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Byars SG, Prestes P, Suphapimol V, Takeuchi F, De Vries N, Maier MC, Melo M, Balding D, Samani N, Allen AM, Kato N, Wilkinson-Berka JL, Charchar F, Harrap SB. Four-week inhibition of the renin-angiotensin system in spontaneously hypertensive rats results in persistently lower blood pressure with reduced kidney renin and changes in expression of relevant gene networks. Cardiovasc Res 2024:cvae053. [PMID: 38501595 DOI: 10.1093/cvr/cvae053] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/04/2023] [Revised: 11/06/2023] [Accepted: 12/18/2023] [Indexed: 03/20/2024] Open
Abstract
AIMS Prevention of human hypertension is an important challenge and has been achieved in experimental models. Brief treatment with renin-angiotensin system (RAS) inhibitors permanently reduces the genetic hypertension of the spontaneously hypertensive rat (SHR). The kidney is involved in this fascinating phenomenon, but relevant changes in gene expression are unknown. METHODS In SHR, we studied the effect of treatment between 10 and 14 weeks of age with the angiotensin receptor blocker, losartan, or the angiotensin-converting enzyme (ACE) inhibitor, perindopril (with controls for non-specific effects of lowering BP) on differential RNA expression, DNA methylation and renin immunolabelling in the kidney at 20 weeks of age. RESULTS RNA sequencing revealed a 6-fold increase in renin gene (Ren) expression during losartan treatment (P < 0.0001). Six weeks after losartan, arterial pressure remained lower (P = 0.006), yet kidney Ren showed reduced expression by 23% after losartan (P = 0.03) and by 43% after perindopril (P = 1.4 x 10-6) associated with increased DNA methylation (P = 0.04). Immunolabelling confirmed reduced cortical renin after earlier RAS blockade (P = 0.002). RNA sequencing identified differential expression of mRNAs, miRNAs and lncRNAs with evidence of networking and co-regulation. These included 13 candidate genes (Grhl1, Ammecr1l, Hs6st1, Nfil3, Fam221a, Lmo4, Adamts1, Cish, Hif3a, Bcl6, Rad54l2, Adap1, Dok4), the miRNA miR-145-3p and the lncRNA AC115371. Gene ontogeny analyses revealed that these networks were enriched with genes relevant to BP, RAS and the kidneys. CONCLUSIONS Early RAS inhibition in SHR resets genetic pathways and networks resulting in a legacy of reduced Ren expression and BP persisting for a minimum of 6 weeks.
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Affiliation(s)
- Sean G Byars
- The Florey Institute of Neuroscience & Mental Health, Parkville, Victoria, Australia
| | - Priscilla Prestes
- Health Innovation and Transformation Centre, Federation University, Ballarat, Victoria, Australia
| | - Vara Suphapimol
- Department of Anatomy & Physiology, School of Biomedical Sciences, University of Melbourne, Victoria, Australia
| | - Fumihiko Takeuchi
- Department of Gene Diagnostics and Therapeutics, National Center for Global Health and Medicine, Tokyo, Japan
| | - Nathan De Vries
- Health Innovation and Transformation Centre, Federation University, Ballarat, Victoria, Australia
| | - Michelle C Maier
- Health Innovation and Transformation Centre, Federation University, Ballarat, Victoria, Australia
| | - Mariana Melo
- Department of Anatomy & Physiology, School of Biomedical Sciences, University of Melbourne, Victoria, Australia
| | - David Balding
- Melbourne Integrative Genomic and School of Mathematics & Statistics, University of Melbourne, Victoria, Australia
| | - Nilesh Samani
- Department of Cardiovascular Sciences, University of Leicester, Leicester, UK
| | - Andrew M Allen
- The Florey Institute of Neuroscience & Mental Health, Parkville, Victoria, Australia
| | - Norihiro Kato
- Department of Gene Diagnostics and Therapeutics, National Center for Global Health and Medicine, Tokyo, Japan
| | - Jennifer L Wilkinson-Berka
- Department of Anatomy & Physiology, School of Biomedical Sciences, University of Melbourne, Victoria, Australia
| | - Fadi Charchar
- Health Innovation and Transformation Centre, Federation University, Ballarat, Victoria, Australia
| | - Stephen B Harrap
- Department of Anatomy & Physiology, School of Biomedical Sciences, University of Melbourne, Victoria, Australia
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2
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Mary-Huard T, Balding D. Fast and accurate joint inference of coancestry parameters for populations and/or individuals. PLoS Genet 2023; 19:e1010054. [PMID: 36656906 PMCID: PMC9888729 DOI: 10.1371/journal.pgen.1010054] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2022] [Revised: 01/31/2023] [Accepted: 12/01/2022] [Indexed: 01/20/2023] Open
Abstract
We introduce a fast, new algorithm for inferring from allele count data the FST parameters describing genetic distances among a set of populations and/or unrelated diploid individuals, and a tree with branch lengths corresponding to FST values. The tree can reflect historical processes of splitting and divergence, but seeks to represent the actual genetic variance as accurately as possible with a tree structure. We generalise two major approaches to defining FST, via correlations and mismatch probabilities of sampled allele pairs, which measure shared and non-shared components of genetic variance. A diploid individual can be treated as a population of two gametes, which allows inference of coancestry coefficients for individuals as well as for populations, or a combination of the two. A simulation study illustrates that our fast method-of-moments estimation of FST values, simultaneously for multiple populations/individuals, gains statistical efficiency over pairwise approaches when the population structure is close to tree-like. We apply our approach to genome-wide genotypes from the 26 worldwide human populations of the 1000 Genomes Project. We first analyse at the population level, then a subset of individuals and in a final analysis we pool individuals from the more homogeneous populations. This flexible analysis approach gives advantages over traditional approaches to population structure/coancestry, including visual and quantitative assessments of long-standing questions about the relative magnitudes of within- and between-population genetic differences.
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Affiliation(s)
- Tristan Mary-Huard
- MIA-Paris, INRAE, AgroParisTech, Université Paris-Saclay, Palaiseau, France
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, Génétique Quantitative et Evolution—Le Moulon, Gif-sur-Yvette, France
- * E-mail:
| | - David Balding
- Melbourne Integrative Genomics, School of BioSciences and School of Mathematics & Statistics, University of Melbourne, Parkville, Victoria, Australia
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3
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Berrandou TE, Balding D, Speed D. LDAK-GBAT: Fast and powerful gene-based association testing using summary statistics. Am J Hum Genet 2023; 110:23-29. [PMID: 36480927 PMCID: PMC9892699 DOI: 10.1016/j.ajhg.2022.11.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Accepted: 11/17/2022] [Indexed: 12/12/2022] Open
Abstract
We present LDAK-GBAT, a tool for gene-based association testing using summary statistics from genome-wide association studies that is computationally efficient, produces well-calibrated p values, and is significantly more powerful than existing tools. LDAK-GBAT takes approximately 30 min to analyze imputed data (2.9M common, genic SNPs), requiring less than 10 Gb memory. It shows good control of type 1 error given an appropriate reference panel. Across 109 phenotypes (82 from the UK Biobank, 18 from the Million Veteran Program, and nine from the Psychiatric Genetics Consortium), LDAK-GBAT finds on average 19% (SE: 1%) more significant genes than the existing tool sumFREGAT-ACAT, with even greater gains in comparison with MAGMA, GCTA-fastBAT, sumFREGAT-SKAT-O, and sumFREGAT-PCA.
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Affiliation(s)
- Takiy-Eddine Berrandou
- Quantitative Genetics and Genomics, Aarhus University, Aarhus, Denmark,Corresponding author
| | - David Balding
- Melbourne Integrative Genomics, Melbourne University, Melbourne, VIC, Australia
| | - Doug Speed
- Quantitative Genetics and Genomics, Aarhus University, Aarhus, Denmark,Corresponding author
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4
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White J, Sharma R, Balding D, Cockram J, Mackay IJ. Genome-wide association mapping of Hagberg falling number, protein content, test weight, and grain yield in U.K. wheat. Crop Sci 2022; 62:965-981. [PMID: 35915786 PMCID: PMC9314726 DOI: 10.1002/csc2.20692] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/29/2021] [Accepted: 12/14/2021] [Indexed: 05/12/2023]
Abstract
Association mapping using crop cultivars allows identification of genetic loci of direct relevance to breeding. Here, 150 U.K. wheat (Triticum aestivum L.) cultivars genotyped with 23,288 single nucleotide polymorphisms (SNPs) were used for genome-wide association studies (GWAS) using historical phenotypic data for grain protein content, Hagberg falling number (HFN), test weight, and grain yield. Power calculations indicated experimental design would enable detection of quantitative trait loci (QTL) explaining ≥20% of the variation (PVE) at a relatively high power of >80%, falling to 40% for detection of a SNP with an R2 ≥ .5 with the same QTL. Genome-wide association studies identified marker-trait associations for all four traits. For HFN (h 2 = .89), six QTL were identified, including a major locus on chromosome 7B explaining 49% PVE and reducing HFN by 44 s. For protein content (h 2 = 0.86), 10 QTL were found on chromosomes 1A, 2A, 2B, 3A, 3B, and 6B, together explaining 48.9% PVE. For test weight, five QTL were identified (one on 1B and four on 3B; 26.3% PVE). Finally, 14 loci were identified for grain yield (h 2 = 0.95) on eight chromosomes (1A, 2A, 2B, 2D, 3A, 5B, 6A, 6B; 68.1% PVE), of which five were located within 16 Mbp of genetic regions previously identified as under breeder selection in European wheat. Our study demonstrates the utility of exploiting historical crop datasets, identifying genomic targets for independent validation, and ultimately for wheat genetic improvement.
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Affiliation(s)
- Jon White
- Genetics and Breeding Dep.NIAB93 Lawrence Weaver RoadCambridge, CB3 0LEUK
- Institute of GeneticsUniv. College LondonLondon, WC1E 6BTUK
| | - Rajiv Sharma
- Scotland's Rural College (SRUC)Kings Buildings, West Mains RoadEdinburgh, EH9 3JGUK
| | - David Balding
- Institute of GeneticsUniv. College LondonLondon, WC1E 6BTUK
- Current address: Melbourne Integrative GenomicsUniv. of MelbourneMelbourneAustralia
| | - James Cockram
- Genetics and Breeding Dep.NIAB93 Lawrence Weaver RoadCambridge, CB3 0LEUK
| | - Ian J. Mackay
- Scotland's Rural College (SRUC)Kings Buildings, West Mains RoadEdinburgh, EH9 3JGUK
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Mendoza-Revilla J, Chacón-Duque JC, Fuentes-Guajardo M, Ormond L, Wang K, Hurtado M, Villegas V, Granja V, Acuña-Alonzo V, Jaramillo C, Arias W, Barquera R, Gómez-Valdés J, Villamil-Ramírez H, Silva de Cerqueira CC, Badillo Rivera KM, Nieves-Colón MA, Gignoux CR, Wojcik GL, Moreno-Estrada A, Hünemeier T, Ramallo V, Schuler-Faccini L, Gonzalez-José R, Bortolini MC, Canizales-Quinteros S, Gallo C, Poletti G, Bedoya G, Rothhammer F, Balding D, Fumagalli M, Adhikari K, Ruiz-Linares A, Hellenthal G. Disentangling Signatures of Selection Before and After European Colonization in Latin Americans. Mol Biol Evol 2022; 39:6565306. [PMID: 35460423 PMCID: PMC9034689 DOI: 10.1093/molbev/msac076] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
Throughout human evolutionary history, large-scale migrations have led to intermixing (i.e., admixture) between previously separated human groups. Although classical and recent work have shown that studying admixture can yield novel historical insights, the extent to which this process contributed to adaptation remains underexplored. Here, we introduce a novel statistical model, specific to admixed populations, that identifies loci under selection while determining whether the selection likely occurred post-admixture or prior to admixture in one of the ancestral source populations. Through extensive simulations, we show that this method is able to detect selection, even in recently formed admixed populations, and to accurately differentiate between selection occurring in the ancestral or admixed population. We apply this method to genome-wide SNP data of ∼4,000 individuals in five admixed Latin American cohorts from Brazil, Chile, Colombia, Mexico, and Peru. Our approach replicates previous reports of selection in the human leukocyte antigen region that are consistent with selection post-admixture. We also report novel signals of selection in genomic regions spanning 47 genes, reinforcing many of these signals with an alternative, commonly used local-ancestry-inference approach. These signals include several genes involved in immunity, which may reflect responses to endemic pathogens of the Americas and to the challenge of infectious disease brought by European contact. In addition, some of the strongest signals inferred to be under selection in the Native American ancestral groups of modern Latin Americans overlap with genes implicated in energy metabolism phenotypes, plausibly reflecting adaptations to novel dietary sources available in the Americas.
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Affiliation(s)
- Javier Mendoza-Revilla
- Department of Genetics, Evolution and Environment, and UCL Genetics Institute, University College London, London, United Kingdom.,Human Evolutionary Genetics Unit, Institut Pasteur, UMR2000, CNRS, Paris, France.,Laboratorios de Investigación y Desarrollo, Facultad de Ciencias y Filosofía, Universidad Peruana Cayetano Heredia, Lima, Perú
| | - J Camilo Chacón-Duque
- Centre for Palaeogenetics, Stockholm, Sweden.,Department of Archaeology and Classical Studies, Stockholm University, Stockholm, Sweden
| | - Macarena Fuentes-Guajardo
- Departamento de Tecnología Médica, Facultad de Ciencias de la Salud, Universidad de Tarapacá, Arica, Chile
| | - Louise Ormond
- Department of Genetics, Evolution and Environment, and UCL Genetics Institute, University College London, London, United Kingdom
| | - Ke Wang
- Department of Genetics, Evolution and Environment, and UCL Genetics Institute, University College London, London, United Kingdom.,Department of Archaeogenetics, Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany
| | - Malena Hurtado
- Laboratorios de Investigación y Desarrollo, Facultad de Ciencias y Filosofía, Universidad Peruana Cayetano Heredia, Lima, Perú
| | - Valeria Villegas
- Laboratorios de Investigación y Desarrollo, Facultad de Ciencias y Filosofía, Universidad Peruana Cayetano Heredia, Lima, Perú
| | - Vanessa Granja
- Laboratorios de Investigación y Desarrollo, Facultad de Ciencias y Filosofía, Universidad Peruana Cayetano Heredia, Lima, Perú
| | | | - Claudia Jaramillo
- GENMOL (Genética Molecular), Universidad de Antioquia, Medellín, Colombia
| | - William Arias
- GENMOL (Genética Molecular), Universidad de Antioquia, Medellín, Colombia
| | - Rodrigo Barquera
- Department of Archaeogenetics, Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany.,National School of Anthropology and History, Mexico City, Mexico
| | | | - Hugo Villamil-Ramírez
- Unidad de Genómica de Poblaciones Aplicada a la Salud, Facultad de Química, UNAM-Instituto Nacional de Medicina Genómica, Mexico City, Mexico.,Universidad Nacional Autónoma de México e Instituto Nacional de Medicina Genómica, Mexico City, Mexico
| | | | | | - Maria A Nieves-Colón
- Department of Anthropology, University of Minnesota Twin Cities, Minneapolis, MN, USA
| | - Christopher R Gignoux
- Department of Biostatistics and Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Genevieve L Wojcik
- Bloomberg School of Public Health, John Hopkins University, Baltimore, MD, USA
| | - Andrés Moreno-Estrada
- Laboratorio Nacional de Genómica para la Biodiversidad (UGA-LANGEBIO), CINVESTAV, Irapuato, Guanajuato, Mexico
| | - Tábita Hünemeier
- Departamento de Genética, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil.,Department of Genetics and Evolutionary Biology, University of São Paulo, São Paulo, Brazil
| | - Virginia Ramallo
- Departamento de Genética, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil.,Instituto Patagónico de Ciencias Sociales y Humanas-Centro Nacional Patagónico, CONICET, Puerto Madryn, Argentina
| | | | - Rolando Gonzalez-José
- Instituto Patagónico de Ciencias Sociales y Humanas-Centro Nacional Patagónico, CONICET, Puerto Madryn, Argentina
| | - Maria-Cátira Bortolini
- Departamento de Genética, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
| | - Samuel Canizales-Quinteros
- Unidad de Genómica de Poblaciones Aplicada a la Salud, Facultad de Química, UNAM-Instituto Nacional de Medicina Genómica, Mexico City, Mexico.,Universidad Nacional Autónoma de México e Instituto Nacional de Medicina Genómica, Mexico City, Mexico
| | - Carla Gallo
- Laboratorios de Investigación y Desarrollo, Facultad de Ciencias y Filosofía, Universidad Peruana Cayetano Heredia, Lima, Perú
| | - Giovanni Poletti
- Laboratorios de Investigación y Desarrollo, Facultad de Ciencias y Filosofía, Universidad Peruana Cayetano Heredia, Lima, Perú
| | - Gabriel Bedoya
- GENMOL (Genética Molecular), Universidad de Antioquia, Medellín, Colombia
| | | | - David Balding
- Department of Genetics, Evolution and Environment, and UCL Genetics Institute, University College London, London, United Kingdom.,Schools of BioSciences and Mathematics & Statistics, University of Melbourne, Melbourne, Australia
| | - Matteo Fumagalli
- School of Biological and Behavioural Sciences, Queen Mary University of London, London, United Kingdom
| | - Kaustubh Adhikari
- School of Mathematics and Statistics, Faculty of Science, Technology, Engineering and Mathematics, The Open University, Milton Keynes, United Kingdom
| | - Andrés Ruiz-Linares
- Department of Genetics, Evolution and Environment, and UCL Genetics Institute, University College London, London, United Kingdom.,Ministry of Education Key Laboratory of Contemporary Anthropology and Collaborative Innovation Center of Genetics and Development, Fudan University, Shanghai, China.,Aix-Marseille Université, CNRS, EFS, ADES, Marseille, France
| | - Garrett Hellenthal
- Department of Genetics, Evolution and Environment, and UCL Genetics Institute, University College London, London, United Kingdom
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6
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Mahmoudi A, Koskela J, Kelleher J, Chan YB, Balding D. Bayesian inference of ancestral recombination graphs. PLoS Comput Biol 2022; 18:e1009960. [PMID: 35263345 PMCID: PMC8936483 DOI: 10.1371/journal.pcbi.1009960] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2021] [Revised: 03/21/2022] [Accepted: 02/23/2022] [Indexed: 11/18/2022] Open
Abstract
We present a novel algorithm, implemented in the software ARGinfer, for probabilistic inference of the Ancestral Recombination Graph under the Coalescent with Recombination. Our Markov Chain Monte Carlo algorithm takes advantage of the Succinct Tree Sequence data structure that has allowed great advances in simulation and point estimation, but not yet probabilistic inference. Unlike previous methods, which employ the Sequentially Markov Coalescent approximation, ARGinfer uses the Coalescent with Recombination, allowing more accurate inference of key evolutionary parameters. We show using simulations that ARGinfer can accurately estimate many properties of the evolutionary history of the sample, including the topology and branch lengths of the genealogical tree at each sequence site, and the times and locations of mutation and recombination events. ARGinfer approximates posterior probability distributions for these and other quantities, providing interpretable assessments of uncertainty that we show to be well calibrated. ARGinfer is currently limited to tens of DNA sequences of several hundreds of kilobases, but has scope for further computational improvements to increase its applicability.
