Meiotic recombination (or crossing-over) is advantageous from an evolutionary perspective, as it rearranges existing allelic variation to create new haplotypes in which beneficial alleles are unpaired from harmful alleles. Consequently, beneficial alleles are more likely to spread through populations whilst harmful alleles are lost, meaning that populations with higher crossing-over can respond to selection at a faster rate. Nevertheless, crossing-over may also have a cost: it can create new harmful mutations, and it may break apart beneficial combinations of alleles previously build up by selection. These relative costs and benefits of high and low recombination may vary within and between populations, and if crossover rate itself is heritable, then it may be subject to selection and have the potential to evolve. Therefore, understanding the genetic causes of variation in crossover rate and positioning is important for understanding the potential of a population to respond to selection and more generally, for interpreting and predicting evolutionary phenomena. This project will use next generation sequencing data from a long-term study of Soay sheep (Ovis aries) to investigate mutations driving crossover rate, recombination hotspot positioning and their evolution within this population.
Study system: The Soay sheep of St Kilda is a primitive breed of domestic sheep that has been intensively studied on an individual basis since 1985. SNP data for up to 600K SNPs is currently available for >7,000 individuals.
Key research objectives:
1. Use sequencing data to investigate causal mutations driving variation in crossover rate;
2. Use WGS and ChIP-seq data to identify recombination hotspots and relate this to paralogous variation in PRDM9 (a hotspot-positioning gene);
3. Investigate signatures of selection and association with fitness at identified variants.
Research Training: The project will be mostly computational and will suit students who are willing to learn and further develop bioinformatic skills. The supervisors will provide cutting-edge training in quantitative genetics, evolutionary genomics, statistics and bioinformatics. The first year will include an intensive one-semester course on population genetics, quantitative genetics and statistics at the Institute of Evolutionary Biology which will provide a basis for the analytical work. Further training will be provided by EASTBIO workshops/symposia and Edinburgh Genomics coding and bioinformatics courses. The student will also be able to take advantage of national and international collaborative links of the supervisors in the livestock and wild genomics communities.
Fieldwork: The student will be encouraged to contribute to fieldwork to collect samples and aid the field team to learn about the ecology of the system.
Lab work: The student will be expected to carry out lab work to prepare DNA for sequencing and genotyping. Experience in molecular lab techniques is desirable but not necessary as training will be provided.
Further information: The lead supervisor, Susan Johnston, has extensive experience in quantitative and evolutionary genomics in wild systems, and has developed methods to quantify individual rates of recombination from complex pedigrees. Co-supervisor Konrad Lohse brings expertise in bioinformatics and has developed inference methods for infering demography and recombination from whole genome resequence data.
Johnston SE, C Bérénos, J Slate, JM Pemberton (2016) Conserved genetic architecture underlying individual recombination rate variation in a wild population of Soay sheep. Genetics, 203, 583–598
Zhou Y et al (2018) Construction of PRDM9 allele-specific recombination maps in cattle using large-scale pedigree analysis and genome-wide single sperm genomics, DNA Research, 25, 183–194
Bioinformatics, Ecology & Conservation, Evolution, Genetics, Molecular Biology, Zoology/Animal Science
If you wish to apply for this project, please go to this link.