Statistical Methods for Plant Breeding

Mackay, Ian; Piepho, Hans‐Peter; Garcia, Antônio Augusto Franco

doi:10.1002/9781119487845.ch17

Cited by 18 publications

(20 citation statements)

References 159 publications

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“…maize Cooper et al ., 2014a)) and also used to predict which parents to cross to make the best hybrids (Zhao et al ., 2015). In the short to medium term, improvements in plant breeding are more likely to come from the application of genomic selection than from any other technology (Mackay et al ., 2019).…”

Section: Genomic Selection: ‘The Quantitative Geneticists' Revenge’mentioning

confidence: 99%

“…The statistical methods used in genomic selection generally have little bearing on the accuracy of trait prediction and research in crops is increasingly focused on to how best to implement genomic selection in breeding programmes (Mackay et al ., 2019). In this respect, rather than using methods to predict the trait values of selection candidates directly, extensions to methods have been made to predict the merit of crosses: either the performance of the hybrid, or the distribution of lines descended from the cross.…”

Section: Genomic Selection: ‘The Quantitative Geneticists' Revenge’mentioning

confidence: 99%

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Understanding the classics: the unifying concepts of transgressive segregation, inbreeding depression and heterosis and their central relevance for crop breeding

Mackay

Cockram

Howell

et al. 2020

Plant Biotechnology Journal

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Transgressive segregation and heterosis are the reasons that plant breeding works. Molecular explanations for both phenomena have been suggested and play a contributing role. However, it is often overlooked by molecular genetic researchers that transgressive segregation and heterosis are most simply explained by dispersion of favorable alleles. Therefore, advances in molecular biology will deliver the most impact on plant breeding when integrated with sources of heritable trait variationand this will be best achieved within a quantitative genetics framework. An example of the power of quantitative approaches is the implementation of genomic selection, which has recently revolutionized animal breeding. Genomic selection is now being applied to both hybrid and inbred crops and is likely to be the major source of improvement in plant breeding practice over the next decade. Breeders' ability to efficiently apply genomic selection methodologies is due to recent technology advances in genotyping and sequencing. Furthermore, targeted integration of additional molecular data (such as gene expression, gene copy number and methylation status) into genomic prediction models may increase their performance. In this review, we discuss and contextualize a suite of established quantitative genetics themes relating to hybrid vigour, transgressive segregation and their central relevance to plant breeding, with the aim of informing crop researchers outside of the quantitative genetics discipline of their relevance and importance to crop improvement. Better understanding between molecular and quantitative disciplines will increase the potential for further improvements in plant breeding methodologies and so help underpin future food security.

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Section: Genomic Selection: ‘The Quantitative Geneticists' Revenge’mentioning

confidence: 99%

Section: Genomic Selection: ‘The Quantitative Geneticists' Revenge’mentioning

confidence: 99%

Understanding the classics: the unifying concepts of transgressive segregation, inbreeding depression and heterosis and their central relevance for crop breeding

Mackay

Cockram

Howell

et al. 2020

Plant Biotechnology Journal

View full text Add to dashboard Cite

show abstract

“…High-dimensional trait data is also common in more traditional plant breeding programs. Gene-environment interactions are important in most crops, so breeding programs typically perform trials in many geographic regions and across multiple years before varieties can be released (Mackay et al 2019). In large commercial breeding programs, the number of trials can be on the order of hundreds, with the outcome of each trial representing a separate measure of the variety's performance (Gaffney et al 2015).…”

Section: Introductionmentioning

confidence: 99%

MegaLMM: Mega-scale linear mixed models for genomic predictions with thousands of traits

Runcie

Cheng

et al. 2020

Preprint

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ABSTRACTLarge-scale phenotype data can enhance the power of genomic prediction in plant and animal breeding, as well as human genetics. However, the statistical foundation of multi-trait genomic prediction is based on the multivariate linear mixed effect model, a tool notorious for its fragility when applied to more than a handful of traits. We present MegaLMM, a statistical framework and associated software package for mixed model analyses of a virtually unlimited number of traits. Using three examples with real plant data, we show that MegaLMM can leverage thousands of traits at once to significantly improve genetic value prediction accuracy.

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“…Over the last decade, genome wide association studies (GWAS) has become a prominent method for genetic analysis in plants (4). In crops, GWAS require trait data on large collections of varieties or accessions, which is typically expensive to collect and can therefore result in underpowered studies with relatively low numbers of lines (5, 6). An alternative is to exploit the availability of historical data, such as that collected during varietal development programmes.…”

Section: Introductionmentioning

confidence: 99%

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

Sharma

Cockram

Gardner

et al. 2020

Preprint

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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 world’s 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.

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Statistical Methods for Plant Breeding

Cited by 18 publications

References 159 publications

Understanding the classics: the unifying concepts of transgressive segregation, inbreeding depression and heterosis and their central relevance for crop breeding

Understanding the classics: the unifying concepts of transgressive segregation, inbreeding depression and heterosis and their central relevance for crop breeding

MegaLMM: Mega-scale linear mixed models for genomic predictions with thousands of traits

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

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