DAIRY HERD GENETIC DIFFERENCES FOR LACTATION PRODUCTION

1982 ◽  
Vol 62 (2) ◽  
pp. 323-331 ◽  
Author(s):  
L. R. SCHAEFFER ◽  
A. KERR ◽  
E. B. BURNSIDE
Keyword(s):  
Milk Yield ◽  
Dairy Herd ◽  
Herd Size ◽  
Best Linear Unbiased Prediction ◽  
Genetic Differences ◽  
Phenotypic Variance ◽  
Linear Unbiased Prediction ◽  
Production Program ◽  
Best Linear Unbiased ◽  
Genetic Trends

Cow estimated transmitting abilities (ETA) for milk yield and fat percent derived by best linear unbiased prediction methods were used to compute averages for each herd and year of calving subclass for herds enrolled on the Record of Production program (ROP). Means and variances of herd averages were tabulated according to province, herd size, and year of calving. The genetic trends in herd averages within herd size categories were positive for milk yields and negative for fat percent. The variance of herd averages has increased since 1958 within herd size categories, but has decreased over all herds due to a shift in size of herds since 1958. Herds with fewer than 20 cows represented 31.7% of the ROP herds in Canada in 1977 while in 1958 they represented 72.4%. However, in 1977 the larger herds did not show any genetic advantage over smaller herds. Ontario herds of size 20–9 cows showed greater variability in average ETA for milk and fat percent than herds in other provinces. Correlations among traits on a herd average basis have not changed in the last 10 yr even though herd averages have changed substantially over the same period. Herd genetic differences accounted for only 2.05% of herd phenotypic variance for milk yield and 12.74% for fat percent. Key words: Genetic differences, herds, cow indexing

scholarly journals Genetic evaluation and selection correlated response of growth traits in Japanese quail

2020 ◽  
Vol 50 (2) ◽  
pp. 325-333
Author(s):  
M.M. El-Attrouny ◽  
E.A. Manaa ◽  
S.I. Ramadan
Keyword(s):  
Growth Traits ◽  
Best Linear Unbiased Prediction ◽  
Correlated Response ◽  
Genetic Trend ◽  
Linear Unbiased Prediction ◽  
Phenotypic Correlations ◽  
Genetic And Phenotypic Correlations ◽  
Best Linear Unbiased ◽  
Selection For ◽  
Genetic Trends

Objectives of the current study were to i) investigate effects of selection for bodyweight at four weeks old on bodyweight (BW) and bodyweight gain (BWG) across four generations; ii) estimate correlated response to selection for BW and BWG at different ages; and iii) document best linear unbiased prediction (BLUP) of genetic trends for BW and BWG across four generations of selection. A total of 3540 chicks from 444 sires and 885 dams were used to estimate heritabilities, and genetic and phenotypic correlations for growth traits, including BW at 0, 2, 4, and 6 weeks, and BWG between 0 and 2, 2 and 4, 4 and 6, and 0 and 6 weeks. The selection effects, correlated responses and genetic trend for BW and BWG across generations were quantified by applying the animal model. Estimates of heritability for BW and BWG ranged from 0.22 to 0.42 and from 0.18 to 0.23, respectively. Ranges of genetic and phenotypic correlations for BW varied from 0.31 to 0.92 and 0.05 to 0.65, respectively. Moreover, estimates of genetic and phenotypic correlations for BWG at different ages were from 0.12 to 0.72 and 0.17 to 0.60, respectively. Bodyweight and BWG estimates after four generations of selection were significantly higher than those of the base generation. Moreover, contrasts of generation means were significant across the four generations. The genetic trends across the generations clarified that BLUP estimates for BW and BWG gradually increased with the advance of generations until the fourth generation. Keywords: best linear unbiased prediction, bodyweight, heritability, selection, genetic trend

Genetic evaluation of pedigree beef cattle in Great Britain

1993 ◽  
Vol 1993 ◽  
pp. 106-106
Author(s):  
R E Crump ◽  
J G E Bryan ◽  
D Nicholson ◽  
R Thompson ◽  
G Simm
Keyword(s):  
Great Britain ◽  
Herd Size ◽  
Best Linear Unbiased Prediction ◽  
Individual Animal ◽  
Genetic Progress ◽  
Linear Unbiased Prediction ◽  
Beef Herds ◽  
Best Linear Unbiased ◽  
Improved Performance ◽  
High Level

In order that genetic progress in British beef breeds could be improved, performance traits have been recorded by the Meat and Livestock Commission for many years. A large number of pedigree beef herds have recorded with the Meat and Livestock Commission during this period. Until recently, these records were only made use of via within herd contemporary comparisons such that the results for animals could not be compared across herds or time.Through the use of Individual Animal Model Best Linear Unbiased Prediction (BLUP), differences between herds and contemporary groups within herds can be accounted for provided there are genetic links between herds and contemporary groups. As a result of the small pedigree herd size in Great Britain, typically less than 20, sires are often chosen from outside the herd in order to reduce inbreeding. This practise has resulted in there being a relatively high level of connectedness between contemporary groups and this enables the BLUP procedure to disentangle management and genetic effects.

