that are associated with differential gene expression across 15 brain structures derived from the GTEx database. We show that regulation by these derived variants targets regions under positive selection more often than expected by chance, and that high-frequency derived alleles lie in functional categories related to transcriptional regulation. Our results highlight the role of these variants in gene regulation in specific regions like the cerebellum and pituitary.

Abstract Background Short tandem repeats (STRs) were recently found to have significant impacts on gene expression and diseases in humans, but their roles on gene expression and complex traits in pigs remain unexplored. This study investigates the effects of STRs on gene expression in liver tissues based on the whole-genome sequences and RNA-Seq data of a discovery cohort of 260 F6 individuals and a validation population of 296 F7 individuals from a heterogeneous population generated from crosses among eight pig breeds. Results We identified 5203 and 5868 significantly expression STRs (eSTRs, FDR

Sequence variants modulating gene function or expression affect various heritable traits, including the number of neurons within a population. The present study employed a forward-genetic approach to identify candidate causal genes and their sequence variants controlling the number of one type of retinal neuron, the AII amacrine cell. Data from twenty-six recombinant inbred (RI) strains of mice derived from the parental C57BL/6J (B6/J) and A/J laboratory strains were used to identify genomic loci regulating cell number. Large variation in cell number is present across the RI strains, from a low of ∼57,000 cells to a high of ∼87,000 cells. Quantitative trait locus (QTL) analysis revealed three prospective controlling genomic loci, on Chromosomes (Chrs) 9, 11, and 19, each contributing additive effects that together approach the range of variation observed. Composite interval mapping validated two of these loci, and chromosome substitution strains, in which the A/J genome for Chr 9 or 19 was introgressed on a B6/J genetic background, showed increased numbers of AII amacrine cells as predicted by those two QTL effects. Analysis of the respective genomic loci identified candidate controlling genes defined by their retinal expression, their established biological functions, and by the presence of sequence variants expected to modulate gene function or expression. Two candidate genes, Dtx4 on Chr 19, being a regulator of Notch signaling, and Dixdc1 on Chr 9, a modulator of the WNT-β-catenin signaling pathway, were explored in further detail. Postnatal overexpression of Dtx4 was found to reduce the frequency of amacrine cells, while Dixdc1 knockout retinas contained an excess of AII amacrine cells. Sequence variants in each gene were identified, being the likely sources of variation in gene expression, ultimately contributing to the final number of AII amacrine cells.

Simple Summary: Cathepsin G (CTSG) is a serine protease that participates in the killing of pathogens and tissue remodeling at sites of inflammation. We previously found that the presence of the minor allele in the cis-eQTLs-rs41976219 (AC) was associated with increased CTSG mRNA levels in blood samples from Holstein cows. In this study, we test whether genetic variation in the cis-eQTL-rs41976219 is associated with changes in CTSG protein expression and control of Mycobacterium avium subsp. paratuberculosis (MAP) infection. We demonstrate that the heterozygous genotype for the rs41976219 (AC) results in higher CTSG protein levels in the supernatants of infected CD14+-monocyte-derived macrophages (MDMs) after 2 h of infection and a significantly lower intracellular MAP load at 7 d p.i. than in MDMs from cattle with the AA genotype. Furthermore, the rs41976219 genotype CC is more frequent in healthy cows than in cows with PTB-associated lesions in gut tissues. Higher CTSG levels are detected in plasmas from cows with the CC genotype when compared with cows with the AA + AC genotypes for the rs41976219. We conclude that the presence of the minor allele in the rs41976219 increases the CTSG expression in plasma samples and MAP-infected macrophages, and can influence PTB pathogenesis by modulating MAP load within infected macrophages. An in silico genomic–transcriptomic combined approach allowed the identification of a polymorphism (cis-eQTL-rs41976219) in the Bos taurus genome associated with the CTSG mRNA expression in bovine blood samples, which suggests that individual genetic variation might modulate the CTSG transcriptional response. In the current study, a sandwich ELISA is used to measure the CTSG protein levels in supernatants of monocyte-derived macrophages (MDMs) isolated from cows with the AA (n = 5) and AC (n = 11) genotypes for the rs41976219 and infected ex vivo with MAP. Cows with the AC genotype have significantly higher CTSG protein levels (1.85 ng/mL) in the supernatants of enriched CD14+-MDMs after 2 h of infection when compared with infected CD14+-MDMs from cows with the AA genotype (1.68 ng/mL). Statistically significant differences in the intracellular MAP load at 7 d p.i. are observed between animals with the AA (2.16 log CFUs) and AC (1.44 log CFUs) genotypes. Finally, the association between the rs41976219 allelic variants and resistance to PTB is tested in a larger cattle population (n = 943) classified according to the presence (n = 442) or absence (n = 501) of PTB-associated lesions. The presence of the two minor alleles in the rs41976219 (CC) is more frequent among healthy cows than in cows with PTB-associated lesions in gut tissues (2.2% vs. 1.4%, OR = 0.61). In agreement with this, the CTSG levels in plasma samples of cows without lesions in gut tissues and with the CC (n = 8) genotype are significantly higher than in the plasmas of cows with the AA + AC (n = 36) genotypes. [ABSTRACT FROM AUTHOR]

