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Barreiro RAS, de Almeida TF, Gomes C, Monfardini F, de Farias AA, Tunes GC, de Souza GM, Duim E, de Sá Correia J, Campos Coelho AV, Caraciolo MP, Oliveira Duarte YA, Zatz M, Amaro E, Oliveira JB, Bitarello BD, Brentani H, Naslavsky MS. Assessing the Risk Stratification of Breast Cancer Polygenic Risk Scores in a Brazilian Cohort. J Mol Diagn 2024; 26:825-831. [PMID: 38972593 DOI: 10.1016/j.jmoldx.2024.06.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2023] [Revised: 05/10/2024] [Accepted: 06/11/2024] [Indexed: 07/09/2024] Open
Abstract
Polygenic risk scores (PRSs) for breast cancer have a clear clinical utility in risk prediction. PRS transferability across populations and ancestry groups is hampered by population-specific factors, ultimately leading to differences in variant effects, such as linkage disequilibrium and differences in variant frequency (allele frequency differences). Thus, locally sourced population-based phenotypic and genomic data sets are essential to assess the validity of PRSs derived from signals detected across populations. This study assesses the transferability of a breast cancer PRS composed of 313 risk variants (313-PRS) in a Brazilian trihybrid admixed ancestries (European, African, and Native American) whole-genome sequenced cohort, the Rare Genomes Project. 313-PRS was computed in the Rare Genomes Project (n = 853) using the UK Biobank (UKBB; n = 264,307) as reference. The Brazilian cohorts have a high European ancestry (EA) component, with allele frequency differences and to a lesser extent linkage disequilibrium patterns similar to those found in EA populations. The 313-PRS distribution was found to be inflated when compared with that of the UKBB, leading to potential overestimation of PRS-based risk if EA is taken as a standard. However, case controls lead to equivalent predictive power when compared with UKBB-EA samples with area under the receiver operating characteristic curve values of 0.66 to 0.62 compared with 0.63 for UKBB.
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Affiliation(s)
- Rodrigo A S Barreiro
- Departament of Biochemistry, University of São Paulo, São Paulo, Brazil; Hospital Israelita Albert Einstein, São Paulo, Brazil
| | | | - Catarina Gomes
- Hospital Israelita Albert Einstein, São Paulo, Brazil; Institute of Psychiatry, University of São Paulo, Medical School, São Paulo, Brazil
| | | | | | | | | | - Etienne Duim
- Big Data and Analytics Department, Hospital Israelita Albert Einstein, São Paulo, Brazil
| | | | | | | | - Yeda A Oliveira Duarte
- Medical-Surgical Nursing Department, School of Nursing, University of São Paulo, São Paulo, Brazil; Epidemiology Department, Public Health School, University of São Paulo, São Paulo, Brazil
| | - Mayana Zatz
- Human Genome and Stem Cell Research Center, University of São Paulo, São Paulo, Brazil; Department of Genetics and Evolutionary Biology, Biosciences Institute, University of São Paulo, São Paulo, Brazil
| | - Edson Amaro
- Hospital Israelita Albert Einstein, São Paulo, Brazil
| | | | | | - Helena Brentani
- Hospital Israelita Albert Einstein, São Paulo, Brazil; Institute of Psychiatry, University of São Paulo, Medical School, São Paulo, Brazil
| | - Michel S Naslavsky
- Hospital Israelita Albert Einstein, São Paulo, Brazil; Human Genome and Stem Cell Research Center, University of São Paulo, São Paulo, Brazil; Department of Genetics and Evolutionary Biology, Biosciences Institute, University of São Paulo, São Paulo, Brazil
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Rajawat D, Panigrahi M, Nayak SS, Ghildiyal K, Sharma A, Kumar H, Parida S, Bhushan B, Gaur GK, Mishra BP, Dutt T. Uncovering genes underlying coat color variation in indigenous cattle breeds through genome-wide positive selection. Anim Biotechnol 2023; 34:3920-3933. [PMID: 37493405 DOI: 10.1080/10495398.2023.2240387] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/27/2023]
Abstract
The identification of candidate genes related to pigmentation and under selective sweep provides insights into the genetic basis of pigmentation and the evolutionary forces that have shaped this variation. The selective sweep events in the genes responsible for normal coat color in Indian cattle groups are still unknown. To find coat color genes displaying signs of selective sweeps in the indigenous cattle, we compiled a list of candidate genes previously investigated for their association with coat color and pigmentation. After that, we performed a genome-wide scan of positive selection signatures using the BovineSNP50K Bead Chip in 187 individuals of seven indigenous breeds. We applied a wide range of methods to find evidence of selection, such as Tajima's D, CLR, iHS, varLD, ROH, and FST. We found a total of sixteen genes under selective sweep, that were involved in coat color and pigmentation physiology. These genes are CRIM1 in Gir, MC1R in Sahiwal, MYO5A, PMEL and POMC in Tharparkar, TYRP1, ERBB2, and ASIP in Red Sindhi, MITF, LOC789175, PAX3 and TYR in Ongole, and IRF2, SDR165 and, KIT in Nelore, ADAMTS19 in Hariana. These genes are related to melanin synthesis, the biology of melanocytes and melanosomes, and the migration and survival of melanocytes during development.
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Affiliation(s)
- Divya Rajawat
- Division of Animal Genetics, Indian Veterinary Research Institute, Izatnagar, Bareilly, India
| | - Manjit Panigrahi
- Division of Animal Genetics, Indian Veterinary Research Institute, Izatnagar, Bareilly, India
| | - Sonali Sonejita Nayak
- Division of Animal Genetics, Indian Veterinary Research Institute, Izatnagar, Bareilly, India
| | - Kanika Ghildiyal
- Division of Animal Genetics, Indian Veterinary Research Institute, Izatnagar, Bareilly, India
| | - Anurodh Sharma
- Division of Animal Genetics, Indian Veterinary Research Institute, Izatnagar, Bareilly, India
| | - Harshit Kumar
- Division of Animal Genetics, Indian Veterinary Research Institute, Izatnagar, Bareilly, India
| | - Subhashree Parida
- Pharmacology and Toxicology Division, Indian Veterinary Research Institute, Izatnagar, Bareilly, India
| | - Bharat Bhushan
- Division of Animal Genetics, Indian Veterinary Research Institute, Izatnagar, Bareilly, India
| | - G K Gaur
- Division of Animal Genetics, Indian Veterinary Research Institute, Izatnagar, Bareilly, India
| | - B P Mishra
- Animal Biotechnology Division, Indian Veterinary Research Institute, Izatnagar, Bareilly, India
| | - Triveni Dutt
- Livestock Production and Management Section, Indian Veterinary Research Institute, Izatnagar, Bareilly, India
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Mészárosová M, Mészáros G, Moravčíková N, Pavlík I, Margetín M, Kasarda R. Within- and between-Breed Selection Signatures in the Original and Improved Valachian Sheep. Animals (Basel) 2022; 12:ani12111346. [PMID: 35681809 PMCID: PMC9179888 DOI: 10.3390/ani12111346] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Revised: 05/13/2022] [Accepted: 05/23/2022] [Indexed: 11/16/2022] Open
Abstract
This study explored the genomic diversity and selection signatures in two Slovakian national breeds, the Original Valachian and the Improved Valachian sheep. As they are an important animal genetic resource within the country, but with decreasing population size, our aim is to identify potentially valuable genomic regions. A total of 97 sheep (18 male and 79 female) from the Original Valachian, and 69 sheep (25 male and 44 female) from the Improved Valachian populations were genotyped using the GeneSeek GGP Ovine 50 K chip. The inbreeding levels were assessed with runs of homozygosity (ROH). The selection signatures within breeds were identified based on the top 1% of most homozygous regions within the breed, the so-called ROH islands. The selection signatures between breeds were assessed based on variance in linkage disequilibrium. Overall, we have identified selection signatures with quantitative trait loci (QTL) and genes pointing towards all three production purposes of the Valachian sheep, milk, meat, and wool, including their quality characteristics. Another group with apparent large importance was the various traits related to health and resistance to parasites, which is well in line with the sturdy nature of this breed.
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Affiliation(s)
- Mária Mészárosová
- Faculty of Agrobiology and Food Resources, Institute of Nutrition and Genomics, Slovak University of Agriculture in Nitra, Tr. A. Hlinku 2, 94976 Nitra, Slovakia; (M.M.); (R.K.)
| | - Gábor Mészáros
- Department of Sustainable Agricultural Systems, Division of Livestock Sciences, University of Natural Resources and Life Sciences, Vienna, Gregor-Mendel-Straße 33, 1180 Vienna, Austria;
| | - Nina Moravčíková
- Faculty of Agrobiology and Food Resources, Institute of Nutrition and Genomics, Slovak University of Agriculture in Nitra, Tr. A. Hlinku 2, 94976 Nitra, Slovakia; (M.M.); (R.K.)
- Correspondence:
| | - Ivan Pavlík
- Research Institute of Animal Production—NPPC Slovakia, Hlohovecká 2, 95141 Nitra—Lužianky, Slovakia;
| | - Milan Margetín
- Faculty of Agrobiology and Food Resources, Institute of Animal Science, Slovak University of Agriculture in Nitra, Tr. A. Hlinku 2, 94976 Nitra, Slovakia;
| | - Radovan Kasarda
- Faculty of Agrobiology and Food Resources, Institute of Nutrition and Genomics, Slovak University of Agriculture in Nitra, Tr. A. Hlinku 2, 94976 Nitra, Slovakia; (M.M.); (R.K.)
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Balagué-Dobón L, Cáceres A, González JR. Fully exploiting SNP arrays: a systematic review on the tools to extract underlying genomic structure. Brief Bioinform 2022; 23:bbac043. [PMID: 35211719 PMCID: PMC8921734 DOI: 10.1093/bib/bbac043] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2021] [Revised: 01/25/2022] [Accepted: 01/28/2022] [Indexed: 12/12/2022] Open
Abstract
Single nucleotide polymorphisms (SNPs) are the most abundant type of genomic variation and the most accessible to genotype in large cohorts. However, they individually explain a small proportion of phenotypic differences between individuals. Ancestry, collective SNP effects, structural variants, somatic mutations or even differences in historic recombination can potentially explain a high percentage of genomic divergence. These genetic differences can be infrequent or laborious to characterize; however, many of them leave distinctive marks on the SNPs across the genome allowing their study in large population samples. Consequently, several methods have been developed over the last decade to detect and analyze different genomic structures using SNP arrays, to complement genome-wide association studies and determine the contribution of these structures to explain the phenotypic differences between individuals. We present an up-to-date collection of available bioinformatics tools that can be used to extract relevant genomic information from SNP array data including population structure and ancestry; polygenic risk scores; identity-by-descent fragments; linkage disequilibrium; heritability and structural variants such as inversions, copy number variants, genetic mosaicisms and recombination histories. From a systematic review of recently published applications of the methods, we describe the main characteristics of R packages, command-line tools and desktop applications, both free and commercial, to help make the most of a large amount of publicly available SNP data.
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Hwang MY, Choi NH, Won HH, Kim BJ, Kim YJ. Analyzing the Korean reference genome with meta-imputation increased the imputation accuracy and spectrum of rare variants in the Korean population. Front Genet 2022; 13:1008646. [PMID: 36506321 PMCID: PMC9731225 DOI: 10.3389/fgene.2022.1008646] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Accepted: 11/10/2022] [Indexed: 11/27/2022] Open
Abstract
Genotype imputation is essential for enhancing the power of association-mapping and discovering rare and indels that are missed by most genotyping arrays. Imputation analysis can be more accurate with a population-specific reference panel or a multi-ethnic reference panel with numerous samples. The National Institute of Health, Republic of Korea, initiated the Korean Reference Genome (KRG) project to identify variants in whole-genome sequences of ∼20,000 Korean participants. In the pilot phase, we analyzed the data from 1,490 participants. The genetic characteristics and imputation performance of the KRG were compared with those of the 1,000 Genomes Project Phase 3, GenomeAsia 100K Project, ChinaMAP, NARD, and TOPMed reference panels. For comparison analysis, genotype panels were artificially generated using whole-genome sequencing data from combinations of four different ancestries (Korean, Japanese, Chinese, and European) and two population-specific optimized microarrays (Korea Biobank Array and UK Biobank Array). The KRG reference panel performed best for the Korean population (R 2 = 0.78-0.84, percentage of well-imputed is 91.9% for allele frequency >5%), although the other reference panels comprised a larger number of samples with genetically different background. By comparing multiple reference panels and multi-ethnic genotype panels, optimal imputation was obtained using reference panels from genetically related populations and a population-optimized microarray. Indeed, the reference panels of KRG and TOPMed showed the best performance when applied to the genotype panels of KBA (R 2 = 0.84) and UKB (R 2 = 0.87), respectively. Using a meta-imputation approach to merge imputation results from different reference panels increased the imputation accuracy for rare variants (∼7%) and provided additional well-imputed variants (∼20%) with comparable imputation accuracy to that of the KRG. Our results demonstrate the importance of using a population-specific reference panel and meta-imputation to assess a substantial number of accurately imputed rare variants.
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Affiliation(s)
- Mi Yeong Hwang
- Division of Genome Science, Department of Precision Medicine, National Institute of Health, Cheongju-si, South Korea.,Department of Digital Health, Samsung Advanced Institute for Health Sciences and Technology (SAIHST), Samsung Medical Center, Sungkyunkwan University, Seoul, South Korea
| | - Nak-Hyeon Choi
- Division of Genome Science, Department of Precision Medicine, National Institute of Health, Cheongju-si, South Korea
| | - Hong Hee Won
- Department of Digital Health, Samsung Advanced Institute for Health Sciences and Technology (SAIHST), Samsung Medical Center, Sungkyunkwan University, Seoul, South Korea
| | - Bong-Jo Kim
- Division of Genome Science, Department of Precision Medicine, National Institute of Health, Cheongju-si, South Korea
| | - Young Jin Kim
- Division of Genome Science, Department of Precision Medicine, National Institute of Health, Cheongju-si, South Korea
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Prakrithi P, Lakra P, Sundar D, Kapoor M, Mukerji M, Gupta I, The Indian Genome Variation Consortium. Genetic Risk Prediction of COVID-19 Susceptibility and Severity in the Indian Population. Front Genet 2021; 12:714185. [PMID: 34707636 PMCID: PMC8543005 DOI: 10.3389/fgene.2021.714185] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2021] [Accepted: 09/08/2021] [Indexed: 01/09/2023] Open
Abstract
Host genetic variants can determine their susceptibility to COVID-19 infection and severity as noted in a recent Genome-wide Association Study (GWAS). Given the prominent genetic differences in Indian sub-populations as well as differential prevalence of COVID-19, here, we compute genetic risk scores in diverse Indian sub-populations that may predict differences in the severity of COVID-19 outcomes. We utilized the top 100 most significantly associated single-nucleotide polymorphisms (SNPs) from a GWAS by Pairo-Castineira et al. determining the genetic susceptibility to severe COVID-19 infection, to compute population-wise polygenic risk scores (PRS) for populations represented in the Indian Genome Variation Consortium (IGVC) database. Using a generalized linear model accounting for confounding variables, we found that median PRS was significantly associated (p < 2 x 10−16) with COVID-19 mortality in each district corresponding to the population studied and had the largest effect on mortality (regression coefficient = 10.25). As a control we repeated our analysis on randomly selected 100 non-associated SNPs several times and did not find significant association. Therefore, we conclude that genetic susceptibility may play a major role in determining the differences in COVID-19 outcomes and mortality across the Indian sub-continent. We suggest that combining PRS with other observed risk-factors in a Bayesian framework may provide a better prediction model for ascertaining high COVID-19 risk groups and to design more effective public health resource allocation and vaccine distribution schemes.
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Affiliation(s)
- P Prakrithi
- Genomics and Molecular Medicine, CSIR Institute of Genomics and Integrative Biology, New Delhi, India.,Department of Biochemical Engineering and Biotechnology, Indian Institute of Technology Delhi, New Delhi, India
| | - Priya Lakra
- Department of Biochemical Engineering and Biotechnology, Indian Institute of Technology Delhi, New Delhi, India
| | - Durai Sundar
- Department of Biochemical Engineering and Biotechnology, Indian Institute of Technology Delhi, New Delhi, India
| | - Manav Kapoor
- Department of Neuroscience, Icahn School of Medicine at Mt. Sinai, New York, NY, United States
| | - Mitali Mukerji
- Genomics and Molecular Medicine, CSIR Institute of Genomics and Integrative Biology, New Delhi, India
| | - Ishaan Gupta
- Department of Biochemical Engineering and Biotechnology, Indian Institute of Technology Delhi, New Delhi, India
| | - The Indian Genome Variation Consortium
- Genomics and Molecular Medicine, CSIR Institute of Genomics and Integrative Biology, New Delhi, India.,Department of Biochemical Engineering and Biotechnology, Indian Institute of Technology Delhi, New Delhi, India.,Department of Neuroscience, Icahn School of Medicine at Mt. Sinai, New York, NY, United States
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7
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Casto-Rebollo C, Argente MJ, García ML, Blasco A, Ibáñez-Escriche N. Selection for environmental variance of litter size in rabbits involves genes in pathways controlling animal resilience. Genet Sel Evol 2021; 53:59. [PMID: 34256696 PMCID: PMC8276493 DOI: 10.1186/s12711-021-00653-y] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2020] [Accepted: 07/06/2021] [Indexed: 11/16/2022] Open
Abstract
Background Environmental variance (VE) is partially under genetic control, which means that the VE of individuals that share the same environment can differ because they have different genotypes. Previously, a divergent selection experiment for VE of litter size (LS) during 13 generations in rabbit yielded a successful response and revealed differences in resilience between the divergent lines. The aim of the current study was to identify signatures of selection in these divergent lines to better understand the molecular mechanisms and pathways that control VE of LS and animal resilience. Three methods (FST, ROH and varLD) were used to identify signatures of selection in a set of 473 genotypes from these rabbit lines (377) and a base population (96). A whole-genome sequencing (WGS) analysis was performed on 54 animals to detect genes with functional mutations. Results By combining signatures of selection and WGS data, we detected 373 genes with functional mutations in their transcription units, among which 111 had functions related to the immune system, stress response, reproduction and embryo development, and/or carbohydrate and lipid metabolism. The genes TTC23L, FBXL20, GHDC, ENSOCUG00000031631, SLC18A1, CD300LG, MC2R, and ENSOCUG00000006264 were particularly relevant, since each one carried a functional mutation that was fixed in one of the rabbit lines and absent in the other line. In the 3ʹUTR region of the MC2R and ENSOCUG00000006264 genes, we detected a novel insertion/deletion (INDEL) variant. Conclusions Our findings provide further evidence in favour of VE as a measure of animal resilience. Signatures of selection were identified for VE of LS in genes that have a functional mutation in their transcription units and are mostly implicated in the immune response and stress response pathways. However, the real implications of these genes for VE and animal resilience will need to be assessed through functional analyses. Supplementary Information The online version contains supplementary material available at 10.1186/s12711-021-00653-y.
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Affiliation(s)
- Cristina Casto-Rebollo
- Institute for Animal Science and Technology, Universitat Politècnica de València, Valencia, Spain
| | - María José Argente
- Departamento de Tecnología Agroalimentaria, Universidad Miguel Hernández de Elche, Orihuela, Spain
| | - María Luz García
- Departamento de Tecnología Agroalimentaria, Universidad Miguel Hernández de Elche, Orihuela, Spain
| | - Agustín Blasco
- Institute for Animal Science and Technology, Universitat Politècnica de València, Valencia, Spain
| | - Noelia Ibáñez-Escriche
- Institute for Animal Science and Technology, Universitat Politècnica de València, Valencia, Spain.
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Cardoso DF, Fernandes Júnior GA, Scalez DCB, Alves AAC, Magalhães AFB, Bresolin T, Ventura RV, Li C, de Sena Oliveira MC, Porto-Neto LR, Carvalheiro R, de Oliveira HN, Tonhati H, Albuquerque LG. Uncovering Sub-Structure and Genomic Profiles in Across-Countries Subpopulations of Angus Cattle. Sci Rep 2020; 10:8770. [PMID: 32471998 PMCID: PMC7260210 DOI: 10.1038/s41598-020-65565-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2019] [Accepted: 05/04/2020] [Indexed: 11/09/2022] Open
Abstract
Highlighting genomic profiles for geographically distinct subpopulations of the same breed may provide insights into adaptation mechanisms to different environments, reveal genomic regions divergently selected, and offer initial guidance to joint genomic analysis. Here, we characterized similarities and differences between the genomic patterns of Angus subpopulations, born and raised in Canada (N = 382) and Brazil (N = 566). Furthermore, we systematically scanned for selection signatures based on the detection of autozygosity islands common between the two subpopulations, and signals of divergent selection, via FST and varLD tests. The principal component analysis revealed a sub-structure with a close connection between the two subpopulations. The averages of genomic relationships, inbreeding coefficients, and linkage disequilibrium at varying genomic distances were rather similar across them, suggesting non-accentuated differences in overall genomic diversity. Autozygosity islands revealed selection signatures common to both subpopulations at chromosomes 13 (63.77-65.25 Mb) and 14 (22.81-23.57 Mb), which are notably known regions affecting growth traits. Nevertheless, further autozygosity islands along with FST and varLD tests unravel particular sites with accentuated population subdivision at BTAs 7 and 18 overlapping with known QTL and candidate genes of reproductive performance, thermoregulation, and resistance to infectious diseases. Our findings indicate overall genomic similarity between Angus subpopulations, with noticeable signals of divergent selection in genomic regions associated with the adaptation in different environments.
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Affiliation(s)
- Diercles Francisco Cardoso
- Department of Animal Science, School of Agricultural and Veterinarian Science, São Paulo State University (UNESP), Jaboticabal, SP, Brazil.
- Centre for Genetic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, Guelph, ON, Canada.
| | - Gerardo Alves Fernandes Júnior
- Department of Animal Science, School of Agricultural and Veterinarian Science, São Paulo State University (UNESP), Jaboticabal, SP, Brazil
| | - Daiane Cristina Becker Scalez
- Department of Animal Science, School of Agricultural and Veterinarian Science, São Paulo State University (UNESP), Jaboticabal, SP, Brazil
- Centre for Genetic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, Guelph, ON, Canada
| | - Anderson Antonio Carvalho Alves
- Department of Animal Science, School of Agricultural and Veterinarian Science, São Paulo State University (UNESP), Jaboticabal, SP, Brazil
| | - Ana Fabrícia Braga Magalhães
- Department of Animal Science, School of Agricultural and Veterinarian Science, São Paulo State University (UNESP), Jaboticabal, SP, Brazil
| | - Tiago Bresolin
- Department of Animal Science, School of Agricultural and Veterinarian Science, São Paulo State University (UNESP), Jaboticabal, SP, Brazil
| | - Ricardo Vieira Ventura
- Department of Animal Nutrition and Production, School of Veterinary Medicine and Animal Science (FMVZ), University of Sao Paulo (USP), Pirassununga, SP, Brazil
| | - Changxi Li
- Department of Agricultural Food and Nutritional Science, Faculty of Agricultural, Life & Environmental Sciences, University of Alberta, Edmonton, AB, Canada
- Lacombe Research and Development Centre, Agriculture and Agri-Food Canada, Lacombe, AB, Canada
| | | | | | - Roberto Carvalheiro
- Department of Animal Science, School of Agricultural and Veterinarian Science, São Paulo State University (UNESP), Jaboticabal, SP, Brazil
- National Council for Science and Technological Development, Brasília, Distrito Federal, Brazil
| | - Henrique Nunes de Oliveira
- Department of Animal Science, School of Agricultural and Veterinarian Science, São Paulo State University (UNESP), Jaboticabal, SP, Brazil
- National Council for Science and Technological Development, Brasília, Distrito Federal, Brazil
| | - Humberto Tonhati
- Department of Animal Science, School of Agricultural and Veterinarian Science, São Paulo State University (UNESP), Jaboticabal, SP, Brazil
- National Council for Science and Technological Development, Brasília, Distrito Federal, Brazil
| | - Lucia Galvão Albuquerque
- Department of Animal Science, School of Agricultural and Veterinarian Science, São Paulo State University (UNESP), Jaboticabal, SP, Brazil.
- National Council for Science and Technological Development, Brasília, Distrito Federal, Brazil.
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Selection in Australian Thoroughbred horses acts on a locus associated with early two-year old speed. PLoS One 2020; 15:e0227212. [PMID: 32049967 PMCID: PMC7015314 DOI: 10.1371/journal.pone.0227212] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2019] [Accepted: 12/13/2019] [Indexed: 12/11/2022] Open
Abstract
Thoroughbred horse racing is a global sport with major hubs in Europe, North America, Australasia and Japan. Regional preferences for certain traits have resulted in phenotypic variation that may result from adaptation to the local racing ecosystem. Here, we test the hypothesis that genes selected for regional phenotypic variation may be identified by analysis of selection signatures in pan-genomic SNP genotype data. Comparing Australian to non-Australian Thoroughbred horses (n = 99), the most highly differentiated loci in a composite selection signals (CSS) analysis were on ECA6 (34.75–34.85 Mb), ECA14 (33.2–33.52 Mb and 35.52–36.94 Mb) and ECA16 (24.28–26.52 Mb) in regions containing candidate genes for exercise adaptations including cardiac function (ARHGAP26, HBEGF, SRA1), synapse development and locomotion (APBB3, ATXN7, CLSTN3), stress response (NR3C1) and the skeletal muscle response to exercise (ARHGAP26, NDUFA2). In a genome-wide association study for field-measured speed in two-year-olds (n = 179) SNPs contained within the single association peak (33.2–35.6 Mb) overlapped with the ECA14 CSS signals and spanned a protocadherin gene cluster. Association tests using higher density SNP genotypes across the ECA14 locus identified a SNP within the PCDHGC5 gene associated with elite racing performance (n = 922). These results indicate that there may be differential selection for racing performance under racing and management conditions that are specific to certain geographic racing regions. In Australia breeders have principally selected horses for favourable genetic variants at loci containing genes that modulate behaviour, locomotion and skeletal muscle physiology that together appear to be contributing to early two-year-old speed.
