Abstract
The phenotype is the result of the interaction of the genotype with epigenetic factors and the environment. It has been considered that genetics is responsible for >70% of facial phenotype. Interestingly, besides sequencing nucleic acids, it is possible to generate DNA methylation maps of ancient remains, to determine phenotypes. Other application areas are medicine, forensics and law-enforcement. Thus, breakthroughs in nucleic-acid analyses are allowing to determine the phenotype from genotypic data. That is also being facilitated by developments in software (including parallel processing) and hardware, including neural engines (neural-network hardware). New strategies involving artificial intelligence and machine learning have been also deployed to reach such a goal. Phenotyping is a challenging task, on the edge of current technology. Promising results have already been obtained, including prediction of Neanderthal (Homo sapiens neandertha/ensis) and Denisovan (Homo sapiens denisova) faces. The future is promising in this research area, in which ethical and legal implications should also be considered.
Key words: mutations, DNA, RNA, genome-wide association studies, single-nucleotide polymorphisms, quantitative-trait loci, molecular photofitting, physical appearance, biogeographic ancestry, paleogenomics, paleotranscriptomics.
Resumen
El fenotipo es el resultado de la interacción del genotipo con factores epigenéticos y el medio ambiente. Se ha considerado que la genética es responsable de >70% del fenotipo facial. Curiosamente, además de secuenciar ácidos nucleicos, es posible generar mapas de metilación de ADN de restos antiguos, para determinar fenotipos. Otras áreas de aplicación son la medicina, ciencia forense y control del cumplimiento de la ley. Así, los avances en el estudio de ácidos nucleicos están permitiendo determinar el fenotipo a partir de datos genotípicos. Ello también se ve facilitado por los desarrollos en software (incluido el procesamiento paralelo) y hardware, incluidos los motores neuronales (hardware de redes neuronales). También se han implementado nuevas estrategias, involucrando inteligencia artificial y aprendizaje automático, para alcanzar dicho objetivo. El fenotipado es una tarea desafiante, a la vanguardia de la tecnología actual. Ya se han obtenido resultados prometedores, incluida la predicción de caras de neandertales (Homo sapiens neanderthaiensis) y denisovanos (Homo sapiens denisova). El futuro es prometedor en esta área de investigación, en la que también se deben considerar las implicaciones éticas y legales.
Palabras clava: mutaciones, ADN, ARN, estudios de asociación del genoma completo, polimorfismos de un solo nucleótido, loci de rasgos cuantitativos, fototipificación molecular, apariencia física, ascendencia biogeográfica, paleogenómica, paleotranscriptómica.
Introduction
The genotype is the genomic composition of biological entities like virusoids, viroids, viruses and cells. It includes: i) main genome; ii) plasmids (mostly in eubacteria prokaryotes, but sometimes also in archaea prokaryotes and eukaryotes); iii) organelle (mitochondria and chloroplasts) genomes in eukaryotes; and iv) plasmids of organelles. The word genotype was coined in 1903 by the Danish botanist Wilhelm Johannsen (Johannsen, 1903). Genes within the genome are unique for haploid cells, but may exhibit the same (homozygous) or different (heterozygous) alleles if two (diploid) or more (polyploid) sets are present within the same cell, or across the species population. August Weismann (1834-1914) noticed that pluricellular organisms may contain somatic cells (that build the body), as well as germ cells (carrying heredity) (Winther, 2001).
On the other hand, observable features of biological entities are called phenotype. The genotype-phenotype duality was proposed by Wilhelm Johannsen (Johannsen, 1911). Yet, the phenotype may not be determined by the genotype alone. Other involved elements may be the environment (which is not inherited) and epigenetic factors, which may be inherited. Therefore, organisms with the same genotype may look or behave differently. On the other hand, organisms with different genotypes may look alike. It has been considered that genetics is
responsible for >70% of facial phenotype (Djordjevic et al, 2016). Phenotypes are visible for current biological entities, but may not be available for forensic or ancient samples. Thus, it may be useful to infer phenotypes from the genotypes in such scenarios. This topic is currently in the frontier of knowledge, being actively investigated. There is a wide interest in this area. That includes basic knowledge for studies of both modern and ancient samples, as well as applications in medicine, besides forensics and law-enforcement areas. Indeed, interesting research results have recently been published in this fascinating topic, as described below.
