Beaufort trout MicroPlex: a high‐throughput multiplex platform comprising 38 informative...

Journal of Fish Biology (2013) 82, 1789–1804

doi:10.1111/jfb.12095, available online at wileyonlinelibrary.com

Beaufort trout MicroPlex: a high-throughput multiplexplatform comprising 38 informative microsatellite loci foruse in resident and anadromous (sea trout) brown trout

Salmo trutta genetic studies

K. Keenan*, C. R. Bradley*, J. J. Magee*, R. A. Hynes*,R. J. Kennedy†, W. W. Crozier†, R. Poole‡, T. F. Cross§,

P. McGinnity§ and P. A. Prodohl*‖*Institute for Global Food Security, School of Biological Sciences, Queen’s University,

Belfast BT9 7BL, U.K., †Agri-Food and Biosciences Institute, Newforge Lane, Belfast BT95PX, U.K., ‡Marine Institute Catchment Research Facility, Furnace, Newport, Co. Mayo,

Ireland and §School of Biological, Earth and Environmental Sciences/Aquaculture &Fisheries Development Centre, University College, Cork, Ireland

(Received 12 October 2012, Accepted 4 February 2013)

A flexible panel consisting of 38 informative microsatellite markers for Salmo trutta is described.These markers were selected from a pool of over 150 candidate loci that can be readily amplifiedin four multiplex PCR groups but other permutations are also possible. The basic properties ofeach markers were assessed in six population samples from both the Burrishoole catchment, in thewest of Ireland, and Lough Neagh, in Northern Ireland. A method to assess the relative utility ofindividual markers for the detection of population genetic structuring is also described. Given itsflexibility, technical reliability and high degree of informativeness, the use of this panel of markersis advocated as a standard for S. trutta genetic studies. © 2013 The Authors

Journal of Fish Biology © 2013 The Fisheries Society of the British Isles

Key words: conservation; DNA profiling; management; marker panel; population genetics.

INTRODUCTION

The brown trout Salmo trutta L. 1758 is one of the most polytypic vertebrate speciesdescribed, with extensive variation observed in morphological, behavioural, ecolog-ical and genetic traits (Ferguson, 1989; McKeown et al ., 2010). This high levelof variation has made S. trutta a model species for population and evolutionarygenetic studies (Hendry & Stearns, 2004). These studies include investigations oflife-history variation (Hansen et al ., 2000; Olsen & Vøllestad, 2001; Klemetsen& Amundsen, 2003; Jonsson & Jonsson, 2006; Robertsen et al ., 2011), micro-evolutionary processes (Hansen & Mensberg, 1996; Hynes et al ., 1996; Prodohl

‖Author to whom correspondence should be addressed. Tel.: +44 2890972267; email: [email protected]

1789© 2013 The AuthorsJournal of Fish Biology © 2013 The Fisheries Society of the British Isles

1790 K . K E E NA N E T A L .

et al ., 1997), natural selection (Coughlan et al ., 2006; Jensen et al ., 2008; Hansenet al ., 2010), kin selection (Carlsson et al ., 2004; O’Farrell et al ., 2011), interspecifichybridization (Matthews et al ., 2000) and mating systems (Tiira et al ., 2005; Jacobet al ., 2007).

Given its biological and evolutionary complexity, in addition to its socioeconomicimportance throughout its distribution range (Youngson et al ., 2003), the identifi-cation and description of S. trutta population genetic structure are of the utmostimportance for the conservation and management of the species. To ensure that thistask is achieved successfully, the availability of effective and reliable moleculargenetic tools is essential.

To date, microsatellite marker loci have been the tool of choice for most of thegenetic applications in S. trutta . Despite their utility, the use of these markers has notbeen without complications. These include problems associated with the reproducibil-ity of genotypes derived from different research laboratories, and also difficulties indetermining mutation models for specific loci (Buschiazzo & Gemmell, 2006). Theformer issue, in particular, has notably hampered efforts in the application of meta-analyses of generated data (e.g . scoring inconsistencies). In recent years, however,this problem has been minimized as the result of a better understanding of technicalissues associated with microsatellite screening and parallel developments in scoringsoftware, along with a higher degree of collaboration between research groups thathas promoted exchange of control samples and protocols. Thus, allele bin calls canbe predefined, readily allowing the standardization of allele size calls across researchprojects (Ellis et al ., 2011).

There are a large number of salmonid microsatellites available in the publishedliterature, many of which have been found to cross-amplify in S. trutta (Scribneret al ., 1996; Paterson et al ., 2004; King et al ., 2005; Vasemagi et al ., 2005a).While such a large pool of markers is potentially advantageous, it is not withoutproblems. For instance, for logistic and economic reasons, it is often impractical toscreen >20–30 markers in any study. Thus, researchers are often forced to use asub-set of markers that are typically chosen ad hoc. This variation in markers usedby different research groups has prevented meaningful comparisons of populationsand locus trends across the range of the species (Carlsson et al ., 1999; Swatdiponget al ., 2010; Meier et al ., 2011).

