Prediction of eye, hair and skin colour in Latin Americans
- Autores
- Palmal, Sagnik; Kaustubh Adhikari; Mendoza Revilla, Javier; Fuentes Guajardo, Macarena; Silva de Cerqueira, Caio Cesar; Bonfante, Betty; Chacón Duque, Juan Camilo; Sohail, Anood; Hurtadeo, Malena; Villegas, Valeria; Granja, Vanessa; Jaramillo, Claudia; Arias, Williams; Barquera Lozano, Rodrigo; Everardo Martínez, Paola; Gómez Valdés, Jorge; Villamil Ramirez, Hugo; Hünemeier, Tábita; Ramallo, Virginia; Parolin, María Laura; Gonzalez, Rolando Jose; Schüler-Faccini, Lavinia; Bortolini, María Cátira; Acuña Alonzo, Victor; Canizales Quinteros, Samuel; Gallo, Carla; Poletti, Giovanni; Bedoya, Gabriel; Rothhammer, Francisco; Balding, David; Faux, Pierre; Ruiz Linares, Andrés
- Año de publicación
- 2021
- Idioma
- inglés
- Tipo de recurso
- artículo
- Estado
- versión publicada
- Descripción
- 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.
Fil: Palmal, Sagnik. Centre National de la Recherche Scientifique; Francia. Aix-Marseille Université; Francia
Fil: Kaustubh Adhikari. The Open University; Reino Unido. University College London; Estados Unidos
Fil: Mendoza Revilla, Javier. Universidad Peruana Cayetano Heredia; Perú. Institut Pasteur de Paris.; Francia
Fil: Fuentes Guajardo, Macarena. Universidad de Tarapacá; Chile
Fil: Silva de Cerqueira, Caio Cesar. Scientific Police of São Paulo State; Brasil
Fil: Bonfante, Betty. Aix-Marseille Université; Francia. Centre National de la Recherche Scientifique; Francia
Fil: Chacón Duque, Juan Camilo. Natural History Museum; Reino Unido
Fil: Sohail, Anood. Kinnaird College for Women. Department of Biotechnology; Pakistán
Fil: Hurtadeo, Malena. Universidad Peruana Cayetano Heredia; Perú
Fil: Villegas, Valeria. Universidad Peruana Cayetano Heredia; Perú
Fil: Granja, Vanessa. Universidad Peruana Cayetano Heredia; Perú
Fil: Jaramillo, Claudia. Kinnaird College for Women. Department of Biotechnology; Pakistán. Universidad de Antioquia; Colombia
Fil: Arias, Williams. Universidad de Antioquia; Colombia
Fil: Barquera Lozano, Rodrigo. Instituto Nacional de Antropología e Historia; México. Max Planck Institute for the Science of Human History. Department of Archaeogenetics; Alemania
Fil: Everardo Martínez, Paola. Instituto Nacional de Antropología e Historia; México
Fil: Gómez Valdés, Jorge. Instituto Nacional de Antropología e Historia; México
Fil: Villamil Ramirez, Hugo. Universidad Nacional Autónoma de México; México
Fil: Hünemeier, Tábita. Universidade de Sao Paulo; Brasil
Fil: Ramallo, Virginia. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Centro Nacional Patagónico. Instituto Patagónico de Ciencias Sociales y Humanas; Argentina. Universidade Federal do Rio Grande do Sul; Brasil
Fil: Parolin, María Laura. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Centro Nacional Patagónico. Instituto de Diversidad y Evolución Austral; Argentina
Fil: Gonzalez, Rolando Jose. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Centro Nacional Patagónico. Instituto Patagónico de Ciencias Sociales y Humanas; Argentina
Fil: Schüler-Faccini, Lavinia. Universidade Federal do Rio Grande do Sul, Porto Alegre; Brasil
Fil: Bortolini, María Cátira. Universidade Federal do Rio Grande do Sul; Brasil
Fil: Acuña Alonzo, Victor. Instituto Nacional de Antropología e Historia; México. Universidade Federal do Rio Grande do Sul; Brasil
Fil: Canizales Quinteros, Samuel. Universidad Nacional Autónoma de México; México
Fil: Gallo, Carla. Universidad Peruana Cayetano Heredia; Perú
