scholarly journals Heritability, Genetic and Phenotypic Correlations, and Predicted Selection Response of Quantitative Traits in Peach: I. An Analysis of Several Reproductive Traits

1998 ◽  
Vol 123 (4) ◽  
pp. 598-603 ◽  
Author(s):  
Valdomiro A.B. de Souza ◽  
David H. Byrne ◽  
Jeremy F. Taylor
Keyword(s):  
Fixed Effects ◽  
Prunus Persica ◽  
Fruit Set ◽  
Genetic Correlations ◽  
Selection Response ◽  
Direct Selection ◽  
Multiple Trait ◽  
Narrow Sense Heritability ◽  
Phenotypic Correlations ◽  
Flower Density

Seedlings of 108 families from crosses among 42 peach [Prunus persica (L.) Batsch] cultivars and selections were evaluated for six plant characteristics in 1993, 1994, and 1995. The data were analyzed by using a mixed linear model, with years treated as fixed and additive genotypes as random factors. Best linear unbiased prediction (BLUP) was used to estimate fixed effects. Restricted maximum likelihood (REML) was used to estimate variance components, and a multiple trait model was used to estimate genetic and phenotypic covariances among traits. The narrow-sense heritability estimates were 0.41, 0.29, 0.48, 0.47, 0.43, and 0.23 for flower density, flowers per node, node density, fruit density, fruit set, and blind node propensity, respectively. Most genetic correlations among pairs of traits were ≥0.30 and were, in general, much higher than the corresponding phenotypic correlations. Flower density and flowers per node (ra = 0.95), fruit density and fruit set (ra = 0.84) and flower density and fruit density (ra = 0.71) were the combinations of traits that had the highest genetic correlation estimates. Direct selection practiced solely for flower density (either direction) is expected to have a greater effect on fruit density than direct selection for fruit density.

scholarly journals Heritability, Genetic and Phenotypic Correlations, and Predicted Selection Response of Quantitative Traits in Peach: II. An Analysis of Several Fruit Traits

1998 ◽  
Vol 123 (4) ◽  
pp. 604-611 ◽  
Author(s):  
Valdomiro A.B. de Souza ◽  
David H. Byrne ◽  
Jeremy F. Taylor
Keyword(s):  
Fruit Development ◽  
Fixed Effects ◽  
Prunus Persica ◽  
Genetic Correlations ◽  
Soluble Solids ◽  
Direct Selection ◽  
Multiple Trait ◽  
Phenotypic Correlations ◽  
Development Period ◽  
Genetic And Phenotypic Correlations

Thirteen peach [Prunus persica (L.) Batsch] fruit characteristics were investigated for 3 years, 1993, 1994, and 1995, in College Station, Texas, to determine heritability, genetic and phenotypic correlations, and predicted response to selection. Seedlings of 108 families resulting from crosses among 42 peach cultivars and selections were used in the evaluations. A mixed linear model, with years treated as fixed and additive genotypes as random factors, was employed to analyze the data. Best linear unbiased prediction (BLUP) was used to estimate fixed effects. Restricted maximum likelihood (REML) was used to estimate variance components, and a multiple trait model was used to estimate genetic and phenotypic covariances between traits. Genetic and phenotypic correlations ≥0.65 and <0.30 were considered strong or very strong and weak, respectively. Date of ripening, fruit development period (FDP) and date of full bloom had the highest heritability (h2) estimates, 0.94, 0.91, and 0.78, respectively. Fruit cheek diameter and titratable acidity (h2 = 0.31) were the traits with the lowest estimates. Fruit development period, fruit blush, and date of ripening had the highest predicted selection responses, whereas fruit suture, fruit cheek, L/W12 (ratio fruit length to average fruit diameters), and fruit tip had the lowest values. Most genetic correlations were ≥0.30 and were, in general, much higher than the corresponding phenotypic correlations. All four measures of fruit size were genetically and phenotypically very strongly correlated. Important genetic correlation estimates were also observed for date of ripening with FDP (ra = 0.93), date of ripening and FDP with fruit blush (ra = -0.77, ra = -0.72), SS (percent soluble solids) (ra = 0.63, ra = 0.62) and TA (ra = 0.55, ra = 0.64), and SS with TA (ra = -0.56). Direct selection practiced solely for early ripening and short FDP is expected to have a greater effect on correlated traits than direct selection for early bloom and large fruit mass.

scholarly journals Heritability, Genetic, and Phenotypic Correlations of Several Peach Traits

