Other search tools

About this data

The publication data currently available has been vetted by Vanderbilt faculty, staff, administrators and trainees. The data itself is retrieved directly from NCBI's PubMed and is automatically updated on a weekly basis to ensure accuracy and completeness.

If you have any questions or comments, please contact us.

Results: 1 to 10 of 152

Publication Record

Connections

Fine-mapping of 150 breast cancer risk regions identifies 191 likely target genes.
Fachal L, Aschard H, Beesley J, Barnes DR, Allen J, Kar S, Pooley KA, Dennis J, Michailidou K, Turman C, Soucy P, Lemaçon A, Lush M, Tyrer JP, Ghoussaini M, Moradi Marjaneh M, Jiang X, Agata S, Aittomäki K, Alonso MR, Andrulis IL, Anton-Culver H, Antonenkova NN, Arason A, Arndt V, Aronson KJ, Arun BK, Auber B, Auer PL, Azzollini J, Balmaña J, Barkardottir RB, Barrowdale D, Beeghly-Fadiel A, Benitez J, Bermisheva M, Białkowska K, Blanco AM, Blomqvist C, Blot W, Bogdanova NV, Bojesen SE, Bolla MK, Bonanni B, Borg A, Bosse K, Brauch H, Brenner H, Briceno I, Brock IW, Brooks-Wilson A, Brüning T, Burwinkel B, Buys SS, Cai Q, Caldés T, Caligo MA, Camp NJ, Campbell I, Canzian F, Carroll JS, Carter BD, Castelao JE, Chiquette J, Christiansen H, Chung WK, Claes KBM, Clarke CL, GEMO Study Collaborators, EMBRACE Collaborators, Collée JM, Cornelissen S, Couch FJ, Cox A, Cross SS, Cybulski C, Czene K, Daly MB, de la Hoya M, Devilee P, Diez O, Ding YC, Dite GS, Domchek SM, Dörk T, Dos-Santos-Silva I, Droit A, Dubois S, Dumont M, Duran M, Durcan L, Dwek M, Eccles DM, Engel C, Eriksson M, Evans DG, Fasching PA, Fletcher O, Floris G, Flyger H, Foretova L, Foulkes WD, Friedman E, Fritschi L, Frost D, Gabrielson M, Gago-Dominguez M, Gambino G, Ganz PA, Gapstur SM, Garber J, García-Sáenz JA, Gaudet MM, Georgoulias V, Giles GG, Glendon G, Godwin AK, Goldberg MS, Goldgar DE, González-Neira A, Tibiletti MG, Greene MH, Grip M, Gronwald J, Grundy A, Guénel P, Hahnen E, Haiman CA, Håkansson N, Hall P, Hamann U, Harrington PA, Hartikainen JM, Hartman M, He W, Healey CS, Heemskerk-Gerritsen BAM, Heyworth J, Hillemanns P, Hogervorst FBL, Hollestelle A, Hooning MJ, Hopper JL, Howell A, Huang G, Hulick PJ, Imyanitov EN, KConFab Investigators, HEBON Investigators, ABCTB Investigators, Isaacs C, Iwasaki M, Jager A, Jakimovska M, Jakubowska A, James PA, Janavicius R, Jankowitz RC, John EM, Johnson N, Jones ME, Jukkola-Vuorinen A, Jung A, Kaaks R, Kang D, Kapoor PM, Karlan BY, Keeman R, Kerin MJ, Khusnutdinova E, Kiiski JI, Kirk J, Kitahara CM, Ko YD, Konstantopoulou I, Kosma VM, Koutros S, Kubelka-Sabit K, Kwong A, Kyriacou K, Laitman Y, Lambrechts D, Lee E, Leslie G, Lester J, Lesueur F, Lindblom A, Lo WY, Long J, Lophatananon A, Loud JT, Lubiński J, MacInnis RJ, Maishman T, Makalic E, Mannermaa A, Manoochehri M, Manoukian S, Margolin S, Martinez ME, Matsuo K, Maurer T, Mavroudis D, Mayes R, McGuffog L, McLean C, Mebirouk N, Meindl A, Miller A, Miller N, Montagna M, Moreno F, Muir K, Mulligan AM, Muñoz-Garzon VM, Muranen TA, Narod SA, Nassir R, Nathanson KL, Neuhausen SL, Nevanlinna H, Neven P, Nielsen FC, Nikitina-Zake L, Norman