Supporting Information

Single Nucleotide Polymorphism markers for analysis of historical and contemporary samples of Arctic char (Salvelinus alpinus)

Magnus W. Jacobsen1, Camilla Christensen1, Rikke Madsen1, Shenglin Lin1, Rasmus Nygaard2, Bjarni Jónsson3, Kim Præbel4, and Michael M. Hansen1

1Department of Bioscience, Aarhus University, Ny Munkegade 114, DK-8000 Aarhus C, Denmark.

2Greenland Institute of Natural Resources, Kivioq 2, P.O. Box 570, 3900 Nuuk, Greenland.

3Northwest Iceland Nature Research Centre, Adalgata 2, IS-550, Saudárkrókur, Iceland.

4UiT The Arctic University of Norway, N-9037 Tromsø, Norway.

1

Tables

Table S2 Information about sample size (N), Observed (Ho) and expected heterozygosity (He) for each of the 53 SNP loci for each population. HW-test denotes p-values for Hardy-Weinberg tests. Values marked by bold denote significant deviations following False Discovery Rate correction.

SNP name

Populations

Biggijavri Præstefjord Vatnshilidarvatn Sermeerlat Kangerluat Kobbefjord Ekaluit Historical (1952)

Landlocked Anadromous Landlocked Anadromous Anadromous AnadromousNo

. Ho He HW-test No. Ho He HW-test No. Ho He HW-test No

. Ho He HW-test No. Ho He HW-test No

. Ho He HW-test

Cath2_KC590659 16 0.1250 0.3958 0.0157 20 0.4500 0.4816 1.0000 20 0.2500 0.3605 0.2115 20 0.4000 0.4316 1.0000 20 0.2000 0.4737 0.0155 22 0.2727 0.3052 0.5356

Cath2_ KC596075 16 0.1250 0.3958 0.0169 20 0.4500 0.4816 1.0000 20 0.2500 0.3605 0.2140 20 0.4000 0.4316 1.0000 19 0.2105 0.4854 0.0220 22 0.2727 0.2403 1.0000

Contig11261 16 0 0 NA 20 0 0 NA 20 0 0 NA 20 0.2500 0.2974 0.4682 20 0.05000 0.1447 0.0784 22 0 0 NA

Contig214_63 16 0 0 NA 20 0.3000 0.2605 1.0000 20 0 0 NA 20 0.1000 0.1868 0.1508 20 0 0 NA 22 0.0909 0.0887 1.0000

Contig2980_70 16 0 0 NA 20 0.2500 0.2237 1.0000 20 0 0 NA 20 0.0500 0.0500 NA 20 0.2000 0.1842 1.0000 22 0 0 NA

Contig6336_73 16 0 0 NA 20 0.5500 0.5105 1.0000 20 0 0 NA 20 0.4500 0.4079 1.0000 20 0.6000 0.4632 0.3280 22 0.4091 0.4264 1.0000

Contig7751_81 16 0 0 NA 20 0.4500 0.4816 1.0000 20 0.2000 0.1842 1.0000 20 0.4000 0.3842 1.0000 20 0.5000 0.5079 1.0000 18 0.3889 0.5033 0.3817

Contig92_84 16 0 0 NA 20 0.1000 0.0974 1.0000 20 0 0 NA 20 0.4500 0.4816 1.0000 20 0.1500 0.2263 0.2466 22 0 0 NA

Contig11263_71 16 0.5625 0.4125 0.2565 20 0.3000 0.3289 1.0000 20 0.3500 0.2947 1.0000 20 0.3500 0.4842 0.3406 19 0.4737 0.3684 0.5260 21 0.4286 0.4952 0.6591

Contig12050 16 0 0 NA 20 0.2500 0.4132 0.1081 20 0 0 NA 20 0.3500 0.4526 0.3482 20 0.4000 0.3842 1.0000 21 0.3810 0.4190 1.0000

Contig1776_87 16 0 0 NA 20 0.0500 0.0500 NA 20 0 0 NA 20 0.5000 0.4289 0.6182 20 0.1000 0.0974 1.0000 22 0.2273 0.2056 1.0000

Contig2194_67 16 0 0 NA 20 0.3500 0.4105 0.5916 20 0 0 NA 20 0.4500 0.5132 0.6687 20 0.5000 0.5079 1.0000 22 0.1818 0.1688 1.0000

Contig9220 16 0.3750 0.4458 0.5903 20 0.2500 0.3605 0.2174 20 0.7500 0.4947 0.0595 20 0.5000 0.4658 1.0000 20 0.5000 0.4921 1.0000 22 0.1818 0.2424 0.3242

Contig11431_72 16 0 0 NA 20 0 0 NA 20 0 0 NA 20 0 0 NA 20 0.1000 0.0974 1.0000 22 0 0 NA

Contig1821_63 16 0 0 NA 20 0.2000 0.1842 1.0000 20 0 0 NA 20 0.3500 0.2947 1.0000 20 0.1000 0.0974 1.0000 22 0.1364 0.1299 1.0000

Contig2997 16 0 0.2333 0.0040 20 0.2500 0.4132 0.1087 20 0 0 NA 20 0.9000 0.4974 0.0007 20 0.4500 0.4079 1.0000 21 0.1429 0.1357 1.0000

Contig4510_74 16 0 0 NA 20 0.5000 0.5132 1.0000 20 0.1000 0.974 1.0000 20 0.2000 0.1842 1.0000 20 0.4500 0.4079 1.0000 22 0.3636 0.4069 0.6218

Contig6593 16 0 0 NA 20 0.4000 0.4684 0.6309 20 0 0 NA 20 0.3000 0.3289 1.0000 20 0.3500 0.4842 0.3368 22 0.1364 0.1299 1.0000

Contig8674_69 16 0.3125 0.4208 0.5336 20 0.4000 0.4947 0.6361 20 0.5000 0.4289 0.6187 20 0.1500 0.2263 0.2474 20 0.3500 0.4526 0.3480 22 0.4091 0.5043 0.4176

Contig9346_76 16 0 0 NA 20 0.1000 0.0974 1.0000 20 0 0 NA 20 0.6000 0.4632 0.3323 20 0.1500 0.1421 1.0000 22 0 0 NA

Contig11566 16 0 0 NA 20 0.3500 0.4105 0.5898 20 0 0 NA 20 0.6000 0.5053 0.6499 20 0.1500 0.2263 0.2473 21 0.0476 0.0476 NA

Contig12176_62 7 0 0 NA 20 0.5000 0.4289 0.6209 20 0.4000 0.5105 0.3935 20 0.6000 0.5053 0.6491 20 0.4500 0.4500 1.0000 20 0.6000 0.4895 0.3820

Contig3057_86 16 0 0 NA 20 0.1500 0.1421 1.0000 20 0 0 NA 20 0.5500 0.5000 1.0000 20 0.1500 0.1421 1.0000 22 0.0909 0.1710 0.1378

2

Contig5808_61 16 0 0 NA 20 0.4000 0.3842 1.0000 20 0 0 NA 20 0 0 NA 20 0.2000 0.2632 0.3532 22 0.1364 0.2078 0.2226

Contig7991 16 0 0 NA 20 0.5000 0.5079 1.0000 20 0 0 NA 20 0.1500 0.2263 0.2450 20 0.6500 0.4974 0.3555 22 0.0455 0.0455 NA

Contig8752 16 0 0 NA 20 0.4500 0.5132 0.6712 20 0 0 NA 20 0.3500 0.3579 1.0000 20 0.5500 0.5105 1.0000 21 0.3333 0.3452 1.0000

Contig3343 16 0 0 NA 20 0.3000 0.2605 1.0000 20 0 0 NA 20 0.4000 0.4316 1.0000 20 0.1500 0.1421 1.0000 21 0.0476 0.0476 NA

Contig12281 16 0 0 NA 20 0.1500 0.1421 1.0000 20 0 0 NA 20 0.3000 0.2605 1.0000 20 0.3000 0.4342 0.2778 22 0.0455 0.0455 NA

