scholarly journals Genetic analysis of clinical mastitis during different risk periods in Finnish Ayrshire

2008 ◽  
Vol 16 (2) ◽  
pp. 115 ◽  
Author(s):  
E. NEGUSSIE ◽  
I. STRANDÉN ◽  
E. MÄNTYSAARI
Keyword(s):  
Genetic Variation ◽  
Genetic Information ◽  
Genetic Parameters ◽  
Genetic Correlations ◽  
Clinical Mastitis ◽  
Data Sets ◽  
Threshold Models ◽  
Early Lactation ◽  
Sire Evaluation ◽  
Using Data

Clinical mastitis (CM) records from first-lactation Finnish Ayrshire were analysed by linear and threshold models to assess the effects trait definition on estimates of genetic parameters and sire evaluation. The studied CM traits were defined by dividing lactation into six lactation stages (risk periods) by days (d) after calving: CM1 (-7 to 150 d), CM2 (-30 to 30 d), CM3 (-30 to 150 d), CM4 (31 to 150 d), CM5 (150 to 300 d), CM6 (-30 to 300 d). In addition, two data sets were prepared to assess the effect of excluding (Data I) or including (Data II) records of culled cows on estimates of genetic parameters. Sire variances and heritabilities were larger using Data II. When data from longer intervals was used heritabilities of CM were slightly higher than shorter intervals indicating that longer intervals tend to obscure genetic variation between animals. Of all CM traits, heritability of liability to CM with threshold-liability model was highest for CM2 (h2=0.083) implying that most of the genetic information on CM is in early lactation. In sire evaluation, a multitrait index calculated by combining CM2, CM4 and CM5 had the highest correlation with all other univariate CM trait evaluations. This and the magnitude (less than 1.0) of genetic correlations between CM traits suggest that a multitrait model considering CM from the different risk periods would be appropriate for CM sire evaluation.;

Effects of trait definition on genetic parameter estimates and sire evaluation for clinical mastitis with threshold models

2004 ◽  
Vol 79 (3) ◽  
pp. 355-363 ◽  
Author(s):  
Y. M. Chang ◽  
D. Gianola ◽  
B. Heringstad ◽  
G. Klemetsdal
Keyword(s):  
Genetic Parameter ◽  
Genetic Correlations ◽  
Clinical Mastitis ◽  
Residual Effects ◽  
Parameter Estimates ◽  
Threshold Models ◽  
Posterior Probabilities ◽  
Sire Evaluation ◽  
Single Interval ◽  
The Impact

AbstractClinical mastitis records on 36 178 first-lactation Norwegian dairy cattle (NRF) cows, daughters of 245 sires from 5286 herds, were analysed to study the impact of trait definition on estimates of genetic parameters and sire evaluations for clinical mastitis. The opportunity interval for infection, going from 30 days pre-calving to 300 days post partum, was divided into either 11 periods (each 30 days long); four periods ((-30, 0), (1, 30), (31, 120), (121, 300)); a single period (-30, 300) or defined as the interval currently used for sire evaluation in Norway (-15,120). Within each period, clinical mastitis was scored as 1 if it occurred at least once and 0 otherwise. Analysis was with Bayesian threshold models, assuming that mastitis (presence v. absence) was a different trait in each period. By use of multivariate or univariate normal link functions, unobserved liabilities to disease were modelled as a linear function of year of calving, age-season of calving, herd, sire of cow and residual effects. Estimates of heritability of liability to clinical mastitis ranged from 0-06 to 0-14, depending on the model and stage of lactation. In multi-period models, estimates of genetic correlations between periods were positive and ranged from 0-13 to 0-55. This suggests that clinical mastitis resistance is not the same trait in different periods of the first lactation, which is not captured by the single-interval models. The single-interval (-30, 300) model gave slightly smaller sire-specific posterior probabilities of clinical mastitis during the first lactation than the multi-period models. Furthermore, the interval used in current Norwegian sire evaluation understated the posterior probabilities of clinical mastitis, relative to the multi-period specifications. This led to some differences in sire rankings between the four models, although there was agreement between the four- and 11-period models. In conclusion, the multi-period models captured more genetic variation than the single-interval models, but the four-period model gave sire rankings that differed little from those obtained with an 11-period definition of clinical mastitis.

scholarly journals Genetic analysis of clinical mastitis in different risk periods by linear and threshold models

2006 ◽  
pp. 1-7
Author(s):  
Enyew Negussie ◽  
Ismo Strandén ◽  
Esa Mäntysaari
Keyword(s):  
Genetic Analysis ◽  
Variance Components ◽  
Genetic Correlations ◽  
Heritability Estimate ◽  
Clinical Mastitis ◽  
Breeding Value ◽  
Threshold Models ◽  
Early Lactation ◽  
Data Set ◽  
Risk Period

