Genetic Ancestry and Natural Selection Drive Population Differences in Immune Responses to Pathogens.

Individuals from different populations vary considerably in their susceptibility to immune-related diseases. To understand how genetic variation and natural selection contribute to these differences, we tested for the effects of African versus European ancestry on the transcriptional response of primary macrophages to live bacterial pathogens. A total of 9.3% of macrophage-expressed genes show ancestry-associated differences in the gene regulatory response to infection, and African ancestry specifically predicts a stronger inflammatory response and reduced intracellular bacterial growth. A large proportion of these differences are under genetic control: for 804 genes, more than 75% of ancestry effects on the immune response can be explained by a single cis- or trans-acting expression quantitative trait locus (eQTL). Finally, we show that genetic effects on the immune response are strongly enriched for recent, population-specific signatures of adaptation. Together, our results demonstrate how historical selective events continue to shape human phenotypic diversity today, including for traits that are key to controlling infection.

Dissecting human population variation in single-cell responses to SARS-CoV-2.

Humans display substantial interindividual clinical variability after SARS-CoV-2 infection1-3, the genetic and immunological basis of which has begun to be deciphered4. However, the extent and drivers of population differences in immune responses to SARS-CoV-2 remain unclear. Here we report single-cell RNA-sequencing data for peripheral blood mononuclear cells-from 222 healthy donors of diverse ancestries-that were stimulated with SARS-CoV-2 or influenza A virus. We show that SARS-CoV-2 induces weaker, but more heterogeneous, interferon-stimulated gene activity compared with influenza A virus, and a unique pro-inflammatory signature in myeloid cells. Transcriptional responses to viruses display marked population differences, primarily driven by changes in cell abundance including increased lymphoid differentiation associated with latent cytomegalovirus infection. Expression quantitative trait loci and mediation analyses reveal a broad effect of cell composition on population disparities in immune responses, with genetic variants exerting a strong effect on specific loci. Furthermore, we show that natural selection has increased population differences in immune responses, particularly for variants associated with SARS-CoV-2 response in East Asians, and document the cellular and molecular mechanisms by which Neanderthal introgression has altered immune functions, such as the response of myeloid cells to viruses. Finally, colocalization and transcriptome-wide association analyses reveal an overlap between the genetic basis of immune responses to SARS-CoV-2 and COVID-19 severity, providing insights into the factors contributing to current disparities in COVID-19 risk.

DNA sequence-dependent compartmentalization and silencing of chromatin at the nuclear lamina.

A large fraction of the mammalian genome is organized into inactive chromosomal domains along the nuclear lamina. The mechanism by which these lamina associated domains (LADs) are established remains to be elucidated. Using genomic repositioning assays, we show that LADs, spanning the developmentally regulated IgH and Cyp3a loci contain discrete DNA regions that associate chromatin with the nuclear lamina and repress gene activity in fibroblasts. Lamina interaction is established during mitosis and likely involves the localized recruitment of Lamin B during late anaphase. Fine-scale mapping of LADs reveals numerous lamina-associating sequences (LASs), which are enriched for a GAGA motif. This repeated motif directs lamina association and is bound by the transcriptional repressor cKrox, in a complex with HDAC3 and Lap2ß. Knockdown of cKrox or HDAC3 results in dissociation of LASs/LADs from the nuclear lamina. These results reveal a mechanism that couples nuclear compartmentalization of chromatin domains with the control of gene activity.

Probabilistic integration of transcriptome-wide association studies and colocalization analysis identifies key molecular pathways of complex traits.

