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1.
Cell ; 184(11): 3006-3021.e17, 2021 05 27.
Article in English | MEDLINE | ID: mdl-33930287

ABSTRACT

Genetic studies have revealed many variant loci that are associated with immune-mediated diseases. To elucidate the disease pathogenesis, it is essential to understand the function of these variants, especially under disease-associated conditions. Here, we performed a large-scale immune cell gene-expression analysis, together with whole-genome sequence analysis. Our dataset consists of 28 distinct immune cell subsets from 337 patients diagnosed with 10 categories of immune-mediated diseases and 79 healthy volunteers. Our dataset captured distinctive gene-expression profiles across immune cell types and diseases. Expression quantitative trait loci (eQTL) analysis revealed dynamic variations of eQTL effects in the context of immunological conditions, as well as cell types. These cell-type-specific and context-dependent eQTLs showed significant enrichment in immune disease-associated genetic variants, and they implicated the disease-relevant cell types, genes, and environment. This atlas deepens our understanding of the immunogenetic functions of disease-associated variants under in vivo disease conditions.


Subject(s)
Gene Expression Regulation/genetics , Gene Expression/immunology , Immune System Diseases/genetics , Adult , Female , Gene Expression/genetics , Gene Expression Regulation/immunology , Genetic Predisposition to Disease/genetics , Genome-Wide Association Study/methods , Humans , Immune System/cytology , Immune System/metabolism , Immune System Diseases/metabolism , Immune System Diseases/physiopathology , Male , Middle Aged , Polymorphism, Single Nucleotide/genetics , Quantitative Trait Loci/genetics , Quantitative Trait Loci/immunology , Transcriptome/genetics , Whole Genome Sequencing/methods
2.
Cell ; 184(10): 2633-2648.e19, 2021 05 13.
Article in English | MEDLINE | ID: mdl-33864768

ABSTRACT

Long non-coding RNA (lncRNA) genes have well-established and important impacts on molecular and cellular functions. However, among the thousands of lncRNA genes, it is still a major challenge to identify the subset with disease or trait relevance. To systematically characterize these lncRNA genes, we used Genotype Tissue Expression (GTEx) project v8 genetic and multi-tissue transcriptomic data to profile the expression, genetic regulation, cellular contexts, and trait associations of 14,100 lncRNA genes across 49 tissues for 101 distinct complex genetic traits. Using these approaches, we identified 1,432 lncRNA gene-trait associations, 800 of which were not explained by stronger effects of neighboring protein-coding genes. This included associations between lncRNA quantitative trait loci and inflammatory bowel disease, type 1 and type 2 diabetes, and coronary artery disease, as well as rare variant associations to body mass index.


Subject(s)
Disease/genetics , Multifactorial Inheritance/genetics , Population/genetics , RNA, Long Noncoding/genetics , Transcriptome , Coronary Artery Disease/genetics , Diabetes Mellitus, Type 1/genetics , Diabetes Mellitus, Type 2/genetics , Gene Expression Profiling , Genetic Variation , Humans , Inflammatory Bowel Diseases/genetics , Organ Specificity/genetics , Quantitative Trait Loci
3.
Cell ; 179(3): 750-771.e22, 2019 10 17.
Article in English | MEDLINE | ID: mdl-31626773

ABSTRACT

Tissue-specific regulatory regions harbor substantial genetic risk for disease. Because brain development is a critical epoch for neuropsychiatric disease susceptibility, we characterized the genetic control of the transcriptome in 201 mid-gestational human brains, identifying 7,962 expression quantitative trait loci (eQTL) and 4,635 spliceQTL (sQTL), including several thousand prenatal-specific regulatory regions. We show that significant genetic liability for neuropsychiatric disease lies within prenatal eQTL and sQTL. Integration of eQTL and sQTL with genome-wide association studies (GWAS) via transcriptome-wide association identified dozens of novel candidate risk genes, highlighting shared and stage-specific mechanisms in schizophrenia (SCZ). Gene network analysis revealed that SCZ and autism spectrum disorder (ASD) affect distinct developmental gene co-expression modules. Yet, in each disorder, common and rare genetic variation converges within modules, which in ASD implicates superficial cortical neurons. More broadly, these data, available as a web browser and our analyses, demonstrate the genetic mechanisms by which developmental events have a widespread influence on adult anatomical and behavioral phenotypes.


