1
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Orchard P, Manickam N, Ventresca C, Vadlamudi S, Varshney A, Rai V, Kaplan J, Lalancette C, Mohlke KL, Gallagher K, Burant CF, Parker SCJ. Human and rat skeletal muscle single-nuclei multi-omic integrative analyses nominate causal cell types, regulatory elements, and SNPs for complex traits. Genome Res 2021; 31:2258-2275. [PMID: 34815310 PMCID: PMC8647829 DOI: 10.1101/gr.268482.120] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2020] [Accepted: 09/16/2021] [Indexed: 12/12/2022]
Abstract
Skeletal muscle accounts for the largest proportion of human body mass, on average, and is a key tissue in complex diseases and mobility. It is composed of several different cell and muscle fiber types. Here, we optimize single-nucleus ATAC-seq (snATAC-seq) to map skeletal muscle cell-specific chromatin accessibility landscapes in frozen human and rat samples, and single-nucleus RNA-seq (snRNA-seq) to map cell-specific transcriptomes in human. We additionally perform multi-omics profiling (gene expression and chromatin accessibility) on human and rat muscle samples. We capture type I and type II muscle fiber signatures, which are generally missed by existing single-cell RNA-seq methods. We perform cross-modality and cross-species integrative analyses on 33,862 nuclei and identify seven cell types ranging in abundance from 59.6% to 1.0% of all nuclei. We introduce a regression-based approach to infer cell types by comparing transcription start site-distal ATAC-seq peaks to reference enhancer maps and show consistency with RNA-based marker gene cell type assignments. We find heterogeneity in enrichment of genetic variants linked to complex phenotypes from the UK Biobank and diabetes genome-wide association studies in cell-specific ATAC-seq peaks, with the most striking enrichment patterns in muscle mesenchymal stem cells (∼3.5% of nuclei). Finally, we overlay these chromatin accessibility maps on GWAS data to nominate causal cell types, SNPs, transcription factor motifs, and target genes for type 2 diabetes signals. These chromatin accessibility profiles for human and rat skeletal muscle cell types are a useful resource for nominating causal GWAS SNPs and cell types.
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Affiliation(s)
- Peter Orchard
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan 48109, USA
| | - Nandini Manickam
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan 48109, USA
| | - Christa Ventresca
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan 48109, USA
- Department of Human Genetics, University of Michigan, Ann Arbor, Michigan 48109, USA
| | - Swarooparani Vadlamudi
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, USA
| | - Arushi Varshney
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan 48109, USA
| | - Vivek Rai
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan 48109, USA
| | - Jeremy Kaplan
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan 48109, USA
| | - Claudia Lalancette
- Epigenomics Core, University of Michigan, Ann Arbor, Michigan 48109, USA
| | - Karen L Mohlke
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, USA
| | - Katherine Gallagher
- Department of Surgery, University of Michigan, Ann Arbor, Michigan 48109, USA
- Department of Microbiology and Immunology, University of Michigan, Ann Arbor, Michigan 48109, USA
| | - Charles F Burant
- Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan 48109, USA
| | - Stephen C J Parker
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan 48109, USA
- Department of Human Genetics, University of Michigan, Ann Arbor, Michigan 48109, USA
- Department of Biostatistics, University of Michigan, Ann Arbor, Michigan 48109, USA
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Shi J, Boehnke M, Lee S. Trans-ethnic meta-analysis of rare variants in sequencing association studies. Biostatistics 2021; 22:706-722. [PMID: 31883325 DOI: 10.1093/biostatistics/kxz061] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2018] [Revised: 11/06/2019] [Accepted: 12/02/2019] [Indexed: 11/15/2022] Open
Abstract
Trans-ethnic meta-analysis is a powerful tool for detecting novel loci in genetic association studies. However, in the presence of heterogeneity among different populations, existing gene-/region-based rare variants meta-analysis methods may be unsatisfactory because they do not consider genetic similarity or dissimilarity among different populations. In response, we propose a score test under the modified random effects model for gene-/region-based rare variants associations. We adapt the kernel regression framework to construct the model and incorporate genetic similarities across populations into modeling the heterogeneity structure of the genetic effect coefficients. We use a resampling-based copula method to approximate asymptotic distribution of the test statistic, enabling efficient estimation of p-values. Simulation studies show that our proposed method controls type I error rates and increases power over existing approaches in the presence of heterogeneity. We illustrate our method by analyzing T2D-GENES consortium exome sequence data to explore rare variant associations with several traits.
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Affiliation(s)
- Jingchunzi Shi
- Thomas Francis, Jr. School of Public Health II, 1420 Washington Heights, Ann Arbor, MI 48109, USA
| | - Michael Boehnke
- Thomas Francis, Jr. School of Public Health II, 1420 Washington Heights, Ann Arbor, MI 48109, USA
| | - Seunggeun Lee
- Thomas Francis, Jr. School of Public Health II, 1420 Washington Heights, Ann Arbor, MI 48109, USA
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Sun C, Kovacs P, Guiu-Jurado E. Genetics of Body Fat Distribution: Comparative Analyses in Populations with European, Asian and African Ancestries. Genes (Basel) 2021; 12:genes12060841. [PMID: 34072523 PMCID: PMC8228180 DOI: 10.3390/genes12060841] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2021] [Revised: 05/26/2021] [Accepted: 05/27/2021] [Indexed: 12/16/2022] Open
Abstract
Preferential fat accumulation in visceral vs. subcutaneous depots makes obese individuals more prone to metabolic complications. Body fat distribution (FD) is regulated by genetics. FD patterns vary across ethnic groups independent of obesity. Asians have more and Africans have less visceral fat compared with Europeans. Consequently, Asians tend to be more susceptible to type 2 diabetes even with lower BMIs when compared with Europeans. To date, genome-wide association studies (GWAS) have identified more than 460 loci related to FD traits. However, the majority of these data were generated in European populations. In this review, we aimed to summarize recent advances in FD genetics with a focus on comparisons between European and non-European populations (Asians and Africans). We therefore not only compared FD-related susceptibility loci identified in three ethnicities but also discussed whether known genetic variants might explain the FD pattern heterogeneity across different ancestries. Moreover, we describe several novel candidate genes potentially regulating FD, including NID2, HECTD4 and GNAS, identified in studies with Asian populations. It is of note that in agreement with current knowledge, most of the proposed FD candidate genes found in Asians belong to the group of developmental genes.
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Affiliation(s)
- Chang Sun
- Medical Department III-Endocrinology, Nephrology, Rheumatology, University of Leipzig Medical Center, 04103 Leipzig, Germany
| | - Peter Kovacs
- Medical Department III-Endocrinology, Nephrology, Rheumatology, University of Leipzig Medical Center, 04103 Leipzig, Germany
| | - Esther Guiu-Jurado
- Medical Department III-Endocrinology, Nephrology, Rheumatology, University of Leipzig Medical Center, 04103 Leipzig, Germany
- Deutsches Zentrum für Diabetesforschung, 85764 Neuherberg, Germany
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4
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Mušo M, Dumbell R, Pulit S, Sinnott-Armstrong N, Laber S, Zolkiewski L, Bentley L, Claussnitzer M, Cox RD. A lead candidate functional single nucleotide polymorphism within the WARS2 gene associated with waist-hip-ratio does not alter RNA stability. BIOCHIMICA ET BIOPHYSICA ACTA. GENE REGULATORY MECHANISMS 2020; 1863:194640. [PMID: 33007465 PMCID: PMC7695619 DOI: 10.1016/j.bbagrm.2020.194640] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/10/2020] [Revised: 09/22/2020] [Accepted: 09/22/2020] [Indexed: 11/06/2022]
Abstract
We have prioritised a single nucleotide polymorphism (SNP) rs2645294 as one candidate functional SNP in the TBX15-WARS2 waist-hip-ratio locus using posterior probability analysis. This SNP is located in the 3' untranslated region of the WARS2 (tryptophanyl tRNA synthetase 2, mitochondrial) gene with which it has an expression quantitative trait in subcutaneous white adipose tissue. We show that transcripts of the WARS2 gene in a human white adipose cell line, heterozygous for the rs2645294 SNP, showed allelic imbalance. We tested whether the rs2645294 SNP altered WARS2 RNA stability using three different methods: actinomycin-D inhibition and RNA decay, mature and nascent RNA analysis and luciferase reporter assays. We found no evidence of a difference in RNA stability between the rs2645294 alleles indicating that the allelic expression imbalance was likely due to transcriptional regulation.
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Affiliation(s)
- Milan Mušo
- MRC Harwell Institute, Mammalian Genetics Unit, Harwell Campus, Oxfordshire OX11 0RD, UK
| | - Rebecca Dumbell
- MRC Harwell Institute, Mammalian Genetics Unit, Harwell Campus, Oxfordshire OX11 0RD, UK
| | - Sara Pulit
- Department of Genetics, Center for Molecular Medicine, University Medical Center Utrecht, Utrecht, the Netherlands; Big Data Institute, Li Ka Shing Center for Health Information and Discovery, Oxford University, Oxford, UK; Program in Medical Population Genetics, Broad Institute, Cambridge, MA, USA
| | | | - Samantha Laber
- MRC Harwell Institute, Mammalian Genetics Unit, Harwell Campus, Oxfordshire OX11 0RD, UK
| | - Louisa Zolkiewski
- MRC Harwell Institute, Mammalian Genetics Unit, Harwell Campus, Oxfordshire OX11 0RD, UK
| | - Liz Bentley
- MRC Harwell Institute, Mammalian Genetics Unit, Harwell Campus, Oxfordshire OX11 0RD, UK
| | - Melina Claussnitzer
- The Broad Institute of MIT and Harvard, Cambridge, MA, USA; Gerontology Division, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA; Institute of Nutritional Science, University of Hohenheim, Stuttgart, Germany
| | - Roger D Cox
- MRC Harwell Institute, Mammalian Genetics Unit, Harwell Campus, Oxfordshire OX11 0RD, UK.
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Zhang X, Ehrlich KC, Yu F, Hu X, Meng XH, Deng HW, Shen H, Ehrlich M. Osteoporosis- and obesity-risk interrelationships: an epigenetic analysis of GWAS-derived SNPs at the developmental gene TBX15. Epigenetics 2020; 15:728-749. [PMID: 31975641 PMCID: PMC7574382 DOI: 10.1080/15592294.2020.1716491] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Abstract
A major challenge in translating findings from genome-wide association studies (GWAS) to biological mechanisms is pinpointing functional variants because only a very small percentage of variants associated with a given trait actually impact the trait. We used an extensive epigenetics, transcriptomics, and genetics analysis of the TBX15/WARS2 neighbourhood to prioritize this region's best-candidate causal variants for the genetic risk of osteoporosis (estimated bone density, eBMD) and obesity (waist-hip ratio or waist circumference adjusted for body mass index). TBX15 encodes a transcription factor that is important in bone development and adipose biology. Manual curation of 692 GWAS-derived variants gave eight strong candidates for causal SNPs that modulate TBX15 transcription in subcutaneous adipose tissue (SAT) or osteoblasts, which highly and specifically express this gene. None of these SNPs were prioritized by Bayesian fine-mapping. The eight regulatory causal SNPs were in enhancer or promoter chromatin seen preferentially in SAT or osteoblasts at TBX15 intron-1 or upstream. They overlap strongly predicted, allele-specific transcription factor binding sites. Our analysis suggests that these SNPs act independently of two missense SNPs in TBX15. Remarkably, five of the regulatory SNPs were associated with eBMD and obesity and had the same trait-increasing allele for both. We found that WARS2 obesity-related SNPs can be ascribed to high linkage disequilibrium with TBX15 intron-1 SNPs. Our findings from GWAS index, proxy, and imputed SNPs suggest that a few SNPs, including three in a 0.7-kb cluster, act as causal regulatory variants to fine-tune TBX15 expression and, thereby, affect both obesity and osteoporosis risk.
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Affiliation(s)
- Xiao Zhang
- Tulane Center for Bioinformatics and Genomics, Department of Biostatistics and Data Science, School of Public Health and Tropical Medicine, Tulane University , New Orleans, LA, USA
| | - Kenneth C Ehrlich
- Tulane Center for Bioinformatics and Genomics, Department of Biostatistics and Data Science, School of Public Health and Tropical Medicine, Tulane University , New Orleans, LA, USA
| | - Fangtang Yu
- Tulane Center for Bioinformatics and Genomics, Department of Biostatistics and Data Science, School of Public Health and Tropical Medicine, Tulane University , New Orleans, LA, USA
| | - Xiaojun Hu
- Tulane Center for Bioinformatics and Genomics, Department of Biostatistics and Data Science, School of Public Health and Tropical Medicine, Tulane University , New Orleans, LA, USA.,Department of Orthopedics, People's Hospital of Rongchang District , Chongqing, China
| | - Xiang-He Meng
- Laboratory of Molecular and Statistical Genetics, College of Life Sciences, Hunan Normal University , Changsha, Hunan, China
| | - Hong-Wen Deng
- Tulane Center for Bioinformatics and Genomics, Department of Biostatistics and Data Science, School of Public Health and Tropical Medicine, Tulane University , New Orleans, LA, USA
| | - Hui Shen
- Tulane Center for Bioinformatics and Genomics, Department of Biostatistics and Data Science, School of Public Health and Tropical Medicine, Tulane University , New Orleans, LA, USA
| | - Melanie Ehrlich
- Tulane Center for Bioinformatics and Genomics, Department of Biostatistics and Data Science, School of Public Health and Tropical Medicine, Tulane University , New Orleans, LA, USA.,Tulane Cancer Center, Hayward Human Genetics Program, Tulane University Health Sciences , New Orleans, LA, USA
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Li Y, Zhao W, Li D, Tao X, Xiong Z, Liu J, Zhang W, Ji A, Tang K, Liu F, Li C. EDAR, LYPLAL1, PRDM16, PAX3, DKK1, TNFSF12, CACNA2D3, and SUPT3H gene variants influence facial morphology in a Eurasian population. Hum Genet 2019; 138:681-689. [PMID: 31025105 DOI: 10.1007/s00439-019-02023-7] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2019] [Accepted: 04/20/2019] [Indexed: 12/19/2022]
Abstract
In human society, the facial surface is visible and recognizable based on the facial shape variation which represents a set of highly polygenic and correlated complex traits. Understanding the genetic basis underlying facial shape traits has important implications in population genetics, developmental biology, and forensic science. A number of single nucleotide polymorphisms (SNPs) are associated with human facial shape variation, mostly in European populations. To bridge the gap between European and Asian populations in term of the genetic basis of facial shape variation, we examined the effect of these SNPs in a European-Asian admixed Eurasian population which included a total of 612 individuals. The coordinates of 17 facial landmarks were derived from high resolution 3dMD facial images, and 136 Euclidean distances between all pairs of landmarks were quantitatively derived. DNA samples were genotyped using the Illumina Infinium Global Screening Array and imputed using the 1000 Genomes reference panel. Genetic association between 125 previously reported facial shape-associated SNPs and 136 facial shape phenotypes was tested using linear regression. As a result, a total of eight SNPs from different loci demonstrated significant association with one or more facial shape traits after adjusting for multiple testing (significance threshold p < 1.28 × 10-3), together explaining up to 6.47% of sex-, age-, and BMI-adjusted facial phenotype variance. These included EDAR rs3827760, LYPLAL1 rs5781117, PRDM16 rs4648379, PAX3 rs7559271, DKK1 rs1194708, TNFSF12 rs80067372, CACNA2D3 rs56063440, and SUPT3H rs227833. Notably, the EDAR rs3827760 and LYPLAL1 rs5781117 SNPs displayed significant association with eight and seven facial phenotypes, respectively (2.39 × 10-5 < p < 1.28 × 10-3). The majority of these SNPs showed a distinct allele frequency between European and East Asian reference panels from the 1000 Genomes Project. These results showed the details of above eight genes influence facial shape variation in a Eurasian population.
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Affiliation(s)
- Yi Li
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Beijing, China
| | - Wenting Zhao
- Key Laboratory of Forensic Genetics, National Engineering Laboratory for Forensic Science, Institute of Forensic Science, Beijing, China
| | - Dan Li
- CAS-MPG Partner Institute and Key Laboratory for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Xianming Tao
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Beijing, China
| | - Ziyi Xiong
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Beijing, China
| | - Jing Liu
- Key Laboratory of Forensic Genetics, National Engineering Laboratory for Forensic Science, Institute of Forensic Science, Beijing, China
| | - Wei Zhang
- Key Laboratory of Forensic Genetics, National Engineering Laboratory for Forensic Science, Institute of Forensic Science, Beijing, China
| | - Anquan Ji
- Key Laboratory of Forensic Genetics, National Engineering Laboratory for Forensic Science, Institute of Forensic Science, Beijing, China
| | - Kun Tang
- CAS-MPG Partner Institute and Key Laboratory for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Fan Liu
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Beijing, China.
- Department of Genetic Identification, Erasmus MC University Medical Center Rotterdam, Rotterdam, The Netherlands.
| | - Caixia Li
- Key Laboratory of Forensic Genetics, National Engineering Laboratory for Forensic Science, Institute of Forensic Science, Beijing, China.