Understanding the genetic basis of clinical mastitis (CM) during lactation is essential in deciding on the best measure of CM for breeding value evaluation. CM is one of the leading reasons for premature disposal of dairy cows. Culled cows have incomplete lactation records, and in statistical analysis this may cause sampling biases, because culled cows are seldom a random sample. It is, therefore, essential to study the effects of culling information on the genetic analysis of CM. The objectives of this study were first: to estimate heritability and genetic association between CM during the different stages of first lactation using linear and threshold models, second: to assess the effects culling information on the estimates of variance (co)variance components and associated genetic parameters. First lactation was divided into six lactation stages (risk periods) by days (d) after calving: CM1, (-7 to 150 d); CM2, (-30 to 30 d); CM3, (-30 to 150 d); CM4, (31 to 150 d); CM5, (150 to 300 d); CM6, (-30 to 300 d). To assess the effect of including or excluding records of cows that had been culled from the herd before the end of the risk period, two data sets were prepared. In the first data set (Data I), records of cows culled before the end of the risk period were not included. In the second data set (Data II), records of cows culled during the risk period were included; if the cow had been culled for mastitis reasons or had completed two-thirds of the opportunity period if culled for other reasons. Variance (co)variance components were estimated by linear and threshold-liability models employing Bayesian approach. Heritability estimates using the linear model ranged from 0.005 to 0.024 for Data I, and from 0.005 to 0.029 for Data II, depending on the stages of lactation or risk period defined. The corresponding estimates from the threshold-liability model ranged from 0.034 to 0.076, and from 0.043 to 0.083 for Data I and Data II, respectively. In general, increased sire variance and higher heritability were observed with the inclusion of culling information. Estimated genetic correlations between the CM traits were medium to high, and ranged from 0.42 to 0.99 in Data I, and from 0.61 to 0.99 in Data II. Thus CM cannot be regarded the same trait in the course of lactation. Results from the threshold analyses showed that the heritability of liability to CM was highest for CM2 and followed by CM6, CM3 and CM1with estimates of 0.083, 0.073, 0.073 and 0.072, respectively. Estimates for CM4 and CM 5 were the lowest with heritability of 0.043 and 0.047, respectively. The high heritability estimate for CM2 indicates that most genetic information is in early lactation. Thus, the best measure of CM would be to consider only cases in early lactation. However, as the genetic correlation between CM traits defined over the different risk periods were less than one, mastitis cannot be regarded as the same trait in the different parts of lactation. Thus, a multivariate model, treating mastitis in different stages of lactation as different traits, would be the best model for sire evaluation.

Genetic Parameters and Genotype-by-Environment Interactions in Regional Progeny Tests of Pinus taeda L. in the Southern USA

2020 ◽  
Author(s):  
Edwin Lauer ◽  
Andrew Sims ◽  
Steven McKeand ◽  
Fikret Isik
Keyword(s):  
Genetic Variation ◽  
Pinus Taeda ◽  
Genetic Parameters ◽  
Genetic Correlations ◽  
Breeding Program ◽  
Model Fit ◽  
Genotype By Environment Interactions ◽  
Genotype By Environment ◽  
Progeny Tests ◽  
Pinus Taeda L

Abstract Genetic parameters were estimated using a five-series multienvironment trial of Pinus taeda L. in the southern USA. There were 324 half-sib families planted in five test series across 37 locations. A set of six variance/covariance matrices for the genotype-by-environment (G × E) effect for tree height and diameter were compared on the basis of model fit. In single-series analysis, extended factor analytical models provided generally superior model fit to simpler models for both traits; however, in the combined-series analysis, diameter was optimally modeled using simpler variance/covariance structures. A three-way compound term for modeling G × E interactions among and within series yielded substantial improvements in terms of model fit and standard errors of predictions. Heritability of family means ranged between 0.63 and 0.90 for both height and diameter. Average additive genetic correlations among sites were 0.70 and 0.61 for height and diameter, respectively, suggesting the presence of some G × E interaction. Pairs of sites with the lowest additive genetic correlations were located at opposite ends of the latitude range. Latent factor regression revealed a small number of parents with large factor scores that changed ranks significantly between southern and northern environments. Study Implications Multienvironmental progeny tests of loblolly pine (Pinus taeda L.) were established over 10 years in the southern United States to understand the genetic variation for the traits of economic importance. There was substantial genetic variation between open-pollinated families, suggesting that family selection would be efficient in the breeding program. Genotype-by-environment interactions were negligible among sites in the deployment region but became larger between sites at the extremes of the distribution. The data from these trials are invaluable in informing the breeding program about the genetic merit of selection candidates and their potential interaction with the environment. These results can be used to guide deployment decisions in the southern USA, helping landowners match germplasm with geography to achieve optimal financial returns and conservation outcomes.

