Introgression from Gossypium mustelinum and G. tomentosum into upland cotton, G. hirusutum

To increase genetic diversity with elite upland cotton, introgression populations with wild species of cotton, Gossypium mustelinum and G. tomentosum, were created. To accomplish this objective, F1, F2, BC1F1, and BC1F2 generations were developed along with random mating populations (BC1rm1 and BC1rm2) and grown in a randomized complete block design with four replications in College Station, Texas during 2003 and 2004, and in Mexico during 2005 for G. mustelinum introgression populations. These generations were tested with microsatellite markers from chromosome 11 in order to measure the effects of selection and recombination. Later generations (BC2F1, BC2rm1, BC2F2, BC3F1, BC3rm1 and BC3F2) and composite generations were evaluated in a randomized complete block design with four replications during 2004 and 2005 for agronomic properties. Introgression barriers for G. mustelinum were found to include daylength sensitivity and hybrid breakdown, which was only apparent in Mexico. Backcross generations had improved fiber quality. Random mating populations did not have increased variance and means differed little from BC1F1 levels. Microsatellite markers showed decreased frequency of G. mustelinum alleles and decreasing heterozygosity, but no increase in map distances in random mating populations. Upper-half mean length and upper quartile length by weight were highly heritable, as measured with parent-offspring regression. Most other agronomic traits had moderate heritabilities. Composite generations were found to be favorable for selection and breeding. For G. tomentosum populations, hybrid breakdown was also a problem with low yields for F2 and BC1F2 generations, but day length sensitivity was not. Little or no increase in variance was found in random mating populations when compared to BC1F1 levels. G. tomentosum populations did not show improvements in fiber length as seen in G. mustelinum populations, but did have increased strength in BC1F1 and F1 generations over TM-1. Mapping distances increased in the random mating populations for G. tomentosum, and the frequency of alien alleles did not decrease in random mating populations. Generation means approached recurrent parental values for most traits within three backcrosses. Composite generations were found to be the most useful for breeding and selection

Genomic selection using random regressions on known and latent environmental covariates

KEY MESSAGE: The integration of known and latent environmental covariates within a single-stage genomic selection approach provides breeders with an informative and practical framework to utilise genotype by environment interaction for prediction into current and future environments. ABSTRACT: This paper develops a single-stage genomic selection approach which integrates known and latent environmental covariates within a special factor analytic framework. The factor analytic linear mixed model of Smith et al. (2001) is an effective method for analysing multi-environment trial (MET) datasets, but has limited practicality since the underlying factors are latent so the modelled genotype by environment interaction (GEI) is observable, rather than predictable. The advantage of using random regressions on known environmental covariates, such as soil moisture and daily temperature, is that the modelled GEI becomes predictable. The integrated factor analytic linear mixed model (IFA-LMM) developed in this paper includes a model for predictable and observable GEI in terms of a joint set of known and latent environmental covariates. The IFA-LMM is demonstrated on a late-stage cotton breeding MET dataset from Bayer CropScience. The results show that the known covariates predominately capture crossover GEI and explain 34.4% of the overall genetic variance. The most notable covariates are maximum downward solar radiation (10.1%), average cloud cover (4.5%) and maximum temperature (4.0%). The latent covariates predominately capture non-crossover GEI and explain 40.5% of the overall genetic variance. The results also show that the average prediction accuracy of the IFA-LMM is [Formula: see text] higher than conventional random regression models for current environments and [Formula: see text] higher for future environments. The IFA-LMM is therefore an effective method for analysing MET datasets which also utilises crossover and non-crossover GEI for genomic prediction into current and future environments. This is becoming increasingly important with the emergence of rapidly changing environments and climate change. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00122-022-04186-w

Editorial: Genomic selection: Lessons learned and perspectives

Genomic selection (GS) has been one of the most prominent Research Topics in breeding science in the last two decades after the milestone paper by Meuwissen et al. (2001). Its huge potential for increasing the efficiency of breeding programs attracted scientific curiosity and research funding. Many different statistical prediction methods have been tested, and different use cases have been explored. We organized this Research Topic to look both back and forward. The objectives were to review the developments of the last 20 years, to provide a snapshot of current hot topics, and potentially also to define areas on which more (or less) focus should be put in the future, thereby supporting readers with formulating and prioritizing their ideas for future research. Several questions were brought up when organizing this Research Topic including: How did GS change breeding schemes? Which impact did GS have on realized selection gain? What, considering the context of particularities of different crops, may be optimal breeding schemes to leverage the full potential of GS? What has been the impact of and what is the potential of hybrid prediction, statistical epistasis models, deep learning and other methods? What are the long-term effects of GS? Can predictive breeding approaches also be used to harness genetic resources from germplasm banks in a more efficient way