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Abstract Selection that acts in a sex-specific manner causes the evolution of sexual dimorphism. Sex-specific phenotypic selection has been demonstrated in many taxa and can be in the same direction in the two sexes (differing only in magnitude), limited to one sex, or in opposing directions (antagonistic). Attempts to detect the signal of sex-specific selection from genomic data have confronted numerous difficulties. These challenges highlight the utility of “direct approaches,” in which fitness is predicted from individual genotype within each sex. Here, we directly measured selection on Single Nucleotide Polymorphisms (SNPs) in a natural population of the sexually dimorphic, dioecious plant, Silene latifolia. We measured flowering phenotypes, estimated fitness over one reproductive season, as well as survival to the next year, and genotyped all adults and a subset of their offspring for SNPs across the genome. We found that while phenotypic selection was congruent (fitness covaried similarly with flowering traits in both sexes), SNPs showed clear evidence for sex-specific selection. SNP-level selection was particularly strong in males and may involve an important gametic component (e.g., pollen competition). While the most significant SNPs under selection in males differed from those under selection in females, paternity selection showed a highly polygenic tradeoff with female survival. Alleles that increased male mating success tended to reduce female survival, indicating sexual antagonism at the genomic level. Perhaps most importantly, this experiment demonstrates that selection within natural populations can be strong enough to measure sex-specific fitness effects of individual loci. Males and females typically differ phenotypically, a phenomenon known as sexual dimorphism. These differences arise when selection on males differs from selection on females, either in magnitude or direction. Estimated relationships between traits and fitness indicate that sex-specific selection is widespread, occurring in both plants and animals, and explains why so many species exhibit sexual dimorphism. Finding the specific loci experiencing sex-specific selection is a challenging prospect but one worth undertaking given the extensive evolutionary consequences. Flowering plants with separate sexes are ideal organisms for such studies, given that the fitness of females can be estimated by counting the number of seeds they produce. Determination of fitness for males has been made easier as thousands of genetic markers can now be used to assign paternity to seeds. We undertook just such a study in S. latifolia, a short-lived, herbaceous plant. We identified loci under sex-specific selection in this species and found more loci affecting fitness in males than females. Importantly, loci with major effects on male fitness were distinct from the loci with major effects on females. We detected sexual antagonism only when considering the aggregate effect of many loci. Hence, even though males and females share the same genome, this does not necessarily impose a constraint on their independent evolution.
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SPIN: sex-specific and pathway-based interpretable neural network for sexual dimorphism analysis
Abstract Sexual dimorphism in prevalence, severity and genetic susceptibility exists for most common diseases. However, most genetic and clinical outcome studies are designed in sex-combined framework considering sex as a covariate. Few sex-specific studies have analyzed males and females separately, which failed to identify gene-by-sex interaction. Here, we propose a novel unified biologically interpretable deep learning-based framework (named SPIN) for sexual dimorphism analysis. We demonstrate that SPIN significantly improved the C-index up to 23.6% in TCGA cancer datasets, and it was further validated using asthma datasets. In addition, SPIN identifies sex-specific and -shared risk loci that are often missed in previous sex-combined/-separate analysis. We also show that SPIN is interpretable for explaining how biological pathways contribute to sexual dimorphism and improve risk prediction in an individual level, which can result in the development of precision medicine tailored to a specific individual’s characteristics.
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- Award ID(s):
- 2117941
- PAR ID:
- 10548057
- Publisher / Repository:
- Oxford
- Date Published:
- Journal Name:
- Briefings in Bioinformatics
- Volume:
- 25
- Issue:
- 4
- ISSN:
- 1467-5463
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1093/bib/bbae239