Phenotyping modem intact DNA
Phenotype prediction from genetic information is called phenotyping. That can be accomplished using genotyping data generated with molecular markers, including nucleic-acid sequencing (Scudder et al, 2018), which actually is the ultimate genotyping technology, as we have reviewed (Dorado et al, 2021). Indeed, the First-Generation Sequencing (FGS) represented a revolution, to which we have contributed (Lario et al, 1997), since it allowed to read genetic information for the first time, including the Human Genome Project. The Second-Generation Sequencing (SGS) further improved throughput and reduced cost, allowing to sequence ancient genomes for the first time, as we have reviewed (Dorado et al, 2015). Finally, the Third-Generation Sequencing (TGS) allowed to directly sequence nucleic acids, without previous retrotranscription or amplification. That makes possible to directly sequence ancient RNA (aRNA), as we have reviewed (Dorado et al, 2016; 2020). Phenotyping is also know as molecular photofitting in forensic science, when applied to infer the physical appearance and biogeographic ancestry. But phenotyping is not an easy task. The rationale is that we do not fully understand how genes work and interact with the environment, to produce phenotypes.
To gain knowledge on this research area, significant genetic variants associated with a particular trait can be discovered, using Genome-Wide Association Studies (GWAS). Thus, molecular markers associated to traits of interest can be identified (Fagertun et al, 2015; Kayser, 2015; Marcinska et al, 2015; Wolinsky, 2015; Adhikari et al, 2016; Cole et al, 2016; Roosenboom et al, 2016, 2018; Shaffer et al, 2016; Lee et al, 2017; Tsagkrasoulis et al, 2017; Cha et al, 2018; Claes et al, 2018; Indencleef et al, 2018; Qiao et al, 2018; Richmond et al, 2018; Rolfe et al, 2018; Wang, 2018; Weinberg et al, 2018; Bohringer and DeJong, 2019; Hebbring, 2019; Li et al, 2019; Long et al, 2019; Sero et al, 2019; Wu et al, 2019; Xiong et al, 2019; Balanovska et al, 2020; Pospiech et al, 2020; White et al, 2020; Bonfante et al, 2021; Liu et al, 2021; Naqvi et al, 2021). Among them, Single-Nucleotide Polymorphisms (SNP) can be particularly relevant, since they are usually abundant across genomes. Likewise, Quantitative Trait Loci (QTl) can be useful. Thus, they allow to link molecular markers with quantitative traits in phenotypes. Additionally, mathematical models can be designed to predict phenotypes, from genotypic data.
Recent developments in computing, in general, and bioinformatics, in particular, can be also useful in phenotyping research (DeJong et al, 2018). Among them are multivariate statistical approaches, like Principal-Components Analysis (PCA) (Shui et al, 2017; Crouch et al, 2018), multilevel PCA (mPCA) (Farnell et al, 2020) and toolboxes for integrative analyses (White et al, 2019; Li et al, 2020). Additionally, the term Artificial Intelligence (AI) was coined by John McCarthy in 1956. Thus, AI tries to analyze data and generate results to achieve a particular goal (Legg and Hutter, 2007). Therefore, it mimics human cognitive functions, like learning and problem solving (Russell and Norvig, 2020). On the other hand, the term Machine Learning (ML) was coined by Arthur Samuel in 1959. ML is the part of AI that develops algorithms that can be empirically and automatically improved. This way, the machine is trained with data, gaining new experience to optimize results. Thus, predictions can be made for new scenarios, that may not have been specifically programmed in advance. That differentiates ML from traditional computing, that only works with pre-programmed algorithms (Alpaydin, 2020; Hu et al, 2020).
Such developments in computing software have been facilitated thanks to hardware improvements, in general, and microprocessors, in particular. Among them are: i) increasing microprocessor clock frequency to generate pulses (clock rate); ii) reducing microprocessor lithographic node; and iii) incrementing the number of cores in multicore (a few) and manycore (high number) of CentralProcessing Units (CPU) and Graphics-Processing Units (GPU), allowing parallel processing. Dedicated neural-network hardware is another interesting development. That includes the Neural Engine from manufacturers like Apple <https://www.apple.com>. For instance, the one of the ARM-based Apple Silicon M1 microprocessor is capable of executing 11,000 milliard operations per second, being used for machine learning tasks. Indeed, phenotyping is a multidisciplinary science, including biology, bioinformatics, ethics and law (Claes and Shriver, 2014) (Fig. 1).
