scholarly journals Some observations on asymmetrical correlated responses to selection

1966 ◽  
Vol 7 (1) ◽  
pp. 44-57 ◽  
Author(s):  
B. B. Bohren ◽  
W. G. Hill ◽  
A. Robertson
Keyword(s):  
Model Selection ◽  
Gene Frequency ◽  
Genetic Parameters ◽  
Correlated Response ◽  
Correlated Responses ◽  
Gene Frequencies ◽  
Genetic Covariance ◽  
Gene Effects ◽  
Selection Experiments ◽  
Relative Change

The pattern of changes of the genetic covariance between two characters on selection was examined in an effort to explain the asymmetry of correlated responses in two traits, or of the same trait in two environments, frequently observed in experimental results.The algebraic conclusions were further examined by model selection experiments using a computer. The computer was programmed to calculate the change in gene frequency from generation to generation and to calculate from it the expected changes in genetic variances and covariance as selection proceeded. This procedure was carried out with several models of gene effects and gene frequencies.Asymmetry of the genetic covariance, and consequently of the correlated responses, resulted when the relative change in gene frequency at the loci contributing positively and negatively to the covariance depended on the trait selected. The conditions necessary for the development of asymmetry were examined and the results suggest that any symmetry found in an experiment is perhaps more surprising than asymmetry. Probably the most frequent contribution to asymmetry in practice will be from loci contributing negatively to the covariance and having frequencies other than 0·5.Accurate prediction of correlated response over many generations is therefore not possible without prior knowledge of the composition of the genetic covariance, as well as its magnitude. The validity of existing theory for the prediction of correlated responses is likely to be much poorer than for the prediction of direct responses. Predictions would then have to be based on the genetic parameters estimated in each generation.

scholarly journals The use of selection experiments for detecting quantitative trait loci

1997 ◽  
Vol 69 (3) ◽  
pp. 227-232 ◽  
Author(s):  
L. OLLIVIER ◽  
L. A. MESSER ◽  
M. F. ROTHSCHILD ◽  
C. LEGAULT
Keyword(s):  
Quantitative Trait ◽  
Marker Locus ◽  
Frequency Change ◽  
Gene Frequencies ◽  
Marker Allele ◽  
Gene Effects ◽  
Selection Experiments ◽  
Frequency Changes ◽  
Combined Selection ◽  
Number Of Individuals

Gene frequency changes following selection may reveal the existence of gene effects on the trait selected. Loci for the selected quantitative trait (SQTL) may thus be detected. Additionally, one can estimate the average effect (α) of a marker allele associated with an SQTL from the allele frequency change (Δq) due to selection of given intensity (i). In a sample of unrelated individuals, it is optimal to select the upper and lower 27% for generating Δq in order to estimate α. For a given number of individuals genotyped, this estimator is 0·25i2 times more efficient than the classical estimator of α, based on the regression of the trait on the genotype at the marker locus. The method is extended to selection criteria using information from relatives, showing that combined selection considerably increases the efficiency of estimation for traits of low heritability. The method has been applied to the detection of SQTL in a selection experiment in which the trait selected was pig litter size averaged over the first four parities, with i=3. Results for four genes are provided, one of which yielded a highly significant effect. The conditions required for valid application of the method are discussed, including selection experiments over several generations. Additional advantages of the method can be anticipated from determining gene frequencies on pooled samples of blood or DNA.

scholarly journals Estimation of Gene Frequencies in Clinical Research

2018 ◽  
Vol 7 (4.10) ◽  
pp. 508
Author(s):  
N. Vijayalakshmi ◽  
Dr. P.Sekhar ◽  
Dr. G.Mokesh Rayalu
Keyword(s):  
Quantitative Genetics ◽  
Gene Frequency ◽  
Genetic Parameters ◽  
Estimation Methods ◽  
Biological Research ◽  
Blood Group System ◽  
Gene Frequencies ◽  
Multiple Alleles ◽  
Segregation Ratios ◽  
Research Problems

Biometrics is a branch of statistics in which various mathematical and statistical techniques can be applied to biological research problems. These are two main areas of specialization of Biometry namely, Bioassays and Quantitative Genetics. Genetics concerns with Heredity and variation. Quantitative Genetics is concerned with the inheritances of quantitative differences between individuals.The essence of Quantitative Genetics is to estimate the genetic parameters such as Gene frequencies, segregation Ratios, Recombination of Genes and so on. Among them, the estimation of Gene Frequencies in the population is an important one. The proportion or percentage of genes in the population is called gene Frequency. In the present research articles, the ABO blood group system of man has been described by discussing the multiple alleles; genotypes, Frequencies and phenotypes of blood groups. The various estimation methods for estimating gene frequencies have gene presents in the present study. 

