Optimizing the Power to Identify the Genetic Basis of Complex Traits with Evolve and Resequence Studies.
Mol Biol Evol
; 36(12): 2890-2905, 2019 12 01.
Article
em En
| MEDLINE
| ID: mdl-31400203
ABSTRACT
Evolve and resequence (E&R) studies are frequently used to dissect the genetic basis of quantitative traits. By subjecting a population to truncating selection for several generations and estimating the allele frequency differences between selected and nonselected populations using next-generation sequencing (NGS), the loci contributing to the selected trait may be identified. The role of different parameters, such as, the population size or the number of replicate populations has been examined in previous works. However, the influence of the selection regime, that is the strength of truncating selection during the experiment, remains little explored. Using whole genome, individual based forward simulations of E&R studies, we found that the power to identify the causative alleles may be maximized by gradually increasing the strength of truncating selection during the experiment. Notably, such an optimal selection regime comes at no or little additional cost in terms of sequencing effort and experimental time. Interestingly, we also found that a selection regime which optimizes the power to identify the causative loci is not necessarily identical to a regime that maximizes the phenotypic response. Finally, our simulations suggest that an E&R study with an optimized selection regime may have a higher power to identify the genetic basis of quantitative traits than a genome-wide association study, highlighting that E&R is a powerful approach for finding the loci underlying complex traits.
Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Seleção Genética
/
Técnicas Genéticas
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Evolução Molecular
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Característica Quantitativa Herdável
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Modelos Genéticos
Tipo de estudo:
Evaluation_studies
/
Prognostic_studies
Limite:
Animals
Idioma:
En
Revista:
Mol Biol Evol
Assunto da revista:
BIOLOGIA MOLECULAR
Ano de publicação:
2019
Tipo de documento:
Article
País de afiliação:
Áustria