College of Agriculture & Natural Resources
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The collections in this community comprise faculty research works, as well as graduate theses and dissertations.
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Item Weighted Single-Step GWAS Identifies Genes Influencing Fillet Color in Rainbow Trout(MDPI, 2022-07-26) Ahmed, Ridwan O.; Ali, Ali; Al-Tobasei, Rafet; Leeds, Tim; Kenney, Brett; Salem, MohamedThe visual appearance of the fish fillet is a significant determinant of consumers’ purchase decisions. Depending on the rainbow trout diet, a uniform bright white or reddish/pink fillet color is desirable. Factors affecting fillet color are complex, ranging from the ability of live fish to accumulate carotenoids in the muscle to preharvest environmental conditions, early postmortem muscle metabolism, and storage conditions. Identifying genetic markers of fillet color is a desirable goal but a challenging task for the aquaculture industry. This study used weighted, single-step GWAS to explore the genetic basis of fillet color variation in rainbow trout. We identified several SNP windows explaining up to 3.5%, 2.5%, and 1.6% of the additive genetic variance for fillet redness, yellowness, and whiteness, respectively. SNPs are located within genes implicated in carotenoid metabolism (β,β-carotene 15,15′-dioxygenase, retinol dehydrogenase) and myoglobin homeostasis (ATP synthase subunit β, mitochondrial (ATP5F1B)). These genes are involved in processes that influence muscle pigmentation and postmortem flesh coloration. Other identified genes are involved in the maintenance of muscle structural integrity (kelch protein 41b (klh41b), collagen α-1(XXVIII) chain (COL28A1), and cathepsin K (CTSK)) and protection against lipid oxidation (peroxiredoxin, superoxide dismutase 2 (SOD2), sestrin-1, Ubiquitin carboxyl-terminal hydrolase-10 (USP10)). A-to-G single-nucleotide polymorphism in β,β-carotene 15,15′-dioxygenase, and USP10 result in isoleucine-to-valine and proline-to-leucine non-synonymous amino acid substitutions, respectively. Our observation confirms that fillet color is a complex trait regulated by many genes involved in carotenoid metabolism, myoglobin homeostasis, protection against lipid oxidation, and maintenance of muscle structural integrity. The significant SNPs identified in this study could be prioritized via genomic selection in breeding programs to improve fillet color in rainbow trout.Item Genome-wide identification of loci associated with growth in rainbow trout(Springer Nature, 2020-03-05) Ali, Ali; Al-Tobasei, Rafet; Lourenco, Daniela; Leeds, Tim; Kenney, Brett; Salem, MohamedGrowth is a major economic production trait in aquaculture. Improvements in growth performance will reduce time and cost for fish to reach market size. However, genes underlying growth have not been fully explored in rainbow trout. A previously developed 50 K gene-transcribed SNP chip, containing ~ 21 K SNPs showing allelic imbalances potentially associated with important aquaculture production traits including body weight, muscle yield, was used for genotyping a total of 789 fish with available phenotypic data for bodyweight gain. Genotyped fish were obtained from two consecutive generations produced in the NCCCWA growth-selection breeding program. Weighted single-step GBLUP (WssGBLUP) was used to perform a genome-wide association (GWA) analysis to identify quantitative trait loci (QTL) associated with bodyweight gain. Using genomic sliding windows of 50 adjacent SNPs, 247 SNPs associated with bodyweight gain were identified. SNP-harboring genes were involved in cell growth, cell proliferation, cell cycle, lipid metabolism, proteolytic activities, chromatin modification, and developmental processes. Chromosome 14 harbored the highest number of SNPs (n = 50). An SNP window explaining the highest additive genetic variance for bodyweight gain (~ 6.4%) included a nonsynonymous SNP in a gene encoding inositol polyphosphate 5-phosphatase OCRL-1. Additionally, based on a single-marker GWA analysis, 33 SNPs were identified in association with bodyweight gain. The highest SNP explaining variation in bodyweight gain was identified in a gene coding for thrombospondin-1 (THBS1) (R2 = 0.09). The majority of SNP-harboring genes, including OCRL-1 and THBS1, were involved in developmental processes. Our results suggest that development-related genes are important determinants for growth and could be prioritized and used for genomic selection in breeding programs.Item Genome-wide scan for common variants associated with intramuscular fat and moisture content in rainbow trout(Springer Nature, 2020-08-25) Ali, Ali; Al-Tobasei, Rafet; Lourenco, Daniela; Leeds, Tim; Kenney, Brett; Salem, MohamedGenetic improvement of fillet quality attributes is a priority of the aquaculture industry. Muscle composition impacts quality attributes such as flavor, appearance, texture, and juiciness. Fat and moisture make up about ~ 80% of the tissue weight. The genetic architecture underlying the fat and moisture content of the muscle is still to be fully explored in fish. A 50 K gene transcribed SNP chip was used for genotyping 789 fish with available phenotypic data for fat and moisture content. Genotyped fish were obtained from two consecutive generations produced in the National Center for Cool and Cold Water Aquaculture (NCCCWA) growth-selective breeding program. Estimates of SNP effects from weighted single-step GBLUP (WssGBLUP) were used to perform genome-wide association (GWA) analysis to identify quantitative trait loci (QTL) associated with the studied traits. Using genomic sliding windows of 50 adjacent SNPs, 137 and 178 SNPs were identified as associated with fat and moisture content, respectively. Chromosomes 19 and 29 harbored the highest number of SNPs explaining at least 2% of the genetic variation in fat and moisture content. A total of 61 common SNPs on chromosomes 19 and 29 affected the aforementioned traits; this association suggests common mechanisms underlying intramuscular fat and moisture content. Additionally, based on single-marker GWA analyses, 8 and 24 SNPs were identified in association with fat and moisture content, respectively. SNP-harboring genes were primarily involved in lipid metabolism, cytoskeleton remodeling, and protein turnover. This work provides putative SNP markers that could be prioritized and used for genomic selection in breeding programs.Item Distinct microbial assemblages associated with genetic selection for high- and low- muscle yield in rainbow trout(Springer Nature, 2020-11-23) Chapagain, Pratima; Walker, Donald; Leeds, Tim; Cleveland, Beth M.; Salem, MohamedFish gut microbial assemblages play a crucial role in the growth rate, metabolism, and immunity of the host. We hypothesized that the gut microbiota of rainbow trout was correlated with breeding program based genetic selection for muscle yield. To test this hypothesis, fecal samples from 19 fish representing an F2 high-muscle genetic line (ARS-FY-H) and 20 fish representing an F1 low-muscle yield genetic line (ARS-FY-L) were chosen for microbiota profiling using the 16S rRNA gene. Significant differences in microbial assemblages between these two genetic lines might represent the effect of host genetic selection in structuring the gut microbiota of the host. Tukey’s transformed inverse Simpson indices indicated that high muscle yield genetic line (ARS-FY-H) samples have higher microbial diversity compared to those of the low muscle yield genetic line (ARS-FY-L) (LMM, χ2(1) =14.11, p < 0.05). The fecal samples showed statistically distinct structure in microbial assemblages between the genetic lines (F1,36 = 4.7, p < 0.05, R2 = 11.9%). Functional profiling of bacterial operational taxonomic units predicted characteristic functional capabilities of the microbial communities in the high (ARS-FY-H) and low (ARS-FY-L) muscle yield genetic line samples. The significant differences of the microbial assemblages between high (ARS-FY-H) and low (ARS-FY-L) muscle yield genetic lines indicate a possible effect of genetic selection on the microbial diversity of the host. The functional composition of taxa demonstrates a correlation between bacteria and improving the muscle accretion in the host, probably, by producing various metabolites and enzymes that might aid in digestion. Further research is required to elucidate the mechanisms involved in shaping the microbial community through host genetic selection.Item Genomic predictions for fillet yield and firmness in rainbow trout using reduced-density SNP panels(Springer Nature, 2021-01-30) Al-Tobasei, Rafet; Ali, Ali; Garcia, Andre L. S.; Lourenco, Daniela; Leeds, Tim; Salem, MohamedOne of the most important goals for the rainbow trout aquaculture industry is to improve fillet yield and fillet quality. Previously, we showed that a 50 K transcribed-SNP chip can be used to detect quantitative trait loci (QTL) associated with fillet yield and fillet firmness. In this study, data from 1568 fish genotyped for the 50 K transcribed-SNP chip and ~ 774 fish phenotyped for fillet yield and fillet firmness were used in a single-step genomic BLUP (ssGBLUP) model to compute the genomic estimated breeding values (GEBV). In addition, pedigree-based best linear unbiased prediction (PBLUP) was used to calculate traditional, family-based estimated breeding values (EBV). The genomic predictions outperformed the traditional EBV by 35% for fillet yield and 42% for fillet firmness. The predictive ability for fillet yield and fillet firmness was 0.19–0.20 with PBLUP, and 0.27 with ssGBLUP. Additionally, reducing SNP panel densities indicated that using 500–800 SNPs in genomic predictions still provides predictive abilities higher than PBLUP. These results suggest that genomic evaluation is a feasible strategy to identify and select fish with superior genetic merit within rainbow trout families, even with low-density SNP panels.