A, Offit K, Olah E, Olopade OI, Olsson H, Orr N, Osorio A, Pankratz VS, Papp J, Park SK, Park-Simon TW, Parsons MT, Paul J, Pedersen IS, Peissel B, Peshkin B, Peterlongo P, Peto J, Plaseska-Karanfilska D, Prajzendanc K, Prentice R, Presneau N, Prokofyeva D, Pujana MA, Pylkäs K, Radice P, Ramus SJ, Rantala J, Rau-Murthy R, Rennert G, Risch HA, Robson M, Romero A, Rossing M, Saloustros E, Sánchez-Herrero E, Sandler DP, Santamariña M, Saunders C, Sawyer EJ, Scheuner MT, Schmidt DF, Schmutzler RK, Schneeweiss A, Schoemaker MJ, Schöttker B, Schürmann P, Scott C, Scott RJ, Senter L, Seynaeve CM, Shah M, Sharma P, Shen CY, Shu XO, Singer CF, Slavin TP, Smichkoska S, Southey MC, Spinelli JJ, Spurdle AB, Stone J, Stoppa-Lyonnet D, Sutter C, Swerdlow AJ, Tamimi RM, Tan YY, Tapper WJ, Taylor JA, Teixeira MR, Tengström M, Teo SH, Terry MB, Teulé A, Thomassen M, Thull DL, Tischkowitz M, Toland AE, Tollenaar RAEM, Tomlinson I, Torres D, Torres-Mejía G, Troester MA, Truong T, Tung N, Tzardi M, Ulmer HU, Vachon CM, van Asperen CJ, van der Kolk LE, van Rensburg EJ, Vega A, Viel A, Vijai J, Vogel MJ, Wang Q, Wappenschmidt B, Weinberg CR, Weitzel JN, Wendt C, Wildiers H, Winqvist R, Wolk A, Wu AH, Yannoukakos D, Zhang Y, Zheng W, Hunter D, Pharoah PDP, Chang-Claude J, García-Closas M, Schmidt MK, Milne RL, Kristensen VN, French JD, Edwards SL, Antoniou AC, Chenevix-Trench G, Simard J, Easton DF, Kraft P, Dunning AM
(2020) Nat Genet 52: 56-73
MeSH Terms: Bayes Theorem, Biomarkers, Tumor, Breast Neoplasms, Chromosome Mapping, Female, Genetic Predisposition to Disease, Genome-Wide Association Study, Humans, Linkage Disequilibrium, Polymorphism, Single Nucleotide, Quantitative Trait Loci, Regulatory Sequences, Nucleic Acid, Risk Factors
Show Abstract · Added March 3, 2020
Genome-wide association studies have identified breast cancer risk variants in over 150 genomic regions, but the mechanisms underlying risk remain largely unknown. These regions were explored by combining association analysis with in silico genomic feature annotations. We defined 205 independent risk-associated signals with the set of credible causal variants in each one. In parallel, we used a Bayesian approach (PAINTOR) that combines genetic association, linkage disequilibrium and enriched genomic features to determine variants with high posterior probabilities of being causal. Potentially causal variants were significantly over-represented in active gene regulatory regions and transcription factor binding sites. We applied our INQUSIT pipeline for prioritizing genes as targets of those potentially causal variants, using gene expression (expression quantitative trait loci), chromatin interaction and functional annotations. Known cancer drivers, transcription factors and genes in the developmental, apoptosis, immune system and DNA integrity checkpoint gene ontology pathways were over-represented among the highest-confidence target genes.
0 Communities
1 Members
0 Resources
13 MeSH Terms
On Using Local Ancestry to Characterize the Genetic Architecture of Human Traits: Genetic Regulation of Gene Expression in Multiethnic or Admixed Populations.
