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.

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.

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 The limits of normal approximation for adult height

2021 ◽  
Author(s):  
Sergei A. Slavskii ◽  
Ivan A. Kuznetsov ◽  
Tatiana I. Shashkova ◽  
Georgii A. Bazykin ◽  
Tatiana I. Axenovich ◽  
...  
Keyword(s):  
Complex Traits ◽  
Adult Height ◽  
Normal Approximation ◽  
Classical Model ◽  
Model Complexity ◽  
Test Case ◽  
Genetic Studies ◽  
Quantitative Genetic ◽  
Profound Influence ◽  
Log Normal

AbstractAdult height inspired the first biometrical and quantitative genetic studies and is a test-case trait for understanding heritability. The studies of height led to formulation of the classical polygenic model, that has a profound influence on the way we view and analyse complex traits. An essential part of the classical model is an assumption of additivity of effects and normality of the distribution of the residuals. However, it may be expected that the normal approximation will become insufficient in bigger studies. Here, we demonstrate that when the height of hundreds of thousands of individuals is analysed, the model complexity needs to be increased to include non-additive interactions between sex, environment and genes. Alternatively, the use of log-normal approximation allowed us to still use the additive effects model. These findings are important for future genetic and methodologic studies that make use of adult height as an exemplar trait.

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 Genomic Breeding Values for Claw Diseases/Disorders in Czech Holstein Cows

2019 ◽  
Vol 67 (5) ◽  
pp. 1245-1251
Author(s):  
Ludmila Zavadilová ◽  
Eva Kašná ◽  
Zuzana Krupová
Keyword(s):  
Infectious Diseases ◽  
Fixed Effects ◽  
Genetic Correlations ◽  
Genetic Effect ◽  
Holstein Cows ◽  
Additive Genetic Effect ◽  
Genomic Information ◽  
White Line ◽  
Genomic Breeding ◽  
Breeding Values

Genomic breeding values (GEBV) were predicted for claw diseases/disorders in Holstein cows. The data sets included 6,498, 6,641 and 16,208 cows for the three groups of analysed disorders. The analysed traits were infectious diseases (ID), including digital and interdigital dermatitis and interdigital phlegmon, and non-infectious diseases (NID), including ulcers, white line disease, horn fissures, and double sole and overall claw disease (OCD), comprising all recorded disorders. Claw diseases/disorders were defined as 0/1 occurrence per lactation. Linear animal models were employed for prediction of conventional breeding values (BV) and genomic breeding values (GEBV), including the random additive genetic effect of animal and the permanent environmental effect of cow and fixed effects of parity, herd, year and month of calving. Both high and intermediate weights (80% and 50%, respectively) of genomic information were employed for GEBV50 and GEBV80 prediction. The estimated heritability for ID was 3.47%, whereas that for NID 4.61% and for OCD was 2.29%. Approximate genetic correlations among claw diseases/disorders traits ranged from 19% (ID x NID) to 81% (NID x OCD). The correlations between predicted BV and GEBV50 (84–99%) were higher than those between BV and GEBV80 (70–98%). Reliability of breeding values was low for each claw disease/disorder (on average, 3.7 to 14.8%) and increased with the weight of genomic information employed.

scholarly journals Genetic-Quantitative Uni- and Bi-Trait Analysis for Growth Traits in the Colombian Creole Breed Blanco Orejinegro (BON)

2021 ◽  
Author(s):  
Marisol Londoño-Gil ◽  
Juan Carlos Rincón Flórez ◽  
Albeiro López-Herrera ◽  
Luis Gabriel Gonzalez-Herrera
Keyword(s):  
Growth Traits ◽  
Genetic Correlations ◽  
Genetic Effect ◽  
Cattle Breed ◽  
Live Weight ◽  
Daily Weight Gain ◽  
Additive Genetic Effect ◽  
Selection Processes ◽  
Heritability Estimates ◽  
Maternal Heritability

Abstract The Blanco Orejinegro (BON) is a Colombian creole cattle breed that is not genetically well characterized for growth traits. The aim of this work was to estimate genetic parameters for birth weight (BW), weaning weight (WW), yearling weight (YW), daily weight gain between birth and weaning (DWG), time to reach 120 kg of live weight (T120), and time to reach 60% of adult weight (T60%), and establish the selection criteria for growth traits in the BON population of Colombia. Genealogical and phenotypic information for BW, WW, YW, DWG, T120, and T60% traits of BON animals from 14 Colombian herds were used. These traits were analyzed with the AIREML method in a uni- and bi-trait animal model including the maternal effect for BW, WW, DWG, and T120. The direct heritability estimates values were 0.22 ± 0.059 (BW), 0.20 ± 0.057 (WW), 0.20 ± 0.153 (YW), 0.17 ± 0.07 (DWG), 0.26 (T120), and 0.44 ± 0.03 (T60%). The maternal heritability estimates values were 0.14 ± 0.040 (BW), 0.15 ± 0.039 (WW), 0.25 ± 0.06 (DWG), and 0.16 (T120). The direct genetic correlations were high (>|0.60|) among all the traits, except between T60% with BW, WW, YW, and DWG (ranged from -0.02 to -0.51), all in a favorable direction. The results showed that there is genetic variation in the growth traits associated with the additive genetic effect and they might respond to selection processes. Furthermore, genetic gains would improve through selection, especially for YW and T60% when WW is used as criterion.

