Biology subjects

Mackay, I. J.

Publications and source records attributed to Mackay, I. J..

5 recordsLinked to original sources

bioRxiv2022

Multi-trait ensemble genomic prediction and simulations of recurrent selection highlight importance of complex trait genetic architecture in long-term genetic gains in wheat

Cereal crop breeders have achieved considerable genetic gain in genetically complex traits, such as grain yield, while maintaining genetic diversity. However, focus on selection for yield has negatively impacted other important traits. To better understand selection within a breeding context, and how it might be optimised, we analysed genotypic and phenotypic data from a diverse, 16-founder wheat multi-parent advanced generation inter-cross (MAGIC) population. Compared to single-trait models, multi-trait ensemble genomic prediction models increased prediction accuracy for almost 90% of traits, improving grain yield prediction accuracy by 3-52%. For complex traits, non-parametric models (Random Forest) also outperformed simplified, additive models (LASSO), increasing grain yield prediction accuracy by 10-36%. Simulations of recurrent genomic selection then showed that sustained greater forward prediction accuracy optimised long-term genetic gains. Simulations of selection on grain yield found indirect responses in related traits, which involved optimisation of antagonistic trait relationships. We found multi-trait selection indices could be used to optimise undesirable relationships, such as the trade-off between grain yield and protein content, or combine traits of interest, such as yield and weed competitive ability. Simulations of phenotypic selection found that including Random Forest rather than LASSO genetic models, and multi-trait rather than single-trait models as the true genetic model, accelerated and extended long-term genetic gain whilst maintaining genetic diversity. These results suggest important roles of pleiotropy and epistasis in the wider context of wheat breeding programmes and provide insights into mechanisms for continued genetic gain in a limited genepool and optimisation of multiple traits for crop improvement.

genetics↗

bioRxiv2021

Genetic resistance to yellow rust infection of the wheat ear is controlled by genes controlling foliar resistance and flowering time

Yellow rust (YR), or stripe rust, is a fungal infection of wheat (Triticum aestivum L.) caused by the pathogen Puccinia striiformis Westend f. sp. tritici (Pst). While much research has focused on YR infection of wheat leaves, we are not aware of reports investigating the genetic control of YR resistance in other wheat structures, such as the ears. Here we use an eight-founder population to undertake genetic analysis of glume YR infection in wheat ears. Five quantitative trait loci (QTL) were identified, each explaining between ~3-7% of the phenotypic variation. Of these, three (QYrg.niab-2D.2, QYrg.niab-4D.1 and QYrg.niab-5A.1) co-located with QTL for leaf YR resistance previously identified in the same population, with evidence suggesting QYrg.niab-5A.1 may correspond to the adult plant resistance locus Yr34 which originates from T. monococcum ssp. monococcum and that resistance at QYrg.niab-2D.2 may be conferred by chromosomal introgression from a wheat relative. Additional leaf YR resistance QTL previously identified in the population were not detected as controlling glume resistance, with the remaining two glume YR QTL linked to genetic loci controlling flowering time. The first of these, QYrg.niab-2D.1, mapped to the major flowering time locus Photoperiod-D1 (Ppd-D1), with the early-flowering allele from the MAGIC founder Soissons conferring reduced glume YR resistance. The second, QYrg.niab-4A.1, was identified in one trial only, and was located close to a flowering time QTL. This indicates earlier flowering results in increased glume YR susceptibility, likely due to exposure of tissues during environmental conditions more favourable for Pst infection. Collectively, our results provide first insights into the genetic control of YR resistance in glumes, controlled by subsets of QTL for leaf YR resistance and flowering time. This work provides specific genetic targets for the control of YR resistance in both the leaves and the glumes, and may be especially relevant in Pst-prone agricultural environments where earlier flowering is favoured. Core ideasO_LIPuccinia striiformis Westend f. sp. tritici (Pst) causes yellow rust (YR) in wheat leaves and ears. C_LIO_LIWe present the first reports for the genetic control of YR on the wheat ear. C_LIO_LIEar YR infection is controlled by subsets of QTL controlling leaf resistance and flowering time. C_LIO_LIThe findings are relevant to wheat breeding for Pst-prone environments. C_LI