Background: Prostatitis is characterized by high prevalence, low cure rates, and frequent recurrences, and remains one of the most clinically challenging problems. Hence, in this article, we first integrated Mendelian randomization (MR) with expression quantitative trait loci (eQTL) and protein quantitative trait loci (pQTL) data to identify novel therapeutic targets and their potential metabolic mechanisms for prostatitis., Methods: Prostatitis-related genetic data, eQTLs, pQTLs, and 1400 metabolites were downloaded from online databases. MR, or summary data-based MR (SMR) analyses were applied to assess the potential causal relationships between exposures and predicted outcomes. Sensitivity analysis was conducted using pleiotropy, heterogeneity, and leave-one-out analysis to evaluate the robustness of our results., Results: Based on our results, we first identified and validated GNLY as a novel cis-eQTL and cis-pQTL-mediated susceptibility gene for reducing prostatitis risk in five independent datasets (one discovery dataset and four validation datasets) (all p <0.05). Meanwhile, we also found that the GNLY eQTL could increase the metabolite of sphingomyelin level (d18:0/20:0, d16:0/22:0) risks (p <0.05), and the metabolite of sphingomyelin level (d18:0/20:0, d16:0/22:0) could reduce the risk of prostatitis (p <0.05). According to the above-mentioned relationships, we finally revealed the potential metabolic mechanism of GNLY eQTL in suppressing prostatitis via regulating the metabolite of sphingomyelin level (d18:0/20:0, d16:0/22:0)., Conclusions: We successfully identified GNLY as a novel cis-eQTL and cis-pQTL-mediated susceptibility gene in suppressing prostatitis and its potential metabolic mechanism via regulating sphingomyelin (d18:0/20:0, d16:0/22:0) levels, providing a novel therapeutic target and paving the way for future GNLY-related studies in prostatitis., (Copyright © 2024 Instituto Mexicano del Seguro Social (IMSS). Published by Elsevier Inc. All rights reserved.)

To date, more than 1456 associations have been identified for Chronic Obstructive Pulmonary Disease (COPD) risk through Genome-Wide Association Studies (GWAS). However, target genes for COPD susceptibility in the Indian population and the mechanism underlying remains largely unexplored and no GWAS studies on COPD are available on the Indian population till now. This study was conducted using the existing public data on GWAS of different parts of the world, and the genetic polymorphisms to understand the possible mechanisms of these polymorphisms using available data from the Genotype-Tissue Expression (GTEx) project. We jotted down 16 important genes and 28 Single Nucleotide Polymorphisms (SNPs) in the Indian population from 1456 variants. Pathway analysis showed that these relevant genes are mostly associated with immune responses and activation, which is a key factor in COPD development. Our investigation revealed possible target genes associated with COPD in the context of the Indian population. [ABSTRACT FROM AUTHOR]

Background: A genome-wide association study (GWAS) correlates variation in the genotype with variation in the phenotype across a cohort, but the causal gene mediating that impact is often unclear. When the phenotype is protein abundance, a reasonable hypothesis is that the gene encoding that protein is the causal gene. However, as variants impacting protein levels can occur thousands or even millions of base pairs from the gene encoding the protein, it is unclear at what distance this simple hypothesis breaks down. Results: By making the simple assumption that cis-pQTLs should be distance dependent while trans-pQTLs are distance independent, we arrive at a simple and empirical distance cutoff separating cis- and trans-pQTLs. Analyzing a recent large-scale pQTL study (Pietzner in Science 374:eabj1541, 2021) we arrive at an estimated distance cutoff of 944 kilobasepairs (95% confidence interval: 767–1,161) separating the cis and trans regimes. Conclusions: We demonstrate that this simple model can be applied to other molecular GWAS traits. Since much of biology is built on molecular traits like protein, transcript and metabolite abundance, we posit that the mathematical models for cis and trans distance distributions derived here will also apply to more complex phenotypes and traits. [ABSTRACT FROM AUTHOR]

Sick, male, and older populations are more vulnerable to COVID-19. However, it remains unclear whether a common mechanism exists across different demographic characteristics. SARS-CoV-2 infection is initiated by the specific binding of the viral spike protein to angiotensin-converting enzyme 2 (ACE2). This study analyzed the demographics of pulmonary ACE2 expression, Mendelian randomization (MR) of ACE2 and COVID-19, and comparative tropism of SARS-CoV-2. The key features of SARS-CoV-2 tropism, including pulmonary ACE2 expression and ACE2-expressing cell types, showed distinct subphenotypes associated with the demographics of vulnerable COVID-19 populations, suggesting a hypothesis centered on "ACE2" to explain their interplay. Next, by integrating multiple COVID-19 cohorts of genome-wide association studies (GWASs) and cisexpression quantitative trait loci (cis-eQTLs) of ACE2, MR analysis demonstrated that ACE2 played a causal role in COVID-19 susceptibility and severity, suggesting ACE2 as a promising target for early COVID-19 treatment. Next, by analyzing the expression of host cell receptors using single-cell RNA sequencing (scRNA-seq) data of human lung tissues, comparative tropism analysis showed that SARS-CoV-2 and other respiratory viruses, but not non-respiratory viruses, had remarkably overlapping and enriched cellular tropism in alveolar type 2 (AT2) cells. This finding indicates the possibility of coinfection with SARS-CoV-2 and other respiratory viruses, perhaps implying sociovirology at the cellular level. Moreover, the binding of viral entry proteins to the compatible host cell receptors is under strong natural selection pressure. Therefore, comparative tropism might reveal the footprint of natural selection that shapes the virus population, which provides a novel perspective for understanding zoonotic spillover events. [ABSTRACT FROM AUTHOR]