To alleviate some of these logistic trade-offs, a panel of 38 informativemicrosatellite loci is presented. This panel avails of new technological and analyticaladvances and is intended to foster a more collaborative approach to S. truttagenetic research. It is anticipated that this marker panel will be useful in a range ofresearch areas, including genetic mapping, parentage analysis, quantitative trait loci(QTL) analysis, ancestry assignment and population structure detection. The markerpanel, referred to as the Beaufort trout MicroPlex (BTMP), has been designedto allow the efficient and reliable amplification of many loci in relatively fewPCRs, through multiplexing. Although the main aim of this study was to maximizethroughput, hence the particular configuration of multiplex groups presented, thenature of the multiplexing system employed provides other users of the panel withsufficient flexibility to rearrange loci to suit specific demands of any given study(notably, in the event of locus range overlap as a result of diversity not observed inthis study).

© 2013 The AuthorsJournal of Fish Biology © 2013 The Fisheries Society of the British Isles, Journal of Fish Biology 2013, 82, 1789–1804

B E AU F O RT T RO U T M I C RO P L E X F O R S A L M O T RU T TA G E N E T I C S 1791

MATERIALS AND METHODS

I N I T I A L M A R K E R L O C I S E L E C T I O N

All initial steps in the development of the BTMP (e.g . reliability of PCR amplificationand allele resolution) were carried out on a random sub-set of S. trutta specimens collectedthroughout Britain and Ireland (n = 24). Following the identification of reliably amplifyingloci, 150 candidate salmonid microsatellite primer sets were further evaluated with respectto a number of criteria relevant to their suitability for S. trutta population genetic studies.All candidate loci were either obtained from the literature, developed using in-house cloning,or designed de novo from sequences sourced from GenBank. Loci obtained using the lattermethod are prefixed with ‘m’, indicating that the sequences presented in Table I differ fromthe original sequences presented in the references provided. The evaluation criteria used were(1) reliability of amplification, (2) consistency of automated allelic calls, (3) equal or greaterthan two alleles and (4) allele size range. Loci with very large size ranges (i.e. spanning c.100–600 bp) were excluded due to their unsuitability for size-based multiplexing.

M U LT I P L E X D E V E L O P M E N T

Following extensive evaluation of marker compatibility, loci fulfilling the set criteriawere subsequently grouped into four separate multiplex panels that were optimizedfor screening on a 96 capillary ABI-3730XL DNA analyser (Applied Biosystems;www.appliedbiosystems.com). The main criteria for the inclusion of markers in each ofthe four panels were co-amplification reliability and non-overlapping size compatibility,while also making use of the distinct ABI fluorescent labels. The cycling conditions of allfour multiplex PCRs were as follows: (95◦ C for 15 min) ×1 cycle (95◦ C for 45 s, 55◦C for 1 min 30 s and 72◦ C for 1 min) ×5 cycles, (95◦ C for 45 s, 57◦ C for 1 min 30 sand 72◦ C for 1 min) ×22 cycles, (60◦ C for 30 min) ×1 cycle. Each multiplex reactionconsisted of 1 μl of template DNA (concentration ranging from c. 0·5 to 5 ng), 0·15 μMof each primer, 1·75 μl of PCR mastermix (Qiagen Multiplex PCR Kit; www.qiagen.com)and double-distilled H2O as required to make a final volume of 3·5 μl. PCRs were carriedout in 96 well microtitre plates and were overlain with 10 μl of mineral oil to preventevaporation. Following PCR, amplified fragments were subsequently diluted one-tenthwith double-distilled H2O and 1 μl of this dilution was added to 9 μl of HiDi formamide(Life Technologies; www.lifetechnologies.com) mixed with Gene Scan 600-LIZ (LifeTechnologies), as per standard ABI 3730xl genotyping protocol.

A S S E S S M E N T O F B T M P F O R P O P U L AT I O N S T U D I E S

Although the BTMP is expected to be useful for a range of S. trutta genetic researchquestions as outlined earlier, given its commercial and conservation relevance, it is likely thatits widest application will be in population genetic research, specifically population structuredetection. To provide an initial evaluation of the utility of the BTMP when applied to thisspecific research area, an assessment of marker performance, in the form of basic populationgenetic statistical description and statistical power analyses, was carried out.

To investigate the properties of individual microsatellites [e.g . amplification reliabilityunder multiplex conditions, polymorphism level, informativeness for the inference of ances-try, statistical power to detect genetic differentiation and linkage disequilibrium (LD)], sixfreshwater S. trutta population samples (n = 265) were screened for all loci in the BTMP.These population samples were collected from three geographical regions within the islandof Ireland. For the purposes of this study, these regions are referred to as: west of Ireland(WOI), north Lough Neagh (NLN) and south Lough Neagh (SLN). Two putative popu-lation samples were collected from each of these three geographical regions to accountfor within-region pair-wise population comparisons also. These samples were as follows:WOI = Altahooney (ALT) + Lower Goulaun (LG), NLN = Cloghmills (CM) + Kells Water(KEL) and SLN = Blackwater (BW) + River Fury (FUR) (see Fig. 1 for details of samplinglocations). All fish (0+ and 1+ year juveniles) were caught via electrofishing. Genomic DNA


1792 K . K E E NA N E T A L .

Tab

leI.

