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Salvatore G, Chibani Bahi Amar A, Canale-Tabet K, Fridi R, Tabet Aoul N, Saci S, Labarthe E, Palombo V, D'Andrea M, Vignal A, Faux P. Natural clines and human management impact the genetic structure of Algerian honey bee populations. Genet Sel Evol 2023; 55:94. [PMID: 38114899 PMCID: PMC10729559 DOI: 10.1186/s12711-023-00864-5] [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] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Accepted: 12/04/2023] [Indexed: 12/21/2023] Open
Abstract
BACKGROUND The Algerian honey bee population is composed of two described subspecies A. m. intermissa and A. m. sahariensis, of which little is known regarding population genomics, both in terms of genetic differentiation and of possible contamination by exogenous stock. Moreover, the phenotypic differences between the two subspecies are expected to translate into genetic differences and possible adaptation to heat and drought in A. m. sahariensis. To shed light on the structure of this population and to integrate these two subspecies in the growing dataset of available haploid drone sequences, we performed whole-genome sequencing of 151 haploid drones. RESULTS Integrated analysis of our drone sequences with a similar dataset of European reference populations did not detect any significant admixture in the Algerian honey bees. Interestingly, most of the genetic variation was not found between the A. m. intermissa and A. m. sahariensis subspecies; instead, two main genetic clusters were found along an East-West axis. We found that the correlation between genetic and geographic distances was higher in the Western cluster and that close-family relationships were mostly detected in the Eastern cluster, sometimes at long distances. In addition, we selected a panel of 96 ancestry-informative markers to decide whether a sampled bee is Algerian or not, and tested this panel in simulated cases of admixture. CONCLUSIONS The differences between the two main genetic clusters suggest differential breeding management between eastern and western Algeria, with greater exchange of genetic material over long distances in the east. The lack of detected admixture events suggests that, unlike what is seen in many places worldwide, imports of queens from foreign countries do not seem to have occurred on a large scale in Algeria, a finding that is relevant for conservation purposes. In addition, the proposed panel of 96 markers was found effective to distinguish Algerian from European honey bees. Therefore, we conclude that applying this approach to other taxa is promising, in particular when genetic differentiation is difficult to capture.
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Affiliation(s)
- Giovanna Salvatore
- Department of Agricultural, Environmental and Food Sciences, University of Molise, Via De Sanctis Snc, 86100, Campobasso, Italy.
- GenPhySE, Université de Toulouse, INRAE, INPT, INP-ENVT, 31326, Castanet-Tolosan, France.
| | - Amira Chibani Bahi Amar
- Laboratoire de Génétique Moléculaire et Cellulaire (LGMC), Département de Génétique Moléculaire Appliquée, Université des Sciences et de la Technologie d'Oran Mohamed Boudiaf, USTOMB, BP 1505, El M'naouer, 31000, Oran, Algeria
| | - Kamila Canale-Tabet
- GenPhySE, Université de Toulouse, INRAE, INPT, INP-ENVT, 31326, Castanet-Tolosan, France
| | - Riad Fridi
- Laboratoire de Génétique Moléculaire et Cellulaire (LGMC), Département de Génétique Moléculaire Appliquée, Université des Sciences et de la Technologie d'Oran Mohamed Boudiaf, USTOMB, BP 1505, El M'naouer, 31000, Oran, Algeria
| | - Nacera Tabet Aoul
- Laboratoire de Génétique Moléculaire et Cellulaire (LGMC), Département de Génétique Moléculaire Appliquée, Université des Sciences et de la Technologie d'Oran Mohamed Boudiaf, USTOMB, BP 1505, El M'naouer, 31000, Oran, Algeria
- Department of Biotechnology, Faculty SNV, University of Oran1 Ahmed Ben Bella, Oran, Algeria
| | - Soumia Saci
- National Institute of Agronomic Research of Algeria (INRAA), El Harrach, Alger, Algeria
| | - Emmanuelle Labarthe
- GenPhySE, Université de Toulouse, INRAE, INPT, INP-ENVT, 31326, Castanet-Tolosan, France
| | - Valentino Palombo
- Department of Agricultural, Environmental and Food Sciences, University of Molise, Via De Sanctis Snc, 86100, Campobasso, Italy
| | - Mariasilvia D'Andrea
- Department of Agricultural, Environmental and Food Sciences, University of Molise, Via De Sanctis Snc, 86100, Campobasso, Italy
| | - Alain Vignal
- GenPhySE, Université de Toulouse, INRAE, INPT, INP-ENVT, 31326, Castanet-Tolosan, France
| | - Pierre Faux
- GenPhySE, Université de Toulouse, INRAE, INPT, INP-ENVT, 31326, Castanet-Tolosan, France.
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Campos DP, Granger-Neto HP, Júnior JES, Faux P, Santos FR. Population Genomics of the Critically Endangered Brazilian Merganser. Animals (Basel) 2023; 13:3759. [PMID: 38136797 PMCID: PMC10741106 DOI: 10.3390/ani13243759] [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] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Revised: 11/20/2023] [Accepted: 11/30/2023] [Indexed: 12/24/2023] Open
Abstract
The Brazilian merganser (Mergus octosetaceus) is one of the most endangered bird species in South America and comprises less than 250 mature individuals in wild environments. This is a species extremely sensitive to environmental disturbances and restricted to a few "pristine" freshwater habitats in Brazil, and it has been classified as Critically Endangered on the IUCN Red List since 1994. Thus, biological conservation studies are vital to promote adequate management strategies and to avoid the decline of merganser populations. In this context, to understand the evolutionary dynamics and the current genetic diversity of remaining Brazilian merganser populations, we used the "Genotyping by Sequencing" approach to genotype 923 SNPs in 30 individuals from all known areas of occurrence. These populations revealed a low genetic diversity and high inbreeding levels, likely due to the recent population decline associated with habitat loss. Furthermore, it showed a moderate level of genetic differentiation between all populations located in four separated areas of the highly threatened Cerrado biome. The results indicate that urgent actions for the conservation of the species should be accompanied by careful genetic monitoring to allow appropriate in situ and ex situ management to increase the long-term species' survival in its natural environment.
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Affiliation(s)
- Davidson P. Campos
- Department of Genetics, Ecology and Evolution, Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais, Belo Horizonte 31270-901, MG, Brazil; (D.P.C.); (H.P.G.-N.); (J.E.S.J.)
| | - Henry Paul Granger-Neto
- Department of Genetics, Ecology and Evolution, Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais, Belo Horizonte 31270-901, MG, Brazil; (D.P.C.); (H.P.G.-N.); (J.E.S.J.)
| | - José E. Santos Júnior
- Department of Genetics, Ecology and Evolution, Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais, Belo Horizonte 31270-901, MG, Brazil; (D.P.C.); (H.P.G.-N.); (J.E.S.J.)
| | - Pierre Faux
- GenPhySE, Université de Toulouse, INRAE, ENVT, 31326 Castanet-Tolosan, France;
| | - Fabrício R. Santos
- Department of Genetics, Ecology and Evolution, Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais, Belo Horizonte 31270-901, MG, Brazil; (D.P.C.); (H.P.G.-N.); (J.E.S.J.)
