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The impact of sex on gene expression across human tissues.
Oliva M, Muñoz-Aguirre M, Kim-Hellmuth S, Wucher V, Gewirtz ADH, Cotter DJ, Parsana P, Kasela S, Balliu B, Viñuela A, Castel SE, Mohammadi P, Aguet F, Zou Y, Khramtsova EA, Skol AD, Garrido-Martín D, Reverter F, Brown A, Evans P, Gamazon ER, Payne A, Bonazzola R, Barbeira AN, Hamel AR, Martinez-Perez A, Soria JM, GTEx Consortium, Pierce BL, Stephens M, Eskin E, Dermitzakis ET, Segrè AV, Im HK, Engelhardt BE, Ardlie KG, Montgomery SB, Battle AJ, Lappalainen T, Guigó R, Stranger BE
(2020) Science 369:
MeSH Terms: Chromosomes, Human, X, Disease, Epigenesis, Genetic, Female, Gene Expression, Gene Expression Regulation, Genetic Variation, Genome-Wide Association Study, Humans, Male, Organ Specificity, Promoter Regions, Genetic, Quantitative Trait Loci, Sex Characteristics, Sex Factors
Show Abstract · Added September 15, 2020
Many complex human phenotypes exhibit sex-differentiated characteristics. However, the molecular mechanisms underlying these differences remain largely unknown. We generated a catalog of sex differences in gene expression and in the genetic regulation of gene expression across 44 human tissue sources surveyed by the Genotype-Tissue Expression project (GTEx, v8 release). We demonstrate that sex influences gene expression levels and cellular composition of tissue samples across the human body. A total of 37% of all genes exhibit sex-biased expression in at least one tissue. We identify cis expression quantitative trait loci (eQTLs) with sex-differentiated effects and characterize their cellular origin. By integrating sex-biased eQTLs with genome-wide association study data, we identify 58 gene-trait associations that are driven by genetic regulation of gene expression in a single sex. These findings provide an extensive characterization of sex differences in the human transcriptome and its genetic regulation.
Copyright © 2020 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works.
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15 MeSH Terms
Combined linkage and association analysis identifies rare and low frequency variants for blood pressure at 1q31.
Wang H, Nandakumar P, Tekola-Ayele F, Tayo BO, Ware EB, Gu CC, Lu Y, Yao J, Zhao W, Smith JA, Hellwege JN, Guo X, Edwards TL, Loos RJF, Arnett DK, Fornage M, Rotimi C, Kardia SLR, Cooper RS, Rao DC, Ehret G, Chakravarti A, Zhu X
(2019) Eur J Hum Genet 27: 269-277
MeSH Terms: African Americans, Chromosomes, Human, Pair 1, Gene Frequency, Genome-Wide Association Study, Humans, Hypertension, Linkage Disequilibrium, Polymorphism, Single Nucleotide
Show Abstract · Added March 3, 2020
High blood pressure (BP) is a major risk factor for cardiovascular disease (CVD) and is more prevalent in African Americans as compared to other US groups. Although large, population-based genome-wide association studies (GWAS) have identified over 300 common polymorphisms modulating inter-individual BP variation, largely in European ancestry subjects, most of them do not localize to regions previously identified through family-based linkage studies. This discrepancy has remained unexplained despite the statistical power differences between current GWAS and prior linkage studies. To address this issue, we performed genome-wide linkage analysis of BP traits in African-American families from the Family Blood Pressure Program (FBPP) and genotyped on the Illumina Human Exome BeadChip v1.1. We identified a genomic region on chromosome 1q31 with LOD score 3.8 for pulse pressure (PP), a region we previously implicated in DBP studies of European ancestry families. Although no reported GWAS variants map to this region, combined linkage and association analysis of PP identified 81 rare and low frequency exonic variants accounting for the linkage evidence. Replication analysis in eight independent African ancestry cohorts (N = 16,968) supports this specific association with PP (P = 0.0509). Additional association and network analyses identified multiple potential candidate genes in this region expressed in multiple tissues and with a strong biological support for a role in BP. In conclusion, multiple genes and rare variants on 1q31 contribute to PP variation. Beyond producing new insights into PP, we demonstrate how family-based linkage and association studies can implicate specific rare and low frequency variants for complex traits.
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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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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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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
African genetic ancestry interacts with body mass index to modify risk for uterine fibroids.