Contig11742_67 16 0 0 NA 20 0.2000 0.1842 1.0000 20 0 0 NA 20 0.2000 0.2632 0.3553 20 0.1000 0.0974 1.0000 22 0 0 NA

Contig9421 16 0.2500 0.2250 1.0000 20 0.2500 0.4553 0.1169 20 0.2000 0.2632 0.3527 20 0 0.1000 0.0263 20 0.6000 0.5053 0.6506 21 0.4762 0.4548 1.0000

Contig8976_82 16 0 0 NA 20 0.2500 0.2237 1.0000 20 0 0 NA 20 0.0500 0.0500 NA 20 0.4000 0.3842 1.0000 22 0.0909 0.0887 1.0000

Contig711_65 16 0.0625 0.1792 0.1015 20 0.4000 0.4684 0.6287 20 0.2000 0.1842 1.0000 20 0.0500 0.0500 NA 20 0.2000 0.3316 0.1323 21 0.3810 0.4857 0.3803

Contig481 16 0.3125 0.3542 1.0000 20 0.1500 0.2263 0.2485 20 0 0 NA 20 0.0500 0.0500 NA 20 0.2000 0.2632 0.3556 22 0.0909 0.0887 1.0000

Contig3493_74 16 0 0 NA 20 0.0500 0.1447 0.0776 20 0 0 NA 20 0 0 NA 20 0.1500 0.1421 1.0000 22 0.4091 0.3312 0.5374

Contig2680_72 16 0 0 NA 20 0.1500 0.1421 1.0000 20 0 0 NA 20 0 0 NA 20 0.2000 0.1842 1.0000 21 0.3333 0.2833 1.0000

Contig1973 16 0.3750 0.5125 0.3512 20 0.4000 0.5158 0.3909 20 0.6500 0.5079 0.3702 20 0.4500 0.4079 1.0000 20 0.5000 0.5079 1.0000 22 0.4545 0.4048 1.0000

Contig1373 16 0 0 NA 20 0.4000 0.4316 1.0000 20 0 0 NA 20 0.2500 0.5079 0.0310 20 0.1500 0.1421 1.0000 22 0.0909 0.0887 1.0000

Contig10740_78 16 0 0 NA 20 0.3000 0.3289 1.0000 20 0 0 NA 20 0.2500 0.2974 0.4677 20 0.2000 0.1842 1.0000 22 0.0909 0.0887 1.0000

Contig959_76 16 0 0 NA 20 0.1000 0.1868 0.1524 20 0 0 NA 20 0 0 NA 20 0.1500 0.1421 1.0000 21 0.0952 0.0929 1.0000

Contig8978_60 16 0 0 NA 20 0.4000 0.3842 1.0000 20 0 0 NA 20 0.0500 0.0500 NA 20 0.5500 0.4789 0.6437 22 0.5455 0.4719 0.6512

Contig5917_74 16 0 0 NA 20 0 0 NA 20 0 0 NA 20 0 0 NA 20 0.1000 0.0974 1.0000 19 0.1579 0.1491 1.0000

Contig4954 16 0 0 NA 20 0.1500 0.1421 1.0000 20 0.0500 0.0500 NA 20 0 0 NA 20 0.0500 0.1447 0.0740 22 0 0 NA

Contig3498 16 0 0 NA 20 0.2500 0.2237 1.0000 20 0 0 NA 20 0.0500 0.1447 0.0761 19 0.2000 0.1842 1.0000 21 0.1818 0.2424 0.3207

Contig2705 16 0 0 NA 20 0.3500 0.4842 0.3385 20 0 0 NA 20 0.5263 0.3947 0.2624 20 0.3500 0.5158 0.1972 22 0.0476 0.0476 NA

Contig1525_59 16 0 0 NA 20 0.2500 0.2237 1.0000 20 0 0 NA 20 0.6000 0.5105 0.6577 20 0.3000 0.2605 1.0000 21 0.0952 0.0929 1.0000

Contig11854_70 16 0.3750 0.3125 1.0000 20 0.6000 0.4263 0.1181 20 0 0 NA 20 0.0500 0.0500 NA 20 0.2500 0.2237 1.0000 22 0.1818 0.1688 1.0000

Contig10812 16 0 0 NA 20 0.2500 0.2237 1.0000 20 0 0 NA 20 0 0 NA 20 0.2000 0.2632 0.3527 22 0.1818 0.1688 1.0000

Contig9609 16 0 0 NA 20 0.4000 0.3263 0.5500 20 0 0 NA 20 0.1500 0.1421 1.0000 20 0.4500 0.3553 0.5301 21 0.1429 0.1357 1.0000

Contig609_67 16 0 0 NA 20 0.6500 0.4763 0.1592 20 0 0 NA 20 0.2500 0.2237 1.0000 20 0.5000 0.4913 1.0000 22 0.2273 0.4913 0.0211

Contig3603_79 16 0 0 NA 20 0.1000 0.0974 1.0000 20 0 0 NA 20 0 0 NA 20 0.0500 0.0500 NA 22 0.0455 0.0455 NA

Contig2925 16 0 0 NA 20 0.3000 0.2605 1.0000 20 0 0 NA 20 0.4500 0.4079 1.0000 20 0.3000 0.3289 1.0000 22 0.0909 0.1710 0.1348

Contig1570 16 0.3750 0.3125 1.0000 20 0.1500 0.2263 0.2458 20 0 0 NA 20 0.3500 0.3579 1.0000 20 0.1500 0.2263 0.2466 22 0.1364 0.1299 1.0000

Contig850 16 0 0 1.0000 20 0.2000 0.1842 1.0000 20 0.3000 0.4342 0.2814 20 0.5500 0.4474 0.6070 20 0.1000 0.0974 1.0000 22 0.3182 0.3333 1.0000

All loci - 0.0608 0.0786 0.0258 - 0.2796 0.2972 1 - 0.0778 0.0772 0.6229 - 0.2669 0.2638 0.8600 - 0.2746 0.2904 0.9989 - 0.1878 0.1993 1

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Note 1

Samples and DNA extraction

Adipose fin clips were taken between 2008-2016 and stored in 96% ethanol from a total of 186 individuals, representing five Greenlandic, one Icelandic and one Norwegian population (N=20 per population) (see Fig. 1 in the main paper). Also, a historical sample from 1952 (Ekaluit, N=22, Fig 1) consisting of char otoliths was included. From one of the Greenlandic populations (Kobbefjord) six family crosses were analyzed (between 2 and 35 offspring per family). DNA was extracted using the E.Z.N.A. DNA Tissue Extraction (fresh samples) and MicroElute Genomic DNA kits (historical samples) (OMEGA Bio-Tek, CA, USA). In order to minimize contamination risk, extraction of DNA from historical samples was conducted in a clean laboratory facility where no contemporary DNA samples have been processed.

Note 2

ddRAD protocol using gel cut for size selection

Digestion

Measure the DNA concentrations of the extracted samples on the Quibit and dilute to 50 ng/µl

Master mix 1xR:

10xNEB4 2,0 µl

Sbf1 0,2 µl (20 000U)

Msp1 0,2 µl (20 000U)

BSA 0,25 µl (20 mg/ml)

H2O 7,35 µl

Mix 10 µl master mix with 10µl DNA (50 ng/µl)

Program: 37 °C for 12 hours, followed by 65 °C for 20 min., 4 °C hold

Check for successful digestions by running 3 µl of the digested DNA on a 1 % agarose gel. You want to see a smear without any clear bands (none-digested DNA)

If the digested DNA samples look different in regards to concentrations then re-measure the concentrations (use the Quibit) and dilute to equal concentrations. Use 1 x NEB4 buffer to reach a final volume of 20 µl. We suggest re-measuring the DNA concentrations, as it allows for more accurate pooling of the samples.

If clear bands are present for some samples these should be re-digested.

