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Functionally oriented analysis of cardiometabolic traits in a trans-ethnic sample.
Petty LE, Highland HM, Gamazon ER, Hu H, Karhade M, Chen HH, de Vries PS, Grove ML, Aguilar D, Bell GI, Huff CD, Hanis CL, Doddapaneni H, Munzy DM, Gibbs RA, Ma J, Parra EJ, Cruz M, Valladares-Salgado A, Arking DE, Barbeira A, Im HK, Morrison AC, Boerwinkle E, Below JE
(2019) Hum Mol Genet 28: 1212-1224
MeSH Terms: Adult, Aged, Blood Pressure, Body Mass Index, Chromosome Mapping, Ethnic Groups, European Continental Ancestry Group, Female, Forecasting, Genetic Association Studies, Genome-Wide Association Study, Humans, Male, Metabolome, Middle Aged, Multifactorial Inheritance, Phenotype, Polymorphism, Single Nucleotide, Transcriptome
Show Abstract · Added February 15, 2019
Interpretation of genetic association results is difficult because signals often lack biological context. To generate hypotheses of the functional genetic etiology of complex cardiometabolic traits, we estimated the genetically determined component of gene expression from common variants using PrediXcan (1) and determined genes with differential predicted expression by trait. PrediXcan imputes tissue-specific expression levels from genetic variation using variant-level effect on gene expression in transcriptome data. To explore the value of imputed genetically regulated gene expression (GReX) models across different ancestral populations, we evaluated imputed expression levels for predictive accuracy genome-wide in RNA sequence data in samples drawn from European-ancestry and African-ancestry populations and identified substantial predictive power using European-derived models in a non-European target population. We then tested the association of GReX on 15 cardiometabolic traits including blood lipid levels, body mass index, height, blood pressure, fasting glucose and insulin, RR interval, fibrinogen level, factor VII level and white blood cell and platelet counts in 15 755 individuals across three ancestry groups, resulting in 20 novel gene-phenotype associations reaching experiment-wide significance across ancestries. In addition, we identified 18 significant novel gene-phenotype associations in our ancestry-specific analyses. Top associations were assessed for additional support via query of S-PrediXcan (2) results derived from publicly available genome-wide association studies summary data. Collectively, these findings illustrate the utility of transcriptome-based imputation models for discovery of cardiometabolic effect genes in a diverse dataset.
© The Author(s) 2019. Published by Oxford University Press.
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19 MeSH Terms
Examining How the MAFB Transcription Factor Affects Islet β-Cell Function Postnatally.
Cyphert HA, Walker EM, Hang Y, Dhawan S, Haliyur R, Bonatakis L, Avrahami D, Brissova M, Kaestner KH, Bhushan A, Powers AC, Stein R
(2019) Diabetes 68: 337-348
MeSH Terms: Animals, Cells, Cultured, Chromatin Immunoprecipitation, Chromosomes, Artificial, Bacterial, DNA Methylation, Female, Humans, In Vitro Techniques, Insulin-Secreting Cells, Maf Transcription Factors, Large, MafB Transcription Factor, Mice, Mice, Transgenic, Pregnancy, Tryptophan Hydroxylase
Show Abstract · Added January 8, 2019
The sustained expression of the MAFB transcription factor in human islet β-cells represents a distinct difference in mice. Moreover, mRNA expression of closely related and islet β-cell-enriched MAFA does not peak in humans until after 9 years of age. We show that the MAFA protein also is weakly produced within the juvenile human islet β-cell population and that expression is postnatally restricted in mouse β-cells by de novo DNA methylation. To gain insight into how MAFB affects human β-cells, we developed a mouse model to ectopically express in adult mouse β-cells using transcriptional control sequences. Coexpression of MafB with MafA had no overt impact on mouse β-cells, suggesting that the human adult β-cell MAFA/MAFB heterodimer is functionally equivalent to the mouse MafA homodimer. However, MafB alone was unable to rescue the islet β-cell defects in a mouse mutant lacking MafA in β-cells. Of note, transgenic production of MafB in β-cells elevated tryptophan hydroxylase 1 mRNA production during pregnancy, which drives the serotonin biosynthesis critical for adaptive maternal β-cell responses. Together, these studies provide novel insight into the role of MAFB in human islet β-cells.