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Affiliation(s)
- Ali Mahmoudi
- Melbourne Integrative Genomics / School of Mathematics and Statistics, The University of Melbourne, Melbourne, Australia
| | - Jere Koskela
- Department of Statistics, The University of Warwick, Coventry, United Kingdom
| | - Jerome Kelleher
- Big Data Institute, The University of Oxford, Oxford, United Kingdom
| | - Yao-ban Chan
- Melbourne Integrative Genomics / School of Mathematics and Statistics, The University of Melbourne, Melbourne, Australia
| | - David Balding
- Melbourne Integrative Genomics / School of Mathematics and Statistics, The University of Melbourne, Melbourne, Australia
- School of BioSciences, The University of Melbourne, Melbourne, Australia
- * E-mail:
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7
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Mallawaarachchi S, Tonkin-Hill G, Croucher NJ, Turner P, Speed D, Corander J, Balding D. Genome-wide association, prediction and heritability in bacteria with application to Streptococcus pneumoniae. NAR Genom Bioinform 2022; 4:lqac011. [PMID: 35211669 PMCID: PMC8862724 DOI: 10.1093/nargab/lqac011] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2021] [Revised: 01/06/2022] [Accepted: 02/01/2022] [Indexed: 11/14/2022] Open
Abstract
Whole-genome sequencing has facilitated genome-wide analyses of association, prediction and heritability in many organisms. However, such analyses in bacteria are still in their infancy, being limited by difficulties including genome plasticity and strong population structure. Here we propose a suite of methods including linear mixed models, elastic net and LD-score regression, adapted to bacterial traits using innovations such as frequency-based allele coding, both insertion/deletion and nucleotide testing and heritability partitioning. We compare and validate our methods against the current state-of-art using simulations, and analyse three phenotypes of the major human pathogen Streptococcus pneumoniae, including the first analyses of minimum inhibitory concentrations (MIC) for penicillin and ceftriaxone. We show that the MIC traits are highly heritable with high prediction accuracy, explained by many genetic associations under good population structure control. In ceftriaxone MIC, this is surprising because none of the isolates are resistant as per the inhibition zone criteria. We estimate that half of the heritability of penicillin MIC is explained by a known drug-resistance region, which also contributes a quarter of the ceftriaxone MIC heritability. For the within-host carriage duration phenotype, no associations were observed, but the moderate heritability and prediction accuracy indicate a moderately polygenic trait.
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Affiliation(s)
| | - Gerry Tonkin-Hill
- Parasites and Microbes, Wellcome Sanger Institute, Cambridge CB10 1SA, UK
| | - Nicholas J Croucher
- Faculty of Medicine, School of Public Health, Imperial College, London SW7 2AZ, UK
| | - Paul Turner
- Cambodia-Oxford Medical Research Unit, Angkor Hospital for Children, Siem Reap 1710, Cambodia,Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford OX3 7LG, UK
| | - Doug Speed
- Aarhus Institute of Advanced Studies (AIAS), Aarhus University, 8000 Aarhus, Denmark,Bioinformatics Research Centre, Aarhus University, 8000 Aarhus, Denmark,UCL Genetics Institute, University College London, London WC1E 6BT, United Kingdom
| | - Jukka Corander
- Parasites and Microbes, Wellcome Sanger Institute, Cambridge CB10 1SA, UK,Department of Biostatistics, Faculty of Medicine, University of Oslo, 0372 Oslo, Norway,Helsinki Institute of Information Technology, Department of Mathematics and Statistics, University of Helsinki, Helsinki 00014, Finland
| | - David Balding
- Correspondence may also be addressed to David Balding.
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8
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Brook C, Lynøe N, Eriksson A, Balding D. Retraction of a peer reviewed article suggests ongoing problems with Australian forensic science. Forensic Sci Int Synerg 2021; 3:100208. [PMID: 34805819 PMCID: PMC8585618 DOI: 10.1016/j.fsisyn.2021.100208] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2021] [Revised: 10/21/2021] [Accepted: 10/25/2021] [Indexed: 11/26/2022]
Abstract
We describe events arising from the case of Joby Rowe, convicted of the homicide of his three month old daughter, and explore what they illustrate about systemic problems in the forensic science community in Australia. A peer reviewed journal article that scrutinized the forensic evidence presented in the Rowe case was retracted by a forensic science journal for reasons unrelated to quality or accuracy, under pressure from forensic medical experts criticized in the article. Details of the retraction obtained through freedom of information mechanisms reveal improper pressure and subversion of publishing processes in order to avoid scrutiny. The retraction was supported by the editorial board and two Australian forensic science societies, which is indicative of serious deficiencies in the leadership of forensic science in Australia. We propose paths forward including blind peer review, publication of expert reports, and a criminal cases review authority, that would help stimulate a culture that encourages scrutiny, and relies on evidence-based rather than eminence-based knowledge.
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Affiliation(s)
- Chris Brook
- Universidad de La Laguna, Av. Del Astrofísico Francisco Sánchez, ES-38206, La Laguna, Tenerife, Spain
| | - Niels Lynøe
- Centre for Healthcare Ethics, Karolinska Institutet, SE-171 77, Stockholm, Sweden
| | - Anders Eriksson
- Department of Community Medicine and Rehabilitation, Forensic Medicine, Umeå University, SE-907 12, Umeå, Sweden
| | - David Balding
- School of BioSciences and School of Mathematics & Statistics, University of Melbourne, VIC, 3010, Australia
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9
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Chen Y, André M, Adhikari K, Blin M, Bonfante B, Mendoza-Revilla J, Fuentes-Guajardo M, Palmal S, Chacón-Duque JC, Hurtado M, Villegas V, Granja V, Jaramillo C, Arias W, Lozano RB, Everardo-Martínez P, Gómez-Valdés J, Villamil-Ramírez H, de Cerqueira CCS, Hünemeier T, Ramallo V, Gonzalez-José R, Schüler-Faccini L, Bortolini MC, Acuña-Alonzo V, Canizales-Quinteros S, Gallo C, Poletti G, Bedoya G, Rothhammer F, Balding D, Tobin DJ, Wang S, Faux P, Ruiz-Linares A. A genome-wide association study identifies novel gene associations with facial skin wrinkling and mole count in Latin Americans. Br J Dermatol 2021; 185:988-998. [PMID: 33959940 DOI: 10.1111/bjd.20436] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/04/2021] [Indexed: 12/01/2022]
Abstract
BACKGROUND Genome-wide association studies (GWASs) have identified genes influencing skin ageing and mole count in Europeans, but little is known about the relevance of these (or other genes) in non-Europeans. OBJECTIVES To conduct a GWAS for facial skin ageing and mole count in adults < 40 years old, of mixed European, Native American and African ancestry, recruited in Latin America. METHODS Skin ageing and mole count scores were obtained from facial photographs of over 6000 individuals. After quality control checks, three wrinkling traits and mole count were retained for genetic analyses. DNA samples were genotyped with Illumina's HumanOmniExpress chip. Association testing was performed on around 8 703 729 single-nucleotide polymorphisms (SNPs) across the autosomal genome. RESULTS Genome-wide significant association was observed at four genome regions: two were associated with wrinkling (in 1p13·3 and 21q21·2), one with mole count (in 1q32·3) and one with both wrinkling and mole count (in 5p13·2). Associated SNPs in 5p13·2 and in 1p13·3 are intronic within SLC45A2 and VAV3, respectively, while SNPs in 1q32·3 are near the SLC30A1 gene, and those in 21q21·2 occur in a gene desert. Analyses of SNPs in IRF4 and MC1R are consistent with a role of these genes in skin ageing. CONCLUSIONS We replicate the association of wrinkling with variants in SLC45A2, IRF4 and MC1R reported in Europeans. We identify VAV3 and SLC30A1 as two novel candidate genes impacting on wrinkling and mole count, respectively. We provide the first evidence that SLC45A2 influences mole count, in addition to variants in this gene affecting melanoma risk in Europeans.
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Affiliation(s)
- Y Chen
- Ministry of Education Key Laboratory of Contemporary Anthropology and Collaborative Innovation Center of Genetics and Development, School of Life Sciences and Human Phenome Institute, Fudan University, Yangpu District, Shanghai, China
| | - M André
- UMR 7268 ADES, CNRS, Aix-Marseille Université, EFS, Faculté de Médecine Timone, Marseille, 13005, France.,Estonian Biocentre, Institute of Genomics, University of Tartu, Tartu, 51010, Estonia
| | - K Adhikari
- School of Mathematics and Statistics, Faculty of Science, Technology, Engineering and Mathematics, The Open University, Milton Keynes, MK7 6AA, UK.,Department of Genetics, Evolution and Environment, and UCL Genetics Institute, University College London, London, WC1E 6BT, UK
| | - M Blin
- UMR 7268 ADES, CNRS, Aix-Marseille Université, EFS, Faculté de Médecine Timone, Marseille, 13005, France
| | - B Bonfante
- UMR 7268 ADES, CNRS, Aix-Marseille Université, EFS, Faculté de Médecine Timone, Marseille, 13005, France
| | - J Mendoza-Revilla
- Laboratorios de Investigación y Desarrollo, Facultad de Ciencias y Filosofía, Universidad Peruana Cayetano Heredia, Lima 31, Perú.,Unit of Human Evolutionary Genetics, Institut Pasteur, Paris, 75015, France
| | - M Fuentes-Guajardo
- Departamento de Tecnología Médica, Facultad de Ciencias de la Salud, Universidad de Tarapacá, Arica, 1000000, Chile
| | - S Palmal
- UMR 7268 ADES, CNRS, Aix-Marseille Université, EFS, Faculté de Médecine Timone, Marseille, 13005, France
| | - J C Chacón-Duque
- Division of Vertebrates and Anthropology, Department of Earth Sciences, Natural History Museum, London, SW7 5BD, UK
| | - M Hurtado
- Laboratorios de Investigación y Desarrollo, Facultad de Ciencias y Filosofía, Universidad Peruana Cayetano Heredia, Lima 31, Perú
| | - V Villegas
- Laboratorios de Investigación y Desarrollo, Facultad de Ciencias y Filosofía, Universidad Peruana Cayetano Heredia, Lima 31, Perú
| | - V Granja
- Laboratorios de Investigación y Desarrollo, Facultad de Ciencias y Filosofía, Universidad Peruana Cayetano Heredia, Lima 31, Perú
| | - C Jaramillo
- GENMOL (Genética Molecular), Universidad de Antioquia, Medellín, 5001000, Colombia
| | - W Arias
- GENMOL (Genética Molecular), Universidad de Antioquia, Medellín, 5001000, Colombia
| | - R B Lozano
- National Institute of Anthropology and History, Mexico City, MC, 6600, Mexico.,Department of Archaeogenetics, Max Planck Institute for the Science of Human History (MPI-SHH), Jena, 07745, Germany
| | - P Everardo-Martínez
- National Institute of Anthropology and History, Mexico City, MC, 6600, Mexico
| | - J Gómez-Valdés
- National Institute of Anthropology and History, Mexico City, MC, 6600, Mexico
| | - H Villamil-Ramírez
- Unidad de Genomica de Poblaciones Aplicada a la Salud, Facultad de Química, UNAM-Instituto Nacional de Medicina Genómica, Mexico City, MC, 4510, Mexico
| | | | - T Hünemeier
- Departamento de Genética e Biologia Evolutiva, Instituto de Biociências, Universidade de São Paulo, São Paulo, SP, 05508-090, Brazil
| | - V Ramallo
- Departamento de Genética, Universidade Federal do Rio Grande do Sul, Porto Alegre, RS, 90040-060, Brazil.,Instituto Patagónico de Ciencias Sociales y Humanas, Centro Nacional Patagónico, CONICET, Puerto Madryn, U9129ACD, Argentina
| | - R Gonzalez-José
- Instituto Patagónico de Ciencias Sociales y Humanas, Centro Nacional Patagónico, CONICET, Puerto Madryn, U9129ACD, Argentina
| | - L Schüler-Faccini
- Departamento de Genética, Universidade Federal do Rio Grande do Sul, Porto Alegre, RS, 90040-060, Brazil
| | - M-C Bortolini
- Departamento de Genética, Universidade Federal do Rio Grande do Sul, Porto Alegre, RS, 90040-060, Brazil
| | - V Acuña-Alonzo
- National Institute of Anthropology and History, Mexico City, MC, 6600, Mexico
| | - S Canizales-Quinteros
- Unidad de Genomica de Poblaciones Aplicada a la Salud, Facultad de Química, UNAM-Instituto Nacional de Medicina Genómica, Mexico City, MC, 4510, Mexico
| | - C Gallo
- Laboratorios de Investigación y Desarrollo, Facultad de Ciencias y Filosofía, Universidad Peruana Cayetano Heredia, Lima 31, Perú
| | - G Poletti
- Laboratorios de Investigación y Desarrollo, Facultad de Ciencias y Filosofía, Universidad Peruana Cayetano Heredia, Lima 31, Perú
| | - G Bedoya
- GENMOL (Genética Molecular), Universidad de Antioquia, Medellín, 5001000, Colombia
| | - F Rothhammer
- Instituto de Alta Investigación, Universidad de Tarapacá, Arica, 1000000, Chile
| | - D Balding
- Melbourne Integrative Genomics, Schools of BioSciences and Mathematics & Statistics, University of Melbourne, Melbourne, VIC, 3010, Australia
| | - D J Tobin
- The Charles Institute of Dermatology, University College Dublin, Dublin, Ireland
| | - S Wang
- Chinese Academy of Sciences Key Laboratory of Computational Biology, Shanghai Institutes for Biological Sciences, University of the Chinese Academy of Sciences, Shanghai, 200031, China
| | - P Faux
- UMR 7268 ADES, CNRS, Aix-Marseille Université, EFS, Faculté de Médecine Timone, Marseille, 13005, France
| | - A Ruiz-Linares
- Ministry of Education Key Laboratory of Contemporary Anthropology and Collaborative Innovation Center of Genetics and Development, School of Life Sciences and Human Phenome Institute, Fudan University, Yangpu District, Shanghai, China.,UMR 7268 ADES, CNRS, Aix-Marseille Université, EFS, Faculté de Médecine Timone, Marseille, 13005, France.,Department of Genetics, Evolution and Environment, and UCL Genetics Institute, University College London, London, WC1E 6BT, UK
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10
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Palmal S, Adhikari K, Mendoza-Revilla J, Fuentes-Guajardo M, Silva de Cerqueira CC, Bonfante B, Chacón-Duque JC, Sohail A, Hurtado M, Villegas V, Granja V, Jaramillo C, Arias W, Lozano RB, Everardo-Martínez P, Gómez-Valdés J, Villamil-Ramírez H, Hünemeier T, Ramallo V, Parolin ML, Gonzalez-José R, Schüler-Faccini L, Bortolini MC, Acuña-Alonzo V, Canizales-Quinteros S, Gallo C, Poletti G, Bedoya G, Rothhammer F, Balding D, Faux P, Ruiz-Linares A. Prediction of eye, hair and skin colour in Latin Americans. Forensic Sci Int Genet 2021; 53:102517. [PMID: 33865096 DOI: 10.1016/j.fsigen.2021.102517] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2020] [Revised: 03/19/2021] [Accepted: 03/30/2021] [Indexed: 10/21/2022]
Abstract
Here we evaluate the accuracy of prediction for eye, hair and skin pigmentation in a dataset of > 6500 individuals from Mexico, Colombia, Peru, Chile and Brazil (including genome-wide SNP data and quantitative/categorical pigmentation phenotypes - the CANDELA dataset CAN). We evaluated accuracy in relation to different analytical methods and various phenotypic predictors. As expected from statistical principles, we observe that quantitative traits are more sensitive to changes in the prediction models than categorical traits. We find that Random Forest or Linear Regression are generally the best performing methods. We also compare the prediction accuracy of SNP sets defined in the CAN dataset (including 56, 101 and 120 SNPs for eye, hair and skin colour prediction, respectively) to the well-established HIrisPlex-S SNP set (including 6, 22 and 36 SNPs for eye, hair and skin colour prediction respectively). When training prediction models on the CAN data, we observe remarkably similar performances for HIrisPlex-S and the larger CAN SNP sets for the prediction of hair (categorical) and eye (both categorical and quantitative), while the CAN sets outperform HIrisPlex-S for quantitative, but not for categorical skin pigmentation prediction. The performance of HIrisPlex-S, when models are trained in a world-wide sample (although consisting of 80% Europeans, https://hirisplex.erasmusmc.nl), is lower relative to training in the CAN data (particularly for hair and skin colour). Altogether, our observations are consistent with common variation of eye and hair colour having a relatively simple genetic architecture, which is well captured by HIrisPlex-S, even in admixed Latin Americans (with partial European ancestry). By contrast, since skin pigmentation is a more polygenic trait, accuracy is more sensitive to prediction SNP set size, although here this effect was only apparent for a quantitative measure of skin pigmentation. Our results support the use of HIrisPlex-S in the prediction of categorical pigmentation traits for forensic purposes in Latin America, while illustrating the impact of training datasets on its accuracy.