Genetic Trends from a Selection Experiment in Broiler Breeder Lines Using Best Linear Unbiased Prediction Methodology.

1995 ◽  
Vol 32 (6) ◽  
pp. 379-386
Author(s):  
Gariba DANBARO ◽  
Kenji OYAMA ◽  
Fumio MUKAI ◽  
Soichi TSUJI ◽  
Tomonori TATEISHI ◽  
...  
Keyword(s):  
Best Linear Unbiased Prediction ◽  
Selection Experiment ◽  
Linear Unbiased Prediction ◽  
Broiler Breeder ◽  
Best Linear Unbiased ◽  
Genetic Trends ◽  
Unbiased Prediction

scholarly journals Genomic Predictions With Nonadditive Effects Improved Estimates of Additive Effects and Predictions of Total Genetic Values in Pinus sylvestris

2021 ◽  
Vol 12 ◽  
Author(s):  
Ainhoa Calleja-Rodriguez ◽  
ZhiQiang Chen ◽  
Mari Suontama ◽  
Jin Pan ◽  
Harry X. Wu
Keyword(s):  
Pinus Sylvestris ◽  
Scots Pine ◽  
Growth Traits ◽  
Best Linear Unbiased Prediction ◽  
Genetic Effects ◽  
Phenotypic Variance ◽  
Linear Unbiased Prediction ◽  
Genomic Marker ◽  
Best Linear Unbiased ◽  
Unbiased Prediction

Genomic selection study (GS) focusing on nonadditive genetic effects of dominance and the first order of epistatic effects, in a full-sib family population of 695 Scots pine (Pinus sylvestris L.) trees, was undertaken for growth and wood quality traits, using 6,344 single nucleotide polymorphism markers (SNPs) generated by genotyping-by-sequencing (GBS). Genomic marker-based relationship matrices offer more effective modeling of nonadditive genetic effects than pedigree-based models, thus increasing the knowledge on the relevance of dominance and epistatic variation in forest tree breeding. Genomic marker-based models were compared with pedigree-based models showing a considerable dominance and epistatic variation for growth traits. Nonadditive genetic variation of epistatic nature (additive × additive) was detected for growth traits, wood density (DEN), and modulus of elasticity (MOEd) representing between 2.27 and 34.5% of the total phenotypic variance. Including dominance variance in pedigree-based Best Linear Unbiased Prediction (PBLUP) and epistatic variance in genomic-based Best Linear Unbiased Prediction (GBLUP) resulted in decreased narrow-sense heritability and increased broad-sense heritability for growth traits, DEN and MOEd. Higher genetic gains were reached with early GS based on total genetic values, than with conventional pedigree selection for a selection intensity of 1%. This study indicates that nonadditive genetic variance may have a significant role in the variation of selection traits of Scots pine, thus clonal deployment could be an attractive alternative for the species. Additionally, confidence in the role of nonadditive genetic effects in this breeding program should be pursued in the future, using GS.

Comparative efficiency of different multiple linear regression prediction equations of first lactation 305-day milk yield for sire evaluation in Murrah buffaloes

2018 ◽  
Author(s):  
Saroj Kumar Sahoo ◽  
Avtar Singh ◽  
G.S. Ambhore ◽  
S.K. Dash ◽  
P.P. Dubey
Keyword(s):  
Least Squares ◽  
Milk Yield ◽  
Best Linear Unbiased Prediction ◽  
Regression Equations ◽  
Linear Unbiased Prediction ◽  
Murrah Buffaloes ◽  
Sire Evaluation ◽  
Best Linear Unbiased ◽  
Animal Model Blup ◽  
Unbiased Prediction

