MEN_3157 653..660 RAD sequencing yields a high success rate for westslope cutthroat and rainbow trout species-diagnostic SNP assays S T E P H E N J . A M I S H , * P A U L A . H O H E N L O H E , † S A L L Y P A I N T E R , * R O B B F . L E A R Y , ‡ C L I N T M U H L F E L D , §¶ F R E D W . A L L E N D O R F * a n d G O R D O N L U I K A R T * ¶* * *Fish and Wildlife Genomics Group, Division of Biological Sciences, University of Montana, Missoula, MT 59812, USA, †Department of Biological Sciences, University of Idaho, Moscow, ID 83844-3051, USA, ‡Montana Fish, Wildlife & Parks, University of Montana, Missoula, MT 59812, USA, §U.S. Geological Survey, Northern Rocky Mountain Science Center, Glacier National Park, West Glacier, MT 59936, USA, ¶Flathead Lake Biological Station, University of Montana, Polson, MT 59860, USA, **CIBIO, Centro de Investigação em Biodiversidade e Recursos Genéticos, Universidade do Porto, Campus Agrário de Vairão, 4485-661 Vairão, Portugal Abstract Hybridization with introduced rainbow trout threatens most native westslope cutthroat trout populations. Understanding the genetic effects of hybridization and introgression requires a large set of high-throughput, diagnostic genetic markers to inform conservation and management. Recently, we identified several thousand candidate single-nucleotide polymor- phism (SNP) markers based on RAD sequencing of 11 westslope cutthroat trout and 13 rainbow trout individuals. Here, we used flanking sequence for 56 of these candidate SNP markers to design high-throughput genotyping assays. We vali- dated the assays on a total of 92 individuals from 22 populations and seven hatchery strains. Forty-six assays (82%) ampli- fied consistently and allowed easy identification of westslope cutthroat and rainbow trout alleles as well as heterozygote controls. The 46 SNPs will provide high power for early detection of population admixture and improved identification of hybrid and nonhybridized individuals. This technique shows promise as a very low-cost, reliable and relatively rapid method for developing and testing SNP markers for nonmodel organisms with limited genomic resources. Keywords: conservation genomics, hybridization, introgression, invasive species, microfluidic PCR, salmonids, SNP, trout species identification Received 22 November 2011; revision received 14 February 2012; accepted 17 February 2012 Introduction Rainbow trout (RBT; Oncorhynchus mykiss), the most widely introduced salmonid in the world (Lever 1996), produce fertile offspring when crossed with cutthroat trout (O. clarkii), and introgression often continues until a hybrid swarm is formed and the native cutthroat genomes are lost (Allendorf & Leary 1988). A major consequence of such interspecific hybridization may be outbreeding depression because of the break-up of co-adapted gene complexes and disruption of local adap- tations (Allendorf et al. 2004; Muhlfeld et al. 2009). Introgression poses a serious threat to all subspecies of cutthroat trout in western North America owing to wide- spread stocking of rainbow trout and invasion by rainbow trout and hybrids into historical cutthroat trout habitats. Currently, range-wide estimates of hybridization in many of the 12 cutthroat trout subspecies and popula- tions are incomplete. Westslope cutthroat trout (WCT; Oncorhynchus clarkii lewisi), the most widely distributed subspecies of cutthroat trout, historically occupied aqua- tic habitats throughout the Columbia, Fraser, Missouri and Hudson Bay drainages of the United States and Can- ada (Behnke 2002). However, nonhybridized populations are estimated to persist in <10% of their historical range (Shepard et al. 2005). While over half of the population genetic samples in Shepard et al. (2005) found no evi- dence of admixture, only 30% had enough individuals sampled to detect 1% admixture at the 95% level of confi- dence. As a result, only 15% of the population genetic samples showed no evidence of admixture (<1%) with a high degree of confidence. Markers detecting low amounts of