The genetic and mechanistic basis for variation in gene regulation

Systematic evaluation of single-cell RNA-seq analyses performance based on long-read sequencing platforms

INTRODUCTION

The rapid development of next-generation sequencing (NGS)-based single-cell RNA sequencing (scRNA-seq) allows for detecting and quantifying gene expression in a high-throughput manner, providing a powerful tool for comprehensively understanding cellular function in various biological processes. However, the NGS-based scRNA-seq only quantifies gene expression and cannot reveal the exact transcript structures (isoforms) of each gene due to the limited read length. On the other hand, the long read length of third-generation sequencing (TGS) technologies, including Oxford Nanopore Technologies (ONT) and Pacific Biosciences (PacBio), enable direct reading of intact cDNA molecules.

OBJECTIVES

Both ONT and PacBio have been used in conjunction with scRNA-seq, but their performance in single-cell analyses has not been systematically evaluated.

METHODS

To address this, we generated ONT and PacBio data from the same single-cell cDNA libraries containing different amount of cells.

RESULTS

Using NGS as a control, we assessed the performance of each platform in cell type identification. Additionally, the reliability in identifying novel isoforms and allele-specific gene/isoform expression by both platforms was verified, providing a systematic evaluation to design the sequencing strategies in single-cell transcriptome studies.

CONCLUSION

Beyond gene expression analysis, which the NGS-based scRNA-seq only affords, TGS-based scRNA-seq achieved gene splicing analyses, identifying novel isoforms. Attribute to higher sequencing quality of PacBio, it outperforms ONT in accuracy of novel transcripts identification and allele-specific gene/isoform expression.

Shaping immunity: The influence of natural selection on population immune diversity

Humans exhibit considerable variability in their immune responses to the same immune challenges. Such variation is widespread and affects individual and population-level susceptibility to infectious diseases and immune disorders. Although the factors influencing immune response diversity are partially understood, what mechanisms lead to the wide range of immune traits in healthy individuals remain largely unexplained. Here, we discuss the role that natural selection has played in driving phenotypic differences in immune responses across populations and present-day susceptibility to immune-related disorders. Further, we touch on future directions in the field of immunogenomics, highlighting the value of expanding this work to human populations globally, the utility of modeling the immune response as a dynamic process, and the importance of considering the potential polygenic nature of natural selection. Identifying loci acted upon by evolution may further pinpoint variants critically involved in disease etiology, and designing studies to capture these effects will enrich our understanding of the genetic contributions to immunity and immune dysregulation.

Gene expression variation underlying tissue-specific responses to copper stress in Drosophila melanogaster

Copper is one of a handful of biologically necessary heavy metals that is also a common environmental pollutant. Under normal conditions, copper ions are required for many key physiological processes. However, in excess, copper results in cell and tissue damage ranging in severity from temporary injury to permanent neurological damage. Because of its biological relevance, and because many conserved copper-responsive genes respond to nonessential heavy metal pollutants, copper resistance in Drosophila melanogaster is a useful model system with which to investigate the genetic control of the heavy metal stress response. Because heavy metal toxicity has the potential to differently impact specific tissues, we genetically characterized the control of the gene expression response to copper stress in a tissue-specific manner in this study. We assessed the copper stress response in head and gut tissue of 96 inbred strains from the Drosophila Synthetic Population Resource using a combination of differential expression analysis and expression quantitative trait locus mapping. Differential expression analysis revealed clear patterns of tissue-specific expression. Tissue and treatment specific responses to copper stress were also detected using expression quantitative trait locus mapping. Expression quantitative trait locus associated with MtnA, Mdr49, Mdr50, and Sod3 exhibited both genotype-by-tissue and genotype-by-treatment effects on gene expression under copper stress, illuminating tissue- and treatment-specific patterns of gene expression control. Together, our data build a nuanced description of the roles and interactions between allelic and expression variation in copper-responsive genes, provide valuable insight into the genomic architecture of susceptibility to metal toxicity, and highlight candidate genes for future functional characterization.

MRPS6 modulates glucose-stimulated insulin secretion in mouse islet cells through mitochondrial unfolded protein response

Lack of efficient insulin secretion from the pancreas can lead to impaired glucose tolerance (IGT), prediabetes, and diabetes. We have previously identified two IGT-associated single nucleotide polymorphisms (SNPs) rs62212118 and rs13052524 located at two overlapping genes: MRPS6 and SLC5A3. In this study, we show that MRPS6 but not SLC5A3 regulates glucose-stimulated insulin secretion (GSIS) in primary human β-cell and a mouse pancreatic insulinoma β-cell line. Data mining and biochemical studies reveal that MRPS6 is positively regulated by the mitochondrial unfolded protein response (UPRmt), but feedback inhibits UPRmt. Disruption of such feedback by MRPS6 knockdown causes UPRmt hyperactivation in high glucose conditions, hence elevated ROS levels, increased apoptosis, and impaired GSIS. Conversely, MRPS6 overexpression reduces UPRmt, mitigates high glucose-induced ROS levels and apoptosis, and enhances GSIS in an ATF5-dependent manner. Consistently, UPRmt up-regulation or down-regulation by modulating ATF5 expression is sufficient to decrease or increase GSIS. The negative role of UPRmt in GSIS is further supported by analysis of public transcriptomic data from murine islets. In all, our studies identify MRPS6 and UPRmt as novel modulators of GSIS and apoptosis in β-cells, contributing to our understanding of the molecular and cellular mechanisms of IGT, prediabetes, and diabetes.

Non-Random Enrichment of Single-Nucleotide Polymorphisms Associated with Clopidogrel Resistance within Risk Loci Linked to the Severity of Underlying Cardiovascular Diseases: The Role of Admixture

Cardiovascular disease (CVD) is one of the leading causes of death in Puerto Rico, where clopidogrel is commonly prescribed to prevent ischemic events. Genetic contributors to both a poor clopidogrel response and the severity of CVD have been identified mainly in Europeans. However, the non-random enrichment of single-nucleotide polymorphisms (SNPs) associated with clopidogrel resistance within risk loci linked to underlying CVDs, and the role of admixture, have yet to be tested. This study aimed to assess the possible interaction between genetic biomarkers linked to CVDs and those associated with clopidogrel resistance among admixed Caribbean Hispanics. We identified 50 SNPs significantly associated with CVDs in previous genome-wide association studies (GWASs). These SNPs were combined with another ten SNPs related to clopidogrel resistance in Caribbean Hispanics. We developed Python scripts to determine whether SNPs related to CVDs are in close proximity to those associated with the clopidogrel response. The average and individual local ancestry (LAI) within each locus were inferred, and 60 random SNPs with their corresponding LAIs were generated for enrichment estimation purposes. Our results showed no CVD-linked SNPs in close proximity to those associated with the clopidogrel response among Caribbean Hispanics. Consequently, no genetic loci with a dual predictive role for the risk of CVD severity and clopidogrel resistance were found in this population. Native American ancestry was the most enriched within the risk loci linked to CVDs in this population. The non-random enrichment of disease susceptibility loci with drug-response SNPs is a new frontier in Precision Medicine that needs further attention.

Analysis of subcellular RNA fractions demonstrates significant genetic regulation of gene expression in human brain post-transcriptionally

Gaining insight into the genetic regulation of gene expression in human brain is key to the interpretation of genome-wide association studies for major neurological and neuropsychiatric diseases. Expression quantitative trait loci (eQTL) analyses have largely been used to achieve this, providing valuable insights into the genetic regulation of steady-state RNA in human brain, but not distinguishing between molecular processes regulating transcription and stability. RNA quantification within cellular fractions can disentangle these processes in cell types and tissues which are challenging to model in vitro. We investigated the underlying molecular processes driving the genetic regulation of gene expression specific to a cellular fraction using allele-specific expression (ASE). Applying ASE analysis to genomic and transcriptomic data from paired nuclear and cytoplasmic fractions of anterior prefrontal cortex, cerebellar cortex and putamen tissues from 4 post-mortem neuropathologically-confirmed control human brains, we demonstrate that a significant proportion of genetic regulation of gene expression occurs post-transcriptionally in the cytoplasm, with genes undergoing this form of regulation more likely to be synaptic. These findings have implications for understanding the structure of gene expression regulation in human brain, and importantly the interpretation of rapidly growing single-nucleus brain RNA-sequencing and eQTL datasets, where cytoplasm-specific regulatory events could be missed.

