Accuracy of genotype imputation and genomic predictions in a two-generation farmed Atlantic salmon population using high-density and low-density SNP panels

Genomic selection enables cumulative genetic gains in key production traits such as disease resistance, playing an important role in the economic and environmental sustainability of aquaculture production. However, it requires genome-wide genetic marker data on large populations, which can be prohibitively expensive. Genotype imputation is a cost-effective method for obtaining high-density genotypes, but its value in aquaculture breeding programs which are characterized by large full-sibling families has yet to be fully assessed. The aim of this study was to optimize the use of low-density genotypes and evaluate genotype imputation strategies for cost-effective genomic prediction. Phenotypes and genotypes (78,362 SNPs) were obtained for 610 individuals from a Scottish Atlantic salmon breeding program population (Landcatch, UK) challenged with sea lice, Lepeophtheirus salmonis. The genomic prediction accuracy of genomic selection was calculated using GBLUP approaches and compared across SNP panels of varying densities and composition, with and without imputation. Imputation was tested when parents were genotyped for the optimal SNP panel, and offspring were genotyped for a range of lower density imputation panels. Reducing SNP density had little impact on prediction accuracy until 5,000 SNPs, below which the accuracy dropped. Imputation accuracy increased with increasing imputation panel density. Genomic prediction accuracy when offspring were genotyped for just 200 SNPs, and parents for 5,000 SNPs, was 0.53. This accuracy was similar to the full high density and optimal density dataset, and markedly higher than using 200 SNPs without imputation. These results suggest that imputation from very low to medium density can be a cost-effective tool for genomic selection in Atlantic salmon breeding programs.

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Genome wide association and genomic prediction for growth traits in juvenile farmed Atlantic salmon using a high density SNP array

BMC Genomics ◽

10.1186/s12864-015-2117-9 ◽

2015 ◽

Vol 16 (1) ◽

Cited By ~ 109

Author(s):

Hsin-Yuan Tsai ◽

Alastair Hamilton ◽

Alan E. Tinch ◽

Derrick R. Guy ◽

Karim Gharbi ◽

...

Keyword(s):

Atlantic Salmon ◽

Genomic Prediction ◽

Growth Traits ◽

Snp Array ◽

High Density ◽

Genome Wide Association ◽

Genome Wide ◽

Farmed Atlantic Salmon

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A multi-breed reference panel and additional rare variants maximize imputation accuracy in cattle

Genetics Selection Evolution ◽

10.1186/s12711-019-0519-x ◽

2019 ◽

Vol 51 (1) ◽

Cited By ~ 8

Author(s):

Troy N. Rowan ◽

Jesse L. Hoff ◽

Tamar E. Crum ◽

Jeremy F. Taylor ◽

Robert D. Schnabel ◽

...

Keyword(s):

Rare Variants ◽

Imputation Accuracy ◽

High Density ◽

Genotype Imputation ◽

Reference Panel ◽

Low Density ◽

Single Nucleotide ◽

Functional Variants ◽

Causal Variants ◽

Downstream Analysis

Abstract Background During the last decade, the use of common-variant array-based single nucleotide polymorphism (SNP) genotyping in the beef and dairy industries has produced an astounding amount of medium-to-low density genomic data. Although low-density assays work well in the context of genomic prediction, they are less useful for detecting and mapping causal variants and the effects of rare variants are not captured. The objective of this project was to maximize the accuracies of genotype imputation from medium- and low-density assays to the marker set obtained by combining two high-density research assays (~ 850,000 SNPs), the Illumina BovineHD and the GGP-F250 assays, which contains a large proportion of rare and potentially functional variants and for which the assay design is described here. This 850 K SNP set is useful for both imputation to sequence-level genotypes and direct downstream analysis. Results We found that a large multi-breed composite imputation reference panel that includes 36,131 samples with either BovineHD and/or GGP-F250 genotypes significantly increased imputation accuracy compared with a within-breed reference panel, particularly at variants with low minor allele frequencies. Individual animal imputation accuracies were maximized when more genetically similar animals were represented in the composite reference panel, particularly with complete 850 K genotypes. The addition of rare variants from the GGP-F250 assay to our composite reference panel significantly increased the imputation accuracy of rare variants that are exclusively present on the BovineHD assay. In addition, we show that an assay marker density of 50 K SNPs balances cost and accuracy for imputation to 850 K. Conclusions Using high-density genotypes on all available individuals in a multi-breed reference panel maximized imputation accuracy for tested cattle populations. Admixed animals or those from breeds with a limited representation in the composite reference panel were still imputed at high accuracy, which is expected to further increase as the reference panel expands. We anticipate that the addition of rare variants from the GGP-F250 assay will increase the accuracy of imputation to sequence level.

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Data-Driven Network Modeling as a Framework to Evaluate the Transmission of Piscine Myocarditis Virus (PMCV) in the Irish Farmed Atlantic Salmon Population and the Impact of Different Mitigation Measures

Frontiers in Veterinary Science ◽

10.3389/fvets.2020.00385 ◽

2020 ◽

Vol 7 ◽

Author(s):

Tadaishi Yatabe ◽

Beatriz Martínez-López ◽

José Manuel Díaz-Cao ◽

Fiona Geoghegan ◽

Neil M. Ruane ◽

...

Keyword(s):

Atlantic Salmon ◽

Network Modeling ◽

Data Driven ◽

Mitigation Measures ◽

Salmon Population ◽

Farmed Atlantic Salmon ◽

The Impact

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Family-specific genotype arrays increase the accuracy of pedigree based imputation at very low marker densities

10.1101/502989 ◽

2018 ◽

Author(s):

Andrew Whalen ◽

John M Hickey ◽

Gregor Gorjanc

Keyword(s):

Imputation Accuracy ◽

Population Based ◽

High Density ◽

Genotype Imputation ◽

Low Density ◽

Marker Selection ◽

Selection Strategies ◽

Statistical Regularities ◽

Missing Genotypes ◽

Density Marker

In this paper we evaluate the performance of using a family-specific low-density genotype arrays to increase the accuracy of pedigree based imputation. Genotype imputation is a widely used tool that decreases the costs of genotyping a population by genotyping the majority of individuals using a low-density array and using statistical regularities between the low-density and high-density individuals to fill in the missing genotypes. Previous work on population based imputation has found that it is possible to increase the accuracy of imputation by maximizing the number of informative markers on an array. In the context of pedigree based imputation, where the informativeness of a marker depends only on the genotypes of an individual's parents, it may be beneficial to select the markers on each low-density array on a family-by-family basis. In this paper we examined four family-specific low-density marker selection strategies, and evaluated their performance in the context of a real pig breeding dataset. We found that family-specific or sire-specific arrays could increase imputation accuracy by 0.11 at 1 marker per chromosome, by 0.027 at 25 markers per chromosome and by 0.007 at 100 markers per chromosome. These results suggest that there may be a room to use family-specific genotyping for very-low-density arrays particularly if a given sire or sire-dam pairing have a large number of offspring.

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