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Pravenec M, Zídek V, Landa V, Mlejnek P, Šilhavý J, Šimáková M, Trnovská J, Škop V, Marková I, Malínská H, Hüttl M, Kazdová L, Bardová K, Tauchmannová K, Vrbacký M, Nůsková H, Mráček T, Kopecký J, Houštěk J. Mutant Wars2 gene in spontaneously hypertensive rats impairs brown adipose tissue function and predisposes to visceral obesity. Physiol Res 2018; 66:917-924. [PMID: 29261326 DOI: 10.33549/physiolres.933811] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022] Open
Abstract
Brown adipose tissue (BAT) plays an important role in lipid and glucose metabolism in rodents and possibly also in humans. Identification of genes responsible for BAT function would shed light on underlying pathophysiological mechanisms of metabolic disturbances. Recent linkage analysis in the BXH/HXB recombinant inbred (RI) strains, derived from Brown Norway (BN) and spontaneously hypertensive rats (SHR), identified two closely linked quantitative trait loci (QTL) associated with glucose oxidation and glucose incorporation into BAT lipids in the vicinity of Wars2 (tryptophanyl tRNA synthetase 2 (mitochondrial)) gene on chromosome 2. The SHR harbors L53F WARS2 protein variant that was associated with reduced angiogenesis and Wars2 thus represents a prominent positional candidate gene. In the current study, we validated this candidate as a quantitative trait gene (QTG) using transgenic rescue experiment. SHR-Wars2 transgenic rats with wild type Wars2 gene when compared to SHR, showed more efficient mitochondrial proteosynthesis and increased mitochondrial respiration, which was associated with increased glucose oxidation and incorporation into BAT lipids, and with reduced weight of visceral fat. Correlation analyses in RI strains showed that increased activity of BAT was associated with amelioration of insulin resistance in muscle and white adipose tissue. In summary, these results demonstrate important role of Wars2 gene in regulating BAT function and consequently lipid and glucose metabolism.
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Affiliation(s)
- M Pravenec
- Institute of Physiology of the Czech Academy of Sciences, Prague, Czech Republic.
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Ramos-Lopez O, Riezu-Boj JI, Milagro FI, Martinez JA. Dopamine gene methylation patterns are associated with obesity markers and carbohydrate intake. Brain Behav 2018; 8:e01017. [PMID: 29998543 PMCID: PMC6085894 DOI: 10.1002/brb3.1017] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/18/2018] [Accepted: 05/08/2018] [Indexed: 12/18/2022] Open
Abstract
INTRODUCTION Dopamine (DA) is a neurotransmitter that regulates the rewarding and motivational processes underlying food intake and eating behaviors. This study hypothesized associations of DNA methylation signatures at genes modulating DA signaling with obesity features, metabolic profiles, and dietary intake. METHODS An adult population within the Methyl Epigenome Network Association project was included (n = 473). DNA methylation levels in white blood cells were measured by microarray (450K). Differentially methylated genes were mapped within the dopaminergic synapse pathway using the KEGG reference database (map04728). Subsequently, network enrichment analyses were run in the pathDIP portal. Associations of methylation patterns with anthropometric markers of general (BMI) and abdominal obesity (waist circumference), the blood metabolic profile, and daily dietary intakes were screened. RESULTS After applying a correction for multiple comparisons, 12 CpG sites were strongly associated (p < 0.0001) with BMI: cg03489495 (ITPR3), cg22851378 (PPP2R2D), cg04021127 (PPP2R2D), cg22441882 (SLC18A1), cg03045635 (DRD5), cg23341970 (ITPR2), cg13051970 (DDC), cg08943004 (SLC6A3), cg20557710 (CACNA1C), cg24085522 (GNAL), cg16846691 (ITPR2), and cg09691393 (SLC6A3). Moreover, average methylation levels of these genes differed according to the presence or absence of abdominal obesity. Pathway analyses revealed a statistically significant contribution of the aforementioned genes to dopaminergic synapse transmission (p = 4.78E-08). Furthermore, SLC18A1 and SLC6A3 gene methylation signatures correlated with total energy (p < 0.001) and carbohydrate (p < 0.001) intakes. CONCLUSIONS The results of this investigation reveal that methylation status on DA signaling genes may underlie epigenetic mechanisms contributing to carbohydrate and calorie consumption and fat deposition.
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Affiliation(s)
- Omar Ramos-Lopez
- Department of Nutrition, Food Science and Physiology, Center for Nutrition Research, University of Navarra, Pamplona, Spain
| | - Jose I Riezu-Boj
- Department of Nutrition, Food Science and Physiology, Center for Nutrition Research, University of Navarra, Pamplona, Spain.,Navarra Institute for Health Research (IdiSNA), Pamplona, Spain
| | - Fermin I Milagro
- Department of Nutrition, Food Science and Physiology, Center for Nutrition Research, University of Navarra, Pamplona, Spain.,CIBERobn, Fisiopatología de la Obesidad y la Nutrición, Carlos III Health Institute, Madrid, Spain
| | - J Alfredo Martinez
- Department of Nutrition, Food Science and Physiology, Center for Nutrition Research, University of Navarra, Pamplona, Spain.,Navarra Institute for Health Research (IdiSNA), Pamplona, Spain.,CIBERobn, Fisiopatología de la Obesidad y la Nutrición, Carlos III Health Institute, Madrid, Spain.,Madrid Institute of Advanced Studies (IMDEA Food), Madrid, Spain
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Sahibdeen V, Crowther NJ, Soodyall H, Hendry LM, Munthali RJ, Hazelhurst S, Choudhury A, Norris SA, Ramsay M, Lombard Z. Genetic variants in SEC16B are associated with body composition in black South Africans. Nutr Diabetes 2018; 8:43. [PMID: 30026463 PMCID: PMC6053407 DOI: 10.1038/s41387-018-0050-0] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/12/2017] [Revised: 05/14/2018] [Accepted: 05/23/2018] [Indexed: 12/12/2022] Open
Abstract
Objective The latest genome-wide association studies of obesity-related traits have identified several genetic loci contributing to body composition (BC). These findings have not been robustly replicated in African populations, therefore, this study aimed to assess whether European BC-associated gene loci played a similar role in a South African black population. Methods A replication and fine-mapping study was performed in participants from the Birth to Twenty cohort (N = 1,926) using the Metabochip. Measurements included body mass index (BMI), waist and hip circumference, waist-to-hip ratio (WHR), total fat mass, total lean mass and percentage fat mass (PFM). Results SNPs in several gene loci, including SEC16B (Padj < 9.48 × 10−7), NEGR1 (Padj < 1.64 × 10−6), FTO (Padj < 2.91 × 10−5), TMEM18 (Padj < 2.27 × 10−5), and WARS2(Padj < 3.25 × 10−5) were similarly associated (albeit not at array-wide signficance (P ≤ 6.7 × 10−7) with various phenotypes including fat mass, PFM, WHR linked to BC in this African cohort, however the associations were driven by different sentinel SNPs. More importantly, DXA-derived BC measures revealed stronger genetic associations than simple anthropometric measures. Association signals generated in this study were shared by European and African populations, as well as unique to this African cohort. Moreover, sophisticated estimates like DXA measures enabled an enhanced characterisation of genetic associations for BC traits. Conclusion Results from this study suggest that in-depth genomic studies in larger African cohorts may reveal novel SNPs for body composition and adiposity, which will provide greater insight into the aetiology of obesity.
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Affiliation(s)
- Venesa Sahibdeen
- Division of Human Genetics, School of Pathology, Faculty of Health Sciences, National Health Laboratory Service & University of the Witwatersrand, Johannesburg, South Africa. .,Sydney Brenner Institute for Molecular Bioscience, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa.
| | - Nigel J Crowther
- Department of Chemical Pathology, School of Pathology, Faculty of Health Sciences, National Health Laboratory Service & University of the Witwatersrand, Johannesburg, South Africa
| | - Himla Soodyall
- Division of Human Genetics, School of Pathology, Faculty of Health Sciences, National Health Laboratory Service & University of the Witwatersrand, Johannesburg, South Africa
| | - Liesl M Hendry
- Sydney Brenner Institute for Molecular Bioscience, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa.,School of Molecular and Cell Biology, Faculty of Science, University of the Witwatersrand, Johannesburg, South Africa
| | - Richard J Munthali
- Sydney Brenner Institute for Molecular Bioscience, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa.,School of Molecular and Cell Biology, Faculty of Science, University of the Witwatersrand, Johannesburg, South Africa.,MRC/Wits Developmental Pathways for Health Research Unit, Department of Paediatrics, School of Clinical Medicine, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Scott Hazelhurst
- Sydney Brenner Institute for Molecular Bioscience, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa.,School of Electrical and Information Engineering, University of the Witwatersrand, Johannesburg, South Africa
| | - Ananyo Choudhury
- Sydney Brenner Institute for Molecular Bioscience, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Shane A Norris
- MRC/Wits Developmental Pathways for Health Research Unit, Department of Paediatrics, School of Clinical Medicine, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Michèle Ramsay
- Division of Human Genetics, School of Pathology, Faculty of Health Sciences, National Health Laboratory Service & University of the Witwatersrand, Johannesburg, South Africa.,Sydney Brenner Institute for Molecular Bioscience, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Zané Lombard
- Division of Human Genetics, School of Pathology, Faculty of Health Sciences, National Health Laboratory Service & University of the Witwatersrand, Johannesburg, South Africa.,Sydney Brenner Institute for Molecular Bioscience, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa.,School of Molecular and Cell Biology, Faculty of Science, University of the Witwatersrand, Johannesburg, South Africa
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10
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A fine-mapping study of central obesity loci incorporating functional annotation and imputation. Eur J Hum Genet 2018; 26:1369-1377. [PMID: 29967334 DOI: 10.1038/s41431-018-0168-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2017] [Revised: 03/08/2018] [Accepted: 04/11/2018] [Indexed: 01/02/2023] Open
Abstract
A recent genome-wide association study (GWAS) of central obesity identified 27 loci, from sex-combined analysis, associated with waist-to-hip ratio adjusted for body-mass index (WHRadjBMI) in European-ancestry individuals. Nevertheless, the identified variants may not be the biological causal ones due to the presence of linkage disequilibrium (LD). To better understand the mechanisms underlying the identified loci from the GWAS meta-analysis, we first imputed summary statistics at GWAS loci to increase genetic resolution, and then we applied a Bayesian statistical fine-mapping method through PAINTOR, incorporating LD structure and functional annotations to select and prioritize the most plausible causal variants across WHRadjBMI-associated regions. Using adipose tissue- and cell-specific annotations that showed significant associations with WHRadjBMI, we identified 33 single-nucleotide polymorphisms (SNPs) from 27 sex-combined fine-mapping loci with posterior probability of causality greater than 0.9. Six of the selected 33 SNPs belong to at least one of the top five identified annotations. SNPs rs1440372 (SMAD6) and rs12608504 (JUND) are particularly important since they not only have associated functional annotations but are also GWA hits in the original study. Incorporation of functional annotations helps identify additional plausible causal variants, such as rs2213731 (DNM3-PIGC) and rs4531856 (JUND), that did not reach genome-wide significance in GWAS. Our results provide promising candidates for future functional validation experiments.
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11
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Endo C, Johnson TA, Morino R, Nakazono K, Kamitsuji S, Akita M, Kawajiri M, Yamasaki T, Kami A, Hoshi Y, Tada A, Ishikawa K, Hine M, Kobayashi M, Kurume N, Tsunemi Y, Kamatani N, Kawashima M. Genome-wide association study in Japanese females identifies fifteen novel skin-related trait associations. Sci Rep 2018; 8:8974. [PMID: 29895819 PMCID: PMC5997657 DOI: 10.1038/s41598-018-27145-2] [Citation(s) in RCA: 46] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2017] [Accepted: 05/25/2018] [Indexed: 12/27/2022] Open
Abstract
Skin trait variation impacts quality-of-life, especially for females from the viewpoint of beauty. To investigate genetic variation related to these traits, we conducted a GWAS of various skin phenotypes in 11,311 Japanese women and identified associations for age-spots, freckles, double eyelids, straight/curly hair, eyebrow thickness, hairiness, and sweating. In silico annotation with RoadMap Epigenomics epigenetic state maps and colocalization analysis of GWAS and GTEx Project eQTL signals provided information about tissue specificity, candidate causal variants, and functional target genes. Novel signals for skin-spot traits neighboured AKAP1/MSI2 (rs17833789; P = 2.2 × 10-9), BNC2 (rs10810635; P = 2.1 × 10-22), HSPA12A (rs12259842; P = 7.1 × 10-11), PPARGC1B (rs251468; P = 1.3 × 10-21), and RAB11FIP2 (rs10444039; P = 5.6 × 10-21). HSPA12A SNPs were the only protein-coding gene eQTLs identified across skin-spot loci. Double edged eyelid analysis identified that a signal around EMX2 (rs12570134; P = 8.2 × 10-15) was also associated with expression of EMX2 and the antisense-RNA gene EMX2OS in brain putamen basal ganglia tissue. A known hair morphology signal in EDAR was associated with both eyebrow thickness (rs3827760; P = 1.7 × 10-9) and straight/curly hair (rs260643; P = 1.6 × 10-103). Excessive hairiness signals' top SNPs were also eQTLs for TBX15 (rs984225; P = 1.6 × 10-8), BCL2 (rs7226979; P = 7.3 × 10-11), and GCC2 and LIMS1 (rs6542772; P = 2.2 × 10-9). For excessive sweating, top variants in two signals in chr2:28.82-29.05 Mb (rs56089836; P = 1.7 × 10-11) were eQTLs for either PPP1CB or PLB1, while a top chr16:48.26-48.45 Mb locus SNP was a known ABCC11 missense variant (rs6500380; P = 6.8 × 10-10). In total, we identified twelve loci containing sixteen association signals, of which fifteen were novel. These findings will help dermatologic researchers better understand the genetic underpinnings of skin-related phenotypic variation in human populations.
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Affiliation(s)
- Chihiro Endo
- Department of Dermatology, School of Medicine, Tokyo Women's Medical University, Shinjuku, Tokyo, 162-8666, Japan
| | | | - Ryoko Morino
- EverGene Ltd., Shinjuku-ku, Tokyo, 163-1435, Japan
| | | | | | | | | | - Tatsuya Yamasaki
- Life Science Group, Healthcare Division, Department of Healthcare Business, MTI Ltd., Shinjuku-ku, Tokyo, 163-1435, Japan
| | - Azusa Kami
- EverGene Ltd., Shinjuku-ku, Tokyo, 163-1435, Japan
| | - Yuria Hoshi
- Life Science Group, Healthcare Division, Department of Healthcare Business, MTI Ltd., Shinjuku-ku, Tokyo, 163-1435, Japan
| | - Asami Tada
- EverGene Ltd., Shinjuku-ku, Tokyo, 163-1435, Japan
| | | | - Maaya Hine
- LunaLuna Division, Department of Healthcare Business, MTI Ltd., Shinjuku-ku, Tokyo, 163-1435, Japan
| | - Miki Kobayashi
- LunaLuna Division, Department of Healthcare Business, MTI Ltd., Shinjuku-ku, Tokyo, 163-1435, Japan
| | - Nami Kurume
- LunaLuna Division, Department of Healthcare Business, MTI Ltd., Shinjuku-ku, Tokyo, 163-1435, Japan
| | - Yuichiro Tsunemi
- Department of Dermatology, School of Medicine, Tokyo Women's Medical University, Shinjuku, Tokyo, 162-8666, Japan
| | | | - Makoto Kawashima
- Department of Dermatology, School of Medicine, Tokyo Women's Medical University, Shinjuku, Tokyo, 162-8666, Japan
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12
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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 JMJ, 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 YDI, 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. Association analyses of East Asian individuals and trans-ancestry analyses with European individuals reveal new loci associated with cholesterol and triglyceride levels. Hum Mol Genet 2017; 26:1770-1784. [PMID: 28334899 DOI: 10.1093/hmg/ddx062] [Citation(s) in RCA: 107] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2016] [Accepted: 02/16/2017] [Indexed: 12/28/2022] Open
Abstract
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.