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Neville MDC, Choi J, Lieberman J, Duan QL. Identification of deleterious and regulatory genomic variations in known asthma loci. Respir Res 2018; 19:248. [PMID: 30541564 PMCID: PMC6292105 DOI: 10.1186/s12931-018-0953-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2018] [Accepted: 11/23/2018] [Indexed: 11/25/2022] Open
Abstract
Background Candidate gene and genome-wide association studies have identified hundreds of asthma risk loci. The majority of associated variants, however, are not known to have any biological function and are believed to represent markers rather than true causative mutations. We hypothesized that many of these associated markers are in linkage disequilibrium (LD) with the elusive causative variants. Methods We compiled a comprehensive list of 449 asthma-associated variants previously reported in candidate gene and genome-wide association studies. Next, we identified all sequence variants located within the 305 unique genes using whole-genome sequencing data from the 1000 Genomes Project. Then, we calculated the LD between known asthma variants and the sequence variants within each gene. LD variants identified were then annotated to determine those that are potentially deleterious and/or functional (i.e. coding or regulatory effects on the encoded transcript or protein). Results We identified 10,130 variants in LD (r2 > 0.6) with known asthma variants. Annotations of these LD variants revealed that several have potentially deleterious effects including frameshift, alternate splice site, stop-lost, and missense. Moreover, 24 of the LD variants have been reported to regulate gene expression as expression quantitative trait loci (eQTLs). Conclusions This study is proof of concept that many of the genetic loci previously associated with complex diseases such as asthma are not causative but represent markers of disease, which are in LD with the elusive causative variants. We hereby report a number of potentially deleterious and regulatory variants that are in LD with the reported asthma loci. These reported LD variants could account for the original association signals with asthma and represent the true causative mutations at these loci. Electronic supplementary material The online version of this article (10.1186/s12931-018-0953-2) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Matthew D C Neville
- Department of Biomedical and Molecular Sciences, Queen's University, Botterell Hall, Room 530 - 18 Stuart St, Kingston, ON, K7L3N6, Canada
| | - Jihoon Choi
- Department of Biomedical and Molecular Sciences, Queen's University, Botterell Hall, Room 530 - 18 Stuart St, Kingston, ON, K7L3N6, Canada
| | - Jonathan Lieberman
- Department of Biomedical and Molecular Sciences, Queen's University, Botterell Hall, Room 530 - 18 Stuart St, Kingston, ON, K7L3N6, Canada
| | - Qing Ling Duan
- Department of Biomedical and Molecular Sciences, Queen's University, Botterell Hall, Room 530 - 18 Stuart St, Kingston, ON, K7L3N6, Canada. .,School of Computing, Queen's University, 557 Goodwin Hall, Room 531, Kingston, ON, K7L 2N8, Canada.
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11
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Mokhber M, Moradi-Shahrbabak M, Sadeghi M, Moradi-Shahrbabak H, Stella A, Nicolzzi E, Rahmaninia J, Williams JL. A genome-wide scan for signatures of selection in Azeri and Khuzestani buffalo breeds. BMC Genomics 2018; 19:449. [PMID: 29890939 PMCID: PMC5996463 DOI: 10.1186/s12864-018-4759-x] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2017] [Accepted: 05/04/2018] [Indexed: 01/25/2023] Open
Abstract
Background Identification of genomic regions that have been targets of selection may shed light on the genetic history of livestock populations and help to identify variation controlling commercially important phenotypes. The Azeri and Kuzestani buffalos are the most common indigenous Iranian breeds which have been subjected to divergent selection and are well adapted to completely different regions. Examining the genetic structure of these populations may identify genomic regions associated with adaptation to the different environments and production goals. Results A set of 385 water buffalo samples from Azeri (N = 262) and Khuzestani (N = 123) breeds were genotyped using the Axiom® Buffalo Genotyping 90 K Array. The unbiased fixation index method (FST) was used to detect signatures of selection. In total, 13 regions with outlier FST values (0.1%) were identified. Annotation of these regions using the UMD3.1 Bos taurus Genome Assembly was performed to find putative candidate genes and QTLs within the selected regions. Putative candidate genes identified include FBXO9, NDFIP1, ACTR3, ARHGAP26, SERPINF2, BOLA-DRB3, BOLA-DQB, CLN8, and MYOM2. Conclusions Candidate genes identified in regions potentially under selection were associated with physiological pathways including milk production, cytoskeleton organization, growth, metabolic function, apoptosis and domestication-related changes include immune and nervous system development. The QTL identified are involved in economically important traits in buffalo related to milk composition, udder structure, somatic cell count, meat quality, and carcass and body weight. Electronic supplementary material The online version of this article (10.1186/s12864-018-4759-x) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Mahdi Mokhber
- Department of Animal Science, Faculty of Agriculture, Urmia University, 11Km Sero Road, P. O. Box: 165, Urmia, 5756151818, Iran.
| | - Mohammad Moradi-Shahrbabak
- Department of Animal Science, Faculty of Agricultural Science and Engineering, University College of Agriculture and Natural Resources (UTCAN), University of Tehran, P. O. Box: 4111, Karaj, 1417614418, Iran
| | - Mostafa Sadeghi
- Department of Animal Science, Faculty of Agricultural Science and Engineering, University College of Agriculture and Natural Resources (UTCAN), University of Tehran, P. O. Box: 4111, Karaj, 1417614418, Iran
| | - Hossein Moradi-Shahrbabak
- Department of Animal Science, Faculty of Agricultural Science and Engineering, University College of Agriculture and Natural Resources (UTCAN), University of Tehran, P. O. Box: 4111, Karaj, 1417614418, Iran
| | - Alessandra Stella
- Parco Tecnologico Padano (PTP), Via Einstein, Cascina Codazza, 26900, Lodi, Italy
| | - Ezequiel Nicolzzi
- Parco Tecnologico Padano (PTP), Via Einstein, Cascina Codazza, 26900, Lodi, Italy
| | - Javad Rahmaninia
- Department of Animal Breeding and Genetics, Animal Science Research Institute of Iran (ASRI), Karaj, 3146618361, Iran
| | - John L Williams
- Davies Research Centre, School of Animal and Veterinary Sciences, University of Adelaide, Roseworthy, SA, 5371, Australia
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12
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Gilchrist JJ, Rautanen A, Fairfax BP, Mills TC, Naranbhai V, Trochet H, Pirinen M, Muthumbi E, Mwarumba S, Njuguna P, Mturi N, Msefula CL, Gondwe EN, MacLennan JM, Chapman SJ, Molyneux ME, Knight JC, Spencer CCA, Williams TN, MacLennan CA, Scott JAG, Hill AVS. Risk of nontyphoidal Salmonella bacteraemia in African children is modified by STAT4. Nat Commun 2018. [PMID: 29523850 PMCID: PMC5844948 DOI: 10.1038/s41467-017-02398-z] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
Abstract
Nontyphoidal Salmonella (NTS) is a major cause of bacteraemia in Africa. The disease typically affects HIV-infected individuals and young children, causing substantial morbidity and mortality. Here we present a genome-wide association study (180 cases, 2677 controls) and replication analysis of NTS bacteraemia in Kenyan and Malawian children. We identify a locus in STAT4, rs13390936, associated with NTS bacteraemia. rs13390936 is a context-specific expression quantitative trait locus for STAT4 RNA expression, and individuals carrying the NTS-risk genotype demonstrate decreased interferon-γ (IFNγ) production in stimulated natural killer cells, and decreased circulating IFNγ concentrations during acute NTS bacteraemia. The NTS-risk allele at rs13390936 is associated with protection against a range of autoimmune diseases. These data implicate interleukin-12-dependent IFNγ-mediated immunity as a determinant of invasive NTS disease in African children, and highlight the shared genetic architecture of infectious and autoimmune disease.
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Affiliation(s)
- James J Gilchrist
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, OX3 7BN, UK. .,Department of Paediatrics, University of Oxford, Oxford, OX3 9DU, UK.
| | - Anna Rautanen
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, OX3 7BN, UK
| | - Benjamin P Fairfax
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, OX3 7BN, UK
| | - Tara C Mills
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, OX3 7BN, UK
| | - Vivek Naranbhai
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, OX3 7BN, UK
| | - Holly Trochet
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, OX3 7BN, UK
| | - Matti Pirinen
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, OX3 7BN, UK.,Institute for Molecular Medicine, Finland (FIMM) University of Helsinki, FI-00014, Helsinki, Finland
| | - Esther Muthumbi
- KEMRI-Wellcome Trust Research Programme, Kilifi, 80108, Kenya
| | - Salim Mwarumba
- KEMRI-Wellcome Trust Research Programme, Kilifi, 80108, Kenya
| | | | - Neema Mturi
- KEMRI-Wellcome Trust Research Programme, Kilifi, 80108, Kenya
| | - Chisomo L Msefula
- Malawi-Liverpool-Wellcome Trust Clinical Research Programme, College of Medicine, P.O. Box 30096, Chichiri, Blantyre, Malawi.,Pathology Department, College of Medicine, P.O. Box 360, Chichiri, Blantyre, Malawi
| | - Esther N Gondwe
- Malawi-Liverpool-Wellcome Trust Clinical Research Programme, College of Medicine, P.O. Box 30096, Chichiri, Blantyre, Malawi
| | - Jenny M MacLennan
- Malawi-Liverpool-Wellcome Trust Clinical Research Programme, College of Medicine, P.O. Box 30096, Chichiri, Blantyre, Malawi.,Department of Zoology, University of Oxford, South Parks Road, Oxford, OX1 3PS, UK
| | - Stephen J Chapman
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, OX3 7BN, UK.,Oxford Centre for Respiratory Medicine, Churchill Hospital Site, Oxford University Hospitals, Oxford, OX3 7LE, UK
| | - Malcolm E Molyneux
- Malawi-Liverpool-Wellcome Trust Clinical Research Programme, College of Medicine, P.O. Box 30096, Chichiri, Blantyre, Malawi
| | - Julian C Knight
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, OX3 7BN, UK
| | - Chris C A Spencer
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, OX3 7BN, UK
| | - Thomas N Williams
- KEMRI-Wellcome Trust Research Programme, Kilifi, 80108, Kenya.,Department of Medicine, Imperial College, Norfolk Place, London, W2 1PG, UK
| | - Calman A MacLennan
- Malawi-Liverpool-Wellcome Trust Clinical Research Programme, College of Medicine, P.O. Box 30096, Chichiri, Blantyre, Malawi.,The Jenner Institute, University of Oxford, Old Road Campus Research Building, Oxford, OX3 7DQ, UK
| | - J Anthony G Scott
- KEMRI-Wellcome Trust Research Programme, Kilifi, 80108, Kenya.,Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London, WC1E 7HT, UK
| | - Adrian V S Hill
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, OX3 7BN, UK. .,The Jenner Institute, University of Oxford, Old Road Campus Research Building, Oxford, OX3 7DQ, UK.
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13
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Urayama KY, Takagi M, Kawaguchi T, Matsuo K, Tanaka Y, Ayukawa Y, Arakawa Y, Hasegawa D, Yuza Y, Kaneko T, Noguchi Y, Taneyama Y, Ota S, Inukai T, Yanagimachi M, Keino D, Koike K, Toyama D, Nakazawa Y, Kurosawa H, Nakamura K, Moriwaki K, Goto H, Sekinaka Y, Morita D, Kato M, Takita J, Tanaka T, Inazawa J, Koh K, Ishida Y, Ohara A, Mizutani S, Matsuda F, Manabe A. Regional evaluation of childhood acute lymphoblastic leukemia genetic susceptibility loci among Japanese. Sci Rep 2018; 8:789. [PMID: 29335448 PMCID: PMC5768812 DOI: 10.1038/s41598-017-19127-7] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2017] [Accepted: 12/20/2017] [Indexed: 12/20/2022] Open
Abstract
Genome-wide association studies (GWAS) performed mostly in populations of European and Hispanic ancestry have confirmed an inherited genetic basis for childhood acute lymphoblastic leukemia (ALL), but these associations are less clear in other races/ethnicities. DNA samples from ALL patients (aged 0–19 years) previously enrolled onto a Tokyo Children’s Cancer Study Group trial were collected during 2013–2015, and underwent single nucleotide polymorphism (SNP) microarray genotyping resulting in 527 B-cell ALL for analysis. Cases and control data for 3,882 samples from the Nagahama Study Group and Aichi Cancer Center Study were combined, and association analyses across 10 previous GWAS-identified regions were performed after targeted SNP imputation. Linkage disequilibrium (LD) patterns in Japanese and other populations were evaluated using the varLD score based on 1000 Genomes data. Risk associations for ARID5B (rs10821936, OR = 1.84, P = 6 × 10−17) and PIP4K2A (rs7088318, OR = 0.76, P = 2 × 10−4) directly transferred to Japanese, and the IKZF1 association was detected by an alternate SNP (rs1451367, OR = 1.52, P = 2 × 10−6). Marked regional LD differences between Japanese and Europeans was observed for most of the remaining loci for which associations did not transfer, including CEBPE, CDKN2A, CDKN2B, and ELK3. This study represents a first step towards characterizing the role of genetic susceptibility in childhood ALL risk in Japanese.
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Affiliation(s)
- Kevin Y Urayama
- Department of Social Medicine, National Center for Child Health and Development, Tokyo, Japan. .,Graduate School of Public Health, St. Luke's International University, Tokyo, Japan.
| | - Masatoshi Takagi
- Department of Pediatrics and Developmental Biology, Tokyo Medical and Dental University, Tokyo, Japan
| | - Takahisa Kawaguchi
- Center for Genomic Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Keitaro Matsuo
- Division of Molecular and Clinical Epidemiology, Aichi Cancer Center Research Institute, Aichi, Japan
| | - Yoichi Tanaka
- Department of Clinical Pharmacy, Center for Clinical Pharmacy and Sciences, School of Pharmacy, Kitasato University, Tokyo, Japan
| | - Yoko Ayukawa
- Department of Social Medicine, National Center for Child Health and Development, Tokyo, Japan
| | - Yuki Arakawa
- Department of Hematology/Oncology, Saitama Children's Medical Center, Saitama, Japan
| | - Daisuke Hasegawa
- Department of Pediatrics, St. Luke's International Hospital, Tokyo, Japan
| | - Yuki Yuza
- Department of Hematology/Oncology, Tokyo Metropolitan Children's Medical Center, Tokyo, Japan
| | - Takashi Kaneko
- Department of Hematology/Oncology, Tokyo Metropolitan Children's Medical Center, Tokyo, Japan
| | - Yasushi Noguchi
- Department of Pediatrics, Japanese Red Cross Narita Hospital, Chiba, Japan
| | - Yuichi Taneyama
- Department of Hematology/Oncology, Chiba Children's Hospital, Chiba, Japan
| | - Setsuo Ota
- Department of Pediatrics, Teikyo University Chiba Medical Center, Chiba, Japan
| | - Takeshi Inukai
- Department of Pediatrics, University of Yamanashi, Yamanashi, Japan
| | - Masakatsu Yanagimachi
- Department of Pediatrics and Developmental Biology, Tokyo Medical and Dental University, Tokyo, Japan.,Department of Pediatrics, Yokohama City University Graduate School of Medicine, Yokohama, Japan
| | - Dai Keino
- Department of Pediatrics, St. Marianna University School of Medicine, Kawasaki, Japan
| | - Kazutoshi Koike
- Division of Pediatric Hematology and Oncology, Ibaraki Children's Hospital, Mito, Japan
| | - Daisuke Toyama
- Division of Pediatrics, Showa University Fujigaoka Hospital, Yokohama, Japan
| | - Yozo Nakazawa
- Department of Pediatrics, Shinshu University School of Medicine, Matsumoto, Japan
| | | | - Kozue Nakamura
- Department of Pediatrics, Teikyo University Hospital, Tokyo, Japan
| | - Koichi Moriwaki
- Department of Pediatrics, Saitama Medical Center, Saitama Medical University, Saitama, Japan
| | - Hiroaki Goto
- Division of Hematology/Oncology & Regenerative Medicine, Kanagawa Children's Medical Center, Yokohama, Japan
| | - Yujin Sekinaka
- Department of Pediatrics, National Defense Medical College, Saitama, Japan
| | - Daisuke Morita
- Department of Pediatrics, Shinshu University School of Medicine, Matsumoto, Japan
| | - Motohiro Kato
- Children's Cancer Center, National Center for Child Health and Development, Tokyo, Japan
| | - Junko Takita
- Department of Pediatrics, The University of Tokyo Hospital, Tokyo, Japan
| | - Toshihiro Tanaka
- Department of Human Genetics and Disease Diversity, Tokyo Medical Dental University, Tokyo, Japan.,Bioresource Research Center, Tokyo Medical and Dental University, Tokyo, Japan
| | - Johji Inazawa
- Bioresource Research Center, Tokyo Medical and Dental University, Tokyo, Japan
| | - Katsuyoshi Koh
- Department of Hematology/Oncology, Saitama Children's Medical Center, Saitama, Japan
| | - Yasushi Ishida
- Pediatric Medical Center, Ehime Prefectural Central Hospital, Matsuyama, Japan
| | - Akira Ohara
- Department of Pediatrics, Toho University, Tokyo, Japan
| | - Shuki Mizutani
- Department of Pediatrics and Developmental Biology, Tokyo Medical and Dental University, Tokyo, Japan
| | - Fumihiko Matsuda
- Center for Genomic Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Atsushi Manabe
- Department of Pediatrics, St. Luke's International Hospital, Tokyo, Japan
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14
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Chacón-Sánchez MI, Martínez-Castillo J. Testing Domestication Scenarios of Lima Bean ( Phaseolus lunatus L.) in Mesoamerica: Insights from Genome-Wide Genetic Markers. FRONTIERS IN PLANT SCIENCE 2017; 8:1551. [PMID: 28955351 PMCID: PMC5601060 DOI: 10.3389/fpls.2017.01551] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/05/2017] [Accepted: 08/24/2017] [Indexed: 05/03/2023]
Abstract
Plant domestication can be seen as a long-term process that involves a complex interplay among demographic processes and evolutionary forces. Previous studies have suggested two domestication scenarios for Lima bean in Mesoamerica: two separate domestication events, one from gene pool MI in central-western Mexico and another one from gene pool MII in the area Guatemala-Costa Rica, or a single domestication from gene pool MI in central-western Mexico followed by post-domestication gene flow with wild populations. In this study we evaluated the genetic structure of the wild gene pool and tested these two competing domestication scenarios of Lima bean in Mesoamerica by applying an ABC approach to a set of genome-wide SNP markers. The results confirm the existence of three gene pools in wild Lima bean, two Mesoamerican gene pools (MI and MII) and the Andean gene pool (AI), and suggest the existence of another gene pool in central Colombia. The results indicate that although both domestication scenarios may be supported by genetic data, higher statistical support was given to the single domestication scenario in central-western Mexico followed by admixture with wild populations. Domestication would have involved strong founder effects reflected in loss of genetic diversity and increased LD levels in landraces. Genomic regions affected by selection were detected and these may harbor candidate genes related to domestication.
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Affiliation(s)
- María I. Chacón-Sánchez
- Departamento de Agronomía, Facultad de Ciencias Agrarias, Universidad Nacional de ColombiaBogotá, Colombia
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15
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Yan Q, Brehm J, Pino-Yanes M, Forno E, Lin J, Oh SS, Acosta-Perez E, Laurie CC, Cloutier MM, Raby BA, Stilp AM, Sofer T, Hu D, Huntsman S, Eng CS, Conomos MP, Rastogi D, Rice K, Canino G, Chen W, Barr RG, Burchard EG, Celedón JC. A meta-analysis of genome-wide association studies of asthma in Puerto Ricans. Eur Respir J 2017; 49:49/5/1601505. [PMID: 28461288 DOI: 10.1183/13993003.01505-2016] [Citation(s) in RCA: 38] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2016] [Accepted: 12/18/2016] [Indexed: 12/15/2022]
Abstract
Puerto Ricans are disproportionately affected with asthma in the USA. In this study, we aim to identify genetic variants that confer susceptibility to asthma in Puerto Ricans.We conducted a meta-analysis of genome-wide association studies (GWAS) of asthma in Puerto Ricans, including participants from: the Genetics of Asthma in Latino Americans (GALA) I-II, the Hartford-Puerto Rico Study and the Hispanic Community Health Study. Moreover, we examined whether susceptibility loci identified in previous meta-analyses of GWAS are associated with asthma in Puerto Ricans.The only locus to achieve genome-wide significance was chromosome 17q21, as evidenced by our top single nucleotide polymorphism (SNP), rs907092 (OR 0.71, p=1.2×10-12) at IKZF3 Similar to results in non-Puerto Ricans, SNPs in genes in the same linkage disequilibrium block as IKZF3 (e.g. ZPBP2, ORMDL3 and GSDMB) were significantly associated with asthma in Puerto Ricans. With regard to results from a meta-analysis in Europeans, we replicated findings for rs2305480 at GSDMB, but not for SNPs in any other genes. On the other hand, we replicated results from a meta-analysis of North American populations for SNPs at IL1RL1, TSLP and GSDMB but not for IL33Our findings suggest that common variants on chromosome 17q21 have the greatest effects on asthma in Puerto Ricans.
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Affiliation(s)
- Qi Yan
- Division of Pediatric Pulmonary Medicine, Allergy, and Immunology, Children's Hospital of Pittsburgh of UPMC, University of Pittsburgh, Pittsburgh, PA, USA
| | - John Brehm
- Division of Pediatric Pulmonary Medicine, Allergy, and Immunology, Children's Hospital of Pittsburgh of UPMC, University of Pittsburgh, Pittsburgh, PA, USA
| | - Maria Pino-Yanes
- CIBER de Enfermedades Respiratorias, Instituto de Salud Carlos III, Madrid, Spain.,Research Unit, Hospital Universitario N.S. de Candelaria, Universidad de La Laguna, Santa Cruz de Tenerife, Spain
| | - Erick Forno
- Division of Pediatric Pulmonary Medicine, Allergy, and Immunology, Children's Hospital of Pittsburgh of UPMC, University of Pittsburgh, Pittsburgh, PA, USA
| | - Jerome Lin
- Dept of Human Genetics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA
| | - Sam S Oh
- Dept of Medicine, University of California San Francisco, San Francisco, CA, USA
| | - Edna Acosta-Perez
- Behavioral Sciences Research Institute, University of Puerto Rico, San Juan, Puerto Rico
| | - Cathy C Laurie
- Dept of Biostatistics, University of Washington, Seattle, WA, USA
| | | | - Benjamin A Raby
- Channing Division of Network Medicine, Dept of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Adrienne M Stilp
- Dept of Biostatistics, University of Washington, Seattle, WA, USA
| | - Tamar Sofer
- Dept of Biostatistics, University of Washington, Seattle, WA, USA
| | - Donglei Hu
- Dept of Medicine, University of California San Francisco, San Francisco, CA, USA
| | - Scott Huntsman
- Dept of Medicine, University of California San Francisco, San Francisco, CA, USA
| | - Celeste S Eng
- Dept of Medicine, University of California San Francisco, San Francisco, CA, USA
| | | | - Deepa Rastogi
- Dept of Pediatrics, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Kenneth Rice
- Dept of Biostatistics, University of Washington, Seattle, WA, USA
| | - Glorisa Canino
- Behavioral Sciences Research Institute, University of Puerto Rico, San Juan, Puerto Rico
| | - Wei Chen
- Division of Pediatric Pulmonary Medicine, Allergy, and Immunology, Children's Hospital of Pittsburgh of UPMC, University of Pittsburgh, Pittsburgh, PA, USA
| | - R Graham Barr
- Dept of Epidemiology, Columbia University, New York, NY, USA.,These authors contributed equally to this work
| | - Esteban G Burchard
- Dept of Medicine, University of California San Francisco, San Francisco, CA, USA.,Dept of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA.,These authors contributed equally to this work
| | - Juan C Celedón
- Division of Pediatric Pulmonary Medicine, Allergy, and Immunology, Children's Hospital of Pittsburgh of UPMC, University of Pittsburgh, Pittsburgh, PA, USA .,These authors contributed equally to this work
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16
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Álvarez-Álvarez MM, Zanetti D, Carreras-Torres R, Moral P, Athanasiadis G. A survey of sub-Saharan gene flow into the Mediterranean at risk loci for coronary artery disease. Eur J Hum Genet 2017; 25:472-476. [PMID: 28098150 DOI: 10.1038/ejhg.2016.200] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2016] [Revised: 11/24/2016] [Accepted: 12/14/2016] [Indexed: 12/31/2022] Open
Abstract
This study tries to find detectable signals of gene flow of Sub-Saharan origin into the Mediterranean in four genomic regions previously associated with coronary artery disease. A total of 366 single-nucleotide polymorphisms were genotyped in 772 individuals from 10 Mediterranean countries. Population structure analyses were performed, in which a noticeable Sub-Saharan component was found in the studied samples. The overall percentage of this Sub-Saharan component presents differences between the two Mediterranean coasts. D-statistics suggest possible Sub-Saharan introgression into one of the studied genomic regions (10q11). We also found differences in linkage disequilibrium patterns between the two Mediterranean coasts, possibly attributable to differential Sub-Saharan admixture. Our results confirm the potentially important role of human demographic history when performing epidemiological studies.
