Sex-dependent dominance maintains migration supergene in rainbow trout

Abstract

Males and females often differ in their fitness optima for shared traits that have a shared genetic basis, leading to sexual conflict. Morphologically differentiated sex chromosomes can resolve this conflict and protect sexually antagonistic variation, but they accumulate deleterious mutations. However, how sexual conflict is resolved in species that lack differentiated sex chromosomes is largely unknown. Here we present a chromosome-anchored genome assembly for rainbow trout (Oncorhynchus mykiss) and characterize a 55-Mb double-inversion supergene that mediates sex-specific migratory tendency through sex-dependent dominance reversal, an alternative mechanism for resolving sexual conflict. The double inversion contains key photosensory, circadian rhythm, adiposity and sex-related genes and displays a latitudinal frequency cline, indicating environmentally dependent selection. Our results show sex-dependent dominance reversal across a large autosomal supergene, a mechanism for sexual conflict resolution capable of protecting sexually antagonistic variation while avoiding the homozygous lethality and deleterious mutations associated with typical heteromorphic sex chromosomes. Methodology ReplicatesDescribe the experimental replicates, specifying number, type and replicate agreement. Sequencing depthDescribe the sequencing depth for each experiment, providing the total number of reads, uniquely mapped reads, length of reads and whether they were paired-or single-end. AntibodiesDescribe the antibodies used for the ChIP-seq experiments; as applicable, provide supplier name, catalog number, clone name, and lot number. Peak calling parametersSpecify the command line program and parameters used for read mapping and peak calling, including the ChIP, control and index files used. Data qualityDescribe the methods used to ensure data quality in full detail, including how many peaks are at FDR 5% and above 5-fold enrichment. SoftwareDescribe the software used to collect and analyze the ChIP-seq data. For custom code that has been deposited into a community repository, provide accession details. Flow Cytometry PlotsConfirm that:The axis labels state the marker and fluorochrome used (e.g. CD4-FITC).The axis scales are clearly visible. Include numbers along axes only for bottom left plot of group (a 'group' is an analysis of identical markers).All plots are contour plots with outliers or pseudocolor plots.A numerical value for number of cells or percentage (with statistics) is provided. Methodology Sample preparationDescribe the sample preparation, detailing the biological source of the cells and any tissue processing steps used. InstrumentIdentify the instrument used for data collection, specifying make and model number. SoftwareDescribe the software used to collect and analyze the flow cytometry data. For custom code that has been deposited into a community repository, provide accession details.Cell population abundance Describe the abundance of the relevant cell populations within post-sort fractions, providing details on the...

Temporal dynamics of migration‐linked genetic variation are driven by streamflows and riverscape permeability

“…We found that Omy05 SNPs were characterized by lower heterozygosity than expected (H 0 = 0.43, H E = 0.46, p < .01 in a paired t test), and this was the only chromosome where this pattern was observed ( Figure S1), which is consistent with positive assortative mating with respect to this chromosome (i.e., individuals are more likely to mate if they have similar Omy05 genotypes and migration phenotypes; Miller et al, 2012;Pearse et al, 2014;Pearse et al, 2019). F ST comparisons using putatively neutral markers (i.e., with Omy05 excluded) between the streams (within regions of Elder Creek) and Fox Creek were all < 0.02 and not statistically significant.…”

“…While large barriers mark step-wise transitions between migratory and resident life history forms (Pearse et al, 2009), it is less clear how small partial barriers influence the distribution of the two forms in streams where they co-occur. The recent discovery of a genomic region associated with life history type (i.e., migratory vs. resident) in O. mykiss (Pearse et al, 2019;Pearse, Miller, Abadía-Cardoso, & Garza, 2014) has opened the door to exploring the influence of partial barriers on genetic diversity associated with life history in O. mykiss at fine spatial and temporal scales. Landscape features can shape both neutral genetic structure and the distribution of adaptive variation within a species (Davis, Epps, Flitcroft, & Banks, 2018;Grummer et al, 2019;Orsini, Andrew, & Eizaguirre, 2013).…”

Cis-regulatory differences in isoform expression associate with life history strategy variation in Atlantic salmon

“…enhancers) may underlie the functional differences among salmon vgll3 genotypes by changing their expression level or pattern. In other species, a similar pattern of linked non-coding changes is often observed in association studies for life history traits [6,7,10,18,19], suggesting that regulatory changes may be a common driver of evolution in life history strategies. However, non-coding and coding hypotheses pertaining the control of maturation age are nuanced and not mutually exclusive; enhancer mutations can have effects unlinked to their genotype (they can act in trans) [20,21] and non-coding mutations can alter transcript structure, stability and translation via alternative transcription start sites and splicing [22][23][24].…”

A mobile sex-determining region, male-specific haplotypes, and rearing environment influence age at maturity in Chinook salmon

“…Chromosome inversions are also increasingly found to be associated with live-history variation across taxa (Wellenreuther et al 2019). In salmonids there is strong heterochiasmy in which male recombination is restricted to telomeres (Lien et al 2011) and large chromosome inversions have been detected in multiple species (Pearse et al 2019), including on the sex chromosome in chum salmon (McKinney et al 2020). The large LD-blocks that we identified could be due to strong heterochiasmy or chromosome inversions but we cannot attribute the large LD-blocks to any particular cause with the data available.…”