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.

scholarly journals A note on genetic parameters and accuracy of estimated breeding values in honey bees

2019 ◽  
Vol 51 (1) ◽  
Author(s):  
Evert W. Brascamp ◽  
Piter Bijma
Keyword(s):  
Honey Bees ◽  
Variance Components ◽  
Genetic Parameters ◽  
Additive Genetic Variance ◽  
Breeding Value ◽  
Phenotypic Variance ◽  
Breeding Values ◽  
Estimated Breeding Values ◽  
Additive Genetic Relationship ◽  
The Relationship

Abstract Background In honey bees, observations are usually made on colonies. The phenotype of a colony is affected by the average breeding value for the worker effect of the thousands of workers in the colony (the worker group) and by the breeding value for the queen effect of the queen of the colony. Because the worker group consists of multiple individuals, interpretation of the variance components and heritabilities of phenotypes observed on the colony and of the accuracy of selection is not straightforward. The additive genetic variance among worker groups depends on the additive genetic relationship between the drone-producing queens (DPQ) that produce the drones that mate with the queen. Results Here, we clarify how the relatedness between DPQ affects phenotypic variance, heritability and accuracy of the estimated breeding values of replacement queens. Second, we use simulation to investigate the effect of assumptions about the relatedness between DPQ in the base population on estimates of genetic parameters. Relatedness between DPQ in the base generation may differ considerably between populations because of their history. Conclusions Our results show that estimates of (co)variance components and derived genetic parameters were seriously biased (25% too high or too low) when assumptions on the relationship between DPQ in the statistical analysis did not agree with reality.

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.

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.

scholarly journals GIBBSTHUR: Software for Estimating Variance Components and Predicting Breeding Values for Ranking Traits Based on a Thurstonian Model

Animals ◽  
2020 ◽  
Vol 10 (6) ◽  
pp. 1001
Author(s):  
Luis Varona ◽  
Andrés Legarra
Keyword(s):  
Markov Chain ◽  
Variance Components ◽  
Data Augmentation ◽  
Genetic Effect ◽  
Additive Genetic Effect ◽  
Public Repository ◽  
Breeding Values ◽  
Average Performance ◽  
Covariance Components ◽  
Variance And Covariance Components

(1) Background: Ranking traits are used commonly for breeding purposes in several equine populations; however, implementation is complex, because the position of a horse in a competition event is discontinuous and is influenced by the performance of its competitors. One approach to overcoming these limitations is to assume an underlying Gaussian liability that represents a horse’s performance and dictates the observed classification in a competition event. That approach can be implemented using Montecarlo Markov Chain (McMC) techniques with a procedure known as the Thurstonian model. (2) Methods: We have developed software (GIBBSTHUR) that analyses ranking traits along with other continuous or threshold traits. The software implements a Gibbs Sampler scheme with a data-augmentation step for the liability of the ranking traits and provides estimates of the variance and covariance components and predictions of the breeding values and the average performance of the competitors in competition events. (3) Results: The results of a simple example are presented, in which it is shown that the procedure can recover the simulated variance and covariance components. In addition, the correlation between the simulated and predicted breeding values and between the estimates of the event effects and the average additive genetic effect of the competitors demonstrates the ability of the software to produce useful predictions for breeding purposes. (4) Conclusions: the GIBBSTHUR software provides a useful tool for the breeding evaluation of ranking traits in horses and is freely available in a public repository (https://github.com/lvaronaunizar/Gibbsthur).

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.

The formation of simple linear models for predicting the breeding values of animals

2020 ◽  
Author(s):  
А.Е. КАЛАШНИКОВ ◽  
J. PŘIBYL ◽  
А.А. КОЧЕТКОВ ◽  
Т.М. АХМЕТОВ ◽  
Р.Р. ШАЙДУЛЛИН ◽  
...  
Keyword(s):  
Information System ◽  
Basic Principle ◽  
Linear Models ◽  
Residual Error ◽  
International Standards ◽  
Rating System ◽  
Breeding Value ◽  
Matrix Equations ◽  
Mathematical Apparatus ◽  
Breeding Values

