Austin Daigle
@adaigle.bsky.social
PhD Candidate in Bioinformatics & Computational Biology at UNC, coadvised by Dan Schrider & Parul Johri. I work on population genetics, transposon detection, and simulation-based inference—simulating evolution because real evolution takes way too long.
These results highlight a key challenge for population-genetic analyses in highly selfing species or low-recombination genomic regions. Check out the paper for a deeper dive into other potentially relevant factors like beneficial mutations, dominance coefficients, and population structure!
March 5, 2025 at 6:36 PM
These results highlight a key challenge for population-genetic analyses in highly selfing species or low-recombination genomic regions. Check out the paper for a deeper dive into other potentially relevant factors like beneficial mutations, dominance coefficients, and population structure!
If HRI was the cause mis-inference, we’d expect it to be caused by the reduced levels of effective recombination in selfers. Indeed, when we ran simulations with lower recombination (but no selfing), we saw the same patterns of DFE mis-inference.
March 5, 2025 at 6:36 PM
If HRI was the cause mis-inference, we’d expect it to be caused by the reduced levels of effective recombination in selfers. Indeed, when we ran simulations with lower recombination (but no selfing), we saw the same patterns of DFE mis-inference.
We hypothesized this mis-inference was caused by HRI, where linked deleterious mutations interact and reduce the efficacy of selection. The site frequency spectrum (SFS) had a U-shape at high selfing rates, a pattern often linked to HRI and not modeled by current DFE inference approaches.
March 5, 2025 at 6:36 PM
We hypothesized this mis-inference was caused by HRI, where linked deleterious mutations interact and reduce the efficacy of selection. The site frequency spectrum (SFS) had a U-shape at high selfing rates, a pattern often linked to HRI and not modeled by current DFE inference approaches.
In simulated highly selfing populations, the DFE was mis-inferred by two unique DFE inference methods—nearly neutral and strongly deleterious mutations were overestimated, while mildly deleterious ones were underestimated.
March 5, 2025 at 6:36 PM
In simulated highly selfing populations, the DFE was mis-inferred by two unique DFE inference methods—nearly neutral and strongly deleterious mutations were overestimated, while mildly deleterious ones were underestimated.