GENOME-WIDE SCAN FOR COMMON VARIANTS ASSOCIATED WITH INTRAMUSCULAR FAT AND MOISTURE CONTENT IN RAINBOW TROUT
Background Genetic 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 50K 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.
Results 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.
Conclusion 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.
Figure 1
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Figure 3
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Figure 5
Due to technical limitations, full-text HTML conversion of this manuscript could not be completed. However, the manuscript can be downloaded and accessed as a PDF.
Due to technical limitations, Tables 1-5 are provided in the Supplementary Files sections.
CAPTIONS:
Table 1. SNP markers in genomic sliding windows explaining at least 2% of the genetic variance for fat and moisture content and involved in lipid metabolism. A color gradient on the left indicates differences in additive genetic variance explained by windows containing the representative SNP marker (green is the highest and red is the lowest). SNPs are sorted according to their chromosome positions.
Table 2. SNP markers in genomic sliding windows explaining at least 2% of the genetic variance for fat and moisture content and involvement in transmembrane transport and cytoskeleton regulation. A color gradient on the left indicates differences in additive genetic variance explained by windows containing the representative SNP marker (green is the highest and red is the lowest). SNPs are sorted according to their chromosome positions.
Table 3. SNP markers in genomic sliding windows explaining at least 2% of the genetic variance for moisture content and involved in cell cycle and cytoskeleton regulation. A color gradient on the left indicates differences in additive genetic variance explained by windows containing the representative SNP marker (green is the highest and red is the lowest). SNPs are sorted according to their chromosome positions.
Table 4. SNP markers in genomic sliding windows explaining at least 2% of the genetic variance for moisture content and involved in proteolytic activities. A color gradient on the left indicates differences in additive genetic variance explained by windows containing the representative SNP marker (green is the highest and red is the lowest). SNPs are sorted according to their chromosome positions.
Table 5. SNP markers significantly associated with variability in fat and moisture content using family-based association analysis. A color gradient on the left shows the phenotypic variation explained by each SNP marker (green is the highest and red is the lowest). SNPs associated with the phenotypes are sorted according to their chromosome positions.
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Posted 12 Jun, 2020
On 31 Jul, 2020
On 09 Jun, 2020
On 08 Jun, 2020
On 08 Jun, 2020
On 13 May, 2020
Received 04 May, 2020
Received 03 May, 2020
On 16 Apr, 2020
On 15 Apr, 2020
On 14 Apr, 2020
Invitations sent on 08 Apr, 2020
On 18 Mar, 2020
On 18 Mar, 2020
On 17 Mar, 2020
On 17 Mar, 2020
GENOME-WIDE SCAN FOR COMMON VARIANTS ASSOCIATED WITH INTRAMUSCULAR FAT AND MOISTURE CONTENT IN RAINBOW TROUT
Posted 12 Jun, 2020
On 31 Jul, 2020
On 09 Jun, 2020
On 08 Jun, 2020
On 08 Jun, 2020
On 13 May, 2020
Received 04 May, 2020
Received 03 May, 2020
On 16 Apr, 2020
On 15 Apr, 2020
On 14 Apr, 2020
Invitations sent on 08 Apr, 2020
On 18 Mar, 2020
On 18 Mar, 2020
On 17 Mar, 2020
On 17 Mar, 2020
Background Genetic 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 50K 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.
Results 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.
Conclusion 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.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Due to technical limitations, full-text HTML conversion of this manuscript could not be completed. However, the manuscript can be downloaded and accessed as a PDF.
Due to technical limitations, Tables 1-5 are provided in the Supplementary Files sections.
CAPTIONS:
Table 1. SNP markers in genomic sliding windows explaining at least 2% of the genetic variance for fat and moisture content and involved in lipid metabolism. A color gradient on the left indicates differences in additive genetic variance explained by windows containing the representative SNP marker (green is the highest and red is the lowest). SNPs are sorted according to their chromosome positions.
Table 2. SNP markers in genomic sliding windows explaining at least 2% of the genetic variance for fat and moisture content and involvement in transmembrane transport and cytoskeleton regulation. A color gradient on the left indicates differences in additive genetic variance explained by windows containing the representative SNP marker (green is the highest and red is the lowest). SNPs are sorted according to their chromosome positions.
Table 3. SNP markers in genomic sliding windows explaining at least 2% of the genetic variance for moisture content and involved in cell cycle and cytoskeleton regulation. A color gradient on the left indicates differences in additive genetic variance explained by windows containing the representative SNP marker (green is the highest and red is the lowest). SNPs are sorted according to their chromosome positions.
Table 4. SNP markers in genomic sliding windows explaining at least 2% of the genetic variance for moisture content and involved in proteolytic activities. A color gradient on the left indicates differences in additive genetic variance explained by windows containing the representative SNP marker (green is the highest and red is the lowest). SNPs are sorted according to their chromosome positions.
Table 5. SNP markers significantly associated with variability in fat and moisture content using family-based association analysis. A color gradient on the left shows the phenotypic variation explained by each SNP marker (green is the highest and red is the lowest). SNPs associated with the phenotypes are sorted according to their chromosome positions.