Design of multivariate selection experiments to estimate genetic parameters

1986 ◽  
Vol 1986 ◽  
pp. 26-26
Author(s):  
N.D. Cameron ◽  
R. Thompson
Keyword(s):  
Genetic Parameters ◽  
Classical Method ◽  
Phenotypic Correlation ◽  
Phenotypic Variance ◽  
Genetic Covariance ◽  
Breeding Programs ◽  
Genetic Variances ◽  
Rates Of Change ◽  
Selection Experiments ◽  
Unbiased Estimates

Precise, unbiased estimates of genetic parameters, such as heritability and genetic covariance, are necessary to optimise breeding programs and to predict rates of change for various selection schemes. A classical method of estimation is to use high and low selection experiments. We consider two generation selection experiments when observations in the parental generation are only taken on one sex, resulting in half-sib family information. We consider cases of two standardised traits, with zero mean and unit variance, and assume that the traits are normally distributed. The genetic and phenotypic variance-covariance matrices for the traits are denoted by G and P respectively. The genetic variances and covariances of the standardised traits are then heritabilities and co-heritabilities.The construction of other designs is examined, assuming the phenotypic correlation of rp between traits is known. For simplicity, comparisons of designs are developed by considering the variance of the genetic variances when the traits are uncorrelated both phenotypically and genetically.

scholarly journals A comparison of two methods for predicting changes in the distribution of gene frequency when selection is applied repeatedly to a finite population

1969 ◽  
Vol 13 (2) ◽  
pp. 117-126 ◽  
Author(s):  
Derek J. Pike
Keyword(s):  
Iterative Procedure ◽  
Gene Frequency ◽  
Finite Population ◽  
Transition Matrices ◽  
Single Cycle ◽  
Gene Frequencies ◽  
Mean And Variance ◽  
The Mean ◽  
The One ◽  
Selection Of

Robertson (1960) used probability transition matrices to estimate changes in gene frequency when sampling and selection are applied to a finite population. Curnow & Baker (1968) used Kojima's (1961) approximate formulae for the mean and variance of the change in gene frequency from a single cycle of selection applied to a finite population to develop an iterative procedure for studying the effects of repeated cycles of selection and regeneration. To do this they assumed a beta distribution for the unfixed gene frequencies at each generation.These two methods are discussed and a result used in Kojima's paper is proved. A number of sets of calculations are carried out using both methods and the results are compared to assess the accuracy of Curnow & Baker's method in relation to Robertson's approach.It is found that the one real fault in the Curnow-Baker method is its tendency to fix too high a proportion of the genes, particularly when the initial gene frequency is near to a fixation point. This fault is largely overcome when more individuals are selected. For selection of eight or more individuals the Curnow-Baker method is very accurate and appreciably faster than the transition matrix method.

Selection for rate and efficiency of lean gain in Hereford cattle 1. Selection pressure applied and direct responses

1990 ◽  
Vol 51 (1) ◽  
pp. 23-34 ◽  
Author(s):  
R. A. Mrode ◽  
C. Smith ◽  
R. Thompson
Keyword(s):  
Genetic Parameters ◽  
Control Line ◽  
Correlated Responses ◽  
Direct Response ◽  
Lean Growth ◽  
Hereford Cattle ◽  
Frozen Semen ◽  
Phenotypic Standard Deviation ◽  
Selection For ◽  
Selection Of

ABSTRACTSelection of bulls for rate and efficiency of lean gain was studied in a herd of Hereford cattle. There were two selection lines, one selected for lean growth rate (LGR) from birth to 400 days and the other for lean food conversion ratio (LFCR) from 200 to 400 days of age, for a period of 8 years. A control line bred by frozen semen from foundation bulls was also maintained. Generation interval was about 2·4 years and average male selection differentials, per generation were 1·2 and — 1·1 phenotypic standard deviation units for LGR and LFCR respectively.Genetic parameters and responses to selection were estimated from the deviation of the selected lines from a control line and by restricted maximum likelihood (REML) techniques on the same material. Realized heritabilities were 0·40 (s.e. 0·12) for LGR and 0·40 (s.e. 0·13) for LFCR using the control line. Corresponding estimates from REML were 0·42 (s.e. 0·10) and 0·37 (s.e. 0·14). The estimate of the genetic correlation between LGR and LFCR was about — 0·69 (s.e. 0·12) using REML.The estimates of direct annual genetic change using deviations from the control were 3·6 (s.e. 1·3) g/day for LGR and — 0·14 (s.e. 0·07) kg food per kg lean gain for LFCR. Corrsponding estimates from REML were similar but more precisely estimated. The correlated responses for LFCR in the LGR line was higher than the direct response for LFCR.