Role of non-coding variants in cardiovascular disease

Cardiovascular diseases (CVDs) constitute one of the significant causes of death worldwide. Different pathological states are linked to CVDs, which despite interventions and treatments, still have poor prognoses. The genetic component, as a beneficial tool in the risk stratification of CVD development, plays a role in the pathogenesis of this group of diseases. The emergence of genome-wide association studies (GWAS) have led to the identification of non-coding parts associated with cardiovascular traits and disorders. Variants located in functional non-coding regions, including promoters/enhancers, introns, miRNAs and 5'/3' UTRs, account for 90% of all identified single-nucleotide polymorphisms associated with CVDs. Here, for the first time, we conducted a comprehensive review on the reported non-coding variants for different CVDs, including hypercholesterolemia, cardiomyopathies, congenital heart diseases, thoracic aortic aneurysms/dissections and coronary artery diseases. Additionally, we present the most commonly reported genes involved in each CVD. In total, 1469 non-coding variants constitute most reports on familial hypercholesterolemia, hypertrophic cardiomyopathy and dilated cardiomyopathy. The application and identification of non-coding variants are beneficial for the genetic diagnosis and better therapeutic management of CVDs.

ADGR: Admixture-Informed Differential Gene Regulation

The regulatory elements in proximal and distal regions of genes are involved in the regulation of gene expression. Risk alleles in intronic and intergenic regions may alter gene expression by modifying the binding affinity and stability of diverse DNA-binding proteins implicated in gene expression regulation. By focusing on the local ancestral structure of coding and regulatory regions using the paired whole-genome sequence and tissue-wide transcriptome datasets from the Genotype-Tissue Expression project, we investigated the impact of genetic variants, in aggregate, on tissue-specific gene expression regulation. Local ancestral origins of the coding region, immediate and distant upstream regions, and distal regulatory region were determined using RFMix with the reference panel from the 1000 Genomes Project. For each tissue, inter-individual variation of gene expression levels explained by concordant or discordant local ancestry between coding and regulatory regions was estimated. Compared to European, African descent showed more frequent change in local ancestral structure, with shorter haplotype blocks. The expression level of the Adenosine Deaminase Like (ADAL) gene was significantly associated with admixed ancestral structure in the regulatory region across multiple tissue types. Further validations are required to understand the impact of the local ancestral structure of regulatory regions on gene expression regulation in humans and other species.

Leveraging drug perturbation to reveal genetic regulators of hepatic gene expression in African Americans

Expression quantitative locus (eQTL) studies have paved the way in identifying genetic variation impacting gene expression levels. African Americans (AAs) are disproportionately underrepresented in eQTL studies, resulting in a lack of power to identify population-specific regulatory variants especially related to drug response. Specific drugs are known to affect the biosynthesis of drug metabolism enzymes as well as other genes. We used drug perturbation in cultured primary hepatocytes derived from AAs to determine the effect of drug treatment on eQTL mapping and to identify the drug response eQTLs (reQTLs) that show altered effect size following drug treatment. Whole-genome genotyping (Illumina MEGA array) and RNA sequencing were performed on 60 primary hepatocyte cultures after treatment with six drugs (Rifampin, Phenytoin, Carbamazepine, Dexamethasone, Phenobarbital, and Omeprazole) and at baseline (no treatment). eQTLs were mapped by treatment and jointly with Meta-Tissue. We found varying transcriptional changes across different drug treatments and identified Nrf2 as a potential general transcriptional regulator. We jointly mapped eQTLs with gene expression data across all drug treatments and baseline, which increased our power to detect eQTLs by 2.7-fold. We also identified 2,988 reQTLs (eQTLs with altered effect size after drug treatment). reQTLs were more likely to overlap transcription factor binding sites, and we uncovered reQTLs for drug metabolizing genes such as CYP3A5. Our results provide insights into the genetic regulation of gene expression in hepatocytes through drug perturbation and provide insight into SNPs that effect the liver's ability to respond to transcription upregulation.

DNA methylation QTL mapping across diverse human tissues provides molecular links between genetic variation and complex traits

Studies of DNA methylation (DNAm) in solid human tissues are relatively scarce; tissue-specific characterization of DNAm is needed to understand its role in gene regulation and its relevance to complex traits. We generated array-based DNAm profiles for 987 human samples from the Genotype-Tissue Expression (GTEx) project, representing 9 tissue types and 424 subjects. We characterized methylome and transcriptome correlations (eQTMs), genetic regulation in cis (mQTLs and eQTLs) across tissues and e/mQTLs links to complex traits. We identified mQTLs for 286,152 CpG sites, many of which (>5%) show tissue specificity, and mQTL colocalizations with 2,254 distinct GWAS hits across 83 traits. For 91% of these loci, a candidate gene link was identified by integration of functional maps, including eQTMs, and/or eQTL colocalization, but only 33% of loci involved an eQTL and mQTL present in the same tissue type. With this DNAm-focused integrative analysis, we contribute to the understanding of molecular regulatory mechanisms in human tissues and their impact on complex traits.

Variation in heat shock protein 40 kDa relates to divergence in thermotolerance among cryptic rotifer species

Genetic divergence and the frequency of hybridization are central for defining species delimitations, especially among cryptic species where morphological differences are merely absent. Rotifers are known for their high cryptic diversity and therefore are ideal model organisms to investigate such patterns. Here, we used the recently resolved Brachionus calyciflorus species complex to investigate whether previously observed between species differences in thermotolerance and gene expression are also reflected in their genomic footprint. We identified a Heat Shock Protein gene (HSP 40 kDa) which exhibits cross species pronounced sequence variation. This gene exhibits species-specific fixed sites, alleles, and sites putatively under positive selection. These sites are located in protein binding regions involved in chaperoning and may therefore reflect adaptive diversification. By comparing three genetic markers (ITS, COI, HSP 40 kDa), we revealed hybridization events between the cryptic species. The low frequency of introgressive haplotypes/alleles suggest a tight, but not fully impermeable boundary between the cryptic species.

The missing link between genetic association and regulatory function

The genetic basis of most traits is highly polygenic and dominated by non-coding alleles. It is widely assumed that such alleles exert small regulatory effects on the expression of cis-linked genes. However, despite the availability of gene expression and epigenomic datasets, few variant-to-gene links have emerged. It is unclear whether these sparse results are due to limitations in available data and methods, or to deficiencies in the underlying assumed model. To better distinguish between these possibilities, we identified 220 gene-trait pairs in which protein-coding variants influence a complex trait or its Mendelian cognate. Despite the presence of expression quantitative trait loci near most GWAS associations, by applying a gene-based approach we found limited evidence that the baseline expression of trait-related genes explains GWAS associations, whether using colocalization methods (8% of genes implicated), transcription-wide association (2% of genes implicated), or a combination of regulatory annotations and distance (4% of genes implicated). These results contradict the hypothesis that most complex trait-associated variants coincide with homeostatic expression QTLs, suggesting that better models are needed. The field must confront this deficit and pursue this 'missing regulation.'

