Interspecific Sample Prioritization Can Improve QTL Detection With Tree-Based Predictive Models

Due to increasing demand for new advanced crops, considerable efforts have been made to explore the improvement of stress and disease resistance cultivar traits through the study of wild crops. When both wild and interspecific hybrid materials are available, a common approach has been to study two types of materials separately and simply compare the quantitative trait locus (QTL) regions. However, combining the two types of materials can potentially create a more efficient method of finding predictive QTLs. In this simulation study, we focused on scenarios involving causal marker expression suppressed by trans-regulatory mechanisms, where the otherwise easily lost associated signals benefit the most from combining the two types of data. A probabilistic sampling approach was used to prioritize consistent genotypic phenotypic patterns across both types of data sets. We chose random forest and gradient boosting to apply the prioritization scheme and found that both facilitated the investigation of predictive causal markers in most of the biological scenarios simulated.

Evaluation of Traits Associated With Seed Characteristics in Arkansas Restorer Lines

Rice Grain dimension and weight are two critical factors for marketing and increasing yield capacity. Seed shape is measured by its length, width, thickness, and ratio of length-width. In this study, an experiment was conducted in a controlled condition from fall 2017 to 2020 to identify QTL and candidate genes associated with seed dimension and weight using a bi-parental population resulting from two University of Arkansas developed genotypes: a restorer line 367R and an advanced breeding line RU1501139, in Stuttgart, Arkansas. Five seed dimension traits, including seed length, seed width, seed thickness, seed length-width ratio, and 100-seeds weight, were obtained for QTL detection. The study detected a total of 17 QTL. Four QTL associated with seed length were identified, in which two were positioned on chr. 3, one on chr. 7, and one on chr. 11. Two QTL related to seed length-width ratio were detected on chr. 3 and 7. Whereas a total of three QTL were identified for seed thickness, one each on chr. 5, 6, and 8. Eight QTL associated with seed weight were found, of which four QTL were detected on chr. 12, two each on chr. 1 and 10, and one on chr. 3. Of 17 QTL, four QTL originated from RU1501139, while the origin of the other 13 QTL was 367R. Since multiple genes could control the yield and seed physical characteristics, the detected QTL can play a role in introducing superior parental lines for developing conventional and hybrid rice production.

Harnessing multivariate, penalized regression methods for genomic prediction and QTL detection of drought-related traits in grapevine

Viticulture has to cope with climate change and to decrease pesticide inputs, while maintaining yield and wine quality. Breeding is a key lever to meet this challenge, and genomic prediction a promising tool to accelerate breeding programs. Multivariate methods are potentially more accurate than univariate ones. Moreover, some prediction methods also provide marker selection, thus allowing quantitative trait loci (QTLs) detection and the identification of positional candidate genes. To study both genomic prediction and QTL detection for drought-related traits in grapevine, we applied several methods, interval mapping as well as univariate and multivariate penalized regression, in a bi-parental progeny. With a dense genetic map, we simulated two traits under four QTL configurations. The penalized regression method Elastic Net (EN) for genomic prediction, and controlling the marginal False Discovery Rate on EN selected markers to prioritize the QTLs. Indeed, penalized methods were more powerful than interval mapping for QTL detection across various genetic architectures. Multivariate prediction did not perform better than its univariate counterpart, despite strong genetic correlation between traits. Using 14 traits measured in semi-controlled conditions under different watering conditions, penalized regression methods proved very efficient for intra-population prediction whatever the genetic architecture of the trait, with predictive abilities reaching 0.68. Compared to a previous study on the same traits, these methods applied on a denser map found new QTLs controlling traits linked to drought tolerance and provided relevant candidate genes. Overall, these findings provide a strong evidence base for implementing genomic prediction in grapevine breeding.

Assessing the effect of phenotyping scoring systems and SNP calling and filtering methods on detection of QTL associated with reaction of Brassica napus to Sclerotinia sclerotiorum

The polyploid nature of canola (Brassica napus) represents a challenge for the accurate identification of single nucleotide polymorphisms (SNPs) and the detection of quantitative trait loci (QTL). In this study, combinations of eight phenotyping scoring systems and six SNP calling and filtering parameters were evaluated for their efficiency in detection of QTL associated with response to Sclerotinia stem rot, caused by Sclerotinia sclerotiorum, in two doubled haploid (DH) canola mapping populations. Most QTL were detected in lesion length, relative areas under the disease progress curve (rAUDPC) for lesion length, and binomial-plant mortality data sets. Binomial data derived from lesion size were less efficient in QTL detection. Inclusion of additional phenotypic sets to the analysis increased the numbers of significant QTL by 2.3-fold; however, the continuous data sets were more efficient. Between two filtering parameters used to analyze genotyping by sequencing (GBS) data, imputation of missing data increased QTL detection in one population with a high level of missing data but not in the other. Inclusion of segregation-distorted SNPs increased QTL detection but did not impact their R2 values significantly. Twelve of the 16 detected QTL were on chromosomes A02 and C01, and the rest were on A07, A09, and C03. Marker A02-7594120, associated with a QTL on chromosome A02 was detected in both populations. Results of this study suggest the impact of genotypic variant calling and filtering parameters may be population dependent while deriving additional phenotyping scoring systems such as rAUDPC datasets and mortality binary may improve QTL detection efficiency.

The influence of QTL allelic diversity on QTL detection in multi-parent populations: a simulation study in sugar beet

Background Multi-parent populations (MPPs) are important resources for studying plant genetic architecture and detecting quantitative trait loci (QTLs). In MPPs, the QTL effects can show various levels of allelic diversity, which can be an important factor influencing the detection of QTLs. In MPPs, the allelic effects can be more or less specific. They can depend on an ancestor, a parent or the combination of parents in a cross. In this paper, we evaluated the effect of QTL allelic diversity on the QTL detection power in MPPs. Results We simulated: a) cross-specific QTLs; b) parental and ancestral QTLs; and c) bi-allelic QTLs. Inspired by a real application in sugar beet, we tested different MPP designs (diallel, chessboard, factorial, and NAM) derived from five or nine parents to explore the ability to sample genetic diversity and detect QTLs. Using a fixed total population size, the QTL detection power was larger in MPPs with fewer but larger crosses derived from a reduced number of parents. The use of a larger set of parents was useful to detect rare alleles with a large phenotypic effect. The benefit of using a larger set of parents was however conditioned on an increase of the total population size. We also determined empirical confidence intervals for QTL location to compare the resolution of different designs. For QTLs representing 6% of the phenotypic variation, using 1600 F2 offspring individuals, we found average 95% confidence intervals over different designs of 49 and 25 cM for cross-specific and bi-allelic QTLs, respectively. Conclusions MPPs derived from less parents with few but large crosses generally increased the QTL detection power. Using a larger set of parents to cover a wider genetic diversity can be useful to detect QTLs with a reduced minor allele frequency when the QTL effect is large and when the total population size is increased.