scholarly journals Genetic analysis of tolerance to infections using random regressions: a simulation study

2011 ◽  
Vol 93 (4) ◽  
pp. 291-302 ◽  
Author(s):  
ANTTI KAUSE
Keyword(s):  
Genetic Analysis ◽  
Genetic Correlation ◽  
Simulation Study ◽  
Genetic Variance ◽  
Genetic Correlations ◽  
Experimental Conditions ◽  
Covariance Functions ◽  
Threshold Trait ◽  
Pathogen Burden ◽  
The Impact

SummaryTolerance to infections is the ability of a host to limit the impact of a given pathogen burden on host performance. This simulation study demonstrated the merit of using random regressions to estimate unbiased genetic variances for tolerance slope and its genetic correlations with other traits, which could not be obtained using the previously implemented statistical methods. Genetic variance in tolerance was estimated as genetic variance in regression slopes of host performance along an increasing pathogen burden level. Random regressions combined with covariance functions allowed genetic variance for host performance to be estimated at any point along the pathogen burden trajectory, providing a novel means to analyse infection-induced changes in genetic variation of host performance. Yet, the results implied that decreasing family size as well as a non-zero environmental or genetic correlation between initial host performance before infection and pathogen burden led to biased estimates for tolerance genetic variance. In both cases, genetic correlation between tolerance slope and host performance in a pathogen-free environment became artificially negative, implying a genetic trade-off when it did not exist. Moreover, recording a normally distributed pathogen burden as a threshold trait is not a realistic way of obtaining unbiased estimates for tolerance genetic variance. The results show that random regressions are suitable for the genetic analysis of tolerance, given suitable data structure collected either under field or experimental conditions.

scholarly journals Genetic Analysis of Several Economically Important Disease Traits in German Holstein Cows

2006 ◽  
Vol 49 (3) ◽  
pp. 209-221
Author(s):  
D. Hinrichs ◽  
E. Stamer ◽  
W. Junge ◽  
E. Kalm
Keyword(s):  
Genetic Structure ◽  
Genetic Analysis ◽  
Genetic Correlation ◽  
Metabolic Diseases ◽  
Genetic Correlations ◽  
Holstein Cows ◽  
Data Recording ◽  
The Impact ◽  
All Diseases ◽  
Important Disease

Abstract. In the present study several disease categories were analysed. Data recording ends after 50, 100, or 300 days in milk. Furthermore, the impact of increasing numbers of daughters per sire (improved genetic structure of the data) was examined and genetic correlations between disease categories were estimated. Diseases were clustered into fertility diseases, udder diseases, metabolic diseases, and claw and leg diseases. In addition, all diseases were analysed simultaneously. Frequencies of the disease categories were moderately high and vary between 7% and 78%. The most frequent disease categories were fertility diseases and udder diseases. Heritabilities for all diseases varied between 0.03 and 0.05, and were 0.02 to 0.05 for fertility diseases, 0.06 to 0.08 for udder diseases, 0.08 to 0.16 for metabolic diseases, and 0.01 to 0.03 for claw and leg diseases, respectively. The genetic correlation between disease categories ranged from – 0.18 to 0.82.

scholarly journals Genetic analysis and clonal stability of two yellow cypress clonal populations in British Columbia

2013 ◽  
Vol 62 (1-6) ◽  
pp. 173-186 ◽  
Author(s):  
B. S. Baltunis ◽  
J. H. Russell ◽  
A.Van Niejenhuis ◽  
J. Barker ◽  
Y. A. El-Kassaby
Keyword(s):  
British Columbia ◽  
Genetic Analysis ◽  
Combining Ability ◽  
Variance Components ◽  
Genetic Variance ◽  
Genetic Correlations ◽  
Specific Combining Ability ◽  
Type B ◽  
Height Range ◽  
Coastal British Columbia

AbstractGenetic analysis of height and form at age 12 years of 697 yellow cypress (Callitropsis nootkatensis [D. Don] Oerst.) clones tested across seven sites in coastal British Columbia (BC) were explored in populations: Population 1 - No Pedigree and Population 2 - Reconstructed Pedigree. Genetic variances were statistically significant but generally higher σ̂g2was observed for Population 2. Height and form were under low to moderate genetic control as indicated by clonal repeatability and estimates were relatively similar between populations. For example, average Ĥ2in Population 2 was 0.31 for height (range: 0.18-0.45) and 0.22 for form (range: 0.06-0.32). While average Ĥ2in Population 1 was 0.25 for height (range: 0.19-0.35) and 0.18 for form (range: 0.09-0.27). The reconstructed pedigree in Population 2 allowed partitioning the genetic variance (σ̂g2) into component parts of additive (σ̂a2), specific combining ability (σ̂s2), and clone (σ̂c2); however, general lack of structure within the population resulted in variance components to be estimated with little precision for additive and specific combining ability. The majority of genetic variation was associated with clone for both traits. For example, σ̂c2accounted for 57.6% and 62.5% of the total genetic variance for height and form, respectively. Growth and form responses of clones across test environments were relatively stable and overall type-B genetic correlations were in excess of 0.8 for both traits implying clones selected for production populations should respond favorably across the seed planning zone for yellow cypress in coastal BC.

