Tim Beissinger is the lead of modeling for a plant biology project at Google X, "The Moonshot Factory".

Topic: Shooting for the moon – applying genomic prediction forward, backward, and one plant at a time.

Abstract: The pace at which humanity invented and applied new technologies to go from an earth-bound species to one capable of traveling to the moon is remarkable. Such progress is possible by bringing together huge problems, breakthrough technologies, and radical solutions. The world of plant breeding and plant genetics today is ripe for moonshot thinking. Food security is a problem for millions of people around the world, DNA sequencing and other genotyping technologies have become routinely applied across even niche crops, and thousands of scientists across the globe are developing the radical biological and computational solutions that will bring about the next frontier in agriculture. To this end, this talk will explore the application of genomic prediction in three radical ways. Forward: Tim Beissinger will discuss the use of machine-learning based models to make phenotypic predictions for specific environmental conditions. Backward: He will show how decoding signals of prior polygenic selection can inform and drive breeding decisions in the future. One plant at a time: He will describe a framework to do breeding with data from individual segregating plants instead of working with plot-level data. Lastly, Tim Beissinger will describe Google X, The Moonshot Factory, and how Google is cultivating a “factory” of moonshot solutions to some of the world’s biggest problems.

Biography: Tim Beissinger previously was a professor at the University of Göttingen where he held the chair of Plant Breeding Methodology and served as the director of the Center for Integrated Breeding Research. He received his PhD in Statistical and Quantitative Genetics from the University of Wisconsin. Tim Beissinger's research involves developing quantitative and evolutionary genetic methods for plant breeding applications, especially in the areas of genomic prediction, experimental evolution and artificial intelligence.

Guusje Bonnema is the Research Group Leader (Reader) of the 'Quality and Development group' in Plant Breeding, Wageningen University and Research.

Topic: Lettuce Breeding for Vertical Farming: Modulating plant circadian rhythms to maximise yield potential

Abstract: The circadian clock is an endogeneous timekeeping mechanism that enables plants to synchronise their metabolic and physiological processes with the daily cycle of the Earth. It influences many agronomic traits and has recurrently been targeted through artificial selection during domestication and improvement of crops. In our study, we discovered that during the domestication of lettuce (Lactuca sativa), the circadian clock period was altered, resulting in a shift from a 24-hour cycle in wild lettuce to a 27-hour cycle in cultivated lettuce. Our genome-wide association studies (GWAS) revealed that this change was achieved through mutations in PHYTOCHROME C (PHYC). This protein plays a role in the plant's circadian clock and flowering processes. We propose that a slower clock in lettuce is a result of breeding efforts aimed at delaying bolting, a process that precedes flowering and is generally considered undesireable. By altering the activity of PHYC, it was possible to modify the timing of bolting and delay flowering while indirectly slowing the plant's circadian clock. These findings suggest that PHYC likely promotes photoperiodic flowering in lettuce and provide another example of the importance of the circadian clock in plant biology and the development of crops. We hypothesise that this prolonged circadian clock period increases lettuce vegetative growth when cultivated under long day (photoperiod) conditions, despite the loss of circadian resonance.

Biography: Guusje Bonnema's research focuses on understanding the genetic diversity and molecular/genetic regulation of domestication traits of very diverse diploid Brassica morphotypes. In addition, she investigates what traits optimise crops for cultivation in novel cultivation systems. This includes strip cultivation, a sustainable cropping system that reduces the need for external inputs while maintaining or increasing yield per area. In addition, she investigates traits that optimise lettuce for growth in vertical farms, with a special focus on genetic variation in circadian clock parameters.

Sreekala Chellamma is a laureate and discovery research lead at Corteva Agriscience in des Moines, Iowa, USA.

Topic: Doubled Haploids - Technology advancements and applications

Abstract: Doubled haploids (DH) have been known in the literature for decades. Various methods of producing DHs have been employed widely for genetic gain in plant breeding programs for a few crops. Moreover, this provides a system for enhancing biological understandings in both breeding and plant cell biology. Most recently, haploids and DHs have been considered tools for incorporating targeted breeding in crop species. Past research focused on protocol developments using either chromosome elimination techniques or tissue culture methods. Advancements in technological space, especially in cell biology, engineering, and digital technologies, make it possible to envision rapid advancements in this space. A few examples will be discussed in this seminar.

