Genomic Prediction Accuracy of Seven Breeding Selection Traits Improved by QTL Identification in Flax

Abstract

Molecular markers are one of the major factors affecting genomic prediction accuracy and the cost of genomic selection (GS). Previous studies have indicated that the use of quantitative trait loci (QTL) as markers in GS significantly increases prediction accuracy compared with genome-wide random single nucleotide polymorphism (SNP) markers. To optimize the selection of QTL markers in GS, a set of 260 lines from bi-parental populations with 17,277 genome-wide SNPs were used to evaluate the prediction accuracy for seed yield (YLD), days to maturity (DTM), iodine value (IOD), protein (PRO), oil (OIL), linoleic acid (LIO), and linolenic acid (LIN) contents. These seven traits were phenotyped over four years at two locations. Identification of quantitative trait nucleotides (QTNs) for the seven traits was performed using three types of statistical models for genome-wide association study: two SNP-based single-locus (SS), seven SNP-based multi-locus (SM), and one haplotype-block-based multi-locus (BM) models. The identified QTNs were then grouped into QTL based on haplotype blocks. For all seven traits, 133, 355, and 1208 unique QTL were identified by SS, SM, and BM, respectively. A total of 1420 unique QTL were obtained by SS+SM+BM, ranging from 254 (OIL, LIO) to 361 (YLD) for individual traits, whereas a total of 427 unique QTL were achieved by SS+SM, ranging from 56 (YLD) to 128 (LIO). SS models alone did not identify sufficient QTL for GS. The highest prediction accuracies were obtained using single-trait QTL identified by SS+SM+BM for OIL (0.929 ± 0.016), PRO (0.893 ± 0.023), YLD (0.892 ± 0.030), and DTM (0.730 ± 0.062), and by SS+SM for LIN (0.837 ± 0.053), LIO (0.835 ± 0.049), and IOD (0.835 ± 0.041). In terms of the number of QTL markers and prediction accuracy, SS+SM outperformed other models or combinations thereof. The use of all SNPs or QTL of all seven traits significantly reduced the prediction accuracy of traits. The results further validated that QTL outperformed high-density genome-wide random markers, and demonstrated that the combined use of single and multi-locus models can effectively identify a comprehensive set of QTL that improve prediction accuracy, but further studies on detection and removal of redundant or false-positive QTL to maximize prediction accuracy and minimize the number of QTL markers in GS are warranted.

Genome-wide association study for grain mineral content in a Brazilian common bean diversity panel

“…Although some of the QTNs identi ed in the present study were characterized as having a small effect, these may prove useful for genomic selection (GS) (He et al 2019a). In GS models, instead of using a complete panel of randomized SNPs, the use of markers associated with traits of interest may decrease the number of markers and, consequently, the costs of genotyping large breeding populations (Lan et al 2020). Moreover, the use of speci c trait-associated markers in GS models can improve prediction accuracy by reducing background noise in model construction (He et al 2019b;Ali et al 2020).…”

Technological Innovations for Improving Cassava Production in Sub-Saharan Africa

“…GS could be more robust by integrating biological knowledge. The inclusion of QTL markers associated with the trait of interest could increase the robustness of genetic evaluation ( Ozimati et al, 2018 ; Lan et al, 2020 ). Other variables affecting the precision of the prediction model include the size of the training population, the number of markers used in the model, the trait genetic architecture, and heritability ( de Oliveira et al, 2012 ; Ly et al, 2013 ; Wolfe et al, 2017 ; Somo et al, 2020 ).…”

Achievements and Challenges of Genomics-Assisted Breeding in Forest Trees: From Marker-Assisted Selection to Genome Editing

“…In the context of animal breeding, some authors suggest that the haplotype approach may be especially beneficial for predicting traits of relatively high heritability [205,212,213]. However, in plants, prediction based on haplotypes has been especially beneficial for predicting low-heritability traits [31,33,34]. For example, Matias et al (2017) [31] reported that the predictive accuracy of the models based on haplotypes was higher than the accuracy based on SNPs (not grouped into haplotypes) at predicting the yield of corn grains, but this result was not observed in the genomic prediction of plant height.…”

“…According to Edwards (2015) [27], haplotypes can establish more precise and reliable genealogical relationships than a relationship matrix made purely from SNPs because they allow us to examine the identity by descent that exists among the individuals of a population. Interestingly, haplotypes have been used in GS models, covering extensive regions of the genome of some annual plants (such as wheat and maize), which has increased the predictive power of complex phenotypic traits [30][31][32][33][34]. Interestingly, the haplotype approach can be especially beneficial for predicting traits with a relatively low heritability.…”