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Multi-tissue transcriptome analyses identify genetic mechanisms underlying neuropsychiatric traits.
Gamazon ER, Zwinderman AH, Cox NJ, Denys D, Derks EM
(2019) Nat Genet 51: 933-940
MeSH Terms: Algorithms, Computational Biology, Gene Expression Profiling, Gene Expression Regulation, Gene Regulatory Networks, Genetic Association Studies, Genetic Predisposition to Disease, Genome-Wide Association Study, Humans, Mental Disorders, Organ Specificity, Polymorphism, Single Nucleotide, Quantitative Trait Loci, Quantitative Trait, Heritable, Transcriptome
Show Abstract · Added July 17, 2019
The genetic architecture of psychiatric disorders is characterized by a large number of small-effect variants located primarily in non-coding regions, suggesting that the underlying causal effects may influence disease risk by modulating gene expression. We provide comprehensive analyses using transcriptome data from an unprecedented collection of tissues to gain pathophysiological insights into the role of the brain, neuroendocrine factors (adrenal gland) and gastrointestinal systems (colon) in psychiatric disorders. In each tissue, we perform PrediXcan analysis and identify trait-associated genes for schizophrenia (n associations = 499; n unique genes = 275), bipolar disorder (n associations = 17; n unique genes = 13), attention deficit hyperactivity disorder (n associations = 19; n unique genes = 12) and broad depression (n associations = 41; n unique genes = 31). Importantly, both PrediXcan and summary-data-based Mendelian randomization/heterogeneity in dependent instruments analyses suggest potentially causal genes in non-brain tissues, showing the utility of these tissues for mapping psychiatric disease genetic predisposition. Our analyses further highlight the importance of joint tissue approaches as 76% of the genes were detected only in difficult-to-acquire tissues.
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Genome-wide association and transcriptome studies identify target genes and risk loci for breast cancer.
Ferreira MA, Gamazon ER, Al-Ejeh F, Aittomäki K, Andrulis IL, Anton-Culver H, Arason A, Arndt V, Aronson KJ, Arun BK, Asseryanis E, Azzollini J, Balmaña J, Barnes DR, Barrowdale D, Beckmann MW, Behrens S, Benitez J, Bermisheva M, Białkowska K, Blomqvist C, Bogdanova NV, Bojesen SE, Bolla MK, Borg A, Brauch H, Brenner H, Broeks A, Burwinkel B, Caldés T, Caligo MA, Campa D, Campbell I, Canzian F, Carter J, Carter BD, Castelao JE, Chang-Claude J, Chanock SJ, Christiansen H, Chung WK, Claes KBM, Clarke CL, EMBRACE Collaborators, GC-HBOC Study Collaborators, GEMO Study Collaborators, Couch FJ, Cox A, Cross SS, Czene K, Daly MB, de la Hoya M, Dennis J, Devilee P, Diez O, Dörk T, Dunning AM, Dwek M, Eccles DM, Ejlertsen B, Ellberg C, Engel C, Eriksson M, Fasching PA, Fletcher O, Flyger H, Friedman E, Frost D, Gabrielson M, Gago-Dominguez M, Ganz PA, Gapstur SM, Garber J, García-Closas M, García-Sáenz JA, Gaudet MM, Giles GG, Glendon G, Godwin AK, Goldberg MS, Goldgar DE, González-Neira A, Greene MH, Gronwald J, Guénel P, Haiman CA, Hall P, Hamann U, He W, Heyworth J, Hogervorst FBL, Hollestelle A, Hoover RN, Hopper JL, Hulick PJ, Humphreys K, Imyanitov EN, ABCTB Investigators, HEBON Investigators, BCFR Investigators, Isaacs C, Jakimovska M, Jakubowska A, James PA, Janavicius R, Jankowitz RC, John EM, Johnson N, Joseph V, Karlan BY, Khusnutdinova E, Kiiski JI, Ko YD, Jones ME, Konstantopoulou I, Kristensen VN, Laitman Y, Lambrechts D, Lazaro C, Leslie G, Lester J, Lesueur F, Lindström S, Long J, Loud JT, Lubiński J, Makalic E, Mannermaa A, Manoochehri M, Margolin S, Maurer T, Mavroudis D, McGuffog L, Meindl A, Menon U, Michailidou K, Miller A, Montagna M, Moreno F, Moserle L, Mulligan AM, Nathanson KL, Neuhausen SL, Nevanlinna H, Nevelsteen I, Nielsen FC, Nikitina-Zake L, Nussbaum RL, Offit K, Olah E, Olopade OI, Olsson H, Osorio A, Papp J, Park-Simon TW, Parsons