Enzyme Prodrug Therapy with Photo-Cross-Linkable Anti-EGFR Affibodies Conjugated to Upconverting Nanoparticles

In this work, we demonstrate that a photo-cross-linkable conjugate of upconverting nanoparticles and cytosine deaminase can catalyze prodrug conversion specifically at tumor sites in vivo. Non-covalent association of proteins and peptides with cellular surfaces leads to receptor-mediated endocytosis and catabolic degradation. Recently, we showed that covalent attachment of proteins such as affibodies to cell receptors yields extended expression on cell surfaces with preservation of protein function. To adapt this technology for in vivo applications, conjugates were prepared from upconverting nanoparticles and fusion proteins of affibody and cytosine deaminase enzyme (UC-ACD). The affibody allows covalent photo-cross-linking to epidermal growth factor receptors (EGFRs) overexpressed on Caco-2 human colorectal cancer cells under near-infrared (NIR) light. Once bound, the cytosine deaminase portion of the fusion protein converts the prodrug 5-fluorocytosine (5-FC) to the anticancer drug 5-fluorouracil (5-FU). NIR covalent photoconjugation of UC-ACD to Caco-2 cells showed 4-fold higher retention than observed with cells that were not irradiated in vitro. Next, athymic mice expressing Caco-2 tumors showed 5-fold greater UC-ACD accumulation in the tumors than either conjugates without the CD enzyme or UC-ACDs in the absence of NIR excitation. With oral administration of 5-FC prodrug, tumors with photoconjugated UC-ACD yielded 2-fold slower growth than control groups, and median mouse survival increased from 28 days to 35 days. These experiments demonstrate that enzyme-decorated nanoparticles can remain viable after a single covalent photoconjugation in vivo, which can in turn localize prodrug conversion to tumor sites for multiple weeks.

Characterization of sequence determinants of enhancer function using natural genetic variation

Sequence variation in enhancers that control cell-type-specific gene transcription contributes significantly to phenotypic variation within human populations. However, it remains difficult to predict precisely the effect of any given sequence variant on enhancer function due to the complexity of DNA sequence motifs that determine transcription factor (TF) binding to enhancers in their native genomic context. Using F₁-hybrid cells derived from crosses between distantly related inbred strains of mice, we identified thousands of enhancers with allele-specific TF binding and/or activity. We find that genetic variants located within the central region of enhancers are most likely to alter TF binding and enhancer activity. We observe that the AP-1 family of TFs (Fos/Jun) are frequently required for binding of TEAD TFs and for enhancer function. However, many sequence variants outside of core motifs for AP-1 and TEAD also impact enhancer function, including sequences flanking core TF motifs and AP-1 half sites. Taken together, these data represent one of the most comprehensive assessments of allele-specific TF binding and enhancer function to date and reveal how sequence changes at enhancers alter their function across evolutionary timescales.

A comprehensive comparison of multilocus association methods with summary statistics in genome-wide association studies

BACKGROUND

Multilocus analysis on a set of single nucleotide polymorphisms (SNPs) pre-assigned within a gene constitutes a valuable complement to single-marker analysis by aggregating data on complex traits in a biologically meaningful way. However, despite the existence of a wide variety of SNP-set methods, few comprehensive comparison studies have been previously performed to evaluate the effectiveness of these methods.

RESULTS

We herein sought to fill this knowledge gap by conducting a comprehensive empirical comparison for 22 commonly-used summary-statistics based SNP-set methods. We showed that only seven methods could effectively control the type I error, and that these well-calibrated approaches had varying power performance under the simulation scenarios. Overall, we confirmed that the burden test was generally underpowered and score-based variance component tests (e.g., sequence kernel association test) were much powerful under the polygenic genetic architecture in both common and rare variant association analyses. We further revealed that two linkage-disequilibrium-free P value combination methods (e.g., harmonic mean P value method and aggregated Cauchy association test) behaved very well under the sparse genetic architecture in simulations and real-data applications to common and rare variant association analyses as well as in expression quantitative trait loci weighted integrative analysis. We also assessed the scalability of these approaches by recording computational time and found that all these methods can be scalable to biobank-scale data although some might be relatively slow.

CONCLUSION

In conclusion, we hope that our findings can offer an important guidance on how to choose appropriate multilocus association analysis methods in post-GWAS era. All the SNP-set methods are implemented in the R package called MCA, which is freely available at https://github.com/biostatpzeng/ .

Differentially Active and Conserved Neural Enhancers Define Two Forms of Adaptive Noncoding Evolution in Humans

The human and chimpanzee genomes are strikingly similar, but our neural phenotypes are very different. Many of these differences are likely driven by changes in gene expression, and some of those changes may have been adaptive during human evolution. Yet, the relative contributions of positive selection on regulatory regions or other functional regulatory changes are unclear. Where are these changes located throughout the human genome? Are functional regulatory changes near genes or are they in distal enhancer regions? In this study, we experimentally combined both human and chimpanzee cis-regulatory elements (CREs) that showed either (1) signs of accelerated evolution in humans or (2) that have been shown to be active in the human brain. Using a massively parallel reporter assay, we tested the ability of orthologous human and chimpanzee CREs to activate transcription in induced pluripotent stem-cell-derived neural progenitor cells and neurons. With this assay, we identified 179 CREs with differential activity between human and chimpanzee; in contrast, we found 722 CREs with signs of positive selection in humans. Selection and differentially expressed CREs strikingly differ in level of expression, size, and genomic location. We found a subset of 69 CREs in loci with genetic variants associated with neuropsychiatric diseases, which underscores the consequence of regulatory activity in these loci for proper neural development and function. By combining CREs that either experienced recent selection in humans or CREs that are functional brain enhancers, presents a novel way of studying the evolution of noncoding elements that contribute to human neural phenotypes.

An enhancer of Agouti contributes to parallel evolution of cryptically colored beach mice

Identifying the genetic basis of repeatedly evolved traits provides a way to reconstruct their evolutionary history and ultimately investigate the predictability of evolution. Here, we focus on the oldfield mouse (Peromyscus polionotus), which occurs in the southeastern United States, where it exhibits considerable color variation. Dorsal coats range from dark brown in mainland mice to near white in mice inhabiting sandy beaches; this light pelage has evolved independently on Florida's Gulf and Atlantic coasts as camouflage from predators. To facilitate genomic analyses, we first generated a chromosome-level genome assembly of Peromyscus polionotus subgriseus. Next, in a uniquely variable mainland population (Peromyscus polionotus albifrons), we scored 23 pigment traits and performed targeted resequencing in 168 mice. We find that pigment variation is strongly associated with an ∼2-kb region ∼5 kb upstream of the Agouti signaling protein coding region. Using a reporter-gene assay, we demonstrate that this regulatory region contains an enhancer that drives expression in the dermis of mouse embryos during the establishment of pigment prepatterns. Moreover, extended tracts of homozygosity in this Agouti region indicate that the light allele experienced recent and strong positive selection. Notably, this same light allele appears fixed in both Gulf and Atlantic coast beach mice, despite these populations being separated by >1,000 km. Together, our results suggest that this identified Agouti enhancer allele has been maintained in mainland populations as standing genetic variation and from there, has spread to and been selected in two independent beach mouse lineages, thereby facilitating their rapid and parallel evolution.

A genome-wide association study for varicose veins

Background

The aim was to compare the genetic information of varicose vein patients with that of a healthy population attempting to identify certain significant genetic associations.

Method

Patients’ clinical characteristics and demographics were collected, and their genetic samples were examined. The results were compared to the genetic information of one thousand sex-matched healthy controls from Taiwan Biobank database. The Clinical-Etiology-Anatomy-Pathophysiology classification was applied for further subgroup analysis.

Results

After comparison of genetic information of ninety-six patients to that of healthy controls, two significant single nucleotide polymorphisms (SNPs) were identified. One was in DPYSL2 gene, and the other was in VSTM2L gene. A further comparison between C2-3 patient subgroup and C4-6 subgroup identified another four significant SNPs, which were located in ZNF664-FAM101A, PHF2, ACOT11, and TOM1L1 genes.

Conclusion

Our preliminary result identified six significant SNPs located in six different genes. All of them and their genetic products may warrant further investigations.