Zhong Y, Perera MA, Gamazon ER
(2019) Am J Hum Genet 104: 1097-1115
MeSH Terms: Ethnic Groups, Gene Expression Regulation, Genetics, Population, Genome-Wide Association Study, Humans, Linkage Disequilibrium, Models, Genetic, Multifactorial Inheritance, Phenotype, Polymorphism, Single Nucleotide, Quantitative Trait Loci
Show Abstract · Added July 17, 2019
Understanding the nature of the genetic regulation of gene expression promises to advance our understanding of the genetic basis of disease. However, the methodological impact of the use of local ancestry on high-dimensional omics analyses, including, most prominently, expression quantitative trait loci (eQTL) mapping and trait heritability estimation, in admixed populations remains critically underexplored. Here, we develop a statistical framework that characterizes the relationships among the determinants of the genetic architecture of an important class of molecular traits. We provide a computationally efficient approach to local ancestry analysis in eQTL mapping while increasing control of type I and type II error over traditional approaches. Applying our method to National Institute of General Medical Sciences (NIGMS) and Genotype-Tissue Expression (GTEx) datasets, we show that the use of local ancestry can improve eQTL mapping in admixed and multiethnic populations, respectively. We estimate the trait variance explained by ancestry by using local admixture relatedness between individuals. By using simulations of diverse genetic architectures and degrees of confounding, we show improved accuracy in estimating heritability when accounting for local ancestry similarity. Furthermore, we characterize the sparse versus polygenic components of gene expression in admixed individuals. Our study has important methodological implications for genetic analysis of omics traits across a range of genomic contexts, from a single variant to a prioritized region to the entire genome. Our findings highlight the importance of using local ancestry to better characterize the heritability of complex traits and to more accurately map genetic associations.
Copyright © 2019 American Society of Human Genetics. Published by Elsevier Inc. All rights reserved.
0 Communities
1 Members
0 Resources
11 MeSH Terms
Sequence-based HLA-A, B, C, DP, DQ, and DR typing of 496 adults from San Diego, California, USA.
Moore E, Grifoni A, Weiskopf D, Schulten V, Arlehamn CSL, Angelo M, Pham J, Leary S, Sidney J, Broide D, Frazier A, Phillips E, Mallal S, Mack SJ, Sette A
(2018) Hum Immunol 79: 821-822
MeSH Terms: Adolescent, Adult, Alleles, California, Female, Gene Frequency, Genotype, Genotyping Techniques, HLA-A Antigens, HLA-B Antigens, HLA-C Antigens, HLA-DP Antigens, HLA-DQ Antigens, HLA-DR Antigens, Histocompatibility Testing, Humans, Linkage Disequilibrium, Male, Middle Aged, Sequence Analysis, DNA, T-Lymphocytes, Young Adult
Show Abstract · Added March 30, 2020
DNA sequence-based typing at the HLA-A, -B, -C, -DPB1, -DQA1, -DQB1, and -DRB1 loci was performed on 496 healthy adult donors from San Diego, California, to characterize allele frequencies in support of studies of T cell responses to common allergens. Deviations from Hardy Weinberg proportions were detected at each locus except A and C. Several alleles were found in more than 15% of individuals, including the class II alleles DPB1∗02:01, DPB1∗04:01, DQA1∗01:02, DQA1∗05:01, DQB1∗03:01, and the class I allele A∗02:01. Genotype data will be available in the Allele Frequencies Net Database (AFND 3562).
Copyright © 2018. Published by Elsevier Inc.
0 Communities
1 Members
0 Resources
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.
0 Communities
1 Members
0 Resources
MeSH Terms
PR interval genome-wide association meta-analysis identifies 50 loci associated with atrial and atrioventricular electrical activity.
