Publications by authors named "Brian Cullis"

25 Publications

  • Page 1 of 1

Information Based Diagnostic for Genetic Variance Parameter Estimation in Multi-Environment Trials.

Front Plant Sci 2021 7;12:785430. Epub 2021 Dec 7.

Centre for Biometrics and Data Science for Sustainable Primary Industries, School of Mathematics and Applied Statistics, National Institute for Applied Statistics Research Australia, University of Wollongong, Wollongong, NSW, Australia.

Plant breeding programs evaluate varieties in series of field trials across years and locations, referred to as multi-environment trials (METs). These are an essential part of variety evaluation with the key aim of the statistical analysis of these datasets to accurately estimate the variety by environment (VE) effects. It has previously been thought that the number of varieties in common between environments, referred to as "variety connectivity," was a key driver of the reliability of genetic variance parameter estimation and that this in turn affected the reliability of predictions of VE effects. In this paper we have provided the link between the objectives of this work and those in model-based experimental design. We propose the use of the -optimality criterion as a diagnostic to capture the information available for the residual maximum likelihood (REML) estimation of the genetic variance parameters. We demonstrate the methods for a dataset with pedigree information as well as evaluating the performance of the diagnostic using two simulation studies. This measure is shown to provide a superior diagnostic to the traditional connectivity type measure in the sense of better forecasting the uncertainty of genetic variance parameter estimates.
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http://dx.doi.org/10.3389/fpls.2021.785430DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8688772PMC
December 2021

Evidence for the Application of Emerging Technologies to Accelerate Crop Improvement - A Collaborative Pipeline to Introgress Herbicide Tolerance Into Chickpea.

Front Plant Sci 2021 3;12:779122. Epub 2021 Dec 3.

Tamworth Agricultural Institute, New South Wales Department of Primary Industries, Tamworth, NSW, Australia.

Accelerating genetic gain in crop improvement is required to ensure improved yield and yield stability under increasingly challenging climatic conditions. This case study demonstrates the effective confluence of innovative breeding technologies within a collaborative breeding framework to develop and rapidly introgress imidazolinone Group 2 herbicide tolerance into an adapted Australian chickpea genetic background. A well-adapted, high-yielding desi cultivar PBA HatTrick was treated with ethyl methanesulfonate to generate mutations in the () gene. After 2 years of field screening with imidazolinone herbicide across >20 ha and controlled environment progeny screening, two selections were identified which exhibited putative herbicide tolerance. Both selections contained the same single amino acid substitution, from alanine to valine at position 205 (AV) in the AHAS1 protein, and KASP™ markers were developed to discriminate between tolerant and intolerant genotypes. A pipeline combining conventional crossing and F production with accelerated single seed descent from F and marker-assisted selection at F rapidly introgressed the herbicide tolerance trait from one of the mutant selections, D15PAHI002, into PBA Seamer, a desi cultivar adapted to Australian cropping areas. Field evaluation of the derivatives of the D15PAHI002 × PBA Seamer cross was analyzed using a factor analytic mixed model statistical approach designed to accommodate low seed numbers resulting from accelerated single seed descent. To further accelerate trait introgression, field evaluation trials were undertaken concurrent with crop safety testing trials. In 2020, 4 years after the initial cross, an advanced line selection CBA2061, bearing acetohydroxyacid synthase (AHAS) inhibitor tolerance and agronomic and disease resistance traits comparable to parent PBA Seamer, was entered into Australian National Variety Trials as a precursor to cultivar registration. The combination of cross-institutional collaboration and the application of novel pre-breeding platforms and statistical technologies facilitated a 3-year saving compared to a traditional breeding approach. This breeding pipeline can be used as a model to accelerate genetic gain in other self-pollinating species, particularly food legumes.
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http://dx.doi.org/10.3389/fpls.2021.779122DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8678039PMC
December 2021

Plant Variety Selection Using Interaction Classes Derived From Factor Analytic Linear Mixed Models: Models With Independent Variety Effects.

Front Plant Sci 2021 9;12:737462. Epub 2021 Sep 9.

Centre for Biometrics and Data Science for Sustainable Primary Industries, School of Mathematics and Applied Statistics, National Institute for Applied Statistics Research Australia, University of Wollongong, Wollongong, NSW, Australia.