PCRMS: a database of predicted cis-regulatory modules and constituent transcription factor binding sites in genomes

More accurate and more complete predictions of cis-regulatory modules (CRMs) and constituent transcription factor (TF) binding sites (TFBSs) in genomes can facilitate characterizing functions of regulatory sequences. Here, we developed a database predicted cis-regulatory modules (PCRMS) (https://cci-bioinfo.uncc.edu) that stores highly accurate and unprecedentedly complete maps of predicted CRMs and TFBSs in the human and mouse genomes. The web interface allows the user to browse CRMs and TFBSs in an organism, find the closest CRMs to a gene, search CRMs around a gene and find all TFBSs of a TF. PCRMS can be a useful resource for the research community to characterize regulatory genomes. Database URL: https://cci-bioinfo.uncc.edu/.

In Silico Analysis Identified Putative Pathogenic Missense nsSNPs in Human SLITRK1 Gene

Human DNA contains several variations, which can affect the structure and normal functioning of a protein. These variations could be single nucleotide polymorphisms (SNPs) or insertion-deletions (InDels). SNPs, as opposed to InDels, are more commonly present in DNA and may cause genetic disorders. In the current study, several bioinformatic tools were used to prioritize the pathogenic variants in the SLITRK1 gene. Out of all of the variants, 16 were commonly predicted to be pathogenic by these tools. All the variants had very low frequency, i.e., <0.0001 in the global population. The secondary structure of all filtered variants was predicted, but no structural change was observed at the site of variation in any variant. Protein stability analysis of these variants was then performed, which determined a decrease in protein stability of 10 of the variants. Amino acid conservation analysis revealed that all the amino acids were highly conserved, indicating their structural and functional importance. Protein 3D structure of wildtype SLITRK1 and all of its variants was predicted using I-TASSER, and the effect of variation on 3D structure of the protein was observed using the Missense3D tool, which presented the probable structural loss in three variants, i.e., Asn529Lys, Leu496Pro and Leu94Phe. The wildtype SLITRK1 protein and these three variants were independently docked with their close interactor protein PTPRD, and remarkable differences were observed in the docking sites of normal and variants, which will ultimately affect the functional activity of the SLITRK1 protein. Previous studies have shown that mutations in SLITRK1 are involved in Tourette syndrome. The present study may assist a molecular geneticist in interpreting the variant pathogenicity in research as well as diagnostic setup.

Simultaneous test and estimation of total genetic effect in eQTL integrative analysis through mixed models

Integration of expression quantitative trait loci (eQTL) into genome-wide association studies (GWASs) is a promising manner to reveal functional roles of associated single-nucleotide polymorphisms (SNPs) in complex phenotypes and has become an active research field in post-GWAS era. However, how to efficiently incorporate eQTL mapping study into GWAS for prioritization of causal genes remains elusive. We herein proposed a novel method termed as Mixed transcriptome-wide association studies (TWAS) and mediated Variance estimation (MTV) by modeling the effects of cis-SNPs of a gene as a function of eQTL. MTV formulates the integrative method and TWAS within a unified framework via mixed models and therefore includes many prior methods/tests as special cases. We further justified MTV from another two statistical perspectives of mediation analysis and two-stage Mendelian randomization. Relative to existing methods, MTV is superior for pronounced features including the processing of direct effects of cis-SNPs on phenotypes, the powerful likelihood ratio test for assessment of joint effects of cis-SNPs and genetically regulated gene expression (GReX), two useful quantities to measure relative genetic contributions of GReX and cis-SNPs to phenotypic variance, and the computationally efferent parameter expansion expectation maximum algorithm. With extensive simulations, we identified that MTV correctly controlled the type I error in joint evaluation of the total genetic effect and proved more powerful to discover true association signals across various scenarios compared to existing methods. We finally applied MTV to 41 complex traits/diseases available from three GWASs and discovered many new associated genes that had otherwise been missed by existing methods. We also revealed that a small but substantial fraction of phenotypic variation was mediated by GReX. Overall, MTV constructs a robust and realistic modeling foundation for integrative omics analysis and has the advantage of offering more attractive biological interpretations of GWAS results.

Genomic variants affecting homoeologous gene expression dosage contribute to agronomic trait variation in allopolyploid wheat

Allopolyploidy greatly expands the range of possible regulatory interactions among functionally redundant homoeologous genes. However, connection between the emerging regulatory complexity and expression and phenotypic diversity in polyploid crops remains elusive. Here, we use diverse wheat accessions to map expression quantitative trait loci (eQTL) and evaluate their effects on the population-scale variation in homoeolog expression dosage. The relative contribution of cis- and trans-eQTL to homoeolog expression variation is strongly affected by both selection and demographic events. Though trans-acting effects play major role in expression regulation, the expression dosage of homoeologs is largely influenced by cis-acting variants, which appear to be subjected to selection. The frequency and expression of homoeologous gene alleles showing strong expression dosage bias are predictive of variation in yield-related traits, and have likely been impacted by breeding for increased productivity. Our study highlights the importance of genomic variants affecting homoeolog expression dosage in shaping agronomic phenotypes and points at their potential utility for improving yield in polyploid crops.

Profiling of open chromatin in developing pig (Sus scrofa) muscle to identify regulatory regions

There is very little information about how the genome is regulated in domestic pigs (Sus scrofa). This lack of knowledge hinders efforts to define and predict the effects of genetic variants in pig breeding programs. To address this knowledge gap, we need to identify regulatory sequences in the pig genome starting with regions of open chromatin. We used the "Improved Protocol for the Assay for Transposase-Accessible Chromatin (Omni-ATAC-Seq)" to identify putative regulatory regions in flash-frozen semitendinosus muscle from 24 male piglets. We collected samples from the smallest-, average-, and largest-sized male piglets from each litter through five developmental time points. Of the 4661 ATAC-Seq peaks identified that represent regions of open chromatin, >50% were within 1 kb of known transcription start sites. Differential read count analysis revealed 377 ATAC-Seq defined genomic regions where chromatin accessibility differed significantly across developmental time points. We found regions of open chromatin associated with downregulation of genes involved in muscle development that were present in small-sized fetal piglets but absent in large-sized fetal piglets at day 90 of gestation. The dataset that we have generated provides a resource for studies of genome regulation in pigs and contributes valuable functional annotation information to filter genetic variants for use in genomic selection in pig breeding programs.

Antifibrotic factor KLF4 is repressed by the miR-10/TFAP2A/TBX5 axis in dermal fibroblasts: insights from twins discordant for systemic sclerosis

OBJECTIVES

Systemic sclerosis (SSc) is a complex disease of unknown aetiology in which inflammation and fibrosis lead to multiple organ damage. There is currently no effective therapy that can halt the progression of fibrosis or reverse it, thus studies that provide novel insights into disease pathogenesis and identify novel potential therapeutic targets are critically needed.

METHODS

We used global gene expression and genome-wide DNA methylation analyses of dermal fibroblasts (dFBs) from a unique cohort of twins discordant for SSc to identify molecular features of this pathology. We validated the findings using in vitro, ex vivo and in vivo models.

RESULTS

Our results revealed distinct differentially expressed and methylated genes, including several transcription factors involved in stem cell differentiation and developmental programmes (KLF4, TBX5, TFAP2A and homeobox genes) and the microRNAs miR-10a and miR-10b which target several of these deregulated genes. We show that KLF4 expression is reduced in SSc dFBs and its expression is repressed by TBX5 and TFAP2A. We also show that KLF4 is antifibrotic, and its conditional knockout in fibroblasts promotes a fibrotic phenotype.

CONCLUSIONS

Our data support a role for epigenetic dysregulation in mediating SSc susceptibility in dFBs, illustrating the intricate interplay between CpG methylation, miRNAs and transcription factors in SSc pathogenesis, and highlighting the potential for future use of epigenetic modifiers as therapies.

Epigenome editing of the CFTR-locus for treatment of cystic fibrosis

BACKGROUND

Mechanisms governing the diversity of CFTR gene expression throughout the body are complex. Multiple intronic and distal regulatory elements are responsible for regulating differential CFTR expression across tissues.

METHODS

Drawing on published data, 18 high-priority genomic regions were identified and interrogated for CFTR-enhancer function using CRISPR/dCas9-based epigenome editing tools. Each region was evaluated by dCas9p300 and dCas9KRAB for its ability to enhance or repress CFTR expression, respectively.