van Setten J, Brody JA, Jamshidi Y, Swenson BR, Butler AM, Campbell H, Del Greco FM, Evans DS, Gibson Q, Gudbjartsson DF, Kerr KF, Krijthe BP, Lyytikäinen LP, Müller C, Müller-Nurasyid M, Nolte IM, Padmanabhan S, Ritchie MD, Robino A, Smith AV, Steri M, Tanaka T, Teumer A, Trompet S, Ulivi S, Verweij N, Yin X, Arnar DO, Asselbergs FW, Bader JS, Barnard J, Bis J, Blankenberg S, Boerwinkle E, Bradford Y, Buckley BM, Chung MK, Crawford D, den Hoed M, Denny JC, Dominiczak AF, Ehret GB, Eijgelsheim M, Ellinor PT, Felix SB, Franco OH, Franke L, Harris TB, Holm H, Ilaria G, Iorio A, Kähönen M, Kolcic I, Kors JA, Lakatta EG, Launer LJ, Lin H, Lin HJ, Loos RJF, Lubitz SA, Macfarlane PW, Magnani JW, Leach IM, Meitinger T, Mitchell BD, Munzel T, Papanicolaou GJ, Peters A, Pfeufer A, Pramstaller PP, Raitakari OT, Rotter JI, Rudan I, Samani NJ, Schlessinger D, Silva Aldana CT, Sinner MF, Smith JD, Snieder H, Soliman EZ, Spector TD, Stott DJ, Strauch K, Tarasov KV, Thorsteinsdottir U, Uitterlinden AG, Van Wagoner DR, Völker U, Völzke H, Waldenberger M, Jan Westra H, Wild PS, Zeller T, Alonso A, Avery CL, Bandinelli S, Benjamin EJ, Cucca F, Dörr M, Ferrucci L, Gasparini P, Gudnason V, Hayward C, Heckbert SR, Hicks AA, Jukema JW, Kääb S, Lehtimäki T, Liu Y, Munroe PB, Parsa A, Polasek O, Psaty BM, Roden DM, Schnabel RB, Sinagra G, Stefansson K, Stricker BH, van der Harst P, van Duijn CM, Wilson JF, Gharib SA, de Bakker PIW, Isaacs A, Arking DE, Sotoodehnia N
(2018) Nat Commun 9: 2904
MeSH Terms: Atrial Function, Atrioventricular Node, Electrocardiography, Electrophysiological Phenomena, Female, Genome-Wide Association Study, Humans, Linkage Disequilibrium, Male, Mutation, Missense, Risk Factors
Show Abstract · Added March 24, 2020
Electrocardiographic PR interval measures atrio-ventricular depolarization and conduction, and abnormal PR interval is a risk factor for atrial fibrillation and heart block. Our genome-wide association study of over 92,000 European-descent individuals identifies 44 PR interval loci (34 novel). Examination of these loci reveals known and previously not-yet-reported biological processes involved in cardiac atrial electrical activity. Genes in these loci are over-represented in cardiac disease processes including heart block and atrial fibrillation. Variants in over half of the 44 loci were associated with atrial or blood transcript expression levels, or were in high linkage disequilibrium with missense variants. Six additional loci were identified either by meta-analysis of ~105,000 African and European-descent individuals and/or by pleiotropic analyses combining PR interval with heart rate, QRS interval, and atrial fibrillation. These findings implicate developmental pathways, and identify transcription factors, ion-channel genes, and cell-junction/cell-signaling proteins in atrio-ventricular conduction, identifying potential targets for drug development.
0 Communities
1 Members
0 Resources
MeSH Terms
Sequence-based HLA-A, B, C, DP, DQ, and DR typing of 339 adults from Managua, Nicaragua.
Weiskopf D, Grifoni A, Arlehamn CSL, Angelo M, Leary S, Sidney J, Frazier A, Mack SJ, Phillips E, Mallal S, Cerpas C, Balmaseda A, Harris E, Sette A
(2018) Hum Immunol 79: 1-2
MeSH Terms: Adult, Databases, Genetic, Dengue, Gene Frequency, Genetics, Population, Genotype, HLA-A Antigens, HLA-B Antigens, HLA-C Antigens, HLA-DP Antigens, HLA-DQ Antigens, HLA-DR Antigens, Histocompatibility Testing, Humans, Linkage Disequilibrium, Nicaragua, Sequence Analysis, DNA, T-Lymphocytes
Show Abstract · Added March 30, 2020
DNA sequence-based typing at the HLA-A, -B, -C, -DPB1, -DQA1, -DQB1, and -DRB1 loci was performed on anonymized samples provided by 339 healthy adult blood bank donors in Managua, Nicaragua. The purpose of the study was to characterize allele frequencies in the local population to support studies of T cell immunity against pathogens, including Dengue virus. Deviations from Hardy Weinberg proportions were detected for all class II loci (HLA-DPB1, -DQA1, -DQB1 and -DRB1), and at the class I C locus, but not at the class I A and B loci. The genotype data will be available in the Allele Frequencies Net Database.