A major challenge in the analysis of plant breeding multi-environment datasets is the provision of meaningful and concise information for variety selection in the presence of variety by environment interaction (VEI). This is addressed in the current paper by fitting a factor analytic linear mixed model (FALMM) then using the fundamental factor analytic parameters to define groups of environments in the dataset within which there is minimal crossover VEI, but between which there may be substantial crossover VEI. These groups are consequently called interaction classes (iClasses). Given that the environments within an iClass exhibit minimal crossover VEI, it is then valid to obtain predictions of overall variety performance (across environments) for each iClass. These predictions can then be used not only to select the best varieties within each iClass but also to match varieties in terms of their patterns of VEI across iClasses. The latter is aided with the use of a new graphical tool called an iClass Interaction Plot. The ideas are introduced in this paper within the framework of FALMMs in which the genetic effects for different varieties are assumed independent. The application to FALMMs which include information on genetic relatedness is the subject of a subsequent paper.
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http://dx.doi.org/10.3389/fpls.2021.737462DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8460066PMC
September 2021

A Multi-Environment Trial Analysis of Frost Susceptibility in Wheat and Barley Under Australian Frost-Prone Field Conditions.

Front Plant Sci 2021 19;12:722637. Epub 2021 Aug 19.

School of Agriculture, Food and Wine, Waite Research Institute, The University of Adelaide, Urrbrae, SA, Australia.

Low temperatures during the flowering period of cereals can lead to floret sterility, yield reduction, and economic losses in Australian crops. In order to breed for improved frost susceptibility, selection methods are urgently required to identify novel sources of frost tolerant germplasm. However, the presence of genotype by environment interactions (i.e. variety responses to a change in environment) is a major constraint to select the most appropriate varieties in any given target environment. An advanced method of analysis for multi-environment trials that includes factor analytic selection tools to summarize overall performance and stability to a specific trait across the environments could deliver useful information to guide growers and plant breeding programs in providing the most appropriate decision making-strategy. In this study, the updated selection tools approached in this multi-environment trials (MET) analysis have allowed variety comparisons with similar frost susceptibility but which have a different response to changes in the environment or vice versa. This MET analysis included a wide range of sowing dates grown at multiple locations from 2010 to 2019, respectively. These results, as far as we are aware, show for the first-time genotypic differences to frost damage through a MET analysis by phenotyping a vast number of accurate empirical measurements that reached in excess of 557,000 spikes. This has resulted in a substantial number of experimental units (10,317 and 5,563 in wheat and barley, respectively) across a wide range of sowing times grown at multiple locations from 2010 to 2019. Varieties with low frost overall performance (OP) and low frost stability (root mean square deviation -RMSD) were less frost susceptible, with performance more consistent across all environments, while varieties with low OP and high RMSD were adapted to specific environmental conditions.
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http://dx.doi.org/10.3389/fpls.2021.722637DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8417324PMC
August 2021

Use of Contemporary Groups in the Construction of Multi-Environment Trial Datasets for Selection in Plant Breeding Programs.

Front Plant Sci 2020 2;11:623586. Epub 2021 Feb 2.

Centre for Bioinformatics and Biometrics, School of Mathematics and Applied Statistics, National Institute for Applied Statistics Research Australia, University of Wollongong, Wollongong, NSW, Australia.

Plant breeding programs use multi-environment trial (MET) data to select superior lines, with the ultimate aim of increasing genetic gain. Selection accuracy can be improved with the use of advanced statistical analysis methods that employ informative models for genotype by environment interaction, include information on genetic relatedness and appropriately accommodate within-trial error variation. The gains will only be achieved, however, if the methods are applied to suitable MET datasets. In this paper we present an approach for constructing MET datasets that optimizes the information available for selection decisions. This is based on two new concepts that characterize the structure of a breeding program. The first is that of "contemporary groups," which are defined to be groups of lines that enter the initial testing stage of the breeding program in the same year. The second is that of "data bands," which are sequences of trials that correspond to the progression through stages of testing from year to year. MET datasets are then formed by combining bands of data in such a way as to trace the selection histories of lines within contemporary groups. Given a specified dataset, we use the A-optimality criterion from the model-based design literature to quantify the information for any given selection decision. We demonstrate the methods using two motivating examples from a durum and chickpea breeding program. Datasets constructed using contemporary groups and data bands are shown to be superior to other forms, in particular those that relate to a single year alone.
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http://dx.doi.org/10.3389/fpls.2020.623586DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7884452PMC
February 2021

Genetic variation for fusarium crown rot tolerance in durum wheat.

PLoS One 2021 12;16(2):e0240766. Epub 2021 Feb 12.

Centre for Bioinformatics and Biometrics, National Institute for Applied Statistics Research Australia, University of Wollongong, Wollongong, New South Wales, Australia.