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Affiliation(s)
| | - Peng Chen
- Saw Swee Hock School of Public Health, National University Health System, National University of Singapore, Singapore, Singapore.,Key Laboratory of Pathobiology, Ministry of Education, Jilin University, Changchun, China
| | - Young Jin Kim
- Division of Structural and Functional Genomics, Center for Genome Science, Korean National Institute of Health, Osong, Chungchungbuk-do, South Korea
| | - Xu Wang
- Life Sciences Institute, National University of Singapore, Singapore, Singapore
| | - Hui Cai
- Division of Epidemiology, Department of Medicine, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Shengxu Li
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA, USA
| | - Jirong Long
- Division of Epidemiology, Department of Medicine, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Ying Wu
- Department of Genetics, University of North Carolina, Chapel Hill, NC, USA
| | - Ya Xing Wang
- Beijing Institute of Ophthalmology, Beijing Ophthalmology and Visual Science Key Lab, Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | | | - Jer-Yuarn Wu
- Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan.,School of Chinese Medicine, China Medical University, Taichung, Taiwan
| | - Keum-Ji Jung
- Institute for Health Promotion, Graduate School of Public Health, Yonsei University, Seoul, South Korea
| | - Cheng Hu
- Shanghai Diabetes Institute, Shanghai Jiaotong University Affiliated Sixth People's Hospital, Shanghai, China
| | - Koichi Akiyama
- National Center for Global Health and Medicine, Tokyo, Japan
| | - Yonghong Zhang
- Department of Epidemiology, School of Public Health and Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Medical College of Soochow University, Suzhou, China
| | - Sanghoon Moon
- Division of Structural and Functional Genomics, Center for Genome Science, Korean National Institute of Health, Osong, Chungchungbuk-do, South Korea
| | - Todd A Johnson
- Laboratory for Medical Science Mathematics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Huaixing Li
- Institute for Nutritional Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, University of the Chinese Academy of Sciences, Shanghai, China
| | - Rajkumar Dorajoo
- Genome Institute of Singapore, Agency for Science, Technology and Research, Singapore, Singapore
| | - Meian He
- Department of Occupational and Environmental Health and State Key Laboratory of Environmental Health for Incubating, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Maren E Cannon
- Department of Genetics, University of North Carolina, Chapel Hill, NC, USA
| | - Tamara S Roman
- Department of Genetics, University of North Carolina, Chapel Hill, NC, USA
| | - Elias Salfati
- Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Keng-Hung Lin
- Department of Ophthalmology, Taichung Veterans General Hospital, Taichung, Taiwan
| | - Xiuqing Guo
- Department of Pediatrics, Institute for Translational Genomics and Population Sciences, LABioMed at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Wayne H H Sheu
- Division of Endocrinology and Metabolism, Department of Internal Medicine, Taichung Veterans General Hospital, Taichung, Taiwan.,School of Medicine, National Yang-Ming University, Taipei, Taiwan.,School of Medicine, National Defense Medical Center, Taipei, Taiwan.,Institute of Medical Technology, National Chung-Hsing University, Taichung, Taiwan
| | - Devin Absher
- HudsonAlpha Institute for Biotechnology, Huntsville, AL, USA
| | - Linda S Adair
- Department of Nutrition, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, North Carolina, USA
| | | | - Tin Aung
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore.,Duke-NUS Medical School Singapore, Singapore, Singapore.,Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Qiuyin Cai
- Division of Epidemiology, Department of Medicine, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Li-Ching Chang
- Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan
| | - Chien-Hsiun Chen
- Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan.,School of Chinese Medicine, China Medical University, Taichung, Taiwan
| | - Li-Hsin Chien
- Institute of Population Health Sciences, National Health Research Institutes, Miaoli, Taiwan
| | - Lee-Ming Chuang
- Division of Endocrinology & Metabolism, Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan.,College of Medicine, National Taiwan University, Taipei, Taiwan.,Institute of Preventive Medicine, School of Public Health, National Taiwan University, Taipei, Taiwan
| | - Shu-Chun Chuang
- Institute of Population Health Sciences, National Health Research Institutes, Miaoli, Taiwan
| | - Shufa Du
- Department of Nutrition, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, North Carolina, USA.,Carolina Population Center, University of North Carolina, Chapel Hill, NC, USA
| | - Qiao Fan
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
| | - Cathy S J Fann
- Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan
| | - Alan B Feranil
- USC-Office of Population Studies Foundation, Inc, University of San Carlos, Cebu City, Philippines.,Department of Anthropology, Sociology, and History, University of San Carlos, Cebu City, Philippines
| | - Yechiel Friedlander
- Unit of Epidemiology, Hebrew University-Hadassah Braun School of Public Health, Jerusalem, Israel
| | - Penny Gordon-Larsen
- Department of Nutrition, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, North Carolina, USA.,Carolina Population Center, University of North Carolina, Chapel Hill, NC, USA
| | - Dongfeng Gu
- Department of Epidemiology and Population Genetics, Fuwai Hospital, Beijing, China
| | - Lixuan Gui
- Department of Occupational and Environmental Health and State Key Laboratory of Environmental Health for Incubating, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Zhirong Guo
- Department of Epidemiology, School of Public Health and Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Medical College of Soochow University, Suzhou, China
| | - Chew-Kiat Heng
- Department of Paediatrics, Yong Loo Lin School of Medicine, National University of Singapore, Singapore.,Khoo Teck Puat-National University Children's Medical Institute, National University Health System, Singapore, Singapore
| | - James Hixson
- Human Genetics Center, University of Texas School of Public Health, Houston, TX, USA
| | - Xuhong Hou
- Shanghai Diabetes Institute, Shanghai Jiaotong University Affiliated Sixth People's Hospital, Shanghai, China
| | - Chao Agnes Hsiung
- Institute of Population Health Sciences, National Health Research Institutes, Miaoli, Taiwan
| | - Yao Hu
- Institute for Nutritional Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, University of the Chinese Academy of Sciences, Shanghai, China
| | - Mi Yeong Hwang
- Division of Structural and Functional Genomics, Center for Genome Science, Korean National Institute of Health, Osong, Chungchungbuk-do, South Korea
| | - Chii-Min Hwu
- School of Medicine, National Yang-Ming University, Taipei, Taiwan.,Section of Endocrinology and Metabolism, Department of Medicine, Taipei Veterans General Hospital, Taipei, Taiwan
| | - Masato Isono
- National Center for Global Health and Medicine, Tokyo, Japan
| | - Jyh-Ming Jimmy Juang
- College of Medicine, National Taiwan University, Taipei, Taiwan.,Cardiovascular Center and Division of Cardiology, Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan
| | - Chiea-Chuen Khor
- Genome Institute of Singapore, Agency for Science, Technology and Research, Singapore, Singapore.,Department of Biochemistry, National University of Singapore, Singapore, Singapore
| | - Yun Kyoung Kim
- Division of Structural and Functional Genomics, Center for Genome Science, Korean National Institute of Health, Osong, Chungchungbuk-do, South Korea
| | - Woon-Puay Koh
- Saw Swee Hock School of Public Health, National University Health System, National University of Singapore, Singapore, Singapore.,Duke-NUS Medical School Singapore, Singapore, Singapore
| | - Michiaki Kubo
- RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - I-Te Lee
- Division of Endocrinology and Metabolism, Department of Internal Medicine, Taichung Veterans General Hospital, Taichung, Taiwan.,School of Medicine, National Yang-Ming University, Taipei, Taiwan.,School of Medicine, Chung Shan Medical University, Taichung, Taiwan
| | - Sun-Ju Lee
- Institute for Health Promotion, Graduate School of Public Health, Yonsei University, Seoul, South Korea
| | - Wen-Jane Lee
- Department of Medical Research, Taichung Veterans General Hospital, Taichung, Taiwan.,Department of Social Work, Tunghai University, Taichung, Taiwan
| | - Kae-Woei Liang
- School of Medicine, National Yang-Ming University, Taipei, Taiwan.,Cardiovascular Center, Taichung Veterans General Hospital, Taichung, Taiwan.,Department of Medicine, China Medical University, Taichung, Taiwan
| | - Blanche Lim
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
| | - Sing-Hui Lim
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
| | - Jianjun Liu
- Saw Swee Hock School of Public Health, National University Health System, National University of Singapore, Singapore, Singapore.,Genome Institute of Singapore, Agency for Science, Technology and Research, Singapore, Singapore.,Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Toru Nabika
- Department of Functional Pathology, Shimane University School of Medicine, Izumo, Japan
| | - Wen-Harn Pan
- Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan
| | - Hao Peng
- Department of Epidemiology, School of Public Health and Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Medical College of Soochow University, Suzhou, China
| | - Thomas Quertermous
- Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Charumathi Sabanayagam
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore.,Duke-NUS Medical School Singapore, Singapore, Singapore
| | - Kevin Sandow
- Department of Pediatrics, Institute for Translational Genomics and Population Sciences, LABioMed at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Jinxiu Shi
- Department of Genetics, Shanghai-MOST Key Laboratory of Health and Disease Genomics, Chinese National Human Genome Center and Shanghai Industrial Technology Institute, Shanghai, China
| | - Liang Sun
- Institute for Nutritional Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, University of the Chinese Academy of Sciences, Shanghai, China
| | - Pok Chien Tan
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
| | - Shu-Pei Tan
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
| | - Kent D Taylor
- Department of Pediatrics, Institute for Translational Genomics and Population Sciences, LABioMed at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Yik-Ying Teo
- Saw Swee Hock School of Public Health, National University Health System, National University of Singapore, Singapore, Singapore.,Life Sciences Institute, National University of Singapore, Singapore, Singapore.,Genome Institute of Singapore, Agency for Science, Technology and Research, Singapore, Singapore.,NUS Graduate School for Integrative Science and Engineering, National University of Singapore, Singapore, Singapore.,Department of Statistics and Applied Probability, National University of Singapore, Singapore, Singapore
| | - Sue-Anne Toh
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Tatsuhiko Tsunoda
- Laboratory for Medical Science Mathematics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan.,Department of Medical Science Mathematics, Medical Research Institute, Tokyo Medical and Dental University, Tokyo, Japan
| | - Rob M van Dam
- Saw Swee Hock School of Public Health, National University Health System, National University of Singapore, Singapore, Singapore
| | - Aili Wang
- Department of Epidemiology, School of Public Health and Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Medical College of Soochow University, Suzhou, China
| | - Feijie Wang
- Institute for Nutritional Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, University of the Chinese Academy of Sciences, Shanghai, China
| | - Jie Wang
- Shanghai Diabetes Institute, Shanghai Jiaotong University Affiliated Sixth People's Hospital, Shanghai, China
| | - Wen Bin Wei
- Beijing Tongren Eye Center, Beijing Tongren Hospital, Beijing Ophthalmology and Visual Science Key Lab, Beijing Key Laboratory of Intraocular Tumor Diagnosis and Treatment, Capital Medical University, Beijing, China
| | - Yong-Bing Xiang
- Department of Epidemiology, Shanghai Cancer Institute, Shanghai, China
| | - Jie Yao
- Department of Pediatrics, Institute for Translational Genomics and Population Sciences, LABioMed at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Jian-Min Yuan
- Department of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA.,Division of Cancer Control and Population Sciences, University of Pittsburgh Cancer Institute, Pittsburgh, PA, USA
| | - Rong Zhang
- Shanghai Diabetes Institute, Shanghai Jiaotong University Affiliated Sixth People's Hospital, Shanghai, China
| | - Wanting Zhao
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore.,Duke-NUS Medical School Singapore, Singapore, Singapore
| | - Yii-Der Ida Chen
- Department of Pediatrics, Institute for Translational Genomics and Population Sciences, LABioMed at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Stephen S Rich
- Center for Public Health Genomics, University of Virginia School of Medicine, Charlottesville, VA, USA
| | - Jerome I Rotter
- Department of Pediatrics, Institute for Translational Genomics and Population Sciences, LABioMed at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Tzung-Dau Wang
- College of Medicine, National Taiwan University, Taipei, Taiwan.,Cardiovascular Center and Division of Cardiology, Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan
| | - Tangchun Wu
- Department of Occupational and Environmental Health and State Key Laboratory of Environmental Health for Incubating, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Xu Lin
- Institute for Nutritional Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, University of the Chinese Academy of Sciences, Shanghai, China
| | - Bok-Ghee Han
- Center for Genome Science, Korean National Institute of Health, Osong, Chungchungbuk-do, South Korea
| | - Toshihiro Tanaka
- Laboratory for Cardiovascular Diseases, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan.,Department of Human Genetics and Disease Diversity, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, Tokyo, Japan
| | - Yoon Shin Cho
- Department of Biomedical Science, Hallym University, Chuncheon, South Korea
| | - Tomohiro Katsuya
- Department of Clinical Gene Therapy, Osaka University Graduate School of Medicine, Suita, Japan
| | - Weiping Jia
- Shanghai Diabetes Institute, Shanghai Jiaotong University Affiliated Sixth People's Hospital, Shanghai, China
| | - Sun-Ha Jee
- Institute for Health Promotion, Graduate School of Public Health, Yonsei University, Seoul, South Korea
| | - Yuan-Tsong Chen
- Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan
| | - Norihiro Kato
- National Center for Global Health and Medicine, Tokyo, Japan
| | - Jost B Jonas
- Beijing Institute of Ophthalmology, Beijing Ophthalmology and Visual Science Key Lab, Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University, Beijing, China.,Department of Ophthalmology, Medical Faculty Mannheim of the Ruprecht-Karls-University of Heidelberg, Mannheim, Germany
| | - Ching-Yu Cheng
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore.,Duke-NUS Medical School Singapore, Singapore, Singapore.,Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Xiao-Ou Shu
- Division of Epidemiology, Department of Medicine, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Jiang He
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA, USA
| | - Wei Zheng
- Division of Epidemiology, Department of Medicine, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Tien-Yin Wong
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore.,Duke-NUS Medical School Singapore, Singapore, Singapore.,Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Wei Huang
- Department of Genetics, Shanghai-MOST Key Laboratory of Health and Disease Genomics, Chinese National Human Genome Center and Shanghai Industrial Technology Institute, Shanghai, China
| | - Bong-Jo Kim
- Division of Structural and Functional Genomics, Center for Genome Science, Korean National Institute of Health, Osong, Chungchungbuk-do, South Korea
| | - E-Shyong Tai
- Saw Swee Hock School of Public Health, National University Health System, National University of Singapore, Singapore, Singapore.,Duke-NUS Medical School Singapore, Singapore, Singapore.,Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Karen L Mohlke
- Department of Genetics, University of North Carolina, Chapel Hill, NC, USA
| | - Xueling Sim
- Saw Swee Hock School of Public Health, National University Health System, National University of Singapore, Singapore, Singapore
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13
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McAllister K, Mechanic LE, Amos C, Aschard H, Blair IA, Chatterjee N, Conti D, Gauderman WJ, Hsu L, Hutter CM, Jankowska MM, Kerr J, Kraft P, Montgomery SB, Mukherjee B, Papanicolaou GJ, Patel CJ, Ritchie MD, Ritz BR, Thomas DC, Wei P, Witte JS. Current Challenges and New Opportunities for Gene-Environment Interaction Studies of Complex Diseases. Am J Epidemiol 2017; 186:753-761. [PMID: 28978193 PMCID: PMC5860428 DOI: 10.1093/aje/kwx227] [Citation(s) in RCA: 106] [Impact Index Per Article: 15.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2016] [Revised: 03/14/2017] [Accepted: 03/16/2017] [Indexed: 12/25/2022] Open
Abstract
Recently, many new approaches, study designs, and statistical and analytical methods have emerged for studying gene-environment interactions (G×Es) in large-scale studies of human populations. There are opportunities in this field, particularly with respect to the incorporation of -omics and next-generation sequencing data and continual improvement in measures of environmental exposures implicated in complex disease outcomes. In a workshop called "Current Challenges and New Opportunities for Gene-Environment Interaction Studies of Complex Diseases," held October 17-18, 2014, by the National Institute of Environmental Health Sciences and the National Cancer Institute in conjunction with the annual American Society of Human Genetics meeting, participants explored new approaches and tools that have been developed in recent years for G×E discovery. This paper highlights current and critical issues and themes in G×E research that need additional consideration, including the improved data analytical methods, environmental exposure assessment, and incorporation of functional data and annotations.
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Affiliation(s)
| | - Leah E. Mechanic
- Correspondence to Dr. Leah E. Mechanic, Genomic Epidemiology Branch, Epidemiology and Genomics Research Program, Division of Cancer Control and Population Sciences, National Cancer Institute, 9609 Medical Center Drive, Room 4E104, MSC 9763, Bethesda, MD 20892 (e-mail: )
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14
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Lange LA, Graff M, Lange EM, Young KL, Richardson AS, Mohlke KL, North KE, Harris KM, Gordon-Larsen P. Evidence for Association between SH2B1 Gene Variants and Glycated Hemoglobin in Nondiabetic European American Young Adults: The Add Health Study. Ann Hum Genet 2017; 80:294-305. [PMID: 27530450 DOI: 10.1111/ahg.12165] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2015] [Revised: 05/16/2016] [Accepted: 07/05/2016] [Indexed: 12/25/2022]
Abstract
Glycated hemoglobin (HbA1c) is used to classify glycaemia and type 2 diabetes (T2D). Body mass index (BMI) is a predictor of HbA1c levels and T2D. We tested 43 established BMI and obesity loci for association with HbA1c in a nationally representative multiethnic sample of young adults from the National Longitudinal Study of Adolescent to Adult Health [Add Health: age 24-34 years; n = 5641 European Americans (EA); 1740 African Americans (AA); 1444 Hispanic Americans (HA)] without T2D, using two levels of covariate adjustment (Model 1: age, sex, smoking, and geographic region; Model 2: Model 1 covariates plus BMI). Bonferroni adjustment was made for 43 SNPs and we considered P < 0.0011 statistically significant. Means (SD) for HbA1c were 5.4% (0.3) in EA, 5.7% (0.4) in AA, and 5.5% (0.3) in HA. We observed significant evidence for association with HbA1c for two variants near SH2B1 in EA (rs4788102, P = 2.2 × 10(-4) ; rs7359397, P = 9.8 × 10(-4) ) for Model 1. Both results were attenuated after adjustment for BMI (rs4788102, P = 1.7 × 10(-3) ; rs7359397, P = 4.6 × 10(-3) ). No variant reached Bonferroni-corrected significance in AA or HA. These results suggest that SH2B1 polymorphisms are associated with HbA1c, largely independent of BMI, in EA young adults.
