Genetic Analysis of Milk Production Traits and Mid-Infrared Spectra in Chinese Holstein Population

Milk composition always serves as an indicator for the cow’s health status and body condition. Some non-genetic factors such as parity, days in milk (DIM), and calving season, which obviously affect milk performance, therefore, need to be considered in dairy farm management. However, only a few milk compositions are used in the current animal selection programs. The mid-infrared (MIR) spectroscopy can reflect the global composition of milk, but this information is currently underused. The objectives of this study were to detect the effect of some non-genetic factors on milk production traits as well as 1060 individual spectral points covering from 925.92 cm−1 to 5011.54 cm−1, estimate heritabilities of milk production traits and MIR spectral wavenumbers, and explore the genetic correlations between milk production traits and 1060 individual spectral points in a Chinese Holstein population. The mixed models procedure of SAS software was used to test the non-genetic factors. Single-trait animal models were used to estimate heritabilities and bivariate animal models were used to estimate genetic correlations using the package of ASReml in R software. The results showed that herd, parity, calving season, and lactation stage had significant effects on the percentages of protein and lactose, whereas herd and lactation stage had significant effects on fat percentage. Moreover, the herd showed a significant effect on all of the 1060 individual wavenumbers, whereas lactation stage, parity, and calving season had significant effect on most of the wavenumbers of the lactose-region (925 cm−1 to 1200 cm−1), protein-region (1240 cm−1 to 1600 cm−1), and fat-regions (1680 cm−1 to 1770 cm−1 and 2800 cm−1 to 3015 cm−1). The estimated heritabilities for protein percentage (PP), fat percentage (FP), and lactose percentage (LP) were 0.08, 0.05, and 0.09, respectively. Further, the milk spectrum was heritable but low for most individual points. Heritabilities of 1060 individual spectral points were 0.04 on average, ranging from 0 to 0.11. In particular, heritabilities for wavenumbers of spectral regions related to water absorption were very low and even null, and heritabilities for wavenumbers of specific MIR regions associated with fat-I, fat-II, protein, and lactose were 0.04, 0.06, 0.05, and 0.06 on average, respectively. The genetic correlations between PP and FP, PP and LP, FP, and LP were 0.78, −0.29, and −0.14, respectively. In addition, PP, FP, and LP shared the similar patterns of genetic correlations with the spectral wavenumbers. The genetic correlations between milk production traits and spectral regions related to important milk components varied from weak to very strong (0.01 to 0.94, and −0.01 to −0.96). The current study could be used as a management tool for dairy farms and also provides a further understanding of the genetic background of milk MIR spectra.

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Effect of Breed and Some Non - Genetic Factors on Milk Production and Some Proportions of Its Chemical Components in Two Breeds of Local Sheep

Al-Qadisiyah Journal For Agriculture Sciences (QJAS) (P-ISSN 2077-5822 E-ISSN 2617-1479) ◽

10.33794/qjas.vol10.iss1.102 ◽

2020 ◽

Vol 10 (1) ◽

pp. 227-231

Author(s):

Rahman Hussein AL-Qasimi ◽

Shatha Mohammed Abbas ◽

Allawi L.D. AL-Khauzai

Keyword(s):

Milk Production ◽

Genetic Factors ◽

Chemical Components ◽

Lactation Period ◽

Production Traits ◽

Milk Production Traits ◽

Fat Percentage ◽

Awassi Sheep ◽

Total Milk ◽

Milk Components

The study was carried out on 19 ewes of local Awassi sheep and 12ewes local Arabi sheep in the Al-kafeel sheep station Karbala, to determine the effect of breed and some non-genetic factors such as (sex of the lamb, type of birth, age and weight of ewes at birth) on daily and total milk production and lactation period and some of milk components (fat, protein and lactose). The results showed that a significant effect (P <0.05) of the breed on milk production traits where Awassi sheep recorded the highest mean (0.91 kg , 101.63 kg , 104.86 day) compared to the Arabi sheep she was means (0.77 kg , 88.15 kg , 99.15 day) respectively. As well as in proportions of milk components with mean( 5.1 , 4.90 , 5.51) % respectively compared to the Arabi sheep (4.70 . 4.20 . 4.89) ewes with male lambs also exceeded superior ewes with female lambs in daily and total milk production and the lactation period the sex of the lamb did not affect the proportions of milk components the weight of the ewes had a significant effect (P <0.05) in milk production attributes with superior weight of ewes on lower ewes and did not affect the proportions of milk ingredients except for lactose. The type of birth and the age of the ewes did not have a significant effect in all the studied traits except for the superiority (P<0.05) of young ewes on age ewes in the fat percentage of milk.

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Genetic correlations of mid-infrared-predicted milk fatty acid groups with milk production traits

Journal of Dairy Science ◽

10.3168/jds.2017-14089 ◽

2018 ◽

Vol 101 (5) ◽

pp. 4295-4306 ◽

Cited By ~ 7

Author(s):

A. Fleming ◽

F.S. Schenkel ◽

F. Malchiodi ◽

R.A. Ali ◽

B. Mallard ◽

...