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Affiliation(s)
- Miguel M Álvarez-Álvarez
- Faculty of Biology, Department of Evolutionary Biology, Ecology and Environmental Sciences, Biodiversity Research Institute, University of Barcelona,Barcelona, Spain
| | - Daniela Zanetti
- Faculty of Biology, Department of Evolutionary Biology, Ecology and Environmental Sciences, Biodiversity Research Institute, University of Barcelona,Barcelona, Spain
| | - Robert Carreras-Torres
- Faculty of Biology, Department of Evolutionary Biology, Ecology and Environmental Sciences, Biodiversity Research Institute, University of Barcelona,Barcelona, Spain
| | - Pedro Moral
- Faculty of Biology, Department of Evolutionary Biology, Ecology and Environmental Sciences, Biodiversity Research Institute, University of Barcelona,Barcelona, Spain
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17
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González-Rodríguez A, Munilla S, Mouresan EF, Cañas-Álvarez JJ, Díaz C, Piedrafita J, Altarriba J, Baro JÁ, Molina A, Varona L. On the performance of tests for the detection of signatures of selection: a case study with the Spanish autochthonous beef cattle populations. Genet Sel Evol 2016; 48:81. [PMID: 27793093 PMCID: PMC5084421 DOI: 10.1186/s12711-016-0258-1] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2016] [Accepted: 10/18/2016] [Indexed: 01/05/2023] Open
Abstract
Background Procedures for the detection of signatures of selection can be classified according to the source of information they use to reject the null hypothesis of absence of selection. Three main groups of tests can be identified that are based on: (1) the analysis of the site frequency spectrum, (2) the study of the extension of the linkage disequilibrium across the length of the haplotypes that surround the polymorphism, and (3) the differentiation among populations. The aim of this study was to compare the performance of a subset of these procedures by using a dataset on seven Spanish autochthonous beef cattle populations. Results Analysis of the correlations between the logarithms of the statistics that were obtained by 11 tests for detecting signatures of selection at each single nucleotide polymorphism confirmed that they can be clustered into the three main groups mentioned above. A factor analysis summarized the results of the 11 tests into three canonical axes that were each associated with one of the three groups. Moreover, the signatures of selection identified with the first and second groups of tests were shared across populations, whereas those with the third group were more breed-specific. Nevertheless, an enrichment analysis identified the metabolic pathways that were associated with each group; they coincided with canonical axes and were related to immune response, muscle development, protein biosynthesis, skin and pigmentation, glucose metabolism, fat metabolism, embryogenesis and morphology, heart and uterine metabolism, regulation of the hypothalamic–pituitary–thyroid axis, hormonal, cellular cycle, cell signaling and extracellular receptors. Conclusions We show that the results of the procedures used to identify signals of selection differed substantially between the three groups of tests. However, they can be classified using a factor analysis. Moreover, each canonical factor that coincided with a group of tests identified different signals of selection, which could be attributed to processes of selection that occurred at different evolutionary times. Nevertheless, the metabolic pathways that were associated with each group of tests were similar, which suggests that the selection events that occurred during the evolutionary history of the populations probably affected the same group of traits. Electronic supplementary material The online version of this article (doi:10.1186/s12711-016-0258-1) contains supplementary material, which is available to authorized users.
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Affiliation(s)
| | - Sebastián Munilla
- Departamento de Anatomía, Embriología y Genética, Universidad de Zaragoza, 50013, Saragossa, Spain.,Departamento de Producción Animal, Facultad de Agronomía, Universidad de Buenos Aires, 1417, Buenos Aires, Argentina
| | - Elena F Mouresan
- Departamento de Anatomía, Embriología y Genética, Universidad de Zaragoza, 50013, Saragossa, Spain
| | - Jhon J Cañas-Álvarez
- Grup de Recerca en Remugants, Departament de Ciència Animal i dels Aliments, Universitat Autònoma de Barcelona, 08193, Bellaterra, Barcelona, Spain
| | - Clara Díaz
- Departamento de Mejora Genética Animal, INIA, 28040, Madrid, Spain
| | - Jesús Piedrafita
- Grup de Recerca en Remugants, Departament de Ciència Animal i dels Aliments, Universitat Autònoma de Barcelona, 08193, Bellaterra, Barcelona, Spain
| | - Juan Altarriba
- Departamento de Anatomía, Embriología y Genética, Universidad de Zaragoza, 50013, Saragossa, Spain.,Instituto Agroalimentario de Aragón (IA2), 50013, Saragossa, Spain
| | - Jesús Á Baro
- Departamento de Ciencias Agroforestales, Universidad de Valladolid, 34004, Palencia, Spain
| | | | - Luis Varona
- Departamento de Anatomía, Embriología y Genética, Universidad de Zaragoza, 50013, Saragossa, Spain. .,Instituto Agroalimentario de Aragón (IA2), 50013, Saragossa, Spain.
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18
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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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Gan W, Walters RG, Holmes MV, Bragg F, Millwood IY, Banasik K, Chen Y, Du H, Iona A, Mahajan A, Yang L, Bian Z, Guo Y, Clarke RJ, Li L, McCarthy MI, Chen Z. Evaluation of type 2 diabetes genetic risk variants in Chinese adults: findings from 93,000 individuals from the China Kadoorie Biobank. Diabetologia 2016; 59:1446-1457. [PMID: 27053236 PMCID: PMC4901105 DOI: 10.1007/s00125-016-3920-9] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/29/2015] [Accepted: 02/22/2016] [Indexed: 01/19/2023]
Abstract
AIMS/HYPOTHESIS Genome-wide association studies (GWAS) have discovered many risk variants for type 2 diabetes. However, estimates of the contributions of risk variants to type 2 diabetes predisposition are often based on highly selected case-control samples, and reliable estimates of population-level effect sizes are missing, especially in non-European populations. METHODS The individual and cumulative effects of 59 established type 2 diabetes risk loci were measured in a population-based China Kadoorie Biobank (CKB) study of 93,000 Chinese adults, including >7,100 diabetes cases. RESULTS Association signals were directionally consistent between CKB and the original discovery GWAS: of 56 variants passing quality control, 48 showed the same direction of effect (binomial test, p = 2.3 × 10(-8)). We observed a consistent overall trend towards lower risk variant effect sizes in CKB than in case-control samples of GWAS meta-analyses (mean 19-22% decrease in log odds, p ≤ 0.0048), likely to reflect correction of both 'winner's curse' and spectrum bias effects. The association with risk of diabetes of a genetic risk score, based on lead variants at 25 loci considered to act through beta cell function, demonstrated significant interactions with several measures of adiposity (BMI, waist circumference [WC], WHR and percentage body fat [PBF]; all p interaction < 1 × 10(-4)), with a greater effect being observed in leaner adults. CONCLUSIONS/INTERPRETATION Our study provides further evidence of shared genetic architecture for type 2 diabetes between Europeans and East Asians. It also indicates that even very large GWAS meta-analyses may be vulnerable to substantial inflation of effect size estimates, compared with those observed in large-scale population-based cohort studies. ACCESS TO RESEARCH MATERIALS Details of how to access China Kadoorie Biobank data and details of the data release schedule are available from www.ckbiobank.org/site/Data+Access .
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Affiliation(s)
- Wei Gan
- Oxford Centre for Diabetes, Endocrinology, and Metabolism, University of Oxford, Churchill Hospital Campus, Old Road, Headington, Oxford, OX3 7LJ, UK
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK
| | - Robin G Walters
- Clinical Trial Service Unit and Epidemiological Studies Unit, University of Oxford, Old Road Campus, Oxford, OX3 7LF, UK
| | - Michael V Holmes
- Clinical Trial Service Unit and Epidemiological Studies Unit, University of Oxford, Old Road Campus, Oxford, OX3 7LF, UK
| | - Fiona Bragg
- Clinical Trial Service Unit and Epidemiological Studies Unit, University of Oxford, Old Road Campus, Oxford, OX3 7LF, UK
| | - Iona Y Millwood
- Clinical Trial Service Unit and Epidemiological Studies Unit, University of Oxford, Old Road Campus, Oxford, OX3 7LF, UK
| | - Karina Banasik
- Oxford Centre for Diabetes, Endocrinology, and Metabolism, University of Oxford, Churchill Hospital Campus, Old Road, Headington, Oxford, OX3 7LJ, UK
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK
- The Novo Nordisk Foundation Center for Basic Metabolic Research, Section of Metabolic Genetics, University of Copenhagen, Copenhagen, Denmark
| | - Yiping Chen
- Clinical Trial Service Unit and Epidemiological Studies Unit, University of Oxford, Old Road Campus, Oxford, OX3 7LF, UK
| | - Huaidong Du
- Clinical Trial Service Unit and Epidemiological Studies Unit, University of Oxford, Old Road Campus, Oxford, OX3 7LF, UK
| | - Andri Iona
- Clinical Trial Service Unit and Epidemiological Studies Unit, University of Oxford, Old Road Campus, Oxford, OX3 7LF, UK
| | - Anubha Mahajan
- Oxford Centre for Diabetes, Endocrinology, and Metabolism, University of Oxford, Churchill Hospital Campus, Old Road, Headington, Oxford, OX3 7LJ, UK
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK
| | - Ling Yang
- Clinical Trial Service Unit and Epidemiological Studies Unit, University of Oxford, Old Road Campus, Oxford, OX3 7LF, UK
| | - Zheng Bian
- Chinese Academy of Medical Sciences, Dong Cheng District, Beijing, People's Republic of China
| | - Yu Guo
- Chinese Academy of Medical Sciences, Dong Cheng District, Beijing, People's Republic of China
| | - Robert J Clarke
- Clinical Trial Service Unit and Epidemiological Studies Unit, University of Oxford, Old Road Campus, Oxford, OX3 7LF, UK
| | - Liming Li
- Chinese Academy of Medical Sciences, Dong Cheng District, Beijing, People's Republic of China
- School of Public Health, Peking University Health Sciences Center, Beijing, People's Republic of China
| | - Mark I McCarthy
- Oxford Centre for Diabetes, Endocrinology, and Metabolism, University of Oxford, Churchill Hospital Campus, Old Road, Headington, Oxford, OX3 7LJ, UK.
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK.
- National Institute of Health Research Oxford Biomedical Research Centre, Oxford, UK.
| | - Zhengming Chen
- Clinical Trial Service Unit and Epidemiological Studies Unit, University of Oxford, Old Road Campus, Oxford, OX3 7LF, UK.
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Randhawa IAS, Khatkar MS, Thomson PC, Raadsma HW. A Meta-Assembly of Selection Signatures in Cattle. PLoS One 2016; 11:e0153013. [PMID: 27045296 PMCID: PMC4821596 DOI: 10.1371/journal.pone.0153013] [Citation(s) in RCA: 58] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2015] [Accepted: 03/22/2016] [Indexed: 12/31/2022] Open
Abstract
Since domestication, significant genetic improvement has been achieved for many traits of commercial importance in cattle, including adaptation, appearance and production. In response to such intense selection pressures, the bovine genome has undergone changes at the underlying regions of functional genetic variants, which are termed “selection signatures”. This article reviews 64 recent (2009–2015) investigations testing genomic diversity for departure from neutrality in worldwide cattle populations. In particular, we constructed a meta-assembly of 16,158 selection signatures for individual breeds and their archetype groups (European, African, Zebu and composite) from 56 genome-wide scans representing 70,743 animals of 90 pure and crossbred cattle breeds. Meta-selection-scores (MSS) were computed by combining published results at every given locus, within a sliding window span. MSS were adjusted for common samples across studies and were weighted for significance thresholds across and within studies. Published selection signatures show extensive coverage across the bovine genome, however, the meta-assembly provides a consensus profile of 263 genomic regions of which 141 were unique (113 were breed-specific) and 122 were shared across cattle archetypes. The most prominent peaks of MSS represent regions under selection across multiple populations and harboured genes of known major effects (coat color, polledness and muscle hypertrophy) and genes known to influence polygenic traits (stature, adaptation, feed efficiency, immunity, behaviour, reproduction, beef and dairy production). As the first meta-assembly of selection signatures, it offers novel insights about the hotspots of selective sweeps in the bovine genome, and this method could equally be applied to other species.
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Affiliation(s)
- Imtiaz A. S. Randhawa
- Reprogen - Animal Bioscience Group, Faculty of Veterinary Science, The University of Sydney, 425 Werombi Road, Camden, 2570, NSW, Australia
- * E-mail:
| | - Mehar S. Khatkar
- Reprogen - Animal Bioscience Group, Faculty of Veterinary Science, The University of Sydney, 425 Werombi Road, Camden, 2570, NSW, Australia
| | - Peter C. Thomson
- Reprogen - Animal Bioscience Group, Faculty of Veterinary Science, The University of Sydney, 425 Werombi Road, Camden, 2570, NSW, Australia
| | - Herman W. Raadsma
- Reprogen - Animal Bioscience Group, Faculty of Veterinary Science, The University of Sydney, 425 Werombi Road, Camden, 2570, NSW, Australia
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Claudio-Campos K, Duconge J, Cadilla CL, Ruaño G. Pharmacogenetics of drug-metabolizing enzymes in US Hispanics. Drug Metab Pers Ther 2016; 30:87-105. [PMID: 25431893 DOI: 10.1515/dmdi-2014-0023] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2014] [Accepted: 10/02/2014] [Indexed: 12/19/2022]
Abstract
Although the Hispanic population is continuously growing in the United States, they are underrepresented in pharmacogenetic studies. This review addresses the need for compiling available pharmacogenetic data in US Hispanics, discussing the prevalence of clinically relevant polymorphisms in pharmacogenes encoding for drug-metabolizing enzymes. CYP3A5*3 (0.245-0.867) showed the largest frequency in a US Hispanic population. A higher prevalence of CYP2C9*3, CYP2C19*4, and UGT2B7 IVS1+985 A>G was observed in US Hispanic vs. non-Hispanic populations. We found interethnic and intraethnic variability in frequencies of genetic polymorphisms for metabolizing enzymes, which highlights the need to define the ancestries of participants in pharmacogenetic studies. New approaches should be integrated in experimental designs to gain knowledge about the clinical relevance of the unique combination of genetic variants occurring in this admixed population. Ethnic subgroups in the US Hispanic population may harbor variants that might be part of multiple causative loci or in linkage-disequilibrium with functional variants. Pharmacogenetic studies in Hispanics should not be limited to ascertain commonly studied polymorphisms that were originally identified in their parental populations. The success of the Personalized Medicine paradigm will depend on recognizing genetic diversity between and within US Hispanics and the uniqueness of their genetic backgrounds.
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22
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Zanetti D, Via M, Carreras-Torres R, Esteban E, Chaabani H, Anaibar F, Harich N, Habbal R, Ghalim N, Moral P. Analysis of Genomic Regions Associated With Coronary Artery Disease Reveals Continent-Specific Single Nucleotide Polymorphisms in North African Populations. J Epidemiol 2016; 26:264-71. [PMID: 26780859 PMCID: PMC4848325 DOI: 10.2188/jea.je20150034] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Background In recent years, several genomic regions have been robustly associated with coronary artery disease (CAD) in different genome-wide association studies (GWASs) conducted mainly in people of European descent. These kinds of data are lacking in African populations, even though heart diseases are a major cause of premature death and disability. Methods Here, 384 single nucleotide polymorphisms (SNPs) in the top four CAD risk regions (1p13, 1q41, 9p21, and 10q11) were genotyped in 274 case-control samples from Morocco and Tunisia, with the aim of analyzing for the first time if the associations found in European populations were transferable to North Africans. Results The results indicate that, as in Europe, these four genetic regions are also important for CAD risk in North Africa. However, the individual SNPs associated with CAD in Africa are different from those identified in Europe in most cases (1p13, 1q41, and 9p21). Moreover, the seven risk variants identified in North Africans are efficient in discriminating between cases and controls in North African populations, but not in European populations. Conclusions This study indicates a disparity in markers associated to CAD susceptibility between North Africans and Europeans that may be related to population differences in the chromosomal architecture of these risk regions.
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Affiliation(s)
- Daniela Zanetti
- Department of Animal Biology-Anthropology, University of Barcelona
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23
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Kato N, Loh M, Takeuchi F, Verweij N, Wang X, Zhang W, Kelly TN, Saleheen D, Lehne B, Leach IM, Drong AW, Abbott J, Wahl S, Tan ST, Scott WR, Campanella G, Chadeau-Hyam M, Afzal U, Ahluwalia TS, Bonder MJ, Chen P, Dehghan A, Edwards TL, Esko T, Go MJ, Harris SE, Hartiala J, Kasela S, Kasturiratne A, Khor CC, Kleber ME, Li H, Yu Mok Z, Nakatochi M, Sapari NS, Saxena R, Stewart AFR, Stolk L, Tabara Y, Teh AL, Wu Y, Wu JY, Zhang Y, Aits I, Da Silva Couto Alves A, Das S, Dorajoo R, Hopewell JC, Kim YK, Koivula RW, Luan J, Lyytikäinen LP, Nguyen QN, Pereira MA, Postmus I, Raitakari OT, Bryan MS, Scott RA, Sorice R, Tragante V, Traglia M, White J, Yamamoto K, Zhang Y, Adair LS, Ahmed A, Akiyama K, Asif R, Aung T, Barroso I, Bjonnes A, Braun TR, Cai H, Chang LC, Chen CH, Cheng CY, Chong YS, Collins R, Courtney R, Davies G, Delgado G, Do LD, Doevendans PA, Gansevoort RT, Gao YT, Grammer TB, Grarup N, Grewal J, Gu D, Wander GS, Hartikainen AL, Hazen SL, He J, Heng CK, Hixson JE, Hofman A, Hsu C, Huang W, Husemoen LLN, Hwang JY, Ichihara S, Igase M, Isono M, Justesen JM, Katsuya T, Kibriya MG, Kim YJ, Kishimoto M, Koh WP, Kohara K, Kumari M, Kwek K, Lee NR, Lee J, Liao J, Lieb W, Liewald DCM, Matsubara T, Matsushita Y, Meitinger T, Mihailov E, Milani L, Mills R, Mononen N, Müller-Nurasyid M, Nabika T, Nakashima E, Ng HK, Nikus K, Nutile T, Ohkubo T, Ohnaka K, Parish S, Paternoster L, Peng H, Peters A, Pham ST, Pinidiyapathirage MJ, Rahman M, Rakugi H, Rolandsson O, Ann Rozario M, Ruggiero D, Sala CF, Sarju R, Shimokawa K, Snieder H, Sparsø T, Spiering W, Starr JM, Stott DJ, Stram DO, Sugiyama T, Szymczak S, Tang WHW, Tong L, Trompet S, Turjanmaa V, Ueshima H, Uitterlinden AG, Umemura S, Vaarasmaki M, van Dam RM, van Gilst WH, van Veldhuisen DJ, Viikari JS, Waldenberger M, Wang Y, Wang A, Wilson R, Wong TY, Xiang YB, Yamaguchi S, Ye X, Young RD, Young TL, Yuan JM, Zhou X, Asselbergs FW, Ciullo M, Clarke R, Deloukas P, Franke A, Franks PW, Franks S, Friedlander Y, Gross MD, Guo Z, Hansen T, Jarvelin MR, Jørgensen T, Jukema JW, kähönen M, Kajio H, Kivimaki M, Lee JY, Lehtimäki T, Linneberg A, Miki T, Pedersen O, Samani NJ, Sørensen TIA, Takayanagi R, Toniolo D, Ahsan H, Allayee H, Chen YT, Danesh J, Deary IJ, Franco OH, Franke L, Heijman BT, Holbrook JD, Isaacs A, Kim BJ, Lin X, Liu J, März W, Metspalu A, Mohlke KL, Sanghera DK, Shu XO, van Meurs JBJ, Vithana E, Wickremasinghe AR, Wijmenga C, Wolffenbuttel BHW, Yokota M, Zheng W, Zhu D, Vineis P, Kyrtopoulos SA, Kleinjans JCS, McCarthy MI, Soong R, Gieger C, Scott J, Teo YY, He J, Elliott P, Tai ES, van der Harst P, Kooner JS, Chambers JC. Trans-ancestry genome-wide association study identifies 12 genetic loci influencing blood pressure and implicates a role for DNA methylation. Nat Genet 2015; 47:1282-1293. [PMID: 26390057 PMCID: PMC4719169 DOI: 10.1038/ng.3405] [Citation(s) in RCA: 248] [Impact Index Per Article: 27.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2015] [Accepted: 08/21/2015] [Indexed: 12/17/2022]
Abstract
We carried out a trans-ancestry genome-wide association and replication study of blood pressure phenotypes among up to 320,251 individuals of East Asian, European and South Asian ancestry. We find genetic variants at 12 new loci to be associated with blood pressure (P = 3.9 × 10(-11) to 5.0 × 10(-21)). The sentinel blood pressure SNPs are enriched for association with DNA methylation at multiple nearby CpG sites, suggesting that, at some of the loci identified, DNA methylation may lie on the regulatory pathway linking sequence variation to blood pressure. The sentinel SNPs at the 12 new loci point to genes involved in vascular smooth muscle (IGFBP3, KCNK3, PDE3A and PRDM6) and renal (ARHGAP24, OSR1, SLC22A7 and TBX2) function. The new and known genetic variants predict increased left ventricular mass, circulating levels of NT-proBNP, and cardiovascular and all-cause mortality (P = 0.04 to 8.6 × 10(-6)). Our results provide new evidence for the role of DNA methylation in blood pressure regulation.