Tolerance to the cereal disease Fusarium crown rot (FCR) was investigated in a set of 34 durum wheat genotypes, with Suntop, (bread wheat) and EGA Bellaroi (durum) as tolerant and intolerant controls, in a series of replicated field trials over four years with inoculated (FCR-i) and non-inoculated (FCR-n) plots of the genotypes. The genotypes included conventional durum lines and lines derived from crossing durum with 2-49, a bread wheat genotype with the highest level of partial resistance to FCR. A split plot trial design was chosen to optimize the efficiency for the prediction of FCR tolerance for each genotype. A multi-environment trial (MET) analysis was undertaken which indicated that there was good repeatability of FCR tolerance across years. Based on an FCR tolerance index, Suntop was the most tolerant genotype and EGA Bellaroi was very intolerant, but some durum wheats had FCR tolerance indices which were comparable to Suntop. These included some conventional durum genotypes, V101030, TD1702, V11TD013*3X-63 and DBA Bindaroi, as well as genotypes from crosses with 2-49 (V114916 and V114942). The correlation between FCR tolerance and FCR-n yield predictions was moderately negative indicating it could be somewhat difficult to develop FCR-tolerant genotypes that are high yielding under low disease pressure. However, FCR tolerance showed a positive correlation with FCR-i yield predictions in seasons of high disease expression indicating it could be possible to screen for FCR tolerance using only FCR-i treatments. These results are the first demonstration of genetic diversity in durum germplasm for FCR tolerance and they provide a basis for breeding for this trait.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0240766PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7880437PMC
July 2021

Genome-Wide Association Mapping Identifies Novel Loci for Quantitative Resistance to Blackleg Disease in Canola.

Front Plant Sci 2020 11;11:1184. Epub 2020 Aug 11.

Centre for Bioinformatics and Biometrics, National Institute for Applied Statistics Research Australia, University of Wollongong, Wollongong, NSW, Australia.

Blackleg disease, caused by the fungal pathogen , continues to be a major concern for sustainable production of canola ( L.) in many parts of the world. The deployment of effective quantitative resistance (QR) is recognized as a durable strategy in providing natural defense to pathogens. Herein, we uncover loci for resistance to blackleg in a genetically diverse panel of canola accessions by exploiting historic recombination events which occurred during domestication and selective breeding by genome-wide association analysis (GWAS). We found extensive variation in resistance to blackleg at the adult plant stage, including for upper canopy infection. Using the linkage disequilibrium and genetic relationship estimates from 12,414 high quality SNPs, GWAS identified 59 statistically significant and "suggestive" SNPs on 17 chromosomes of genome that underlie variation in resistance to blackleg, evaluated under field and shade-house conditions. Each of the SNP association accounted for up to 25.1% of additive genetic variance in resistance among diverse panel of accessions. To understand the homology of QR genomic regions with genome, we searched the synteny between QR regions with 22 ancestral blocks of Brassicaceae. Comparative analyses revealed that 25 SNP associations for QR were localized in nine ancestral blocks, as a result of genomic rearrangements. We further showed that phenological traits such as flowering time, plant height, and maturity confound the genetic variation in resistance. Altogether, these findings provided new insights on the complex genetic control of the blackleg resistance and further expanded our understanding of its genetic architecture.
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http://dx.doi.org/10.3389/fpls.2020.01184DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7432127PMC
August 2020

Genomic selection in multi-environment plant breeding trials using a factor analytic linear mixed model.

J Anim Breed Genet 2019 Jul;136(4):279-300

National Institute for Applied Statistics Research Australia, Centre for Bioinformatics and Biometrics, University of Wollongong, Wollongong, New South Wales, Australia.

Genomic selection (GS) is a statistical and breeding methodology designed to improve genetic gain. It has proven to be successful in animal breeding; however, key points of difference have not been fully considered in the transfer of GS from animal to plant breeding. In plant breeding, individuals (varieties) are typically evaluated across a number of locations in multiple years (environments) in formally designed comparative experiments, called multi-environment trials (METs). The design structure of individual trials can be complex and needs to be modelled appropriately. Another key feature of MET data sets is the presence of variety by environment interaction (VEI), that is the differential response of varieties to a change in environment. In this paper, a single-step factor analytic linear mixed model is developed for plant breeding MET data sets that incorporates molecular marker data, appropriately accommodates non-genetic sources of variation within trials and models VEI. A recently developed set of selection tools, which are natural derivatives of factor analytic models, are used to facilitate GS for a motivating data set from an Australian plant breeding company. The power and versatility of these tools is demonstrated for the variety by environment and marker by environment effects.
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http://dx.doi.org/10.1111/jbg.12404DOI Listing
July 2019

Determining the Genetic Architecture of Reproductive Stage Drought Tolerance in Wheat Using a Correlated Trait and Correlated Marker Effect Model.

G3 (Bethesda) 2019 02 7;9(2):473-489. Epub 2019 Feb 7.

National Institute for Applied Statistics Research Australia (NIASRA), School of Mathematics & Applied Statistics, Faculty of Engineering & Information Sciences, University of Wollongong NSW 2522, Australia.

