Genome-wide associations for feed utilisation complex in primiparous Holstein–Friesian dairy cows from experimental research herds in four European countries

Veerkamp, R.F.; Coffey, M.P.; Berry, D.P.; Haas, Y. de; Strandberg, E.; Bovenhuis, H.; Calus, M.P.L.; Wall, E.

doi:10.1017/s1751731112001152

Cited by 44 publications

(58 citation statements)

References 52 publications

(52 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…For all 529 cows used as selection candidate and reference individual, precorrected phenotypes were available. A detailed description of the precorrection is given by Veerkamp et al (2012).…”

Section: Ldmentioning

confidence: 99%

The Effect of Linkage Disequilibrium and Family Relationships on the Reliability of Genomic Prediction

2013

View full text Add to dashboard Cite

Although the concept of genomic selection relies on linkage disequilibrium (LD) between quantitative trait loci and markers, reliability of genomic predictions is strongly influenced by family relationships. In this study, we investigated the effects of LD and family relationships on reliability of genomic predictions and the potential of deterministic formulas to predict reliability using population parameters in populations with complex family structures. Five groups of selection candidates were simulated by taking different information sources from the reference population into account: (1) allele frequencies, (2) LD pattern, (3) haplotypes, (4) haploid chromosomes, and (5) individuals from the reference population, thereby having real family relationships with reference individuals. Reliabilities were predicted using genomic relationships among 529 reference individuals and their relationships with selection candidates and with a deterministic formula where the number of effective chromosome segments (M e ) was estimated based on genomic and additive relationship matrices for each scenario. At a heritability of 0.6, reliabilities based on genomic relationships were 0.002 6 0.0001 (allele frequencies), 0.022 6 0.001 (LD pattern), 0.018 6 0.001 (haplotypes), 0.100 6 0.008 (haploid chromosomes), and 0.318 6 0.077 (family relationships). At a heritability of 0.1, relative differences among groups were similar. For all scenarios, reliabilities were similar to predictions with a deterministic formula using estimated M e . So, reliabilities can be predicted accurately using empirically estimated M e and level of relationship with reference individuals has a much higher effect on the reliability than linkage disequilibrium per se. Furthermore, accumulated length of shared haplotypes is more important in determining the reliability of genomic prediction than the individual shared haplotype length. C URRENTLY, it is feasible in most plant and animal breeding programs to genotype individuals at low costs for many thousands of single-nucleotide polymorphisms (SNPs) spread across the whole genome. With a sufficiently large reference population containing individuals with phenotypes and genotypes, SNP effects can be estimated. Subsequently, estimated SNP effects and an individual's genotype for each SNP can be used for genomic prediction of breeding values. Selection based on those genomic breeding values is called genomic selection (Meuwissen et al. 2001) and this method has high potential both in animal (e.g., Hayes et al. 2009a) and plant breeding (e.g., Heffner et al. 2009;Jannink et al. 2010). Many studies demonstrated higher reliabilities for direct genomic breeding values compared to breeding values based on pedigree information only, especially for juvenile individuals without phenotypic information (e.g., Meuwissen et al. 2001;Calus et al. 2008;VanRaden 2008).The response to genomic selection relies on linkage disequilibrium (LD) between specific alleles of SNPs and quantitative trait loci (QTL) (Meuwiss...

show abstract

Section: Ldmentioning

confidence: 99%

The Effect of Linkage Disequilibrium and Family Relationships on the Reliability of Genomic Prediction

2013

View full text Add to dashboard Cite

show abstract

“…The pre-corrected phenotypes used here were obtained from Veerkamp et al (2012) who described in detail the steps taken to adjust the original phenotypes. In short, phenotypic measurements were corrected for the mean lactation curve, herd, nutritional treatment, milking frequency, year and month of milk test by management group, and experimental treatments using random regression.…”

Section: Phenotypic Datamentioning

confidence: 99%

“…Animals with conflicting information were removed. The SNP quality control criteria were as described by Veerkamp et al (2012), that is, GenCall (GC) score .0.20 and GenTrain (GT) score .0.55 for individual genotypes; call rate .95%; minor allele frequency .0.01 in each country; and no extreme deviation from Hardy-Weinberg Equilibrium (i.e. x 2 , 600).…”

Section: Genomic Datamentioning

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

Self Cite

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

“…Banos et al (2012) previously described an approach for the collation of research data from 4 European countries. Veerkamp et al (2012) used these collated data to undertake a genome-wide association study for feed intake complex and were able to detect significant putative QTL for feed intake, which could not have been found in the individual data sets. de collated DMI data from Australia, the Netherlands, and the United Kingdom and demonstrated that the accuracy of genomic selection could be improved by combining data from several research populations.…”

Section: Introductionmentioning

confidence: 99%

International genetic evaluations for feed intake in dairy cattle through the collation of data from multiple sources

Berry

Coffey

Pryce

et al. 2014

Journal of Dairy Science

Self Cite

112

110

View full text Add to dashboard Cite

Feed represents a large proportion of the variable costs in dairy production systems. The omission of feed intake measures explicitly from national dairy cow breeding objectives is predominantly due to a lack of information from which to make selection decisions. However, individual cow feed intake data are available in different countries, mostly from research or nucleus herds. None of these data sets are sufficiently large enough on their own to generate accurate genetic evaluations. In the current study, we collate data from 10 populations in 9 countries and estimate genetic parameters for dry matter intake (DMI). A total of 224,174 test-day records from 10,068 parity 1 to 5 records of 6,957 cows were available, as well as records from 1,784 growing heifers. Random regression models were fit to the lactating cow test-day records and predicted feed intake at 70 d postcalving was extracted from these fitted profiles. The random regression model included a fixed polynomial regression for each lactation separately, as well as herd-year-season of calving and experimental treatment as fixed effects; random effects fit in the model included individual animal deviation from the fixed regression for each parity as well as mean herdspecific deviations from the fixed regression. Predicted DMI at 70 d postcalving was used as the phenotype for the subsequent genetic analyses undertaken using an animal repeatability model. Heritability estimates of predicted cow feed intake 70 d postcalving was 0.34 across the entire data set and varied, within population, from 0.08 to 0.52. Repeatability of feed intake across lactations was 0.66. Heritability of feed intake in the growing heifers was 0.20 to 0.34 in the 2 populations with heifer data. The genetic correlation between feed intake in lactating cows and growing heifers was 0.67. A combined pedigree and genomic relationship matrix was used to improve linkages between populations for the estimation of genetic correlations of DMI in lactating cows; genotype information was available on 5,429 of the animals. Populations were categorized as North America, grazing, other low input, and high input European Union. Albeit associated with large standard errors, genetic correlation estimates for DMI between populations varied from 0.14 to 0.84 but were stronger (0.76 to 0.84) between the populations representative of high-input production systems. Genetic correlations with the grazing populations were weak to moderate, varying from 0.14 to 0.57. Genetic evaluations for DMI can be undertaken using data collated from international populations; however, genotype-by-environment interactions with grazing production systems need to be considered.

show abstract

Genome-wide associations for feed utilisation complex in primiparous Holstein–Friesian dairy cows from experimental research herds in four European countries

Cited by 44 publications

References 52 publications

The Effect of Linkage Disequilibrium and Family Relationships on the Reliability of Genomic Prediction

The Effect of Linkage Disequilibrium and Family Relationships on the Reliability of Genomic Prediction

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

International genetic evaluations for feed intake in dairy cattle through the collation of data from multiple sources

Contact Info

Product

Resources

About