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Affiliation(s)
- Sagnik Palmal
- UMR 7268 ADES, CNRS, Aix-Marseille Université, EFS, Faculté de Médecine Timone, Marseille 13005, France
| | - Kaustubh Adhikari
- School of Mathematics and Statistics, Faculty of Science, Technology, Engineering and Mathematics, The Open University, Milton Keynes MK7 6AA, UK; Department of Genetics, Evolution and Environment, and UCL Genetics Institute, University College London, London WC1E 6BT, UK
| | - Javier Mendoza-Revilla
- Laboratorios de Investigación y Desarrollo, Facultad de Ciencias y Filosofía, Universidad Peruana Cayetano Heredia, Lima 31, Perú; Unit of Human Evolutionary Genetics, Institut Pasteur, Paris 75015, France
| | - Macarena Fuentes-Guajardo
- Departamento de Tecnología Médica, Facultad de Ciencias de la Salud, Universidad de Tarapacá, Arica 1000000, Chile
| | | | - Betty Bonfante
- UMR 7268 ADES, CNRS, Aix-Marseille Université, EFS, Faculté de Médecine Timone, Marseille 13005, France
| | - Juan Camilo Chacón-Duque
- Division of Vertebrates and Anthropology, Department of Earth Sciences, Natural History Museum, London SW7 5BD, UK
| | - Anood Sohail
- Department of Biotechnology, Kinnaird College for Women, 93 - Jail Road, Lahore 54000, Pakistan
| | - Malena Hurtado
- Laboratorios de Investigación y Desarrollo, Facultad de Ciencias y Filosofía, Universidad Peruana Cayetano Heredia, Lima 31, Perú
| | - Valeria Villegas
- Laboratorios de Investigación y Desarrollo, Facultad de Ciencias y Filosofía, Universidad Peruana Cayetano Heredia, Lima 31, Perú
| | - Vanessa Granja
- Laboratorios de Investigación y Desarrollo, Facultad de Ciencias y Filosofía, Universidad Peruana Cayetano Heredia, Lima 31, Perú
| | - Claudia Jaramillo
- Department of Biotechnology, Kinnaird College for Women, 93 - Jail Road, Lahore 54000, Pakistan; GENMOL (Genética Molecular), Universidad de Antioquia, Medellín 5001000, Colombia
| | - William Arias
- GENMOL (Genética Molecular), Universidad de Antioquia, Medellín 5001000, Colombia
| | - Rodrigo Barquera Lozano
- National Institute of Anthropology and History, Mexico City 6600, Mexico; Department of Archaeogenetics, Max Planck Institute for the Science of Human History (MPI-SHH), Jena 07745, Germany
| | | | - Jorge Gómez-Valdés
- National Institute of Anthropology and History, Mexico City 6600, Mexico
| | - Hugo Villamil-Ramírez
- Unidad de Genomica de Poblaciones Aplicada a la Salud, Facultad de Química, UNAM-Instituto Nacional de Medicina Genómica, Mexico City 4510, Mexico
| | - Tábita Hünemeier
- Departamento de Genética e Biologia Evolutiva, Instituto de Biociências, Universidade de São Paulo, São Paulo, SP 05508-090, Brazil
| | - Virginia Ramallo
- Departamento de Genética, Universidade Federal do Rio Grande do Sul, Porto Alegre 90040-060, Brazil; Instituto Patagónico de Ciencias Sociales y Humanas, Centro Nacional Patagónico, CONICET, Puerto Madryn U9129ACD, Argentina
| | - Maria-Laura Parolin
- Instituto de Diversidad y Evolución Austral (IDEAus), Centro Nacional Patagónico, CONICET, Puerto Madryn, Argentina
| | - Rolando Gonzalez-José
- Instituto Patagónico de Ciencias Sociales y Humanas, Centro Nacional Patagónico, CONICET, Puerto Madryn U9129ACD, Argentina
| | - Lavinia Schüler-Faccini
- Departamento de Genética, Universidade Federal do Rio Grande do Sul, Porto Alegre 90040-060, Brazil
| | - Maria-Cátira Bortolini
- Departamento de Genética, Universidade Federal do Rio Grande do Sul, Porto Alegre 90040-060, Brazil
| | | | - Samuel Canizales-Quinteros
- Unidad de Genomica de Poblaciones Aplicada a la Salud, Facultad de Química, UNAM-Instituto Nacional de Medicina Genómica, Mexico City 4510, Mexico
| | - Carla Gallo
- Laboratorios de Investigación y Desarrollo, Facultad de Ciencias y Filosofía, Universidad Peruana Cayetano Heredia, Lima 31, Perú
| | - Giovanni Poletti
- Laboratorios de Investigación y Desarrollo, Facultad de Ciencias y Filosofía, Universidad Peruana Cayetano Heredia, Lima 31, Perú
| | - Gabriel Bedoya
- GENMOL (Genética Molecular), Universidad de Antioquia, Medellín 5001000, Colombia
| | - Francisco Rothhammer
- Instituto de Alta Investigación, Universidad de Tarapacá, Arica 1000000, Chile; Programa de Genetica Humana, ICBM, Facultad de Medicina, Universidad de Chile, Santiago, Arica 1000000, Chile
| | - David Balding
- Department of Genetics, Evolution and Environment, and UCL Genetics Institute, University College London, London WC1E 6BT, UK; Melbourne Integrative Genomics, Schools of BioSciences and Mathematics & Statistics, University of Melbourne, Melbourne, VIC 3010, Australia
| | - Pierre Faux
- UMR 7268 ADES, CNRS, Aix-Marseille Université, EFS, Faculté de Médecine Timone, Marseille 13005, France.
| | - Andrés Ruiz-Linares
- UMR 7268 ADES, CNRS, Aix-Marseille Université, EFS, Faculté de Médecine Timone, Marseille 13005, France; Department of Genetics, Evolution and Environment, and UCL Genetics Institute, University College London, London WC1E 6BT, UK; Ministry of Education Key Laboratory of Contemporary Anthropology and Collaborative Innovation Center of Genetics and Development, School of Life Sciences and Human Phenome Institute, Fudan University, Yangpu District, Shanghai, China.
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11
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Bonfante B, Faux P, Navarro N, Mendoza-Revilla J, Dubied M, Montillot C, Wentworth E, Poloni L, Varón-González C, Jones P, Xiong Z, Fuentes-Guajardo M, Palmal S, Chacón-Duque JC, Hurtado M, Villegas V, Granja V, Jaramillo C, Arias W, Barquera R, Everardo-Martínez P, Sánchez-Quinto M, Gómez-Valdés J, Villamil-Ramírez H, Silva de Cerqueira CC, Hünemeier T, Ramallo V, Liu F, Weinberg SM, Shaffer JR, Stergiakouli E, Howe LJ, Hysi PG, Spector TD, Gonzalez-José R, Schüler-Faccini L, Bortolini MC, Acuña-Alonzo V, Canizales-Quinteros S, Gallo C, Poletti G, Bedoya G, Rothhammer F, Thauvin-Robinet C, Faivre L, Costedoat C, Balding D, Cox T, Kayser M, Duplomb L, Yalcin B, Cotney J, Adhikari K, Ruiz-Linares A. A GWAS in Latin Americans identifies novel face shape loci, implicating VPS13B and a Denisovan introgressed region in facial variation. Sci Adv 2021; 7:eabc6160. [PMID: 33547071 PMCID: PMC7864580 DOI: 10.1126/sciadv.abc6160] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/05/2020] [Accepted: 12/17/2020] [Indexed: 05/25/2023]
Abstract
To characterize the genetic basis of facial features in Latin Americans, we performed a genome-wide association study (GWAS) of more than 6000 individuals using 59 landmark-based measurements from two-dimensional profile photographs and ~9,000,000 genotyped or imputed single-nucleotide polymorphisms. We detected significant association of 32 traits with at least 1 (and up to 6) of 32 different genomic regions, more than doubling the number of robustly associated face morphology loci reported until now (from 11 to 23). These GWAS hits are strongly enriched in regulatory sequences active specifically during craniofacial development. The associated region in 1p12 includes a tract of archaic adaptive introgression, with a Denisovan haplotype common in Native Americans affecting particularly lip thickness. Among the nine previously unidentified face morphology loci we identified is the VPS13B gene region, and we show that variants in this region also affect midfacial morphology in mice.
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Affiliation(s)
- Betty Bonfante
- Aix-Marseille Université, CNRS, EFS, ADES, Marseille 13005, France
| | - Pierre Faux
- Aix-Marseille Université, CNRS, EFS, ADES, Marseille 13005, France
| | - Nicolas Navarro
- Biogéosciences, UMR 6282 CNRS, EPHE, Université Bourgogne Franche-Comté, Dijon 21078, France
- EPHE, PSL University, Paris 75014, France
| | - Javier Mendoza-Revilla
- Laboratorios de Investigación y Desarrollo, Facultad de Ciencias y Filosofía, Universidad Peruana Cayetano Heredia, Lima, 31, Perú
- Unit of Human Evolutionary Genetics, Institut Pasteur, Paris 75015, France
| | - Morgane Dubied
- Biogéosciences, UMR 6282 CNRS, EPHE, Université Bourgogne Franche-Comté, Dijon 21078, France
| | - Charlotte Montillot
- INSERM UMR 1231 Génétique des Anomalies du Développement, Université Bourgogne Franche-Comté, Dijon 21000, France
| | - Emma Wentworth
- Department of Genetics and Genome Sciences, University of Connecticut Health, Farmington, CT 06030, USA
| | - Lauriane Poloni
- Biogéosciences, UMR 6282 CNRS, EPHE, Université Bourgogne Franche-Comté, Dijon 21078, France
- EPHE, PSL University, Paris 75014, France
| | - Ceferino Varón-González
- Institut de Systématique, Évolution, Biodiversité, ISYEB-UMR 7205-CNRS, MNHN, UPMC, EPHE, UA, Muséum National d'Histoire Naturelle, Sorbonne Universités, Paris 75005, France
| | - Philip Jones
- Department of Genetics, Evolution and Environment, and UCL Genetics Institute, University College London, London WC1E 6BT, UK
| | - Ziyi Xiong
- Department of Genetic Identification, Erasmus MC University Medical Center Rotterdam, Rotterdam 3015GD, Netherlands
| | - Macarena Fuentes-Guajardo
- Departamento de Tecnología Médica, Facultad de Ciencias de la Salud, Universidad de Tarapacá, Arica 1000000, Chile
| | - Sagnik Palmal
- Aix-Marseille Université, CNRS, EFS, ADES, Marseille 13005, France
| | - Juan Camilo Chacón-Duque
- Division of Vertebrates and Anthropology, Department of Earth Sciences, Natural History Museum, London SW7 5BD, UK
| | - Malena Hurtado
- Laboratorios de Investigación y Desarrollo, Facultad de Ciencias y Filosofía, Universidad Peruana Cayetano Heredia, Lima, 31, Perú
| | - Valeria Villegas
- Laboratorios de Investigación y Desarrollo, Facultad de Ciencias y Filosofía, Universidad Peruana Cayetano Heredia, Lima, 31, Perú
| | - Vanessa Granja
- Laboratorios de Investigación y Desarrollo, Facultad de Ciencias y Filosofía, Universidad Peruana Cayetano Heredia, Lima, 31, Perú
| | - Claudia Jaramillo
- GENMOL (Genética Molecular), Universidad de Antioquia, Medellín 5001000, Colombia
| | - William Arias
- GENMOL (Genética Molecular), Universidad de Antioquia, Medellín 5001000, Colombia
| | - Rodrigo Barquera
- Molecular Genetics Laboratory, National School of Anthropology and History, Mexico City 14050, Mexico
- Department of Archaeogenetics, Max Planck Institute for the Science of Human History (MPI-SHH), Jena 07745, Germany
| | - Paola Everardo-Martínez
- Molecular Genetics Laboratory, National School of Anthropology and History, Mexico City 14050, Mexico
| | - Mirsha Sánchez-Quinto
- Forensic Science, Faculty of Medicine, UNAM (Universidad Nacional Autónoma de México), Mexico City 06320, Mexico
| | - Jorge Gómez-Valdés
- Molecular Genetics Laboratory, National School of Anthropology and History, Mexico City 14050, Mexico
| | - Hugo Villamil-Ramírez
- Unidad de Genomica de Poblaciones Aplicada a la Salud, Facultad de Química, UNAM-Instituto Nacional de Medicina Genómica, Mexico City 4510, Mexico
| | | | - Tábita Hünemeier
- Departamento de Genética e Biologia Evolutiva, Instituto de Biociências, Universidade de São Paulo, São Paulo, SP 05508-090, Brazil
| | - Virginia Ramallo
- Departamento de Genética, Universidade Federal do Rio Grande do Sul, Porto Alegre 90040-060, Brasil
- Instituto Patagónico de Ciencias Sociales y Humanas, Centro Nacional Patagónico, CONICET, Puerto Madryn U9129ACD, Argentina
| | - Fan Liu
- Department of Genetic Identification, Erasmus MC University Medical Center Rotterdam, Rotterdam 3015GD, Netherlands
- Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100864, China
- University of Chinese Academy of Sciences, Beijing 100864, China
| | - Seth M Weinberg
- Department of Human Genetics, University of Pittsburgh, Pittsburgh, PA 15260, USA
- Center for Craniofacial and Dental Genetics, Department of Oral Biology, University of Pittsburgh, Pittsburgh, PA 15260, USA
- Department of Anthropology, University of Pittsburgh, Pittsburgh, PA 15260, USA
| | - John R Shaffer
- Department of Human Genetics, University of Pittsburgh, Pittsburgh, PA 15260, USA
- Center for Craniofacial and Dental Genetics, Department of Oral Biology, University of Pittsburgh, Pittsburgh, PA 15260, USA
| | - Evie Stergiakouli
- Medical Research Council Integrative Epidemiology Unit, Population Health Sciences, University of Bristol, Bristol BS1 2LY, UK
- School of Oral and Dental Sciences, University of Bristol, Bristol BS1 2LY, UK
| | - Laurence J Howe
- Medical Research Council Integrative Epidemiology Unit, Population Health Sciences, University of Bristol, Bristol BS1 2LY, UK
| | - Pirro G Hysi
- Department of Twin Research and Genetic Epidemiology, King's College London, London WC2R 2LS, UK
| | - Timothy D Spector
- Department of Twin Research and Genetic Epidemiology, King's College London, London WC2R 2LS, UK
| | - Rolando Gonzalez-José
- Instituto Patagónico de Ciencias Sociales y Humanas, Centro Nacional Patagónico, CONICET, Puerto Madryn U9129ACD, Argentina
| | - Lavinia Schüler-Faccini
- Departamento de Genética, Universidade Federal do Rio Grande do Sul, Porto Alegre 90040-060, Brasil
| | - Maria-Cátira Bortolini
- Departamento de Genética, Universidade Federal do Rio Grande do Sul, Porto Alegre 90040-060, Brasil
| | - Victor Acuña-Alonzo
- Molecular Genetics Laboratory, National School of Anthropology and History, Mexico City 14050, Mexico
| | - Samuel Canizales-Quinteros
- Unidad de Genomica de Poblaciones Aplicada a la Salud, Facultad de Química, UNAM-Instituto Nacional de Medicina Genómica, Mexico City 4510, Mexico
| | - Carla Gallo
- Laboratorios de Investigación y Desarrollo, Facultad de Ciencias y Filosofía, Universidad Peruana Cayetano Heredia, Lima, 31, Perú
| | - Giovanni Poletti
- Laboratorios de Investigación y Desarrollo, Facultad de Ciencias y Filosofía, Universidad Peruana Cayetano Heredia, Lima, 31, Perú
| | - Gabriel Bedoya
- GENMOL (Genética Molecular), Universidad de Antioquia, Medellín 5001000, Colombia
| | - Francisco Rothhammer
- Instituto de Alta Investigación, Universidad de Tarapacá, Arica, Arica 1000000, Chile
| | - Christel Thauvin-Robinet
- INSERM UMR 1231 Génétique des Anomalies du Développement, Université Bourgogne Franche-Comté, Dijon 21000, France
- Centre de Référence Maladies Rares "Anomalies du Développement et Syndromes Malformatifs" de l'Est, Centre de Génétique, FHU TRANSLAD, CHU Dijon, Dijon 21000, France
| | - Laurence Faivre
- INSERM UMR 1231 Génétique des Anomalies du Développement, Université Bourgogne Franche-Comté, Dijon 21000, France
- Centre de Référence Maladies Rares "Anomalies du Développement et Syndromes Malformatifs" de l'Est, Centre de Génétique, FHU TRANSLAD, CHU Dijon, Dijon 21000, France
| | | | - David Balding
- Department of Genetics, Evolution and Environment, and UCL Genetics Institute, University College London, London WC1E 6BT, UK
- Melbourne Integrative Genomics, Schools of BioSciences and Mathematics & Statistics, University of Melbourne, Melbourne, VIC 3052, Australia
| | - Timothy Cox
- Department of Oral and Craniofacial Sciences, School of Dentistry and Department of Pediatrics, School of Medicine, University of Missouri, Kansas City, MO 64108, USA
| | - Manfred Kayser
- Department of Genetic Identification, Erasmus MC University Medical Center Rotterdam, Rotterdam 3015GD, Netherlands
| | - Laurence Duplomb
- INSERM UMR 1231 Génétique des Anomalies du Développement, Université Bourgogne Franche-Comté, Dijon 21000, France
| | - Binnaz Yalcin
- INSERM UMR 1231 Génétique des Anomalies du Développement, Université Bourgogne Franche-Comté, Dijon 21000, France
| | - Justin Cotney
- Department of Genetics and Genome Sciences, University of Connecticut Health, Farmington, CT 06030, USA
| | - Kaustubh Adhikari
- School of Mathematics and Statistics, Faculty of Science, Technology, Engineering and Mathematics, The Open University, Milton Keynes MK7 6AA, UK.
- Department of Genetics, Evolution and Environment, and UCL Genetics Institute, University College London, London WC1E 6BT, UK
| | - Andrés Ruiz-Linares
- Aix-Marseille Université, CNRS, EFS, ADES, Marseille 13005, France.
