scholarly journals Genomic evaluation with multibreed and crossbred data

2022 ◽  
Author(s):  
I. Misztal ◽  
Y. Stein ◽  
D.A.L. Lourenco
Keyword(s):  
Genomic Evaluation

scholarly journals 353 ASAS-EAAP Talk: Low-coverage whole-genome sequencing in local livestock breeds

2020 ◽  
Vol 98 (Supplement_4) ◽  
pp. 81-82
Author(s):  
Joaquim Casellas ◽  
Melani Martín de Hijas-Villalba ◽  
Marta Vázquez-Gómez ◽  
Samir Id Lahoucine
Keyword(s):  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
Error Rate ◽  
Allele Frequencies ◽  
Paternity Testing ◽  
Sequencing Error ◽  
Whole Genome ◽  
Genomic Evaluation ◽  
Sequencing Error Rate ◽  
Low Coverage

Abstract Current European regulations for autochthonous livestock breeds put a special emphasis on pedigree completeness, which requires laboratory paternity testing by genetic markers in most cases. This entails significant economic expenditure for breed societies and precludes other investments in breeding programs, such as genomic evaluation. Within this context, we developed paternity testing through low-coverage whole-genome data in order to reuse these data for genomic evaluation at no cost. Simulations relied on diploid genomes composed by 30 chromosomes (100 cM each) with 3,000,000 SNP per chromosome. Each population evolved during 1,000 non-overlapping generations with effective size 100, mutation rate 10–4, and recombination by Kosambi’s function. Only those populations with 1,000,000 ± 10% polymorphic SNP per chromosome in generation 1,000 were retained for further analyses, and expanded to the required number of parents and offspring. Individuals were sequenced at 0.01, 0.05, 0.1, 0.5 and 1X depth, with 100, 500, 1,000 or 10,000 base-pair reads and by assuming a random sequencing error rate per SNP between 10–2 and 10–5. Assuming known allele frequencies in the population and sequencing error rate, 0.05X depth sufficed to corroborate the true father (85,0%) and to discard other candidates (96,3%). Those percentages increased up to 99,6% and 99,9% with 0,1X depth, respectively (read length = 10,000 bp; smaller read lengths slightly improved the results because they increase the number of sequenced SNP). Results were highly sensitive to biases in allele frequencies and robust to inaccuracies regarding sequencing error rate. Low-coverage whole-genome sequencing data could be subsequently integrated into genomic BLUP equations by appropriately constructing the genomic relationship matrix. This approach increased the correlation between simulated and predicted breeding values by 1.21% (h2 = 0.25; 100 parents and 900 offspring; 0.1X depth by 10,000 bp reads). Although small, this increase opens the door to genomic evaluation in local livestock breeds.

scholarly journals Actionable clinical decisions based on comprehensive genomic evaluation in asymptomatic adults

2015 ◽  
Vol 3 (5) ◽  
pp. 433-439 ◽  
Author(s):  
Nir Pillar ◽  
Ofer Isakov ◽  
Daphna Weissglas‐Volkov ◽  
Shay Botchan ◽  
Eitan Friedman ◽  
...  
Keyword(s):  
Clinical Decisions ◽  
Genomic Evaluation

scholarly journals Genomic selection in French dairy cattle

2012 ◽  
Vol 52 (3) ◽  
pp. 115 ◽  
Author(s):  
D. Boichard ◽  
F. Guillaume ◽  
A. Baur ◽  
P. Croiseau ◽  
M. N. Rossignol ◽  
...  
Keyword(s):  
Linkage Disequilibrium ◽  
Genomic Selection ◽  
Reference Population ◽  
Specific Reference ◽  
Nucleotide Polymorphisms ◽  
Genomic Evaluation ◽  
Linear Unbiased Prediction ◽  
Best Linear Unbiased ◽  
Estimated Breeding Values ◽  
Restricted Use

Genomic selection is implemented in French Holstein, Montbéliarde, and Normande breeds (70%, 16% and 12% of French dairy cows). A characteristic of the model for genomic evaluation is the use of haplotypes instead of single-nucleotide polymorphisms (SNPs), so as to maximise linkage disequilibrium between markers and quantitative trait loci (QTLs). For each trait, a QTL-BLUP model (i.e. a best linear unbiased prediction model including QTL random effects) includes 300–700 trait-dependent chromosomal regions selected either by linkage disequilibrium and linkage analysis or by elastic net. This model requires an important effort to phase genotypes, detect QTLs, select SNPs, but was found to be the most efficient one among all tested ones. QTLs are defined within breed and many of them were found to be breed specific. Reference populations include 1800 and 1400 bulls in Montbéliarde and Normande breeds. In Holstein, the very large reference population of 18 300 bulls originates from the EuroGenomics consortium. Since 2008, ~65 000 animals have been genotyped for selection by Labogena with the 50k chip. Bulls genomic estimated breeding values (GEBVs) were made official in June 2009. In 2010, the market share of the young bulls reached 30% and is expected to increase rapidly. Advertising actions have been undertaken to recommend a time-restricted use of young bulls with a limited number of doses. In January 2011, genomic selection was opened to all farmers for females. Current developments focus on the extension of the method to a multi-breed context, to use all reference populations simultaneously in genomic evaluation.