Integrative genetic association methods have shown great promise in post-GWAS (genome-wide association study) analyses, in which one of the most challenging tasks is identifying putative causal genes and uncovering molecular mechanisms of complex traits. Recent studies suggest that prevailing computational approaches, including transcriptome-wide association studies (TWASs) and colocalization analysis, are individually imperfect, but their joint usage can yield robust and powerful inference results. This paper presents INTACT, a computational framework to integrate probabilistic evidence from these distinct types of analyses and implicate putative causal genes. This procedure is flexible and can work with a wide range of existing integrative analysis approaches. It has the unique ability to quantify the uncertainty of implicated genes, enabling rigorous control of false-positive discoveries. Taking advantage of this highly desirable feature, we further propose an efficient algorithm, INTACT-GSE, for gene set enrichment analysis based on the integrated probabilistic evidence. We examine the proposed computational methods and illustrate their improved performance over the existing approaches through simulation studies. We apply the proposed methods to analyze the multi-tissue eQTL data from the GTEx project and eight large-scale complex- and molecular-trait GWAS datasets from multiple consortia and the UK Biobank. Overall, we find that the proposed methods markedly improve the existing putative gene implication methods and are particularly advantageous in evaluating and identifying key gene sets and biological pathways underlying complex traits.

Genetic control of the dynamic transcriptional response to immune stimuli and glucocorticoids at single-cell resolution.

Synthetic glucocorticoids, such as dexamethasone, have been used as a treatment for many immune conditions, such as asthma and, more recently, severe COVID-19. Single-cell data can capture more fine-grained details on transcriptional variability and dynamics to gain a better understanding of the molecular underpinnings of inter-individual variation in drug response. Here, we used single-cell RNA-seq to study the dynamics of the transcriptional response to glucocorticoids in activated peripheral blood mononuclear cells from 96 African American children. We used novel statistical approaches to calculate a mean-independent measure of gene expression variability and a measure of transcriptional response pseudotime. Using these approaches, we showed that glucocorticoids reverse the effects of immune stimulation on both gene expression mean and variability. Our novel measure of gene expression response dynamics, based on the diagonal linear discriminant analysis, separated individual cells by response status on the basis of their transcriptional profiles and allowed us to identify different dynamic patterns of gene expression along the response pseudotime. We identified genetic variants regulating gene expression mean and variability, including treatment-specific effects, and showed widespread genetic regulation of the transcriptional dynamics of the gene expression response.

Analyzing and reconciling colocalization and transcriptome-wide association studies from the perspective of inferential reproducibility.

Transcriptome-wide association studies and colocalization analysis are popular computational approaches for integrating genetic-association data from molecular and complex traits. They show the unique ability to go beyond variant-level genetic-association evidence and implicate critical functional units, e.g., genes, in disease etiology. However, in practice, when the two approaches are applied to the same molecular and complex-trait data, the inference results can be markedly different. This paper systematically investigates the inferential reproducibility between the two approaches through theoretical derivation, numerical experiments, and analyses of four complex trait GWAS and GTEx eQTL data. We identify two classes of inconsistent inference results. We find that the first class of inconsistent results (i.e., genes with strong colocalization but weak transcriptome-wide association study [TWAS] signals) might suggest an interesting biological phenomenon, i.e., horizontal pleiotropy; thus, the two approaches are truly complementary. The inconsistency in the second class (i.e., genes with weak colocalization but strong TWAS signals) can be understood and effectively reconciled. To this end, we propose a computational approach for locus-level colocalization analysis. We demonstrate that the joint TWAS and locus-level colocalization analysis improves specificity and sensitivity for implicating biologically relevant genes.

Probabilistic colocalization of genetic variants from complex and molecular traits: promise and limitations.