- Department of Genetics, Evolution and Environment, and UCL Genetics Institute, University College London, London WC1E 6BT, UK
- Ministry of Education Key Laboratory of Contemporary Anthropology and Collaborative Innovation Center of Genetics and Development, School of Life Sciences and Human Phenome Institute, Fudan University, Yangpu District, Shanghai, China
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12
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Taylor D, Balding D. How can courts take into account the uncertainty in a likelihood ratio? Forensic Sci Int Genet 2020; 48:102361. [PMID: 32769057 DOI: 10.1016/j.fsigen.2020.102361] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2020] [Revised: 05/17/2020] [Accepted: 07/22/2020] [Indexed: 11/19/2022]
Abstract
As legal practitioners and courts become more aware of scientific methods and evidence evaluation, they are demanding measures of the reliability of expert opinion. In particular, there are calls for error rates to accompany opinion evidence in comparative forensic sciences. While error rates or confidence intervals can be useful for those disciplines that claim to identify the source of a trace, the call for these statistical tools has extended to sciences that present opinions in the form of a likelihood ratio. In this article we argue against presenting both a likelihood ratio and numerical measures of its uncertainty. We explain how the LR already encapsulates uncertainty. Instead we consider how sensitivity analyses can be used to guide the presentation of LRs that are informative to the court and not unfair to defendants.
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Affiliation(s)
- Duncan Taylor
- School of Biological Sciences, Flinders University, GPO Box 2100 Adelaide, SA, 5001, Australia; Forensic Science SA, PO Box 2790, Adelaide, SA, 5000, Australia.
| | - David Balding
- Melbourne Integrative Genomics, School of BioSciences and School of Mathematics & Statistics, University of Melbourne, Australia
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13
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You Y, Balding D. A comparison of software for the evaluation of complex DNA profiles. Forensic Sci Int Genet 2019; 40:114-119. [DOI: 10.1016/j.fsigen.2019.02.014] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2018] [Revised: 01/22/2019] [Accepted: 02/13/2019] [Indexed: 10/27/2022]
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14
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Adhikari K, Mendoza-Revilla J, Sohail A, Fuentes-Guajardo M, Lampert J, Chacón-Duque JC, Hurtado M, Villegas V, Granja V, Acuña-Alonzo V, Jaramillo C, Arias W, Lozano RB, Everardo P, Gómez-Valdés J, Villamil-Ramírez H, Silva de Cerqueira CC, Hunemeier T, Ramallo V, Schuler-Faccini L, Salzano FM, Gonzalez-José R, Bortolini MC, Canizales-Quinteros S, Gallo C, Poletti G, Bedoya G, Rothhammer F, Tobin DJ, Fumagalli M, Balding D, Ruiz-Linares A. A GWAS in Latin Americans highlights the convergent evolution of lighter skin pigmentation in Eurasia. Nat Commun 2019; 10:358. [PMID: 30664655 PMCID: PMC6341102 DOI: 10.1038/s41467-018-08147-0] [Citation(s) in RCA: 94] [Impact Index Per Article: 18.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2017] [Accepted: 12/20/2018] [Indexed: 12/17/2022] Open
Abstract
We report a genome-wide association scan in >6,000 Latin Americans for pigmentation of skin and eyes. We found eighteen signals of association at twelve genomic regions. These include one novel locus for skin pigmentation (in 10q26) and three novel loci for eye pigmentation (in 1q32, 20q13 and 22q12). We demonstrate the presence of multiple independent signals of association in the 11q14 and 15q13 regions (comprising the GRM5/TYR and HERC2/OCA2 genes, respectively) and several epistatic interactions among independently associated alleles. Strongest association with skin pigmentation at 19p13 was observed for an Y182H missense variant (common only in East Asians and Native Americans) in MFSD12, a gene recently associated with skin pigmentation in Africans. We show that the frequency of the derived allele at Y182H is significantly correlated with lower solar radiation intensity in East Asia and infer that MFSD12 was under selection in East Asians, probably after their split from Europeans. Pigmentation variation in humans is influenced by complex genetic architecture in different populations. Here, the authors perform a genome-wide association analysis involving > 6,000 Latin Americans for pigmentation of skin and eyes, and identify known and novel genetic associations.
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Affiliation(s)
- Kaustubh Adhikari
- Department of Genetics, Evolution and Environment, and UCL Genetics Institute, University College London, London, WC1E 6BT, UK
| | - Javier Mendoza-Revilla
- Department of Genetics, Evolution and Environment, and UCL Genetics Institute, University College London, London, WC1E 6BT, UK.,Laboratorios de Investigación y Desarrollo, Facultad de Ciencias y Filosofía, Universidad Peruana Cayetano Heredia, Lima, 31, Peru
| | - Anood Sohail
- Department of Genetics, Cambridge University, Cambridge, CB2 3EH, UK
| | - Macarena Fuentes-Guajardo
- Department of Genetics, Evolution and Environment, and UCL Genetics Institute, University College London, London, WC1E 6BT, UK.,Departamento de Tecnología Médica, Facultad de Ciencias de la Salud, Universidad de Tarapacá, Arica, 1000000, Chile
| | - Jodie Lampert
- Department of Genetics and Genome Biology, University of Leicester, Leicester, LE1 7RH, UK
| | - Juan Camilo Chacón-Duque
- Department of Genetics, Evolution and Environment, and UCL Genetics Institute, University College London, London, WC1E 6BT, UK
| | - Malena Hurtado
- Laboratorios de Investigación y Desarrollo, Facultad de Ciencias y Filosofía, Universidad Peruana Cayetano Heredia, Lima, 31, Peru
| | - Valeria Villegas
- Laboratorios de Investigación y Desarrollo, Facultad de Ciencias y Filosofía, Universidad Peruana Cayetano Heredia, Lima, 31, Peru
| | - Vanessa Granja
- Laboratorios de Investigación y Desarrollo, Facultad de Ciencias y Filosofía, Universidad Peruana Cayetano Heredia, Lima, 31, Peru
| | - Victor Acuña-Alonzo
- Department of Genetics, Evolution and Environment, and UCL Genetics Institute, University College London, London, WC1E 6BT, UK.,National Institute of Anthropology and History, Mexico City, 4510, Mexico
| | - Claudia Jaramillo
- GENMOL (Genética Molecular), Universidad de Antioquia, Medellín, 5001000, Colombia
| | - William Arias
- GENMOL (Genética Molecular), Universidad de Antioquia, Medellín, 5001000, Colombia
| | - Rodrigo Barquera Lozano
- National Institute of Anthropology and History, Mexico City, 4510, Mexico.,Department of Archaeogenetics, Max Planck Institute for the Science of Human History, Jena, 07745, Germany
| | - Paola Everardo
- National Institute of Anthropology and History, Mexico City, 4510, Mexico
| | - Jorge Gómez-Valdés
- National Institute of Anthropology and History, Mexico City, 4510, Mexico
| | - Hugo Villamil-Ramírez
- Unidad de Genomica de Poblaciones Aplicada a la Salud, Facultad de Química, UNAM-Instituto Nacional de Medicina Genómica, Mexico City, 4510, Mexico
| | - Caio C Silva de Cerqueira
- Departamento de Genética, Universidade Federal do Rio Grande do Sul, Porto Alegre, 91501-970, Brazil
| | - Tábita Hunemeier
- Departamento de Genética, Universidade Federal do Rio Grande do Sul, Porto Alegre, 91501-970, Brazil
| | - Virginia Ramallo
- Departamento de Genética, Universidade Federal do Rio Grande do Sul, Porto Alegre, 91501-970, Brazil.,Instituto Patagonico de Ciencias Sociales y Humanas, Centro Nacional Patagonico, CONICET, Puerto Madryn, U9129ACD, Argentina
| | - Lavinia Schuler-Faccini
- Departamento de Genética, Universidade Federal do Rio Grande do Sul, Porto Alegre, 91501-970, Brazil
| | - Francisco M Salzano
- Departamento de Genética, Universidade Federal do Rio Grande do Sul, Porto Alegre, 91501-970, Brazil
| | - Rolando Gonzalez-José
- Instituto Patagonico de Ciencias Sociales y Humanas, Centro Nacional Patagonico, CONICET, Puerto Madryn, U9129ACD, Argentina
| | - Maria-Cátira Bortolini
- Departamento de Genética, Universidade Federal do Rio Grande do Sul, Porto Alegre, 91501-970, Brazil
| | - Samuel Canizales-Quinteros
- Unidad de Genomica de Poblaciones Aplicada a la Salud, Facultad de Química, UNAM-Instituto Nacional de Medicina Genómica, Mexico City, 4510, Mexico
| | - Carla Gallo
- Laboratorios de Investigación y Desarrollo, Facultad de Ciencias y Filosofía, Universidad Peruana Cayetano Heredia, Lima, 31, Peru
| | - Giovanni Poletti
- Laboratorios de Investigación y Desarrollo, Facultad de Ciencias y Filosofía, Universidad Peruana Cayetano Heredia, Lima, 31, Peru
| | - Gabriel Bedoya
- GENMOL (Genética Molecular), Universidad de Antioquia, Medellín, 5001000, Colombia
| | - Francisco Rothhammer
- Instituto de Alta Investigación, Universidad de Tarapaca, Arica, 1000000, Chile.,Programa de Genetica Humana, ICBM, Facultad de Medicina, Universidad de Chile, Santiago, 8320000, Chile
| | - Desmond J Tobin
- Centre for Skin Sciences, Faculty of Life Sciences, University of Bradford, Bradford, BD7 1DP, West Yorkshire, UK.,The Charles Institute of Dermatology, University College Dublin, Dublin, D4, Ireland
| | - Matteo Fumagalli
- Department of Life Sciences, Silwood Park campus, Imperial College London, Ascot, SL5 7PY, UK
| | - David Balding
- Department of Genetics, Evolution and Environment, and UCL Genetics Institute, University College London, London, WC1E 6BT, UK.,Melbourne Integrative Genomics, Schools of BioSciences and Mathematics & Statistics, University of Melbourne, Melbourne, VIC, 3010, Australia
| | - Andrés Ruiz-Linares
- Ministry of Education Key Laboratory of Contemporary Anthropology and Collaborative Innovation Center of Genetics and Development, School of Life Sciences and Human Phenome Institute, Fudan University, Shanghai, 200438, China. .,Aix-Marseille Université, CNRS, EFS, ADES, Marseille, 13005, France.
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15
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Chacón-Duque JC, Adhikari K, Fuentes-Guajardo M, Mendoza-Revilla J, Acuña-Alonzo V, Barquera R, Quinto-Sánchez M, Gómez-Valdés J, Everardo Martínez P, Villamil-Ramírez H, Hünemeier T, Ramallo V, Silva de Cerqueira CC, Hurtado M, Villegas V, Granja V, Villena M, Vásquez R, Llop E, Sandoval JR, Salazar-Granara AA, Parolin ML, Sandoval K, Peñaloza-Espinosa RI, Rangel-Villalobos H, Winkler CA, Klitz W, Bravi C, Molina J, Corach D, Barrantes R, Gomes V, Resende C, Gusmão L, Amorim A, Xue Y, Dugoujon JM, Moral P, González-José R, Schuler-Faccini L, Salzano FM, Bortolini MC, Canizales-Quinteros S, Poletti G, Gallo C, Bedoya G, Rothhammer F, Balding D, Hellenthal G, Ruiz-Linares A. Latin Americans show wide-spread Converso ancestry and imprint of local Native ancestry on physical appearance. Nat Commun 2018; 9:5388. [PMID: 30568240 PMCID: PMC6300600 DOI: 10.1038/s41467-018-07748-z] [Citation(s) in RCA: 90] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2018] [Accepted: 11/19/2018] [Indexed: 12/15/2022] Open
Abstract
Historical records and genetic analyses indicate that Latin Americans trace their ancestry mainly to the intermixing (admixture) of Native Americans, Europeans and Sub-Saharan Africans. Using novel haplotype-based methods, here we infer sub-continental ancestry in over 6,500 Latin Americans and evaluate the impact of regional ancestry variation on physical appearance. We find that Native American ancestry components in Latin Americans correspond geographically to the present-day genetic structure of Native groups, and that sources of non-Native ancestry, and admixture timings, match documented migratory flows. We also detect South/East Mediterranean ancestry across Latin America, probably stemming mostly from the clandestine colonial migration of Christian converts of non-European origin (Conversos). Furthermore, we find that ancestry related to highland (Central Andean) versus lowland (Mapuche) Natives is associated with variation in facial features, particularly nose morphology, and detect significant differences in allele frequencies between these groups at loci previously associated with nose morphology in this sample.
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Affiliation(s)
- Juan-Camilo Chacón-Duque
- Department of Genetics, Evolution and Environment and UCL Genetics Institute, University College London, London, WC1E 6BT, UK
| | - Kaustubh Adhikari
- Department of Genetics, Evolution and Environment and UCL Genetics Institute, University College London, London, WC1E 6BT, UK
| | - Macarena Fuentes-Guajardo
- Department of Genetics, Evolution and Environment and UCL Genetics Institute, University College London, London, WC1E 6BT, UK
- Departamento de Tecnología Médica, Facultad de Ciencias de la Salud, Universidad de Tarapacá, Arica, 1000009, Chile
| | - Javier Mendoza-Revilla
- Department of Genetics, Evolution and Environment and UCL Genetics Institute, University College London, London, WC1E 6BT, UK
- Laboratorios de Investigación y Desarrollo, Facultad de Ciencias y Filosofía, Universidad Peruana Cayetano Heredia, Lima, 31, Peru
| | - Victor Acuña-Alonzo
- Department of Genetics, Evolution and Environment and UCL Genetics Institute, University College London, London, WC1E 6BT, UK
- Molecular Genetics Laboratory, Escuela Nacional de Antropología e Historia, Mexico City, 14030, Mexico
| | - Rodrigo Barquera
- Molecular Genetics Laboratory, Escuela Nacional de Antropología e Historia, Mexico City, 14030, Mexico
- Department of Archaeogenetics, Max Planck Institute for the Science of Human History, Jena, 07745, Germany
| | - Mirsha Quinto-Sánchez
- Ciencia Forense, Facultad de Medicina, Universidad Nacional Autónoma de México, Mexico City, 04510, Mexico
| | - Jorge Gómez-Valdés
- Posgrado en Antropología Física, Escuela Nacional de Antropología e Historia, Mexico City, 14030, Mexico
| | - Paola Everardo Martínez
- Posgrado en Antropología, Universidad Nacional Autónoma de México, Mexico City, 04510, Mexico
| | - Hugo Villamil-Ramírez
- Unidad de Genómica de Poblaciones Aplicada a la Salud, Facultad de Química, Universidad Nacional Autónoma de México e Instituto Nacional de Medicina Genómica, Mexico City, 04510, Mexico
| | - Tábita Hünemeier
- Departamento de Genética e Biología Evolutiva, Instituto de Biociências, Universidade de São Paulo, Sao Paulo, 05508-090, Brazil
| | - Virginia Ramallo
- Instituto Patagónico de Ciencias Sociales y Humanas-Centro Nacional Patagónico, CONICET, Puerto Madryn, U912OACD, Argentina
- Departamento de Genética, Universidade Federal do Rio Grande do Sul, Porto Alegre, 91501-970, Brazil
| | - Caio C Silva de Cerqueira
- Departamento de Genética, Universidade Federal do Rio Grande do Sul, Porto Alegre, 91501-970, Brazil
| | - Malena Hurtado
- Laboratorios de Investigación y Desarrollo, Facultad de Ciencias y Filosofía, Universidad Peruana Cayetano Heredia, Lima, 31, Peru
| | - Valeria Villegas
- Laboratorios de Investigación y Desarrollo, Facultad de Ciencias y Filosofía, Universidad Peruana Cayetano Heredia, Lima, 31, Peru
| | - Vanessa Granja
- Laboratorios de Investigación y Desarrollo, Facultad de Ciencias y Filosofía, Universidad Peruana Cayetano Heredia, Lima, 31, Peru
| | - Mercedes Villena
- Instituto Boliviano de Biología de Altura (IBBA), Universidad Mayor de San Andrés (UMSA), La Paz, 2070, Bolivia
| | - René Vásquez
- Instituto Boliviano de Biología de Altura (IBBA), Universidad Autónoma Tomás Frías, Potosí, 53820, Bolivia
| | - Elena Llop
- Programa de Genetica Humana, ICBM, Facultad de Medicina, Universidad de Chile, Santiago, 1027, Chile
| | - José R Sandoval
- Facultad de Medicina Humana, Universidad de San Martín de Porres, Lima, 12, Peru
| | | | - Maria-Laura Parolin
- Instituto de Diversidad y Evolución Austral (IDEAus), Centro Nacional Patagónico, CONICET, Puerto Madryn, U912OACD, Argentina
| | - Karla Sandoval
- National Laboratory of Genomics and Biodiversity (LANGEBIO), CINVESTAV, Irapuato, 36821, Mexico
| | - Rosenda I Peñaloza-Espinosa
- Department of Biological Systems, Division of Biological and Health Sciences, Universidad Autónoma Metropolitana-Xochimilco, Mexico City, 04960, Mexico
| | - Hector Rangel-Villalobos
- Instituto de Investigación en Genética Molecular, Universidad de Guadalajara, Ocotlán, 1115, Mexico
| | - Cheryl A Winkler
- Basic Research Laboratory, National Cancer Institute, Frederick National Laboratory, Frederick, MD, 21702, USA
| | - William Klitz
- Integrative Biology, University of California, Berkeley, CA, 94720, USA
| | - Claudio Bravi
- Instituto Multidisciplinario de Biología Celular, CONICET, La Plata, B1906APO, Argentina
| | - Julio Molina
- Centro de Investigaciones Biomédicas de Guatemala, Ciudad de Guatemala, 01011, Guatemala
| | - Daniel Corach
- Servicio de Huellas Digitales Genéticas and CONICET, Universidad de Buenos Aires, Buenos Aires, C1113AAD, Argentina
| | - Ramiro Barrantes
- Escuela de Biología, Universidad de Costa Rica, San José, 2060, Costa Rica
| | - Verónica Gomes
- Instituto de Patologia e Imunologia Molecular da Universidade do Porto (IPATIMUP), Porto, 4200-135, Portugal
- Instituto de Investigação e Inovação em Saúde (i3S), Universidade do Porto, Porto, 4200-135, Portugal
| | - Carlos Resende
- Instituto de Patologia e Imunologia Molecular da Universidade do Porto (IPATIMUP), Porto, 4200-135, Portugal
- Instituto de Investigação e Inovação em Saúde (i3S), Universidade do Porto, Porto, 4200-135, Portugal
| | - Leonor Gusmão
- Instituto de Patologia e Imunologia Molecular da Universidade do Porto (IPATIMUP), Porto, 4200-135, Portugal
- Instituto de Investigação e Inovação em Saúde (i3S), Universidade do Porto, Porto, 4200-135, Portugal
- DNA Diagnostic Laboratory (LDD), Universidade do Estado do Rio de Janeiro, Rio de Janeiro, 23968-000, Brazil
| | - Antonio Amorim
- Instituto de Patologia e Imunologia Molecular da Universidade do Porto (IPATIMUP), Porto, 4200-135, Portugal
- Instituto de Investigação e Inovação em Saúde (i3S), Universidade do Porto, Porto, 4200-135, Portugal
- Faculdade de Ciências, Universidade do Porto, Porto, 4169-007, Portugal
| | - Yali Xue
- The Wellcome Trust Sanger Institute, Hinxton, CB10 1SA, UK
| | - Jean-Michel Dugoujon
- Centre National de la Recherche Scientifique, Université Toulouse 3 Paul Sabatier, Toulouse, 31330, France
| | - Pedro Moral
- Departamento de Biología Evolutiva, Ecología y Ciencias Ambientales, Universitat de Barcelona, Barcelona, 08007, Spain
| | - Rolando González-José
- Instituto Patagónico de Ciencias Sociales y Humanas-Centro Nacional Patagónico, CONICET, Puerto Madryn, U912OACD, Argentina
| | - Lavinia Schuler-Faccini
- Departamento de Genética, Universidade Federal do Rio Grande do Sul, Porto Alegre, 91501-970, Brazil
| | - Francisco M Salzano
- Departamento de Genética, Universidade Federal do Rio Grande do Sul, Porto Alegre, 91501-970, Brazil
| | - Maria-Cátira Bortolini
- Departamento de Genética, Universidade Federal do Rio Grande do Sul, Porto Alegre, 91501-970, Brazil
| | - Samuel Canizales-Quinteros
- Unidad de Genómica de Poblaciones Aplicada a la Salud, Facultad de Química, Universidad Nacional Autónoma de México e Instituto Nacional de Medicina Genómica, Mexico City, 04510, Mexico
| | - Giovanni Poletti
- Laboratorios de Investigación y Desarrollo, Facultad de Ciencias y Filosofía, Universidad Peruana Cayetano Heredia, Lima, 31, Peru
| | - Carla Gallo
- Laboratorios de Investigación y Desarrollo, Facultad de Ciencias y Filosofía, Universidad Peruana Cayetano Heredia, Lima, 31, Peru
| | - Gabriel Bedoya
- Genética Molecular (GENMOL), Universidad de Antioquia, Medellín, 5001000, Colombia
| | - Francisco Rothhammer
- Programa de Genetica Humana, ICBM, Facultad de Medicina, Universidad de Chile, Santiago, 1027, Chile
- Instituto de Alta Investigación, Universidad de Tarapacá, Arica, 1000009, Chile
| | - David Balding
- Department of Genetics, Evolution and Environment and UCL Genetics Institute, University College London, London, WC1E 6BT, UK
- Schools of BioSciences and Mathematics & Statistics, University of Melbourne, Melbourne, VIC, 3010, Australia
| | - Garrett Hellenthal
- Department of Genetics, Evolution and Environment and UCL Genetics Institute, University College London, London, WC1E 6BT, UK.
