Comparison of genomic-enabled cross selection criteria for the improvement of inbred line breeding populations

Abstract

A crucial step in inbred plant breeding is the choice of mating design to derive high-performing inbred varieties while also maintaining a competitive breeding population to secure sufficient genetic gain in future generations. In practice, the mating design usually relies on crosses involving the best parental inbred lines to ensure high mean progeny performance. This excludes crosses involving lower performing but more complementary parents in terms of favorable alleles. We predicted the ability of crosses to produce putative outstanding progenies (high mean and high variance progeny distribution) using genomic prediction models. This study compared the benefits and drawbacks of 7 genomic cross selection criteria (CSC) in terms of genetic gain for 1 trait and genetic diversity in the next generation. Six CSC were already published, and we propose an improved CSC that can estimate the proportion of progeny above a threshold defined for the whole mating plan. We simulated mating designs optimized using different CSC. The 835 elite parents came from a real breeding program and were evaluated between 2000 and 2016. We applied constraints on parental contributions and genetic similarities between selected parents according to usual breeder practices. Our results showed that CSC based on progeny variance estimation increased the genetic value of superior progenies by up to 5% in the next generation compared to CSC based on the progeny mean estimation (i.e. parental genetic values) alone. It also increased the genetic gain (up to 4%) and/or maintained more genetic diversity at QTLs (up to 4% more genic variance when the marker effects were perfectly estimated).

Keywords: GenPred; Genomic Prediction; Shared Data Resources; bread wheat; cross value; diversity management; genetic gain; mating design.

Fig. 1.

Different tested scenarios. The scenarios considered 2 marker effect estimation accuracy levels (TRUE, in which QTL effects were known, and ESTIMATED, with marker effects being estimated by GS); 2 types of populations (Unselected populations corresponding to the 835 INRAE-AO founders and Selected populations starting from those founders, followed by 3 random crossing and selection cycles); and 2 mating design constraint levels (CONSTRAINT and NO CONSTRAINT). Each scenario was simulated for 30 different genetic architectures (characterized by a set of 300 QTLs with random position and effect) using INRAE-AO historical breeding lines as the parental population.

Fig. 2.

Relative increase in the 7% best progeny TBV using CSC instead of PM for CONSTRAINT scenarios. The vertical dashed line represents a 7% selection rate, as used in Fig. 3.

Fig. 3.

Trade-off between the relative increase in the 7% best progeny TBV and genic variance using CSC instead of PM for CONSTRAINT scenarios. Gray lines link criteria belonging to the set of best trade-offs, i.e. the best relative increase in the mean TBV for each level of relative increase in genic variance.

Fig. 4.

Relative increase in the mean TBV of selected progeny compared to progeny using the PM criterion for NO CONSTRAINT scenarios.