Genetic parameters for terminal sires estimated using data of progeny from Border Leicester × Merino ewes

1995 ◽  
Vol 46 (4) ◽  
pp. 703
Author(s):  
PA Kenney ◽  
ME Goddard ◽  
LP Thatcher
Keyword(s):  
Genetic Parameters ◽  
Selection Index ◽  
Genetic Correlations ◽  
Subcutaneous Fat ◽  
Muscle Area ◽  
Eye Muscle ◽  
Bone Length ◽  
Selection For ◽  
Using Data ◽  
Omental Fat

Three and a half thousand lambs from Border Leicester x Merino ewes mated to 133 sires from five Poll Dorset, one White Suffolk, one Siromt, two Meridale and four Merino studs were slaughtered, their carcasses halved and one side divided into six primals. Subcutaneous fat was dissected from all six primals, and bone from only the three rear primals. There were four slaughter groups: average slaughter weights of 30 and 35 kg for ewes and 35 and 45 kg for cryptorchids. Heritabilities and phenotypic and genetic correlations for all traits measured (>50) are published in an appendix. Where comparisons were available, estimates were similar to those for purebred animals. Genetic parameters for various assessments of fat were similar except for channel and omental fat. The GR fat depth was the best predictor for total subcutaneous fat, cannon bone length for total bone, and eye muscle area for total soft tissue. Carcass weight and GR appear to be the most important measurements for use in selection for breeding of sires for the prime lamb industry. Slaughter weight and fat depth at the C site could be used as suitable alternatives on live animals. Production of lean meat is not likely to be increased greatly by including measurements other than liveweight and GR in a selection index. Of the other measurements bone length and eye muscle measurements showed most promise.

scholarly journals Comment on ‘Large-Scale Cognitive GWAS Meta-Analysis Reveals Tissue-Specific Neural Expression and Potential Nootropic Drug Targets’ by Lam et al.

2018 ◽  
Vol 21 (2) ◽  
pp. 84-88 ◽  
Author(s):  
W. David Hill
Keyword(s):  
Genetic Information ◽  
Drug Targets ◽  
Large Scale ◽  
Association Studies ◽  
Meta Analysis ◽  
Genetic Correlations ◽  
Data Sets ◽  
Genome Wide Association Studies ◽  
Nootropic Drug ◽  
Genome Wide

Intelligence and educational attainment are strongly genetically correlated. This relationship can be exploited by Multi-Trait Analysis of GWAS (MTAG) to add power to Genome-wide Association Studies (GWAS) of intelligence. MTAG allows the user to meta-analyze GWASs of different phenotypes, based on their genetic correlations, to identify association's specific to the trait of choice. An MTAG analysis using GWAS data sets on intelligence and education was conducted by Lam et al. (2017). Lam et al. (2017) reported 70 loci that they described as ‘trait specific’ to intelligence. This article examines whether the analysis conducted by Lam et al. (2017) has resulted in genetic information about a phenotype that is more similar to education than intelligence.

Multitrait and repeatability estimates of random effects on litter size in sheep

2002 ◽  
Vol 74 (2) ◽  
pp. 209-216 ◽  
Author(s):  
C. Hagger
Keyword(s):  
Litter Size ◽  
Random Effects ◽  
Genetic Gain ◽  
Genetic Parameters ◽  
Genetic Correlations ◽  
Parameter Estimates ◽  
Data Sets ◽  
Phenotypic Variance ◽  
The Third ◽  
Meat Sheep

