Multisite genetic parameter estimates from a Callitropsis nootkatensis diallel study with clonally replicated progeny

2015 ◽  
Vol 45 (6) ◽  
pp. 689-697 ◽  
Author(s):  
John H. Russell ◽  
João Costa e Silva ◽  
Brian S. Baltunis
Keyword(s):  
Genetic Variance ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Additive Genetic Variance ◽  
Genetic Parameter ◽  
Genetic Correlations ◽  
Parameter Estimates ◽  
Narrow Sense ◽  
Spatially Correlated ◽  
Crown Form

Clonally replicated Callitropsis nootkatensis (D. Don) D.P. Little progeny from partial diallels were established in nine trials on coastal British Columbia, Canada. The trials were assessed for height, diameter, and crown form at age 12 years. An individual-genotype, linear mixed model with spatially correlated residuals was used to estimate the variance components and related genetic parameters. The majority of the estimated genetic variance for all traits was additive, and nonadditive genetic variance was predominantly due to dominance effects. Narrow-sense heritabilities for height and diameter at individual sites varied from 0.07 to 0.39, whereas for crown form, they were all less than 0.1. Dominance and epistasis ratios were, for the most part, lower than narrow-sense heritabilities. Common across-site additive and nonadditive genetic correlations were strongly positive and not significantly different from 1.0 for the majority of traits across sites within a series. Significant levels of additive genetic variance, coupled with insignificant to low nonadditive genetic variance for growth and crown form, would seem to be contrary to developing a clonal testing and deployment program. However, the lack of viable orchard seed and the faster delivery of genetic gain to reforestation, as well as more accurate forward selections based on additive genetic effects, makes this strategy viable for C. nootkatensis.

Genetic parameter estimates (full sib and half sib analysis) of fertility and hatchability in two strains of Rhode Island chickens

2021 ◽  
Vol 38 (1) ◽  
pp. 14-22
Author(s):  
M. Orunmuyi ◽  
I. A. Adeyinka ◽  
O.O Oni
Keyword(s):  
Rhode Island ◽  
Additive Genetic Variance ◽  
Genetic Parameter ◽  
Genetic Correlations ◽  
Reproductive Traits ◽  
Parameter Estimates ◽  
Phenotypic Correlations ◽  
Poultry Breeding ◽  
Genetic And Phenotypic Correlations ◽  
Heritability Estimates

A study was conducted to estimate the genetic parameters of fertility and hatchability in two strains of Rhode Island Red (RIR) Chickens denoted as Strain A and Strain B respectively using the full-sib (sire +dam variance) and maternal half-sib (dam variance) components. The birds were obtained from the selected populations of RIR Chickens kept at the poultry breeding programme of National Animal Production Research Institute, Shika, Zaria, Nigeria. Settable eggs were collected from mating 28 cocks to 252 hens in a ratio of 1cock:9 hens from each strain. Eggs were pedigreed according to sire and dam. Results showed that values obtained for number of egg set (EGGSET), number of fertile eggs (NFERT), number of hatched chicks (NHATCH), percentage of chicks hatched from total eggs set (PHATCH) and percentage of chicks hatched from fertile eggs (PHATCHBL) were all higher in strain A than strain B. Heritability estimates obtained from the full-sib and maternal half-sib analysis ranged from medium to high for the two strains (0.24-0.96). The maternal half sib estimates were higher (0.40-0.96) than the estimates obtained from full sibs (0.24- 0.48). Genetic and phenotypic correlations obtained for both strains were positive and similar regardless of method of estimation. Genetic correlations between EGGSET and PFERT were low in strain A using both full-sib and maternal half-sib analyses (0.09-0.14). Phenotypic correlations between EGGSET and PFERT, PHATCH and PHATCHBL were also low in both strains and regardless of method of analyses. Moderate to high heritability estimates suggest that genetic improvement can be obtained by selection of these reproductive traits. The full-sib analysis for estimating heritability will be preferred since it is assumed that only additive genetic variance contributes to the covariance between family members.

scholarly journals Genetic architecture and major genes for backfat thickness in pig lines of diverse genetic backgrounds

