Subgenome contributions to quantitative genetic variation in bread wheat and durum wheat populations

Abstract

Bread wheat (Triticum aestivum L.) has the A, B, and D genomes, whereas durum wheat (Triticum turgidum L. ssp. durum) has the A and B genomes. This study aimed to assess the usefulness of the square of predictive ability for subgenome i [r MP(i) 2 ] for estimating subgenome contributions to trait variation and to determine which subgenomes in bread wheat and durum wheat populations had the largest and smallest contributions for different traits. Phenotypic and marker data were obtained for four populations used in published studies to map quantitative trait loci (QTL). Subgenome r MP(i) was calculated by analyzing only the markers within each subgenome. Clear differences were found in subgenome contributions for several but not all traits in the populations studied. In the Louise × Penawawa bread wheat population, the r MP(i) 2 values indicated that the B genome had the largest contribution to variation for milling and baking quality traits, and these results were consistent with prior results showing QTL for such traits on chromosome 3B. For yield, no significant differences in subgenome contributions were found in any of the populations. The D genome, despite its lower polymorphism, had the largest r MP(i) 2 for heading date in the 'Seri' × 'Babax' bread wheat population. In a bread wheat association mapping panel, the r MP(i) 2 values were problematic in that their sum exceeded 1.0 and the subgenome r MP(i) values were not significantly different from the wholegenome r MP. This result was probably caused by linkage disequilibrium between markers found in different subgenomes in the association mapping panel. Abbreviations: h 2 , heritability; JK-Omrabi, Jennah Khetifa/Cham1//Triticum dicoccoides 600545/2 × Omrabi5; N M , number of markers; QTL, quantitative trait locus (or loci); r MP , predictive ability; r MP(i) 2 , square of predictive ability for the ith subgenome; RR-BLUP, ridge regression-best linear unbiased prediction; SNP, single nucleotide polymorphism; SRC water, solvent retention capacity for water.

Whole‐genome versus per‐chromosome targeted recombination: Simulations and predicted gains in maize with an integer programming model

“…For all three maize traits, the high correlations (0.76–0.91) between the number of recombinations and chromosome length suggested a highly polygenic nature and a uniform distribution of favorable alleles across the genome. It remains to be seen whether this result will hold true for traits such as baking and milling quality in wheat ( Triticum aestivum L.), for which certain chromosomes contribute much more strongly than other chromosomes to trait variation (Bernardo, 2021; Carter et al., 2012).…”