Rieseberg, L. H. — Page 3

bioRxiv2019

Ancestral reconstruction of sunflower karyotypes reveals dramatic chromosomal evolution

Mapping the chromosomal rearrangements between species can inform our understanding of genome evolution, reproductive isolation, and speciation. Here we present a novel algorithm for identifying regions of synteny in pairs of genetic maps, which is implemented in the accompanying R package, syntR. The syntR algorithm performs as well as previous ad-hoc methods while being systematic, repeatable, and is applicable to mapping chromosomal rearrangements in any group of species. In addition, we present a systematic survey of chromosomal rearrangements in the annual sunflowers, which is a group known for extreme karyotypic diversity. We build high-density genetic maps for two subspecies of the prairie sunflower, Helianthus petiolaris ssp. petiolaris and H. petiolaris ssp. fallax. Using syntR, and we identify blocks of synteny between these two subspecies and previously published high-density genetic maps. We reconstruct ancestral karyotypes for annual sunflowers using those synteny blocks and conservatively estimate that there have been 7.9 chromosomal rearrangements per million years - a high rate of chromosomal evolution. Although the rate of inversion is even higher than the rate of translocation in this group, we further find that every extant karyotype is distinguished by between 1 and 3 translocations involving only 8 of the 17 chromosomes. This non-random exchange suggests that specific chromosomes are prone to translocation and may thus contribute disproportionately to widespread hybrid sterility in sunflowers. These data deepen our understanding of chromosome evolution and confirm that Helianthus has an exceptional rate of chromosomal rearrangement that may facilitate similarly rapid diversification.

evolutionary biology↗

bioRxiv2017

A Novel Post Hoc Method For Detecting Index Switching Finds No Evidence For Increased Switching On The Illumina HiSeq X

High throughput sequencing using the Illumina HiSeq platform is a pervasive and critical molecular ecology resource, and has provided the data underlying many recent advances. A recent study has suggested that index switching, where reads are misattributed to the wrong sample, may be higher in new versions of the HiSeq platform. This has the potential to invalidate both published and in-progress work across the field. Here, we test for evidence of index switching in an exemplar whole genome shotgun dataset sequenced on both the Illumina HiSeq 2500, which should not have the problem, and the Illumina HiSeq X, which may. We leverage unbalanced heterozygotes, which may be produced by index switching, and ask whether the under-sequenced allele is more likely to be found in other samples in the same lane than expected based on the allele frequency. Although we validate the sensitivity of this method using simulations, we find that neither the HiSeq 2500 nor the HiSeq X have evidence of index switching. This suggests that, thankfully, index switching may not be a ubiquitous problem in HiSeq X sequence data. Lastly, we provide scripts for applying our method so that index switching can be tested for in other datasets.

bioinformatics↗