Genetic Studies of the Offspring of Identical Twins - A Model for the Analysis of Quantitative Inheritance in Man

1976 ◽  
Vol 25 (1) ◽  
pp. 103-113 ◽  
Author(s):  
Walter E. Nance
Keyword(s):  
Birth Weight ◽  
Genetic Variance ◽  
Genetic Model ◽  
Genetic Effect ◽  
Identical Twins ◽  
Additive Genetic Effect ◽  
Quantitative Inheritance ◽  
Ridge Count ◽  
Environmental Variance ◽  
Genetic Studies

In conjunction with full-sib and parental observations, half-sib analysis permits an estimation of the genetic and environmental variance as well as a partitioning of the genetic variance into its additive, dominance and epistatic components. The offspring of identical twins are a unique class of human half-sibs who provide an unusual opportunity to resolve and measure several additional potentially important sources of human variation including maternal effects, the influences of common environmental factors and assortative mating.The genetic model thus developed for the analysis of quantitative inheritance in man has been applied to the analysis of total ridge count and birth weight, confirming the existence of a major additive genetic effect on ridge count and a significant maternal effect on birth weight.

Potential biases of incomplete linear models in heritability estimation and breeding value prediction

1999 ◽  
Vol 29 (6) ◽  
pp. 724-736 ◽  
Author(s):  
P X Lu ◽  
D A Huber ◽  
T L White
Keyword(s):  
Genetic Variance ◽  
Linear Models ◽  
Additive Genetic Variance ◽  
Genetic Effect ◽  
Breeding Value ◽  
Additive Genetic Effect ◽  
Unbalanced Data ◽  
Breeding Values ◽  
Incomplete Model ◽  
Heritability Estimation

Potential biases associated with incomplete linear models in the estimation of heritability and the prediction of breeding values have been investigated. Results indicate that estimates of additive genetic variance and heritability as well as predicted parental breeding values from incomplete models will inevitably be biased as long as the true variance components of ignored effects are not zero. While models ignoring the interaction effect of males and females (SCA) × environment (E) interaction downwardly biased the estimates of additive genetic variance and heritability, models ignoring SCA and (or) the additive genetic effect (GCA) × E interaction yielded upward biases. The magnitudes of biases are functions of population genetic architecture, mating design, and field experimental design and can be precisely assessed with formulae derived for balanced data. Numerical simulations using unbalanced data of different mating and field experimental designs suggest that the formulae from balanced data can be used to approximate the minimum biases associated with unbalanced data. Because of the magnitudes of biases for some typical forest genetic scenarios, it is suggested that models ignoring SCA and (or) GCA × E should be avoided when the numbers of test sites and crosses per parent are small. However, incomplete model ignoring SCA × E interaction may be used to reduce computational demand with only negligible consequences.

FACTORIAL GENETIC ANALYSIS OF A TWO-LOCUS SYSTEM IN BARLEY

1973 ◽  
Vol 15 (3) ◽  
pp. 473-482 ◽  
Author(s):  
S. Jana
Keyword(s):  
Dominance Effect ◽  
Early Stage ◽  
Genotypic Variation ◽  
Genetic Effect ◽  
Additive Genetic Effect ◽  
Genetic Studies ◽  
Genetic Method ◽  
Quantitative Genetic ◽  
Single Character ◽  
Locus System

Backcross-derived homozygous lines of Atlas barley, isogenic except for two unlinked loci, A/a and B/b, each with two alleles, were crossed to produce five heterozygous genotypes. The nine possible genotypes were then used for detailed quantitative genetic studies at various stages in the life cycle of the plant. Components of genotypic variation attributable to additive, dominance and epistatic effects of genes were estimated by the use of the factorial genetic method. The relative magnitudes of these components for a single character were found to change considerably with the age of the plant and they also changed from character to character at the same age. Additive genetic effect, particularly of the A/a locus was the largest component of genotypic variation in the first 6 weeks of growth of the seedling. Epistasis was important at the very early stage of growth, but decreased strikingly in size at a time immediately following jointing. In general, the A/a locus was found to be genetically more active than the B/b locus for a number of metrical characters. Dominance effect of the A/a locus was responsible for about 50% of the total gene controlled variability for grain yield.