2021 ◽  
Vol 53 (1) ◽  
Author(s):  
Miguel Gozalo-Marcilla ◽  
Jaap Buntjer ◽  
Martin Johnsson ◽  
Lorena Batista ◽  
Federico Diez ◽  
...  
Keyword(s):  
Candidate Genes ◽  
Genetic Variance ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Insulin Signalling ◽  
Additive Genetic Variance ◽  
Genomic Region ◽  
Carcass Composition ◽  
Backfat Thickness ◽  
Genomic Regions

Abstract Background Backfat thickness is an important carcass composition trait for pork production and is commonly included in swine breeding programmes. In this paper, we report the results of a large genome-wide association study for backfat thickness using data from eight lines of diverse genetic backgrounds. Methods Data comprised 275,590 pigs from eight lines with diverse genetic backgrounds (breeds included Large White, Landrace, Pietrain, Hampshire, Duroc, and synthetic lines) genotyped and imputed for 71,324 single-nucleotide polymorphisms (SNPs). For each line, we estimated SNP associations using a univariate linear mixed model that accounted for genomic relationships. SNPs with significant associations were identified using a threshold of p < 10–6 and used to define genomic regions of interest. The proportion of genetic variance explained by a genomic region was estimated using a ridge regression model. Results We found significant associations with backfat thickness for 264 SNPs across 27 genomic regions. Six genomic regions were detected in three or more lines. The average estimate of the SNP-based heritability was 0.48, with estimates by line ranging from 0.30 to 0.58. The genomic regions jointly explained from 3.2 to 19.5% of the additive genetic variance of backfat thickness within a line. Individual genomic regions explained up to 8.0% of the additive genetic variance of backfat thickness within a line. Some of these 27 genomic regions also explained up to 1.6% of the additive genetic variance in lines for which the genomic region was not statistically significant. We identified 64 candidate genes with annotated functions that can be related to fat metabolism, including well-studied genes such as MC4R, IGF2, and LEPR, and more novel candidate genes such as DHCR7, FGF23, MEDAG, DGKI, and PTN. Conclusions Our results confirm the polygenic architecture of backfat thickness and the role of genes involved in energy homeostasis, adipogenesis, fatty acid metabolism, and insulin signalling pathways for fat deposition in pigs. The results also suggest that several less well-understood metabolic pathways contribute to backfat development, such as those of phosphate, calcium, and vitamin D homeostasis.

scholarly journals Additive and Dominance Genomic Analysis for Litter Size in Purebred and Crossbred Iberian Pigs

Genes ◽  
2021 ◽  
Vol 13 (1) ◽  
pp. 12
Author(s):  
Houssemeddine Srihi ◽  
José Luis Noguera ◽  
Victoria Topayan ◽  
Melani Martín de Hijas ◽  
Noelia Ibañez-Escriche ◽  
...  
Keyword(s):  
Litter Size ◽  
Genetic Variance ◽  
Mixed Model ◽  
Additive Genetic Variance ◽  
Genetic Correlations ◽  
Genomic Analysis ◽  
Gxe Interaction ◽  
Genomic Regions ◽  
Additive Genetic Effects ◽  
Selection Of

INGA FOOD S. A., as a Spanish company that produces and commercializes fattened pigs, has produced a hybrid Iberian sow called CASTÚA by crossing the Retinto and Entrepelado varieties. The selection of the parental populations is based on selection criteria calculated from purebred information, under the assumption that the genetic correlation between purebred and crossbred performance is high; however, these correlations can be less than one because of a GxE interaction or the presence of non-additive genetic effects. This study estimated the additive and dominance variances of the purebred and crossbred populations for litter size, and calculated the additive genetic correlations between the purebred and crossbred performances. The dataset consisted of 2030 litters from the Entrepelado population, 1977 litters from the Retinto population, and 1958 litters from the crossbred population. The individuals were genotyped with a GeneSeek® GGP Porcine70K HDchip. The model of analysis was a ‘biological’ multivariate mixed model that included additive and dominance SNP effects. The estimates of the additive genotypic variance for the total number born (TNB) were 0.248, 0.282 and 0.546 for the Entrepelado, Retinto and Crossbred populations, respectively. The estimates of the dominance genotypic variances were 0.177, 0.172 and 0.262 for the Entrepelado, Retinto and Crossbred populations. The results for the number born alive (NBA) were similar. The genetic correlations between the purebred and crossbred performance for TNB and NBA—between the brackets—were 0.663 in the Entrepelado and 0.881 in Retinto poplulations. After backsolving to obtain estimates of the SNP effects, the additive genetic variance associated with genomic regions containing 30 SNPs was estimated, and we identified four genomic regions that each explained > 2% of the additive genetic variance in chromosomes (SSC) 6, 8 and 12: one region in SSC6, two regions in SSC8, and one region in SSC12.