Colocalization analysis has emerged as a powerful tool to uncover the overlapping of causal variants responsible for both molecular and complex disease phenotypes. The findings from colocalization analysis yield insights into the molecular pathways of complex diseases. In this paper, we conduct an in-depth investigation of the promise and limitations of the available colocalization analysis approaches. Focusing on variant-level colocalization approaches, we first establish the connections between various existing methods. We proceed to discuss the impacts of various controllable analytical factors and uncontrollable practical factors on outcomes of colocalization analysis through realistic simulations and real data examples. We identify a single analytical factor, the specification of prior enrichment levels, which can lead to severe inflation of false-positive colocalization findings. Meanwhile, the combination of many other analytical and practical factors all lead to diminished power. Consequently, we recommend the following strategies for the best practice of colocalization analysis: (1) estimating prior enrichment level from the observed data and (2) separating fine-mapping and colocalization analysis. Our analysis of 4,091 complex traits and the multi-tissue expression quantitative trait loci (eQTL) data from the GTEx (v.8) suggests that colocalizations of molecular QTLs and causal complex trait associations are widespread. However, only a small proportion can be confidently identified from currently available data due to a lack of power. Our findings set a benchmark for current and future integrative genetic association analysis applications.

Single-Cell Immunobiology of the Maternal-Fetal Interface.

Pregnancy success requires constant dialogue between the mother and developing conceptus. Such crosstalk is facilitated through complex interactions between maternal and fetal cells at distinct tissue sites, collectively termed the "maternal-fetal interface." The emergence of single-cell technologies has enabled a deeper understanding of the unique processes taking place at the maternal-fetal interface as well as the discovery of novel pathways and immune and nonimmune cell types. Single-cell approaches have also been applied to decipher the cellular dynamics throughout pregnancy, in parturition, and in obstetrical syndromes such as recurrent spontaneous abortion, preeclampsia, and preterm labor. Furthermore, single-cell technologies have been used during the recent COVID-19 pandemic to evaluate placental viral cell entry and the impact of SARS-CoV-2 infection on maternal and fetal immunity. In this brief review, we summarize the current knowledge of cellular immunobiology in pregnancy and its complications that has been generated through single-cell investigations of the maternal-fetal interface.

IL-22 Plays a Dual Role in the Amniotic Cavity: Tissue Injury and Host Defense against Microbes in Preterm Labor.

IL-22 is a multifaceted cytokine with both pro- and anti-inflammatory functions that is implicated in multiple pathologies. However, the role of IL-22 in maternal-fetal immunity in late gestation is poorly understood. In this study, we first showed that IL-22+ T cells coexpressing retinoic acid-related orphan receptor γt (ROR-γt) are enriched at the human maternal-fetal interface of women with preterm labor and birth, which was confirmed by in silico analysis of single-cell RNA sequencing data. T cell activation leading to preterm birth in mice was preceded by a surge in IL-22 in the maternal circulation and amniotic cavity; however, systemic administration of IL-22 in mice did not induce adverse perinatal outcomes. Next, using an ex vivo human system, we showed that IL-22 can cross from the choriodecidua to the intra-amniotic space, where its receptors (Il22ra1, Il10rb, and Il22ra2) are highly expressed by murine gestational and fetal tissues in late pregnancy. Importantly, amniotic fluid concentrations of IL-22 were elevated in women with sterile or microbial intra-amniotic inflammation, suggesting a dual role for this cytokine. The intra-amniotic administration of IL-22 alone shortened gestation and caused neonatal death in mice, with the latter outcome involving lung maturation and inflammation. IL-22 plays a role in host response by participating in the intra-amniotic inflammatory milieu preceding Ureaplasma parvum-induced preterm birth in mice, which was rescued by the deficiency of IL-22. Collectively, these data show that IL-22 alone is capable of causing fetal injury leading to neonatal death and can participate in host defense against microbial invasion of the amniotic cavity leading to preterm labor and birth.

A signature of Neanderthal introgression on molecular mechanisms of environmental responses.

