High-density marker imputation accuracy in sixteen French cattle breeds

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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High-density marker profiling confirms ancestral genomes of Avena species and identifies D-genome chromosomes of hexaploid oat

Theoretical and Applied Genetics ◽

10.1007/s00122-016-2762-7 ◽

2016 ◽

Vol 129 (11) ◽

pp. 2133-2149 ◽

Cited By ~ 30

Author(s):

Honghai Yan ◽

Wubishet A. Bekele ◽

Charlene P. Wight ◽

Yuanying Peng ◽

Tim Langdon ◽

...

Keyword(s):

High Density ◽

D Genome ◽

Avena Species ◽

Density Marker

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Assessment of the lower limb soft tissue artefact at marker-cluster level with a high-density marker set during walking

Journal of Biomechanics ◽

10.1016/j.jbiomech.2017.04.036 ◽

2017 ◽

Vol 62 ◽

pp. 21-26 ◽

Cited By ~ 6

Author(s):

Arnaud Barré ◽

Rachid Aissaoui ◽

Kamiar Aminian ◽

Raphaël Dumas

Keyword(s):

Soft Tissue ◽

Lower Limb ◽

High Density ◽

Marker Cluster ◽

Cluster Level ◽

Density Marker

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High-density marker loss of heterozygosity analysis of rat chromosome 10 in endometrial adenocarcinoma

Genes Chromosomes and Cancer ◽

10.1002/gcc.1198 ◽

2001 ◽

Vol 32 (4) ◽

pp. 330-341 ◽

Cited By ~ 8

Author(s):

Afrouz Behboudi ◽

G�ran Levan ◽

Hans J. Hedrich ◽

Karin Klinga-Levan

Keyword(s):

Loss Of Heterozygosity ◽

Endometrial Adenocarcinoma ◽

High Density ◽

Chromosome 10 ◽

Density Marker

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Improving accuracy of genomic predictions within and between dairy cattle breeds with imputed high-density single nucleotide polymorphism panels

Journal of Dairy Science ◽

10.3168/jds.2011-5019 ◽

2012 ◽

Vol 95 (7) ◽

pp. 4114-4129 ◽

Cited By ~ 329

Author(s):

M. Erbe ◽

B.J. Hayes ◽

L.K. Matukumalli ◽

S. Goswami ◽

P.J. Bowman ◽

...

Keyword(s):

Single Nucleotide Polymorphism ◽

Dairy Cattle ◽

High Density ◽

Nucleotide Polymorphism ◽

Single Nucleotide ◽

Cattle Breeds ◽

Improving Accuracy ◽

Genomic Predictions

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Selection signatures in two oldest Russian native cattle breeds revealed using high-density single nucleotide polymorphism analysis

PLoS ONE ◽

10.1371/journal.pone.0242200 ◽

2020 ◽

Vol 15 (11) ◽

pp. e0242200

Author(s):

Natalia Anatolievna Zinovieva ◽

Arsen Vladimirovich Dotsev ◽

Alexander Alexandrovich Sermyagin ◽

Tatiana Evgenievna Deniskova ◽

Alexandra Sergeevna Abdelmanova ◽

...

Keyword(s):

Single Nucleotide Polymorphism ◽

Candidate Genes ◽

High Density ◽

Fixation Index ◽

Nucleotide Polymorphism ◽

Selection Signatures ◽

Single Nucleotide ◽

Cattle Breeds ◽

Native Cattle ◽

Genomic Regions

Native cattle breeds can carry specific signatures of selection reflecting their adaptation to the local environmental conditions and response to the breeding strategy used. In this study, we comprehensively analysed high-density single nucleotide polymorphism (SNP) genotypes to characterise the population structure and detect the selection signatures in Russian native Yaroslavl and Kholmogor dairy cattle breeds, which have been little influenced by introgression with transboundary breeds. Fifty-six samples of pedigree-recorded purebred animals, originating from different breeding farms and representing different sire lines, of the two studied breeds were genotyped using a genome-wide bovine genotyping array (Bovine HD BeadChip). Three statistical analyses—calculation of fixation index (FST) for each SNP for the comparison of the pairs of breeds, hapFLK analysis, and estimation of the runs of homozygosity (ROH) islands shared in more than 50% of animals—were combined for detecting the selection signatures in the genome of the studied cattle breeds. We confirmed nine and six known regions under putative selection in the genomes of Yaroslavl and Kholmogor cattle, respectively; the flanking positions of most of these regions were elucidated. Only two of the selected regions (localised on BTA 14 at 24.4–25.1 Mbp and on BTA 16 at 42.5–43.5 Mb) overlapped in Yaroslavl, Kholmogor and Holstein breeds. In addition, we detected three novel selection sweeps in the genome of Yaroslavl (BTA 4 at 4.74–5.36 Mbp, BTA 15 at 17.80–18.77 Mbp, and BTA 17 at 45.59–45.61 Mbp) and Kholmogor breeds (BTA 12 at 82.40–81.69 Mbp, BTA 15 at 16.04–16.62 Mbp, and BTA 18 at 0.19–1.46 Mbp) by using at least two of the above-mentioned methods. We expanded the list of candidate genes associated with the selected genomic regions and performed their functional annotation. We discussed the possible involvement of the identified candidate genes in artificial selection in connection with the origin and development of the breeds. Our findings on the Yaroslavl and Kholmogor breeds obtained using high-density SNP genotyping and three different statistical methods allowed the detection of novel putative genomic regions and candidate genes that might be under selection. These results might be useful for the sustainable development and conservation of these two oldest Russian native cattle breeds.

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Optimizing Low-Cost Genotyping and Imputation Strategies for Genomic Selection in Atlantic Salmon

G3 Genes|Genome|Genetics ◽

10.1534/g3.119.400800 ◽

2019 ◽

Vol 10 (2) ◽

pp. 581-590 ◽

Cited By ~ 4

Author(s):

Smaragda Tsairidou ◽

Alastair Hamilton ◽

Diego Robledo ◽

James E. Bron ◽

Ross D. Houston

Keyword(s):

Atlantic Salmon ◽

Genomic Selection ◽

Environmental Sustainability ◽

Genomic Prediction ◽

Prediction Accuracy ◽

Imputation Accuracy ◽

Cost Effective ◽

High Density ◽

Genotype Imputation ◽

Breeding Programs

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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