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Affiliation(s)
- Leslie A Lange
- Department of Genetics, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, USA.,Carolina Center for Genome Sciences, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, USA
| | - Mariaelisa Graff
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, USA.,Carolina Population Center, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, USA
| | - Ethan M Lange
- Department of Genetics, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, USA.,Carolina Center for Genome Sciences, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, USA.,Deptartment of Nutrition, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, USA
| | - Kristin L Young
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, USA.,Carolina Population Center, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, USA
| | - Andrea S Richardson
- Carolina Population Center, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, USA.,Department of Sociology, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, USA
| | - Karen L Mohlke
- Department of Genetics, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, USA.,Carolina Center for Genome Sciences, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, USA
| | - Kari E North
- Carolina Center for Genome Sciences, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, USA.,Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, USA
| | - Kathleen M Harris
- Carolina Population Center, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, USA.,Department of Biostatistics, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, USA
| | - Penny Gordon-Larsen
- Carolina Population Center, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, USA.,Department of Sociology, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, USA
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15
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Bigdeli TB, Ripke S, Peterson RE, Trzaskowski M, Bacanu SA, Abdellaoui A, Andlauer TFM, Beekman ATF, Berger K, Blackwood DHR, Boomsma DI, Breen G, Buttenschøn HN, Byrne EM, Cichon S, Clarke TK, Couvy-Duchesne B, Craddock N, de Geus EJC, Degenhardt F, Dunn EC, Edwards AC, Fanous AH, Forstner AJ, Frank J, Gill M, Gordon SD, Grabe HJ, Hamilton SP, Hardiman O, Hayward C, Heath AC, Henders AK, Herms S, Hickie IB, Hoffmann P, Homuth G, Hottenga JJ, Ising M, Jansen R, Kloiber S, Knowles JA, Lang M, Li QS, Lucae S, MacIntyre DJ, Madden PAF, Martin NG, McGrath PJ, McGuffin P, McIntosh AM, Medland SE, Mehta D, Middeldorp CM, Milaneschi Y, Montgomery GW, Mors O, Müller-Myhsok B, Nauck M, Nyholt DR, Nöthen MM, Owen MJ, Penninx BWJH, Pergadia ML, Perlis RH, Peyrot WJ, Porteous DJ, Potash JB, Rice JP, Rietschel M, Riley BP, Rivera M, Schoevers R, Schulze TG, Shi J, Shyn SI, Smit JH, Smoller JW, Streit F, Strohmaier J, Teumer A, Treutlein J, Van der Auwera S, van Grootheest G, van Hemert AM, Völzke H, Webb BT, Weissman MM, Wellmann J, Willemsen G, Witt SH, Levinson DF, Lewis CM, Wray NR, Flint J, Sullivan PF, Kendler KS. Genetic effects influencing risk for major depressive disorder in China and Europe. Transl Psychiatry 2017; 7:e1074. [PMID: 28350396 PMCID: PMC5404611 DOI: 10.1038/tp.2016.292] [Citation(s) in RCA: 48] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/19/2016] [Accepted: 11/27/2016] [Indexed: 11/24/2022] Open
Abstract
Major depressive disorder (MDD) is a common, complex psychiatric disorder and a leading cause of disability worldwide. Despite twin studies indicating its modest heritability (~30-40%), extensive heterogeneity and a complex genetic architecture have complicated efforts to detect associated genetic risk variants. We combined single-nucleotide polymorphism (SNP) summary statistics from the CONVERGE and PGC studies of MDD, representing 10 502 Chinese (5282 cases and 5220 controls) and 18 663 European (9447 cases and 9215 controls) subjects. We determined the fraction of SNPs displaying consistent directions of effect, assessed the significance of polygenic risk scores and estimated the genetic correlation of MDD across ancestries. Subsequent trans-ancestry meta-analyses combined SNP-level evidence of association. Sign tests and polygenic score profiling weakly support an overlap of SNP effects between East Asian and European populations. We estimated the trans-ancestry genetic correlation of lifetime MDD as 0.33; female-only and recurrent MDD yielded estimates of 0.40 and 0.41, respectively. Common variants downstream of GPHN achieved genome-wide significance by Bayesian trans-ancestry meta-analysis (rs9323497; log10 Bayes Factor=8.08) but failed to replicate in an independent European sample (P=0.911). Gene-set enrichment analyses indicate enrichment of genes involved in neuronal development and axonal trafficking. We successfully demonstrate a partially shared polygenic basis of MDD in East Asian and European populations. Taken together, these findings support a complex etiology for MDD and possible population differences in predisposing genetic factors, with important implications for future genetic studies.
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Affiliation(s)
- T B Bigdeli
- Department of Psychiatry, Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University School of Medicine, Richmond, VA, USA
| | - S Ripke
- Department of Psychiatry, Charite Universitatsmedizin Berlin Campus Benjamin Franklin, Berlin, Germany
- Medical and Population Genetics, Broad Institute, Cambridge, MA, USA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
| | - R E Peterson
- Department of Psychiatry, Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University School of Medicine, Richmond, VA, USA
| | - M Trzaskowski
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia
- Queensland Brain Institute, The University of Queensland, Brisbane, QLD, Australia
| | - S-A Bacanu
- Department of Psychiatry, Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University School of Medicine, Richmond, VA, USA
| | - A Abdellaoui
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - T F M Andlauer
- Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Munich, Germany
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
| | - A T F Beekman
- Department of Psychiatry, VU University Medical Center and GGZ inGeest, Amsterdam, The Netherlands
| | - K Berger
- Institute of Epidemiology and Social Medicine, University of Muenster, Münster, Germany
| | - D H R Blackwood
- Division of Psychiatry, University of Edinburgh, Edinburgh, UK
| | - D I Boomsma
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - G Breen
- King's College London, NIHR BRC for Mental Health, London, UK
- King's College London, MRC Social Genetic and Developmental Psychiatry Centre, London, UK
| | - H N Buttenschøn
- Department of Clinical Medicine, Translational Neuropsychiatry Unit, Aarhus University, Aarhus, Denmark
| | - E M Byrne
- Queensland Brain Institute, The University of Queensland, Brisbane, QLD, Australia
| | - S Cichon
- Department of Biomedicine, University of Basel, Basel, Switzerland
- Division of Medical Genetics, University of Basel, Basel, Switzerland
- Institute of Neuroscience and Medicine (INM-1), Research Center Juelich, Jülich, Germany
- Institute of Human Genetics, University of Bonn, Bonn, Germany
| | - T-K Clarke
- Division of Psychiatry, University of Edinburgh, Edinburgh, UK
| | - B Couvy-Duchesne
- Queensland Brain Institute, The University of Queensland, Brisbane, QLD, Australia
- Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
- Centre for Advanced Imaging, University of Queensland, Brisbane, QLD, Australia
| | - N Craddock
- Department of Psychological Medicine, Cardiff University, Cardiff, UK
| | - E J C de Geus
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- EMGO+ Institute, VU University Medical Center, Amsterdam, The Netherlands
| | - F Degenhardt
- Institute of Human Genetics, University of Bonn, Bonn, Germany
- Department of Genomics, Life and Brain Center, University of Bonn, Bonn, Germany
| | - E C Dunn
- Stanley Center for Psychiatric Research, Broad Institute, Cambridge, MA, USA
- Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA
- Psychiatric and Neurodevelopmental Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
| | - A C Edwards
- Department of Psychiatry, Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University School of Medicine, Richmond, VA, USA
| | - A H Fanous
- Department of Psychiatry and Behavioral Sciences, State University of New York Downstate Medical Center, Brooklyn, NY, USA
| | - A J Forstner
- Institute of Human Genetics, University of Bonn, Bonn, Germany
- Department of Genomics, Life and Brain Center, University of Bonn, Bonn, Germany
| | - J Frank
- Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Heidelberg, Germany
| | - M Gill
- Department of Psychiatry, Trinity College Dublin, Dublin, Ireland
| | - S D Gordon
- Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - H J Grabe
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, Germany
| | - S P Hamilton
- Department of Psychiatry, Kaiser-Permanente Northern California, San Fransisco, CA, USA
| | - O Hardiman
- Institute of Neuroscience, Trinity College Dublin, Dublin, Ireland
| | - C Hayward
- Medical Research Council Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK
| | - A C Heath
- Department of Psychiatry, Washington University in Saint Louis School of Medicine, St Louis, MO, USA
| | - A K Henders
- Queensland Brain Institute, The University of Queensland, Brisbane, QLD, Australia
| | - S Herms
- Institute of Human Genetics, University of Bonn, Bonn, Germany
- Department of Genomics, Life and Brain Center, University of Bonn, Bonn, Germany
- Division of Medical Genetics, Department of Biomedicine, University of Basel, Basel, Switzerland
| | - I B Hickie
- Brain and Mind Research Institute, University of Sydney, Sydney, NSW, Australia
| | - P Hoffmann
- Institute of Human Genetics, University of Bonn, Bonn, Germany
- Department of Genomics, Life and Brain Center, University of Bonn, Bonn, Germany
- Human Genomics Research Group, Department of Biomedicine, University of Basel, Basel, Switzerland
| | - G Homuth
- Department of Functional Genomics, Interfaculty Institute for Genetics and Functional Genomics, University Medicine and Ernst Moritz Arndt University Greifswald, Greifswald, Germany
| | - J-J Hottenga
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - M Ising
- Max Planck Institute of Psychiatry, Munich, Germany
| | - R Jansen
- Department of Psychiatry, VU University Medical Center and GGZ inGeest, Amsterdam, The Netherlands
| | - S Kloiber
- Max Planck Institute of Psychiatry, Munich, Germany
| | - J A Knowles
- Department of Psychiatry and The Behavioral Sciences, University of Southern California, Los Angeles, CA, USA
| | - M Lang
- Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Heidelberg, Germany
| | - Q S Li
- Neuroscience Therapeutic Area, Janssen Research and Development, LLC, Titusville, NJ, USA
| | - S Lucae
- Max Planck Institute of Psychiatry, Munich, Germany
| | - D J MacIntyre
- Division of Psychiatry, University of Edinburgh, Edinburgh, UK
| | - P A F Madden
- Department of Psychiatry, Washington University in Saint Louis School of Medicine, St Louis, MO, USA
| | - N G Martin
- Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
- School of Psychology, University of Queensland, Brisbane, QLD, Australia
| | - P J McGrath
- Department of Psychiatry, New York State Psychiatric Institute, Columbia University College of Physicians and Surgeons, New York, NY, USA
| | - P McGuffin
- King's College London, MRC Social Genetic and Developmental Psychiatry Centre, London, UK
| | - A M McIntosh
- Division of Psychiatry, University of Edinburgh, Edinburgh, UK
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK
| | - S E Medland
- Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - D Mehta
- Queensland Brain Institute, The University of Queensland, Brisbane, QLD, Australia
| | - C M Middeldorp
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Y Milaneschi
- Department of Psychiatry, VU University Medical Center and GGZ inGeest, Amsterdam, The Netherlands
| | - G W Montgomery
- Institute for Molecular Biology, University of Queensland, Brisbane, QLD, Australia
| | - O Mors
- Psychosis Research Unit, Aarhus University Hospital, Risskov, Denmark
| | - B Müller-Myhsok
- Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Munich, Germany
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
- Department of Molecular and Clinical Pharmacology, University of Liverpool, Liverpool, UK
| | - M Nauck
- Institute of Clinical Chemistry and Laboratory Medicine, University Medicine Greifswald, Greifswald, Germany
| | - D R Nyholt
- Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, QLD, Australia
| | - M M Nöthen
- Institute of Human Genetics, University of Bonn, Bonn, Germany
- Department of Genomics, Life and Brain Center, University of Bonn, Bonn, Germany
| | - M J Owen
- MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff University School of Medicine, Cardiff, UK
| | - B W J H Penninx
- Department of Psychiatry, VU University Medical Center and GGZ inGeest, Amsterdam, The Netherlands
| | - M L Pergadia
- Charles E. Schmidt College of Medicine, Florida Atlantic University, Boca Raton, FL, USA
| | - R H Perlis
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
- Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA
| | - W J Peyrot
- Department of Psychiatry, VU University Medical Center and GGZ inGeest, Amsterdam, The Netherlands
| | - D J Porteous
- Medical Genetics Section, CGEM, IGMM, University of Edinburgh, Edinburgh, UK
| | - J B Potash
- Department of Psychiatry, University of Iowa, Iowa, IA, USA
| | - J P Rice
- Department of Psychiatry, Washington University in Saint Louis, St Louis, MO, USA
| | - M Rietschel
- Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Heidelberg, Germany
| | - B P Riley
- Department of Psychiatry, Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University School of Medicine, Richmond, VA, USA
- Department of Human and Molecular Genetics, Virginia Commonwealth University School of Medicine, Richmond, VA, USA
| | - M Rivera
- Department of Biochemistry and Molecular Biology II, Institute of Neurosciences, Center for Biomedical Research, University of Granada, Granada, Spain
- King's College London, MRC Social Genetic and Developmental Psychiatry Centre, London, UK
| | - R Schoevers
- Department of Psychiatry, University of Groningen, University of Medical Center Groningen, Groningen, The Netherlands
| | - T G Schulze
- Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Heidelberg, Germany
- Institute of Psychiatric Phenomics and Genomics, Medical Center of the University of Munich, Campus Innenstadt, Munich, Germany
- Department of Psychiatry and Psychotherapy, University Medical Center Göttingen, Göttingen, The Netherlands
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University, Baltimore, MD, USA
- Human Genetics Branch, NIMH Division of Intramural Research Programs, Bethesda, MD, USA
| | - J Shi
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
| | - S I Shyn
- Division of Psychiatry, Group Health, Seattle, WA, USA
| | - J H Smit
- Department of Psychiatry, VU University Medical Center and GGZ inGeest, Amsterdam, The Netherlands
| | - J W Smoller
- Stanley Center for Psychiatric Research, Broad Institute, Cambridge, MA, USA
- Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA
- Psychiatric and Neurodevelopmental Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
| | - F Streit
- Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Heidelberg, Germany
| | - J Strohmaier
- Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Heidelberg, Germany
| | - A Teumer
- Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany
| | - J Treutlein
- Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Heidelberg, Germany
| | - S Van der Auwera
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, Germany
| | - G van Grootheest
- Department of Psychiatry, VU University Medical Center and GGZ inGeest, Amsterdam, The Netherlands
| | - A M van Hemert
- Department of Psychiatry, Leiden University Medical Center, Leiden, The Netherlands
| | - H Völzke
- Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany
| | - B T Webb
- Department of Psychiatry, Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University School of Medicine, Richmond, VA, USA
| | - M M Weissman
- Division of Epidemiology, New York State Psychiatric Institute, New York, NY, USA
- Department of Psychiatry, Columbia University College of Physicians and Surgeons, New York, NY, USA
| | - J Wellmann
- Institute of Epidemiology and Social Medicine, University of Muenster, Münster, Germany
| | - G Willemsen
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - S H Witt
- Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Heidelberg, Germany
| | - D F Levinson
- Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA
| | - C M Lewis
- King's College London, MRC Social Genetic and Developmental Psychiatry Centre, London, UK
- King's College London, Department of Medical and Molecular Genetics, London, UK
| | - N R Wray
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia
- Queensland Brain Institute, The University of Queensland, Brisbane, QLD, Australia
| | - J Flint
- Merton College, University of Oxford, Oxford, UK
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK
| | - P F Sullivan
- Medical Epidemiology and Biostatistics, Karolinska Institutet, Solna, Sweden
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - K S Kendler
- Department of Psychiatry, Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University School of Medicine, Richmond, VA, USA
- Department of Human and Molecular Genetics, Virginia Commonwealth University School of Medicine, Richmond, VA, USA
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16
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Racimo F, Gokhman D, Fumagalli M, Ko A, Hansen T, Moltke I, Albrechtsen A, Carmel L, Huerta-Sánchez E, Nielsen R. Archaic Adaptive Introgression in TBX15/WARS2. Mol Biol Evol 2017; 34:509-524. [PMID: 28007980 PMCID: PMC5430617 DOI: 10.1093/molbev/msw283] [Citation(s) in RCA: 57] [Impact Index Per Article: 8.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
Abstract
A recent study conducted the first genome-wide scan for selection in Inuit from Greenland using single nucleotide polymorphism chip data. Here, we report that selection in the region with the second most extreme signal of positive selection in Greenlandic Inuit favored a deeply divergent haplotype that is closely related to the sequence in the Denisovan genome, and was likely introgressed from an archaic population. The region contains two genes, WARS2 and TBX15, and has previously been associated with adipose tissue differentiation and body-fat distribution in humans. We show that the adaptively introgressed allele has been under selection in a much larger geographic region than just Greenland. Furthermore, it is associated with changes in expression of WARS2 and TBX15 in multiple tissues including the adrenal gland and subcutaneous adipose tissue, and with regional DNA methylation changes in TBX15.
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Affiliation(s)
- Fernando Racimo
- Department of Integrative Biology, University of California Berkeley, Berkeley, CA
| | - David Gokhman
- Department of Genetics, The Alexander Silberman Institute of Life Sciences, Faculty of Science, The Hebrew University of Jerusalem, Edmond J. Safra Campus, Givat Ram, Jerusalem, Israel
| | - Matteo Fumagalli
- Department of Genetics, Evolution, and Environment, University College London, London, United Kingdom
| | - Amy Ko
- Department of Integrative Biology, University of California Berkeley, Berkeley, CA
| | - Torben Hansen
- The Novo Nordisk Foundation Center for Basic Metabolic Research, Section of Metabolic Genetics, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Ida Moltke
- The Bioinformatics Centre, Department of Biology, University of Copenhagen, Copenhagen, Denmark
| | - Anders Albrechtsen
- The Bioinformatics Centre, Department of Biology, University of Copenhagen, Copenhagen, Denmark
| | - Liran Carmel
- Department of Genetics, The Alexander Silberman Institute of Life Sciences, Faculty of Science, The Hebrew University of Jerusalem, Edmond J. Safra Campus, Givat Ram, Jerusalem, Israel
| | | | - Rasmus Nielsen
- Department of Integrative Biology, University of California Berkeley, Berkeley, CA
- Department of Statistics, University of California Berkeley, Berkeley, CA
- Museum of Natural History, University of Copenhagen, Copenhagen, Denmark
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17
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Racimo F, Marnetto D, Huerta-Sánchez E. Signatures of Archaic Adaptive Introgression in Present-Day Human Populations. Mol Biol Evol 2017; 34:296-317. [PMID: 27756828 PMCID: PMC5400396 DOI: 10.1093/molbev/msw216] [Citation(s) in RCA: 76] [Impact Index Per Article: 10.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023] Open
Abstract
Comparisons of DNA from archaic and modern humans show that these groups interbred, and in some cases received an evolutionary advantage from doing so. This process-adaptive introgression-may lead to a faster rate of adaptation than is predicted from models with mutation and selection alone. Within the last couple of years, a series of studies have identified regions of the genome that are likely examples of adaptive introgression. In many cases, once a region was ascertained as being introgressed, commonly used statistics based on both haplotype as well as allele frequency information were employed to test for positive selection. Introgression by itself, however, changes both the haplotype structure and the distribution of allele frequencies, thus confounding traditional tests for detecting positive selection. Therefore, patterns generated by introgression alone may lead to false inferences of positive selection. Here we explore models involving both introgression and positive selection to investigate the behavior of various statistics under adaptive introgression. In particular, we find that the number and allelic frequencies of sites that are uniquely shared between archaic humans and specific present-day populations are particularly useful for detecting adaptive introgression. We then examine the 1000 Genomes dataset to characterize the landscape of uniquely shared archaic alleles in human populations. Finally, we identify regions that were likely subject to adaptive introgression and discuss some of the most promising candidate genes located in these regions.
