scholarly journals Advantage of including Genomic Information to Predict Breeding Values for Lactation Yields of Milk, Fat, and Protein or Somatic Cell Score in a New Zealand Dairy Goat Herd

Animals ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 24
Author(s):  
Megan Scholtens ◽  
Nicolas Lopez-Villalobos ◽  
Klaus Lehnert ◽  
Russell Snell ◽  
Dorian Garrick ◽  
...  
Keyword(s):  
New Zealand ◽  
Somatic Cell ◽  
Milk Fat ◽  
Single Step ◽  
Somatic Cell Score ◽  
Dairy Goat ◽  
Nucleotide Polymorphisms ◽  
Genomic Information ◽  
Breeding Values ◽  
Goat Population

Selection on genomic breeding values (GBVs) is now readily available for ranking candidates in improvement schemes. Our objective was to quantify benefits in terms of accuracy of prediction from including genomic information in the single-trait estimation of breeding values (BVs) for a New Zealand mixed breed dairy goat herd. The dataset comprised phenotypic and pedigree records of 839 does. The phenotypes comprised estimates of 305-day lactation yields of milk, fat, and protein and average somatic cell score from the 2016 production season. Only 388 of the goats were genotyped with a Caprine 50K SNP chip and 41,981 of the single nucleotide polymorphisms (SNPs) passed quality control. Pedigree-based best linear unbiased prediction (PBLUP) was used to obtain across-breed breeding values (EBVs), whereas a single-step BayesC model (ssBC) was used to estimate across-breed GBVs. The average prediction accuracies ranged from 0.20 to 0.22 for EBVs and 0.34 to 0.43 for GBVs. Accuracies of GBVs were up to 103% greater than EBVs. Breed effects were more reliably estimated in the ssBC model compared with the PBLUP model. The greatest benefit of genomic prediction was for individuals with no pedigree or phenotypic records. Including genomic information improved the prediction accuracy of BVs compared with the current pedigree-based BLUP method currently implemented in the New Zealand dairy goat population.

Genetic parameters for total lactation yields of milk, fat, protein, and somatic cell score in New Zealand dairy goats

2019 ◽  
Vol 91 (1) ◽  
Author(s):  
Megan R. Scholtens ◽  
Nicolas Lopez‐Villalobos ◽  
Dorian Garrick ◽  
Hugh Blair ◽  
Klaus Lehnert ◽  
...  
Keyword(s):  
New Zealand ◽  
Somatic Cell ◽  
Milk Fat ◽  
Genetic Parameters ◽  
Somatic Cell Score ◽  
Dairy Goats

scholarly journals Comparison of models for missing pedigree in single-step genomic prediction

2021 ◽  
Vol 99 (2) ◽  
Author(s):  
Yutaka Masuda ◽  
Shogo Tsuruta ◽  
Matias Bermann ◽  
Heather L Bradford ◽  
Ignacy Misztal
Keyword(s):  
Density Function ◽  
Single Step ◽  
Joint Density ◽  
Pedigree Information ◽  
Relationship Matrix ◽  
Genomic Information ◽  
Breeding Values ◽  
Joint Density Function ◽  
Information Updates ◽  
Genetic Trends

Abstract Pedigree information is often missing for some animals in a breeding program. Unknown-parent groups (UPGs) are assigned to the missing parents to avoid biased genetic evaluations. Although the use of UPGs is well established for the pedigree model, it is unclear how UPGs are integrated into the inverse of the unified relationship matrix (H-inverse) required for single-step genomic best linear unbiased prediction. A generalization of the UPG model is the metafounder (MF) model. The objectives of this study were to derive 3 H-inverses and to compare genetic trends among models with UPG and MF H-inverses using a simulated purebred population. All inverses were derived using the joint density function of the random breeding values and genetic groups. The breeding values of genotyped animals (u2) were assumed to be adjusted for UPG effects (g) using matrix Q2 as u2∗=u2+Q2g before incorporating genomic information. The Quaas–Pollak-transformed (QP) H-inverse was derived using a joint density function of u2∗ and g updated with genomic information and assuming nonzero cov(u2∗,g′). The modified QP (altered) H-inverse also assumes that the genomic information updates u2∗ and g, but cov(u2∗,g′)=0. The UPG-encapsulated (EUPG) H-inverse assumed genomic information updates the distribution of u2∗. The EUPG H-inverse had the same structure as the MF H-inverse. Fifty percent of the genotyped females in the simulation had a missing dam, and missing parents were replaced with UPGs by generation. The simulation study indicated that u2∗ and g in models using the QP and altered H-inverses may be inseparable leading to potential biases in genetic trends. Models using the EUPG and MF H-inverses showed no genetic trend biases. These 2 H-inverses yielded the same genomic EBV (GEBV). The predictive ability and inflation of GEBVs from young genotyped animals were nearly identical among models using the QP, altered, EUPG, and MF H-inverses. Although the choice of H-inverse in real applications with enough data may not result in biased genetic trends, the EUPG and MF H-inverses are to be preferred because of theoretical justification and possibility to reduce biases.

scholarly journals Genomic prediction of lactation curves for milk, fat, protein, and somatic cell score in Holstein cattle

