Dataset Information

Integrated Genomics and Metagenomics Predictions and Modulation of the Gut Microbiota-Muscel Axis to Improve Fillet Yield in Rainbow Trout

ABSTRACT: This VCF file contains high-confidence single nucleotide polymorphisms (SNPs) and small indels identified from whole-genome sequencing of rainbow trout used in the “Integrated Genomics and Metagenomics Predictions and Modulation of the Gut Microbiota–Muscle Axis to Improve Fillet Yield” project. These variants support analyses integrating host genomic, phenotypic, and functional metagenomic data to explore the genetic basis of growth and muscle yield traits. The dataset enables genome-wide association studies and multi-omics prediction models that combine host genetics and microbiome functional profiles to account for previously unexplained phenotypic variation and improve predictive ability for aquaculture-relevant traits.

INSTRUMENT(S): -

ORGANISM(S): Oncorhynchus Mykiss

SUBMITTER: Department of Animal and Avian Sciences, University of Maryland, College Park, MD 20742

PROVIDER: PRJEB109732 | EVA | 2026-03-12

REPOSITORIES: EVA

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Dataset's files

Source:

			Action	DRS
	G0424-02-UMD_Trout_GT90.accessioned.vcf.gz	Vcf
	G0424-02-UMD_Trout_GT90.accessioned.vcf.gz.csi	Vcf
	G0424-02-UMD_Trout_GT90.vcf.csi	Other
	G0424-02-UMD_Trout_GT90.vcf.gz	Vcf
	G0824-027-UMD_Trout_GP90.accessioned.vcf.gz	Vcf

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Project description:Alpine goat phenotypes for quality components have been routinely recorded for many years and deposited in the Council on Dairy Cattle Breeding (CDCB) repository. The data collected were used to conduct an exploratory genome-wide association study (GWAS) from 72 female Alpine goats originating from locations throughout the U.S. Genotypes were identified with the Illumina Goat 50K single nucleotide polymorphisms (SNP) Beadchip. The analysis used a polygenic model where the dropping criteria was the Call Rate ≥ 0.95. The initial dataset was composed of ~ 60,000 rows of SNPs, 21 columns of phenotypic traits and composed of 53,384 scaffolds containing other informative data points used for genomic predictive power. Phenotypic association with the 50KBeadchip revealed 26,074 reads of candidate genes. These candidate genes segregated as separate novel SNPs and were identified as statistically significant regions for genome and chromosome level trait associations. Candidate genes associated differently for each of the following phenotypic traits: test day milk yield (13,469 candidate genes), test day protein yield (25,690 candidate genes), test day fat yield (25,690 candidate genes), percentage protein (25,690 candidate genes), percentage fat (25,690 candidate genes), and percentage lactose content (25,690 candidate genes). The outcome of this study supports elucidation of novel genes that are important for livestock species in association to key phenotypic traits. Validation towards the development of marker-based selection that provide precision breeding methods will thereby increase breeding value. Specific aims: 1) Improve on contributions to the phenotype repository, the Council on Dairy Cattle Breeding (CDCB) for milk quality traits that are economically important for goat production while developing a corresponding DNA repository for each of the animals with significant genotype-phenotype associations. 2) Develop genomic prediction tools and provide data for a better database for tools to predict phenotypic traits by initially using the high density Goat50KSNP BeadChip for the selection of more specific SNPs associated with select signatures (genes) for phenotypic traits in American Alpine goats. 3) To establish whether a low number of goat subjects (< 300 goats) will provide statistically significant (p < 0.05) predictive capabilities for desired breeding traits in American Alpine dairy goats.

Project description:BACKGROUND: Genomic prediction (GP) based on single nucleotide polymorphisms (SNP) has become a broadly used tool to increase the gain of selection in plant breeding. However, using predictors that are biologically closer to the phenotypes such as transcriptome and metabolome may increase the prediction ability in GP. The objectives of this study were to (i) assess the prediction ability for three phenotypic traits using different omic datasets including sequence variants (SV), deleterious SV (dSV), tolerant SV (tSV), expression presence/absence variation (ePAV), gene expression (GE), transcript expression (TE), and metabolites (M) as single predictors in comparison to those using a SNP array; (ii) investigate the improvement in prediction ability when combining multiple omic datasets information to predict phenotypic variation in barley breeding programs; (iii) explore the relationship between genes and metabolites to unravel the metabolic pathway of the three above mentioned phenotypic traits.RESULTS: The prediction ability from genomic best linear unbiased prediction (GBLUP) for the three traits using dSV information was higher than when using tSV, all SV information, or the SNP array. Any predictors from the transcriptome (GE, TE, as well as ePAV) and metabolome provided higher prediction abilities compared to the SNP array and SV on average across the three traits. In addition, some (di)-similarity existed between different omic datasets, and therefore provided complementary biological perspectives to phenotypic variation. Optimal combining the information of dSV, TE, ePAV, as well as metabolites into GP models could improve the prediction ability over that of the single predictors alone.CONCLUSIONS: The use of integrated omic datasets in GP model is highly recommended. Furthermore, we evaluated a cost-effective approach generating 3’end mRNA sequencing with transcriptome data extracted from seedling without losing prediction ability in comparison to the full-length mRNA sequencing, paving the path for the use of such prediction methods in commercial breeding programs.

Dataset Information

Integrated Genomics and Metagenomics Predictions and Modulation of the Gut Microbiota-Muscel Axis to Improve Fillet Yield in Rainbow Trout

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