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Affiliation(s)
- Fernando Racimo
- Department of Integrative Biology, University of California Berkeley, Berkeley, CA
| | - Davide Marnetto
- Department of Molecular Biotechnology and Health Sciences, University of Torino, Turin, Italy
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18
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Pasaniuc B, Price AL. Dissecting the genetics of complex traits using summary association statistics. Nat Rev Genet 2017; 18:117-127. [PMID: 27840428 PMCID: PMC5449190 DOI: 10.1038/nrg.2016.142] [Citation(s) in RCA: 256] [Impact Index Per Article: 36.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
During the past decade, genome-wide association studies (GWAS) have been used to successfully identify tens of thousands of genetic variants associated with complex traits and diseases. These studies have produced extensive repositories of genetic variation and trait measurements across large numbers of individuals, providing tremendous opportunities for further analyses. However, privacy concerns and other logistical considerations often limit access to individual-level genetic data, motivating the development of methods that analyse summary association statistics. Here, we review recent progress on statistical methods that leverage summary association data to gain insights into the genetic basis of complex traits and diseases.
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Affiliation(s)
- Bogdan Pasaniuc
- Departments of Human Genetics, and Pathology and Laboratory Medicine, University of California, Los Angeles, California 90095, USA
| | - Alkes L Price
- Departments of Epidemiology and Biostatistics, Harvard T. H. Chan School of Public Health, Boston, Massachusetts 02115, USA
- Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts 02142, USA
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19
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Yoneyama S, Yao J, Guo X, Fernandez-Rhodes L, Lim U, Boston J, Buzková P, Carlson CS, Cheng I, Cochran B, Cooper R, Ehret G, Fornage M, Gong J, Gross M, Gu CC, Haessler J, Haiman CA, Henderson B, Hindorff LA, Houston D, Irvin MR, Jackson R, Kuller L, Leppert M, Lewis CE, Li R, Le Marchand L, Matise TC, Nguyen KDH, Chakravarti A, Pankow JS, Pankratz N, Pooler L, Ritchie MD, Bien SA, Wassel CL, Chen YDI, Taylor KD, Allison M, Rotter JI, Schreiner PJ, Schumacher F, Wilkens L, Boerwinkle E, Kooperberg C, Peters U, Buyske S, Graff M, North KE. Generalization and fine mapping of European ancestry-based central adiposity variants in African ancestry populations. Int J Obes (Lond) 2017; 41:324-331. [PMID: 27867202 PMCID: PMC5296276 DOI: 10.1038/ijo.2016.207] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/19/2016] [Revised: 10/12/2016] [Accepted: 10/23/2016] [Indexed: 12/19/2022]
Abstract
BACKGROUND/OBJECTIVES Central adiposity measures such as waist circumference (WC) and waist-to-hip ratio (WHR) are associated with cardiometabolic disorders independently of body mass index (BMI) and are gaining clinically utility. Several studies report genetic variants associated with central adiposity, but most utilize only European ancestry populations. Understanding whether the genetic associations discovered among mainly European descendants are shared with African ancestry populations will help elucidate the biological underpinnings of abdominal fat deposition. SUBJECTS/METHODS To identify the underlying functional genetic determinants of body fat distribution, we conducted an array-wide association meta-analysis among persons of African ancestry across seven studies/consortia participating in the Population Architecture using Genomics and Epidemiology (PAGE) consortium. We used the Metabochip array, designed for fine-mapping cardiovascular-associated loci, to explore novel array-wide associations with WC and WHR among 15 945 African descendants using all and sex-stratified groups. We further interrogated 17 known WHR regions for African ancestry-specific variants. RESULTS Of the 17 WHR loci, eight single-nucleotide polymorphisms (SNPs) located in four loci were replicated in the sex-combined or sex-stratified meta-analyses. Two of these eight independently associated with WHR after conditioning on the known variant in European descendants (rs12096179 in TBX15-WARS2 and rs2059092 in ADAMTS9). In the fine-mapping assessment, the putative functional region was reduced across all four loci but to varying degrees (average 40% drop in number of putative SNPs and 20% drop in genomic region). Similar to previous studies, the significant SNPs in the female-stratified analysis were stronger than the significant SNPs from the sex-combined analysis. No novel associations were detected in the array-wide analyses. CONCLUSIONS Of 17 previously identified loci, four loci replicated in the African ancestry populations of this study. Utilizing different linkage disequilibrium patterns observed between European and African ancestries, we narrowed the suggestive region containing causative variants for all four loci.
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Affiliation(s)
- Sachiko Yoneyama
- Department of Epidemiology, University of North Carolina, Chapel Hill, NC, 27514, USA
| | - Jie Yao
- Institute for Translational Genomics and Population Sciences, Department of Pediatrics, LABioMed at Harbor-UCLA Medical Center, Torrance, CA, 90502, USA
| | - Xiuqing Guo
- Institute for Translational Genomics and Population Sciences, Department of Pediatrics, LABioMed at Harbor-UCLA Medical Center, Torrance, CA, 90502, USA
| | | | - Unhee Lim
- Epidemiology Program, University of Hawaii Cancer Center, Honolulu, HI, 96813, USA
| | - Jonathan Boston
- Center for Human Genetics Research, Vanderbilt University Medical Center, Nashville, TN, 37203, USA
| | - Petra Buzková
- Department of Biostatistics, University of Washington, Seattle, WA, 98195, USA
| | - Christopher S. Carlson
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, 98109, USA
| | - Iona Cheng
- Cancer Prevention Institute of California, Fremont, CA, 94538, USA
| | - Barbara Cochran
- Baylor College of Medicine, Houston, TX, 77030, USA
- Division of Cardiology, Geneva University Hospital, Genève, 1205, Switzerland
| | - Richard Cooper
- Preventive Medicine and Epidemiology, Loyola University, Chicago, IL, 60153, USA
| | - Georg Ehret
- Center for Complex Disease Genomics, McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, 21205, USA
| | - Myriam Fornage
- The Human Genetics Center and Institute of Molecular Medicine, The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA
| | - Jian Gong
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, 98109, USA
| | - Myron Gross
- Masonic Cancer Center, University of Minnesota, Minneapolis, MN, 55455, USA
| | - C. Charles Gu
- Department of Biostatistics, Washington University, St. Louis, MO, 63110, USA
| | - Jeff Haessler
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, 98109, USA
| | - Christopher A. Haiman
- Department of Preventive Medicine, Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, CA, 90089, USA
| | - Brian Henderson
- Department of Preventive Medicine, Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, CA, 90089, USA
| | - Lucia A. Hindorff
- Division of Genomic Medicine, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Denise Houston
- Department of Internal Medicine, Wake Forest School of Medicine, Winston-Salem, NC, 27157, USA
| | - Marguerite R. Irvin
- Department of Biostatistics, University of Alabama at Birmingham, Birmingham, AL, 35294, USA
| | - Rebecca Jackson
- Department of Internal Medicine, Ohio State Medical Center, Columbus, OH, 43210, USA
| | - Lew Kuller
- Department of Medicine, University of Pittsburgh, Pittsburgh, PA, 15261, USA
| | - Mark Leppert
- Department of Human Genetics, University of Utah, Salt Lake City, UT, 84112, USA
| | - Cora E. Lewis
- Department of Medicine, University of Alabama, Birmingham, AL, 35294, USA
| | - Rongling Li
- Division of Genomic Medicine, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Loic Le Marchand
- Epidemiology Program, University of Hawaii Cancer Center, Honolulu, HI, 96813, USA
| | - Tara C. Matise
- Department of Genetics, Rutgers University, Piscataway, NJ, 08854, USA
| | - Khanh-Dung H. Nguyen
- Center for Complex Disease Genomics, McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, 21205, USA
| | - Aravinda Chakravarti
- Center for Complex Disease Genomics, McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, 21205, USA
| | - James S. Pankow
- Division of Epidemiology & Community Health, School of Public Health, University of Minnesota, Minneapolis, MN, 55455, USA
| | - Nathan Pankratz
- Masonic Cancer Center, University of Minnesota, Minneapolis, MN, 55455, USA
| | - Loreall Pooler
- Department of Preventive Medicine, Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, CA, 90089, USA
| | - Marylyn D. Ritchie
- Biochemistry and Molecular Biology, Pennsylvania State University, State College, PA, 16801, USA
| | - Stephanie A. Bien
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, 98109, USA
| | - Christina L. Wassel
- University of Vermont College of Medicine, Department of Pathology and Laboratory Medicine, Colchester, VT, 05446, USA
| | - Yii-Der I. Chen
- Institute for Translational Genomics and Population Sciences, Department of Pediatrics, LABioMed at Harbor-UCLA Medical Center, Torrance, CA, 90502, USA
| | - Kent D. Taylor
- Institute for Translational Genomics and Population Sciences, Department of Pediatrics, LABioMed at Harbor-UCLA Medical Center, Torrance, CA, 90502, USA
| | - Matthew Allison
- School of Medicine, University of California San Diego, Department of Preventive Medicine, San Diego, CA, 92110, USA
| | - Jerome I. Rotter
- Institute for Translational Genomics and Population Sciences, Department of Pediatrics, LABioMed at Harbor-UCLA Medical Center, Torrance, CA, 90502, USA
| | - Pamela J. Schreiner
- Division of Epidemiology & Community Health, School of Public Health, University of Minnesota, Minneapolis, MN, 55455, USA
| | - Fredrick Schumacher
- Department of Preventive Medicine, Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, CA, 90089, USA
| | - Lynne Wilkens
- Epidemiology Program, University of Hawaii Cancer Center, Honolulu, HI, 96813, USA
| | - Eric Boerwinkle
- The Human Genetics Center and Institute of Molecular Medicine, Houston, TX, 77030, USA
- Department of Statistics and Biostatistics, Rutgers University, Piscataway, NJ, 8854, USA
| | - Charles Kooperberg
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, 98109, USA
| | - Ulrike Peters
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, 98109, USA
| | - Steven Buyske
- Department of Genetics, Rutgers University, Piscataway, NJ, 08854, USA
| | - Mariaelisa Graff
- Department of Epidemiology, University of North Carolina, Chapel Hill, NC, 27514, USA
| | - Kari E. North
- Department of Epidemiology, University of North Carolina, Chapel Hill, NC, 27514, USA
- Carolina Center for Genome Sciences, Chapel Hill, NC, 27514, USA
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20
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van Rooij FJ, Qayyum R, Smith AV, Zhou Y, Trompet S, Tanaka T, Keller MF, Chang LC, Schmidt H, Yang ML, Chen MH, Hayes J, Johnson AD, Yanek LR, Mueller C, Lange L, Floyd JS, Ghanbari M, Zonderman AB, Jukema JW, Hofman A, van Duijn CM, Desch KC, Saba Y, Ozel AB, Snively BM, Wu JY, Schmidt R, Fornage M, Klein RJ, Fox CS, Matsuda K, Kamatani N, Wild PS, Stott DJ, Ford I, Slagboom PE, Yang J, Chu AY, Lambert AJ, Uitterlinden AG, Franco OH, Hofer E, Ginsburg D, Hu B, Keating B, Schick UM, Brody JA, Li JZ, Chen Z, Zeller T, Guralnik JM, Chasman DI, Peters LL, Kubo M, Becker DM, Li J, Eiriksdottir G, Rotter JI, Levy D, Grossmann V, Patel KV, Chen CH, Ridker PM, Tang H, Launer LJ, Rice KM, Li-Gao R, Ferrucci L, Evans MK, Choudhuri A, Trompouki E, Abraham BJ, Yang S, Takahashi A, Kamatani Y, Kooperberg C, Harris TB, Jee SH, Coresh J, Tsai FJ, Longo DL, Chen YT, Felix JF, Yang Q, Psaty BM, Boerwinkle E, Becker LC, Mook-Kanamori DO, Wilson JG, Gudnason V, O'Donnell CJ, Dehghan A, Cupples LA, Nalls MA, Morris AP, Okada Y, Reiner AP, Zon LI, Ganesh SK, Ganesh SK. Genome-wide Trans-ethnic Meta-analysis Identifies Seven Genetic Loci Influencing Erythrocyte Traits and a Role for RBPMS in Erythropoiesis. Am J Hum Genet 2017; 100:51-63. [PMID: 28017375 DOI: 10.1016/j.ajhg.2016.11.016] [Citation(s) in RCA: 36] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2016] [Accepted: 11/16/2016] [Indexed: 11/26/2022] Open
Abstract
Genome-wide association studies (GWASs) have identified loci for erythrocyte traits in primarily European ancestry populations. We conducted GWAS meta-analyses of six erythrocyte traits in 71,638 individuals from European, East Asian, and African ancestries using a Bayesian approach to account for heterogeneity in allelic effects and variation in the structure of linkage disequilibrium between ethnicities. We identified seven loci for erythrocyte traits including a locus (RBPMS/GTF2E2) associated with mean corpuscular hemoglobin and mean corpuscular volume. Statistical fine-mapping at this locus pointed to RBPMS at this locus and excluded nearby GTF2E2. Using zebrafish morpholino to evaluate loss of function, we observed a strong in vivo erythropoietic effect for RBPMS but not for GTF2E2, supporting the statistical fine-mapping at this locus and demonstrating that RBPMS is a regulator of erythropoiesis. Our findings show the utility of trans-ethnic GWASs for discovery and characterization of genetic loci influencing hematologic traits.
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Affiliation(s)
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | - Santhi K Ganesh
- Division of Cardiovascular Medicine, Department of Internal Medicine, Department of Human Genetics, University of Michigan, 1500 E. Medical Center Drive, Ann Arbor, MI 48109, USA.
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21
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Laber S, Cox RD. Mouse Models of Human GWAS Hits for Obesity and Diabetes in the Post Genomic Era: Time for Reevaluation. Front Endocrinol (Lausanne) 2017; 8:11. [PMID: 28223964 PMCID: PMC5294391 DOI: 10.3389/fendo.2017.00011] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/04/2016] [Accepted: 01/13/2017] [Indexed: 12/12/2022] Open
Affiliation(s)
- Samantha Laber
- Mammalian Genetics Unit, Medical Research Council Harwell Institute, Oxfordshire, UK
- Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford, UK
- *Correspondence: Samantha Laber, ; Roger D. Cox,
| | - Roger D. Cox
- Mammalian Genetics Unit, Medical Research Council Harwell Institute, Oxfordshire, UK
- *Correspondence: Samantha Laber, ; Roger D. Cox,
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22
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Velázquez-Fernández D, Mercado-Celis G, Flores-Morales J, Clavellina-Gaytán D, Vidrio R, Vidrio E, Mosti M, Sánchez-Aguilar H, Rodriguez D, León P, Herrera MF. Analysis of Gene Candidate SNP and Ancestral Origin Associated to Obesity and Postoperative Weight Loss in a Cohort of Obese Patients Undergoing RYGB. Obes Surg 2016; 27:1481-1492. [DOI: 10.1007/s11695-016-2501-9] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
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23
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Hernández-Tobías EA, Torres-Sánchez L, Noris G, Santana C, Samano MR, Arellano-Galindo J, Arenas-Sordo MDLL, Brooks D, Rodríguez-Ventura AL, Meraz-Ríos MA, Gómez R. PPARG-LYPLAL1 Multi-Allelic Combination Associated with Obesity and Overweight in Mexican Adolescent Females. Ethn Dis 2016; 26:477-484. [PMID: 27773974 DOI: 10.18865/ed.26.4.477] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
OBJECTIVE We studied multi-loci variants to identify the contribution of six candidate genes (ADIPOQ, CDH13, LYPLAL1, MC4R, PPARG and PGC1A) in the development of obesity and overweight. DESIGN We genotyped 404 chromosomes with eleven SNPs in Mexican female adolescents, who were subdivided into two groups (obesity-overweight and normal-weight) using the World Health Organization parameters. Genomic (800 chromosomes) and ancestral (208 chromosomes) controls were included to reduce the population bias. Anthropometric measurements, biochemical parameters, and caloric intake were obtained only in the groups of Mexican female adolescents. RESULTS A positive genotype-phenotype association was found that involves the multi-allelic combination of three risk alleles (one in PPARG and two in LYPLAL1) with obesity and overweight (OR=3.1, P=.010). This combination also exhibited a significant association with waist circumference (P=.030) and triglycerides levels (P=.030). These associations were supported by a logistic regression analysis adjusted for several confounding variables. CONCLUSIONS Our data suggest the joint participation of PPARG-LYPLAL1 genes in metabolic disorders development. Hence, these genes could act as potential biomarkers in obesity and overweight. Our findings underscore the complexity of metabolic disorders and provide evidence about the importance of multi-loci analysis to study complex diseases.