MT, Pedersen IS, Peixoto A, Peterlongo P, Pharoah PDP, Plaseska-Karanfilska D, Poppe B, Presneau N, Radice P, Rantala J, Rennert G, Risch HA, Saloustros E, Sanden K, Sawyer EJ, Schmidt MK, Schmutzler RK, Sharma P, Shu XO, Simard J, Singer CF, Soucy P, Southey MC, Spinelli JJ, Spurdle AB, Stone J, Swerdlow AJ, Tapper WJ, Taylor JA, Teixeira MR, Terry MB, Teulé A, Thomassen M, Thöne K, Thull DL, Tischkowitz M, Toland AE, Torres D, Truong T, Tung N, Vachon CM, van Asperen CJ, van den Ouweland AMW, van Rensburg EJ, Vega A, Viel A, Wang Q, Wappenschmidt B, Weitzel JN, Wendt C, Winqvist R, Yang XR, Yannoukakos D, Ziogas A, Kraft P, Antoniou AC, Zheng W, Easton DF, Milne RL, Beesley J, Chenevix-Trench G
(2019) Nat Commun 10: 1741
MeSH Terms: Breast Neoplasms, Female, Gene Expression Profiling, Genetic Predisposition to Disease, Genome-Wide Association Study, Humans, Quantitative Trait Loci
Show Abstract · Added July 17, 2019
Genome-wide association studies (GWAS) have identified more than 170 breast cancer susceptibility loci. Here we hypothesize that some risk-associated variants might act in non-breast tissues, specifically adipose tissue and immune cells from blood and spleen. Using expression quantitative trait loci (eQTL) reported in these tissues, we identify 26 previously unreported, likely target genes of overall breast cancer risk variants, and 17 for estrogen receptor (ER)-negative breast cancer, several with a known immune function. We determine the directional effect of gene expression on disease risk measured based on single and multiple eQTL. In addition, using a gene-based test of association that considers eQTL from multiple tissues, we identify seven (and four) regions with variants associated with overall (and ER-negative) breast cancer risk, which were not reported in previous GWAS. Further investigation of the function of the implicated genes in breast and immune cells may provide insights into the etiology of breast cancer.
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Gene expression imputation across multiple brain regions provides insights into schizophrenia risk.
Huckins LM, Dobbyn A, Ruderfer DM, Hoffman G, Wang W, Pardiñas AF, Rajagopal VM, Als TD, T Nguyen H, Girdhar K, Boocock J, Roussos P, Fromer M, Kramer R, Domenici E, Gamazon ER, Purcell S, CommonMind Consortium, Schizophrenia Working Group of the Psychiatric Genomics Consortium, iPSYCH-GEMS Schizophrenia Working Group, Demontis D, Børglum AD, Walters JTR, O'Donovan MC, Sullivan P, Owen MJ, Devlin B, Sieberts SK, Cox NJ, Im HK, Sklar P, Stahl EA
(2019) Nat Genet 51: 659-674
MeSH Terms: Brain, Case-Control Studies, Gene Expression, Genetic Predisposition to Disease, Genome-Wide Association Study, Genotype, Humans, Polymorphism, Single Nucleotide, Quantitative Trait Loci, Risk, Schizophrenia, Transcriptome
Show Abstract · Added July 17, 2019
Transcriptomic imputation approaches combine eQTL reference panels with large-scale genotype data in order to test associations between disease and gene expression. These genic associations could elucidate signals in complex genome-wide association study (GWAS) loci and may disentangle the role of different tissues in disease development. We used the largest eQTL reference panel for the dorso-lateral prefrontal cortex (DLPFC) to create a set of gene expression predictors and demonstrate their utility. We applied DLPFC and 12 GTEx-brain predictors to 40,299 schizophrenia cases and 65,264 matched controls for a large transcriptomic imputation study of schizophrenia. We identified 413 genic associations across 13 brain regions. Stepwise conditioning identified 67 non-MHC genes, of which 14 did not fall within previous GWAS loci. We identified 36 significantly enriched pathways, including hexosaminidase-A deficiency, and multiple porphyric disorder pathways. We investigated developmental expression patterns among the 67 non-MHC genes and identified specific groups of pre- and postnatal expression.