Summary: Standard transcriptome-wide association study (TWAS) methods first train gene expression prediction models using reference transcriptomic data and then test the association between the predicted genetically regulated gene expression and phenotype of interest. Most existing TWAS tools require cumbersome preparation of genotype input files and extra coding to enable parallel computation. To improve the efficiency of TWAS tools, we developed Transcriptome-Integrated Genetic Association Resource V2 (TIGAR-V2), which directly reads Variant Call Format (VCF) files, enables parallel computation, and reduces up to 90% of computation cost (mainly due to loading genotype data) compared to the original version. TIGAR-V2 can train gene expression imputation models using either nonparametric Bayesian Dirichlet process regression (DPR) or Elastic-Net (as used by PrediXcan), perform TWASs using either individual-level or summary-level genome-wide association study (GWAS) data, and implement both burden and variance-component statistics for gene-based association tests. We trained gene expression prediction models by DPR for 49 tissues using Genotype-Tissue Expression (GTEx) V8 by TIGAR-V2 and illustrated the usefulness of these Bayesian cis-expression quantitative trait locus (eQTL) weights through TWASs of breast and ovarian cancer utilizing public GWAS summary statistics. We identified 88 and 37 risk genes, respectively, for breast and ovarian cancer, most of which are either known or near previously identified GWAS (∼95%) or TWAS (∼40%) risk genes and three novel independent TWAS risk genes with known functions in carcinogenesis. These findings suggest that TWASs can provide biological insight into the transcriptional regulation of complex diseases. The TIGAR-V2 tool, trained Bayesian cis-eQTL weights, and linkage disequilibrium (LD) information from GTEx V8 are publicly available, providing a useful resource for mapping risk genes of complex diseases.

Carcass yield traits are of considerable economic importance for farm animals, which act as a major contributor to the world’s food supply. Genome-wide association studies (GWASs) have identified many genetic variants associated with carcass yield traits in beef cattle. However, their functions are not effectively illustrated. In this study, we performed an integrative analysis of gene-based GWAS with expression quantitative trait locus (eQTL) analysis to detect candidate genes for carcass yield traits and validate their effects on bovine skeletal muscle satellite cells (BSCs). The gene-based GWAS and cis-eQTL analysis revealed 1780 GWAS and 1538 cis-expression genes. Among them, we identified 153 shared genes that may play important roles in carcass yield traits. Notably, the identified cis-eQTLs of PON3 and PRIM2 were significantly (p < 0.001) enriched in previous GWAS loci for carcass traits. Furthermore, overexpression of PON3 and PRIM2 promoted the BSCs’ proliferation, increased the expression of MYOD and downregulated the expression of MYOG, which indicated that these genes may inhibit myogenic differentiation. In contrast, PON3 and PRIM2 were significantly downregulated during the differentiation of BSCs. These findings suggested that PON3 and PRIM2 may promote the proliferation of BSCs and inhibit them in the pre-differentiation stage. Our results further contribute to the understanding of the molecular mechanisms of carcass yield traits in beef cattle.

Characterization of genetic variations that are associated with gene expression levels is essential to understand cellular mechanisms that underline human complex traits. Expression quantitative trait loci (eQTL) mapping attempts to identify genetic variants, such as single nucleotide polymorphisms (SNPs), that affect the expression of one or more genes. With the availability of a large volume of gene expression data, it is necessary and important to develop fast and efficient statistical and computational methods to perform eQTL mapping for such large scale data. In this paper, we proposed a new method, the low rank penalized regression method (LORSEN), for eQTL mapping. We evaluated and compared the performance of LORSEN with two existing methods for eQTL mapping using extensive simulations as well as real data from the HapMap3 project. Simulation studies showed that our method outperformed two commonly used methods for eQTL mapping, LORS and FastLORS, in many scenarios in terms of area under the curve (AUC). We illustrated the usefulness of our method by applying it to SNP variants data and gene expression levels on four chromosomes from the HapMap3 Project. [ABSTRACT FROM AUTHOR]

Characterization of genetic variations that are associated with gene expression levels is essential to understand cellular mechanisms that underline human complex traits. Expression quantitative trait loci (eQTL) mapping attempts to identify genetic variants, such as single nucleotide polymorphisms (SNPs), that affect the expression of one or more genes. With the availability of a large volume of gene expression data, it is necessary and important to develop fast and efficient statistical and computational methods to perform eQTL mapping for such large scale data. In this paper, we proposed a new method, the low rank penalized regression method (LORSEN), for eQTL mapping. We evaluated and compared the performance of LORSEN with two existing methods for eQTL mapping using extensive simulations as well as real data from the HapMap3 project. Simulation studies showed that our method outperformed two commonly used methods for eQTL mapping, LORS and FastLORS, in many scenarios in terms of area under the curve (AUC). We illustrated the usefulness of our method by applying it to SNP variants data and gene expression levels on four chromosomes from the HapMap3 Project.

Alzheimer's disease (AD) develops relentlessly in affected individuals and its occurrence is increasing. A clinical test to diagnose early-stage AD could be an important means of enabling interventions to slow its progression. However, available neuroimaging and cerebrospinal fluid-based diagnoses are very costly. Therefore, detecting AD from blood transcripts that mirror the expression of brain transcripts in the AD could improve the diagnosis. To achieve this goal, we employed a transcriptional analysis of affected tissues and integrated them with cis-eQTL data. In this study, we analyzed microarray gene expression data of brain and blood cells from AD patients and control individuals. Differentially expressed genes (DEGs) common to both brain tissue and blood cells were identified. Potential common genes and molecular pathways were identified using overlapping DEGs through the pathway and gene ontology enrichment analysis. We identified 18 significantly dysregulated genes shared by both brain and blood cells in AD affected individuals. We validated these candidates as disease-associated genes using gold-standard benchmarking databases (gene SNP-disease linkage). Significant molecular pathway and gene ontology indicating AD progression were identified. This study also identified regulatory factors, including transcription factors (TFs), microRNAs and candidate drugs. In sum, we identified new putative links between pathological processes in brain tissue and blood cells in AD that may allow assessment of AD status using blood samples. Thus, our formulated methodologies demonstrate the power of gene and gene expression analysis for brain-related pathologies transcriptomics, cis-eQTL, and epigenetics data from brain and blood cells.