Some interesting examples of phenotyping have been already published, exploiting such technologies. For instance, just 24 SNP associated to facial variation were used for the first time to infer human faces. They used Bootstrapped Response-based Imputation Modeling (BRIM). As the authors acknowledged, facial prediction using genotyping data is challenging, but results are promising (Claes et al, 2014a-b). A further step in DNA phenotyping was carried out sequencing whole human genomes, involving the prestigious Craig Venter Institute (Lippert et al, 2017).
Phenotyping forensic and ancient DNA
It is known that the melanocortin 1 receptor (MC1R) is related to pigmentation. Thus, a fragment of the MC1R gen from Neanderthal bone remains was amplified, by Polymerase Chain-Reaction (PCR). Interestingly, amplicon sequencing revealed that they contained a mutation producing pale skin and red hair (redhead). It was concluded that at least 1% of homozygous Neanderthals may have had such phenotype (Lalueza-Fox et al, 2007). Since phenotyping modern intact DNA is challenging, much more can be using forensic and ancient DNA (aDNA), which is typically damaged, both physically (short fragments) and chemically (modified nucleotide bases).
A first step to overcome such problems was carried out generating DNA methylation maps of Neanderthal (Homo sapiens neandertha/ensis) and Denisovan (Homo sapiens denisova) remains. Yet, such a goal may not be directly reached, as can be accomplished with modern DNA. Indeed, as said, aDNA may be chemically damaged. Thus, cytosine deamination generates either uracils or thymines (from unmethylated or methylated cytosines, respectively). Uracils can be trimmed, but higher thymine reads are expected in positions with premortem methylated cytosines, as compared to unmethylated positions. Therefore, CpG ^ TpG transitions are a useful proxy for aDNA methylation in ancient DNA (Gokhman et al, 2014, 2016; Hernando-Herraez et al, 2O15; Orlando et al, 2015; Seguin-Orlando et al, 2015; Smith et al, 2015; Hanghoj et al, 2016, 2019) (Fig. 2).
This methodology was further used to infer the skeletal and facial anatomy of Neanderthals and Denisovans. Thus, methylation changes in archaic humans, chimpanzees and modern humans were identified. Gene expression was scored, considering that promoter hypermethylation represses genes. Such downregulation is associated to known mutations causing loss-of-function. Three unidirectional filters allow to predict morphological changes. Skeletal profiles of Neanderthals and chimpanzees were reconstructed, taking into account known morphologies. Furthermore, the accuracy, precision and sensitivity of the method were evaluated (Gokhman et al, 2019) (Fig. 3).
Interestingly, differential hypermethylation of voice- and face-related genes have also been found between modern humans, when compared to ancient hominids (Neanderthals and Denisovans), as well as modern great apes (chimpanzees). Therefore, it has been proposed that they played a key role in human evolution, shaping our vocal tract and face (Gokhman et al, 2020). DNA methylation patters can also be used to predict age (Zbiec-Piekarska et al, 2015) and diseases like schizophrenia (Banerjee et al, 2018, 2019), as well as gene expression of ancient samples (Batyrev et al, 2019; Hahn et al, 2020; Liu et al, 2020; Mathov et al 2020; Rubi et al, 2020). That has implications for ancient environments and life styles (Gokhman et al 2017). Likewise, gene regulation in modern and archaic samples can be inferred using indirect approaches (Yan and McCoy, 2020) and trained statistical models (Colbran et al, 2019).
On the other hand, self-domestication is defined as a behavioral process involving reduced aggression and increased collaboration, as shown by hominids like bonobos and humans (Wrangham, 2003). Interestingly, molecular biology applied to archaic and modern humans have shown that the Bromodomain Adjacent to Zinc-finger domain 1B (BAZ1B) gene was involved in selfdomestication, being a master regulator of modern human face (Zanella et al, 2019). Therefore, methodologies involving both archaeology and molecular biology, including paleogenomics, paleotranscriptomics and paleoproteomics, as we have reviewed (Dorado et al, 2007-2014, 2017, 2018, 2019), have allowed to infer faces of archaic hominids, like Neanderthals and Denisovans (Fig 4).
