Genetic Drivers of Epigenetic and Transcriptional Variation in Human Immune Cells

Translating non-coding genetic associations into a better understanding of immune-mediated disease

Genome-wide association studies have identified hundreds of genetic loci that are associated with immune-mediated diseases. Most disease-associated variants are non-coding, and a large proportion of these variants lie within enhancers. As a result, there is a pressing need to understand how common genetic variation might affect enhancer function and thereby contribute to immune-mediated (and other) diseases. In this Review, we first describe statistical and experimental methods to identify causal genetic variants that modulate gene expression, including statistical fine-mapping and massively parallel reporter assays. We then discuss approaches to characterise the mechanisms by which these variants modulate immune function, such as clustered regularly interspaced short palindromic repeats (CRISPR)-based screens. We highlight examples of studies that, by elucidating the effects of disease variants within enhancers, have provided important insights into immune function and uncovered key pathways of disease.

eQTL Studies: from Bulk Tissues to Single Cells

An expression quantitative trait locus (eQTL) is a chromosomal region where genetic variants are associated with the expression levels of certain genes that can be both nearby or distant. The identifications of eQTLs for different tissues, cell types, and contexts have led to better understanding of the dynamic regulations of gene expressions and implications of functional genes and variants for complex traits and diseases. Although most eQTL studies to date have been performed on data collected from bulk tissues, recent studies have demonstrated the importance of cell-type-specific and context-dependent gene regulations in biological processes and disease mechanisms. In this review, we discuss statistical methods that have been developed to enable the detections of cell-type-specific and context-dependent eQTLs from bulk tissues, purified cell types, and single cells. We also discuss the limitations of the current methods and future research opportunities.

Epigenomic charting and functional annotation of risk loci in renal cell carcinoma

While the mutational and transcriptional landscapes of renal cell carcinoma (RCC) are well-known, the epigenome is poorly understood. We characterize the epigenome of clear cell (ccRCC), papillary (pRCC), and chromophobe RCC (chRCC) by using ChIP-seq, ATAC-Seq, RNA-seq, and SNP arrays. We integrate 153 individual data sets from 42 patients and nominate 50 histology-specific master transcription factors (MTF) to define RCC histologic subtypes, including EPAS1 and ETS-1 in ccRCC, HNF1B in pRCC, and FOXI1 in chRCC. We confirm histology-specific MTFs via immunohistochemistry including a ccRCC-specific TF, BHLHE41. FOXI1 overexpression with knock-down of EPAS1 in the 786-O ccRCC cell line induces transcriptional upregulation of chRCC-specific genes, TFCP2L1, ATP6V0D2, KIT, and INSRR, implicating FOXI1 as a MTF for chRCC. Integrating RCC GWAS risk SNPs with H3K27ac ChIP-seq and ATAC-seq data reveals that risk-variants are significantly enriched in allelically-imbalanced peaks. This epigenomic atlas in primary human samples provides a resource for future investigation.

Integrative genetic analysis illuminates ALS heritability and identifies risk genes

Amyotrophic lateral sclerosis (ALS) has substantial heritability, in part shared with fronto-temporal dementia (FTD). We show that ALS heritability is enriched in splicing variants and in binding sites of 6 RNA-binding proteins including TDP-43 and FUS. A transcriptome wide association study (TWAS) identified 6 loci associated with ALS, including in NUP50 encoding for the nucleopore basket protein NUP50. Independently, rare variants in NUP50 were associated with ALS risk (P = 3.71.10-03; odds ratio = 3.29; 95%CI, 1.37 to 7.87) in a cohort of 9,390 ALS/FTD patients and 4,594 controls. Cells from one patient carrying a NUP50 frameshift mutation displayed a decreased level of NUP50. Loss of NUP50 leads to death of cultured neurons, and motor defects in Drosophila and zebrafish. Thus, our study identifies alterations in splicing in neurons as critical in ALS and provides genetic evidence linking nuclear pore defects to ALS.

Epigenetic age acceleration correlates with BMI in young adults

INTRODUCTION

Obesity increases the risk of Type 2 diabetes, cardiovascular disease, several types of cancer, and other age-related disorders. Among older adults, obesity is also related to epigenetic age, typically measured with DNA methylation (DNAm). Because less is known about obesity and epigenetic aging earlier in the lifespan, this study examined the relationship between obesity and DNAm in young adulthood and whether these relationships vary by sex.

METHODS

A cross-sectional community sample of 290 healthy young adults (mean age 27.39 years, 60% female; 80% African American, 18% White) had their BMI and waist circumference measured. Four epigenetic age estimators were derived from salivary DNA: Hannum DNAm, Horvath DNAm, Phenoage DNAm, and GrimAge DNAm. Sociodemographic covariates included age, sex, race, parental education, and income-to-needs ratio.

RESULTS

After adjusting for covariates, higher BMI and waist were associated with higher DNAm PhenoAge in both sexes, with a stronger effect on BMI in males (β = 0.35, p < .001) compared to females (β = 0.13, p = .002). Higher waist, but not BMI, was associated with higher Horvath DNA methylation age. Both BMI and waist circumference were associated with higher Hannum DNAm age in men but not in women. Neither BMI nor waist circumference were related to GrimAge.

DISCUSSION

This study extends prior research by linking obesity with accelerated epigenetic aging in young adulthood, replicating the associations across two measures of obesity and four indices of salivary epigenetic aging. The results add to evidence that higher BMI accelerates aging early in the lifespan.

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.

Using epigenomics to understand cellular responses to environmental influences in diseases

It is a generally accepted model that environmental influences can exert their effects, at least in part, by changing the molecular regulators of transcription that are described as epigenetic. As there is biochemical evidence that some epigenetic regulators of transcription can maintain their states long term and through cell division, an epigenetic model encompasses the idea of maintenance of the effect of an exposure long after it is no longer present. The evidence supporting this model is mostly from the observation of alterations of molecular regulators of transcription following exposures. With the understanding that the interpretation of these associations is more complex than originally recognised, this model may be oversimplistic; therefore, adopting novel perspectives and experimental approaches when examining how environmental exposures are linked to phenotypes may prove worthwhile. In this review, we have chosen to use the example of nonalcoholic fatty liver disease (NAFLD), a common, complex human disease with strong environmental and genetic influences. We describe how epigenomic approaches combined with emerging functional genetic and single-cell genomic techniques are poised to generate new insights into the pathogenesis of environmentally influenced human disease phenotypes exemplified by NAFLD.

Multi-omic modeling of antidepressant response implicates dynamic immune and inflammatory changes in individuals who respond to treatment

BACKGROUND

Major depressive disorder (MDD) is a leading cause of disability worldwide, and is commonly treated with antidepressant drugs (AD). Although effective, many patients fail to respond to AD treatment, and accordingly identifying factors that can predict AD response would greatly improve treatment outcomes. In this study, we developed a machine learning tool to integrate multi-omic datasets (gene expression, DNA methylation, and genotyping) to identify biomarker profiles associated with AD response in a cohort of individuals with MDD.

