Genomic Predictions for Muscle Yield and Fillet Firmness in Rainbow Trout using Reduced-Density SNP Panels

Al-Tobasei, Rafet; Ali, Ali R.; Garcia, André; Lourenco, Daniela; Leeds, Timothy D.; Salem, Mohamed

doi:10.21203/rs.3.rs-36925/v1

Cited by 5 publications

(6 citation statements)

References 53 publications

(98 reference statements)

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“…In rainbow trout, GWAS has been used to identify significant QTLs associated with such traits of interest as growth or disease resistance ( Laghari et al, 2014 ; Kocmarek et al, 2015 ; Liu et al, 2015 ; Abdelrahman et al, 2017 ; Ashton et al, 2017 ; Vallejo et al, 2017 ; Fraslin et al, 2018 ; Reis Neto et al, 2019 ; Ali et al, 2020b ). Recently, studies have begun to focus on quality traits like fillet yield ( Gonzalez-Pena et al, 2016 ; Al-Tobasei et al, 2017 ), muscle yield ( Salem et al, 2018 ), skinned and trimmed fillet yield ( Al-Tobasei et al, 2020 ), shear force and fillet firmness ( Al-Tobasei et al, 2017 , 2020 ; Ali et al, 2019 ), fillet whiteness as determined by L ∗ a ∗ b ∗ characteristics ( Al-Tobasei et al, 2017 ), and intramuscular fat content and moisture ( Ali et al, 2020a ). However, these studies were carried out on two experimental lines of trout that had been previously selected for growth or for resistance to Flavobacterium psychrophilum by the NCCCWA breeding program in the United States ( Leeds et al, 2016 ) and in China ( Hu et al, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

Genetic Parameters and Genome-Wide Association Studies of Quality Traits Characterised Using Imaging Technologies in Rainbow Trout, Oncorhynchus mykiss

et al. 2021

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One of the top priorities of the aquaculture industry is the genetic improvement of economically important traits in fish, such as those related to processing and quality. However, the accuracy of genetic evaluations has been hindered by a lack of data on such traits from a sufficiently large population of animals. The objectives of this study were thus threefold: (i) to estimate genetic parameters of growth-, yield-, and quality-related traits in rainbow trout (Oncorhynchus mykiss) using three different phenotyping technologies [invasive and non-invasive: microwave-based, digital image analysis, and magnetic resonance imaging (MRI)], (ii) to detect quantitative trait loci (QTLs) associated with these traits, and (iii) to identify candidate genes present within these QTL regions. Our study collected data from 1,379 fish on growth, yield-related traits (body weight, condition coefficient, head yield, carcass yield, headless gutted carcass yield), and quality-related traits (total fat, percentage of fat in subcutaneous adipose tissue, percentage of fat in flesh, flesh colour); genotypic data were then obtained for all fish using the 57K SNP Axiom® Trout Genotyping array. Heritability estimates for most of the 14 traits examined were moderate to strong, varying from 0.12 to 0.67. Most traits were clearly polygenic, but our genome-wide association studies (GWASs) identified two genomic regions on chromosome 8 that explained up to 10% of the genetic variance (cumulative effects of two QTLs) for several traits (weight, condition coefficient, subcutaneous and total fat content, carcass and headless gutted carcass yields). For flesh colour traits, six QTLs explained 1–4% of the genetic variance. Within these regions, we identified several genes (htr1, gnpat, ephx1, bcmo1, and cyp2x) that have been implicated in adipogenesis or carotenoid metabolism, and thus represent good candidates for further functional validation. Finally, of the three techniques used for phenotyping, MRI demonstrated particular promise for measurements of fat content and distribution, while the digital image analysis-based approach was very useful in quantifying colour-related traits. This work provides new insights that may aid the development of commercial breeding programmes in rainbow trout, specifically with regard to the genetic improvement of yield and flesh-quality traits as well as the use of invasive and/or non-invasive technologies to predict such traits.

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Section: Introductionmentioning

confidence: 99%

Genetic Parameters and Genome-Wide Association Studies of Quality Traits Characterised Using Imaging Technologies in Rainbow Trout, Oncorhynchus mykiss

et al. 2021

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“…The predictions by GS tended to be more accurate than phenotypic selection because it considers the real genetic relatedness between genotypes rather than the average expectation. Studies have been conducted with empirical data (Al-Tobasei et al ., 2020) and with simulations (Hou et al ., 2020) whether for animal or plant breeding, to understand the markers’ effect on the accuracy of genomic predictions. However, they only consider the additive genetic effects to identify marker subsets and, consequently, make the predictions.…”

Section: Discussionmentioning

confidence: 99%

“…With larger datasets of phenotyped and genotyped individuals, GS’s predictive ability may be increasingly driven by LD rather than by linkage information (Hickey et al ., 2014). Furthermore, several studies claim that it is possible to substantially reduce the number of markers and maintain high predictive ability (Tayeh et al ., 2015; Ma et al ., 2016; Sousa et al ., 2019; Al-Tobasei et al ., 2020). Thus, the use of genotyping techniques with highly discriminative markers could be a viable alternative to current low-cost SNP array strategies, with the potential to increase the fraction of the genome captured in a cost-efficient manner (Elshire et al ., 2011).…”

