Korol Laboratory

Lab Members | Collaborators | Alumni Graduate students| Alumni-Postdocs

The main research themes of the lab include:

  • Evolutionary adaptation to stressful environments using Drosophila as a model
  • Population genetics of multilocus systems (including non-linear dynamics)
  • Recombination variability, evolution of sex and recombination
  • Genome evolution and structure on the above-gene level
  • Genome mapping, including multilocus mapping and physical mapping
  • Genetics of quantitative traits and its evolutionary and practical applications

Corresponding studies of the lab are based on intimate interaction between field observations, laboratory experimentation, and theoretical analysis (using both mathematical modeling and computer simulations). Beside Drosophila, our studies target other organisms (in collaboration with other labs, both within and outside the Institute of Evolution) that include plants, fungi, mammals, and humans. The main fields of activity are related to evolutionary genetics and genomics (experimental and theoretical), genome structural and functional analysis, and bioinformatics.

Population Dynamics: The goal of research is to the study, at the model level, the conditions of sustaining polymorphism in (infinite) multi-locus populations, depending on different external factors, particularly, on selection.

  • The main result of studying population dynamics with constant selection was the development of a general theory of phenotype selection. In the framework of this theory, it has been shown that the set of stable polymorphous points is finite for “almost all” selection coefficients.
  • For the case of a two-locus diallele population, an important type of system with constant and variable selection has been discovered and studied. General statements about trajectory convergence have been obtained and applied to the case of moving-optimum selection. The role of the log-convex up and log-convex down selection functions has been revealed.
  • A new type of behavior of standard genetic models, extremely complex limiting trajectories (T-cycles, "supercycles," strange attractors, etc.) has been found. Such behavior results from the simple cyclic selection or combination of different breeding systems.
  • The model of interacting populations, in particular, of the host-parasite type, has been suggested and studied.

Evolution of sex and recombination: We are interested in testing theoretical models aimed at explaining the factors responsible of sex and recombination maintenance, the role of sex and recombination in population adaptation and genome evolution, and adaptive values of major properties of recombination and mutation (DNA repair) systems. A part of these studies is devoted to the role of sex and recombination in population polymorphism and complexity of population dynamics. We found that standard multilocus models with cyclical selection may demonstrate extremely complex dynamic patterns (including dynamic chaos), and this complexity does not show negative correlation with sex.

Our previous attempts were also concentrated on producing empirical evidence on the effect of selection for fitness related traits on recombination, using Drosophila melanogaster as a model organism. In particular, it was demonstrated that directional selection for tolerance for daily temperature fluctuations with amplitudes increasing by generations, as well as two-way selection for geotaxis, resulted in increased rates of recombination and relaxed interference. These findings on evolving recombination can be interpreted as an indication that genetic variation may limit the population response to selection. Our more recent studies of reproduction mode are based on the assessment of DNA sequence variation in nature using fungal populations along stress gradients.

Another important objective is unravelling ecological-genetic regulation of recombination and mutation. Rich evidence is available in the literature on changes in recombination rates under stress (radiation, temperature, starvation, chemicals). Moreover, some effects may be transmitted over many cell division cycles (we found such an effect in Drosophila and called it "ontogenetic memory") and even over generations. We speculate that these effects may reflect some regulation of genetic variation in the progeny of stressed individuals. Likewise, we found that stress-induced changes in recombination rate may depend on individual fitness: the higher the tolerance the lower the changes in recombination. Such dependence is applied also to mutation variation and can be interpreted as negative feedback regulation of genetic variation. This principle was recently employed by several authors in theoretical models with "fitness-associated recombination" (FAR) and extended to other aspects related to regulation of genetic variation.

Molecular-genetic basis of adaptation to stress: Our targets include analysis of sympatric differentiation of Drosophila natural populations inhabiting ecologically contrasting opposite slopes in “Evolution Canyon”, Mt. Carmel. Despite easy migration, significant interslope divergence was established involving habitat choice, mate choice (link 1, link 2), thermal and drought tolerances, allele frequencies (link 1, link 2) or expression of some candidate genes (period, desaturase2, hsp70, hsp40, hsp83, meiotic-9, patched, p53). Parallel patterns of stress tolerance, habitat choice, and mate choice were demonstrated in D. simulans. However, some tests for interslope genetic differentiation in Drosophila, derived from the opposite EC slopes, gave somewhat controversial results. New candidate genes are involved in further tests for association with adaptive reactions (physiological and behavioral) based on sequence organization in coding and non-coding genome regions and stress-induced changes in gene expression.