genetics↗

bioRxiv2020

Trends of genetic changes uncovered by Env- and Eigen-GWAS in wheat and barley

The process of crop breeding over the last century has delivered new varieties with increased genetic gains, resulting in higher crop performance and yield. However in many cases, the underlying alleles and genomic regions that have underpinned this success remain unknown. This is due, in part, to the difficulty in generating sufficient phenotypic data on large numbers of historical varieties to allow such analyses to be undertaken. Here we demonstrate the ability to circumvent such bottlenecks by identifying genomic regions selected over 100 years of crop breeding using the age of a variety as a surrogate for yield. Using environmental genome-wide association scans (EnvGWAS) on variety age in two of the worlds most important crops, wheat and barley, we found strong signals of selection across the genomes of our target crops. EnvGWAS identified 16 genomic regions in barley and 10 in wheat with contrasting patterns between spring and winter types of the two crops. To further examine changes in genome structure in wheat and barley over the past century, we used the same genotypic data to derive eigenvectors for deployment in EigenGWAS. This resulted in the detection of seven major chromosomal introgressions that contributed to adaptation in wheat. The deployment of both EigenGWAS and EnvGWAS based on variety age avoids costly phenotyping and will facilitate the identification of genomic tracts that have been under selection during plant breeding in underutilized historical cultivar collections. Our results not only demonstrate the potential of using historical cultivar collections coupled with genomic data to identify chromosomal regions that have been under selection but to also guide future plant breeding strategies to maximise the rate of genetic gain and adaptation in crop improvement programs. Significance Statement100 years of plant breeding have greatly improved crop adaptation, resilience, and productivity. Generating the trait data required for these studies is prohibitively expensive and can be impossible on large historical traits. This study reports using variety age and eigenvectors of the genomic relationship matrix as surrogate traits in GWAS to locate the genomic regions that have undergone selection during varietal development in wheat and barley. In several cases these were confirmed as associated with yield and other selected traits. The success and the simplicity of the approach means it can easily be extended to other crops with a recent recorded history of plant breeding and available genomic resources.

genetics↗

bioRxiv2020

On the origin of photoperiod non-responsiveness in barley

In barley, the transition from the vegetative to reproductive phase is complex and under the control of photoperiodic and temperature conditions. One major gene involved is PPD-H1, a PSEUDO-RESPONSE REGULATOR 7 (PRR7) that encodes a component of the circadian clock. Mutation at PPD-H1 resulted in the photoperiod non-responsive ppd-H1 alleles that are beneficial under high latitudinal environments as they allow vegetative growth during the long-day summer conditions whereby higher yields are harvested by farmers. Utilizing a diverse GWAS panel of world-wide origin and a genome-wide gene-based set of 50K SNP markers, a strong association of days to heading with the PPD-H1 gene was detected in multi-location field trials. Re-sequencing of the gene spanning putative causative SNPs, SNP22 (Turner et al. 2005) and SNP48 (Jones et al. 2008), detected recombination between the two, previously reported to be in complete LD. Phenotyping of the recombinants and phylogenetic relationships among haplotypes supported the original conclusion of Turner et al. (2005) that SNP22, present in the CCT domain, is the most likely causative SNP. To infer the origin of non-responsiveness, the PPD-H1 gene was re-sequenced in a geo-referenced collection of 2057 wild and domesticated barleys and compared with the allelic status of the 6000-year-old barley sample from the Yoram cave in the Masada cliff. A monophyletic and post-domestication origin in the Fertile Crescent was found in contrast to the pre-domestication origin proposed by Jones et al. (2008). We show that the photoperiod non-responsiveness originated from Desert type wild barley in the Southern Levant.Competing Interest StatementThe authors have declared no competing interest.View Full Text

plant biology↗

bioRxiv2018

The effects of training population design on genomic prediction accuracy in wheat

Genomic selection offers several routes for increasing genetic gain or efficiency of plant breeding programs. In various species of livestock there is empirical evidence of increased rates of genetic gain from the use of genomic selection to target different aspects of the breeders equation. Accurate predictions of genomic breeding value are central to this and the design of training sets is in turn central to achieving sufficient levels of accuracy. In summary, small numbers of close relatives and very large numbers of distant relatives are expected to enable accurate predictions.\n\nTo quantify the effect of some of the properties of training sets on the accuracy of genomic selection in crops we performed an extensive field-based winter wheat trial. In summary, this trial involved the construction of 44 F2:4 bi- and triparental populations, from which 2992 lines were grown on four field locations and yield was measured. For each line, genotype data were generated for 25,000 segregating single nucleotide polymorphism markers. The overall heritability of yield was estimated to 0.65, and estimates within individual families ranged between 0.10 and 0.85. Within cross genomic prediction accuracies of yield BLUEs were 0.125 - 0.127 using two different cross-validation approaches, and generally increased with training set size. Using related crosses in training and validation sets generally resulted in higher prediction accuracies than using unrelated crosses. The results of this study emphasize the importance of the training set design in relation to the genetic material to which the resulting prediction model is to be applied.

genetics↗