Subject(s)
Autism Spectrum Disorder/genetics , Quantitative Trait Loci/genetics , Schizophrenia/genetics , Transcriptome/genetics , Autism Spectrum Disorder/metabolism , Autism Spectrum Disorder/pathology , Brain/growth & development , Brain/metabolism , Female , Fetus/metabolism , Gene Expression Regulation, Developmental , Genetic Predisposition to Disease , Genome-Wide Association Study , Gestational Age , Humans , Male , Neurons/metabolism , Polymorphism, Single Nucleotide/genetics , RNA Splicing/genetics , Schizophrenia/metabolism , Schizophrenia/pathology
4.
Cell ; 176(1-2): 377-390.e19, 2019 01 10.
Article in English | MEDLINE | ID: mdl-30612741

ABSTRACT

Over one million candidate regulatory elements have been identified across the human genome, but nearly all are unvalidated and their target genes uncertain. Approaches based on human genetics are limited in scope to common variants and in resolution by linkage disequilibrium. We present a multiplex, expression quantitative trait locus (eQTL)-inspired framework for mapping enhancer-gene pairs by introducing random combinations of CRISPR/Cas9-mediated perturbations to each of many cells, followed by single-cell RNA sequencing (RNA-seq). Across two experiments, we used dCas9-KRAB to perturb 5,920 candidate enhancers with no strong a priori hypothesis as to their target gene(s), measuring effects by profiling 254,974 single-cell transcriptomes. We identified 664 (470 high-confidence) cis enhancer-gene pairs, which were enriched for specific transcription factors, non-housekeeping status, and genomic and 3D conformational proximity to their target genes. This framework will facilitate the large-scale mapping of enhancer-gene regulatory interactions, a critical yet largely uncharted component of the cis-regulatory landscape of the human genome.


Subject(s)
Chromosome Mapping/methods , Enhancer Elements, Genetic/genetics , Gene Expression Regulation/genetics , CRISPR-Cas Systems/genetics , Clustered Regularly Interspaced Short Palindromic Repeats/genetics , Gene Expression Profiling , Gene Regulatory Networks/genetics , Genome, Human , Genome-Wide Association Study , Genomics , Humans , Quantitative Trait Loci , Transcription Factors/genetics
5.
Cell ; 174(3): 576-589.e18, 2018 07 26.
Article in English | MEDLINE | ID: mdl-30033361

ABSTRACT

Genome-wide association studies (GWAS) have identified rs11672691 at 19q13 associated with aggressive prostate cancer (PCa). Here, we independently confirmed the finding in a cohort of 2,738 PCa patients and discovered the biological mechanism underlying this association. We found an association of the aggressive PCa-associated allele G of rs11672691 with elevated transcript levels of two biologically plausible candidate genes, PCAT19 and CEACAM21, implicated in PCa cell growth and tumor progression. Mechanistically, rs11672691 resides in an enhancer element and alters the binding site of HOXA2, a novel oncogenic transcription factor with prognostic potential in PCa. Remarkably, CRISPR/Cas9-mediated single-nucleotide editing showed the direct effect of rs11672691 on PCAT19 and CEACAM21 expression and PCa cellular aggressive phenotype. Clinical data demonstrated synergistic effects of rs11672691 genotype and PCAT19/CEACAM21 gene expression on PCa prognosis. These results provide a plausible mechanism for rs11672691 associated with aggressive PCa and thus lay the ground work for translating this finding to the clinic.