AbstractFive data sets with records of first, second and third lambings of the White Alpine sheep (WAS1, WAS2), the Brown-Headed Meat sheep (BFS), the Black-Brown Mountain sheep (SBS) and the Valais Black-Nose sheep (SNS) of Switzerland were used to estimate phenotypic and genetic parameters for litter size using a multitrait and a repeatability model by the REML method. The sets contained litter information from 26 274, 25 165, 18 913, 14 953 and 21 726 ewes, respectively. Average numbers of litters per ewe were between 2·09 and 2·31. Average litter sizes at birth were between 1·36 and 1·57 lambs in first, between 1·52 and 1·75 in second and, between 1·56 and 1·86 in third parities. Multitrait estimates of heritability for size of first litters were 0·164, 0·157, 0·117, 0·223 and 0·116 for the WAS1, WAS2, BFS, SBS and SNS data, respectively. The corresponding estimates were 0·176, 0·165, 0·140, 0·208 and 0·134 for second and, 0·141, 0·155, 0·121, 0·145 and 0·107 for third litters. The systematic increase in phenotypic variances from first to third litter within data sets favoured the multivariate over the repeatability approach. Genetic correlations between size of the first three litters were, with one exception, above 0·927. Random flock ✕ year and sire of litter effects contributed between 2·2% and 13·2% and between 0·7% and 4·7% to the phenotypic variance of the traits, respectively. Residuals contributed between 70·6% and 84·2% to this parameter, estimates for the third litter were always highest. Heritability estimates from the repeatability model were smaller than the smallest multivariate estimates. Expected genetic gain in litter size from selection on the multitrait model was equal to the achieved response from the repeatability approach.

The genetic foundation of fitness and reproduction traits in Australian pig populations. 2. Relationships between weaning to conception interval, farrowing interval, stayability, and other common reproduction and production traits.

1996 ◽  
Vol 47 (8) ◽  
pp. 1275 ◽  
Author(s):  
E Tholen ◽  
KL Bunter ◽  
S Hermesch ◽  
HU Graser
Keyword(s):  
Genetic Parameters ◽  
Performance Test ◽  
Genetic Relationships ◽  
Average Daily Gain ◽  
Genetic Correlations ◽  
Data Sets ◽  
Production Traits ◽  
Breeding Programs ◽  
Reproduction Traits ◽  
Daily Gain

Data sets from 2 large Australian piggeries were used to estimate genetic parameters for the traits weaning to conception interval (WCIi-l,i) and farrowing interval (FIi-l,i), number born alive (NBAI), average piglet birthweight (BWi), 21-day litter weight (W21i), and sow stayability (STAYli) recorded for each ith parity, as well as sow average daily gain (ADG) and backfat (BF) recorded at the end of performance test. Over parities and herds, heritabilities for each trait were in the ranges: WCI/FI, 0.0-0.10; NBA, 0.09-0.16; BW, 0.11-0.35; W21, 0.12-0.23; STAYli, 0.02-0.09; ADG, 0.35-0.37; BF, 0.36-0.45. Genetic correlations between NBAl and NBA from later parities were significantly different from 1. In addition, in 1 herd negative genetic correlations (rg = -0.04 to -0.25) were found between sow stayability traits and NBA1, but not NBA recorded in later parities. Stayability was Unfavourably correlated with ADG and BF, and favourably correlated with WCI12. However, WCI12 was unfavourably correlated genetically with BF (rg = -0.24) but uncorrelated with ADG. Antagonistic relationships also existed between NBA and BW, NBA and W21, and BW and STAY. In addition to the traditional traits currently included in pig-breeding programs (e.g. ADG, BF, and NBA), traits such as WCI, BW, and STAY should also be considered as selection criteria to minimise the detrimental effects of antagonistic genetic relationships between traits.

Genetic parameters of milk cholesterol content in Holstein cattle

2018 ◽  
Vol 98 (4) ◽  
pp. 714-722 ◽  
Author(s):  
Duy N. Do ◽  
Allison Fleming ◽  
Flavio S. Schenkel ◽  
Filippo Miglior ◽  
Xin Zhao ◽  
...  
Keyword(s):  
Milk Fat ◽  
Genetic Parameters ◽  
Genetic Correlations ◽  
Cholesterol Content ◽  
Production Traits ◽  
Fat Percentage ◽  
Bivariate Models ◽  
Estimate Heritability ◽  
Fat Yield ◽  
Using Data

This study aimed to estimate heritability for milk cholesterol (CHL) and genetic correlations between milk CHL and other production traits (test-day milk, fat, and protein yields, fat and protein percentages, and somatic cell score). Milk CHL content was determined by gas chromatography and expressed as mg of CHL in 100 g of fat (CHL_fat) or in 100 mg of milk (CHL_milk). Univariate models were used to estimate variances and heritability, whereas bivariate models were used to compute correlations using data from 1793 cows. The average concentrations (standard deviation) of CHL_fat and CHL_milk were 275.63 (75) mg and 11.16 (3.63) mg, respectively. Milk CHL content was significantly affected by days in milk and herd (P < 0.05), but not by parity, regardless of the scale of expression. Heritability estimates for CHL_fat and CHL_milk were 0.06 ± 0.04 and 0.17 ± 0.06, respectively. Phenotypic and genetic correlations between CHL_fat and CHL_milk were 0.82 and 0.44 ± 0.24, respectively. CHL_fat had nonsignificant genetic correlations with all production traits, whereas CHL_milk had significant (P < 0.05) genetic correlations with milk yield (−0.47), fat yield (0.51), protein percentage (0.56), and fat percentage (0.88). This is the first study to estimate genetic parameters for milk CHL content. Further studies are required to assess the possibility of genetically selecting cows with lower milk CHL content.