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Affiliation(s)
- Norihiro Kato
- Department of Gene Diagnostics and Therapeutics, Research Institute, National Center for Global Health and Medicine, Tokyo, Japan
| | - Marie Loh
- Institute of Health Sciences, University of Oulu, Oulu, Finland
- Department of Epidemiology and Biostatistics, Imperial College London, London, UK
- Translational Laboratory in Genetic Medicine, Agency for Science, Technology and Research (A*STAR), Singapore
| | - Fumihiko Takeuchi
- Department of Gene Diagnostics and Therapeutics, Research Institute, National Center for Global Health and Medicine, Tokyo, Japan
| | - Niek Verweij
- Department of Cardiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Xu Wang
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore
| | - Weihua Zhang
- Department of Epidemiology and Biostatistics, Imperial College London, London, UK
- Ealing Hospital National Health Service (NHS) Trust, Middlesex, UK
| | - Tanika N Kelly
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, Louisiana, USA
| | - Danish Saleheen
- Center for Non-Communicable Diseases, Karachi, Pakistan
- Department of Public Health and Primary Care, University of Cambridge, Strangeways Research Laboratory, Cambridge, UK
- Cardiovascular Institute, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Benjamin Lehne
- Department of Epidemiology and Biostatistics, Imperial College London, London, UK
| | - Irene Mateo Leach
- Department of Cardiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Alexander W Drong
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK
| | - James Abbott
- Bioinformatics Support Service, Imperial College London, London, UK
| | - Simone Wahl
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München–German Research Center for Environmental Health, Neuherberg, Germany
- German Center for Diabetes Research (DZD), Neuherberg, Germany
| | - Sian-Tsung Tan
- Ealing Hospital National Health Service (NHS) Trust, Middlesex, UK
- National Heart and Lung Institute, Imperial College London, London, UK
| | - William R Scott
- Department of Epidemiology and Biostatistics, Imperial College London, London, UK
- National Heart and Lung Institute, Imperial College London, London, UK
| | - Gianluca Campanella
- Department of Epidemiology and Biostatistics, Imperial College London, London, UK
| | - Marc Chadeau-Hyam
- Department of Epidemiology and Biostatistics, Imperial College London, London, UK
| | - Uzma Afzal
- Department of Epidemiology and Biostatistics, Imperial College London, London, UK
- Ealing Hospital National Health Service (NHS) Trust, Middlesex, UK
| | - Tarunveer S Ahluwalia
- Novo Nordisk Foundation Centre for Basic Metabolic Research, Section of Metabolic Genetics, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Copenhagen Prospective Studies on Asthma in Childhood (COSPAC), Herlev and Gentofte Hospital, University of Copenhagen, Copenhagen, Denmark
- Steno Diabetes Center, Gentofte, Denmark
| | - Marc Jan Bonder
- Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Peng Chen
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore
| | - Abbas Dehghan
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Todd L Edwards
- Vanderbilt Epidemiology Center, Center for Human Genetics Research, Division of Epidemiology, Department of Medicine, Vanderbilt University, Nashville, Tennessee, USA
| | - Tõnu Esko
- Estonian Genome Center, University of Tartu, Tartu, Estonia
- Division of Endocrinology, Children’s Hospital Boston, Boston, Massachusetts, USA
- Department of Genetics, Harvard Medical School, Boston, Massachusetts, USA
- Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts, USA
| | - Min Jin Go
- Center for Genome Science, National Institute of Health, Osong Health Technology Administration Complex, Chungcheongbuk-do, Republic of Korea
| | - Sarah E Harris
- Medical Genetics Section, University of Edinburgh Centre for Genomic and Experimental Medicine and Medical Research Council (MRC) Institute of Genetics and Molecular Medicine, Western General Hospital, Edinburgh, UK
- Centre for Cognitive Aging and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK
| | - Jaana Hartiala
- Department of Preventive Medicine, University of Southern California Keck School of Medicine, Los Angeles, California, USA
- Institute for Genetic Medicine, University of Southern California Keck School of Medicine, Los Angeles, California, USA
| | - Silva Kasela
- Estonian Genome Center, University of Tartu, Tartu, Estonia
| | | | - Chiea-Chuen Khor
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore
- Genome Institute of Singapore, A*STAR, Singapore
- Department of Ophthalmology, National University of Singapore, Singapore
- Department of Paediatrics, National University of Singapore, Singapore
| | - Marcus E Kleber
- Medical Clinic V, Mannheim Medical Faculty, University of Heidelberg, Mannheim, Germany
| | - Huaixing Li
- Key Laboratory of Nutrition and Metabolism, Institute for Nutritional Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Zuan Yu Mok
- Cancer Science Institute of Singapore, National University of Singapore, Singapore
| | - Masahiro Nakatochi
- Center for Advanced Medicine and Clinical Research, Nagoya University Hospital, Nagoya, Japan
| | - Nur Sabrina Sapari
- Cancer Science Institute of Singapore, National University of Singapore, Singapore
| | - Richa Saxena
- Center for Human Genetic Research, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Alexandre F R Stewart
- University of Ottawa Heart Institute, Cardiovascular Research Methods Centre, Ottawa, Ontario, Canada
- Ruddy Canadian Cardiovascular Genetics Centre, Ottawa, Ontario, Canada
| | - Lisette Stolk
- Department of Internal Medicine, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Yasuharu Tabara
- Center for Genomic Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Ai Ling Teh
- Singapore Institute for Clinical Sciences (SICS), A*STAR, Singapore
| | - Ying Wu
- Department of Genetics, University of North Carolina, Chapel Hill, North Carolina, USA
| | - Jer-Yuarn Wu
- Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan
- School of Chinese Medicine, China Medical University, Taichung, Taiwan
| | - Yi Zhang
- State Key Laboratory of Medical Genetics, Shanghai Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Shanghai Institute of Hypertension, Shanghai, China
| | - Imke Aits
- Institute of Epidemiology and Biobank popgen, Christian Albrechts University of Kiel, Kiel, Germany
| | - Alexessander Da Silva Couto Alves
- Department of Epidemiology and Biostatistics, MRC Health Protection Agency (PHE) Centre for Environment and Health, School of Public Health, Imperial College London, London, UK
| | - Shikta Das
- Department of Epidemiology and Biostatistics, MRC Health Protection Agency (PHE) Centre for Environment and Health, School of Public Health, Imperial College London, London, UK
| | | | - Jemma C Hopewell
- Clinical Trials Support Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Yun Kyoung Kim
- Center for Genome Science, National Institute of Health, Osong Health Technology Administration Complex, Chungcheongbuk-do, Republic of Korea
| | - Robert W Koivula
- Department of Clinical Sciences, Genetic and Molecular Epidemiology Unit, Skåne University Hospital Malmö, Malmö, Sweden
| | - Jian’an Luan
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - Leo-Pekka Lyytikäinen
- Department of Clinical Chemistry, Fimlab Laboratories, Tampere, Finland
- Department of Clinical Chemistry, University of Tampere School of Medicine, Tampere, Finland
| | - Quang N Nguyen
- Vietnam National Heart Institute, Bach Mai Hospital, Hanoi, Vietnam
| | - Mark A Pereira
- School of Public Health, University of Minnesota, Minneapolis, Minnesota, USA
| | - Iris Postmus
- Department of Gerontology and Geriatrics, Leiden University Medical Center, Leiden, the Netherlands
- Netherlands Consortium for Healthy Ageing, Leiden, the Netherlands
| | - Olli T Raitakari
- Department of Clinical Physiology and Nuclear Medicine, Turku University Hospital, Turku, Finland
- Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Turku, Finland
| | - Molly Scannell Bryan
- Department of Public Health Sciences, University of Chicago, Chicago, Illinois, USA
| | - Robert A Scott
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - Rossella Sorice
- Institute of Genetics and Biophysics A Buzzati-Traverso, CNR, Naples, Italy
| | - Vinicius Tragante
- Division of Heart and Lungs, Department of Cardiology, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Michela Traglia
- Division of Genetics and Cell Biology, San Raffaele Scientific Institute, Milan, Italy
- Institute for Maternal and Child Health, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) ‘Burlo Garofolo’, Trieste, Italy
| | - Jon White
- University College London Genetics Institute, Department of Genetics, Environment and Evolution, University College London, London, UK
| | - Ken Yamamoto
- Division of Genomics, Medical Institute of Bioregulation, Kyushu University, Fukuoka, Japan
| | - Yonghong Zhang
- Department of Epidemiology, School of Public Health, Medical College of Soochow University, Suzhou, China
| | - Linda S Adair
- Department of Nutrition, University of North Carolina, Chapel Hill, North Carolina, USA
| | | | - Koichi Akiyama
- Department of Gene Diagnostics and Therapeutics, Research Institute, National Center for Global Health and Medicine, Tokyo, Japan
| | - Rasheed Asif
- Center for Non-Communicable Diseases, Karachi, Pakistan
| | - Tin Aung
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
| | - Inês Barroso
- Metabolic Disease Group, Wellcome Trust Sanger Institute, Cambridge, UK
- National Institute for Health Research (NIHR) Cambridge Biomedical Research Centre, Institute of Metabolic Science, Addenbrooke’s Hospital, Cambridge, UK
- University of Cambridge Metabolic Research Laboratories, Institute of Metabolic Science, Addenbrooke’s Hospital, Cambridge, UK
| | - Andrew Bjonnes
- Center for Human Genetic Research, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Timothy R Braun
- Department of Pediatrics, University of Oklahoma Health Sciences Center, Oklahoma City, Oklahoma, USA
| | - Hui Cai
- Vanderbilt Epidemiology Center, Division of Epidemiology, Department of Medicine, Vanderbilt Genetics Institute, Vanderbilt University, Nashville, Tennessee, USA
- Vanderbilt-Ingram Cancer Center, Division of Epidemiology, Department of Medicine, Vanderbilt University, 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
| | - Ching-Yu Cheng
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore
- Department of Ophthalmology, National University of Singapore, Singapore
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
- Centre for Quantitative Medicine, Office of Clinical Sciences, Duke–National University of Singapore Graduate Medical School, Singapore
| | - Yap-Seng Chong
- Singapore Institute for Clinical Sciences (SICS), A*STAR, Singapore
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Rory Collins
- Clinical Trials Support Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Regina Courtney
- Vanderbilt Epidemiology Center, Division of Epidemiology, Department of Medicine, Vanderbilt Genetics Institute, Vanderbilt University, Nashville, Tennessee, USA
- Vanderbilt-Ingram Cancer Center, Division of Epidemiology, Department of Medicine, Vanderbilt University, Nashville, Tennessee, USA
| | - Gail Davies
- Centre for Cognitive Aging and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK
- Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - Graciela Delgado
- Medical Clinic V, Mannheim Medical Faculty, University of Heidelberg, Mannheim, Germany
| | - Loi D Do
- Vietnam National Heart Institute, Bach Mai Hospital, Hanoi, Vietnam
| | - Pieter A Doevendans
- Division of Heart and Lungs, Department of Cardiology, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Ron T Gansevoort
- Department of Internal Medicine, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Yu-Tang Gao
- Department of Epidemiology, Shanghai Cancer Institute, Shanghai, China
| | - Tanja B Grammer
- Medical Clinic V, Mannheim Medical Faculty, University of Heidelberg, Mannheim, Germany
| | - Niels Grarup
- Novo Nordisk Foundation Centre for Basic Metabolic Research, Section of Metabolic Genetics, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Jagvir Grewal
- Department of Epidemiology and Biostatistics, Imperial College London, London, UK
- Ealing Hospital National Health Service (NHS) Trust, Middlesex, UK
| | - Dongfeng Gu
- Fu Wai Hospital and Cardiovascular Institute, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Gurpreet S Wander
- Dayanand Medical College and Hospital Unit, Hero DMC Heart Institute, Ludhiana, India
| | - Anna-Liisa Hartikainen
- Department of Obstetrics and Gynecology, Oulu University Hospital, Oulu, Finland
- Medical Research Center, University of Oulu, Oulu, Finland
- Unit of Primary Care, Oulu University Hospital, Oulu, Finland
| | - Stanley L Hazen
- Department of Cardiovascular Medicine, Heart and Vascular Institute, Cleveland Clinic, Cleveland, Ohio, USA
- Department of Cell Biology, Lerner Research Institute, Cleveland Clinic, Cleveland, Ohio, USA
| | - Jing He
- Department of Biostatistics, Vanderbilt University, Nashville, Tennessee, USA
| | - Chew-Kiat Heng
- Department of Paediatrics, Yong Loo Lin School of Medicine, Singapore
| | - James E Hixson
- Human Genetics Center, University of Texas School of Public Health at Houston, Houston, Texas, USA
| | - Albert Hofman
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Chris Hsu
- University of Southern California Keck School of Medicine, Los Angeles, California, USA
| | - Wei Huang
- Department of Genetics, Chinese National Human Genomic Center, Shanghai, China
| | - Lise L N Husemoen
- Research Centre for Prevention and Health, Glostrup University Hospital, Glostrup, Denmark
| | - Joo-Yeon Hwang
- Center for Genome Science, National Institute of Health, Osong Health Technology Administration Complex, Chungcheongbuk-do, Republic of Korea
| | - Sahoko Ichihara
- Graduate School of Regional Innovation Studies, Mie University, Tsu, Japan
| | - Michiya Igase
- Department of Geriatric Medicine, Ehime University Graduate School of Medicine, Toon, Ehime, Japan
| | - Masato Isono
- Department of Gene Diagnostics and Therapeutics, Research Institute, National Center for Global Health and Medicine, Tokyo, Japan
| | - Johanne M Justesen
- Novo Nordisk Foundation Centre for Basic Metabolic Research, Section of Metabolic Genetics, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Tomohiro Katsuya
- Department of Clinical Gene Therapy, Osaka University Graduate School of Medicine, Suita, Japan
- Department of Geriatric Medicine and Nephrology, Osaka University Graduate School of Medicine, Suita, Japan
| | - Muhammad G Kibriya
- Department of Public Health Sciences, University of Chicago, Chicago, Illinois, USA
| | - Young Jin Kim
- Center for Genome Science, National Institute of Health, Osong Health Technology Administration Complex, Chungcheongbuk-do, Republic of Korea
| | | | - Woon-Puay Koh
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore
- Duke–National University of Singapore Graduate Medical School, Singapore
| | - Katsuhiko Kohara
- Department of Geriatric Medicine, Ehime University Graduate School of Medicine, Toon, Ehime, Japan
| | - Meena Kumari
- Department of Epidemiology and Public Health, University College London, London, UK
| | | | - Nanette R Lee
- University of San Carlos Office of Population Studies Foundation, University of San Carlos, Cebu City, Philippines
- Department of Anthropology, Sociology and History, University of San Carlos, Cebu City, Philippines
| | - Jeannette Lee
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore
| | - Jiemin Liao
- Department of Ophthalmology, National University of Singapore, Singapore
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
| | - Wolfgang Lieb
- Institute of Epidemiology and Biobank popgen, Christian Albrechts University of Kiel, Kiel, Germany
| | - David C M Liewald
- Centre for Cognitive Aging and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK
- Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - Tatsuaki Matsubara
- Department of Internal Medicine, Aichi-Gakuin University School of Dentistry, Nagoya, Japan
| | - Yumi Matsushita
- National Center for Global Health and Medicine, Toyama, Japan
| | - Thomas Meitinger
- Institute of Human Genetics, Helmholtz Zentrum München–German Research Center for Environmental Health, Neuherberg, Germany
- Institute of Human Genetics, Technische Universität München, Munich, Germany
| | | | - Lili Milani
- Estonian Genome Center, University of Tartu, Tartu, Estonia
| | - Rebecca Mills
- Ealing Hospital National Health Service (NHS) Trust, Middlesex, UK
| | - Nina Mononen
- Department of Clinical Chemistry, Fimlab Laboratories, Tampere, Finland
- Department of Clinical Chemistry, University of Tampere School of Medicine, Tampere, Finland
| | - Martina Müller-Nurasyid
- Institute of Genetic Epidemiology, Helmholtz Zentrum München–German Research Center for Environmental Health, Neuherberg, Germany
- Department of Medicine I, Ludwig Maximilians University Munich, Munich, Germany
- German Center for Cardiovascular Research (DZHK), partner site Munich Heart Alliance, Munich, Germany
| | - Toru Nabika
- Department of Functional Pathology, Shimane University Faculty of Medicine, Izumo, Japan
| | - Eitaro Nakashima
- Division of Endocrinology and Diabetes, Department of Internal Medicine, Nagoya University Graduate School of Medicine, Nagoya, Japan
- Department of Diabetes and Endocrinology, Chubu Rosai Hospital, Nagoya, Japan
| | - Hong Kiat Ng
- Cancer Science Institute of Singapore, National University of Singapore, Singapore
| | - Kjell Nikus
- Heart Centre, Department of Cardiology, Tampere University Hospital and University of Tampere School of Medicine, Tampere, Finland
| | - Teresa Nutile
- Institute of Genetics and Biophysics A Buzzati-Traverso, CNR, Naples, Italy
| | - Takayoshi Ohkubo
- Department of Hygiene and Public Health, Teikyo University School of Medicine, Tokyo, Japan
| | - Keizo Ohnaka
- Department of Geriatric Medicine, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Sarah Parish
- Clinical Trials Support Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | | | - Hao Peng
- Department of Epidemiology, School of Public Health, Medical College of Soochow University, Suzhou, China
| | - Annette Peters
- Institute of Epidemiology II, Helmholtz Zentrum München–German Research Center for Environmental Health, Neuherberg, Germany
- German Center for Cardiovascular Research (DZHK), partner site Munich Heart Alliance, Munich, Germany
| | - Son T Pham
- Vietnam National Heart Institute, Bach Mai Hospital, Hanoi, Vietnam
| | | | - Mahfuzar Rahman
- UChicago Research Bangladesh, Uttara, Dhaka, Bangladesh
- Research and Evaluation Division, Bangladesh Rehabilitation Assistance Committee (BRAC), Dhaka, Bangladesh
| | - Hiromi Rakugi
- Department of Geriatric Medicine and Nephrology, Osaka University Graduate School of Medicine, Suita, Japan
| | - Olov Rolandsson
- Department of Public Health and Clinical Medicine, Section for Family Medicine, Umeå Universitet, Umeå, Sweden
| | - Michelle Ann Rozario
- Cancer Science Institute of Singapore, National University of Singapore, Singapore
| | - Daniela Ruggiero
- Institute of Genetics and Biophysics A Buzzati-Traverso, CNR, Naples, Italy
| | - Cinzia F Sala
- Division of Genetics and Cell Biology, San Raffaele Scientific Institute, Milan, Italy
| | - Ralhan Sarju
- Dayanand Medical College and Hospital Unit, Hero DMC Heart Institute, Ludhiana, India
| | - Kazuro Shimokawa
- Department of Gene Diagnostics and Therapeutics, Research Institute, National Center for Global Health and Medicine, Tokyo, Japan
| | - Harold Snieder
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Thomas Sparsø
- Novo Nordisk Foundation Centre for Basic Metabolic Research, Section of Metabolic Genetics, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Wilko Spiering
- Department of Vascular Medicine, University Medical Center Utrecht, Utrecht, the Netherlands
| | - John M Starr
- Centre for Cognitive Aging and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK
- Alzheimer Scotland Dementia Research Centre, University of Edinburgh, Edinburgh, UK
| | - David J Stott
- Academic Section of Geriatric Medicine, Institute of Cardiovascular and Medical Sciences, Faculty of Medicine, University of Glasgow, Glasgow, UK
| | - Daniel O Stram
- University of Southern California Keck School of Medicine, Los Angeles, California, USA
| | - Takao Sugiyama
- Institute for Adult Diseases, Asahi Life Foundation, Tokyo, Japan
| | - Silke Szymczak
- Institute of Clinical Molecular Biology, Christian Albrechts University of Kiel, Kiel, Germany
| | - W H Wilson Tang
- Department of Cardiovascular Medicine, Heart and Vascular Institute, Cleveland Clinic, Cleveland, Ohio, USA
- Department of Cellular and Molecular Medicine, Lerner Research Institute, Cleveland Clinic, Cleveland, Ohio, USA
| | - Lin Tong
- Department of Public Health Sciences, University of Chicago, Chicago, Illinois, USA
| | - Stella Trompet
- Department of Cardiology, Leiden University Medical Center, Leiden, the Netherlands
| | - Väinö Turjanmaa
- Department of Clinical Physiology, Tampere University Hospital, Tampere, Finland
- Department of Clinical Physiology, University of Tampere School of Medicine, Tampere, Finland
| | - Hirotsugu Ueshima
- Department of Health Science, Shiga University of Medical Science, Otsu, Japan
- Center for Epidemiologic Research in Asia, Shiga University of Medical Science, Otsu, Japan
| | - André G Uitterlinden
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, the Netherlands
- Department of Internal Medicine, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Satoshi Umemura
- Department of Medical Science and Cardiorenal Medicine, Yokohama City University Graduate School of Medicine, Yokohama, Japan
| | - Marja Vaarasmaki
- Department of Obstetrics and Gynecology, Oulu University Hospital, Oulu, Finland
- Medical Research Center, University of Oulu, Oulu, Finland
- Unit of Primary Care, Oulu University Hospital, Oulu, Finland
| | - Rob M van Dam
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore
| | - Wiek H van Gilst
- Department of Cardiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Dirk J van Veldhuisen
- Department of Cardiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Jorma S Viikari
- Division of Medicine, Turku University Hospital, Turku, Finland
- Department of Medicine, University of Turku, Turku, Finland
| | - Melanie Waldenberger
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München–German Research Center for Environmental Health, Neuherberg, Germany
- Institute of Epidemiology II, Helmholtz Zentrum München–German Research Center for Environmental Health, Neuherberg, Germany
| | - Yiqin Wang
- Key Laboratory of Nutrition and Metabolism, Institute for Nutritional Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Aili Wang
- Department of Epidemiology, School of Public Health, Medical College of Soochow University, Suzhou, China
| | - Rory Wilson
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München–German Research Center for Environmental Health, Neuherberg, Germany
- Institute of Epidemiology II, Helmholtz Zentrum München–German Research Center for Environmental Health, Neuherberg, Germany
| | - Tien-Yin Wong
- Department of Ophthalmology, National University of Singapore, Singapore
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
| | - Yong-Bing Xiang
- Department of Epidemiology, Shanghai Cancer Institute, Shanghai, China
| | - Shuhei Yamaguchi
- Third Department of Internal Medicine, Shimane University Faculty of Medicine, Izumo, Japan
| | - Xingwang Ye
- Key Laboratory of Nutrition and Metabolism, Institute for Nutritional Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Robin D Young
- Department of Public Health and Primary Care, University of Cambridge, Strangeways Research Laboratory, Cambridge, UK
| | - Terri L Young
- Neuroscience and Behavioural Disorders (NBD) Program, Duke–National University of Singapore Graduate Medical School, Singapore
- Duke Eye Center, Duke University Medical Center, Durham, North Carolina, USA
| | - Jian-Min Yuan
- Cancer Control and Population Sciences, University of Pittsburgh Cancer Institute, Pittsburgh, Pennsylvania, USA
| | - Xueya Zhou
- Bioinformatics Division, Tsinghua National Laboratory for Informatics Science and Technology (TNLIST), Ministry of Education Key Laboratory of Bioinformatics, Department of Automation, Tsinghua University, Beijing, China
- Center for Synthetic and Systems Biology, TNLIST, Ministry of Education Key Laboratory of Bioinformatics, Department of Automation, Tsinghua University, Beijing, China
- Department of Psychiatry, University of Hong Kong, Hong Kong
| | - Folkert W Asselbergs
- Division of Heart and Lungs, Department of Cardiology, University Medical Center Utrecht, Utrecht, the Netherlands
- Durrer Center for Cardiogenetic Research, Interuniversity Cardiology Institute of the Netherlands (ICIN)–Netherlands Heart Institute, Utrecht, the Netherlands
- Institute of Cardiovascular Science, Faculty of Population Health Sciences, University College London, London, UK
| | - Marina Ciullo
- Institute of Genetics and Biophysics A Buzzati-Traverso, CNR, Naples, Italy
| | - Robert Clarke
- Clinical Trials Support Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Panos Deloukas
- William Harvey Research Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, UK
- King Abdulaziz University, Jeddah, Saudi Arabia
| | - Andre Franke
- Institute of Clinical Molecular Biology, Christian Albrechts University of Kiel, Kiel, Germany
| | - Paul W Franks
- Department of Clinical Sciences, Genetic and Molecular Epidemiology Unit, Skåne University Hospital Malmö, Malmö, Sweden
- Department of Public Health and Clinical Medicine, Section for Family Medicine, Umeå Universitet, Umeå, Sweden
- Department of Nutrition, Harvard School of Public Health, Boston, Massachusetts, USA
| | - Steve Franks
- Institute of Reproductive and Developmental Biology, Imperial College London, Hammersmith Hospital, London, UK
| | | | - Myron D Gross
- School of Medicine, University of Minnesota, Minneapolis, Minnesota, USA
| | - Zhirong Guo
- Department of Epidemiology, School of Public Health, Medical College of Soochow University, Suzhou, China
| | - Torben Hansen
- Novo Nordisk Foundation Centre for Basic Metabolic Research, Section of Metabolic Genetics, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Marjo-Riitta Jarvelin
- Institute of Health Sciences, University of Oulu, Oulu, Finland
- Department of Epidemiology and Biostatistics, MRC Health Protection Agency (PHE) Centre for Environment and Health, School of Public Health, Imperial College London, London, UK
- Unit of Primary Care, Oulu University Hospital, Oulu, Finland
- Biocenter Oulu, University of Oulu, Oulu, Finland
- Center for Life Course Epidemiology, Faculty of Medicine, University of Oulu, Oulu, Finland
| | - Torben Jørgensen
- Research Centre for Prevention and Health, Glostrup University Hospital, Glostrup, Denmark
| | - J Wouter Jukema
- Department of Cardiology, Leiden University Medical Center, Leiden, the Netherlands
- Durrer Center for Cardiogenetic Research, Interuniversity Cardiology Institute of the Netherlands (ICIN)–Netherlands Heart Institute, Utrecht, the Netherlands
- ICIN, Utrecht, the Netherlands
| | - Mika kähönen
- Department of Clinical Physiology, Tampere University Hospital, Tampere, Finland
- Department of Clinical Physiology, University of Tampere School of Medicine, Tampere, Finland
| | - Hiroshi Kajio
- National Center for Global Health and Medicine, Toyama, Japan
| | - Mika Kivimaki
- Department of Epidemiology and Public Health, University College London, London, UK
| | - Jong-Young Lee
- Ministry of Health and Welfare, Seoul, Republic of Korea
- THERAGEN ETEX Bio Institute, Suwon, Republic of Korea
| | - Terho Lehtimäki
- Department of Clinical Chemistry, Fimlab Laboratories, Tampere, Finland
- Department of Clinical Chemistry, University of Tampere School of Medicine, Tampere, Finland
| | - Allan Linneberg
- Research Centre for Prevention and Health, Glostrup University Hospital, Glostrup, Denmark
- Department of Clinical Experimental Research, Rigshospitalet, Copenhagen, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Tetsuro Miki
- Department of Geriatric Medicine, Ehime University Graduate School of Medicine, Toon, Ehime, Japan
| | - Oluf Pedersen
- Novo Nordisk Foundation Centre for Basic Metabolic Research, Section of Metabolic Genetics, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Nilesh J Samani
- Department of Cardiovascular Sciences, University of Leicester, Glenfield Hospital, Leicester, UK
- NIHR Leicester Cardiovascular Biomedical Research Unit, Glenfield Hospital, Leicester, UK
| | - Thorkild I A Sørensen
- Novo Nordisk Foundation Centre for Basic Metabolic Research, Section of Metabolic Genetics, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Institute of Preventive Medicine, Bispebjerg and Frederiksberg Hospital, Copenhagen, Denmark
| | - Ryoichi Takayanagi
- Department of Medicine and Bioregulatory Science, Graduate School of Medical Sciences, Kyushu University, Kyushu, Japan
| | - Daniela Toniolo
- Division of Genetics and Cell Biology, San Raffaele Scientific Institute, Milan, Italy
- Institute of Molecular Genetics, National Research Council (CNR), Pavia, Italy
| | | | | | | | | | - Habibul Ahsan
- Department of Public Health Sciences, University of Chicago, Chicago, Illinois, USA
| | - Hooman Allayee
- Department of Preventive Medicine, University of Southern California Keck School of Medicine, Los Angeles, California, USA
- Institute for Genetic Medicine, University of Southern California Keck School of Medicine, Los Angeles, California, USA
| | - Yuan-Tsong Chen
- Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan
| | - John Danesh
- Department of Public Health and Primary Care, University of Cambridge, Strangeways Research Laboratory, Cambridge, UK
- Wellcome Trust Sanger Institute, Hinxton, UK
| | - Ian J Deary
- Centre for Cognitive Aging and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK
- Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - Oscar H Franco
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Lude Franke
- Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Bastiaan T Heijman
- Molecular Epidemiology, Leiden University Medical Center, Leiden, the Netherlands
| | | | - Aaron Isaacs
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Bong-Jo Kim
- Center for Genome Science, National Institute of Health, Osong Health Technology Administration Complex, Chungcheongbuk-do, Republic of Korea
| | - Xu Lin
- Key Laboratory of Nutrition and Metabolism, Institute for Nutritional Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Jianjun Liu
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore
- Genome Institute of Singapore, A*STAR, Singapore
| | - Winfried März
- Medical Clinic V, Mannheim Medical Faculty, University of Heidelberg, Mannheim, Germany
- Clinical Institute of Medical and Chemical Laboratory Diagnostics, Medical University of Graz, Graz, Austria
- Synlab Academy, Synlab Services, Mannheim, Germany
| | | | - Karen L Mohlke
- Department of Genetics, University of North Carolina, Chapel Hill, North Carolina, USA
| | - Dharambir K Sanghera
- Department of Pediatrics, University of Oklahoma Health Sciences Center, Oklahoma City, Oklahoma, USA
| | - Xiao-Ou Shu
- Vanderbilt Epidemiology Center, Division of Epidemiology, Department of Medicine, Vanderbilt Genetics Institute, Vanderbilt University, Nashville, Tennessee, USA
- Vanderbilt-Ingram Cancer Center, Division of Epidemiology, Department of Medicine, Vanderbilt University, Nashville, Tennessee, USA
| | - Joyce B J van Meurs
- Department of Internal Medicine, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Eranga Vithana
- Department of Ophthalmology, National University of Singapore, Singapore
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
- Neuroscience and Behavioural Disorders (NBD) Program, Duke–National University of Singapore Graduate Medical School, Singapore
| | | | - Cisca Wijmenga
- Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Bruce H W Wolffenbuttel
- Department of Endocrinology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Mitsuhiro Yokota
- Department of Genome Science, Aichi-Gakuin University School of Dentistry, Nagoya, Japan
| | - Wei Zheng
- Vanderbilt Epidemiology Center, Division of Epidemiology, Department of Medicine, Vanderbilt Genetics Institute, Vanderbilt University, Nashville, Tennessee, USA
- Vanderbilt-Ingram Cancer Center, Division of Epidemiology, Department of Medicine, Vanderbilt University, Nashville, Tennessee, USA
| | - Dingliang Zhu
- State Key Laboratory of Medical Genetics, Shanghai Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Shanghai Institute of Hypertension, Shanghai, China
| | - Paolo Vineis
- Department of Epidemiology and Biostatistics, Imperial College London, London, UK
| | - Soterios A Kyrtopoulos
- National Hellenic Research Foundation, Institute of Biology, Medicinal Chemistry and Biotechnology, Athens, Greece
| | - Jos C S Kleinjans
- Department of Toxicogenomics, Maastricht University, Maastricht, the Netherlands
| | - Mark I McCarthy
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK
- Oxford Centre for Diabetes Endocrinology and Metabolism, University of Oxford, Oxford, UK
| | - Richie Soong
- Cancer Science Institute of Singapore, National University of Singapore, Singapore
- Department of Pathology, National University of Singapore, Singapore
| | - Christian Gieger
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München–German Research Center for Environmental Health, Neuherberg, Germany
- Institute of Epidemiology II, Helmholtz Zentrum München–German Research Center for Environmental Health, Neuherberg, Germany
| | - James Scott
- National Heart and Lung Institute, Imperial College London, London, UK
| | - Yik-Ying Teo
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore
- Genome Institute of Singapore, A*STAR, Singapore
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
- National University of Singapore Graduate School for Integrative Science and Engineering, National University of Singapore, Singapore
- Life Sciences Institute, National University of Singapore, Singapore
- Department of Statistics and Applied Probability, National University of Singapore, Singapore
| | - Jiang He
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, Louisiana, USA
| | - Paul Elliott
- Department of Epidemiology and Biostatistics, Imperial College London, London, UK
| | - E Shyong Tai
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore
- Department of Clinical Gene Therapy, Osaka University Graduate School of Medicine, Suita, Japan
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Pim van der Harst
- Department of Cardiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
- Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
- Durrer Center for Cardiogenetic Research, Interuniversity Cardiology Institute of the Netherlands (ICIN)–Netherlands Heart Institute, Utrecht, the Netherlands
| | - Jaspal S Kooner
- Ealing Hospital National Health Service (NHS) Trust, Middlesex, UK
- National Heart and Lung Institute, Imperial College London, London, UK
- Imperial College Healthcare NHS Trust, London, UK
| | - John C Chambers
- Department of Epidemiology and Biostatistics, Imperial College London, London, UK
- Ealing Hospital National Health Service (NHS) Trust, Middlesex, UK
- Imperial College Healthcare NHS Trust, London, UK
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Zanetti D, Carreras-Torres R, Esteban E, Via M, Moral P. Potential Signals of Natural Selection in the Top Risk Loci for Coronary Artery Disease: 9p21 and 10q11. PLoS One 2015; 10:e0134840. [PMID: 26252781 PMCID: PMC4529309 DOI: 10.1371/journal.pone.0134840] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2015] [Accepted: 07/15/2015] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND Coronary artery disease (CAD) is a complex disease and the leading cause of death in the world. Populations of different ancestry do not always share the same risk markers. Natural selective processes may be the cause of some of the population differences detected for specific risk mutations. OBJECTIVE In this study, 384 single nucleotide polymorphisms (SNPs) located in four genomic regions associated with CAD (1p13, 1q41, 9p21 and 10q11) are analysed in a set of 19 populations from Europe, Middle East and North Africa and also in Asian and African samples from the 1000 Genomes Project. The aim of this survey is to explore for the first time whether the genetic variability in these genomic regions is better explained by demography or by natural selection. RESULTS The results indicate significant differences in the structure of genetic variation and in the LD patterns among populations that probably explain the population disparities found in markers of susceptibility to CAD. CONCLUSIONS The results are consistent with potential signature of positive selection in the 9p21 region and of balancing selection in the 9p21 and 10q11. Specifically, in Europe three CAD risk markers in the 9p21 region (rs9632884, rs1537371 and rs1333042) show consistent signals of positive selection. The results of this study are consistent with a potential selective role of CAD in the configuration of genetic diversity in current human populations.