Copyright © 2017 American Society for Histocompatibility and Immunogenetics. All rights reserved.
0 Communities
1 Members
0 Resources
MeSH Terms
Population Stratification in Genetic Association Studies.
Hellwege JN, Keaton JM, Giri A, Gao X, Velez Edwards DR, Edwards TL
(2017) Curr Protoc Hum Genet 95: 1.22.1-1.22.23
MeSH Terms: Alleles, Chromosome Mapping, Evolution, Molecular, Gene Frequency, Genetic Association Studies, Genetics, Population, Humans, Linkage Disequilibrium, Models, Genetic, Models, Statistical, Quantitative Trait, Heritable
Show Abstract · Added March 3, 2020
Population stratification (PS) is a primary consideration in studies of genetic determinants of human traits. Failure to control for PS may lead to confounding, causing a study to fail for lack of significant results, or resources to be wasted following false-positive signals. Here, historical and current approaches for addressing PS when performing genetic association studies in human populations are reviewed. Methods for detecting the presence of PS, including global and local ancestry methods, are described. Also described are approaches for accounting for PS when calculating association statistics, such that measures of association are not confounded. Many traits are being examined for the first time in minority populations, which may inherently feature PS. © 2017 by John Wiley & Sons, Inc.
Copyright © 2017 John Wiley and Sons, Inc.
0 Communities
1 Members
0 Resources
MeSH Terms
Evidence of selection as a cause for racial disparities in fibroproliferative disease.
Hellwege JN, Torstenson ES, Russell SB, Edwards TL, Velez Edwards DR
(2017) PLoS One 12: e0182791
MeSH Terms: African Continental Ancestry Group, Connective Tissue Diseases, Databases, Genetic, European Continental Ancestry Group, Female, Gene Frequency, Genetic Predisposition to Disease, Humans, Linkage Disequilibrium, Models, Genetic, Neoplasms, Connective and Soft Tissue, Polymorphism, Single Nucleotide, Prevalence, Quantitative Trait Loci, Risk Assessment, Selection, Genetic
Show Abstract · Added March 3, 2020
Fibroproliferative diseases are common complex traits featuring scarring and overgrowth of connective tissue which vary widely in presentation because they affect many organ systems. Most fibroproliferative diseases are more prevalent in African-derived populations than in European populations, leading to pronounced health disparities. It is hypothesized that the increased prevalence of these diseases in African-derived populations is due to selection for pro-fibrotic alleles that are protective against helminth infections. We constructed a genetic risk score (GRS) of fibroproliferative disease risk-increasing alleles using 147 linkage disequilibrium-pruned variants identified through genome-wide association studies of seven fibroproliferative diseases with large African-European prevalence disparities. A comparison of the fibroproliferative disease GRS between 1000 Genomes Phase 3 populations detected a higher mean GRS in AFR (mean = 148 risk alleles) than EUR (mean = 136 risk alleles; T-test p-value = 1.75x10-123). To test whether differences in GRS burden are systematic and may be due to selection, we employed the quantitative trait loci (QTL) sign test. The QTL sign test result indicates that population differences in risk-increasing allele burdens at these fibroproliferative disease variants are systematic and support a model featuring selective pressure (p-value = 0.011). These observations were replicated in an independent sample and were more statistically significant (T-test p-value = 7.26x10-237, sign test p-value = 0.015). This evidence supports the role of selective pressure acting to increase frequency of fibroproliferative alleles in populations of African relative to European ancestry populations.
0 Communities
1 Members
0 Resources
MeSH Terms
Discovery and fine-mapping of adiposity loci using high density imputation of genome-wide association studies in individuals of African ancestry: African Ancestry Anthropometry Genetics Consortium.