Expression quantitative trait locus (eQTL) analyses are critical in understanding the complex functional regulatory natures of genetic variation and have been widely used in the interpretation of disease-associated variants identified by genome-wide association studies (GWAS). Emerging evidence has shown that trans-eQTL effects on remote gene expression could be mediated by local transcripts, which is known as the mediation effects. To discover the genome-wide eQTL mediation effects combing genomic and transcriptomic profiles, it is necessary to develop novel computational methods to rapidly scan large number of candidate associations while controlling for multiple testing appropriately. Here, we present eQTLMAPT, an R package aiming to perform eQTL mediation analysis with implementation of efficient permutation procedures in multiple testing correction. eQTLMAPT is advantageous in threefold. First, it accelerates mediation analysis by effectively pruning the permutation process through adaptive permutation scheme. Second, it can efficiently and accurately estimate the significance level of mediation effects by modeling the null distribution with generalized Pareto distribution (GPD) trained from a few permutation statistics. Third, eQTLMAPT provides flexible interfaces for users to combine various permutation schemes with different confounding adjustment methods. Experiments on real eQTL dataset demonstrate that eQTLMAPT provides higher resolution of estimated significance of mediation effects and is an order of magnitude faster than compared methods with similar accuracy.

Transcriptome-wide association studies (TWASs) have been widely used to integrate gene expression and genetic data for studying complex traits. Due to the computational burden, existing TWAS methods do not assess distant trans -expression quantitative trait loci (eQTL) that are known to explain important expression variation for most genes. We propose a Bayesian genome-wide TWAS (BGW-TWAS) method that leverages both cis - and trans -eQTL information for a TWAS. Our BGW-TWAS method is based on Bayesian variable selection regression, which not only accounts for cis - and trans -eQTL of the target gene but also enables efficient computation by using summary statistics from standard eQTL analyses. Our simulation studies illustrated that BGW-TWASs achieved higher power compared to existing TWAS methods that do not assess trans -eQTL information. We further applied BWG-TWAS to individual-level GWAS data (N = ∼3.3K), which identified significant associations between the genetically regulated gene expression (GReX) of ZC3H12B and Alzheimer dementia (AD) (p value = 5.42 × 10−13), neurofibrillary tangle density (p value = 1.89 × 10−6), and global measure of AD pathology (p value = 9.59 × 10−7). These associations for ZC3H12B were completely driven by trans -eQTL. Additionally, the GReX of KCTD12 was found to be significantly associated with β -amyloid (p value = 3.44 × 10−8) which was driven by both cis - and trans -eQTL. Four of the top driven trans -eQTL of ZC3H12B are located within APOC1 , a known major risk gene of AD and blood lipids. Additionally, by applying BGW-TWAS with summary-level GWAS data of AD (N = ∼54K), we identified 13 significant genes including known GWAS risk genes HLA-DRB1 and APOC1 , as well as ZC3H12B. [ABSTRACT FROM AUTHOR]

Expression quantitative trait locus (eQTL) analyses are critical in understanding the complex functional regulatory natures of genetic variation and have been widely used in the interpretation of disease-associated variants identified by genome-wide association studies (GWAS). Emerging evidence has shown that trans -eQTL effects on remote gene expression could be mediated by local transcripts, which is known as the mediation effects. To discover the genome-wide eQTL mediation effects combing genomic and transcriptomic profiles, it is necessary to develop novel computational methods to rapidly scan large number of candidate associations while controlling for multiple testing appropriately. Here, we present eQTLMAPT, an R package aiming to perform eQTL mediation analysis with implementation of efficient permutation procedures in multiple testing correction. eQTLMAPT is advantageous in threefold. First, it accelerates mediation analysis by effectively pruning the permutation process through adaptive permutation scheme. Second, it can efficiently and accurately estimate the significance level of mediation effects by modeling the null distribution with generalized Pareto distribution (GPD) trained from a few permutation statistics. Third, eQTLMAPT provides flexible interfaces for users to combine various permutation schemes with different confounding adjustment methods. Experiments on real eQTL dataset demonstrate that eQTLMAPT provides higher resolution of estimated significance of mediation effects and is an order of magnitude faster than compared methods with similar accuracy. [ABSTRACT FROM AUTHOR]

The knowledge of genetic variants shaping human placental transcriptome is limited and they are not cataloged in the Genotype-Tissue Expression project. So far, only one whole genome analysis of placental expression quantitative trait loci (eQTLs) has been published by Peng et al. (2017) with no external independent validation. We report the second study on the landscape of placental eQTLs. The study aimed to generate a high-confidence list of placental cis-eQTLs and to investigate their potential functional implications. Analysis of cis-eQTLs (±100 kbp from the gene) utilized 40 placental RNA sequencing and respective whole genome genotyping datasets. The identified 199 placental cis-eSNPs represented 88 independent eQTL signals (FDR < 5%). The most significant placental eQTLs (FDR < 10-5) modulated the expression of ribosomal protein RPL9, transcription factor ZSCAN9 and aminopeptidase ERAP2. The analysis confirmed 50 eSNP-eGenes pairs reported by Peng et al. (2017) and thus, can be claimed as robust placental eQTL signals. The study identified also 13 novel placental eGenes. Among these, ZSCAN9 is modulated by several eSNPs (experimentally validated: rs1150707) that have been also shown to affect the methylation level of genes variably escaping X-chromosomal inactivation. The identified 63 placental eGenes exhibited mostly mixed or ubiquitous expression. Functional enrichment analysis highlighted 35 Gene Ontology categories with the top ranking pathways “ruffle membrane” (FDR = 1.81 × 10-2) contributing to the formation of motile cell surface and “ATPase activity, coupled” (FDR = 2.88 × 10-2), critical for the membrane transport. Placental eGenes were also significantly enriched in pathways implicated in development, signaling and immune function. However, this study was not able to confirm a significant overrepresentation of genome-wide association studies top hits among the placental eSNP and eGenes, reported by Peng et al. (2017). The identified eSNPs were further analyzed in association with newborn and pregnancy traits. In the discovery step, a suggestive association was detected between the eQTL of ALPG (rs11678251) and reduced placental, newborn’s and infant’s weight. Meta-analysis across REPROMETA, HAPPY PREGNANCY, ALSPAC cohorts (n = 6830) did not replicate these findings. In summary, the study emphasizes the role of genetic variation in driving the transcriptome profile of the human placenta and the importance to explore further its functional implications.