admixture in popu- lations and individuals will provide an understanding of the mechanisms causing the spread of hybridization, help protect nonhybridized populations from invasion, and aid in identifying nonhybridized populations suitable as sources for hatchery brood stocks or other conservation actions (Allendorf et al. 2001). Besides estimates of Correspondence: Steve Amish, Fax: 406-243-4384; E-mail: stephen.amish@umontana.edu � 2012 Blackwell Publishing Ltd Molecular Ecology Resources (2012) 12, 653–660 doi: 10.1111/j.1755-0998.2012.03157.x individual or population levels of admixture, the distri- bution and the frequency of introgressed genotypes within a population or sample can illuminate the dura- tion and extent of hybridization (Jiggins & Mallet 2000). For example, a bimodal distribution is thought to result from selection against intermediate genotypes or assorta- tive mating between the species (Jiggins & Mallet 2000; Weigel et al. 2003). Currently, the number of available species-diagnostic loci for addressing these questions in native cutthroat trout and rainbow trout is limited. The development of additional species-diagnostic genotyping assays and high-throughput SNP genotyping systems will provide increased power for detecting hybridization at the individual level and more precisely estimating the structure of hybrid zones. Currently, 10–15 diagnostic microsatellite markers are often used to detect cutthroat and rainbow trout hybridization at the population level. If we assume that each marker sorts independently, there is no linkage disequilibrium affect- ing the markers, and if genotypes are distributed accord- ing to Hardy–Weinberg ratios (i.e. the samples are representative of a breeding aggregation or population), then we can calculate the likelihood of failing to detect a single RBT allele in any given fish or in a sample of unre- lated fish using binomial probability (Rasmussen et al. 2010). The probability of detecting 1% hybridization in a population with 95% certainty and 20 individual samples requires only eight independent diagnostic markers. In contrast, to detect 1% admixture in an individual with 95% certainty will require 150 independent diagnostic markers (Table 1). Similarly, an increase in the number of diagnostic markers will also improve our ability to differ- entiate between parental back-crosses and later genera- tion hybrid crosses. Recently, we identified a large set of candidate diag- nostic SNPs using restriction-site-associated DNA (RAD) sequencing (Hohenlohe et al. 2011). Briefly, we sequenced a single RAD library (Baird et al. 2008; Etter et al. 2011a) created from 24 fish [11 WCT, 12 coastal rain- bow trout (O. m. mykiss, CRT), and 1 inland or redband rainbow trout (O. m. gairdneri, IRT)] and applied strict fil- tering based on observed heterozygosity and deviations from Hardy–Weinberg equilibrium to remove homeolo- gous loci (paralogs resulting from the ancestral salmonid whole-genome duplication; Lie et al. 1994). That analysis produced a total of 2923 RAD markers at which there was a single candidate SNP fixed between the two spe- cies, and no other polymorphism, within the informative 48-bp RAD tag sequence (Hohenlohe et al. 2011). Here, we expand the list of candidate SNP markers, and we use microfluidic PCR assays to verify a subset of them for high-throughput estimates of hybridization in trout. We chose to develop a bioinformatics pipeline for this pur- pose because of its cost-effectiveness and because the rainbow trout genome would subsequently be available for the development of additional markers. Materials and methods In addition to the 2923 single-SNP candidate markers from the study by Hohenlohe et al. (2011), we identified candidate diagnostic SNPs in RAD tags containing two putative fixed SNPs in the 48-bp sequence (an additional 643 markers) and those containing one putative fixed SNP and one additional site polymorphic within one of the taxa (an additional 1348 markers). We aligned the total set of 4914 RAD tag sequences against a