Selection for lean growth rate and correlated responses in litter traits in a synthetic line of Yorkshire-Meishan pigs

2001 ◽  
Vol 81 (2) ◽  
pp. 205-214 ◽  
Author(s):  
P. Chen ◽  
T. J. Baas ◽  
J. C. M. Dekkers ◽  
L. L. Christian
Keyword(s):  
Growth Rate ◽  
Regression Coefficients ◽  
Correlated Response ◽  
Selection Line ◽  
Control Line ◽  
Correlated Responses ◽  
Lean Growth ◽  
Litter Traits ◽  
Selection For ◽  
Synthetic Line

Selection for lean growth rate (LGR) was conducted for four generations in a synthetic line of Yorkshire-Meishan pigs to study the effectiveness of selection for LGR and correlated responses in litter traits. Lean growth rate was estimated from ultrasound measurements of 10th-rib backfat thickness and longissimus muscle area. In the selection line, 7 boars and 20 gilts with the highest LGR were selected to produce the next generation. The generation interval was 13 mo and the average selection differential per generation was 1.1 phenotypic standard deviation units. A contemporaneous control line was maintained by randomly selecting 5 boars and 15 gilts. Data from a total of 1057 pigs sired by 58 boars and out of 133 sows were available from the two lines. Selection responses were estimated from deviations of the selection line from the control line using least squares (LS) and by multiple trait derivative-free restricted maximum likelihood analysis using an animal model (AM). The estimate of response to selection per generation using LS was 9.4 ± 0.95 g d–1 for LGR. The corresponding estimate from the AM was 9.8 ± 0.51 g d–1. Correlated responses in litter traits were regressed on generation. For the LS method, regression coefficients were negative but not significant (P > 0.05) for total number born, number born alive, and number at 21 d and at 42 d. Significant, positive correlated responses occurred in 42-d litter weight and 21-d piglet weight (P < 0.05). For the AM method, the regression coefficients were also negative, but were not significant (P > 0.05) for numberalive at birth, at 21 d, and at 42 d. A significant positive correlated response occurred only for 42-d litter weight (P < 0.05). Although results are based on a population of limited size, it can be concluded that selection for LGR in a synthetic line is effective and should have little effect on litter traits. Key words: Pigs, selection, lean growth rate, correlated response

scholarly journals Estimation of direct and maternal genetic parameters for weaning weight in Hungarian Simmental cattle

2021 ◽  
pp. 17-22
Author(s):  
Afees Abiola Ajasa ◽  
Imre Füller ◽  
Barnabás Vágó ◽  
István Komlósi ◽  
János Posta
Keyword(s):  
Fixed Effects ◽  
Genetic Parameters ◽  
Additive Genetic Variance ◽  
Random Effect ◽  
Ratio Test ◽  
Genetic Covariance ◽  
Weaning Weight ◽  
Simmental Cattle ◽  
Estimate Variance ◽  
Best Fit

The aim of the current research was to estimate variance components and genetic parameters of weaning weight in Hungarian Simmental cattle. Weaning weight records were obtained from the Association of Hungarian Simmental Breeders. The dataset comprised of 44,278 animals born from 1975 to 2020. The data was analyzed using the restricted maximum likelihood methodology of the Wombat software. We fitted a total of six models to the weaning weight data of Hungarian Simmental cattle. Models ranged from a simple model with animals as the only random effect to a model that had maternal environmental effects as additional random effects as well as direct maternal genetic covariance. Fixed effects in the model comprised of herd, birth year, calving order and sex. Likelihood ratio test was used to determine the best fit model for the data. Results indicated that allowing for direct-maternal genetic covariance increases the direct and maternal effect dramatically. The best fit model had direct and maternal genetic effects as the only random effect with non-zero direct-maternal genetic correlation. Direct heritability, maternal heritability and direct maternal correlation of the best fit model was 0.57, 0.16 and -0.78 respectively. The result indicates that problem of (co-)sampling variation occurs when attempting to partition additive genetic variance into direct and maternal components.

scholarly journals Correlated Response on Growth Traits and Their Variabilities to Selection for Ovulation Rate in Rabbits Using Genetic Trends and a Cryopreserved Control Population

