Dataset Information

The impact of genetic relationship information on genomic breeding values in German Holstein cattle.

ABSTRACT:

Background

The impact of additive-genetic relationships captured by single nucleotide polymorphisms (SNPs) on the accuracy of genomic breeding values (GEBVs) has been demonstrated, but recent studies on data obtained from Holstein populations have ignored this fact. However, this impact and the accuracy of GEBVs due to linkage disequilibrium (LD), which is fairly persistent over generations, must be known to implement future breeding programs.

Materials and methods

The data set used to investigate these questions consisted of 3,863 German Holstein bulls genotyped for 54,001 SNPs, their pedigree and daughter yield deviations for milk yield, fat yield, protein yield and somatic cell score. A cross-validation methodology was applied, where the maximum additive-genetic relationship (amax) between bulls in training and validation was controlled. GEBVs were estimated by a Bayesian model averaging approach (BayesB) and an animal model using the genomic relationship matrix (G-BLUP). The accuracy of GEBVs due to LD was estimated by a regression approach using accuracy of GEBVs and accuracy of pedigree-based BLUP-EBVs.

Results

Accuracy of GEBVs obtained by both BayesB and G-BLUP decreased with decreasing amax for all traits analyzed. The decay of accuracy tended to be larger for G-BLUP and with smaller training size. Differences between BayesB and G-BLUP became evident for the accuracy due to LD, where BayesB clearly outperformed G-BLUP with increasing training size.

Conclusions

GEBV accuracy of current selection candidates varies due to different additive-genetic relationships relative to the training data. Accuracy of future candidates can be lower than reported in previous studies because information from close relatives will not be available when selection on GEBVs is applied. A Bayesian model averaging approach exploits LD information considerably better than G-BLUP and thus is the most promising method. Cross-validations should account for family structure in the data to allow for long-lasting genomic based breeding plans in animal and plant breeding.

SUBMITTER: Habier D

PROVIDER: S-EPMC2838754 | biostudies-literature | 2010 Feb

REPOSITORIES: biostudies-literature

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Publications

The impact of genetic relationship information on genomic breeding values in German Holstein cattle.

Habier David D Tetens Jens J Seefried Franz-Reinhold FR Lichtner Peter P Thaller Georg G

Genetics, selection, evolution : GSE 20100219

<h4>Background</h4>The impact of additive-genetic relationships captured by single nucleotide polymorphisms (SNPs) on the accuracy of genomic breeding values (GEBVs) has been demonstrated, but recent studies on data obtained from Holstein populations have ignored this fact. However, this impact and the accuracy of GEBVs due to linkage disequilibrium (LD), which is fairly persistent over generations, must be known to implement future breeding programs.<h4>Materials and methods</h4>The data set us ...[more]

PMID: 20170500

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Project description:BackgroundFor infectious diseases, the probability that an animal gets infected depends on its own susceptibility, and on the number of infectious herd mates and their infectivity. Together with the duration of the infectious period, susceptibility and infectivity determine the basic reproduction ratio of the disease ([Formula: see text]). [Formula: see text] is the average number of secondary cases caused by a typical infectious individual in an otherwise uninfected population. An infectious disease dies out when [Formula: see text] is lower than 1. Thus, breeding strategies that aim at reducing disease prevalence should focus on reducing [Formula: see text], preferably to a value lower than 1. In animal breeding, however, [Formula: see text] has received little attention. Here, we estimate the additive genetic variance in host susceptibility, host infectivity, and [Formula: see text] for the endemic claw disease digital dermatitis (DD) in Holstein Friesian dairy cattle, and estimate genomic breeding values (GEBV) for these traits. We recorded DD disease status of both hind claws of 1513 cows from 12 Dutch dairy farms, every 2 weeks, 11 times. The genotype data consisted of 75,904 single nucleotide polymorphisms (SNPs) for 1401 of the cows. We modelled the probability that a cow got infected between recordings, and compared four generalized linear mixed models. All models included a genetic effect for susceptibility; Models 2 and 4 also included a genetic effect for infectivity, while Models 1 and 2 included a farm*period interaction. We corrected for variation in exposure to infectious herd mates via an offset.ResultsGEBV for [Formula: see text] from the model that included genetic effects for susceptibility only had an accuracy of ~ 0.39 based on cross-validation between farms, which is very high given the limited amount of data and the complexity of the trait. Models with a genetic effect for infectivity showed a larger bias, but also a slightly higher accuracy of GEBV. Additive genetic standard deviation for [Formula: see text] was large, i.e. ~ 1.17, while the mean [Formula: see text] was 2.36.ConclusionsGEBV for [Formula: see text] showed substantial variation. The mean [Formula: see text] was only about one genetic standard deviation greater than 1. These results suggest that lowering DD prevalence by selective breeding is promising.

