Genomic Prediction of Gene Bank Wheat Landraces

Abstract

This study examines genomic prediction within 8416 Mexican landrace accessions and 2403 Iranian landrace accessions stored in gene banks. The Mexican and Iranian collections were evaluated in separate field trials, including an optimum environment for several traits, and in two separate environments (drought, D and heat, H) for the highly heritable traits, days to heading (DTH), and days to maturity (DTM). Analyses accounting and not accounting for population structure were performed. Genomic prediction models include genotype × environment interaction (G × E). Two alternative prediction strategies were studied: (1) random cross-validation of the data in 20% training (TRN) and 80% testing (TST) (TRN20-TST80) sets, and (2) two types of core sets, “diversity” and “prediction”, including 10% and 20%, respectively, of the total collections. Accounting for population structure decreased prediction accuracy by 15–20% as compared to prediction accuracy obtained when not accounting for population structure. Accounting for population structure gave prediction accuracies for traits evaluated in one environment for TRN20-TST80 that ranged from 0.407 to 0.677 for Mexican landraces, and from 0.166 to 0.662 for Iranian landraces. Prediction accuracy of the 20% diversity core set was similar to accuracies obtained for TRN20-TST80, ranging from 0.412 to 0.654 for Mexican landraces, and from 0.182 to 0.647 for Iranian landraces. The predictive core set gave similar prediction accuracy as the diversity core set for Mexican collections, but slightly lower for Iranian collections. Prediction accuracy when incorporating G × E for DTH and DTM for Mexican landraces for TRN20-TST80 was around 0.60, which is greater than without the G × E term. For Iranian landraces, accuracies were 0.55 for the G × E model with TRN20-TST80. Results show promising prediction accuracies for potential use in germplasm enhancement and rapid introgression of exotic germplasm into elite materials.

Comparison of Representative and Custom Methods of Generating Core Subsets of a Carrot Germplasm Collection

“…Isidro et al (2015) found that for the rice dataset, an approach to developing a training population based on a stratified sampling strategy similar to our approach in the genotypic core gave the highest predictive accuracy compared with training populations chosen by different optimization algorithms. Similarly, Crossa et al (2016) found that prediction accuracy was similar for all traits using a diversity core set and one chosen to minimize prediction error variance when using a method outlined by Akdemir et al (2015). This suggests that stratification based on diversity is a straightforward and effective method to generate subsets of germplasm to be used in genomic prediction.…”

“…Methods to develop the best subset of a collection to use in model-based prediction have been tested in an inbred rice (Oryza sativa L.) diversity panel characterized by strong population structure (Isidro et al, 2015) and in wheat (Triticum aestivum L.) landrace populations (Crossa et al, 2016). Isidro et al (2015) found that for the rice dataset, an approach to developing a training population based on a stratified sampling strategy similar to our approach in the genotypic core gave the highest predictive accuracy compared with training populations chosen by different optimization algorithms.…”

Comparison of array‐ and sequencing‐based markers for genome‐wide association mapping and genomic prediction in spring wheat

“…To dissect the genetic basis of traits through GWAS, several studies were conducted using array-based platforms (Liu, Pinto, Cossani, Sukumaran, & Reynolds, 2019;Lopes, Dreisigacker, Pena, Sukumaran, & Reynolds, 2015;Sukumaran, Dreisigacker, Lopes, Chavez, & Reynolds, 2015, 2018bValluru, Reynolds, Davies, & Sukumaran, 2017) in spring wheat and sequencing-based platforms (Sukumaran, Reynolds, & Sansaloni, 2018c) in durum wheat (Triticum durum Desf.). Genomic prediction studies were also conducted using the I90K and DArTseq platforms (Christy et al, 2018;Crossa et al, 2014Crossa et al, , 2016aCrossa et al, , 2016bCrossa et al, , 2017Juliana et al, 2018Juliana et al, , 2019Rutkoski et al, 2016;Sukumaran, Crossa, Jarquin, Lopes, & Reynolds, 2017a, 2017b.…”

Genomic selection for lentil breeding: empirical evidence

“…Fitting all markers simultaneously avoids multiple testing and the need to identify markers-trait associations based on an arbitrarily chosen significance threshold. Genomic selection was initially suggested for livestock breeding (Meuwissen et al 2001), and later evaluated and implemented in crop breeding, especially for wheat and maize (Bernardo 2009;Bernardo and Yu 2007;Beyene et al 2015;Crossa et al 2016;Crossa et al 2014). The goal of genomic selection is to enhance the genetic gain of quantitative traits by accelerating the breeding cycle and increasing selection intensity (Battenfield et al 2016;Heffner et al 2010).…”