Marker-Based Estimation of Heritability in Immortal Populations

  title={Marker-Based Estimation of Heritability in Immortal Populations},
  author={Willem Kruijer and Martin P. Boer and Marcos Malosetti and P{\'a}draic J. Flood and Bas Engel and Rik Kooke and Joost J. B. Keurentjes and Fred A. van Eeuwijk},
  pages={379 - 398}
Heritability is a central parameter in quantitative genetics, from both an evolutionary and a breeding perspective. For plant traits heritability is traditionally estimated by comparing within- and between-genotype variability. This approach estimates broad-sense heritability and does not account for different genetic relatedness. With the availability of high-density markers there is growing interest in marker-based estimates of narrow-sense heritability, using mixed models in which genetic… 
Confidence intervals for heritability via Haseman-Elston regression
  • T. Sofer
  • Mathematics
    Statistical applications in genetics and molecular biology
  • 2017
This paper proposes to estimate variance components via a Haseman-Elston regression, find the asymptotic distribution of the variance components and proportions of variance, and use them to construct confidence intervals (CIs) and demonstrates the approach on data from the Hispanic Community Health Study/Study of Latinos.
Genome-Wide Association Mapping and Genomic Prediction Elucidate the Genetic Architecture of Morphological Traits in Arabidopsis1
Genomic prediction models revealed different genetic architectures for the morphological traits in Arabidopsis and good indications that natural allelic variation in many of these genes contributes to phenotypic variation were obtained.
Reconstruction of networks with direct and indirect genetic effects
This work proposes an alternative strategy, where genetic effects are formally included in the graph, which has important advantages: genetic effects can be directly incorporated in causal inference, implemented via the PCgen algorithm, which can analyze many more traits; and it is shown that reconstruction is much more accurate if individual plant or plot data are used, instead of genotypic means.
Integrating genomics and multivariate evolutionary quantitative genetics: A case study of multivariate constraints on sexual selection in Drosophila serrata
This work integrates multivariate GWAS with G and β estimation in a well-studied system of multivariate constraint; sexual selection on male cuticular hydrocarbons (CHCs) in Drosophila serrata and discusses potential factors leading to varied results including multivariate stabilising selection and mutational bias.
Fast and accurate construction of confidence intervals for heritability
It is shown that often the probability that the genetic component is estimated as zero is high even when the true heritability is bounded away from zero, emphasizing the need for accurate confidence intervals.
An Effective Strategy for Trait Combinations in Multiple-Trait Genomic Selection
Cross-validation experimental results indicate that single trait predictions could be used as reference for trait combinations in multi-trait genomic selection.
Lessons from a GWAS study of a wheat pre-breeding program: pyramiding resistance alleles to Fusarium crown rot
It is demonstrated that QTL detection using breeding populations under selection for the target trait can identify QTL controlling the target traits and that the frequency of the favourable alleles was increased as a response to selection, thereby validating the QTL detected.
Genome-wide association analyses of 54 traits identified multiple loci for the determination of floret fertility in wheat.
A genetic network underlying floret fertility and related traits and several candidate genes involved in carbohydrate metabolism, phytohormones or floral development colocalized with such QTLs, thereby providing potential targets for selection are proposed.
Misspecification in Mixed-Model-Based Association Analysis
This work shows that the presence of any nonadditive genetic effect increases only the residual variance and does not affect estimates of additive genetic variance for panels of unrelated individuals.


  • K. Ritland
  • Biology
    Evolution; international journal of organic evolution
  • 1996
This marker‐based method makes possible studies with long‐lived organisms or with organisms difficult to culture, and opens the possibility that quantitative trait expression in natural environments can be analyzed in an unmanipulative way.
Assumption-Free Estimation of Heritability from Genome-Wide Identity-by-Descent Sharing between Full Siblings
The application shows that it is feasible to estimate genetic variance solely from within-family segregation and provides an independent validation of previously untestable assumptions, and will allow partitioning of genetic variation into additive and non-additive components.
Estimating Effects and Making Predictions from Genome-Wide Marker Data
In genome-wide association studies (GWAS), hundreds of thousands of genetic markers (SNPs) are tested for association with a trait or phenotype. Reported effects tend to be larger in magnitude than
Improved heritability estimation from genome-wide SNPs.
Parametric and Nonparametric Statistical Methods for Genomic Selection of Traits with Additive and Epistatic Genetic Architectures
Parametric methods were unable to predict phenotypic values when the underlying genetic architecture was based entirely on epistasis, and were slightly better than nonparametric methods for additive genetic architectures.
Common SNPs explain a large proportion of the heritability for human height
Evidence is provided that the remaining heritability is due to incomplete linkage disequilibrium between causal variants and genotyped SNPs, exacerbated by causal variants having lower minor allele frequency than the SNPs explored to date.
Estimation of heritability and prediction of selection response in plant populations
A unified presentation, a synthesis, and an evaluation of the methods employed in the estimation of heritability in plants are presented, discussing the use of both collateral and lineal relatives and the biases present in the estimators.
Ridge Regression and Other Kernels for Genomic Selection with R Package rrBLUP
A new software package for R called rrBLUP, which is a fast maximum‐likelihood algorithm for mixed models with a single variance component besides the residual error, which allows for efficient prediction with unreplicated training data.