Joint modeling of additive and non-additive genetic line effects in single field trials

Oakey, Helena; Verbyla, A. P.; Pitchford, W. S.; Cullis, Brian R; Kuchel, Haydn

doi:10.1007/s00122-006-0333-z

Cited by 186 publications

(217 citation statements)

References 27 publications

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“…Mixed-model analysis can then be performed either on the individual plant (or plot) data or on genotypic means. In the literature on multi-environment trials (Smith et al 2001(Smith et al , 2005Oakey et al 2006;Piepho and Williams 2006;Piepho et al , 2012Boer et al 2007;Verbyla et al 2007;Stich et al 2008;Möhring and Piepho 2009;Van Eeuwijk et al 2010;Welham et al 2010;Malosetti et al 2013) these approaches are referred to as respectively one-stage and two-stage. These works consider mostly populations for which a pedigree is available, typically experimental populations.…”

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confidence: 99%

Marker-Based Estimation of Heritability in Immortal Populations

et al. 2014

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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...

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confidence: 99%

Marker-Based Estimation of Heritability in Immortal Populations

et al. 2014

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“…The data were coded and models were specified to partition the variability due to entries into a fixed difference between the mean of all inbred checks and the mean of all Tusón families, fixed effects of the check lines, fixed effects of the generations for the generation model (1), and random effects of the families for the family model (2) (Oakey et al, 2006;Piepho et al, 2006).…”

Section: Discussionmentioning

confidence: 99%

Hallauer’s Tusón: a decade of selection for tropical-to-temperate phenological adaptation in maize

et al. 2014

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Crop species exhibit an astounding capacity for environmental adaptation, but genetic bottlenecks resulting from intense selection for adaptation and productivity can lead to a genetically vulnerable crop. Improving the genetic resiliency of temperate maize depends upon the use of tropical germplasm, which harbors a rich source of natural allelic diversity. Here, the adaptation process was studied in a tropical maize population subjected to 10 recurrent generations of directional selection for early flowering in a single temperate environment in Iowa, USA. We evaluated the response to this selection across a geographical range spanning from 43.05°(WI) to 18.00°(PR) latitude. The capacity for an all-tropical maize population to become adapted to a temperate environment was revealed in a marked fashion: on average, families from generation 10 flowered 20 days earlier than families in generation 0, with a nine-day separation between the latest generation 10 family and the earliest generation 0 family. Results suggest that adaptation was primarily due to selection on genetic main effects tailored to temperature-dependent plasticity in flowering time. Genotype-by-environment interactions represented a relatively small component of the phenotypic variation in flowering time, but were sufficient to produce a signature of localized adaptation that radiated latitudinally, in partial association with daylength and temperature, from the original location of selection. Furthermore, the original population exhibited a maladaptive syndrome including excessive ear and plant heights along with later flowering; this was reduced in frequency by selection for flowering time.

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“…However, when the e j vector is modelled using spatial effects, there is no longer a single estimate for the trial residual variance, r 2 . To overcome this Cullis et al (2006) and Oakey et al (2006) formulated the generalised heritability as:…”

Section: Phenotypic Analysismentioning

confidence: 99%

“…The generalised heritability can be interpreted as the proportion of total phenotypic variance explained by the genotypic component and can be used to calculate the expected genetic gain Piepho and Möhring 2007). The above formulation can be extended to calculate the heritability for an across-site analysis and to include the additive relationship matrix to calculate the narrow sense heritability (Oakey et al 2006) as breeders would generally select on performance across sites (Hanson 1963). The trial mean and generalised heritability on a single trial basis for each trait were calculated from the best spatial models where the genotypes were fitted as random effects (Table 1).…”

Section: Phenotypic Analysismentioning

confidence: 99%

Multi-environment QTL mixed models for drought stress adaptation in wheat

et al. 2008

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Many quantitative trait loci (QTL) detection methods ignore QTL-by-environment interaction (QEI) and are limited in accommodation of error and environment-specific variance. This paper outlines a mixed model approach using a recombinant inbred spring wheat population grown in six drought stress trials. Genotype estimates for yield, anthesis date and height were calculated using the best design and spatial effects model for each trial. Parsimonious factor analytic models best captured the variance-covariance structure, including genetic correlations, among environments. The 1RS.1BL rye chromosome translocation (from one parent) which decreased progeny yield by 13.8 g m -2 was explicitly included in the QTL model. Simple interval mapping (SIM) was used in a genome-wide scan for significant QTL, where QTL effects were fitted as fixed environmentspecific effects. All significant environment-specific QTL were subsequently included in a multi-QTL model and evaluated for main and QEI effects with non-significant QEI effects being dropped. QTL effects (either consistent or environment-specific) included eight yield, four anthesis, and six height QTL. One yield QTL co-located (or was linked) to an anthesis QTL, while another co-located with a height QTL. In the final multi-QTL model, only one QTL for yield (6 g m -2 ) was consistent across environments (no QEI), while the remaining QTL had significant QEI effects (average size per environment of 5.1 g m -2 ). Compared to single trial analyses, the described framework allowed explicit modelling and detection of QEI effects and incorporation of additional classification information about genotypes.

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Joint modeling of additive and non-additive genetic line effects in single field trials

Cited by 186 publications

References 27 publications

Marker-Based Estimation of Heritability in Immortal Populations

Marker-Based Estimation of Heritability in Immortal Populations

Hallauer’s Tusón: a decade of selection for tropical-to-temperate phenological adaptation in maize

Multi-environment QTL mixed models for drought stress adaptation in wheat

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