RESULTS

Multiple genomic regions were tested for enhancer activity using CRISPR/dCas9 epigenome editing. dCas9p300 mediates a significant increase in CFTR mRNA levels when targeted to the promoter and a region 44 kb upstream of the transcriptional start site in a CFTR-low expressing cell line. Multiple gRNAs targeting the promoter induced a robust increase in CFTR protein levels. In contrast, dCas9KRAB-mediated repression is much more robust with 10 of the 18 evaluated genomic regions inducing CFTR protein knockdown. To evaluate the therapeutic efficacy of modulating CFTR gene regulation, dCas9p300 was used to induce elevated levels of CFTR from the endogenous locus in ΔF508/ΔF508 human bronchial epithelial cells. Ussing chamber studies demonstrated a synergistic increase in ion transport in response to CRISPR-induced expression of ΔF508 CFTR mRNA along with VX809 treatment.

CONCLUSIONS

CRISPR/dCas9-based epigenome-editing provides a previously unexplored tool for interrogating CFTR enhancer function. Here, we demonstrate that therapeutic interventions that increase the expression of CFTR may improve the efficacy of CFTR modulators. A better understanding CFTR regulatory mechanisms could uncover novel therapeutic interventions for the development of cystic fibrosis therapies.

BACH1 Binding Links the Genetic Risk for Severe Periodontitis with ST8SIA1

Genome-wide association studies identified various loci associated with periodontal diseases, but assigning causal alleles remains difficult. Likewise, the generation of biological meaning underlying a statistical association has been challenging. Here, we characterized the genetic association at the gene ST8SIA1 that increases the risk for severe periodontitis in smokers. We used CRISPR/dCas9 activation and RNA-sequencing to identify genetic interaction partners of ST8SIA1 and to determine its function in the cell. We used reporter gene assays to identify regulatory elements at the associated single-nucleotide polymorphisms (SNPs) and to determine effect directions and allele-specific changes of enhancer activity. Antibody electrophoretic mobility shift assays proved allele-specific transcription factor binding at the putative causal SNPs. We found the reported periodontitis risk gene ABCA1 as the top upregulated gene following ST8SIA1 activation. Gene set enrichment analysis showed highest effects on integrin cell surface interactions (area under the curve [AUC] = 0.85; q = 4.9 × 10-6) and cell cycle regulation (AUC = 0.89; q = 1.6 × 10-5). We identified 2 associated repressor elements in the introns of ST8SIA1 that bind the transcriptional repressor BACH1. The putative causative variant rs2012722 decreased BACH1 binding by 40%. We also pinpointed ST8SIA1 as the target gene of the association. ST8SIA1 inhibits cell adhesion with extracellular matrix proteins, integrins, and cell cycle, as well as enhances apoptosis. Likewise, tobacco smoke reportedly results in an inhibition of cell adhesion and a decrease in integrin-positive cells and cell growth. We conclude that impaired ST8SIA1 repression, independently caused by reduced BACH1 binding at the effect T allele, as well as by tobacco smoke, contributes to higher ST8SIA1 levels, and in smokers who carry the effect T allele, both factors would be additive with damaging effects on the gingival barrier integrity. The activity of ST8SIA1 is also linked with the periodontitis risk gene ABCA1.

Investigation of the genetic effect of 56 tobacco-smoking susceptibility genes on DNA methylation and RNA expression in human brain

Although various susceptibility genes have been revealed to influence tobacco smoking, the underlying regulatory mechanisms between genetic variants and smoking are poorly understood. In this study, we investigated cis-expression quantitative trait loci (cis-eQTLs) and methylation quantitative trait loci (mQTLs) for 56 candidate smoking-linked genes using the BrainCloud cohort samples. An eQTL was revealed to significantly affect EGLN2 expression in the European sample and two mQTLs were respectively detected in CpG sites in NRXN1 and CYP2A7. Interestingly, we found for the first time that the minor allele of the single nucleotide polymorphism (SNP) rs3745277 located in CYP2A7P1 (downstream of CYP2B6) significantly decreased methylation at the CpG site for CYP2A7 (cg25427638; P = 5.31 × 10^-7), reduced expression of CYP2B6 (P = 0.03), and lowered the percentage of smokers (8.8% vs. 42.3%; Odds Ratio (OR) = 0.14, 95% Confidence Interval (CI): 0.02-0.62; P = 4.47 × 10^-3) in a dominant way for the same cohort sample. Taken together, our findings resulted from analyzing genetic variation, DNA methylation, mRNA expression, and smoking status together using the same participants revealed a regulatory mechanism linking mQTLs to the smoking phenotype. Moreover, we demonstrated the presence of different regulatory effects of low-frequency and common variants on mRNA expression and DNA methylation.

Multi-Omic Approaches to Identify Genetic Factors in Metabolic Syndrome

Metabolic syndrome (MetS) is a highly heritable disease and a major public health burden worldwide. MetS diagnosis criteria are met by the simultaneous presence of any three of the following: high triglycerides, low HDL/high LDL cholesterol, insulin resistance, hypertension, and central obesity. These diseases act synergistically in people suffering from MetS and dramatically increase risk of morbidity and mortality due to stroke and cardiovascular disease, as well as certain cancers. Each of these component features is itself a complex disease, as is MetS. As a genetically complex disease, genetic risk factors for MetS are numerous, but not very powerful individually, often requiring specific environmental stressors for the disease to manifest. When taken together, all sequence variants that contribute to MetS disease risk explain only a fraction of the heritable variance, suggesting additional, novel loci have yet to be discovered. In this article, we will give a brief overview on the genetic concepts needed to interpret genome-wide association studies (GWAS) and quantitative trait locus (QTL) data, summarize the state of the field of MetS physiological genomics, and to introduce tools and resources that can be used by the physiologist to integrate genomics into their own research on MetS and any of its component features. There is a wealth of phenotypic and molecular data in animal models and humans that can be leveraged as outlined in this article. Integrating these multi-omic QTL data for complex diseases such as MetS provides a means to unravel the pathways and mechanisms leading to complex disease and promise for novel treatments. © 2022 American Physiological Society. Compr Physiol 12:1-40, 2022.

DNA Methylation Signature in Monozygotic Twins Discordant for Psoriatic Disease

Background: Psoriatic disease is a multifactorial inflammatory condition spanning from skin and nail psoriasis (Pso) to spine and joint involvement characterizing psoriatic arthritis (PsA). Monozygotic twins provide a model to investigate genetic, early life environmental exposure and stochastic influences to complex diseases, mainly mediated by epigenetics.

Methods: We performed a genome-wide DNA methylation study on whole blood of monozygotic twins from 7 pairs discordant for Pso/PsA using the Infinium Methylation EPIC array (Illumina). MeDiP—qPCR was used to confirm specific signals. Data were replicated in an independent cohort of seven patients with Pso/PsA and 3 healthy controls. Transcriptomic profiling was performed by RNAsequence on the same 7 monozygotic twin pairs.

Results: We identified 2,564 differentially methylated positions between psoriatic disease and controls, corresponding to 1,703 genes, 59% within gene bodies. There were 19 regions with at least two DMPs within 1 kb of distance and significant within-pair Δβ-values (p < 0.005), among them SNX25, BRG1 and SMAD3 genes, all involved in TGF-β signaling pathway, were identified. Co-expression analyses on transcriptome data identified IL-6/JAK/STAT3 and TNF-α pathways as important signaling axes involved in the disease, and they also suggested an altered glucose metabolism in patients’ immune cells, characteristic of pro-inflammatory T lymphocytes.

Conclusion: The study suggests the presence of an epigenetic signature in affected individuals, pointing to genes involved in immunological and inflammatory responses. This result is also supported by transcriptome data, that altogether suggest a higher activation state of the immune system, that could promote the disease status.

