Verifying polymorphisms associated with long and short sleep using polycistronic CRISPR coupled with extreme QTL mapping
2020-04-20T22:59:43Z (GMT) by
Artificial selection and genome-wide association mapping have isolated unprecedented numbers of candidate polymorphisms putatively involved in sleep. One of the greatest challenges in functional genomics is to understand how these polymorphic variants affect sleep. CRISPR technology offers the possibility of replacing alleles to directly observe their effects; however, the task can be daunting for traits having large numbers of predicted polymorphic targets. Previously, we identified 126 polymorphisms for long and short night sleep using artificial selection. Here we apply a new approach to verify these polymorphisms. Using a polycistronic CRISPR gRNA design, we expressed multiple gRNAs per polycistronic construct to create indels near target polymorphisms in a long-sleeping line of the Sleep Inbred Panel, SIP_L1_9. We cloned four gRNAs into a pCFD5 plasmid and injected the plasmid into SIP_L1_9. We allowed the resulting transformants to mate randomly for two generations in order to recombine the transformed chromosomes. We then measured sleep in the transformed populations. Night sleep ranged from 136.7 min. ± 25.2 to 706.3 min. ± 7.4 for progeny from one polycistronic construct and from 266.9 min. ± 103.0 to 696.9 min. ± 9.7 for a second polycistronic construct, exceeding the range of night sleep in the unperturbed SIP_L1_9 background. This suggests that efficient transformation occurred in both populations. We collected the 10% shortest-sleeping and 10% longest-sleeping flies for each construct and extracted their DNA. We are currently sequencing the genomic DNA from each of the high/low 10% and will associate the sleep phenotypes with the number and combination of genomic breaks. In principle, more breaks should be present in the short sleeping flies than in the long sleepers as long sleepers represent the unperturbed background of the injected strain. In this way, we can quickly verify sleep-relevant polymorphisms with the greatest effects, screening out any false positives.