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Quantile regression for genome-wide association study of flowering time-related traits in common bean

Author

Listed:
  • Moysés Nascimento
  • Ana Carolina Campana Nascimento
  • Fabyano Fonseca e Silva
  • Leiri Daiane Barili
  • Naine Martins do Vale
  • José Eustáquio Carneiro
  • Cosme Damião Cruz
  • Pedro Crescêncio Souza Carneiro
  • Nick Vergara Lopes Serão

Abstract

Flowering is an important agronomic trait. Quantile regression (QR) can be used to fit models for all portions of a probability distribution. In Genome-wide association studies (GWAS), QR can estimate SNP (Single Nucleotide Polymorphism) effects on each quantile of interest. The objectives of this study were to estimate genetic parameters and to use QR to identify genomic regions for phenological traits (Days to first flower—DFF; Days for flowering—DTF; Days to end of flowering—DEF) in common bean. A total of 80 genotypes of common beans, with 3 replicates were raised at 4 locations and seasons. Plants were genotyped for 384 SNPs. Traditional single-SNP and 9 QR models, ranging from equally spaced quantiles (τ) 0.1 to 0.9, were used to associate SNPs to phenotype. Heritabilities were moderate high, ranging from 0.32 to 0.58. Genetic and phenotypic correlations were all high, averaging 0.66 and 0.98, respectively. Traditional single-SNP GWAS model was not able to find any SNP-trait association. On the other hand, when using QR methodology considering one extreme quantile (τ = 0.1) we found, respectively 1 and 7, significant SNPs associated for DFF and DTF. Significant SNPs were found on Pv01, Pv02, Pv03, Pv07, Pv10 and Pv11 chromosomes. We investigated potential candidate genes in the region around these significant SNPs. Three genes involved in the flowering pathways were identified, including Phvul.001G214500, Phvul.007G229300 and Phvul.010G142900.1 on Pv01, Pv07 and Pv10, respectively. These results indicate that GWAS-based QR was able to enhance the understanding on genetic architecture of phenological traits (DFF and DTF) in common bean.

Suggested Citation

  • Moysés Nascimento & Ana Carolina Campana Nascimento & Fabyano Fonseca e Silva & Leiri Daiane Barili & Naine Martins do Vale & José Eustáquio Carneiro & Cosme Damião Cruz & Pedro Crescêncio Souza Carne, 2018. "Quantile regression for genome-wide association study of flowering time-related traits in common bean," PLOS ONE, Public Library of Science, vol. 13(1), pages 1-14, January.
    • Handle: RePEc:plo:pone00:0190303
    DOI: 10.1371/journal.pone.0190303
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    Cited by:

    1. Gabriela França Oliveira & Ana Carolina Campana Nascimento & Moysés Nascimento & Isabela de Castro Sant'Anna & Juan Vicente Romero & Camila Ferreira Azevedo & Leonardo Lopes Bhering & Eveline Teixeira, 2021. "Quantile regression in genomic selection for oligogenic traits in autogamous plants: A simulation study," PLOS ONE, Public Library of Science, vol. 16(1), pages 1-12, January.
    2. Li-Chu Chien, 2020. "A rank-based normalization method with the fully adjusted full-stage procedure in genetic association studies," PLOS ONE, Public Library of Science, vol. 15(6), pages 1-16, June.

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