Look-alike humans identified by facial recognition algorithms show genetic similarities

Joshi, Ricky S.; Rigau, Maria; García-Prieto, Carlos A.; Castro de Moura, Manuel; Piñeyro, David; Moran, Sebastian; Davalos, Veronica; Carrión, Pablo; Ferrando-Bernal, Manuel, 1990-; Olalde Marquínez, Íñigo, 1987-; Lalueza Fox, Carles, 1965-; Navarro i Cuartiellas, Arcadi, 1969-; Fernández-Tena, Carles; Aspandi, Decky; Sukno, Federico Mateo; Binefa i Valls, Xavier; Valencia, Alfonso; Esteller, Manel

doi:http://dx.doi.org/10.1016/j.celrep.2022.111257

Look-alike humans identified by facial recognition algorithms show genetic similarities

Citation

Joshi RS, Rigau M, García-Prieto CA, Castro de Moura M, Piñeyro D, Moran S, Davalos V, Carrión P, Ferrando-Bernal M, Olalde I, Lalueza-Fox C, Navarro A, Fernández-Tena C, Aspandi D, Sukno FM, Binefa X, Valencia A, Esteller M. Look-alike humans identified by facial recognition algorithms show genetic similarities. Cell Rep. 2022 Aug 23;40(8):111257. DOI: 10.1016/j.celrep.2022.111257

Abstract

The human face is one of the most visible features of our unique identity as individuals. Interestingly, monozygotic twins share almost identical facial traits and the same DNA sequence but could exhibit differences in other biometrical parameters. The expansion of the world wide web and the possibility to exchange pictures of humans across the planet has increased the number of people identified online as virtual twins or doubles that are not family related. Herein, we have characterized in detail a set of "look-alike" humans, defined by facial recognition algorithms, for their multiomics landscape. We report that these individuals share similar genotypes and differ in their DNA methylation and microbiome landscape. These results not only provide insights about the genetics that determine our face but also might have implications for the establishment of other human anthropometric properties and even personality characteristics.