Status and advances in mining for blackleg (Leptosphaeria maculans) quantitative resistance (QR) in oilseed rape (Brassica napus)

KEY MESSAGE

Quantitative resistance (QR) loci discovered through genetic and genomic analyses are abundant in the Brassica napus genome, providing an opportunity for their utilization in enhancing blackleg resistance. Quantitative resistance (QR) has long been utilized to manage blackleg in Brassica napus (canola, oilseed rape), even before major resistance genes (R-genes) were extensively explored in breeding programmes. In contrast to R-gene-mediated qualitative resistance, QR reduces blackleg symptoms rather than completely eliminating the disease. As a polygenic trait, QR is controlled by numerous genes with modest effects, which exerts less pressure on the pathogen to evolve; hence, its effectiveness is more durable compared to R-gene-mediated resistance. Furthermore, combining QR with major R-genes has been shown to enhance resistance against diseases in important crops, including oilseed rape. For these reasons, there has been a renewed interest among breeders in utilizing QR in crop improvement. However, the mechanisms governing QR are largely unknown, limiting its deployment. Advances in genomics are facilitating the dissection of the genetic and molecular underpinnings of QR, resulting in the discovery of several loci and genes that can be potentially deployed to enhance blackleg resistance. Here, we summarize the efforts undertaken to identify blackleg QR loci in oilseed rape using linkage and association analysis. We update the knowledge on the possible mechanisms governing QR and the advances in searching for the underlying genes. Lastly, we lay out strategies to accelerate the genetic improvement of blackleg QR in oilseed rape using improved phenotyping approaches and genomic prediction tools.

Accurate prediction of cis-regulatory modules reveals a prevalent regulatory genome of humans

cis-regulatory modules(CRMs) formed by clusters of transcription factor (TF) binding sites (TFBSs) are as important as coding sequences in specifying phenotypes of humans. It is essential to categorize all CRMs and constituent TFBSs in the genome. In contrast to most existing methods that predict CRMs in specific cell types using epigenetic marks, we predict a largely cell type agonistic but more comprehensive map of CRMs and constituent TFBSs in the gnome by integrating all available TF ChIP-seq datasets. Our method is able to partition 77.47% of genome regions covered by available 6092 datasets into a CRM candidate (CRMC) set (56.84%) and a non-CRMC set (43.16%). Intriguingly, the predicted CRMCs are under strong evolutionary constraints, while the non-CRMCs are largely selectively neutral, strongly suggesting that the CRMCs are likely cis-regulatory, while the non-CRMCs are not. Our predicted CRMs are under stronger evolutionary constraints than three state-of-the-art predictions (GeneHancer, EnhancerAtlas and ENCODE phase 3) and substantially outperform them for recalling VISTA enhancers and non-coding ClinVar variants. We estimated that the human genome might encode about 1.47M CRMs and 68M TFBSs, comprising about 55% and 22% of the genome, respectively; for both of which, we predicted 80%. Therefore, the cis-regulatory genome appears to be more prevalent than originally thought.

Igh Locus Polymorphism May Dictate Topological Chromatin Conformation and V Gene Usage in the Ig Repertoire

Vast repertoires of unique antigen receptors are created in developing B and T lymphocytes. The antigen receptor loci contain many variable (V), diversity (D) and joining (J) gene segments that are arrayed across very large genomic expanses and are joined to form variable-region exons of expressed immunoglobulins and T cell receptors. This process creates the potential for an organism to respond to large numbers of different pathogens. Here, we consider the possibility that genetic polymorphisms with alterations in a vast array of regulatory elements in the immunoglobulin heavy chain (IgH) locus lead to changes in locus topology and impact immune-repertoire formation.

Genetic impacts on DNA methylation: research findings and future perspectives

Multiple recent studies highlight that genetic variants can have strong impacts on a significant proportion of the human DNA methylome. Methylation quantitative trait loci, or meQTLs, allow for the exploration of biological mechanisms that underlie complex human phenotypes, with potential insights for human disease onset and progression. In this review, we summarize recent milestones in characterizing the human genetic basis of DNA methylation variation over the last decade, including heritability findings and genome-wide identification of meQTLs. We also discuss challenges in this field and future areas of research geared to generate insights into molecular processes underlying human complex traits.

Genetic Variants in Transcription Factor Binding Sites in Humans: Triggered by Natural Selection and Triggers of Diseases

Variants of transcription factor binding sites (TFBSs) constitute an important part of the human genome. Current evidence demonstrates close links between nucleotides within TFBSs and gene expression. There are multiple pathways through which genomic sequences located in TFBSs regulate gene expression, and recent genome-wide association studies have shown the biological significance of TFBS variation in human phenotypes. However, numerous challenges remain in the study of TFBS polymorphisms. This article aims to cover the current state of understanding as regards the genomic features of TFBSs and TFBS variants; the mechanisms through which TFBS variants regulate gene expression; the approaches to studying the effects of nucleotide changes that create or disrupt TFBSs; the challenges faced in studies of TFBS sequence variations; the effects of natural selection on collections of TFBSs; in addition to the insights gained from the study of TFBS alleles related to gout, its associated comorbidities (increased body mass index, chronic kidney disease, diabetes, dyslipidemia, coronary artery disease, ischemic heart disease, hypertension, hyperuricemia, osteoporosis, and prostate cancer), and the treatment responses of patients.

Cis-acting variation is common across regulatory layers but is often buffered during embryonic development

Precise patterns of gene expression are driven by interactions between transcription factors, regulatory DNA sequences, and chromatin. How DNA mutations affecting any one of these regulatory "layers" are buffered or propagated to gene expression remains unclear. To address this, we quantified allele-specific changes in chromatin accessibility, histone modifications, and gene expression in F1 embryos generated from eight Drosophila crosses at three embryonic stages, yielding a comprehensive data set of 240 samples spanning multiple regulatory layers. Genetic variation (allelic imbalance) impacts gene expression more frequently than chromatin features, with metabolic and environmental response genes being most often affected. Allelic imbalance in cis-regulatory elements (enhancers) is common and highly heritable, yet its functional impact does not generally propagate to gene expression. When it does, genetic variation impacts RNA levels through two alternative mechanisms involving either H3K4me3 or chromatin accessibility and H3K27ac. Changes in RNA are more predictive of variation in H3K4me3 than vice versa, suggesting a role for H3K4me3 downstream from transcription. The impact of a substantial proportion of genetic variation is consistent across embryonic stages, with 50% of allelic imbalanced features at one stage being also imbalanced at subsequent developmental stages. Crucially, buffering, as well as the magnitude and evolutionary impact of genetic variants, is influenced by regulatory complexity (i.e., number of enhancers regulating a gene), with transcription factors being most robust to cis-acting, but most influenced by trans-acting, variation.

Battle for supremacy: nucleic acid interactions between viruses and cells

Since the COVID-19 pandemic swept across the globe, researchers have been trying to understand its origin, life cycle, and pathogenesis. There is a striking variability in the phenotypic response to infection with SARS-CoV-2 that may reflect differences in host genetics and/or immune response. It is known that the human epigenome is influenced by ethnicity, age, lifestyle, and environmental factors, including previous viral infections. This Review examines the influence of viruses on the host epigenome. We describe general lessons and methodologies that can be used to understand how the virus evades the host immune response. We consider how variation in the epigenome may contribute to heterogeneity in the response to SARS-CoV-2 and may identify a precision medicine approach to treatment.

