Biology subjects

Podlich, D.

Publications and source records attributed to Podlich, D..

4 recordsLinked to original sources

bioRxiv2021

Can we harness digital technologies and physiology to hasten genetic gain in U.S. maize breeding?

Genetic gain in breeding programs depends on the predictive skill of genotype-to-phenotype algorithms and precision of phenotyping, both integrated with well-defined breeding objectives for a target population of environments (TPE). The integration of physiology and genomics could improve predictive skill by capturing additive and non-additive interaction effects of genotype (G), environment (E), and management (M). Precision phenotyping at managed stress environments (MSEs) can elicit physiological expression of processes that differentiate germplasm for performance in target environments, thus enabling algorithm training. Gap analysis methodology enables design of GxM technologies for target environments by assessing the difference between current and attainable yields within physiological limits. Harnessing digital technologies such as crop growth model-whole genome prediction (CGM-WGP) and gap analysis, and MSEs, can hasten genetic gain by improving predictive skill and definition of breeding goals in the U.S. maize production TPE. A half-diallel maize experiment resulting from crossing 9 elite maize inbreds was conducted at 17 locations in the TPE and 6 locations at MSEs between 2017 and 2019. Analyses over 35 families represented by 2367 hybrids demonstrated that CGM-WGP offered a predictive advantage (y) compared to WGP that increased with occurrence of drought as measured by decreasing whole-season evapotranspiration (ET; log(y) = 0.80({+/-}0.6) - 0.006({+/-}0.001) x ET; r2 = 0.59; df = 21). Predictions of unobserved physiological traits using the CGM, akin to digital phenotyping, were stable. This understanding of germplasm response to ET enables predictive design of opportunities to close productivity gaps. We conclude that enabling physiology through digital methods can hasten genetic gain by improving predictive skill and defining breeding objectives bounded by physiological realities.

plant biology↗

bioRxiv2020

Two decades of creating drought tolerant maize and underpinning prediction technologies in the US corn-belt: Review and perspectives on the future of crop design

Over the last decade, society witnessed the largest expansion of agricultural land planted with drought tolerant (DT) maize (Zea mays L.) Dedicated efforts to drought breeding led to development of DT maize. Here we show that after two decades of sustained breeding efforts the rate of crop improvement under drought is in the range 1.0-1.6% yr-1, which is higher than rates (0.7% yr-1) reported prior to drought breeding. Prediction technologies that leverage biological understanding and statistical learning to improve upon the quantitative genetics framework will further accelerate genetic gain. A review of published and unpublished analyses conducted on data including 138 breeding populations and 93 environments between 2009 and 2019 demonstrated an average prediction skill (r) improvement around 0.2. These methods applied to pre-commercial stages showed accuracies higher that current statistical approaches (0.85 vs. 0.70). Improvement in hybrid and management choice can increase water productivity. Digital gap analyses are applicable at field scale suggesting the possibility of transition from evaluating hybrids to designing genotype x management (GxM) technologies for target cropping systems in drought prone areas. Due to the biocomplexity of drought, research and development efforts should be sustained to advance knowledge and iteratively improve models. HighlightCrop improvement rate in maize increased after implementation of drought breeding efforts. Harnessing crop, quantitative genetics and gap models will enable the transition from genetic evaluation to crop design.

plant biology↗

bioRxiv2020

Back to the future: Implications of genetic complexity for hybrid breeding strategies

Commercial hybrid breeding operations can be described as decentralized networks of smaller, more or less isolated breeding programs. There is further a tendency for the disproportionate use of successful inbred lines for generating the next generation of recombinants, which has led to a series of significant bottlenecks, particularly in the history of the North American and European maize germplasm. Both the decentralization and the disproportionate inbred use reduce effective population size and constrain the accessible genetic space. Under these conditions, long term response to selection is not expected to be optimal under the classical infinitesimal model of quantitative genetics. In this study we therefore aim to propose an alternative rational for the success of large breeding operations in the context of genetic complexity arising from the structure and properties of interactive genetic networks. For this we use simulations based on the NK model of genetic architecture. We indeed found that constraining genetic space and reducing effective population size, through program decentralization and disproportionate inbred use, is required to expose additive genetic variation and thus facilitate heritable genetic gains. These results introduce new insights into why the historically grown structure of hybrid breeding programs was successful in improving the yield potential of hybrid crops over the last century. We also hope that a renewed appreciation for "why things worked" in the past can guide the adoption of novel technologies and the design of future breeding strategies for navigating biological complexity.

genetics↗

bioRxiv2020

Modelling selection response in plant breeding programs using crop models as mechanistic gene-to-phenotype (CGM-G2P) multi-trait link functions

Plant breeding programs are designed and operated over multiple cycles to systematically change the genetic makeup of plants to achieve improved trait performance for a Target Population of Environments (TPE). Within each cycle, selection applied to the standing genetic variation within a structured reference population of genotypes (RPG) is the primary mechanism by which breeding programs make the desired genetic changes. Selection operates to change the frequencies of the alleles of the genes controlling trait variation within the RPG. The structure of the RPG and the TPE has important implications for the design of optimal breeding strategies. The breeders equation, together with the quantitative genetic theory behind the equation, informs many of the principles for design of breeding programs. The breeders equation can take many forms depending on the details of the breeding strategy. Through the genetic changes achieved by selection, the cultivated varieties of crops (cultivars) are improved for use in agriculture. From a breeding perspective, selection for specific trait combinations requires a quantitative link between the effects of the alleles of the genes impacted by selection and the trait phenotypes of plants and their breeding value. This gene-to-phenotype link function provides the G2P map for one to many traits. For complex traits controlled by many genes, the infinitesimal model for trait genetic variation is the dominant G2P model of quantitative genetics. Here we consider motivations and potential benefits of using the hierarchical structure of crop models as CGM-G2P trait link functions in combination with the infinitesimal model for the design and optimisation of selection in breeding programs.

genetics↗