In this study, first lactation 39059 weekly test-day milk yield records of 961 Murrah buffaloes were used to predict first lactation 305-day milk yield (FL305DMY) by stepwise backward regression method. The best single, two, three and four test day combinations were selected for prediction of FL305DMY based on adjusted R2 and RMSE values. The sires were evaluated for 305-day actual and predicted first lactation milk yield based on derived multiple regression equations using four methods viz. least squares (LSQ), simple regressed least squares (SRLS), best linear unbiased prediction sire model (BLUP-SM) and best linear unbiased prediction animal model (BLUP-AM) methods. The effectiveness of different sire evaluation methods were judged by error variance, coefficient of determination, coefficient of variation and spearman’s rank correlation. The accuracy of prediction of FL305DMY from weekly test day milk yields were observed to be best for TD-7 (48th day) and TD-22 (153rd day) combination with BLUP-AM as the most efficient method for sire evaluation. It was concluded that the FL305DMY can be predicted as early as 153rd day of lactation and further can be used for early genetic evaluation of Murrah sires.

GENETIC TRENDS FOR MILK AND FAT PRODUCTION IN DAIRY CATTLE

1979 ◽  
Vol 59 (1) ◽  
pp. 203-206 ◽  
Author(s):  
T. R. BATRA
Keyword(s):  
Dairy Cattle ◽  
Prediction Method ◽  
Weighted Average ◽  
Best Linear Unbiased Prediction ◽  
Linear Unbiased Prediction ◽  
Sire Evaluation ◽  
Best Linear Unbiased ◽  
Jersey Cows ◽  
Genetic Trends ◽  
Unbiased Prediction

The Best Linear Unbiased Prediction method is used to evaluate dairy sires in Canada. Milk and fat production records of 2-yr-old Ayrshire, Guernsey, Holstein and Jersey cows calved from 1958 through 1975 were used in the sire evaluation done in November 1976. Genetic trends were estimated as twice the change in weighted average of sire proofs per year. Genetic trends for milk and fat production were 1.32 and 1.62 BCA for Ayrshire; 1.50 and.88 BCA for Guernsey;.72 and.80 BCA for Holstein; and.60 and.54 BCA for Jersey, respectively.

scholarly journals Estimating Breeding values for milk production and mastitis traits for Holstein cattle in Egypt

2019 ◽  
Vol 23 (4) ◽  
pp. 159
Author(s):  
Eslam Faid-Allah
Keyword(s):  
Milk Production ◽  
Milk Yield ◽  
Holstein Cattle ◽  
Phenotypic Variance ◽  
Production Traits ◽  
Linear Unbiased Prediction ◽  
Breeding Values ◽  
Time Parity ◽  
Best Linear Unbiased ◽  
Selection Of

<p class="abstrak2">This study was carried out to evaluate the sires and dams genetically for milk production and mastitis traits in Egyptian 12 herds of Holstein cattle using Best Linear Unbiased Prediction via MTDFREML program. The data was obtained from a commercial farm called Dena, located in Cairo-Alex Desert Road (80 Km), Menofia, Egypt. Data included 4791 cows, 4227 dams and 248 sires that represented the period from 2007 to 2014. Estimating breeding values for milk production traits as cumulative milk yield at 90 days (90-DM), cumulative milk yield at 180 days (180-DM), cumulative milk yield at 270 days (270-DM), cumulative milk yield at 305 days (305-DM), and number of mastitis infection around the season of lactation (MAST). The averages of the 90-DM, 180-DM, 270-DM, 305-DM and MAST were 3026.3±655.1 kg, 5873.3±1081.1 kg, 7891.1±2692.2 kg, 9611.2±1897.9 kg, and 0.712±1.2 time/parity, respectively. Estimates of heritability for the previous traits were 0.11±0.016, 0.15±0.014, 0.18±0.012, 0.22±0.015, and 0.09±0.029, respectively; genetic variance were 47206.2 kg, 175300.6 kg, 1304654.4 kg, 792411.6 kg and 0.12 time/parity, respectively; and phenotypic variance were 429147.6 kg, 1168670.6 kg, 7248079.9 kg, 3601870.9 kg, and 1.35 time/parity, respectively. The EBV values as average, SD, (Min: Max) for sires were 0.0±0.179 (-0.4: 0.66) for MAST, 0.0±86.176 (-263.1: 245.4) for 90-DM, 0.0±227.523 (-600.3: 800.3) for 180-DM, 0.0±413.48 (-323.3: 1277.7) for 270-DM and 0.0±440.26 (-1280.9: 1565.1) for 305-DM. Also, The EBVs for dams were 0.0±0.055 (-0.14: 0.45) for MAST, 0.033±26.24 (-142.8: 103.0) for 90-DM, 0.074±76.81 (-360.2: 289.6) for 180-DM, -0.045±139.66 (-591.9: 529.2) for 270-DM and 0.266±154.1 (-666.3: 617.6) for 305-DM. These results provide that the selection of sires and dams will improve the traits of milk production and mastitis in this herd because of the wide differences in genetic potential among sires and dams.</p>

scholarly journals Correction: Weighted Genomic Best Linear Unbiased Prediction for Carcass Traits in Hanwoo Cattle. Genes 2019, 10, 1019

