RESUMEN
Personalized genome sequencing has revealed millions of genetic differences between individuals, but our understanding of their clinical relevance remains largely incomplete. To systematically decipher the effects of human genetic variants, we obtained whole-genome sequencing data for 809 individuals from 233 primate species and identified 4.3 million common protein-altering variants with orthologs in humans. We show that these variants can be inferred to have nondeleterious effects in humans based on their presence at high allele frequencies in other primate populations. We use this resource to classify 6% of all possible human protein-altering variants as likely benign and impute the pathogenicity of the remaining 94% of variants with deep learning, achieving state-of-the-art accuracy for diagnosing pathogenic variants in patients with genetic diseases.
Asunto(s)
Variación Genética , Primates , Animales , Humanos , Secuencia de Bases , Frecuencia de los Genes , Primates/genética , Secuenciación Completa del GenomaRESUMEN
Personalized genome sequencing has revealed millions of genetic differences between individuals, but our understanding of their clinical relevance remains largely incomplete. To systematically decipher the effects of human genetic variants, we obtained whole genome sequencing data for 809 individuals from 233 primate species, and identified 4.3 million common protein-altering variants with orthologs in human. We show that these variants can be inferred to have non-deleterious effects in human based on their presence at high allele frequencies in other primate populations. We use this resource to classify 6% of all possible human protein-altering variants as likely benign and impute the pathogenicity of the remaining 94% of variants with deep learning, achieving state-of-the-art accuracy for diagnosing pathogenic variants in patients with genetic diseases. One Sentence Summary: Deep learning classifier trained on 4.3 million common primate missense variants predicts variant pathogenicity in humans.
RESUMEN
The 2030 Agenda was set in 2015 by the United Nations, with 17 Sustainable Development Goals. The Amazonian riverine people are recognized as traditional communities that have their own culture and use the local natural resources of their territories in an ancestral and traditional way. The Sustainable Development Reserve is a Brazilian protected area category which aims to ensure the protection of the natural environment while allowing the residence and the use of these lands by traditional populations. This article reports and discusses the achievements and challenges of the Sustainable Development Goals in two sustainable development reserves in Central Amazonia. The goals were evaluated in the Mamirauá and Amanã Sustainable Development Reserves, due to the large research programs developed in those areas along the past 20 years. The 17 Sustainable Development Goals have a clear connection with the mission of these sustainable development reserves in Central Amazon. Despite the many achievements conquered over the years, there are many challenges yet to overcome; and while striving to achieve the goals from the 2030 Agenda, new challenges will emerge. The current main challenges to reach the Sustainable Development Goals in the Mamirauá and Amanã Sustainable Development Reserves, in Central Amazon, are connecting to the reality of rural areas.
