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GENOME-WIDE SCAN FOR COMMON VARIANTS ASSOCIATED WITH INTRAMUSCULAR FAT AND MOISTURE CONTENT IN RAINBOW TROUT
Mohamed Salem
Department of Animal and Avian Sciences, University of Maryland, college park
Corresponding Author
ORCiD: https://orcid.org/0000-0003-2142-6716
License:
This work is licensed under a CC BY 4.0 License. Read Full License
View the published version at BMC Genomics.
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.
Keywords
fat content, moisture, GWAS, single-step, QTL
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.
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Review #1 received
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Reviewer #3 agreed
On 16 Apr, 2020
Reviewer #2 agreed
On 15 Apr, 2020
Reviewer #1 agreed
On 14 Apr, 2020
Reviewers invited
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On 18 Mar, 2020
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Learn more about In Review
Research article
GENOME-WIDE SCAN FOR COMMON VARIANTS ASSOCIATED WITH INTRAMUSCULAR FAT AND MOISTURE CONTENT IN RAINBOW TROUT
Mohamed Salem
Department of Animal and Avian Sciences, University of Maryland, college park
Corresponding Author
ORCiD: https://orcid.org/0000-0003-2142-6716
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Published
Version 2
Posted 12 Jun, 2020
Published
On 31 Jul, 2020
No community comments so far
Editor assigned
On 09 Jun, 2020
Submission checks complete
On 08 Jun, 2020
Editor invited
On 08 Jun, 2020
No comments provided
Editorial decision: Major revision
On 13 May, 2020
Review #1 received
Received 04 May, 2020
Review #2 received
Received 03 May, 2020
Reviewer #3 agreed
On 16 Apr, 2020
Reviewer #2 agreed
On 15 Apr, 2020
Reviewer #1 agreed
On 14 Apr, 2020
Reviewers invited
Invitations sent on 08 Apr, 2020
First submitted
On 18 Mar, 2020
Editor assigned
On 18 Mar, 2020
Submission checks complete
On 17 Mar, 2020
Editor invited
On 17 Mar, 2020
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Epigenetics & Genomics
License:
This work is licensed under a CC BY 4.0 License. Read Full License
View the published version at BMC Genomics.
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.