© 2018 by the American Diabetes Association.
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15 MeSH Terms
The CeNGEN Project: The Complete Gene Expression Map of an Entire Nervous System.
Hammarlund M, Hobert O, Miller DM, Sestan N
(2018) Neuron 99: 430-433
MeSH Terms: Animals, Caenorhabditis elegans, Caenorhabditis elegans Proteins, Chromosome Mapping, Gene Expression Profiling, National Institute of Neurological Disorders and Stroke (U.S.), Nervous System, Nervous System Physiological Phenomena, United States
Show Abstract · Added March 26, 2019
Differential gene expression defines individual neuron types and determines how each contributes to circuit physiology and responds to injury and disease. The C. elegans Neuronal Gene Expression Map & Network (CeNGEN) will establish a comprehensive gene expression atlas of an entire nervous system at single-neuron resolution.
Copyright © 2018. Published by Elsevier Inc.
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9 MeSH Terms
Haploinsufficiency for Microtubule Methylation Is an Early Driver of Genomic Instability in Renal Cell Carcinoma.
Chiang YC, Park IY, Terzo EA, Tripathi DN, Mason FM, Fahey CC, Karki M, Shuster CB, Sohn BH, Chowdhury P, Powell RT, Ohi R, Tsai YS, de Cubas AA, Khan A, Davis IJ, Strahl BD, Parker JS, Dere R, Walker CL, Rathmell WK
(2018) Cancer Res 78: 3135-3146
MeSH Terms: Animals, Carcinogenesis, Carcinoma, Renal Cell, Cell Line, Tumor, Chromosomes, Human, Pair 3, Fibroblasts, Gene Knockdown Techniques, Genomic Instability, Haploinsufficiency, Histone-Lysine N-Methyltransferase, Histones, Humans, Kidney Neoplasms, Kidney Tubules, Proximal, Lysine, Methylation, Mice, Micronuclei, Chromosome-Defective, Microtubules
Show Abstract · Added October 30, 2019
Loss of the short arm of chromosome 3 (3p) occurs early in >95% of clear cell renal cell carcinoma (ccRCC). Nearly ubiquitous 3p loss in ccRCC suggests haploinsufficiency for 3p tumor suppressors as early drivers of tumorigenesis. We previously reported methyltransferase , which trimethylates H3 histones on lysine 36 (H3K36me3) and is located in the 3p deletion, to also trimethylate microtubules on lysine 40 (αTubK40me3) during mitosis, with αTubK40me3 required for genomic stability. We now show that monoallelic, -deficient cells retaining H3K36me3, but not αTubK40me3, exhibit a dramatic increase in mitotic defects and micronuclei count, with increased viability compared with biallelic loss. In -inactivated human kidney cells, rescue with a pathogenic mutant deficient for microtubule (αTubK40me3), but not histone (H3K36me3) methylation, replicated this phenotype. Genomic instability (micronuclei) was also a hallmark of patient-derived cells from ccRCC. These data show that the tumor suppressor displays a haploinsufficiency phenotype disproportionately impacting microtubule methylation and serves as an early driver of genomic instability. Loss of a single allele of a chromatin modifier plays a role in promoting oncogenesis, underscoring the growing relevance of tumor suppressor haploinsufficiency in tumorigenesis. .
©2018 American Association for Cancer Research.
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Cell-of-Origin Patterns Dominate the Molecular Classification of 10,000 Tumors from 33 Types of Cancer.