Bea

ufor

ttr

out

Mic

roPl

ex(B

TM

P)pa

nel

info

rmat

ion,

incl

udin

gpr

imer

sequ

ence

s(w

ithA

BI-

labe

lled

prim

er),

tota

lnu

mbe

rof

alle

les

obse

rved

(na)

,alle

lesi

zera

nge

per

locu

san

dor

igin

allit

erat

ure

refe

renc

es.L

oci

nam

espr

efixe

dw

ith‘m

’ha

vebe

enm

odifi

edin

this

stud

yfr

omth

eir

orig

inal

sour

ces

for

use

inSa

lmo

trut

ta.

All

unla

belle

dpr

imer

sar

e‘p

igta

iled’

(pre

fixed

with

‘gttt

’),

follo

win

gB

row

nste

inet

al.

(199

6).

Num

ber

ofal

lele

san

dsi

zera

nges

are

thos

eob

serv

edfo

rth

ein

itial

24in

divi

dual

sus

edto

asse

sslo

cus

ampl

ifica

tion

and

the

six

popu

latio

nsa

mpl

esus

edto

test

the

BT

MP

mul

tiple

xes

Pane

l/loc

usFo

rwar

dpr

imer

Rev

erse

prim

ern

a¶

Size

*ra

nge

(bp)

BT

linka

gem

ap†

Ref

eren

ce

BT

MP

-Pan

el-1

Ssa

85N

ED

-AG

GT

GG

GT

CC

TC

CA

AG

CTA

Cgt

ttAC

CC

GC

TC

CT

CA

CT

TAA

TC

910

0–

125

BT

-04

O’R

eilly

etal

.(1

996)

mO

ne10

2aN

ED

-GG

GA

TTA

TT

CT

TAC

TT

TG

GC

TG

TT

gtttC

CT

GG

TT

GG

GA

AT

CA

CT

GC

316

0–

176

NA

Ols

enet

al.

(200

0)‡

mO

ne10

2b§

––

1817

4–

280

NA

Ols

enet

al.

(200

0)‡

Ssa

406U

oSN

ED

-AC

CA

AC

CT

GC

AC

AT

GT

CT

TC

TAT

Ggt

ttGC

TG

CC

GC

CT

GT

TG

TC

TC

TT

T38

412

–58

0B

T-1

1C

airn

eyet

al.

(200

0)M

HC

-IPE

T-A

GG

AA

GG

TG

CT

GA

AG

AG

GA

AC

gtttC

AA

TTA

CC

AC

AA

GC

CC

GC

TC

1411

0–

150

NA

Gri

mho

ltet

al.

(200

2)C

A04

8302

PET

-TT

GC

CA

CC

TC

TAA

AC

GC

TT

Cgt

ttAA

AT

GA

AC

CC

CA

GC

CA

TAC

A9

172

–21

9N

AV

asem

agi

etal

.(2

005b

)Ss

a41

9UoS

PET

-GG

TC

GTA

TC

GC

GT

TT

CA

GG

Agt

ttGC

TG

CA

ATA

AA

GA

GA

TG

CT

TG

TT

723

0–

570

BT

-33

Cai

rney

etal

.(2

000)

Ssa

416U

oSFA

M-T

GA

CC

AA

CA

AC

AA

AC

GC

AC

AT

gtttC

CC

AC

CC

AT

TAA

CA

CA

AC

TAT

410

0–

160

NA

Cai

rney

etal

.(2

000)

Sssp

2201

FAM

-TT

TAG

AT

GG

TG

GG

ATA

CT

GG

GA

GG

Cgt

ttCG

GG

AG

CC

CC

ATA

AC

CC

TAC

TAA

TAA

C44

180

–38

0N

APa

ters

onet

al.

(200

4)C

A04

8828

VIC

-GA

GG

GC

TT

CC

CA

TAC

AA

CA

Agt

ttGT

TTA

AG

CG

GT

GA

GT

TG

AC

GA

GA

G35

245

–32

6N

AV

asem

agi

etal

.(2

005b

)C

ocl-

lav-

4V

IC-T

GG

TG

TAA

TG

GC

TT

TT

CC

TG

gtttG

GG

AG

CA

AC

AT

TG

GA

CT

CT

C8

145

–17

0N

AR

oger

set

al.

(200

4)B

TM

P-P

anel

-2O

neμ

9N

ED

-CT

CT

CT

TT

GG

CT

CG

GG

GA

AT

GT

Tgt

ttGC

AT

GT

TC

TG

AC

AG

CC

TAC

AG

CT

1317

5–

227

BT

-19

Scri

bner

etal

.(1

996)

Ssa

D15

7N

ED

-AT

CG

AA

AT

GG

AA

CT

TT

TG

AA

TG

gtttG

CT

TAG

GG

CT

GA

GA

GA

GG

AA

TAC

3823

6–

391

NA

Kin

get

al.

(200

5)Ss

sp22

16PE

T-G

GC

CC

AG

AC

AG

ATA

AA

CA

AA

CA

CG

Cgt

ttGC

CA

AC

AG

CA

GC

AT

CTA

CA

CC

CA

G8

120

–16

8N

APa

ters

onet

al.