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Affiliation(s)
- Daniela Zanetti
- Department of Animal Biology-Anthropology, University of Barcelona, Barcelona, Spain
| | | | - Esther Esteban
- Department of Animal Biology-Anthropology, University of Barcelona, Barcelona, Spain
- Biodiversity Research Institute, University of Barcelona, Spain
| | - Marc Via
- Department of Psychiatry and Clinical Psychobiology and Institute for Brain, Cognition and Behavior (IR3C), University of Barcelona, Barcelona, Spain
| | - Pedro Moral
- Department of Animal Biology-Anthropology, University of Barcelona, Barcelona, Spain
- Biodiversity Research Institute, University of Barcelona, Spain
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Sorbolini S, Marras G, Gaspa G, Dimauro C, Cellesi M, Valentini A, Macciotta NP. Detection of selection signatures in Piemontese and Marchigiana cattle, two breeds with similar production aptitudes but different selection histories. Genet Sel Evol 2015; 47:52. [PMID: 26100250 PMCID: PMC4476081 DOI: 10.1186/s12711-015-0128-2] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2014] [Accepted: 05/20/2015] [Indexed: 12/21/2022] Open
Abstract
BACKGROUND Domestication and selection are processes that alter the pattern of within- and between-population genetic variability. They can be investigated at the genomic level by tracing the so-called selection signatures. Recently, sequence polymorphisms at the genome-wide level have been investigated in a wide range of animals. A common approach to detect selection signatures is to compare breeds that have been selected for different breeding goals (i.e. dairy and beef cattle). However, genetic variations in different breeds with similar production aptitudes and similar phenotypes can be related to differences in their selection history. METHODS In this study, we investigated selection signatures between two Italian beef cattle breeds, Piemontese and Marchigiana, using genotyping data that was obtained with the Illumina BovineSNP50 BeadChip. The comparison was based on the fixation index (Fst), combined with a locally weighted scatterplot smoothing (LOWESS) regression and a control chart approach. In addition, analyses of Fst were carried out to confirm candidate genes. In particular, data were processed using the varLD method, which compares the regional variation of linkage disequilibrium between populations. RESULTS Genome scans confirmed the presence of selective sweeps in the genomic regions that harbour candidate genes that are known to affect productive traits in cattle such as DGAT1, ABCG2, CAPN3, MSTN and FTO. In addition, several new putative candidate genes (for example ALAS1, ABCB8, ACADS and SOD1) were detected. CONCLUSIONS This study provided evidence on the different selection histories of two cattle breeds and the usefulness of genomic scans to detect selective sweeps even in cattle breeds that are bred for similar production aptitudes.
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Affiliation(s)
- Silvia Sorbolini
- Dipartimento di Agraria, Sezione di Scienze Zootecniche Università degli Studi di Sassari, 07100, Sassari, Italy.
| | - Gabriele Marras
- Dipartimento di Agraria, Sezione di Scienze Zootecniche Università degli Studi di Sassari, 07100, Sassari, Italy.
| | - Giustino Gaspa
- Dipartimento di Agraria, Sezione di Scienze Zootecniche Università degli Studi di Sassari, 07100, Sassari, Italy.
| | - Corrado Dimauro
- Dipartimento di Agraria, Sezione di Scienze Zootecniche Università degli Studi di Sassari, 07100, Sassari, Italy.
| | - Massimo Cellesi
- Dipartimento di Agraria, Sezione di Scienze Zootecniche Università degli Studi di Sassari, 07100, Sassari, Italy.
| | - Alessio Valentini
- Dipartimento per l'Innovazione dei Sistemi Biologici Agroalimentari e Forestali DIBAF, Università della Tuscia, Viterbo, Italy.
| | - Nicolò Pp Macciotta
- Dipartimento di Agraria, Sezione di Scienze Zootecniche Università degli Studi di Sassari, 07100, Sassari, Italy.
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26
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Tosto G, Fu H, Vardarajan BN, Lee JH, Cheng R, Reyes-Dumeyer D, Lantigua R, Medrano M, Jimenez-Velazquez IZ, Elkind MSV, Wright CB, Sacco RL, Pericak-Vance M, Farrer L, Rogaeva E, St George-Hyslop P, Reitz C, Mayeux R. F-box/LRR-repeat protein 7 is genetically associated with Alzheimer's disease. Ann Clin Transl Neurol 2015; 2:810-20. [PMID: 26339675 PMCID: PMC4554442 DOI: 10.1002/acn3.223] [Citation(s) in RCA: 49] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2015] [Revised: 05/05/2015] [Accepted: 05/14/2015] [Indexed: 01/28/2023] Open
Abstract
Objective In the context of late-onset Alzheimer’s disease (LOAD) over 20 genes have been identified but, aside APOE, all show small effect sizes, leaving a large part of the genetic component unexplained. Admixed populations, such as Caribbean Hispanics, can provide a valuable contribution because of their unique genetic profile and higher incidence of the disease. We aimed to identify novel loci associated with LOAD. Methods About 4514 unrelated Caribbean Hispanics (2451 cases and 2063 controls) were selected for genome-wide association analysis. Significant loci were further tested in the expanded cohort that also included related family members (n = 5300). Two AD-like transgenic mice models (J20 and rTg4510) were used to study gene expression. Independent data sets of non-Hispanic Whites and African Americans were used to further validate findings, along with publicly available brain expression data sets. Results A novel locus, rs75002042 in FBXL7 (5p15.1), was found genome-wide significant in the case–control cohort (odd ratio [OR] = 0.61, P = 6.19E-09) and confirmed in the related members cohorts (OR = 0.63, P = 4.7E-08). Fbxl7 protein was overexpressed in both AD-like transgenic mice compared to wild-type littermates. Publicly available microarray studies also showed significant overexpression of Fbxl7 in LOAD brains compared to nondemented controls. single-nucleotide polymorphism (SNP) rs75002042 was in complete linkage disequilibrium with other variants in two independent non-Hispanic White and African American data sets (0.0005 < P < 0.02) used for replication. Interpretation FBXL7, encodes a subcellular protein involved in phosphorylation-dependent ubiquitination processes and displays proapoptotic activity. F-box proteins also modulate inflammation and innate immunity, which may be important in LOAD pathogenesis. Further investigations are needed to validate and understand its role in this and other populations.
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Affiliation(s)
- Giuseppe Tosto
- The Taub Institute for Research on Alzheimer's Disease and the Aging Brain, School of Public Health, Columbia University New York, New York ; The Gertrude H. Sergievsky Center, School of Public Health, Columbia University New York, New York
| | - Hongjun Fu
- The Taub Institute for Research on Alzheimer's Disease and the Aging Brain, School of Public Health, Columbia University New York, New York
| | - Badri N Vardarajan
- The Taub Institute for Research on Alzheimer's Disease and the Aging Brain, School of Public Health, Columbia University New York, New York ; The Gertrude H. Sergievsky Center, School of Public Health, Columbia University New York, New York
| | - Joseph H Lee
- The Taub Institute for Research on Alzheimer's Disease and the Aging Brain, School of Public Health, Columbia University New York, New York ; The Gertrude H. Sergievsky Center, School of Public Health, Columbia University New York, New York
| | - Rong Cheng
- The Taub Institute for Research on Alzheimer's Disease and the Aging Brain, School of Public Health, Columbia University New York, New York ; The Gertrude H. Sergievsky Center, School of Public Health, Columbia University New York, New York
| | - Dolly Reyes-Dumeyer
- The Taub Institute for Research on Alzheimer's Disease and the Aging Brain, School of Public Health, Columbia University New York, New York ; The Gertrude H. Sergievsky Center, School of Public Health, Columbia University New York, New York
| | - Rafael Lantigua
- Department of Medicine College of Physicians and Surgeons, School of Public Health, Columbia University New York, New York
| | - Martin Medrano
- School of Medicine, Pontificia Universidad Catolica Madre y Maestra Santiago, Dominican Republic
| | - Ivonne Z Jimenez-Velazquez
- Department of Medicine, Geriatrics Program, School of Medicine, University of Puerto Rico San Juan, Puerto Rico
| | - Mitchell S V Elkind
- The Gertrude H. Sergievsky Center, School of Public Health, Columbia University New York, New York ; Department of Neurology, Columbia University New York, New York
| | - Clinton B Wright
- Evelyn F. McKnight Brain Institute and Departments of Neurology, Public Health Sciences, John T. McDonald Department of Human Genetics, University of Miami Miami, Florida, 33136
| | - Ralph L Sacco
- Evelyn F. McKnight Brain Institute and Departments of Neurology, Public Health Sciences, John T. McDonald Department of Human Genetics, University of Miami Miami, Florida, 33136 ; Neuroscience Program, Leonard M. Miller School of Medicine, University of Miami Miami, Florida, 33136
| | - Margaret Pericak-Vance
- Neuroscience Program, Leonard M. Miller School of Medicine, University of Miami Miami, Florida, 33136 ; The John P. Hussman Institute for Human Genomics, University of Miami Miami, Florida, 33136
| | - Lindsay Farrer
- Departments of Medicine (Biomedical Genetics), Neurology, Ophthalmology, Biostatistics and Epidemiology, Boston University Schools of Medicine and Public Health Boston, Massachusetts
| | - Ekaterina Rogaeva
- Tanz Centre for Research in Neurodegenerative Diseases, and Department of Medicine, University of Toronto Krembil Discovery Tower, 60 Leonard Avenue, Toronto, Ontario, Canada, M5T 2S8
| | - Peter St George-Hyslop
- Tanz Centre for Research in Neurodegenerative Diseases, and Department of Medicine, University of Toronto Krembil Discovery Tower, 60 Leonard Avenue, Toronto, Ontario, Canada, M5T 2S8 ; Department of Clinical Neurosciences, Cambridge Institute for Medical Research, University of Cambridge Cambridge, CB2 0XY, United Kingdom
| | - Christiane Reitz
- The Taub Institute for Research on Alzheimer's Disease and the Aging Brain, School of Public Health, Columbia University New York, New York ; The Gertrude H. Sergievsky Center, School of Public Health, Columbia University New York, New York ; Department of Neurology, Columbia University New York, New York
| | - Richard Mayeux
- The Taub Institute for Research on Alzheimer's Disease and the Aging Brain, School of Public Health, Columbia University New York, New York ; The Gertrude H. Sergievsky Center, School of Public Health, Columbia University New York, New York ; Department of Neurology, Columbia University New York, New York ; Department of Psychiatry, School of Public Health, Columbia University New York, New York ; Department of Epidemiology, School of Public Health, Columbia University New York, New York
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27
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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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28
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Liao J, Su X, Chen P, Wang X, Xu L, Li X, Thean L, Tan C, Tan AG, Tay WT, Jun G, Zheng Y, Chew M, Wang YX, Tan QS, Barathi VA, Klein BE, Saw SM, Vithana EN, Tai ES, Iyengar SK, Mitchell P, Khor CC, Aung T, Wang JJ, Jonas JB, Teo YY, Wong TY, Cheng CY. Meta-analysis of genome-wide association studies in multiethnic Asians identifies two loci for age-related nuclear cataract. Hum Mol Genet 2014; 23:6119-28. [DOI: 10.1093/hmg/ddu315] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023] Open
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29
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Li C, Yang X, He J, Hixson JE, Gu D, Rao DC, Shimmin LC, Huang J, Gu CC, Chen J, Li J, Kelly TN. A gene-based analysis of variants in the serum/glucocorticoid regulated kinase (SGK) genes with blood pressure responses to sodium intake: the GenSalt Study. PLoS One 2014; 9:e98432. [PMID: 24878720 PMCID: PMC4039502 DOI: 10.1371/journal.pone.0098432] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2014] [Accepted: 04/22/2014] [Indexed: 11/18/2022] Open
Abstract
BACKGROUND Serum and glucocorticoid regulated kinase (SGK) plays a critical role in the regulation of renal sodium transport. We examined the association between SGK genes and salt sensitivity of blood pressure (BP) using single-marker and gene-based association analysis. METHODS A 7-day low-sodium (51.3 mmol sodium/day) followed by a 7-day high-sodium intervention (307.8 mmol sodium/day) was conducted among 1,906 Chinese participants. BP measurements were obtained at baseline and each intervention using a random-zero sphygmomanometer. Additive associations between each SNP and salt-sensitivity phenotypes were assessed using a mixed linear regression model to account for family dependencies. Gene-based analyses were conducted using the truncated p-value method. The Bonferroni-method was used to adjust for multiple testing in all analyses. RESULTS In single-marker association analyses, SGK1 marker rs2758151 was significantly associated with diastolic BP (DBP) response to high-sodium intervention (P = 0.0010). DBP responses (95% confidence interval) to high-sodium intervention for genotypes C/C, C/T, and T/T were 2.04 (1.57 to 2.52), 1.79 (1.42 to 2.16), and 0.85 (0.30 to 1.41) mmHg, respectively. Similar trends were observed for SBP and MAP responses although not significant (P = 0.15 and 0.0026, respectively). In addition, gene-based analyses demonstrated significant associations between SGK1 and SBP, DBP and MAP responses to high sodium intervention (P = 0.0002, 0.0076, and 0.00001, respectively). Neither SGK2 nor SGK3 were associated with the salt-sensitivity phenotypes in single-maker or gene-based analyses. CONCLUSIONS The current study identified association of the SGK1 gene and BP salt-sensitivity in the Han Chinese population. Further studies are warranted to identify causal SGK1 gene variants.
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Affiliation(s)
- Changwei Li
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, Louisiana, United States of America
| | - Xueli Yang
- State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center of Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Jiang He
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, Louisiana, United States of America
- Department of Medicine, Tulane University School of Medicine, New Orleans, Louisiana, United States of America
| | - James E. Hixson
- Department of Epidemiology, Human Genetics and Environmental Sciences, University of Texas School of Public Health, Houston, Texas, United States of America
| | - Dongfeng Gu
- State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center of Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Dabeeru C. Rao
- Division of Biostatistics, Washington University School of Medicine, St. Louis, Missouri, United States of America
| | - Lawrence C. Shimmin
- Department of Epidemiology, Human Genetics and Environmental Sciences, University of Texas School of Public Health, Houston, Texas, United States of America
| | - Jianfeng Huang
- State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center of Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Charles C. Gu
- Division of Biostatistics, Washington University School of Medicine, St. Louis, Missouri, United States of America
| | - Jichun Chen
- State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center of Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Jianxin Li
- State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center of Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Tanika N. Kelly
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, Louisiana, United States of America
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Abraham G, Inouye M. Fast principal component analysis of large-scale genome-wide data. PLoS One 2014; 9:e93766. [PMID: 24718290 PMCID: PMC3981753 DOI: 10.1371/journal.pone.0093766] [Citation(s) in RCA: 183] [Impact Index Per Article: 18.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2014] [Accepted: 03/07/2014] [Indexed: 01/22/2023] Open
Abstract
Principal component analysis (PCA) is routinely used to analyze genome-wide single-nucleotide polymorphism (SNP) data, for detecting population structure and potential outliers. However, the size of SNP datasets has increased immensely in recent years and PCA of large datasets has become a time consuming task. We have developed flashpca, a highly efficient PCA implementation based on randomized algorithms, which delivers identical accuracy in extracting the top principal components compared with existing tools, in substantially less time. We demonstrate the utility of flashpca on both HapMap3 and on a large Immunochip dataset. For the latter, flashpca performed PCA of 15,000 individuals up to 125 times faster than existing tools, with identical results, and PCA of 150,000 individuals using flashpca completed in 4 hours. The increasing size of SNP datasets will make tools such as flashpca essential as traditional approaches will not adequately scale. This approach will also help to scale other applications that leverage PCA or eigen-decomposition to substantially larger datasets.
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Affiliation(s)
- Gad Abraham
- Medical Systems Biology, Department of Pathology and Department of Microbiology & Immunology, University of Melbourne, Parkville, Victoria, Australia
- * E-mail:
| | - Michael Inouye
- Medical Systems Biology, Department of Pathology and Department of Microbiology & Immunology, University of Melbourne, Parkville, Victoria, Australia
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31
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Pérez O'Brien AM, Utsunomiya YT, Mészáros G, Bickhart DM, Liu GE, Van Tassell CP, Sonstegard TS, Da Silva MVB, Garcia JF, Sölkner J. Assessing signatures of selection through variation in linkage disequilibrium between taurine and indicine cattle. Genet Sel Evol 2014; 46:19. [PMID: 24592996 PMCID: PMC4014805 DOI: 10.1186/1297-9686-46-19] [Citation(s) in RCA: 60] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2013] [Accepted: 01/09/2014] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Signatures of selection are regions in the genome that have been preferentially increased in frequency and fixed in a population because of their functional importance in specific processes. These regions can be detected because of their lower genetic variability and specific regional linkage disequilibrium (LD) patterns. METHODS By comparing the differences in regional LD variation between dairy and beef cattle types, and between indicine and taurine subspecies, we aim at finding signatures of selection for production and adaptation in cattle breeds. The VarLD method was applied to compare the LD variation in the autosomal genome between breeds, including Angus and Brown Swiss, representing taurine breeds, and Nelore and Gir, representing indicine breeds. Genomic regions containing the top 0.01 and 0.1 percentile of signals were characterized using the UMD3.1 Bos taurus genome assembly to identify genes in those regions and compared with previously reported selection signatures and regions with copy number variation. RESULTS For all comparisons, the top 0.01 and 0.1 percentile included 26 and 165 signals and 17 and 125 genes, respectively, including TECRL, BT.23182 or FPPS, CAST, MYOM1, UVRAG and DNAJA1. CONCLUSIONS The VarLD method is a powerful tool to identify differences in linkage disequilibrium between cattle populations and putative signatures of selection with potential adaptive and productive importance.
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Affiliation(s)
| | | | | | | | | | | | | | | | | | - Johann Sölkner
- University of Natural Resources and Life Sciences, Vienna, Austria.
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32
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Charles BA, Shriner D, Rotimi CN. Accounting for linkage disequilibrium in association analysis of diverse populations. Genet Epidemiol 2014; 38:265-73. [PMID: 24464495 DOI: 10.1002/gepi.21788] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2013] [Revised: 11/14/2013] [Accepted: 12/03/2013] [Indexed: 12/25/2022]
Abstract
The National Human Genome Research Institute's catalog of published genome-wide association studies (GWAS) lists over 10,000 genetic variants collectively associated with over 800 human diseases or traits. Most of these GWAS have been conducted in European-ancestry populations. Findings gleaned from these studies have led to identification of disease-associated loci and biologic pathways involved in disease etiology. In multiple instances, these genomic findings have led to the development of novel medical therapies or evidence for prescribing a given drug as the appropriate treatment for a given individual beyond phenotypic appearances or socially defined constructs of race or ethnicity. Such findings have implications for populations throughout the globe and GWAS are increasingly being conducted in more diverse populations. A major challenge for investigators seeking to follow up genomic findings between diverse populations is discordant patterns of linkage disequilibrium (LD). We provide an overview of common measures of LD and opportunities for their use in novel methods designed to address challenges associated with following up GWAS conducted in European-ancestry populations in African-ancestry populations or, more generally, between populations with discordant LD patterns. We detail the strengths and weaknesses associated with different approaches. We also describe application of these strategies in follow-up studies of populations with concordant LD patterns (replication) or discordant LD patterns (transferability) as well as fine-mapping studies. We review application of these methods to a variety of traits and diseases.