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3
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Paganini J, Faux P, Beley S, Picard C, Chiaroni J, Di Cristofaro J. HLA-F transcriptional and protein differential expression according to its genetic polymorphisms. HLA 2023; 102:578-589. [PMID: 37166140 DOI: 10.1111/tan.15087] [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] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2022] [Revised: 03/21/2023] [Accepted: 04/20/2023] [Indexed: 05/12/2023]
Abstract
Many specificities single out HLA-F: its structure, expression regulation at cell membrane and function. HLA-F mRNA is detected in the most cell types and the protein is localized in the ER and Golgi apparatus. When expressed at cell surface, HLA-F may be associated to β2-microglobulin and peptide or expressed as an open-conformer molecule. HLA-F reaches the membrane upon activation of different primary cell types and cell-lines. HLA-F has its highest affinity for the KIR3DS1-activating NK receptor, but also binds inhibitory immune receptors. Some studies reported that HLA-F expression is associated with its genotype. Higher HLA-F mRNA expression associated with F*01:01:02, and 3 noncoding SNPs, rs1362126, rs2523405, and rs2523393, located in HLA-F-AS1 or upstream the HLA-F sequence were associated with HLA-F mRNA expression. Given the implication of HLA-F in many clinical setting, and the undisclosed process of its expression regulation, we aim to confirm the effect of the aforementioned SNPs with HLA-F transcriptional and protein expression. We analyzed the distribution, frequency and linkage disequilibrium of these SNPs at worldwide scale in the 1000 Genomes Project samples. Influence on the genotype of each SNP on HLA-F expression was explored using RNAseq data from the 1000 Genomes Project, and using Q-PCR and intracellular cytometry in PBMC from healthy individuals. Our results show that the SNPs under studied displayed remarkably different allelic proportion according to geography and confirm that rs1362126, rs2523405, and rs2523393 displayed the most concordant results, with the highest effect size and a double-dose effect.
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Affiliation(s)
| | - Pierre Faux
- GenPhySE, Université de Toulouse, INRAE, INPT, INP-ENVT, Castanet Tolosan, France
- Aix Marseille University, CNRS, EFS, ADES, UMR7268, Marseille, France
| | - Sophie Beley
- Aix Marseille University, CNRS, EFS, ADES, UMR7268, Marseille, France
- Etablissement Français du Sang PACA Corse, Marseille, France
| | - Christophe Picard
- Aix Marseille University, CNRS, EFS, ADES, UMR7268, Marseille, France
- Etablissement Français du Sang PACA Corse, Marseille, France
| | - Jacques Chiaroni
- Aix Marseille University, CNRS, EFS, ADES, UMR7268, Marseille, France
- Etablissement Français du Sang PACA Corse, Marseille, France
| | - Julie Di Cristofaro
- Aix Marseille University, CNRS, EFS, ADES, UMR7268, Marseille, France
- Etablissement Français du Sang PACA Corse, Marseille, France
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Faux P, Ding L, Ramirez-Aristeguieta LM, Chacón-Duque JC, Comini M, Mendoza-Revilla J, Fuentes-Guajardo M, Jaramillo C, Arias W, Hurtado M, Villegas V, Granja V, Barquera R, Everardo-Martínez P, Quinto-Sánchez M, Gómez-Valdés J, Villamil-Ramírez H, Silva de Cerqueira CC, Hünemeier T, Ramallo V, Gonzalez-José R, Schüler-Faccini L, Bortolini MC, Acuña-Alonzo V, Canizales-Quinteros S, Poletti G, Gallo C, Rothhammer F, Rojas W, Schmid AB, Adhikari K, Bennett DL, Ruiz-Linares A. Neanderthal introgression in SCN9A impacts mechanical pain sensitivity. Commun Biol 2023; 6:958. [PMID: 37816865 PMCID: PMC10564861 DOI: 10.1038/s42003-023-05286-z] [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] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2023] [Accepted: 08/25/2023] [Indexed: 10/12/2023] Open
Abstract
The Nav1.7 voltage-gated sodium channel plays a key role in nociception. Three functional variants in the SCN9A gene (encoding M932L, V991L, and D1908G in Nav1.7), have recently been identified as stemming from Neanderthal introgression and to associate with pain symptomatology in UK BioBank data. In 1000 genomes data, these variants are absent in Europeans but common in Latin Americans. Analysing high-density genotype data from 7594 Latin Americans, we characterized Neanderthal introgression in SCN9A. We find that tracts of introgression occur on a Native American genomic background, have an average length of ~123 kb and overlap the M932L, V991L, and D1908G coding positions. Furthermore, we measured experimentally six pain thresholds in 1623 healthy Colombians. We found that Neanderthal ancestry in SCN9A is significantly associated with a lower mechanical pain threshold after sensitization with mustard oil and evidence of additivity of effects across Nav1.7 variants. Our findings support the reported association of Neanderthal Nav1.7 variants with clinical pain, define a specific sensory modality affected by archaic introgression in SCN9A and are consistent with independent effects of the Neanderthal variants on Nav1.7 function.
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Affiliation(s)
- Pierre Faux
- 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, 200438, Shanghai, China
- UMR ADES, Aix-Marseille Université, CNRS, EFS, 13005, Marseille, France
- UMR GenPhySE, INRAE, INP, ENVT, Université de Toulouse, 31326, Castanet-Tolosan, France
| | - Li Ding
- 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, 200438, Shanghai, China
| | | | - J Camilo Chacón-Duque
- Centre for Palaeogenetics, Svante Arrhenius väg 20C, SE-10691, Stockholm, Sweden
- Department of Archaeology and Classical Studies, Stockholm University, SE-1069, Stockholm, Sweden
- Department of Genetics, Evolution and Environment, University College London, London, WC1E 6BT, UK
| | - Maddalena Comini
- Nuffield Department of Clinical Neurosciences, Oxford University, Oxford, OX3 9DU, UK
| | - Javier Mendoza-Revilla
- Laboratorios de Investigación y Desarrollo, Facultad de Ciencias y Filosofía, Universidad Peruana Cayetano Heredia, 31, Lima, Perú
- Unit of Human Evolutionary Genetics, Institut Pasteur, 75015, Paris, France
| | - Macarena Fuentes-Guajardo
- Departamento de Tecnología Médica, Facultad de Ciencias de la Salud, Universidad de Tarapacá, 1000000, Arica, Chile
| | - Claudia Jaramillo
- GENMOL (Genética Molecular), Universidad de Antioquia, 5001000, Medellín, Colombia
| | - William Arias
- GENMOL (Genética Molecular), Universidad de Antioquia, 5001000, Medellín, Colombia
| | - Malena Hurtado
- Laboratorios de Investigación y Desarrollo, Facultad de Ciencias y Filosofía, Universidad Peruana Cayetano Heredia, 31, Lima, Perú
| | - Valeria Villegas
- Laboratorios de Investigación y Desarrollo, Facultad de Ciencias y Filosofía, Universidad Peruana Cayetano Heredia, 31, Lima, Perú
| | - Vanessa Granja
- Laboratorios de Investigación y Desarrollo, Facultad de Ciencias y Filosofía, Universidad Peruana Cayetano Heredia, 31, Lima, Perú
| | - Rodrigo Barquera
- Molecular Genetics Laboratory, National School of Anthropology and History, Mexico City, 14050, 6600, Mexico, Mexico
- Department of Archaeogenetics, Max Planck Institute for the Science of Human History (MPI-SHH), 07745, Jena, Germany
| | - Paola Everardo-Martínez
- Molecular Genetics Laboratory, National School of Anthropology and History, Mexico City, 14050, 6600, Mexico, Mexico
| | - Mirsha Quinto-Sánchez
- Forensic Science, Faculty of Medicine, UNAM (Universidad Nacional Autónoma de México), 06320, Mexico City, Mexico
| | - Jorge Gómez-Valdés
- Molecular Genetics Laboratory, National School of Anthropology and History, Mexico City, 14050, 6600, Mexico, Mexico