Concluding remarks and future prospects
Recent developments in archaeology, molecular biology, software and hardware are allowing to carry out scientific projects that were not previously possible. One of them is to infer faces of modern or archaic humans, from their genomes. This genotypic-based phenotypic prediction is challenging, being in the limit of what is currently possible. Yet, some interesting accomplishments in this area have already been published, with promising results. A more accurate phenotypic prediction should be possible with the optimization of current technologies and development of new ones. Among them are: i) structural genomics, including non-coding DNA and identification of all genes present in genomes; ii) functional genomics, including implications of spurious or generalized transcription, as we have reviewed (Dorado et al, 2020); and iii) epigenetics, including genomic-methylation maps. Finally, the ethical and legal implications of genomic research should be taken into account (Berkman et al, 2016).
Acknowledgements. Supported by "Ministerio de Economía y Competitividad" (MINECO grant BIO2015-64737-R) and "Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria" (MINECO and INIA RF2012-00002-C02-02); "Consejería de Agricultura y Pesca" (041/C/2007, 75/C/2009 and 56/C/2010), "Consejería de Economía, Innovación y Ciencia" (P11-AGR-7322) and "Grupo PAI" (AGR-248) of "Junta de Andalucía"; and "Universidad de Córdoba" ("Ayuda a Grupos"), Spain.
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Biología molecular para inferir fenotipos de restos forenses y antiguos en bioarqueología - Revisión
Introducción
El genotipo es la composición genómica de entidades biológicas como virusoides, viroides, virus y células. Incluye: i) genoma principal; ii) plásmidos (principalmente en eubacterias procariotas, pero a veces también en arqueas procariotas y en eucariotas); iii) genomas de orgánulos (mitocondrias y cloroplastos) en eucariotas; y iv) plásmidos de orgánulos. La palabra genotipo fue acuñada en 1903 por el botánico danés Wilhelm Johannsen (Johannsen, 1903). Los genes dentro del genoma son únicos para las células haploides, pero pueden exhibir los mismos alelos (homocigotos) o diferentes (heterocigotos) si dos conjuntos (diploides) o más (poliploides) están presentes dentro de la misma célula, o en toda la población biológica. August Weismann (1834-1914) observó que los organismos pluricelulares pueden contener células somáticas (que construyen el cuerpo), así como células germinales (portadoras de herencia) (Winther, 2001).
Por otro lado, las características observables de las entidades biológicas se denominan fenotipo. La dualidad genotipo-fenotipo fue propuesta por Wilhelm Johannsen (Johannsen, 1911). Sin embargo, es posible que el fenotipo no esté determinado solo por el genotipo. Otros elementos involucrados pueden ser el medio ambiente (que no se hereda) y factores epigenéticos, que pueden heredarse. Por lo tanto, organismos con el mismo genotipo pueden tener aspecto o comportamiento diferentes. Por otro lado, organismos con diferentes genotipos pueden parecerse. Se ha considerado que la genética es responsable de >70% del fenotipo facial (Djordjevic et al, 2016). Los fenotipos son visibles para las entidades biológicas actuales, pero es posible que no estén disponibles para muestras antiguas o forenses. Por lo tanto, puede ser útil inferir fenotipos de los genotipos en tales escenarios. Este tema se encuentra actualmente en la frontera del conocimiento, siendo investigado activamente. Existe un amplio interés en esta área. Ello abarca conocimientos básicos para el estudio de muestras tanto modernas como antiguas, así como aplicaciones en medicina, además de las áreas forenses y policiales. De hecho, recientemente se han publicado interesantes resultados de investigaciones sobre este fascinante tema, como se describe a continuación.
Fenotipado del ADN modemo intacto
La predicción del fenotipo a partir de la información genética se denomina fenotipado. Eso se puede lograr utilizando datos de genotipado generados con marcadores moleculares, incluyendo secuenciación de ácidos nucleicos (Scudder et al, 2018), que realmente es la tecnología de genotipado definitiva, como hemos revisado (Dorado et al, 2021). En efecto, la Secuenciación de Primera Generación (FGS; del inglés, "First-Generation Sequencing") representó una revolución, a la que hemos contribuido (Lario et al, 1997), ya que permitió leer información genética por primera vez, incluido el Proyecto Genoma Humano. La secuenciación de segunda generación (SGS; del inglés, "Second-Generation Sequencing") mejoró aún más el rendimiento y redujo el costo, lo que permitió secuenciar genomas antiguos por primera vez, como hemos revisado (Dorado et al, 2015). Finalmente, la Secuenciación de Tercera Generación (TGS; del inglés, "Third-Generation Sequencing") permitió secuenciar directamente ácidos nucleicos, sin retrotranscripción ni amplificación previa. Eso hace posible secuenciar directamente ARN antiguo (ARNa), como hemos revisado (Dorado et al, 2016; 2020). El fenotipado también se conoce como fototipificación molecular en ciencia forense, cuando se aplica para inferir la apariencia física y la ascendencia biogeográfica. Pero el fenotipado no es una tarea fácil. La razón es que no comprendemos completamente cómo funcionan los genes e interactúan con el medio ambiente, para producir fenotipos.