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Affiliation(s)
- Bashira A Charles
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, Bethesda, Maryland, United States of America
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33
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Kelly TN, Takeuchi F, Tabara Y, Edwards TL, Kim YJ, Chen P, Li H, Wu Y, Yang CF, Zhang Y, Gu D, Katsuya T, Ohkubo T, Gao YT, Go MJ, Teo YY, Lu L, Lee NR, Chang LC, Peng H, Zhao Q, Nakashima E, Kita Y, Shu XO, Kim NH, Tai ES, Wang Y, Adair LS, Chen CH, Zhang S, Li C, Nabika T, Umemura S, Cai Q, Cho YS, Wong TY, Zhu J, Wu JY, Gao X, Hixson JE, Cai H, Lee J, Cheng CY, Rao DC, Xiang YB, Cho MC, Han BG, Wang A, Tsai FJ, Mohlke K, Lin X, Ikram MK, Lee JY, Zheng W, Tetsuro M, Kato N, He J. Genome-wide association study meta-analysis reveals transethnic replication of mean arterial and pulse pressure loci. Hypertension 2013; 62:853-9. [PMID: 24001895 DOI: 10.1161/hypertensionaha.113.01148] [Citation(s) in RCA: 61] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Abstract
We conducted a genome-wide association study meta-analysis of mean arterial pressure and pulse pressure among 26,600 East Asian participants (stage 1) followed by replication study of up to 28,783 participants (stage 2). For novel loci, statistical significance was determined by a P<5.0×10(-8) in joint analysis of stage 1 and stage 2 data. For loci reported by the previous mean arterial and pulse pressure genome-wide association study meta-analysis in Europeans, evidence of transethnic replication was determined by consistency in effect direction and a Bonferroni-corrected P<1.4×10(-3). No novel loci were identified by the current study. Five independent mean arterial pressure variants demonstrated robust evidence for transethnic replication including rs17249754 at ATP2B1 (P=7.5×10(-15)), rs2681492 at ATP2B1 (P=3.4×10(-7)), rs11191593 at NT5C2 (1.1×10(-6)), rs3824755 at CYP17A1 (P=1.2×10(-6)), and rs13149993 at FGF5 (P=2.4×10(-4)). Two additional variants showed suggestive evidence of transethnic replication (consistency in effect direction and P<0.05), including rs319690 at MAP4 (P=0.014) and rs1173771 at NPR3 (P=0.018). For pulse pressure, robust evidence of replication was identified for 2 independent variants, including rs17249754 at ATP2B1 (P=1.2×10(-5)) and rs11191593 at NT5C2 (P=1.1×10(-3)), with suggestive evidence of replication among an additional 2 variants including rs3824755 at CYP17A1 (P=6.1×10(-3)) and rs2681492 at ATP2B1 (P=9.0×10(-3)). Replicated variants demonstrated consistency in effect sizes between East Asian and European samples, with effect size differences ranging from 0.03 to 0.24 mm Hg for mean arterial pressure and from 0.03 to 0.21 mm Hg for pulse pressure. In conclusion, we present the first evidence of transethnic replication of several mean arterial and pulse pressure loci in an East Asian population.
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Affiliation(s)
- Tanika N Kelly
- Department of Epidemiology, Tulane University, 1440 Canal St, Suite 2000, New Orleans, LA 70112.
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
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Dorajoo R, Li R, Ikram MK, Liu J, Froguel P, Lee J, Sim X, Ong RTH, Tay WT, Peng C, Young TL, Blakemore AIF, Cheng CY, Aung T, Mitchell P, Wang JJ, Klaver CC, Boerwinkle E, Klein R, Siscovick DS, Jensen RA, Gudnason V, Smith AV, Teo YY, Wong TY, Tai ES, Heng CK, Friedlander Y. Are C-reactive protein associated genetic variants associated with serum levels and retinal markers of microvascular pathology in Asian populations from Singapore? PLoS One 2013; 8:e67650. [PMID: 23844046 PMCID: PMC3699653 DOI: 10.1371/journal.pone.0067650] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2013] [Accepted: 05/22/2013] [Indexed: 11/19/2022] Open
Abstract
Introduction C-reactive protein (CRP) levels are associated with cardiovascular disease and systemic inflammation. We assessed whether CRP-associated loci were associated with serum CRP and retinal markers of microvascular disease, in Asian populations. Methods Genome-wide association analysis (GWAS) for serum CRP was performed in East-Asian Chinese (N = 2,434) and Malays (N = 2,542) and South-Asian Indians (N = 2,538) from Singapore. Leveraging on GWAS data, we assessed, in silico, association levels among the Singaporean datasets for 22 recently identified CRP-associated loci. At loci where directional inconsistencies were observed, quantification of inter-ethnic linkage disequilibrium (LD) difference was determined. Next, we assessed association for a variant at CRP and retinal vessel traits [central retinal artery equivalent (CRAE) and central retinal vein equivalent (CRVE)] in a total of 24,132 subjects of East-Asian, South-Asian and European ancestry. Results Serum CRP was associated with SNPs in/near APOE, CRP, HNF1A and LEPR (p-values ≤4.7×10−8) after meta-analysis of Singaporean populations. Using a candidate-SNP approach, we further replicated SNPs at 4 additional loci that had been recently identified to be associated with serum CRP (IL6R, GCKR, IL6 and IL1F10) (p-values ≤0.009), in the Singaporean datasets. SNPs from these 8 loci explained 4.05% of variance in serum CRP. Two SNPs (rs2847281 and rs6901250) were detected to be significant (p-value ≤0.036) but with opposite effect directions in the Singaporean populations as compared to original European studies. At these loci we did not detect significant inter-population LD differences. We further did not observe a significant association between CRP variant and CRVE or CRAE levels after meta-analysis of all Singaporean and European datasets (p-value >0.058). Conclusions Common variants associated with serum CRP, first detected in primarily European studies, are also associated with CRP levels in East-Asian and South-Asian populations. We did not find a causal link between CRP and retinal measures of microvascular disease.
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Affiliation(s)
- Rajkumar Dorajoo
- Genome Institute of Singapore, Agency for Science, Technology and Research, Singapore, Singapore
| | - Ruoying Li
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Mohammad Kamran Ikram
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
- Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Centre for Quantitative Medicine, Office of Clinical Sciences, Duke-NUS Graduate Medical School, Singapore, Singapore
- Department of Epidemiology and Ophthalmology, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Jianjun Liu
- Genome Institute of Singapore, Agency for Science, Technology and Research, Singapore, Singapore
| | - Philippe Froguel
- Department of Genomics of Common Disease, School of Public Health, Imperial College London, Hammersmith Hospital, London, United Kingdom
- CNRS-UMR-8199, Univ Lille Nord de France, UDSL, Lille, France
| | - Jeannette Lee
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore
| | - Xueling Sim
- Centre for Molecular Epidemiology, National University of Singapore, Singapore, Singapore
- Centre for Statistical Genetics, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Rick Twee-Hee Ong
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore
- National University of Singapore Graduate School for Integrative Science and Engineering, National University of Singapore, Singapore, Singapore
| | - Wan Ting Tay
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
| | - Chen Peng
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore
| | - Terri L. Young
- Duke–National University of Singapore Graduate Medical School, Singapore, Singapore
- Duke Centre for Human Genetics, Duke University Medical Center, Durham, North Carolina, United States of America
| | - Alexandra I. F. Blakemore
- Section of Investigative Medicine, Division of Diabetes, Endocrinology and Metabolism, Imperial College London, London, United Kingdom
| | - Ching Yu Cheng
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
- Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Centre for Quantitative Medicine, Office of Clinical Sciences, Duke-NUS Graduate Medical School, Singapore, Singapore
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore
| | - Tin Aung
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
| | - Paul Mitchell
- Centre for Vision Research, Department of Ophthalmology and Westmead Millennium Institute, University of Sydney, Sydney, Australia
| | - Jie Jin Wang
- Centre for Vision Research, Department of Ophthalmology and Westmead Millennium Institute, University of Sydney, Sydney, Australia
- Centre for Eye Research Australia, University of Melbourne, Royal Victorian Eye and Ear Hospital, Melbourne, Australia
| | - Caroline C. Klaver
- Department of Epidemiology and Ophthalmology, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Eric Boerwinkle
- Human Genetics Center and Human Genome Sequencing Center, University of Texas and Baylor College of Medicine, Houston, Texas, United States of America
| | - Ronald Klein
- Department of Ophthalmology and Visual Science, University of Wisconsin, Madison, Wisconsin, United States of America
| | - David S. Siscovick
- Cardiovascular Health Research Unit, University of Washington, Seattle, Washington, United States of America
- Department of Medicine, University of Washington, Seattle, Washington, United States of America
- Department of Epidemiology, University of Washington, Seattle, Washington, United States of America
| | - Richard A. Jensen
- Cardiovascular Health Research Unit, University of Washington, Seattle, Washington, United States of America
- Department of Medicine, University of Washington, Seattle, Washington, United States of America
| | - Vilmundur Gudnason
- Department of Medicine, University of Iceland, Reykjavik, Iceland
- Icelandic Heart Association, Kopavogur, Iceland, University of Iceland, Reykjavik, Iceland
| | - Albert Vernon Smith
- Icelandic Heart Association, Kopavogur, Iceland, University of Iceland, Reykjavik, Iceland
| | - Yik Ying Teo
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore
- Department of Epidemiology and Public Health, National University of Singapore, Singapore, Singapore
- * E-mail: (YYT); (TYW)); (EST); (CKH); (YF)
| | - Tien Yin Wong
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
- Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Centre for Eye Research Australia, University of Melbourne, Royal Victorian Eye and Ear Hospital, Melbourne, Australia
- * E-mail: (YYT); (TYW)); (EST); (CKH); (YF)
| | - E-Shyong Tai
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Duke–National University of Singapore Graduate Medical School, Singapore, Singapore
- * E-mail: (YYT); (TYW)); (EST); (CKH); (YF)
| | - Chew-Kiat Heng
- Department of Pediatrics, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- * E-mail: (YYT); (TYW)); (EST); (CKH); (YF)
| | - Yechiel Friedlander
- Epidemiology Unit, Hebrew University-Hadassah School of Public Health and Community Medicine, Jerusalem, Israel
- * E-mail: (YYT); (TYW)); (EST); (CKH); (YF)
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High trans-ethnic replicability of GWAS results implies common causal variants. PLoS Genet 2013; 9:e1003566. [PMID: 23785302 PMCID: PMC3681663 DOI: 10.1371/journal.pgen.1003566] [Citation(s) in RCA: 160] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2013] [Accepted: 04/26/2013] [Indexed: 12/02/2022] Open
Abstract
Genome-wide association studies (GWAS) have detected many disease associations. However, the reported variants tend to explain small fractions of risk, and there are doubts about issues such as the portability of findings over different ethnic groups or the relative roles of rare versus common variants in the genetic architecture of complex disease. Studying the degree of sharing of disease-associated variants across populations can help in solving these issues. We present a comprehensive survey of GWAS replicability across 28 diseases. Most loci and SNPs discovered in Europeans for these conditions have been extensively replicated using peoples of European and East Asian ancestry, while the replication with individuals of African ancestry is much less common. We found a strong and significant correlation of Odds Ratios across Europeans and East Asians, indicating that underlying causal variants are common and shared between the two ancestries. Moreover, SNPs that failed to replicate in East Asians map into genomic regions where Linkage Disequilibrium patterns differ significantly between populations. Finally, we observed that GWAS with larger sample sizes have detected variants with weaker effects rather than with lower frequencies. Our results indicate that most GWAS results are due to common variants. In addition, the sharing of disease alleles and the high correlation in their effect sizes suggest that most of the underlying causal variants are shared between Europeans and East Asians and that they tend to map close to the associated marker SNPs. Describing and identifying the genetic variants that increase risk for complex diseases remains a central focus of human genetics and is fundamental for the emergent field of personalized medicine. Over the last six years, GWAS have revolutionized the field, discovering hundreds of disease loci. However, with only a handful of exceptions, the causal variants that generate the associations unveiled by GWAS have not been identified, and their frequency and degree of sharing across populations remains unknown. Here, we present a comprehensive comparison of GWAS results designed to try to understand the nature of causal variants. By examining the results of GWAS for 28 diseases that have been performed with peoples of European, East Asian, and African ancestries, we conclude that a large fraction of associations are caused by common causal variants that should map relatively close to the associated markers. Our results indicate that many of the disease risk variants discovered by GWAS are shared across Eurasians.
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Ma RCW, Hu C, Tam CH, Zhang R, Kwan P, Leung TF, Thomas GN, Go MJ, Hara K, Sim X, Ho JSK, Wang C, Li H, Lu L, Wang Y, Li JW, Wang Y, Lam VKL, Wang J, Yu W, Kim YJ, Ng DP, Fujita H, Panoutsopoulou K, Day-Williams AG, Lee HM, Ng ACW, Fang YJ, Kong APS, Jiang F, Ma X, Hou X, Tang S, Lu J, Yamauchi T, Tsui SKW, Woo J, Leung PC, Zhang X, Tang NLS, Sy HY, Liu J, Wong TY, Lee JY, Maeda S, Xu G, Cherny SS, Chan TF, Ng MCY, Xiang K, Morris AP, Keildson S, Hu R, Ji L, Lin X, Cho YS, Kadowaki T, Tai ES, Zeggini E, McCarthy MI, Hon KL, Baum L, Tomlinson B, So WY, Bao Y, Chan JCN, Jia W. Genome-wide association study in a Chinese population identifies a susceptibility locus for type 2 diabetes at 7q32 near PAX4. Diabetologia 2013; 56:1291-305. [PMID: 23532257 PMCID: PMC3648687 DOI: 10.1007/s00125-013-2874-4] [Citation(s) in RCA: 71] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/21/2012] [Accepted: 01/31/2013] [Indexed: 12/18/2022]
Abstract
AIMS/HYPOTHESIS Most genetic variants identified for type 2 diabetes have been discovered in European populations. We performed genome-wide association studies (GWAS) in a Chinese population with the aim of identifying novel variants for type 2 diabetes in Asians. METHODS We performed a meta-analysis of three GWAS comprising 684 patients with type 2 diabetes and 955 controls of Southern Han Chinese descent. We followed up the top signals in two independent Southern Han Chinese cohorts (totalling 10,383 cases and 6,974 controls), and performed in silico replication in multiple populations. RESULTS We identified CDKN2A/B and four novel type 2 diabetes association signals with p < 1 × 10(-5) from the meta-analysis. Thirteen variants within these four loci were followed up in two independent Chinese cohorts, and rs10229583 at 7q32 was found to be associated with type 2 diabetes in a combined analysis of 11,067 cases and 7,929 controls (p meta = 2.6 × 10(-8); OR [95% CI] 1.18 [1.11, 1.25]). In silico replication revealed consistent associations across multiethnic groups, including five East Asian populations (p meta = 2.3 × 10(-10)) and a population of European descent (p = 8.6 × 10(-3)). The rs10229583 risk variant was associated with elevated fasting plasma glucose, impaired beta cell function in controls, and an earlier age at diagnosis for the cases. The novel variant lies within an islet-selective cluster of open regulatory elements. There was significant heterogeneity of effect between Han Chinese and individuals of European descent, Malaysians and Indians. CONCLUSIONS/INTERPRETATION Our study identifies rs10229583 near PAX4 as a novel locus for type 2 diabetes in Chinese and other populations and provides new insights into the pathogenesis of type 2 diabetes.
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Affiliation(s)
- R. C. W. Ma
- Department of Medicine and Therapeutics, Chinese University of Hong Kong, Prince of Wales Hospital, Shatin, New Territories, Hong Kong, SAR People’s Republic of China
- Hong Kong Institute of Diabetes and Obesity, Chinese University of Hong Kong, Hong Kong, SAR People’s Republic of China
- Li Ka Shing Institute of Life Sciences, Chinese University of Hong Kong, Hong Kong, SAR People’s Republic of China
| | - C. Hu
- Department of Endocrinology and Metabolism, Shanghai Diabetes Institute, Shanghai Key Laboratory of Diabetes Mellitus, Shanghai Clinical Center for Diabetes, Shanghai Key Clinical Center for Metabolic Disease, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, 600 Yishan Road, Shanghai, 200233 People’s Republic of China
- Shanghai Jiao Tong University Affiliated Sixth People’s Hospital South Campus, Shanghai, People’s Republic of China
| | - C. H. Tam
- Department of Medicine and Therapeutics, Chinese University of Hong Kong, Prince of Wales Hospital, Shatin, New Territories, Hong Kong, SAR People’s Republic of China
| | - R. Zhang
- Department of Endocrinology and Metabolism, Shanghai Diabetes Institute, Shanghai Key Laboratory of Diabetes Mellitus, Shanghai Clinical Center for Diabetes, Shanghai Key Clinical Center for Metabolic Disease, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, 600 Yishan Road, Shanghai, 200233 People’s Republic of China
| | - P. Kwan
- Department of Medicine and Therapeutics, Chinese University of Hong Kong, Prince of Wales Hospital, Shatin, New Territories, Hong Kong, SAR People’s Republic of China
| | - T. F. Leung
- Department of Paediatrics, Chinese University of Hong Kong, Hong Kong, People’s Republic of China
| | - G. N. Thomas
- Department of Public Health, Epidemiology and Biostatistics, University of Birmingham, Birmingham, UK
| | - M. J. Go
- Center for Genome Science, National Institute of Health, Osong Health Technology Administration Complex, Gangoe-myeon, Yeonje-ri, Cheongwon-gun, Chungcheongbuk-do Republic of Korea
| | - K. Hara
- Department of Diabetes and Metabolic Diseases, Graduate School of Medicine, University of Tokyo, Tokyo, Japan
- Department of Integrated Molecular Science on Metabolic Diseases, University of Tokyo Hospital, Tokyo, Japan
| | - X. Sim
- Centre for Molecular Epidemiology, Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Republic of Singapore
- Center for Statistical Genetics and Department of Biostatistics, University of Michigan, Ann Arbor, MI USA
| | - J. S. K. Ho
- Department of Medicine and Therapeutics, Chinese University of Hong Kong, Prince of Wales Hospital, Shatin, New Territories, Hong Kong, SAR People’s Republic of China
| | - C. Wang
- Department of Endocrinology and Metabolism, Shanghai Diabetes Institute, Shanghai Key Laboratory of Diabetes Mellitus, Shanghai Clinical Center for Diabetes, Shanghai Key Clinical Center for Metabolic Disease, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, 600 Yishan Road, Shanghai, 200233 People’s Republic of China
| | - H. Li
- Key Laboratory of Nutrition and Metabolism, Institute for Nutritional Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences and Graduate School of the Chinese Academy of Sciences, Shanghai, People’s Republic of China
| | - L. Lu
- Key Laboratory of Nutrition and Metabolism, Institute for Nutritional Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences and Graduate School of the Chinese Academy of Sciences, Shanghai, People’s Republic of China
| | - Y. Wang
- Key Laboratory of Nutrition and Metabolism, Institute for Nutritional Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences and Graduate School of the Chinese Academy of Sciences, Shanghai, People’s Republic of China
| | - J. W. Li
- School of Life Sciences, Chinese University of Hong Kong, Hong Kong, SAR People’s Republic of China
| | - Y. Wang
- Department of Medicine and Therapeutics, Chinese University of Hong Kong, Prince of Wales Hospital, Shatin, New Territories, Hong Kong, SAR People’s Republic of China
| | - V. K. L. Lam
- Department of Medicine and Therapeutics, Chinese University of Hong Kong, Prince of Wales Hospital, Shatin, New Territories, Hong Kong, SAR People’s Republic of China
| | - J. Wang
- Department of Endocrinology and Metabolism, Shanghai Diabetes Institute, Shanghai Key Laboratory of Diabetes Mellitus, Shanghai Clinical Center for Diabetes, Shanghai Key Clinical Center for Metabolic Disease, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, 600 Yishan Road, Shanghai, 200233 People’s Republic of China
| | - W. Yu
- Department of Endocrinology and Metabolism, Shanghai Diabetes Institute, Shanghai Key Laboratory of Diabetes Mellitus, Shanghai Clinical Center for Diabetes, Shanghai Key Clinical Center for Metabolic Disease, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, 600 Yishan Road, Shanghai, 200233 People’s Republic of China
| | - Y. J. Kim
- Center for Genome Science, National Institute of Health, Osong Health Technology Administration Complex, Gangoe-myeon, Yeonje-ri, Cheongwon-gun, Chungcheongbuk-do Republic of Korea
| | - D. P. Ng
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Republic of Singapore
| | - H. Fujita
- Department of Diabetes and Metabolic Diseases, Graduate School of Medicine, University of Tokyo, Tokyo, Japan
| | - K. Panoutsopoulou
- Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, UK
| | - A. G. Day-Williams
- Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, UK
| | - H. M. Lee
- Department of Medicine and Therapeutics, Chinese University of Hong Kong, Prince of Wales Hospital, Shatin, New Territories, Hong Kong, SAR People’s Republic of China
| | - A. C. W. Ng
- Department of Medicine and Therapeutics, Chinese University of Hong Kong, Prince of Wales Hospital, Shatin, New Territories, Hong Kong, SAR People’s Republic of China
| | - Y-J. Fang
- Department of Colorectal Surgery, State Key Laboratory of Oncology in South China, Sun Yat-sen University Cancer Center, Guangzhou, People’s Republic of China
| | - A. P. S. Kong
- Department of Medicine and Therapeutics, Chinese University of Hong Kong, Prince of Wales Hospital, Shatin, New Territories, Hong Kong, SAR People’s Republic of China
| | - F. Jiang
- Department of Endocrinology and Metabolism, Shanghai Diabetes Institute, Shanghai Key Laboratory of Diabetes Mellitus, Shanghai Clinical Center for Diabetes, Shanghai Key Clinical Center for Metabolic Disease, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, 600 Yishan Road, Shanghai, 200233 People’s Republic of China
| | - X. Ma
- Department of Endocrinology and Metabolism, Shanghai Diabetes Institute, Shanghai Key Laboratory of Diabetes Mellitus, Shanghai Clinical Center for Diabetes, Shanghai Key Clinical Center for Metabolic Disease, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, 600 Yishan Road, Shanghai, 200233 People’s Republic of China
| | - X. Hou
- Department of Endocrinology and Metabolism, Shanghai Diabetes Institute, Shanghai Key Laboratory of Diabetes Mellitus, Shanghai Clinical Center for Diabetes, Shanghai Key Clinical Center for Metabolic Disease, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, 600 Yishan Road, Shanghai, 200233 People’s Republic of China
| | - S. Tang
- Department of Endocrinology and Metabolism, Shanghai Diabetes Institute, Shanghai Key Laboratory of Diabetes Mellitus, Shanghai Clinical Center for Diabetes, Shanghai Key Clinical Center for Metabolic Disease, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, 600 Yishan Road, Shanghai, 200233 People’s Republic of China
| | - J. Lu
- Department of Endocrinology and Metabolism, Shanghai Diabetes Institute, Shanghai Key Laboratory of Diabetes Mellitus, Shanghai Clinical Center for Diabetes, Shanghai Key Clinical Center for Metabolic Disease, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, 600 Yishan Road, Shanghai, 200233 People’s Republic of China
| | - T. Yamauchi
- Department of Diabetes and Metabolic Diseases, Graduate School of Medicine, University of Tokyo, Tokyo, Japan
| | - S. K. W. Tsui
- School of Biomedical Sciences, Chinese University of Hong Kong, Hong Kong, SAR People’s Republic of China
| | - J. Woo
- Department of Medicine and Therapeutics, Chinese University of Hong Kong, Prince of Wales Hospital, Shatin, New Territories, Hong Kong, SAR People’s Republic of China
| | - P. C. Leung
- Department of Orthopaedics, Chinese University of Hong Kong, Hong Kong, SAR People’s Republic of China
| | - X. Zhang
- Shanghai Jiao Tong University Affiliated Sixth People’s Hospital South Campus, Shanghai, People’s Republic of China
| | - N. L. S. Tang
- Department of Chemical Pathology, Chinese University of Hong Kong, Hong Kong, SAR People’s Republic of China
| | - H. Y. Sy
- Department of Paediatrics, Chinese University of Hong Kong, Hong Kong, People’s Republic of China
| | - J. Liu
- Genome Institute of Singapore, Agency for Science, Technology and Research, Singapore, Republic of Singapore
| | - T. Y. Wong
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Republic of Singapore
- Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Republic of Singapore
- Centre for Eye Research Australia, University of Melbourne, East Melbourne, VIC Australia
| | - J. Y. Lee
- Center for Genome Science, National Institute of Health, Osong Health Technology Administration Complex, Gangoe-myeon, Yeonje-ri, Cheongwon-gun, Chungcheongbuk-do Republic of Korea
| | - S. Maeda
- Laboratory for Endocrinology and Metabolism, RIKEN Center for Genomic Medicine, Yokohama, Japan
| | - G. Xu
- Department of Medicine and Therapeutics, Chinese University of Hong Kong, Prince of Wales Hospital, Shatin, New Territories, Hong Kong, SAR People’s Republic of China
| | - S. S. Cherny
- Department of Psychiatry and State Key Laboratory of Brain and Cognitive Sciences, University of Hong Kong, Hong Kong, SAR People’s Republic of China
| | - T. F. Chan
- School of Life Sciences, Chinese University of Hong Kong, Hong Kong, SAR People’s Republic of China
| | - M. C. Y. Ng
- Center for Genomics and Personalized Medicine Research, Center for Diabetes Research, Wake Forest School of Medicine, Winston-Salem, NC USA
| | - K. Xiang
- Department of Endocrinology and Metabolism, Shanghai Diabetes Institute, Shanghai Key Laboratory of Diabetes Mellitus, Shanghai Clinical Center for Diabetes, Shanghai Key Clinical Center for Metabolic Disease, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, 600 Yishan Road, Shanghai, 200233 People’s Republic of China
| | - A. P. Morris
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK
| | | | - S. Keildson
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK
| | | | - R. Hu
- Institute of Endocrinology and Diabetology, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, People’s Republic of China
| | - L. Ji
- Department of Endocrinology and Metabolism, Peking University People’s Hospital, Beijing, People’s Republic of China
| | - X. Lin
- Key Laboratory of Nutrition and Metabolism, Institute for Nutritional Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences and Graduate School of the Chinese Academy of Sciences, Shanghai, People’s Republic of China
| | - Y. S. Cho
- Department of Biomedical Science, Hallym University, Chuncheon, Gangwon-do Republic of Korea
| | - T. Kadowaki
- Department of Diabetes and Metabolic Diseases, Graduate School of Medicine, University of Tokyo, Tokyo, Japan
| | - E. S. Tai
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Republic of Singapore
- Graduate Medical School, Duke-National University of Singapore, Singapore, Republic of Singapore
| | - E. Zeggini
- Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, UK
| | - M. I. McCarthy
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK
- Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Churchill Hospital, Oxford, UK
| | - K. L. Hon
- Department of Paediatrics, Chinese University of Hong Kong, Hong Kong, People’s Republic of China
| | - L. Baum
- School of Pharmacy, Chinese University of Hong Kong, Hong Kong, SAR People’s Republic of China
| | - B. Tomlinson
- Department of Medicine and Therapeutics, Chinese University of Hong Kong, Prince of Wales Hospital, Shatin, New Territories, Hong Kong, SAR People’s Republic of China
| | - W. Y. So
- Department of Medicine and Therapeutics, Chinese University of Hong Kong, Prince of Wales Hospital, Shatin, New Territories, Hong Kong, SAR People’s Republic of China
| | - Y. Bao
- Department of Endocrinology and Metabolism, Shanghai Diabetes Institute, Shanghai Key Laboratory of Diabetes Mellitus, Shanghai Clinical Center for Diabetes, Shanghai Key Clinical Center for Metabolic Disease, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, 600 Yishan Road, Shanghai, 200233 People’s Republic of China
| | - J. C. N. Chan
- Department of Medicine and Therapeutics, Chinese University of Hong Kong, Prince of Wales Hospital, Shatin, New Territories, Hong Kong, SAR People’s Republic of China
- Hong Kong Institute of Diabetes and Obesity, Chinese University of Hong Kong, Hong Kong, SAR People’s Republic of China
- Li Ka Shing Institute of Life Sciences, Chinese University of Hong Kong, Hong Kong, SAR People’s Republic of China
| | - W. Jia
- Department of Endocrinology and Metabolism, Shanghai Diabetes Institute, Shanghai Key Laboratory of Diabetes Mellitus, Shanghai Clinical Center for Diabetes, Shanghai Key Clinical Center for Metabolic Disease, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, 600 Yishan Road, Shanghai, 200233 People’s Republic of China
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Utsunomiya YT, Pérez O’Brien AM, Sonstegard TS, Van Tassell CP, do Carmo AS, Mészáros G, Sölkner J, Garcia JF. Detecting loci under recent positive selection in dairy and beef cattle by combining different genome-wide scan methods. PLoS One 2013; 8:e64280. [PMID: 23696874 PMCID: PMC3655949 DOI: 10.1371/journal.pone.0064280] [Citation(s) in RCA: 75] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2012] [Accepted: 04/13/2013] [Indexed: 12/21/2022] Open
Abstract
As the methodologies available for the detection of positive selection from genomic data vary in terms of assumptions and execution, weak correlations are expected among them. However, if there is any given signal that is consistently supported across different methodologies, it is strong evidence that the locus has been under past selection. In this paper, a straightforward frequentist approach based on the Stouffer Method to combine P-values across different tests for evidence of recent positive selection in common variations, as well as strategies for extracting biological information from the detected signals, were described and applied to high density single nucleotide polymorphism (SNP) data generated from dairy and beef cattle (taurine and indicine). The ancestral Bovinae allele state of over 440,000 SNP is also reported. Using this combination of methods, highly significant (P<3.17×10−7) population-specific sweeps pointing out to candidate genes and pathways that may be involved in beef and dairy production were identified. The most significant signal was found in the Cornichon homolog 3 gene (CNIH3) in Brown Swiss (P = 3.82×10−12), and may be involved in the regulation of pre-ovulatory luteinizing hormone surge. Other putative pathways under selection are the glucolysis/gluconeogenesis, transcription machinery and chemokine/cytokine activity in Angus; calpain-calpastatin system and ribosome biogenesis in Brown Swiss; and gangliosides deposition in milk fat globules in Gyr. The composite method, combined with the strategies applied to retrieve functional information, may be a useful tool for surveying genome-wide selective sweeps and providing insights in to the source of selection.
