• Asian-Australasian Journal of Animal Sciences
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• v.24 no.11
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• pp.1514-1524
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• 2011

A total of 4,752 monthly lactation records of Friesian cows during the period from 2000 to 2005 were used to estimate genetic parameters and to determine the effect of udder health on milk production traits. Three milk production traits were studied: 305-day milk yield (305-dMY), 305-day fat yield (305-dFY) and 305-day protein yield (305-dPY). Four udder health traits were studied: somatic cell count (SCC), mastitis (MAST), udder health status (UDHS) with 10 categories and udder quarter infection (UDQI) with 7 categories. Mixed model least square analysis was used to estimate the fixed effects of month and year of calving and parity (P) on different studied traits. Sire and dam within sire were included in the model as random effects. Data were analyzed using Multi-trait Derivative Free Restricted Maximum Likelihood methodology (MTDFREML) to estimate genetic parameters. Unadjusted means of 305-dMY, 305-dFY, 305-dPY and SCC were 3,936, 121, 90 kg and 453,000 cells/ml, respectively. Increasing SCC from 300,000 to 2,000,000 cells/ml increased UDQI from 5.51 to 23.2%. Losses in monthly and lactationally milk yields per cow ranged from 17 to 93 and from 135 to 991 kg, respectively. The corresponding losses in monthly and lactationally milk yields return per cow at the same level of SCC ranged from 29.8 to 163 and from 236 to 1,734 Egyptian pounds, respectively. Heritability estimates of 305-dMY, 305-dFY, 305-dPY, SCC, MAST, UDHS, UDQI were 0.31${\pm}$0.4, 0.33${\pm}$0.03, 0.35${\pm}$0.05, 0.23${\pm}$0.02, 0.14${\pm}$0.02, 0.13${\pm}$0.03, and 0.09${\pm}$0.01, respectively. All milk production traits showed slightly unfavorable negative phenotypic and genetic correlations with SCC, MAST, UDHS and UDQI. There were positive and high genetic correlations between SCC and each of MAST (0.85${\pm}$0.7), UDHS (0.87${\pm}$0.10) and UDQI (0.77${\pm}$0.06) and between MAST and each of UDHS (0.91${\pm}$0.11) and UDQI (0.83${\pm}$0.07). It could be concluded that the economic losses from mastitis and high SCC are considerable. The high genetic correlation between SCC and clinical mastitis (CM) suggest that the selection for lower SCC would help to reduce or eliminate the undesirable correlated responses of clinical mastitis associated with selection for increasing milk yield. Additionally, it is recommended also that if direct information on under health traits is not available, measures of SCC can be inclusion in a selection criteria to improve the income from dairy cows.

• Asian-Australasian Journal of Animal Sciences
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• v.29 no.9
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• pp.1222-1228
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• 2016

The objective of this research was to estimate genetic parameters and trends for length of productive life (LPL), lifetime number of piglets born alive (LBA), lifetime number of piglets weaned (LPW), lifetime litter birth weight (LBW), and lifetime litter weaning weight (LWW) in a commercial swine farm in Northern Thailand. Data were gathered during a 24-year period from July 1989 to August 2013. A total of 3,109 phenotypic records from 2,271 Landrace (L) and 838 Yorkshire sows (Y) were analyzed. Variance and covariance components, heritabilities and correlations were estimated using an Average Information Restricted Maximum Likelihood (AIREML) procedure. The 5-trait animal model contained the fixed effects of first farrowing year-season, breed group, and age at first farrowing. Random effects were sow and residual. Estimates of heritabilities were medium for all five traits ($0.17{\pm}0.04$ for LPL and LBA to $0.20{\pm}0.04$ for LPW). Genetic correlations among these traits were high, positive, and favorable (p<0.05), ranging from $0.93{\pm}0.02$ (LPL-LWW) to $0.99{\pm}0.02$ (LPL-LPW). Sow genetic trends were non-significant for LPL and all lifetime production traits. Sire genetic trends were negative and significant for LPL ($-2.54{\pm}0.65d/yr$; p = 0.0007), LBA ($-0.12{\pm}0.04piglets/yr$; p = 0.0073), LPW ($-0.14{\pm}0.04piglets/yr$; p = 0.0037), LBW ($-0.13{\pm}0.06kg/yr$; p = 0.0487), and LWW ($-0.69{\pm}0.31kg/yr$; p = 0.0365). Dam genetic trends were positive, small and significant for all traits ($1.04{\pm}0.42d/yr$ for LPL, p = 0.0217; $0.16{\pm}0.03piglets/yr$ for LBA, p<0.0001; $0.12{\pm}0.03piglets/yr$ for LPW, p = 0.0002; $0.29{\pm}0.04kg/yr$ for LBW, p<0.0001 and $1.23{\pm}0.19kg/yr$ for LWW, p<0.0001). Thus, the selection program in this commercial herd managed to improve both LPL and lifetime productive traits in sires and dams. It was ineffective to improve LPL and lifetime productive traits in sows.