Comparison between instrumental variable and mediation-based methods for reconstructing causal gene networks in yeast

Causal gene networks model the flow of information within a cell. Reconstructing causal networks from omics data is challenging because correlation does not imply causation. When genomics and transcriptomics data from a segregating population are combined, genomic variants can be used to orient the direction of causality between gene expression traits. Instrumental variable methods use a local expression quantitative trait locus (eQTL) as a randomized instrument for a gene's expression level, and assign target genes based on distal eQTL associations. Mediation-based methods additionally require that distal eQTL associations are mediated by the source gene. A detailed comparison between these methods has not yet been conducted, due to the lack of a standardized implementation of different methods, the limited sample size of most multi-omics datasets, and the absence of ground-truth networks for most organisms. Here we used Findr, a software package providing uniform implementations of instrumental variable, mediation, and coexpression-based methods, a recent dataset of 1012 segregants from a cross between two budding yeast strains, and the Yeastract database of known transcriptional interactions to compare causal gene network inference methods. We found that causal inference methods result in a significant overlap with the ground-truth, whereas coexpression did not perform better than random. A subsampling analysis revealed that the performance of mediation saturates at large sample sizes, due to a loss of sensitivity when residual correlations become significant. Instrumental variable methods on the other hand contain false positive predictions, due to genomic linkage between eQTL instruments. Instrumental variable and mediation-based methods also have complementary roles for identifying causal genes underlying transcriptional hotspots. Instrumental variable methods correctly predicted STB5 targets for a hotspot centred on the transcription factor STB5, whereas mediation failed due to Stb5p auto-regulating its own expression. Mediation suggests a new candidate gene, DNM1, for a hotspot on Chr XII, whereas instrumental variable methods could not distinguish between multiple genes located within the hotspot. In conclusion, causal inference from genomics and transcriptomics data is a powerful approach for reconstructing causal gene networks, which could be further improved by the development of methods to control for residual correlations in mediation analyses, and for genomic linkage and pleiotropic effects from transcriptional hotspots in instrumental variable analyses.

Nongenetic Bioconjugation Strategies for Modifying Cell Membranes and Membrane Proteins: A Review

The cell membrane possesses an extensive library of proteins, carbohydrates, and lipids that control a significant portion of inter- and intracellular functions, including signaling, proliferation, migration, and adhesion, among others. Augmenting the cell surface composition would open possibilities for advances in therapy, tissue engineering, and probing fundamental cell processes. While genetic engineering has proven effective for many in vitro applications, these techniques result in irreversible changes to cells and are difficult to apply in vivo. Another approach is to instead attach exogenous functional groups to the cell membrane without changing the genetic nature of the cell. This review focuses on more recent approaches of nongenetic methods of cell surface modification through metabolic pathways, anchorage by hydrophobic interactions, and chemical conjugation. Benefits and drawbacks of each approach are considered, followed by a discussion of potential applications for nongenetic cell surface modification and an outlook on the future of the field.

Sex differences in human adipose tissue gene expression and genetic regulation involve adipogenesis

Sex differences in adipose tissue distribution and function are associated with sex differences in cardiometabolic disease. While many studies have revealed sex differences in adipocyte cell signaling and physiology, there is a relative dearth of information regarding sex differences in transcript abundance and regulation. We investigated sex differences in subcutaneous adipose tissue transcriptional regulation using omic-scale data from ∼3000 geographically and ethnically diverse human samples. We identified 162 genes with robust sex differences in expression. Differentially expressed genes were implicated in oxidative phosphorylation and adipogenesis. We further determined that sex differences in gene expression levels could be related to sex differences in the genetics of gene expression regulation. Our analyses revealed sex-specific genetic associations, and this finding was replicated in a study of 98 inbred mouse strains. The genes under genetic regulation in human and mouse were enriched for oxidative phosphorylation and adipogenesis. Enrichment analysis showed that the associated genetic loci resided within binding motifs for adipogenic transcription factors (e.g., PPARG and EGR1). We demonstrated that sex differences in gene expression could be influenced by sex differences in genetic regulation for six genes (e.g., FADS1 and MAP1B). These genes exhibited dynamic expression patterns during adipogenesis and robust expression in mature human adipocytes. Our results support a role for adipogenesis-related genes in subcutaneous adipose tissue sex differences in the genetic and environmental regulation of gene expression.

Primo: integration of multiple GWAS and omics QTL summary statistics for elucidation of molecular mechanisms of trait-associated SNPs and detection of pleiotropy in complex traits

To provide a comprehensive mechanistic interpretation of how known trait-associated SNPs affect complex traits, we propose a method, Primo, for integrative analysis of GWAS summary statistics with multiple sets of omics QTL summary statistics from different cellular conditions or studies. Primo examines association patterns of SNPs to complex and omics traits. In gene regions harboring known susceptibility loci, Primo performs conditional association analysis to account for linkage disequilibrium. Primo allows for unknown study heterogeneity and sample correlations. We show two applications using Primo to examine the molecular mechanisms of known susceptibility loci and to detect and interpret pleiotropic effects.

Gene expression models based on transcription factor binding events confer insight into functional cis-regulatory variants

Motivation

Deciphering the functional roles of cis-regulatory variants is a critical challenge in genome analysis and interpretation. It has been hypothesized that altered transcription factor (TF) binding events are a central mechanism by which cis-regulatory variants impact gene expression levels. However, we lack a computational framework to understand and quantify such mechanistic contributions.

Results

We present TF2Exp, a gene-based framework to predict the impact of altered TF-binding events on gene expression levels. Using data from lymphoblastoid cell lines, TF2Exp models were applied successfully to predict the expression levels of 3196 genes. Alterations within DNase I hypersensitive, CTCF-bound and tissue-specific TF-bound regions were the greatest contributing features to the models. TF2Exp models performed as well as models based on common variants, both in cross-validation and external validation. Combining TF alteration and common variant features can further improve model performance. Unlike variant-based models, TF2Exp models have the unique advantage to evaluate the functional impact of variants in linkage disequilibrium and uncommon variants. We find that adding TF-binding events altered only by uncommon variants could increase the number of predictable genes (R² > 0.05). Taken together, TF2Exp represents a key step towards interpreting the functional roles of cis-regulatory variants in the human genome.

Availability and implementation

The code and model training results are publicly available at https://github.com/wqshi/TF2Exp.

Supplementary information

Supplementary data are available at Bioinformatics online.

A framework for integrating directed and undirected annotations to build explanatory models of cis-eQTL data

A longstanding goal of regulatory genetics is to understand how variants in genome sequences lead to changes in gene expression. Here we present a method named Bayesian Annotation Guided eQTL Analysis (BAGEA), a variational Bayes framework to model cis-eQTLs using directed and undirected genomic annotations. We used BAGEA to integrate directed genomic annotations with eQTL summary statistics from tissues of various origins. This analysis revealed epigenetic marks that are relevant for gene expression in different tissues and cell types. We estimated the predictive power of the models that were fitted based on directed genomic annotations. This analysis showed that, depending on the underlying eQTL data used, the directed genomic annotations could predict up to 1.5% of the variance observed in the expression of genes with top nominal eQTL association p-values < 10⁻⁷. For genes with estimated effect sizes in the top 25% quantile, up to 5% of the expression variance could be predicted. Based on our results, we recommend the use of BAGEA for the analysis of cis-eQTL data to reveal annotations relevant to expression biology.

Many geneticists wish to map changes in DNA sequences to changes in human traits and to understand these processes mechanistically. Here we present BAGEA, a framework to study this question for gene expression. Specifically, BAGEA models a genome variant’s impact on gene expression based on established genome annotations. BAGEA predicts not only whether a variant has an impact on gene expression, but also the sign of the effect. We applied BAGEA to datasets from different tissues and cell types and found that annotations most predictive of gene expression in a given tissue were typically derived from similar tissues. Based on our results, we recommend the use of BAGEA to reveal annotations relevant to expression biology and to build predictive models of gene expression.

Enhancer Domains Predict Gene Pathogenicity and Inform Gene Discovery in Complex Disease

Non-coding transcriptional regulatory elements are critical for controlling the spatiotemporal expression of genes. Here, we demonstrate that the sizes and number of enhancers linked to a gene reflect its disease pathogenicity. Moreover, genes with redundant enhancer domains are depleted of cis-acting genetic variants that disrupt gene expression, and they are buffered against the effects of disruptive non-coding mutations. Our results demonstrate that dosage-sensitive genes have evolved a robustness to the disruptive effects of genetic variation by expanding their regulatory domains. This solves a puzzle about why genes associated with human disease are depleted of cis-eQTLs (cis-expression quantitative trait loci), suggesting that this relationship might complicate gene identification in causal genome-wide association studies (GWASs) using eQTL information, and establishes a framework for identifying non-coding regulatory variation with phenotypic consequences.