| | - Hugo Villamil-Ramírez
- Unidad de Genomica de Poblaciones Aplicada a la Salud, Facultad de Química, UNAM-Instituto Nacional de Medicina Genómica, 4510, Mexico City, Mexico
| | | | - Tábita Hünemeier
- Departamento de Genética e Biologia Evolutiva, Instituto de Biociências, Universidade de São Paulo, 05508-090, São Paulo, SP, Brazil
| | - Virginia Ramallo
- Departamento de Genética, Universidade Federal do Rio Grande do Sul, 90040-060, Porto Alegre, Brasil
- Instituto Patagónico de Ciencias Sociales y Humanas, Centro Nacional Patagónico, CONICET, U9129ACD, Puerto Madryn, Argentina
| | - Rolando Gonzalez-José
- Instituto Patagónico de Ciencias Sociales y Humanas, Centro Nacional Patagónico, CONICET, U9129ACD, Puerto Madryn, Argentina
| | - Lavinia Schüler-Faccini
- Departamento de Genética, Universidade Federal do Rio Grande do Sul, 90040-060, Porto Alegre, Brasil
| | - Maria-Cátira Bortolini
- Departamento de Genética, Universidade Federal do Rio Grande do Sul, 90040-060, Porto Alegre, Brasil
| | - Victor Acuña-Alonzo
- Molecular Genetics Laboratory, National School of Anthropology and History, Mexico City, 14050, 6600, Mexico, Mexico
| | - Samuel Canizales-Quinteros
- Unidad de Genomica de Poblaciones Aplicada a la Salud, Facultad de Química, UNAM-Instituto Nacional de Medicina Genómica, 4510, Mexico City, Mexico
| | - Giovanni Poletti
- Laboratorios de Investigación y Desarrollo, Facultad de Ciencias y Filosofía, Universidad Peruana Cayetano Heredia, 31, Lima, Perú
| | - Carla Gallo
- Laboratorios de Investigación y Desarrollo, Facultad de Ciencias y Filosofía, Universidad Peruana Cayetano Heredia, 31, Lima, Perú
| | - Francisco Rothhammer
- Instituto de Alta Investigación, Universidad de Tarapacá, Arica, 1000000, Arica, Chile
| | - Winston Rojas
- GENMOL (Genética Molecular), Universidad de Antioquia, 5001000, Medellín, Colombia
| | - Annina B Schmid
- Nuffield Department of Clinical Neurosciences, Oxford University, Oxford, OX3 9DU, UK
| | - Kaustubh Adhikari
- Department of Genetics, Evolution and Environment, University College London, London, WC1E 6BT, UK.
- School of Mathematics and Statistics, Faculty of Science, Technology, Engineering and Mathematics, The Open University, Milton Keynes, MK7 6AA, UK.
- Department of Cell and Developmental Biology, University College London, London, WC1E 6BT, UK.
| | - David L Bennett
- Nuffield Department of Clinical Neurosciences, Oxford University, Oxford, OX3 9DU, 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, Yangpu District, 200438, Shanghai, China.
- UMR ADES, Aix-Marseille Université, CNRS, EFS, 13005, Marseille, France.
- Department of Genetics, Evolution and Environment, University College London, London, WC1E 6BT, UK.
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5
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Li Q, Chen J, Faux P, Delgado ME, Bonfante B, Fuentes-Guajardo M, Mendoza-Revilla J, 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, Wu S, Du S, Giardina A, Paria SS, Khokan MR, Gonzalez-José R, Schüler-Faccini L, Bortolini MC, Acuña-Alonzo V, Canizales-Quinteros S, Gallo C, Poletti G, Rojas W, Rothhammer F, Navarro N, Wang S, Adhikari K, Ruiz-Linares A. Automatic landmarking identifies new loci associated with face morphology and implicates Neanderthal introgression in human nasal shape. Commun Biol 2023; 6:481. [PMID: 37156940 PMCID: PMC10167347 DOI: 10.1038/s42003-023-04838-7] [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] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Accepted: 04/12/2023] [Indexed: 05/10/2023] Open
Abstract
We report a genome-wide association study of facial features in >6000 Latin Americans based on automatic landmarking of 2D portraits and testing for association with inter-landmark distances. We detected significant associations (P-value <5 × 10-8) at 42 genome regions, nine of which have been previously reported. In follow-up analyses, 26 of the 33 novel regions replicate in East Asians, Europeans, or Africans, and one mouse homologous region influences craniofacial morphology in mice. The novel region in 1q32.3 shows introgression from Neanderthals and we find that the introgressed tract increases nasal height (consistent with the differentiation between Neanderthals and modern humans). Novel regions include candidate genes and genome regulatory elements previously implicated in craniofacial development, and show preferential transcription in cranial neural crest cells. The automated approach used here should simplify the collection of large study samples from across the world, facilitating a cosmopolitan characterization of the genetics of facial features.
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Affiliation(s)
- Qing Li
- 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, 200438, China
| | - Jieyi 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, 200438, China
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, 320 Yue Yang Road, Shanghai, 200031, China
| | - Pierre Faux
- Aix-Marseille Université, CNRS, EFS, ADES, Marseille, 13005, France
| | - Miguel Eduardo Delgado
- 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, 200438, China
- División Antropología, Facultad de Ciencias Naturales y Museo, Universidad Nacional de La Plata, La Plata, República Argentina
- Consejo Nacional de Investigaciones Científicas y Técnicas, CONICET, Buenos Aires, República Argentina
| | - Betty Bonfante
- Aix-Marseille Université, CNRS, EFS, ADES, Marseille, 13005, France
| | - Macarena Fuentes-Guajardo
- Departamento de Tecnología Médica, Facultad de Ciencias de la Salud, Universidad de Tarapacá, Arica, 1000000, Chile
| | - 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
| | - J 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, 6600, 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, 6600, 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, 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
| | - Sijie Wu
- 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, 200438, China
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, 320 Yue Yang Road, Shanghai, 200031, China
| | - Siyuan Du
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, 320 Yue Yang Road, Shanghai, 200031, China
| | - Andrea Giardina
- School of Mathematics and Statistics, Faculty of Science, Technology, Engineering and Mathematics, The Open University, Milton Keynes, MK7 6AA, United Kingdom
| | - Soumya Subhra Paria
- School of Mathematics and Statistics, Faculty of Science, Technology, Engineering and Mathematics, The Open University, Milton Keynes, MK7 6AA, United Kingdom
| | - Mahfuzur Rahman Khokan
- School of Mathematics and Statistics, Faculty of Science, Technology, Engineering and Mathematics, The Open University, Milton Keynes, MK7 6AA, United Kingdom
| | - 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
| | - Victor Acuña-Alonzo
- Molecular Genetics Laboratory, National School of Anthropology and History, Mexico City, 14050, Mexico, 6600, 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
| | - Winston Rojas
- GENMOL (Genética Molecular), Universidad de Antioquia, Medellín, 5001000, Colombia
| | - Francisco Rothhammer
- Instituto de Alta Investigación, Universidad de Tarapacá, Arica, Arica, 1000000, Chile
| | - Nicolas Navarro
- Biogéosciences, UMR 6282 CNRS, Université de Bourgogne, Dijon, 21000, France
- EPHE, PSL University, Paris, 75014, France
| | - Sijia Wang
- 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, 200438, China
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, 320 Yue Yang Road, Shanghai, 200031, China
| | - Kaustubh Adhikari
- School of Mathematics and Statistics, Faculty of Science, Technology, Engineering and Mathematics, The Open University, Milton Keynes, MK7 6AA, United Kingdom.