• Asian-Australasian Journal of Animal Sciences
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• v.30 no.3
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• pp.320-327
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• 2017

Objective: The objective of this research was to estimate genetic correlations between number of piglets born alive in the first parity (NBA1), litter birth weight in the first parity (LTBW1), number of piglets weaned in the first parity (NPW1), litter weaning weight in the first parity (LTWW1), number of piglets born alive from second to last parity (NBA2+), litter birth weight from second to last parity (LTBW2+), number of piglets weaned from second to last parity (NPW2+) and litter weaning weight from second to last parity (LTWW2+), and to identify the percentages of animals (the top 10%, 25%, and 50%) for first parity and sums of second and later parity traits. Methods: The 9,830 records consisted of 2,124 Landrace (L), 724 Yorkshire (Y), 2,650 LY, and 4,332 YL that had their first farrowing between July 1989 and December 2013. The 8-trait animal model included the fixed effects of first farrowing year-season, additive genetic group, heterosis of the sow and the litter, age at first farrowing, and days to weaning (NPW1, LTWW1, NPW2+, and LTWW2+). Random effects were animal and residual. Results: Heritability estimates ranged from $0.08{\pm}0.02$ (NBA1 and NPW1) to $0.29{\pm}0.02$ (NPW2+). Genetic correlations between reproduction traits in the first parity and from second to last parity ranged from $0.17{\pm}0.08$ (LTBW1 and LTBW2+) to $0.67{\pm}0.06$ (LTWW1 and LTWW2+). Phenotypic correlations between reproduction traits in the first parity and from second to last parity were close to zero. Rank correlations between LTWW1 and LTWW2+ estimated breeding value tended to be higher than for other pairs of traits across all replacement percentages. Conclusion: These rank correlations indicated that selecting boars and sows using genetic predictions for first parity reproduction traits would help improve reproduction traits in the second and later parities as well as lifetime productivity in this swine population.

• Journal of agriculture & life science
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• v.46 no.1
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• pp.113-121
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• 2012

A total of 150,624 records of Holstein milk production collected from 2005 to 2009 were analyzed to investigate the effects of two different contemporary group definitions, parity and somatic cell score (SCS). The first definition (H BY S) of contemporary group was milking cows and heifers born in the same year and season. And the second thing (H CY S) was milking cow and heifers that delivered calves in the same year and season. Effects of contemporary group, parity and regression effect on SCS from two models were highly significant sources of variation. Coverage of variation ($R^2$) was somewhat higher in models with H BY S as contemporary group. From multivariate models with H BY S, phenotypic correlation coefficients of milk components were estimated high and positive. However, the phenotypic correlation coefficient between milk yield and SCS was -0.09, which was low enough to evidence no correlation between them. Phenotypic correlation between SCS and butter fat or between SCS and protein were also negligible but negative. From multivariate models with H CY S as contemporary group, phenotypic correlation among milk traits and SCS were similar to the estimates from models with H BY S. However, SCS in these models were lowly but negatively correlated with milk yield, milk protein, butter fat or SNF, and the phenotypic correlation coefficients of which were -0.10, -0.08, -0.08, -0.11, respectively.

• Journal of agriculture & life science
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• v.44 no.2
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• pp.29-38
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• 2010

This study looked into estimation of variance component over swine's economic traits by multiple animal model and maternal effect model using on-farm test data of total 31,455 swine of Duroc, Landrace and Yorkshire species that were born between 2000 and 2008. Heritability by estimated additive genetic effect showed higher than one by maternal genetic effect using multivariate maternal animal model in each trait examined by each breed and most heritability when considering only additive genetic effect in multiple traits animal model was estimated to be higher than one by estimated additive genetic effect in multivariate maternal animal model. In correlation between breeding value by estimated maternal genetic effect and phenotypic value using multivariate maternal animal model, rank correlation and simple correlation of breeding value and phenotypic value by maternal genetic effect also showed low positive correlation or strong negative correlation, which can be considered that if correlation with phenotype were increased properly considering maternal genetic effect in each trait by each breed, even better improvement could be promoted.

• Asian-Australasian Journal of Animal Sciences
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• v.18 no.9
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• pp.1231-1236
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• 2005

Genetic parameters for first lactation milk production based on test day (TD) records of 56319 Iranian Holstein cows from 655 herds that first calved between 1991 and 2001 were estimated with restricted maximum likelihood method under an Animal model. Traits analyzed were milk yield and milk fat percentage. Heritability for TD records were highest in second half of the lactation, ranging from 0.11 to 0.19 for milk yield and 0.038 to 0.094 for milk fat percentage respectively. Estimates for lactation records for these traits were 0.24 and 0.26 respectively. Genetic correlations between individual TD records were high for consecutive TD records (>0.9) and decreased as the interval between tests increased. Estimates of genetic correlations of TD yield with corresponding lactation yield were highest (0.78 to 0.86) for mid-lactation (TD3 to TD8). Phenotypic correlations were lower than corresponding genetic correlations, but both followed the same pattern. For milk fat percentage no clear pattern was found. Results of this study suggested that TD yields especially in mid-lactation may be used for genetic evaluation instead of 305-day yield.