Genes ◽  
2020 ◽  
Vol 11 (9) ◽  
pp. 1013
Author(s):  
Bryan Irvine Lopez ◽  
Seung-Hwan Lee ◽  
Jong-Eun Park ◽  
Dong-Hyun Shin ◽  
Jae-Don Oh ◽  
...  
Keyword(s):  
Carcass Traits ◽  
Best Linear Unbiased Prediction ◽  
Linear Unbiased Prediction ◽  
Best Linear Unbiased ◽  
Hanwoo Cattle ◽  
Unbiased Prediction

The authors wish to make the following corrections to this paper [...]

scholarly journals Genomic Prediction Using Alternative Strategies of Weighted Single-Step Genomic BLUP for Yearling Weight and Carcass Traits in Hanwoo Beef Cattle

Genes ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 266
Author(s):  
Hossein Mehrban ◽  
Masoumeh Naserkheil ◽  
Deuk Hwan Lee ◽  
Chungil Cho ◽  
Taejeong Choi ◽  
...  
Keyword(s):  
Quantitative Trait Loci ◽  
Beef Cattle ◽  
Genomic Prediction ◽  
Quantitative Trait ◽  
Carcass Traits ◽  
Best Linear Unbiased Prediction ◽  
Single Step ◽  
Linear Unbiased Prediction ◽  
Single Nucleotide ◽  
Best Linear Unbiased

The weighted single-step genomic best linear unbiased prediction (GBLUP) method has been proposed to exploit information from genotyped and non-genotyped relatives, allowing the use of weights for single-nucleotide polymorphism in the construction of the genomic relationship matrix. The purpose of this study was to investigate the accuracy of genetic prediction using the following single-trait best linear unbiased prediction methods in Hanwoo beef cattle: pedigree-based (PBLUP), un-weighted (ssGBLUP), and weighted (WssGBLUP) single-step genomic methods. We also assessed the impact of alternative single and window weighting methods according to their effects on the traits of interest. The data was comprised of 15,796 phenotypic records for yearling weight (YW) and 5622 records for carcass traits (backfat thickness: BFT, carcass weight: CW, eye muscle area: EMA, and marbling score: MS). Also, the genotypic data included 6616 animals for YW and 5134 for carcass traits on the 43,950 single-nucleotide polymorphisms. The ssGBLUP showed significant improvement in genomic prediction accuracy for carcass traits (71%) and yearling weight (99%) compared to the pedigree-based method. The window weighting procedures performed better than single SNP weighting for CW (11%), EMA (11%), MS (3%), and YW (6%), whereas no gain in accuracy was observed for BFT. Besides, the improvement in accuracy between window WssGBLUP and the un-weighted method was low for BFT and MS, while for CW, EMA, and YW resulted in a gain of 22%, 15%, and 20%, respectively, which indicates the presence of relevant quantitative trait loci for these traits. These findings indicate that WssGBLUP is an appropriate method for traits with a large quantitative trait loci effect.

A method for reducing inbreeding with Best Linear Unbiased Prediction

1993 ◽  
Vol 1993 ◽  
pp. 33-33
Author(s):  
B Grundy ◽  
WG Hill
Keyword(s):  
Best Linear Unbiased Prediction ◽  
Breeding Value ◽  
Best Linear Unbiased Predictor ◽  
Linear Unbiased Prediction ◽  
Genetic Merit ◽  
Selection Decisions ◽  
Best Linear Unbiased ◽  
Estimated Breeding Value ◽  
Commercial Environment ◽  
Unbiased Prediction

An optimum way of selecting animals is through a prediction of their genetic merit (estimated breeding value, EBV), which can be achieved using a best linear unbiased predictor (BLUP) (Henderson, 1975). Selection decisions in a commercial environment, however, are rarely made solely on genetic merit but also on additional factors, an important example of which is to limit the accumulation of inbreeding. Comparison of rates of inbreeding under BLUP for a range of hentabilities highlights a trend of increasing inbreeding with decreasing heritability. It is therefore proposed that selection using a heritability which is artificially raised would yield lower rates of inbreeding than would otherwise be the case.

Sign in / Sign up

Export Citation Format

Share Document