published database of rainbow trout sequence contigs (Sanchez et al. 2009) using the program Bowtie (Langmead et al. 2009). We allowed up to three mismatches between the WCT or the CRT and the reference sequence. The data set from the study by Sanchez et al. (2009) contained 47 526 contigs ranging in size from 185 to 1978 bp, produced by 454 sequencing of a reduced representation genomic library in rainbow trout. A total of 66 (1.3%) of our candi- date RAD tags aligned against one or more of the contigs from the study by Sanchez et al. (2009) with at least 50bp of flanking sequence on either side of the diagnostic SNP. Ten of these loci were dropped after preliminary data suggested one of the primers or probes was not amplify- ing. Sequences for the remaining 56 candidate markers were submitted to KBioscience for the design of KASPar SNP genotyping assays. A total of 92 individuals from 22 populations and seven hatchery strains plus two heterozygous positive controls (F1s) were then used to validate the 56 assays on Fluidigm 96.96 microfluidic PCR chips. The individuals included two cutthroat trout species, WCT and YCT (Yel- lowstone cutthroat trout, O. c. bouvieri), as well as IRT and CRT (Table 2). All samples came from putatively nonhybridized populations, based on a current panel of seven diagnostic microsatellite loci and seven indel loci, Table 1 The likelihood of detecting a single RBT allele in any given fish or in a population of unrelated fish using binomial probability for a number of independent species-diagnostic loci, samples and percent hybridization Number of markers Number of samples %Hybridization Probability of detection 8 1 17 95.0 8 20 1 95.0 46 1 3.1 95.0 46 4 1 97.5 77 1 1.9 95.0 96 1 1.6 95.0 150 1 1 95.0 � 2012 Blackwell Publishing Ltd 654 S . J . A M I S H E T A L . except for fish from Lake Koocanusa, which appear to have both a CRT and IRT genetic component (R. Leary, personal communication), and the South Fork of the Jocko River, which appear to have a CRT component. WCT samples included two year classes from the Washoe Park State Trout Hatchery, Anaconda Montana, and samples from 18 wild populations, including three populations from the Missouri River basin east of the Continental Divide. YCT samples were from the Yellow- stone River State Trout Hatchery, Big Timber, Montana, and a population in Slough Creek. IRT samples were from two populations in the Kootenai River drainage in Montana. CRT were taken from hatchery stock from across the country to account for the multitude of poten- tial sources used currently and historically for stocking. Results Forty-six of fifty-six assays (82%) were diagnostic for the identification WCT, RBT and hybrids (Table 3). An assay was considered diagnostic if both heterozygous positive controls showed separation from the homozygous genotype clusters and >95% of samples had concordant genotypes (i.e. genotype agreed with expectation estab- lished by earlier microsatellite ⁄ indel data). Eight of ten assay failures were attributed to the design process, including poor quality or inadequate sequence data being used to design the assay (e.g. using sequence from a paralogous region or sequence containing errors) or errors in the primer or probe design and manufacture process. These failures included three assays where one or both probes did not amplify, and one assay appears to have amplified a homeolog. In fact, this locus had an elevated depth of sequence reads in the original RAD sequencing run, in the 95th percentile among the 2923 fixed single-SNP candidate markers (Hohenlohe et al. 2011), consistent with the interpretation that it represents two incorrectly assembled homeologs. Given a total of 46 assays for detecting RBT and WCT hybridization and four samples from a population, we have a 97.5% probability of detecting 1% introgression in a hybrid swarm. In an individual fish, we have a 95% probability of detecting >3.1% introgression with the same number of assays (Table 1). However, very low Table 2 Location name, species, whether the