Subject(s)
Prostatic Neoplasms/genetics , RNA, Long Noncoding/genetics , RNA, Untranslated/genetics , Adult , Alleles , Cell Line, Tumor , Chromosomes, Human, Pair 19/genetics , Cohort Studies , Gene Expression Regulation, Neoplastic/genetics , Gene Frequency/genetics , Genetic Predisposition to Disease/genetics , Genome-Wide Association Study , Genotype , Homeodomain Proteins , Humans , Male , Middle Aged , Polymorphism, Single Nucleotide/genetics , Prognosis
6.
Cell ; 167(3): 643-656.e17, 2016 Oct 20.
Article in English | MEDLINE | ID: mdl-27768888

ABSTRACT

Humans differ in the outcome that follows exposure to life-threatening pathogens, yet the extent of population differences in immune responses and their genetic and evolutionary determinants remain undefined. Here, we characterized, using RNA sequencing, the transcriptional response of primary monocytes from Africans and Europeans to bacterial and viral stimuli-ligands activating Toll-like receptor pathways (TLR1/2, TLR4, and TLR7/8) and influenza virus-and mapped expression quantitative trait loci (eQTLs). We identify numerous cis-eQTLs that contribute to the marked differences in immune responses detected within and between populations and a strong trans-eQTL hotspot at TLR1 that decreases expression of pro-inflammatory genes in Europeans only. We find that immune-responsive regulatory variants are enriched in population-specific signals of natural selection and show that admixture with Neandertals introduced regulatory variants into European genomes, affecting preferentially responses to viral challenges. Together, our study uncovers evolutionarily important determinants of differences in host immune responsiveness between human populations.


Subject(s)
Adaptation, Physiological/genetics , Adaptation, Physiological/immunology , Adaptive Immunity , Neanderthals/genetics , Neanderthals/immunology , Adaptive Immunity/genetics , Alleles , Animals , Bacterial Infections/genetics , Bacterial Infections/immunology , Base Sequence , Biological Evolution , Black People/genetics , Gene Expression Regulation , Genetic Variation , Humans , Immune System , Quantitative Trait Loci , RNA/genetics , Selection, Genetic , Sequence Analysis, RNA , Toll-Like Receptors/genetics , Transcription, Genetic , Virus Diseases/genetics , Virus Diseases/immunology , White People/genetics
7.
Cell ; 167(3): 657-669.e21, 2016 Oct 20.
Article in English | MEDLINE | ID: mdl-27768889

ABSTRACT

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.

8.
Annu Rev Genet ; 56: 41-62, 2022 11 30.
Article in English | MEDLINE | ID: mdl-35697043

ABSTRACT

Since the identification of sickle cell trait as a heritable form of resistance to malaria, candidate gene studies, linkage analysis paired with sequencing, and genome-wide association (GWA) studies have revealed many examples of genetic resistance and susceptibility to infectious diseases. GWA studies enabled the identification of many common variants associated with small shifts in susceptibility to infectious diseases. This is exemplified by multiple loci associated with leprosy, malaria, HIV, tuberculosis, and coronavirus disease 2019 (COVID-19), which illuminate genetic architecture and implicate pathways underlying pathophysiology. Despite these successes, most of the heritability of infectious diseases remains to be explained. As the field advances, current limitations may be overcome by applying methodological innovations such as cellular GWA studies and phenome-wide association (PheWA) studies as well as by improving methodological rigor with more precise case definitions, deeper phenotyping, increased cohort diversity, and functional validation of candidate loci in the laboratory or human challenge studies.


Subject(s)
COVID-19 , Communicable Diseases , Humans , Genome-Wide Association Study , COVID-19/genetics , Communicable Diseases/genetics , Human Genetics
9.
Annu Rev Genet ; 54: 189-211, 2020 11 23.
Article in English | MEDLINE | ID: mdl-32867542

ABSTRACT

Canalization refers to the evolution of populations such that the number of individuals who deviate from the optimum trait, or experience disease, is minimized. In the presence of rapid cultural, environmental, or genetic change, the reverse process of decanalization may contribute to observed increases in disease prevalence. This review starts by defining relevant concepts, drawing distinctions between the canalization of populations and robustness of individuals. It then considers evidence pertaining to three continuous traits and six domains of disease. In each case, existing genetic evidence for genotype-by-environment interactions is insufficient to support a strong inference of decanalization, but we argue that the advent of genome-wide polygenic risk assessment now makes an empirical evaluation of the role of canalization in preventing disease possible. Finally, the contributions of both rare and common variants to congenital abnormality and adult onset disease are considered in light of a new kerplunk model of genetic effects.