Keyword(s):

Fatty Acid ◽

Milk Production ◽

Genetic Correlations ◽

Milk Fatty Acid ◽

Production Traits ◽

Milk Production Traits ◽

Mid Infrared

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QTL mapping for production traits in Czech Fleckvieh cattle

Czech Journal of Animal Science ◽

10.17221/6939-cjas ◽

2013 ◽

Vol 58 (No. 9) ◽

pp. 396-403 ◽

Cited By ~ 2

Author(s):

J. Matějíčková ◽

M. Štípková ◽

G. Sahana ◽

T. Kott ◽

J. Kyseľová ◽

...

Keyword(s):

Maximum Likelihood ◽

Qtl Mapping ◽

Milk Production ◽

Variance Component ◽

Variance Component Analysis ◽

Production Traits ◽

Milk Production Traits ◽

Chromosome 14 ◽

Fat Percentage ◽

Mapping Model

The objective of this study was to find QTL for milk production traits in Czech Fleckvieh cattle on chromosomes 6, 7, 11, 14, and 23 where QTL were previously identified in other dairy cattle populations. Sixteen grandsire families were genotyped for 38 microsatellite markers on the selected chromosomes. A QTL mapping model based on variance component analysis was implemented via restricted maximum likelihood (REML) to estimate QTL positions and their effects. A significant QTL affecting fat percentage was found at the beginning of chromosome 14 (0 cM), near marker ILSTS039. Suggestive QTL associated with milk production traits appeared on other studied chromosomes (BTA6, BTA7, BTA11, and BTA23). This first QTL search on five chromosomes in Czech Fleckvieh population showed several suggestive QTL that can be promising for further studies and contribute to better understanding of genetics of milk production in the Czech Fleckvieh cattle.  

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Using quantile regression for fitting lactation curve in dairy cows

Journal of Dairy Research ◽

10.1017/s0022029919000013 ◽

2019 ◽

Vol 86 (1) ◽

pp. 19-24

Author(s):

Hossein Naeemipour Younesi ◽

Mohammad Mahdi Shariati ◽

Saeed Zerehdaran ◽

Mehdi Jabbari Nooghabi ◽

Peter Løvendahl

Keyword(s):

Quantile Regression ◽

Dairy Cows ◽

Milk Production ◽

Milk Yield ◽

Information Criterion ◽

Production Traits ◽

Milk Production Traits ◽

Fat Percentage ◽

Protein Percentage ◽

Breeding Center

AbstractThe main objective of this study was to compare the performance of different ‘nonlinear quantile regression’ models evaluated at theτth quantile (0·25, 0·50, and 0·75) of milk production traits and somatic cell score (SCS) in Iranian Holstein dairy cows. Data were collected by the Animal Breeding Center of Iran from 1991 to 2011, comprising 101 051 monthly milk production traits and SCS records of 13 977 cows in 183 herds. Incomplete gamma (Wood), exponential (Wilmink), Dijkstra and polynomial (Ali & Schaeffer) functions were implemented in the quantile regression. Residual mean square, Akaike information criterion and log-likelihood from different models and quantiles indicated that in the same quantile, the best models were Wilmink for milk yield, Dijkstra for fat percentage and Ali & Schaeffer for protein percentage. Over all models the best model fit occurred at quantile 0·50 for milk yield, fat and protein percentage, whereas, for SCS the 0·25th quantile was best. The best model to describe SCS was Dijkstra at quantiles 0·25 and 0·50, and Ali & Schaeffer at quantile 0·75. Wood function had the worst performance amongst all traits. Quantile regression is specifically appropriate for SCS which has a mixed multimodal distribution.

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Effect of Non-Genetic Factors on Persistency and Milk Production Traits in Murrah Buffaloes

Journal of Animal Research ◽

10.5958/2277-940x.2015.00084.4 ◽

2015 ◽

Vol 5 (3) ◽

pp. 493

Author(s):

Nawal Kishor Pareek ◽

Raman Narang

Keyword(s):

Milk Production ◽

Genetic Factors ◽

Production Traits ◽

Milk Production Traits ◽

Murrah Buffaloes

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Lactation curves of Sarda breed goats estimated with test day models

Journal of Dairy Research ◽

10.1017/s0022029905001366 ◽

2005 ◽

Vol 72 (4) ◽

pp. 470-475 ◽

Cited By ~ 11

Author(s):

Nicolò PP Macciotta ◽

Pancrazio Fresi ◽

Graziano Usai ◽

Aldo Cappio-Borlino

Keyword(s):