Ng MCY, Graff M, Lu Y, Justice AE, Mudgal P, Liu CT, Young K, Yanek LR, Feitosa MF, Wojczynski MK, Rand K, Brody JA, Cade BE, Dimitrov L, Duan Q, Guo X, Lange LA, Nalls MA, Okut H, Tajuddin SM, Tayo BO, Vedantam S, Bradfield JP, Chen G, Chen WM, Chesi A, Irvin MR, Padhukasahasram B, Smith JA, Zheng W, Allison MA, Ambrosone CB, Bandera EV, Bartz TM, Berndt SI, Bernstein L, Blot WJ, Bottinger EP, Carpten J, Chanock SJ, Chen YI, Conti DV, Cooper RS, Fornage M, Freedman BI, Garcia M, Goodman PJ, Hsu YH, Hu J, Huff CD, Ingles SA, John EM, Kittles R, Klein E, Li J, McKnight B, Nayak U, Nemesure B, Ogunniyi A, Olshan A, Press MF, Rohde R, Rybicki BA, Salako B, Sanderson M, Shao Y, Siscovick DS, Stanford JL, Stevens VL, Stram A, Strom SS, Vaidya D, Witte JS, Yao J, Zhu X, Ziegler RG, Zonderman AB, Adeyemo A, Ambs S, Cushman M, Faul JD, Hakonarson H, Levin AM, Nathanson KL, Ware EB, Weir DR, Zhao W, Zhi D, Bone Mineral Density in Childhood Study (BMDCS) Group, Arnett DK, Grant SFA, Kardia SLR, Oloapde OI, Rao DC, Rotimi CN, Sale MM, Williams LK, Zemel BS, Becker DM, Borecki IB, Evans MK, Harris TB, Hirschhorn JN, Li Y, Patel SR, Psaty BM, Rotter JI, Wilson JG, Bowden DW, Cupples LA, Haiman CA, Loos RJF, North KE
(2017) PLoS Genet 13: e1006719
MeSH Terms: Adiposity, African Continental Ancestry Group, Anthropometry, Body Mass Index, Chromosome Mapping, European Continental Ancestry Group, Female, Gene Frequency, Genetic Predisposition to Disease, Genome-Wide Association Study, Humans, Linkage Disequilibrium, Male, Obesity, Polymorphism, Single Nucleotide, Serine Endopeptidases, Transcription Factor 7-Like 2 Protein, Waist-Hip Ratio
Show Abstract · Added August 22, 2017
Genome-wide association studies (GWAS) have identified >300 loci associated with measures of adiposity including body mass index (BMI) and waist-to-hip ratio (adjusted for BMI, WHRadjBMI), but few have been identified through screening of the African ancestry genomes. We performed large scale meta-analyses and replications in up to 52,895 individuals for BMI and up to 23,095 individuals for WHRadjBMI from the African Ancestry Anthropometry Genetics Consortium (AAAGC) using 1000 Genomes phase 1 imputed GWAS to improve coverage of both common and low frequency variants in the low linkage disequilibrium African ancestry genomes. In the sex-combined analyses, we identified one novel locus (TCF7L2/HABP2) for WHRadjBMI and eight previously established loci at P < 5×10-8: seven for BMI, and one for WHRadjBMI in African ancestry individuals. An additional novel locus (SPRYD7/DLEU2) was identified for WHRadjBMI when combined with European GWAS. In the sex-stratified analyses, we identified three novel loci for BMI (INTS10/LPL and MLC1 in men, IRX4/IRX2 in women) and four for WHRadjBMI (SSX2IP, CASC8, PDE3B and ZDHHC1/HSD11B2 in women) in individuals of African ancestry or both African and European ancestry. For four of the novel variants, the minor allele frequency was low (<5%). In the trans-ethnic fine mapping of 47 BMI loci and 27 WHRadjBMI loci that were locus-wide significant (P < 0.05 adjusted for effective number of variants per locus) from the African ancestry sex-combined and sex-stratified analyses, 26 BMI loci and 17 WHRadjBMI loci contained ≤ 20 variants in the credible sets that jointly account for 99% posterior probability of driving the associations. The lead variants in 13 of these loci had a high probability of being causal. As compared to our previous HapMap imputed GWAS for BMI and WHRadjBMI including up to 71,412 and 27,350 African ancestry individuals, respectively, our results suggest that 1000 Genomes imputation showed modest improvement in identifying GWAS loci including low frequency variants. Trans-ethnic meta-analyses further improved fine mapping of putative causal variants in loci shared between the African and European ancestry populations.