| | - Andrés Ruiz-Linares
- Ministry of Education Key Laboratory of Contemporary Anthropology and Collaborative Innovation Center of Genetics and Development, School of Life Sciences and Human Phenome Institute, Fudan University, Shanghai, 200433, China.
- Aix-Marseille Univ, CNRS, EFS, ADES, Marseille, 13007, France.
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16
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Heine K, Beskos A, Jasra A, Balding D, De Iorio M. Bridging trees for posterior inference on ancestral recombination graphs. Proc Math Phys Eng Sci 2018; 474:20180568. [PMID: 30602937 PMCID: PMC6304023 DOI: 10.1098/rspa.2018.0568] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2018] [Accepted: 11/01/2018] [Indexed: 11/08/2023] Open
Abstract
We present a new Markov chain Monte Carlo algorithm, implemented in the software Arbores, for inferring the history of a sample of DNA sequences. Our principal innovation is a bridging procedure, previously applied only for simple stochastic processes, in which the local computations within a bridge can proceed independently of the rest of the DNA sequence, facilitating large-scale parallelization.
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Affiliation(s)
- K. Heine
- Department of Mathematical Sciences, University of Bath, Claverton Down, Bath BA2 7AY, UK
| | - A. Beskos
- Department of Statistical Science, University College London, Gower Street, London WC1E 6BT, UK
| | - A. Jasra
- Department of Statistics and Applied Probability, National University of Singapore, 6 Science Drive 2, 117546, Singapore
| | - D. Balding
- Centre for Systems Genomics, School of BioSciences, University of Melbourne, Victoria 3010, Australia
- School of Mathematics and Statistics, University of Melbourne, Victoria 3010, Australia
| | - M. De Iorio
- Department of Statistical Science, University College London, Gower Street, London WC1E 6BT, UK
- Yale-NUS College, 16 College Avenue West, 138527, Singapore
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17
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Salvatori R, Radian S, Diekmann Y, Iacovazzo D, David A, Gabrovska P, Grassi G, Bussell AM, Stals K, Weber A, Quinton R, Crowne EC, Corazzini V, Metherell L, Kearney T, Du Plessis D, Sinha AK, Baborie A, Lecoq AL, Chanson P, Ansorge O, Ellard S, Trainer PJ, Balding D, Thomas MG, Korbonits M. In-frame seven amino-acid duplication in AIP arose over the last 3000 years, disrupts protein interaction and stability and is associated with gigantism. Eur J Endocrinol 2017; 177. [PMID: 28634279 PMCID: PMC5510572 DOI: 10.1530/eje-17-0293] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Abstract
OBJECTIVE Mutations in the aryl hydrocarbon receptor-interacting protein (AIP) gene are associated with pituitary adenoma, acromegaly and gigantism. Identical alleles in unrelated pedigrees could be inherited from a common ancestor or result from recurrent mutation events. DESIGN AND METHODS Observational, inferential and experimental study, including: AIP mutation testing; reconstruction of 14 AIP-region (8.3 Mbp) haplotypes; coalescent-based approximate Bayesian estimation of the time to most recent common ancestor (tMRCA) of the derived allele; forward population simulations to estimate current number of allele carriers; proposal of mutation mechanism; protein structure predictions; co-immunoprecipitation and cycloheximide chase experiments. RESULTS Nine European-origin, unrelated c.805_825dup-positive pedigrees (four familial, five sporadic from the UK, USA and France) included 16 affected (nine gigantism/four acromegaly/two non-functioning pituitary adenoma patients and one prospectively diagnosed acromegaly patient) and nine unaffected carriers. All pedigrees shared a 2.79 Mbp haploblock around AIP with additional haploblocks privately shared between subsets of the pedigrees, indicating the existence of an evolutionarily recent common ancestor, the 'English founder', with an estimated median tMRCA of 47 generations (corresponding to 1175 years) with a confidence interval (9-113 generations, equivalent to 225-2825 years). The mutation occurred in a small tandem repeat region predisposed to slipped strand mispairing. The resulting seven amino-acid duplication disrupts interaction with HSP90 and leads to a marked reduction in protein stability. CONCLUSIONS The c.805_825dup allele, originating from a common ancestor, associates with a severe clinical phenotype and a high frequency of gigantism. The mutation is likely to be the result of slipped strand mispairing and affects protein-protein interactions and AIP protein stability.
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Affiliation(s)
| | - Serban Radian
- William Harvey Research InstituteBarts and the London School of Medicine, Queen Mary University of London, London, UK
- Department of EndocrinologyC.I. Parhon National Institute of Endocrinology, ‘C. Davila’ University of Medicine and Pharmacy, Bucharest, Romania
| | - Yoan Diekmann
- Research Department of GeneticsEvolution and Environment, University College London, London, UK
| | - Donato Iacovazzo
- William Harvey Research InstituteBarts and the London School of Medicine, Queen Mary University of London, London, UK
| | - Alessia David
- Centre of Bioinformatics and System BiologyDepartment of Life Sciences, Imperial College London, London, UK
| | - Plamena Gabrovska
- William Harvey Research InstituteBarts and the London School of Medicine, Queen Mary University of London, London, UK
| | - Giorgia Grassi
- William Harvey Research InstituteBarts and the London School of Medicine, Queen Mary University of London, London, UK
| | - Anna-Marie Bussell
- Department of Molecular GeneticsRoyal Devon and Exeter Foundation Trust, Exeter, UK
| | - Karen Stals
- Department of Molecular GeneticsRoyal Devon and Exeter Foundation Trust, Exeter, UK
| | - Astrid Weber
- Department of Clinical GeneticsLiverpool Women’s Hospital, Liverpool, UK
| | - Richard Quinton
- Department of EndocrinologyNewcastle-upon-Tyne Hospitals & Institute of Genetic Medicine, Newcastle University, Newcastle, UK
| | - Elizabeth C Crowne
- Bristol Royal Hospital for ChildrenUniversity Hospitals Bristol Foundation Trust, Bristol, UK
| | | | - Lou Metherell
- William Harvey Research InstituteBarts and the London School of Medicine, Queen Mary University of London, London, UK
| | - Tara Kearney
- Endocrinology and Neuropathology UnitSalford Royal Hospital, Manchester, UK
| | - Daniel Du Plessis
- Endocrinology and Neuropathology UnitSalford Royal Hospital, Manchester, UK
| | | | - Atik Baborie
- The Walton Centre for Neurology and NeurosurgeryLiverpool, UK
| | - Anne-Lise Lecoq
- Assistance Publique-Hôpitaux de ParisHôpital de Bicêtre, Service d’Endocrinologie et des Maladies de la Reproduction and Centre de Référence des Maladies Endocriniennes Rares de la Croissance, Le Kremlin-Bicêtre, France
- Inserm 1185Fac Med Paris Sud, Univ Paris-Sud, Université Paris-Saclay, Le Kremlin-Bicêtre, France
| | - Philippe Chanson
- Assistance Publique-Hôpitaux de ParisHôpital de Bicêtre, Service d’Endocrinologie et des Maladies de la Reproduction and Centre de Référence des Maladies Endocriniennes Rares de la Croissance, Le Kremlin-Bicêtre, France
- Inserm 1185Fac Med Paris Sud, Univ Paris-Sud, Université Paris-Saclay, Le Kremlin-Bicêtre, France
| | | | - Sian Ellard
- Department of Molecular GeneticsRoyal Devon and Exeter Foundation Trust, Exeter, UK
- Institute of Biomedical and Clinical ScienceUniversity of Exeter Medical School, Exeter, UK
| | - Peter J Trainer
- Department of EndocrinologyChristie Hospital, Manchester, UK
| | - David Balding
- Research Department of GeneticsEvolution and Environment, University College London, London, UK
- Centre for Systems GenomicsSchools of Biosciences and of Mathematics & Statistics, University of Melbourne, Melbourne, Australia
| | - Mark G Thomas
- Research Department of GeneticsEvolution and Environment, University College London, London, UK
| | - Márta Korbonits
- William Harvey Research InstituteBarts and the London School of Medicine, Queen Mary University of London, London, UK
- Correspondence should be addressed to M Korbonits;
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18
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Abstract
One-dimensional, periodic and annihilating systems of Brownian motions and random walks are defined and interpreted in terms of sizeless particles which vanish on contact. The generating function and moments of the number pairs of particles which have vanished, given an arbitrary initial arrangement, are derived in terms of known two-particle survival probabilities. Three important special cases are considered: Brownian motion with the particles initially (i) uniformly distributed and (ii) equally spaced on a circle and (iii) random walk on a lattice with initially each site occupied. Results are also given for the infinite annihilating particle systems obtained in the limit as the number of particles and the size of the circle or lattice increase. Application of the results to the theory of diffusion-limited reactions is discussed.
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19
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Adhikari K, Fuentes-Guajardo M, Quinto-Sánchez M, Mendoza-Revilla J, Camilo Chacón-Duque J, Acuña-Alonzo V, Jaramillo C, Arias W, Lozano RB, Pérez GM, Gómez-Valdés J, Villamil-Ramírez H, Hunemeier T, Ramallo V, Silva de Cerqueira CC, Hurtado M, Villegas V, Granja V, Gallo C, Poletti G, Schuler-Faccini L, Salzano FM, Bortolini MC, Canizales-Quinteros S, Cheeseman M, Rosique J, Bedoya G, Rothhammer F, Headon D, González-José R, Balding D, Ruiz-Linares A. A genome-wide association scan implicates DCHS2, RUNX2, GLI3, PAX1 and EDAR in human facial variation. Nat Commun 2016; 7:11616. [PMID: 27193062 PMCID: PMC4874031 DOI: 10.1038/ncomms11616] [Citation(s) in RCA: 116] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2015] [Accepted: 04/14/2016] [Indexed: 12/28/2022] Open
Abstract
We report a genome-wide association scan for facial features in ∼6,000 Latin Americans. We evaluated 14 traits on an ordinal scale and found significant association (P values<5 × 10−8) at single-nucleotide polymorphisms (SNPs) in four genomic regions for three nose-related traits: columella inclination (4q31), nose bridge breadth (6p21) and nose wing breadth (7p13 and 20p11). In a subsample of ∼3,000 individuals we obtained quantitative traits related to 9 of the ordinal phenotypes and, also, a measure of nasion position. Quantitative analyses confirmed the ordinal-based associations, identified SNPs in 2q12 associated to chin protrusion, and replicated the reported association of nasion position with SNPs in PAX3. Strongest association in 2q12, 4q31, 6p21 and 7p13 was observed for SNPs in the EDAR, DCHS2, RUNX2 and GLI3 genes, respectively. Associated SNPs in 20p11 extend to PAX1. Consistent with the effect of EDAR on chin protrusion, we documented alterations of mandible length in mice with modified Edar funtion. Humans show great diversity in facial appearance and this variation is highly heritable. Here, Andres Ruiz-Linares and colleagues examined facial features in admixed Latin Americans and identify genome-wide associations for 14 facial traits, including four gene loci (RUNX2, GLI3, DCHS2 and PAX1) influencing nose morphology.
