Prof. Milan Lstibůrek graduated from the Swedish University of Agricultural Sciences, the Czech University of Life Sciences (undergraduate degrees forest biology and forestry), and the North Carolina State University, USA (Ph.D. in quantitative forest genetics). Following his postdoc experience at the University of British Columbia in Vancouver, he pursued his academic career at the CULS, eventually reaching the full professorship in 2015. He is also serving as an Adjunct Professor at the North Carolina State University.
His original research covers the following topics: development of in-situ breeding and conservation applications in forest trees ("Breeding without Breeding" and alternatives); seed orchard spatial layouts ("Minimum Inbreeding Seed Orchard Design", "Optimum Neighborhood Seed Orchard Design"); development of optimum deployment and selection algorithms; development of alternative approaches to estimating genetic parameters (concept “Realized Heritability in Panmictic Populations”); studies on the genetics of fluorescence and spectral reflectance in forest trees; integration of genomics in tree breeding; and evaluation of positive assortative mating in forest tree breeding.
Scientific publications: Google Scholar
I am the principal investigator in two large projects:
Follow @KGFLD_Prague for updates about new publications. Beyond this, I am also involved in https://extemit.fld.czu.cz/en
My research in a nutshell
I get frequent questions about my research from the scientific community and forest tree breeders. This page provides basic information on my research in a broader context. I am adding links to scientific papers for a more detailed treatment of each topic. The reader should be familiar with fundamental concepts in quantitative and population genetics. I recommend the following three books: "Principles of Population Genetics" by Hartl and Clark, "Introduction to Quantitative Genetics" by Falconer and Mackay, and "Forest Genetics" by White, Adams, and Neale.
(1) Breeding-without-breeding (BWB)
Tree breeding is challenging both financially and logistically. Most tree breeding programs involve repeated cycles of three activities: breeding (crosses), testing, and selection. Historically, breeders conducted controlled crosses, followed by the establishment of large progeny trials replicated across a relatively small number of test sites. One drawback of these methods is that breeders have to isolate and label each strobilus and conduct crosses with known pollen, which must be collected from individual trees. The mating scheme is optimized to facilitate the estimation of genetic parameters and provide information for selecting the top-ranking individuals.
The BWB concept is based on natural pollination, meaning breeders typically collect cones in seed orchards and establish progeny trials without knowing the parentage of individual seedlings. Later, they use simple DNA analyses (e.g., SSR markers) and reconstruct the pedigree. Estimation of genetic parameters and selection then follows in the same trajectory.
Collecting bulk seed from orchards allows establishing commercial forest stands that can be regarded as "progeny trials". Large number of such trials facilitates much bigger candidate populations for selection (higher selection intensity) and evaluation of multiple adaptive traits across vast and complex environmental gradients. With climate change, this is a prerequisite for efficient multi-trait selection.
The fundaments are introduced here, followed by a retrospective (small-scale) study:
El-Kassaby, Y. A., & Lstibůrek, M. (2009). Breeding without breeding. Genetics Research 91(2): 111-120.
One of the questions people asked was about pollen contamination and its effect on the BWB efficiency. Using the phenotypic preselection, it is not difficult to show that the male parents of high-ranking offspring are typically located in seed orchards, and the effect is substantial. Theoretical reasoning is here:
Lstibůrek, M., Klápště, J., Kobliha, J., & El-Kassaby, Y. A. (2012). Breeding without Breeding. Tree Genetics & Genomes 8(4): 873-877.
The reasoning was later empirically demonstrated here:
Korecký, J., Lstibůrek, M., & El-Kassaby, Y. A. (2014). Congruence between theory and practice: reduced contamination rate following phenotypic preselection within the Breeding without Breeding framework. Scandinavian Journal of Forest Research 29(6): 552-554.
Another question was how many offspring should be genotyped to satisfy the declared selection diversity constraint. This is somewhat more complicated, but I recommend reading both the Introduction and Discussion chapters:
Lstibůrek, M., Ivanková, K., Kadlec, J., Kobliha, J., Klápště, J., & El-Kassaby, Y. A. (2011). Breeding without breeding: minimum fingerprinting effort with respect to the effective population size. Tree Genetics & Genomes, 7(5), 1069-1078.
For a thorough treatment of the BWB concept and its efficiency, see
Lstibůrek, M., Hodge, G. R., & Lachout, P. (2015). Uncovering genetic information from commercial forest plantations—making up for lost time using "breeding without breeding". Tree Genetics & Genomes 11(3): 1-12.
In the paper, we provide stochastic and deterministic gain predictions. We provide reasoning to quantify the proportion of offspring that should be phenotyped and genotyped. It is essential to understand that in BWB, DNA sampling is conducted in a small proportion of the offspring, thus minimizing the genotyping cost.
The BWB concept is now popular in many countries. We discuss the large-scale application in Norway's spruce breeding program in Norway. This is where we first introduce the "landscape" adjective to the BWB.
Lstibůrek, M., El-Kassaby, Y. A., Skrøppa, T., Hodge, G. R., Sønstebø, J. H., & Steffenrem, A. (2017). Dynamic gene-resource landscape management of Norway spruce: combining utilization and conservation. Frontiers in Plant Science 8: 1810.
First large-scale empirical evidence of the BWB's efficiency is provided in the following paper. I am pleased to say that BWB works, and the efficiency is comparable to traditional breeding at a fraction of the cost. Selected offspring were already grafted and planted in the second generation seed orchard (using the Minimum-Inbreeding design) and will provide improved seed to Austrian forestry.