Giri A, Edwards TL, Hartmann KE, Torstenson ES, Wellons M, Schreiner PJ, Velez Edwards DR
(2017) PLoS Genet 13: e1006871
MeSH Terms: Adolescent, Adult, African Continental Ancestry Group, Body Mass Index, Case-Control Studies, Chromosome Mapping, Chromosomes, Human, Pair 2, Chromosomes, Human, Pair 6, European Continental Ancestry Group, Female, Genotyping Techniques, Humans, Leiomyoma, Logistic Models, Obesity, Polymorphism, Single Nucleotide, Prospective Studies, Risk Factors, Young Adult
Show Abstract · Added February 21, 2019
Race, specifically African ancestry, and obesity are important risk factors for uterine fibroids, and likely interact to provide the right conditions for fibroid growth. However, existing studies largely focus on the main-effects rather than their interaction. Here, we firstly provide evidence for interaction between categories of body mass index (BMI) and reported-race in relation to uterine fibroids. We then investigate whether the association between inferred local European ancestry and fibroid risk is modified by BMI in African American (AA) women in the Vanderbilt University Medical Center bio-repository (BioVU) (539 cases and 794 controls) and the Coronary Artery Risk Development in Young Adults study (CARDIA, 264 cases and 173 controls). We used multiple logistic regression to evaluate interactions between local European ancestry and BMI in relation to fibroid risk, then performed fixed effects meta-analysis. Statistical significance threshold for local-ancestry and BMI interactions was empirically estimated with 10,000 permutations (p-value = 1.18x10-4). Admixture mapping detected an association between European ancestry and fibroid risk which was modified by BMI (continuous-interaction p-value = 3.75x10-5) around ADTRP (chromosome 6p24); the strongest association was found in the obese category (ancestry odds ratio (AOR) = 0.51, p-value = 2.23x10-5). Evaluation of interaction between genotyped/imputed variants and BMI in this targeted region suggested race-specific interaction, present in AAs only; strongest evidence was found for insertion/deletion variant (6:11946435), again in the obese category (OR = 1.66, p-value = 1.72x10-6). We found nominal evidence for interaction between local ancestry and BMI at a previously reported region in chromosome 2q31-32, which includes COL5A2, and TFPI, an immediate downstream target of ADTRP. Interactions between BMI and SNPs (single nucleotide polymorphisms) found in this region in AA women were also detected in an independent European American population of 1,195 cases and 1,164 controls. Findings from our study provide an example of how modifiable and non-modifiable factors may interact to influence fibroid risk and suggest a biological role for BMI in fibroid etiology.
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Type 2 Diabetes Variants Disrupt Function of SLC16A11 through Two Distinct Mechanisms.
Rusu V, Hoch E, Mercader JM, Tenen DE, Gymrek M, Hartigan CR, DeRan M, von Grotthuss M, Fontanillas P, Spooner A, Guzman G, Deik AA, Pierce KA, Dennis C, Clish CB, Carr SA, Wagner BK, Schenone M, Ng MCY, Chen BH, MEDIA Consortium, SIGMA T2D Consortium, Centeno-Cruz F, Zerrweck C, Orozco L, Altshuler DM, Schreiber SL, Florez JC, Jacobs SBR, Lander ES
(2017) Cell 170: 199-212.e20
MeSH Terms: Basigin, Cell Membrane, Chromosomes, Human, Pair 17, Diabetes Mellitus, Type 2, Gene Knockdown Techniques, Haplotypes, Hepatocytes, Heterozygote, Histone Code, Humans, Liver, Models, Molecular, Monocarboxylic Acid Transporters
Show Abstract · Added September 20, 2017
Type 2 diabetes (T2D) affects Latinos at twice the rate seen in populations of European descent. We recently identified a risk haplotype spanning SLC16A11 that explains ∼20% of the increased T2D prevalence in Mexico. Here, through genetic fine-mapping, we define a set of tightly linked variants likely to contain the causal allele(s). We show that variants on the T2D-associated haplotype have two distinct effects: (1) decreasing SLC16A11 expression in liver and (2) disrupting a key interaction with basigin, thereby reducing cell-surface localization. Both independent mechanisms reduce SLC16A11 function and suggest SLC16A11 is the causal gene at this locus. To gain insight into how SLC16A11 disruption impacts T2D risk, we demonstrate that SLC16A11 is a proton-coupled monocarboxylate transporter and that genetic perturbation of SLC16A11 induces changes in fatty acid and lipid metabolism that are associated with increased T2D risk. Our findings suggest that increasing SLC16A11 function could be therapeutically beneficial for T2D. VIDEO ABSTRACT.
Copyright © 2017 Elsevier Inc. All rights reserved.
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13 MeSH Terms