MATERIALS AND METHODS

Individuals with MDD (N = 111) were treated for 8 weeks with antidepressants and were separated into responders and non-responders based on the Montgomery-Åsberg Depression Rating Scale (MADRS). Using peripheral blood samples, we performed RNA-sequencing, assessed DNA methylation using the Illumina EPIC array, and performed genotyping using the Illumina PsychArray. To address this rich multi-omic dataset with high dimensional features, we developed integrative Geneset-Embedded non-negative Matrix factorization (iGEM), a non-negative matrix factorization (NMF) based model, supplemented with auxiliary information regarding gene sets and gene-methylation relationships. In particular, we factorize the subjects by features (i.e., gene expression or DNA methylation) into subjects-by-factors and factors-by-features. We define the factors as the meta-phenotypes as they represent integrated composite scores of the molecular measurements for each subject.

RESULTS

Using our model, we identified a number of meta-phenotypes which were related to AD response. By integrating geneset information into the model, we were able to relate these meta-phenotypes to biological processes, including a meta-phenotype related to immune and inflammatory functions as well as other genes related to depression or AD response. The meta-phenotype identified several genes including immune interleukin 1 receptor like 1 (IL1RL1) and interleukin 5 receptor (IL5) subunit alpha (IL5RA), AKT/PIK3 pathway related phosphoinositide-3-kinase regulatory subunit 6 (PIK3R6), and sphingomyelin phosphodiesterase 3 (SMPD3), which has been identified as a target of AD treatment.

CONCLUSIONS

The derived meta-phenotypes and associated biological functions represent both biomarkers to predict response, as well as potential new treatment targets. Our method is applicable to other diseases with multi-omic data, and the software is open source and available on Github (https://github.com/li-lab-mcgill/iGEM).

Causal relationship between atrial fibrillation and leukocyte telomere length: A two sample, bidirectional Mendelian randomization study

Background

Atrial fibrillation (AF) is an age-related disease, while telomeres play a central role in aging. But the relationship between AF and telomere length (LTL) is still controversial. This study aims to examine the potential causal association between AF and LTL by using Mendelian randomization (MR).

Methods

Bidirectional two-sample MR, expression and protein quantitative trait loci (eQTL and pQTL)-based MR were performed using genetic variants from United Kingdom Biobank, FinnGen, and a meta-analysis study, which comprised nearly 1 million participants in the Atrial Fibrillation Study and 470,000 participants in the Telomere Length Study. Apart from the inverse variance weighted (IVW) approach as the main MR analysis, complementary analysis approaches and sensitivity analysis were applied.

Results

The forward MR revealed a significant causal estimate for the genetically predicted AF with LTL shortening [IVW: odds ratio (OR) = 0.989, p = 0.007; eQTL-IVW: OR = 0.988, p = 0.005; pQTL-IVW: OR = 0.975, p < 0.005]. But in the reverse MR analysis, genetically predicted LTL has no significant correlation with AF (IVW: OR = 0.995, p = 0.916; eQTL-IVW: OR = 0.999, p = 0.995; pQTL-IVW: OR = 1.055, p = 0.570). The FinnGen replication data yielded similar findings. Sensitivity analysis ensured the stability of the results.

Conclusion

The presence of AF leads to LTL shortening rather than the other way around. Aggressive intervention for AF may delay the telomere attrition.

Neutrophil dynamics and inflammaging in acute ischemic stroke: A transcriptomic review

Stroke is among the leading causes of death and disability worldwide. Restoring blood flow through recanalization is currently the only acute treatment for cerebral ischemia. Unfortunately, many patients that achieve a complete recanalization fail to regain functional independence. Recent studies indicate that activation of peripheral immune cells, particularly neutrophils, may contribute to microcirculatory failure and futile recanalization. Stroke primarily affects the elderly population, and mortality after endovascular therapies is associated with advanced age. Previous analyses of differential gene expression across injury status and age identify ischemic stroke as a complex age-related disease. It also suggests robust interactions between stroke injury, aging, and inflammation on a cellular and molecular level. Understanding such interactions is crucial in developing effective protective treatments. The global stroke burden will continue to increase with a rapidly aging human population. Unfortunately, the mechanisms of age-dependent vulnerability are poorly defined. In this review, we will discuss how neutrophil-specific gene expression patterns may contribute to poor treatment responses in stroke patients. We will also discuss age-related transcriptional changes that may contribute to poor clinical outcomes and greater susceptibility to cerebrovascular diseases.

Reversal of splicing infidelity is a pre-activation step in B cell differentiation

Introduction

B cell activation and differentiation is central to the adaptive immune response. Changes in exon usage can have major impacts on cellular signaling and differentiation but have not been systematically explored in differentiating B cells.

Methods

We analyzed exon usage and intron retention in RNA-Seq data from subsets of human B cells at various stages of differentiation, and in an in vitro laboratory model of B cell activation and differentiation (Epstein Barr virus infection).

Results

Blood naïve B cells were found to have an unusual splicing profile, with unannotated splicing events in over 30% of expressed genes. Splicing changed substantially upon naïve B cell entry into secondary lymphoid tissue and before activation, involving significant increases in exon commitment and reductions in intron retention. These changes preferentially involved short introns with weak splice sites and were likely mediated by an overall increase in splicing efficiency induced by the lymphoid environment. The majority of transcripts affected by splicing changes showed restoration of encoded conserved protein domains and/or reduced targeting to the nonsense-mediated decay pathway. Affected genes were enriched in functionally important immune cell activation pathways such as antigen-mediated signaling, cell cycle control and mRNA processing and splicing.

Discussion

Functional observations from donor B cell subsets in progressive states of differentiation and from timecourse experiments using the in vitro model suggest that these widespread changes in mRNA splicing play a role in preparing naïve B cells for the decisive step of antigen-mediated activation and differentiation.

Genetic variation in histone modifications and gene expression identifies regulatory variants in the mammary gland of cattle

BACKGROUND

Causal variants for complex traits, such as eQTL are often found in non-coding regions of the genome, where they are hypothesised to influence phenotypes by regulating gene expression. Many regulatory regions are marked by histone modifications, which can be assayed by chromatin immunoprecipitation followed by sequencing (ChIP-seq). Sequence reads from ChIP-seq form peaks at putative regulatory regions, which may reflect the amount of regulatory activity at this region. Therefore, eQTL which are also associated with differences in histone modifications are excellent candidate causal variants.

RESULTS

We assayed the histone modifications H3K4Me3, H3K4Me1 and H3K27ac and mRNA in the mammary gland of up to 400 animals. We identified QTL for peak height (histone QTL), exon expression (eeQTL), allele specific expression (aseQTL) and allele specific binding (asbQTL). By intersecting these results, we identify variants which may influence gene expression by altering regulatory regions of the genome, and may be causal variants for other traits. Lastly, we find that these variants are found in putative transcription factor binding sites, identifying a mechanism for the effect of many eQTL.

CONCLUSIONS

We find that allele specific and traditional QTL analysis often identify the same genetic variants and provide evidence that many eQTL are regulatory variants which alter activity at regulatory regions of the bovine genome. Our work provides methodological and biological updates on how regulatory mechanisms interplay at multi-omics levels.

