Genomic Selection for Processing and End‐Use Quality Traits in the CIMMYT Spring Bread Wheat Breeding Program

Abstract

Wheat (Triticum aestivum L.) cultivars must possess suitable enduse quality for release and consumer acceptability. However, breeding for quality traits is often considered a secondary target relative to yield largely because of amount of seed needed and expense. Without testing and selection, many undesirable materials are advanced, expending additional resources. Here, we develop and validate whole-genome prediction models for end-use quality phenotypes in the CIMMYT bread wheat breeding program. Model accuracy was tested using forward prediction on breeding lines (n = 5520) tested in unbalanced yield trials from 2009 to 2015 at Ciudad Obregon, Sonora, Mexico. Quality parameters included test weight, 1000-kernel weight, hardness, grain and flour protein, flour yield, sodium dodecyl sulfate sedimentation, Mixograph and Alveograph performance, and loaf volume. In general, prediction accuracy substantially increased over time as more data was available to train the model. Reflecting practical implementation of genomic selection (GS) in the breeding program, forward prediction accuracies (r) for quality parameters were assessed in 2015 and ranged from 0.32 (grain hardness) to 0.62 (mixing time). Increased selection intensity was possible with GS since more entries can be genotyped than phenotyped and expected genetic gain was 1.4 to 2.7 times higher across all traits than phenotypic selection. Given the limitations in measuring many lines for quality, we conclude that GS is a powerful tool to facilitate early generation selection for end-use quality in wheat, leaving larger populations for selection on yield during advanced testing and leading to better gain for both quality and yield in bread wheat breeding programs.

Haplotype‐based genotyping‐by‐sequencing in oat genome research

“…Figure S5 shows an example of the predictive accuracy of CORE heading date data in the 2010 Ottawa home‐test population. The accuracies obtained for these independent validations were lower than cross‐validation accuracies, which are in agreement with previous independent validation comparisons (Asoro et al ., ; Battenfield et al ., ) and reflect additional variance in environment and/or genotype‐by‐environment interaction between years, as well as potential differences in population parameters. Differential response to environment is a confounding factor in all types of selection; thus, this reduced accuracy of genomic prediction probably reflects a more realistic metric for the selection of stable and predictable performance in practical breeding schemes.…”

Glycolipid ranking of bread quality hard wheat breeding stock cultivars by tandem mass spectrometry of total lipid extract

“…The ability to accurately profile lipids allows for inclusion of these compounds in wheat breeding selection criteria. Many programs are working toward using genomic selection for prediction of end use quality (Battenfield et al 2016). The use of phenotypic data, such as loaf volume, can be problematic as multiple factors influence the trait.…”

Wheat quality improvement at CIMMYT and the use of genomic selection on it

“…Due to advances in sequencing technology, genome-wide single nucleotide polymorphism (SNP) markers are available through genotyping-by-sequencing (GBS) at a cost conducive to application for genomic selection (Poland and Rife, 2012, Poland et al, 2012). Currently, the cost of genotyping all ~ 10,000 first year yield trials lines is equivalent to phenotyping 2000 lines for processing and end-use quality (Battenfield et al, 2016). Thus, genomic selection, for quality alone will assist in selecting for quality at CIMMYT since all first-year yield trials can be predicted, rather than only the yield trial winners.…”

“…Battenfield et al (2016) applied genomic selection to all of the processing and end-use quality traits regularly tested in the CIMMYT spring wheat breeding program as listed above. These traits have variable levels of prediction accuracy, however, they demonstrated that most expensive traits, dough rheology and baking final product, can be predicted with a high degree of confidence.…”