(200

4)St

r2Q

UB

PET

-CT

GG

GG

TC

CA

CA

GC

CTA

TAA

gtttG

AG

CTA

CA

AC

CT

GA

TC

CA

CC

A40

173

–35

0N

AT

his

stud

ySt

r3Q

UB

FAM

-CT

GA

CC

GC

TG

CA

CA

CTA

Agt

ttGG

CT

CTA

AT

CG

AC

TG

GC

AG

A5

115

–17

5N

AT

his

stud

ySs

a42

0UoS

FAM

-GC

AG

GA

GA

GT

CG

CTA

CA

Ggt

ttGA

TC

TAT

GC

CC

AC

AA

AC

AG

3218

0–

365

NA

Cai

rney

etal

.(2

000)

mO

ne10

4FA

M-G

GG

TAC

CC

AC

TG

AC

GC

TAT

Ggt

tTC

TG

GTA

CT

TC

CC

TG

AT

GC

2229

0–

387

NA

Ols

enet

al.

(200

0)†

Ssa

197

VIC

-GG

GT

TG

AG

TAG

GG

AG

GC

TT

Ggt

tTG

GC

AG

GG

AT

TT

GA

CA

TAA

C11

120

–17

6B

T-2

8O

’Rei

llyet

al.

(199

6)m

Oki

10V

IC-A

TG

CA

AT

TT

CC

AA

AC

TC

GT

GT

gtttC

CC

CT

TT

CT

CC

CC

TT

TC

TC

TAC

3220

2–

360

BT

-32

Smith

etal

.(1

998)

‡


B E AU F O RT T RO U T M I C RO P L E X F O R S A L M O T RU T TA G E N E T I C S 1793T

able

I.C

ontin

ued

Pane

l/loc

usFo

rwar

dpr

imer

Rev

erse

prim

ern

a¶

Size

*ra

nge

(bp)

BT

linka

gem

ap†

Ref

eren

ce

BT

MP

-Pan

el-3

BG

9354

88gt

tTG

AC

CC

CA

CC

AA

GT

TT

TT

CT

NE

D-A

AA

CA

CA

GTA

AG

CC

CA

TC

TAT

TG

1011

0–

165

NA

Vas

emag

iet

al.

(200

5b)

Ssa

D71

NE

D-A

AC

GT

GA

AA

CA

TAA

AT

CG

AT

GG

gtT

TAA

GA

AT

GG

GT

TG

CC

TAT

GA

G19

170

–25

7N

AK

ing

etal

.(2

005)

Sasa

-TA

P2A

gtttG

TC

CT

GA

TG

TT

GG

CT

CC

CA

GG

NE

D-G

CG

GG

AC

AC

CG

TC

AG

GG

CA

GT

1328

0–

447

NA

Gri

mho

ltet

al.

(200

2)C

A05

3293

PET

-TC

TC

AT

GG

TG

AG

CA

AC

AA

AC

Agt

ttAC

TC

TG

GG

GC

AT

TC

AT

TC

AG

914

0–

171

NA

Vas

emag

iet

al.

(200

5a,b

)Ss

a41

0UoS

gtttG

GA

AA

ATA

AT

CA

AT

GC

TG

CT

GG

TT

PET

-CTA

CA

AT

CT

GG

AC

TAT

CT

TC

TT

CA

3116

8–

326

NA

Cai

rney

etal

.(2

000)

Ssa

422U

oSgt

TTA

TG

GG

CG

TC

CA

CC

TC

TG

AC

AFA

M-C

AC

CC

CA

GC

CT

CC

TC

AA

CC

TT

C15

143

–24

8N

AC

airn

eyet

al.

(200

0)C

A06

0208

VIC

-GC

AA

CA

AT

TC

CC

TT

TT

GA

CC

gtttC

GT

GC

AG

TAG

GA

AA

GG

GG

TA8

145

–19

1N

AV

asem

agi

etal

.(2

005b

)M

HC

-I-U

TR

VIC

-TG

CC

CA

GA

TG

AC

TT

GA

GA

GA

Cgt

ttCC

AA

CC

TC

CT

GT

GT

TG

TG

TG

1922

6–

475

NA

Vas

emag

iet

al.

(200

5a)

BT

MP

-Pan

el-4

Ssa

D17

0N

ED

-GG

AG

GC

AG

TTA

AG

AG

AA

CA

AA

AG

gttT

CA

CC

TAC

CC

TT

CT

CA

TT

CA

AG

2514

8–

217

NA

Kin

get

al.

(200

5)Sa

sa-U

BA

NE

D-G

GA

GA

GC

TG

CC

CA

GA

TG

AC

TT

gtttC

AA

TTA

CC

AC

AA

GC

CC

GC

TC

1426

8–

523

NA

Gri

mho

ltet

al.

(200

2)Ss

a41

3UoS

PET

-GTA

GA

CG

CC

AT

CG

GTA

TT

GT

Ggt

ttCG

TG

AT

GC

CG

CT

GTA

GA

CT

TG

1122

5–

282

BT

-13

Cai

rney

etal

.(2

000)

Ssa

407U

oSFA

M-T

GT

GTA

GG

CA

GG

TG

TG

GA

Cgt

ttCA

CT

GC

TG

TTA

CT

TT

GG

TG

AT

TC

3120

4–

320

NA

Cai

rney

etal

.(2

000)

Ssa

D48

FAM

-GA

GC

CT

GT

TC

AG

AG

AA

AT

GA

Ggt

ttCA

GA

GG

TG

TT

GA

GT

CA

GA

GA

AG

100

304

–55

8N

AK

ing

etal

.(2

005)

CA

0545

65a

VIC

-TC

TG

TG

GT

TC

CC

GA

TC

TT

TC

gtttC

AA

CA

TT

TG

CC

TAG

CC

CA

GA

110

1–

120

NA

Vas

emag

iet

al.

