PT - JOURNAL ARTICLE AU - Rami-Petteri Apuli AU - Carolina Bernhardsson AU - Bastian Schiffthaler AU - Kathryn M. Robinson AU - Stefan Jansson AU - Nathaniel R. Street AU - Pär K. Ingvarsson TI - Constructing a high-density linkage map to infer the genomic landscape of recombination rate variation in European Aspen <em>(Populus tremula)</em> AID - 10.1101/664037 DP - 2019 Jan 01 TA - bioRxiv PG - 664037 4099 - http://biorxiv.org/content/early/2019/06/10/664037.short 4100 - http://biorxiv.org/content/early/2019/06/10/664037.full AB - The rate of meiotic recombination is one of the central factors determining levels of linkage disequilibrium and the efficiency of natural selection, and many organisms show a positive correlation between local rates of recombination and levels of nucleotide diversity indicating that linked selection is an important factor determining genome-wide levels of nucleotide diversity. Several methods for estimating recombination rates from segregating polymorphisms in natural populations have recently been developed. These methods have been extensively used in part because they are relatively simple to implement even in many non-model organisms, but also because they potentially offer higher resolution than traditional map-based methods. However, thorough comparisons of LD and map-based estimates of recombination are not readily available in plants. Here we present a new, high-resolution linkage map for Populus tremula and use this to estimate variation in recombination rates across the P. tremula genome. We compare these results to recombination rates estimated based on linkage disequilibrium in a large number of unrelated individuals. We also assess how variation in recombination rates is associated with genomic features, such as gene density, repeat density and methylation levels. We find that recombination rates obtained from the two methods largely agree, although the LD-based method identify a number of genomic regions with very high recombination rates that the map-based method fail to detect. Linkage map and LD-based estimates of recombination rates are positively correlated and show similar correlations with other genomic features, showing that both methods can accurately infer recombination rate variation across the genome.