Water stress during reproductive growth is a major yield constraint for wheat ( L). We previously established a controlled environment drought tolerance phenotyping method targeting the young microspore stage of pollen development. This method eliminates stress avoidance based on flowering time. We substituted soil drought treatments by a reproducible osmotic stress treatment using hydroponics and NaCl as osmolyte. Salt exclusion in hexaploid wheat avoids salt toxicity, causing osmotic stress. A Cranbrook x Halberd doubled haploid (DH) population was phenotyped by scoring spike grain numbers of unstressed (SGNCon) and osmotically stressed (SGNTrt) plants. Grain number data were analyzed using a linear mixed model (LMM) that included genetic correlations between the SGNCon and SGNTrt traits. Viewing this as a genetic regression of SGNTrt on SGNCon allowed derivation of a stress tolerance trait (SGNTol). Importantly, and by definition of the trait, the genetic effects for SGNTol are statistically independent of those for SGNCon. Thus they represent non-pleiotropic effects associated with the stress treatment that are independent of the control treatment. QTL mapping was conducted using a whole genome approach in which the LMM included all traits and all markers simultaneously. The marker effects within chromosomes were assumed to follow a spatial correlation model. This resulted in smooth marker profiles that could be used to identify positions of putative QTL. The most influential QTL were located on chromosome 5A for SGNTol (126cM; contributed by Halberd), 5A for SGNCon (141cM; Cranbrook) and 2A for SGNTrt (116cM; Cranbrook). Sensitive and tolerant population tail lines all showed matching soil drought tolerance phenotypes, confirming that osmotic stress is a valid surrogate screening method.
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http://dx.doi.org/10.1534/g3.118.200835DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6385972PMC
February 2019

Increased accuracy of starch granule type quantification using mixture distributions.

Plant Methods 2017 6;13:107. Epub 2017 Dec 6.

CSIRO Agriculture and Food, 1600 Clunies Ross Street, Canberra, 2601 Australia.

Background: The proportion of granule types in wheat starch is an important characteristic that can affect its functionality. It is widely accepted that granule types are either large, disc-shaped A-type granules or small, spherical B-type granules. Additionally, there are some reports of the tiny C-type granules. The differences between these granule types are due to its carbohydrate composition and crystallinity which is highly, but not perfectly, correlated with the granule size. A majority of the studies that have considered granule types analyse them based on a size threshold rather than chemical composition. This is understandable due to the expense of separating starch into different types. While the use of a size threshold to classify granule type is a low-cost measure, this results in misclassification. We present an alternative, statistical method to quantify the proportion of granule types by a fit of the mixture distribution, along with an R package, a web based app and a video tutorial for how to use the web app to enable its straightforward application.

Results: Our results show that the reliability of the genotypic effects increase approximately 60% using the proportions of the A-type and B-type granule estimated by the mixture distribution over the standard size-threshold measure. Although there was a marginal drop in reliability for C-type granules. The latter is likely due to the low observed genetic variance for C-type granules.

Conclusions: The determination of the proportion of granule types from size-distribution is better achieved by using the mixing probabilities from the fit of the mixture distribution rather than using a size-threshold.
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http://dx.doi.org/10.1186/s13007-017-0259-2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5718142PMC
December 2017

Multiple Factors Affect Socioeconomics and Wellbeing of Artisanal Sea Cucumber Fishers.

PLoS One 2016 8;11(12):e0165633. Epub 2016 Dec 8.

Partners in Community Development Fiji, Suva, Fiji.

Small-scale fisheries are important to livelihoods and subsistence seafood consumption of millions of fishers. Sea cucumbers are fished worldwide for export to Asia, yet few studies have assessed factors affecting socioeconomics and wellbeing among fishers. We interviewed 476 men and women sea cucumber fishers at multiple villages within multiple locations in Fiji, Kiribati, Tonga and New Caledonia using structured questionnaires. Low rates of subsistence consumption confirmed a primary role of sea cucumbers in income security. Prices of sea cucumbers sold by fishers varied greatly among countries, depending on the species. Gender variation in landing prices could be due to women catching smaller sea cucumbers or because some traders take advantage of them. Dissatisfaction with fishery income was common (44% of fishers), especially for i-Kiribati fishers, male fishers, and fishers experiencing difficulty selling their catch, but was uncorrelated with sale prices. Income dissatisfaction worsened with age. The number of livelihood activities averaged 2.2-2.5 across countries, and varied significantly among locations. Sea cucumbers were often a primary source of income to fishers, especially in Tonga. Other common livelihood activities were fishing other marine resources, copra production in Kiribati, agriculture in Fiji, and salaried jobs in New Caledonia. Fishing other coastal and coral reef resources was the most common fall-back livelihood option if fishers were forced to exit the fishery. Our data highlight large disparities in subsistence consumption, gender-related price equity, and livelihood diversity among parallel artisanal fisheries. Improvement of supply chains in dispersed small-scale fisheries appears as a critical need for enhancing income and wellbeing of fishers. Strong evidence for co-dependence among small-scale fisheries, through fall-back livelihood preferences of fishers, suggests that resource managers must mitigate concomitant effects on other fisheries when considering fishery closures. That is likely to depend on livelihood diversification programs to take pressure off co-dependent fisheries.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0165633PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5145150PMC
July 2017

Genomic Selection in Multi-environment Crop Trials.

