DOI: 10.5061/dryad.34tmpg4xx
title: Balancing selection maintains intraspecific diversity in a deep-sea fish

description: Segregating alleles in natural populations can be driven to fixation or
 loss by genetic drift or directional selection, or may be maintained in a
 polymorphic state by balancing selection. Balancing selection in a
 panmictic population is theoretically well established, but not widely
 understood at the molecular level. In this study, we focus on the
 evolutionary processes affecting non-synonymous variants at eight
 functionally relevant loci (based on candidate SNP genotyping) in a
 deep-sea fish species (Coryphaenoides rupestris) that lives across habitat
 zones ranging from ~200 m to ~2000 m depth. At each of these loci, one
 allele is predominant in the deeper water. Across a shallower depth range,
 we find that minor allele frequencies show a highly significant increase
 or decline progressively across five defined age categories. At single
 depths below a threshold depth, the deep-water allele declines in
 frequency with age. Together, these data indicate segregatio n to
 different depths, either shallow or deep, and balancing selection to
 retain variants needed for each depth range. This is supported by signals
 for long-term balancing selection at these loci (based on published
 genomic data). We discuss alternative interpretations and conclude that
 balancing selection, maintaining ecotype diversity, is the best supported
 mechanism.
description: # Balancing selection maintains intraspecific diversity in a deep-sea fish
 Dataset DOI:
 [10.5061/dryad.34tmpg4xx](https://doi.org/10.5061/dryad.34tmpg4xx) ##
 Description of the data and file structure Specimens of
 290 *Coryphaenoides rupestris* were collected from eight depths ranging
 from 750m to 1900m and run on a MiSeq (Illumina Inc.) with a 2 x 75 bp
 paired-end sequencing protocol. Raw reads were automatically
 de-multiplexed by the MiSeq (Illumina Inc.) Analysis Software using the
 individual-specific index barcodes. Paired-end reads were combined using
 FLASH (min overlap of 4 and max overlap of 50; Magoč and Salzberg 2011).
 Merged reads were then mapped to the compiled consensus sequences for the
 target loci using BWA-MEM v0.7.17-r1188 (Li and Durbin 2009). Mapped reads
 were converted from Sequence Alignment/Map (SAM) files to BAM files with
 SAMtools v1.13 (Li et al., 2009). Variable sites were identified using
 FreeBayes v1.3.6 (--haplotype-length 0 -kwVa –no-mnps –no-complex;
 Garrison and Marth 2012); the positions of all SNPs for each locus were
 recorded in a VCF file. Raw genotypes were extracted directly from the VCF
 using vcfR v1.15.0 (Knaus and Grunwald 2017) and custom R (CRAN) scripts.
 ### Files and variables #### File: grenadier.vcf **Description:** A
 Variant Calling Format file that includes the genotypes of 12 loci across
 290 specimens. | Loci | Gene name | | :----------------- | :-------- | |
 Crup\_1041\_33310 | ROCK1 | | Crup\_1041\_367049 | EGFR1 | |
 Crup\_10763\_28387 | b4galt2 | | Crup\_20180\_17073 | adgalt2 | |
 Crup\_3193\_255831 | EEFR1 | | Crup\_4264\_107730 | ATG9 | |
 Crup\_5367\_301064 | OBSL1 | | Crup\_9875\_168788 | CAC1E | |
 Crup\_1964\_323785 | LRRTM4 | | Crup\_3137\_404746 | PKMYT1 | |
 Crup\_875\_62292 | Grid1a | ## Code/software The file can be opened with a
 text editor and every other free software related to VCF view/handling.