Hunting for Beneficial Mutations: Conditioning on SIFT Scores When Estimating the Distribution of Fitness Effect of New Mutations

Abstract

The Distribution of Fitness Effects (DFE) of new mutations is a key parameter of molecular evolution. The DFE can in principle be estimated by comparing the Site Frequency Spectra (SFS) of putatively neutral and functional polymorphisms. Unfortunately the DFE is intrinsically hard to estimate, especially for beneficial mutations since these tend to be exceedingly rare. There is therefore a strong incentive to find out whether conditioning on properties of mutations that are independent of the SFS could provide additional information. In the present study, we developed a new measure based on SIFT scores. SIFT scores are assigned to nucleotide sites based on their level of conservation across a multi species alignment: the more conserved a site, the more likely mutations occurring at this site are deleterious and the lower the SIFT score. If one knows the ancestral state at a given site, one can assign a value to new mutations occurring at the site based on the change of SIFT score associated with the mutation. We called this new measure δ. We show that properties of the DFE as well as the flux of beneficial mutations across classes covary with δ and, hence, that SIFT scores are informative when estimating the fitness effect of new mutations. In particular, conditioning on SIFT scores can help to characterize beneficial mutations.

A Novel NCSTN Mutation in a Three-Generation Chinese Family with Hidradenitis Suppurative

“…MagicViewer was used to view the short read alignment and validate the candidate SNPs and InDels [ 31 ]. Nonsynonymous variants were evaluated by four algorithms: Ployphen, SIFT, PANTHER, and Pmut, as described previously to determine pathogenicity [ 32 – 34 ].…”

Genetic load: genomic estimates and applications in non-model animals

“…Such data may become available for example, as specific distributions for different bins of selection coefficients [114][115][116][117][118] with the whole-genome sequencing of thousands of species [119][120][121] . The improved alignments and annotations of large datasets of whole genomes 42,79,120,122,123 and the integration of phylogenomic and population genomics approaches 124 will help to rapidly advance the field. Genomic sequences generated for individuals with fitness data across their entire life history, from deceased embryos to healthy adults, could be very useful for validating the genomic measures of realized load.…”

What does the distribution of fitness effects of new mutations reflect? Insights from plants

“…Another promising path is to use knowledge about the genome structure to improve estimation of the DFE. Whereas some methods rely on machine learning and require a large amount of information that is only available in a handful of species (Huang & Siepel, 2019), others should be easily implemented in numerous less well studied species (Chen et al ., 2021). Ultimately, this should help to tease apart the effects of life history and genomic traits vs demography (basically N e ) on the DFE.…”

“…Fig.2Box plots of (a) the π 0 /π 4 ratio and (b) the shape parameter β of the negative part of the distribution of fitness effects (DFE) for annual and perennial plant species. Data are from J Chen et al (2017Chen et al ( , 2021…”