Ancient human migrations led to the settlement of population groups in varied environmental contexts worldwide. The extent to which adaptation to local environments has shaped human genetic diversity is a longstanding question in human evolution. Recent studies have suggested that introgression of archaic alleles in the genome of modern humans may have contributed to adaptation to environmental pressures such as pathogen exposure. Functional genomic studies have demonstrated that variation in gene expression across individuals and in response to environmental perturbations is a main mechanism underlying complex trait variation. We considered gene expression response to in vitro treatments as a molecular phenotype to identify genes and regulatory variants that may have played an important role in adaptations to local environments. We investigated if Neanderthal introgression in the human genome may contribute to the transcriptional response to environmental perturbations. To this end we used eQTLs for genes differentially expressed in a panel of 52 cellular environments, resulting from 5 cell types and 26 treatments, including hormones, vitamins, drugs, and environmental contaminants. We found that SNPs with introgressed Neanderthal alleles (N-SNPs) disrupt binding of transcription factors important for environmental responses, including ionizing radiation and hypoxia, and for glucose metabolism. We identified an enrichment for N-SNPs among eQTLs for genes differentially expressed in response to 8 treatments, including glucocorticoids, caffeine, and vitamin D. Using Massively Parallel Reporter Assays (MPRA) data, we validated the regulatory function of 21 introgressed Neanderthal variants in the human genome, corresponding to 8 eQTLs regulating 15 genes that respond to environmental perturbations. These findings expand the set of environments where archaic introgression may have contributed to adaptations to local environments in modern humans and provide experimental validation for the regulatory function of introgressed variants.

A termination-independent role of Rat1 in cotranscriptional splicing.

Rat1 is a 5'→3' exoribonuclease in budding yeast. It is a highly conserved protein with homologs being present in fission yeast, flies, worms, mice and humans. Rat1 and its human homolog Xrn2 have been implicated in multiple nuclear processes. Here we report a novel role of Rat1 in mRNA splicing. We observed an increase in the level of unspliced transcripts in mutants of Rat1. Accumulation of unspliced transcripts was not due to the surveillance role of Rat1 in degrading unspliced mRNA, or an indirect effect of Rat1 function in termination of transcription or on the level of splicing factors in the cell, or due to an increased elongation rate in Rat1 mutants. ChIP-Seq analysis revealed Rat1 crosslinking to the introns of a subset of yeast genes. Mass spectrometry and coimmunoprecipitation revealed an interaction of Rat1 with the Clf1, Isy1, Yju2, Prp43 and Sub2 splicing factors. Furthermore, recruitment of splicing factors on the intron was compromised in the Rat1 mutant. Based on these findings we propose that Rat1 has a novel role in splicing of mRNA in budding yeast. Rat1, however, is not a general splicing factor as it crosslinks to only long introns with an average length of 400 nucleotides.

Human Plasma Proteome During Normal Pregnancy.

The human plasma proteome is underexplored despite its potential value for monitoring health and disease. Herein, using a recently developed aptamer-based platform, we profiled 7288 proteins in 528 plasma samples from 91 normal pregnancies (Gene Expression Omnibus identifier GSE206454). The coefficient of variation was <20% for 93% of analytes (median 7%), and a cross-platform correlation for selected key angiogenic and anti-angiogenic proteins was significant. Gestational age was associated with changes in 953 proteins, including highly modulated placenta- and decidua-specific proteins, and they were enriched in biological processes including regulation of growth, angiogenesis, immunity, and inflammation. The abundance of proteins corresponding to RNAs specific to populations of cells previously described by single-cell RNA-Seq analysis of the placenta was highly modulated throughout gestation. Furthermore, machine learning-based prediction of gestational age and of time from sampling to term delivery compared favorably with transcriptomic models (mean absolute error of 2 weeks). These results suggested that the plasma proteome may provide a non-invasive readout of placental cellular dynamics and serve as a blueprint for investigating obstetrical disease.

Transcriptome changes in maternal peripheral blood during term parturition mimic perturbations preceding spontaneous preterm birth†.

