Increased accuracy of artificial selection by using the realized relationship matrix

Hayes, Ben J.; Visscher, Peter M.; Goddard, Michael E.

doi:10.1017/s0016672308009981

Cited by 574 publications

(636 citation statements)

References 25 publications

Supporting

Mentioning

596

Contrasting

Unclassified

Order By: Relevance

“…It has been noted (e.g., Hayes et al 2009) that using model (A2) with genetic relatedness matrix (1) is equivalent to assuming that the effects of the standardized marker scores are drawn independently from Gaussian distributions with variance s 2 A =p: Consequently, the model can account only for additive genetic effects, and nonadditive effects will get into the residual variance. This is whyĥ 2 is an estimate of narrow-sense heritability.…”

Section: Marker-based Estimation Of Heritabilitymentioning

confidence: 99%

Marker-Based Estimation of Heritability in Immortal Populations

et al. 2014

View full text Add to dashboard Cite

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 relatedness is estimated from genetic markers. Such estimates have received much attention in human genetics but are rarely reported for plant traits. A major obstacle is that current methodology and software assume a single phenotypic value per genotype, hence requiring genotypic means. An alternative that we propose here is to use mixed models at the individual plant or plot level. Using statistical arguments, simulations, and real data we investigate the feasibility of both approaches and how these affect genomic prediction with the best linear unbiased predictor and genome-wide association studies. Heritability estimates obtained from genotypic means had very large standard errors and were sometimes biologically unrealistic. Mixed models at the individual plant or plot level produced more realistic estimates, and for simulated traits standard errors were up to 13 times smaller. Genomic prediction was also improved by using these mixed models, with up to a 49% increase in accuracy. For genome-wide association studies on simulated traits, the use of individual plant data gave almost no increase in power. The new methodology is applicable to any complex trait where multiple replicates of individual genotypes can be scored. This includes important agronomic crops, as well as bacteria and fungi.KEYWORDS marker-based estimation of heritability; GWAS; genomic prediction; Arabidopsis thaliana; one-vs. two-stage approaches N ARROW-SENSE heritability is an important parameter in quantitative genetics, determining the response to selection and representing the proportion of phenotypic variance that is due to additive genetic effects (Jacquard 1983;Ritland 1996;Visscher et al. 2006Visscher et al. , 2008Holland et al. 2010;Sillanpaa 2011). This definition of heritability goes back to Fisher (1918) and Wright (1920) almost a century ago. In plant species for which replicates of the same genotype are available (inbred lines, doubled haploids, clones), a different form of heritability, broadsense heritability, is traditionally estimated by the intraclass correlation coefficient for genotypic effects, using estimates for within-and between-genotype variance. Broad-sense heritability is also referred to as repeatability and gives the proportion of phenotypic variance explained by heritable (additive) and nonheritable (dominance, epistasis) genetic variance.With the arrival of high-density genotyping there is growing interest in marker-based estimation of narrow-sense heritability (WTCCC 2007;Yang et al. 2010Yang et al. , 2011Vatti...

show abstract

Section: Marker-based Estimation Of Heritabilitymentioning

confidence: 99%

Marker-Based Estimation of Heritability in Immortal Populations

et al. 2014

View full text Add to dashboard Cite

show abstract

“…Model 3 is known as a linear mixed model with a random effect having the covariance matrix K causal = 1 M XX T (9). It is also known as a Gaussian process with a linear covariance (or kernel) function (10,11).…”

Section: Resultsmentioning

confidence: 99%

Linear mixed model for heritability estimation that explicitly addresses environmental variation

Heckerman

Gurdasani

Kadie

et al. 2016

Proc. Natl. Acad. Sci. U.S.A.