• Asian-Australasian Journal of Animal Sciences
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• v.16 no.9
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• pp.1247-1253
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• 2003

Selection for increased milk production in dairy cows has often resulted in a higher incidence of disease and thus incurred a greater health costs. Considerable interests have been shown in breeding dairy cattle for disease resistance in recent years. This paper discusses the limitations of breeding dairy cattle for genetic resistance in six parts: 1) complexity of disease resistance, 2) difficulty in estimating genetic parameters for planning breeding programs against disease, 3) undesirable relationship between production traits and disease, 4) disease as affected by recessive genes, 5) new mutation of the pathogens, and 6) variable environmental factors. The hidden problems of estimating genetic and phenotypic parameters involving disease incidence were examined in terms of categorical nature, non-independence, heterogeneity of error variance, non-randomness, and automatic relationship between disease and production traits. In light of these limitations, the prospect for increasing genetic resistance by conventional breeding methods would not be so bright as we like. Since the phenomenon of disease is the result of a joint interaction among host genotype, pathogen genotype and environment, it becomes essential to adopt an integrated approach of increasing genetic resistance of the host animals, manipulating the pathogen genotypes, developing effective vaccines and drugs, and improving the environmental conditions. The advances in DNA-based technology show considerable promise in directly manipulating host and pathogen genomes for genetic resistance and producing vaccines and drugs for prevention and medication to promote the wellbeing of the animals.

• Korean Journal of Plant Resources
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• v.20 no.6
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• pp.504-510
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• 2007

GE interaction is the expression of differential genotypic adaptation across environments. GE interactions through different stability parameters and performance of the traits of genotypes were studied. The traits were days to maturity, pod length, number of pods/ plant, 100-seed weight and seed yield/plant in ten soybean genotypes across five environments. Significant differences were observed for genotypes, environments and GE interactions. Stability analysis after Eberhart and Russell's model suggested that the genotypes used in this study were all more or less responsive to environmental changes. Most of the genotypes perform better in Env.3. Based on phenotypic indices(Pi), regression ($S^2di$) genotype Garurab was found fairly stable for days to maturity. BS-23 and G-2120 may be considered as stable genotype for pod length. All the genotypes except G-2120 showed that the genotypes were relatively unstable under environmental fluctuation for the number of pod/plant. Genotype BS-23 was found most stable among all the genotypes for 100-seed weight. BS-3 and Gaurab was the most stable and desirable genotypes for seed yield in soybean.

• Journal of Crop Science and Biotechnology
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• v.11 no.3
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• pp.157-162
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• 2008

A total of 2,765 accessions were used as the initial set having both seed coat color and 100-seed weight data. As a result of molecular profiling using six SSR markers followed by stratification based on their usages, 335 accessions(12.1%) were selected by clustering based on UPGMA. Since 75 out of 335 accessions were mixed in phenotypic traits as a result of characterization, 260 accessions were finally set as a core set. This core set revealed nearly the same diversity compared with the other results on morphological traits of Korean soybean landraces. In total, 115 alleles(19.2 alleles per locus) were detected in the initial set and 79 alleles(13.2 alleles per locus) were detected in the core set. All 30 major alleles were present in the initial set and in the core set as well. In allele coverage, the core set was 71.4% of the initial set. These comparisons of number of alleles, gene diversity and coverage indicated that the core set represented the entire set well.

• Plant Breeding and Biotechnology
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• v.6 no.4
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• pp.454-462
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• 2018

Yield and grade are the key factors that affect production value of peanut. The objective of this study was to identify QTLs for pod yield, hundred-seed weight, and total sound mature kernel (TSMK). A total of 90 recombinant inbred lines, derived from Tamrun OL07 and a breeding line Tx964117, were used as a mapping population and planted in Brownfield and Stephenville, Texas. A genetic map was developed using 1,211 SNP markers based on double digest restriction-site associated DNA sequencing (ddRAD-seq). A total of 10 QTLs were identified above the permutation threshold, three for yield, three for hundred-seed weight and four for TSMK, with LOD score values of 3.7 - 6.9 and phenotypic variance explained of 12.2% - 35.9%. Among those, there were several QTLs that were detected in more than one field experiment. The commonly detected QTLs in this study may be used as potential targets for future breeding program to incorporate yield and grade related traits through molecular breeding.