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Affiliation(s)
- Kaustubh Adhikari
- Department of Genetics, Evolution and Environment, UCL Genetics Institute, University College London, London WC1E 6BT, UK
| | - Macarena Fuentes-Guajardo
- Department of Genetics, Evolution and Environment, UCL Genetics Institute, University College London, London WC1E 6BT, UK.,Departamento de Tecnología Médica, Facultad de Ciencias de la Salud, Universidad de Tarapacá, Arica 1000009, Chile
| | - Mirsha Quinto-Sánchez
- Centro Nacional Patagónico, CONICET, Unidad de Diversidad, Sistematica y Evolucion, Puerto Madryn U912OACD, Argentina
| | - Javier Mendoza-Revilla
- Department of Genetics, Evolution and Environment, UCL Genetics Institute, University College London, London WC1E 6BT, UK.,Laboratorios de Investigación y Desarrollo, Facultad de Ciencias y Filosofía, Universidad Peruana Cayetano Heredia, Lima 31, Perú
| | - Juan Camilo Chacón-Duque
- Department of Genetics, Evolution and Environment, UCL Genetics Institute, University College London, London WC1E 6BT, UK
| | - Victor Acuña-Alonzo
- Department of Genetics, Evolution and Environment, UCL Genetics Institute, University College London, London WC1E 6BT, UK.,Laboratorio de Genética Molecular, Escuela Nacional de Antropologia e Historia, México City 14030, México
| | - Claudia Jaramillo
- GENMOL (Genética Molecular), Universidad de Antioquia, Medellín 5001000, Colombia
| | - William Arias
- GENMOL (Genética Molecular), Universidad de Antioquia, Medellín 5001000, Colombia
| | - Rodrigo Barquera Lozano
- Laboratorio de Genética Molecular, Escuela Nacional de Antropologia e Historia, México City 14030, México.,Unidad de Genómica de Poblaciones Aplicada a la Salud, Facultad de Química, UNAM-Instituto Nacional de Medicina Genómica, México City 4510, México
| | - Gastón Macín Pérez
- Laboratorio de Genética Molecular, Escuela Nacional de Antropologia e Historia, México City 14030, México.,Unidad de Genómica de Poblaciones Aplicada a la Salud, Facultad de Química, UNAM-Instituto Nacional de Medicina Genómica, México City 4510, México
| | - Jorge Gómez-Valdés
- Departamento de Anatomía, Facultad de Medicina, Universidad Nacional Autónoma de México (UNAM), México City 04510, México
| | - Hugo Villamil-Ramírez
- Unidad de Genómica de Poblaciones Aplicada a la Salud, Facultad de Química, UNAM-Instituto Nacional de Medicina Genómica, México City 4510, México
| | - Tábita Hunemeier
- Departamento de Genética, Universidade Federal do Rio Grande do Sul, Porto Alegre 91501-970, Brasil
| | - Virginia Ramallo
- Centro Nacional Patagónico, CONICET, Unidad de Diversidad, Sistematica y Evolucion, Puerto Madryn U912OACD, Argentina.,Departamento de Genética, Universidade Federal do Rio Grande do Sul, Porto Alegre 91501-970, Brasil
| | - Caio C Silva de Cerqueira
- Centro Nacional Patagónico, CONICET, Unidad de Diversidad, Sistematica y Evolucion, Puerto Madryn U912OACD, Argentina.,Departamento de Genética, Universidade Federal do Rio Grande do Sul, Porto Alegre 91501-970, Brasil
| | - Malena Hurtado
- Laboratorios de Investigación y Desarrollo, Facultad de Ciencias y Filosofía, Universidad Peruana Cayetano Heredia, Lima 31, Perú
| | - Valeria Villegas
- Laboratorios de Investigación y Desarrollo, Facultad de Ciencias y Filosofía, Universidad Peruana Cayetano Heredia, Lima 31, Perú
| | - Vanessa Granja
- Laboratorios de Investigación y Desarrollo, Facultad de Ciencias y Filosofía, Universidad Peruana Cayetano Heredia, Lima 31, Perú
| | - Carla Gallo
- Laboratorios de Investigación y Desarrollo, Facultad de Ciencias y Filosofía, Universidad Peruana Cayetano Heredia, Lima 31, Perú
| | - Giovanni Poletti
- Laboratorios de Investigación y Desarrollo, Facultad de Ciencias y Filosofía, Universidad Peruana Cayetano Heredia, Lima 31, Perú
| | - Lavinia Schuler-Faccini
- Departamento de Genética, Universidade Federal do Rio Grande do Sul, Porto Alegre 91501-970, Brasil
| | - Francisco M Salzano
- Departamento de Genética, Universidade Federal do Rio Grande do Sul, Porto Alegre 91501-970, Brasil
| | - Maria-Cátira Bortolini
- Departamento de Genética, Universidade Federal do Rio Grande do Sul, Porto Alegre 91501-970, Brasil
| | - Samuel Canizales-Quinteros
- Unidad de Genómica de Poblaciones Aplicada a la Salud, Facultad de Química, UNAM-Instituto Nacional de Medicina Genómica, México City 4510, México
| | - Michael Cheeseman
- Division of Developmental Biology, The Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, Midlothian EH25 9RG, UK
| | - Javier Rosique
- Departamento de Antropología, Universidad de Antioquia, Medellín 5001000, Colombia
| | - Gabriel Bedoya
- GENMOL (Genética Molecular), Universidad de Antioquia, Medellín 5001000, Colombia
| | | | - Denis Headon
- Division of Developmental Biology, The Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, Midlothian EH25 9RG, UK
| | - Rolando González-José
- Centro Nacional Patagónico, CONICET, Unidad de Diversidad, Sistematica y Evolucion, Puerto Madryn U912OACD, Argentina
| | - David Balding
- Department of Genetics, Evolution and Environment, UCL Genetics Institute, University College London, London WC1E 6BT, UK.,Schools of BioSciences and Mathematics and Statistics, University of Melbourne, Melbourne, Victoria 3010, Australia
| | - Andrés Ruiz-Linares
- Department of Genetics, Evolution and Environment, UCL Genetics Institute, University College London, London WC1E 6BT, UK
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20
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Adhikari K, Fontanil T, Cal S, Mendoza-Revilla J, Fuentes-Guajardo M, Chacón-Duque JC, Al-Saadi F, Johansson JA, Quinto-Sanchez M, Acuña-Alonzo V, Jaramillo C, Arias W, Barquera Lozano R, Macín Pérez G, Gómez-Valdés J, Villamil-Ramírez H, Hunemeier T, Ramallo V, Silva de Cerqueira CC, Hurtado M, Villegas V, Granja V, Gallo C, Poletti G, Schuler-Faccini L, Salzano FM, Bortolini MC, Canizales-Quinteros S, Rothhammer F, Bedoya G, Gonzalez-José R, Headon D, López-Otín C, Tobin DJ, Balding D, Ruiz-Linares A. A genome-wide association scan in admixed Latin Americans identifies loci influencing facial and scalp hair features. Nat Commun 2016; 7:10815. [PMID: 26926045 PMCID: PMC4773514 DOI: 10.1038/ncomms10815] [Citation(s) in RCA: 118] [Impact Index Per Article: 14.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2015] [Accepted: 01/25/2016] [Indexed: 12/20/2022] Open
Abstract
We report a genome-wide association scan in over 6,000 Latin Americans for features of scalp hair (shape, colour, greying, balding) and facial hair (beard thickness, monobrow, eyebrow thickness). We found 18 signals of association reaching genome-wide significance (P values 5 × 10(-8) to 3 × 10(-119)), including 10 novel associations. These include novel loci for scalp hair shape and balding, and the first reported loci for hair greying, monobrow, eyebrow and beard thickness. A newly identified locus influencing hair shape includes a Q30R substitution in the Protease Serine S1 family member 53 (PRSS53). We demonstrate that this enzyme is highly expressed in the hair follicle, especially the inner root sheath, and that the Q30R substitution affects enzyme processing and secretion. The genome regions associated with hair features are enriched for signals of selection, consistent with proposals regarding the evolution of human hair.
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Affiliation(s)
- Kaustubh Adhikari
- Department of Genetics, Evolution and Environment, and UCL Genetics Institute, University College London, London WC1E 6BT, UK
| | - Tania Fontanil
- Departamento de Bioquímica y Biología Molecular, IUOPA, Universidad de Oviedo, Oviedo 33006, Spain
| | - Santiago Cal
- Departamento de Bioquímica y Biología Molecular, IUOPA, Universidad de Oviedo, Oviedo 33006, Spain
| | - Javier Mendoza-Revilla
- Department of Genetics, Evolution and Environment, and UCL Genetics Institute, University College London, London WC1E 6BT, UK
- Laboratorios de Investigación y Desarrollo, Facultad de Ciencias y Filosofía, Universidad Peruana Cayetano Heredia, Lima, 31, Perú
| | - Macarena Fuentes-Guajardo
- Department of Genetics, Evolution and Environment, and UCL Genetics Institute, University College London, London WC1E 6BT, UK
- Departamento de Tecnología Médica, Facultad de Ciencias de la Salud, Universidad de Tarapacá, Arica 1000009, Chile
| | - Juan-Camilo Chacón-Duque
- Department of Genetics, Evolution and Environment, and UCL Genetics Institute, University College London, London WC1E 6BT, UK
| | - Farah Al-Saadi
- Department of Genetics, Evolution and Environment, and UCL Genetics Institute, University College London, London WC1E 6BT, UK
| | - Jeanette A. Johansson
- Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, Midlothian EH25 9RG, UK
| | | | - Victor Acuña-Alonzo
- Department of Genetics, Evolution and Environment, and UCL Genetics Institute, University College London, London WC1E 6BT, UK
- National Institute of Anthropology and History, México 4510, México
| | - Claudia Jaramillo
- GENMOL (Genética Molecular), Universidad de Antioquia, Medellín 5001000, Colombia
| | - William Arias
- GENMOL (Genética Molecular), Universidad de Antioquia, Medellín 5001000, Colombia
| | - Rodrigo Barquera Lozano
- National Institute of Anthropology and History, México 4510, México
- Unidad de Genómica de Poblaciones Aplicada a la Salud, Facultad de Química, UNAM-Instituto Nacional de Medicina Genómica, México 4510, México
| | - Gastón Macín Pérez
- National Institute of Anthropology and History, México 4510, México
- Unidad de Genómica de Poblaciones Aplicada a la Salud, Facultad de Química, UNAM-Instituto Nacional de Medicina Genómica, México 4510, México
| | | | - Hugo Villamil-Ramírez
- Unidad de Genómica de Poblaciones Aplicada a la Salud, Facultad de Química, UNAM-Instituto Nacional de Medicina Genómica, México 4510, México
| | - Tábita Hunemeier
- Departamento de Genética, Universidade Federal do Rio Grande do Sul, Porto Alegre 91501-970, Brasil
| | - Virginia Ramallo
- Centro Nacional Patagónico, CONICET, Puerto Madryn U9129ACD, Argentina
- Departamento de Genética, Universidade Federal do Rio Grande do Sul, Porto Alegre 91501-970, Brasil
| | - Caio C. Silva de Cerqueira
- Centro Nacional Patagónico, CONICET, Puerto Madryn U9129ACD, Argentina
- Departamento de Genética, Universidade Federal do Rio Grande do Sul, Porto Alegre 91501-970, Brasil
| | - Malena Hurtado
- Laboratorios de Investigación y Desarrollo, Facultad de Ciencias y Filosofía, Universidad Peruana Cayetano Heredia, Lima, 31, Perú
| | - Valeria Villegas
- Laboratorios de Investigación y Desarrollo, Facultad de Ciencias y Filosofía, Universidad Peruana Cayetano Heredia, Lima, 31, Perú
| | - Vanessa Granja
- Laboratorios de Investigación y Desarrollo, Facultad de Ciencias y Filosofía, Universidad Peruana Cayetano Heredia, Lima, 31, Perú
| | - Carla Gallo
- Laboratorios de Investigación y Desarrollo, Facultad de Ciencias y Filosofía, Universidad Peruana Cayetano Heredia, Lima, 31, Perú
| | - Giovanni Poletti
- Laboratorios de Investigación y Desarrollo, Facultad de Ciencias y Filosofía, Universidad Peruana Cayetano Heredia, Lima, 31, Perú
| | - Lavinia Schuler-Faccini
- Departamento de Genética, Universidade Federal do Rio Grande do Sul, Porto Alegre 91501-970, Brasil
| | - Francisco M. Salzano
- Departamento de Genética, Universidade Federal do Rio Grande do Sul, Porto Alegre 91501-970, Brasil
| | - Maria-Cátira Bortolini
- Departamento de Genética, Universidade Federal do Rio Grande do Sul, Porto Alegre 91501-970, Brasil
| | - Samuel Canizales-Quinteros
- Unidad de Genómica de Poblaciones Aplicada a la Salud, Facultad de Química, UNAM-Instituto Nacional de Medicina Genómica, México 4510, México
| | | | - Gabriel Bedoya
- GENMOL (Genética Molecular), Universidad de Antioquia, Medellín 5001000, Colombia
| | | | - Denis Headon
- Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, Midlothian EH25 9RG, UK
| | - Carlos López-Otín
- Departamento de Bioquímica y Biología Molecular, IUOPA, Universidad de Oviedo, Oviedo 33006, Spain
| | - Desmond J. Tobin
- Centre for Skin Sciences, Faculty of Life Sciences, University of Bradford, Bradford BD7 1DP, Victoria, UK
| | - David Balding
- Department of Genetics, Evolution and Environment, and UCL Genetics Institute, University College London, London WC1E 6BT, UK
- Schools of BioSciences and Mathematics and Statistics, University of Melbourne, Melbourne 3010, Australia
| | - Andrés Ruiz-Linares
- Department of Genetics, Evolution and Environment, and UCL Genetics Institute, University College London, London WC1E 6BT, UK
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21
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van Dorp L, Balding D, Myers S, Pagani L, Tyler-Smith C, Bekele E, Tarekegn A, Thomas MG, Bradman N, Hellenthal G. Evidence for a Common Origin of Blacksmiths and Cultivators in the Ethiopian Ari within the Last 4500 Years: Lessons for Clustering-Based Inference. PLoS Genet 2015; 11:e1005397. [PMID: 26291793 PMCID: PMC4546361 DOI: 10.1371/journal.pgen.1005397] [Citation(s) in RCA: 49] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2014] [Accepted: 06/26/2015] [Indexed: 01/02/2023] Open
Abstract
The Ari peoples of Ethiopia are comprised of different occupational groups that can be distinguished genetically, with Ari Cultivators and the socially marginalised Ari Blacksmiths recently shown to have a similar level of genetic differentiation between them (FST ≈ 0.023 − 0.04) as that observed among multiple ethnic groups sampled throughout Ethiopia. Anthropologists have proposed two competing theories to explain the origins of the Ari Blacksmiths as (i) remnants of a population that inhabited Ethiopia prior to the arrival of agriculturists (e.g. Cultivators), or (ii) relatively recently related to the Cultivators but presently marginalized in the community due to their trade. Two recent studies by different groups analysed genome-wide DNA from samples of Ari Blacksmiths and Cultivators and suggested that genetic patterns between the two groups were more consistent with model (i) and subsequent assimilation of the indigenous peoples into the expanding agriculturalist community. We analysed the same samples using approaches designed to attenuate signals of genetic differentiation that are attributable to allelic drift within a population. By doing so, we provide evidence that the genetic differences between Ari Blacksmiths and Cultivators can be entirely explained by bottleneck effects consistent with hypothesis (ii). This finding serves as both a cautionary tale about interpreting results from unsupervised clustering algorithms, and suggests that social constructions are contributing directly to genetic differentiation over a relatively short time period among previously genetically similar groups. While it is widely recognized that DNA patterns vary across world-wide human populations, the primary features that drive these differences are less well understood. As an example, the Ari peoples of Ethiopia are presently socially divided according to occupation, with Ari Blacksmiths marginalised relative to Ari Cultivators. Two competing theories proposed by anthropologists to explain the existence of these occupational groupings suggest very different histories: (i) the Cultivators reflect migrants who moved into the region occupied by ancestors of the Blacksmiths perhaps many thousands of years ago, versus (ii) the Blacksmiths and Cultivators comprised the same ancestral group before the former was marginalised due solely to their trade. Recent genetic studies showed that Blacksmiths and Cultivators are distinguishable by their DNA, and suggested that overall DNA patterns among the two groups were consistent with (i). However, we demonstrate here that interpreting the results of currently popular algorithms that compare DNA is not always straight-forward. Instead we use a variety of analyses to show that (ii) seems a more likely explanation, perhaps illustrating how social marginalisation can lead to groups becoming genetically distinguishable over a relatively short time period.
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Affiliation(s)
- Lucy van Dorp
- University College London Genetics Institute (UGI), University College London, London, United Kingdom
- Centre for Mathematics and Physics in the Life Sciences and EXperimental Biology (CoMPLEX), University College London, London, United Kingdom
| | - David Balding
- University College London Genetics Institute (UGI), University College London, London, United Kingdom
- Schools of BioSciences and of Mathematics & Statistics, University of Melbourne, Melbourne, Australia
| | - Simon Myers
- Department of Statistics, University of Oxford, Oxford, United Kingdom
| | - Luca Pagani
- The Wellcome Trust Sanger Institute, Hinxton, United Kingdom
- Department of Archaeology and Anthropology, University of Cambridge, Cambridge, United Kingdom
| | | | | | | | - Mark G. Thomas
- Research Department of Genetics, Evolution and Environment, University College London, London, United Kingdom
| | | | - Garrett Hellenthal
- University College London Genetics Institute (UGI), University College London, London, United Kingdom
- * E-mail:
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22
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Adhikari K, Reales G, Smith AJP, Konka E, Palmen J, Quinto-Sanchez M, Acuña-Alonzo V, Jaramillo C, Arias W, Fuentes M, Pizarro M, Barquera Lozano R, Macín Pérez G, Gómez-Valdés J, Villamil-Ramírez H, Hunemeier T, Ramallo V, Silva de Cerqueira CC, Hurtado M, Villegas V, Granja V, Gallo C, Poletti G, Schuler-Faccini L, Salzano FM, Bortolini MC, Canizales-Quinteros S, Rothhammer F, Bedoya G, Calderón R, Rosique J, Cheeseman M, Bhutta MF, Humphries SE, Gonzalez-José R, Headon D, Balding D, Ruiz-Linares A. A genome-wide association study identifies multiple loci for variation in human ear morphology. Nat Commun 2015; 6:7500. [PMID: 26105758 PMCID: PMC4491814 DOI: 10.1038/ncomms8500] [Citation(s) in RCA: 56] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2014] [Accepted: 05/14/2015] [Indexed: 11/09/2022] Open
Abstract
Here we report a genome-wide association study for non-pathological pinna morphology in over 5,000 Latin Americans. We find genome-wide significant association at seven genomic regions affecting: lobe size and attachment, folding of antihelix, helix rolling, ear protrusion and antitragus size (linear regression P values 2 × 10(-8) to 3 × 10(-14)). Four traits are associated with a functional variant in the Ectodysplasin A receptor (EDAR) gene, a key regulator of embryonic skin appendage development. We confirm expression of Edar in the developing mouse ear and that Edar-deficient mice have an abnormally shaped pinna. Two traits are associated with SNPs in a region overlapping the T-Box Protein 15 (TBX15) gene, a major determinant of mouse skeletal development. Strongest association in this region is observed for SNP rs17023457 located in an evolutionarily conserved binding site for the transcription factor Cartilage paired-class homeoprotein 1 (CART1), and we confirm that rs17023457 alters in vitro binding of CART1.