The present study interrogated a quantitative trait locus (QTL) on Chr 4 associated with the population sizes of two types of bipolar cell in the mouse retina. This locus was identified by quantifying the number of rod bipolar cells and Type 2 cone bipolar cells across a panel of recombinant inbred (RI) strains of mice derived from two inbred laboratory strains, C57BL/6J (B6/J) and A/J, and mapping a proportion of that variation in cell number, for each cell type, to this shared locus. There, we identified the candidate gene X Kell blood group precursor related family member 8 homolog (Xkr8). While Xkr8 has no documented role in the retina, we localize robust expression in the mature retina via in situ hybridization, confirm its developmental presence via immunolabeling, and show that it is differentially regulated during the postnatal period between the B6/J and A/J strains using qPCR. Microarray analysis, derived from whole eye mRNA from the entire RI strain set, demonstrates significant negative correlation of Xkr8 expression with the number of each of these two types of bipolar cells, and the variation in Xkr8 expression across the strains maps a cis-eQTL, implicating a regulatory variant discriminating the parental genomes. Xkr8 plasmid electroporation during development yielded a reduction in the number of bipolar cells in the retina, while sequence analysis of Xkr8 in the two parental strain genomes identified a structural variant in the 3′ UTR that may disrupt mRNA stability, and two SNPs in the promoter that create transcription factor binding sites. We propose that Xkr8, via its participation in mediating cell death, plays a role in the specification of bipolar cell number in the retina.

Genome-wide association studies (GWASs) have identified hundreds of genetic risk variants for human cancers. However, target genes for the majority of risk loci remain largely unexplored. It is also unclear whether GWAS risk-loci-associated genes contribute to mutational signatures and tumor mutational burden (TMB) in cancer tissues. We systematically conducted cis-expression quantitative trait loci (cis-eQTL) analyses for 294 GWAS-identified variants for six major types of cancer—colorectal, lung, ovary, prostate, pancreas, and melanoma—by using transcriptome data from the Genotype-Tissue Expression (GTEx) Project, the Cancer Genome Atlas (TCGA), and other public data sources. By using integrative analysis strategies, we identified 270 candidate target genes, including 99 with previously unreported associations, for six cancer types. By analyzing functional genomic data, our results indicate that 180 genes (66.7% of 270) had evidence of cis-regulation by putative functional variants via proximal promoter or distal enhancer-promoter interactions. Together with our previously reported associations for breast cancer risk, our results show that 24 genes are shared by at least two cancer types, including four genes for both breast and ovarian cancer. By integrating mutation data from TCGA, we found that expression levels of 33 and 66 putative susceptibility genes were associated with specific mutational signatures and TMB of cancer-driver genes, respectively, at a Bonferroni-corrected p < 0.05. Together, these findings provide further insight into our understanding of how genetic risk variants might contribute to carcinogenesis through the regulation of susceptibility genes that are related to the biogenesis of somatic mutations. [ABSTRACT FROM AUTHOR]

Sequence variants modulating gene function or expression affect various heritable traits, including the number of neurons within a population. The present study employed a forward-genetic approach to identify candidate causal genes and their sequence variants controlling the number of one type of retinal neuron, the AII amacrine cell. Data from twenty-six recombinant inbred (RI) strains of mice derived from the parental C57BL/6J (B6/J) and A/J laboratory strains were used to identify genomic loci regulating cell number. Large variation in cell number is present across the RI strains, from a low of ∼57,000 cells to a high of ∼87,000 cells. Quantitative trait locus (QTL) analysis revealed three prospective controlling genomic loci, on Chromosomes (Chrs) 9, 11, and 19, each contributing additive effects that together approach the range of variation observed. Composite interval mapping validated two of these loci, and chromosome substitution strains, in which the A/J genome for Chr 9 or 19 was introgressed on a B6/J genetic background, showed increased numbers of AII amacrine cells as predicted by those two QTL effects. Analysis of the respective genomic loci identified candidate controlling genes defined by their retinal expression, their established biological functions, and by the presence of sequence variants expected to modulate gene function or expression. Two candidate genes, Dtx4 on Chr 19, being a regulator of Notch signaling, and Dixdc1 on Chr 9, a modulator of the WNT-β-catenin signaling pathway, were explored in further detail. Postnatal overexpression of Dtx4 was found to reduce the frequency of amacrine cells, while Dixdc1 knockout retinas contained an excess of AII amacrine cells. Sequence variants in each gene were identified, being the likely sources of variation in gene expression, ultimately contributing to the final number of AII amacrine cells., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Kulesh, Bozadjian, Parisi, Leong, Kautzman, Reese and Keeley.)