Adaptor annealing

4

Dissolve the oligos in ddH20 to a final concentration of 100 pmol/µl = 100 µM.

Annealing:

10xAB buffer 10 µl

Oligonucleotid F 10 µl

Oligonucleotid R 10 µl

H2O 70 µl

Program: 95 °C for 5 min. followed by 6 °C/min to 25 °C

Measure the concentrations of the annealed adaptors using the Qubit

The adaptors with barcodes should be diluted to ca. 3,2 ng/µl (dependent on the restriction enzyme and species)

Adaptor 2 should have a concentration of ca. 150 ng/µl

Adaptor ligation

Master mix 1x R:

10xNEB4 2,0 µl

ATP 10mM 4,0 µl

Adaptor 2 (undiluted) 3,0 µl

T4 DNA ligase 0,1 µl (2.000.000 U/ml)

H2O 8,9 µl

The digested DNA samples are pipetted into PCR strips with separate lids. There should be a total of 18 µl master mix per sample.

Ligation:

DNA 20 µl

Master mix 18 µl

Barcoded adaptor 2 µl

Program: Incubate 22 °C 2 Hrs, 65 °C for 20 min., 4 °C hold

5

Pooling of barcoded samples and purification using OMEGA E.Z.N.A Gel Extraction Kit

Pool 20 µl of each sample into an eppendorf tube or a 15 ml tube depending on the total volume.

The pooled samples should be purified on an OMEGA purification column in order to get rid of excess adaptors.

Follow the OMEGA E.Z.N.A Gel Extraction Kit protocol. This can be found in the box together with purification columns and reagents.

Purify the pooled sample using only one purification column by loading quantities of 700 µl until the entire sample is used.

Hereafter, follow the OMEGA E.Z.N.A Gel Extraction Kit protocol. Elute in 30 µl EB buffer (E1). The final quantity of DNA can be increased by using 70 °C EB-buffer and subsequently incubate the column at 70 °C for 15 min. A second elution (E2) should be carried out in a separate eppendorf tube. Like last time a volume of 30 µl 70 °C EB-buffer should be used. However, this time incubation can be carried out for only 2 min at room temperature.

Measure DNA concentration on both samples (E1 and E2) using the Quibit.

This second sample (E2) should be put in the freezer and can be used in case of need – if more barcoded product is needed for subsequent library amplification.

Size selection

Load the entire E1 sample mixed with 7 µl loading buffer on a 1,5 % agarose gel. Load a 100 bp (10 µL) ladder on each side on the sample. Important; leave one well empty between ladders and the closest samples.

Before running the gel, the electrophoresis camber should be bleached for 10 min and exposed to 2 x 10 min of UV radiation in order to decrease the risk of DNA contamination. Use new none-used TAE buffer.

Run the gel for one hour, 80 Volt and cut out the DNA from 200-500 bp. Use a ruler.

Weigh the gel piece and use the OMEGA E.Z.N.A Gel Extraction Kit for purifying the DNA sample. Like during the last purification step only one column should be used in order to ensure high DNA concentration of the library.

Elute using 2x40 µl EB buffer into a single eppendorf tube (Total 80 µl).

Measure the DNA concentration using the quibit. It should be around 5 ng/µl. The quantity used for PCR should be around 20 ng.

Make one PCR reaction. Measure the concentration on the Quibit. It should be in the range >20 ng/µl.

During PCR, smaller fragments can be over-amplified, which can lead to increased error rate and skewed coverage of ddRAD loci. To check for such bias you can run several PCR reactions using different number of PCR cycles. The PCRs can then be evaluated on a gel and the PCR showing the least amplification bias (in theory you would want to use a PCR with 10-14 cycles showing a smear with no or few minor distinct bands). The PCRs should be run on a 1.5% gel by loading 10 µl PCR product.

Choose the PCR setup that gives the best result.

6

PCR

5x multiplex master 4 µl

IlluminaF_PE 1 µl

IlluminaR_PE 1 µl

DNA 8 µl

Make 6-8 independent reactions. This allows for high DNA yield and smaller impact of PCR amplification errors compared to a library build using one/few PCR reactions.

PCR program: 95 °C 1 min; (95 °C 30 s, 62 °C 20 s, 68 °C 25 s)12 cycles (will differ from library to library according to methods (restrictions enzymes used) and species, the number of cycles will normally be around 12-14 cycler), 72 °C 5 min, 4 °C hold

Purification using AMpure beads

Pool the PCR reactions and purify the sample using 1.5 x AMpure

Make 5 ml 80 % ethanol (has to be made each time)

The AMpure bead mix should be in room temperature

1. Measure the volume of the pooled library sample2. Vortex the bottle containing the AMpure beads3. Add 1.5 x volume of AMpure and mix4. Incubate for 5 min. in room temperature5. Puls-spin and put the eppendorf tube in the magnet stand6. When the liquid is clear (ca. 3-5 min) remove the supernatant (save it in case something went wrong)7. Leave the tube in the magnet stand and add 500 µl 80 % ethanol8. Remove the ethanol after 30 s9. Wash again with ethanol as in step 7-8 10. Puls-spin and put the tube back in the magnet11. Dry the bead pellet for ca. 5 min.12. Elute in 30 µl 0,1xTE or TE by removing the eppendorf tube from the magnet stand and mix

thoroughly. Afterwards the tube is put back into the magnet stand and the supernatant (containing the DNA) is removed. (save the tube containing the bead in case something went wrong).

Concentration of the final library should be measured using the Qubit and the library size profile should be analysed using the Bioanalyser

If the concentration is high enough (see kit) use the DNA 1000 kit.

7

Note 3

Pipeline – methods and results

Methods

Finding SNPs

First adaptor sequences were removed from the data and replaced by Ns using a costume made unix script. Subsequently, the RAD data was de-multiplexed, trimmed to 80 bp and finally quality filtered using the program PROCESS_RADTAGS v1.4 implemented in the stacks software package (Catchen et al. 2011; Catchen et al. 2013a). We removed all reads showing single position phred-scores <10 and any reads with uncalled bases (Ns) which allowed us to remove reads with leftover adaptor sequence. Subsequently all read pairs from the parent fish (N=9) showing ≥4 bp overlap were merged into contigs using PEAR v0.9.6 (Zhang et al. 2014) and used to create a "synthetic genome". First 1) within and among individual contigs were analyzed using PyRAD v3.0.66 (Eaton 2014) using a clustering threshold of 97%, a ploidy level of two and allowing a maximum of five heterozygote sites per contig per each individual. Only contigs showing a coverage ≥3 within individuals and which were observed in ≥50% of the parents were kept. Subsequently, 2) consensus contigs were generated. In case SNPs were present among the parents, bases were called according to a majority rule criterion.

Subsequently, reads from all individuals were aligned to the "synthetic genome" using BOWTIE v1.1.2 (Langmead et al. 2009) allowing one unique hit and three mismatches. SNPs were called in all individuals using STACKS v1.4 (Catchen et al. 2011; Catchen et al. 2013b), calling SNPs by position in the "synthetic genome". SNPs were called in individuals from the five sampled populations from the Nuuk area (see Fig 1 in the main paper). Only RAD loci showing a minimum coverage of 3X per individual and called in minimum 66.7% of the individuals in each population were used for calling SNPs. Following SNP calling, filtering was conducted to remove spurious loci caused by sequencing errors or alignment of paralog reads. All RAD-loci showing >2 haplotypes were discarded. Also, filtering was conducted using a test for Hardy-Weinberg equilibrium, where the test statistic is FIS×√(N) (Brown 1970); FIS is Wright's inbreeding coefficient relative to the subpopulation and N denotes sample size. The test statistic is approximately standard normally distributed, i.e. with mean of 0 and variance of 1. We discarded loci with values >2 or <-2, indicating significant heterozygote deficit or excess, respectively. All SNPs with a Minor Allele Frequency (MAF) <0.05 were discarded from further analysis and only SNPs located on contigs 121-156 bp and with at least 50 bp flanking sequence on each side were retained.

