Decreased recent adaptation at human mendelian disease genes as a possible consequence of interference between advantageous and deleterious variants

Abstract

Advances in genome sequencing have dramatically improved our understanding of the genetic basis of human diseases, and thousands of human genes have been associated with different diseases. Despite our expanding knowledge of gene-disease associations, and despite the medical importance of disease genes, their recent evolution has not been thoroughly studied across diverse human populations. In particular, recent genomic adaptation at disease genes has not been characterized as well as purifying selection and long-term adaptation. Understanding the relationship between disease and adaptation at the gene level in the human genome is hampered by the fact that we don’t know whether disease genes have experienced more, less, or as much adaptation as non-disease genes during the last ~50,000 years of recent human evolution. Here, we compare the rate of strong recent adaptation in the form of selective sweeps between mendelian, non-infectious disease genes and non-disease genes across 26 distinct human populations from the 1,000 Genomes Project. We find that mendelian disease genes have experienced far less selective sweeps compared to non-disease genes especially in Africa. This sweep deficit at mendelian disease genes is less visible in East Asia or Europe. Investigating further the possible causes of the sweep deficit at disease genes, we find that this deficit is very strong at disease genes with both low recombination rates and with high numbers of associated disease variants, but is almost non-existent at disease genes with higher recombination rates or lower numbers of associated disease variants. Because segregating recessive deleterious variants have the ability to interfere with adaptive ones, these observations strongly suggest that adaptation has been slowed down by the presence of interfering recessive deleterious variants at disease genes. This is further supported by population simulations that show that interference at disease genes is expected to be lower in East Asia and Europe. These results clarify the evolutionary relationship between disease genes and recent genomic adaptation, and suggest that disease genes suffer not only from a higher load of segregating deleterious mutations, but also from a transient inability to adapt as much, and/or as fast as the rest of the genome.

Strong evidence for the adaptive walk model of gene evolution in Drosophila and Arabidopsis

“…This higher load of segregating deleterious mutations can interfere with nearby beneficial mutations, preventing their fixation and slowing down adaptation. Indeed, previous studies have shown that segregating recessive deleterious mutations can substantially decrease the fixation rate of advantageous mutations, leading to a weaker adaptive signal in some parts of the genome [90,91]. In contrast, the large effective population sizes of Drosophila species make them more efficient in removing deleterious mutations and prone to stronger positive selection [92][93][94].…”

Timesweeper: Accurately Identifying Selective Sweeps Using Population Genomic Time Series

“…Such efforts can help reveal the extend and mode of recent adaptation, as well as clues about the genetic and phenotypic targets of selection [9,20,24]. Moreover, signatures of selective sweeps have been shown to co-localize with disease-associated mutations [11, 56], but see [57]. One possible explanation is that deleterious alleles on the positively selected haplotype may hitchhike to higher frequencies than they might otherwise reach [58], although other potential explanations exist [59, 60].…”

Mixture Density Regression reveals frequent recent adaptation in the human genome

“…These mutations can interfere with adaptation when they are in linkage disequilibrium with advantageous mutations. This would be especially relevant for genomic regions with low recombination, where the linkage disequilibrium between deleterious and advantageous mutations is more likely [19,52]. Therefore, a higher coding density could favor or hinder positive selection depending on the circumstances, complicating the detection of an association between coding density and selection with our approach.…”