Background Alcohol and nicotine codependence can be considered as a more severe subtype of alcohol dependence. A portion of its risk may be attributable to genetic factors. Methods We searched for significant risk genomic regions specific for this disorder using a genome-wide association study. A total of 8,847 subjects underwent gene-disease association analysis, including (i) a discovery cohort of 818 European American cases with alcohol and nicotine codependence and 1,396 European American controls, (ii) a replication cohort of 5,704 Australian family subjects with 907 affected offspring, and (iii) a replication cohort of 449 African American cases and 480 African American controls. Additionally, a total of 38,714 subjects of European or African descent in 18 independent cohorts with 10 other nonalcoholism neuropsychiatric disorders were analyzed as contrast. Furthermore, 90 unrelated Hap Map CEU individuals, 93 European brain tissue samples, and 80 European peripheral blood mononuclear cell samples underwent cis-acting expression quantitative locus ( cis-e QTL) analysis. Results We identified a significant risk region for alcohol and nicotine codependence between IPO11 and HTR1 A on chromosome 5q that was reported to be suggestively associated with alcohol dependence previously. In the European American discovery cohort, 381 single nucleotide polymorphisms (SNPs) in this region were nominally associated with alcohol and nicotine codependence ( p < 0.05); 57 associations of them survived region- and cohort-wide correction (α = 3.6 × 10−6); and the top SNP (rs7445832) was significantly associated with alcohol and nicotine codependence at the genome-wide significance level ( p = 6.2 × 10−9). Furthermore, associations for 34 and 11 SNPs were replicated in the Australian and African American replication cohorts, respectively. Among these replicable associations, 4 reached genome-wide significance level in the meta-analysis of European Americans and European Australians: rs7445832 ( p = 9.6 × 10−10), rs13361996 ( p = 8.2 × 10−9), rs62380518 ( p = 2.3 × 10−8), and rs7714850 ( p = 3.4 × 10−8). Cis-e QTL analysis showed that many risk SNPs in this region had nominally significant cis-acting regulatory effects on HTR1 A or IPO11 m RNA expression. Finally, no markers were significantly associated with any other neuropsychiatric disorder examined. Conclusions We speculate that this IPO11- HTR1 A region might harbor a causal variant for alcohol and nicotine codependence. [ABSTRACT FROM AUTHOR]

Abstract: Single nucleotide polymorphisms (SNPs) between microarray probes and RNA targets can affect the performance of expression array by weakening the hybridization. In this paper, we examined the effect of the SNPs on Affymetrix GeneChip probe set summaries and the expression quantitative trait loci (eQTL) mapping results in two eQTL datasets, one from mouse and one from human. We showed that removing SNP-containing probes significantly changed the probe set summaries and the more SNP-containing probes we removed the greater the change. Comparison of the eQTL mapping results between with and without SNP-containing probes showed that less than 70% of the significant eQTL peaks were concordant regardless of the significance threshold. These results indicate that SNPs do affect both probe set summaries and eQTLs (both cis and trans), thus SNP-containing probes should be filtered out to improve the performance of eQTL mapping. [Copyright &y& Elsevier]

Non-synonymous coding mutations in a gene change the resulting protein, no matter where it is expressed, but the effects of cis-regulatory mutations could be spatially or temporally limited – a phenomenon termed limited pleiotropy. Here, we report the genome-wide occurrence of limited pleiotropy of cis-regulatory mutations in barley ( Hordeum vulgare L.) using Affymetrix analysis of 22 840 genes in a population of 139 doubled haploid lines derived from a cross between the cultivars Steptoe (St) and Morex (Mx). We identified robust cis-acting expression regulators that segregate as major genes in two successive ontogenetic stages: germinating embryo tissues and seedling leaves from the embryonic axis. We show that these polymorphisms may be consistent in both tissues or may cause a dramatic change in transcript abundance in one tissue but not in another. We also show that the parental allele that increases expression can vary with the tissue, suggesting nucleotide polymorphism in enhancer sequences. Because of the limited pleiotropy of cis-regulating mutations, the number of cis expression quantitative trait loci ( cis-eQTLs) discovered by ‘genetical genomics’ is strongly affected by the particular tissue or developmental stage studied. Given that limited pleiotropy is a common feature of cis-regulatory mutations in barley, we predict that the phenomenon would be relevant to developmental and/or tissue-specific interactions across wide taxonomic boundaries in both plants and animals. [ABSTRACT FROM AUTHOR]

Sick, male, and older populations are more vulnerable to COVID-19. However, it remains unclear whether a common mechanism exists across different demographic characteristics. SARS-CoV-2 infection is initiated by the specific binding of the viral spike protein to angiotensin-converting enzyme 2 (ACE2). This study analyzed the demographics of pulmonary ACE2 expression, Mendelian randomization (MR) of ACE2 and COVID-19, and comparative tropism of SARS-CoV-2. The key features of SARS-CoV-2 tropism, including pulmonary ACE2 expression and ACE2-expressing cell types, showed distinct subphenotypes associated with the demographics of vulnerable COVID-19 populations, suggesting a hypothesis centered on "ACE2" to explain their interplay. Next, by integrating multiple COVID-19 cohorts of genome-wide association studies (GWASs) and cis-expression quantitative trait loci (cis-eQTLs) of ACE2, MR analysis demonstrated that ACE2 played a causal role in COVID-19 susceptibility and severity, suggesting ACE2 as a promising target for early COVID-19 treatment. Next, by analyzing the expression of host cell receptors using single-cell RNA sequencing (scRNA-seq) data of human lung tissues, comparative tropism analysis showed that SARS-CoV-2 and other respiratory viruses, but not non-respiratory viruses, had remarkably overlapping and enriched cellular tropism in alveolar type 2 (AT2) cells. This finding indicates the possibility of coinfection with SARS-CoV-2 and other respiratory viruses, perhaps implying sociovirology at the cellular level. Moreover, the binding of viral entry proteins to the compatible host cell receptors is under strong natural selection pressure. Therefore, comparative tropism might reveal the footprint of natural selection that shapes the virus population, which provides a novel perspective for understanding zoonotic spillover events., Competing Interests: Conflicts of interest The authors disclose no potential conflicts of interest., (Copyright: © 2022 Zheng et al.)

Genome-wide association studies have successfully mapped thousands of loci associated with complex traits. During the last decade, functional genomics approaches combining genotype information with bulk RNA-sequencing data have identified genes regulated by GWAS loci through expression quantitative trait locus (eQTL) analysis. Single-cell RNA-Sequencing (scRNA-Seq) technologies have created new exciting opportunities for spatiotemporal assessment of changes in gene expression at the single-cell level in complex and inherited conditions. A growing number of studies have demonstrated the power of scRNA-Seq in eQTL mapping across different cell types, developmental stages and stimuli that could be obscured when using bulk RNA-Seq methods. In this review, we outline the methodological principles, advantages, limitations and the future experimental and analytical considerations of single-cell eQTL studies. We look forward to the explosion of single-cell eQTL studies applied to large-scale population genetics to take us one step closer to understanding the molecular mechanisms of disease.