HortScience ◽  
1995 ◽  
Vol 30 (4) ◽  
pp. 763B-763
Author(s):  
Valdomiro A.B. de Souza ◽  
David H. Byrne ◽  
Jeremy F. Taylor
Keyword(s):  
Skin Color ◽  
Fixed Effects ◽  
Prunus Persica ◽  
Mixed Linear Model ◽  
Fruit Weight ◽  
Soluble Solids ◽  
Genetic Progress ◽  
Multiple Trait ◽  
Phenotypic Correlations ◽  
Genetic And Phenotypic Correlations

Heritability estimates are useful to predict genetic progress among offspring when the parents are selected on their performance, but they also provide information about major changes in the amount and nature of genetic variability through generations. Genetic and phenotypic correlations, on the other hand, are useful for better planning of selection programs. In this research, seedlings of 39 families resulting from crosses among 27 peach [Prunus persica (L.) Batsch] cultivars and selections were evaluated for date of full bloom (DFB), date of ripening (DR), fruit period development (FDP), flower density (FD), node density (ND), fruit density (FRD), fruit weight (WT), soluble solids content (SS), apical protuberance (TIP), red skin color (BLUSH), and shape (SH) in 1993 and 1994. The data were analyzed using the mixed linear model. The best linear unbiased prediction (BLUP) was used to estimate fixed effects and predict breeding values (BV). Restricted maximum likelihood (REML) was used to estimate variance components, and a multiple-trait model to estimate genetic and phenotypic covariances between traits. The data indicates high heritability for DFB, DR, FDP, and BLUSH, intermediate heritability for WT, TIP, and SH, and low heritability for FD, ND, FRD, and SS. They also indicate year effect as a major environmental component affecting seedling performance. High correlation estimates were found between some traits, but further analysis is needed to determine their significance.

scholarly journals Corrigendum to: Consequences of genetic selection for environmental impact traits on economically important traits in dairy cows

2018 ◽  
Vol 58 (10) ◽  
pp. 1966
Author(s):  
Purna Kandel ◽  
Sylvie Vanderick ◽  
Marie-Laure Vanrobays ◽  
Hélène Soyeurt ◽  
Nicolas Gengler
Keyword(s):  
Dairy Cows ◽  
Body Condition Score ◽  
Genetic Correlations ◽  
Genetic Selection ◽  
Selection Response ◽  
Direct Selection ◽  
Correlated Response ◽  
Production Traits ◽  
Ch4 Emissions ◽  
Natural Logarithm

Methane (CH4) emission is an important environmental trait in dairy cows. Breeding aiming to mitigate CH4 emissions require the estimation of genetic correlations with other economically important traits and the prediction of their selection response. In this study, test-day CH4 emissions were predicted from milk mid-infrared spectra of Holstein cows. Predicted CH4 emissions (PME) and log-transformed CH4 intensity (LMI) computed as the natural logarithm of PME divided by milk yield (MY). Genetic correlations of PME and LMI with traits used currently were approximated from correlations between estimated breeding values of sires. Values were for PME with MY 0.06, fat yield (FY) 0.09, protein yield (PY) 0.13, fertility 0.17; body condition score (BCS) –0.02; udder health (UDH) 0.22; and longevity 0.22. As expected by its definition, values were negative for LMI with production traits (MY –0.61; FY –0.15 and PY –0.40) and positive with fertility (0.36); BCS (0.20); UDH (0.08) and longevity (0.06). The genetic correlations of 33 type traits with PME ranged from –0.12 to 0.25 and for LMI ranged from –0.22 to 0.18. Without selecting PME and LMI (status quo) the relative genetic change through correlated responses of other traits were in PME by 2% and in LMI by –15%, but only due to the correlated response to MY. Results showed for PME that direct selection of this environmental trait would reduce milk carbon foot print but would also affect negatively fertility. Therefore, more profound changes in current indexes will be required than simply adding environmental traits as these traits also affect the expected progress of other traits.

RELATIONSHIPS OF REPRODUCTION TRAITS, BODY WEIGHT AND MILK YIELD IN DAIRY CATTLE

1986 ◽  
Vol 66 (1) ◽  
pp. 53-65 ◽  
Author(s):  
T. R. BATRA ◽  
A. J. LEE ◽  
A. J. McALLISTER
Keyword(s):  
Body Weight ◽  
Dairy Cattle ◽  
Milk Yield ◽  
Fixed Effects ◽  
Genetic Correlations ◽  
Calving Interval ◽  
Phenotypic Correlations ◽  
Reproduction Traits ◽  
Heritability Estimates ◽  
National Cooperative