Insights gained from single-cell analysis of immune cells in tofacitinib treatment of Vogt-Koyanagi-Harada disease

Vogt-Koyanagi-Harada disease (VKH) is an important refractory uveitis mediated by pathological T cells (TCs). Tofacitinib (TOFA) is a JAK- targeted therapy for several autoimmune diseases. However, the specific pathogenesis and targeted therapeutics for VKH remain largely unknown. Based on single-cell RNA sequencing and mass cytometry, we present what we believe is the first multimodal, high-dimensional analysis to generate a comprehensive human immune atlas regarding subset composition, gene signatures, enriched pathways, and intercellular interactions of VKH patients undergoing TOFA therapy. Patients with VKH are characterized by TCs' polarization from naive to effector and memory subsets, together with accrued monocytes and upregulated cytokines and JAK/STAT signaling pathways. In vitro, TOFA reversed Th17/Treg imbalance and inhibited IL-2-induced STAT1/3 phosphorylation. TOFA alleviated VKH symptoms by restoring pathological TCs' polarization and functional marker expression and downregulating cytokine signaling and lymphocyte function. Remarkably, inflammation-related responses and intercellular interactions decreased after TOFA treatment, particularly in monocytes. Notably, we identified 2 inflammation- and JAK-associated monocyte subpopulations that were strongly implicated in VKH pathogenesis and mechanisms involved in TOFA treatment. Here, we provide a potentially novel JAK-targeted therapy for VKH and elaborate on the possible therapeutic mechanisms of TOFA, expanding our knowledge of VKH pathological patterns.

The design and implementation of natural population cohort study Biobank: A multiple-center project cooperation with medical consortia in Southwest China

Objective

The West China Hospital of Sichuan University collaborated with regional medical consortia in Sichuan Province to launch a natural population cohort study (NPCS) to investigate the health status of residents and collect public health data in southwest China.

Methods

Up to 80,000 participants will be enrolled by the NPCS from 11 regional medical consortia over five years. Individuals are invited to visit one of 11 participating medical consortia to fill out questionnaires, receive a free health exam, and donate biospecimens upon enrolment. All participating medical facilities adhered to standard operating procedures for collecting and processing biospecimens to ensure uniformity (serum, lithium heparinized plasma, ethylene diamine tetraacetie acid plasma, and buffy coat). The Electronic Data Capture System, Picture Archiving and Communication System, Laboratory Information Management System, Biospecimen Quality Control System, Biobank Information Management System, and will be used to sort and classify clinical indices, imaging data, laboratory parameters, pre-analytical variables, and biospecimen information, respectively. All quality assurance and quality control procedures in the NPCS biobank adhered to the "DAIDS Guidelines for Good Clinical Laboratory Practice Standards". This project will integrate high-dimensional multi-omics data, laboratory data, clinical data, questionnaire data, and environmental risk factors.

Results

An estimated 2,240,000 aliquots of the sample will be stored by the end of the study. These samples are linked with comprehensively collected clinical indices, imaging data, and laboratory parameters. Big data analysis can be implemented to create predictive algorithms, explore pathogenesis mechanisms, uncover potential biomarkers, and provide information on public health.

Conclusions

NPCS will provide an integrative approach to research risk factors and pathogenesis of major chronic or endemic diseases in Sichuan Province and provide key scientific evidence to support the formulation of health management policies in China.

Investigating the prediction of CpG methylation levels from SNP genotype data to help elucidate relationships between methylation, gene expression and complex traits

As popularised by PrediXcan (and related methods), transcriptome-wide association studies (TWAS), in which gene expression is imputed from single-nucleotide polymorphism (SNP) genotypes and tested for association with a phenotype, are a popular approach for investigating the role of gene expression in complex traits. Like gene expression, DNA methylation is an important biological process and, being under genetic regulation, may be imputable from SNP genotypes. Here, we investigate prediction of CpG methylation levels from SNP genotype data to help elucidate relationships between methylation, gene expression and complex traits. We start by examining how well CpG methylation can be predicted from SNP genotypes, comparing three penalised regression approaches and examining whether changing the window size improves prediction accuracy. Although methylation at most CpG sites cannot be accurately predicted from SNP genotypes, for a subset it can be predicted well. We next apply our methylation prediction models (trained using the optimal method and window size) to carry out a methylome-wide association study (MWAS) of primary biliary cholangitis. We intersect the regions identified via MWAS with those identified via TWAS, providing insight into the interplay between CpG methylation, gene expression and disease status. We conclude that MWAS has the potential to improve understanding of biological mechanisms in complex traits.

GWAS and autoimmunity: What have we learned and what next

Autoimmune diseases are common conditions characterized by loss of tolerance, female predominance and a remarkable heterogeneity among different populations. Most often they are polygenic and several genetic loci have been linked with the risk of developing autoimmune diseases. However, causal inference is difficult. When the genomic revolution began there were high hopes of translating fast genetic analyses to the bedside but this has proven to be challenging. Nonetheless, over the last decade, fine-mapping strategies have greatly improved; one of the most significant research lines focuses on the in vivo and ex vivo definition of the effect of genetic variants within the target tissues and within specific subpopulations of immune cells that are involved in the disease pathogenesis. This strategy also includes the longitudinal tracking of a large number of immunophenotypes in many individuals to build a large reference atlas for variant characterization. In this review, we discuss the results obtained by GWAS in autoimmune diseases and review recent advances in fine mapping strategies. More importantly, we discuss gaps and future directions.

A family-based study of genetic and epigenetic effects across multiple neurocognitive, motor, social-cognitive and social-behavioral functions

Many psychiatric and neurodevelopmental disorders are known to be heritable, but studies trying to elucidate the genetic architecture of such traits often lag behind studies of somatic traits and diseases. The reasons as to why relatively few genome-wide significant associations have been reported for such traits have to do with the sample sizes needed for the detection of small effects, the difficulty in defining and characterizing the phenotypes, partially due to overlaps in affected underlying domains (which is especially true for cognitive phenotypes), and the complex genetic architectures of the phenotypes, which are not wholly captured in traditional case-control GWAS designs. We aimed to tackle the last two issues by performing GWASs of eight quantitative neurocognitive, motor, social-cognitive and social-behavioral traits, which may be considered endophenotypes for a variety of psychiatric and neurodevelopmental conditions, and for which we employed models capturing both general genetic association and parent-of-origin effects, in a family-based sample comprising 402 children and their parents (mostly family trios). We identified 48 genome-wide significant associations across several traits, of which 3 also survived our strict study-wide quality criteria. We additionally performed a functional annotation of implicated genes, as most of the 48 associations were with variants within protein-coding genes. In total, our study highlighted associations with five genes (TGM3, CACNB4, ANKS1B, CSMD1 and SYNE1) associated with measures of working memory, processing speed and social behavior. Our results thus identify novel associations, including previously unreported parent-of-origin associations with relevant genes, and our top results illustrate new potential gene → endophenotype → disorder pathways.

mir152 hypomethylation as a mechanism for non-syndromic cleft lip and palate

Non-syndromic cleft lip with or without cleft palate (NSCLP), the most common human craniofacial malformation, is a complex disorder given its genetic heterogeneity and multifactorial component revealed by genetic, epidemiological, and epigenetic findings. Epigenetic variations associated with NSCLP have been identified; however, functional investigation has been limited. Here, we combined a reanalysis of NSCLP methylome data with genetic analysis and used both in vitro and in vivo approaches to dissect the functional effects of epigenetic changes. We found a region in mir152 that is frequently hypomethylated in NSCLP cohorts (21-26%), leading to mir152 overexpression. mir152 overexpression in human neural crest cells led to downregulation of spliceosomal, ribosomal, and adherens junction genes. In vivo analysis using zebrafish embryos revealed that mir152 upregulation leads to craniofacial cartilage impairment. Also, we suggest that zebrafish embryonic hypoxia leads to mir152 upregulation combined with mir152 hypomethylation and also analogous palatal alterations. We therefore propose that mir152 hypomethylation, potentially induced by hypoxia in early development, is a novel and frequent predisposing factor to NSCLP.

