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Genetic Determinants of Fiber-Associated Traits in Flax Identified by Omics Data Integration.

Alexander KanapinTatyana RozhminaMikhail BankinSvetlana Yu SurkovaMaria DukEkaterina OsyaginaMaria Samsonova
Published in: International journal of molecular sciences (2022)
In this paper, we explore potential genetic factors in control of flax phenotypes associated with fiber by mining a collection of 306 flax accessions from the Federal Research Centre of the Bast Fiber Crops, Torzhok, Russia. In total, 11 traits were assessed in the course of 3 successive years. A genome-wide association study was performed for each phenotype independently using six different single-locus models implemented in the GAPIT3 R package. Moreover, we applied a multivariate linear mixed model implemented in the GEMMA package to account for trait correlations and potential pleiotropic effects of polymorphisms. The analyses revealed a number of genomic variants associated with different fiber traits, implying the complex and polygenic control. All stable variants demonstrate a statistically significant allelic effect across all 3 years of the experiment. We tested the validity of the predicted variants using gene expression data available for the flax fiber studies. The results shed new light on the processes and pathways associated with the complex fiber traits, while the pinpointed candidate genes may be further used for marker-assisted selection.
Keyphrases
  • single cell
  • genome wide
  • copy number
  • gene expression
  • dna methylation
  • genome wide association study
  • electronic health record
  • big data
  • machine learning
  • climate change
  • deep learning
  • neural network