Представлен основной принцип оценки племенной ценности при помощи линейных моделей (матричных уравнений) согласно общепринятым мировым стандартам (BLUP). Дан алгоритм и описан математический аппарат для реализации системы оценки и выбора лучших племенных животных для дальнейшей селекции. Представлены основные принципы построения работоспособной системы оценки племенной ценности в современном формате в соответствии с требованиями ISAG/ICAR (Interbull). Описана необходимость формирования единой информационной системы баз данных (ИС БД) для хранения информации о первичном учете, идентификации и данных о продуктивности животных. Представлено описание принципа проверки достоверности линейных моделей по величине остаточной ошибки и соответствия модели наследования по Менделю. The basic principle of estimating breeding value using linear models (matrix equations) according to generally accepted international standards (BLUP) is performed. An algorithm is showed and a mathematical apparatus is described for implementing a system for estimating and selecting the best breeding animals for further reproduction. The main principles of building a workable breeding values rating system in a modern format in concordance with the claims of ISAG / ICAR (Interbull) are given down. The necessity of forming a unified database information system (IS DB) for keeping information about primary registration, identification and data on animal productivity is declared. The description of the principle of checking up the reliability of linear models by the value of the residual error and the conformity of the Mendel model of inheritance is presented.

scholarly journals Estimation of inbreeding effect on conception in Czech Holstein

2019 ◽  
Vol 64 (No. 7) ◽  
pp. 309-316
Author(s):  
Michala Hofmannová ◽  
Josef Přibyl ◽  
Emil Krupa ◽  
Petr Pešek
Keyword(s):  
Genetic Parameters ◽  
Negative Impact ◽  
Additive Genetic Variance ◽  
Random Effect ◽  
Inbreeding Coefficient ◽  
Conception Rate ◽  
Breeding Value ◽  
Breeding Values ◽  
Binary Model ◽  
Animal Model Blup

The influence of calculated inbreeding coefficients on the conception of heifers and cows was analysed by a two-trait binary model for conceived/not conceived after each insemination on 677 234 Czech Holstein cows and heifers with 3 248 299 insemination records as the covariable in a statistical model. The data between 1996 and 2014 were analysed. Various effects and their statistical influence on traits were tested by a generalized linear model. Consequently, genetic parameters were estimated by the Gibbs sampling method and used in predicting breeding values using the best linear unbiased prediction by animal model (BLUP-AM). The mean for the conception rate of cows averaged over lactations was 33.7% and for heifers it reached 53.8%. Average inbreeding coefficient increased from 1% in 1996 to almost 5% by 2013 and was in the range of 0–45%. The rate of inbreeding per generation was 0.20%. Although the effect of inbreeding was statistically significant (P = 0.05) for both traits, the proportion of variability explained by the models was relatively low. Estimated genetic parameters were low for both traits. Coefficient of heritability was 2.00% and 1.30% for cows and heifers, respectively, whereas coefficients of repeatability reached 6.09% and 7.08% for cows and heifers, respectively. The random effect of the permanent environment (PE) reached higher values than the additive genetic variance (G) and explained 5.67% and 4.09% of variability for cows and heifers, respectively. A negative impact of inbreeding on heifer and cow conception was observed, whereby every 10% increase in inbreeding coefficient resulted in a conception decline by 2.23%. Calculated Spearman’s rank correlation coefficient between estimated breeding values considering and not considering the effect of inbreeding was close to one. Presented results indicate that inbreeding has a negligible influence on the breeding values of conception. The results also indicate that it is not necessary to include inbreeding coefficient in the routine breeding value evaluation of conception rate of heifers and cows. On the other hand, monitoring of inbreeding is necessary to avoid an increase of its rate.

scholarly journals Narrow-sense heritability estimation of complex traits using identity-by-descent information

2017 ◽  
Author(s):  
Luke M. Evans ◽  
Rasool Tahmasbi ◽  
Matthew Jones ◽  
Scott I. Vrieze ◽  
Gonçalo R. Abecasis ◽  
...  
Keyword(s):  
Complex Traits ◽  
Genetic Variance ◽  
Additive Genetic Variance ◽  
Fundamental Parameter ◽  
Last Common Ancestor ◽  
Narrow Sense ◽  
Narrow Sense Heritability ◽  
Heritability Estimation ◽  
Causal Variants ◽  
Heritability Estimates