Subject(s)
Genetic Diseases, Inborn/genetics , Genome/genetics , Human Genetics/methods , Genetic Variation/genetics , Genotype , Humans , Phylogeny
10.
Am J Hum Genet ; 111(6): 1084-1099, 2024 06 06.
Article in English | MEDLINE | ID: mdl-38723630

ABSTRACT

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


Subject(s)
Breast Neoplasms , Genetic Predisposition to Disease , Genome-Wide Association Study , Ovarian Neoplasms , Polymorphism, Single Nucleotide , Quantitative Trait Loci , Humans , Female , Ovarian Neoplasms/genetics , Ovarian Neoplasms/pathology , Breast Neoplasms/genetics , Breast Neoplasms/pathology , Bayes Theorem , Transcriptome , Gene Expression Regulation, Neoplastic
11.
Am J Hum Genet ; 111(2): 323-337, 2024 02 01.
Article in English | MEDLINE | ID: mdl-38306997

ABSTRACT

Genome-wide association studies (GWASs) have uncovered susceptibility loci associated with psychiatric disorders such as bipolar disorder (BP) and schizophrenia (SCZ). However, most of these loci are in non-coding regions of the genome, and the causal mechanisms of the link between genetic variation and disease risk is unknown. Expression quantitative trait locus (eQTL) analysis of bulk tissue is a common approach used for deciphering underlying mechanisms, although this can obscure cell-type-specific signals and thus mask trait-relevant mechanisms. Although single-cell sequencing can be prohibitively expensive in large cohorts, computationally inferred cell-type proportions and cell-type gene expression estimates have the potential to overcome these problems and advance mechanistic studies. Using bulk RNA-seq from 1,730 samples derived from whole blood in a cohort ascertained from individuals with BP and SCZ, this study estimated cell-type proportions and their relation with disease status and medication. For each cell type, we found between 2,875 and 4,629 eGenes (genes with an associated eQTL), including 1,211 that are not found on the basis of bulk expression alone. We performed a colocalization test between cell-type eQTLs and various traits and identified hundreds of associations that occur between cell-type eQTLs and GWASs but that are not detected in bulk eQTLs. Finally, we investigated the effects of lithium use on the regulation of cell-type expression loci and found examples of genes that are differentially regulated according to lithium use. Our study suggests that applying computational methods to large bulk RNA-seq datasets of non-brain tissue can identify disease-relevant, cell-type-specific biology of psychiatric disorders and psychiatric medication.


Subject(s)
Genome-Wide Association Study , Lithium , Humans , Genome-Wide Association Study/methods , RNA-Seq , Quantitative Trait Loci/genetics , Phenotype , Polymorphism, Single Nucleotide , Genetic Predisposition to Disease
12.
Am J Hum Genet ; 111(3): 445-455, 2024 03 07.
Article in English | MEDLINE | ID: mdl-38320554

ABSTRACT

Regulation of transcription and translation are mechanisms through which genetic variants affect complex traits. Expression quantitative trait locus (eQTL) studies have been more successful at identifying cis-eQTL (within 1 Mb of the transcription start site) than trans-eQTL. Here, we tested the cis component of gene expression for association with observed plasma protein levels to identify cis- and trans-acting genes that regulate protein levels. We used transcriptome prediction models from 49 Genotype-Tissue Expression (GTEx) Project tissues to predict the cis component of gene expression and tested the predicted expression of every gene in every tissue for association with the observed abundance of 3,622 plasma proteins measured in 3,301 individuals from the INTERVAL study. We tested significant results for replication in 971 individuals from the Trans-omics for Precision Medicine (TOPMed) Multi-Ethnic Study of Atherosclerosis (MESA). We found 1,168 and 1,210 cis- and trans-acting associations that replicated in TOPMed (FDR < 0.05) with a median expected true positive rate (π1) across tissues of 0.806 and 0.390, respectively. The target proteins of trans-acting genes were enriched for transcription factor binding sites and autoimmune diseases in the GWAS catalog. Furthermore, we found a higher correlation between predicted expression and protein levels of the same underlying gene (R = 0.17) than observed expression (R = 0.10, p = 7.50 × 10-11). This indicates the cis-acting genetically regulated (heritable) component of gene expression is more consistent across tissues than total observed expression (genetics + environment) and is useful in uncovering the function of SNPs associated with complex traits.