(200

5b)

CA

0545

65b

§–

–7

125

–16

3N

AV

asem

agi

etal

.(2

005b

)m

One

101

VIC

-TG

CTA

AA

TG

AC

TG

AA

AT

GT

TG

AG

Agt

ttGA

GA

AT

GA

AT

GG

CT

GA

AT

GG

A7

155

–19

4N

AO

lsen

etal

.(2

000)

‡C

A06

0177

VIC

-CG

CT

TC

CT

GG

AC

AA

AA

AT

TAgt

ttGA

GC

AC

AC

CC

AT

TC

TC

A12

234

–31

5N

AV

asem

agi

etal

.(2

005b

)m

One

108

VIC

-GT

CA

TAC

TAC

TC

AT

TC

CA

CA

TTA

gtttA

CA

CA

GT

CA

CC

TC

AG

TC

TAT

TC

3837

1–

518

NA

Ols

enet

al.

(200

0)‡

NA

,no

tap

plic

able

.*S

izes

rang

esre

pres

ent

tota

lob

serv

edsi

zera

nge

acro

ssal

lpo

pula

tion

sam

ples

scre

ened

sinc

eth

ede

velo

pmen

tof

the

BT

MP

topr

ovid

ea

bette

rid

eaof

poss

ible

size

rang

ein

othe

rpo

pula

tions

.†G

harb

iet

al.

(200

6).

‡The

orig

inal

sour

ces

ofth

ese

loci

are

asin

dica

ted,

how

ever

PCR

prim

erse

quen

ces

have

been

rede

sign

edin

this

stud

yfo

rus

ein

S.tr

utta

.§C

o-am

plifi

esus

ing

prim

erse

tre

port

edfo

rth

ere

spec

tive

‘a’

locu

s.¶T

otal

num

ber

ofal

lele

sob

serv

edac

ross

all

six

popu

latio

nsa

mpl

es.


1794 K . K E E NA N E T A L .

N

WOI

ALT (47)LG (42)

BW (53)

FUR (41)

CM (41)

KEL (41)

SLN

Ireland

NLN

Fig. 1. Sampling locations of the six Salmo trutta populations used to validate the Beaufort trout MicroPlex.Shaded areas represent regional grouping of population samples. The three regions are west of Ireland(WOI), north Lough Neagh (NLN) and south Lough Neagh (SLN); from each of these three regions,two putative population samples were collected as follows: WOI = Altahooney (ALT) + Lower Goulaun(LG), NLN = Cloghmills (CM) + Kells Water (KEL) and SLN = Blackwater (BW) + River Fury (FUR).Numbers in parentheses represent sample sizes.

was extracted from adipose tissue using the Promega, Wizard SV Genomic DNA PurificationSystem (www.promega.com).

Following PCR amplification of samples for the BTMP using the above protocol, allelicfragment sizes were determined using GeneMapper 4.1 (Applied Biosystems). To ensure theaccuracy of genotype data, two initial reference allele bin sets were independently producedusing a sub-sample of 96 individuals. Subsequent genotyping was carried out by the sameperson to ensure concordance of allele calls and binning consistency. Two population sampleswere independently amplified and genotyped twice for a sub-sample of microsatellite loci inorder to ensure run-to-run genotype accuracy.

Basic within-population sample genetic diversity statistics [i.e. observed (H O) and expected(H E) heterozygosities] were estimated using Arlequin 3.1 (Excoffier et al ., 2005). Allelicrichness was calculated in MSA 4.05 (Dieringer & Schlotterer, 2003). Tests for departuresfrom Hardy–Weinberg equilibrium (HWE) were carried out using Genepop 4.1 (Rousset,2008). This software was also used to test for LD for all locus pairs across populationsamples. Where appropriate, Bonferroni corrections for multiple tests were carried out.

Locus informativeness for inference of ancestry was determined using the statistic In(Rosenberg et al ., 2003). In was calculated using the R package, diveRsity (K. Keenan &P. A. Prodohl, unpubl. data). Power to detect genetic divergence within each of the abovesampling regions, using the BTMP, was assessed using POWSIM 4.0 (Ryman & Palm, 2006).The actual genetic divergence between within-region population samples used in POWSIM



simulations was calculated using G st (Nei & Chesser, 1983), again in the R package, diveR-sity. All sample sizes were set to n = 50 in order to represent a typical population geneticexperimental design, and also due to its close approximation to the actual size of samplescollected from each of the populations studied here (Fig. 1).