2019 ◽  
Vol 102 (1) ◽  
pp. 452-463 ◽  
Author(s):  
H.R. Oliveira ◽  
L.F. Brito ◽  
F.F. Silva ◽  
D.A.L. Lourenco ◽  
J. Jamrozik ◽  
...  
Keyword(s):  
Somatic Cell ◽  
Genomic Prediction ◽  
Milk Fat ◽  
Somatic Cell Score ◽  
Holstein Cattle

scholarly journals Milk, Fat, Protein, Somatic Cell Score, and Days Open Among Holstein, Brown Swiss, and Their Crosses

2007 ◽  
Vol 90 (7) ◽  
pp. 3542-3549 ◽  
Author(s):  
C.D. Dechow ◽  
G.W. Rogers ◽  
J.B. Cooper ◽  
M.I. Phelps ◽  
A.L. Mosholder
Keyword(s):  
Somatic Cell ◽  
Milk Fat ◽  
Somatic Cell Score ◽  
Brown Swiss ◽  
Days Open

scholarly journals L-selectin gene polymorphism and its association with clinical mastitis, somatic cell score, and milk production in Polish Holstein-Friesian cattle

2018 ◽  
Vol 63 (No. 7) ◽  
pp. 256-262 ◽  
Author(s):  
M. Dusza ◽  
J. Pokorska ◽  
J. Makulska ◽  
D. Kulaj ◽  
M. Cupial
Keyword(s):  
Somatic Cell ◽  
Milk Production ◽  
Milk Fat ◽  
Bacterial Infections ◽  
Clinical Mastitis ◽  
Somatic Cell Score ◽  
Production Traits ◽  
Milk Production Traits ◽  
Holstein Friesian ◽  
Friesian Cows

Bovine mastitis is a widespread disease of the mammary gland, highly contributing to the increase in veterinary costs in dairy industry. In the present study, the genetic polymorphism within bovine L-selectin gene was analysed and its impact on clinical mastitis occurrence, somatic cell score (SCS), and milk production traits in Polish Holstein-Friesian cows was examined. Polymorphism within L-selectin gene, molecule responsible for neutrophil attachment to endothelium, might have a potential role in immune response to bacterial infections and udder health. Two hundred and six Polish Holstein-Friesian cows were genotyped by polymerase chain reaction-restriction fragment length polymorphism method. Two single nucleotide polymorphisms mutations within the coding sequence of L-selectin gene were identified (c.165G>A and c.567C>T). The effect of c.165G>A and c.567C>T mutations on SCS was highly significant (P = 0.0019 and P = 0.0003, respectively). Strong associations (P ≤ 0.0001) were also observed between L-selectin polymorphism and milk production traits (milk yield, milk fat percentage, and milk protein percentage). However, the polymorphism in the analysed gene had no influence on the resistance or susceptibility of cows to clinical mastitis (only the tendency toward significance, P = 0.06 for c.567C>T mutation was found). Potential exploitation of the information on the identified associations in genetic selection needs to confirm the obtained results in further investigations.

scholarly journals The Effect of Integrating Genomic Information into Genetic Evaluations of Chinese Merino Sheep

Animals ◽  
2020 ◽  
Vol 10 (4) ◽  
pp. 569
Author(s):  
Chen Wei ◽  
Hanpeng Luo ◽  
Bingru Zhao ◽  
Kechuan Tian ◽  
Xixia Huang ◽  
...  
Keyword(s):  
Rank Correlation ◽  
Best Linear Unbiased Prediction ◽  
Single Step ◽  
Breeding Value ◽  
Genomic Information ◽  
Linear Unbiased Prediction ◽  
Breeding Values ◽  
Merino Sheep ◽  
Best Linear Unbiased ◽  
Unbiased Prediction

Genomic evaluations are a method for improving the accuracy of breeding value estimation. This study aimed to compare estimates of genetic parameters and the accuracy of breeding values for wool traits in Merino sheep between pedigree-based best linear unbiased prediction (PBLUP) and single-step genomic best linear unbiased prediction (ssGBLUP) using Bayesian inference. Data were collected from 28,391 yearlings of Chinese Merino sheep (classified in 1992–2018) at the Xinjiang Gonaisi Fine Wool Sheep-Breeding Farm, China. Subjectively-assessed wool traits, namely, spinning count (SC), crimp definition (CRIM), oil (OIL), and body size (BS), and objectively-measured traits, namely, fleece length (FL), greasy fleece weight (GFW), mean fiber diameter (MFD), crimp number (CN), and body weight pre-shearing (BWPS), were analyzed. The estimates of heritability for wool traits were low to moderate. The largest h2 values were observed for FL (0.277) and MFD (0.290) with ssGBLUP. The heritabilities estimated for wool traits with ssGBLUP were slightly higher than those obtained with PBLUP. The accuracies of breeding values were low to moderate, ranging from 0.362 to 0.573 for the whole population and from 0.318 to 0.676 for the genotyped subpopulation. The correlation between the estimated breeding values (EBVs) and genomic EBVs (GEBVs) ranged from 0.717 to 0.862 for the whole population, and the relative increase in accuracy when comparing EBVs with GEBVs ranged from 0.372% to 7.486% for these traits. However, in the genotyped population, the rank correlation between the estimates obtained with PBLUP and ssGBLUP was reduced to 0.525 to 0.769, with increases in average accuracy of 3.016% to 11.736% for the GEBVs in relation to the EBVs. Thus, genomic information could allow us to more accurately estimate the relationships between animals and improve estimates of heritability and the accuracy of breeding values by ssGBLUP.

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