Subject(s)
Proteome , Transcriptome , Humans , Transcriptome/genetics , Proteome/genetics , Multifactorial Inheritance , Quantitative Trait Loci/genetics , Genome-Wide Association Study , Polymorphism, Single Nucleotide/genetics
13.
Am J Hum Genet ; 111(9): 1899-1913, 2024 Sep 05.
Article in English | MEDLINE | ID: mdl-39173627

ABSTRACT

Understanding the molecular mechanisms of complex traits is essential for developing targeted interventions. We analyzed liver expression quantitative-trait locus (eQTL) meta-analysis data on 1,183 participants to identify conditionally distinct signals. We found 9,013 eQTL signals for 6,564 genes; 23% of eGenes had two signals, and 6% had three or more signals. We then integrated the eQTL results with data from 29 cardiometabolic genome-wide association study (GWAS) traits and identified 1,582 GWAS-eQTL colocalizations for 747 eGenes. Non-primary eQTL signals accounted for 17% of all colocalizations. Isolating signals by conditional analysis prior to coloc resulted in 37% more colocalizations than using marginal eQTL and GWAS data, highlighting the importance of signal isolation. Isolating signals also led to stronger evidence of colocalization: among 343 eQTL-GWAS signal pairs in multi-signal regions, analyses that isolated the signals of interest resulted in higher posterior probability of colocalization for 41% of tests. Leveraging allelic heterogeneity, we predicted causal effects of gene expression on liver traits for four genes. To predict functional variants and regulatory elements, we colocalized eQTL with liver chromatin accessibility QTL (caQTL) and found 391 colocalizations, including 73 with non-primary eQTL signals and 60 eQTL signals that colocalized with both a caQTL and a GWAS signal. Finally, we used publicly available massively parallel reporter assays in HepG2 to highlight 14 eQTL signals that include at least one expression-modulating variant. This multi-faceted approach to unraveling the genetic underpinnings of liver-related traits could lead to therapeutic development.


Subject(s)
Genome-Wide Association Study , Liver , Quantitative Trait Loci , Humans , Alleles , Cardiovascular Diseases/genetics , Genetic Predisposition to Disease , Liver/metabolism , Phenotype , Polymorphism, Single Nucleotide
14.
Am J Hum Genet ; 111(7): 1448-1461, 2024 07 11.
Article in English | MEDLINE | ID: mdl-38821058

ABSTRACT

Both trio and population designs are popular study designs for identifying risk genetic variants in genome-wide association studies (GWASs). The trio design, as a family-based design, is robust to confounding due to population structure, whereas the population design is often more powerful due to larger sample sizes. Here, we propose KnockoffHybrid, a knockoff-based statistical method for hybrid analysis of both the trio and population designs. KnockoffHybrid provides a unified framework that brings together the advantages of both designs and produces powerful hybrid analysis while controlling the false discovery rate (FDR) in the presence of linkage disequilibrium and population structure. Furthermore, KnockoffHybrid has the flexibility to leverage different types of summary statistics for hybrid analyses, including expression quantitative trait loci (eQTL) and GWAS summary statistics. We demonstrate in simulations that KnockoffHybrid offers power gains over non-hybrid methods for the trio and population designs with the same number of cases while controlling the FDR with complex correlation among variants and population structure among subjects. In hybrid analyses of three trio cohorts for autism spectrum disorders (ASDs) from the Autism Speaks MSSNG, Autism Sequencing Consortium, and Autism Genome Project with GWAS summary statistics from the iPSYCH project and eQTL summary statistics from the MetaBrain project, KnockoffHybrid outperforms conventional methods by replicating several known risk genes for ASDs and identifying additional associations with variants in other genes, including the PRAME family genes involved in axon guidance and which may act as common targets for human speech/language evolution and related disorders.


