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Environmental adaptation in Chinook salmon
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(Oncorhynchus tshawytscha) throughout their North American range
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Benjamin C. Hecht1,2, Andrew P. Matala1, Jon E. Hess1, and Shawn R. Narum1
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Columbia River Inter-Tribal Fish Commission and 2University of Idaho, Hagerman Fish Culture
Experiment Station, Hagerman, ID 83332, USA
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Supplementary File S1
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RAD library preparation and Sequencing
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Restriction-site associated DNA (RAD) (Miller et al. 2007; Baird et al. 2008) libraries were prepared for
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Illumina HiSeq 1500 sequencing using a protocol similar to those previously published (Baird et al. 2008;
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Miller et al. 2012) but modified as described in Hecht et al. (Hecht et al. 2013) to allow for lower starting
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DNA concentrations when tissue was limited for some individuals or populations. Here libraries were
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prepared with a starting DNA concentration per sample of 150, 250, or 500ng depending on sample
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quality and quantity. Samples were digested individually with the restriction enzyme SbfI-HF (NEB,
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Ipswich, MA, USA) and individually barcoded using a 6nt barcode adapter sequence. Digested and
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barcoded samples of the same starting concentration were pooled into libraries of between 36 and 96
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samples, where no two samples within a library were assigned the same barcode sequence, and each
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barcode sequence within a library differed by at least two bases from another barcode sequence.
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Libraries were mechanically sheared to generate DNA fragment lengths between 200-700bp using a
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Bioruptor 300 sonicator (Diagenode, Denville, NJ, USA) and fragments were size selected and isolated
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using an Agencourt AMPure XP bead purification system (Beckman Coulter, Brea, CA, USA). The
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remainder of the RAD library preparation follows the previously defined protocol of Miller et al. (2012).
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Prior to sequencing, RAD libraries were quantified using real time PCR and a Kapa Illumina Library
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Quantification Kit following recommended protocols (Kapa Biosystems Inc., Woburn, MA, USA) on an
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ABI 7900HT Sequence Detection System (Life Technologies, Grand Island, NY, USA). Libraries were
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sequenced on an Illumina HiSeq 1500 sequencer (Illumina Inc., San Diego, CA, USA) at a single read
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length of 100bp. Depending on the quality and quantity of the sequence generated, some libraries were
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sequenced in more than one lane to reach target read depths per individual of approximately 2 million
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reads. In total 2,775 samples were sequenced in 44 libraries across 63 Illumina flow cell lanes.
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de Novo SNP discovery and genotyping
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While a RADtag based SNP catalog had previously been constructed for Chinook salmon (Brieuc et al.
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2014), here we included samples from a broader range of populations from Alaska to California in order
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to identify informative loci throughout the entire species range. We therefore identified and genotyped
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SNP loci de novo, by constructing a SNP catalog from individuals spanning the geographic range of
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populations in our collection. This was performed using the software pipeline Stacks v.1.03 (Catchen et
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al. 2011, 2013). Raw Illumina reads were first scrutinized for quality using the software program FastQC
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(http://www.bioinformatics.babraham.ac.uk/projects/fastqc/). It was determined that the 3’ 15-20
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bases of Illumina sequence reads had in general reduced quality scores relative to the 5’ 80-85 base
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positions across our sequence data. We therefore truncated our sequence reads to 80 bases by
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removing the 3’ sequence most prone to error. In addition to truncating, our reads were quality filtered
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and de-multiplexed using the ‘process_radtags’ program of the Stacks pipeline and included options for
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cleaning the data by discarding any read with an uncalled base (-c), discarding reads with low quality
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scores (-q), and rescuing barcodes and partial restriction enzyme recognition sites (-r). All other
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parameters and options were executed with the default values as outlined in the manual for the
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program (http://creskolab.uoregon.edu/stacks).