fish is of wild or hatchery origin, and the number of samples, basin and subbasin information for the screening panel used to validate species-diagnostic assays. Species are labelled as westslope cutthroat trout (WCT), Yellowstone cutthroat trout (YCT), inland or redband rainbow trout (IRT), and coastal rainbow trout (CRT) Location name Species Wild ⁄ Hatchery N Basin Subbasin Anaconda, MT WCT H 12 NA* NA Big Foot Creek WCT W 2 Columbia Upper Kootenai Copper Creek WCT W 2 Columbia Flint-Rock Cottonwood Creek WCT W 3 Columbia Lower Flathead Davis Creek WCT W 4 Columbia Bitterroot Flat Creek WCT W 3 Columbia Upper Kootenai Gillispie Creek WCT W 3 Columbia Flint-Rock Hawk Creek WCT W 2 Columbia N. F. Flathead Humbug Creek WCT W 2 Columbia Blackfoot McGinnis Creek WCT W 3 Columbia Lower Clark Fork Morrison Creek WCT W 3 Columbia Middle Flathead Ringeye Creek WCT W 2 Columbia Blackfoot Runt Creek WCT W 3 Columbia Yaak S. Fork Jocko WCT W 3 Columbia Lower Flathead Six Mile Creek WCT W 3 Columbia Middle Clark Fork Werner Creek WCT W 3 Columbia N. F. Flathead Bear Creek WCT W 1 Missouri Red Rock McClellan Creek WCT W 1 Missouri Upper Missouri McVey Creek WCT W 1 Missouri Big Hole Big Timber, MT YCT H 6 NA NA Slough Creek YCT W 4 Missouri Yellowstone Lake Koocanusa, BC IRT W 4 Columbia Yaak Yahk River, BC IRT W 5 Columbia Yaak Abbot Creek CRT W 2 Columbia Middle Flathead Arlee, MT CRT H 7 NA NA Eagle Lake, CA CRT H 2 NA NA McConaughy, NE CRT H 2 NA NA Fish Lake, UT CRT H 2 NA NA Erwin ⁄ Arlee Cross, TN CRT H 2 NA NA *Basin and Subbasin designations were not made for hatchery stocks. � 2012 Blackwell Publishing Ltd T R O U T S P E C I E S - D I A G N O S T I C S N P S F R O M R A D S E Q U E N C I N G 655 T a b le 3 S e q u e n ce in fo rm a ti o n fo r th e v a li d a te d a ss a y s. T h e n u m b e r o f a d d it io n a l v a ri a b le si te s in th e R A D ta g a n d th e re fe re n ce se q u e n ce a re li st e d , a lo n g w it h th e ca ll ra te (% o f sa m p le s a ss ig n e d to a g e n o ty p e cl u st e r) a n d th e ca ll co n co rd a n ce (% o f sa m p le s a ss ig n e d to th e co rr e ct cl u st e r) . S e q u e n ce s g iv e n a re fo r th e K b io sc ie n ce s K A S P a ss a y fo rm a t. R B T se q u e n ce n a m e is th e ra in b o w tr o u t re fe re n ce se q u e n ce (S a n ch e z et al . 2 0 0 9 ) fr o m th e R A D lo ci a li g n m e n ts u se d fo r fl a n k in g se q u e n ce in th e S N P a ss a y d e si g n A ss a y n a m e A d d it io n a l V a ri a b le S it e s S N P N % C a ll ra te % C o n co r- d a n ce S e q u e n ce (5 ¢-3 ¢) R A D G e n B a n k A cc e ss io n N o . R B T R e f S e q u e n ce G e n B a n k T ra ce A rc h iv e N o . R A D T a g R e f S e q P ri m e r F A M A ll e le P ri m e r V IC A ll e le C o m m o n P ri m e r R B T W C T O m y _ R A D _ 1 7 0 6 3 _ 3 1 0 0 a ⁄c 6 6 1 0 0 .0 1 0 0 .0 G T C A G T A G G A G G T G C T A T T G A G A T C A G T A G G A G G T G C T A T T G A G C G C C T G C A G G C T G T C C A G T A G T T JQ 7 5 5 4 3 2 JQ 7 5 5 4 7 8 g n l| ti | 5 0 9 0 1 8 2 4 8 _ 6 4 8 3 7 0 O m y _ R A D _ 1 7 8 0 6 _ 2 4 0 0 t ⁄a 6 6 1 0 0 .0 1 0 0 .0 T G G G C T G T G T G A G A G A C A G A G A T G G G C T G T G T G A G A G A C A G A G T A C A C C T G C A G G G C C T G T C T G A A JQ 7 5 5 4 3 3 JQ 7 5 5 4 7 9 g n l| ti | 5 0 9 6 1 9 9 3 8 _ 6 5 0 8 3 1 O m y _ R A D _ 1 9 2 3 4 _ 5 1 0 0 t ⁄c 6 6 1 0 0 .0 9 8 .5 T T C C T G T G T A A A G C A G T G G T G G A C T T C C T G T G T A A A G C A G T G G T G A G T G C T C G T A C C A T C C A C C G T C A A JQ 7 5 5 4 3 4 JQ 7 5 5 4 8 0 g n l| ti | 5 0 9 6 2 0 5 9 7 _ 6 5 1 6 6 1 O m y _ R A D _ 2 0 6 6 3 _ 4 6 0 2 t ⁄g 6 6 9 8 .5 1 0 0 .0 G G A G C A A A G C A T T A A A A G T G T G C T G A T G G A G C A A A G C A T T A A A A G T G T G C T T G T T G T T G A T G A G C C A G G G T C T G T T T JQ 7 5 5 4 3 5 JQ 7 5 5 4 8 1 g n l| ti | 5 1 4 6 5 3 6 9 9 _ 6 2 5 6 8 6 O m y _ R A D _ 2 1 2 1 2 _ 5 0 0 0 a ⁄t 6 6 9 3 .9 1 0 0 .0 T T A A T A A T C A C T A C A T T T C A C A T A G A A T T G C T T T T A A T A A T C A C T A C