bims-plasge Biomed News
on Plastid genes
Issue of 2022‒01‒16
two papers selected by
Vera S. Bogdanova
Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences

  1. Front Genet. 2021 ;12 707754
      Phenotypic evaluation and efficient utilization of germplasm collections can be time-intensive, laborious, and expensive. However, with the plummeting costs of next-generation sequencing and the addition of genomic selection to the plant breeder's toolbox, we now can more efficiently tap the genetic diversity within large germplasm collections. In this study, we applied and evaluated genomic prediction's potential to a set of 482 pea (Pisum sativum L.) accessions-genotyped with 30,600 single nucleotide polymorphic (SNP) markers and phenotyped for seed yield and yield-related components-for enhancing selection of accessions from the USDA Pea Germplasm Collection. Genomic prediction models and several factors affecting predictive ability were evaluated in a series of cross-validation schemes across complex traits. Different genomic prediction models gave similar results, with predictive ability across traits ranging from 0.23 to 0.60, with no model working best across all traits. Increasing the training population size improved the predictive ability of most traits, including seed yield. Predictive abilities increased and reached a plateau with increasing number of markers presumably due to extensive linkage disequilibrium in the pea genome. Accounting for population structure effects did not significantly boost predictive ability, but we observed a slight improvement in seed yield. By applying the best genomic prediction model (e.g., RR-BLUP), we then examined the distribution of genotyped but nonphenotyped accessions and the reliability of genomic estimated breeding values (GEBV). The distribution of GEBV suggested that none of the nonphenotyped accessions were expected to perform outside the range of the phenotyped accessions. Desirable breeding values with higher reliability can be used to identify and screen favorable germplasm accessions. Expanding the training set and incorporating additional orthogonal information (e.g., transcriptomics, metabolomics, physiological traits, etc.) into the genomic prediction framework can enhance prediction accuracy.
    Keywords:  genomic prediction; genomic selection; germplasm accessions; next-generation sequencing; pea (Pisum sativum L); reliability criteria
  2. Curr Opin Plant Biol. 2022 Jan 09. pii: S1369-5266(21)00168-0. [Epub ahead of print]66 102166
      Breakthroughs in assembly of whole-genome sequencing and targeted sequence capture data have accelerated comparative genomics analyses in cereals with big and complex genomes such as wheat. This newly acquired information has revealed unexpected expansions in two large gene families linked to restoration of fertility in species that exhibit cytoplasmic male sterility. Extreme levels of copy-number and structural variation detected within and between species illustrate the genetic diversity among the family members and reveal the evolutionary mechanisms at work. This new knowledge will greatly facilitate the development of hybrid production strategies in wheat and related species.
    Keywords:  CMS; Cytoplasmic male sterility; Hybrid breeding; Mitochondrial transcription termination factors; Mitochondrial-nuclear genome interactions; PPR; Pentatricopeptide repeat proteins; RFL; Restorer-of-fertility-like genes; Trticeae genomes; mTERF