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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
Trans-ethnic association study of blood pressure determinants in over 750,000 individuals.
Giri A, Hellwege JN, Keaton JM, Park J, Qiu C, Warren HR, Torstenson ES, Kovesdy CP, Sun YV, Wilson OD, Robinson-Cohen C, Roumie CL, Chung CP, Birdwell KA, Damrauer SM, DuVall SL, Klarin D, Cho K, Wang Y, Evangelou E, Cabrera CP, Wain LV, Shrestha R, Mautz BS, Akwo EA, Sargurupremraj M, Debette S, Boehnke M, Scott LJ, Luan J, Zhao JH, Willems SM, Thériault S, Shah N, Oldmeadow C, Almgren P, Li-Gao R, Verweij N, Boutin TS, Mangino M, Ntalla I, Feofanova E, Surendran P, Cook JP, Karthikeyan S, Lahrouchi N, Liu C, Sepúlveda N, Richardson TG, Kraja A, Amouyel P, Farrall M, Poulter NR, Understanding Society Scientific Group, International Consortium for Blood Pressure, Blood Pressure-International Consortium of Exome Chip Studies, Laakso M, Zeggini E, Sever P, Scott RA, Langenberg C, Wareham NJ, Conen D, Palmer CNA, Attia J, Chasman DI, Ridker PM, Melander O, Mook-Kanamori DO, Harst PV, Cucca F, Schlessinger D, Hayward C, Spector TD, Jarvelin MR, Hennig BJ, Timpson NJ, Wei WQ, Smith JC, Xu Y, Matheny ME, Siew EE, Lindgren C, Herzig KH, Dedoussis G, Denny JC, Psaty BM, Howson JMM, Munroe PB, Newton-Cheh C, Caulfield MJ, Elliott P, Gaziano JM, Concato J, Wilson PWF, Tsao PS, Velez Edwards DR, Susztak K, Million Veteran Program, O'Donnell CJ, Hung AM, Edwards TL
(2019) Nat Genet 51: 51-62
MeSH Terms: Adolescent, Animals, Blood Pressure, Ethnic Groups, Female, Gene Expression, Genome-Wide Association Study, Humans, Kidney Tubules, Male, Mice, Middle Aged, Polymorphism, Single Nucleotide, Transcriptome, Up-Regulation
Show Abstract · Added January 3, 2019
In this trans-ethnic multi-omic study, we reinterpret the genetic architecture of blood pressure to identify genes, tissues, phenomes and medication contexts of blood pressure homeostasis. We discovered 208 novel common blood pressure SNPs and 53 rare variants in genome-wide association studies of systolic, diastolic and pulse pressure in up to 776,078 participants from the Million Veteran Program (MVP) and collaborating studies, with analysis of the blood pressure clinical phenome in MVP. Our transcriptome-wide association study detected 4,043 blood pressure associations with genetically predicted gene expression of 840 genes in 45 tissues, and mouse renal single-cell RNA sequencing identified upregulated blood pressure genes in kidney tubule cells.
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15 MeSH Terms
Probing the Virtual Proteome to Identify Novel Disease Biomarkers.