Fil: Poletti, Giovanni. Universidad Peruana Cayetano Heredia; Perú
Fil: Bedoya, Gabriel. Universidad de Antioquia; Colombia
Fil: Rothhammer, Francisco. Universidad de Tarapacá; Chile. Universidad de Chile; Chile
Fil: Balding, David. University College London; Reino Unido. University of Melbourne; Australia
Fil: Faux, Pierre. Aix-Marseille Université; Francia. Centre National de la Recherche Scientifique; Francia
Fil: Ruiz Linares, Andrés. Aix-Marseille Université; Francia. Centre National de la Recherche Scientifique; Francia. University College London; Reino Unido. Fudan University; República de China - Materia
-
ADMIXTURE
DNA PHENOTYPING
EYE-COLOUR
HAIR-COLOUR
LATIN AMERICANS
PIGMENTATION PREDICTION
SKIN-COLOUR - Nivel de accesibilidad
- acceso abierto
- Condiciones de uso
- https://creativecommons.org/licenses/by/2.5/ar/
- Repositorio
.jpg)
- Institución
- Consejo Nacional de Investigaciones Científicas y Técnicas
- OAI Identificador
- oai:ri.conicet.gov.ar:11336/148751
Ver los metadatos del registro completo
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Prediction of eye, hair and skin colour in Latin AmericansPalmal, SagnikKaustubh AdhikariMendoza Revilla, JavierFuentes Guajardo, MacarenaSilva de Cerqueira, Caio CesarBonfante, BettyChacón Duque, Juan CamiloSohail, AnoodHurtadeo, MalenaVillegas, ValeriaGranja, VanessaJaramillo, ClaudiaArias, WilliamsBarquera Lozano, RodrigoEverardo Martínez, PaolaGómez Valdés, JorgeVillamil Ramirez, HugoHünemeier, TábitaRamallo, VirginiaParolin, María LauraGonzalez, Rolando JoseSchüler-Faccini, LaviniaBortolini, María CátiraAcuña Alonzo, VictorCanizales Quinteros, SamuelGallo, CarlaPoletti, GiovanniBedoya, GabrielRothhammer, FranciscoBalding, DavidFaux, PierreRuiz Linares, AndrésADMIXTUREDNA PHENOTYPINGEYE-COLOURHAIR-COLOURLATIN AMERICANSPIGMENTATION PREDICTIONSKIN-COLOURhttps://purl.org/becyt/ford/1.6https://purl.org/becyt/ford/1Here 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.Fil: Palmal, Sagnik. Centre National de la Recherche Scientifique; Francia. Aix-Marseille Université; FranciaFil: Kaustubh Adhikari. The Open University; Reino Unido. University College London; Estados UnidosFil: Mendoza Revilla, Javier. Universidad Peruana Cayetano Heredia; Perú. Institut Pasteur de Paris.; FranciaFil: Fuentes Guajardo, Macarena. Universidad de Tarapacá; ChileFil: Silva de Cerqueira, Caio Cesar. Scientific Police of São Paulo State; BrasilFil: Bonfante, Betty. Aix-Marseille Université; Francia. Centre National de la Recherche Scientifique; FranciaFil: Chacón Duque, Juan Camilo. Natural History Museum; Reino UnidoFil: Sohail, Anood. Kinnaird College for Women. Department of Biotechnology; PakistánFil: Hurtadeo, Malena. Universidad Peruana Cayetano Heredia; PerúFil: Villegas, Valeria. Universidad Peruana Cayetano Heredia; PerúFil: Granja, Vanessa. Universidad Peruana Cayetano Heredia; PerúFil: Jaramillo, Claudia. Kinnaird College for Women. Department of Biotechnology; Pakistán. Universidad de Antioquia; ColombiaFil: Arias, Williams. Universidad de Antioquia; ColombiaFil: Barquera Lozano, Rodrigo. Instituto Nacional de Antropología e Historia; México. Max Planck Institute for the Science of Human History. Department of Archaeogenetics; AlemaniaFil: Everardo Martínez, Paola. Instituto Nacional de Antropología e Historia; MéxicoFil: Gómez Valdés, Jorge. Instituto Nacional de Antropología e Historia; MéxicoFil: Villamil Ramirez, Hugo. Universidad Nacional Autónoma de México; MéxicoFil: Hünemeier, Tábita. Universidade de Sao Paulo; BrasilFil: Ramallo, Virginia. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Centro Nacional Patagónico. Instituto Patagónico de Ciencias Sociales y Humanas; Argentina. Universidade Federal do Rio Grande do Sul; BrasilFil: Parolin, María Laura. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Centro Nacional Patagónico. Instituto de Diversidad y Evolución Austral; ArgentinaFil: Gonzalez, Rolando Jose. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Centro Nacional Patagónico. Instituto Patagónico de Ciencias Sociales y Humanas; ArgentinaFil: Schüler-Faccini, Lavinia. Universidade Federal do Rio Grande do Sul, Porto Alegre; BrasilFil: Bortolini, María Cátira. Universidade Federal do Rio Grande do Sul; BrasilFil: Acuña Alonzo, Victor. Instituto Nacional de Antropología e Historia; México. Universidade Federal do Rio Grande do Sul; BrasilFil: Canizales Quinteros, Samuel. Universidad Nacional Autónoma de México; MéxicoFil: Gallo, Carla. Universidad Peruana Cayetano Heredia; PerúFil: Poletti, Giovanni. Universidad Peruana Cayetano Heredia; PerúFil: Bedoya, Gabriel. Universidad de Antioquia; ColombiaFil: Rothhammer, Francisco. Universidad de Tarapacá; Chile. Universidad de Chile; ChileFil: Balding, David. University College London; Reino Unido. University of Melbourne; AustraliaFil: Faux, Pierre. Aix-Marseille Université; Francia. Centre National de la Recherche Scientifique; FranciaFil: Ruiz Linares, Andrés. Aix-Marseille Université; Francia. Centre National de la Recherche Scientifique; Francia. University College London; Reino Unido. Fudan University; República de ChinaElsevier Ireland2021-07info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/148751Palmal, Sagnik; Kaustubh Adhikari; Mendoza Revilla, Javier; Fuentes Guajardo, Macarena; Silva de Cerqueira, Caio Cesar; et al.; Prediction of eye, hair and skin colour in Latin Americans; Elsevier Ireland; Forensic Science International: Genetics; 53; 7-2021; 1-122041-17231872-4973CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://www.fsigenetics.com/article/S1872-4973(21)00055-7/fulltextinfo:eu-repo/semantics/altIdentifier/doi/10.1016/j.fsigen.2021.102517info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T09:50:47Zoai:ri.conicet.gov.ar:11336/148751instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-29 09:50:48.109CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse |
| dc.title.none.fl_str_mv |
Prediction of eye, hair and skin colour in Latin Americans |
| title |
Prediction of eye, hair and skin colour in Latin Americans |
| spellingShingle |
Prediction of eye, hair and skin colour in Latin Americans Palmal, Sagnik ADMIXTURE DNA PHENOTYPING EYE-COLOUR HAIR-COLOUR LATIN AMERICANS PIGMENTATION PREDICTION SKIN-COLOUR |
| title_short |
Prediction of eye, hair and skin colour in Latin Americans |
| title_full |
Prediction of eye, hair and skin colour in Latin Americans |
| title_fullStr |
Prediction of eye, hair and skin colour in Latin Americans |
| title_full_unstemmed |
Prediction of eye, hair and skin colour in Latin Americans |
| title_sort |
Prediction of eye, hair and skin colour in Latin Americans |
| dc.creator.none.fl_str_mv |
Palmal, Sagnik Kaustubh Adhikari Mendoza Revilla, Javier Fuentes Guajardo, Macarena Silva de Cerqueira, Caio Cesar Bonfante, Betty Chacón Duque, Juan Camilo Sohail, Anood Hurtadeo, Malena Villegas, Valeria Granja, Vanessa Jaramillo, Claudia Arias, Williams Barquera Lozano, Rodrigo Everardo Martínez, Paola Gómez Valdés, Jorge Villamil Ramirez, Hugo Hünemeier, Tábita Ramallo, Virginia Parolin, María Laura Gonzalez, Rolando Jose Schüler-Faccini, Lavinia Bortolini, María Cátira Acuña Alonzo, Victor Canizales Quinteros, Samuel Gallo, Carla Poletti, Giovanni Bedoya, Gabriel Rothhammer, Francisco Balding, David Faux, Pierre Ruiz Linares, Andrés |