The relationships between reproduction traits, body weight and milk yield were investigated using data from 1611 heifers and 733 cows from two lines of the National Cooperative Dairy Cattle Breeding Project. The data were analyzed separately for heifers and cows within lines using a mixed linear model containing fixed effects for station, year of birth, season of birth and random effect of sires. Heritability estimates and genetic correlations were estimated by a paternal half-sib analysis. Heritability estimates for heifer and cow reproduction traits ranged between 0 and 26% while those of body weights at calving and 112 d postpartum and milk yield ranged from 24 to 43%. Heifers with difficult calving had a higher incidence of retained placenta than those with normal calving. Phenotypic correlations between heifer reproduction traits and milk yield during first lactation were small. High milk production in cows was associated with longer calving interval. Phenotypic correlations between heifer's and cow's reproduction traits were small. Difficult calving in heifers impairs reproductive performance after calving resulting in greater number of days from calving to first and last breeding and leading to a longer calving interval. Key words: Reproduction traits, heifers, cows, milk yield, dairy cattle

scholarly journals EFFECT OF FIXED FACTORS AND ESTIMATION OF GENETIC PARAMETERS OF GROWTH TRAITS FOR MOUNTAIN KIDS

2019 ◽  
Vol 50 (6) ◽  
Author(s):  
Hermiz & Baper
Keyword(s):  
Variance Components ◽  
Fixed Effects ◽  
Genetic Parameters ◽  
Maximum Likelihood Method ◽  
Growth Traits ◽  
Genetic Correlations ◽  
Likelihood Method ◽  
Phenotypic Correlations ◽  
Body Weights ◽  
Genetic And Phenotypic Correlations

Body weights at birth (469), weaning (394) and at six month of age (358) for kids utilized in this study were raised at private project in Duhok governorate, Iraq during two kidding season (2016-2017) and (2017-2018). GLM within SAS programme was used to analyze the data which include the fixed effects (age of doe, year and season of kidding, sex of kid and type of birth, regression on doe weight at kidding, and the regression of later weights of kids on earlier weights) influencing the studied traits. Restricted Maximum Likelihood Method was used to estimate repeatability, heritability, genetic and phenotypic correlations after adjusting the records for fixed effects. Variance components of random effects were tested for positive definite. Overall mean of weights at birth (BWT), weaning (WWT) and 6 month of age (WT6M) were 2.92, 15.32 and 24.45 kg, respectively. Differences among groups of age of doe in all studied traits were not significant, while year of kidding and sex of kid affect all traits significantly (p<0.01). Season of kidding affect BWT and WWT significantly (P<0.01). Single born kids were heavier (P<0.01) than twins in BWT only. Regression of BWT on doe weight at kidding (0.033 kg/kg) was significant (P<0.01), while the regressions of WWT and WT6M were not significant. The regression coefficients of WWT on BWT (1.906 kg/kg) and of WT6M on WWT (0.835 kg/kg) were highly significant (P<0.01). Repeatability estimates for BWT, WWT and WT6M were 0.47, 0.45 and 0.35, respectively; on the same order the estimates of heritability were 0.41, 0.61 and 0.79. Genetic correlations between BWT with each of WWT (0.45) and WT6M (0.55), and between WWT and WT6M (0.68) were highly significant. All phenotypic correlations between each pair of body weights were higher than genetic correlations and ranged between 0.48 and 0.73.

scholarly journals 12 Genetic and phenotypic parameters for Aleutian disease tests and their correlations with growth and pelt quality traits in American mink

2020 ◽  
Vol 98 (Supplement_4) ◽  
pp. 17-18
Author(s):  
Guoyu Hu ◽  
Duy Ngoc Do ◽  
Janine Gray ◽  
Karim Karimi ◽  
Younes Miar
Keyword(s):  
Fixed Effects ◽  
American Mink ◽  
Genetic Correlations ◽  
Direct Selection ◽  
Enzyme Linked Immunosorbent Assay ◽  
Quality Traits ◽  
Genetically Improved ◽  
Significant Heritability ◽  
Improvement Programs ◽  
Selection For