scholarly journals Genetic Parameter Estimates for Growth Traits from Diallel Tests of Loblolly Pine Throughout the Southeastern United States

2008 ◽  
Vol 57 (1-6) ◽  
pp. 101-110 ◽  
Author(s):  
S. E. Mckeand ◽  
B. Li ◽  
J. E. Grissom ◽  
F. Isik ◽  
K. J. S. Jayawickrama
Keyword(s):  
United States ◽  
Loblolly Pine ◽  
Growth Traits ◽  
Additive Genetic Variance ◽  
Southeastern United States ◽  
Genetic Correlations ◽  
Parameter Estimates ◽  
Narrow Sense ◽  
Improvement Program ◽  
Individual Tree

Abstract Variation in heritability and in genetic correlation estimates were evaluated for juvenile tree height and volume for six testing areas of loblolly pine (Pinus taeda L.) in the southeastern United States. Variance components and their functions (heritability and type B genetic correlations) were estimated from 265 six-parent disconnected diallel series, tested in almost 1000 trials (4 tests per diallel series). Original data were collected at age 6 years from about one million trees (265 diallel series x 30 crosses x 36 trees per cross/site x 4 sites) planted in field tests. Genetic tests were from the second cycle of breeding in the North Carolina State University - Industry Cooperative Tree Improvement Program. The overall unbiased individual-tree narrow-sense heritability for height was 0.19 and for volume was 0.16. The broad-sense heritabilities for height (0.24) and for volume (0.22) were higher than narrow-sense heritabilities due to the presence of non-additive genetic variance. There were moderate regional differences in these estimates, with tests in the Lower Gulf Coastal Plain tending to have the highest heritabilities for growth traits. There was very little association between site index and heritability, but heritabilities were higher on sites with the highest survival and highest test precision. Genotype x environment interactions were generally low both for half-sib and full-sib families, indicating that families can be operationally deployed to different sites with little concern about unpredictable performance.

Genetic parameter estimates and principal component analysis of breeding values of reproduction and growth traits in female Canchim cattle

2013 ◽  
Vol 25 (5) ◽  
pp. 775 ◽  
Author(s):  
M. E. Buzanskas ◽  
R. P. Savegnago ◽  
D. A. Grossi ◽  
G. C. Venturini ◽  
S. A. Queiroz ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Mixed Model ◽  
Growth Traits ◽  
Genetic Parameter ◽  
Genetic Correlations ◽  
Principal Component ◽  
Component Analysis ◽  
Parameter Estimates ◽  
Breeding Values ◽  
Heritability Estimates

Phenotypic data from female Canchim beef cattle were used to obtain estimates of genetic parameters for reproduction and growth traits using a linear animal mixed model. In addition, relationships among animal estimated breeding values (EBVs) for these traits were explored using principal component analysis. The traits studied in female Canchim cattle were age at first calving (AFC), age at second calving (ASC), calving interval (CI), and bodyweight at 420 days of age (BW420). The heritability estimates for AFC, ASC, CI and BW420 were 0.03 ± 0.01, 0.07 ± 0.01, 0.06 ± 0.02, and 0.24 ± 0.02, respectively. The genetic correlations for AFC with ASC, AFC with CI, AFC with BW420, ASC with CI, ASC with BW420, and CI with BW420 were 0.87 ± 0.07, 0.23 ± 0.02, –0.15 ± 0.01, 0.67 ± 0.13, –0.07 ± 0.13, and 0.02 ± 0.14, respectively. Standardised EBVs for AFC, ASC and CI exhibited a high association with the first principal component, whereas the standardised EBV for BW420 was closely associated with the second principal component. The heritability estimates for AFC, ASC and CI suggest that these traits would respond slowly to selection. However, selection response could be enhanced by constructing selection indices based on the principal components.

