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Natural selection—probably the most well-known evolutionary mechanism—increases the prevalence of traits that enhance survival and reproduction. However, evolution does not merely propagate favorable traits, nor does it always benefit populations.
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Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...
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The genomes of eukaryotes are punctuated by long stretches of sequence which do not code for proteins or RNAs. Although some of these regions do contain crucial regulatory sequences, the vast majority of this DNA serves no known function. Typically, these regions of the genome are the ones in which the fastest change, in evolutionary terms, is observed, because there is typically little to no selection pressure acting on these regions to preserve their sequences.
In contrast, regions which code...
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Genome-wide Association Studies-GWAS01:11

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Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
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In a population that is not at Hardy-Weinberg equilibrium, the frequency of alleles changes over time. Therefore, any deviations from the five conditions of Hardy-Weinberg equilibrium can alter the genetic variation of a given population. Conditions that change the genetic variability of a population include mutations, natural selection, non-random mating, gene flow, and genetic drift (small population size).
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Genetic variation is the diversity in DNA sequences found among individuals of the same species. This diversity is crucial for a species' survival because it helps organisms adapt to environmental changes. Genetic variation begins with fertilization, where an egg and sperm cell merge. Each of these cells carries 23 chromosomes, up to 46 in the fertilized egg. Chromosomes are long DNA strands that contain genes, the basic units of heredity.
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Video Experimental Relacionado

Updated: Jan 7, 2026

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Pastrami: un algoritmo rápido y eficiente para la inferencia de ascendencia genética a pequeña escala

Andrew B Conley1,2, Lavanya Rishishwar1,2, Shivam Sharma3

  • 1National Institute of Minority Health and Health Disparities, National Institutes of Health, Bethesda, MD 20892, United States.

NAR genomics and bioinformatics
|December 25, 2025
PubMed
Resumen

Un nuevo algoritmo, Pastrami, ofrece una inferencia de ascendencia genética rápida y eficiente para biobancos grandes. Logra una precisión similar a los métodos existentes pero se ejecuta aproximadamente 45 veces más rápido, lo que reduce significativamente el tiempo computacional para la investigación genómica.

Palabras clave:
ascendencia genéticainferencia de ascendenciabiobancosalgoritmo computacionalgenómicainvestigación genómica

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Área de la Ciencia:

  • Genómica
  • Genética de Poblaciones
  • Bioinformática

Sus antecedentes:

  • La investigación genómica a gran escala se basa en biobancos con numerosos participantes.
  • Los métodos actuales de inferencia de ascendencia genética son demasiado lentos para estos grandes conjuntos de datos.

Objetivo del estudio:

  • Desarrollar un algoritmo computacionalmente eficiente para la inferencia de ascendencia genética a pequeña escala en cohortes del tamaño de un biobanco.
  • Abordar las limitaciones de los métodos existentes en términos de velocidad y requisitos de recursos.

Principales métodos:

  • Desarrolló el algoritmo Pastrami para la inferencia de ascendencia genética supervisada.
  • Pastrami compara haplotipos, crea vectores de copia y utiliza la regresión de mínimos cuadrados no negativos.
  • Evaluó Pastrami en conjuntos de datos genómicos de África, las Américas y el Reino Unido, comparándolo con ChromoPainter y RFMix.

Principales resultados:

  • Pastrami demostró estimaciones de ascendencia muy similares en comparación con ChromoPainter y RFMix.
  • El algoritmo presenta un aumento lineal en el tiempo de CPU con el tamaño de la muestra.
  • Pastrami logró un tiempo de ejecución aproximadamente 45 veces más rápido que ChromoPainter, procesando grandes conjuntos de datos significativamente más rápido.

Conclusiones:

  • Pastrami proporciona una solución rápida y eficiente para la inferencia de ascendencia genética en biobancos grandes.
  • El rendimiento del algoritmo hace que los estudios genómicos a gran escala sean más factibles computacionalmente.
  • Pastrami está disponible gratuitamente en GitHub, lo que promueve una mayor adopción en la comunidad investigadora.