Data from: Whole-genome resequencing reveals polygenic signatures of directional and balancing selection on alternative migratory life histories
Data files
Oct 16, 2024 version files 69.45 KB
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00_Moran_2024_Suppl_Tables.xlsx
65.56 KB
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README.md
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Nov 04, 2024 version files 73.10 KB
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00_Moran_2024_Suppl_Tables_v2.xlsx
68.57 KB
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README.md
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Abstract
Migration in animals and associated adaptations to contrasting environments are underpinned by complex genetic architecture. Here, we explore the genomic basis of facultative anadromy in brown trout (Salmo trutta), wherein some individuals migrate to sea while others remain resident in natal rivers, to better understand how alternative migratory tactics (AMTs) are maintained evolutionarily. To identify genomic variants associated with AMTs, we sequenced whole genomes for 194 individual trout from five anadromous–resident population pairs, situated above and below waterfalls, in five different Irish rivers. These waterfalls act as natural barriers to upstream migration and hence we predicted that loci underpinning AMTs should be under similar divergent selection across these replicate pairs. A sliding windows based analysis revealed a highly polygenic adaptive divergence between anadromous and resident populations, encompassing 329 differentiated genomic regions. These regions were associated with 292 genes involved in various processes crucial for AMTs, including energy homeostasis, reproduction, osmoregulation, immunity, circadian rhythm and neural function. Furthermore, examining patterns of diversity we were able to link specific genes and biological processes to putative AMT trait classes: migratory-propensity, migratory-lifestyle and residency. Importantly, AMT outlier regions possessed higher genetic diversity than the background genome, particularly in the anadromous group, suggesting balancing selection may play a role in maintaining genetic variation. Overall, the results from this study provide important insights into the genetic architecture of migration and the evolutionary mechanisms shaping genomic diversity within and across populations.
We have submitted our supplementary tables and main code (bash and R scripts) used for analyses.
Description of the data and file structure
The Supplementary tables are provided in a spreadsheet (00_Moran_2024_Suppl_Tables_v2.xlsx) and captions for the tabs are below:
Table S1. Per sample summary of raw read quality and predicted coverage based on genome length.
Table S2. Global summary of read mapping (clean and filtered reads from fastp) based on Qualimap and multiQC reports.
Table S3. Genes (n=292) within 10kb up or downstream of AMT outliers (regions of overlap for FST windows and C2 SNPs). Chromosome name (chr), gene name, ensemble gene id, gene start and end positions, distance from outlier windows to gene (bp, 0=range overlap), and gene description.
Table S4. GO term enrichment results for 292 AMT genes. Go terms with p<0.01 (weight Fihser i.e. not padjusted) shown.
Table S5. Genes that overlapped between putative AMT genes (n=292) with differentially expressed (DE) genes in smolts vs. non-smolts (Wynne et al., 2021).
Table S6. The number of AMT outlier windows (n=329) and associated genes (n=292) assigned to one of four groups based on TD. Note nine genes were assigned to more than one ZTD group.
Table S7. Genes associated with (within 10kb up or downstream) AMT outliers assigned to the four ZTD groups. Columns indicate chromosome name (chr), gene name, ensemble gene id, gene start and end positions, distance from outlier windows to gene (bp, 0=range overlap), and gene description.
Table S7.A) Genes (n=4) associated with (within 10kb up or downstream) AMT outliers assigned to the ZTD LA_LR group (lower tertile for both anadromous and resident pools).
Table S7.B) Genes (n=39) associated with (within 10kb up or downstream) AMT outliers assigned to the ZTD LA_MHR group (lower tertile for anadromous pool versus middle and upper tertiles for resident pool).
Table S7.C) Genes (n=102) associated with (within 10kb up or downstream) AMT outliers assigned to the ZTD MHA_LR group (middle and upper tertiles for anadromous pool versus lower tertile for resident pool).
Table S7.D) Genes (n=176) associated with (within 10kb up or downstream) AMT outliers assigned to the ZTD MHA_MHR group (middle and upper tertiles for anadromous and resident pools).
Table S8.A-B) Genes associated with (within 10kb up or downstream) AMT outliers assigned to the top 1% of ZTD in the (A) anadromous and (B) resident pools. Columns indicate chromosome name (chr), gene name, ensemble gene id, gene start and end positions and gene description.
Table S9.A-B) Genes associated with (within 10kb up or downstream) AMT outliers assigned to the top 1% of Betascores in the (A) anadromous and (B) resident pools. Columns indicate chromosome name (chr), gene name, ensemble gene id, gene start and end positions and gene description.
Table S10.A-B) Genes associated with (within 10kb up or downstream) AMT outliers assigned to the top 1% of intersex FST in the (A) anadromous and (B) resident pools. Columns indicate chromosome name (chr), gene name, ensemble gene id, gene start and end positions and gene description.
Note gene IDs were retrieved from Ensembl using the biomaRt package (v.2.57.1; Durinck et al., 2009) as of May 2024. In some cases, the ‘Gene’ column may have empty cells despite the presence of an Ensembl ID. This occurs when genes have not yet been functionally annotated or assigned a name.
Code/software
Main bash scripts for running software and R code used for analyses. Additional custom scripts are available from the corresponding authors upon request.
Access information
Other publicly accessible locations of the data:
- Related data: Whole genome resequencing data used in this study is available from the European Nucleotide Archive (ENA) (PRJEB72781).
Version changes
04/11/2024: We identified a minor issue in the initial BayPass analysis where two chromosomes (chr 33 and chr 39) were unintentionally excluded due to a data merging error. After resolving this and rerunning the analysis, the results remained highly consistent with our original findings, with no effect on the main results or conclusions. However, this reanalysis led to small adjustments in the outlier data: ATM Outlier Windows: Increased from 310 to 329. Number of Genes: Increased from 273 to 292. These changes are now reflected in the updated supplementary data tables.