Selection index: economic weights for maximum simultaneous genetic gain

Estimation of the genetic parameters of sheep growth traits based on machine vision acquisition

In the realm of animal phenotyping, manual measurements are frequently utilised. While machine-generated data show potential for enhancing high-throughput breeding, additional research and validation are imperative before incorporating them into genetic evaluation processes. This research presents a method for managing meat sheep and collecting data, utilising the Sheep Data Recorder system for data input and the Sheep Body Size Collector system for image capture. The study aimed to investigate the genetic parameter changes of growth traits in Ujumqin sheep by comparing machine-generated measurements with manual measurements. The dataset consisted of 552 data points from the offspring of 75 breeding rams and 399 breeding ewes. Six distinct random regression models were assessed to pinpoint the most suitable model for estimating genetic parameters linked to growth traits. These models were distinguished based on the inclusion or exclusion of maternal genetic effects, maternal permanent environmental effects, and covariance between maternal and direct genetic effects. Fixed factors such as individual age, individual sex, and ewe age were taken into account in the analysis. The genetic parameters for the yearling growth traits of Ujumqin sheep were calculated using ASReml software. The Akaike information criterion, the Bayesian information criterion, and fivefold cross-validation were employed to identify the optimal model. Research findings indicate that the most accurate models for manually measured data revealed heritability estimates of 0.12 ± 0.15 for BW, 0.05 ± 0.07 for body slanting length, 0.03 ± 0.07 for withers height, 0.15 ± 0.12 for hip height, 0.11 ± 0.11 for chest depth, 0.13 ± 0.13 for shoulder width, and 0.53 ± 0.15 for chest circumference. The optimal models for machine-predicted data showed heritability estimates of 0.1 ± 0.09 for body slanting length, 0.14 ± 0.12 for withers height, 0.55 ± 0.15 for hip height, 0.34 ± 0.15 for chest depth, 0.26 ± 0.15 for shoulder width, and 0.47 ± 0.16 for chest circumference. In manually measured data, genetic correlations ranged from 0.35 to 0.99, while phenotypic correlations ranged from 0.07 to 0.90. In machine data, genetic correlations ranged from -0.05 to 0.99, while phenotypic correlations ranged from 0.03 to 0.84. The results suggest that machine-based estimations may lead to an overestimation of heritability, but this discrepancy does not impact the selection of breeding models.

Genetic Basis of Potato Tuber Defects and Identification of Heat-Tolerant Clones

Heat stress during the potato growing season reduces tuber marketable yield and quality. Tuber quality deterioration includes external (heat sprouts, chained tubers, knobs) and internal (vascular discoloration, hollow heart, internal heat necrosis) tuber defects, as well as a reduction in their specific gravity and increases in reducing sugars that result in suboptimal (darker) processed products (french fries and chips). Successfully cultivating potatoes under heat-stress conditions requires planting heat-tolerant varieties that can produce high yields of marketable tubers, few external and internal tuber defects, high specific gravity, and low reducing sugars (in the case of processing potatoes). Heat tolerance is a complex trait, and understanding its genetic basis will aid in developing heat-tolerant potato varieties. A panel of 217 diverse potato clones was evaluated for yield and quality attributes in Dalhart (2019 and 2020) and Springlake (2020 and 2021), Texas, and genotyped with the Infinium 22 K V3 Potato Array. A genome-wide association study was performed to identify genomic regions associated with heat-tolerance traits using the GWASpoly package. Quantitative trait loci were identified on chromosomes 1, 3, 4, 6, 8, and 11 for external defects and on chromosomes 1, 2, 3, 10, and 11 for internal defects. Yield-related quantitative trait loci were detected on chromosomes 1, 6, and 10 pertaining to the average tuber weight and tuber number per plant. Genomic-estimated breeding values were calculated using the StageWise package. Clones with low genomic-estimated breeding values for tuber defects were identified as donors of good traits to improve heat tolerance. The identified genomic regions associated with heat-tolerance attributes and the genomic-estimated breeding values will be helpful to develop new potato cultivars with enhanced heat tolerance in potatoes.

Genomic selection and genome-wide association studies in tetraploid chipping potatoes

Potato is a major food crop in the United States and around the world. Most potatoes grown in the United States are destined for processing. Genomic selection can speed up breeding progress for important traits, including those with complex inheritance by guiding the identification of the best parents and guiding selection to advance clones in the breeding program. However, the application of genomic selection in polyploid species has been challenging. In this study, we obtained breeding values of 384 chipping clones evaluated in Texas between 2017 and 2020. The mean reliability of the genomic-estimated breeding values obtained were 0.77, 0.41, 0.61, 0.71, and 0.24 for chip color, chip quality, specific gravity, vine maturity, and total yield, respectively. Potato clones with good chip quality, high yield, high specific gravity, and light-color chips were identified using a multi-trait selection index based on weighted standardized genomic-estimated breeding values. Genome-wide association studies identified quantitative trait loci on chromosome 5 for vine maturity and chromosomes 1, 3, and 7 for chip color. This research has laid the groundwork for implementing genomic selection in tetraploid potato breeding and understanding the genetic basis of chip processing traits in potatoes.