Abstract Aleutian disease brings tremendous financial losses to the mink industry. The ineffective immunoprophylaxis, medication, and culling strategies have urged the mink industry to select mink with low quantitative enzyme-linked immunosorbent assay (qELISA) score or negative counterimmunoelectrophoresis (CIEP) test result. However, little is known about the heritabilities of qELISA and CEIP as well as their relationships with growth and pelt quality traits. The traits, including qELISA, CIEP, body length at harvest (HLEN), the size of dried pelt (SIZE), the overall quality of dried pelt (QUA), and the nap length of dried pelt (NAP), were measured on 1,683 American mink from the Canadian Center for Fur Animal Research (Nova Scotia, Canada) and Millbank Fur Farm (Ontario, Canada). Significance (P &lt; 0.05) of fixed effects (sex, farm, age, and color) and random effects (common litter, permanent environment, and dam) were determined by univariate analyses, while genetic and phenotypic parameters for all traits were estimated under bivariate analyses using ASREML 4.1. Estimated heritabilities (±SE) were 0.41±0.07 for qELISA, 0.06±0.06 for CIEP, 0.39±0.06 for HLEN, 0.46±0.07 for SIZE, 0.25±0.06 for QUA, and 0.46±0.08 for NAP. The qELISA showed non-significant (P &gt; 0.05) genetic correlations with HLEN (0.05±0.13) and dried pelt traits (0.02±0.18 with SIZE, -0.21±0.20 with QUA, and -0.13±0.16 with NAP). The CIEP only showed a significant (P &lt; 0.05) negative genetic correlation with SIZE (-0.85±0.33). The moderate-to-high heritabilities of qELISA, HLEN, SIZE, QUA, and NAP indicated that these traits can be genetically improved through a genetic/genomic selection. The low and non-significant heritability of CIEP indicated the ineffectiveness of direct selection for this trait. The estimated genetic parameters for qELISA suggested that selection for lower qELISA scores may not interfere with the selection of pelt size and quality in the genetic improvement programs of American mink.

scholarly journals Genetic Parameters of Conformation Traits in Young Polish Holstein-Friesian Bulls

2014 ◽  
Vol 14 (4) ◽  
pp. 831-840
Author(s):  
Agnieszka Otwinowska-Mindur ◽  
Ewa Ptak ◽  
Wojciech Jagusiak ◽  
Andrzej Żarnecki
Keyword(s):  
Fixed Effects ◽  
Genetic Parameters ◽  
Dominant Role ◽  
Genetic Correlations ◽  
Progeny Testing ◽  
Multiple Trait ◽  
Holstein Friesian ◽  
Type Traits ◽  
Genetic And Phenotypic Correlations ◽  
Final Score

Abstract The objective of this study was to estimate the genetic parameters of conformation traits in Polish Holstein-Friesian bulls evaluated for registration in the herd book and for entry into progeny testing. Data were 8 linearly scored (1-9 scale) and 6 composite (scored from 50 to 100) conformation traits of 2,738 young bulls born between 2001 and 2011. The multiple-trait REML method was applied for (co)variance component estimation. The linear model included fixed linear regressions on age at evaluation (from 10 to 23 months), fixed effects of year of birth, fixed effects of herd-classifier, and random animal effect. Heritability estimates for all analysed traits were within the range of 0.04-0.37. Among the 6 composite type traits, heritability was highest for size and for overall conformation score. The lowest heritability was for feet and legs. Among the linearly scored traits, heritability was the lowest for rear legs - side view and foot angle, and the highest for rump angle and muscularity of front end. Composite traits showed the highest genetic correlations with muscularity and final score playing the dominant role. Genetic correlations among linear traits were low and moderate (0.02-0.53). The relatively low genetic and phenotypic correlations indicated that no conformation trait of bulls can be improved by indirect selection alone. More research is needed to establish relationship between bull conformation traits and the conformation of their progeny.

scholarly journals Consequences of genetic selection for environmental impact traits on economically important traits in dairy cows

2018 ◽  
Vol 58 (10) ◽  
pp. 1779
Author(s):  
Purna Kandel ◽  
Sylvie Vanderick ◽  
Marie-Laure Vanrobays ◽  
Hélène Soyeurt ◽  
Nicolas Gengler
Keyword(s):  
Dairy Cows ◽  
Body Condition Score ◽  
Genetic Correlations ◽  
Genetic Selection ◽  
Selection Response ◽  
Direct Selection ◽  
Correlated Response ◽  
Production Traits ◽  
Ch4 Emissions ◽  
Natural Logarithm