Animals ◽  
2021 ◽  
Vol 11 (9) ◽  
pp. 2591
Author(s):  
Rosa Peiró ◽  
Celia Quirino ◽  
Agustín Blasco ◽  
María Antonia Santacreu
Keyword(s):  
Growth Traits ◽  
Ovulation Rate ◽  
Control Population ◽  
Individual Growth ◽  
Correlated Response ◽  
Phenotypic Variance ◽  
Correlated Responses ◽  
Growth Variability ◽  
Selection For ◽  
Genetic Trends

The aim of this work was to estimate correlated responses in growth traits and their variabilities in an experiment of selection for ovulation rate during 10 generations in rabbits. Individual weight at 28 days old (IW28, kg) and at 63 days old (IW63, kg) was analyzed, as well as individual growth rate (IGR = IW63 − IW28, kg). The variability of each growth trait was calculated as the absolute value of the difference between the individual value and the mean value of their litter. Data were analyzed using Bayesian methodology. The estimated heritabilities of IW28, IW63 and IGR were low, whereas negligible heritabilities were obtained for growth variability traits. The common litter effect was high for all growth traits, around 30% of the phenotypic variance, whereas low maternal effect for all growth traits was obtained. Low genetic correlations between ovulation rate and growth traits were found, and also between ovulation rate and the variability of growth traits. Therefore, genetic trends methods did not show correlated responses in growth traits. A similar result was also obtained using a cryopreserved control population.

scholarly journals On sojourn times at particular gene frequencies

1975 ◽  
Vol 25 (2) ◽  
pp. 89-94 ◽  
Author(s):  
Edward Pollak ◽  
Barry C. Arnold
Keyword(s):  
Markov Chains ◽  
Gene Frequency ◽  
Overlapping Generations ◽  
Finite Population ◽  
Diffusion Method ◽  
Gene Frequencies ◽  
Sojourn Times ◽  
The Mean ◽  
Number Of Visits ◽  
Finite Markov Chains

SUMMARYThe distribution of visits to a particular gene frequency in a finite population of size N with non-overlapping generations is derived. It is shown, by using well-known results from the theory of finite Markov chains, that all such distributions are geometric, with parameters dependent only on the set of bij's, where bij is the mean number of visits to frequency j/2N, given initial frequency i/2N. The variance of such a distribution does not agree with the value suggested by the diffusion method. An improved approximation is derived.

scholarly journals Model comparison and estimation of genetic parameters for body weight in commercial broilers

2013 ◽  
Vol 93 (1) ◽  
pp. 67-77 ◽  
Author(s):  
G. Maniatis ◽  
N. Demiris ◽  
A. Kranis ◽  
G. Banos ◽  
A. Kominakis
Keyword(s):  
Body Weight ◽  
Akaike Information Criterion ◽  
Model Evaluation ◽  
Model Comparison ◽  
Genetic Parameters ◽  
Genetic Correlations ◽  
Information Criterion ◽  
Biological Properties ◽  
Genetic Covariance ◽  
Estimation Of Genetic Parameters

Maniatis, G., Demiris, N., Kranis, A., Banos, G. and Kominakis, A. 2013. Model comparison and estimation of genetic parameters for body weight in commercial broilers. Can. J. Anim. Sci. 93: 67–77. The availability of powerful computing and advances in algorithmic efficiency allow for the consideration of increasingly complex models. Consequently, the development and application of appropriate statistical procedures for model evaluation is becoming increasingly important. This paper is concerned with the application of an alternative model determination criterion (conditional Akaike Information Criterion, cAIC) in a large dataset comprising 203 323 body weights of broilers, pertaining to 7 (BW7) and 35 (BW35) days of age. Seven univariate and seven bivariate models were applied. Direct genetic, maternal genetic and maternal environmental (c2) effects were estimated via REML. The model evaluation criteria included conditional Akaike Information Criterion (cAIC), Bayesian Information Criterion (BIC) and the standard Akaike Information Criterion (henceforth marginal; mAIC). According to cAIC the best-fitting model included direct genetic, maternal genetic and c2 effects. Maternal heritabilities were low (0.10 and 0.03) compared to the direct heritabilities (0.17 and 0.21), while c2 was 0.05 and 0.04 for BW7 and BW35, respectively. BIC and mAIC favoured a model that additionally included a direct-maternal genetic covariance, resulting in highly negative direct-maternal genetic correlations (−0.47 and −0.64 for BW7 and BW35, respectively) and higher direct heritabilities (0.25 and 0.28 for BW7 and BW35, respectively). Results suggest that cAIC can select different animal models than mAIC and BIC with different biological properties.

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