Genome structure, sequence comparisons on the above-gene level: Revealing the interplay of functional and structural genome organization is one of the main objectives of genomics. Current comparative genomics is mainly constrained to coding sequences or simple predictors (like GC isochors), thus the majority of information generated in sequencing projects remains mainly untouched. Recent discoveries that large conserved blocks of noncoding DNA may have strong phenotypic impacts indicating that gene-based analysis is not sufficient for understanding organization, regulation, and evolution of eukaryotic genomes. For sequence comparisons on above-gene levels, we employ “compositional spectra” (CS) analysis based on fuzzy linguistics. Our main hypothesis is that both genic and non-genic parts of genomes of higher eukaryotes are highly structured, with a restricted number of distinct CS-types (genomic states). We further hypothesize that the distribution of these genomic states has evolutionary and functional consequences. The main objectives include: classifying major patterns of sequence organization within eukaryotic genomes; correlating the revealed compositional patterns with biologically meaningful characteristics (mutation and recombination hot/cold spots, gene-rich regions, LD blocks, SNP rich regions, centromeric- or telomeric-like patterns); and detecting conserved and ultraconserved compositional patterns as new candidates for functionally meaningful controlling elements.

Genome mapping (genetic and physical): Our objective is developing efficient tools for multilocus mapping allowing reliable ordering of hundreds and thousands of markers per chromosome, complemented by computing-intensive map verification. As a tool for efficient discrete optimization of this challenging problem (with complexity ~n!) new heuristics for Evolution Strategy algorithms were developed in our lab. These tools are implemented in MultiPoint mapping package (http://multiqtl.com). Even more challenging is another targeted problem: building consensus maps using parallel data from different labs and/or for different mapping populations. Our group is developing new adaptive algorithms for physical mapping (contig assembly for BAC libraries based on fingerprinting or DNA-DNA hybridization data) and the integration of genetic and physical maps within the framework of FP7 Triticeae Genome Mapping consortium (http://www.triticeaegenome.eu).

Genetic architecture of quantitative traits: We are developing methods and algorithms for genetic mapping of quantitative traits, including joint analysis of multiple trait complexes, and using data scored in different developmental and ecological conditions. These tools are implemented in MultiQTL mapping package (http://multiqtl.com). Multiple-trait QTL analysis may be of special importance for revealing genomic determinants of microarray expression (eQTL mapping). We are interested in the analysis of domestication-evolution traits, agriculturally important stress-tolerance traits, and medically important traits of the rat and mouse. To increase the efficiency of QTL analysis, we have suggested a new cost-effective mapping design based on fractionated selective DNA pooling analysis, which is now being extended to association mapping.


Main Recent Grants


Triticeae genome physical mapping (FP7 project, www.triticeaegenome.eu)


Population genomics and association mapping of wheat disease resistance genes (BARD, with A. Braiman, J. Dvorak, and E. Akhunov)


Buildingconsensus genetic maps for maize and wheat (BARD, with P. Schnable and S. Aluru)


A genomic approach to trypanotolerance in cattle (US-AID CDR, with M. Soller, M. Ndungu & J.P. Gibson)


Incipient sympatric speciation in Drosophila caused by microclimatic contrasts (ISF)


Utilization of wild cereal germplasm from the Israeli Center of Diversity for wheat and barley improvement (DIP, with E. Nevo, T. Fahima, M. Roder, Y. Gutterman, P. Neumann, D. Yakir)


Molecular and behavioral adaptation to a natural gradient of thermal stress in “Evolution Canyon”, Mt. Carmel, Israel (BSF, with M. Feder)


Genetic basis of adaptation to stress in Drosophila natural populations (ISF)


QTL mapping of drought resistance derived from wild barley (US-AID CDR, with T. Fahima, Y. Turuspekov & E. Nevo)








You are here: Home Laboratories Korol laboratory Korol Laboratory