Hoadley KA, Yau C, Hinoue T, Wolf DM, Lazar AJ, Drill E, Shen R, Taylor AM, Cherniack AD, Thorsson V, Akbani R, Bowlby R, Wong CK, Wiznerowicz M, Sanchez-Vega F, Robertson AG, Schneider BG, Lawrence MS, Noushmehr H, Malta TM, Cancer Genome Atlas Network, Stuart JM, Benz CC, Laird PW
(2018) Cell 173: 291-304.e6
MeSH Terms: Aneuploidy, Chromosomes, Cluster Analysis, CpG Islands, DNA Methylation, Databases, Factual, Humans, MicroRNAs, Mutation, Neoplasm Proteins, Neoplasms, RNA, Messenger
Show Abstract · Added October 30, 2019
We conducted comprehensive integrative molecular analyses of the complete set of tumors in The Cancer Genome Atlas (TCGA), consisting of approximately 10,000 specimens and representing 33 types of cancer. We performed molecular clustering using data on chromosome-arm-level aneuploidy, DNA hypermethylation, mRNA, and miRNA expression levels and reverse-phase protein arrays, of which all, except for aneuploidy, revealed clustering primarily organized by histology, tissue type, or anatomic origin. The influence of cell type was evident in DNA-methylation-based clustering, even after excluding sites with known preexisting tissue-type-specific methylation. Integrative clustering further emphasized the dominant role of cell-of-origin patterns. Molecular similarities among histologically or anatomically related cancer types provide a basis for focused pan-cancer analyses, such as pan-gastrointestinal, pan-gynecological, pan-kidney, and pan-squamous cancers, and those related by stemness features, which in turn may inform strategies for future therapeutic development.
Copyright © 2018 Elsevier Inc. All rights reserved.
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Genomic and Functional Approaches to Understanding Cancer Aneuploidy.
Taylor AM, Shih J, Ha G, Gao GF, Zhang X, Berger AC, Schumacher SE, Wang C, Hu H, Liu J, Lazar AJ, Cancer Genome Atlas Research Network, Cherniack AD, Beroukhim R, Meyerson M
(2018) Cancer Cell 33: 676-689.e3
MeSH Terms: Aneuploidy, Carcinoma, Squamous Cell, Cell Cycle, Cell Proliferation, Chromosome Aberrations, Chromosome Deletion, Chromosomes, Human, Pair 3, Databases, Genetic, Genomics, Humans, Mutation Rate, Tumor Suppressor Protein p53
Show Abstract · Added October 30, 2019
Aneuploidy, whole chromosome or chromosome arm imbalance, is a near-universal characteristic of human cancers. In 10,522 cancer genomes from The Cancer Genome Atlas, aneuploidy was correlated with TP53 mutation, somatic mutation rate, and expression of proliferation genes. Aneuploidy was anti-correlated with expression of immune signaling genes, due to decreased leukocyte infiltrates in high-aneuploidy samples. Chromosome arm-level alterations show cancer-specific patterns, including loss of chromosome arm 3p in squamous cancers. We applied genome engineering to delete 3p in lung cells, causing decreased proliferation rescued in part by chromosome 3 duplication. This study defines genomic and phenotypic correlates of cancer aneuploidy and provides an experimental approach to study chromosome arm aneuploidy.
Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.
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Local ancestry transitions modify snp-trait associations.
Fish AE, Crawford DC, Capra JA, Bush WS
(2018) Pac Symp Biocomput 23: 424-435
MeSH Terms: Adult, African Continental Ancestry Group, Chromosomes, Human, Computational Biology, Epistasis, Genetic, European Continental Ancestry Group, Evolution, Molecular, Gene Frequency, Genetics, Population, Genome-Wide Association Study, Haplotypes, Humans, Linear Models, Models, Genetic, Polymorphism, Single Nucleotide, Recombination, Genetic
Show Abstract · Added March 14, 2018
Genomic maps of local ancestry identify ancestry transitions - points on a chromosome where recent recombination events in admixed individuals have joined two different ancestral haplotypes. These events bring together alleles that evolved within separate continential populations, providing a unique opportunity to evaluate the joint effect of these alleles on health outcomes. In this work, we evaluate the impact of genetic variants in the context of nearby local ancestry transitions within a sample of nearly 10,000 adults of African ancestry with traits derived from electronic health records. Genetic data was located using the Metabochip, and used to derive local ancestry. We develop a model that captures the effect of both single variants and local ancestry, and use it to identify examples where local ancestry transitions significantly interact with nearby variants to influence metabolic traits. In our most compelling example, we find that the minor allele of rs16890640 occuring on a European background with a downstream local ancestry transition to African ancestry results in significantly lower mean corpuscular hemoglobin and volume. This finding represents a new way of discovering genetic interactions, and is supported by molecular data that suggest changes to local ancestry may impact local chromatin looping.