Subject(s)
Autism Spectrum Disorder , Genome-Wide Association Study , Linkage Disequilibrium , Quantitative Trait Loci , Genome-Wide Association Study/methods , Humans , Autism Spectrum Disorder/genetics , Genetic Predisposition to Disease , Polymorphism, Single Nucleotide , Computer Simulation , Models, Genetic
15.
Hum Mol Genet ; 33(7): 624-635, 2024 Mar 20.
Article in English | MEDLINE | ID: mdl-38129112

ABSTRACT

Transcriptome-wide association studies (TWAS) integrate gene expression prediction models and genome-wide association studies (GWAS) to identify gene-trait associations. The power of TWAS is determined by the sample size of GWAS and the accuracy of the expression prediction model. Here, we present a new method, the Summary-level Unified Method for Modeling Integrated Transcriptome using Functional Annotations (SUMMIT-FA), which improves gene expression prediction accuracy by leveraging functional annotation resources and a large expression quantitative trait loci (eQTL) summary-level dataset. We build gene expression prediction models in whole blood using SUMMIT-FA with the comprehensive functional database MACIE and eQTL summary-level data from the eQTLGen consortium. We apply these models to GWAS for 24 complex traits and show that SUMMIT-FA identifies significantly more gene-trait associations and improves predictive power for identifying "silver standard" genes compared to several benchmark methods. We further conduct a simulation study to demonstrate the effectiveness of SUMMIT-FA.


Subject(s)
Genome-Wide Association Study , Transcriptome , Humans , Transcriptome/genetics , Genome-Wide Association Study/methods , Computer Simulation , Quantitative Trait Loci/genetics , Phenotype , Polymorphism, Single Nucleotide , Genetic Predisposition to Disease
16.
Hum Mol Genet ; 2024 Sep 06.
Article in English | MEDLINE | ID: mdl-39239979

ABSTRACT

Common variants in the MicroRNA 137 host gene MIR137HG and its adjacent gene DPYD have been associated with schizophrenia risk and the latest Psychiatric Genomics Consortium (PGC). Genome-Wide Association Study on schizophrenia has confirmed and extended these findings. To elucidate the association of schizophrenia risk-associated SNPs in this genomic region, we examined the expression of both mature and immature transcripts of the miR-137 host gene (MIR137HG) in the dorsolateral prefrontal cortex (DLPFC) and subgenual anterior cingulate cortex (sgACC) of postmortem brain samples of donors with schizophrenia and psychiatrically-unaffected controls using qPCR and RNA-Seq approaches. No differential expression of miR-137, MIR137HG, or its transcripts was observed. Two schizophrenia risk-associated SNPs identified in the PGC study, rs11165917 (DLPFC: P = 2.0e-16; sgACC: P = 6.4e-10) and rs4274102 (DLPFC: P = 0.036; sgACC: P = 0.002), were associated with expression of the MIR137HG long non-coding RNA transcript MIR137HG-203 (ENST00000602672.2) in individuals of European ancestry. Carriers of the minor (risk) allele of rs11165917 had significantly lower expression of MIR137HG-203 compared with those carrying the major allele. However, we were unable to validate this result by short-read sequencing of RNA extracted from DLPFC or sgACC tissue. This finding suggests that immature transcripts of MIR137HG may contribute to genetic risk for schizophrenia.

17.
Annu Rev Genomics Hum Genet ; 24: 277-303, 2023 08 25.
Article in English | MEDLINE | ID: mdl-37196361

ABSTRACT

Recent advancements in single-cell technologies have enabled expression quantitative trait locus (eQTL) analysis across many individuals at single-cell resolution. Compared with bulk RNA sequencing, which averages gene expression across cell types and cell states, single-cell assays capture the transcriptional states of individual cells, including fine-grained, transient, and difficult-to-isolate populations at unprecedented scale and resolution. Single-cell eQTL (sc-eQTL) mapping can identify context-dependent eQTLs that vary with cell states, including some that colocalize with disease variants identified in genome-wide association studies. By uncovering the precise contexts in which these eQTLs act, single-cell approaches can unveil previously hidden regulatory effects and pinpoint important cell states underlying molecular mechanisms of disease. Here, we present an overview of recently deployed experimental designs in sc-eQTL studies. In the process, we consider the influence of study design choices such as cohort, cell states, and ex vivo perturbations. We then discuss current methodologies, modeling approaches, and technical challenges as well as future opportunities and applications.