Biography: Sreekala Chellamma received her Bachelor of Science in agriculture and her Master of Science in horticulture at Kerala Agricultural University in Thrissur, India, and her PhD in agriculture at the Indian Agricultural Research Institute, New Delhi. Her PhD focus was among the first research on exploiting heterosis for carotenoids in African marigold, which is highly valued today for animal field and its pharmaceutical value. She pursued her post-doctoral research as a research officer at the Temasek Life Sciences Laboratories in Singapore, a research fellow at the National Institute of Agrobiological Sciences, Japan, and as a biologist at Agriculture and AgriFood Canada. During this period, she worked on rice disease resistance, rice stress biology, and carotenoid metabolic engineering. She joined Dow Agrosciences in 2009 as a trait manager for canola and continued with similar roles in corn and cotton. She made key contributions to the development of the Enlist™ cotton trait that is currently in the market. Later she became the monocot transformation leader at Dow Agrosciences. Most recently, she assumed the discovery lead position at Corteva Agriscience, where her interest is using cell and reproductive biology to develop novel breeding technologies for crops of interest to Corteva.

Gregor Gorjanc is a Group Leader (Reader) at the Roslin Institute of the University of Edinburgh.

Topic: Application of tree sequences to boost breeding in the era of mega-scale genomics

Abstract: The use of genomic data in agriculture is rapidly growing. Many breeding programs now already have millions of individuals genotyped with SNP arrays. At the same time, the cost of whole-genome sequencing is continuing to drop, promising another significant increase in the amount of genomic data. With both the number of individuals and the number of loci in millions, we face the quadratic complexity problem of storing genotype data, let alone analysing it. Gregor Gorjanc will present his and his team's experience using the new tree sequence data format to manage storage and analyses of mega-scale genomic datasets. The essence of the tree sequence data format is to represent the genotype data by describing its generating process - DNA copying, recombination, and mutation. He will show applications of inferring tree sequences in different species and analysing tree sequences from a population genetics perspective as well as a quantitative genetics perspective.

Biography: Gregor Gorjanc leads the Highlander Lab, which focuses on managing and improving agricultural populations using data science, genetics, and breeding. The lab is particularly interested in (i) methods for genetics and breeding, (ii) desing and optimisation of breeding programs, and (iii) analysis of data to unravel biology and find new ways of improving populations. These interests are applied to a range of species, including livestock, crops, and insects. See www.ed.ac.uk/roslin/highlanderlab for more information.

Carus John-Bejai is responsible for managing the KWS global wheat pre-breeding activities, which he took over in 2021 from Dr. Lage.

Topic: Something old, something new and something borrowed: deployment of plant genetic resources in commercial Triticum aestivum breeding programs

Abstract: The global districbution of T. aestivum genetic subgroups has been primarily influenced by historical and political factors, with several reports of the transfer of breeding material between contrasting agro-climatic zones. We at KWS advocate for the sustained global exchange of breeding material and established a commercial pre-breeding program to systematically use foreign germplasm in our target regions. Foreign elite cultivars (something borrowed) continue the base of our pre-breeding program.
Despite lacking adaptation, some spring wheat cultivars show a positive additive effect in grain yield in environments they were not bred for or in. This suggests that yield potential is not intrinsically linked to adaptation and that beneficial alleles exist suitable for deployment across the globe. Our pre-breeding program has been successful at serving ad a bridge for introducing novel and useful genetic diversity into our elite germplasm pools. We believe this is the underlying principle that had facilitated our success.
How can we address the emergence of new pests and deseases? Or climatic conditions not currently experienced? We believe that the development of synthetic hexaploid wheat and alien introgression lines (something new) bearing the genetics of T. aestivum's progenitors and relatives will provide us with the solution to these problems. Within the UK, public and private partnerships and collaborative research between academia and industry have shown the value of this material.
As mentioned previously, the dominance of a genetic subgroup in a region does not mean it is the only subgroup that is or was appropriate for the region. With the increased  focus on hybrid wheat breeding, these neglected subgroups (something old) may offer an avenue to develop heterotic groups. But, what is the most efficient way to utilize these subgroups?

