Multiple-trait genomic evaluation of linear type traits using genomic and phenotypic data in US Holsteins

Tsuruta, S.; Misztal, I.; Aguilar, Ignácio; Lawlor, T.J.

doi:10.3168/jds.2011-4256

Cited by 129 publications

(139 citation statements)

References 11 publications

Supporting

Mentioning

126

Contrasting

Unclassified

Order By: Relevance

“…It is also clear that when genomic selection is applied in a population, all traits considered by the recording systems of that population would be included in the new evaluation process and optimisation of the whole genomic evaluation for all parameters would be sometimes difficult due to their different characteristics. Difficulties arise with multiple-trait genomic analyses, although application with single-step procedures was reported in literature in other species for a large number of traits (Tsuruta et al 2011). …”

Section: Genomic Selection In Pig Populationsmentioning

confidence: 99%

Genomic selection in pigs: state of the art and perspectives

Samoré

Fontanesi

2016

Italian Journal of Animal Science

View full text Add to dashboard Cite

Genomic selection, that is based on the prediction of the breeding value (the genomic breeding value or GEBV) of the animals considering genomic information, is changing breeding strategies and approaches in dairy cattle and in several other livestock species. The starting points for the application of genomic selection in pigs were the development of a first commercial singlenucleotide polymorphism panel for high throughput genotyping, the sequencing of the pig genome and the application of statistical and methodological approaches first developed in dairy cattle and then adapted to the peculiarities of the pig breeding industry. In this review, we focused on the specific applications of the genomic selection in pigs, considering its limits, its advantages and its perspectives throughout an analysis of the simulation studies and field applications already available targeting many different traits (with high and low heritability). In addition, we presented an overview of the problems related to the implementation of genomic selection in crossbred and multi-breed pig populations and the potential solutions taking into account the economic aspects that should be faced in including genomic selection in pig breeding programmes. ARTICLE HISTORY

show abstract

Section: Genomic Selection In Pig Populationsmentioning

confidence: 99%

Genomic selection in pigs: state of the art and perspectives

Samoré

Fontanesi

2016

Italian Journal of Animal Science

View full text Add to dashboard Cite

show abstract

“…Large-scale genetic evaluations with the single-step approach were run for either single trait or multiple-trait analyses (Aguilar et al, 2011b;Tsuruta et al, 2011). Computing time for genetic evaluations using genomic information took twice as long as evaluations without genomic data (Tsuruta et al, 2011), but could be reduced by 4 to 11% compared with regular genetic evaluations if custom libraries are used [I. Aguilar, I. Misztal (University of Georgia, Athens), A. Legarra (University of Georgia, Athens), and S. Tsuruta (University of Georgia, Athens), unpublished data].…”

Section: Genomic Evaluationmentioning

confidence: 99%

BREEDING AND GENETICS SYMPOSIUM: Really big data: Processing and analysis of very large data sets1

Cole

Newman

Foertter

et al. 2012

Journal of Animal Science

Self Cite

View full text Add to dashboard Cite

Modern animal breeding data sets are large and getting larger, due in part to recent availability of high-density SNP arrays and cheap sequencing technology. High-performance computing methods for efficient data warehousing and analysis are under development. Financial and security considerations are important when using shared clusters. Sound software engineering practices are needed, and it is better to use existing solutions when possible. Storage requirements for genotypes are modest, although full-sequence data will require greater storage capacity. Storage requirements for intermediate and results files for genetic evaluations are much greater, particularly when multiple runs must be stored for research and validation studies. The greatest gains in accuracy from genomic selection have been realized for traits of low heritability, and there is increasing interest in new health and management traits. The collection of sufficient phenotypes to produce accurate evaluations may take many years, and high-reliability proofs for older bulls are needed to estimate marker effects. Data mining algorithms applied to large data sets may help identify unexpected relationships in the data, and improved visualization tools will provide insights. Genomic selection using large data requires a lot of computing power, particularly when large fractions of the population are genotyped. Theoretical improvements have made possible the inversion of large numerator relationship matrices, permitted the solving of large systems of equations, and produced fast algorithms for variance component estimation. Recent work shows that single-step approaches combining BLUP with a genomic relationship (G) matrix have similar computational requirements to traditional BLUP, and the limiting factor is the construction and inversion of G for many genotypes. A naïve algorithm for creating G for 14,000 individuals required almost 24 h to run, but custom libraries and parallel computing reduced that to 15 m. Large data sets also create challenges for the delivery of genetic evaluations that must be overcome in a way that does not disrupt the transition from conventional to genomic evaluations. Processing time is important, especially as real-time systems for on-farm decisions are developed. The ultimate value of these systems is to decrease time-to-results in research, increase accuracy in genomic evaluations, and accelerate rates of genetic improvement.