Genetic parameters for clinical mastitis in Holstein-Friesians in the United Kingdom: a Bayesian analysis

2001 ◽  
Vol 73 (2) ◽  
pp. 229-240 ◽  
Author(s):  
H. N. Kadarmideen ◽  
R. Rekaya ◽  
D. Gianola
Keyword(s):  
Genetic Parameters ◽  
Genetic Parameter ◽  
Mixed Linear Model ◽  
Clinical Mastitis ◽  
Parameter Estimates ◽  
Data Sets ◽  
Data Sampling ◽  
Environmental Variance ◽  
Data Set ◽  
Monte Carlo Techniques

AbstractA Bayesian threshold-liability model with Markov chain Monte Carlo techniques was used to infer genetic parameters for clinical mastitis records collected on Holstein-Friesian cows by one of the United Kingdom’s national recording schemes. Four data sets were created to investigate the effect of data sampling methods on genetic parameter estimates for first and multi-lactation cows, separately. The data sets were: (1) cows with complete first lactations only (8671 cows); (2) all cows, with first lactations whether complete or incomplete (10 967 cows); (3) cows with complete multi-lactations (32 948 records); and (4) all cows with multiple lactations whether complete or incomplete (44 268 records). A Gaussian mixed linear model with sire effects was adopted for liability. Explanatory variables included in the model varied for each data set. Analyses were conducted using Gibbs sampling and estimates were on the liability scale. Posterior means of heritability for clinical mastitis were higher for first lactations (0·11 and 0·10 for data sets 1 and 2, respectively) than for multiple lactations (0·09 and 0·07, for data sets 3 and 4, respectively). For multiple lactations, estimates of permanent environmental variance were higher for complete than incomplete lactations. Repeatability was 0·21 and 0·17 for data sets 3 and 4, respectively. This suggests the existence of effects, other than additive genetic effects, on susceptibility to mastitis that are common to all lactations. In first or multi-lactation data sets, heritability was proportionately 0·10 to 0·19 lower for data sets with all records (in which case the models had days in milk as a covariate) than for data with only complete lactation records (models without days in milk as a covariate). This suggests an effect of data sampling on genetic parameter estimates. The regression of liability on days in milk differed from zero, indicating that the probability of mastitis is higher for longer lactations, as expected. Results also indicated that a regression on days in milk should be included in a model for genetic evaluation of sires for mastitis resistance based on records in progress.

scholarly journals Genetic analysis of reproductive characteristics in Iran-Black sheep

2020 ◽  
Vol 42 ◽  
pp. e47380
Author(s):  
Hasan Baneh ◽  
Javad Ahmadpanah ◽  
Yahya Mohammadi
Keyword(s):  
Reproductive Performance ◽  
Genetic Parameters ◽  
Genetic Correlations ◽  
Environmental Correlations ◽  
Black Sheep ◽  
Litter Weight ◽  
Breeding Station ◽  
Reproduction Traits ◽  
Using Data ◽  
Size At Birth

This study was conducted to estimate genetic parameters and trends for reproduction traits using data collected at the breeding station of Iran-Black sheep during 1980 to 2004. The traits included in the analyses were litter size at birth (LSB) and weaning (LSW) and litter mean weight per lamb born (LMWLB) and weaned (LMWLW) as basic traits, and total litter weight at birth (TLWB) and weaning (TLWW) as composite traits. Direct heritability estimates for LSB, TLWB, LMWLB, LSW, TLWW and LMWLW were 0.11, 0.07, 0.33, 0.08, 0.09 and 0.11, respectively. The permanent environmental effects had significant impact on all traits and ranged from 0.05 to 0.16. Effect of service sire was highly significant (p < 0.01) for all traits except LMWLW. Estimates of genetic correlations ranged from -0.76 (LSB-LMWLB) to 0.98 (LSB-LSW). Phenotypic and environmental correlations were generally lower than those of genetic correlations. Environmental correlations ranged from -0.55 (LSW-LMWLW) to 0.99 (LSB-LSW). Also, the estimated correlation for the effect of service sire ranged from -0.77 (LMWLB-TLWW) to 0.96 (LSB-LSW and LSB-TLWW). The results suggest that selection based on TLWB could be more effective than the other traits to enhance reproductive performance in Iran-Black ewes.

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