- 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, Yangpu District, Shanghai, 200438, China.
- 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.
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6
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Trendafilova T, Adhikari K, Schmid AB, Patel R, Polgár E, Chisholm KI, Middleton SJ, Boyle K, Dickie AC, Semizoglou E, Perez-Sanchez J, Bell AM, Ramirez-Aristeguieta LM, Khoury S, Ivanov A, Wildner H, Ferris E, Chacón-Duque JC, Sokolow S, Saad Boghdady MA, Herchuelz A, Faux P, Poletti G, Gallo C, Rothhammer F, Bedoya G, Zeilhofer HU, Diatchenko L, McMahon SB, Todd AJ, Dickenson AH, Ruiz-Linares A, Bennett DL. Sodium-calcium exchanger-3 regulates pain "wind-up": From human psychophysics to spinal mechanisms. Neuron 2022; 110:2571-2587.e13. [PMID: 35705078 PMCID: PMC7613464 DOI: 10.1016/j.neuron.2022.05.017] [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] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Revised: 03/31/2022] [Accepted: 05/16/2022] [Indexed: 11/17/2022]
Abstract
Repeated application of noxious stimuli leads to a progressively increased pain perception; this temporal summation is enhanced in and predictive of clinical pain disorders. Its electrophysiological correlate is "wind-up," in which dorsal horn spinal neurons increase their response to repeated nociceptor stimulation. To understand the genetic basis of temporal summation, we undertook a GWAS of wind-up in healthy human volunteers and found significant association with SLC8A3 encoding sodium-calcium exchanger type 3 (NCX3). NCX3 was expressed in mouse dorsal horn neurons, and mice lacking NCX3 showed normal, acute pain but hypersensitivity to the second phase of the formalin test and chronic constriction injury. Dorsal horn neurons lacking NCX3 showed increased intracellular calcium following repetitive stimulation, slowed calcium clearance, and increased wind-up. Moreover, virally mediated enhanced spinal expression of NCX3 reduced central sensitization. Our study highlights Ca2+ efflux as a pathway underlying temporal summation and persistent pain, which may be amenable to therapeutic targeting.
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Affiliation(s)
| | - Kaustubh Adhikari
- School of Mathematics and Statistics, Faculty of Science, Technology, Engineering and Mathematics, The Open University, Milton Keynes, UK; Department of Genetics, Evolution and Environment, University College London, London, UK; Department of Cell and Developmental Biology, University College London, London, UK
| | - Annina B Schmid
- Nuffield Department of Clinical Neurosciences, Oxford University, Oxford, UK
| | - Ryan Patel
- Department of Neuroscience, Physiology and Pharmacology, University College London, London, UK
| | - Erika Polgár
- School of Psychology and Neuroscience, University of Glasgow, Glasgow, UK
| | - Kim I Chisholm
- Wolfson Centre for Age-Related Diseases, King's College London, London, UK
| | - Steven J Middleton
- Nuffield Department of Clinical Neurosciences, Oxford University, Oxford, UK
| | - Kieran Boyle
- School of Psychology and Neuroscience, University of Glasgow, Glasgow, UK
| | - Allen C Dickie
- School of Psychology and Neuroscience, University of Glasgow, Glasgow, UK
| | | | | | - Andrew M Bell
- School of Psychology and Neuroscience, University of Glasgow, Glasgow, UK
| | | | - Samar Khoury
- McGill University and Genome Quebec Innovation Centre, Montreal, QC, Canada
| | - Aleksandar Ivanov
- Department of Physiology Anatomy and Genetics, University of Oxford, Oxford, UK
| | - Hendrik Wildner
- Institute of Pharmacology and Toxicology, University of Zurich, Zurich, Switzerland
| | - Eleanor Ferris
- Nuffield Department of Clinical Neurosciences, Oxford University, Oxford, UK
| | - Juan-Camilo Chacón-Duque
- Department of Genetics, Evolution and Environment, University College London, London, UK; Centre for Palaeogenetics, Stockholm, Sweden; Department of Archaeology and Classical Studies, Stockholm University, Stockholm, Sweden
| | - Sophie Sokolow
- Laboratoire de Pharmacodynamie et de Thérapeutique Faculté de Médecine Université Libre de Bruxelles, Brussels, Belgium; School of Nursing, University of California, Los Angeles, Los Angeles, CA, USA
| | | | - André Herchuelz
- Laboratoire de Pharmacodynamie et de Thérapeutique Faculté de Médecine Université Libre de Bruxelles, Brussels, Belgium
| | - Pierre Faux
- CNRS, EFS, ADES, Aix-Marseille Université, Marseille, France
| | - Giovanni Poletti
- Unidad de Neurobiologia Molecular y Genética, Laboratorios de Investigación y Desarrollo, Facultad de Ciencias y Filosofía, Universidad Peruana Cayetano Heredia, Lima, Peru
| | - Carla Gallo
- Unidad de Neurobiologia Molecular y Genética, Laboratorios de Investigación y Desarrollo, Facultad de Ciencias y Filosofía, Universidad Peruana Cayetano Heredia, Lima, Peru
| | | | - Gabriel Bedoya
- GENMOL (Genética Molecular), Universidad de Antioquia, Medellin, Colombia
| | - Hanns Ulrich Zeilhofer
- Institute of Pharmacology and Toxicology, University of Zurich, Zurich, Switzerland; Institute of Pharmaceutical Sciences, Swiss Federal Institute of Technology (ETH) Zurich, Zurich, Switzerland
| | - Luda Diatchenko
- McGill University and Genome Quebec Innovation Centre, Montreal, QC, Canada
| | - Stephen B McMahon
- Wolfson Centre for Age-Related Diseases, King's College London, London, UK
| | - Andrew J Todd
- School of Psychology and Neuroscience, University of Glasgow, Glasgow, UK
| | - Anthony H Dickenson
- Department of Neuroscience, Physiology and Pharmacology, University College London, London, UK
| | - Andres Ruiz-Linares
- Department of Genetics, Evolution and Environment, University College London, London, UK; CNRS, EFS, ADES, Aix-Marseille Université, Marseille, France; 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, China.
| | - David L Bennett
- Nuffield Department of Clinical Neurosciences, Oxford University, Oxford, UK.
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7
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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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8
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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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9
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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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10
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Alemu SW, Kadri NK, Harland C, Faux P, Charlier C, Caballero A, Druet T. An evaluation of inbreeding measures using a whole-genome sequenced cattle pedigree. Heredity (Edinb) 2020; 126:410-423. [PMID: 33159183 PMCID: PMC8027009 DOI: 10.1038/s41437-020-00383-9] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [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: 04/24/2020] [Revised: 10/23/2020] [Accepted: 10/23/2020] [Indexed: 11/13/2022] Open
Abstract
The estimation of the inbreeding coefficient (F) is essential for the study of inbreeding depression (ID) or for the management of populations under conservation. Several methods have been proposed to estimate the realized F using genetic markers, but it remains unclear which one should be used. Here we used whole-genome sequence data for 245 individuals from a Holstein cattle pedigree to empirically evaluate which estimators best capture homozygosity at variants causing ID, such as rare deleterious alleles or loci presenting heterozygote advantage and segregating at intermediate frequency. Estimators relying on the correlation between uniting gametes (FUNI) or on the genomic relationships (FGRM) presented the highest correlations with these variants. However, homozygosity at rare alleles remained poorly captured. A second group of estimators relying on excess homozygosity (FHOM), homozygous-by-descent segments (FHBD), runs-of-homozygosity (FROH) or on the known genealogy (FPED) was better at capturing whole-genome homozygosity, reflecting the consequences of inbreeding on all variants, and for young alleles with low to moderate frequencies (0.10 < . < 0.25). The results indicate that FUNI and FGRM might present a stronger association with ID. However, the situation might be different when recessive deleterious alleles reach higher frequencies, such as in populations with a small effective population size. For locus-specific inbreeding measures or at low marker density, the ranking of the methods can also change as FHBD makes better use of the information from neighboring markers. Finally, we confirmed that genomic measures are in general superior to pedigree-based estimates. In particular, FPED was uncorrelated with locus-specific homozygosity.