In an effort to establish a robust locus selection method for researchers wishing to recon-figuring the BTMP into multiplex groups that better address their particular questions, poweranalysis was carried out in a sequential manner. Contributions from each locus to overallpower were assessed by iteratively adding a single locus to the power-analysis simulation inan order defined by loci ranked from high to low for: (1) number of alleles (na), (2) infor-mativeness for the inference of ancestry (In) and (3) randomly ranked loci (i.e. alphabeticalorder of locus names). With the addition of individual loci in sequential order for each of thethree rank criteria, statistical power was independently assessed in POWSIM. This processwas repeated until all available loci were included in the final power analysis simulation.The rationale of this method is based on the observation by Kalinowski (2002) that highna leads to higher statistical power or accuracy. In can also be interpreted as an assessmentof the decrease in uncertainty resulting from information contributed by a particular locus(Rosenberg et al ., 2003), suggesting that both measures may be useful in the selection of themost informative loci for population genetic studies. For the purposes of the power analy-ses, the loci Sasa-UBA, CA054565a and SsaD48 were excluded because of deviations fromHWE, monomorphism and having more than the permissible 50 alleles by POWSIM 4.0,respectively.

As an additional test of the generality (i.e. applicability to populations beyond thosesurveyed here) of na and In as predictors of statistical power, correlations between locusranks were carried out across within-region comparisons using Spearman’s rank correla-tion, calculated in the statistical programming environment R (R Development Core Team;www.r-project.org).

RESULTS

M A R K E R L O C I S E L E C T I O N A N D M U LT I P L E XD E V E L O P M E N T

From the original 150 candidate loci assessed, 38 met the specified selectioncriteria. Following extensive testing involving multiple permutations of marker com-binations, the minimum number of multiplex groups required to reliably amplify the38 loci was found to be four. This particular configuration of the BTMP can befound in Table I. Microsatellites within the BTMP were found to reliably amplify inall six population samples with a mean ± s.d. amplification success of 96·6 ± 1·9%(individual locus amplification success per population sample is presented in TableSI, Supporting Information). All primer sequence information, including those formarkers derived from the literature, are presented in Table I. Trace file figures (chro-matograms) for each microsatellite locus, in addition to notes pertinent to successfulallele scoring, are provided in Appendix SI (Supporting Information).

B T M P F L E X I B I L I T Y

The flexibility of the panel of markers comprising the BTMP was tested as part ofa parallel on-going project investigating the population genetics of sea trout (anadro-mous S. trutta). During these tests, it was noted that the allelic size range of many ofthe microsatellite markers comprising the BTMP panel varied considerably betweenfreshwater resident and anadromous S. trutta , leading to extensive marker overlap(unpubl. data). To address this issue, an alternative marker configuration was tested.


1796 K . K E E NA N E T A L .

This alternative panel comprised 22 of the original 38 BTMP loci organized intotwo unique multiplex groups. An initial screening of anadromous S. trutta , as part ofthis parallel study, has shown that this new configuration of loci does indeed over-come the overlap issue, while more than adequately meeting the statistical powerrequirement. Details of anadromous S. trutta configuration of the BTMP markers arepresented in Table SII (Supporting Information).

S TAT I S T I C A L A S S E S S M E N T O F T H E B T M P

Within the BTMP, the mean observed single locus heterozygosity ranged from0·20 to 0·92 (Ssa416UoS and SSsp2201). The mean ± s.d. number of alleles perlocus was 20·4 ± 17·9 and any single population sample possessed between 23 and100% of the total alleles observed at a particular locus across all population samplesscreened (Table SI, Supporting Information).

As expected from presumed neutral markers, with one exception (Sasa-UBA),all loci conform to HWE expectations within all six population samples (Table SI,Supporting Information). The small number of deviations, observed after Bonferronicorrection, had no consistent pattern related to samples or loci. Accordingly, they arelikely to be due to sampling error rather than true population deviations from HWEexpectations. For the Sasa-UBA locus, however, five of the six samples were foundto deviate from HWE expectations. While the occurrence of null alleles cannot bedismissed, it is interesting to note that Sasa-UBA is embedded in the 3′-untranslatedregion of the major histocompatibility complex (MHC) class I locus in Salmo salar .Protein modelling of several Sasa-UBA alleles has demonstrated differences in theirpeptide-binding domains (Grimholt et al ., 2002). Thus, selective constraints cannotbe excluded as a possible explanation for the observed deviations from HWE. It waselected to leave this particular marker locus in the BTMP panel as it may be usefulfor investigations examining adaptive differences among S. trutta populations. It isimportant to emphasize, however, that care should be taken when using this particularlocus to estimate population parameters which assume neutrality.

An additional feature was identified during marker assessment with regard to locimOne102 and CA054565 . Both loci primer sets appeared to amplify two indepen-dently segregating loci. The two presumed paralogues for each primer set were sub-sequently renamed as mOne102a and mOne102b and CA054565a and CA054565b,respectively. In both cases, the locus with the smaller allele size distribution wasdenoted with the suffix ‘a’ and the larger with ‘b’. The two mOne102 loci are easilydistinguished as they do not overlap with any other loci within their fluorophore group(Table I). This, however, is not the case for the CA054565 loci (i.e. CA054565b).Thus, alleles at the CA054565b locus tend to fall within the range of the mOne108locus within the same fluorophore group (VIC; see Table I). Despite this overlap,alleles from each locus can be easily identified based on their distinct stutter patternsand peak shape profiles.

In this study, CA054565a was excluded from analyses as it was found to bemonomorphic for the same allele in all six population samples examined. Variationat this locus, however, was observed in the samples previously used to carry outthe initial locus selection and in other S. trutta populations examined, including theanadromous S. trutta samples mentioned above.