Efecto de dos estrategias de agrupación de padres fantasmas en la evaluación genética de rasgos de crecimiento en el ganado Braunvieh mexicano

2021 ◽  
Vol 12 (3) ◽  
pp. 878-892
Author(s):  
Luis Antonio Saavedra-Jiménez ◽  
Rodolfo Ramírez-Valverde ◽  
Rafael Núñez-Domínguez ◽  
Agustín Ruíz-Flores ◽  
José Guadalupe García-Muñiz ◽  
...  
Keyword(s):  
Fixed Effects ◽  
Growth Traits ◽  
Genetic Effect ◽  
Additive Genetic Effect ◽  
Genetic Trend ◽  
Phenotypic Data ◽  
Genetic Groups ◽  
Grouping Strategies ◽  
Parent Groups ◽  
Different Sources

The study aimed to compare two grouping strategies for unknown parents or phantom parent groups (PPG) on the genetic evaluation of growth traits for Mexican Braunvieh cattle. Phenotypic data included birth (BW), weaning (WW) and yearling (YW) weights. Pedigree included 57,341 animals. The first strategy involved 12 PPG (G12) based on the birth year of the unknown parent’s progeny and the sex of the unknown parent, while the second involved 24 PPG (G24) based on the birth year of the unknown parent’s progeny and 4-selection pathways. The animal models included fixed effects and the random direct additive genetic effect; WW also included random maternal genetic and maternal permanent environmental effects. Product-moment correlations between EBV from G0 (no PPG) and G12 were 0.96, 0.77 and 0.69 for BW, WW and YW, respectively, and between EBV from G0 and G24 were 0.91, 0.54, and 0.53, respectively. Corresponding rank correlations between G0 and G12 were 0.94, 0.77, and 0.72, and between G0 and G24 were 0.89, 0.61, and 0.60. Genetic trends showed a base deviation from the genetic trend of G0, except for BW of G12. The results did not support the use of the two grouping strategies on the studied population and traits, and further research is required. Introducing PPG to the model, enough phenotype contribution from descendants to PPG, and avoiding collinearity between PPG and fixed effects are important. Genetic groups should reflect changes in the genetic structure of the population to the unknown parents, including different sources of genetic materials, and changes made by selection over time.

Systemic and Genetic Risk Factors in Diabetic Retinopathy and Diabetic Macular Edema

2018 ◽  
pp. 102-107
Keyword(s):  
Risk Factors ◽  
Diabetic Retinopathy ◽  
Macular Edema ◽  
Diabetic Macular Edema ◽  
Systemic Risk ◽  
Genetic Effect ◽  
Current Treatment ◽  
Genetic Studies ◽  
Patient Subgroups ◽  
Systemic Risk Factors

Diabetic retinopathy (DR) is an increasingly common health problem in our country as it is all over the world. DR is a leading cause of loss of vision patients at a productive age. Current treatment of diabetic macular edema (DME) is distressing, expensive, and not suitable for some patient subgroups. For this reason, the development and progression of DR and DME are affected by many systemic risk factors. It is important to increase the understanding of these responsible risk factors and develop preventive strategies. However, the presence of systemic risk factors is inadequate to predict the progression of the disease on an individual basis. It indicates the presence of a genetic effect. In this review, we have summarized the known systemic risk factors as well as the genetic basis of the disease under the light of genetic studies.

scholarly journals Random regression models to estimate genetic parameters for milk production of Guzerat cows using orthogonal Legendre polynomials

2014 ◽  
Vol 49 (5) ◽  
pp. 372-383 ◽  
Author(s):  
Maria Gabriela Campolina Diniz Peixoto ◽  
Daniel Jordan de Abreu Santos ◽  
Rusbel Raul Aspilcueta Borquis ◽  
Frank Ângelo Tomita Bruneli ◽  
João Cláudio do Carmo Panetto ◽  
...  
Keyword(s):  
Milk Production ◽  
Legendre Polynomial ◽  
Legendre Polynomials ◽  
Regression Models ◽  
Environmental Effect ◽  
Genetic Parameters ◽  
Genetic Effect ◽  
Random Regression ◽  
Additive Genetic Effect ◽  
Fifth Order

The objective of this work was to compare random regression models for the estimation of genetic parameters for Guzerat milk production, using orthogonal Legendre polynomials. Records (20,524) of test-day milk yield (TDMY) from 2,816 first-lactation Guzerat cows were used. TDMY grouped into 10-monthly classes were analyzed for additive genetic effect and for environmental and residual permanent effects (random effects), whereas the contemporary group, calving age (linear and quadratic effects) and mean lactation curve were analized as fixed effects. Trajectories for the additive genetic and permanent environmental effects were modeled by means of a covariance function employing orthogonal Legendre polynomials ranging from the second to the fifth order. Residual variances were considered in one, four, six, or ten variance classes. The best model had six residual variance classes. The heritability estimates for the TDMY records varied from 0.19 to 0.32. The random regression model that used a second-order Legendre polynomial for the additive genetic effect, and a fifth-order polynomial for the permanent environmental effect is adequate for comparison by the main employed criteria. The model with a second-order Legendre polynomial for the additive genetic effect, and that with a fourth-order for the permanent environmental effect could also be employed in these analyses.

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