Standard transcriptome-wide association study (TWAS) methods first train gene expression prediction models using reference transcriptomic data and then test the association between the predicted genetically regulated gene expression and phenotype of interest. Most existing TWAS tools require cumbersome preparation of genotype input files and extra coding to enable parallel computation. To improve the efficiency of TWAS tools, we developed Transcriptome-Integrated Genetic Association Resource V2 (TIGAR-V2), which directly reads Variant Call Format (VCF) files, enables parallel computation, and reduces up to 90% of computation cost (mainly due to loading genotype data) compared to the original version. TIGAR-V2 can train gene expression imputation models using either nonparametric Bayesian Dirichlet process regression (DPR) or Elastic-Net (as used by PrediXcan), perform TWASs using either individual-level or summary-level genome-wide association study (GWAS) data, and implement both burden and variance-component statistics for gene-based association tests. We trained gene expression prediction models by DPR for 49 tissues using Genotype-Tissue Expression (GTEx) V8 by TIGAR-V2 and illustrated the usefulness of these Bayesian cis -expression quantitative trait locus (eQTL) weights through TWASs of breast and ovarian cancer utilizing public GWAS summary statistics. We identified 88 and 37 risk genes, respectively, for breast and ovarian cancer, most of which are either known or near previously identified GWAS (∼95%) or TWAS (∼40%) risk genes and three novel independent TWAS risk genes with known functions in carcinogenesis. These findings suggest that TWASs can provide biological insight into the transcriptional regulation of complex diseases. The TIGAR-V2 tool, trained Bayesian cis -eQTL weights, and linkage disequilibrium (LD) information from GTEx V8 are publicly available, providing a useful resource for mapping risk genes of complex diseases., Competing Interests: The authors declare no competing interests., (© 2021 The Author(s).)

Identification of genes whose regulation of expression is functionally similar in both brain tissue and blood cells could in principle enable monitoring of significant neurological traits and disorders by analysis of blood samples. We thus employed transcriptional analysis of pathologically affected tissues, using agnostic approaches to identify overlapping gene functions and integrating this transcriptomic information with expression quantitative trait loci (eQTL) data. Here, we estimate the correlation of gene expression in the top-associated cis-eQTLs of brain tissue and blood cells in Parkinson's Disease (PD). We introduced quantitative frameworks to reveal the complex relationship of various biasing genetic factors in PD, a neurodegenerative disease. We examined gene expression microarray and RNA-Seq datasets from human brain and blood tissues from PD-affected and control individuals. Differentially expressed genes (DEG) were identified for both brain and blood cells to determine common DEG overlaps. Based on neighborhood-based benchmarking and multilayer network topology approaches we then developed genetic associations of factors with PD. Overlapping DEG sets underwent gene enrichment using pathway analysis and gene ontology methods, which identified candidate common genes and pathways. We identified 12 significantly dysregulated genes shared by brain and blood cells, which were validated using dbGaP (gene SNP-disease linkage) database for gold-standard benchmarking of their significance in disease processes. Ontological and pathway analyses identified significant gene ontology and molecular pathways that indicate PD progression. In sum, we found possible novel links between pathological processes in brain tissue and blood cells by examining cell pathway commonalities, corroborating these associations using well validated datasets. This demonstrates that for brain-related pathologies combining gene expression analysis and blood cell cis-eQTL is a potentially powerful analytical approach. Thus, our methodologies facilitate data-driven approaches that can advance knowledge of disease mechanisms and may, with clinical validation, enable prediction of neurological dysfunction using blood cell transcript profiling., (Copyright © 2019 Elsevier Ltd. All rights reserved.)

The knowledge of genetic variants shaping human placental transcriptome is limited and they are not cataloged in the Genotype-Tissue Expression project. So far, only one whole genome analysis of placental expression quantitative trait loci (eQTLs) has been published by Peng et al. (2017) with no external independent validation. We report the second study on the landscape of placental eQTLs. The study aimed to generate a high-confidence list of placental cis -eQTLs and to investigate their potential functional implications. Analysis of cis -eQTLs (±100 kbp from the gene) utilized 40 placental RNA sequencing and respective whole genome genotyping datasets. The identified 199 placental cis -eSNPs represented 88 independent eQTL signals (FDR < 5%). The most significant placental eQTLs (FDR < 10 -5 ) modulated the expression of ribosomal protein RPL9, transcription factor ZSCAN9 and aminopeptidase ERAP2. The analysis confirmed 50 eSNP-eGenes pairs reported by Peng et al. (2017) and thus, can be claimed as robust placental eQTL signals. The study identified also 13 novel placental eGenes. Among these, ZSCAN9 is modulated by several eSNPs (experimentally validated: rs1150707) that have been also shown to affect the methylation level of genes variably escaping X-chromosomal inactivation. The identified 63 placental eGenes exhibited mostly mixed or ubiquitous expression. Functional enrichment analysis highlighted 35 Gene Ontology categories with the top ranking pathways "ruffle membrane" (FDR = 1.81 × 10 -2 ) contributing to the formation of motile cell surface and "ATPase activity, coupled" (FDR = 2.88 × 10 -2 ), critical for the membrane transport. Placental eGenes were also significantly enriched in pathways implicated in development, signaling and immune function. However, this study was not able to confirm a significant overrepresentation of genome-wide association studies top hits among the placental eSNP and eGenes, reported by Peng et al. (2017). The identified eSNPs were further analyzed in association with newborn and pregnancy traits. In the discovery step, a suggestive association was detected between the eQTL of ALPG (rs11678251) and reduced placental, newborn's and infant's weight. Meta-analysis across REPROMETA, HAPPY PREGNANCY, ALSPAC cohorts ( n = 6830) did not replicate these findings. In summary, the study emphasizes the role of genetic variation in driving the transcriptome profile of the human placenta and the importance to explore further its functional implications.