A T T T C A C A T A G A A T T G C T A G C T T A G A T T G T A T A T T C T G C T G C T A G G T T JQ 7 5 5 4 3 6 JQ 7 5 5 4 8 2 g n l| ti | 5 0 9 7 0 1 9 9 3 _ 6 6 9 8 6 8 O m y _ R A D _ 2 1 3 6 2 _ 2 8 0 0 g ⁄t 6 6 1 0 0 .0 1 0 0 .0 G C T G C T C T G C T G A C G G T T C C T G C T C T G C T G A C G G T T A G G T T G A A C T G G A C G A G C C G G A A JQ 7 5 5 4 3 7 JQ 7 5 5 4 8 3 g n l| ti | 5 1 4 6 5 7 7 1 7 _ 6 3 0 6 3 0 O m y _ R A D _ 2 1 4 3 1 _ 3 0 0 0 a ⁄g 6 6 1 0 0 .0 1 0 0 .0 C T G T T C A G G G T G A T G A T G C T G T C T G T T C A G G G T G A T G A T G C T G C C A G G A T G A G G G T T G - C C T G G T C A T JQ 7 5 5 4 3 8 JQ 7 5 5 4 8 4 g n l| ti | 5 0 9 7 0 8 9 1 6 _ 6 7 8 8 3 3 O m y _ R A D _ 2 2 1 1 1 _ 3 4 0 2 t ⁄g 6 6 1 0 0 .0 1 0 0 .0 G A A C T T T G C T G G G C A T G T G G G G A A C T T T G C T G G G C A T G T G G T T G C A C G G A T A A C A T G G T C T T T G A T A A C T T JQ 7 5 5 4 3 9 JQ 7 5 5 4 8 5 g n l| ti | 5 1 4 6 4 8 3 9 9 _ 4 9 8 9 8 5 O m y _ R A D _ 2 3 2 4 7 _ 2 2 0 0 c ⁄t 6 6 1 0 0 .0 9 8 .5 C C C A C G G G A T A C T G G G T G C C C C A C G G G A T A C T G G G T A C C T G C A G G A G C T G G T C A G C T A T JQ 7 5 5 4 4 0 JQ 7 5 5 4 8 6 g n l| ti | 5 1 4 6 4 9 7 8 9 _ 6 2 0 8 0 3 O m y _ R A D _ 2 3 9 1 0 _ 1 4 0 0 c ⁄t 6 6 1 0 0 .0 1 0 0 .0 T C C T G C A G G G T G T C G G C G C T C C T G C A G G G T G T C G G C A C G C T T T A A A C A G C T G G T G G A C A G T A JQ 7 5 5 4 4 1 JQ 7 5 5 4 8 7 g n l| ti | 5 1 4 6 5 8 4 0 5 _ 6 3 1 5 5 8 O m y _ R A D _ 2 6 3 5 2 _ 3 8 1 0 g ⁄a 7 0 9 4 .3 1 0 0 .0 A T G C A C A C C A C T G C A T C C A G A T A T G C A C A C C A C T G C A T C C A G A C C T A C T G T T A C A A C C T G C A G G A G C T A JQ 7 5 5 4 6 7 JQ 7 5 5 5 1 3 g n l| ti | 5 0 9 7 0 0 0 1 8 _ 6 6 7 2 9 4 O m y _ R A D _ 2 7 3 3 7 _ 1 8 0 0 c ⁄t 6 6 1 0 0 .0 1 0 0 .0 G G T A G A T T T C C G A C G T A A A T A C G G G G T A G A T T T C C G A C G T A A A T A C G A G A G G C C C T G C A G G A A A T A A C G A T T JQ 7 5 5 4 4 2 JQ 7 5 5 4 8 8 g n l| ti | 5 0 9 6 2 7 1 6 5 _ 6 5 9 9 2 2 O m y _ R A D _ 2 8 0 8 0 _ 2 7 0 0 g ⁄a 6 6 1 0 0 .0 1 0 0 .0 G A T G T G T G G C T G T T G G T C A A C C A A T G T G T G G C T G T T G G T C A A C C G G C A A G A C C C T C A G A A T C C T C T T C A A JQ 7 5 5 4 4 3 JQ 7 5 5 4 8 9 g n l| ti | 5 0 9 0 1 7 2 7 9 _ 6 4 7 1 2 7 O m y _ R A D _ 2 9 2 5 2 _ 3 4 1 0 g ⁄t 6 6 1 0 0 .0 1 0 0 .0 G T C G T T C T T C T G G C C C A G G A C T G T C G T T C T T C T G G C C C A G G A A G T C A G G C T C T G A C G G C C T A C T T JQ 7 5 5 4 6 8 JQ 7 5 5 5 1 4 g n l| ti | 5 0 9 6 2 9 7 3 5 _ 6 6 3 2 8 4 O m y _ R A D _ 2 9 4 1 9 _ 2 3 1 0 c ⁄t 6 9 9 4 .2 1 0 0 .0 C C C G C C A G A T G G C C A G G C C C C G C C A G A T G G C C A G A C A T G G A G G A C C T G A G T G C T C T A A A JQ 7 5 5 4 6 9 JQ 7 5 5 5 1 5 g n l| ti | 5 1 4 6 4 8 4 2 4 _ 4 9 9 0 1 3 O m y _ R A D _ 3 0 3 7 8 _ 1 5 1 0 a ⁄c 6 9 1 0 0 .0 9 1 .3 G G T C T G T C C C C C T G T C C G T T C T G T C C C C C T G T C C G G G C A G T G T G A C C C T G C A G G A C A JQ 7 5 5 4 7 0 JQ 7 5 5 5 1 6 g n l| ti | 5 0 9 6 2 7 6 0 5 _ 6 6 0 4 5 7 � 2012 Blackwell Publishing Ltd 656 S . 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G A T G C A G G A G A T JQ 7 5 5 4 5 2 JQ 7 5 5 4 9 8 g n l| ti | 5 0 9 6 2 0 9 6 1 _ 6 5 2 1 6 8 O m y _ R A D _ 4 9 7 5 9 _ 2 1 1 1 g ⁄t 6 9 9 2 .8 1 0 0 .0 G T C T T T G T T G G A A T T T A T T G C C A T A T T C T C T T T G T T G G A A T T T A T T G C C A T A T T A A T A T C T C A C C T G C A G G T T T A A G T A C C A A A JQ 7 5 5 4 7 4 JQ 7 5 5 5 2 0 g n l| ti | 5 0 9 7 0 0 5 2 1 _ 6 6 7 9 1 3 O m y _ R A D _ 5 1 7 4 0 _ 9 0 0 c ⁄g 6 9 9 4 .2 1 0 0 .0 T A T C G G G T A C C T G C A G G T G A C T A T C G G G T A C C T G C A G G T G A G G C C T T G A C A G T A C A A C A G G C A C T T T JQ 7 5 5 4 5 3 JQ 7 5 5 4 9 9 g n l| ti | 5 1 4 6 5 5 3 3 0 _ 6 2 7 6 1 4 O m y _ R A D _ 5 1 8 2 1 _ 4 7 0 0 g ⁄t 6 6 1 0 0 .0 1 0 0 .0 A A C T C C A C A A G G T C A G A G G T A A C A A C T C C A C A A G G T C A G A G G T A A A C T A C T C T G C C G A C A T C C T A T C A G A A JQ 7 5 5 4 5 4 JQ 