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Affiliation(s)
| | | | - Gino Noris
- Laboratorio BIMODI (Biología Molecular Diagnóstica), Querétaro, Qro., México
| | - Carla Santana
- Laboratorio BIMODI (Biología Molecular Diagnóstica), Querétaro, Qro., México
| | - María Reyna Samano
- Departamento de Nutrición y Bioprogramación, Instituto Nacional de Perinatología, México, D.F., México
| | | | | | - Daniel Brooks
- Departamento de Toxicología, Cinvestav-IPN, México D.F., México; Department of Anthropology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | | | | | - Rocío Gómez
- Departamento de Toxicología, Cinvestav-IPN, México D.F., México
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24
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Gupta J, Johansson E, Bernstein JA, Chakraborty R, Khurana Hershey GK, Rothenberg ME, Mersha TB. Resolving the etiology of atopic disorders by using genetic analysis of racial ancestry. J Allergy Clin Immunol 2016; 138:676-699. [PMID: 27297995 PMCID: PMC5014679 DOI: 10.1016/j.jaci.2016.02.045] [Citation(s) in RCA: 46] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2015] [Revised: 02/09/2016] [Accepted: 02/25/2016] [Indexed: 12/23/2022]
Abstract
Atopic dermatitis (AD), food allergy, allergic rhinitis, and asthma are common atopic disorders of complex etiology. The frequently observed atopic march from early AD to asthma, allergic rhinitis, or both later in life and the extensive comorbidity of atopic disorders suggest common causal mechanisms in addition to distinct ones. Indeed, both disease-specific and shared genomic regions exist for atopic disorders. Their prevalence also varies among races; for example, AD and asthma have a higher prevalence in African Americans when compared with European Americans. Whether this disparity stems from true genetic or race-specific environmental risk factors or both is unknown. Thus far, the majority of the genetic studies on atopic diseases have used populations of European ancestry, limiting their generalizability. Large-cohort initiatives and new analytic methods, such as admixture mapping, are currently being used to address this knowledge gap. Here we discuss the unique and shared genetic risk factors for atopic disorders in the context of ancestry variations and the promise of high-throughput "-omics"-based systems biology approach in providing greater insight to deconstruct their genetic and nongenetic etiologies. Future research will also focus on deep phenotyping and genotyping of diverse racial ancestry, gene-environment, and gene-gene interactions.
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Affiliation(s)
- Jayanta Gupta
- Division of Asthma Research, Department of Pediatrics, Cincinnati Children's Hospital Medical Center, University of Cincinnati, Cincinnati, Ohio
| | - Elisabet Johansson
- Division of Asthma Research, Department of Pediatrics, Cincinnati Children's Hospital Medical Center, University of Cincinnati, Cincinnati, Ohio
| | - Jonathan A Bernstein
- Division of Immunology/Allergy Section, Department of Internal Medicine, University of Cincinnati, Cincinnati, Ohio
| | - Ranajit Chakraborty
- Center for Computational Genomics, Institute of Applied Genetics, Department of Molecular and Medical Genetics, University of North Texas Health Science Center, Fort Worth, Tex
| | - Gurjit K Khurana Hershey
- Division of Asthma Research, Department of Pediatrics, Cincinnati Children's Hospital Medical Center, University of Cincinnati, Cincinnati, Ohio
| | - Marc E Rothenberg
- Division of Allergy and Immunology, Department of Pediatrics, Cincinnati Children's Hospital Medical Center, University of Cincinnati, Cincinnati, Ohio
| | - Tesfaye B Mersha
- Division of Asthma Research, Department of Pediatrics, Cincinnati Children's Hospital Medical Center, University of Cincinnati, Cincinnati, Ohio.
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25
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Liu CT, Raghavan S, Maruthur N, Kabagambe EK, Hong J, Ng MCY, Hivert MF, Lu Y, An P, Bentley AR, Drolet AM, Gaulton KJ, Guo X, Armstrong LL, Irvin MR, Li M, Lipovich L, Rybin DV, Taylor KD, Agyemang C, Palmer ND, Cade BE, Chen WM, Dauriz M, Delaney JAC, Edwards TL, Evans DS, Evans MK, Lange LA, Leong A, Liu J, Liu Y, Nayak U, Patel SR, Porneala BC, Rasmussen-Torvik LJ, Snijder MB, Stallings SC, Tanaka T, Yanek LR, Zhao W, Becker DM, Bielak LF, Biggs ML, Bottinger EP, Bowden DW, Chen G, Correa A, Couper DJ, Crawford DC, Cushman M, Eicher JD, Fornage M, Franceschini N, Fu YP, Goodarzi MO, Gottesman O, Hara K, Harris TB, Jensen RA, Johnson AD, Jhun MA, Karter AJ, Keller MF, Kho AN, Kizer JR, Krauss RM, Langefeld CD, Li X, Liang J, Liu S, Lowe WL, Mosley TH, North KE, Pacheco JA, Peyser PA, Patrick AL, Rice KM, Selvin E, Sims M, Smith JA, Tajuddin SM, Vaidya D, Wren MP, Yao J, Zhu X, Ziegler JT, Zmuda JM, Zonderman AB, Zwinderman AH, Adeyemo A, Boerwinkle E, Ferrucci L, Hayes MG, Kardia SLR, Miljkovic I, Pankow JS, Rotimi CN, Sale MM, Wagenknecht LE, Arnett DK, Chen YDI, Nalls MA, Province MA, Kao WHL, Siscovick DS, Psaty BM, Wilson JG, Loos RJF, Dupuis J, Rich SS, Florez JC, Rotter JI, Morris AP, Meigs JB. Trans-ethnic Meta-analysis and Functional Annotation Illuminates the Genetic Architecture of Fasting Glucose and Insulin. Am J Hum Genet 2016; 99:56-75. [PMID: 27321945 PMCID: PMC5005440 DOI: 10.1016/j.ajhg.2016.05.006] [Citation(s) in RCA: 45] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2016] [Accepted: 05/02/2016] [Indexed: 12/11/2022] Open
Abstract
Knowledge of the genetic basis of the type 2 diabetes (T2D)-related quantitative traits fasting glucose (FG) and insulin (FI) in African ancestry (AA) individuals has been limited. In non-diabetic subjects of AA (n = 20,209) and European ancestry (EA; n = 57,292), we performed trans-ethnic (AA+EA) fine-mapping of 54 established EA FG or FI loci with detailed functional annotation, assessed their relevance in AA individuals, and sought previously undescribed loci through trans-ethnic (AA+EA) meta-analysis. We narrowed credible sets of variants driving association signals for 22/54 EA-associated loci; 18/22 credible sets overlapped with active islet-specific enhancers or transcription factor (TF) binding sites, and 21/22 contained at least one TF motif. Of the 54 EA-associated loci, 23 were shared between EA and AA. Replication with an additional 10,096 AA individuals identified two previously undescribed FI loci, chrX FAM133A (rs213676) and chr5 PELO (rs6450057). Trans-ethnic analyses with regulatory annotation illuminate the genetic architecture of glycemic traits and suggest gene regulation as a target to advance precision medicine for T2D. Our approach to utilize state-of-the-art functional annotation and implement trans-ethnic association analysis for discovery and fine-mapping offers a framework for further follow-up and characterization of GWAS signals of complex trait loci.
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Affiliation(s)
- Ching-Ti Liu
- Department of Biostatistics, School of Public Health, Boston University, Boston, MA 02118, USA.
| | - Sridharan Raghavan
- Division of General Internal Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA; Department of Veterans Affairs Medical Center, Eastern Colorado Health Care System, Denver, CO 80220, USA; Division of General Internal Medicine, Department of Medicine, University of Colorado School of Medicine, Denver, CO 80220, USA
| | - Nisa Maruthur
- Division of General Internal Medicine, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA; Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University, Baltimore, MD 21287, USA; Department of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD 21287, USA
| | - Edmond Kato Kabagambe
- Division of Epidemiology, Department of Medicine, School of Medicine, Vanderbilt University Medical Center, Nashville, TN 37203, USA
| | - Jaeyoung Hong
- Department of Biostatistics, School of Public Health, Boston University, Boston, MA 02118, USA
| | - Maggie C Y Ng
- Center for Genomics and Personalized Medicine Research, Wake Forest University School of Medicine, Winston-Salem, NC 27157, USA; Center for Diabetes Research, Wake Forest University School of Medicine, Winston-Salem, NC 27157, USA
| | - Marie-France Hivert
- Department of Population Medicine, Harvard Pilgrim Health Care Institute, Harvard Medical School, Boston, MA 02215, USA; Diabetes Unit, Massachusetts General Hospital, Boston, MA 02114, USA; Department of Medicine, Universite de Sherbrooke, Sherbrooke, QC J1G 0A2, Canada
| | - Yingchang Lu
- The Charles Bronfman Institute for Personalized Medicine, The Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; The Genetics of Obesity and Related Metabolic Traits Program, The Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Ping An
- Division of Statistical Genomics, Department of Genetics, School of Medicine, Washington University, St Louis, MO 63108, USA
| | - Amy R Bentley
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, NIH, Bethesda, MD 20892, USA
| | - Anne M Drolet
- Center for Molecular Medicine and Genetics, School of Medicine, Wayne State University, Detroit, MI 48201, USA
| | - Kyle J Gaulton
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK
| | - Xiuqing Guo
- Institute for Translational Genomics and Population Sciences, Department of Pediatrics, Los Angeles Biomedical Research Institute, Harbor-UCLA Medical Center, Torrance, CA 90502, USA
| | - Loren L Armstrong
- Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA
| | - Marguerite R Irvin
- Department of Epidemiology, School of Public Health, University of Alabama - Birmingham, Birmingham, AL 35294, USA
| | - Man Li
- Department of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD 21287, USA
| | - Leonard Lipovich
- Center for Molecular Medicine and Genetics, School of Medicine, Wayne State University, Detroit, MI 48201, USA; Department of Neurology, School of Medicine, Wayne State University, Detroit, MI 48201, USA
| | - Denis V Rybin
- Department of Biostatistics, School of Public Health, Boston University, Boston, MA 02118, USA
| | - Kent D Taylor
- Institute for Translational Genomics and Population Sciences, Department of Pediatrics, Los Angeles Biomedical Research Institute, Harbor-UCLA Medical Center, Torrance, CA 90502, USA
| | - Charles Agyemang
- Department of Public Health, Academic Medical Center Amsterdam, Meibergdreef 15, 1105 AZ Amsterdam, the Netherlands
| | - Nicholette D Palmer
- Center for Genomics and Personalized Medicine Research, Wake Forest University School of Medicine, Winston-Salem, NC 27157, USA; Department of Biochemistry, Wake Forest University School of Medicine, Winston-Salem, NC 27157, USA
| | - Brian E Cade
- Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Wei-Min Chen
- Center for Public Health Genomics, Department of Public Health Sciences, School of Medicine, University of Virginia, Charlottesville, VA 22908, USA
| | - Marco Dauriz
- Division of Endocrinology, Diabetes & Metabolism, Department of Medicine, University of Verona, 37126 Verona, Italy
| | - Joseph A C Delaney
- Department of Epidemiology, University of Washington, Seattle, WA 98195, USA
| | - Todd L Edwards
- Division of Epidemiology, Department of Medicine, School of Medicine, Vanderbilt University Medical Center, Nashville, TN 37203, USA
| | - Daniel S Evans
- California Pacific Medical Center Research Institute, San Francisco, CA 94107, USA
| | - Michele K Evans
- Health Disparities Research Section, Laboratory of Epidemiology and Population Sciences, National Institute on Aging, NIH, Baltimore, MD 21224, USA
| | - Leslie A Lange
- Department of Genetics, University of North Carolina, Chapel Hill, NC 27607, USA
| | - Aaron Leong
- Division of General Internal Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
| | - Jingmin Liu
- Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA
| | - Yongmei Liu
- Center for Human Genetics, Division of Public Health Sciences, Wake Forest University School of Medicine, Winston-Salem, NC 27157, USA
| | - Uma Nayak
- Center for Public Health Genomics, Department of Public Health Sciences, School of Medicine, University of Virginia, Charlottesville, VA 22908, USA
| | - Sanjay R Patel
- Department of Medicine, University of Pittsburgh, Pittsburgh, PA 15213, USA
| | - Bianca C Porneala
- Division of General Internal Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
| | - Laura J Rasmussen-Torvik
- Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA
| | - Marieke B Snijder
- Department of Public Health, Academic Medical Center Amsterdam, Meibergdreef 15, 1105 AZ Amsterdam, the Netherlands
| | - Sarah C Stallings
- Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, TN 37203, USA
| | - Toshiko Tanaka
- Translational Gerontology Branch, National Institute of Aging at Harbor Hospital, Baltimore, MD 21225, USA
| | - Lisa R Yanek
- GeneSTAR Research Program, Division of General Internal Medicine, Department of Medicine, Johns Hopkins University, Baltimore, MD 21287, USA
| | - Wei Zhao
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI 48109, USA
| | - Diane M Becker
- GeneSTAR Research Program, Division of General Internal Medicine, Department of Medicine, Johns Hopkins University, Baltimore, MD 21287, USA; Department of Health Policy and Management, Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD 21287, USA
| | - Lawrence F Bielak
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI 48109, USA
| | - Mary L Biggs
- Department of Biostatistics, University of Washington, Seattle, WA 98195, USA; Cardiovascular Health Research Unit, Department of Medicine, School of Medicine, University of Washington, Seattle, WA 98195, USA
| | - Erwin P Bottinger
- The Charles Bronfman Institute for Personalized Medicine, The Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Donald W Bowden
- Center for Genomics and Personalized Medicine Research, Wake Forest University School of Medicine, Winston-Salem, NC 27157, USA; Center for Diabetes Research, Wake Forest University School of Medicine, Winston-Salem, NC 27157, USA; Department of Biochemistry, Wake Forest University School of Medicine, Winston-Salem, NC 27157, USA
| | - Guanjie Chen
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, NIH, Bethesda, MD 20892, USA
| | - Adolfo Correa
- Department of Medicine, University of Mississippi Medical Center, Jackson, MS 39216, USA
| | - David J Couper
- Collaborative Studies Coordinating Center, Department of Biostatistics, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC 27514, USA
| | - Dana C Crawford
- Department of Epidemiology and Biostatistics, Institute for Computational Biology, Case Western Reserve University, Cleveland, OH 44106, USA
| | - Mary Cushman
- Department of Medicine and Pathology, University of Vermont, College of Medicine, Burlington, VT 05405, USA
| | - John D Eicher
- National Heart, Lung, and Blood Institute's Framingham Heart Study, Framingham, MA 01702, USA; Population Sciences Branch, Division of Intramural Research, National Heart, Lung, and Blood Institute, NIH, Framingham, MA 01702, USA
| | - Myriam Fornage
- Institute of Molecular Medicine and Human Genetics Center, University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Nora Franceschini
- Department of Epidemiology, University of North Carolina, Chapel Hill, NC 27514, USA
| | - Yi-Ping Fu
- Cardiovascular Epidemiology and Human Genomics Branch, National Heart, Lung, and Blood Institute, NIH, Framingham, MA 01702, USA
| | - Mark O Goodarzi
- Division of Endocrinology, Diabetes & Metabolism, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA
| | - Omri Gottesman
- The Charles Bronfman Institute for Personalized Medicine, The Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Kazuo Hara
- The Charles Bronfman Institute for Personalized Medicine, The Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Department of Diabetes and Metabolic Diseases, Graduate School of Medicine, The University of Tokyo, Tokyo 113-8655, Japan; Department of Diabetes, Endocrinology, and Metabolism, Tokyo Medical University, Tokyo 163-0023, Japan
| | - Tamara B Harris
- Laboratory of Epidemiology and Population Sciences, NIH, Bethesda, MD 20892, USA
| | - Richard A Jensen
- Cardiovascular Health Research Unit, Department of Medicine, School of Medicine, University of Washington, Seattle, WA 98195, USA
| | - Andrew D Johnson
- Population Sciences Branch, Division of Intramural Research, National Heart, Lung, and Blood Institute, NIH, Framingham, MA 01702, USA
| | - Min A Jhun
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI 48109, USA
| | - Andrew J Karter
- Division of Research, Kaiser Permanente, Northern California Region, Oakland, CA 94612, USA
| | - Margaux F Keller
- Department of Genetics and Pharmacogenomics, Merck Research Laboratories, 33 Avenue Louis Pasteur, Boston, MA 02115, USA
| | - Abel N Kho
- Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA
| | - Jorge R Kizer
- Department of Medicine, Albert Einstein College of Medicine, Montefiore Medical Center, Bronx, NY 10461, USA; Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY 10461, USA
| | - Ronald M Krauss
- Children's Hospital Oakland Research Institute, Oakland, CA 94609, USA
| | - Carl D Langefeld
- Center for Public Health Genomics, Wake Forest University School of Medicine, Winston-Salem, NC 27157, USA; Department of Biostatistical Sciences, Wake Forest University School of Medicine, Winston-Salem, NC 27157, USA
| | - Xiaohui Li
- Institute for Translational Genomics and Population Sciences, Department of Pediatrics, Los Angeles Biomedical Research Institute, Harbor-UCLA Medical Center, Torrance, CA 90502, USA
| | - Jingling Liang
- Department of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, OH 44106, USA
| | - Simin Liu
- Department of Epidemiology, Brown University, Providence, RI 02912, USA; Department of Medicine, Brown University, Providence, RI 02903, USA
| | - William L Lowe
- Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA
| | - Thomas H Mosley
- Division of Geriatrics/Gerontology, Department of Medicine, University of Mississippi Medical Center, Jackson, MS 39216, USA
| | - Kari E North
- Department of Epidemiology, University of North Carolina, Chapel Hill, NC 27514, USA
| | - Jennifer A Pacheco
- Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA
| | - Patricia A Peyser
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI 48109, USA
| | - Alan L Patrick
- Tobago Health Studies Office, Scarborough, Tobago, Trinidad and Tobago
| | - Kenneth M Rice
- Department of Biostatistics, University of Washington, Seattle, WA 98195, USA
| | - Elizabeth Selvin
- Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University, Baltimore, MD 21287, USA; Department of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD 21287, USA
| | - Mario Sims
- Department of Medicine, University of Mississippi Medical Center, Jackson, MS 39216, USA
| | - Jennifer A Smith
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI 48109, USA
| | - Salman M Tajuddin
- Health Disparities Research Section, Laboratory of Epidemiology and Population Sciences, National Institute on Aging, NIH, Baltimore, MD 21224, USA
| | - Dhananjay Vaidya
- Department of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD 21287, USA; GeneSTAR Research Program, Division of General Internal Medicine, Department of Medicine, Johns Hopkins University, Baltimore, MD 21287, USA
| | - Mary P Wren
- Department of Biochemistry, Wake Forest University School of Medicine, Winston-Salem, NC 27157, USA
| | - Jie Yao
- Institute for Translational Genomics and Population Sciences, Department of Pediatrics, Los Angeles Biomedical Research Institute, Harbor-UCLA Medical Center, Torrance, CA 90502, USA
| | - Xiaofeng Zhu
- Department of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, OH 44106, USA
| | - Julie T Ziegler
- Center for Public Health Genomics, Wake Forest University School of Medicine, Winston-Salem, NC 27157, USA; Department of Biostatistical Sciences, Wake Forest University School of Medicine, Winston-Salem, NC 27157, USA
| | - Joseph M Zmuda
- Department of Epidemiology, University of Pittsburgh, Pittsburgh, PA 15213, USA
| | - Alan B Zonderman
- Behavioral Epidemiology Section, Laboratory of Epidemiology & Population Science, Intramural Research Program, National Institute on Aging, NIH, Baltimore, MD 21224, US
| | - Aeilko H Zwinderman
- Department of Public Health, Academic Medical Center Amsterdam, Meibergdreef 15, 1105 AZ Amsterdam, the Netherlands
| | - Adebowale Adeyemo
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, NIH, Bethesda, MD 20892, USA
| | - Eric Boerwinkle
- Institute of Molecular Medicine and Human Genetics Center, University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Luigi Ferrucci
- Translational Gerontology Branch, National Institute of Aging at Harbor Hospital, Baltimore, MD 21225, USA
| | - M Geoffrey Hayes
- Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA
| | - Sharon L R Kardia
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI 48109, USA
| | - Iva Miljkovic
- Department of Epidemiology, University of Pittsburgh, Pittsburgh, PA 15213, USA
| | - James S Pankow
- Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, MN 55455, USA
| | - Charles N Rotimi
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, NIH, Bethesda, MD 20892, USA
| | - Michele M Sale
- Center for Public Health Genomics, Department of Public Health Sciences, School of Medicine, University of Virginia, Charlottesville, VA 22908, USA
| | - Lynne E Wagenknecht
- Division of Public Health Sciences, Wake Forest University School of Medicine, Winston-Salem, NC 27157, USA
| | - Donna K Arnett
- University of Kentucky College of Public Health, Lexington, KY 40563, USA
| | - Yii-Der Ida Chen
- Institute for Translational Genomics and Population Sciences, Department of Pediatrics, Los Angeles Biomedical Research Institute, Harbor-UCLA Medical Center, Torrance, CA 90502, USA
| | - Michael A Nalls
- Laboratory of Neurogenetics, National Institute on Aging, NIH, Bethesda, MD 20892, USA
| | - Michael A Province
- Division of Statistical Genomics, Department of Genetics, School of Medicine, Washington University, St Louis, MO 63108, USA
| | - W H Linda Kao
- Department of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD 21287, USA
| | - David S Siscovick
- Department of Epidemiology, University of Washington, Seattle, WA 98195, USA; Cardiovascular Health Research Unit, Department of Medicine, School of Medicine, University of Washington, Seattle, WA 98195, USA; The New York Academy of Medicine, New York, NY 10029, USA
| | - Bruce M Psaty
- Department of Epidemiology, University of Washington, Seattle, WA 98195, USA; Cardiovascular Health Research Unit, Department of Medicine, School of Medicine, University of Washington, Seattle, WA 98195, USA; Department of Health Services, University of Washington, Seattle, WA 98195, USA; Group Health Research Institute, Group Health Cooperative, Seattle, WA 98101, USA
| | - James G Wilson
- Department of Physiology and Biophysics, University of Mississippi Medical Center, Jackson, MS 39216, USA
| | - Ruth J F Loos
- The Charles Bronfman Institute for Personalized Medicine, The Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; The Genetics of Obesity and Related Metabolic Traits Program, The Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; The Mindich Child Health and Development Institute, The Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Josée Dupuis
- Department of Biostatistics, School of Public Health, Boston University, Boston, MA 02118, USA; National Heart, Lung, and Blood Institute's Framingham Heart Study, Framingham, MA 01702, USA
| | - Stephen S Rich
- Center for Public Health Genomics, Department of Public Health Sciences, School of Medicine, University of Virginia, Charlottesville, VA 22908, USA
| | - Jose C Florez
- Diabetes Unit, Massachusetts General Hospital, Boston, MA 02114, USA; Center for Human Genetic Research, Massachusetts General Hospital, Boston, MA 02114, USA; Programs in Metabolism and Medical & Population Genetics, Broad Institute, Cambridge, MA 02142, USA; Department of Medicine, Harvard Medical School, Boston, MA 02115, USA
| | - Jerome I Rotter
- Institute for Translational Genomics and Population Sciences, Department of Pediatrics, Los Angeles Biomedical Research Institute, Harbor-UCLA Medical Center, Torrance, CA 90502, USA
| | - Andrew P Morris
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK; Institute of Translational Medicine, Department of Biostatistics, University of Liverpool, Liverpool L69 3BX, UK
| | - James B Meigs
- Division of General Internal Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA.
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Schmit SL, Schumacher FR, Edlund CK, Conti DV, Ihenacho U, Wan P, Van Den Berg D, Casey G, Fortini BK, Lenz HJ, Tusié-Luna T, Aguilar-Salinas CA, Moreno-Macías H, Huerta-Chagoya A, Ordóñez-Sánchez ML, Rodríguez-Guillén R, Cruz-Bautista I, Rodríguez-Torres M, Muñóz-Hernández LL, Arellano-Campos O, Gómez D, Alvirde U, González-Villalpando C, González-Villalpando ME, Le Marchand L, Haiman CA, Figueiredo JC. Genome-wide association study of colorectal cancer in Hispanics. Carcinogenesis 2016; 37:547-556. [PMID: 27207650 PMCID: PMC4876992 DOI: 10.1093/carcin/bgw046] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2015] [Accepted: 04/13/2016] [Indexed: 01/01/2023] Open
Abstract
This manuscript describes the first large-scale genome-wide association study of colorectal cancer in Hispanics and Latinos. Our results demonstrate the broad replication of known susceptibility regions and the importance of fine-mapping in ethnic minority populations. Genome-wide association studies (GWAS) have identified 58 susceptibility alleles across 37 regions associated with the risk of colorectal cancer (CRC) with P < 5×10−8. Most studies have been conducted in non-Hispanic whites and East Asians; however, the generalizability of these findings and the potential for ethnic-specific risk variation in Hispanic and Latino (HL) individuals have been largely understudied. We describe the first GWAS of common genetic variation contributing to CRC risk in HL (1611 CRC cases and 4330 controls). We also examine known susceptibility alleles and implement imputation-based fine-mapping to identify potential ethnicity-specific association signals in known risk regions. We discovered 17 variants across 4 independent regions that merit further investigation due to suggestive CRC associations (P < 1×10−6) at 1p34.3 (rs7528276; Odds Ratio (OR) = 1.86 [95% confidence interval (CI): 1.47–2.36); P = 2.5×10−7], 2q23.3 (rs1367374; OR = 1.37 (95% CI: 1.21–1.55); P = 4.0×10−7), 14q24.2 (rs143046984; OR = 1.65 (95% CI: 1.36–2.01); P = 4.1×10−7) and 16q12.2 [rs142319636; OR = 1.69 (95% CI: 1.37–2.08); P=7.8×10−7]. Among the 57 previously published CRC susceptibility alleles with minor allele frequency ≥1%, 76.5% of SNPs had a consistent direction of effect and 19 (33.3%) were nominally statistically significant (P < 0.05). Further, rs185423955 and rs60892987 were identified as novel secondary susceptibility variants at 3q26.2 (P = 5.3×10–5) and 11q12.2 (P = 6.8×10−5), respectively. Our findings demonstrate the importance of fine mapping in HL. These results are informative for variant prioritization in functional studies and future risk prediction modeling in minority populations.
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Affiliation(s)
- Stephanie L Schmit
- Department of Preventive Medicine.,University of Southern California Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, CA 90089, USA.,Department of Cancer Epidemiology, Moffitt Cancer Center, Tampa, FL 33612, USA
| | - Fredrick R Schumacher
- Department of Preventive Medicine.,University of Southern California Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, CA 90089, USA
| | - Christopher K Edlund
- Department of Preventive Medicine.,University of Southern California Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, CA 90089, USA
| | - David V Conti
- Department of Preventive Medicine.,University of Southern California Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, CA 90089, USA
| | - Ugonna Ihenacho
- Department of Preventive Medicine.,University of Southern California Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, CA 90089, USA
| | - Peggy Wan
- Department of Preventive Medicine.,University of Southern California Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, CA 90089, USA
| | | | - Graham Casey
- Department of Preventive Medicine.,University of Southern California Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, CA 90089, USA
| | - Barbara K Fortini
- Department of Biology, Claremont McKenna College, Claremont, CA 91711, USA
| | - Heinz-Josef Lenz
- Department of Preventive Medicine.,University of Southern California Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, CA 90089, USA
| | - Teresa Tusié-Luna
- Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Sección XVI, Tlalpan, 14000 México City, México.,Instituto de Investigaciones Biomédicas, UNAM. Unidad de Biología Molecular y Medicina Genómica, UNAM/INCMNSZ, Coyoacán, 04510 México City, México
| | - Carlos A Aguilar-Salinas
- Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Sección XVI, Tlalpan, 14000 México City, México
| | | | - Alicia Huerta-Chagoya
- Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Sección XVI, Tlalpan, 14000 México City, México.,Instituto de Investigaciones Biomédicas, UNAM. Unidad de Biología Molecular y Medicina Genómica, UNAM/INCMNSZ, Coyoacán, 04510 México City, México
| | | | | | | | | | | | - Olimpia Arellano-Campos
- Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Sección XVI, Tlalpan, 14000 México City, México
| | - Donají Gómez
- Universidad Autónoma Metropolitana, Tlalpan 14387, México City, México
| | - Ulices Alvirde
- Universidad Autónoma Metropolitana, Tlalpan 14387, México City, México
| | - Clicerio González-Villalpando
- Unidad de Investigación en Diabetes, Instituto Nacional de Salud Pública, México City, México.,Centro de Estudios en Diabetes, 01120 México City, México and
| | | | - Loic Le Marchand
- Epidemiology Program, University of Hawaii Cancer Center, Honolulu, HI 96813, USA
| | - Christopher A Haiman
- Department of Preventive Medicine.,University of Southern California Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, CA 90089, USA
| | - Jane C Figueiredo
- Department of Preventive Medicine.,University of Southern California Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, CA 90089, USA
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27
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Hong J, Lunetta KL, Cupples LA, Dupuis J, Liu CT. Evaluation of a Two-Stage Approach in Trans-Ethnic Meta-Analysis in Genome-Wide Association Studies. Genet Epidemiol 2016; 40:284-92. [PMID: 27061095 DOI: 10.1002/gepi.21963] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2015] [Revised: 12/08/2015] [Accepted: 01/29/2016] [Indexed: 01/02/2023]
Abstract
Meta-analysis of genome-wide association studies (GWAS) has achieved great success in detecting loci underlying human diseases. Incorporating GWAS results from diverse ethnic populations for meta-analysis, however, remains challenging because of the possible heterogeneity across studies. Conventional fixed-effects (FE) or random-effects (RE) methods may not be most suitable to aggregate multiethnic GWAS results because of violation of the homogeneous effect assumption across studies (FE) or low power to detect signals (RE). Three recently proposed methods, modified RE (RE-HE) model, binary-effects (BE) model and a Bayesian approach (Meta-analysis of Transethnic Association [MANTRA]), show increased power over FE and RE methods while incorporating heterogeneity of effects when meta-analyzing trans-ethnic GWAS results. We propose a two-stage approach to account for heterogeneity in trans-ethnic meta-analysis in which we clustered studies with cohort-specific ancestry information prior to meta-analysis. We compare this to a no-prior-clustering (crude) approach, evaluating type I error and power of these two strategies, in an extensive simulation study to investigate whether the two-stage approach offers any improvements over the crude approach. We find that the two-stage approach and the crude approach for all five methods (FE, RE, RE-HE, BE, MANTRA) provide well-controlled type I error. However, the two-stage approach shows increased power for BE and RE-HE, and similar power for MANTRA and FE compared to their corresponding crude approach, especially when there is heterogeneity across the multiethnic GWAS results. These results suggest that prior clustering in the two-stage approach can be an effective and efficient intermediate step in meta-analysis to account for the multiethnic heterogeneity.
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Affiliation(s)
- Jaeyoung Hong
- Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts, United States of America
| | - Kathryn L Lunetta
- Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts, United States of America
| | - L Adrienne Cupples
- Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts, United States of America.,National Heart, Lung and Blood Institute's Framingham Heart Study, Framingham, Massachusetts, United States of America
| | - Josée Dupuis
- Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts, United States of America.,National Heart, Lung and Blood Institute's Framingham Heart Study, Framingham, Massachusetts, United States of America
| | - Ching-Ti Liu
- Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts, United States of America
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28
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Asimit JL, Hatzikotoulas K, McCarthy M, Morris AP, Zeggini E. Trans-ethnic study design approaches for fine-mapping. Eur J Hum Genet 2016; 24:1330-6. [PMID: 26839038 PMCID: PMC4856879 DOI: 10.1038/ejhg.2016.1] [Citation(s) in RCA: 62] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2015] [Revised: 10/22/2015] [Accepted: 12/22/2015] [Indexed: 01/03/2023] Open
Abstract
Studies that traverse ancestrally diverse populations may increase power to detect novel loci and improve fine-mapping resolution of causal variants by leveraging linkage disequilibrium differences between ethnic groups. The inclusion of African ancestry samples may yield further improvements because of low linkage disequilibrium and high genetic heterogeneity. We investigate the fine-mapping resolution of trans-ethnic fixed-effects meta-analysis for five type II diabetes loci, under various settings of ancestral composition (European, East Asian, African), allelic heterogeneity, and causal variant minor allele frequency. In particular, three settings of ancestral composition were compared: (1) single ancestry (European), (2) moderate ancestral diversity (European and East Asian), and (3) high ancestral diversity (European, East Asian, and African). Our simulations suggest that the European/Asian and European ancestry-only meta-analyses consistently attain similar fine-mapping resolution. The inclusion of African ancestry samples in the meta-analysis leads to a marked improvement in fine-mapping resolution.
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Affiliation(s)
| | | | - Mark McCarthy
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK
| | - Andrew P Morris
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK.,Department of Biostatistics, University of Liverpool, Liverpool, UK
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29
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Maddaloni E, D'Onofrio L, Pozzilli P. Frailty and geography: should these two factors be added to the ABCDE contemporary guide to diabetes therapy? Diabetes Metab Res Rev 2016; 32:169-75. [PMID: 26484614 DOI: 10.1002/dmrr.2762] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/14/2015] [Revised: 09/13/2015] [Accepted: 10/09/2015] [Indexed: 12/19/2022]
Abstract
On the road towards personalized treatments for type 2 diabetes, we suggest here that two parameters could be added to the ABCDE algorithm, 'F' for frailty and 'G' for geography. Indeed, the progressive ageing of population is causing a simultaneous increase of frailty worldwide. The identification of the optimal therapeutic approach is often difficult in frail subjects because of the complexity of 'frailty syndrome'. Nevertheless, given the relevance of diabetes in the development and progression of frailty, a safe and effective cure of diabetes is extremely important to guarantee a good medical outcome. There are few data about diabetes treatment in this delicate category of patients, and the choice of the appropriate therapy mostly remains a challenge. Moreover, type 2 diabetes affects more than 382 million people of different countries, races and ethnicities. To face the lack of solid evidence-based medicine for the treatment of diabetes in different ethnic groups, it is extremely important to increase knowledge about the different pathophysiology of diabetes according to ethnicity. In this way, a tailored approach to treatment of various ethnic groups living in the same or different regions can eventually be developed. Copyright © 2016 John Wiley & Sons, Ltd.