The complex physiologic process of parturition includes the onset of labor, which requires the orchestrated stimulation of a common pathway involving uterine contractility, cervical ripening, and chorioamniotic membrane activation. However, the labor-specific processes taking place in these tissues have limited use as predictive biomarkers unless they can be probed in non-invasive samples, such as the peripheral blood. Herein, we utilized a transcriptomic dataset to assess labor-specific changes in the peripheral blood of women who delivered at term. We identified a set of genes that were differentially expressed with labor and enriched for immunological processes, and these gene expression changes were strongly correlated with results from prior studies, providing in silico validation of our findings. We then identified significant correlations between labor-specific transcriptomic changes in the maternal circulation and those detected in the chorioamniotic membranes, myometrium, and cervix of women at term, demonstrating that tissue-specific labor signatures are partly mirrored in the peripheral blood. Finally, we demonstrated a significant overlap between the peripheral blood transcriptomic changes in term parturition and those observed in asymptomatic women, prior to the diagnosis of preterm prelabor rupture of the membranes, who ultimately delivered preterm. Collectively, we provide evidence that the normal process of labor at term is characterized by a unique immunological expression signature, which may serve as a useful tool for assessing labor status and for potentially identifying women at risk for preterm birth.

Social status alters chromatin accessibility and the gene regulatory response to glucocorticoid stimulation in rhesus macaques.

Low social status is an important predictor of disease susceptibility and mortality risk in humans and other social mammals. These effects are thought to stem in part from dysregulation of the glucocorticoid (GC)-mediated stress response. However, the molecular mechanisms that connect low social status and GC dysregulation to downstream health outcomes remain elusive. Here, we used an in vitro GC challenge to investigate the consequences of experimentally manipulated social status (i.e., dominance rank) for immune cell gene regulation in female rhesus macaques, using paired control and GC-treated peripheral blood mononuclear cell samples. We show that social status not only influences immune cell gene expression but also chromatin accessibility at hundreds of regions in the genome. Social status effects on gene expression were less pronounced following GC treatment than under control conditions. In contrast, social status effects on chromatin accessibility were stable across conditions, resulting in an attenuated relationship between social status, chromatin accessibility, and gene expression after GC exposure. Regions that were more accessible in high-status animals and regions that become more accessible following GC treatment were enriched for a highly concordant set of transcription factor binding motifs, including motifs for the GC receptor cofactor AP-1. Together, our findings support the hypothesis that social status alters the dynamics of GC-mediated gene regulation and identify chromatin accessibility as a mechanism involved in social stress-driven GC resistance. More broadly, they emphasize the context-dependent nature of social status effects on gene regulation and implicate epigenetic remodeling of chromatin accessibility as a contributing factor.

Proteoglycans: Systems-Level Insight into Their Expression in Healthy and Diseased Placentas.

Proteoglycan macromolecules play key roles in several physiological processes (e.g., adhesion, proliferation, migration, invasion, angiogenesis, and apoptosis), all of which are important for placentation and healthy pregnancy. However, their precise roles in human reproduction have not been clarified. To fill this gap, herein, we provide an overview of the proteoglycans' expression and role in the placenta, in trophoblast development, and in pregnancy complications (pre-eclampsia, fetal growth restriction), highlighting one of the most important members of this family, syndecan-1 (SDC1). Microarray data analysis showed that of 34 placentally expressed proteoglycans, SDC1 production is markedly the highest in the placenta and that SDC1 is the most upregulated gene during trophoblast differentiation into the syncytiotrophoblast. Furthermore, placental transcriptomic data identified dysregulated proteoglycan genes in pre-eclampsia and in fetal growth restriction, including SDC1, which is supported by the lower concentration of syndecan-1 in maternal blood in these syndromes. Overall, our clinical and in vitro studies, data analyses, and literature search pointed out that proteoglycans, as important components of the placenta, may regulate various stages of placental development and participate in the maintenance of a healthy pregnancy. Moreover, syndecan-1 may serve as a useful marker of syncytialization and a prognostic marker of adverse pregnancy outcomes. Further studies are warranted to explore the role of proteoglycans in healthy and complicated pregnancies, which may help in diagnostic or therapeutic developments.

High-throughput characterization of genetic effects on DNA-protein binding and gene transcription.