Evaluation genetic parameters for weight at different ages in Baluchi breed of sheep

2003 ◽  
Vol 2003 ◽  
pp. 145-145
Author(s):  
M. Hosseinpour Mashhadi ◽  
F. Eftekhari Shahroudi ◽  
R. Valizadeh
Keyword(s):  
Birth Weight ◽  
Genetic Parameters ◽  
Genetic Effect ◽  
Additive Genetic Effect ◽  
Maternal Environment ◽  
Breeding Programs ◽  
Breeding Values ◽  
Genetic Potential ◽  
Different Ages ◽  
Maternal Genetic Effect

Improving breeding values and breeding programs should be done based on genetic potential. The range of additive direct heritability and maternal environment heritability for birth weight is about 0.07 to 0.22 and 0.1 to 0.33 respectively the range of these values for the following weights are 0.09- 0.58 and 0.01- 0.17. the objective of this study was to predict the direct additive genetic effect, maternal genetic effect and heritabilities of lamb weight traits in baluchi breed of sheep.

scholarly journals Genetic and Non-genetic Factors Affecting Gestation Length and Calve Birth Weight of Sahiwal-Friesian Crossbred Groups

2010 ◽  
pp. 1-8
Author(s):  
MAI Talukder ◽  
JM Panandam
Keyword(s):  
Birth Weight ◽  
Genetic Factors ◽  
Genetic Effect ◽  
Additive Genetic Effect ◽  
Gestation Length ◽  
Factors Affecting ◽  
Breed Group ◽  
Significant Difference ◽  
Calf Mortality ◽  
Sire Breed

This study focused on the dairy unit at the Ladang Pusat Ternakan Haiwan Ayer Hitam in Johor, Malaysia. Eight crossbreed groups namely M50, M50-1, M50-2, M50-3, M56, M63, M75 and M75-1 were evaluated. Retrospective data on gestation length and calf birth weight were extracted for evaluation of 1346 animals and were analyzed between 1981 and 2001. Effects of breed group, parity, calf sex and age at calving were non significant for GL. Year of birth was only significant (P<0.05) affected for GL. The GL for the breed groups ranged between 279 - 283 days. The cow breed group x parity interaction effect was significant (P<0.05) for CBW. Sire breed group, calf sex and age at calving significantly (P<0.05) affected the CBW. There was no significant difference in CBW of the cow breed groups for the first two parities. M50, M50-1, M63 and M75-1 had significantly (P<0.05) higher CBW in the third and fourth parity (26.76-28.98 kg). M50-3 and M56 had significantly (P<0.05) lower CBW than M50 and M63. M56 had the lowest (P<0.05) CBW in the fourth parity (22.22 ± 1.24 kg). Individual additive genetic effect, maternal additive genetic effect, individual heterosis and maternal heterosis were non-significant for GL and CBW. Calf sex significantly (P<0.01) affected the CBW in all breed groups except M56 and M63. Male calves weighed significantly (P<0.01) heavier than female calves in the earlier breed group. Calf mortality ranged between 3.49 - 7.27%. The highest calf mortality at birth was observed in M75 (7.27%) followed by M50-3 (6.8%) and M75-1 (5.66%). The lowest mortality was observed in M50-1. M50, M50-1, M63 and M75-1 had better performance on CBW. Higher Friesian grades calf mortality rate was higher than lower Friesian grades. The non genetic factor year of birth only affected GL, but most of the genetic and non genetic factors significantly (P<0.05) affected the CBW.