Leveraging pleiotropy to discover and interpret GWAS results for sleep-associated traits

Genetic association studies of many heritable traits resulting from physiological testing often have modest sample sizes due to the cost and burden of the required phenotyping. This reduces statistical power and limits discovery of multiple genetic associations. We present a strategy to leverage pleiotropy between traits to both discover new loci and to provide mechanistic hypotheses of the underlying pathophysiology. Specifically, we combine a colocalization test with a locus-level test of pleiotropy. In simulations, we show that this approach is highly selective for identifying true pleiotropy driven by the same causative variant, thereby improves the chance to replicate the associations in underpowered validation cohorts and leads to higher interpretability. Here, as an exemplar, we use Obstructive Sleep Apnea (OSA), a common disorder diagnosed using overnight multi-channel physiological testing. We leverage pleiotropy with relevant cellular and cardio-metabolic phenotypes and gene expression traits to map new risk loci in an underpowered OSA GWAS. We identify several pleiotropic loci harboring suggestive associations to OSA and genome-wide significant associations to other traits, and show that their OSA association replicates in independent cohorts of diverse ancestries. By investigating pleiotropic loci, our strategy allows proposing new hypotheses about OSA pathobiology across many physiological layers. For example, we identify and replicate the pleiotropy across the plateletcrit, OSA and an eQTL of DNA primase subunit 1 (PRIM1) in immune cells. We find suggestive links between OSA, a measure of lung function (FEV1/FVC), and an eQTL of matrix metallopeptidase 15 (MMP15) in lung tissue. We also link a previously known genome-wide significant peak for OSA in the hexokinase 1 (HK1) locus to hematocrit and other red blood cell related traits. Thus, the analysis of pleiotropic associations has the potential to assemble diverse phenotypes into a chain of mechanistic hypotheses that provide insight into the pathogenesis of complex human diseases.

Genetic regulation of serum IgA levels and susceptibility to common immune, infectious, kidney, and cardio-metabolic traits

Immunoglobulin A (IgA) mediates mucosal responses to food antigens and the intestinal microbiome and is involved in susceptibility to mucosal pathogens, celiac disease, inflammatory bowel disease, and IgA nephropathy. We performed a genome-wide association study of serum IgA levels in 41,263 individuals of diverse ancestries and identified 20 genome-wide significant loci, including 9 known and 11 novel loci. Co-localization analyses with expression QTLs prioritized candidate genes for 14 of 20 significant loci. Most loci encoded genes that produced immune defects and IgA abnormalities when genetically manipulated in mice. We also observed positive genetic correlations of serum IgA levels with IgA nephropathy, type 2 diabetes, and body mass index, and negative correlations with celiac disease, inflammatory bowel disease, and several infections. Mendelian randomization supported elevated serum IgA as a causal factor in IgA nephropathy. African ancestry was consistently associated with higher serum IgA levels and greater frequency of IgA-increasing alleles compared to other ancestries. Our findings provide novel insights into the genetic regulation of IgA levels and its potential role in human disease.

QTLbase2: an enhanced catalog of human quantitative trait loci on extensive molecular phenotypes

Deciphering the fine-scale molecular mechanisms that shape the genetic effects at disease-associated loci from genome-wide association studies (GWAS) remains challenging. The key avenue is to identify the essential molecular phenotypes that mediate the causal variant and disease under particular biological conditions. Therefore, integrating GWAS signals with context-specific quantitative trait loci (QTLs) (such as different tissue/cell types, disease states, and perturbations) from extensive molecular phenotypes would present important strategies for full understanding of disease genetics. Via persistent curation and systematic data processing of large-scale human molecular trait QTLs (xQTLs), we updated our previous QTLbase database (now QTLbase2, http://mulinlab.org/qtlbase) to comprehensively analyze and visualize context-specific QTLs across 22 molecular phenotypes and over 95 tissue/cell types. Overall, the resource features the following major updates and novel functions: (i) 960 more genome-wide QTL summary statistics from 146 independent studies; (ii) new data for 10 previously uncompiled QTL types; (iii) variant query scope expanded to fit 195 QTL datasets based on whole-genome sequencing; (iv) supports filtering and comparison of QTLs for different biological conditions, such as stimulation types and disease states; (v) a new linkage disequilibrium viewer to facilitate variant prioritization across tissue/cell types and QTL types.

Multi-ancestry genome-wide association analyses identify novel genetic mechanisms in rheumatoid arthritis

Rheumatoid arthritis (RA) is a highly heritable complex disease with unknown etiology. Multi-ancestry genetic research of RA promises to improve power to detect genetic signals, fine-mapping resolution and performances of polygenic risk scores (PRS). Here, we present a large-scale genome-wide association study (GWAS) of RA, which includes 276,020 samples from five ancestral groups. We conducted a multi-ancestry meta-analysis and identified 124 loci (P < 5 × 10-8), of which 34 are novel. Candidate genes at the novel loci suggest essential roles of the immune system (for example, TNIP2 and TNFRSF11A) and joint tissues (for example, WISP1) in RA etiology. Multi-ancestry fine-mapping identified putatively causal variants with biological insights (for example, LEF1). Moreover, PRS based on multi-ancestry GWAS outperformed PRS based on single-ancestry GWAS and had comparable performance between populations of European and East Asian ancestries. Our study provides several insights into the etiology of RA and improves the genetic predictability of RA.

Genetic risk variants for multiple sclerosis are linked to differences in alternative pre-mRNA splicing

BACKGROUND

Multiple sclerosis (MS) is a chronic immune-mediated disease of the central nervous system to which a genetic predisposition contributes. Over 200 genetic regions have been associated with increased disease risk, but the disease-causing variants and their functional impact at the molecular level are mostly poorly defined. We hypothesized that single-nucleotide polymorphisms (SNPs) have an impact on pre-mRNA splicing in MS.

METHODS

Our study focused on 10 bioinformatically prioritized SNP-gene pairs, in which the SNP has a high potential to alter alternative splicing events (ASEs). We tested for differential gene expression and differential alternative splicing in B cells from MS patients and healthy controls. We further examined the impact of the SNP genotypes on ASEs and on splice isoform expression levels. Novel genotype-dependent effects on splicing were verified with splicing reporter minigene assays.

RESULTS

We were able to confirm previously described findings regarding the relation of MS-associated SNPs with the ASEs of the pre-mRNAs from GSDMB and SP140. We also observed an increased IL7R exon 6 skipping when comparing relapsing and progressive MS patients to healthy subjects. Moreover, we found evidence that the MS risk alleles of the SNPs rs3851808 (EFCAB13), rs1131123 (HLA-C), rs10783847 (TSFM), and rs2014886 (TSFM) may contribute to a differential splicing pattern. Of particular interest is the genotype-dependent exon skipping of TSFM due to the SNP rs2014886. The minor allele T creates a donor splice site, resulting in the expression of the exon 3 and 4 of a short TSFM transcript isoform, whereas in the presence of the MS risk allele C, this donor site is absent, and thus the short transcript isoform is not expressed.