7 5 5 5 0 0 g n l| ti | 5 0 9 6 2 4 1 0 6 _ 6 5 6 1 4 0 � 2012 Blackwell Publishing Ltd T R O U T S P E C I E S - D I A G N O S T I C S N P S F R O M R A D S E Q U E N C I N G 657 T a b le 3 (C o n ti n u e d ) A ss a y n a m e A d d it io n a l V a ri a b le S it e s S N P N % C a ll ra te % C o n co r- d a n ce S e q u e n ce (5 ¢-3 ¢) R A D G e n B a n k A cc e ss io n N o . R B T R e f S e q u e n ce G e n B a n k T ra ce A rc h iv e N o . R A D T a g R e f S e q P ri m e r F A M A ll e le P ri m e r V IC A ll e le C o m m o n P ri m e r R B T W C T O m y _ R A D _ 5 2 9 6 8 _ 1 4 0 0 c ⁄t 6 9 1 0 0 .0 9 8 .6 G G T G A A T T C G G T G T T G T C T G C C G G T G A A T T C G G T G T T G T C T G T G A G T C T C A T C C C T G C A G G G C T T JQ 7 5 5 4 5 5 JQ 7 5 5 5 0 1 g n l| ti | 5 0 9 6 1 9 1 0 7 _ 6 4 9 7 8 1 O m y _ R A D _ 5 3 8 2 2 _ 1 3 0 0 a ⁄c 6 9 1 0 0 .0 1 0 0 .0 A A C A C C G A T A T A C A T A A A T G T G C T G T A A C A C C G A T A T A C A T A A A T G T G C T G G G A C T C A G C C T G C A G G G G T C A T A JQ 7 5 5 4 5 6 JQ 7 5 5 5 0 2 g n l| ti | 5 0 9 6 2 3 4 0 7 _ 6 5 5 2 9 0 O m y _ R A D _ 5 4 1 2 6 _ 3 1 0 0 t ⁄c 6 9 9 7 .1 9 7 .0 T G G T C A A T G C C A T T A T C A A C A G C C T G G T C A A T G C C A T T A T C A A C A G T C T C A C A G T A C C A G C A C G A C C A A T A JQ 9 0 6 7 2 9 JQ 9 0 6 7 2 6 g n l| ti | 5 1 4 6 5 8 8 1 0 _ 6 3 2 0 4 0 O m y _ R A D _ 5 4 5 1 6 _ 3 5 0 0 a ⁄t 7 0 9 2 .9 1 0 0 .0 T G G A C T C A A A C A G A T C C A A T A A C T A C T G G A C T C A A A C A G A T C C A A T A A C A G G T A C T T C T G T G A A A A C C A T T T G T G T G A A JQ 7 5 5 4 5 7 JQ 7 5 5 5 0 3 g n l| ti | 5 1 4 6 5 8 8 7 1 _ 6 3 2 1 1 4 O m y _ R A D _ 5 4 5 8 4 _ 1 7 0 0 c ⁄a 6 9 8 1 .2 1 0 0 .0 G T A C C T G C A G G G A A A G C T A C T C T T A C C T G C A G G G A A A G C T A C T C G G G A T C C A C C A G T G T G T A T G T G T A G T T JQ 7 5 5 4 5 8 JQ 7 5 5 5 0 4 g n l| ti | 5 0 9 7 0 6 6 9 6 _ 6 7 6 0 9 3 O m y _ R A D _ 5 5 3 9 1 _ 4 7 0 0 c ⁄t 6 9 9 7 .1 1 0 0 .0 G T C A G T T T T C C T T G T C A G G C T C C T T G T C A G T T T T C C T T G T C A G G C T C T G T C G A A G T C T G C C T C A A C C A C A A T A JQ 7 5 5 4 5 9 JQ 7 5 5 5 0 5 g n l| ti | 5 0 9 6 2 1 0 8 7 _ 6 5 2 3 4 3 O m y _ R A D _ 5 5 8 2 0 _ 2 8 0 0 g ⁄a 6 6 1 0 0 .0 1 0 0 .0 G A G G C C T T A C A G A T T G A T T G C A C A A G G C C T T A C A G A T T G A T T G C A C G G G C A C A G C A G A A G A C C A A T T T C C A T JQ 7 5 5 4 6 0 JQ 7 5 5 5 0 6 g n l| ti | 5 0 9 0 1 0 3 0 3 _ 6 3 8 1 0 5 O m y _ R A D _ 5 6 6 6 _ 9 1 1 c ⁄g 7 0 9 2 .9 1 0 0 .0 G G A G G A A C C T G C A G G T G G C G A G G A A C C T G C A G G T G G G A A A G T C A G T T A A C T A C A C T A C A G A C C A A T T JQ 7 5 5 4 7 5 JQ 7 5 5 5 2 1 g n l| ti | 5 0 9 0 1 8 2 3 6 _ 6 4 8 3 5 6 O m y _ R A D _ 5 7 2 6 2 _ 3 4 0 0 a ⁄g 6 9 9 7 .1 9 7 .0 C C A C A G C G A C C C C A T C G A A C C A C A G C G A C C C C A T C G A G G G T C A A A T G T C A G G G T T A A T C A G A A G T A JQ 7 5 5 4 6 1 JQ 7 5 5 5 0 7 g n l| ti | 5 0 9 0 1 0 9 8 2 _ 6 3 9 0 3 2 O m y _ R A D _ 5 7 6 7 3 _ 4 2 1 0 c ⁄t 6 6 1 0 0 .0 9 8 .5 T C T T A C C A C G A G C T C A G G G A C T C T T A C C A C G A G C T C A G G G A T G C T G G A T C T C A T G G T G G T C C A G A T JQ 7 5 5 4 7 6 JQ 7 5 5 5 2 2 g n l| ti | 5 0 9 0 1 4 1 4 8 _ 6 4 3 1 2 4 O m y _ R A D _ 5 9 5 1 5 _ 1 4 0 2 g ⁄t 6 9 1 0 0 .0 9 8 .6 A G G T G G T G C C A G G A C A G G G C A A G G T G G T G C C A G G A C A G G T C C A G A T C C A G G C C T G C A G G T A A JQ 7 5 5 4 6 2 JQ 7 5 5 5 0 8 g n l| ti | 5 0 9 7 0 7 7 3 2 _ 6 7 7 3 8 2 O m y _ R A D _ 6 0 3 5 _ 1 9 0 0 c ⁄t 7 0 9 5 .7 1 0 0 .0 T T G G A G C G G T A C T C T T T C A G G C T T G G A G C G G T A C T C T T T C A G A G G A G T C C C T G C A G G C C A A T G T A JQ 9 0 6 7 3 2 JQ 9 0 6 7 3 1 g n l| ti | 5 0 9 0 1 1 1 4 0 _ 6 3 9 2 1 7 O m y _ R A D _ 6 0 6 7 4 _ 2 5 0 0 g ⁄a 6 6 1 0 0 .0 1 0 0 .0 T G T G C T G C A G C C C A C A T C A G A A T G C T G C A G C C C A C A T C A G A G T T A A C C T G C A G G A T G A G G A A G G C T T JQ 7 5 5 4 6 3 JQ 7 5 5 5 0 9 g n l| ti | 5 1 4 6 5 5 8 7 8 _ 6 2 8 3 3 2 O m y _ R A D _ 6 9 0 6 1 _ 1 2 0 0 g ⁄a 7 0 9 5 .7 1 0 0 .0 G T C T G T A G C A T A T A C T A T G T T G T C C T T C T G T A G C A T A T A C T A T G T T G T C C C C C T G T C T G G G A A T A A C A G C C G T A T A JQ 7 5 5 4 6 4 JQ 7 5 5 5 1 0 g n l| ti | 5 0 9 6 2 4 8 1 5 _ 6 5 7 0 1 1 O m y _ R A D _ 7 6 6 8 9 _ 9 1 0 c ⁄t 6 6 1 0 0 .0 1 0 0 .0 A C C T C G T C C T G C A G G T C T G C C A C C T C G T C C T G C A G G T C T A C C C T G C T C G A C C C G T G T C T A JQ 7 5 5 4 7 7 JQ 7 5 5 5 2 3 g n l| ti | 5 1 4 6 5 7 1 9 7 _ 6 2 9 9 1 5 O m y _ R A D _ 7 7 1 5 7 _ 4 6 0 0 a ⁄c 7 0 9 8 .6 1 0 0 .0 T G T G T T A C A G C T G C G G G T C C T T T G T T A C A G C T G C G G G T C C T G G G T C A G G C T G C A G T G G A G G A A A JQ 7 5 5 4 6 5 JQ 7 5 5 5 1 1 g n l| ti | 5 0 9 0 1 7 8 9 2 _ 6 4 7 9 2 3 O m y _ R A D _ 8 4 3 6 _ 2 2 0 0 g ⁄t 7 0 9 8 .6 9 7 .1 G A G C C G T C C T T C A G G A A T C G C A G A G C C G T C C T T C A G G A A T C G A C T G C A G G A G A G G G A G G G G C T T JQ 9 0 6 7 2 7 JQ 9 0 6 7 3 0 g n l| ti | 5 0 9 6 9 8 9 4 0 _ 6 6 5 4 6 5 � 2012 Blackwell Publishing Ltd 658 S . J . A M I S H E T A L . levels of introgression will still be difficult to detect at the individual level. Our probability of detecting 1% intro- gression in a fish using 46 markers is only 60%. Because our preliminary screening panel was com- posed of a few individuals from many populations instead of many individuals from a few populations, we have little power to detect alleles at low frequency in these populations. Thus, we cannot exclude the possibil- ity that some of the diagnostic markers may share a low-level polymorphism between WCT and RBT. In 12 species-diagnostic assays, individuals with genotypes indicative of low-level polymorphisms or RBT hybridiza- tion were detected. The SNP from RAD_49331 was not diagnostic in our screening panel. One individual was homozygous for the ‘RBT allele’ at this locus, and 8 others were heterozygotes. These individuals, however, did not possess any other genotypes at the other loci analysed indicative of hybridization. Thus, this locus appears to be polymorphic in WCT. WCT from north- western Montana or south-eastern British Columbia were heterozygous in five additional assays, and the level of introgression in these populations is uncertain due in part to the potential presence of IRT alleles (Table S1). Fish from Lake Koocanusa may have low levels of intro- gression from CRT or WCT. In the fish from Yaak, BC and Runt Creek, these polymorphisms may represent natural levels of introgression between the sympatric WCT and IRT populations. In five of the remaining six assays, the heterozygous fish came from hatchery popu- lations. Because hatchery brood stock samples have been extensively screened for admixture using indels and mi- crosatellites, it is most likely that these alleles represent shared low-level polymorphisms between WCT and RBT. Testing of additional samples will be required to determine their frequency and the usefulness of the assays for species identification and admixture analysis. Discussion Our conversion rate of 82% for diagnostic assays suggests that RAD sequencing offers a reliable and relatively quick and inexpensive way to generate large numbers of SNP markers that does not require a large screening panel (e.g. Seeb et al. 2011). Conversion rates can vary widely and depend on the variability and divergence of the tar- get species, the number of samples sequenced before designing the assays, whether the SNP is in a conserved or highly variable region (e.g. diagnostic between species or polymorphic within species) and on the number and extent of samples used to validate the assay. New sample library protocols and next-generation sequencing tech- niques like RADs promise to make very low-cost marker development available for most organisms (Pennisi 2011) even when no genomic resources are available. The development of additional species-diagnostic genotyping assays provides increased power for detecting hybridization at