Methane (CH4) emission is an important environmental trait in dairy cows. Breeding aiming to mitigate CH4 emissions require the estimation of genetic correlations with other economically important traits and the prediction of their selection response. In this study, test-day CH4 emissions were predicted from milk mid-infrared spectra of Holstein cows. Predicted CH4 emissions (PME) and log-transformed CH4 intensity (LMI) computed as the natural logarithm of PME divided by milk yield (MY). Genetic correlations of PME and LMI with traits used currently were approximated from correlations between estimated breeding values of sires. Values were for PME with MY 0.06, fat yield (FY) 0.09, protein yield (PY) 0.13, fertility 0.17; body condition score (BCS) –0.02; udder health (UDH) 0.22; and longevity 0.22. As expected by its definition, values were negative for LMI with production traits (MY –0.61; FY –0.15 and PY –0.40) and positive with fertility (0.36); BCS (0.20); UDH (0.08) and longevity (0.06). The genetic correlations of 33 type traits with PME ranged from –0.12 to 0.25 and for LMI ranged from –0.22 to 0.18. Without selecting PME and LMI (status quo) the relative genetic change through correlated responses of other traits were in PME by 2% and in LMI by –15%, but only due to the correlated response to MY. Results showed for PME that direct selection of this environmental trait would reduce milk carbon foot print but would also affect negatively fertility. Therefore, more profound changes in current indexes will be required than simply adding environmental traits as these traits also affect the expected progress of other traits.

scholarly journals Genetic parameter of birth and weaning weights for Friesian calves by using an animal model

2005 ◽  
Vol 48 (3) ◽  
pp. 261-269 ◽  
Author(s):  
H. Atil ◽  
A. S. Khattab ◽  
L. Badawy
Keyword(s):  
Animal Model ◽  
Birth Weight ◽  
Fixed Effects ◽  
Genetic Parameter ◽  
Genetic Correlations ◽  
Likelihood Method ◽  
Multiple Trait ◽  
Breeding Values ◽  
Heritability Estimates ◽  
Maternal Heritability

Abstract. Birth and weaning weights of 556 Friesian calves by 41 sires out of 318 different dams over a 11 years period were obtained from a herd of Friesian in Sakha Experimental Farm, Ministry of Agriculture, Egypt were used. The records were analyzed by Multiple Trait Likelihood Method (MTDFREML) by using a repeatability animal model (BOLDMAN et al., 1995). Convergence was attained after 699 iterations. The fixed effects included in the model were season and year of calving, parity and sex and the random effects were direct and maternal genetic, permanent maternal environmental and error. Direct heritability estimates for birth weight (BW) and weaning weight (WW) are 0.28 and 0.13, respectively, while, maternal heritability estimates for the same traits are 0.14 and 0.06, respectively. Repeatability estimates are 0.75 and 0.15 for BW and WW, respectively. Phenotypic and genetic correlations are 0.89 and 0.80, respectively. Estimates of calve breeding values ranged from −3.12 to 4.11 kg for BW and ranged from −4.10 to 5.11 kg for WW. Sire breeding values ranged from −3.40 to 2.99 kg for BW and ranged from −2.50 to 4.47 kg for WW. Dam breeding values ranged from −6.80 to 5.54 kg for BW and ranged from -6.10 to 6.39 kg for WW.

scholarly journals Estimation of genetic parameters for daily milk yields of primiparous Iranian Holstein cows

2013 ◽  
Vol 56 (1) ◽  
pp. 455-466
Author(s):  
K. Kheirabadi ◽  
S. Alijani ◽  
L. Zavadilová ◽  
S. A. Rafat ◽  
G. Moghaddam
Keyword(s):  
Milk Fat ◽  
Fixed Effects ◽  
Genetic Parameters ◽  
National Level ◽  
Genetic Correlations ◽  
Protein Yield ◽  
Random Regression ◽  
Production Traits ◽  
Multiple Traits ◽  
Multiple Trait

Abstract. Applying a multiple trait random regression (MT-RR) in national level and for whole test day records of a country is a great advance in animal breeding context. Having reliable (co) variance components is a critical step in applying multiple traits genetic evaluation especially in developing countries. Genetic parameters of milk, fat and protein yields were estimated for Iranian Holstein dairy cows. Data included 276 692 test day (TD) production traits records collected of 30 705 primiparous cows belonging to 619 sires. An animal multi-trait random regression model was employed in the analyses using the restricted maximum likelihood (REML) method. The model included herd-test-date, age-season of calving (by applying a fixed regression for each subclass of this effect) and year of calving as fixed effects and random regression (RR) coefficients for additive genetic (AG) and permanent environmental (PE) effects. Obtained results showed that daily heritabilities ranged from 0.10 to 0.21 for milk, from 0.05 to 0.08 for fat and from 0.08 to 0.18 for protein yield. Estimated heritability for 305-d milk, fat and protein yields were 0.25, 0.20 and 0.25, respectively. Correlations between individual test day records within traits were high for adjacent tests (nearly 1) and decreased as the interval between tests increased. Correlations between yields of milk, fat and protein on a given test day are also high and greater during late lactation than during early or mid-lactation. Genetic correlations between 305-d yield traits ranged from 0.75 to 0.92. The largest genetic correlation, as well as permanent environmental correlation, was observed between milk and protein yield.

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