CONCLUSION

In summary, we found that genetic variants from MS risk loci affect pre-mRNA splicing. Our findings substantiate the role of ASEs with respect to the genetics of MS. Further studies on how disease-causing genetic variants may modify the interactions between splicing regulatory sequence elements and RNA-binding proteins can help to deepen our understanding of the genetic susceptibility to MS.

Genome-wide association study for systemic lupus erythematosus in an egyptian population

Systemic lupus erythematosus (SLE) susceptibility has a strong genetic component. Genome-wide association studies (GWAS) across trans-ancestral populations show both common and distinct genetic variants of susceptibility across European and Asian ancestries, while many other ethnic populations remain underexplored. We conducted the first SLE GWAS on Egyptians-an admixed North African/Middle Eastern population-using 537 patients and 883 controls. To identify novel susceptibility loci and replicate previously known loci, we performed imputation-based association analysis with 6,382,276 SNPs while accounting for individual admixture. We validated the association analysis using adaptive permutation tests (n = 109). We identified a novel genome-wide significant locus near IRS1/miR-5702 (Pcorrected = 1.98 × 10-8) and eight novel suggestive loci (Pcorrected < 1.0 × 10-5). We also replicated (Pperm < 0.01) 97 previously known loci with at least one associated nearby SNP, with ITGAM, DEF6-PPARD and IRF5 the top three replicated loci. SNPs correlated (r 2 > 0.8) with lead SNPs from four suggestive loci (ARMC9, DIAPH3, IFLDT1, and ENTPD3) were associated with differential gene expression (3.5 × 10-95 < p < 1.0 × 10-2) across diverse tissues. These loci are involved in cellular proliferation and invasion-pathways prominent in lupus and nephritis. Our study highlights the utility of GWAS in an admixed Egyptian population for delineating new genetic associations and for understanding SLE pathogenesis.

The role of macrophage scavenger receptor 1 (MSR1) in inflammatory disorders and cancer

Macrophage scavenger receptor 1 (MSR1), also named CD204, holds key inflammatory roles in multiple pathophysiologic processes. Present primarily on the surface of various types of macrophage, this receptor variably affects processes such as atherosclerosis, innate and adaptive immunity, lung and liver disease, and more recently, cancer. As highlighted throughout this review, the role of MSR1 is often dichotomous, being either host protective or detrimental to the pathogenesis of disease. We will discuss the role of MSR1 in health and disease with a focus on the molecular mechanisms influencing MSR1 expression, how altered expression affects disease process and macrophage function, the limited cell signalling pathways discovered thus far, the emerging role of MSR1 in tumour associated macrophages as well as the therapeutic potential of targeting MSR1.

Predicting Immunotherapy Outcomes in Older Patients with Solid Tumors Using the LIPI Score

Immunotherapy with immune checkpoint blockers (ICB) represents a valid therapeutic option in older patients for several solid cancer types. However, most of the data concerning efficacy and adverse events of ICB available are derived from younger and fitter patients. Reliable biomarkers are needed to better select the population that will benefit from ICB especially in older patients who may be at a higher risk of developing immune-related adverse events (irAEs) with a greater impact on their quality of life. The Lung Immune Prognostic Index (LIPI) is a score that combines pretreatment dNLR (neutrophils/[leukocytes − neutrophils]) and lactate dehydrogenase (LDH) and is correlated with outcomes in patients treated with ICB in non-small-cell lung cancer. We aimed to assess the impact of LIPI in ICB outcomes in a dedicated cohort of older patients. The primary objective was to study the prognostic role of LIPI score in patients aged 70 years or above in a real-life population treated with anti-programmed death-(ligand)1 (anti PD-(L)1). dNLR and LDH were collected in a prospective cohort of patients aged 70 years or above treated with PD-(L)1 inhibitors with metastatic disease between June 2014 and October 2017 at Gustave Roussy. LIPI categorizes the population into three different prognostic groups: good (dNLR ≤ 3 and LDH ≤ ULN—upper normal limit), intermediate (dNLR > 3 or LDH > ULN), and poor (dNLR > 3 and LDH > ULN). Anti PD-(L)1 benefit was analyzed according to overall survival (OS), progression free survival (PFS), and overall response rate (ORR) using RECIST v1.1. criteria. In the 191 older patients treated, most of them (95%) were ICB-naïve, and 160 (84%) had an ECOG performance status of 0−1 with a median age at ICB treatment of 77 (range, 70−93). The most common tumor types were melanoma (66%) and non-small-cell lung cancer (15%). The median follow-up duration was 18.8 months (95% CI 14.7−24.2). LIPI classified the population into three different groups: 38 (23%) patients had a good LIPI score, 84 (51%) had an intermediate LIPI score, and 43 (26%) had a poor LIPI score. The median OS was 20.7 months [95% CI, 12.6−not reached] compared to 11.2 months [95% CI, 8.41−22.2] and 4.7 months [95% CI, 2.2−11.3] in patients with a good, intermediate, and poor LIPI score, respectively (p = 0.0003). The median PFS was 9.2 months [95% CI, 6.2−18.1] in the good LIPI group, 7.2 months [95% CI, 5.4−13] in the intermediate LIPI group, and 3.9 months [95% CI, 2.3−8.2] in the poor LIPI group (p = 0.09). The rate of early death (OS < 3 months) was 37% in the poor LIPI group compared to 5% in the good LIPI group (<0.001). Poor LIPI score was associated with a poorer outcome in older patients treated with anti PD-(L)1. LIPI is a simple and accessible worldwide tool that can serve as a prognostic factor and can be useful for stratification benefit from ICB.

TWAS Atlas: a curated knowledgebase of transcriptome-wide association studies

Transcriptome-wide association studies (TWASs), as a practical and prevalent approach for detecting the associations between genetically regulated genes and traits, are now leading to a better understanding of the complex mechanisms of genetic variants in regulating various diseases and traits. Despite the ever-increasing TWAS outputs, there is still a lack of databases curating massive public TWAS information and knowledge. To fill this gap, here we present TWAS Atlas (https://ngdc.cncb.ac.cn/twas/), an integrated knowledgebase of TWAS findings manually curated from extensive literature. In the current implementation, TWAS Atlas collects 401,266 high-quality human gene-trait associations from 200 publications, covering 22,247 genes and 257 traits across 135 tissue types. In particular, an interactive knowledge graph of the collected gene-trait associations is constructed together with single nucleotide polymorphism (SNP)-gene associations to build up comprehensive regulatory networks at multi-omics levels. In addition, TWAS Atlas, as a user-friendly web interface, efficiently enables users to browse, search and download all association information, relevant research metadata and annotation information of interest. Taken together, TWAS Atlas is of great value for promoting the utility and availability of TWAS results in explaining the complex genetic basis as well as providing new insights for human health and disease research.

Genetic variants associated with psychiatric disorders are enriched at epigenetically active sites in lymphoid cells

Multiple psychiatric disorders have been associated with abnormalities in both the innate and adaptive immune systems. The role of these abnormalities in pathogenesis, and whether they are driven by psychiatric risk variants, remains unclear. We test for enrichment of GWAS variants associated with multiple psychiatric disorders (cross-disorder or trans-diagnostic risk), or 5 specific disorders (cis-diagnostic risk), in regulatory elements in immune cells. We use three independent epigenetic datasets representing multiple organ systems and immune cell subsets. Trans-diagnostic and cis-diagnostic risk variants (for schizophrenia and depression) are enriched at epigenetically active sites in brain tissues and in lymphoid cells, especially stimulated CD4⁺ T cells. There is no evidence for enrichment of either trans-risk or cis-risk variants for schizophrenia or depression in myeloid cells. This suggests a possible model where environmental stimuli activate T cells to unmask the effects of psychiatric risk variants, contributing to the pathogenesis of mental health disorders.