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Affiliation(s)
- Ernesto Maddaloni
- Unit of Endocrinology and Diabetes, Department of Medicine, Università Campus Bio-Medico di Roma, Italy
| | - Luca D'Onofrio
- Unit of Endocrinology and Diabetes, Department of Medicine, Università Campus Bio-Medico di Roma, Italy
| | - Paolo Pozzilli
- Unit of Endocrinology and Diabetes, Department of Medicine, Università Campus Bio-Medico di Roma, Italy
- Centre of Immunobiology, The Blizard Institute, Barts and The London School of Medicine, Queen Mary University of London, London, UK
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30
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Han Y, Hazelett DJ, Wiklund F, Schumacher FR, Stram DO, Berndt SI, Wang Z, Rand KA, Hoover RN, Machiela MJ, Yeager M, Burdette L, Chung CC, Hutchinson A, Yu K, Xu J, Travis RC, Key TJ, Siddiq A, Canzian F, Takahashi A, Kubo M, Stanford JL, Kolb S, Gapstur SM, Diver WR, Stevens VL, Strom SS, Pettaway CA, Al Olama AA, Kote-Jarai Z, Eeles RA, Yeboah ED, Tettey Y, Biritwum RB, Adjei AA, Tay E, Truelove A, Niwa S, Chokkalingam AP, Isaacs WB, Chen C, Lindstrom S, Le Marchand L, Giovannucci EL, Pomerantz M, Long H, Li F, Ma J, Stampfer M, John EM, Ingles SA, Kittles RA, Murphy AB, Blot WJ, Signorello LB, Zheng W, Albanes D, Virtamo J, Weinstein S, Nemesure B, Carpten J, Leske MC, Wu SY, Hennis AJM, Rybicki BA, Neslund-Dudas C, Hsing AW, Chu L, Goodman PJ, Klein EA, Zheng SL, Witte JS, Casey G, Riboli E, Li Q, Freedman ML, Hunter DJ, Gronberg H, Cook MB, Nakagawa H, Kraft P, Chanock SJ, Easton DF, Henderson BE, Coetzee GA, Conti DV, Haiman CA. Integration of multiethnic fine-mapping and genomic annotation to prioritize candidate functional SNPs at prostate cancer susceptibility regions. Hum Mol Genet 2015; 24:5603-18. [PMID: 26162851 PMCID: PMC4572069 DOI: 10.1093/hmg/ddv269] [Citation(s) in RCA: 35] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2015] [Accepted: 07/07/2015] [Indexed: 01/27/2023] Open
Abstract
Interpretation of biological mechanisms underlying genetic risk associations for prostate cancer is complicated by the relatively large number of risk variants (n = 100) and the thousands of surrogate SNPs in linkage disequilibrium. Here, we combined three distinct approaches: multiethnic fine-mapping, putative functional annotation (based upon epigenetic data and genome-encoded features), and expression quantitative trait loci (eQTL) analyses, in an attempt to reduce this complexity. We examined 67 risk regions using genotyping and imputation-based fine-mapping in populations of European (cases/controls: 8600/6946), African (cases/controls: 5327/5136), Japanese (cases/controls: 2563/4391) and Latino (cases/controls: 1034/1046) ancestry. Markers at 55 regions passed a region-specific significance threshold (P-value cutoff range: 3.9 × 10(-4)-5.6 × 10(-3)) and in 30 regions we identified markers that were more significantly associated with risk than the previously reported variants in the multiethnic sample. Novel secondary signals (P < 5.0 × 10(-6)) were also detected in two regions (rs13062436/3q21 and rs17181170/3p12). Among 666 variants in the 55 regions with P-values within one order of magnitude of the most-associated marker, 193 variants (29%) in 48 regions overlapped with epigenetic or other putative functional marks. In 11 of the 55 regions, cis-eQTLs were detected with nearby genes. For 12 of the 55 regions (22%), the most significant region-specific, prostate-cancer associated variant represented the strongest candidate functional variant based on our annotations; the number of regions increased to 20 (36%) and 27 (49%) when examining the 2 and 3 most significantly associated variants in each region, respectively. These results have prioritized subsets of candidate variants for downstream functional evaluation.
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Affiliation(s)
- Ying Han
- Department of Preventive Medicine, Keck School of Medicine
| | | | - Fredrik Wiklund
- Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm, Sweden
| | - Fredrick R Schumacher
- Department of Preventive Medicine, Keck School of Medicine, Norris Comprehensive Cancer Center
| | - Daniel O Stram
- Department of Preventive Medicine, Keck School of Medicine, Norris Comprehensive Cancer Center
| | - Sonja I Berndt
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Zhaoming Wang
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA, Cancer Genomics Research Laboratory, NCI-DCEG, SAIC-Frederick Inc., Frederick, MD, USA
| | - Kristin A Rand
- Department of Preventive Medicine, Keck School of Medicine
| | - Robert N Hoover
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Mitchell J Machiela
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Merideth Yeager
- Cancer Genomics Research Laboratory, NCI-DCEG, SAIC-Frederick Inc., Frederick, MD, USA
| | - Laurie Burdette
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA, Cancer Genomics Research Laboratory, NCI-DCEG, SAIC-Frederick Inc., Frederick, MD, USA
| | - Charles C Chung
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Amy Hutchinson
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA, Cancer Genomics Research Laboratory, NCI-DCEG, SAIC-Frederick Inc., Frederick, MD, USA
| | - Kai Yu
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Jianfeng Xu
- Program for Personalized Cancer Care and Department of Surgery, NorthShore University HealthSystem, Evanston, IL, USA
| | - Ruth C Travis
- Cancer Epidemiology Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Timothy J Key
- Cancer Epidemiology Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Afshan Siddiq
- Department of Genomics of Common Disease, School of Public Health
| | - Federico Canzian
- Genomic Epidemiology Group, German Cancer Research Center, Heidelberg, Germany
| | | | | | - Janet L Stanford
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA, Department of Epidemiology, School of Public Health, University of Washington, Seattle, WA, USA
| | - Suzanne Kolb
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Susan M Gapstur
- Epidemiology Research Program, American Cancer Society, Atlanta, GA, USA
| | - W Ryan Diver
- Epidemiology Research Program, American Cancer Society, Atlanta, GA, USA
| | - Victoria L Stevens
- Epidemiology Research Program, American Cancer Society, Atlanta, GA, USA
| | | | - Curtis A Pettaway
- Department of Urology, University of Texas M.D. Anderson Cancer Center, Houston, TX, USA
| | - Ali Amin Al Olama
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | | | - Rosalind A Eeles
- The Institute of Cancer Research, London, UK, Royal Marsden National Health Services (NHS) Foundation Trust, London and Sutton, UK
| | - Edward D Yeboah
- Korle Bu Teaching Hospital, Accra, Ghana, University of Ghana Medical School, Accra, Ghana
| | - Yao Tettey
- Korle Bu Teaching Hospital, Accra, Ghana, University of Ghana Medical School, Accra, Ghana
| | - Richard B Biritwum
- Korle Bu Teaching Hospital, Accra, Ghana, University of Ghana Medical School, Accra, Ghana
| | - Andrew A Adjei
- Korle Bu Teaching Hospital, Accra, Ghana, University of Ghana Medical School, Accra, Ghana
| | - Evelyn Tay
- Korle Bu Teaching Hospital, Accra, Ghana, University of Ghana Medical School, Accra, Ghana
| | | | | | | | - William B Isaacs
- James Buchanan Brady Urological Institute, Johns Hopkins Hospital and Medical Institution, Baltimore, MD, USA
| | - Constance Chen
- Program in Genetic Epidemiology and Statistical Genetics, Department of Epidemiology
| | - Sara Lindstrom
- Program in Genetic Epidemiology and Statistical Genetics, Department of Epidemiology
| | - Loic Le Marchand
- Epidemiology Program, University of Hawaii Cancer Center, Honolulu, HI, USA
| | | | | | - Henry Long
- Department of Medical Oncology, Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Fugen Li
- Department of Medical Oncology, Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Jing Ma
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | | | - Esther M John
- Cancer Prevention Institute of California, Fremont, CA, USA, Division of Epidemiology, Department of Health Research and Policy, and Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA
| | - Sue A Ingles
- Department of Preventive Medicine, Keck School of Medicine, Norris Comprehensive Cancer Center
| | - Rick A Kittles
- University of Arizona College of Medicine and University of Arizona Cancer Center, Tucson, AZ, USA
| | - Adam B Murphy
- Department of Urology, Northwestern University, Chicago, IL, USA
| | - William J Blot
- International Epidemiology Institute, Rockville, MD, USA, Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt University School of Medicine, Nashville, TN, USA
| | | | - Wei Zheng
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Demetrius Albanes
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Jarmo Virtamo
- Department of Chronic Disease Prevention, National Institute for Health and Welfare, Helsinki, Finland
| | - Stephanie Weinstein
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Barbara Nemesure
- Department of Preventive Medicine, Stony Brook University, Stony Brook, NY, USA
| | - John Carpten
- The Translational Genomics Research Institute, Phoenix, AZ, USA
| | - M Cristina Leske
- Department of Preventive Medicine, Stony Brook University, Stony Brook, NY, USA
| | - Suh-Yuh Wu
- Department of Preventive Medicine, Stony Brook University, Stony Brook, NY, USA
| | - Anselm J M Hennis
- Department of Preventive Medicine, Stony Brook University, Stony Brook, NY, USA, Chronic Disease Research Centre and Faculty of Medical Sciences, University of the West Indies, Bridgetown, Barbados
| | - Benjamin A Rybicki
- Department of Public Health Sciences, Henry Ford Hospital, Detroit, MI, USA
| | | | - Ann W Hsing
- Cancer Prevention Institute of California, Fremont, CA, USA, Division of Epidemiology, Department of Health Research and Policy, and Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA
| | - Lisa Chu
- Cancer Prevention Institute of California, Fremont, CA, USA, Division of Epidemiology, Department of Health Research and Policy, and Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA
| | - Phyllis J Goodman
- SWOG Statistical Center, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Eric A Klein
- Department of Urology, Glickman Urological and Kidney Institute, Cleveland Clinic, Cleveland, OH, USA
| | - S Lilly Zheng
- Center for Cancer Genomics, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - John S Witte
- Department of Epidemiology and Biostatistics, Institute for Human Genetics, University of California, San Francisco, CA, USA and
| | - Graham Casey
- Department of Preventive Medicine, Keck School of Medicine, Norris Comprehensive Cancer Center
| | - Elio Riboli
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College, London, UK
| | - Qiyuan Li
- Medical College, Xiamen University, Xiamen 361102, China
| | | | - David J Hunter
- Program in Genetic Epidemiology and Statistical Genetics, Department of Epidemiology
| | - Henrik Gronberg
- Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm, Sweden
| | - Michael B Cook
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Hidewaki Nakagawa
- Laboratory for Genome Sequencing Analysis, RIKEN Center for Integrative Medical Sciences, Tokyo, Japan
| | - Peter Kraft
- Program in Genetic Epidemiology and Statistical Genetics, Department of Epidemiology, Department of Biostatistics, Harvard School of Public Health, Boston, MA, USA
| | - Stephen J Chanock
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Douglas F Easton
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Brian E Henderson
- Department of Preventive Medicine, Keck School of Medicine, Norris Comprehensive Cancer Center
| | - Gerhard A Coetzee
- Department of Preventive Medicine, Keck School of Medicine, Norris Comprehensive Cancer Center, Department of Urology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - David V Conti
- Department of Preventive Medicine, Keck School of Medicine, Norris Comprehensive Cancer Center
| | - Christopher A Haiman
- Department of Preventive Medicine, Keck School of Medicine, Norris Comprehensive Cancer Center,
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Minghua Y, Huiling Z, Wenyang Z, Chong L, Demin Y, Guiying Z, Yonggang L. Molecular cloning, polymorphism and association of porcine WARS2gene with litter size. BIOTECHNOL BIOTEC EQ 2015. [DOI: 10.1080/13102818.2015.1060867] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022] Open
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Kichaev G, Pasaniuc B. Leveraging Functional-Annotation Data in Trans-ethnic Fine-Mapping Studies. Am J Hum Genet 2015; 97:260-71. [PMID: 26189819 DOI: 10.1016/j.ajhg.2015.06.007] [Citation(s) in RCA: 126] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2015] [Accepted: 06/09/2015] [Indexed: 01/10/2023] Open
Abstract
Localization of causal variants underlying known risk loci is one of the main research challenges following genome-wide association studies. Risk loci are typically dissected through fine-mapping experiments in trans-ethnic cohorts for leveraging the variability in the local genetic structure across populations. More recent works have shown that genomic functional annotations (i.e., localization of tissue-specific regulatory marks) can be integrated for increasing fine-mapping performance within single-population studies. Here, we introduce methods that integrate the strength of association between genotype and phenotype, the variability in the genetic backgrounds across populations, and the genomic map of tissue-specific functional elements to increase trans-ethnic fine-mapping accuracy. Through extensive simulations and empirical data, we have demonstrated that our approach increases fine-mapping resolution over existing methods. We analyzed empirical data from a large-scale trans-ethnic rheumatoid arthritis (RA) study and showed that the functional genetic architecture of RA is consistent across European and Asian ancestries. In these data, we used our proposed methods to reduce the average size of the 90% credible set from 29 variants per locus for standard non-integrative approaches to 22 variants.
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Affiliation(s)
- Gleb Kichaev
- Bioinformatics Interdepartmental Program, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Bogdan Pasaniuc
- Bioinformatics Interdepartmental Program, University of California, Los Angeles, Los Angeles, CA 90095, USA; Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA; Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA.
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33
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LIN28A Modulates Splicing and Gene Expression Programs in Breast Cancer Cells. Mol Cell Biol 2015; 35:3225-43. [PMID: 26149387 DOI: 10.1128/mcb.00426-15] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2015] [Accepted: 06/26/2015] [Indexed: 12/22/2022] Open
Abstract
LIN28 is an evolutionarily conserved RNA-binding protein with critical functions in developmental timing and cancer. However, the molecular mechanisms underlying LIN28's oncogenic properties are yet to be described. RNA-protein immunoprecipitation coupled with genome-wide sequencing (RIP-Seq) analysis revealed significant LIN28 binding within 843 mRNAs in breast cancer cells. Many of the LIN28-bound mRNAs are implicated in the regulation of RNA and cell metabolism. We identify heterogeneous nuclear ribonucleoprotein A1 (hnRNP A1), a protein with multiple roles in mRNA metabolism, as a LIN28-interacting partner. Subsequently, we used a custom computational method to identify differentially spliced gene isoforms in LIN28 and hnRNP A1 small interfering RNA (siRNA)-treated cells. The results reveal that these proteins regulate alternative splicing and steady-state mRNA expression of genes implicated in aspects of breast cancer biology. Notably, cells lacking LIN28 undergo significant isoform switching of the ENAH gene, resulting in a decrease in the expression of the ENAH exon 11a isoform. The expression of ENAH isoform 11a has been shown to be elevated in breast cancers that express HER2. Intriguingly, analysis of publicly available array data from the Cancer Genome Atlas (TCGA) reveals that LIN28 expression in the HER2 subtype is significantly different from that in other breast cancer subtypes. Collectively, our data suggest that LIN28 may regulate splicing and gene expression programs that drive breast cancer subtype phenotypes.
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34
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Smith AJP, Humphries SE, Talmud PJ. Identifying functional noncoding variants from genome-wide association studies for cardiovascular disease and related traits. Curr Opin Lipidol 2015; 26:120-6. [PMID: 25692342 DOI: 10.1097/mol.0000000000000158] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
PURPOSE OF REVIEW Genome-wide association studies have identified many novel loci for cardiovascular disease and related traits. Attention is now shifting towards the analysis of these loci for causal variants, with a view to identify the novel mechanisms leading to disease. RECENT FINDINGS This review focuses on the approaches to identify causal, noncoding variants for coronary artery disease, lipid traits and other cardiovascular risk factors. Fine-mapping studies are discussed, along with the novel statistical approaches to produce 'credible sets'. The use of combining genome-wide association study datasets with experimental methods such as expression quantitative trait loci and allele-specific chromatin accessibility are explored, with recent examples discussed. Mapping long-range chromatin interactions and evolving genome-editing technologies such as clustered regularly interspaced short palindromic repeats combined with clustered regularly interspaced short palindromic repeats-associated (Cas9) nuclease promise to aid considerably the search for causal variants. SUMMARY Identification of causal variants for cardiovascular disease and related traits is still in the early stages, but with technologies evolving and increasingly relevant tissue samples undergoing analysis, there are favourable prospects that many new mechanisms for disease will be uncovered by the end of this decade.
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Affiliation(s)
- Andrew J P Smith
- British Heart Foundation Laboratories, Institute of Cardiovascular Sciences, Centre for Cardiovascular Genetics, University College London, London, UK
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35
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Li YR, Keating BJ. Trans-ethnic genome-wide association studies: advantages and challenges of mapping in diverse populations. Genome Med 2014; 6:91. [PMID: 25473427 PMCID: PMC4254423 DOI: 10.1186/s13073-014-0091-5] [Citation(s) in RCA: 124] [Impact Index Per Article: 12.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022] Open
Abstract
Genome-wide association studies (GWASs) are the method most often used by geneticists to interrogate the human genome, and they provide a cost-effective way to identify the genetic variants underpinning complex traits and diseases. Most initial GWASs have focused on genetically homogeneous cohorts from European populations given the limited availability of ethnic minority samples and so as to limit population stratification effects. Transethnic studies have been invaluable in explaining the heritability of common quantitative traits, such as height, and in examining the genetic architecture of complex diseases, such as type 2 diabetes. They provide an opportunity for large-scale signal replication in independent populations and for cross-population meta-analyses to boost statistical power. In addition, transethnic GWASs enable prioritization of candidate genes, fine-mapping of functional variants, and potentially identification of SNPs associated with disease risk in admixed populations, by taking advantage of natural differences in genomic linkage disequilibrium across ethnically diverse populations. Recent efforts to assess the biological function of variants identified by GWAS have highlighted the need for large-scale replication, meta-analyses and fine-mapping across worldwide populations of ethnically diverse genetic ancestries. Here, we review recent advances and new approaches that are important to consider when performing, designing or interpreting transethnic GWASs, and we highlight existing challenges, such as the limited ability to handle heterogeneity in linkage disequilibrium across populations and limitations in dissecting complex architectures, such as those found in recently admixed populations.
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Affiliation(s)
- Yun R Li
- />The Center for Applied Genomics, 1,016 Abramson Building, The Children’s Hospital of Philadelphia, Philadelphia, 19104 PA USA
- />Medical Scientist Training Program, Perelman School of Medicine, University of Pennsylvania, Philadelphia, 19104 PA USA
| | - Brendan J Keating
- />The Center for Applied Genomics, 1,016 Abramson Building, The Children’s Hospital of Philadelphia, Philadelphia, 19104 PA USA
- />Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, 19104 PA USA
- />Department of Surgery, Division of Transplantation, Perelman School of Medicine, University of Pennsylvania, Philadelphia, 19104 PA USA
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