scholarly journals GENETIC ANALYSIS OF SIZE-SCALING PATTERNS IN THE MOUSE MANDIBLE

Genetics ◽  
1985 ◽  
Vol 111 (3) ◽  
pp. 579-595
Author(s):  
William R Atchley ◽  
A Alison Plummer ◽  
Bruce Riska
Keyword(s):  
Genetic Variance ◽  
Adult Mouse ◽  
Genetic Correlations ◽  
Covariance Structure ◽  
Ontogenetic Changes ◽  
Skeletal Morphology ◽  
Quantitative Genetic ◽  
Functional Aspects ◽  
The Impact ◽  
The Relationship

ABSTRACT The relationship between multidimensional form of the adult mouse mandible and body size is examined from an ontogenetic perspective. The origin and ontogeny of phenotypic correlations are described in terms of genetic and environmental covariance patterns between adult skeletal morphology and growth in body weight. Different ontogenetic patterns are observed in the genetic correlations, and these can be related to the developmental as well as the functional aspects of mandibular form. The quantitative genetic aspects of craniomandibular growth and morphogenesis are explored, together with an examination of the impact of ontogenetic changes in the genetic variance-covariance structure on morphogenetic integration and evolution by selection.

Micron blowout: heritability and genetic correlations with fibre diameter and secondary follicle diameter

1999 ◽  
Vol 50 (8) ◽  
pp. 1375 ◽  
Author(s):  
J. A. Hill ◽  
R. W. Ponzoni ◽  
J. W. James
Keyword(s):  
Genetic Correlation ◽  
Scale Effect ◽  
Genetic Variance ◽  
Genetic Correlations ◽  
Fibre Diameter ◽  
The Other ◽  
Follicle Diameter ◽  
The Difference ◽  
Biased Estimates ◽  
Practical Implications

Calculation of micron blowout as the difference between fibre diameter records taken at different ages can produce ‘biased’ estimates of the heritability and genetic correlations due to a scale effect. In some instances, standardisation of the fibre diameter records to a common genetic variance (i.e. removal of the scale effect) changed the heritability and the genetic correlation estimates. The effect of standardisation on the heritability of micron blowout was determined to a large extent by the difference in the genetic variance between the 2 fibre diameter measurements, whereas in the case of the genetic correlation between micron blowout and another trait, it was also dependent on the genetic correlation between the other trait and the two fibre diameters. It is recommended that heritabilities and genetic correlations involving micron blowout be calculated after standardising the fibre diameter measurements to a common genetic variance. The practical implications of the results are briefly discussed.

scholarly journals The Impact on Estimations of Genetic Correlations of the Use, in Genome Wide Case-Control Studies, of Super-Normal, Unscreened and Family-History Screened Controls

2019 ◽  
Author(s):  
Kenneth S. Kendler ◽  
Chris Chatzinakos ◽  
Silviu-Alin Bacanu
Keyword(s):  
Genetic Correlation ◽  
Genetic Correlations ◽  
Case Control ◽  
Case Control Studies ◽  
Minimal Impact ◽  
Genome Wide ◽  
Unbiased Estimates ◽  
The Impact ◽  
Gwa Studies ◽  
Normal Controls

ABSTRACTTraditionally, in normal case-control studies of disorder A, individuals with disorder A are screened-out of controls. However, in genome wide association (GWA) studies, controls are sometimes unscreened or screened for disorder A and disorder B, producing super-normal controls. Using simulations, we examine how the observed genetic correlations between two disorders (A and B) are influenced by the use of unscreened, normal, and super-normal controls. Normal controls produce unbiased estimates of the genetic correlation. However, unscreened and super-normal controls both bias upward the genetic correlations. The strength of the bias increases with increasing population prevalences for the two disorders. With super-normal controls, the magnitude of bias is stronger when the true genetic correlation is low. The opposite is seen with the use of unscreened controls. Adding screening of first-degree relatives of controls substantially increases the bias in genetic correlations with super-normal controls but has minimal impact when normal controls are used.