the individual level and more precisely estimating the structure of hybrid zones. With the addi- tion of our 46 assays to the 31 previously available SNPs (Finger et al. 2009; McGlauflin et al. 2010, Harwood & Phillips 2011; Kalinowski et al. 2011), the number of cur- rently available diagnostic SNPs between WCT and RBT has increased to 77. With 77 diagnostic SNPs, we can detect 1.9% introgression with 95% certainty at the indi- vidual level. Our probability of detecting 1% introgression in a fish using 96 markers is only 85%, reaching 95% with 150 markers (Table 1). The ability to detect low levels of hybridization at the individual level increases sampling scheme flexibility, removing the requirement that aggre- gations of 20-30 samples be considered a population. We developed a bioinformatic pipeline using publicly available 454 reads (Sanchez et al. 2009) for identifying flanking sequence required for assay design that will be eas- ily applied to the rainbow trout genome sequence when it is published (Miller et al. 2011). This reduced our set of candi- date loci from 4914 to 66 (1.3%). At the time of this experi- ment, using 454 sequencing to produce reads >100-nt reads required for SNP assay development was beyond our bud- get. The reference genome sequence will allow assay design for most of the SNPs identified in our RAD loci. An alternative approach to using published long read sequence data is to generate longer contiguous sequence reads at each RAD tag using over-lapping paired-end sequencing (Etter et al. 2011b). This technique holds great promise for allowing assay design on the full set of candi- date SNP markers for any species. In addition, this approach should have a higher validation rate, because SNP detection and flanking sequence would come from the same individuals and populations. RAD sequencing is one of a family of approaches applying high-throughput sequencing to a reduced rep- resentation of a genome to identify and genotype large numbers of SNP markers in organisms without substan- tial genetic resources (Cosart et al. 2011; Davey et al. 2011). Next-generation sequencing approaches require slightly more bioinformatic effort compared with tradi- tional marker discovery, but a number of publicly avail- able tools are being developed to handle these types of data (Catchen et al. 2011; Davey et al. 2011). One advan- tage of RAD over related restriction-enzyme-reduced representation sequencing techniques in taxa with com- plex, repetitive genomes is that the set of markers does not depend on a fragment size selection step, so that it is more consistent across libraries (Davey et al. 2011). This helps reduce variation between runs and allows the com- pilation and re-analysis of large sequence databases across related species, populations and individuals gen- erated using the same RAD library technique. We con- � 2012 Blackwell Publishing Ltd T R O U T S P E C I E S - D I A G N O S T I C S N P S F R O M R A D S E Q U E N C I N G 659 clude that the emerging techniques for the generation and analysis of RAD sequencing data provide a relatively quick and cost-effective method for the identification of large numbers of species-diagnostic SNPs. Acknowledgements PAH was supported by NIH COBRE grant 5P20RR016448 (L. Forney, PI). Funding was provided in part by the Great Northern Landscape Conservation Cooperative (US Department of Interior). Any use of trade, product or firm names is for descriptive purposes only and does not imply endorsement by the U.S. Government. This research was conducted in accordance with the Animal Welfare Act and its subsequent amendments. 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Table S1 The sample location and number, RAD locus, species and number of heterozygous genotypes (Hets) are reported from the validated assays. Please note: Wiley-Blackwell are not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing material) should be directed to the corresponding author for the article. � 2012 Blackwell Publishing Ltd 660 S . J . A M I S H E T A L .