0·0

0·2

0·4

0·6

0·8

1·0(a)

0·0

0·2

0·4

0·6

0·8

1·0(b)

0·00 5 10 15 20

Number of loci

25 30 35

0·2

0·4

0·6

0·8

1·0(c)

Pow

er to

det

ect d

iver

genc

e

Fig. 2. Sequential power analysis results for each of the within-region pair-wise Salmo trutta populationcomparisons: (a) WOI - Gst = 0.002, (b) SLN - Gst = 0.003 and (c) NLN - Gst = 0.022 (see Fig. 1). Linesrepresent the different rank orders used when sequentially adding loci to assess statistical power ( ,number of alleles; , informativeness for the inference of ancestry In; , randomly ranked loci). TheG st values represent the actual values used in POWSIM simulations. n = 50 for all population samples.

The statistical power to detect genetic divergence (i.e. population structuring)at the actual levels measured for each region was shown to be generally high(i.e. >0·7; Fig. 2). Using the sequential approach described, it was observed thatna and In were suitable predictors of the statistical power provided by a givenlocus. Thus, both parameters performed much better than the randomly rankedloci as a proxy to locus statistical power in all regional comparisons. Loci ranksbased on In and na were strongly positively correlated across pair-wise compar-isons (Table II). This observation is perhaps not surprising as loci characteristics arelikely to be inherently conserved, at least over the relatively small geographical scaleexamined here.


1798 K . K E E NA N E T A L .

Table II. Spearman’s rank correlation ρ-scores with significance levels for loci ranked forIn (informativeness for the inference of ancestry) and na (total number of observed alleles).

See Fig. 1 for population sample code

In

Populationsample WOI SLN NLN

WOI – *** ***SLN 0·781 – ***NLN 0·780 0·756 –

na Population sample WOI SLN NLNWOI – *** ***SLN 0·940 – ***NLN 0·936 0·964 –

***P -value < 0·001.

DISCUSSION

In this study, a high-throughput set of informative microsatellite marker loci ispresented for general use in both freshwater and anadromous S. trutta genetic inves-tigations. While many microsatellite markers have been previously described forS. trutta , this is the first attempt to compile a highly flexible, high-throughput andcost-effective group of markers suitable for addressing a range of research questions.

T E C H N I C A L B E N E F I T S O F T H E B T M P

The BTMP ensures maximum throughput in conjunction with high data qualityand consistency for microsatellite genotyping. In this study, for example, screeningthroughput was extremely high, with up to 922 genotypes (assuming 96% ampli-fication success) being produced from a single 96 well plate PCR. This level ofthroughput for microsatellite multiplexing is much higher than that observed else-where. For example, the multiplex panels presented in Renshaw et al . (2006) andused in Hansen et al . (2010), assuming the same amplification success achievedhere, generate only 645 and 368 genotypes, respectively. Such high throughput alsoreduces the time for reaction preparation, thus resulting in tangible reductions ofboth financial costs associated with personnel and also PCR reagents. Optimizationof each of the four multiplex reactions in a total volume of only 3·5 μl providesadditional savings on PCR reagent usage. Given that as little as 0·5 ng of genomicDNA is required per PCR, tissue requirements are also minimal.

One of the major benefits of the BTMP is high quality data, as demonstrated bythe levels of genotyping accuracy and amplification success observed. For example,following the establishment of the initial reference allele bin sets, no genotypeinconsistencies were observed for replicated individuals (i.e. same individualsscreened more than once for the same loci). This high degree of reproducibilityis a particularly useful feature of the BTMP, especially for parentage studieswhere allelic mismatches can be particularly troublesome (Wang, 2010). Thegenotyping platform used to develop the BTMP also allows for standardization ofmicrosatellite scoring through the development of a universal reference allele binset. This consensus bin set overcomes one of the major disadvantages commonly



associated with microsatellites. Thus, it is now possible to share a reference allelebin set among research groups to ensure that independently derived genotypesare both accurate and comparable (Ellis et al ., 2011). This advantage also lendsitself to meta-analyses and should be seen as beneficial for the establishment of anunderstanding of S. trutta population structure and evolution throughout its range.

B T M P A P P L I C AT I O N S

Different genetic approaches have distinct experimental requirements; thus, locusselection for inclusion in the BTMP panels was carefully planned to ensure thegeneral applicability of the markers. For instance, levels of polymorphism of locifor population genetic structure studies and population assignment have constraintsin the sense that there is no discriminative value in using monomorphic markers.Where markers are too polymorphic, however, practical constraints relating to alleletyping and sample size requirements will be encountered. Loci used for parentageassignment analyses, on the other hand, should be sufficiently polymorphic to allowunambiguous individual assignment to family and family group. Therefore, thereare no explicit constraints on how high or low the levels of polymorphism can be.Taking these factors into consideration, the BTMP was developed to be flexiblefor researchers who wish to reorganize the loci to meet the requirements of theirparticular research project. This feature of the BTMP is shown in Table SII (Sup-porting Information), where two unique multiplex groups (derived from the originalpool of 38 markers) were developed for specific application to anadromous S. truttapopulations, with allelic distribution ranges distinct from those of the surveyed fresh-water S. trutta populations. The inclusion of loci which have the potential to deviatefrom neutrality as a result of their association to regions of the genome involved inadaptive changes (e.g. Sasa-UBA and the other MHC-linked loci) also provides theopportunity for investigations of adaptive evolution in S. trutta populations.