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Affiliation(s)
- Yuri Tani Utsunomiya
- Departamento de Medicina Veterinária Preventiva e Reprodução Animal, Faculdade de Ciências Agrárias e Veterinárias, UNESP - Univ Estadual Paulista, Jaboticabal, São Paulo, Brazil
| | - Ana Maria Pérez O’Brien
- Division of Livestock Sciences, Department of Sustainable Agricultural Systems, BOKU - University of Natural Resources and Life Sciences, Vienna, Austria
| | - Tad Stewart Sonstegard
- Bovine Functional Genomics Laboratory, ARS-USDA - Agricultural Research Service - United States Department of Agriculture, Beltsville, Maryland, United States of America
| | - Curtis Paul Van Tassell
- Bovine Functional Genomics Laboratory, ARS-USDA - Agricultural Research Service - United States Department of Agriculture, Beltsville, Maryland, United States of America
| | - Adriana Santana do Carmo
- Departamento de Medicina Veterinária Preventiva e Reprodução Animal, Faculdade de Ciências Agrárias e Veterinárias, UNESP - Univ Estadual Paulista, Jaboticabal, São Paulo, Brazil
| | - Gábor Mészáros
- Division of Livestock Sciences, Department of Sustainable Agricultural Systems, BOKU - University of Natural Resources and Life Sciences, Vienna, Austria
| | - Johann Sölkner
- Division of Livestock Sciences, Department of Sustainable Agricultural Systems, BOKU - University of Natural Resources and Life Sciences, Vienna, Austria
- * E-mail: (JS); (JFG)
| | - José Fernando Garcia
- Departamento de Medicina Veterinária Preventiva e Reprodução Animal, Faculdade de Ciências Agrárias e Veterinárias, UNESP - Univ Estadual Paulista, Jaboticabal, São Paulo, Brazil
- Departamento de Apoio, Saúde e Produção Animal, Faculdade de Medicina Veterinária de Araçatuba, UNESP - Univ Estadual Paulista, Araçatuba, São Paulo, Brazil
- * E-mail: (JS); (JFG)
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Weissglas-Volkov D, Aguilar-Salinas CA, Nikkola E, Deere KA, Cruz-Bautista I, Arellano-Campos O, Muñoz-Hernandez LL, Gomez-Munguia L, Ordoñez-Sánchez ML, Reddy PMVL, Lusis AJ, Matikainen N, Taskinen MR, Riba L, Cantor RM, Sinsheimer JS, Tusie-Luna T, Pajukanta P. Genomic study in Mexicans identifies a new locus for triglycerides and refines European lipid loci. J Med Genet 2013; 50:298-308. [PMID: 23505323 DOI: 10.1136/jmedgenet-2012-101461] [Citation(s) in RCA: 94] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
BACKGROUND The Mexican population and others with Amerindian heritage exhibit a substantial predisposition to dyslipidemias and coronary heart disease. Yet, these populations remain underinvestigated by genomic studies, and to date, no genome-wide association (GWA) studies have been reported for lipids in these rapidly expanding populations. METHODS AND FINDINGS We performed a two-stage GWA study for hypertriglyceridemia and low high-density lipoprotein cholesterol (HDL-C) in Mexicans (n=4361), and identified a novel Mexican-specific genome-wide significant locus for serum triglycerides (TGs) near the Niemann-Pick type C1 protein gene (p=2.43×10(-08)). Furthermore, three European loci for TGs (APOA5, GCKR and LPL), and four loci for HDL-C (ABCA1, CETP, LIPC and LOC55908) reached genome-wide significance in Mexicans. We used cross-ethnic mapping to narrow three European TG GWA loci, APOA5, MLXIPL, and CILP2 that were wide and contained multiple candidate variants in the European scan. At the APOA5 locus, this reduced the most likely susceptibility variants to one, rs964184. Importantly, our functional analysis demonstrated a direct link between rs964184 and postprandial serum apoAV protein levels, supporting rs964184 as the causative variant underlying the European and Mexican GWA signal. Overall, 52 of the 100 reported associations from European lipid GWA meta-analysis generalised to Mexicans. However, in 82 of the 100 European GWA loci, a different variant other than the European lead/best-proxy variant had the strongest regional evidence of association in Mexicans. CONCLUSIONS This first Mexican GWA study of lipids identified a novel GWA locus for high TG levels; used the interpopulation heterogeneity to significantly restrict three previously known European GWA signals, and surveyed whether the European lipid GWA SNPs extend to the Mexican population.
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Affiliation(s)
- Daphna Weissglas-Volkov
- Department of Human Genetics, David Geffen School of Medicine at UCLA, Gonda Center, Room 6335B, 695 Charles E. Young Drive South, Los Angeles, CA 90095-7088, USA
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Ng MCY, Saxena R, Li J, Palmer ND, Dimitrov L, Xu J, Rasmussen-Torvik LJ, Zmuda JM, Siscovick DS, Patel SR, Crook ED, Sims M, Chen YDI, Bertoni AG, Li M, Grant SFA, Dupuis J, Meigs JB, Psaty BM, Pankow JS, Langefeld CD, Freedman BI, Rotter JI, Wilson JG, Bowden DW. Transferability and fine mapping of type 2 diabetes loci in African Americans: the Candidate Gene Association Resource Plus Study. Diabetes 2013; 62:965-76. [PMID: 23193183 PMCID: PMC3581206 DOI: 10.2337/db12-0266] [Citation(s) in RCA: 52] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/02/2012] [Accepted: 08/21/2012] [Indexed: 01/08/2023]
Abstract
Type 2 diabetes (T2D) disproportionally affects African Americans (AfA) but, to date, genetic variants identified from genome-wide association studies (GWAS) are primarily from European and Asian populations. We examined the single nucleotide polymorphism (SNP) and locus transferability of 40 reported T2D loci in six AfA GWAS consisting of 2,806 T2D case subjects with or without end-stage renal disease and 4,265 control subjects from the Candidate Gene Association Resource Plus Study. Our results revealed that seven index SNPs at the TCF7L2, KLF14, KCNQ1, ADCY5, CDKAL1, JAZF1, and GCKR loci were significantly associated with T2D (P < 0.05). The strongest association was observed at TCF7L2 rs7903146 (odds ratio [OR] 1.30; P = 6.86 × 10⁻⁸). Locus-wide analysis demonstrated significant associations (P(emp) < 0.05) at regional best SNPs in the TCF7L2, KLF14, and HMGA2 loci as well as suggestive signals in KCNQ1 after correction for the effective number of SNPs at each locus. Of these loci, the regional best SNPs were in differential linkage disequilibrium (LD) with the index and adjacent SNPs. Our findings suggest that some loci discovered in prior reports affect T2D susceptibility in AfA with similar effect sizes. The reduced and differential LD pattern in AfA compared with European and Asian populations may facilitate fine mapping of causal variants at loci shared across populations.
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Affiliation(s)
- Maggie C Y Ng
- Center for Genomics and Personalized Medicine Research, Wake Forest School of Medicine, Winston-Salem, North Carolina, USA.
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40
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Li H, Gan W, Lu L, Dong X, Han X, Hu C, Yang Z, Sun L, Bao W, Li P, He M, Sun L, Wang Y, Zhu J, Ning Q, Tang Y, Zhang R, Wen J, Wang D, Zhu X, Guo K, Zuo X, Guo X, Yang H, Zhou X, Zhang X, Qi L, Loos RJ, Hu FB, Wu T, Liu Y, Liu L, Yang Z, Hu R, Jia W, Ji L, Li Y, Lin X. A genome-wide association study identifies GRK5 and RASGRP1 as type 2 diabetes loci in Chinese Hans. Diabetes 2013; 62:291-8. [PMID: 22961080 PMCID: PMC3526061 DOI: 10.2337/db12-0454] [Citation(s) in RCA: 140] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
Substantial progress has been made in identification of type 2 diabetes (T2D) risk loci in the past few years, but our understanding of the genetic basis of T2D in ethnically diverse populations remains limited. We performed a genome-wide association study and a replication study in Chinese Hans comprising 8,569 T2D case subjects and 8,923 control subjects in total, from which 10 single nucleotide polymorphisms were selected for further follow-up in a de novo replication sample of 3,410 T2D case and 3,412 control subjects and an in silico replication sample of 6,952 T2D case and 11,865 control subjects. Besides confirming seven established T2D loci (CDKAL1, CDKN2A/B, KCNQ1, CDC123, GLIS3, HNF1B, and DUSP9) at genome-wide significance, we identified two novel T2D loci, including G-protein-coupled receptor kinase 5 (GRK5) (rs10886471: P = 7.1 × 10(-9)) and RASGRP1 (rs7403531: P = 3.9 × 10(-9)), of which the association signal at GRK5 seems to be specific to East Asians. In nondiabetic individuals, the T2D risk-increasing allele of RASGRP1-rs7403531 was also associated with higher HbA(1c) and lower homeostasis model assessment of β-cell function (P = 0.03 and 0.0209, respectively), whereas the T2D risk-increasing allele of GRK5-rs10886471 was also associated with higher fasting insulin (P = 0.0169) but not with fasting glucose. Our findings not only provide new insights into the pathophysiology of T2D, but may also shed light on the ethnic differences in T2D susceptibility.
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Affiliation(s)
- Huaixing Li
- Key Laboratory of Nutrition and Metabolism, Institute for Nutritional Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences and Graduate School of the Chinese Academy of Sciences, Shanghai, China
| | - Wei Gan
- Key Laboratory of Nutrition and Metabolism, Institute for Nutritional Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences and Graduate School of the Chinese Academy of Sciences, Shanghai, China
| | - Ling Lu
- Key Laboratory of Nutrition and Metabolism, Institute for Nutritional Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences and Graduate School of the Chinese Academy of Sciences, Shanghai, China
| | - Xiao Dong
- Key Laboratory of Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences and Graduate School of the Chinese Academy of Sciences, Shanghai, China
| | - Xueyao Han
- Department of Endocrinology and Metabolism, Peking University People's Hospital, Beijing, China
| | - Cheng Hu
- Department of Endocrinology and Metabolism, Shanghai Diabetes Institute, Shanghai Key Laboratory of Diabetes Mellitus, Shanghai Clinical Center for Diabetes, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, China
| | - Zhen Yang
- Institute of Endocrinology and Diabetology, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China
| | - Liang Sun
- Key Laboratory of Geriatrics, Beijing Hospital and Beijing Institute of Geriatrics, Ministry of Health, Beijing, China
| | - Wei Bao
- Department of Nutrition and Food Hygiene, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Ministry of Education Key Laboratory for Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Pengtao Li
- State Key Laboratory of Medical Molecular Biology, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences, School of Basic Medicine, Peking Union Medical College, Beijing, China
| | - Meian He
- Ministry of Education Key Laboratory for Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
- Institute of Occupational Medicine, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Liangdan Sun
- Institute of Dermatology, No. 1 Hospital, Anhui Medical University, Hefei, Anhui, China
- Department of Dermatology, No. 1 Hospital, Anhui Medical University, Hefei, Anhui, China
- State Key Laboratory Incubation Base of Dermatology, Ministry of National Science and Technology, Hefei, Anhui, China
| | - Yiqin Wang
- Key Laboratory of Nutrition and Metabolism, Institute for Nutritional Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences and Graduate School of the Chinese Academy of Sciences, Shanghai, China
| | - Jingwen Zhu
- Key Laboratory of Nutrition and Metabolism, Institute for Nutritional Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences and Graduate School of the Chinese Academy of Sciences, Shanghai, China
| | - Qianqian Ning
- Key Laboratory of Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences and Graduate School of the Chinese Academy of Sciences, Shanghai, China
| | - Yong Tang
- Department of Endocrinology and Metabolism, Peking University People's Hospital, Beijing, China
| | - Rong Zhang
- Department of Endocrinology and Metabolism, Shanghai Diabetes Institute, Shanghai Key Laboratory of Diabetes Mellitus, Shanghai Clinical Center for Diabetes, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, China
| | - Jie Wen
- Institute of Endocrinology and Diabetology, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China
| | - Di Wang
- Department of Nutrition and Food Hygiene, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Ministry of Education Key Laboratory for Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Xilin Zhu
- State Key Laboratory of Medical Molecular Biology, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences, School of Basic Medicine, Peking Union Medical College, Beijing, China
| | - Kunquan Guo
- Ministry of Education Key Laboratory for Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
- Institute of Occupational Medicine, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Xianbo Zuo
- Institute of Dermatology, No. 1 Hospital, Anhui Medical University, Hefei, Anhui, China
- Department of Dermatology, No. 1 Hospital, Anhui Medical University, Hefei, Anhui, China
- State Key Laboratory Incubation Base of Dermatology, Ministry of National Science and Technology, Hefei, Anhui, China
| | - Xiaohui Guo
- Peking University First Hospital, Beijing, China
| | - Handong Yang
- Dongfeng Central Hospital, Dongfeng Motor Corporation and Hubei University of Medicine, Shiyan, Hubei, China
| | - Xianghai Zhou
- Department of Endocrinology and Metabolism, Peking University People's Hospital, Beijing, China
| | | | | | - Xuejun Zhang
- Institute of Dermatology, No. 1 Hospital, Anhui Medical University, Hefei, Anhui, China
- Department of Dermatology, No. 1 Hospital, Anhui Medical University, Hefei, Anhui, China
- State Key Laboratory Incubation Base of Dermatology, Ministry of National Science and Technology, Hefei, Anhui, China
| | - Lu Qi
- Department of Nutrition, Harvard School of Public Health, Boston, Massachusetts
| | - Ruth J.F. Loos
- Department of Preventive Medicine, Charles R. Bronfman Institute of Personalized Medicine, Mount Sinai School of Medicine, New York, New York
- Department of Preventive Medicine, Child Health and Development Institute, Mount Sinai School of Medicine, New York, New York
| | - Frank B. Hu
- Department of Nutrition, Harvard School of Public Health, Boston, Massachusetts
| | - Tangchun Wu
- Ministry of Education Key Laboratory for Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
- Institute of Occupational Medicine, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Ying Liu
- State Key Laboratory of Medical Molecular Biology, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences, School of Basic Medicine, Peking Union Medical College, Beijing, China
| | - Liegang Liu
- Department of Nutrition and Food Hygiene, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Ministry of Education Key Laboratory for Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Ze Yang
- Key Laboratory of Geriatrics, Beijing Hospital and Beijing Institute of Geriatrics, Ministry of Health, Beijing, China
| | - Renming Hu
- Institute of Endocrinology and Diabetology, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China
| | - Weiping Jia
- Department of Endocrinology and Metabolism, Shanghai Diabetes Institute, Shanghai Key Laboratory of Diabetes Mellitus, Shanghai Clinical Center for Diabetes, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, China
| | - Linong Ji
- Department of Endocrinology and Metabolism, Peking University People's Hospital, Beijing, China
- Corresponding author: Xu Lin, , Yixue Li, , or Linong Ji,
| | - Yixue Li
- Key Laboratory of Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences and Graduate School of the Chinese Academy of Sciences, Shanghai, China
- Shanghai Center for Bioinformation Technology, Shanghai, China
- College of Life Science and Biotechnology, Shanghai Jiaotong University, Shanghai, China
- Corresponding author: Xu Lin, , Yixue Li, , or Linong Ji,
| | - Xu Lin
- Key Laboratory of Nutrition and Metabolism, Institute for Nutritional Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences and Graduate School of the Chinese Academy of Sciences, Shanghai, China
- Corresponding author: Xu Lin, , Yixue Li, , or Linong Ji,
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Liu CT, Ng MCY, Rybin D, Adeyemo A, Bielinski SJ, Boerwinkle E, Borecki I, Cade B, Chen YDI, Djousse L, Fornage M, Goodarzi MO, Grant SFA, Guo X, Harris T, Kabagambe E, Kizer JR, Liu Y, Lunetta KL, Mukamal K, Nettleton JA, Pankow JS, Patel SR, Ramos E, Rasmussen-Torvik L, Rich SS, Rotimi CN, Sarpong D, Shriner D, Sims M, Zmuda JM, Redline S, Kao WH, Siscovick D, Florez JC, Rotter JI, Dupuis J, Wilson JG, Bowden DW, Meigs JB. Transferability and fine-mapping of glucose and insulin quantitative trait loci across populations: CARe, the Candidate Gene Association Resource. Diabetologia 2012; 55:2970-84. [PMID: 22893027 PMCID: PMC3804308 DOI: 10.1007/s00125-012-2656-4] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/20/2012] [Accepted: 06/14/2012] [Indexed: 01/22/2023]
Abstract
AIMS/HYPOTHESIS Hyperglycaemia disproportionately affects African-Americans (AfAs). We tested the transferability of 18 single-nucleotide polymorphisms (SNPs) associated with glycaemic traits identified in European ancestry (EuA) populations in 5,984 non-diabetic AfAs. METHODS We meta-analysed SNP associations with fasting glucose (FG) or insulin (FI) in AfAs from five cohorts in the Candidate Gene Association Resource. We: (1) calculated allele frequency differences, variations in linkage disequilibrium (LD), fixation indices (F(st)s) and integrated haplotype scores (iHSs); (2) tested EuA SNPs in AfAs; and (3) interrogated within ± 250 kb around each EuA SNP in AfAs. RESULTS Allele frequency differences ranged from 0.6% to 54%. F(st) exceeded 0.15 at 6/16 loci, indicating modest population differentiation. All iHSs were <2, suggesting no recent positive selection. For 18 SNPs, all directions of effect were the same and 95% CIs of association overlapped when comparing EuA with AfA. For 17 of 18 loci, at least one SNP was nominally associated with FG in AfAs. Four loci were significantly associated with FG (GCK, p = 5.8 × 10(-8); MTNR1B, p = 8.5 × 10(-9); and FADS1, p = 2.2 × 10(-4)) or FI (GCKR, p = 5.9 × 10(-4)). At GCK and MTNR1B the EuA and AfA SNPs represented the same signal, while at FADS1, and GCKR, the EuA and best AfA SNPs were weakly correlated (r(2) <0.2), suggesting allelic heterogeneity for association with FG at these loci. CONCLUSIONS/INTERPRETATION Few glycaemic SNPs showed strict evidence of transferability from EuA to AfAs. Four loci were significantly associated in both AfAs and those with EuA after accounting for varying LD across ancestral groups, with new signals emerging to aid fine-mapping.