show abstract

“…Therefore, for other traits than those analysed here, the benefit of using the predictor traits may be different. For example, differences in accuracy for various type traits, while using single-or multi-trait genomic analyses shown by Tsuruta et al (2011), were up to 9%.…”

Section: Accuracymentioning

confidence: 99%

“…For example, Aguilar et al (2011) showed an increase in accuracy of conception rate when enriching A with G and an additional substantial increase when multitrait approach was used instead of the single-trait one. Tsuruta et al (2011), who analysed linear type traits of US Holsteins, showed that, in terms of accuracy, the benefit of genomic analyses instead of pedigree was persistent across all analysed traits. Another explanation of small differences in accuracy between multi-trait pedigree and genomic scenarios is that recording own phenotypes for predictor traits on the evaluated animals gives information about the Mendelian sampling in the evaluated animals.…”

Section: Accuracymentioning

confidence: 99%

Effect of predictor traits on accuracy of genomic breeding values for feed intake based on a limited cow reference population

Pszczoła

Veerkamp

Haas

et al. 2013

Animal

View full text Add to dashboard Cite

The genomic breeding value accuracy of scarcely recorded traits is low because of the limited number of phenotypic observations. One solution to increase the breeding value accuracy is to use predictor traits. This study investigated the impact of recording additional phenotypic observations for predictor traits on reference and evaluated animals on the genomic breeding value accuracy for a scarcely recorded trait. The scarcely recorded trait was dry matter intake (DMI, n 5 869) and the predictor traits were fat-protein-corrected milk (FPCM, n 5 1520) and live weight (LW, n 5 1309). All phenotyped animals were genotyped and originated from research farms in Ireland, the United Kingdom and the Netherlands. Multi-trait REML was used to simultaneously estimate variance components and breeding values for DMI using available predictors. In addition, analyses using only pedigree relationships were performed. Breeding value accuracy was assessed through cross-validation (CV) and prediction error variance (PEV). CV groups (n 5 7) were defined by splitting animals across genetic lines and management groups within country. With no additional traits recorded for the evaluated animals, both CV-and PEV-based accuracies for DMI were substantially higher for genomic than for pedigree analyses (CV: max. 0.26 for pedigree and 0.33 for genomic analyses; PEV: max. 0.45 and 0.52, respectively). With additional traits available, the differences between pedigree and genomic accuracies diminished. With additional recording for FPCM, pedigree accuracies increased from 0.26 to 0.47 for CV and from 0.45 to 0.48 for PEV. Genomic accuracies increased from 0.33 to 0.50 for CV and from 0.52 to 0.53 for PEV. With additional recording for LW instead of FPCM, pedigree accuracies increased to 0.54 for CV and to 0.61 for PEV. Genomic accuracies increased to 0.57 for CV and to 0.60 for PEV. With both FPCM and LW available for evaluated animals, accuracy was highest (0.62 for CV and 0.61 for PEV in pedigree, and 0.63 for CV and 0.61 for PEV in genomic analyses). Recording predictor traits for only the reference population did not increase DMI breeding value accuracy. Recording predictor traits for both reference and evaluated animals significantly increased DMI breeding value accuracy and removed the bias observed when only reference animals had records. The benefit of using genomic instead of pedigree relationships was reduced when more predictor traits were used. Using predictor traits may be an inexpensive way to significantly increase the accuracy and remove the bias of (genomic) breeding values of scarcely recorded traits such as feed intake.

show abstract

Multiple-trait genomic evaluation of linear type traits using genomic and phenotypic data in US Holsteins

Cited by 129 publications

References 11 publications

Genomic selection in pigs: state of the art and perspectives

Genomic selection in pigs: state of the art and perspectives

BREEDING AND GENETICS SYMPOSIUM: Really big data: Processing and analysis of very large data sets1

Effect of predictor traits on accuracy of genomic breeding values for feed intake based on a limited cow reference population

Contact Info

Product

Resources

About