Milk Production ◽

Milk Yield ◽

Linear Models ◽

Morphological Structure ◽

Production Traits ◽

Mixed Linear Models ◽

Milk Production Traits ◽

Milk Traits ◽

Lactation Stage ◽

Parity Number

Test day records of milk yield (38765), fat and protein contents (11357) of Sarda goats (the most numerous Italian goat breed) were analysed with mixed linear models in order to estimate the effects of test date (month and year of kidding for fat and protein contents) parity, number of kids born, altitude of location of flocks (<200 m asl, 200–500 m asl, >500 m asl), flocks within altitude and lactation stage (eight days-in-milk intervals of 30 d each) on milk production. All factors considered in the models affected milk traits significantly. Milk yield was lower in first parity goats than in higher parities whereas fat and protein contents showed an opposite trend. Goats with two kids at parturition had a higher milk yield than goats with one kid and tended to have lower fat and protein percentages. Repeatability between test days within lactation was 0·34, 0·17 and 0·45 for milk yield, fat content and protein content, respectively. Lactation curves of goats farmed at different altitudes were clearly separated, especially for milk yield. Results of the present study highlight differences in milk production traits among the three subpopulations that have been previously identified within the Sarda breed on the basis of the morphological structure of animals and altitude of location of flocks.

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Associations between polymorphisms of SLC22A7, NGFR, ARNTL and PPP2R2B genes and Milk production traits in Chinese Holstein

BMC Genomic Data ◽

10.1186/s12863-021-01002-0 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Ruike Jia ◽

Yihan Fu ◽

Lingna Xu ◽

Houcheng Li ◽

Yanhua Li ◽

...

Keyword(s):

Dairy Cattle ◽

Milk Production ◽

Association Analysis ◽

Milk Yield ◽

Production Traits ◽

Milk Production Traits ◽

Fat Percentage ◽

Protein Percentage ◽

Fat Yield ◽

Percentage Protein

Abstract Background Our preliminary work confirmed that, SLC22A7 (solute carrier family 22 member 7), NGFR (nerve growth factor receptor), ARNTL (aryl hydrocarbon receptor nuclear translocator like) and PPP2R2B (protein phosphatase 2 regulatory subunit Bβ) genes were differentially expressed in dairy cows during different stages of lactation, and involved in the lipid metabolism through insulin, PI3K-Akt, MAPK, AMPK, mTOR, and PPAR signaling pathways, so we considered these four genes as the candidates affecting milk production traits. In this study, we detected polymorphisms of the four genes and verified their genetic effects on milk yield and composition traits in a Chinese Holstein cow population. Results By resequencing the whole coding region and part of the flanking region of SLC22A7, NGFR, ARNTL and PPP2R2B, we totally found 20 SNPs, of which five were located in SLC22A7, eight in NGFR, three in ARNTL, and four in PPP2R2B. Using Haploview4.2, we found three haplotype blocks including five SNPs in SLC22A7, eight in NGFR and three in ARNTL. Single-SNP association analysis showed that 19 out of 20 SNPs were significantly associated with at least one of milk yield, fat yield, fat percentage, protein yield or protein percentage in the first and second lactations (P < 0.05). Haplotype-based association analysis showed that the three haplotypes were significantly associated with at least one of milk yield, fat yield, fat percentage, protein yield or protein percentage (P < 0.05). Further, we used SOPMA software to predict a SNP, 19:g.37095131C > T in NGFR, changed the structure of NGFR protein. In addition, we used Jaspar software to found that four SNPs, 19:g.37113872C > G,19:g.37113157C > T, and 19:g.37112276C > T in NGFR and 15:g.39320936A > G in ARNTL, could change the transcription factor binding sites and might affect the expression of the corresponding genes. These five SNPs might be the potential functional mutations for milk production traits in dairy cattle. Conclusions In summary, we proved that SLC22A7, NGFR, ARNTL and PPP2R2B have significant genetic effects on milk production traits. The valuable SNPs can be used as candidate genetic markers for genomic selection of dairy cattle, and the effects of these SNPs on other traits need to be further verified.

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Effect of DGAT1 and TG gene polymorphisms on intramuscular fat and on milk production traits in different cattle breeds in Hungary

Acta Veterinaria Hungarica ◽

10.1556/avet.56.2008.2.5 ◽

2008 ◽

Vol 56 (2) ◽

pp. 181-186 ◽

Cited By ~ 11

Author(s):

István Anton ◽

Katalin Kovács ◽

László Fésüs ◽

József Várhegyi ◽

László Lehel ◽

...

Keyword(s):

Milk Production ◽

Milk Fat ◽

Longissimus Dorsi ◽

Production Traits ◽

Milk Production Traits ◽

Fat Percentage ◽

Longissimus Dorsi Muscle ◽

Cattle Breeds ◽

Pcr Rflp ◽

The Difference

The objective of this study was to estimate the effect of the thyroglobulin (TG) locus on beef quality traits in some beef cattle breeds and to investigate the effect of the DGAT1 locus on milk production traits in the Hungarian Holstein Friesian population. TG and DGAT1 genotypes were determined by polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) assay. At the TG locus TT bulls showed the highest fat percentage values in the longissimus dorsi muscle (m. longissimus dorsi); the difference between CC and TT genotypes was significant. DGAT1 GC/GC cows had the highest milk, fat and protein yield values. Due to the relatively small number of GC/GC cows the difference proved to be significant only between AA/AA and AA/GC genotypes.

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