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Affiliation(s)
- Kaustubh Adhikari
- Department of Genetics, Evolution and Environment, UCL Genetics Institute, University College London, London WC1E 6BT, UK
| | - Guillermo Reales
- Department of Genetics, Evolution and Environment, UCL Genetics Institute, University College London, London WC1E 6BT, UK
| | - Andrew J P Smith
- Centre for Cardiovascular Genetics, BHF Laboratories, Institute Cardiovascular Sciences, University College London, Rayne Building, London WC1E 6JF, UK
| | - Esra Konka
- Department of Genetics, Evolution and Environment, UCL Genetics Institute, University College London, London WC1E 6BT, UK
| | - Jutta Palmen
- Centre for Cardiovascular Genetics, BHF Laboratories, Institute Cardiovascular Sciences, University College London, Rayne Building, London WC1E 6JF, UK
| | | | - Victor Acuña-Alonzo
- 1] Department of Genetics, Evolution and Environment, UCL Genetics Institute, University College London, London WC1E 6BT, UK [2] National Institute of Anthropology and History, Mexico City 4510, Mexico
| | - Claudia Jaramillo
- GENMOL (Genética Molecular), Universidad de Antioquia, Medellín 5001000, Colombia
| | - William Arias
- GENMOL (Genética Molecular), Universidad de Antioquia, Medellín 5001000, Colombia
| | - Macarena Fuentes
- Instituto de Alta Investigación, Universidad de Tarapacá, Programa de Genética Humana ICBM Facultad de Medicina Universidad de Chile and Centro de Investigaciones del Hombre en el Desierto, Arica 1000000, Chile
| | - María Pizarro
- Instituto de Alta Investigación, Universidad de Tarapacá, Programa de Genética Humana ICBM Facultad de Medicina Universidad de Chile and Centro de Investigaciones del Hombre en el Desierto, Arica 1000000, Chile
| | - Rodrigo Barquera Lozano
- 1] National Institute of Anthropology and History, Mexico City 4510, Mexico [2] Unidad de Genómica de Poblaciones Aplicada a la Salud, Facultad de Química, UNAM-Instituto Nacional de Medicina Genómica, Mexico City 4510, Mexico
| | - Gastón Macín Pérez
- 1] National Institute of Anthropology and History, Mexico City 4510, Mexico [2] Unidad de Genómica de Poblaciones Aplicada a la Salud, Facultad de Química, UNAM-Instituto Nacional de Medicina Genómica, Mexico City 4510, Mexico
| | | | - Hugo Villamil-Ramírez
- Unidad de Genómica de Poblaciones Aplicada a la Salud, Facultad de Química, UNAM-Instituto Nacional de Medicina Genómica, Mexico City 4510, Mexico
| | - Tábita Hunemeier
- Departamento de Genética, Universidade Federal do Rio Grande do Sul, Porto Alegre 91501-970, Brazil
| | - Virginia Ramallo
- 1] Centro Nacional Patagónico, CONICET, Puerto Madryn U9129ACD, Argentina [2] Departamento de Genética, Universidade Federal do Rio Grande do Sul, Porto Alegre 91501-970, Brazil
| | | | - Malena Hurtado
- Laboratorios de Investigación y Desarrollo, Facultad de Ciencias y Filosofía, Universidad Peruana Cayetano Heredia, Lima 31, Peru
| | - Valeria Villegas
- Laboratorios de Investigación y Desarrollo, Facultad de Ciencias y Filosofía, Universidad Peruana Cayetano Heredia, Lima 31, Peru
| | - Vanessa Granja
- Laboratorios de Investigación y Desarrollo, Facultad de Ciencias y Filosofía, Universidad Peruana Cayetano Heredia, Lima 31, Peru
| | - Carla Gallo
- Laboratorios de Investigación y Desarrollo, Facultad de Ciencias y Filosofía, Universidad Peruana Cayetano Heredia, Lima 31, Peru
| | - Giovanni Poletti
- Laboratorios de Investigación y Desarrollo, Facultad de Ciencias y Filosofía, Universidad Peruana Cayetano Heredia, Lima 31, Peru
| | - Lavinia Schuler-Faccini
- Departamento de Genética, Universidade Federal do Rio Grande do Sul, Porto Alegre 91501-970, Brazil
| | - Francisco M Salzano
- Departamento de Genética, Universidade Federal do Rio Grande do Sul, Porto Alegre 91501-970, Brazil
| | - Maria-Cátira Bortolini
- Departamento de Genética, Universidade Federal do Rio Grande do Sul, Porto Alegre 91501-970, Brazil
| | - Samuel Canizales-Quinteros
- Unidad de Genómica de Poblaciones Aplicada a la Salud, Facultad de Química, UNAM-Instituto Nacional de Medicina Genómica, Mexico City 4510, Mexico
| | - Francisco Rothhammer
- Instituto de Alta Investigación, Universidad de Tarapacá, Programa de Genética Humana ICBM Facultad de Medicina Universidad de Chile and Centro de Investigaciones del Hombre en el Desierto, Arica 1000000, Chile
| | - Gabriel Bedoya
- GENMOL (Genética Molecular), Universidad de Antioquia, Medellín 5001000, Colombia
| | - Rosario Calderón
- Departamento de Zoología y Antropología Física, Universidad Complutense de Madrid, Madrid 28040, Spain
| | - Javier Rosique
- Departamento de Antropología, Facultad de Ciencias Sociales y Humanas, Universidad de Antioquia, Medellín 5001000, Colombia
| | - Michael Cheeseman
- The Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, Midlothian EH25 9RG, UK
| | - Mahmood F Bhutta
- 1] UCL Ear Institute, University College London, London WC1X 8EE, UK [2] Royal National Throat Nose and Ear Hospital, London WC1X 8EE, UK
| | - Steve E Humphries
- 1] Department of Genetics, Evolution and Environment, UCL Genetics Institute, University College London, London WC1E 6BT, UK [2] Centre for Cardiovascular Genetics, BHF Laboratories, Institute Cardiovascular Sciences, University College London, Rayne Building, London WC1E 6JF, UK
| | | | - Denis Headon
- The Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, Midlothian EH25 9RG, UK
| | - David Balding
- 1] Department of Genetics, Evolution and Environment, UCL Genetics Institute, University College London, London WC1E 6BT, UK [2] Schools of BioSciences and Mathematics &Statistics, University of Melbourne, Melbourne, Victoria 3010, Australia
| | - Andrés Ruiz-Linares
- Department of Genetics, Evolution and Environment, UCL Genetics Institute, University College London, London WC1E 6BT, UK
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23
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Abstract
We observe n sequences at each of m sites and assume that they have evolved from an ancestral sequence that forms the root of a binary tree of known topology and branch lengths, but the sequence states at internal nodes are unknown. The topology of the tree and branch lengths are the same for all sites, but the parameters of the evolutionary model can vary over sites. We assume a piecewise constant model for these parameters, with an unknown number of change-points and hence a transdimensional parameter space over which we seek to perform Bayesian inference. We propose two novel ideas to deal with the computational challenges of such inference. Firstly, we approximate the model based on the time machine principle: the top nodes of the binary tree (near the root) are replaced by an approximation of the true distribution; as more nodes are removed from the top of the tree, the cost of computing the likelihood is reduced linearly in n. The approach introduces a bias, which we investigate empirically. Secondly, we develop a particle marginal Metropolis-Hastings (PMMH) algorithm, that employs a sequential Monte Carlo (SMC) sampler and can use the first idea. Our time-machine PMMH algorithm copes well with one of the bottle-necks of standard computational algorithms: the transdimensional nature of the posterior distribution. The algorithm is implemented on simulated and real data examples, and we empirically demonstrate its potential to outperform competing methods based on approximate Bayesian computation (ABC) techniques.
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Affiliation(s)
- Adam Persing
- 1 Department of Statistical Science, University College London , London, United Kingdom
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24
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King TE, Fortes GG, Balaresque P, Thomas MG, Balding D, Delser PM, Neumann R, Parson W, Knapp M, Walsh S, Tonasso L, Holt J, Kayser M, Appleby J, Forster P, Ekserdjian D, Hofreiter M, Schürer K. Identification of the remains of King Richard III. Nat Commun 2014; 5:5631. [PMID: 25463651 PMCID: PMC4268703 DOI: 10.1038/ncomms6631] [Citation(s) in RCA: 126] [Impact Index Per Article: 12.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2014] [Accepted: 10/21/2014] [Indexed: 12/21/2022] Open
Abstract
In 2012, a skeleton was excavated at the presumed site of the Grey Friars friary in Leicester, the last-known resting place of King Richard III. Archaeological, osteological and radiocarbon dating data were consistent with these being his remains. Here we report DNA analyses of both the skeletal remains and living relatives of Richard III. We find a perfect mitochondrial DNA match between the sequence obtained from the remains and one living relative, and a single-base substitution when compared with a second relative. Y-chromosome haplotypes from male-line relatives and the remains do not match, which could be attributed to a false-paternity event occurring in any of the intervening generations. DNA-predicted hair and eye colour are consistent with Richard's appearance in an early portrait. We calculate likelihood ratios for the non-genetic and genetic data separately, and combined, and conclude that the evidence for the remains being those of Richard III is overwhelming.
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Affiliation(s)
- Turi E. King
- Department of Genetics, University of Leicester, Leicester LE1 7RH, UK
- School of Archaeology and Ancient History, University of Leicester, Leicester LE1 7RH, UK
| | - Gloria Gonzalez Fortes
- Department of Biology, University of York, York YO10 5DD, UK
- Institute of Biochemistry and Biology, Universität Potsdam, Karl-Liebknechtstr. 24-25, 14476 Potsdam, Germany
| | - Patricia Balaresque
- UMR5288-CNRS/Université Paul Sabatier-Toulouse 3 Laboratoire Anthropologie Moléculaire et Imagerie de Synthèse Faculté de Médecine Purpam 37, allées Jules Guesde, 31073 Toulouse, France
| | - Mark G. Thomas
- Research Department of Genetics, Evolution and Environment, University College London, London WC1E 6BT, UK
| | - David Balding
- Research Department of Genetics, Evolution and Environment, University College London, London WC1E 6BT, UK
| | | | - Rita Neumann
- Department of Genetics, University of Leicester, Leicester LE1 7RH, UK
| | - Walther Parson
- Institute of Legal Medicine, Innsbruck Medical University, Muellerstraße 44, A-6020 Innsbruck, Austria
- Pennsylvania State University, Eberly College of Science, Thomas Bldg, #517, University Park, Pennsylvania 16802, USA
| | - Michael Knapp
- School of Biological Sciences, Bangor University, Bangor LL57 2UW, UK
| | - Susan Walsh
- Molecular Anthropology Laboratory, Department of Anthropology, Yale University, Yale, New Haven, Connecticut 06511, USA
- Department of Forensic Molecular Biology, Erasmus MC University Medical Centre Rotterdam, 3000 CA Rotterdam, The Netherlands
| | - Laure Tonasso
- UMR5288-CNRS/Université Paul Sabatier-Toulouse 3 Laboratoire Anthropologie Moléculaire et Imagerie de Synthèse Faculté de Médecine Purpam 37, allées Jules Guesde, 31073 Toulouse, France
| | - John Holt
- Space Research Centre, University of Leicester, Leicester LE1 7RH, UK
| | - Manfred Kayser
- Department of Forensic Molecular Biology, Erasmus MC University Medical Centre Rotterdam, 3000 CA Rotterdam, The Netherlands
| | - Jo Appleby
- School of Archaeology and Ancient History, University of Leicester, Leicester LE1 7RH, UK
| | - Peter Forster
- McDonald Institute for Archaeological Research, University of Cambridge, Cambridge CB2 3ER, UK
- Murray Edwards College, University of Cambridge, Cambridge CB3 0DF, UK
| | - David Ekserdjian
- Department of the History of Art and Film, University of Leicester, Leicester LE1 7RH, UK
| | - Michael Hofreiter
- Department of Biology, University of York, York YO10 5DD, UK
- Institute of Biochemistry and Biology, Universität Potsdam, Karl-Liebknechtstr. 24-25, 14476 Potsdam, Germany
| | - Kevin Schürer
- Centre for English Local History, University of Leicester, Leicester LE1 7RH, UK
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25
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Bentley AR, Scutari M, Gosman N, Faure S, Bedford F, Howell P, Cockram J, Rose GA, Barber T, Irigoyen J, Horsnell R, Pumfrey C, Winnie E, Schacht J, Beauchêne K, Praud S, Greenland A, Balding D, Mackay IJ. Applying association mapping and genomic selection to the dissection of key traits in elite European wheat. Theor Appl Genet 2014; 127:2619-33. [PMID: 25273129 DOI: 10.1007/s00122-014-2403-y] [Citation(s) in RCA: 40] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/30/2014] [Accepted: 09/20/2014] [Indexed: 05/18/2023]
Abstract
We show the application of association mapping and genomic selection for key breeding targets using a large panel of elite winter wheat varieties and a large volume of agronomic data. The heightening urgency to increase wheat production in line with the needs of a growing population, and in the face of climatic uncertainty, mean new approaches, including association mapping (AM) and genomic selection (GS) need to be validated and applied in wheat breeding. Key adaptive responses are the cornerstone of regional breeding. There is evidence that new ideotypes for long-standing traits such as flowering time may be required. In order to detect targets for future marker-assisted improvement and validate the practical application of GS for wheat breeding we genotyped 376 elite wheat varieties with 3,046 DArT, single nucleotide polymorphism and gene markers and measured seven traits in replicated yield trials over 2 years in France, Germany and the UK. The scale of the phenotyping exceeds the breadth of previous AM and GS studies in these key economic wheat production regions of Northern Europe. Mixed-linear modelling (MLM) detected significant marker-trait associations across and within regions. Genomic prediction using elastic net gave low to high prediction accuracies depending on the trait, and could be experimentally increased by modifying the constituents of the training population (TP). We also tested the use of differentially penalised regression to integrate candidate gene and genome-wide markers to predict traits, demonstrating the validity and simplicity of this approach. Overall, our results suggest that whilst AM offers potential for application in both research and breeding, GS represents an exciting opportunity to select key traits, and that optimisation of the TP is crucial to its successful implementation.
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Affiliation(s)
- Alison R Bentley
- The John Bingham Laboratory, NIAB, Huntingdon Road, Cambridge, CB3 0LE, UK,
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26
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Ke X, Cortina-Borja M, Silva BC, Lowe R, Rakyan V, Balding D. Integrated analysis of genome-wide genetic and epigenetic association data for identification of disease mechanisms. Epigenetics 2014; 8:1236-44. [DOI: 10.4161/epi.26407] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023] Open
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27
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Abstract
We investigate two approaches to increase the efficiency of phenotypic prediction from genome-wide markers, which is a key step for genomic selection (GS) in plant and animal breeding. The first approach is feature selection based on Markov blankets, which provide a theoretically-sound framework for identifying non-informative markers. Fitting GS models using only the informative markers results in simpler models, which may allow cost savings from reduced genotyping. We show that this is accompanied by no loss, and possibly a small gain, in predictive power for four GS models: partial least squares (PLS), ridge regression, LASSO and elastic net. The second approach is the choice of kinship coefficients for genomic best linear unbiased prediction (GBLUP). We compare kinships based on different combinations of centring and scaling of marker genotypes, and a newly proposed kinship measure that adjusts for linkage disequilibrium (LD). We illustrate the use of both approaches and examine their performances using three real-world data sets with continuous phenotypic traits from plant and animal genetics. We find that elastic net with feature selection and GBLUP using LD-adjusted kinships performed similarly well, and were the best-performing methods in our study.
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Affiliation(s)
- Marco Scutari
- Genetics Institute, University College London (UCL), London, UK
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28
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Puch-Solis R, Rodgers L, Mazumder A, Pope S, Evett I, Curran J, Balding D. Evaluating forensic DNA profiles using peak heights, allowing for multiple donors, allelic dropout and stutters. Forensic Sci Int Genet 2013; 7:555-63. [DOI: 10.1016/j.fsigen.2013.05.009] [Citation(s) in RCA: 52] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2012] [Revised: 05/20/2013] [Accepted: 05/23/2013] [Indexed: 10/26/2022]
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29
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Speed D, Hoggart C, Petrovski S, Tachmazidou I, Coffey A, Jorgensen A, Eleftherohorinou H, De Iorio M, Todaro M, De T, Smith D, Smith PE, Jackson M, Cooper P, Kellett M, Howell S, Newton M, Yerra R, Tan M, French C, Reuber M, Sills GE, Chadwick D, Pirmohamed M, Bentley D, Scheffer I, Berkovic S, Balding D, Palotie A, Marson A, O'Brien TJ, Johnson MR. A genome-wide association study and biological pathway analysis of epilepsy prognosis in a prospective cohort of newly treated epilepsy. Hum Mol Genet 2013; 23:247-58. [PMID: 23962720 DOI: 10.1093/hmg/ddt403] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023] Open
Abstract
We present the analysis of a prospective multicentre study to investigate genetic effects on the prognosis of newly treated epilepsy. Patients with a new clinical diagnosis of epilepsy requiring medication were recruited and followed up prospectively. The clinical outcome was defined as freedom from seizures for a minimum of 12 months in accordance with the consensus statement from the International League Against Epilepsy (ILAE). Genetic effects on remission of seizures after starting treatment were analysed with and without adjustment for significant clinical prognostic factors, and the results from each cohort were combined using a fixed-effects meta-analysis. After quality control (QC), we analysed 889 newly treated epilepsy patients using 472 450 genotyped and 6.9 × 10(6) imputed single-nucleotide polymorphisms. Suggestive evidence for association (defined as Pmeta < 5.0 × 10(-7)) with remission of seizures after starting treatment was observed at three loci: 6p12.2 (rs492146, Pmeta = 2.1 × 10(-7), OR[G] = 0.57), 9p23 (rs72700966, Pmeta = 3.1 × 10(-7), OR[C] = 2.70) and 15q13.2 (rs143536437, Pmeta = 3.2 × 10(-7), OR[C] = 1.92). Genes of biological interest at these loci include PTPRD and ARHGAP11B (encoding functions implicated in neuronal development) and GSTA4 (a phase II biotransformation enzyme). Pathway analysis using two independent methods implicated a number of pathways in the prognosis of epilepsy, including KEGG categories 'calcium signaling pathway' and 'phosphatidylinositol signaling pathway'. Through a series of power curves, we conclude that it is unlikely any single common variant explains >4.4% of the variation in the outcome of newly treated epilepsy.
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Affiliation(s)
- Doug Speed
- UCL Genetics Institute, University College London WC1E 6BT, UK
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30
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Affiliation(s)
- David Balding
- UCL Genetics Institute, Gower Street, London WC1E 6BT, UK
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31
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Krane DE, Bahn V, Balding D, Barlow B, Cash H, Desportes BL, D'Eustachio P, Devlin K, Doom TE, Dror I, Ford S, Funk C, Gilder J, Hampikian G, Inman K, Jamieson A, Kent PE, Koppl R, Kornfield I, Krimsky S, Mnookin J, Mueller L, Murphy E, Paoletti DR, Petrov DA, Raymer M, Risinger DM, Roth A, Rudin N, Shields W, Siegel JA, Slatkin M, Song YS, Speed T, Spiegelman C, Sullivan P, Swienton AR, Tarpey T, Thompson WC, Ungvarsky E, Zabell S. Time for DNA disclosure. Science 2010; 326:1631-2. [PMID: 20019271 DOI: 10.1126/science.326.5960.1631] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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32
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Beaumont MA, Nielsen R, Robert C, Hey J, Gaggiotti O, Knowles L, Estoup A, Panchal M, Corander J, Hickerson M, Sisson SA, Fagundes N, Chikhi L, Beerli P, Vitalis R, Cornuet JM, Huelsenbeck J, Foll M, Yang Z, Rousset F, Balding D, Excoffier L. In defence of model-based inference in phylogeography. Mol Ecol 2010; 19:436-446. [PMID: 29284924 DOI: 10.1111/j.1365-294x.2009.04515.x] [Citation(s) in RCA: 123] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Recent papers have promoted the view that model-based methods in general, and those based on Approximate Bayesian Computation (ABC) in particular, are flawed in a number of ways, and are therefore inappropriate for the analysis of phylogeographic data. These papers further argue that Nested Clade Phylogeographic Analysis (NCPA) offers the best approach in statistical phylogeography. In order to remove the confusion and misconceptions introduced by these papers, we justify and explain the reasoning behind model-based inference. We argue that ABC is a statistically valid approach, alongside other computational statistical techniques that have been successfully used to infer parameters and compare models in population genetics. We also examine the NCPA method and highlight numerous deficiencies, either when used with single or multiple loci. We further show that the ages of clades are carelessly used to infer ages of demographic events, that these ages are estimated under a simple model of panmixia and population stationarity but are then used under different and unspecified models to test hypotheses, a usage the invalidates these testing procedures. We conclude by encouraging researchers to study and use model-based inference in population genetics.