ABSTRACTHeritability is a fundamental parameter in genetics. Traditional estimates based on family or twin studies can be biased due to shared environmental or non-additive genetic variance. Alternatively, those based on genotyped or imputed variants typically underestimate narrow-sense heritability contributed by rare or otherwise poorly-tagged causal variants. Identical-by-descent (IBD) segments of the genome share all variants between pairs of chromosomes except new mutations that have arisen since the last common ancestor. Therefore, relating phenotypic similarity to degree of IBD sharing among classically unrelated individuals is an appealing approach to estimating the near full additive genetic variance while avoiding biases that can occur when modeling close relatives. We applied an IBD-based approach (GREML-IBD) to estimate heritability in unrelated individuals using phenotypic simulation with thousands of whole genome sequences across a range of stratification, polygenicity levels, and the minor allele frequencies of causal variants (CVs). IBD-based heritability estimates were unbiased when using unrelated individuals, even for traits with extremely rare CVs, but stratification led to strong biases in IBD-based heritability estimates with poor precision. We used data on two traits in ~120,000 people from the UK Biobank to demonstrate that, depending on the trait and possible confounding environmental effects, GREML-IBD can be applied successfully to very large genetic datasets to infer the contribution of very rare variants lost using other methods. However, we observed apparent biases in this real data that were not predicted from our simulation, suggesting that more work may be required to understand factors that influence IBD-based estimates.

Asymptotic rates of response from index selection

1989 ◽  
Vol 49 (2) ◽  
pp. 217-227 ◽  
Author(s):  
Naomi R. Wray ◽  
W. G. Hill
Keyword(s):  
Genetic Variance ◽  
Additive Genetic Variance ◽  
Index Selection ◽  
Selection Intensity ◽  
Response To Selection ◽  
Multiple Trait ◽  
Breeding Values ◽  
Breeding Programmes ◽  
Breeding Schemes ◽  
Rate Of Response

ABSTRACTThe reduction in additive genetic variance due to selection is investigated when index selection using family records is practised. A population of infinite size with no accumulation of inbreeding, an infinitesimal model and discrete generations are assumed. After several generations of selection, the additive genetic variance and the rate of response to selection reach an asymptote. A prediction of the asymptotic rate of response is considered to be more appropriate for comparing response from alternative breeding programmes and for comparing predicted and realized response than the response following the first generation of selection that is classically used. Algorithms to calculate asymptotic response rate are presented for selection based on indices which include some or all of the records of an individual, its full- and half-sibs and its parental estimated breeding values. An index using all this information is used to predict response when selection is based on breeding values estimated by using a Best Linear Unbiased Prediction (BLUP) animal model, and predictions agree well with simulation results. The predictions are extended to multiple trait selection.Asymptotic responses are compared with one-generation responses for a variety of alternative breeding schemes differing in population structure, selection intensity and heritability of the trait. Asymptotic responses can be up to one-quarter less than one-generation responses, the difference increasing with selection intensity and accuracy of the index. Between family variance is reduced considerably by selection, perhaps to less than half its original value, so selection indices which do not account for this tend to place too much emphasis on family information. Asymptotic rates of response to selection, using indices including family information for traits not measurable on the individuals available for selection, such as sex limited or post-slaughter traits, are found to be as much as two-fifths less than their expected one-generation responses. Despite this, the ranking of the breeding schemes is not greatly altered when compared by one-generation rather than asymptotic responses, so the one-generation prediction is usually likely to be adequate for determining optimum breeding structure.

Competition effects in breeding value prediction of forest trees

2020 ◽  
Author(s):  
Gunnar Jansson ◽  
Richard Kerr ◽  
Gregory Dutkowski ◽  
Johan Kroon
Keyword(s):  
Indirect Effects ◽  
Genetic Parameter ◽  
Genetic Effect ◽  
Tree Breeding ◽  
Breeding Value ◽  
Additive Genetic Effect ◽  
Analytic Structure ◽  
Additive Variance ◽  
Value Prediction ◽  
Genetic Level

Competition is a concern to tree breeding because of its potential to reduce the genetic gain. Competition, if not accounted for in the analytical model, can potentially introduce a source of bias in genetic parameter estimation and breeding value prediction. This study modelled competition between trees in 20 Swedish progeny trials of Norway spruce, Scots pine and lodgepole pine. The competition model assumed a tree has a direct additive genetic effect, which affects the tree’s own phenotype, and an indirect additive effect, which affects the phenotypes of its neighbours. Genetic parameters were estimated via a factor analytic structure (FA) where separate indirect effects were considered for each neighbour, or via a combined indirect effect approach (CIE). We analysed diameter, as it is the trait that can be expected to be affected most by competition. Competition at the genetic level was detected in 17 of the 20 trials analysed. In most cases the ratio of indirect to direct additive variance was less than five percent and no major changes in ranking resulted. At this stage there is little incentive to incorporate indirect effects into program-wide genetic evaluation models. The added complexity is not commensurate with the benefit that would be gained.

Sign in / Sign up

Export Citation Format

Share Document