Genome-wide association studies (GWASs) have identified more than 150 common genetic loci for breast cancer risk. However, the target genes and underlying mechanisms remain largely unknown. We conducted a cis-expression quantitative trait loci (cis-eQTL) analysis using normal or tumor breast transcriptome data from the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC), The Cancer Genome Atlas (TCGA), and the Genotype-Tissue Expression (GTEx) project. We identified a total of 101 genes for 51 lead variants after combing the results of a meta-analysis of METABRIC and TCGA, and the results from GTEx at a Benjamini-Hochberg (BH)-adjusted p < 0.05. Using luciferase reporter assays in both estrogen-receptor positive (ER + ) and negative (ER - ) cell lines, we showed that alternative alleles of potential functional single-nucleotide polymorphisms (SNPs), rs11552449 (DCLRE1B), rs7257932 (SSBP4), rs3747479 (MRPS30), rs2236007 (PAX9), and rs73134739 (ATG10), could significantly change promoter activities of their target genes compared to reference alleles. Furthermore, we performed in vitro assays in breast cancer cell lines, and our results indicated that DCLRE1B, MRPS30, and ATG10 played a vital role in breast tumorigenesis via certain disruption of cell behaviors. Our findings revealed potential target genes for associations of genetic susceptibility risk loci and provided underlying mechanisms for a better understanding of the pathogenesis of breast cancer., (Copyright © 2018 American Society of Human Genetics. Published by Elsevier Inc. All rights reserved.)

The association of classic human leukocyte antigen (HLA) alleles with risk of cervical cancer has been extensively studied, and a protective effect has consistently been found for DRB1*1301, DQA1*0103, and/or DQB1*0603 (these three alleles are in perfect linkage disequilibrium [LD] and often occur on the same haplotype in Europeans), while reports have differed widely with respect to the effect of HLA-B*07, DRB1*1501, and/or DQB1*0602 (the last two alleles are also in perfect LD in Europeans). It is not clear whether the reported HLA alleles are responsible for the differences in cervical cancer susceptibility, or if functional variants at other locations within the major histocompatibility complex (MHC) region may explain the effect. In order to assess the relative contribution of both classic HLA alleles and single-nucleotide polymorphisms (SNPs) within the MHC region to cervical cancer susceptibility, we have imputed classic HLA alleles in 1034 cervical cancer patients and 3948 controls in a Swedish population for an integrated analysis. We found that the protective haplotype DRB1*1301-DQA1*0103-DQB1*0603 has a direct effect on cervical cancer and always occurs together with the C allele of a HLA-DRB1 cis-eQTL (rs9272143), which increases the expression of HLA-DRB1. The haplotype rs9272143C-DRB1*1301-DQA1*0103-DQB1*0603 conferred the strongest protection against cervical cancer (odds ratio [OR] = 0.41, 95% confidence interval [CI] = 0.32-0.52, P = 6.2 × 10(-13)). On the other hand, the associations with HLA-B*0702 and DRB1*1501-DQB1*0602 are attributable to the joint effects of both the HLA-DRB1 cis-eQTL (rs9272143) and a frameshift mutation (G inserion of rs67841474, also known as A5.1) of the MHC class I polypeptide-related sequence A gene (MICA). Variation in LD between the classic HLA loci, rs9272143 and rs67841474 between populations may explain the different associations of HLA-B*07 and DRB1*1501-DQB1*0602 with cervical cancer between studies. The mechanism suggested may also explain similar inconsistent results for other HLA-associated diseases., (© 2014 The Authors. Cancer Medicine published by John Wiley & Sons Ltd.)

In a genome-wide association study, we have previously identified and performed the initial replication of three novel susceptibility loci for cervical cancer: rs9272143 upstream of HLA-DRB1, rs2516448 adjacent to MHC class I polypeptide-related sequence A gene (MICA), and rs3117027 at HLA-DPB2. The risk allele T of rs2516448 is in perfect linkage disequilibrium with a frameshift mutation (A5.1) in MICA exon 5, which results in a truncated protein. To validate these associations in an independent study and extend our prior work to MICA exon 5, we genotyped the single-nucleotide polymorphisms at rs9272143, rs2516448, rs3117027 and the MICA exon 5 microsatellite in a nested case-control study of 961 cervical cancer patients (827 carcinoma in situ and 134 invasive carcinoma) and 1725 controls from northern Sweden. The C allele of rs9272143 conferred protection against cervical cancer (odds ratio [OR] = 0.73, 95% confidence interval [CI] = 0.65-0.82; P = 1.6 × 10(-7)), which is associated with higher expression level of HLA-DRB1, whereas the T allele of rs2516448 increased the susceptibility to cervical cancer (OR = 1.33, 95% CI = 1.19-1.49; P = 5.8 × 10(-7)), with the same association shown with MICA-A5.1. The direction and the magnitude of these associations were consistent with our previous findings. We also identified protective effects of the MICA-A4 (OR = 0.80, 95% CI = 0.68-0.94; P = 6.7 × 10(-3)) and MICA-A5 (OR = 0.60, 95% CI = 0.50-0.72; P = 3.0 × 10(-8)) alleles. The associations with these variants are unlikely to be driven by the nearby human leukocyte antigen (HLA) alleles. No association was observed between rs3117027 and risk of cervical cancer. Our results support the role of HLA-DRB1 and MICA in the pathogenesis of cervical cancer., (© 2014 The Authors. Cancer Medicine published by John Wiley & Sons Ltd.)