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16 MeSH Terms
Landscape of X chromosome inactivation across human tissues.
Tukiainen T, Villani AC, Yen A, Rivas MA, Marshall JL, Satija R, Aguirre M, Gauthier L, Fleharty M, Kirby A, Cummings BB, Castel SE, Karczewski KJ, Aguet F, Byrnes A, GTEx Consortium, Laboratory, Data Analysis &Coordinating Center (LDACC)—Analysis Working Group, Statistical Methods groups—Analysis Working Group, Enhancing GTEx (eGTEx) groups, NIH Common Fund, NIH/NCI, NIH/NHGRI, NIH/NIMH, NIH/NIDA, Biospecimen Collection Source Site—NDRI, Biospecimen Collection Source Site—RPCI, Biospecimen Core Resource—VARI, Brain Bank Repository—University of Miami Brain Endowment Bank, Leidos Biomedical—Project Management, ELSI Study, Genome Browser Data Integration &Visualization—EBI, Genome Browser Data Integration &Visualization—UCSC Genomics Institute, University of California Santa Cruz, Lappalainen T, Regev A, Ardlie KG, Hacohen N, MacArthur DG
(2017) Nature 550: 244-248
MeSH Terms: Chromosomes, Human, X, Female, Genes, X-Linked, Genome, Human, Genomics, Humans, Male, Organ Specificity, Phenotype, Sequence Analysis, RNA, Single-Cell Analysis, Transcriptome, X Chromosome Inactivation
Show Abstract · Added October 27, 2017
X chromosome inactivation (XCI) silences transcription from one of the two X chromosomes in female mammalian cells to balance expression dosage between XX females and XY males. XCI is, however, incomplete in humans: up to one-third of X-chromosomal genes are expressed from both the active and inactive X chromosomes (Xa and Xi, respectively) in female cells, with the degree of 'escape' from inactivation varying between genes and individuals. The extent to which XCI is shared between cells and tissues remains poorly characterized, as does the degree to which incomplete XCI manifests as detectable sex differences in gene expression and phenotypic traits. Here we describe a systematic survey of XCI, integrating over 5,500 transcriptomes from 449 individuals spanning 29 tissues from GTEx (v6p release) and 940 single-cell transcriptomes, combined with genomic sequence data. We show that XCI at 683 X-chromosomal genes is generally uniform across human tissues, but identify examples of heterogeneity between tissues, individuals and cells. We show that incomplete XCI affects at least 23% of X-chromosomal genes, identify seven genes that escape XCI with support from multiple lines of evidence and demonstrate that escape from XCI results in sex biases in gene expression, establishing incomplete XCI as a mechanism that is likely to introduce phenotypic diversity. Overall, this updated catalogue of XCI across human tissues helps to increase our understanding of the extent and impact of the incompleteness in the maintenance of XCI.
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13 MeSH Terms
Genetic effects on gene expression across human tissues.