Comparison of single-trait and multi-trait genomic predictions on agronomic and disease resistance traits in spring wheat

This study performed comprehensive analyses on the predictive abilities of single-trait and two multi-trait models in three populations. Our results demonstrated the superiority of multi-traits over single-trait models across seven agronomic and four to seven disease resistance traits of different genetic architecture. The predictive ability of multi-trait and single-trait prediction models has not been investigated on diverse traits evaluated under organic and conventional management systems. Here, we compared the predictive abilities of 25% of a testing set that has not been evaluated for a single trait (ST), not evaluated for multi-traits (MT1), and evaluated for some traits but not others (MT2) in three spring wheat populations genotyped either with the wheat 90K single nucleotide polymorphisms array or DArTseq. Analyses were performed on seven agronomic traits evaluated under conventional and organic management systems, four to seven disease resistance traits, and all agronomic and disease resistance traits simultaneously. The average prediction accuracies of the ST, MT1, and MT2 models varied from 0.03 to 0.78 (mean 0.41), from 0.05 to 0.82 (mean 0.47), and from 0.05 to 0.92 (mean 0.67), respectively. The predictive ability of the MT2 model was significantly greater than the ST model in all traits and populations except common bunt with the MT1 model being intermediate between them. The MT2 model increased prediction accuracies over the ST and MT1 models in all traits by 9.0-82.4% (mean 37.3%) and 2.9-82.5% (mean 25.7%), respectively, except common bunt that showed up to 7.7% smaller accuracies in two populations. A joint analysis of all agronomic and disease resistance traits further improved accuracies within the MT1 and MT2 models on average by 21.4% and 17.4%, respectively, as compared to either the agronomic or disease resistance traits, demonstrating the high potential of the multi-traits models in improving prediction accuracies.

Identification of Spring Wheat with Superior Agronomic Performance under Contrasting Nitrogen Managements Using Linear Phenotypic Selection Indices

Both the Linear Phenotypic Selection Index (LPSI) and the Restrictive Linear Phenotypic Selection Index (RLPSI) have been widely used to select parents and progenies, but the effect of economic weights on the selection parameters (the expected genetic gain, response to selection, and the correlation between the indices and genetic merits) have not been investigated in detail. Here, we (i) assessed combinations of 2304 economic weights using four traits (maturity, plant height, grain yield and grain protein content) recorded under four organically (low nitrogen) and five conventionally (high nitrogen) managed environments, (ii) compared single-trait and multi-trait selection indices (LPSI vs. RLPSI by imposing restrictions to the expected genetic gain of either yield or grain protein content), and (iii) selected a subset of about 10% spring wheat cultivars that performed very well under organic and/or conventional management systems. The multi-trait selection indices, with and without imposing restrictions, were superior to single trait selection. However, the selection parameters differed quite a lot depending on the economic weights, which suggests the need for optimizing the weights. Twenty-two of the 196 cultivars that showed superior performance under organic and/or conventional management systems were consistently selected using all five of the selected economic weights, and at least two of the selection scenarios. The selected cultivars belonged to the Canada Western Red Spring (16 cultivars), the Canada Northern Hard Red (3), and the Canada Prairie Spring Red (3), and required 83-93 days to maturity, were 72-100 cm tall, and produced from 4.0 to 6.2 t ha-1 grain yield with 14.6-17.7% GPC. The selected cultivars would be highly useful, not only as potential trait donors for breeding under an organic management system, but also for other studies, including nitrogen use efficiency.

Index selection with nonlinear profit function as a tool to achieve simultaneous genetic gain

Simultaneous improvement of several, and often negatively correlated, traits is frequently a desired objective in forest tree breeding. A profit function that includes a combination of both linear weights and weights for the cross-products of trait combinations facilitates the construction of a linear index, with an attractive response in all traits. A detailed algorithm for finding the index coefficients is provided, along with three examples of applications in tree breeding. The index is also a powerful tool in optimizing the selection for a ratio of two traits. It is argued that a more equal progress in several traits provides a safetey net when faced with economic uncertainties. The provided algorithm eliminates the need for direct search techniques. Existence of a dual set of linear weights means that the statistical properties of the index based on nonlinear profit functions are identical to those of the classical Smith-Hazel type of index.