Finally, all candidate SNPs were included in a whitelist that was used to call the same SNPs in the family datasets using STACKS. The genotypes of parents and offspring were analyzed in order to assess Mendelian inheritance. SNPs showing discrepancies from this pattern were discarded. Since more SNPs were needed than could be validated in the family dataset we also chose 31 SNPs randomly among candidates not validated in the family dataset. An overview of the pipeline is provided in Fig. S1 below.

8

Fig. S1. Overview of the pipeline for identifying SNPs in Arctic char from ddRAD data

Two additional SNPs were derived from Arctic char Cathelicidin 2 gene sequences (Kapralova et al. 2013), GenBank Accession Numbers KC590659 and KC596075, respectively, with the former situated in the exon encoding the mature antimicrobial peptide and the latter situated in an untranscribed region.

Sequences for all the chosen candidate SNPs were submitted to the D3 Asssay Design software for primer design (https://d3.fluidigm.com).

Results

Average number of reads per individual constituted 1,508,971 and 1,096,714, respectively, before and after quality filtering, meaning that 27.32% of the reads were removed due to included adaptor sequence, ambiguous base calls or bases with phred-scores <10. A total of 57.45% percent of the paired end reads overlapped in the parent dataset. The merged overlapping loci (now referred to as contigs) showed 4880 putative contigs across the parent individuals after sequence clustering in PyRAD v3.0.66 (Eaton 2014). A total of 20.86% of all quality filtered reads aligned back to the "synthetic reference genome" following BOWTIE v1.1.2 alignment (Langmead et al. 2009). Initially, STACKS v1.4 called 4520 SNPs distributed across 5162 RAD sequence loci of which 2951 showed no variation. Of these SNPs 2160 were removed, due to removal of tri-allelic haplotypes, 334 were removed due to deviations from Hardy-Weinberg equilibrium based on the FIS×√(N) statistic, and 1310 were removed as they exhibited MAF <0.05. Of the final 719 SNPs, 402 were associated with contigs in the "synthetic genome" that were >120 bp long, and 122 had flanking sequence ≥50 bp; 61 had flanking sequence ≥60, the optimal setup for primer design using the D3 software. Among the 122 SNPs, 74 were present in parents and offspring of the family crosses, and 65 of

9

them showed Mendelian inheritance (87.8%). An additional 25 SNPs showing lower MAF (0.02<MAF<0.05) were also found. Of these 8 were polymorphic in some of the crosses and showed Mendelian inheritance.

Among the final 96 SNPs that yielded successful primer design using the D3 Asssay Design software for FLUIDIGM primer design, 57 belonged to SNPs showing MAF >0.05 and ≥60 flanking region (34 validated in offspring); 33 showed MAF >0.05 and had flanking regions between 50-60 bp (25 verified in offspring); four showed MAF between 0.02 and 0.05 and ≥60 flanking region (all validated in offspring) and two were found within the Cathelicidin 2 gene. All sequences and targeted SNPs can be found in Supporting Information, Table S1.

Note 4

Sequences from which SNPs were designed, with the targeted SNP denoted by squared brackets. Other SNPs showing ≥0.05 MAF are denoted by UIPAC codes. Sequences leading to well-functioning SNP assays are listed first and in bold, whereas SNPs that yielded poorly functioning assays (either monomorphic loci, loci showing excessive heterozygosity or yielding insufficient separation on the FLUIDIGM platform) are listed in italics.

Well-functioning assays:

Cath2_KC590659 (Cathelicidin 2 gene): TGGTTAGGACGTAATGCTAACAGTGTCAGAACAGTTGATCTGCAATTTTAAAAAATGCAGATGTGAAAAGTGCATTAAATGCTTAGCAGTTTATGAATTTCATGAATGTGGAAACATGCATCTCTTGAAATAATAACTTTTTCTGATTAGTCTGGTCAAAAAGGTTCTGCTTTTTCCTTTTTAAAAGTTTTAATTCATATTTTTAATGATTACAGCAGAAGATTCGGACAAGAAGAGGCAAGGCCAGTGGAGGCTCT[A/C]GTGACTCTAATATGGGTAGAAAAGATTCCAAGGGGGGTAGGAGAGGTCGTCCTGGGAGTGGCTCTCGTCCAGGGTTTGGCTCCTCCATTGCTGG

Cath2_ KC596075 (Cathelicidin 2 gene): CACTCAAAGTTGTGGTTAATGGGTGAACATGCAACCTTGAGCTTCTTT[A/T]AAAAAATGCTTCTGGTAACTCTTTTGTCTACATTCTGAATTGTTTTGTATTTTGTAGCTATCATTGTTTGTAATAAT

Contig10740_78: CCTGCAGGTCAGTGGTTTGACTGACAGGCGCTCTGATGCTCCGTGACAGGATCTGCTGCTTCCCTTCATTTCCACTTCC[C/T]TTTCCCCCGTCAGGTGACACCCCCACAGGTGCCACAGCAACCGAGACCCCGACTCCTCGGGGTGCGGACCGG

Contig10812: CCTGCAGGTTACCTACAACACACACAGGTTATTACATATCCTGTCTCCATGT[A/G]TGTATGCTCGTTTGCGTATTCGTGTGTGTGTGTCTGCGCGGTACTAACCCAGAGGCTGAGACCAGCCCAGTCTGGCTGCGGTGTCCGG

Contig11261: CCTGCAGGCTCCCTTGCCCGCCACAAGGAGTCGCTGGAGCGAGATGAGCCAAGTTT[T/A]AAAAAATCCTGTCAAACCCTCCCCTAACCTGGACAACACTGGGCCAATTGTGCACCGACCTATGGGACACCCGG

Contig11263_71: CCTGCAGGGAACCACCATTGAAAAACTCCCTCATAGCCTTAACAATAAGGATCGATGGACGTCTAC

10

GGGAGC[A/G]CAGGAGGAAGAGGAGGTCTGCTTTCGGACCCATTCGTTCATCCACGGCTGCTTGCTCGCCGCCGAAAAACCCCGG

Contig11431_72: CCTGCAGGAGCGAGTCACTGCTTTGATGTTTGCAGAGAACGACAGGTTGTTGTCCAGGTTCACGCCAAGGTTC[T/A]TCGCACTCTGGGAAGGGAGAGCATGGAGTTGTCAACCATGATAGAAAGGTCTTGGAGCGGGCAGACCTTCCCCGG

Contig11566: CCTGCAGGACCCCAAGACGGACGGCATCATCCTGATCGGAGAGATCGGAGGCAA[C/T]GCCGAGGAGAACGCTGCCGATTACCTCAAACAGCACAACTCTGTAAGAGGAGACGCACRCATTAACATGCTACCGG

Contig11742_67: CCTGCAGGCTAACAGTCGGGCCAAAAACAGGCAAATAACAGCGCAGTACAACAGTTGTTTTCGGAATG[G/A]CGTCTCGGAATGCACAACTCGTCGGTCCTTGTCACGGATGGGCTATTGCAGCAGACGACCACACCGG

Contig11854_70: CCTGCAGGCACCCGGCCCACCACAAGGAGTCGCTAGAGCACGATGAGCCAAGTAAAGCCCCCCAAACTCTC[C/A]CCTAACCCAGACAACGCTGGGCCAATTGCGCGACGCCCTATGGGACTCCCGGTCCCGGCCGG

Contig12050: CCTGCAGGTCCAGCAGTCATCCCGCAGGAATGCAGCCCTCTAGTGCAGTCAGTACACAACACTATGCTACTCATGTCTT[A/T]GCAAGAGCAGATGGTGACAGGGGTCCCAGCAGTGTCAGGCAGCCAGGGAAGACCGG