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Affiliation(s)
- Mark A Beaumont
- School of Animal and Microbial Sciences, University of Reading, Whiteknights, PO Box 228, Reading, RG6 6AJ, UK
| | - Rasmus Nielsen
- Integrative Biology, UC Berkeley, 3060 Valley Life Sciences Bldg #3140, Berkeley, CA 94720-3140, USA
| | | | - Jody Hey
- Department of Genetics, Rutgers University, 604 Allison Road, Piscataway, NJ 08854, USA
| | - Oscar Gaggiotti
- Laboratoire d'Ecologie Alpine, UMR CNRS 5553, Université Joseph Fourier, BP 53, 38041 GRENOBLE, France
| | - Lacey Knowles
- Department of Ecology and Evolutionary Biology, Museum of Zoology, University of Michigan, Ann Arbor, MI 48109-1079, USA
| | - Arnaud Estoup
- INRA UMR Centre de Biologie et de Gestion des Populations (INRA ⁄ IRD ⁄ Cirad ⁄ Montpellier SupAgro), Campus international de Baillarguet, Montferrier-sur-Lez, France
| | - Mahesh Panchal
- Max Planck Institute for Evolutionary Biology, August-Thienemann-Str. 2, 24306 Plön, Germany
| | - Jukka Corander
- Department of Mathematics and statistics, University of Helsinki, Finland
| | - Mike Hickerson
- Biology Department, Queens College, City University of New York, 65-30 Kissena Boulevard, Flushing, NY 11367-1597, USA
| | - Scott A Sisson
- School of Mathematics and Statistics, University of New South Wales, Sydney, Australia
| | - Nelson Fagundes
- Departamento de Genética, Universidade Federal do Rio Grande do Sul, Porto Alegre, RS, Brazil
| | - Lounès Chikhi
- Université Paul Sabatier-UMR EDB 5174 118, 31062 Toulouse Cedex 09, France
| | - Peter Beerli
- Department of Scientific Computing, Florida State University, Tallahassee, FL 32306, USA
| | - Renaud Vitalis
- CNRS-INRA, CBGP, Campus International de Baillarguet, CS 30016, 34988 Montferrier-sur-Lez, France
| | - Jean-Marie Cornuet
- INRA UMR Centre de Biologie et de Gestion des Populations (INRA ⁄ IRD ⁄ Cirad ⁄ Montpellier SupAgro), Campus international de Baillarguet, Montferrier-sur-Lez, France
| | - John Huelsenbeck
- Integrative Biology, UC Berkeley, 3060 Valley Life Sciences Bldg #3140, Berkeley, CA 94720-3140, USA
| | - Matthieu Foll
- CMPG, Institute of Ecology and Evolution, University of Berne, 3012 Berne, Switzerland.,Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
| | - Ziheng Yang
- Department of Biology, University College London, Gower Street, London WC1E 6BT, UK
| | - Francois Rousset
- Institut des Sciences de l'Évolution, Universté Montpellier 2, CNRS, Place Eugène Bataillon, CC065, Montpellier, Cedex 5, France
| | - David Balding
- Institute of Genetics, University College London, 2nd Floor, Kathleen Lonsdale Building, 5 Gower Place, London WC1E 6BT, UK
| | - Laurent Excoffier
- CMPG, Institute of Ecology and Evolution, University of Berne, 3012 Berne, Switzerland.,Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
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Chambers JC, Zhang W, Zabaneh D, Sehmi J, Jain P, McCarthy MI, Froguel P, Ruokonen A, Balding D, Jarvelin MR, Scott J, Elliott P, Kooner JS. Common genetic variation near melatonin receptor MTNR1B contributes to raised plasma glucose and increased risk of type 2 diabetes among Indian Asians and European Caucasians. Diabetes 2009; 58:2703-8. [PMID: 19651812 PMCID: PMC2768158 DOI: 10.2337/db08-1805] [Citation(s) in RCA: 78] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
OBJECTIVE Fasting plasma glucose and risk of type 2 diabetes are higher among Indian Asians than among European and North American Caucasians. Few studies have investigated genetic factors influencing glucose metabolism among Indian Asians. RESEARCH DESIGN AND METHODS We carried out genome-wide association studies for fasting glucose in 5,089 nondiabetic Indian Asians genotyped with the Illumina Hap610 BeadChip and 2,385 Indian Asians (698 with type 2 diabetes) genotyped with the Illumina 300 BeadChip. Results were compared with findings in 4,462 European Caucasians. RESULTS We identified three single nucleotide polymorphisms (SNPs) associated with glucose among Indian Asians at P < 5 x 10(-8), all near melatonin receptor MTNR1B. The most closely associated was rs2166706 (combined P = 2.1 x 10(-9)), which is in moderate linkage disequilibrium with rs1387153 (r(2) = 0.60) and rs10830963 (r(2) = 0.45), both previously associated with glucose in European Caucasians. Risk allele frequency and effect sizes for rs2166706 were similar among Indian Asians and European Caucasians: frequency 46.2 versus 45.0%, respectively (P = 0.44); effect 0.05 (95% CI 0.01-0.08) versus 0.05 (0.03-0.07 mmol/l), respectively, higher glucose per allele copy (P = 0.84). SNP rs2166706 was associated with type 2 diabetes in Indian Asians (odds ratio 1.21 [95% CI 1.06-1.38] per copy of risk allele; P = 0.006). SNPs at the GCK, GCKR, and G6PC2 loci were also associated with glucose among Indian Asians. Risk allele frequencies of rs1260326 (GCKR) and rs560887 (G6PC2) were higher among Indian Asians compared with European Caucasians. CONCLUSIONS Common genetic variation near MTNR1B influences blood glucose and risk of type 2 diabetes in Indian Asians. Genetic variation at the MTNR1B, GCK, GCKR, and G6PC2 loci may contribute to abnormal glucose metabolism and related metabolic disturbances among Indian Asians.
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Affiliation(s)
- John C. Chambers
- Department of Epidemiology and Public Health, Imperial College London, London, U.K
| | - Weihua Zhang
- Department of Epidemiology and Public Health, Imperial College London, London, U.K
| | - Delilah Zabaneh
- Department of Epidemiology and Public Health, Imperial College London, London, U.K
| | - Joban Sehmi
- National Heart and Lung Institute, Imperial College London, London, U.K
| | - Piyush Jain
- National Heart and Lung Institute, Imperial College London, London, U.K
| | - Mark I. McCarthy
- Oxford Centre for Diabetes, Endocrinology and Metabolism and Oxford National Institute for Health Research, Biomedical Research Centre, Oxford, U.K
| | - Philippe Froguel
- Section of Genomic Medicine, Imperial College London, London, U.K., and the Centre National de la Recherche Scientifique, 8090-Institute of Biology, Pasteur Institute, Lille, France
- UMR 8090-Institute of Biology, Pasteur Institute, Lille, France
| | - Aimo Ruokonen
- Department of Clinical Sciences/Clinical Chemistry, University Hospital Oulu, Oulu, Finland
| | - David Balding
- Department of Epidemiology and Public Health, Imperial College London, London, U.K
| | - Marjo-Riitta Jarvelin
- Department of Epidemiology and Public Health, Imperial College London, London, U.K
- Institute of Health Sciences and Biocenter Oulu, University of Oulu, Oulu, Finland, and Department of Child and Adolescent Health, National Institute of Health and Welfare, Helsinki, Finland
| | - James Scott
- National Heart and Lung Institute, Imperial College London, London, U.K
| | - Paul Elliott
- Department of Epidemiology and Public Health, Imperial College London, London, U.K
| | - Jaspal S. Kooner
- National Heart and Lung Institute, Imperial College London, London, U.K
- Corresponding author: Jaspal S. Kooner,
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Abstract
UNLABELLED PopABC is a computer package for inferring the pattern of demographic divergence of closely related populations and species. The software performs coalescent simulation in the framework of approximate Bayesian computation (ABC). PopABC can also be used to perform Bayesian model choice to discriminate between different demographic scenarios. The program can be used either for research or for education and teaching purposes. AVAILABILITY AND IMPLEMENTATION Source code and binaries are freely available at http://www.reading.ac.uk/ approximately sar05sal/software.htm. The program was implemented in C and can run on UNIX, MacOSX and Windows operating systems.
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Affiliation(s)
- Joao S Lopes
- School of Biological Sciences, University of Reading, Whiteknights, PO Box 228, Reading RG66AJ, UK.
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Meyre D, Delplanque J, Chèvre JC, Lecoeur C, Lobbens S, Gallina S, Durand E, Vatin V, Degraeve F, Proença C, Gaget S, Körner A, Kovacs P, Kiess W, Tichet J, Marre M, Hartikainen AL, Horber F, Potoczna N, Hercberg S, Levy-Marchal C, Pattou F, Heude B, Tauber M, McCarthy MI, Blakemore AIF, Montpetit A, Polychronakos C, Weill J, Coin LJM, Asher J, Elliott P, Järvelin MR, Visvikis-Siest S, Balkau B, Sladek R, Balding D, Walley A, Dina C, Froguel P. Genome-wide association study for early-onset and morbid adult obesity identifies three new risk loci in European populations. Nat Genet 2009; 41:157-9. [PMID: 19151714 DOI: 10.1038/ng.301] [Citation(s) in RCA: 526] [Impact Index Per Article: 35.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2008] [Accepted: 11/20/2008] [Indexed: 11/09/2022]
Abstract
We analyzed genome-wide association data from 1,380 Europeans with early-onset and morbid adult obesity and 1,416 age-matched normal-weight controls. Thirty-eight markers showing strong association were further evaluated in 14,186 European subjects. In addition to FTO and MC4R, we detected significant association of obesity with three new risk loci in NPC1 (endosomal/lysosomal Niemann-Pick C1 gene, P = 2.9 x 10(-7)), near MAF (encoding the transcription factor c-MAF, P = 3.8 x 10(-13)) and near PTER (phosphotriesterase-related gene, P = 2.1 x 10(-7)).
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Affiliation(s)
- David Meyre
- CNRS 8090-Institute of Biology, Pasteur Institute, 59000 Lille, France
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36
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Al-Bustan SA, Alkhalaf M, Al-Rashdan I, Al-Otaibi S, Al-Baker E, Balding D, Alnaqeeb MA. Apolipoprotein E, CI and B gene polymorphisms in a sample of patients with coronary heart disease in the Kuwaiti population. Med Princ Pract 2009; 18:294-9. [PMID: 19494537 DOI: 10.1159/000215727] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/14/2008] [Accepted: 10/27/2008] [Indexed: 11/19/2022] Open
Abstract
OBJECTIVES The objective of this study was to investigate the possible association of clinical variables and apolipoprotein (APOE, APOCI and APOB) polymorphisms with the development of myocardial infraction (MI) and coronary heart disease (CHD) in Kuwaitis. SUBJECTS AND METHODS APOE, APOCI and APOB genotypes were determined by polymerase chain reaction followed by restriction fragment length polymorphism in 143 Kuwaiti CHD patients with (n = 88) and without (n = 55) MI and in 122 controls matched for gender and age. Statistical and genetic analyses of the genotype, allele and haplotype frequencies, as well as regression analyses of genetic and clinical variables were done. RESULTS There was a statistically significant association between CHD and medical history of diabetes mellitus (p < 0.001), hypertension (p < 0.01), high cholesterol (p < 0.05) and family history of CHD (p < 0.001). A highly significant association (p < 0.001) was found, with an adjusted odds ratio of 9.32, for family history and the development of MI. No significant differences were found for allele or genotype frequencies between CHD patients and controls. CONCLUSION The strong effect of family history suggests a major genetic component for the development of CHD in Kuwaitis, but this association does not appear to be related to the APO genes studied here. The results in this study encourages future research into these and other polymorphisms and their potential association with MI and CHD in the Kuwaiti population.
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Affiliation(s)
- Suzanne A Al-Bustan
- Department of Biological Sciences, Faculty of Science, Kuwait University, Safat 13060, Kuwait.
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Balding D. Statistical Approaches for Genome-Wide Association Studies. EJC Suppl 2008. [DOI: 10.1016/s1359-6349(08)71841-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022] Open
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Chambers JC, Elliott P, Zabaneh D, Zhang W, Li Y, Froguel P, Balding D, Scott J, Kooner JS. Common genetic variation near MC4R is associated with waist circumference and insulin resistance. Nat Genet 2008; 40:716-8. [PMID: 18454146 DOI: 10.1038/ng.156] [Citation(s) in RCA: 393] [Impact Index Per Article: 24.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2008] [Accepted: 04/15/2008] [Indexed: 12/13/2022]
Abstract
We carried out a genome-wide association study (318,237 SNPs) for insulin resistance and related phenotypes in 2,684 Indian Asians, with further testing in 11,955 individuals of Indian Asian or European ancestry. We found associations of rs12970134 near MC4R with waist circumference (P = 1.7 x 10(-9)) and, independently, with insulin resistance. Homozygotes for the risk allele of rs12970134 have approximately 2 cm increased waist circumference. Common genetic variation near MC4R is associated with risk of adiposity and insulin resistance.
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Affiliation(s)
- John C Chambers
- Department of Epidemiology and Public Health, Imperial College London, Norfolk Pl., London W2 1PG, UK
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Abstract
The authors present ELB, an easy to programme and computationally fast algorithm for
inferring gametic phase in population samples of multilocus genotypes. Phase updates
are made on the basis of a window of neighbouring loci, and the window size varies
according to the local level of linkage disequilibrium. Thus, ELB is particularly
well suited to problems involving many loci and/or relatively large genomic regions,
including those with variable recombination rate. The authors have simulated
population samples of single nucleotide polymorphism genotypes with varying levels of
recombination and marker density, and find that ELB provides better local estimation
of gametic phase than the PHASE or HTYPER programs, while its global accuracy is
broadly similar. The relative improvement in local accuracy increases both with
increasing recombination and with increasing marker density. Short tandem repeat
(STR, or microsatellite) simulation studies demonstrate ELB's superiority over PHASE
both globally and locally. Missing data are handled by ELB; simulations show that
phase recovery is virtually unaffected by up to 2 per cent of missing data, but that
phase estimation is noticeably impaired beyond this amount. The authors also applied
ELB to datasets obtained from random pairings of 42 human X chromosomes typed at 97
diallelic markers in a 200 kb low-recombination region. Once again, they found ELB to
have consistently better local accuracy than PHASE or HTYPER, while its global
accuracy was close to the best.
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Affiliation(s)
- Laurent Excoffier
- Zoological Institute, University of Bern, Baltzerstrasse 6, CH-3012 Bern, Switzerland.
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Romualdi C, Balding D, Nasidze IS, Risch G, Robichaux M, Sherry ST, Stoneking M, Batzer MA, Barbujani G. Patterns of human diversity, within and among continents, inferred from biallelic DNA polymorphisms. Genome Res 2002; 12:602-12. [PMID: 11932244 PMCID: PMC187513 DOI: 10.1101/gr.214902] [Citation(s) in RCA: 164] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
Previous studies have reported that about 85% of human diversity at Short Tandem Repeat (STR) and Restriction Fragment Length Polymorphism (RFLP) autosomal loci is due to differences between individuals of the same population, whereas differences among continental groups account for only 10% of the overall genetic variance. These findings conflict with popular notions of distinct and relatively homogeneous human races, and may also call into question the apparent usefulness of ethnic classification in, for example, medical diagnostics. Here, we present new data on 21 Alu insertions in 32 populations. We analyze these data along with three other large, globally dispersed data sets consisting of apparently neutral biallelic nuclear markers, as well as with a beta-globin data set possibly subject to selection. We confirm the previous results for the autosomal data, and find a higher diversity among continents for Y-chromosome loci. We also extend the analyses to address two questions: (1) whether differences between continental groups, although small, are nevertheless large enough to confidently assign individuals to their continent on the basis of their genotypes; (2) whether the observed genotypes naturally cluster into continental or population groups when the sample source location is ignored. Using a range of statistical methods, we show that classification errors are at best around 30% for autosomal biallelic polymorphisms and 27% for the Y chromosome. Two data sets suggest the existence of three and four major groups of genotypes worldwide, respectively, and the two groupings are inconsistent. These results suggest that, at random biallelic loci, there is little evidence, if any, of a clear subdivision of humans into biologically defined groups.
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Affiliation(s)
- Chiara Romualdi
- Department of Biology, University of Ferrara, via L. Borsari 46, I-44100 Ferrara, Italy
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Balding D, Richardson S. Special Issue: Modern Statistical Methods for Disease Gene Mapping. Int Stat Rev 2000. [DOI: 10.1111/j.1751-5823.2000.tb00383.x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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Abstract
The corneal limbal vessels of an animal host respond to the presence of a source of Tumour Angiogenesis Factor (TAF) implanted in the cornea by the formation of new capillaries which grow towards the source. This neovasculature can be easily seen and studied and this paper describes a mathematical model of some of the important features of the growth. The model includes the diffusion of TAF, the formation of sprouts from pre-existing vessels and models the movement of these sprouts to form new capillaries as a chemotactic response to the presence of TAF. Numerical results are produced for various values of the parameters which characterize the model and it is suggested that the model might form the framework for further theoretical work on related phenomena such as wound healing or to develop strategies for the investigation of anti-angiogenesis.
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Abstract
A South American anuran (Phyllomedusa sauvagii) produced large amounts of semisolid urate when it was maintained on a diet of insects. Rates of evaporative water loss in Phyllomedusa sauvagii were only about 5 to 10 percent of those other anurans tested and were similar to those of lizards of comparable size.
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