Mosley JD, Benson MD, Smith JG, Melander O, Ngo D, Shaffer CM, Ferguson JF, Herzig MS, McCarty CA, Chute CG, Jarvik GP, Gordon AS, Palmer MR, Crosslin DR, Larson EB, Carrell DS, Kullo IJ, Pacheco JA, Peissig PL, Brilliant MH, Kitchner TE, Linneman JG, Namjou B, Williams MS, Ritchie MD, Borthwick KM, Kiryluk K, Mentch FD, Sleiman PM, Karlson EW, Verma SS, Zhu Y, Vasan RS, Yang Q, Denny JC, Roden DM, Gerszten RE, Wang TJ
(2018) Circulation 138: 2469-2481
MeSH Terms: Adult, Aged, Aged, 80 and over, Biomarkers, Carotid Artery Diseases, Female, Genome-Wide Association Study, Genotype, Humans, Lectins, C-Type, Male, Middle Aged, Odds Ratio, Phenotype, Polymorphism, Single Nucleotide, Proteome, Proteomics, Receptor, Platelet-Derived Growth Factor beta
Show Abstract · Added April 2, 2019
BACKGROUND - Proteomic approaches allow measurement of thousands of proteins in a single specimen, which can accelerate biomarker discovery. However, applying these technologies to massive biobanks is not currently feasible because of the practical barriers and costs of implementing such assays at scale. To overcome these challenges, we used a "virtual proteomic" approach, linking genetically predicted protein levels to clinical diagnoses in >40 000 individuals.
METHODS - We used genome-wide association data from the Framingham Heart Study (n=759) to construct genetic predictors for 1129 plasma protein levels. We validated the genetic predictors for 268 proteins and used them to compute predicted protein levels in 41 288 genotyped individuals in the Electronic Medical Records and Genomics (eMERGE) cohort. We tested associations for each predicted protein with 1128 clinical phenotypes. Lead associations were validated with directly measured protein levels and either low-density lipoprotein cholesterol or subclinical atherosclerosis in the MDCS (Malmö Diet and Cancer Study; n=651).
RESULTS - In the virtual proteomic analysis in eMERGE, 55 proteins were associated with 89 distinct diagnoses at a false discovery rate q<0.1. Among these, 13 associations involved lipid (n=7) or atherosclerosis (n=6) phenotypes. We tested each association for validation in MDCS using directly measured protein levels. At Bonferroni-adjusted significance thresholds, levels of apolipoprotein E isoforms were associated with hyperlipidemia, and circulating C-type lectin domain family 1 member B and platelet-derived growth factor receptor-β predicted subclinical atherosclerosis. Odds ratios for carotid atherosclerosis were 1.31 (95% CI, 1.08-1.58; P=0.006) per 1-SD increment in C-type lectin domain family 1 member B and 0.79 (0.66-0.94; P=0.008) per 1-SD increment in platelet-derived growth factor receptor-β.
CONCLUSIONS - We demonstrate a biomarker discovery paradigm to identify candidate biomarkers of cardiovascular and other diseases.
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18 MeSH Terms
Tissue-Specific Differential Expression of Novel Genes and Long Intergenic Noncoding RNAs in Humans With Extreme Response to Evoked Endotoxemia.
Ferguson JF, Xue C, Gao Y, Tian T, Shi J, Zhang X, Wang Y, Li YD, Wei Z, Li M, Zhang H, Reilly MP
(2018) Circ Genom Precis Med 11: e001907
MeSH Terms: Adipose Tissue, Animals, Endotoxemia, Female, Gene Expression Regulation, Genome-Wide Association Study, Humans, Immunity, Innate, Inflammation, Male, Mice, Monocytes, RNA, Long Noncoding, RNA, Messenger, Sequence Analysis, RNA
Show Abstract · Added April 2, 2019
BACKGROUND - Cytokine responses to activation of innate immunity differ between individuals, yet the genomic and tissue-specific transcriptomic determinants of inflammatory responsiveness are not well understood. We hypothesized that tissue-specific mRNA and long intergenic noncoding RNA (lincRNA) induction differs between individuals with divergent evoked inflammatory responses.
METHODS - In the GENE Study (Genetics of Evoked Response to Niacin and Endotoxemia), we performed an inpatient endotoxin challenge (1 ng/kg lipopolysaccharide [LPS]) in healthy humans. We selected individuals in the top (high responders) and bottom (low responders) extremes of inflammatory responses and applied RNA sequencing to CD14 monocytes (N=15) and adipose tissue (N=25) before and after LPS administration.