Biography: Trinidad and Tobago is his home, where he grew up in a family actively involved in agriculture. This led Carus John-Bejai to become interested in plant breeding as a career and to move to the UK in order to further his studies in this area. He pursued a PhD in Crop Sciences at the University of Nottingham. His research efforts were focused on understanding the genetic and physiological factors that underly the outcrossing ability in hexaploid wheat. After this, he joined KWS UK Ltd in 2017 as an assistant wheat pre-breeder. Carus John-Bejai was mentored by Dr. Jacon Lage and learned the practicalities of a pre-breeding program targeting quantitatively inherited traits such as grain yiel and disease resistance. In addition, he worked on heterotic pools in wheat and the incorporation of male nuclear sterility in breeding pipelines. He is passionate about the use and conservation of plant genetic resources. He is actively trying to apply quantitative genetic theory, statistics, and creativity to better use the genetic resources we have available, now.

Lydia Kienbaum is a senior bioinformatician and plant breeder at Selecta One.

Topic: Bringing phenotyping into the 21st century: Extracting plant traits from images with deep neural networks

Abstract: In modern breeding, there is a stark contrast between the increasing availability of cheap genotypic data and the still mostly manual and labour-intensive task of obtaining phenotypic data - often referred to as the phenotyping bottleneck. Surprisingly, this bottleneck still persists up to today despite our everyday lives being increasingly dominated by artificial intelligence and machine learning algorithms which are capable of transforming entire industries overnight. In this talk, Lydia Kienbaum wants to demonstrate that machine learning techniques can also be applied to plant phenotyping with great success. In particular, she wants to focus on the fruitful combination of applying convolutional neural networks to the analysis of phenotypic images of gene banks and breeding trials. Here, deep learning enables the fast and reliable recognition of plant traits, such as width, length, size and color of maize cobs or quinoa panicles, and provides a high-throughput phenotyping platform that is capable of automatically analysing thousands of images with high accuracy in a short amount of time.

Biography: Lydia Kienbaum finished her Master's degree in plant breeding at the University of Hohenheim in 2020. She was awarded the Rudolf-Mansfeld-Prize 2021 for her thesis, presented by the Association for the promotion of Crop Research Gatersleben. She currently leads and establishes the bioinformatics department within the ornamental plant breeding company Selecta One. Her work encompasses the utilisation of data science techniques for molecular data analysis, GWAS, and KASP marker development. She is responsible for successfully initiating and managing multiple projects focusing on improving data-driven decision-making throughout the organisation with primary emphasis on research and development, product development, and production. In addition to her work at Selecta One, she supported the Institute of Plant Breeding, Seed Science and Population Genetics at the University of Hohenheim as a Bioinformatician. Among others, Lydia established the pipelines for automatic plant phenotyping of genetic resources in maize and quinoa using deep learning techniques and assisted in the data analysis for multiple publication within the scope of PhD projects.

Sandra M. Schmöckel is a Junior Professor at the Department of Physiology of Yield Stability

Topic: The prospectives of Chenopodium quinoa

Abstract: Chenopodium quinoa (quinoa) is a pseudocereal that is known for its beneficial nutritional quality. Quinoa is gluten-free, contains many essential amino acids and has more protein compared to some grains. Quinoa is also characterized by a high tolerance to abiotic stresses; for instance, cold, drought and salt stress. Quinoa stands out for its great diversity: the seeds can be white, beige, red or black and some plants are green while others are pink. There are saponins (bitter-tasting secondary metabolites) in the outer layers of the quinoa seed; these must be removed before consumption and therefore incur considerable costs for post-harvest processing. A high-quality reference genome sequence is available for quinoa, which made it possible to identify the transcription factor that presumably regulates saponin biosynthesis. We use this knowledge, for example, to establish markers for the breeding of non-bitter quinoa. We also characterize the growth of different quinoa ecotypes to evaluate the level of stress tolerance.

Biography: Sandra Schmöckel has been working at the University of Hohenheim since the end of 2018 predominantly with the pseudocereal quinoa and the cereal barley. She is particularly interested in secondary metabolites such as saponins (bitter substances on the outer shell of seeds), as well as drought, heat and salt stress tolerance. As a postdoc (2014 – 2018) she worked with Prof. Mark Tester on the sequencing of the quinoa genome and the regulation of saponins at King Abdullah University of Science and Technology (KAUST, Saudi Arabia). She received her PhD at the University of Adelaide (Australia) where she worked on salt stress tolerance in Arabidopsis.