scholarly journals Multiple-breed genomic evaluation by principal component analysis in small size populations

animal ◽  
2015 ◽  
Vol 9 (5) ◽  
pp. 738-749 ◽  
Author(s):  
G. Gaspa ◽  
H. Jorjani ◽  
C. Dimauro ◽  
M. Cellesi ◽  
P. Ajmone-Marsan ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Genomic Evaluation

Genomic evaluation of dairy heifer livability

2021 ◽  
Author(s):  
M. Neupane ◽  
J.L. Hutchison ◽  
C.P. Van Tassell ◽  
P.M. VanRaden
Keyword(s):  
Genomic Evaluation ◽  
Dairy Heifer

scholarly journals Genomic evaluation of feed efficiency component traits in Duroc pigs using 80K, 650K and whole-genome sequence variants

2018 ◽  
Vol 50 (1) ◽  
Author(s):  
Chunyan Zhang ◽  
Robert Alan Kemp ◽  
Paul Stothard ◽  
Zhiquan Wang ◽  
Nicholas Boddicker ◽  
...  
Keyword(s):  
Genome Sequence ◽  
Feed Efficiency ◽  
Whole Genome Sequence ◽  
Sequence Variants ◽  
Whole Genome ◽  
Genomic Evaluation ◽  
Component Traits

scholarly journals A study of Genomic Prediction across Generations of Two Korean Pig Populations

Animals ◽  
2019 ◽  
Vol 9 (9) ◽  
pp. 672 ◽  
Author(s):  
Beatriz Castro Dias Castro Dias Cuyabano ◽  
Hanna Wackel ◽  
Donghyun Shin ◽  
Cedric Gondro
Keyword(s):  
Genomic Prediction ◽  
Prediction Accuracy ◽  
Reference Population ◽  
Relevant Information ◽  
Production Traits ◽  
Genomic Evaluation ◽  
Genomic Breeding ◽  
Breeding Values ◽  
The Relationship ◽  
Dense Marker

Genomic models that incorporate dense marker information have been widely used for predicting genomic breeding values since they were first introduced, and it is known that the relationship between individuals in the reference population and selection candidates affects the prediction accuracy. When genomic evaluation is performed over generations of the same population, prediction accuracy is expected to decay if the reference population is not updated. Therefore, the reference population must be updated in each generation, but little is known about the optimal way to do it. This study presents an empirical assessment of the prediction accuracy of genomic breeding values of production traits, across five generations in two Korean pig breeds. We verified the decay in prediction accuracy over time when the reference population was not updated. Additionally we compared the prediction accuracy using only the previous generation as the reference population, as opposed to using all previous generations as the reference population. Overall, the results suggested that, although there is a clear need to continuously update the reference population, it may not be necessary to keep all ancestral genotypes. Finally, comprehending how the accuracy of genomic prediction evolves over generations within a population adds relevant information to improve the performance of genomic selection.

scholarly journals DNA Testing and Genetic Evaluation for Poll Breeding in Tropically Adapted Beef Cattle

Proceedings ◽  
2020 ◽  
Vol 36 (1) ◽  
pp. 98
Author(s):  
Imtiaz A.S. Randhawa ◽  
Michael R. McGowan ◽  
Laercio R. Porto-Neto ◽  
Ben J. Hayes ◽  
Russell E. Lyons
Keyword(s):  
Beef Cattle ◽  
Animal Welfare ◽  
Genetic Variants ◽  
Chromosome 1 ◽  
Health Production ◽  
Dna Testing ◽  
Genomic Evaluation ◽  
Cattle Breeding ◽  
Breeding Schemes ◽  
Estimated Breeding Values

In beef cattle, horn management is practiced to physically or surgically remove horns for the safety of animals and workers. However, invasive practices of dehorning and disbudding are a great threat to animal welfare, health, production and human safety, as well as labour intensive and costly. The most effective way to limit the impacts and costs of horns is to prevent their occurrences by breeding naturally polled (hornless) herds. Horn development is complex, although two mutually exclusive genetic variants (Celtic and Friesian) have been found prevalent on each copy of chromosome 1 in most polled cattle. Predicting genotypes in an animal is challenging. Available genetic testing assays were often limited in tropically adapted beef cattle. In this study we present a new optimized poll testing (OPT) assay, which has been bundled with SNP genotyping arrays being used for genomic evaluation in cattle. Breeding schemes can profile future parents for pure-polled stock based on the OPT results. We also evaluated the factors causing complexity in horn conditions. Thus, we coupled OPT predictions with head-status and sex distributions, by modelling genetic and non-genetic impacts, revealing that genetics, sex and sex hormones control horn ontology. Finally, concerns of polledness adversely affecting production and reproduction were investigated by using estimated breeding values of several beef traits. We found no detrimental effects of polledness on production or reproduction. Overall, this research concludes that genetically polled cattle will minimize issues about animal welfare and management costs without reducing production potentials in the tropically adapted beef cattle.

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