Para adquirir conocimientos sobre este ámbito de investigación, se pueden descubrir variantes genéticas significativas asociadas con un rasgo particular, utilizando los estudios de asociación de todo el genoma (GWAS; del inglés, "Genome-Wide Association Studies"). Así, se pueden identificar marcadores moleculares asociados a rasgos de interés (Fagertun et al, 2015; Kayser, 2015; Marcinska et al, 2015; Wolinsky, 2015; Adhikari et al, 2016; Cole et al, 2016; Roosenboom et al, 2016,2018; Shaffer et al, 2016; Lee et al, 2017; Tsagkrasoulis et al, 2017; Cha et al, 2018; Claes et al, 2018; Indencleef et al, 2018; Qiao et al, 2018; Richmond et al, 2018 ; Rolfe et al, 2018; Wang, 2018; Weinberg et al, 2018; Bohringer y DeJong, 2019; Hebbring, 2019; Li et al, 2019; Long et al, 2019; Sero et al, 2019; Wu et al, 2019 ; Xiong et al, 2019; Balanovska et al, 2020; Pospiech et al, 2020; White et al, 2020; Bonfante et al, 2021; Liu et al, 2021; Naqvi et al, 2021). Entre ellos, los polimorfismos de nucleótido único (SNP; del inglés, "SingleNucleotide Polymorphisms") pueden ser particularmente relevantes, ya que suelen abundar en los genomas. Asimismo, los loci de rasgos cuantitativos (QTL; del inglés, "Quantitative Trait Loci") pueden resultar útiles. Así, permiten vincular marcadores moleculares con rasgos cuantitativos en fenotipos. Además, se pueden diseñar modelos matemáticos para predecir fenotipos, a partir de datos genotípicos.
Los desarrollos recientes en computación, en general, y bioinformática, en particular, también pueden ser útiles en la investigación del fenotipado (DeJong et al, 2018). Entre ellos se encuentran las aproximaciones de estadística multivariada, como el análisis de componentes principales (PCA; del inglés, "PrincipalComponents Analysis") (Shui et al, 2017; Crouch et al, 2018), PCA multinivel (mPCA; del inglés, "multilevel PCA") (Farnell et al, 2020) y cajas de herramientas para análisis integradores (White et al, 2019; Li et al, 2020). Además, el término inteligencia artificial (AI; del inglés, "Artificial Intelligence") fue acuñado por John McCarthy en 1956. Así, la AI intenta analizar datos y generar resultados para lograr un objetivo en particular (Legg y Hutter, 2007). Por lo tanto, imita las funciones cognitivas humanas, como aprendizaje y resolución de problemas (Russell y Norvig, 2020). Por otro lado, el término aprendizaje automático (ML; del inglés, "Machine Learning") fue acuñado por Arthur Samuel en 1959. ML es la parte de la AI que desarrolla algoritmos que pueden mejorarse empírica y automáticamente. De esta forma, la máquina se entrena con datos, adquiriendo nueva experiencia para optimizar resultados. Por lo tanto, se pueden hacer predicciones para nuevos escenarios, que pueden no haber sido programados específicamente con anticipación. Eso diferencia al ML de la informática tradicional, que solo funciona con algoritmos preprogramados (Alpaydin, 2020; Hu et al, 2020).
