Augmented p-rep designs

Williams, Emlyn; Piepho, Hans‐Peter; Whitaker, David

doi:10.1002/bimj.201000102

Cited by 125 publications

(107 citation statements)

References 19 publications

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“…The environments were Böhnshausen (latitude 51151 0 N, longitude 10157 0 E, 146 m above sea level (asl), sandy loam soil texture), Hadmersleben (latitude 51159 0 N, longitude 11118 0 E, 88 m asl, silt loam soil texture), Harzhof (latitude 54124 0 N, longitude 9151 0 E, 25 m asl, sandy loam soil texture) and Hohenheim (latitude 48142 0 N, longitude 9112 0 E, 390 m asl, sandy loam soil texture). The experimental designs were partially replicated a-designs (Williams et al, 2011), where all parents, checks as well as 29% of the hybrids were used in both replications (for details, see Longin et al, 2013). Sowing density ranged from 230 to 290 seeds per m 2 and plot size ranged from 7.5 to 9.7 m 2 .…”

Section: Plant Materials and Field Trialsmentioning

confidence: 99%

Bridging the gap between marker-assisted and genomic selection of heading time and plant height in hybrid wheat

et al. 2014

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Based on data from field trials with a large collection of 135 elite winter wheat inbred lines and 1604 F 1 hybrids derived from them, we compared the accuracy of prediction of marker-assisted selection and current genomic selection approaches for the model traits heading time and plant height in a cross-validation approach. For heading time, the high accuracy seen with marker-assisted selection severely dropped with genomic selection approaches RR-BLUP (ridge regression best linear unbiased prediction) and BayesCp, whereas for plant height, accuracy was low with marker-assisted selection as well as RR-BLUP and BayesCp. Differences in the linkage disequilibrium structure of the functional and single-nucleotide polymorphism markers relevant for the two traits were identified in a simulation study as a likely explanation for the different trends in accuracies of prediction. A new genomic selection approach, weighted best linear unbiased prediction (W-BLUP), designed to treat the effects of known functional markers more appropriately, proved to increase the accuracy of prediction for both traits and thus closes the gap between marker-assisted and genomic selection.

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Section: Plant Materials and Field Trialsmentioning

confidence: 99%

Bridging the gap between marker-assisted and genomic selection of heading time and plant height in hybrid wheat

et al. 2014

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“…2006), and augmented p‐rep trial designs (Williams et al. 2011) warrant mention as they are targeted at very low replicate experiments such as early generation breeders' trials. They are therefore also highly suitable for testing large RPs for GS.…”

Section: High Throughput Field Phenotypingmentioning

confidence: 99%

GplusE: beyond genomic selection

Mackay

Ober

Hickey

2015

Food and Energy Security

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GplusE is a strategy for genomic selection in which the accuracy of assessment in the reference population for a primary trait such as yield is increased by the incorporation of data from high‐ throughput field phenotyping platforms. This increase in precision comes from both exploiting genetic relationships between traits and reducing the effect of environmental influences upon them. We describe a collaborative project among researchers and breeders to develop a large reference population of elite UK wheat lines. This will be used to test the method, to study the design of the reference population, and to test genotyping strategies and imputation methods. Finally, it will provide data to pump‐prime the application of genomic selection to UK winter wheat breeding.

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“…5 would be replicated at the desired number of locations and the data analysis would involve the use of spatial statistical methods to adjust each plant's phenotypic observation for spatial variation in rows, columns, and squares [24]. This design belongs to the family of partially replicated or augmented designs, which are highly flexible with regard to size, dimension, and ratio of test plots to control plots [7,26,27]. Such a design provides two levels of error control for estimating allele effects: (1) spatial adjustment to enhance precision and accuracy of phenotypic data and (2) large population size to provide adequate precision for estimation of allele effects.…”

Section: Guidelines For Genetics Researchmentioning

confidence: 99%

Replication Concepts for Bioenergy Research Experiments

2015

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While there are some large and fundamental differences among disciplines related to the conversion of biomass to bioenergy, all scientific endeavors involve the use of biological feedstocks. As such, nearly every scientific experiment conducted in this area, regardless of the specific discipline, is subject to random variation, some of which is unpredictable and unidentifiable (i.e., pure random variation such as variation among plots in an experiment, individuals within a plot, or laboratory samples within an experimental unit) while some is predictable and identifiable (repeatable variation, such as spatial or temporal patterns within an experimental field, a glasshouse or growth chamber, or among laboratory containers). Identifying the scale and sources of this variation relative to the specific hypotheses of interest is a critical component of designing good experiments that generate meaningful and believable hypothesis tests and inference statements. Many bioenergy feedstock experiments are replicated at an incorrect scale, typically by sampling feedstocks to estimate laboratory error or by completely ignoring the errors associated with growing feedstocks in an agricultural area at a field or farmland (micro-or macro-region) scale. As such, actual random errors inherent in experimental materials are frequently underestimated, with unrealistically low standard errors of statistical parameters (e.g., means), leading to improper inferences. The examples and guidelines set forth in this paper and many of the references cited are intended to form the general policy and guidelines for replication of bioenergy feedstock experiments to be published in BioEnergy Research.

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Augmented p-rep designs

Cited by 125 publications

References 19 publications

Bridging the gap between marker-assisted and genomic selection of heading time and plant height in hybrid wheat

Bridging the gap between marker-assisted and genomic selection of heading time and plant height in hybrid wheat

GplusE: beyond genomic selection

Replication Concepts for Bioenergy Research Experiments

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