scholarly journals Realized Sampling Variances of Estimates of Genetic Parameters and the Difference Between Genetic and Phenotypic Correlations

Genetics ◽  
1996 ◽  
Vol 143 (3) ◽  
pp. 1409-1416 ◽  
Author(s):  
Kenneth R Koots ◽  
John P Gibson
Keyword(s):  
Genetic Correlation ◽  
Genetic Parameters ◽  
Genetic Parameter ◽  
Genetic Correlations ◽  
Phenotypic Correlation ◽  
Parameter Estimates ◽  
Sampling Variance ◽  
Phenotypic Correlations ◽  
Genetic And Phenotypic Correlations ◽  
Heritability Estimates

Abstract A data set of 1572 heritability estimates and 1015 pairs of genetic and phenotypic correlation estimates, constructed from a survey of published beef cattle genetic parameter estimates, provided a rare opportunity to study realized sampling variances of genetic parameter estimates. The distribution of both heritability estimates and genetic correlation estimates, when plotted against estimated accuracy, was consistent with random error variance being some three times the sampling variance predicted from standard formulae. This result was consistent with the observation that the variance of estimates of heritabilities and genetic correlations between populations were about four times the predicted sampling variance, suggesting few real differences in genetic parameters between populations. Except where there was a strong biological or statistical expectation of a difference, there was little evidence for differences between genetic and phenotypic correlations for most trait combinations or for differences in genetic correlations between populations. These results suggest that, even for controlled populations, estimating genetic parameters specific to a given population is less useful than commonly believed. A serendipitous discovery was that, in the standard formula for theoretical standard error of a genetic correlation estimate, the heritabilities refer to the estimated values and not, as seems generally assumed, the true population values.

The incorporation of fertility indices in genetic improvement programmes

2001 ◽  
Vol 26 (1) ◽  
pp. 237-249 ◽  
Author(s):  
J.E. Pryce ◽  
R.F. Veerkamp
Keyword(s):  
Genetic Variance ◽  
Selection Index ◽  
Genetic Correlations ◽  
Live Weight ◽  
Parameter Estimates ◽  
Phenotypic Variance ◽  
Production Traits ◽  
Genetic Progress ◽  
Detection Rates ◽  
Days Open

AbstractIn recent years there has been considerable genetic progress in milk production. Yet, increases in yield have been accompanied by an apparent lengthening of calving intervals, days open, days to first heat and a decline in conception rates, which appears to be both at the genetic and phenotypic level. Fertility has a high relative economic value compared to production traits such as protein, making it attractive to include in a breeding programme. To do this there needs to be genetic variance in fertility. Measures of fertility calculated from service dates have a small genetic compared to phenotypic variance, hence heritability estimates are small, typically less than 5%, although coefficients of genetic variance are comparable to those of production traits. Heritabilities of commencement of luteal activity determined using progesterone profiles are generally higher, and have been reported as being from 0.16 to 0.28, which could be because of a more precise quantification of genetic variance, as management influences such as delaying insemination and heat detection rates are excluded. However, it might not be the use of progesterone profiles alone, as days to first heat observed by farm staff has a heritability of 0.15. The most efficient way to breed for improved fertility is to construct a selection index using the genetic and phenotypic parameter estimates of all traits of interest in addition to their respective economic values. Index traits for fertility could include measures such as calving interval, days open, days to first service, or days to first heat but there may also be alternative measures. Examples include traits related to energy balance, such as live weight and condition score (change), both of which have higher heritabilities than fertility measures and have genetic correlations of sufficient magnitude to make genetic progress by using them feasible. To redress the balance between fertility and production, some countries already publish genetic evaluations of fertility including: Denmark, Finland, France, Germany, Israel, The Netherlands, Norway and Sweden.