Personal Goals of Older Female Twins

2009 ◽  
Vol 14 (2) ◽  
pp. 160-167 ◽  
Author(s):  
Katariina Salmela-Aro ◽  
Sanna Read ◽  
Jari-Erik Nurmi ◽  
Markku Koskenvuo ◽  
Jaakko Kaprio ◽  
...  
Keyword(s):  
Physical Exercise ◽  
Independent Living ◽  
Environmental Effect ◽  
Close Relationships ◽  
Environmental Influence ◽  
Genetic Effect ◽  
Additive Genetic Effect ◽  
Personal Goals ◽  
Cultural Activities ◽  
The Individual

This study examined genetic and environmental influences on older women’s personal goals by using data from the Finnish Twin Study on Aging. The interview for the personal goals was completed by 67 monozygotic (MZ) pairs and 75 dizygotic (DZ) pairs. The tetrachoric correlations for personal goals related to health and functioning, close relationships, and independent living were higher in MZ than DZ twins, indicating possible genetic influence. The pattern of tetrachoric correlations for personal goals related to cultural activities, care of others, and physical exercise indicated environmental influence. For goals concerning health and functioning, independent living, and close relationships, additive genetic effect accounted for about half of the individual variation. The rest was the result of a unique environmental effect. Goals concerning physical exercise and care of others showed moderate common environmental effect, while the rest of the variance was the result of a unique environmental effect. Personal goals concerning cultural activities showed unique environmental effects only.

scholarly journals Evolutionary Stability for Interactions among Kin under Quantitative Inheritance.

Genetics ◽  
1989 ◽  
Vol 121 (4) ◽  
pp. 877-889
Author(s):  
A B Harper
Keyword(s):  
Inclusive Fitness ◽  
Fitness Function ◽  
Quantitative Inheritance ◽  
Evolutionarily Stable Strategies ◽  
Environmental Variance ◽  
Pairwise Interactions ◽  
Simplifying Assumptions ◽  
Multilocus Model ◽  
Evolutionarily Stable

Abstract The theory of evolutionarily stable strategies (ESS) predicts the long-term evolutionary outcome of frequency-dependent selection by making a number of simplifying assumptions about the genetic basis of inheritance. I use a symmetrized multilocus model of quantitative inheritance without mutation to analyze the results of interactions between pairs of related individuals and compare the equilibria to those found by ESS analysis. It is assumed that the fitness changes due to interactions can be approximated by the exponential of a quadratic surface. The major results are the following. (1) The evolutionarily stable phenotypes found by ESS analysis are always equilibria of the model studied here. (2) When relatives interact, one of the two conditions for stability of equilibria differs between the two models; this can be accounted for by positing that the inclusive fitness function for quantitative characters is slightly different from the inclusive fitness function for characters determined by a single locus. (3) The inclusion of environmental variance will in general change the equilibrium phenotype, but the equilibria of ESS analysis are changed to the same extent by environmental variance. (4) A class of genetically polymorphic equilibria occur, which in the present model are always unstable. These results expand the range of conditions under which one can validly predict the evolution of pairwise interactions using ESS analysis.

scholarly journals Characterization of Deleterious Mutations in Outcrossing Populations

Genetics ◽  
1998 ◽  
Vol 150 (2) ◽  
pp. 945-956 ◽  
Author(s):  
Hong-Wen Deng
Keyword(s):  
Genetic Variation ◽  
Genetic Variance ◽  
Estimation Bias ◽  
Deleterious Mutations ◽  
Environmental Variance ◽  
Higher Power ◽  
Sib Mating ◽  
The Mean ◽  
Simulation Results