Genotype-phenotype correlation of T-cell subtypes reveals senescent and cytotoxic genes in Alzheimer's disease

Recent studies identifying expression quantitative trait loci (eQTLs) in immune cells have uncovered important links between disease risk alleles and gene expression trends in monocytes, T cells and other cell types. However, these studies are generally done with young, healthy subjects, limiting the utility of their findings for age-related conditions such as Alzheimer's disease (AD). We have performed RNA sequencing on four T-cell subsets in genome-wide genotyped and well-characterized AD subjects and age- and sex-matched controls from the Religious Orders Study/Memory and Aging Project. We correlated gene expression data with AD neuropathological traits and with single-nucleotide polymorphisms to detect eQTLs. We identified several significant genes involved in T-cell senescence and cytotoxicity, consistent with T-cell RNA sequencing studies in aged/AD cohorts. We identified unexpected eQTLs previously associated with neuropsychiatric disease traits. Finally, we discovered that pathways related to axon guidance and synaptic function were enriched among trans-eQTLs in coding regions of the genome. Our data strengthen the potential link between T-cell senescence and age-related neurodegenerative disease. In addition, our eQTL data suggest that T-cell phenotypes may influence neuropsychiatric disorders and can be influenced by genes involved in neurodevelopmental processes.

Genetic determinants of chromatin reveal prostate cancer risk mediated by context-dependent gene regulation

Many genetic variants affect disease risk by altering context-dependent gene regulation. Such variants are difficult to study mechanistically using current methods that link genetic variation to steady-state gene expression levels, such as expression quantitative trait loci (eQTLs). To address this challenge, we developed the cistrome-wide association study (CWAS), a framework for identifying genotypic and allele-specific effects on chromatin that are also associated with disease. In prostate cancer, CWAS identified regulatory elements and androgen receptor-binding sites that explained the association at 52 of 98 known prostate cancer risk loci and discovered 17 additional risk loci. CWAS implicated key developmental transcription factors in prostate cancer risk that are overlooked by eQTL-based approaches due to context-dependent gene regulation. We experimentally validated associations and demonstrated the extensibility of CWAS to additional epigenomic datasets and phenotypes, including response to prostate cancer treatment. CWAS is a powerful and biologically interpretable paradigm for studying variants that influence traits by affecting transcriptional regulation.

A Pan-RNase Inhibitor Enabling CRISPR-mRNA Platforms for Engineering of Primary Human Monocytes

Monocytes and their downstream effectors are critical components of the innate immune system. Monocytes are equipped with chemokine receptors, allowing them to migrate to various tissues, where they can differentiate into macrophage and dendritic cell subsets and participate in tissue homeostasis, infection, autoimmune disease, and cancer. Enabling genome engineering in monocytes and their effector cells will facilitate a myriad of applications for basic and translational research. Here, we demonstrate that CRISPR-Cas9 RNPs can be used for efficient gene knockout in primary human monocytes. In addition, we demonstrate that intracellular RNases are likely responsible for poor and heterogenous mRNA expression as incorporation of pan-RNase inhibitor allows efficient genome engineering following mRNA-based delivery of Cas9 and base editor enzymes. Moreover, we demonstrate that CRISPR-Cas9 combined with an rAAV vector DNA donor template mediates site-specific insertion and expression of a transgene in primary human monocytes. Finally, we demonstrate that SIRPa knock-out monocyte-derived macrophages have enhanced activity against cancer cells, highlighting the potential for application in cellular immunotherapies.

eQTLs as causal instruments for the reconstruction of hormone linked gene networks

Hormones act within in highly dynamic systems and much of the phenotypic response to variation in hormone levels is mediated by changes in gene expression. The increase in the number and power of large genetic association studies has led to the identification of hormone linked genetic variants. However, the biological mechanisms underpinning the majority of these loci are poorly understood. The advent of affordable, high throughput next generation sequencing and readily available transcriptomic databases has shown that many of these genetic variants also associate with variation in gene expression levels as expression Quantitative Trait Loci (eQTLs). In addition to further dissecting complex genetic variation, eQTLs have been applied as tools for causal inference. Many hormone networks are driven by transcription factors, and many of these genes can be linked to eQTLs. In this mini-review, we demonstrate how causal inference and gene networks can be used to describe the impact of hormone linked genetic variation upon the transcriptome within an endocrinology context.

Mapping of promoter usage QTL using RNA-seq data reveals their contributions to complex traits

Genomic variations are associated with gene expression levels, which are called expression quantitative trait loci (eQTL). Most eQTL may affect the total gene expression levels by regulating transcriptional activities of a specific promoter. However, the direct exploration of genomic loci associated with promoter activities using RNA-seq data has been challenging because eQTL analyses treat the total expression levels estimated by summing those of all isoforms transcribed from distinct promoters. Here we propose a new method for identifying genomic loci associated with promoter activities, called promoter usage quantitative trait loci (puQTL), using conventional RNA-seq data. By leveraging public RNA-seq datasets from the lymphoblastoid cell lines of 438 individuals from the GEUVADIS project, we obtained promoter activity estimates and mapped 2,592 puQTL at the 10% FDR level. The results of puQTL mapping enabled us to interpret the manner in which genomic variations regulate gene expression. We found that 310 puQTL genes (16.1%) were not detected by eQTL analysis, suggesting that our pipeline can identify novel variant-gene associations. Furthermore, we identified genomic loci associated with the activity of "hidden" promoters, which the standard eQTL studies have ignored. We found that most puQTL signals were concordant with at least one genome-wide association study (GWAS) signal, enabling novel interpretations of the molecular mechanisms of complex traits. Our results emphasize the importance of the re-analysis of public RNA-seq datasets to obtain novel insights into gene regulation by genomic variations and their contributions to complex traits.

Heritability Estimation of Multiple Sclerosis Related Plasma Protein Levels in Sardinian Families with Immunochip Genotyping Data

This work aimed at estimating narrow-sense heritability, defined as the proportion of the phenotypic variance explained by the sum of additive genetic effects, via Haseman–Elston regression for a subset of 56 plasma protein levels related to Multiple Sclerosis (MS). These were measured in 212 related individuals (with 69 MS cases and 143 healthy controls) obtained from 20 Sardinian families with MS history. Using pedigree information, we found seven statistically significant heritable plasma protein levels (after multiple testing correction), i.e., Gc (h2 = 0.77; 95%CI: 0.36, 1.00), Plat (h2 = 0.70; 95%CI: 0.27, 0.95), Anxa1 (h2 = 0.68; 95%CI: 0.27, 1.00), Sod1 (h2 = 0.58; 95%CI: 0.18, 0.96), Irf8 (h2 = 0.56; 95%CI: 0.19, 0.99), Ptger4 (h2 = 0.45; 95%CI: 0.10, 0.96), and Fadd (h2 = 0.41; 95%CI: 0.06, 0.84). A subsequent analysis was performed on these statistically significant heritable plasma protein levels employing Immunochip genotyping data obtained in 155 healthy controls (92 related and 63 unrelated); we found a meaningful proportion of heritable plasma protein levels’ variability explained by a small set of SNPs. Overall, the results obtained, for these seven MS-related proteins, emphasized a high additive genetic variance component explaining plasma levels’ variability.