View full text Add to dashboard Cite

The linear mixed model (LMM) is now routinely used to estimate heritability. Unfortunately, as we demonstrate, LMM estimates of heritability can be inflated when using a standard model. To help reduce this inflation, we used a more general LMM with two random effects-one based on genomic variants and one based on easily measured spatial location as a proxy for environmental effects. We investigated this approach with simulated data and with data from a Uganda cohort of 4,778 individuals for 34 phenotypes including anthropometric indices, blood factors, glycemic control, blood pressure, lipid tests, and liver function tests. For the genomic random effect, we used identity-by-descent estimates from accurately phased genomewide data. For the environmental random effect, we constructed a covariance matrix based on a Gaussian radial basis function. Across the simulated and Ugandan data, narrow-sense heritability estimates were lower using the more general model. Thus, our approach addresses, in part, the issue of "missing heritability" in the sense that much of the heritability previously thought to be missing was fictional. Software is available at https://github.com/MicrosoftGenomics/ FaST-LMM. A n important causal question comes from the age-old debate about nature versus nurture. For any phenotype such as height or intelligence quotient, how much of the phenotype is inherited and how much is determined by environment? This question was made precise by Fisher (1) and Wright (2) almost a century ago: Given observations of a phenotype from a population of individuals, what is the fraction of variance of the phenotype that is caused by inherited factors relative to the total variance of the phenotype due to both inherited and environmental factors? This fraction, termed "heritability," has been the subject of intense study across various phenotypes and populations since it was defined. Note that, in contrast to how some interpret the informal question around the nature-versus-nurture debate, heritability is not an absolute quantity but rather a quantity relative to a given population. For example, a phenotype in a population where environmental factors have large variation will have a smaller heritability than in an otherwise similar population where environmental factors have a small variation.Over the years, many approaches have been developed to estimate heritability from data (3, 4). Here, we concentrate on an approach made possible by the recent ability to sequence genomes at a modest cost (5, 6). The approach uses a linear mixed model (LMM), a form of multivariate regression of the genomic and environmental factors on the phenotype, which we examine in detail in the next section.In the standard LMM approach, the effects of environmental factors on the phenotype are modeled as noise. Specifically, the phenotype of each individual is assumed to be the sum of two random effects, one based on genomic factors and one based on environmental factors, where the latter is assumed to be mutually independent across indivi...

show abstract

“…The second model, referred to as the "overdominant model," used a nonstandard SNP encoding, where both homozygous classes were represented as 0 and the heterozygous genotype as 1. The genetic similarity between individuals was estimated by computing the realized relationship kinship matrix using SNP encodings specific for each model (48). In addition to fitting two different models to our data, we chose to search for association of variants not only with estimated trait means, but we also estimated the discrepancy of an observed hybrid phenotype from its midparent performance [midparent heterosis (MPH)] for each hybrid-parent trait combination (7).…”

Section: Resultsmentioning

confidence: 99%

Genetic architecture of nonadditive inheritance inArabidopsis thalianahybrids

Seymour

Chae

Grimm

et al. 2016

Proc. Natl. Acad. Sci. U.S.A.

View full text Add to dashboard Cite

The ubiquity of nonparental hybrid phenotypes, such as hybrid vigor and hybrid inferiority, has interested biologists for over a century and is of considerable agricultural importance. Although examples of both phenomena have been subject to intense investigation, no general model for the molecular basis of nonadditive genetic variance has emerged, and prediction of hybrid phenotypes from parental information continues to be a challenge. Here we explore the genetics of hybrid phenotype in 435 Arabidopsis thaliana individuals derived from intercrosses of 30 parents in a half diallel mating scheme. We find that nonadditive genetic effects are a major component of genetic variation in this population and that the genetic basis of hybrid phenotype can be mapped using genome-wide association (GWA) techniques. Significant loci together can explain as much as 20% of phenotypic variation in the surveyed population and include examples that have both classical dominant and overdominant effects. One candidate region inherited dominantly in the half diallel contains the gene for the MADS-box transcription factor AGAMOUS-LIKE 50 (AGL50), which we show directly to alter flowering time in the predicted manner. Our study not only illustrates the promise of GWA approaches to dissect the genetic architecture underpinning hybrid performance but also demonstrates the contribution of classical dominance to genetic variance.T he often observed phenotypic superiority of progeny relative to their parents, or heterosis, is a universal phenomenon and of great importance to plant agriculture. The earliest description of heterosis (also known as hybrid vigor or superiority) dates to Darwin's studies of cross-fertilization in plants. He noticed that intercrossing distantly related individuals gave rise to larger, more vigorous progeny (1). Four decades later, George Shull coined the term "heterosis" (2) for this phenomenon, which he and Edward East had independently described for hybrids of inbred maize in 1908 (3, 4). Heterosis has long been of interest to evolutionary biologists as a potential explanation for the ubiquity of cross-fertilization in plants and animals, but it is also a central component of agricultural breeding programs. The combination of hybrid seed technology and inbred line improvement has driven an unprecedented improvement in maize yield over the past century (5). Despite the economic importance of heterosis and its intensive investigation in a wide spectrum of species, prediction of hybrid performance from parental information remains a major challenge (6).In the terms of quantitative genetics, hybrid vigor (and its opposite, inferiority) describes a deviation of progeny from the phenotypic mean of the parents. This means that heterosis cannot be explained by the addition of the effects of contributing alleles (7). Nonadditive genetic variance can result from a nonlinear phenotypic effect of alleles at one locus, as in the case of dominant/recessive allele pairs in classical genetics, or from epistatic interactions ...

show abstract

Increased accuracy of artificial selection by using the realized relationship matrix

Cited by 574 publications

References 25 publications

Marker-Based Estimation of Heritability in Immortal Populations

Marker-Based Estimation of Heritability in Immortal Populations

Linear mixed model for heritability estimation that explicitly addresses environmental variation

Genetic architecture of nonadditive inheritance inArabidopsis thalianahybrids

Contact Info

Product

Resources

About