G3 (Bethesda) 2016 05 3;6(5):1313-26. Epub 2016 May 3.

Division of Plant Sciences, University of Dundee at the James Hutton Institute, Invergowrie, Dundee DD2 5DA, Scotland, UK Department of Cell and Molecular Sciences, The James Hutton Institute, Invergowrie, Dundee DD2 5DA, Scotland, UK

Genomic selection in crop breeding introduces modeling challenges not found in animal studies. These include the need to accommodate replicate plants for each line, consider spatial variation in field trials, address line by environment interactions, and capture nonadditive effects. Here, we propose a flexible single-stage genomic selection approach that resolves these issues. Our linear mixed model incorporates spatial variation through environment-specific terms, and also randomization-based design terms. It considers marker, and marker by environment interactions using ridge regression best linear unbiased prediction to extend genomic selection to multiple environments. Since the approach uses the raw data from line replicates, the line genetic variation is partitioned into marker and nonmarker residual genetic variation (i.e., additive and nonadditive effects). This results in a more precise estimate of marker genetic effects. Using barley height data from trials, in 2 different years, of up to 477 cultivars, we demonstrate that our new genomic selection model improves predictions compared to current models. Analyzing single trials revealed improvements in predictive ability of up to 5.7%. For the multiple environment trial (MET) model, combining both year trials improved predictive ability up to 11.4% compared to a single environment analysis. Benefits were significant even when fewer markers were used. Compared to a single-year standard model run with 3490 markers, our partitioned MET model achieved the same predictive ability using between 500 and 1000 markers depending on the trial. Our approach can be used to increase accuracy and confidence in the selection of the best lines for breeding and/or, to reduce costs by using fewer markers.
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http://dx.doi.org/10.1534/g3.116.027524DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4856083PMC
May 2016

Ppd-1 is a key regulator of inflorescence architecture and paired spikelet development in wheat.

Nat Plants 2015 Jan 26;1:14016. Epub 2015 Jan 26.

CSIRO Agriculture Flagship, GPO Box 1600, Canberra, ACT 2601, Australia.

The domestication of cereal crops such as wheat, maize, rice and barley has included the modification of inflorescence architecture to improve grain yield and ease harvesting(1). Yield increases have often been achieved through modifying the number and arrangement of spikelets, which are specialized reproductive branches that form part of the inflorescence. Multiple genes that control spikelet development have been identified in maize, rice and barley(2-5). However, little is known about the genetic underpinnings of this process in wheat. Here, we describe a modified spikelet arrangement in wheat, termed paired spikelets. Combining comprehensive QTL and mutant analyses, we show that Photoperiod-1 (Ppd-1), a pseudo-response regulator gene that controls photoperiod-dependent floral induction, has a major inhibitory effect on paired spikelet formation by regulating the expression of FLOWERING LOCUS T (FT)(6,7). These findings show that modulated expression of the two important flowering genes, Ppd-1 and FT, can be used to form a wheat inflorescence with a more elaborate arrangement and increased number of grain producing spikelets.
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http://dx.doi.org/10.1038/nplants.2014.16DOI Listing
January 2015

Factor analytic mixed models for the provision of grower information from national crop variety testing programs.

Theor Appl Genet 2015 Jan 19;128(1):55-72. Epub 2014 Oct 19.

National Institute for Applied Statistics Research Australia, University of Wollongong, Wollongong, Australia,

Key Message: Factor analytic mixed models for national crop variety testing programs have the potential to improve industry productivity through appropriate modelling and reporting to growers of variety by environment interaction. Crop variety testing programs are conducted in many countries world-wide. Within each program, data are combined across locations and seasons, and analysed in order to provide information to assist growers in choosing the best varieties for their conditions. Despite major advances in the statistical analysis of multi-environment trial data, such methodology has not been adopted within national variety testing programs. The most commonly used approach involves a variance component model that includes variety and environment main effects, and variety by environment (V × E) interaction effects. The variety predictions obtained from such an analysis, and subsequently reported to growers, are typically on a long-term regional basis. In Australia, the variance component model has been found to be inadequate in terms of modelling V × E interaction, and the reporting of information at a regional level often masks important local V × E interaction. In contrast, the factor analytic mixed model approach that is widely used in Australian plant breeding programs, has regularly been found to provide a parsimonious and informative model for V × E effects, and accurate predictions. In this paper we develop an approach for the analysis of crop variety evaluation data that is based on a factor analytic mixed model. The information obtained from such an analysis may well be superior, but will only enhance industry productivity if mechanisms exist for successful technology transfer. With this in mind, we offer a suggested reporting format that is user-friendly and contains far greater local information for individual growers than is currently the case.
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http://dx.doi.org/10.1007/s00122-014-2412-xDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4282718PMC
January 2015

Effects of mesh size and escape gaps on discarding in an Australian giant mud crab (Scylla serrata) trap fishery.