A preliminary genetic analysis of breech and tail traits with the aim of improving the welfare of sheep

2007 ◽  
Vol 58 (2) ◽  
pp. 161 ◽  
Author(s):  
D. R. Scobie ◽  
D. O'Connell ◽  
C. A. Morris ◽  
S. M. Hickey
Keyword(s):  
New Zealand ◽  
Genetic Analysis ◽  
Genetic Correlation ◽  
Genetic Correlations ◽  
Phenotypic Correlation ◽  
Linear Scale ◽  
Bare Area ◽  
Phenotypic Correlations ◽  
Tail Docking

The area of naturally bare skin around the perineum was scored at weaning in lambs (n = 2152) from a composite flock of New Zealand crossbred sheep. Breech bareness was scored on a range from 1, where wool was growing right to the edges of the anus, to 5, where a large bare area surrounded the perineum. Bareness on the under surface of the tail was measured on a linear scale at tail docking. Dag score (degree of breech soiling) was recorded at weaning, on a scale of 0–5, where an increasing score indicated more dags. Dag score was taken as a measure of the risk of flystrike in the breech. Female lambs tended to have slightly greater (P < 0.001) breech bareness score (mean score 2.7) than males (mean score 2.6), whereas mean dag score of females was lower than that of males (0.45 v. 0.53; P < 0.05). Breech bareness score had a heritability of 0.33 ± 0.06, and the length of bare skin under the tail had a heritability of 0.59 ± 0.06. The genetic correlation between breech bareness score at weaning and length of bare skin under the tail at docking was positive (0.35 ± 0.10). These 2 traits had phenotypic correlations with dag score of –0.17 ± 0.02 and –0.03 ± 0.03, respectively, and genetic correlations with dag score of –0.30 ± 0.13 and 0.03 ± 0.12, respectively; negative values indicated a favourable relationship. Tails were removed at docking, so the phenotypic correlation of about zero between tail data and dag score at weaning was of little utility. Our results suggest that selecting for these 2 bareness traits could reduce dag formation and the associated risk of breech strike.

scholarly journals Evolution of genetic variability in a spatially heterogeneous environment: effects of genotype–environment interaction

1987 ◽  
Vol 49 (2) ◽  
pp. 147-156 ◽  
Author(s):  
Sara Via ◽  
Russell Lande
Keyword(s):  
Genetic Variability ◽  
Genetic Correlation ◽  
Genetic Variance ◽  
Genetic Correlations ◽  
Covariance Structure ◽  
Genetic Models ◽  
Quantitative Character ◽  
Disruptive Selection ◽  
Environment Interaction ◽  
The Mean

SummaryClassical population genetic models show that disruptive selection in a spatially variable environment can maintain genetic variation. We present quantitative genetic models for the effects of disruptive selection between environments on the genetic covariance structure of a polygenic trait. Our models suggest that disruptive selection usually does not alter the equilibrium genetic variance, although transient changes are predicted. We view a quantitative character as a set of character states, each expressed in one environment. The genetic correlation between character states expressed in different environments strongly affects the evolution of the genetic variability. (1) If the genetic correlation between character states is not ± 1, then the mean phenotype expressed in each environment will eventually attain the optimum value for that environment; this is the evolution of phenotypic plasticity (Via & Lande, 1985). At the joint phenotypic optimum, there is no disruptive selection between environments and thus no increase in the equilibrium genetic variability over that maintained by a balance between mutation and stabilizing selection within each environment. (2) If, however, the genetic correlation between character states is ± 1, the mean phenotype will not evolve to the joint phenotypic optimum and a persistent force of disruptive selection between environments will increase the equilibrium genetic variance. (3) Numerical analyses of the dynamic equations indicate that the mean phenotype can usually be perturbed several phenotypic standard deviations from the optimum without producing transient changes of more than a few per cent in the genetic variances or correlations. It may thus be reasonable to assume a roughly constant covariance structure during phenotypic evolution unless genetic correlations among character states are extremely high or populations are frequently perturbed. (4) Transient changes in the genetic correlations between character states resulting from disruptive selection act to constrain the evolution of the mean phenotype rather than to facilitate it.

Evaluation of repeatability and pre-structured repeatability models for genetic analyses of repeated records of fat and protein contents of milk in Iranian Holstein cows

2018 ◽  
Vol 58 (11) ◽  
pp. 1983
Author(s):  
M. Asadi Fozi
Keyword(s):  
Genetic Analysis ◽  
Genetic Variance ◽  
Additive Genetic Variance ◽  
Genetic Correlations ◽  
Holstein Cows ◽  
Phenotypic Correlation ◽  
Genetic Covariance ◽  
Future Studies ◽  
Phenotypic Correlations ◽  
Variance Structure