GTEx Consortium, Laboratory, Data Analysis &Coordinating Center (LDACC)—Analysis Working Group, Statistical Methods groups—Analysis Working Group, Enhancing GTEx (eGTEx) groups, NIH Common Fund, NIH/NCI, NIH/NHGRI, NIH/NIMH, NIH/NIDA, Biospecimen Collection Source Site—NDRI, Biospecimen Collection Source Site—RPCI, Biospecimen Core Resource—VARI, Brain Bank Repository—University of Miami Brain Endowment Bank, Leidos Biomedical—Project Management, ELSI Study, Genome Browser Data Integration &Visualization—EBI, Genome Browser Data Integration &Visualization—UCSC Genomics Institute, University of California Santa Cruz, Lead analysts:, Laboratory, Data Analysis &Coordinating Center (LDACC):, NIH program management:, Biospecimen collection:, Pathology:, eQTL manuscript working group:, Battle A, Brown CD, Engelhardt BE, Montgomery SB
(2017) Nature 550: 204-213
MeSH Terms: Alleles, Chromosomes, Human, Disease, Female, Gene Expression Profiling, Gene Expression Regulation, Genetic Variation, Genome, Human, Genotype, Humans, Male, Organ Specificity, Quantitative Trait Loci
Show Abstract · Added October 27, 2017
Characterization of the molecular function of the human genome and its variation across individuals is essential for identifying the cellular mechanisms that underlie human genetic traits and diseases. The Genotype-Tissue Expression (GTEx) project aims to characterize variation in gene expression levels across individuals and diverse tissues of the human body, many of which are not easily accessible. Here we describe genetic effects on gene expression levels across 44 human tissues. We find that local genetic variation affects gene expression levels for the majority of genes, and we further identify inter-chromosomal genetic effects for 93 genes and 112 loci. On the basis of the identified genetic effects, we characterize patterns of tissue specificity, compare local and distal effects, and evaluate the functional properties of the genetic effects. We also demonstrate that multi-tissue, multi-individual data can be used to identify genes and pathways affected by human disease-associated variation, enabling a mechanistic interpretation of gene regulation and the genetic basis of disease.
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13 MeSH Terms
Genetic Interactions with Age, Sex, Body Mass Index, and Hypertension in Relation to Atrial Fibrillation: The AFGen Consortium.
Weng LC, Lunetta KL, Müller-Nurasyid M, Smith AV, Thériault S, Weeke PE, Barnard J, Bis JC, Lyytikäinen LP, Kleber ME, Martinsson A, Lin HJ, Rienstra M, Trompet S, Krijthe BP, Dörr M, Klarin D, Chasman DI, Sinner MF, Waldenberger M, Launer LJ, Harris TB, Soliman EZ, Alonso A, Paré G, Teixeira PL, Denny JC, Shoemaker MB, Van Wagoner DR, Smith JD, Psaty BM, Sotoodehnia N, Taylor KD, Kähönen M, Nikus K, Delgado GE, Melander O, Engström G, Yao J, Guo X, Christophersen IE, Ellinor PT, Geelhoed B, Verweij N, Macfarlane P, Ford I, Heeringa J, Franco OH, Uitterlinden AG, Völker U, Teumer A, Rose LM, Kääb S, Gudnason V, Arking DE, Conen D, Roden DM, Chung MK, Heckbert SR, Benjamin EJ, Lehtimäki T, März W, Smith JG, Rotter JI, van der Harst P, Jukema JW, Stricker BH, Felix SB, Albert CM, Lubitz SA
(2017) Sci Rep 7: 11303
MeSH Terms: Age Factors, Aged, Atrial Fibrillation, Body Mass Index, Chromosomes, Human, Pair 4, Epistasis, Genetic, Female, Genetic Loci, Genetic Predisposition to Disease, Genome-Wide Association Study, Humans, Hypertension, Male, Middle Aged, Odds Ratio, Polymorphism, Single Nucleotide, Reproducibility of Results, Risk Factors, Sex Characteristics
Show Abstract · Added March 14, 2018
It is unclear whether genetic markers interact with risk factors to influence atrial fibrillation (AF) risk. We performed genome-wide interaction analyses between genetic variants and age, sex, hypertension, and body mass index in the AFGen Consortium. Study-specific results were combined using meta-analysis (88,383 individuals of European descent, including 7,292 with AF). Variants with nominal interaction associations in the discovery analysis were tested for association in four independent studies (131,441 individuals, including 5,722 with AF). In the discovery analysis, the AF risk associated with the minor rs6817105 allele (at the PITX2 locus) was greater among subjects ≤ 65 years of age than among those > 65 years (interaction p-value = 4.0 × 10). The interaction p-value exceeded genome-wide significance in combined discovery and replication analyses (interaction p-value = 1.7 × 10). We observed one genome-wide significant interaction with body mass index and several suggestive interactions with age, sex, and body mass index in the discovery analysis. However, none was replicated in the independent sample. Our findings suggest that the pathogenesis of AF may differ according to age in individuals of European descent, but we did not observe evidence of statistically significant genetic interactions with sex, body mass index, or hypertension on AF risk.
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19 MeSH Terms