RT Journal Article SR Electronic T1 Constructing a high-density linkage map to infer the genomic landscape of recombination rate variation in European Aspen (Populus tremula) JF bioRxiv FD Cold Spring Harbor Laboratory SP 664037 DO 10.1101/664037 A1 Rami-Petteri Apuli A1 Carolina Bernhardsson A1 Bastian Schiffthaler A1 Kathryn M. Robinson A1 Stefan Jansson A1 Nathaniel R. Street A1 Pär K. Ingvarsson YR 2019 UL http://biorxiv.org/content/early/2019/06/10/664037.abstract 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.