Fat and protein content of milk measurements from first to fifth lactations of Iranian Holstein cows were analysed using repeatability and several pre-structured repeatability models that varied in additive genetic variance structure and fitted heterogeneous residual co (variance). For this research, a total of 257 197 fat and 218 688 protein records were used. The records were measured on 116 531 cows born between 2010 and 2014. The animals originated from 2355 sires and 91 212 dams. Pre-structured repeatability models with heterogeneous residual co (variance) and the respective genetic variance structure were the best models for genetic analysis of the fat and protein data. The results derived from these models showed that heritability of both fat and protein are decreased from first to fifth lactations. Heritability of fat measured at first, second, third, fourth and fifth locations were between 0.10 and 0.19 and those for protein were between 0.07 and 0.24. Moderate to high phenotypic correlations were estimated between the repeated records of the fat and protein. Values of 0.13 and 0.16 were estimated for heritability of fat and protein using repeatability model. Phenotypic correlations among the repeated records of fat and protein were estimated to be 0.30 and 0.33, respectively when this model was applied. The results showed the genetic variance, heritability and phenotypic correlation of the fat and protein are changed over the lactations but the genetic parameters derived from the repeatability model are homogenous whereas in both models unity genetic correlations are assumed among the repeated records. The results of this study show that the repeatability model is not an appropriate model for genetic analysis of the repeated records of fat and protein in the population investigated and can be improved when pre-structured repeatability model is used. In the present study homogenous genetic covariance was assumed among the fat and protein taken at the different lactations which can be modelled in future studies for more improving the models.

Genetic analysis of growth traits in Mecheri sheep

2015 ◽  
Author(s):  
V. Jeichitra ◽  
R. Rajendran ◽  
K. Karunanithi ◽  
P. S. Rahumathulla
Keyword(s):  
Genetic Analysis ◽  
Genetic Correlation ◽  
Genetic Control ◽  
South India ◽  
Growth Traits ◽  
Genetic Correlations ◽  
Research Station ◽  
Body Weights ◽  
Weight Gains

Data on 1763 Mecheri sheep, maintained at the Mecheri Sheep Research Station, Pottaneri, Salem, south India, and recorded between 1991 and 2006, were analysed to study the growth related traits and their genetic control. The average weights at birth and at 12 months of age were 2.25 ± 0.01 and 17.48 ± 0.14 kg, respectively. The pre- and post-weaning (3-6, 3-9 and 3-12) average daily weight gains were 63.40 ± 0.58, 39.57 ± 0.57, 37.48 ± 0.44 and 34.31 ± 0.42 g respectively. The heritabilities of body weights and weight gains were in general moderate to high. The phenotypic and genetic correlations among body weights were positive and moderate to high. The phenotypic and genetic correlations among average daily gains were positive and low to high. The estimates of genetic correlation among average daily gains and body weights were positive and high with few exceptions.

scholarly journals 303 ASAS-EAAP Talk: On the estimation of genetic parameters from molecular data

2020 ◽  
Vol 98 (Supplement_4) ◽  
pp. 33-33
Author(s):  
Miguel Toro Ibáñez
Keyword(s):  
Linkage Disequilibrium ◽  
Genetic Correlation ◽  
Genomic Selection ◽  
Genetic Parameters ◽  
Genetic Variance ◽  
Genetic Correlations ◽  
Molecular Data ◽  
Snp Markers ◽  
Directional Selection ◽  
Recombination Rates

Abstract We deal with several problems that arise when inferring genetic parameters at the level of quantitative trait loci (QTL) from molecular data such as SNP markers. Linkage Disequilibrium (LD) is recognized as a factor creating ambiguity in the partition of genetic variance. Here, using a simple model with three loci (one QTL and two markers), it is shown that the markers generate apparent AxA and DxD epistasis, even if only third order disequilibrium exists and the QTL is dominant. The problem of “phantom epistasis” is not alleviated by larger sample sizes (de los Campos et al., 2019). We also show that markers can give a distorted picture of the genetic correlation between traits: The genomic correlation could be greater, lower or even of opposite sign than the true genetic correlation. Therefore, speculating about genetic correlations and even about their causes (e.g., pleiotropy) using genomic data is often conjectural. Thirdly, we examine the problem of directional selection generating negative linkage disequilibrium (“Bulmer effect”) in the short term from a genomic selection perspective. It seems that the reduction in response due to the Bulmer effect is the same for genomic selection as for selection based on traditional BLUP. However, the reduction in response with genomic selection is greater than when selection is based directly on phenotypes only (Van Grevenhof et al. 2012). It is also expected that directional selection for a polygenic trait should increase recombination rate, provided there is genetic variance for recombination. It is then of relevance to ask whether recombination rates could be manipulated in order to increase selection response (Battagin et al., 2016). Finally, we consider that recombination and epistasis are closely intertwined: Epistasis generate LD and recombination break them up. Then, we should expect genomes to be modular: regions with low recombination containing functionally related genes loosely linked to other regions.

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