Contig12176_62: CCTGCAGGGAGCTCCAGGGGGTGGTGCAGGATACCAGCCAGCGGGTGGAGCAGCAGGGGCGGA[C/G]GCTGGAGGGGGTGGAGAGGCTGATGGTGGGCATGCATCAGGTCATCAGCTTCATCGAAGAGGTGGTAAAGAGCTCCCGG

Contig12281: CCTGCAGGTGTGTGTGASCACAGCCAGGACAGCCTGGTATTCGAGACACTGATTCCCAAGCCCCTGCTGCAGCGCTACGTGTCCCAGCTGCATGACCA[C/T]GGCAGGATCATCCTGTCTGGCCCCAGCGGCACTGGAAAGAGCTTCCTGGCCAGCCGG

Contig1373: CCTGCAGGCTCATCCTGACATGACCATCCATGACATGACAATGCCACCAGC[C/T]ATACGGCTTGTTCTGTGTGTGATTTCCTGCAAGACAGGAATGTCAGTGTTCTGCCATGGCCAGCGAAGAGCCCGG

Contig1525_59: CCTGCAGGCGCCTGGCCAGCCACAAGGAATCGCTCGAGTGCAACCCACGGCCAAACCCCA[A/C]CCGGACAATGCTGGGCCAATTGTGCGCTGCCCTATGGGACTCCCGATCACGGCCGGCTGTGATACAGCCCGG

Contig1570: CCTGCAGGGCAGCAGCGCCACCTGGCCTCCAGGAACGGGGAACCCCAGGGAGC[T/G]AGCAACTCTACAGGGGGCACCACGGCAGCCCTCCCCCTGGCTGGTGCCTCGGACCAGGGAGCAGGGGACCCCTCCGG

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Contig1776_87: CCTGCAGGAMAGCCGCAGCGTGGAGAGTACCCCTGGCCCCGGCCCTGGCTCCCCCAGCGACATGCTCTTCAACGACAGCCAGTCCCCC[G/A]TCACCCCTGGGCCTGGGGGTTGGGCCGCCCGCACCCCGCAGGGCATCATCCCCACCCCCGGCCCCGG

Contig1821_63: CCTGCAGGGAGCTCATGTGCTGGTTGGTTTTCACTATCTGGGCCTGCTTGGTGGGCTGGAGGAC[T/C]AGGCCCTGCTGGGCCATGGGCTCTCTGGGCTCCCTGGGGGGGTAGCGGTGGTGACTCAGCCTTCCAGGGCCTGTGAGGCCGG

Contig1973: CCTGCAGGCCCGCCTCGGAGTCATCAAGAACCGCGGGGGAGGGGTGGGTGCCACCAA[C/T]GACACTGGTGAGAACCAGGATACTGTACTATCGCATCATCACAAACCCTGACGATGTCTCAGCACATTGACAAGGACAGTAGAGCCGG

Contig214_63: CCTGCAGGCGCCCGGCCCGCCACAAGGAGTCGCAAGAGCGCAATGAGCCAAGTAAAGCCCAATT[T/G]TGCGCCGCACTATGGGACTATCGTGACACTATGGGACTATCGTGACACTATGGGACGATCGTCGGATCGAACCCGG

Contig2194_67: CCTGCAGGCGTTTGGTTCTAGGGGGTGGGACCACTTCCCTATTGGTGCCCTCCTTAGTGTCACTATCC[G/A]CCTTCTTCTCTGCAGCGCCCACATTGGTCTCCATTGTGAGGGCCTCATCTTGGATCCGTTGACAGTAGCGTGCAGCGGCCTCCGG

Contig2680_72: CCTGCAGGCTGTGTGTACGGTCCTCAGGGCGGGGCTGCAGGCCCACCCTGACCAGGGCGTAGTAAGGCTGGGG[A/C]ACACTGGAATCTGTAGCCAGGGCCTGGCCCGACTCAACAGTCTGCCTGATCCAACGAGGGTCCACCGG

Contig2705: CCTGCAGGTCCCAGTCAGAGAACAGCCTCAATGGGCAGAGGGGGGTCCCTGA[A/G]CGCAAGTACAACACGGTGGAGAGAGACGGGGGAGGTTCAGGGAGCAGCCGCGGGAGCCAGTCCAAGGGGAAGAGGCCCCAGCCGGGCAGTGCCGG

Contig2925: CCTGCAGGGCTGGACAAAAAGCCTGCAGCCCTGCACACCCAATAGCGCTCCAGTATGTTGTCTTGGCACCTACCTTTTGGCCCAGTGAGCAGGGCCTC[C/A]GTGGCCTTGGCCCCATCCCCACAGTGGGTCTCATCAAATGGCAGACAGTGGTCCCCGG

Contig2980_70: CCTGCAGGCTTGGGTAAAGCCTCGGATCTCTCCGCCATTCCCGCAGAGTACCAGGACTTACGGGAGGTTTT[C/G]AACTCTGGCCGCTCCACATCTCTTCCTCCACATTGGCACTACGACTGCGCAATYGACCTTCTCCCCGG

Contig2997: CCTGCAGGTATCGCCAACACTTCTCTGTCATTCAAAACTGCTGTATTTTAGCT[T/G]AAACAACCTCCAGGCCTTGCCCAGCCCTGTCCTACTCTACTGCAGTCCAGCAGCCCCTTAGTGGGGCAGAGGCTCCCAGACGCAAGGGTGTCCAACAGTCCGG

Contig3057_86: CCTGCAGGCGCCAGGCCCGCCACAAGGAGTCGCTAGAGCGCAGTGAGCCAAGTAAAGCCAAACCC

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TCCCCTGACCCGCACGACGCTG[G/A]GCCAATTGTGCGCCGCCCTATGACTCCCGGGTCACGGCTGCTTGTGATACAGCCTGGGATCGAACCCGG

Contig3343: CCTGCAGGTGCTGGACAGCCAGCTGGGTCAGCTGCAGGACCTGTCCGCCCT[A/G]GCCGTGGACACCCTCACCCTCCTATCGGCCTCCGATAGCCTGCAGCAGGAGGAGGCCCGCCTAGGCCACTGCCGG

Contig3493_74: CCTGCAGGACATCTTCTAGTATGGATGGCCCCAGGTCAAGGTTCAGGGCAAAAGACGAGTCTTCCTCGACCACTG[G/T]AGAGGCCTCCACTGGTGGACAAGGAACCTGTTGGGGGGCCGCGTTCAATTCAAATCCATCCGG

Contig3498: CCTGCAGGCCGGGCTGTGGGAAGCCCACGCAGGGGCCACTGATCTGAGGGTGCCCCTGTGGGCCGAAGGCCTGGTGTTCTTCTACTGCTACGCCCTGCT[A/G]CTGCTGCTGCCCTGCGTGGCCCTCACAGAGCTGGGAGCCGCCGCCATGCCGG

Contig3603_79: CCTGCAGGAGAGGGTCACTGCTTTGATGTTTGCTGAGAATGACAGGGCGTTGTCCAGGATCACGCCATGGTTCTTTGCAC[T/A]CTGGGAGGGCGAAACTGTGGAGTTGTCAACGGTGATGGAGAGGTCTTTGAGCGGGCAGGCCTTCCCCGG

Contig4510_74: CCTGCAGGCTGATCGTTCAGTTGAACGGTGTTTCCTCCGACACATTGGTGCAGCTGGCTTCCGGGTTAAGTGGAC[G/A]GTTGATAAATAGCGCGGTTTGGCGGGTCATGTTTTAGAGGACGCATGACTCGACCTTAGCCTCCTGAGCCCGG

Contig481: CCTGCAGGCGCCCGGCCCGCCACAAGGAGTCGCTAGAGCGCGATGAGACAAATAAAGCCCCCCCCCGGCACAACCCTCTCCTA[A/G]CCCAGACGACGCTGGGCCAATTGTGCGCCGCCCTATAGGACTTCAGATCACGGCCGG