DeCAF: a novel method to identify cell-type specific regulatory variants and their role in cancer risk

Here, we propose DeCAF (DEconvoluted cell type Allele specific Function), a new method to identify cell-fraction (cf) QTLs in tumors by leveraging both allelic and total expression information. Applying DeCAF to RNA-seq data from TCGA, we identify 3664 genes with cfQTLs (at 10% FDR) in 14 cell types, a 5.63× increase in discovery over conventional interaction-eQTL mapping. cfQTLs replicated in external cell-type-specific eQTL data are more enriched for cancer risk than conventional eQTLs. Our new method, DeCAF, empowers the discovery of biologically meaningful cfQTLs from bulk RNA-seq data in moderately sized studies.

Epigenomic and transcriptomic analyses define core cell types, genes and targetable mechanisms for kidney disease

More than 800 million people suffer from kidney disease, yet the mechanism of kidney dysfunction is poorly understood. In the present study, we define the genetic association with kidney function in 1.5 million individuals and identify 878 (126 new) loci. We map the genotype effect on the methylome in 443 kidneys, transcriptome in 686 samples and single-cell open chromatin in 57,229 kidney cells. Heritability analysis reveals that methylation variation explains a larger fraction of heritability than gene expression. We present a multi-stage prioritization strategy and prioritize target genes for 87% of kidney function loci. We highlight key roles of proximal tubules and metabolism in kidney function regulation. Furthermore, the causal role of SLC47A1 in kidney disease is defined in mice with genetic loss of Slc47a1 and in human individuals carrying loss-of-function variants. Our findings emphasize the key role of bulk and single-cell epigenomic information in translating genome-wide association studies into identifying causal genes, cellular origins and mechanisms of complex traits.

Epigenomic analysis reveals a dynamic and context-specific macrophage enhancer landscape associated with innate immune activation and tolerance

Background

Chromatin states and enhancers associate gene expression, cell identity and disease. Here, we systematically delineate the acute innate immune response to endotoxin in terms of human macrophage enhancer activity and contrast with endotoxin tolerance, profiling the coding and non-coding transcriptome, chromatin accessibility and epigenetic modifications.

Results

We describe the spectrum of enhancers under acute and tolerance conditions and the regulatory networks between these enhancers and biological processes including gene expression, splicing regulation, transcription factor binding and enhancer RNA signatures. We demonstrate that the vast majority of differentially regulated enhancers on acute stimulation are subject to tolerance and that expression quantitative trait loci, disease-risk variants and eRNAs are enriched in these regulatory regions and related to context-specific gene expression. We find enrichment for context-specific eQTL involving endotoxin response and specific infections and delineate specific differential regions informative for GWAS variants in inflammatory bowel disease and multiple sclerosis, together with a context-specific enhancer involving a bacterial infection eQTL for KLF4. We show enrichment in differential enhancers for tolerance involving transcription factors NFκB-p65, STATs and IRFs and prioritize putative causal genes directly linking genetic variants and disease risk enhancers. We further delineate similarities and differences in epigenetic landscape between stem cell-derived macrophages and primary cells and characterize the context-specific enhancer activities for key innate immune response genes KLF4, SLAMF1 and IL2RA.

Conclusions

Our study demonstrates the importance of context-specific macrophage enhancers in gene regulation and utility for interpreting disease associations, providing a roadmap to link genetic variants with molecular and cellular functions.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13059-022-02702-1.

Single-cell eQTL models reveal dynamic T cell state dependence of disease loci

Non-coding genetic variants may cause disease by modulating gene expression. However, identifying these expression quantitative trait loci (eQTLs) is complicated by differences in gene regulation across fluid functional cell states within cell types. These states-for example, neurotransmitter-driven programs in astrocytes or perivascular fibroblast differentiation-are obscured in eQTL studies that aggregate cells1,2. Here we modelled eQTLs at single-cell resolution in one complex cell type: memory T cells. Using more than 500,000 unstimulated memory T cells from 259 Peruvian individuals, we show that around one-third of 6,511 cis-eQTLs had effects that were mediated by continuous multimodally defined cell states, such as cytotoxicity and regulatory capacity. In some loci, independent eQTL variants had opposing cell-state relationships. Autoimmune variants were enriched in cell-state-dependent eQTLs, including risk variants for rheumatoid arthritis near ORMDL3 and CTLA4; this indicates that cell-state context is crucial to understanding potential eQTL pathogenicity. Moreover, continuous cell states explained more variation in eQTLs than did conventional discrete categories, such as CD4+ versus CD8+, suggesting that modelling eQTLs and cell states at single-cell resolution can expand insight into gene regulation in functionally heterogeneous cell types.

Immune disease risk variants regulate gene expression dynamics during CD4+ T cell activation

During activation, T cells undergo extensive gene expression changes that shape the properties of cells to exert their effector function. Understanding the regulation of this process could help explain how genetic variants predispose to immune diseases. Here, we mapped genetic effects on gene expression (expression quantitative trait loci (eQTLs)) using single-cell transcriptomics. We profiled 655,349 CD4⁺ T cells, capturing transcriptional states of unstimulated cells and three time points of cell activation in 119 healthy individuals. This identified 38 cell clusters, including transient clusters that were only present at individual time points of activation. We found 6,407 genes whose expression was correlated with genetic variation, of which 2,265 (35%) were dynamically regulated during activation. Furthermore, 127 genes were regulated by variants associated with immune-mediated diseases, with significant enrichment for dynamic effects. Our results emphasize the importance of studying context-specific gene expression regulation and provide insights into the mechanisms underlying genetic susceptibility to immune-mediated diseases.

Challenges in Analyzing Functional Epigenetic Data in Perspective of Adolescent Psychiatric Health

The formative period of adolescence plays a crucial role in the development of skills and abilities for adulthood. Adolescents who are affected by mental health conditions are at risk of suicide and social and academic impairments. Gene-environment complementary contributions to the molecular mechanisms involved in psychiatric disorders have emphasized the need to analyze epigenetic marks such as DNA methylation (DNAm) and non-coding RNAs. However, the large and diverse bioinformatic and statistical methods, referring to the confounders of the statistical models, application of multiple-testing adjustment methods, questions regarding the correlation of DNAm across tissues, and sex-dependent differences in results, have raised challenges regarding the interpretation of the results. Based on the example of generalized anxiety disorder (GAD) and depressive disorder (MDD), we shed light on the current knowledge and usage of methodological tools in analyzing epigenetics. Statistical robustness is an essential prerequisite for a better understanding and interpretation of epigenetic modifications and helps to find novel targets for personalized therapeutics in psychiatric diseases.