RESULTS - Although only a small number of genes were differentially expressed at baseline, there were clear differences in the magnitude of the transcriptional response post-LPS between high and low responders, with a far greater number of genes differentially expressed by endotoxemia in high responders. Furthermore, tissue responses differed during inflammation, and we found a number of tissue-specific differentially expressed lincRNAs post-LPS, which we validated. Relative to nondifferentially expressed lincRNAs, differentially expressed lincRNAs were equally likely to be nonconserved as conserved between human and mouse, indicating that conservation is not a predictor of lincRNAs associated with human inflammatory pathophysiology. Differentially expressed genes also were enriched for signals with inflammatory and cardiometabolic disease in published genome-wide association studies. CTB-41I6.2 ( AC002091.1), a nonconserved human-specific lincRNA, is one of the top lincRNAs regulated by endotoxemia in monocytes, but not in adipose tissue. Knockdown experiments in THP-1 monocytes suggest that this lincRNA enhances LPS-induced interleukin 6 ( IL6) expression in monocytes, and we now refer to this as monocyte LPS-induced lincRNA regulator of IL6 ( MOLRIL6).
CONCLUSIONS - We highlight mRNAs and lincRNAs that represent novel candidates for modulation of innate immune and metabolic responses in humans.
CLINICAL TRIAL REGISTRATION - URL: https://www.clinicaltrials.gov . Unique identifier: NCT00953667.
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15 MeSH Terms
Bid maintains mitochondrial cristae structure and function and protects against cardiac disease in an integrative genomics study.
Salisbury-Ruf CT, Bertram CC, Vergeade A, Lark DS, Shi Q, Heberling ML, Fortune NL, Okoye GD, Jerome WG, Wells QS, Fessel J, Moslehi J, Chen H, Roberts LJ, Boutaud O, Gamazon ER, Zinkel SS
(2018) Elife 7:
MeSH Terms: Animals, Apoptosis, BH3 Interacting Domain Death Agonist Protein, Beclin-1, Cell Respiration, Fibrosis, Gene Expression Regulation, Genome-Wide Association Study, Genomics, Heart Diseases, Heart Ventricles, Humans, Mice, Inbred C57BL, Mitochondria, Mitochondrial Proton-Translocating ATPases, Mutation, Myeloid Progenitor Cells, Myocardial Infarction, Myocytes, Cardiac, Polymorphism, Single Nucleotide, Protein Multimerization, Protein Structure, Secondary, Protein Subunits, Reactive Oxygen Species, Reproducibility of Results, Up-Regulation
Show Abstract · Added December 11, 2018
Bcl-2 family proteins reorganize mitochondrial membranes during apoptosis, to form pores and rearrange cristae. In vitro and in vivo analysis integrated with human genetics reveals a novel homeostatic mitochondrial function for Bcl-2 family protein Bid. Loss of full-length Bid results in apoptosis-independent, irregular cristae with decreased respiration. mice display stress-induced myocardial dysfunction and damage. A gene-based approach applied to a biobank, validated in two independent GWAS studies, reveals that decreased genetically determined BID expression associates with myocardial infarction (MI) susceptibility. Patients in the bottom 5% of the expression distribution exhibit >4 fold increased MI risk. Carrier status with nonsynonymous variation in Bid's membrane binding domain, Bid, associates with MI predisposition. Furthermore, Bid but not Bid associates with Mcl-1, previously implicated in cristae stability; decreased MCL-1 expression associates with MI. Our results identify a role for Bid in homeostatic mitochondrial cristae reorganization, that we link to human cardiac disease.
© 2018, Salisbury-Ruf et al.
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26 MeSH Terms
Genetic Variants Associated with Circulating Fibroblast Growth Factor 23.