Contig4954: CCTGCAGGGAGCAGTCGCTGTTGAAGCTAATGATGCTGGAGGGCCGACCGTTGTCTATGATGCTGCCGATGGACAGCTCCTCCACCACAGTGAACA[C/A]CAGCTCATCCTCTCCGTTCAGCTCYACAGGCTGCTGCAGGGTCACCGTAGCCCGG

Contig5808_61: CCTGCAGGGTGACAGAGAAACCATGGCAGACCACATGATGATGCCCATGTCCCACGGCCCTG[C/T]GACGGCCCTCCACGGCTACAGGATGGGGGGAATGGGTGGCCCTCCACAGCACGCCGTGCCGCAACCCGG

Contig5917_74: CCTGCAGGATCGAGTCACTGATTTGATGTTTGCAGAGAACGACAAGCTGTTGTCCAGGGTCACGCCAAGGTACTT[T/G]GCACTTAGGTATGGGGCACTGTGGATTGTCAACCGTGATGGAGAGGTCTTGGAGCGGGCAGGCCTTTCCCGG

Contig609_67: CCTGCAGGCCGCCAGGGCTCTCACCGTGGTCACCATCATCCTGGGGGTCATAGCTCTCCTCGTGGCTA[C/T]CATTGGGGCTAAGTGCACCAACTGCATCGAGGAGGAGGGGGTGAAGGCCAAGGTGATGGTGTCTGCCGG

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Contig6336_73: CCTGCAGGACTCAGTGTCCGCCTCCGGCCAGGCCCTGCTCTTCTCCCCTTCCAACTACCCCACCCATAACTCCC[A/T]TTCAGCCAGCCCCCTACCACCCCATCAGTCTTTGCAGTCGCAATCCCAGCACCCGTCAGCGTGCAGCCCGG

Contig6593: CCTGCAGGTAAATCAATGGGAAGGCGCAAGGCAGGGATCGATAGTGCTTCTG[C/A]TTTCTCTCTGGTGGATGGAGGCTGTCAGCCCACGGAACATCAGGACATCTGACGGCAGGAATGGGGATCCCGG

Contig711_65: CCTGCAGGTAGCCCAGTGCAGAGTGAGGAGACAGGGCTGGTGGAGTTTCGTATGTCAGGTCCACTC[C/A]AGTACATGGTGTGGTACCACGCTGTGGGCCTCATCTGGATCAGTCAGTTCATCCTTGCCTGCCAGCAGATGACCGTGGCCGG

Contig7751_81: CCTGCAGGACAACAGAATAGAGGGATAGGAACCGCAGGAGGACCGTACAGCAACCAACAGGGGGGAGGTAGAGGTCTAGTTC[A/T]GTTTCTTGGACCCCTCGTCGCCGTAACTARAGCCTGTCTTCTTTACCTCAGTGACACCGATCAGGCGCCGG

Contig7991: CCTGCAGGCTTGTCCCATTGCTCACGGGCAAGGCCTCAGAGGCTTACACGGCAATGGATGAGGGGTTGTCCAAC[T/G]TCTACAAGGGCTTAAAGGAAGCGCTGCTGGTGAAGTTCGACATCTCCCCGG

Contig850: CCTGCAGGGGGACAACAAGAGCCAGTCAGACTCTGCGAAATGCAGCGAACAGAGCAAAACCCAAAAGGTGTGCGAGTTCTCCCTGTACCTGTTGCAG[C/T]GCCGTGTTTGGGTCCATTGGTCCCCTGTGAGAGGGTCATGGCCTGCCCTGGCCCGG

Contig8674_69: CCTGCAGGAGCGGGTCACTGCTTTGATGTTTGCGGAGAACGACAGGGTGTTGTGAAGGGTCACGCCAAGG[G/T]TCTTTGTCCTCTGGGAAGGCGACTGTGTGGAGTTGTCAACCGTGATGGAGAGGTCTTTGAGCGGGCAGGCCTTTCCCGG

Contig8752: CCTGCAGGCCAGGCCTGTCACACTTCCGCTTGCACTCTGTCTCGTTGCTGAAGAGGTGGCGGGGTGTGAGCTGGCAGGCAGGTGGAGAGCA[T/C]AGGGCACGGCTGTGGCTCCACATAAAGAGGGCTGACATGTTGTGCTGTGCCGG

Contig8976_82: CCTGCAGGGCGATGGTATCCCCTCCCCCGTCGTAGGGTAGATAGGGGTCAGCCTCGGTTGAGATATCGTCCATGGTTGACTGA[C/A]AGTCGGTCGCTAAACTAATTTTGCTGTCTCACCGCGTGTTAGCTCCTCGTCGCTAGGCGAACTGCTTATCCGG

Contig8978_60: CCTGCAGGCTTGCCTGCCACGCAGACTCCAGTTAGGCCCAGGCCTTCGTGCGAAAATGCTT[C/T]TGGTGGCCTACGATGGTTCCGGATGTCACCACATTTGTCTCCGCCTGCACGGTCTGAGCTCAGAATAAGACTCCTCGGCATGCTCCGG

Contig92_84: CCTGCAGGAGCGAGTCACTGCTTTGATGTTTGCAGAGAACGACAGGGTGTTGTCCAGGGCCACAGC

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AAGGTTTTTAGCACATTGG[G/A]AGGGGGACACCATGGAGTTGTCAACCATGATGGGGAGGTCTTGGAGTGGGCAGGCTTTTCCCGG

Contig9220: CCTGCAGGCCAGCAGCGCCTCACTGGACGACGGCGAGACAGAGCAGTTGCTCCAAGAGGAGAGGTCGGAGTGCTCGTCCATAAAC[A/C]CGCTGACCCCCGCCATCATCACACCCCAATGCCACCCCCAACACAGCCTGACACCGG

Contig9346_76: CCTGCAGGCACCCGACCCACCACAAGAAGTTGCTAGAGCACGATGAGCCAAGTAAAGCCCCTCCGGCCAAACCCACC[G/A]CTAACCCTGACATAGCTGGGCCAATTGTGCGCCATCCTATGGGACAACCCAGGAATGAACCCGG

Contig9421: CCTGCAGGGCAAGGCAGGCAGTCAGAGAGTTGTGTGTTCCTGGTGGTGTTGCTGTAATATCCTAGAGGACAGGGAC[A/G]TGGCGCTGCAGCCAAGCCCTCTGGACAGTAATGACCTTGGGGACACCGCTCACCGG

Contig959_76: CCTGCAGGGGGTGGTGGAACAGTTGTCCATGGCGTGCAGCATGGGGGGCAGGGCCTCCCAGCAGTCCAGCGCGTGGT[T/C]GTAGCGCTCCGTGCTGTCTGGCGCCACCACGTACAGCTGGCCCTTGAGGACACAGGAACTGTGGTACTCCCGG

Contig9609: CCTGCAGGGATACCACCCTCACCTTCGACTAGCTGGTGGACCTGTTCATTTGGTTGGAT[A/T]ACCTGCTGGTCACCCGTGGATGTCCAGAGAGGGCACCTGTGGATGTCCCTCTCCCAGCACCACCGCTCCGG

Poorly functioning assays:

Contig10150_61: CCTGCAGGCTCATCCTGACATGACCCTCCAACATGACAATGCCACCAGCCATACTGCTCATT[C/A]TGTGTGTGATTTCCTGCAAGACAGGAATGTCCTTGTTCGCCATGGCCAGCGAAGAGCCCGG

Contig1018_73: CCTGCAGGCACCCGGCCCGCCACAAGGAGTTGGTAGAGCGCGATGAGCCAAGTAAAGCCCCACCGACCAAACCC[A/C]TTCCTTAACCCAGACGACGCTGGGCCAATTGTGCGCCGTCCTATGGGAATCCATAGGGCACAGCCTGAGAATTAACCCGG