PLoS One 2014 2;9(9):e106414. Epub 2014 Sep 2.

National Institute Applied Statistics Research Australia, Faculty of Engineering and Information Sciences, University of Wollongong, Wollongong, New South Wales, Australia; Australia and Computational Informatics, CSIRO, Canberra, Australia.

In response to concerns over excessive discarding from Australian recreational round traps (with four funnel entrances) used to target giant mud crabs, Scylla serrata, an experiment was done to assess the independent and cumulative utility of paired, bottom-located horizontal escape gaps (46×120 mm) and increasing mesh size (from 51 to 101 mm). Compared to conventional traps comprising 51-mm mesh throughout, those with the same mesh size and escape gaps caught significantly fewer (by 95%) undersize (<85 mm carapace length--CL) crabs while maintaining legal catches. Traps made from 101-mm mesh (but with the same funnel entrances as conventional designs) and with and without escape gaps similarly retained fewer undersize crabs and also yellowfin bream Acanthopagrus australis (the key bycatch species) by up to 94%, but there were concomitant reductions in fishing power for legal sizes of S. serrata. Although there were no immediate mortalities among any discarded crabs, there was a greater bias towards wounding among post molts than late inter-molts and less damage to individuals in the 101-mm conventional than 51-mm conventional traps (without escape gaps). The results support retrospectively fitting escape gaps in conventional S. serrata traps as a means for reducing discarding, but additional work is required to determine appropriate mesh sizes/configurations that maximize species and size selectivity.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0106414PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4152283PMC
May 2015

Factor analytic and reduced animal models for the investigation of additive genotype-by-environment interaction in outcrossing plant species with application to a Pinus radiata breeding programme.

Theor Appl Genet 2014 Oct 22;127(10):2193-210. Epub 2014 Aug 22.

National Institute for Applied Statistics Research Australia, University of Wollongong, Wollongong, Australia.

Key Message: Modelling additive genotype-by-environment interaction is best achieved with the use of factor analytic models. With numerous environments and for outcrossing plant species, computation is facilitated using reduced animal models. The development of efficient plant breeding strategies requires a knowledge of the magnitude and structure of genotype-by-environment interaction. This information can be obtained from appropriate linear mixed model analyses of phenotypic data from multi-environment trials. The use of factor analytic models for genotype-by-environment effects is known to provide a reliable, parsimonious and holistic approach for obtaining estimates of genetic correlations between all pairs of trials. When breeding for outcrossing species the focus is on estimating additive genetic correlations and effects which is achieved by including pedigree information in the analysis. The use of factor analytic models in this setting may be computationally prohibitive when the number of environments is moderate to large. In this paper, we present an approach that uses an approximate reduced animal model to overcome the computational issues associated with factor analytic models for additive genotype-by-environment effects. The approach is illustrated using a Pinus radiata breeding dataset involving 77 trials, located in environments across New Zealand and south eastern Australia, and with pedigree information on 315,581 trees. Using this approach we demonstrate the existence of substantial additive genotype-by-environment interaction for the trait of stem diameter measured at breast height. This finding has potentially significant implications for both breeding and deployment strategies. Although our approach has been developed for forest tree breeding programmes, it is directly applicable for other outcrossing plant species, including sugarcane, maize and numerous horticultural crops.
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http://dx.doi.org/10.1007/s00122-014-2373-0DOI Listing
October 2014

Identification of crop cultivars with consistently high lignocellulosic sugar release requires the use of appropriate statistical design and modelling.

Biotechnol Biofuels 2013 Dec 21;6(1):185. Epub 2013 Dec 21.

Division of Plant Sciences, College of Life Sciences, University of Dundee at The James Hutton Institute, Invergowrie, Dundee DD2 5DA, UK.

Background: In this study, a multi-parent population of barley cultivars was grown in the field for two consecutive years and then straw saccharification (sugar release by enzymes) was subsequently analysed in the laboratory to identify the cultivars with the highest consistent sugar yield. This experiment was used to assess the benefit of accounting for both the multi-phase and multi-environment aspects of large-scale phenotyping experiments with field-grown germplasm through sound statistical design and analysis.