SIRE BY HERD INTERACTIONS FOR MILK YIELD AND COMPOSITION TRAITS

1977 ◽  
Vol 57 (3) ◽  
pp. 383-388 ◽  
Author(s):  
A. K. W. TONG ◽  
B. W. KENNEDY ◽  
J. E. MOXLEY
Keyword(s):  
Milk Yield ◽  
Milk Fat ◽  
Genetic Parameter ◽  
Genetic Correlations ◽  
Dairy Herd ◽  
Protein Yield ◽  
Reduced Model ◽  
Parameter Estimates ◽  
Full Model ◽  
Reduced Models

A total of 13,561 Holstein 305-day lactation records from 779 Quebec and Maritime herds enrolled on the Dairy Herd Analysis Service were used to evaluate the importance of sire × herd interactions for milk yield and composition traits. Sire × herd interaction accounted for 4.1, 1.1, 0.3, 2.6 and 5.6% of the total variation of milk, fat and protein yield and fat and protein percent, respectively. Genetic parameter estimates obtained under two different models, a full model that accounted for sire × herd interaction and a reduced model that ignored it, were examined. Heritabilities of milk, fat and protein yield and fat and protein percent were, respectively: for the full model, 0.36, 0.47, 0.45, 0.59 and 0.31 and for the reduced model, 0.49, 0.50, 0.46, 0.66 and 0.46. Phenotypic correlations between the traits were not appreciably different when estimated under the full and reduced models. Genetic correlations between the yield traits were also similar when estimated under the full and reduced models, but genetic correlations between yield and percentage traits were more stongly positive, or less negative, when sire × herd interaction was accounted for. The genetic correlation between fat and protein percent was larger under the reduced model than under the full model.

scholarly journals Estimation of Genetic Variance in Fitness, and Inference of Adaptation, When Fitness Follows a Log-Normal Distribution

2019 ◽  
Vol 110 (4) ◽  
pp. 383-395 ◽  
Author(s):  
Timothée Bonnet ◽  
Michael B Morrissey ◽  
Loeske E B Kruuk
Keyword(s):  
Natural Selection ◽  
Animal Models ◽  
Normal Distribution ◽  
Genetic Variance ◽  
Additive Genetic Variance ◽  
Relative Fitness ◽  
Parameter Estimates ◽  
Log Normal ◽  
Change In Mean ◽  
Log Normal Distribution

AbstractAdditive genetic variance in relative fitness (σA2(w)) is arguably the most important evolutionary parameter in a population because, by Fisher’s fundamental theorem of natural selection (FTNS; Fisher RA. 1930. The genetical theory of natural selection. 1st ed. Oxford: Clarendon Press), it represents the rate of adaptive evolution. However, to date, there are few estimates of σA2(w) in natural populations. Moreover, most of the available estimates rely on Gaussian assumptions inappropriate for fitness data, with unclear consequences. “Generalized linear animal models” (GLAMs) tend to be more appropriate for fitness data, but they estimate parameters on a transformed (“latent”) scale that is not directly interpretable for inferences on the data scale. Here we exploit the latest theoretical developments to clarify how best to estimate quantitative genetic parameters for fitness. Specifically, we use computer simulations to confirm a recently developed analog of the FTNS in the case when expected fitness follows a log-normal distribution. In this situation, the additive genetic variance in absolute fitness on the latent log-scale (σA2(l)) equals (σA2(w)) on the data scale, which is the rate of adaptation within a generation. However, due to inheritance distortion, the change in mean relative fitness between generations exceeds σA2(l) and equals (exp⁡(σA2(l))−1). We illustrate why the heritability of fitness is generally low and is not a good measure of the rate of adaptation. Finally, we explore how well the relevant parameters can be estimated by animal models, comparing Gaussian models with Poisson GLAMs. Our results illustrate 1) the correspondence between quantitative genetics and population dynamics encapsulated in the FTNS and its log-normal-analog and 2) the appropriate interpretation of GLAM parameter estimates.

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