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After individual sample reads were quality filtered, trimmed, and de-multiplexed, sequences for each
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sample were submitted to the ‘ustacks’ module of Stacks to identify loci. In ‘ustacks’, the deleveraging (-
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d) and removal (-r) algorithms were applied to filter out those sequences that were likely to be
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paralogous and highly repetitive. We required the minimum depth of coverage at a stack (-m) to be 5
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and allowed a maximum distance (-M) of 2 between stacks, and 4 between secondary reads and primary
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stacks (-N). SNP discovery was carried out using the default SNP model with a chi-square significance
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level of 0.05. We created a de novo catalog of RAD tag loci using the ‘cstacks’ module by selecting two
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individuals from each of 51 populations (n=102) with at least 2.5 million reads (but no greater than 4
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million reads) to represent genetic variation throughout the native range of Chinook salmon (note that
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two populations had poor sequence quality and were excluded from further analysis). Individual
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samples were then aligned to the catalog using the module ‘sstacks’ and genotypes were exported using
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the ‘populations’ module. Genotypes were filtered to exclude 1) any RAD tag locus with more than 4
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SNP sites to remove putative PSV, hyper-variable, or poorly sequenced tags, 2) any RAD tag locus where
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one of the ten doubled haploid samples was observed to be heterozygous at any of the SNP positions in
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order to remove putative PSVs, 3) any SNP marker with more than 2 alleles to remove SNPs with
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sequencing errors, putative PSVs, or loci that do not fit a bi-allelic statistical model, 4) any SNP marker
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missing more than 20% of the genotypes across all of the populations to limit the amount of missing
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data, 5) any SNP marker failing tests of Hardy-Weinberg Equilibrium (HWE; Bonferroni corrected critical
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value at α=0.05, 0.05/19703= 0.00000254) in more than 10% of the populations in order to exclude
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technical artifacts such as null alleles (heterozygote deficit loci) and putative PSVs (heterozygote excess
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loci), and 6) any SNP marker with an average minor allele frequency (MAF) across the populations falling
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below 0.01 in order to exclude spurious rare SNPs or sequencing errors. Since linked SNPs would bias
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population genetics statistics, we only retained one SNP marker per RAD tag, where we kept the SNP
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with the greatest global MAF since this was likely to be the most informative SNP across all the
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populations. Individual samples were also filtered from the dataset if they were missing more than 20%
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of genotypes across all filtered loci and whole populations were removed if fewer than 10 individuals
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remained to represent the population after applying filtering criteria. After applying the first four filters
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outlined above, we identified 29,668 loci, though upon applying the remaining filters we retained 19,703
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SNP loci as our final dataset. Allowing only a single SNP per RAD tag resulted in the most substantial
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reduction in markers as 6,556 SNPs were removed from the data set to reduce SNPs that were physically
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linked within 100 bp. The other filters primarily removed rare SNPs and polymorphisms with unresolved
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technical difficulties such as described by Davey et al. (2013).
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Alignment to Chinook Salmon Linkage Map and Rainbow Trout Genome Assembly
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RADtag sequences from this study were aligned to those from a high density RADtag based linkage map
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in Chinook salmon (Brieuc et al. 2014) in an effort to determine the relative genetic position and linkage
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group assignment of loci. Alignment to the RADtag database of Brieuc et al. (2014) was conducted using
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the short sequence alignment software program Bowtie v.1.0.1 (Langmead et al. 2009) with no more
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than two mismatches between the query sequence and the database sequence. RADtag sequences
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were also aligned to the rainbow trout genome (O. mykiss; Berthelot et al. 2014), which is the most
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closely related species to Chinook salmon with a published genome assembly. Alignment to the rainbow
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trout genome was carried out using Bowtie 2 v.2.2.3 (Langmead & Salzberg 2012) to assign sequences to
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rainbow trout chromosomes and obtain genomic positions, where a RADtag alignment was accepted if
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no more than four mismatches occurred between the RADtag site and the rainbow trout genome, and
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no more than one alignment site was identified for the RADtag sequence within the genome. We then
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queried the rainbow trout genome for coding sequence within a distance of 5kb of the RADtag locus
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alignment sites in an effort to identify putatively linked genes. While linkage disequilibrium on average
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was found to decay after approximately 2 cM in a domesticated strain of rainbow trout (Rexroad III &
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Vallejo 2009), we conservatively opted to only identify coding regions within 5kb of the RADtag
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alignment site, given our limited understanding of the intra-chromosomal micro-rearrangements
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between rainbow trout and Chinook salmon genomes.
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To identify gene functions and annotations, coding sequences were then queried against the
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NCBI nucleotide sequence database using the software program Blast2GO (Conesa et al. 2005). Gene
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functions and annotations to linked adaptive loci (outlier loci from RDA analysis) were compared to the
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functions and annotations of all RADtag linked genes to determine if there was an enrichment of gene
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ontologies in adaptive loci relative to all other genes using a Fisher’s exact test corrected for multiple
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comparisons as implemented in the program Blast2GO (Conesa et al. 2005).
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Detecting Neutral and Outlier Loci
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For all FST outlier tests the “Wenatchee River” population was a pooled population of genetically similar
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samples from Nason Creek, Chiwawa River, and White River (n=51), and samples from the John Day
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River (JDR, n=12) were excluded from analyses because this population size was too low to be
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representative relative to other populations in the study and within the lineage. In total 44 populations
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including 1,945 individually genotyped samples were investigated at 19,703 markers to identify neutral
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and outlier loci. Tests were conducted in four ways, and loci were only considered neutral if they met
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the expectations of neutrality in all four tests. The first test included a range-wide global analysis, where
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all 44 populations (note that the Nason/Chiwawa population was merged with the White River and the
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John Day River population was removed from the analysis) were analyzed together, the second test was
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conducted on a subset of populations previously identified as a putative “North Coastal Lineage”, the
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third test on a subset of populations identified as a putative “South Coastal Lineage”, and the fourth test
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on a subset of populations identified as an “Interior Columbia River Stream-Type Lineage”. Outlier loci
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were identified in each test as those loci which did show excessively higher or lower FST than would be
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expected under the assumptions of neutrality. In this case p-values were corrected for the four multiple
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tests using a Benjamini-Yekutieli correction (Benjamini & Yekutieli 2001) as recommended by Narum
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(2006) and thus included loci with a P-value greater than 0.988 and less than 0.012.
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