Subject(s)
Genome-Wide Association Study , Quantitative Trait Loci , Humans , Genome-Wide Association Study/methods , Chromosome Mapping , Research Design
18.
Am J Hum Genet ; 110(1): 58-70, 2023 01 05.
Article in English | MEDLINE | ID: mdl-36608685

ABSTRACT

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


Subject(s)
Black or African American , Quantitative Trait Loci , Humans , Quantitative Trait Loci/genetics , Black or African American/genetics , Gene Expression Regulation , Liver , Gene Expression , Polymorphism, Single Nucleotide/genetics , Genome-Wide Association Study
19.
Am J Hum Genet ; 110(10): 1718-1734, 2023 10 05.
Article in English | MEDLINE | ID: mdl-37683633

ABSTRACT

Genome-wide association studies of blood pressure (BP) have identified >1,000 loci, but the effector genes and biological pathways at these loci are mostly unknown. Using published association summary statistics, we conducted annotation-informed fine-mapping incorporating tissue-specific chromatin segmentation and colocalization to identify causal variants and candidate effector genes for systolic BP, diastolic BP, and pulse pressure. We observed 532 distinct signals associated with ≥2 BP traits and 84 with all three. For >20% of signals, a single variant accounted for >75% posterior probability, 65 were missense variants in known (SLC39A8, ADRB2, and DBH) and previously unreported BP candidate genes (NRIP1 and MMP14). In disease-relevant tissues, we colocalized >80 and >400 distinct signals for each BP trait with cis-eQTLs and regulatory regions from promoter capture Hi-C, respectively. Integrating mouse, human disorder, gene expression and tissue abundance data, and literature review, we provide consolidated evidence for 436 BP candidate genes for future functional validation and discover several potential drug targets.


Subject(s)
Genome-Wide Association Study , Hypertension , Humans , Animals , Mice , Quantitative Trait Loci/genetics , Multiomics , Genetic Predisposition to Disease , Hypertension/genetics , Polymorphism, Single Nucleotide/genetics
20.
Am J Hum Genet ; 110(2): 284-299, 2023 02 02.
Article in English | MEDLINE | ID: mdl-36693378

ABSTRACT

Insulin secretion is critical for glucose homeostasis, and increased levels of the precursor proinsulin relative to insulin indicate pancreatic islet beta-cell stress and insufficient insulin secretory capacity in the setting of insulin resistance. We conducted meta-analyses of genome-wide association results for fasting proinsulin from 16 European-ancestry studies in 45,861 individuals. We found 36 independent signals at 30 loci (p value < 5 × 10-8), which validated 12 previously reported loci for proinsulin and ten additional loci previously identified for another glycemic trait. Half of the alleles associated with higher proinsulin showed higher rather than lower effects on glucose levels, corresponding to different mechanisms. Proinsulin loci included genes that affect prohormone convertases, beta-cell dysfunction, vesicle trafficking, beta-cell transcriptional regulation, and lysosomes/autophagy processes. We colocalized 11 proinsulin signals with islet expression quantitative trait locus (eQTL) data, suggesting candidate genes, including ARSG, WIPI1, SLC7A14, and SIX3. The NKX6-3/ANK1 proinsulin signal colocalized with a T2D signal and an adipose ANK1 eQTL signal but not the islet NKX6-3 eQTL. Signals were enriched for islet enhancers, and we showed a plausible islet regulatory mechanism for the lead signal in the MADD locus. These results show how detailed genetic studies of an intermediate phenotype can elucidate mechanisms that may predispose one to disease.


Subject(s)
Diabetes Mellitus, Type 2 , Proinsulin , Humans , Proinsulin/genetics , Proinsulin/metabolism , Diabetes Mellitus, Type 2/genetics , Diabetes Mellitus, Type 2/metabolism , Genome-Wide Association Study/methods , Insulin/genetics , Insulin/metabolism , Glucose , Transcription Factors/genetics , Homeodomain Proteins/genetics
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