Robinson-Cohen C, Bartz TM, Lai D, Ikizler TA, Peacock M, Imel EA, Michos ED, Foroud TM, Akesson K, Taylor KD, Malmgren L, Matsushita K, Nethander M, Eriksson J, Ohlsson C, Mellström D, Wolf M, Ljunggren O, McGuigan F, Rotter JI, Karlsson M, Econs MJ, Ix JH, Lutsey PL, Psaty BM, de Boer IH, Kestenbaum BR
(2018) J Am Soc Nephrol 29: 2583-2592
MeSH Terms: African Continental Ancestry Group, Cohort Studies, European Continental Ancestry Group, Female, Fibroblast Growth Factors, Genome-Wide Association Study, Humans, Kidney, Male, Phosphates, Polymorphism, Single Nucleotide, RGS Proteins, Sodium-Phosphate Cotransporter Proteins, Type IIa, Vitamin D, Vitamin D3 24-Hydroxylase
Show Abstract · Added January 3, 2019
BACKGROUND - Fibroblast growth factor 23 (FGF23), a bone-derived hormone that regulates phosphorus and vitamin D metabolism, contributes to the pathogenesis of mineral and bone disorders in CKD and is an emerging cardiovascular risk factor. Central elements of FGF23 regulation remain incompletely understood; genetic variation may help explain interindividual differences.
METHODS - We performed a meta-analysis of genome-wide association studies of circulating FGF23 concentrations among 16,624 participants of European ancestry from seven cohort studies, excluding participants with eGFR<30 ml/min per 1.73 m to focus on FGF23 under normal conditions. We evaluated the association of single-nucleotide polymorphisms (SNPs) with natural log-transformed FGF23 concentration, adjusted for age, sex, study site, and principal components of ancestry. A second model additionally adjusted for BMI and eGFR.
RESULTS - We discovered 154 SNPs from five independent regions associated with FGF23 concentration. The SNP with the strongest association, rs17216707 (=3.0×10), lies upstream of , which encodes the primary catabolic enzyme for 1,25-dihydroxyvitamin D and 25-hydroxyvitamin D. Each additional copy of the T allele at this locus is associated with 5% higher FGF23 concentration. Another locus strongly associated with variations in FGF23 concentration is rs11741640, within and upstream of (a gene involved in renal phosphate transport). Additional adjustment for BMI and eGFR did not materially alter the magnitude of these associations. Another top locus (within , the ABO blood group transferase gene) was no longer statistically significant at the genome-wide level.
CONCLUSIONS - Common genetic variants located near genes involved in vitamin D metabolism and renal phosphate transport are associated with differences in circulating FGF23 concentrations.
Copyright © 2018 by the American Society of Nephrology.
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15 MeSH Terms
Using an atlas of gene regulation across 44 human tissues to inform complex disease- and trait-associated variation.
Gamazon ER, Segrè AV, van de Bunt M, Wen X, Xi HS, Hormozdiari F, Ongen H, Konkashbaev A, Derks EM, Aguet F, Quan J, GTEx Consortium, Nicolae DL, Eskin E, Kellis M, Getz G, McCarthy MI, Dermitzakis ET, Cox NJ, Ardlie KG
(2018) Nat Genet 50: 956-967
MeSH Terms: Disease, Gene Expression, Gene Expression Profiling, Gene Expression Regulation, Genome-Wide Association Study, Genotype, Humans, Phenotype, Polymorphism, Single Nucleotide, Quantitative Trait Loci, Quantitative Trait, Heritable
Show Abstract · Added July 1, 2018
We apply integrative approaches to expression quantitative loci (eQTLs) from 44 tissues from the Genotype-Tissue Expression project and genome-wide association study data. About 60% of known trait-associated loci are in linkage disequilibrium with a cis-eQTL, over half of which were not found in previous large-scale whole blood studies. Applying polygenic analyses to metabolic, cardiovascular, anthropometric, autoimmune, and neurodegenerative traits, we find that eQTLs are significantly enriched for trait associations in relevant pathogenic tissues and explain a substantial proportion of the heritability (40-80%). For most traits, tissue-shared eQTLs underlie a greater proportion of trait associations, although tissue-specific eQTLs have a greater contribution to some traits, such as blood pressure. By integrating information from biological pathways with eQTL target genes and applying a gene-based approach, we validate previously implicated causal genes and pathways, and propose new variant and gene associations for several complex traits, which we replicate in the UK BioBank and BioVU.
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11 MeSH Terms