Many variants associated with complex traits are in noncoding regions and contribute to phenotypes by disrupting regulatory sequences. To characterize these variants, we developed a streamlined protocol for a high-throughput reporter assay, Biallelic Targeted STARR-seq (BiT-STARR-seq), that identifies allele-specific expression (ASE) while accounting for PCR duplicates through unique molecular identifiers. We tested 75,501 oligos (43,500 SNPs) and identified 2720 SNPs with significant ASE (FDR < 10%). To validate disruption of binding as one of the mechanisms underlying ASE, we developed a new high-throughput allele-specific binding assay for NFKB1. We identified 2684 SNPs with allele-specific binding (ASB) (FDR < 10%); 256 of these SNPs also had ASE (OR = 1.97, P-value = 0.0006). Of variants associated with complex traits, 1531 resulted in ASE, and 1662 showed ASB. For example, we characterized that the Crohn's disease risk variant for rs3810936 increases NFKB1 binding and results in altered gene expression.

BAGSE: a Bayesian hierarchical model approach for gene set enrichment analysis.

MOTIVATION: Gene set enrichment analysis has been shown to be effective in identifying relevant biological pathways underlying complex diseases. Existing approaches lack the ability to quantify the enrichment levels accurately, hence preventing the enrichment information to be further utilized in both upstream and downstream analyses. A modernized and rigorous approach for gene set enrichment analysis that emphasizes both hypothesis testing and enrichment estimation is much needed. RESULTS: We propose a novel computational method, Bayesian Analysis of Gene Set Enrichment (BAGSE), for gene set enrichment analysis. BAGSE is built on a Bayesian hierarchical model and fully accounts for the uncertainty embedded in the association evidence of individual genes. We adopt an empirical Bayes inference framework to fit the proposed hierarchical model by implementing an efficient EM algorithm. Through simulation studies, we illustrate that BAGSE yields accurate enrichment quantification while achieving similar power as the state-of-the-art methods. Further simulation studies show that BAGSE can effectively utilize the enrichment information to improve the power in gene discovery. Finally, we demonstrate the application of BAGSE in analyzing real data from a differential expression experiment and a transcriptome-wide association study. Our results indicate that the proposed statistical framework is effective in aiding the discovery of potentially causal pathways and gene networks. AVAILABILITY AND IMPLEMENTATION: BAGSE is implemented using the C++ programing language and is freely available from https://github.com/xqwen/bagse/. Simulated and real data used in this paper are also available at the Github repository for reproducibility purposes. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Genome-wide mapping of regions preferentially targeted by the human DNA-cytosine deaminase APOBEC3A using uracil-DNA pulldown and sequencing.

Activation-induced deaminase (AID) and apolipoprotein B mRNA-editing enzyme catalytic subunit (APOBEC) enzymes convert cytosines to uracils, creating signature mutations that have been used to predict sites targeted by these enzymes. Mutation-based targeting maps are distorted by the error-prone or error-free repair of these uracils and by selection pressures. To directly map uracils created by AID/APOBEC enzymes, here we used uracil-DNA glycosylase and an alkoxyamine to covalently tag and sequence uracil-containing DNA fragments (UPD-Seq). We applied this technique to the genome of repair-defective, APOBEC3A-expressing bacterial cells and created a uracilation genome map, i.e. uracilome. The peak uracilated regions were in the 5'-ends of genes and operons mainly containing tRNA genes and a few protein-coding genes. We validated these findings through deep sequencing of pulldown regions and whole-genome sequencing of independent clones. The peaks were not correlated with high transcription rates or stable RNA:DNA hybrid formation. We defined the uracilation index (UI) as the frequency of occurrence of TT in UPD-Seq reads at different original TC dinucleotides. Genome-wide UI calculation confirmed that APOBEC3A modifies cytosines in the lagging-strand template during replication and in short hairpin loops. APOBEC3A's preference for tRNA genes was observed previously in yeast, and an analysis of human tumor sequences revealed that in tumors with a high percentage of APOBEC3 signature mutations, the frequency of tRNA gene mutations was much higher than in the rest of the genome. These results identify multiple causes underlying selection of cytosines by APOBEC3A for deamination, and demonstrate the utility of UPD-Seq.