Estos desarrollos en software informático se han visto facilitados gracias a las mejoras de hardware, en general, y a los microprocesadores, en particular. Entre ellos se encuentran: i) aumento de la frecuencia de reloj del microprocesador para generar pulsos (velocidad de reloj); ii) reducción del nodo litográfico del microprocesador; y iii) incremento del número de núcleos en multinúcleo (unos pocos; del inglés, "multicore") y muchos núcleos (número elevado; del inglés, "manycore") de unidades de procesamiento central (CPU; del inglés, "CentralProcessing Units") y unidades de procesamiento gráfico (GPU; del inglés, "GraphicsProcessing Units"), lo que permite el procesamiento paralelo. El hardware de red neuronal dedicado es otro desarrollo interesante. Ello incluye el motor neuronal de fabricantes como Apple <https://www.apple.com>. Por ejemplo, el del microprocesador Apple Silicon M1 basado en ARM es capaz de ejecutar 11 billones (11.000 millardos) de operaciones por segundo, y se utiliza para tareas de aprendizaje de máquinas. De hecho, el fenotipado es una ciencia multidisciplinar, que incluye biología, bioinformática, ética y derecho (Claes y Shriver, 2014) (Fig. 1).
Ya se han publicado algunos ejemplos interesantes de fenotipado, explotando estas tecnologías. Por ejemplo, solamente 24 SNP asociados a la variación facial se utilizaron por primera vez para inferir rostros humanos. Utilizaron modelización de imputación basada en la respuesta de remuestreo (BRIM; del inglés, "Bootstrapped Response-based Imputation Modeling"). Como reconocieron los autores, la predicción facial utilizando datos de genotipado es un reto, pero los resultados son prometedores (Claes et al, 2014a-b). Un paso más en la fenotipificación del ADN se llevó a cabo secuenciando genomas humanos completos, con la participación del prestigioso Instituto Craig Venter (Lippert et al, 2017).
Fenotipado de ADN forense y antiguo
Es sabido que el receptor de melanocortina 1 (MC1R) está relacionado con la pigmentación. Por tanto, se amplificó un fragmento del gen MC1R de restos óseos neandertales, mediante la reacción en cadena de la polimerasa (PCR; del inglés, "Polymerase Chain-Reaction"). Curiosamente, la secuenciación de los amplicones reveló que contenían una mutación que produce piel clara y pelo rojo (pelirrojo). Se llegó a la conclusión de que al menos 1% de los neandertales homocigotos podrían haber tenido ese fenotipo (Lalueza-Fox et al, 2007). Dado que el fenotipado del ADN intacto moderno es un desafío, puede serlo mucho más usando ADN forense y antiguo (ADNa), que generalmente está dañado, tanto físicamente (fragmentos cortos) como químicamente (bases de nucleótidos modificadas).
Un primer paso para superar estos problemas se llevó a cabo generando mapas de metilación de ADN de restos de neandertal (Homo sapiens neandertha/ensis) y denisovano (Homo sapiens denisova). Sin embargo, es posible que ese objetivo no se alcance directamente, como puede lograrse con ADN moderno. De hecho, como se ha indicado, el ADNa puede sufrir daños químicos. Así, la desaminación de citosina genera uracilos o timinas (a partir de citosinas no metiladas o metiladas, respectivamente). Los uracilos se pueden recortar, pero se esperan lecturas de timina más altas en posiciones con citosinas metiladas premortem, en comparación con las posiciones no metiladas. Por lo tanto, las transiciones CpG ^ TpG son indicadores o intermediarios (del inglés, "proxies") útiles de metilación del ADN antiguo (Gokhman et al, 2014, 2016; Hernando-Herraez et al, 2015; Orlando et al, 2015; Seguin-Orlando et al, 2015; Smith et al, 2015; Hanghoj et al, 2016, 2019) (Fig. 2).
Esta metodología se utilizó además para inferir la anatomía esquelética y facial de neandertales y denisovanos. Así, se identificaron los cambios de metilación en humanos arcaicos, chimpancés y humanos modernos. La expresión génica se determinó considerando que la hipermetilación del promotor reprime los genes. Tal regulación a la baja está asociada con mutaciones conocidas que causan pérdida de función. Tres filtros unidireccionales permiten predecir cambios morfológicos. Se reconstruyeron perfiles esqueléticos de neandertales y chimpancés, teniendo en cuenta las morfologías conocidas. Además, se evaluó la exactitud, precisión y sensibilidad del método (Gokhman et al, 2019) (Fig. 3).