Results: Complementary designs at both the field and laboratory phases of the experiment ensured that non-genetic sources of variation could be separated from the genetic variation of cultivars, which was the main target of the study. The field phase included biological replication and plot randomisation. The laboratory phase employed re-randomisation and technical replication of samples within a batch, with a subset of cultivars chosen as duplicates that were randomly allocated across batches. The resulting data was analysed using a linear mixed model that incorporated field and laboratory variation and a cultivar by trial interaction, and ensured that the cultivar means were more accurately represented than if the non-genetic variation was ignored. The heritability detected was more than doubled in each year of the trial by accounting for the non-genetic variation in the analysis, clearly showing the benefit of this design and approach.

Conclusions: The importance of accounting for both field and laboratory variation, as well as the cultivar by trial interaction, by fitting a single statistical model (multi-environment trial, MET, model), was evidenced by the changes in list of the top 40 cultivars showing the highest sugar yields. Failure to account for this interaction resulted in only eight cultivars that were consistently in the top 40 in different years. The correspondence between the rankings of cultivars was much higher at 25 in the MET model. This approach is suited to any multi-phase and multi-environment population-based genetic experiment.
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http://dx.doi.org/10.1186/1754-6834-6-185DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3878416PMC
December 2013

Multivariate whole genome average interval mapping: QTL analysis for multiple traits and/or environments.

Theor Appl Genet 2012 Sep 13;125(5):933-53. Epub 2012 Jun 13.

School of Agriculture, Food and Wine, The University of Adelaide, PMB 1, Glen Osmond, SA 5064, Australia.

A major aim in some plant-based studies is the determination of quantitative trait loci (QTL) for multiple traits or across multiple environments. Understanding these QTL by trait or QTL by environment interactions can be of great value to the plant breeder. A whole genome approach for the analysis of QTL is presented for such multivariate applications. The approach is an extension of whole genome average interval mapping in which all intervals on a linkage map are included in the analysis simultaneously. A random effects working model is proposed for the multivariate (trait or environment) QTL effects for each interval, with a variance-covariance matrix linking the variates in a particular interval. The significance of the variance-covariance matrix for the QTL effects is tested and if significant, an outlier detection technique is used to select a putative QTL. This QTL by variate interaction is transferred to the fixed effects. The process is repeated until the variance-covariance matrix for QTL random effects is not significant; at this point all putative QTL have been selected. Unlinked markers can also be included in the analysis. A simulation study was conducted to examine the performance of the approach and demonstrated the multivariate approach results in increased power for detecting QTL in comparison to univariate methods. The approach is illustrated for data arising from experiments involving two doubled haploid populations. The first involves analysis of two wheat traits, α-amylase activity and height, while the second is concerned with a multi-environment trial for extensibility of flour dough. The method provides an approach for multi-trait and multi-environment QTL analysis in the presence of non-genetic sources of variation.
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http://dx.doi.org/10.1007/s00122-012-1884-9DOI Listing
September 2012

Estimation in a multiplicative mixed model involving a genetic relationship matrix.

Genet Sel Evol 2009 Apr 9;41:33. Epub 2009 Apr 9.

Queensland DPI&F, Biometry, Toowoomba, Queensland, Australia.

Genetic models partitioning additive and non-additive genetic effects for populations tested in replicated multi-environment trials (METs) in a plant breeding program have recently been presented in the literature. For these data, the variance model involves the direct product of a large numerator relationship matrix A, and a complex structure for the genotype by environment interaction effects, generally of a factor analytic (FA) form. With MET data, we expect a high correlation in genotype rankings between environments, leading to non-positive definite covariance matrices. Estimation methods for reduced rank models have been derived for the FA formulation with independent genotypes, and we employ these estimation methods for the more complex case involving the numerator relationship matrix. We examine the performance of differing genetic models for MET data with an embedded pedigree structure, and consider the magnitude of the non-additive variance. The capacity of existing software packages to fit these complex models is largely due to the use of the sparse matrix methodology and the average information algorithm. Here, we present an extension to the standard formulation necessary for estimation with a factor analytic structure across multiple environments.
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http://dx.doi.org/10.1186/1297-9686-41-33DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2686677PMC
April 2009

Genetic control of wheat quality: interactions between chromosomal regions determining protein content and composition, dough rheology, and sponge and dough baking properties.

Theor Appl Genet 2009 May 13;118(8):1519-37. Epub 2009 Mar 13.

CSIRO Plant Industry and the Food Futures Flagship, GPO BOX 1600, Canberra, ACT, 2601, Australia.