Contig10510: CCTGCAGGCACCCCGCCTGCCACAAGAACACGATGAGACAAGTAAAGTCCCCCCGGCCA[A/C]ACCCTCCCCTAACAACACTGGGCCAATTGTGAGCTGATCTATGGGACATGTGAGCTGCTCATGGCTGGTTGTGACACAGCCTGGGAACGAACCCGG

Contig11164_73: CCTGCAGGCGCCACAAGGAGTCGCTAGAGCGCGATGAGCCAAGTAAAGCCGCCCCGGCCAAACCCTCCCCTAAC[C/A]CGGACGACGCTTGGCCAATTGTGTGCCGCCCTATGGGACTCCCGATCACAGCAGGTTGTGGTACAGACCGG

Contig11790_88: CCTGCAGGCACCCGGCCCGCCACTAGAAGTCGCTAGAGCGTGATGAGCCAAGTAAAGCCCCCCCGGCCAAACCCTTGCCTAACATGGGC[G/A]ACGCTAGGCCAATTGTGCGCTGTCCAATGGGACTCCCGGCCACGGCTAGTTGTGGCACAGCCCGG

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Contig11924_63: CCTGCAGGTAATTCCTGATGTAGTGCTGCCTGAGCAGATCATTTACAGGGGCCTGGTCGTCCAC[C/G]CCACTGGCTGTGACTACAACAGGCAGGGTCCCTCCATACCTCTTCTTCACCCAGCGCAGCACCCTCCGG

Contig11963_80: CCTGCAGGATGGTAACAACAACTGCCCGAGTTACACCAGGAACGCACAATCCCTCCATCCGTGCTCAGACTGTCCGCAATA[G/A]GCTGAGAGAGGCTGGACTGAGGGCTTGTAGGCCTSTTGTAAGGCAGGTCCTCACCSGACATCACCGG

Contig12126: CCTGCAGGCGCCCGGCCCGCCAGAGTCGCTGGAGTGCGATGAGCCAAGTAAAA[C/T]CCCCCCAGCCAACCCCCCCTGGCCAAACCCTCCCCTAACCCAGACGACGCTGGGTCAATTGTGCGCTGCGCCCTATGGGACTCCCGATACAGCCCGG

Contig1734_76: CCTGCAGGAGCTAGTCACTGCTTTGATGTTTGCAGAGAATTACAAGGTGCTGACCAGGGTCACGCCAAGGTTCTTTA[G/C]ACCTACAAGGGGGACACAGTGGAGTTGTCGACCATGATGGAGAGGTCTTGGAGCGGGCAGGCCTCCTCCGG

Contig1909_67: CCTGCAGGCTCATCCTGACATGACCCTCCAGCATGACAATGCCACCAGCCATACTGCTCGTTCTGTGC[A/G]TGATCTCAATCCCATTGAGCACATCTGGGACCTGTTGGATCGGAGGGTGAAGGCTAGGGCCATTCCTCCCAGAAATGTCCGG

Contig1910_59: CCTGCAGGCAAATCAGTTAAGAACAAATTCTTATTTACAATAGCGGCCTACCCCAGCCAA[A/T]CCCTAACCCAGACGATGATGGGCCAATTGTGCGCCACCCTATGGGACTCCCAATCACAGCCAGTTGTGAAACAGCCGG

Contig2013_74: CCTGCAGGATGACAGCAAAATCATATGCCCCCCAGGCCTCCCGAGTGGCGCAGTGGTCTAAGGCACTGCATCGCA[G/T]TGCTAGCTGTGCCACTAGAGATTCTGGGTTCAAGTCCAGGCTCTGTCGCAGCCGGCCGCGACCGG

Contig2039_77: CCTGCAGGTGTCACAGGTCAGCCAGGACCTCCAGGAAATGACACGGCTGCTCAAGCCCCTCCTCCAGCCCCTCCTCCT[C/T]CCAAAACTCCTTCCAACACCAACTTACTGCTGTTGGAAGATACAGAGATAGAGGGGGAGAGGAGACACCCGG

Contig2223_64: CCTGCAGGAGTGAGTCATTGCTTTGATGTTTGCGGAGAACGACAGGGTGTTGTCCAGGGTCAAGC[C/T]AAGGTTCTTTGCAATCTGGGAGGGGGACACAGTGGAGTTGTCACCCGTGATGGAGAGGTCTTGGAGCGGGCAGGCCTTCCCCGG

Contig2275_84: CCTGCAGGACAGGTACAGGATGGCAACAACAACTGCCCGAGTTACACCAGGAACGCACAATCCCTCAATCAGTGCTCAGACTGTC[C/T]GCAATAGACTGAGAGAGGCTGGACTGAGAGCTTGTAGGCAGGTCTCACCAGACATCACCGG

Contig2481: CCTGCAGGCTCATCCTGACATGACCCTCCAGCATGACAATGCCACCAGCCATACTGCTCGTTCTGTGTGTGT[A/T]TTCCTGCAAGACTTTAATGTCAGTGTTCTGCCATGGCCAGCGACGAACCCGG

Contig2992_69: CCTGCAGGAATCGGAGCTGGCTGCAGTGGGCAGCAGGATGCTCTTCCCCAGTAGCAGTGGCAGCGGAAGC[T/G]CTCTCCACGAGGGCGCCTCCAGAGGCCTGCCAGATGTCAACGACCTGGGCCTGGGCCTCCAGTCCCTTAGCCTGTCCGG

16

Contig3757_62: CCTGCAGGCGCCCGGCCCACCACAAGGAGTCGCTAGAACGCGATGAGCCAAGTAAAGCCCACC[C/T]GGCCAAACCCTCCCCTTCCTTGGGCGACGTTAGGCCAATTTTGCGCCGCCCAATGGGACTCCCGGTCATGGCCGG

Contig3948: CCTGCAGGCTCATCCTGACATGACCCTCCAGCATGACAATGCCACCAGCCATACT[G/A]CTCGTTCTGTACGTGATTTCCTTGAAAGAAAGGAATGTCAGTGTTCTGCCTTGGCCAGCGAAGAGCCCGG

Contig4582_74: CCTGCAGGTGCCCGGCCCAACACAAGGAGTCGCTATAGCGCAATAAGCCAAGTAAAGCTCCCCCGGCCAAACCCT[T/C]CCCTAACCCCGACGACGCTGGGCCAATTGTGCACCGCCCTATGGGACTCACGGTCACGGCCGG

Contig5018_80: CCTGCAGGCGCCTGGCCCACCACAAGGAGTCGCTAGAGCCAAGTAAAGCCACCCAGCCAAACCCTCCTCTAACCCGGACGA[C/T]GCTGGGCCAATTGTGCACCGCCCTATGGGACTTCTGATCACGGCCTGTTATGGTACAGCCCGGGATCGAACCCGG

Contig5309: CCTGCAGGACAGGTACAGGATGGCAGCAACAACTGCCCGAGTTACACCAGGAACGTACAGTCCCTCCATCAGTGCTCAGACTGTCCACAA[G/T]AGGCTGAACTGAGGGCTTGTAGGCCTGTTGTCTGGTGAGGACCTGCCTTACATCACCGG

Contig5343: CCTGCAGGTGCCCGGCCCGTCACAAGAAGTCGCTAGAATGCGATGAGCCCCCCCAGCCAAACCCTACCCTA[A/G]CCCAGACGATGCTGGGTCAATTGTGCACCGCCCTATGGGTCTCCCGGTCGCGGCCGG

Contig5388_62: CCTGCAGGCAGCTGGAGCTGAGCAGAAGCAGTAAACTGAGTTCTGTGGTTTCAGATTGGGGGA[T/C]GGAGGCAGAGAGGCAGGCAGCTGCCTGGGTTGAGCTGAGAAATGGGGTTTAGGGCTGATGCTCCTGGGAGGCGCGGGCCCAGGGGCCCCCCGG

Contig5409_61: CCTGCAGGGATGCCACCGTCACCTTTGACCAACTGGTGGATCTGTCCATTCGGTTGGATAAC[C/G]TGCTGGTCACACACGGACGTCCAGAGAGGGTTCTGTCGGGTCCATCTTCCAGCACCACCGCTCCGG