| author |
Palmal, Sagnik |
| author_facet |
Palmal, Sagnik Kaustubh Adhikari Mendoza Revilla, Javier Fuentes Guajardo, Macarena Silva de Cerqueira, Caio Cesar Bonfante, Betty Chacón Duque, Juan Camilo Sohail, Anood Hurtadeo, Malena Villegas, Valeria Granja, Vanessa Jaramillo, Claudia Arias, Williams Barquera Lozano, Rodrigo Everardo Martínez, Paola Gómez Valdés, Jorge Villamil Ramirez, Hugo Hünemeier, Tábita Ramallo, Virginia Parolin, María Laura Gonzalez, Rolando Jose Schüler-Faccini, Lavinia Bortolini, María Cátira Acuña Alonzo, Victor Canizales Quinteros, Samuel Gallo, Carla Poletti, Giovanni Bedoya, Gabriel Rothhammer, Francisco Balding, David Faux, Pierre Ruiz Linares, Andrés |
| author_role |
author |
| author2 |
Kaustubh Adhikari Mendoza Revilla, Javier Fuentes Guajardo, Macarena Silva de Cerqueira, Caio Cesar Bonfante, Betty Chacón Duque, Juan Camilo Sohail, Anood Hurtadeo, Malena Villegas, Valeria Granja, Vanessa Jaramillo, Claudia Arias, Williams Barquera Lozano, Rodrigo Everardo Martínez, Paola Gómez Valdés, Jorge Villamil Ramirez, Hugo Hünemeier, Tábita Ramallo, Virginia Parolin, María Laura Gonzalez, Rolando Jose Schüler-Faccini, Lavinia Bortolini, María Cátira Acuña Alonzo, Victor Canizales Quinteros, Samuel Gallo, Carla Poletti, Giovanni Bedoya, Gabriel Rothhammer, Francisco Balding, David Faux, Pierre Ruiz Linares, Andrés |
| author2_role |
author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author |
| dc.subject.none.fl_str_mv |
ADMIXTURE DNA PHENOTYPING EYE-COLOUR HAIR-COLOUR LATIN AMERICANS PIGMENTATION PREDICTION SKIN-COLOUR |
| topic |
ADMIXTURE DNA PHENOTYPING EYE-COLOUR HAIR-COLOUR LATIN AMERICANS PIGMENTATION PREDICTION SKIN-COLOUR |
| purl_subject.fl_str_mv |
https://purl.org/becyt/ford/1.6 https://purl.org/becyt/ford/1 |
| dc.description.none.fl_txt_mv |
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. Fil: Palmal, Sagnik. Centre National de la Recherche Scientifique; Francia. Aix-Marseille Université; Francia Fil: Kaustubh Adhikari. The Open University; Reino Unido. University College London; Estados Unidos Fil: Mendoza Revilla, Javier. Universidad Peruana Cayetano Heredia; Perú. Institut Pasteur de Paris.; Francia Fil: Fuentes Guajardo, Macarena. Universidad de Tarapacá; Chile Fil: Silva de Cerqueira, Caio Cesar. Scientific Police of São Paulo State; Brasil Fil: Bonfante, Betty. Aix-Marseille Université; Francia. Centre National de la Recherche Scientifique; Francia Fil: Chacón Duque, Juan Camilo. Natural History Museum; Reino Unido Fil: Sohail, Anood. Kinnaird College for Women. Department of Biotechnology; Pakistán Fil: Hurtadeo, Malena. Universidad Peruana Cayetano Heredia; Perú Fil: Villegas, Valeria. Universidad Peruana Cayetano Heredia; Perú Fil: Granja, Vanessa. Universidad Peruana Cayetano Heredia; Perú Fil: Jaramillo, Claudia. Kinnaird College for Women. Department of Biotechnology; Pakistán. Universidad de Antioquia; Colombia Fil: Arias, Williams. Universidad de Antioquia; Colombia Fil: Barquera Lozano, Rodrigo. Instituto Nacional de Antropología e Historia; México. Max Planck Institute for the Science of Human History. Department of Archaeogenetics; Alemania Fil: Everardo Martínez, Paola. Instituto Nacional de Antropología e Historia; México Fil: Gómez Valdés, Jorge. Instituto Nacional de Antropología e Historia; México Fil: Villamil Ramirez, Hugo. Universidad Nacional Autónoma de México; México Fil: Hünemeier, Tábita. Universidade de Sao Paulo; Brasil Fil: Ramallo, Virginia. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Centro Nacional Patagónico. Instituto Patagónico de Ciencias Sociales y Humanas; Argentina. Universidade Federal do Rio Grande do Sul; Brasil Fil: Parolin, María Laura. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Centro Nacional Patagónico. Instituto de Diversidad y Evolución Austral; Argentina Fil: Gonzalez, Rolando Jose. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Centro Nacional Patagónico. Instituto Patagónico de Ciencias Sociales y Humanas; Argentina Fil: Schüler-Faccini, Lavinia. Universidade Federal do Rio Grande do Sul, Porto Alegre; Brasil Fil: Bortolini, María Cátira. Universidade Federal do Rio Grande do Sul; Brasil Fil: Acuña Alonzo, Victor. Instituto Nacional de Antropología e Historia; México. Universidade Federal do Rio Grande do Sul; Brasil Fil: Canizales Quinteros, Samuel. Universidad Nacional Autónoma de México; México Fil: Gallo, Carla. Universidad Peruana Cayetano Heredia; Perú Fil: Poletti, Giovanni. Universidad Peruana Cayetano Heredia; Perú Fil: Bedoya, Gabriel. Universidad de Antioquia; Colombia Fil: Rothhammer, Francisco. Universidad de Tarapacá; Chile. Universidad de Chile; Chile Fil: Balding, David. University College London; Reino Unido. University of Melbourne; Australia Fil: Faux, Pierre. Aix-Marseille Université; Francia. Centre National de la Recherche Scientifique; Francia Fil: Ruiz Linares, Andrés. Aix-Marseille Université; Francia. Centre National de la Recherche Scientifique; Francia. University College London; Reino Unido. Fudan University; República de China |
| description |
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. |
| publishDate |
2021 |
| dc.date.none.fl_str_mv |
2021-07 |
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info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion http://purl.org/coar/resource_type/c_6501 info:ar-repo/semantics/articulo |
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article |
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publishedVersion |
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http://hdl.handle.net/11336/148751 Palmal, Sagnik; Kaustubh Adhikari; Mendoza Revilla, Javier; Fuentes Guajardo, Macarena; Silva de Cerqueira, Caio Cesar; et al.; Prediction of eye, hair and skin colour in Latin Americans; Elsevier Ireland; Forensic Science International: Genetics; 53; 7-2021; 1-12 2041-1723 1872-4973 CONICET Digital CONICET |
| url |
http://hdl.handle.net/11336/148751 |
| identifier_str_mv |
Palmal, Sagnik; Kaustubh Adhikari; Mendoza Revilla, Javier; Fuentes Guajardo, Macarena; Silva de Cerqueira, Caio Cesar; et al.; Prediction of eye, hair and skin colour in Latin Americans; Elsevier Ireland; Forensic Science International: Genetics; 53; 7-2021; 1-12 2041-1723 1872-4973 CONICET Digital CONICET |
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eng |
| language |
eng |
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Elsevier Ireland |
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Elsevier Ireland |
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CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas |
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dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar |
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