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Affiliation(s)
- Setegn Worku Alemu
- Unit of Animal Genomics, GIGA-R & Faculty of Veterinary Medicine, University of Liège, Liège, Belgium
| | - Naveen Kumar Kadri
- Unit of Animal Genomics, GIGA-R & Faculty of Veterinary Medicine, University of Liège, Liège, Belgium
| | - Chad Harland
- Unit of Animal Genomics, GIGA-R & Faculty of Veterinary Medicine, University of Liège, Liège, Belgium
| | - Pierre Faux
- Unit of Animal Genomics, GIGA-R & Faculty of Veterinary Medicine, University of Liège, Liège, Belgium
| | - Carole Charlier
- Unit of Animal Genomics, GIGA-R & Faculty of Veterinary Medicine, University of Liège, Liège, Belgium
| | - Armando Caballero
- Centro de Investigación Mariña, Departamento de Bioquímica, Genética e Inmunología, Edificio CC Experimentais, Universidade de Vigo, Campus de Vigo, As Lagoas, Marcosende, 36310, Vigo, Spain
| | - Tom Druet
- Unit of Animal Genomics, GIGA-R & Faculty of Veterinary Medicine, University of Liège, Liège, Belgium.
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11
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Faux P, Oliveira JC, Campos DP, Dantas GP, Maia TA, Dergan CG, Cassemiro PM, Hajdu GL, Santos-Júnior JE, Santos FR. Fast genomic analysis of aquatic bird populations from short single-end reads considering sex-related pitfalls. ECOL INFORM 2020. [DOI: 10.1016/j.ecoinf.2020.101058] [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: 11/16/2022]
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12
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Faux P, Geurts P, Druet T. A Random Forests Framework for Modeling Haplotypes as Mosaics of Reference Haplotypes. Front Genet 2019; 10:562. [PMID: 31316542 PMCID: PMC6610336 DOI: 10.3389/fgene.2019.00562] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [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] [Received: 10/08/2018] [Accepted: 05/29/2019] [Indexed: 11/30/2022] Open
Abstract
Many genomic data analyses such as phasing, genotype imputation, or local ancestry inference share a common core task: matching pairs of haplotypes at any position along the chromosome, thereby inferring a target haplotype as a succession of pieces from reference haplotypes, commonly called a mosaic of reference haplotypes. For that purpose, these analyses combine information provided by linkage disequilibrium, linkage and/or genealogy through a set of heuristic rules or, most often, by a hidden Markov model. Here, we develop an extremely randomized trees framework to address the issue of local haplotype matching. In our approach, a supervised classifier using extra-trees (a particular type of random forests) learns how to identify the best local matches between haplotypes using a collection of observed examples. For each example, various features related to the different sources of information are observed, such as the length of a segment shared between haplotypes, or estimates of relationships between individuals, gametes, and haplotypes. The random forests framework was fed with 30 relevant features for local haplotype matching. Repeated cross-validations allowed ranking these features in regard to their importance for local haplotype matching. The distance to the edge of a segment shared by both haplotypes being matched was found to be the most important feature. Similarity comparisons between predicted and true whole-genome sequence haplotypes showed that the random forests framework was more efficient than a hidden Markov model in reconstructing a target haplotype as a mosaic of reference haplotypes. To further evaluate its efficiency, the random forests framework was applied to imputation of whole-genome sequence from 50k genotypes and it yielded average reliabilities similar or slightly better than IMPUTE2. Through this exploratory study, we lay the foundations of a new framework to automatically learn local haplotype matching and we show that extra-trees are a promising approach for such purposes. The use of this new technique also reveals some useful lessons on the relevant features for the purpose of haplotype matching. We also discuss potential improvements for routine implementation.
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Affiliation(s)
- Pierre Faux
- Unit of Animal Genomics, GIGA-R, Faculty of Veterinary Medicine, University of Liège, Liège, Belgium
| | - Pierre Geurts
- Department of Electrical Engineering and Computer Science, Montefiore Institute, University of Liège, Liège, Belgium
| | - Tom Druet
- Unit of Animal Genomics, GIGA-R, Faculty of Veterinary Medicine, University of Liège, Liège, Belgium
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13
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Solé M, Gori AS, Faux P, Bertrand A, Farnir F, Gautier M, Druet T. Age-based partitioning of individual genomic inbreeding levels in Belgian Blue cattle. Genet Sel Evol 2017; 49:92. [PMID: 29273000 PMCID: PMC5741860 DOI: 10.1186/s12711-017-0370-x] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.7] [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: 07/03/2017] [Accepted: 12/13/2017] [Indexed: 11/21/2022] Open
Abstract
Background
Inbreeding coefficients can be estimated either from pedigree data or from genomic data, and with genomic data, they are either global or local (when the linkage map is used). Recently, we developed a new hidden Markov model (HMM) that estimates probabilities of homozygosity-by-descent (HBD) at each marker position and automatically partitions autozygosity in multiple age-related classes (based on the length of HBD segments). Our objectives were to: (1) characterize inbreeding with our model in an intensively selected population such as the Belgian Blue Beef (BBB) cattle breed; (2) compare the properties of the model at different marker densities; and (3) compare our model with other methods.
Results When using 600 K single nucleotide polymorphisms (SNPs), the inbreeding coefficient (probability of sampling an HBD locus in an individual) was on average 0.303 (ranging from 0.258 to 0.375). HBD-classes associated to historical ancestors (with small segments ≤ 200 kb) accounted for 21.6% of the genome length (71.4% of the total length of the genome in HBD segments), whereas classes associated to more recent ancestors accounted for only 22.6% of the total length of the genome in HBD segments. However, these recent classes presented more individual variation than more ancient classes. Although inbreeding coefficients obtained with low SNP densities (7 and 32 K) were much lower (0.060 and 0.093), they were highly correlated with those obtained at higher density (r = 0.934 and 0.975, respectively), indicating that they captured most of the individual variation. At higher SNP density, smaller HBD segments are identified and, thus, more past generations can be explored. We observed very high correlations between our estimates and those based on homozygosity (r = 0.95) or on runs-of-homozygosity (r = 0.95). As expected, pedigree-based estimates were mainly correlated with recent HBD-classes (r = 0.56). Conclusions Although we observed high levels of autozygosity associated with small HBD segments in BBB cattle, recent inbreeding accounted for most of the individual variation. Recent autozygosity can be captured efficiently with low-density SNP arrays and relatively simple models (e.g., two HBD classes). The HMM framework provides local HBD probabilities that are still useful at lower SNP densities. Electronic supplementary material The online version of this article (10.1186/s12711-017-0370-x) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Marina Solé
- Unit of Animal Genomics, GIGA-R & Faculty of Veterinary Medicine, University of Liège, B34 (+1) Avenue de l'Hôpital 1, 4000, Liège, Belgium.