Curiosamente, también se ha encontrado hipermetilación diferencial de genes relacionados con la voz y el rostro entre los humanos modernos, en comparación con homínidos antiguos (neandertales y denisovanos), así como con los grandes simios modernos (chimpancés). Por tanto, se ha propuesto que jugaron un papel clave en la evolución humana, dando forma a nuestro tracto vocal y rostro (Gokhman et al, 2020). Los patrones de metilación del ADN también se pueden utilizar para predecir la edad (Zbiec-Piekarska et al, 2015) y enfermedades como la esquizofrenia (Banerjee et al, 2018, 2019), así como para la expresión génica de muestras antiguas (Batyrev et al, 2019; Hahn et al, 2020; Liu et al, 2020; Mathov et al 2020; Rubi et al, 2020). Ello tiene implicaciones para entornos y estilos de vida antiguos (Gokhman et al 2017). Asimismo, la regulación génica en muestras modernas y arcaicas se puede inferir utilizando enfoques indirectos (Yan y McCoy, 2020) y modelos estadísticos entrenados (Colbran et al, 2019).
Por otro lado, la autodomesticación se define como un proceso conductual que implica una menor agresión y una mayor colaboración, como lo demuestran homínidos como bonobos y humanos (Wrangham, 2003). Curiosamente, la biología molecular aplicada a humanos arcaicos y modernos ha demostrado que el gen bromodominio adyacente al dominio de dedos de zinc 1B (BAZ/B, del inglés, "Bromodomain Adjacent to Zinc-finger domain 1B") estuvo involucrado en la autodomesticación, siendo un regulador maestro del rostro humano moderno (Zanella et al, 2019). Por tanto, metodologías que involucran tanto a la arqueología como a la biología molecular, incluyendo la paleogenómica, paleotranscriptómica y paleoproteómica, como hemos revisado (Dorado et al, 2007-2014, 2017, 2018, 2019), han permitido inferir rostros de homínidos arcaicos, como neandertales y denisovanos. (Figura 4).
Conclusiones finales y perspectivas futuras
Los avances recientes en arqueología, biología molecular, software y hardware están permitiendo realizar proyectos científicos que antes no eran posibles. Uno de ellos es inferir rostros humanos modernos o arcaicos, a partir de sus genomas. Esta predicción fenotípica basada en el genotipo es un desafío, ya que se encuentra en el límite de lo que es posible actualmente. Sin embargo, ya se han publicado algunos logros interesantes en esta área, con resultados prometedores. Debería ser posible una predicción fenotípica más precisa con la optimización de tecnologías actuales y el desarrollo de otras nuevas. Entre ellas se encuentran: i) genómica estructural, incluyendo el ADN no codificante y la identificación de todos los genes presentes en los genomas; ii) genómica funcional, incluidas las implicaciones de la transcripción espuria o generalizada, como hemos revisado (Dorado et al, 2020); y iii) epigenética, incluidos mapas de metilación genómica. Finalmente, se deben tener en cuenta las implicaciones éticas y legales de la investigación genómica (Berkman et al, 2016).
Agradecimientos. Financiado por Ministerio de Economía y Competitividad (MINECO proyecto BIO2015-64737-R) e Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (MINECO e INIA RF2012-00002-C02-02); Consejería de Agricultura y Pesca (041/C/2007, 75/C/2009 y 56/C/2010), Consejería de Economía, Innovación y Ciencia (P11-AGR-7322) y Grupo PAI (AGR-248) de Junta de Andalucía; y Universidad de Córdoba (Ayuda a Grupos), Spain.
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Abstract
El fenotipo es el resultado de la interacción del genotipo con factores epigenéticos y el medio ambiente. Se ha considerado que la genética es responsable de >70% del fenotipo facial. Curiosamente, además de secuenciar ácidos nucleicos, es posible generar mapas de metilación de ADN de restos antiguos, para determinar fenotipos. Otras áreas de aplicación son la medicina, ciencia forense y control del cumplimiento de la ley. Así, los avances en el estudio de ácidos nucleicos están permitiendo determinar el fenotipo a partir de datos genotípicos. Ello también se ve facilitado por los desarrollos en software (incluido el procesamiento paralelo) y hardware, incluidos los motores neuronales (hardware de redes neuronales). También se han implementado nuevas estrategias, involucrando inteligencia artificial y aprendizaje automático, para alcanzar dicho objetivo. El fenotipado es una tarea desafiante, a la vanguardia de la tecnología actual. Ya se han obtenido resultados prometedores, incluida la predicción de caras de neandertales (Homo sapiens neanderthaiensis) y denisovanos (Homo sapiens denisova). El futuro es prometedor en esta área de investigación, en la que también se deben considerar las implicaciones éticas y legales.