QTL Analysis Beyond eQTLs

Expression quantitative trait locus (eQTL) analysis is a powerful method to understand the association between genetic variant and gene expression; it also has potential impact for the study of transcription medicine for human complex disease. In the past two decades, the researchers focus on studying the eQTL, while more and more evidence shows that the regulatory genetic variants locating noncoding region have strong effect for the gene expression. More and more researchers working on eQTL analysis realize the importance of other types of QTLs beyond eQTL. In this chapter, we will explore some QTLs beyond eQTLs that show the regulatory association with eQTLs and explain the underlying link among these types of QTLs.

Exploratory analysis of genetic variants influencing molecular traits in cerebral cortex of suicide completers

Genetic factors have been implicated in suicidal behavior. It has been suggested that one of the roles of genetic factors in suicide could be represented by the effect of genetic variants on gene expression regulation. Alteration in the expression of genes participating in multiple biological systems in the suicidal brain has been demonstrated, so it is imperative to identify genetic variants that could influence gene expression or its regulatory mechanisms. In this study, we integrated DNA methylation, gene expression, and genotype data from the prefrontal cortex of suicides to identify genetic variants that could be factors in the regulation of gene expression, generally called quantitative trait locus (xQTLs). We identify 6,224 methylation quantitative trait loci and 2,239 expression quantitative trait loci (eQTLs) in the prefrontal cortex of suicide completers. The xQTLs identified influence the expression of genes involved in neurodevelopment and cell organization. Two of the eQTLs identified (rs8065311 and rs1019238) were previously associated with cannabis dependence, highlighting a candidate genetic variant for the increased suicide risk in subjects with substance use disorders. Our findings suggest that genetic variants may regulate gene expression in the prefrontal cortex of suicides through the modulation of promoter and enhancer activity, and to a lesser extent, binding transcription factors.

Integrative QTL analysis of gene expression and chromatin accessibility identifies multi-tissue patterns of genetic regulation

Gene transcription profiles across tissues are largely defined by the activity of regulatory elements, most of which correspond to regions of accessible chromatin. Regulatory element activity is in turn modulated by genetic variation, resulting in variable transcription rates across individuals. The interplay of these factors, however, is poorly understood. Here we characterize expression and chromatin state dynamics across three tissues-liver, lung, and kidney-in 47 strains of the Collaborative Cross (CC) mouse population, examining the regulation of these dynamics by expression quantitative trait loci (eQTL) and chromatin QTL (cQTL). QTL whose allelic effects were consistent across tissues were detected for 1,101 genes and 133 chromatin regions. Also detected were eQTL and cQTL whose allelic effects differed across tissues, including local-eQTL for Pik3c2g detected in all three tissues but with distinct allelic effects. Leveraging overlapping measurements of gene expression and chromatin accessibility on the same mice from multiple tissues, we used mediation analysis to identify chromatin and gene expression intermediates of eQTL effects. Based on QTL and mediation analyses over multiple tissues, we propose a causal model for the distal genetic regulation of Akr1e1, a gene involved in glycogen metabolism, through the zinc finger transcription factor Zfp985 and chromatin intermediates. This analysis demonstrates the complexity of transcriptional and chromatin dynamics and their regulation over multiple tissues, as well as the value of the CC and related genetic resource populations for identifying specific regulatory mechanisms within cells and tissues.

Investigating skewness to understand gene expression heterogeneity in large patient cohorts

BACKGROUND

Skewness is an under-utilized statistical measure that captures the degree of asymmetry in the distribution of any dataset. This study applied a new metric based on skewness to identify regulators or genes that have outlier expression in large patient cohorts.

RESULTS

We investigated whether specific patterns of skewed expression were related to the enrichment of biological pathways or genomic properties like DNA methylation status. Our study used publicly available datasets that were generated using both RNA-sequencing and microarray technology platforms. For comparison, the datasets selected for this study also included different samples derived from control donors and cancer patients. When comparing the shift in expression skewness between cancer and control datasets, we observed an enrichment of pathways related to the immune function that reflects an increase towards positive skewness in the cancer relative to control datasets. A significant correlation was also detected between expression skewness and the top 500 genes corresponding to the most significant differential DNA methylation occurring in the promotor regions for four Cancer Genome Atlas cancer cohorts.

CONCLUSIONS

Our results indicate that expression skewness can reveal new insights into transcription based on outlier and asymmetrical behaviour in large patient cohorts.

Genetics' Piece of the PI: Inferring the Origin of Complex Traits and Diseases from Proteome-Wide Protein-Protein Interaction Dynamics

How do common and rare genetic polymorphisms contribute to quantitative traits or disease risk and progression? Multiple human traits have been extensively characterized at the genomic level, revealing their complex genetic architecture. However, it is difficult to resolve the mechanisms by which specific variants contribute to a phenotype. Recently, analyses of variant effects on molecular traits have uncovered intermediate mechanisms that link sequence variation to phenotypic changes. Yet, these methods only capture a fraction of genetic contributions to phenotype. Here, in reviewing the field, it is proposed that complex traits can be understood by characterizing the dynamics of biochemical networks within living cells, and that the effects of genetic variation can be captured on these networks by using protein-protein interaction (PPI) methodologies. This synergy between PPI methodologies and the genetics of complex traits opens new avenues to investigate the molecular etiology of human diseases and to facilitate their prevention or treatment.

Identification and comprehensive characterization of lncRNAs with copy number variations and their driving transcriptional perturbed subpathways reveal functional significance for cancer

Numerous studies have shown that copy number variation (CNV) in lncRNA regions play critical roles in the initiation and progression of cancer. However, our knowledge about their functionalities is still limited. Here, we firstly provided a computational method to identify lncRNAs with copy number variation (lncRNAs-CNV) and their driving transcriptional perturbed subpathways by integrating multidimensional omics data of cancer. The high reliability and accuracy of our method have been demonstrated. Then, the method was applied to 14 cancer types, and a comprehensive characterization and analysis was performed. LncRNAs-CNV had high specificity in cancers, and those with high CNV level may perturb broad biological functions. Some core subpathways and cancer hallmarks widely perturbed by lncRNAs-CNV were revealed. Moreover, subpathways highlighted the functional diversity of lncRNAs-CNV in various cancers. Survival analysis indicated that functional lncRNAs-CNV could be candidate prognostic biomarkers for clinical applications, such as ST7-AS1, CDKN2B-AS1 and EGFR-AS1. In addition, cascade responses and a functional crosstalk model among lncRNAs-CNV, impacted genes, driving subpathways and cancer hallmarks were proposed for understanding the driving mechanism of lncRNAs-CNV. Finally, we developed a user-friendly web interface-LncCASE (http://bio-bigdata.hrbmu.edu.cn/LncCASE/) for exploring lncRNAs-CNV and their driving subpathways in various cancer types. Our study identified and systematically characterized lncRNAs-CNV and their driving subpathways and presented valuable resources for investigating the functionalities of non-coding variations and the mechanisms of tumorigenesis.

Challenges of Translating Gene Regulatory Information into Agronomic Improvements

Improvement of agricultural species has exploited the genetic variation responsible for complex quantitative traits. Much of the functional variation is regulatory, in cis-regulatory elements and trans-acting factors that ultimately contribute to gene expression differences. However, the identification of gene regulatory network components that, when modulated, will increase plant productivity or resilience, is challenging, yet essential to provide increased predictive power for genome engineering approaches that are likely to benefit useful traits. Here, we discuss the opportunities and limitations of using data obtained from gene coexpression, transcription factor binding, and genome-wide association mapping analyses to predict regulatory interactions that impact crop improvement. It is apparent that a combination of information from these data types is necessary for the reliable identification and utilization of important regulatory interactions that underlie complex agronomic traits.