Dataset Information

Genomic Prediction Using Low Density Marker Panels in Aquaculture: Performance Across Species, Traits, and Genotyping Platforms.

ABSTRACT: Genomic selection increases the rate of genetic gain in breeding programs, which results in significant cumulative improvements in commercially important traits such as disease resistance. Genomic selection currently relies on collecting genome-wide genotype data accross a large number of individuals, which requires substantial economic investment. However, global aquaculture production predominantly occurs in small and medium sized enterprises for whom this technology can be prohibitively expensive. For genomic selection to benefit these aquaculture sectors, more cost-efficient genotyping is necessary. In this study the utility of low and medium density SNP panels (ranging from 100 to 9,000 SNPs) to accurately predict breeding values was tested and compared in four aquaculture datasets with different characteristics (species, genome size, genotyping platform, family number and size, total population size, and target trait). The traits show heritabilities between 0.19-0.49, and genomic prediction accuracies using the full density panel of 0.55-0.87. A consistent pattern of genomic prediction accuracy was observed across species with little or no accuracy reduction until SNP density was reduced below 1,000 SNPs (prediction accuracies of 0.44-0.75). Below this SNP density, heritability estimates and genomic prediction accuracies tended to be lower and more variable (93% of maximum accuracy achieved with 1,000 SNPs, 89% with 500 SNPs, and 70% with 100 SNPs). A notable drop in accuracy was observed between 200 SNP panels (0.44-0.75) and 100 SNP panels (0.39-0.66). Now that a multitude of studies have highlighted the benefits of genomic over pedigree-based prediction of breeding values in aquaculture species, the results of the current study highlight that these benefits can be achieved at lower SNP densities and at lower cost, raising the possibility of a broader application of genetic improvement in smaller and more fragmented aquaculture settings.

SUBMITTER: Kriaridou C

PROVIDER: S-EPMC7056899 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Genomic Prediction Using Low Density Marker Panels in Aquaculture: Performance Across Species, Traits, and Genotyping Platforms.

Kriaridou Christina C Tsairidou Smaragda S Houston Ross D RD Robledo Diego D

Frontiers in genetics 20200227

Genomic selection increases the rate of genetic gain in breeding programs, which results in significant cumulative improvements in commercially important traits such as disease resistance. Genomic selection currently relies on collecting genome-wide genotype data accross a large number of individuals, which requires substantial economic investment. However, global aquaculture production predominantly occurs in small and medium sized enterprises for whom this technology can be prohibitively expen ...[more]

PMID: 32174974

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Project description:Genomic prediction and genome-wide association studies are becoming widely employed in potato key performance trait QTL identifications and to support potato breeding using genomic selection. Elite cultivars are tetraploid and highly heterozygous but also share many common ancestors and generation-spanning inbreeding events, resulting from the clonal propagation of potatoes through seed potatoes. Consequentially, many SNP markers are not in a 1:1 relationship with a single allele variant but shared over several alleles that might exert varying effects on a given trait. The impact of such redundant "diluted" predictors on the statistical models underpinning genome-wide association studies (GWAS) and genomic prediction has scarcely been evaluated despite the potential impact on model accuracy and performance. We evaluated the impact of marker location, marker type, and marker density on the genomic prediction and GWAS of five key performance traits in tetraploid potato (chipping quality, dry matter content, length/width ratio, senescence, and yield). A 762-offspring panel of a diallel cross of 18 elite cultivars was genotyped by sequencing, and markers were annotated according to a reference genome. Genomic prediction models (GBLUP) were trained on four marker subsets [non-synonymous (29,553 SNPs), synonymous (31,229), non-coding (32,388), and a combination], and robustness to marker reduction was investigated. Single-marker regression GWAS was performed for each trait and marker subset. The best cross-validated prediction correlation coefficients of 0.54, 0.75, 0.49, 0.35, and 0.28 were obtained for chipping quality, dry matter content, length/width ratio, senescence, and yield, respectively. The trait prediction abilities were similar across all marker types, with only non-synonymous variants improving yield predictive ability by 16%. Marker reduction response did not depend on marker type but rather on trait. Traits with high predictive abilities, e.g., dry matter content, reached a plateau using fewer markers than traits with intermediate-low correlations, such as yield. The predictions were unbiased across all traits, marker types, and all marker densities >100 SNPs. Our results suggest that using non-synonymous variants does not enhance the performance of genomic prediction of most traits. The major known QTLs were identified by GWAS and were reproducible across exonic and whole-genome variant sets for dry matter content, length/width ratio, and senescence. In contrast, minor QTL detection was marker type dependent.