Abstract Deng and Lynch recently proposed estimating the rate and effects of deleterious genomic mutations from changes in the mean and genetic variance of fitness upon selfing/outcrossing in outcrossing/highly selfing populations. The utility of our original estimation approach is limited in outcrossing populations, since selfing may not always be feasible. Here we extend the approach to any form of inbreeding in outcrossing populations. By simulations, the statistical properties of the estimation under a common form of inbreeding (sib mating) are investigated under a range of biologically plausible situations. The efficiencies of different degrees of inbreeding and two different experimental designs of estimation are also investigated. We found that estimation using the total genetic variation in the inbred generation is generally more efficient than employing the genetic variation among the mean of inbred families, and that higher degree of inbreeding employed in experiments yields higher power for estimation. The simulation results of the magnitude and direction of estimation bias under variable or epistatic mutation effects may provide a basis for accurate inferences of deleterious mutations. Simulations accounting for environmental variance of fitness suggest that, under full-sib mating, our extension can achieve reasonably well an estimation with sample sizes of only ∼2000-3000.

scholarly journals A review of deep learning applications for genomic selection

BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Osval Antonio Montesinos-López ◽  
Abelardo Montesinos-López ◽  
Paulino Pérez-Rodríguez ◽  
José Alberto Barrón-López ◽  
Johannes W. R. Martini ◽  
...  
Keyword(s):  
Deep Learning ◽  
Plant Breeding ◽  
Genomic Selection ◽  
Genomic Prediction ◽  
Mixed Model ◽  
Prediction Models ◽  
Genetic Effect ◽  
Training Data ◽  
Additive Genetic Effect ◽  
Main Body

Abstract Background Several conventional genomic Bayesian (or no Bayesian) prediction methods have been proposed including the standard additive genetic effect model for which the variance components are estimated with mixed model equations. In recent years, deep learning (DL) methods have been considered in the context of genomic prediction. The DL methods are nonparametric models providing flexibility to adapt to complicated associations between data and output with the ability to adapt to very complex patterns. Main body We review the applications of deep learning (DL) methods in genomic selection (GS) to obtain a meta-picture of GS performance and highlight how these tools can help solve challenging plant breeding problems. We also provide general guidance for the effective use of DL methods including the fundamentals of DL and the requirements for its appropriate use. We discuss the pros and cons of this technique compared to traditional genomic prediction approaches as well as the current trends in DL applications. Conclusions The main requirement for using DL is the quality and sufficiently large training data. Although, based on current literature GS in plant and animal breeding we did not find clear superiority of DL in terms of prediction power compared to conventional genome based prediction models. Nevertheless, there are clear evidences that DL algorithms capture nonlinear patterns more efficiently than conventional genome based. Deep learning algorithms are able to integrate data from different sources as is usually needed in GS assisted breeding and it shows the ability for improving prediction accuracy for large plant breeding data. It is important to apply DL to large training-testing data sets.

scholarly journals Evaluation of Genetic behavior of Sunflower (Helianthus annuus L.) As Effect by Planting Dates

2019 ◽  
Vol 9 (1) ◽  
pp. 150-155
Author(s):  
Abdullah F. Serheed ◽  
Haider T. Hussein
Keyword(s):  
Helianthus Annuus ◽  
Correlation Coefficient ◽  
Environmental Conditions ◽  
Genetic Variance ◽  
Planting Dates ◽  
Environmental Variance ◽  
Growing Seasons ◽  
The Mean ◽  
Phenotypic Variations ◽  
Coefficient Stability

Afield experiment was carried out during the spring and autumn seasons of 2016 and 2017 in AL- Musaib city / 40 km north of Babylon Provence. Two hybrids of sunflower( shamus, French hybrid (Euroflor) were used to evaluate the performance of the two cultivars at both growing seasons as well as knowledge of genetic behavior by studying the genetic and phenotypic variations, heritability percent, genetic and phenotypic coefficient, stability and persistence of the two cultivars .The results showed significant differences of the studied traits, as the genetic genotype (Shamus) most of the characteristics, especially in yield for two seasons.The genetic variance was higher than the environmental variance for the two seasons indicating that the two cultivars followed the same behavior. The heritability percent the dominant sense was high for most of the traits. The genetic and phenotypic variations between the mean and the high were different for the two seasons, the correlation coefficient was significant, for both cultivars, indicating the appropriateness of the two genotypes for the country's environmental conditions.

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