bioRxivEvolutionary Biology
Quantifying the fraction of new mutations that are recessive lethal
E. E. Wade, C. Kyriazis, M. I. A. Cavassim, K. E. Lohmueller
Abstract
The presence and impact of recessive lethal mutations has been widely documented in diploid outcrossing species. However, precise estimates in different species of the proportion of mutations that are recessive lethal remain limited. Here, we attempt to quantify the fraction of new mutations that are recessive lethal using Fit{partial}a{partial}i, a commonly-used method for inferring the distribution of fitness effects (DFE) using the site frequency spectrum. Using simulations, we demonstrate that Fit{partial}a{partial}i cannot accurately estimate the fraction of recessive lethal mutations, as expected given that Fit{partial}a{partial}i assumes that all mutations are additive by default. Consistent with the idea that mis-specification of the dominance model can explain this performance, we find that Fit{partial}a{partial}i can accurately infer the fraction of additive lethal mutations. Moreover, we demonstrate that in both additive and recessive cases, inference of the deleterious non-lethal portion of the DFE is minimally impacted by a small proportion (<10%) of lethal mutations. Finally, as an alternative approach to estimate the proportion of mutations that are recessive lethal, we employ models of mutation-selection-drift balance using existing genomic parameters and segregating recessive lethals estimates for humans and Drosophila melanogaster. In both species, we find that the segregating recessive lethal load can be explained by a very small fraction (<1%) of new nonsynonymous mutations being recessive lethal. Our results refute recent assertions of a much higher recessive lethal mutation fraction (4-5%), while highlighting the need for additional information on the joint distribution of selection and dominance coefficients.
- organism: human
- organism: Drosophila
- assay: DNA methylation
- assay: whole-genome sequencing
- research focus: variant effect prediction
- research focus: variant calling
- approach: statistical model
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