DOI: 10.5061/dryad.fxpnvx0sv
title: Further evidence from common garden rearing experiments of heritable traits separating lean and siscowet lake charr (Salvelinus namaycush) ecotypes

description: Genetic evidence of selection for complex and polygenically regulated
 phenotypes can easily become masked by neutral population genetic
 structure and phenotypic plasticity. Without direct evidence of
 genotype-phenotype associations, it can be difficult to conclude to what
 degree a phenotype is heritable or a product of environment. Common garden
 laboratory studies control for environmental stochasticity and help to
 determine the mechanism that regulates traits. Here we assess lipid
 content, growth, weight, and length variation in full and hybrid F1
 crosses of deep and shallow water sympatric lake charr ecotypes reared for
 nine years in a common garden experiment. Redundancy analysis (RDA) and
 quantitative-trait-loci (QTL) genomic scans are used to identify
 associations between genotypes at 19,714 single nucleotide polymorphisms
 (SNPs) aligned to the lake charr genome and individual phenotypes to
 determine the role that genetic inheritance plays in ecotype phenotypic
 diversity. Lipid content, growth, length, and weight differed
 significantly among lake charr crosses throughout the experiment
 suggesting that pedigree plays a large role in lake charr development.
 Polygenic scores of 15 SNPs putatively associated with lipid content
 and/or condition factor indicated that ecotype distinguishing traits are
 polygenically regulated and additive. A QTL identified on chromosome 38
 contained &gt;200 genes, some of which were associated with lipid
 metabolism and growth, demonstrating the complex nature of ecotype
 diversity. The results of our common garden study further indicate that
 lake charr ecotypes observed in nature are pre-determined at birth and
 that ecotypes differ fundamentally in lipid metabolism and growth.
description: <strong>Lipid content:</strong>
 From 2015-2019, somatic lipid content (as a percent) was measured
 for each fish using a battery powered, handheld microwave oscillator
 (Distell Model 692 Fish Fatmeter, Distell Inc.). The fatmeter emits a
 low-powered microwave (2 GHz, 2000 MHz, power 2 mW) that interacts with
 water within the somatic tissues and uses the inverse relationship between
 water and lipid to estimate the lipid concentration (as a percent) in the
 tissue (Crossin and Hinch, 2005; Kent, 1990).  Fatmeter readings taken on
 the Research-1 setting were collected on each fish at a site that was in
 the epaxial muscle mass just posterior to the head (site "S1" as
 shown in Sitar et al., 2020). From 2012 to 2014 the fish were too small to
 use the fatmeter on, but a subsample (n<sub>2012</sub>=12;
 n<sub>2013-2014</sub>=20) of fish from each cross were
 lethally sampled and a muscle sample (cross-section of the epaxial muscle
 behind the head and anterior to the dorsal fin) was excised and used for
 lipid analysis by Soxhlett extraction as described in Goetz et al. (2014)
 and Sitar et al. (2020).
 <strong>Pedigree:</strong>
 Parentage of all individuals was assigned using genotypes from a
 six-microsatellite marker panel amplified and genotyped for all parents
 and offspring using previously designed primers for Loci<em>
 SnaMSU</em> 01, 03, 06, 10, 11, and 12 (Rollins et al., 2009).
 Pedigrees were reconstructed using Colony2 Version 2.0.6.5 and a
 full-likelihood approach specifying maternal polygamy without inbreeding
 and no sibship prior (Jones & Wang, 2010).
 <strong>Genotype data:</strong>
 Restriction site associated DNA sequencing (RAD-seq) was
 conducted on 74 parents and 542 F1 offspring using
 <em>SbfI</em> and bestRAD library protocols outlined in Ali et
 al. (2016). An initial library was sequenced at BGI America (Cambridge,
 MA) on one lane of a HiSeq4000 and the remainder of the libraries were
 sent to Novogene (Sacramento, CA) where they were sequenced on seven
 HiSeq4000 lanes for paired-end 150 sequencing. Identification of SNPs and
 genotyping were conducted in STACKS v.2.3 using the <em>de
 novo</em> assembly pipeline (Rochette et al., 2019). Samples were
 demultiplexed with <em>process_radtags</em> (flags = c, -q,
 -r, -t 140). Stacks of similar sequences (loci) for each individual were
 identified with <em>ustacks </em>(flags = -m 3, -M 5, -H
 –max_locus_stacks 4, --model_type bounded, --bound_high 0.05) and a
 catalog of putative loci was generated based on sequences from the
 parents. Individual stacks were then aligned to the catalog in
 <em>sstacks</em>, and genotypes for all putative SNPs were
 assigned using <em>gstacks</em>. Finally, a datafile
 containing genotypes for all SNPs with a minor allele count greater than
 two and all individuals was generated using
 <em>populations</em> and subsequently filtered in VCFTools
 (Danecek, et al., 2011). To ensure that paralogs did not influence our
 findings, we ran HDPlot on unfiltered data for all crosses and removed
 loci identified as potential paralogs (McKinney et al., 2017). Parameters
 for this analysis were set by visually choosing threshold values for read
 depth ratio and proportion of heterozygotes that identified the loci
 conforming to theoretical expectations for singletons (McKinney et al.,
 2017; supplemental figure 1). Once putative paralogs were removed, quality
 filtering was conducted in VCFTools hierarchically. First, all individuals
 with more than 80% missing data were removed from the dataset. Second,
 SNPs missing more than 10% of genotypes were removed. Finally, for loci
 that contained more than one SNP, the SNP with the highest
 minor-allele-frequency was retained leaving a single SNP per-locus. All
 sites that passed the above thresholds were retained in downstream
 analysis. All bioinformatic analysis was conducted using the Turing High
 Performance Computing cluster at Old Dominion University,
 Virginia.