| | - Ann-Stephan Gori
- Unit of Animal Genomics, GIGA-R & Faculty of Veterinary Medicine, University of Liège, B34 (+1) Avenue de l'Hôpital 1, 4000, Liège, Belgium.,Awé Coopérative (Association Wallonne de l'Élevage) - Recherche et Développement, Rue des Champs Elysées 4, 5590, Ciney, Belgium
| | - Pierre Faux
- Unit of Animal Genomics, GIGA-R & Faculty of Veterinary Medicine, University of Liège, B34 (+1) Avenue de l'Hôpital 1, 4000, Liège, Belgium
| | - Amandine Bertrand
- Unit of Animal Genomics, GIGA-R & Faculty of Veterinary Medicine, University of Liège, B34 (+1) Avenue de l'Hôpital 1, 4000, Liège, Belgium
| | - Frédéric Farnir
- BBASV, FARAH-PAD & Faculty of Veterinary Medicine, University of Liège, Quartier Vallée 2, Avenue de Cureghem, (B43 +3), 4000, Liège, Belgium
| | - Mathieu Gautier
- INRA, UMR CBGP (Centre de Biologie pour la Gestion des Populations), Campus International de Baillarguet, 34988, Montferrier sur Lez, France.,IBD (Institut de Biologie Computationnelle), 34095, Montpellier, France
| | - Tom Druet
- Unit of Animal Genomics, GIGA-R & Faculty of Veterinary Medicine, University of Liège, B34 (+1) Avenue de l'Hôpital 1, 4000, Liège, Belgium
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14
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Faux P, Druet T. A strategy to improve phasing of whole-genome sequenced individuals through integration of familial information from dense genotype panels. Genet Sel Evol 2017; 49:46. [PMID: 28511677 PMCID: PMC5434521 DOI: 10.1186/s12711-017-0321-6] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [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: 12/20/2016] [Accepted: 05/05/2017] [Indexed: 11/21/2022] Open
Abstract
Background Haplotype reconstruction (phasing) is an essential step in many applications, including imputation and genomic selection. The best phasing methods rely on both familial and linkage disequilibrium (LD) information. With whole-genome sequence (WGS) data, relatively small samples of reference individuals are generally sequenced due to prohibitive sequencing costs, thus only a limited amount of familial information is available. However, reference individuals have many relatives that have been genotyped (at lower density). The goal of our study was to improve phasing of WGS data by integrating familial information from haplotypes that were obtained from a larger genotyped dataset and to quantify its impact on imputation accuracy. Results Aligning a pre-phased WGS panel [~5 million single nucleotide polymorphisms (SNPs)], which is based on LD information only, to a 50k SNP array that is phased with both LD and familial information (called scaffold) resulted in correctly assigning parental origin for 99.62% of the WGS SNPs, their phase being determined unambiguously based on parental genotypes. Without using the 50k haplotypes as scaffold, that value dropped as expected to 50%. Correctly phased segments were on average longer after alignment to the genotype phase while the number of switches decreased slightly. Most of the incorrectly assigned segments, and subsequent switches, were due to singleton errors. Imputation from 50k SNP array to WGS data with improved phasing had a marginal impact on imputation accuracy (measured as r2), i.e. on average, 90.47% with traditional techniques versus 90.65% with pre-phasing integrating familial information. Differences were larger for SNPs located in chromosome ends and rare variants. Using a denser WGS panel (~13 millions SNPs) that was obtained with traditional variant filtering rules, we found similar results although performances of both phasing and imputation accuracy were lower. Conclusions We present a phasing strategy for WGS data, which indirectly integrates familial information by aligning WGS haplotypes that are pre-phased with LD information only on haplotypes obtained with genotyping data, with both LD and familial information and on a much larger population. This strategy results in very few mismatches with the phase obtained by Mendelian segregation rules. Finally, we propose a strategy to further improve phasing accuracy based on haplotype clusters obtained with genotyping data. Electronic supplementary material The online version of this article (doi:10.1186/s12711-017-0321-6) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Pierre Faux
- Unit of Animal Genomics, GIGA-R and Faculty of Veterinary Medicine, University of Liège, 4000, Liège, Belgium.
| | - Tom Druet
- Unit of Animal Genomics, GIGA-R and Faculty of Veterinary Medicine, University of Liège, 4000, Liège, Belgium
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15
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Charlier C, Li W, Harland C, Littlejohn M, Coppieters W, Creagh F, Davis S, Druet T, Faux P, Guillaume F, Karim L, Keehan M, Kadri NK, Tamma N, Spelman R, Georges M. NGS-based reverse genetic screen for common embryonic lethal mutations compromising fertility in livestock. Genome Res 2016; 26:1333-1341. [PMID: 27646536 PMCID: PMC5052051 DOI: 10.1101/gr.207076.116] [Citation(s) in RCA: 58] [Impact Index Per Article: 7.3] [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: 03/16/2016] [Accepted: 08/19/2016] [Indexed: 01/20/2023]
Abstract
We herein report the result of a large-scale, next generation sequencing (NGS)-based screen for embryonic lethal (EL) mutations in Belgian beef and New Zealand dairy cattle. We estimated by simulation that cattle might carry, on average, ∼0.5 recessive EL mutations. We mined exome sequence data from >600 animals, and identified 1377 stop-gain, 3139 frame-shift, 1341 splice-site, 22,939 disruptive missense, 62,399 benign missense, and 92,163 synonymous variants. We show that cattle have a comparable load of loss-of-function (LoF) variants (defined as stop-gain, frame-shift, or splice-site variants) as humans despite having a more variable exome. We genotyped >40,000 animals for up to 296 LoF and 3483 disruptive missense, breed-specific variants. We identified candidate EL mutations based on the observation of a significant depletion in homozygotes. We estimated the proportion of EL mutations at 15% of tested LoF and 6% of tested disruptive missense variants. We confirmed the EL nature of nine candidate variants by genotyping 200 carrier × carrier trios, and demonstrating the absence of homozygous offspring. The nine identified EL mutations segregate at frequencies ranging from 1.2% to 6.6% in the studied populations and collectively account for the mortality of ∼0.6% of conceptuses. We show that EL mutations preferentially affect gene products fulfilling basic cellular functions. The resulting information will be useful to avoid at-risk matings, thereby improving fertility.