Lstibůrek, M., Schueler, S., El-Kassaby, Y. A., Hodge, G. R., Stejskal, J., Korecký, J., ... & Geburek, T. (2020). In Situ genetic evaluation of European larch across climatic regions using marker-based pedigree reconstruction. Frontiers in Genetics: 28.
I will now return to one of the above papers.
Lstibůrek, M., Klápště, J., Kobliha, J., & El-Kassaby, Y. A. (2012). Breeding without Breeding. Effect of pollen contamination on the fingerprinting effort. Tree Genetics & Genomes 8(4): 873-877.
The equations got stuck in my head for a long time, and I was unsure why. Later, it occurred to me that assuming an identical analytical solution, one may consider heritability as an output variable. It follows that narrow-sense heritability could be alternatively expressed as:
Under the assumptions of Hardy-Weinberg equilibrium, the narrow-sense heritability of a quantitative trait in a given population is directly related to the likelihood of a phenotypic subset of parents passing their respective alleles onto the corresponding phenotypic subset of offspring (Lstibůrek et al. 2018).
I used the original F. Galton's dataset of human height to demonstrate the concept. This was my first big trip outside the forest genetics. If interested, look at the paper.
Lstibůrek, M., Bittner, V., Hodge, G. R., Picek, J., & Mackay, T. F. (2018). Estimating realized heritability in panmictic populations. Genetics 208(1): 89-95.
(3) Seed orchards
I provide a separate link on the left (Software tab) with the presentation on seed orchard spatial layouts and my software developed for tree breeders. These tools have been used in numerous counties (Europe, North America, Africa, Asia, Australia). Upon request, I organize free seminars. Follow @KGFLD_Prague or send me a request.
A long story short, we developed two algorithms: Minimum-inbreeding and Optimum-Allocation-Algorithm, each with its pros and cons. See my presentation for details. I recommend reading the papers in the following sequence.
Lawler, E. L. (1963). The quadratic assignment problem. Management Science 9(4): 586-599.
Lstibůrek, M., & El-Kassaby, Y. A. (2010). Minimum-inbreeding seed orchard design. Forest Science 56(6): 603-608.
Lstibůrek, M., Stejskal, J., Misevicius, A., Korecký, J., & El-Kassaby, Y. A. (2015). Expansion of the minimum-inbreeding seed orchard design to operational scale. Tree Genetics & Genomes 11(1): 1-8.
Chaloupková, K., Stejskal, J., El-Kassaby, Y. A., & Lstibůrek, M. (2016). Optimum neighborhood seed orchard design. Tree Genetics & Genomes 12(6): 1-7.
Chaloupková, K., Stejskal, J., El-Kassaby, Y. A., Frampton, J., & Lstibůrek, M. (2019). Current advances in seed orchard layouts: two case studies in conifers. Forests 10(2): 93.
Chaloupková, K., Lstibůrek, M. (2022). Spatial optimization of genetic thinning in seed orchards. Annals of Forest Science 79(37): 1-8.
(4) Assortative mating
Please, check my dissertation at NC State
There were four papers from the research, three related to the effects of assortative mating in forest tree breeding
Lstibůrek, M., Mullin, T. J., Mackay, T. F. C., Huber, D., & Li, B. (2005). Positive assortative mating with family size as a function of predicted parental breeding values. Genetics 171(3): 1311-1320.
Lstibůrek, M., Mullin, T. J., Lindgren, D., & Rosvall, O. (2004). Open-nucleus breeding strategies compared with population-wide positive assortative mating. I. Equal distribution of testing effort. Theoretical and Applied Genetics 109(6): 1196-1203.
Lstibůrek, M., Mullin, T. J., Lindgren, D., & Rosvall, O. (2004). Open-nucleus breeding strategies compared with population-wide positive assortative mating. II. Unequal distribution of testing effort. Theoretical and Applied Genetics 109(6): 1169–1177.
(5) Statistical inference related to the pedigree reconstruction
This is an ongoing collaboration with the Faculty of Mathematics & Physics, the Charles University in Prague. We aim at two new methods, one with a broader scope for the genetic community (both plants and animals) and another more focused on forestry. Follow @KGFLD_Prague for updates.
(6) Optimum selection algorithms
Initial thoughts were provided here:
Funda, T., Lstibůrek, M., Lachout, P., Klápště, J., & El-Kassaby, Y. A. (2009). Optimization of combined genetic gain and diversity for collection and deployment of seed orchard crops. Tree Genetics & Genomes, 5(4), 583-593.
The method has a severe drawback: in certain instances, the additive relationship matrix (quadratic constraint) is not positive-semidefinite. The same issue can be found in widely cited
Meuwissen, T. H. E. (1997). Maximizing the response of selection with a predefined rate of inbreeding. Journal of Animal Science 75(4): 934-940.
Heuristic algorithms, such as
Mullin, T. J., & Belotti, P. (2016). Using branch-and-bound algorithms to optimize selection of a fixed-size breeding population under a relatedness constraint. Tree Genetics & Genomes 12(1): 1-12.
can not guarantee to find the optimal solution.
For over 15 years, I have been thinking about alternative approaches to this problem. Unlike the similar mathematical programming model in economics, there seems to be a shortcut in genetics. I am working on that now, and I would like to provide a general solution to the broader genetic community. Follow @KGFLD_Prague for updates.