Project description:BACKGROUND:The availability of high-density (HD) marker panels, genome wide variants and sequence data creates an unprecedented opportunity to dissect the genetic basis of complex traits, enhance genomic selection (GS) and identify causal variants of disease. The disproportional increase in the number of parameters in the genetic association model compared to the number of phenotypes has led to further deterioration in statistical power and an increase in co-linearity and false positive rates. At best, HD panels do not significantly improve GS accuracy and, at worst, reduce accuracy. This is true for both regression and variance component approaches. To remedy this situation, some form of single nucleotide polymorphisms (SNP) filtering or external information is needed. Current methods for prioritizing SNP markers (i.e. BayesB, BayesCπ) are sensitive to the increased co-linearity in HD panels which could limit their performance. RESULTS:In this study, the usefulness of FST, a measure of allele frequency variation among populations, as an external source of information in GS was evaluated. A simulation was carried out for a trait with heritability of 0.4. Data was divided into three subpopulations based on phenotype distribution (bottom 5%, middle 90%, top 5%). Marker data were simulated to mimic a 770 K and 1.5 million SNP marker panel. A ten-chromosome genome with 200 K and 400 K SNPs was simulated. Several scenarios with varying distributions for the quantitative trait loci (QTL) effects were simulated. Using all 200 K markers and no filtering, the accuracy of genomic prediction was 0.77. When marker effects were simulated from a gamma distribution, SNPs pre-selected based on the 99.5, 99.0 and 97.5% quantile of the FST score distribution resulted in an accuracy of 0.725, 0.797, and 0.853, respectively. Similar results were observed under other simulation scenarios. Clearly, the accuracy obtained using all SNPs can be easily achieved using only 0.5 to 1% of all markers. CONCLUSIONS:These results indicate that SNP filtering using already available external information could increase the accuracy of GS. This is especially important as next-generation sequencing technology becomes more affordable and accessible to human, animal and plant applications.

Project description:BackgroundGenomic selection has become a standard tool in dairy cattle breeding. However, for other animal species, implementation of this technology is hindered by the high cost of genotyping. One way to reduce the routine costs is to genotype selection candidates with an SNP (single nucleotide polymorphism) panel of reduced density. This strategy is investigated in the present paper. Methods are proposed for the approximation of SNP positions, for selection of SNPs to be included in the low-density panel, for genotype imputation, and for the estimation of the accuracy of genomic breeding values. The imputation method was developed for a situation in which selection candidates are genotyped with an SNP panel of reduced density but have high-density genotyped sires. The dams of selection candidates are not genotyped. The methods were applied to a sire line pig population with 895 German Piétrain boars genotyped with the PorcineSNP60 BeadChip.ResultsGenotype imputation error rates were 0.133 for a 384 marker panel, 0.079 for a 768 marker panel, and 0.022 for a 3000 marker panel. Error rates for markers with approximated positions were slightly larger. Availability of high-density genotypes for close relatives of the selection candidates reduced the imputation error rate. The estimated decrease in the accuracy of genomic breeding values due to imputation errors was 3% for the 384 marker panel and negligible for larger panels, provided that at least one parent of the selection candidates was genotyped at high-density.Genomic breeding values predicted from deregressed breeding values with low reliabilities were more strongly correlated with the estimated BLUP breeding values than with the true breeding values. This was not the case when a shortened pedigree was used to predict BLUP breeding values, in which the parents of the individuals genotyped at high-density were considered unknown.ConclusionsGenomic selection with imputation from very low- to high-density marker panels is a promising strategy for the implementation of genomic selection at acceptable costs. A panel size of 384 markers can be recommended for selection candidates of a pig breeding program if at least one parent is genotyped at high-density, but this appears to be the lower bound.

Project description:BackgroundIt becomes clear that the increase in the density of marker panels and even the use of sequence data didn't result in any meaningful increase in the accuracy of genomic selection (GS) using either regression (RM) or variance component (VC) approaches. This is in part due to the limitations of current methods. Association model are well over-parameterized and suffer from severe co-linearity and lack of statistical power. Even when the variant effects are not directly estimated using VC based approaches, the genomic relationships didn't improve after the marker density exceeded a certain threshold. SNP prioritization-based fixation index (FST) scores were used to track the majority of significant QTL and to reduce the dimensionality of the association model.ResultsTwo populations with average LD between adjacent markers of 0.3 (P1) and 0.7 (P2) were simulated. In both populations, the genomic data consisted of 400 K SNP markers distributed on 10 chromosomes. The density of simulated genomic data mimics roughly 1.2 million SNP markers in the bovine genome. The genomic relationship matrix (G) was calculated for each set of selected SNPs based on their FST score and similar numbers of SNPs were selected randomly for comparison. Using all 400 K SNPs, 46% of the off-diagonal elements (OD) were between - 0.01 and 0.01. The same portion was 31, 23 and 16% when 80 K, 40 K and 20 K SNPs were selected based on FST scores. For randomly selected 20 K SNP subsets, around 33% of the OD fell within the same range. Genomic similarity computed using SNPs selected based on FST scores was always higher than using the same number of SNPs selected randomly. Maximum accuracies of 0.741 and 0.828 were achieved when 20 and 10 K SNPs were selected based on FST scores in P1 and P2, respectively.ConclusionsGenomic similarity could be maximized by the decrease in the number of selected SNPs, but it also leads to a decrease in the percentage of genetic variation explained by the selected markers. Finding the balance between these two parameters could optimize the accuracy of GS in the presence of high density marker panels.

Dataset Information

Genomic Prediction Using Low Density Marker Panels in Aquaculture: Performance Across Species, Traits, and Genotyping Platforms.

Publications

Genomic Prediction Using Low Density Marker Panels in Aquaculture: Performance Across Species, Traits, and Genotyping Platforms.

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