Section: Introductionmentioning

confidence: 99%

Reduction of genotyping marker density for genomic selection is not an affordable approach to long-term breeding in cross-pollinated crops

DoVale¹,

Sabadin

et al. 2021

Preprint

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The selection of informative markers has been studied massively as an alternative to reduce genotyping costs for the genomic selection (GS) application. Low-density marker panels are attractive for GS because they decrease computational time-consuming and multicollinearity beyond more individuals can be genotyped with the same cost. Nevertheless, these inferences are usually made empirically using "static" training sets and populations, which are adequate only to predict a breeding program's initial cycles but might not for long-term cycles. Moreover, to the best of our knowledge, none of these inferences considered the inclusion of dominance into the GS models, which is particularly important to predict cross-pollinated crops. Therefore, that reveals an important and unexplored topic for allogamous long-term breeding. To achieve this goal, we employed two approaches: the former used empirical maize datasets, and the latter simulations of long-term breeding cycles of phenotypic and genomic recurrent selection (intrapopulation and reciprocal). Then, we observed the reducing marker density effect on populations (mean, the best genotypes performance, accuracy, additive variance) over cycles and models (additive, additive-dominance, specific combining ability (SCA)). Our results indicate that the markers reduction based on different linkage disequilibrium (LD) levels is viable only within a cycle and brings a significant decrease in predictive ability over generations. Furthermore, in the long-term, regardless of the selection scheme adopted, the more makers, the better because they buffer LD losses caused by recombination over breeding cycles. Finally, regarding the accuracy, the additive-dominant models tend to outperform the additive ones and perform similar to the SCA.

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“…In this context, marker density reduction has been extensively evaluated as an alternative to reduce the genotyping costs of GS. This reduction can be carried out based on different criteria, for instance, marker effects (e Sousa et al 2019), trait genetic architecture (Zhang et al 2015), haplotype block analysis (Ma et al 2016), genome-wide association studies (GWAS) (Subedi et al 2013), and linkage disequilibrium (LD) (Al-Tobasei et al 2020). Of course, all strategies have advantages and disadvantages.…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, with larger datasets of phenotyped and genotyped individuals, GS' predictive ability may be increasingly driven by LD rather than by linkage information (Hickey et al 2014). Furthermore, several studies claim that it is possible to substantially reduce the number of markers while maintaining high predictive ability (Tayeh et al 2015;Ma et al 2016;e Sousa et al 2019;Al-Tobasei et al 2020). Thus, the use of genotyping techniques with highly discriminative markers could be a viable alternative for current, low-cost SNP array strategies, with the potential to increase the fraction of the genome captured in a cost-efficient manner (Elshire et al 2011).…”

Section: Introductionmentioning

confidence: 99%

Genotyping marker density and prediction models effects in long-term breeding schemes of cross-pollinated crops

et al. 2022

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Reductions of genotyping marker density have been extensively evaluated as potential strategies to reduce the genotyping costs of genomic selection (GS). Low-density marker panels are appealing in GS because they entail lower multicollinearity and computing time and allow more individuals to be genotyped for the same cost. However, statistical models used in GS are usually evaluated with empirical data, using "static" training sets and populations. This may be adequate for making predictions during a breeding program's initial cycles but not for the long-term. Moreover, to the best of our knowledge, no GS models consider the effect of dominance, which is particularly important for breeding outcomes in cross-pollinated crops. Hence, dominance effects are important and unexplored in GS for long-term programs involving allogamous species. To address it, we employed two approaches: analysis of empirical maize datasets and simulations of long-term breeding applying phenotypic and genomic recurrent selection (intrapopulation and reciprocal schemes). In both schemes, we simulated twenty breeding cycles and assessed the effect of marker density reduction on the population mean, the best crosses, additive variance, selective accuracy, and response to selection with models (additive, additive-dominant, general (GCA), and specific combining ability (SCA)). Our results indicate that marker reduction based on linkage disequilibrium levels provides useful predictions only within a cycle, as accuracy significantly decreases over cycles. In the long-term, without training set updating, high-marker density provides the best responses to selection. The model to be used depends on the breeding scheme: additive for intrapopulation and additivedominant or SCA for reciprocal.

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Genomic Predictions for Muscle Yield and Fillet Firmness in Rainbow Trout using Reduced-Density SNP Panels

Cited by 5 publications

References 53 publications

Genetic Parameters and Genome-Wide Association Studies of Quality Traits Characterised Using Imaging Technologies in Rainbow Trout, Oncorhynchus mykiss

Genetic Parameters and Genome-Wide Association Studies of Quality Traits Characterised Using Imaging Technologies in Rainbow Trout, Oncorhynchus mykiss

Reduction of genotyping marker density for genomic selection is not an affordable approach to long-term breeding in cross-pollinated crops

Genotyping marker density and prediction models effects in long-term breeding schemes of cross-pollinated crops

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