While the genetic control of wheat processing characteristics such as dough rheology is well understood, limited information is available concerning the genetic control of baking parameters, particularly sponge and dough (S&D) baking. In this study, a quantitative trait loci (QTL) analysis was performed using a population of doubled haploid lines derived from a cross between Australian cultivars Kukri x Janz grown at sites across different Australian wheat production zones (Queensland in 2001 and 2002 and Southern and Northern New South Wales in 2003) in order to examine the genetic control of protein content, protein expression, dough rheology and sponge and dough baking performance. The study highlighted the inconsistent genetic control of protein content across the test sites, with only two loci (3A and 7A) showing QTL at three of the five sites. Dough rheology QTL were highly consistent across the 5 sites, with major effects associated with the Glu-B1 and Glu-D1 loci. The Glu-D1 5 + 10 allele had consistent effects on S&D properties across sites; however, there was no evidence for a positive effect of the high dough strength Glu-B1-al allele at Glu-B1. A second locus on 5D had positive effects on S&D baking at three of five sites. This study demonstrated that dough rheology measurements were poor predictors of S&D quality. In the absence of robust predictive tests, high heritability values for S&D demonstrate that direct selection is the current best option for achieving genetic gain in this product category.
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http://dx.doi.org/10.1007/s00122-009-1000-yDOI Listing
May 2009

The analysis of QTL by simultaneous use of the full linkage map.

Theor Appl Genet 2007 Dec 20;116(1):95-111. Epub 2007 Oct 20.

School of Agriculture, Food and Wine, The University of Adelaide, PMB 1, Glen Osmond, SA, Australia.

An extension of interval mapping is presented that incorporates all intervals on the linkage map simultaneously. The approach uses a working model in which the sizes of putative QTL for all intervals across the genome are random effects. An outlier detection method is used to screen for possible QTL. Selected QTL are subsequently fitted as fixed effects. This screening and selection approach is repeated until the variance component for QTL sizes is not statistically significant. A comprehensive simulation study is conducted in which map uncertainty is included. The proposed method is shown to be superior to composite interval mapping in terms of power of detection of QTL. There is an increase in the rate of false positive QTL detected when using the new approach, but this rate decreases as the population size increases. The new approach is much simpler computationally. The analysis of flour milling yield in a doubled haploid population illustrates the improved power of detection of QTL using the approach, and also shows how vital it is to allow for sources of non-genetic variation in the analysis.
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http://dx.doi.org/10.1007/s00122-007-0650-xDOI Listing
December 2007

Joint modeling of additive and non-additive (genetic line) effects in multi-environment trials.

Theor Appl Genet 2007 May 11;114(8):1319-32. Epub 2007 Apr 11.

School of Agriculture, Food and Wine, The University of Adelaide, PMB 1, Glen Osmond, SA, 5064, Australia.

A statistical approach for the analysis of multi-environment trials (METs) is presented, in which selection of best performing lines, best parents, and best combination of parents can be determined. The genetic effect of a line is partitioned into additive, dominance and residual non-additive effects. The dominance effects are estimated through the incorporation of the dominance relationship matrix, which is presented under varying levels of inbreeding. A computationally efficient way of fitting dominance effects is presented which partitions dominance effects into between family dominance and within family dominance line effects. The overall approach is applicable to inbred lines, hybrid lines and other general population structures where pedigree information is available.
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http://dx.doi.org/10.1007/s00122-007-0515-3DOI Listing
May 2007

The analysis of longitudinal data using mixed model L-splines.

Biometrics 2006 Jun;62(2):392-401

Rothamsted Research, Harpenden AL5 2JQ, UK.

L-splines are a large family of smoothing splines defined in terms of a linear differential operator. This article develops L-splines within the context of linear mixed models and uses the resulting mixed model L-spline to analyze longitudinal data from a grassland experiment. In the spirit of time-series analysis, a periodic mixed model L-spline is developed, which partitions data into a smooth periodic component plus smooth long-term trend.
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http://dx.doi.org/10.1111/j.1541-0420.2005.00500.xDOI Listing
June 2006

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

Theor Appl Genet 2006 Sep 2;113(5):809-19. Epub 2006 Aug 2.

BiometricsSA, School of Agriculture and Wine, University of Adelaide, PMB 1, Glen Osmond, SA 5064, Australia.

A statistical approach is presented for selection of best performing lines for commercial release and best parents for future breeding programs from standard agronomic trials. The method involves the partitioning of the genetic effect of a line into additive and non-additive effects using pedigree based inter-line relationships, in a similar manner to that used in animal breeding. A difference is the ability to estimate non-additive effects. Line performance can be assessed by an overall genetic line effect with greater accuracy than when ignoring pedigree information and the additive effects are predicted breeding values. A generalized definition of heritability is developed to account for the complex models presented.
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http://dx.doi.org/10.1007/s00122-006-0333-zDOI Listing
September 2006
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