Project description:BackgroundOur aim was to identify genomic regions via genome-wide association studies (GWAS) to improve the predictability of genetic merit in Holsteins for 10 calving and 28 body conformation traits. Animals were genotyped using the Illumina Bovine 50 K BeadChip and imputed to the Illumina BovineHD BeadChip (HD). GWAS were performed on 601,717 real and imputed single nucleotide polymorphism (SNP) genotypes using a single-SNP mixed linear model on 4841 Holstein bulls with breeding value predictions and followed by gene identification and in silico functional analyses. The association results were further validated using five scenarios with different numbers of SNPs.ResultsSeven hundred and eighty-two SNPs were significantly associated with calving performance at a genome-wise false discovery rate (FDR) of 5%. Most of these significant SNPs were on chromosomes 18 (71.9%), 17 (7.4%), 5 (6.8%) and 7 (2.4%) and mapped to 675 genes, among which 142 included at least one significant SNP and 532 were nearby one (100 kbp). For body conformation traits, 607 SNPs were significant at a genome-wise FDR of 5% and most of them were located on chromosomes 5 (30%), 18 (27%), 20 (13%), 6 (6%), 7 (5%), 14 (5%) and 13 (3%). SNP enrichment functional analyses for calving traits at a FDR of 1% suggested potential biological processes including musculoskeletal movement, meiotic cell cycle, oocyte maturation and skeletal muscle contraction. Furthermore, pathway analyses suggested potential pathways associated with calving performance traits including tight junction, oxytocin signaling, and MAPK signaling (P < 0.10). The prediction ability of the 1206 significant SNPs was between 78 and 83% of the prediction ability of the BovineSNP50 SNPs for calving performance traits and between 35 and 79% for body conformation traits.ConclusionsVarious SNPs that are significantly associated with calving performance are located within or nearby genes with potential roles in tight junction, oxytocin signaling, and MAPK signaling. Combining the significant SNPs or SNPs within or nearby gene(s) from the HD panel with the BovineSNP50 panel yielded a marginal increase in the accuracy of prediction of genomic estimated breeding values for all traits compared to the use of the BovineSNP50 panel alone.

Project description:A combined matrix that exploits genealogy together with marker-based information could improve the selection of elite individuals in breeding programs. We present genetic parameters for adaptive and growth traits in beef cattle by exploring linear combinations of pedigree-based (A) and marker-based (G) relationship matrices. We use a data set with 2,111 Brahman (BB) and 2,550 Tropical Composite (TC) cattle with genotypes for 729,068 SNP, and phenotypes for five traits. A weighted relationship matrix (WRM) combining G and A was constructed as WRM = ?G + (1 - ?)A. The weight (?) was explored at values from 0.0 to 1.0, at 0.1 intervals. Additionally, four alternative G matrices, in the WRM, were evaluated according to the selection of SNP used to generate them: 1) Gw: all autosomal SNP with minor allele frequency (MAF) > 1%; 2) Gg: autosomal SNP with MAF > 1% and mapped inside to gene coding regions; 3) Gp: autosomal SNP with MAF > 1% and previously reported to have significant pleiotropic effect in these two populations; and 4) Gc: autosomal SNP with MAF > 1% and with significant correlated effects previously reported in both BB and TC populations. In addition, two A matrices were evaluated: 1) A: all relationships between animals were considered after tracing back known ancestors; and 2) Ad: a distorted A matrix where a random 1% of the off-diagonal nonzero values were set to zero to simulate relationship errors. Five independent Ad matrices were explored each with a different random 1% of relationships masked. Criteria for comparing the resulting WRM included estimates of heritability (h2) and cross-validation accuracy (ACC) of genomic estimated breeding values. The choice of WRM had a greater impact on h2 than on ACC estimates. The 1% errors introduced in pedigree relationships generated large distortion in genetic parameters and ACC estimates. However, employing a ? > 0.7 was an efficient mechanism to compensate for the errors in A. Additionally, although significant (P-value < 0.0001), we found no consistent relationship between the type of SNP used to compute G and h2 or ACC estimates. We devised the optimal value of ? for maximum h2 and ACC at ? = 0.7 suggesting a 70% and 30% weighting to genomic and genealogical information, respectively, as an optimal strategy to compensate for pedigree errors, to improve genetic parameters estimates and lead to more accurate selection decisions.