0 Communities
1 Members
0 Resources
18 MeSH Terms
Association analyses of East Asian individuals and trans-ancestry analyses with European individuals reveal new loci associated with cholesterol and triglyceride levels.
Spracklen CN, Chen P, Kim YJ, Wang X, Cai H, Li S, Long J, Wu Y, Wang YX, Takeuchi F, Wu JY, Jung KJ, Hu C, Akiyama K, Zhang Y, Moon S, Johnson TA, Li H, Dorajoo R, He M, Cannon ME, Roman TS, Salfati E, Lin KH, Guo X, Sheu WHH, Absher D, Adair LS, Assimes TL, Aung T, Cai Q, Chang LC, Chen CH, Chien LH, Chuang LM, Chuang SC, Du S, Fan Q, Fann CSJ, Feranil AB, Friedlander Y, Gordon-Larsen P, Gu D, Gui L, Guo Z, Heng CK, Hixson J, Hou X, Hsiung CA, Hu Y, Hwang MY, Hwu CM, Isono M, Juang JJ, Khor CC, Kim YK, Koh WP, Kubo M, Lee IT, Lee SJ, Lee WJ, Liang KW, Lim B, Lim SH, Liu J, Nabika T, Pan WH, Peng H, Quertermous T, Sabanayagam C, Sandow K, Shi J, Sun L, Tan PC, Tan SP, Taylor KD, Teo YY, Toh SA, Tsunoda T, van Dam RM, Wang A, Wang F, Wang J, Wei WB, Xiang YB, Yao J, Yuan JM, Zhang R, Zhao W, Chen YI, Rich SS, Rotter JI, Wang TD, Wu T, Lin X, Han BG, Tanaka T, Cho YS, Katsuya T, Jia W, Jee SH, Chen YT, Kato N, Jonas JB, Cheng CY, Shu XO, He J, Zheng W, Wong TY, Huang W, Kim BJ, Tai ES, Mohlke KL, Sim X
(2017) Hum Mol Genet 26: 1770-1784
MeSH Terms: Adult, Alleles, Asian Continental Ancestry Group, Cholesterol, Ethnic Groups, European Continental Ancestry Group, Female, Gene Frequency, Genetic Association Studies, Genome-Wide Association Study, Humans, Linkage Disequilibrium, Lipids, Lipoproteins, HDL, Lipoproteins, LDL, Male, Middle Aged, Polymorphism, Single Nucleotide, Quantitative Trait Loci, Triglycerides
Show Abstract · Added April 3, 2018
Large-scale meta-analyses of genome-wide association studies (GWAS) have identified >175 loci associated with fasting cholesterol levels, including total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), and triglycerides (TG). With differences in linkage disequilibrium (LD) structure and allele frequencies between ancestry groups, studies in additional large samples may detect new associations. We conducted staged GWAS meta-analyses in up to 69,414 East Asian individuals from 24 studies with participants from Japan, the Philippines, Korea, China, Singapore, and Taiwan. These meta-analyses identified (P < 5 × 10-8) three novel loci associated with HDL-C near CD163-APOBEC1 (P = 7.4 × 10-9), NCOA2 (P = 1.6 × 10-8), and NID2-PTGDR (P = 4.2 × 10-8), and one novel locus associated with TG near WDR11-FGFR2 (P = 2.7 × 10-10). Conditional analyses identified a second signal near CD163-APOBEC1. We then combined results from the East Asian meta-analysis with association results from up to 187,365 European individuals from the Global Lipids Genetics Consortium in a trans-ancestry meta-analysis. This analysis identified (log10Bayes Factor ≥6.1) eight additional novel lipid loci. Among the twelve total loci identified, the index variants at eight loci have demonstrated at least nominal significance with other metabolic traits in prior studies, and two loci exhibited coincident eQTLs (P < 1 × 10-5) in subcutaneous adipose tissue for BPTF and PDGFC. Taken together, these analyses identified multiple novel lipid loci, providing new potential therapeutic targets.
© The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.
0 Communities
2 Members
0 Resources
MeSH Terms