Causal relationships between NAFLD, T2D and obesity have implications for disease subphenotyping.

BACKGROUND & AIMS: Non-alcoholic fatty liver disease (NAFLD), type 2 diabetes (T2D) and obesity are epidemiologically correlated with each other but the causal inter-relationships between them remain incompletely understood. We aimed to explore the causal relationships between the 3 diseases. METHODS: Using both UK Biobank and publicly available genome-wide association study data, we performed a 2-sample bidirectional Mendelian randomization analysis to test the causal inter-relationships between NAFLD, T2D, and obesity. Transgenic mice expressing the human PNPLA3-I148M isoforms (TghPNPLA3-I148M) were used as an example to validate causal effects and explore underlying mechanisms. RESULTS: Genetically driven NAFLD significantly increased the risk of T2D and central obesity but not insulin resistance or generalized obesity, while genetically driven T2D, body mass index and WHRadjBMI causally increased NAFLD risk. The animal study focusing on PNPLA3 corroborated these causal effects: compared to the TghPNPLA3-I148I controls, the TghPNPLA3-I148M mice developed glucose intolerance and increased visceral fat, but maintained normal insulin sensitivity, reduced body weight, and decreased circulating total cholesterol. Mechanistically, the TghPNPLA3-I148M mice demonstrated decreased pancreatic insulin but increased glucagon secretion, which was associated with increased pancreatic inflammation. In addition, transcription of hepatic cholesterol biosynthesis pathway genes was significantly suppressed, while transcription of thermogenic pathway genes was activated in subcutaneous and brown adipose tissues but not in visceral fat in TghPNPLA3-I148M mice. CONCLUSIONS: Our study suggests that lifelong, genetically driven NAFLD causally promotes T2D with a late-onset type 1-like diabetic subphenotype and central obesity; while genetically driven T2D, obesity, and central obesity all causally increase the risk of NAFLD. This causal relationship revealed new insights into how nature and nurture drive these diseases, providing novel hypotheses for disease subphenotyping. LAY SUMMARY: Non-alcoholic fatty liver disease, type 2 diabetes and obesity are epidemiologically correlated with each other, but their causal relationships were incompletely understood. Herein, we identified causal relationships between these conditions, which suggest that each of these closely related diseases should be further stratified into subtypes. This is important for accurate diagnosis, prevention and treatment of these diseases.

Integrating molecular QTL data into genome-wide genetic association analysis: Probabilistic assessment of enrichment and colocalization.

We propose a novel statistical framework for integrating the result from molecular quantitative trait loci (QTL) mapping into genome-wide genetic association analysis of complex traits, with the primary objectives of quantitatively assessing the enrichment of the molecular QTLs in complex trait-associated genetic variants and the colocalizations of the two types of association signals. We introduce a natural Bayesian hierarchical model that treats the latent association status of molecular QTLs as SNP-level annotations for candidate SNPs of complex traits. We detail a computational procedure to seamlessly perform enrichment, fine-mapping and colocalization analyses, which is a distinct feature compared to the existing colocalization analysis procedures in the literature. The proposed approach is computationally efficient and requires only summary-level statistics. We evaluate and demonstrate the proposed computational approach through extensive simulation studies and analyses of blood lipid data and the whole blood eQTL data from the GTEx project. In addition, a useful utility from our proposed method enables the computation of expected colocalization signals using simple characteristics of the association data. Using this utility, we further illustrate the importance of enrichment analysis on the ability to discover colocalized signals and the potential limitations of currently available molecular QTL data. The software pipeline that implements the proposed computation procedures, enloc, is freely available at https://github.com/xqwen/integrative.