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Affiliation(s)
- Carole Charlier
- Unit of Animal Genomics, GIGA-R & Faculty of Veterinary Medicine, University of Liège (B34), 4000-Liège, Belgium
| | - Wanbo Li
- State Key Laboratory for Pig Genetic Improvement and Production Technology, Jiangxi Agricultural University, Nanchang, 330045, Jiangxi Province, P.R. China
| | - Chad Harland
- Unit of Animal Genomics, GIGA-R & Faculty of Veterinary Medicine, University of Liège (B34), 4000-Liège, Belgium; Livestock Improvement Corporation, Newstead, Hamilton 3240, New Zealand
| | - Mathew Littlejohn
- Livestock Improvement Corporation, Newstead, Hamilton 3240, New Zealand
| | - Wouter Coppieters
- Unit of Animal Genomics, GIGA-R & Faculty of Veterinary Medicine, University of Liège (B34), 4000-Liège, Belgium; Genomics Platform, GIGA, University of Liège (B34), 4000-Liège, Belgium
| | - Frances Creagh
- Livestock Improvement Corporation, Newstead, Hamilton 3240, New Zealand
| | - Steve Davis
- Livestock Improvement Corporation, Newstead, Hamilton 3240, New Zealand
| | - Tom Druet
- Unit of Animal Genomics, GIGA-R & Faculty of Veterinary Medicine, University of Liège (B34), 4000-Liège, Belgium
| | - Pierre Faux
- Unit of Animal Genomics, GIGA-R & Faculty of Veterinary Medicine, University of Liège (B34), 4000-Liège, Belgium
| | - François Guillaume
- Unit of Animal Genomics, GIGA-R & Faculty of Veterinary Medicine, University of Liège (B34), 4000-Liège, Belgium
| | - Latifa Karim
- Unit of Animal Genomics, GIGA-R & Faculty of Veterinary Medicine, University of Liège (B34), 4000-Liège, Belgium; Genomics Platform, GIGA, University of Liège (B34), 4000-Liège, Belgium
| | - Mike Keehan
- Livestock Improvement Corporation, Newstead, Hamilton 3240, New Zealand
| | - Naveen Kumar Kadri
- Unit of Animal Genomics, GIGA-R & Faculty of Veterinary Medicine, University of Liège (B34), 4000-Liège, Belgium
| | - Nico Tamma
- Unit of Animal Genomics, GIGA-R & Faculty of Veterinary Medicine, University of Liège (B34), 4000-Liège, Belgium
| | - Richard Spelman
- Livestock Improvement Corporation, Newstead, Hamilton 3240, New Zealand
| | - Michel Georges
- Unit of Animal Genomics, GIGA-R & Faculty of Veterinary Medicine, University of Liège (B34), 4000-Liège, Belgium
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16
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Faux P, Gengler N. A method to approximate the inverse of a part of the additive relationship matrix. J Anim Breed Genet 2015; 132:229-38. [PMID: 25560252 DOI: 10.1111/jbg.12128] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [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: 03/14/2014] [Accepted: 10/10/2014] [Indexed: 11/28/2022]
Abstract
Single-step genomic predictions need the inverse of the part of the additive relationship matrix between genotyped animals (A22 ). Gains in computing time are feasible with an algorithm that sets up the sparsity pattern of A22-1 (SP algorithm) using pedigree searches, when A22-1 is close to sparse. The objective of this study is to present a modification of the SP algorithm (RSP algorithm) and to assess its use in approximating A22-1 when the actual A22-1 is dense. The RSP algorithm sets up a restricted sparsity pattern of A22-1 by limiting the pedigree search to a maximum number of searched branches. We have tested its use on four different simulated genotyped populations, from 10 000 to 75 000 genotyped animals. Accuracy of approximation is tested by replacing the actual A22-1 by its approximation in an equivalent mixed model including only genotyped animals. Results show that limiting the pedigree search to four branches is enough to provide accurate approximations of A22-1, which contain approximately 80% of zeros. Computing approximations is not expensive in time but may require a great amount of memory (at maximum, approximately 81 min and approximately 55 Gb of RAM for 75 000 genotyped animals using parallel processing on four threads).
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Affiliation(s)
- P Faux
- Animal Science Unit, Gembloux Agro-Bio Tech, University of Liège, Gembloux, Belgium
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Dufrasne M, Faux P, Piedboeuf M, Wavreille J, Gengler N. Estimation of dominance variance for live body weight in a crossbred population of pigs. J Anim Sci 2014; 92:4313-8. [PMID: 25149333 DOI: 10.2527/jas.2014-7833] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [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: 11/13/2022] Open
Abstract
The objective of this study was to estimate the dominance variance for repeated live BW records in a crossbred population of pigs. Data were provided by the Walloon Pig Breeding Association and included 22,197 BW records of 2,999 crossbred Piétrain × Landrace K+ pigs from 50 to 210 d of age. The BW records were standardized and adjusted to 210 d of age for analysis. Three single-trait random regression animal models were used: Model 1 without parental subclass effect, Model 2 with parental subclasses considered unrelated, and Model 3 with the complete parental dominance relationship matrix. Each model included sex, contemporary group, and heterosis as fixed effects as well as additive genetic, permanent environment, and residual as random effects. Variance components and their SE were estimated using a Gibbs sampling algorithm. Heritability tended to increase with age: from 0.50 to 0.64 for Model 1, from 0.19 to 0.42 for Model 2, and from 0.31 to 0.53 for Model 3. Permanent environmental variance tended to decrease with age and accounted for 29 to 44% of total variance for Model 1, 29 to 37% of total variance for Model 2, and 34 to 51% of total variance for Model 3. Residual variance explained <10% of total variance for the 3 models. Dominance variance was computed as 4 times the estimated parental subclass variance. Dominance variance accounted for 22 to 40% of total variance for Model 2 and 5 to 11% of total variance for Model 3, with a decrease with age for both models. Results showed that dominance effects exist for growth traits in pigs and may be reasonably large. The use of the complete dominance relationship matrix may improve the estimation of additive genetic variances and breeding values. Moreover, a dominance effect could be especially useful in selection programs for individual matings through the use of specific combining ability to maximize growth potential of crossbred progeny.
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Affiliation(s)
- M Dufrasne
- Animal Science Unit, Gembloux Agro-Bio Tech, University of Liège, B-5030 Gembloux, Belgium Fonds pour la formation à la Recherche dans l'Industrie et dans l'Agriculture (FRIA), B-1000 Brussels, Belgium
| | - P Faux
- Animal Science Unit, Gembloux Agro-Bio Tech, University of Liège, B-5030 Gembloux, Belgium
| | - M Piedboeuf
- Walloon Pig Breeding Association (AWEP), B-5590 Ciney, Belgium
| | - J Wavreille
- Walloon Agricultural Research Centre (CRA-W), B-5030 Gembloux, Belgium
| | - N Gengler
- Animal Science Unit, Gembloux Agro-Bio Tech, University of Liège, B-5030 Gembloux, Belgium
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Faux P, Gengler N. Inversion of a part of the numerator relationship matrix using pedigree information. Genet Sel Evol 2013; 45:45. [PMID: 24313900 PMCID: PMC3878974 DOI: 10.1186/1297-9686-45-45] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [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: 03/25/2013] [Accepted: 10/29/2013] [Indexed: 11/10/2022] Open
Abstract
Background In recent theoretical developments, the information available (e.g. genotypes) divides the original population into two groups: animals with this information (selected animals) and animals without this information (excluded animals). These developments require inversion of the part of the pedigree-based numerator relationship matrix that describes the genetic covariance between selected animals (A22). Our main objective was to propose and evaluate methodology that takes advantage of any potential sparsity in the inverse of A22 in order to reduce the computing time required for its inversion. This potential sparsity is brought out by searching the pedigree for dependencies between the selected animals. Jointly, we expected distant ancestors to provide relationship ties that increase the density of matrix A22 but that their effect on A22-1 might be minor. This hypothesis was also tested. Methods The inverse of A22 can be computed from the inverse of the triangular factor (T-1) obtained by Cholesky root-free decomposition of A22. We propose an algorithm that sets up the sparsity pattern of T-1 using pedigree information. This algorithm provides positions of the elements of T-1 worth to be computed (i.e. different from zero). A recursive computation of A22-1 is then achieved with or without information on the sparsity pattern and time required for each computation was recorded. For three numbers of selected animals (4000; 8000 and 12 000), A22 was computed using different pedigree extractions and the closeness of the resulting A22-1 to the inverse computed using the fully extracted pedigree was measured by an appropriate norm. Results The use of prior information on the sparsity of T-1 decreased the computing time for inversion by a factor of 1.73 on average. Computational issues and practical uses of the different algorithms were discussed. Cases involving more than 12 000 selected animals were considered. Inclusion of 10 generations was determined to be sufficient when computing A22. Conclusions Depending on the size and structure of the selected sub-population, gains in time to compute A22-1 are possible and these gains may increase as the number of selected animals increases. Given the sequential nature of most computational steps, the proposed algorithm can benefit from optimization and may be convenient for genomic evaluations.