Molecular Quantitative Trait Locus Mapping in Human Complex Diseases

Mapping quantitative trait loci (QTLs) for molecular traits from chromatin to metabolites (i.e., xQTLs) provides insight into the locations and effect modes of genetic variants that influence these molecular phenotypes and the propagation of functional consequences of each variant. xQTL studies indirectly interrogate the functional landscape of the molecular basis of complex diseases, including the impact of non-coding regulatory variants, the tissue specificity of regulatory elements, and their contribution to disease by integrating with genome-wide association studies (GWAS). We summarize a variety of molecular xQTL studies in human tissues and cells. In addition, using the Alzheimer's Disease Sequencing Project (ADSP) as an example, we describe the ADSP xQTL project, a collaborative effort across the ADSP Functional Genomics Consortium (ADSP-FGC). The project's ultimate goal is a reference map of Alzheimer's-related QTLs using existing datasets from multiple omics layers to help us study the consequences of genetic variants identified in the ADSP. xQTL studies enable the identification of the causal genes and pathways in GWAS loci, which will likely aid in the discovery of novel biomarkers and therapeutic targets for complex diseases. © 2022 Wiley Periodicals LLC.

Selective clonal persistence of human retroviruses in vivo: Radial chromatin organization, integration site, and host transcription

The human retroviruses HTLV-1 (human T cell leukemia virus type 1) and HIV-1 persist in vivo as a reservoir of latently infected T cell clones. It is poorly understood what determines which clones survive in the reservoir. We compared >160,000 HTLV-1 integration sites (>40,000 HIV-1 sites) from T cells isolated ex vivo from naturally infected individuals with >230,000 HTLV-1 integration sites (>65,000 HIV-1 sites) from in vitro infection to identify genomic features that determine selective clonal survival. Three statistically independent factors together explained >40% of the observed variance in HTLV-1 clonal survival in vivo: the radial intranuclear position of the provirus, its genomic distance from the centromere, and the intensity of local host genome transcription. The radial intranuclear position of the provirus and its distance from the centromere also explained ~7% of clonal persistence of HIV-1 in vivo. Selection for the intranuclear and intrachromosomal location of the provirus and host transcription intensity favors clonal persistence of human retroviruses in vivo.

Functional Characterization of Genetic Variant Effects on Expression

Thousands of common genetic variants in the human population have been associated with disease risk and phenotypic variation by genome-wide association studies (GWAS). However, the majority of GWAS variants fall into noncoding regions of the genome, complicating our understanding of their regulatory functions, and few molecular mechanisms of GWAS variant effects have been clearly elucidated. Here, we set out to review genetic variant effects, focusing on expression quantitative trait loci (eQTLs), including their utility in interpreting GWAS variant mechanisms. We discuss the interrelated challenges and opportunities for eQTL analysis, covering determining causal variants, elucidating molecular mechanisms of action, and understanding context variability. Addressing these questions can enable better functional characterization of disease-associated loci and provide insights into fundamental biological questions of the noncoding genetic regulatory code and its control of gene expression. Expected final online publication date for the Annual Review of Biomedical Data Science, Volume 5 is August 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Integrative Multi-Omics database (iMOMdb) of Asian Pregnant Women

Asians are underrepresented across many omics databases, thereby limiting the potential of precision medicine in nearly 60% of the global population. As such, there is a pressing need for multi-omics derived quantitative trait loci (QTLs) to fill the knowledge gap of complex traits in populations of Asian ancestry. Here, we provide the first blood-based multi-omics analysis of Asian pregnant women, constituting high-resolution genotyping (N = 1079), DNA methylation (N = 915) and transcriptome profiling (N = 238). Integrative omics analysis identified 219 154 CpGs associated with cis-DNA methylation QTLs (meQTLs) and 3703 RNAs associated with cis-RNA expression QTLs (eQTLs). Ethnicity was the largest contributor of inter-individual variation across all omics datasets, with 2561 genes identified as hotspots of this variation; 395 of these hotspot genes also contained both ethnicity-specific eQTLs and meQTLs. Gene set enrichment analysis of these ethnicity QTL hotspots showed pathways involved in lipid metabolism, adaptive immune system and carbohydrate metabolism. Pathway validation by profiling the lipidome (~480 lipids) of antenatal plasma (N = 752) and placenta (N = 1042) in the same cohort showed significant lipid differences among Chinese, Malay and Indian women, validating ethnicity-QTL gene effects across different tissue types. To develop deeper insights into the complex traits and benefit future precision medicine research in Asian pregnant women, we developed iMOMdb, an open-access database.

Diverse Epigenetic Regulations of Macrophages in Atherosclerosis

Emerging research on epigenetics has resulted in many novel discoveries in atherosclerosis (AS), an inflammaging-associated disease characterized by chronic inflammation primarily driven by macrophages. The bulk of evidence has demonstrated the central role of epigenetic machinery in macrophage polarization to pro- (M1-like) or anti-inflammatory (M2-like) phenotype. An increasing number of epigenetic alterations and their modifiers involved in reprogramming macrophages by regulating DNA methylation or histone modifications (e.g., methylation, acetylation, and recently lactylation) have been identified. They may act to determine or skew the direction of macrophage polarization in AS lesions, thereby representing a promising target. Here we describe the current understanding of the epigenetic machinery involving macrophage polarization, to shed light on chronic inflammation-driving onset and progression of inflammaging-associated diseases, using AS as a prototypic example, and discuss the challenge for developing effective therapies targeting the epigenetic modifiers against these diseases, particularly highlighting a potential strategy based on epigenetically-governed repolarization from M1-like to M2-like phenotype.

Epigenomic alterations in cancer: mechanisms and therapeutic potential

The human cell requires ways to specify its transcriptome without altering the essential sequence of DNA; this is achieved through mechanisms which govern the epigenetic state of DNA and epitranscriptomic state of RNA. These alterations can be found as modified histone proteins, cytosine DNA methylation, non-coding RNAs, and mRNA modifications, such as N6-methyladenosine (m6A). The different aspects of epigenomic and epitranscriptomic modifications require protein complexes to write, read, and erase these chemical alterations. Reflecting these important roles, many of these reader/writer/eraser proteins are either frequently mutated or differentially expressed in cancer. The disruption of epigenetic regulation in the cell can both contribute to cancer initiation and progression, and increase the likelihood of developing resistance to chemotherapies. Development of therapeutics to target proteins involved in epigenomic/epitranscriptomic modifications has been intensive, but further refinement is necessary to achieve ideal treatment outcomes without too many off-target effects for cancer patients. Therefore, further integration of clinical outcomes combined with large-scale genomic analyses is imperative for furthering understanding of epigenomic mechanisms in cancer.

Immune disease variants modulate gene expression in regulatory CD4+ T cells

Identifying cellular functions dysregulated by disease-associated variants could implicate novel pathways for drug targeting or modulation in cell therapies. However, follow-up studies can be challenging if disease-relevant cell types are difficult to sample. Variants associated with immune diseases point toward the role of CD4+ regulatory T cells (Treg cells). We mapped genetic regulation (quantitative trait loci [QTL]) of gene expression and chromatin activity in Treg cells, and we identified 133 colocalizing loci with immune disease variants. Colocalizations of immune disease genome-wide association study (GWAS) variants with expression QTLs (eQTLs) controlling the expression of CD28 and STAT5A, involved in Treg cell activation and interleukin-2 (IL-2) signaling, support the contribution of Treg cells to the pathobiology of immune diseases. Finally, we identified seven known drug targets suitable for drug repurposing and suggested 63 targets with drug tractability evidence among the GWAS signals that colocalized with Treg cell QTLs. Our study is the first in-depth characterization of immune disease variant effects on Treg cell gene expression modulation and dysregulation of Treg cell function.