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Affiliation(s)
- C.-T. Liu
- Department of Biostatistics, Boston University School of Public
Health, Boston, MA, USA
| | - M. C. Y. Ng
- Center for Genomics and Personalized Medicine Research, Center for
Diabetes Research, Wake Forest University School of Medicine, Winston-Salem,
NC, USA
| | - D. Rybin
- Boston University Data Coordinating Center, Boston, MA, USA
| | - A. Adeyemo
- National Human Genome Research Institute, Bethesda, MD, USA
| | | | - E. Boerwinkle
- University of Texas Health Science Center at Houston, Houston, TX,
USA
| | - I. Borecki
- Washington University, St Louis, MO, USA
| | - B. Cade
- Brigham and Women's Hospital, Boston, MA, USA
| | | | - L. Djousse
- Brigham and Women's Hospital, Boston, MA, USA; Department
of Medicine, Harvard Medical School, Boston, MA, USA; Boston VA Healthcare
System, Boston, MA, USA
| | - M. Fornage
- University of Texas Health Science Center at Houston, Houston, TX,
USA
| | | | - S. F. A. Grant
- Children's Hospital of Philadelphia, Philadelphia, PA,
USA
| | - X. Guo
- Cedars Sinai Medical Center, Los Angeles, CA, USA
| | - T. Harris
- National Institute on Aging, Bethesda, MD, USA
| | | | | | - Y. Liu
- Center for Genomics and Personalized Medicine Research, Center for
Diabetes Research, Wake Forest University School of Medicine, Winston-Salem,
NC, USA; Department of Epidemiology and Prevention, Wake Forest University,
Winston-Salem, North Carolina, USA
| | - K. L. Lunetta
- Department of Biostatistics, Boston University School of Public
Health, Boston, MA, USA; National Heart, Lung, and Blood Institute'
Framingham Heart Study, Framingham, MA, USA
| | - K. Mukamal
- Department of Medicine, Harvard Medical School, Boston, MA,
USA
| | - J. A. Nettleton
- University of Texas Health Science Center at Houston, Houston, TX,
USA
| | | | - S. R. Patel
- Brigham and Women's Hospital, Boston, MA, USA
| | - E. Ramos
- National Human Genome Research Institute, Bethesda, MD, USA
| | | | - S. S. Rich
- University of Virginia, Charlottesville, VA, USA
| | - C. N. Rotimi
- National Human Genome Research Institute, Bethesda, MD, USA
| | - D. Sarpong
- Jackson State University, Jackson, MS, USA
| | - D. Shriner
- National Human Genome Research Institute, Bethesda, MD, USA
| | - M. Sims
- University of Mississippi Medical Center, Jackson, MS, USA
| | - J. M. Zmuda
- University of Pittsburgh, Graduate School of Public Health,
Pittsburgh, PA, USA
| | - S. Redline
- Brigham and Women's Hospital, Boston, MA, USA
| | - W. H. Kao
- Johns Hopkins University, Baltimore, MD, USA
| | | | - J. C. Florez
- Department of Medicine, Harvard Medical School, Boston, MA, USA;
Diabetes Unit and Center for Human Genetic Research, Massachusetts General
Hospital, Boston, MA, USA; Program in Medical and Population Genetics, Broad
Institute, Cambridge, MA, USA
| | - J. I. Rotter
- Cedars Sinai Medical Center, Los Angeles, CA, USA
| | - J. Dupuis
- Department of Biostatistics, Boston University School of Public
Health, Boston, MA, USA; National Heart, Lung, and Blood Institute's
Framingham Heart Study, Framingham, MA, USA
| | - J. G. Wilson
- University of Mississippi Medical Center, Jackson, MS, USA
| | - D. W. Bowden
- Center for Genomics and Personalized Medicine Research, Center for
Diabetes Research, Wake Forest University School of Medicine, Winston-Salem,
NC, USA; Departments of Biochemistry and Internal Medicine, Wake Forest
University School of Medicine, Winston-Salem, NC, USA
| | - J. B. Meigs
- Department of Medicine, Harvard Medical School, Boston, MA, USA;
General Medicine Division, Massachusetts General Hospital, 50 Staniford
Street, 9th Flr, Boston, MA, USA
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Teo SM, Ku CS, Salim A, Naidoo N, Chia KS, Pawitan Y. Regions of homozygosity in three Southeast Asian populations. J Hum Genet 2011; 57:101-8. [PMID: 22129560 DOI: 10.1038/jhg.2011.132] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
The genomes of outbred populations were first shown in 2006 to contain regions of homozygosity (ROHs) of several megabases. Further studies have also investigated the characteristics of ROHs in healthy individuals in various populations but there are no studies on Singapore populations to date. This study aims to identify and investigate the characteristics of ROHs in three Singapore populations. A total of 268 samples (96 Chinese, 89 Malays and 83 Indians) are genotyped on Illumina Human 1 M Beadchip and Affymetrix Genome-Wide Human SNP Array 6.0. We use the PennCNV algorithm to detect ROHs. We report an abundance of ROHs (≥500 kb), with an average of more than one hundred regions per individual. On average, the Indian population has the lowest number of ROHs and smallest total length of ROHs per individual compared with the Chinese and Malay populations. We further investigate the relationship between the occurrence of ROHs and haplotype frequency, regional linkage disequilibrium (LD) and positive selection. Based on the results of this data set, we find that the frequency of occurrence of ROHs is positively associated with haplotype frequency and regional LD. The majority of regions detected for recent positive selection and regions with differential LD between populations overlap with the ROH loci. When we consider both the location of the ROHs and the allelic form of the ROHs, we are able to separate the populations by principal component analysis, demonstrating that ROHs contain information on population structure and the demographic history of a population.
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Affiliation(s)
- Shu-Mei Teo
- Centre for Molecular Epidemiology, National University of Singapore, Singapore.
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Large-scale genome-wide association studies in east Asians identify new genetic loci influencing metabolic traits. Nat Genet 2011; 43:990-5. [DOI: 10.1038/ng.939] [Citation(s) in RCA: 226] [Impact Index Per Article: 17.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2010] [Accepted: 08/18/2011] [Indexed: 12/13/2022]
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Kooner JS, Saleheen D, Sim X, Sehmi J, Zhang W, Frossard P, Been LF, Chia KS, Dimas AS, Hassanali N, Jafar T, Jowett JBM, Li X, Radha V, Rees SD, Takeuchi F, Young R, Aung T, Basit A, Chidambaram M, Das D, Grunberg E, Hedman ÅK, Hydrie ZI, Islam M, Khor CC, Kowlessur S, Kristensen MM, Liju S, Lim WY, Matthews DR, Liu J, Morris AP, Nica AC, Pinidiyapathirage JM, Prokopenko I, Rasheed A, Samuel M, Shah N, Shera AS, Small KS, Suo C, Wickremasinghe AR, Wong TY, Yang M, Zhang F, Abecasis GR, Barnett AH, Caulfield M, Deloukas P, Frayling T, Froguel P, Kato N, Katulanda P, Kelly MA, Liang J, Mohan V, Sanghera DK, Scott J, Seielstad M, Zimmet PZ, Elliott P, Teo YY, McCarthy MI, Danesh J, Tai ES, Chambers JC. Genome-wide association study in individuals of South Asian ancestry identifies six new type 2 diabetes susceptibility loci. Nat Genet 2011; 43:984-9. [PMID: 21874001 PMCID: PMC3773920 DOI: 10.1038/ng.921] [Citation(s) in RCA: 393] [Impact Index Per Article: 30.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2011] [Accepted: 08/03/2011] [Indexed: 12/16/2022]
Abstract
We carried out a genome-wide association study of type-2 diabetes (T2D) in individuals of South Asian ancestry. Our discovery set included 5,561 individuals with T2D (cases) and 14,458 controls drawn from studies in London, Pakistan and Singapore. We identified 20 independent SNPs associated with T2D at P < 10(-4) for testing in a replication sample of 13,170 cases and 25,398 controls, also all of South Asian ancestry. In the combined analysis, we identified common genetic variants at six loci (GRB14, ST6GAL1, VPS26A, HMG20A, AP3S2 and HNF4A) newly associated with T2D (P = 4.1 × 10(-8) to P = 1.9 × 10(-11)). SNPs at GRB14 were also associated with insulin sensitivity (P = 5.0 × 10(-4)), and SNPs at ST6GAL1 and HNF4A were also associated with pancreatic beta-cell function (P = 0.02 and P = 0.001, respectively). Our findings provide additional insight into mechanisms underlying T2D and show the potential for new discovery from genetic association studies in South Asians, a population with increased susceptibility to T2D.
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Affiliation(s)
- Jaspal S Kooner
- NHLI, Imperial College London, Hammersmith Hospital, Ducane Road, London, W12 0NN, UK
- Ealing Hospital NHS Trust, Uxbridge Road, Middlesex, UB1 3HW, UK
- Imperial College Healthcare NHS Trust, DuCane Road, London, W12 0HS
| | - Danish Saleheen
- Center for Non-Communicable Diseases Pakistan, Karachi, 75300, Pakistan
- Department of Public Health and Primary Care, University of Cambridge, worts causeway, Cambridge CB1 8RN, UK
| | - Xueling Sim
- Centre for Molecular Epidemiology, National University of Singapore, Singapore
| | - Joban Sehmi
- NHLI, Imperial College London, Hammersmith Hospital, Ducane Road, London, W12 0NN, UK
- Ealing Hospital NHS Trust, Uxbridge Road, Middlesex, UB1 3HW, UK
| | - Weihua Zhang
- Epidemiology and Biostatistics, Imperial College London, Norfolk Place, London, W2 1PG, UK
| | - Philippe Frossard
- Center for Non-Communicable Diseases Pakistan, Karachi, 75300, Pakistan
| | - Latonya F Been
- Department of Pediatrics, Section of Genetics, College of Medicine, University of Oklahoma Health Sciences Center, Oklahoma City, OK 73104, USA
| | - Kee-Seng Chia
- Centre for Molecular Epidemiology, National University of Singapore, Singapore
- Department of Epidemiology and Public Health, National University of Singapore, Singapore
| | - Antigone S Dimas
- Department of Genetic Medicine and Development, University of Geneva Medical School, Geneva, Switzerland
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK
| | - Neelam Hassanali
- Oxford Centre for Diabetes, Endocrinology & Metabolism, University of Oxford, Churchill Hospital, Oxford, UK
| | - Tazeen Jafar
- Department of Community Health Sciences, Aga Khan University, Stadium Road, P.O. Box 3500, Karachi-74800, Pakistan
- Department of Medicine, Aga Khan University, Stadium Road, P.O. Box 3500, Karachi-74800, Pakistan
| | - Jeremy BM Jowett
- Baker IDI Heart and Diabetes Institute, Melbourne, Victoria, 3004, Australia
| | - Xinzhing Li
- NHLI, Imperial College London, Hammersmith Hospital, Ducane Road, London, W12 0NN, UK
| | - Venkatesan Radha
- Department of Molecular Genetics, Madras Diabetes Research Foundation-ICMR Advanced Centre for Genomics of Diabetes, Chennai 603 103, India
| | - Simon D Rees
- College of Medical and Dental Sciences, University of Birmingham
- BioMedical Research Centre, Heart of England NHS Foundation Trust, Birmingham
| | - Fumihiko Takeuchi
- Department of Gene Diagnostics and Therapeutics, Research Institute, National Center for Global Health and Medicine, Tokyo, Japan, 162-8655
| | - Robin Young
- Department of Public Health and Primary Care, University of Cambridge, worts causeway, Cambridge CB1 8RN, UK
| | - Tin Aung
- Department of Ophthalomolgy, National University of Singapore, Singapore
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
| | - Abdul Basit
- Baqai Institute of Diabetology and Endocrinology, Karachi, Pakistan
| | - Manickam Chidambaram
- Department of Molecular Genetics, Madras Diabetes Research Foundation-ICMR Advanced Centre for Genomics of Diabetes, Chennai 603 103, India
| | - Debashish Das
- Ealing Hospital NHS Trust, Uxbridge Road, Middlesex, UB1 3HW, UK
| | - Elin Grunberg
- Department of Twin Research and Genetic Epidemiology, King’s College London, London, UK
| | - Åsa K Hedman
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK
| | - Zafar I Hydrie
- Baqai Institute of Diabetology and Endocrinology, Karachi, Pakistan
| | - Muhammed Islam
- Department of Community Health Sciences, Aga Khan University, Stadium Road, P.O. Box 3500, Karachi-74800, Pakistan
| | - Chiea-Chuen Khor
- Centre for Molecular Epidemiology, National University of Singapore, Singapore
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
- Genome Institute of Singapore, Agency for Science, Technology and Research, Singapore
| | | | - Malene M Kristensen
- Baker IDI Heart and Diabetes Institute, Melbourne, Victoria, 3004, Australia
| | - Samuel Liju
- Department of Molecular Genetics, Madras Diabetes Research Foundation-ICMR Advanced Centre for Genomics of Diabetes, Chennai 603 103, India
| | - Wei-Yen Lim
- Centre for Molecular Epidemiology, National University of Singapore, Singapore
| | - David R Matthews
- Oxford Centre for Diabetes, Endocrinology & Metabolism, University of Oxford, Churchill Hospital, Oxford, UK
| | - Jianjun Liu
- Genome Institute of Singapore, Agency for Science, Technology and Research, Singapore
| | - Andrew P Morris
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK
| | - Alexandra C Nica
- Department of Genetic Medicine and Development, University of Geneva Medical School, Geneva, Switzerland
| | | | - Inga Prokopenko
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK
| | - Asif Rasheed
- Center for Non-Communicable Diseases Pakistan, Karachi, 75300, Pakistan
| | - Maria Samuel
- Center for Non-Communicable Diseases Pakistan, Karachi, 75300, Pakistan
| | - Nabi Shah
- Center for Non-Communicable Diseases Pakistan, Karachi, 75300, Pakistan
| | | | - Kerrin S Small
- Department of Twin Research and Genetic Epidemiology, King’s College London, London, UK
- Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, UK
| | - Chen Suo
- Centre for Molecular Epidemiology, National University of Singapore, Singapore
| | - Ananda R Wickremasinghe
- Department of Public Health, Faculty of Medicine, University of Kelaniya, Ragama, Sri Lanka, 11010
| | - Tien Yin Wong
- Department of Ophthalomolgy, National University of Singapore, Singapore
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
- Center for Eye Research Australia, University of Melbourne, Australia
| | - Mingyu Yang
- Beijing Genomics Institute, Shenzhen 518083, China
| | - Fan Zhang
- Beijing Genomics Institute, Shenzhen 518083, China
| | - DIAGRAM
- Members of the DIAGRAM and MuTHER study are listed in the Supplementary Online Material
| | - MuTHER
- Members of the DIAGRAM and MuTHER study are listed in the Supplementary Online Material
| | - Goncalo R Abecasis
- Center for Statistical Genetics, Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, Michigan
| | - Anthony H Barnett
- College of Medical and Dental Sciences, University of Birmingham
- BioMedical Research Centre, Heart of England NHS Foundation Trust, Birmingham
| | - Mark Caulfield
- Clinical Pharmacology and Barts and the London Genome Centre, William Harvey Research Institute, Barts and the London School of Medicine, Queen Mary University of London, London, UK
| | - Panos Deloukas
- Human Genetics, Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton CB10 1SA UK
| | - Tim Frayling
- Genetics of Complex Traits, Institute of Biomedical and Clinical Science, Peninsula Medical School, University of Exeter, Exeter, UK
| | - Philippe Froguel
- Genomics of Common Diseases, School of Public Health, Imperial College London, Hammersmith Hospital, Du Cane Road, London W12 0NN
| | - Norihiro Kato
- Department of Gene Diagnostics and Therapeutics, Research Institute, National Center for Global Health and Medicine, Tokyo, Japan, 162-8655
| | - Prasad Katulanda
- Oxford Centre for Diabetes, Endocrinology & Metabolism, University of Oxford, Churchill Hospital, Oxford, UK
- Diabetes Research Unit, Dept of Clinical Medicine, Univ of Colombo, Sri Lanka
| | - M Ann Kelly
- College of Medical and Dental Sciences, University of Birmingham
- BioMedical Research Centre, Heart of England NHS Foundation Trust, Birmingham
| | - Junbin Liang
- Beijing Genomics Institute, Shenzhen 518083, China
| | - Viswanathan Mohan
- Department of Molecular Genetics, Madras Diabetes Research Foundation-ICMR Advanced Centre for Genomics of Diabetes, Chennai 603 103, India
- Dr Mohan’s Diabetes Specialties Centre, Chennai 600086, India
| | - Dharambir K Sanghera
- Department of Pediatrics, Section of Genetics, College of Medicine, University of Oklahoma Health Sciences Center, Oklahoma City, OK 73104, USA
| | - James Scott
- NHLI, Imperial College London, Hammersmith Hospital, Ducane Road, London, W12 0NN, UK
| | - Mark Seielstad
- Institure of Human Genetics, University of California, San Francisco
| | - Paul Z Zimmet
- Baker IDI Heart and Diabetes Institute, Melbourne, Victoria, 3004, Australia
| | - Paul Elliott
- Epidemiology and Biostatistics, Imperial College London, Norfolk Place, London, W2 1PG, UK
- MRC-HPA Centre for Environment and Health, Imperial College London, Norfolk Place, London, W2 1PG, UK
| | - Yik Ying Teo
- Centre for Molecular Epidemiology, National University of Singapore, Singapore
- Department of Epidemiology and Public Health, National University of Singapore, Singapore
- Genome Institute of Singapore, Agency for Science, Technology and Research, Singapore
- Department of Statistics and Applied Probability, National University of Singapore, Singapore
- NUS Graduate School for Integrative Science and Engineering, National University of Singapore, Singapore
| | - Mark I McCarthy
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK
- Oxford Centre for Diabetes, Endocrinology & Metabolism, University of Oxford, Churchill Hospital, Oxford, UK
- Oxford NIHR Biomedical Research Centre, Churchill Hospital, Oxford, UK
| | - John Danesh
- Department of Public Health and Primary Care, University of Cambridge, worts causeway, Cambridge CB1 8RN, UK
| | - E Shyong Tai
- Department of Epidemiology and Public Health, National University of Singapore, Singapore
- Department of Medicine, National University of Singapore, Singapore
- Duke-National University of Singapore Graduate Medical School, Singapore 169857,Singapore
| | - John C Chambers
- Ealing Hospital NHS Trust, Uxbridge Road, Middlesex, UB1 3HW, UK
- Imperial College Healthcare NHS Trust, DuCane Road, London, W12 0HS
- Epidemiology and Biostatistics, Imperial College London, Norfolk Place, London, W2 1PG, UK
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Trask JS, Garnica WT, Malhi RS, Kanthaswamy S, Smith DG. High-throughput single-nucleotide polymorphism discovery and the search for candidate genes for long-term SIVmac nonprogression in Chinese rhesus macaques (Macaca mulatta). J Med Primatol 2011; 40:224-32. [PMID: 21781130 PMCID: PMC3144501 DOI: 10.1111/j.1600-0684.2011.00486.x] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
Abstract
BACKGROUND Genetic differences between Indian and Chinese rhesus macaques contribute to the phenotypic variance of clinical trials, including infection with SIVmac. The completion of the rhesus genome has facilitated the discovery of several thousand markers. METHODS We developed a genome-wide SNP map for rhesus macaques containing 3869 validated markers with an average distance of 0.88 Mb and used the program VarLD to identify genomic areas with significant differences in linkage disequilibrium (LD) between Indian-derived and Chinese rhesus macaques. RESULTS Forty-one statistically significant differences in LD between Chinese and Indian-origin rhesus were detected on chromosomes 1, 4, 5 and 11. The region of greatest LD difference was located on the proximal end of chromosome one, which also contained the genes ELAVL4, MAST2 and HIVEP3. CONCLUSION These genomic areas provide entry to more detailed studies of gene function. This method is also applicable to the study of differences in biomarkers between regional populations of other species.
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Affiliation(s)
- J Satkoski Trask
- Department of Anthropology, University of California, Davis, 95616, USA.
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Meta-analysis of genome-wide association studies identifies common variants associated with blood pressure variation in east Asians. Nat Genet 2011; 43:531-8. [PMID: 21572416 DOI: 10.1038/ng.834] [Citation(s) in RCA: 443] [Impact Index Per Article: 34.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2010] [Accepted: 04/20/2011] [Indexed: 12/14/2022]
Abstract
We conducted a meta-analysis of genome-wide association studies of systolic (SBP) and diastolic (DBP) blood pressure in 19,608 subjects of east Asian ancestry from the AGEN-BP consortium followed up with de novo genotyping (n = 10,518) and further replication (n = 20,247) in east Asian samples. We identified genome-wide significant (P < 5 × 10(-8)) associations with SBP or DBP, which included variants at four new loci (ST7L-CAPZA1, FIGN-GRB14, ENPEP and NPR3) and a newly discovered variant near TBX3. Among the five newly discovered variants, we obtained significant replication in the independent samples for all of these loci except NPR3. We also confirmed seven loci previously identified in populations of European descent. Moreover, at 12q24.13 near ALDH2, we observed strong association signals (P = 7.9 × 10(-31) and P = 1.3 × 10(-35) for SBP and DBP, respectively) with ethnic specificity. These findings provide new insights into blood pressure regulation and potential targets for intervention.
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Sim X, Ong RTH, Suo C, Tay WT, Liu J, Ng DPK, Boehnke M, Chia KS, Wong TY, Seielstad M, Teo YY, Tai ES. Transferability of type 2 diabetes implicated loci in multi-ethnic cohorts from Southeast Asia. PLoS Genet 2011; 7:e1001363. [PMID: 21490949 PMCID: PMC3072366 DOI: 10.1371/journal.pgen.1001363] [Citation(s) in RCA: 123] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2010] [Accepted: 03/04/2011] [Indexed: 12/24/2022] Open
Abstract
Recent large genome-wide association studies (GWAS) have identified multiple loci
which harbor genetic variants associated with type 2 diabetes mellitus (T2D),
many of which encode proteins not previously suspected to be involved in the
pathogenesis of T2D. Most GWAS for T2D have focused on populations of European
descent, and GWAS conducted in other populations with different ancestry offer a
unique opportunity to study the genetic architecture of T2D. We performed
genome-wide association scans for T2D in 3,955 Chinese (2,010 cases, 1,945
controls), 2,034 Malays (794 cases, 1,240 controls), and 2,146 Asian Indians
(977 cases, 1,169 controls). In addition to the search for novel variants
implicated in T2D, these multi-ethnic cohorts serve to assess the
transferability and relevance of the previous findings from European descent
populations in the three major ethnic populations of Asia, comprising half of
the world's population. Of the SNPs associated with T2D in previous GWAS,
only variants at CDKAL1 and
HHEX/IDE/KIF11 showed the strongest
association with T2D in the meta-analysis including all three ethnic groups.
However, consistent direction of effect was observed for many of the other SNPs
in our study and in those carried out in European populations. Close examination
of the associations at both the CDKAL1 and
HHEX/IDE/KIF11 loci provided some evidence of locus and
allelic heterogeneity in relation to the associations with T2D. We also detected
variation in linkage disequilibrium between populations for most of these loci
that have been previously identified. These factors, combined with limited
statistical power, may contribute to the failure to detect associations across
populations of diverse ethnicity. These findings highlight the value of
surveying across diverse racial/ethnic groups towards the fine-mapping efforts
for the casual variants and also of the search for variants, which may be
population-specific. Type 2 diabetes mellitus (T2D) is a chronic disease which can lead to
complications such as heart disease, stroke, hypertension, blindness due to
diabetic retinopathy, amputations from peripheral vascular diseases, and kidney
disease from diabetic nephropathy. The increasing prevalence and complications
of T2D are likely to increase the health and economic burden of individuals,
families, health systems, and countries. Our study carried out in three major
Asian ethnic groups (Chinese, Malays, and Indians) in Singapore suggests that
the findings of studies carried out in populations of European ancestry (which
represents most studies to date) may be relevant to populations in Asia.
However, our study also raises the possibility that different genes, and within
the genes different variants, may confer susceptibility to T2D in these
populations. These findings are particularly relevant in Asia, where the
greatest growth of T2D is expected in the coming years, and emphasize the
importance of studying diverse populations when trying to localize the regions
of the genome associated with T2D. In addition, we may need to consider novel
methods for combining data across populations.
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Affiliation(s)
- Xueling Sim
- Centre for Molecular Epidemiology, National University of Singapore,
Singapore, Singapore
| | - Rick Twee-Hee Ong
- Centre for Molecular Epidemiology, National University of Singapore,
Singapore, Singapore
- NUS Graduate School for Integrative Science and Engineering, National
University of Singapore, Singapore, Singapore
- Genome Institute of Singapore, Agency for Science, Technology and
Research, Singapore, Singapore
| | - Chen Suo
- Centre for Molecular Epidemiology, National University of Singapore,
Singapore, Singapore
| | - Wan-Ting Tay
- Singapore Eye Research Institute, Singapore National Eye Centre,
Singapore, Singapore
| | - Jianjun Liu
- Genome Institute of Singapore, Agency for Science, Technology and
Research, Singapore, Singapore
| | - Daniel Peng-Keat Ng
- Department of Epidemiology and Public Health, National University of
Singapore, Singapore, Singapore
| | - Michael Boehnke
- Department of Biostatistics and Center for Statistical Genetics, School
of Public Health, University of Michigan, Ann Arbor, Michigan, United States of
America
| | - Kee-Seng Chia
- Centre for Molecular Epidemiology, National University of Singapore,
Singapore, Singapore
- Department of Epidemiology and Public Health, National University of
Singapore, Singapore, Singapore
| | - Tien-Yin Wong
- Singapore Eye Research Institute, Singapore National Eye Centre,
Singapore, Singapore
- Department of Epidemiology and Public Health, National University of
Singapore, Singapore, Singapore
- Department of Ophthalmology, National University of Singapore, Singapore,
Singapore
- Centre for Eye Research Australia, University of Melbourne, Melbourne,
Australia
| | - Mark Seielstad
- Genome Institute of Singapore, Agency for Science, Technology and
Research, Singapore, Singapore
| | - Yik-Ying Teo
- Centre for Molecular Epidemiology, National University of Singapore,
Singapore, Singapore
- NUS Graduate School for Integrative Science and Engineering, National
University of Singapore, Singapore, Singapore
- Genome Institute of Singapore, Agency for Science, Technology and
Research, Singapore, Singapore
- Department of Epidemiology and Public Health, National University of
Singapore, Singapore, Singapore
- Department of Statistics and Applied Probability, National University of
Singapore, Singapore, Singapore
- * E-mail: (E-ST); (Y-YT)
| | - E-Shyong Tai
- Department of Epidemiology and Public Health, National University of
Singapore, Singapore, Singapore
- Department of Medicine, National University of Singapore, Singapore,
Singapore
- Duke-National University of Singapore Graduate Medical School, Singapore,
Singapore
- * E-mail: (E-ST); (Y-YT)
| |
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