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Affiliation(s)
- Pierre Faux
- Animal Science Unit, Gembloux Agro-Bio Tech, University of Liège, Passage des Déportés, 2, 5030 Gembloux, Belgium.
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Faux P, Gengler N, Misztal I. A recursive algorithm for decomposition and creation of the inverse of the genomic relationship matrix. J Dairy Sci 2012; 95:6093-102. [DOI: 10.3168/jds.2011-5249] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2011] [Accepted: 05/24/2012] [Indexed: 11/19/2022]
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Guehl D, Dehail P, de Sèze MP, Cuny E, Faux P, Tison F, Barat M, Bioulac B, Burbaud P. Evolution of postural stability after subthalamic nucleus stimulation in Parkinson’s disease: a combined clinical and posturometric study. Exp Brain Res 2005; 170:206-15. [PMID: 16328280 DOI: 10.1007/s00221-005-0202-z] [Citation(s) in RCA: 28] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2004] [Accepted: 08/18/2005] [Indexed: 10/25/2022]
Abstract
OBJECTIVES The occurrence of postural and balance disorders is a frequent feature in advanced forms of Parkinson's disease (PD). However, the pathological substrate of these disturbances is poorly understood. METHODS In the present work, we investigated the evolution of posturometric parameters [center of pressure (CoP) displacement and CoP area] and axial scores between the pre-operative period and 3 months post-operative in seven PD patients who underwent bilateral deep brain stimulation (DBS) of the subthalamic nucleus (STN). RESULTS After surgery, the patients leaned backwards much more regardless of the STN stimulation, suggesting that surgery could have a deleterious effect on postural adaptation. During the post-operative period, the improvement in axial and postural scores was similar under levodopatherapy and DBS. On the other hand, DBS of the STN significantly reduced the CoP displacement and the CoP area, whereas levodopatherapy tended only to reduce the CoP displacement and to increase the CoP area significantly. CONCLUSIONS These data suggest that DBS of the STN and levodopa do not act on the same neurological systems involved in posture regulation. DBS of the STN could improve posture via a direct effect on the pedunculopontine nucleus, which is known to be involved in posture regulation.
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Affiliation(s)
- D Guehl
- Service de Neurophysiologie Clinique, Hôpital Pellegrin, Place Amélie-Raba-Léon, CNRS UMR 5543, Université de Bordeaux, 2, Victor Segalen, 146 rue Léo Saignat, 33076, Bordeaux, France.
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Dehail P, Joseph PA, Faux P, Rainfray M, Emeriau JP, Barat M, Bourdel-Marchasson I. Early changes in isokinetic lower limb muscle strength in recovering geriatric subjects on the basis of nutritional status. J Nutr Health Aging 2005; 9:356-63. [PMID: 16222403] [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] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
BACKGROUND In older people, the decrease in muscle mass and strength has a bad effect on functional status. Malnutrition and lack of physical activity exacerbate this phenomenon. OBJECTIVE The main purpose of this study was to estimate isokinetic lower limb muscle strength in recovering older subjects on the basis of nutritional status. DESIGN Twenty-eight elderly subjects hospitalized for an acute event (85.8 +/- 6 years), including 16 malnourished, were enrolled in this study when clinically stable (T0). Re-assessment at one-month was performed in nine after oral supplementation and conventional physiotherapy (T1). The Maximal Peak Torque (MPT) of the ankle plantar flexors was estimated in concentric mode at 30 and 60 degrees /s. The MPT of the knee flexors and extensors was evaluated in the same mode at 30 degrees /s, 60 degrees /s and 120 degrees /s. All patients underwent a nutritional examination with anthropometric measures, dietary intake survey, biochemical indexes and determination of the medial gastrocnemius volume by magnetic resonance imaging. RESULTS At T0, whatever the muscle group tested (except at 120 degrees /s for the knee), the MPT appeared significantly lower in the malnourished group. At T1, the increase in MPT (plantar flexors) in malnourished patients was greater at 60 degrees /s (+23.8 %) than at 30 degrees /s (+14.8 %). Correlations between MPT and nutritional parameters were observed in the malnourished group only at T1 and in the normal-nourished group. CONCLUSION Isokinetic assessment seems to be a pertinent method to estimate lower limb muscle strength in older and frail subjects. Early modifications in strength were observed in malnourished patients who received oral supplementation and physiotherapy.
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Affiliation(s)
- P Dehail
- Departement de Geriatrie, Unite de Soins de Suite et Readaptation - CHU Bordeaux - Hopital Xavier-Arnozan, 33604 Pessac Cedex, France.
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Bourdel-Marchasson I, Joseph PA, Dehail P, Biran M, Faux P, Rainfray M, Emeriau JP, Canioni P, Thiaudière E. Functional and metabolic early changes in calf muscle occurring during nutritional repletion in malnourished elderly patients. Am J Clin Nutr 2001; 73:832-8. [PMID: 11273861 DOI: 10.1093/ajcn/73.4.832] [Citation(s) in RCA: 36] [Impact Index Per Article: 1.6] [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: 12/27/2022] Open
Abstract
BACKGROUND Metabolic alterations in skeletal muscle associated with malnutrition and the potential reversibility of such alterations during refeeding are not fully understood. OBJECTIVE We characterized early changes in muscle during refeeding in malnourished, hospitalized elderly subjects. DESIGN Muscle function, metabolism, and mass were evaluated in 24 clinically stable patients (11 were malnourished) by using isokinetic plantar flexor torque measurements and nuclear magnetic resonance (NMR) imaging for medial gastrocnemius mass assessment and 31P and 13C NMR spectroscopy for inorganic phosphate (Pi), phosphocreatine, and glycogen quantitation. RESULTS Malnourished subjects had lower muscle mass (P < 0.02) and tended to have lower strength than did control subjects. In malnourished subjects, muscle strength increased after refeeding (P < 0.01) whereas muscle mass was unchanged. The ratio of Pi to ATP was lower in malnourished than in control subjects (P < 0.001) and increased during refeeding (P < 0.01). The mean ratio of phosphocreatine to ATP was lower in malnourished than in control subjects (P < 0.01) and increased to control values after refeeding. Muscle glycogen showed a scattered distribution for malnourished subjects; the mean value did not differ significantly from that of control subjects, either at baseline or after refeeding. CONCLUSIONS The lower ratio of phosphocreatine to ATP in malnourished subjects could have resulted from either lower total muscle creatine or reduced oxidative capacities. High or normal glycogen associated with a low Pi-to-ATP ratio in malnourished subjects suggested preferential use of lipid over carbohydrate for energy supply, which is known to reduce muscle performance. The data suggest that normalization of muscle metabolite content after refeeding improves muscle strength in malnourished subjects.
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Affiliation(s)
- I Bourdel-Marchasson
- Département de Gériatrie du Centre Hospitalo-Universitaire de Bordeaux, Hôpital Xavier Arnozan, Centre Hospitalier-Universitaire de Bordeaux, Pessac, France.
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