Project description:BackgroundIn national evaluations, direct genomic breeding values can be considered as correlated traits to those for which phenotypes are available for traditional estimation of breeding values. For this purpose, estimates of the accuracy of direct genomic breeding values expressed as genetic correlations between traits and their respective direct genomic breeding values are required.MethodsWe derived direct genomic breeding values for 2239 registered Limousin and 2703 registered Simmental beef cattle genotyped with either the Illumina BovineSNP50 BeadChip or the Illumina BovineHD BeadChip. For the 264 Simmental animals that were genotyped with the BovineHD BeadChip, genotypes for markers present on the BovineSNP50 BeadChip were extracted. Deregressed estimated breeding values were used as observations in weighted analyses that estimated marker effects to derive direct genomic breeding values for each breed. For each breed, genotyped individuals were clustered into five groups using K-means clustering, with the aim of increasing within-group and decreasing between-group pedigree relationships. Cross-validation was performed five times for each breed, using four groups for training and the fifth group for validation. For each trait, we then applied a weighted bivariate analysis of the direct genomic breeding values of genotyped animals from all five validation sets and their corresponding deregressed estimated breeding values to estimate variance and covariance components.ResultsAfter minimizing relationships between training and validation groups, estimated genetic correlations between each trait and its direct genomic breeding values ranged from 0.39 to 0.76 in Limousin and from 0.29 to 0.65 in Simmental. The efficiency of selection based on direct genomic breeding values relative to selection based on parent average information ranged from 0.68 to 1.28 in genotyped Limousin and from 0.51 to 1.44 in genotyped Simmental animals. The efficiencies were higher for 323 non-genotyped young Simmental animals, born after January 2012, and ranged from 0.60 to 2.04.ConclusionsDirect genomic breeding values show promise for routine use by Limousin and Simmental breeders to improve the accuracy of predicted genetic merit of their animals at a young age and increase response to selection. Benefits from selecting on direct genomic breeding values are greater for breeders who use natural mating sires in their herds than for those who use artificial insemination sires. Producers with unregistered commercial Limousin and Simmental cattle could also benefit from being able to identify genetically superior animals in their herds, an opportunity that has in the past been limited to seed stock animals.

Project description:Fertility and reproductive performance are key drivers of dairy farm profitability. Hence, reproduction traits have been included in a large majority of worldwide dairy cattle selection indexes. The reproductive traits are lowly heritable but can be improved through direct genetic selection. However, most scientific studies and dairy cattle breeding programs have focused solely on the genetic effects of the dam (GED) on reproductive performance and, therefore, ignored the contribution of the service sire in the phenotypic outcomes. This study aimed to investigate the service sire effects on female reproductive traits in Holstein cattle from a genomic perspective. Genetic parameter estimation and genome-wide association studies (GWAS) were performed for the genetic effect of service sire (GESS) on conception rate (CR), 56-day non-return rate (NRR56), calving ease (CE), stillbirth (SB), and gestation length (GL). Our findings indicate that the additive genetic effects of both sire and dam contribute to the phenotypic variance of reproductive traits measured in females (0.0196 vs. 0.0109, 0.0237 vs. 0.0133, 0.0040 vs. 0.0289, 0.0782 vs. 0.0083, and 0.1024 vs. 0.1020 for GESS and GED heritability estimates for CR, NRR56, CE, SB, and GL, respectively), and these two genetic effects are positively correlated for SB (0.1394) and GL (0.7871). Interestingly, the breeding values for GESS on insemination success traits (CR and NRR56) are unfavorably and significantly correlated with some production, health, and type breeding values (ranging from -0.449 to 0.274), while the GESS values on calving traits (CE, SB, and GL) are usually favorably associated with those traits (ranging from -0.493 to 0.313). One hundred sixty-two significant single-nucleotide polymorphisms (SNPs) and their surrounding protein-coding genes were identified as significantly associated with GESS and GED, respectively. Six genes overlapped between GESS and GED for calving traits and 10 genes overlapped between GESS for success traits and calving traits. Our findings indicate the importance of considering the GESS when genetically evaluating the female reproductive traits in Holstein cattle.

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Dataset Information

The impact of genetic relationship information on genomic breeding values in German Holstein cattle.

Background

Materials and methods

Results

Conclusions

Publications

The impact of genetic relationship information on genomic breeding values in German Holstein cattle.

Similar Datasets

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