Genomic prediction using training population design in interspecific soybean populations.

Agronomically important traits generally have complex genetic architecture, where many genes have a small and largely additive effect. Genomic prediction has been demonstrated to increase genetic gain and efficiency in plant breeding programs beyond marker-assisted selection and phenotypic selection. The objective of this study was to evaluate the impact of allelic origin, marker density, training population size, and cross-validation schemes on the accuracy of genomic prediction models in an interspecific soybean nested association mapping (NAM) panel. Three cross-validation schemes were used: (a) Within-Family (WF): training population and predictions are made exclusively within each family; (b) Across All families (AF): all the individuals from the three families were randomly assigned to either the training or validation set; (c) Leave one Family out (LFO): each family is predicted using a training set that contains the other two families. Predictive abilities increased with training population size up to 350 individuals, but no significant gains were noted beyond 250 individuals in the training population. The number of markers had a limited impact on the observed predictive ability across traits; increasing markers used in the model above 1000 revealed no significant increases in prediction accuracy. Predictive abilities for AF were not significantly different from the WF method, and predictive abilities across populations for the WF method had a range of 0.58 to 0.70 for maturity, protein, meal, and oil. Our results also showed encouraging prediction accuracies for grain yield (0.58-0.69) using the WF method. Partitioning genomic prediction between G. max and G. soja alleles revealed useful information to select material with a larger allele contribution from both parents and could accelerate allele introgression from exotic germplasm into the elite soybean gene pool. Supplementary Information: The online version contains supplementary material available at 10.1007/s11032-021-01203-6.

Nested association mapping of important agronomic traits in three interspecific soybean populations.

KEY MESSAGE: Glycine soja germplasm can be used to successfully introduce new alleles with the potential to add valuable new genetic diversity to the current elite soybean gene pool. Given the demonstrated narrow genetic base of the US soybean production, it is essential to identify beneficial alleles from exotic germplasm, such as wild soybean, to enhance genetic gain for favorable traits. Nested association mapping (NAM) is an approach to population development that permits the comparison of allelic effects of the same QTL in multiple parents. Seed yield, plant maturity, plant height and plant lodging were evaluated in a NAM panel consisting of 392 recombinant inbred lines derived from three biparental interspecific soybean populations in eight environments during 2016 and 2017. Nested association mapping, combined with linkage mapping, identified three major QTL for plant maturity in chromosomes 6, 11 and 12 associated with alleles from wild soybean resulting in significant increases in days to maturity. A significant QTL for plant height was identified on chromosome 13 with the allele increasing plant height derived from wild soybean. A significant grain yield QTL was detected on chromosome 17, and the allele from Glycine soja had a positive effect of 166 kg ha-1; RIL's with the wild soybean allele yielded on average 6% more than the lines carrying the Glycine max allele. These findings demonstrate the usefulness and potential of alleles from wild soybean germplasm to enhance important agronomic traits in a soybean breeding program.

A Simple Test Identifies Selection on Complex Traits.

Important traits in agricultural, natural, and human populations are increasingly being shown to be under the control of many genes that individually contribute only a small proportion of genetic variation. However, the majority of modern tools in quantitative and population genetics, including genome-wide association studies and selection-mapping protocols, are designed to identify individual genes with large effects. We have developed an approach to identify traits that have been under selection and are controlled by large numbers of loci. In contrast to existing methods, our technique uses additive-effects estimates from all available markers, and relates these estimates to allele-frequency change over time. Using this information, we generate a composite statistic, denoted [Formula: see text] which can be used to test for significant evidence of selection on a trait. Our test requires pre- and postselection genotypic data but only a single time point with phenotypic information. Simulations demonstrate that [Formula: see text] is powerful for identifying selection, particularly in situations where the trait being tested is controlled by many genes, which is precisely the scenario where classical approaches for selection mapping are least powerful. We apply this test to breeding populations of maize and chickens, where we demonstrate the successful identification of selection on traits that are documented to have been under selection.