Contig5902: CCTGCAGGCGAGCGGCTCGTGGAAGGGTTTGATGCTGATGCTGGCCCTGCGTACTGGTCCGGGCTCGATGGTGGGCA[A/G]GCCCGTAGCTGAGGTGGTGGTGGTCCATGTAGAGGAGATCCTTCTCAACAGCCCCGG

Contig6314_65: CCTGCAGGCGTTATCCAATGAGGCGATGAAGCCAGCTAAACTGAKGCGGTCCGGTCCAGCAACGAG[T/G]GATCCGTTCCAACAGCAGCCCGAAATGAGCGAGGTGATTCTGGTGTAGTACATGGGGATGTATGAAACTTACGCCTCAGCCGG

Contig6496: CCTGCAGGGTTCCCACCTGCCATGCAGCGGAACACCCCAGGCCCCCAGCAGTTTGG[C/G]CCCCAGCAGTCGGGCCAGGGGCCCCCCGTGTCCCCTCGCCCCTCCCTGGGGGGCTCCATGCACCCGG

Contig662_74: CCTGCAGGCACCCGGCCCTCCACAAGGAGTCGCTAGAGCACAATGAGCCAAGTAAAGCCCCACCGGCCAAACCCT[C/T]CCCTAACTCTCCCCTAACGACRCTGGACCAATTGTGTGCCGCCCTATAGGACTTCCAGCCACGGCCGG

Contig7365_82: CCTGCAGGAGCAAGTCACTGCTTTGATGTTTGCAGAGAACGACAGGGTGTTGTCCAGGGTCACGCCAAGGTTCT

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ATGCACTCT[A/G]GGAGGGCGACACTGTAGCGTTGTCAATCGTGATGGAGACGTCTTTGAGCGGTGAAGCCTTCCCCGG

Contig7474: CCTGCAGGTGCCCGGCCCGCCACAAGGAGCTGCTAGAACACAATGAGCCAAGTAAAGCC[A/C]CCCCGGCCAAACCGTCCTCTAACCTGGACGACACTGGTGCGCCGCCCTATGGGATTCCCAATCACGGCAGGTTGTGATACAGCCCGG

Contig777_64: CCTGCAGGTTCATCCTGACATGACCCTCCAGCATGACAATGACACCAGCCATGCTGCTCGTTCTG[T/A]GCGTGATTTCCTGCAAGACAGGAATGTCAGTGTTCTGCCATGGCCAGTGCGAAGAGCCCGG

Contig7854: CCTGCAGGCGCCTGGCCCGCCACAAGGYGTCGCTAAAGCGCGATGAGCCAAGTAAAGCTCCCCCGGCCAAACCCTCCCTTAA[C/A]CCCTCCAACGCTGGGCCAATTGTGTGCCGCCCTATGGGACTCCCAGTCACGGCCGG

Contig8239_63: CCTGCAGGCACTCCTTCACCGCAGCCAACGTGAGTAAAACAGTTAAATGTGTTAACCCTCGCAA[T/G]GCTGCCGGCCCAGATGGCATCCCTAGCCGCATCCTCAGAGCATGCGCAGACCAGCTGGCCGG

Contig8348: CCTGCAGGCTCATCCTGACATGAGCCTCCTGCATGACAATGCCACCAGACA[T/G]ACGGCTCGTTCTGTGCTTGATTTCCTGCAAGACAGGAATGTCAGTGTTCTGCCATGGCCAGCGAAGAGCCCGG

Contig8455: CCTGCAGGGATATCCTTGTCCCATCGTGCATTAGCGACTCCTGTGGCGGGCCG[G/A]GCGCAGTGCACGCTGACACAGTTGCCAGGTGTCGGGTGTTTCCTCCGACACATTGGTGCGTCTGGCTTCCGG

Contig8611_73: CCTGCAGGCGCCCGGCCCACCACAAGGAGTCGCTAGAGCTCAATGGGCCAAGTAAAACTCACCGGCCAAACCCG[T/G]ACGACGCTGGGCCAAATGGGACTACCGACCACGGCCGGTTGTGATACAGCCCGTGATACAGCCCGGGATTGAACCCGG

Contig8660_62: CCTGCAGGCACCCAGCCCGCCACAAGGAGTCGCTAGAGCGCGATGAGCCAAGTACAGCCCCAC[T/C]GGCCAAACCCTCCCCTAACCCAGACGACGCTGGGACAATTGTGCGCCGCCCTCTGGGACACAGCCCGG

Contig9075_71: CCTGCAGGCTGACCTCGGTTGTCAGGTGAACGACATATTGGTGCAGTTGGCTTCCGAGTTAAGCAGGCAGGT[A/G]TTAAGAAGCACGGTTTGGCGGATCATGTTTCGAAGGACGCGTGACTCGACTTTCGCCTCTCCCAAGCCCGG

Contig9099_85: CCTGCAGGAGTGAGTCACTGCTTTGATGTTTGCAGATAACAACAGGGTGTTGTCTAGGGTCACGCCAATGTTCTTTGCACTCTGGA[A/G]GGGGGACACCGTGGAGTTGTCAACCACGAAGGAGAGGTCTTTGAGCGGCCAGGCCTTCCCCGG

Contig9400_74: CCTGCAGGTGGGCAGCGGCCTGCAGCTTCGCACAGCCTCCACCAGCTCCAAAGAGGAGGACTATGAAAATGACGC[T/C]GCTACCATCGTACAGAAATGTGTAAGTATATATGTAACAGTTTAACTTTTAGTCCGTCCCCCCGACCCGG

Contig9811_77: CCTGCAGGAAGGGTACCACATGAGGGAGGAGGATGTCTTCCCTGTTAAMGCTTAGCATTGAGATTGCCTGCAATGACA[A/C]CATGCTCAGTCCGATGATGCTGTGACACACCGCCCCAGACCATGACGGACCCTTCACCTCCAAATCGATCCGG

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Contig7133_66: CCTGCAGGCTAAACTCASACTCCTTAGAGGAGGAGGGCCTGATTCTGCTGGGGCCATCAGCCTCTTG[G/T]ACAGGAGACAGCAGGGGCTTGAGGRTCCTGTTGTTGAGCCCTGCACTGCTGTAGGCCCCGG

References

Brown AHD (1970) Estimation of Wrights fixation index from genotypic frequencies. Genetica, 41, 399-&.Catchen J, Bassham S, Wilson T, Currey M, O'Brien C, Yeates Q, Cresko WA (2013a) The population

structure and recent colonization history of Oregon threespine stickleback determined using restriction-site associated DNA-sequencing. Mol Ecol, 22, 2864-2883.

Catchen J, Hohenlohe PA, Bassham S, Amores A, Cresko WA (2013b) Stacks: an analysis tool set for population genomics. Mol Ecol, 22, 3124-3140.

Catchen JM, Amores A, Hohenlohe P, Cresko W, Postlethwait JH (2011) Stacks: Building and Genotyping Loci De Novo From Short-Read Sequences. G3-Genes Genomes Genetics, 1, 171-182.

Eaton DAR (2014) PyRAD: assembly of de novo RADseq loci for phylogenetic analyses. Bioinformatics, 30, 1844-1849.

Kapralova KH, Gudbrandsson J, Reynisdottir S, Santos CB, Baltanas VC, Maier VH, Snorrason SS, Palsson A (2013) Differentiation at the MHCII alpha and Cath2 Loci in Sympatric Salvelinus alpinus Resource Morphs in Lake Thingvallavatn. Plos One, 8.

Langmead B, Trapnell C, Pop M, Salzberg SL (2009) Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biology, 10.

Zhang JJ, Kobert K, Flouri T, Stamatakis A (2014) PEAR: a fast and accurate Illumina Paired-End reAd mergeR. Bioinformatics, 30, 614-620.

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