The occurrence of putatively duplicated loci, in most instances, has no direct con-sequences for any of the analyses used in this study. Where putatively duplicatedloci were identified, there was no evidence, in the study populations, of LD. Assuch, they could confidently be used as independently segregating marker loci. Theoccurrence of these putative ohnologues is likely to be the result of locus duplica-tion as the ancestor of the salmonids underwent whole genome duplication around50 million years ago and many contemporary salmonid species still exhibit resid-ual tetraploidy (Bailey et al ., 1978). Interestingly, while the locus CA054565a wasmonomorphic and hence uninformative in the context of the present investigation,more than one allele was observed among the individuals used in the developmentof the anadromous S. trutta panels and also in on-going investigations of freshwaterpopulations of S. trutta .

S TAT I S T I C A L P OW E R A N D L O C U S I N F O R M AT I V E N E S S

The POWSIM analysis demonstrated high statistical power for all within-regionpopulation comparisons when all loci of the BTMP were included (Fig. 2). Thishigh statistical power was observed for divergence levels as low as G st = 0·002(ALT v . LG), demonstrating one of the major benefits of the BTMP. When inves-tigating population genetic structuring among populations where F st values are


1800 K . K E E NA N E T A L .

c. >0·005 and n = at least 50, the 38 loci of the BTMP are likely to provide morethan enough statistical power. This conclusion is derived from the observation that,in the SLN population comparison (G st = 0·003), with all 35 eligible loci included,power was found to be 96%. Considering this in view of the fact that three lociwere excluded from the analysis (including the most informative locus SsaD48), itappears reasonable to assume that close to 100% statistical power would be achievedunder the aforementioned conditions. Such superfluous levels of statistical power atrelatively low levels of genetic divergence will undoubtedly provide users of theBTMP with extra flexibility when reconfiguring loci for the specific needs of theirgiven study.

R E C O N F I G U R AT I O N O F T H E B T M P

The BTMP is presented here as a baseline group of microsatellite loci that canbe readily used by researchers for purposes other than population genetics, includ-ing QTL and parentage analyses, inheritance and gene-mapping studies, as well asgenetic stock identification. Thus, it is not presented as a fixed configuration of mark-ers. Certain studies may have a lower requirement for the number of loci (e.g . NLNpopulation comparison) than others. In such cases, it is important to select the mostefficient set of loci to attempt to reduce the overall labour and financial expendi-ture. The main aim of the marker assessment carried out was to evaluate how wellboth qualitative (polymorphism as determined by the number of alleles) and quan-titative (In) features of loci could predict the potential statistical power provided byindividual loci.

Despite the evidence that both In and na can be used to select the most power-ful loci, results should be interpreted cautiously, especially where sample sizes aresmall (i.e. n < 50). This caution is based on the observation that POWSIM assessesstatistical power as a function of the ability to detect true genetic divergence witha particular data set and experimental design. It does not assess the accuracy ofa particular data set in the estimation of a given population parameter (Ryman &Jorde, 2001; Ryman et al ., 2006). This is an important distinction that needs to beemphasized between these two particular tasks.

An alternative way to consider this issue is in terms of sampling error. Wherea locus has many alleles (thus high informativeness as determined by POWSIMand In), most of these occur, necessarily, at low frequencies and are likely to bemisrepresented in samples of insufficient size. Where this occurs, it is often the casethat frequency differences between these alleles are of little biological relevance, yetthey contribute significantly to the overall χ2 test for sample independence (unpubl.data), which is employed in POWSIM. Thus, it is likely that where sample sizesare insufficient to accurately detect alleles, POWSIM will provide reports of highstatistical power, even for data sets with very low biological accuracy (i.e. the abilityto calculate a true parameter which describes a population from a sample of thatpopulation). Despite this issue, it should still be possible to use either polymorphismlevel (i.e. na) or In as a proxy to general relative locus power, providing that samplesizes are adequately large.

The utility of these two features as proxies to power in studies of populations, otherthan those considered here, is supported by the high degree of similarity in locusranks across the three sampling regions. Furthermore, by considering the fact that the



relative polymorphism level at neutral loci is determined almost entirely by mutationrate and genomic range constraints, and that these inherent features are likely to beconserved, at least to the intraspecific scale, it is not unreasonable to assume that theyare generally applicable features of these loci for S. trutta populations throughout thedistribution range. It is possible, however, that the observed high positive correlationsin ranks observed here are restricted to S. trutta from the island of Ireland as a resultof recent common ancestry, relative to S. trutta from elsewhere. In this instance,locus informativeness would have to be assessed on a study-by-study basis. To testthis hypothesis, the methods used here should be replicated in S. trutta populationsfrom other areas.

In summary, the panel of microsatellite markers described is submitted as a valu-able molecular resource facilitating a more rational and collaborative approach tothe study of S. trutta population and evolutionary genetics throughout the speciesdistribution range. Given advances in genotyping technology and software, it is nowpossible to share allele classifications between research groups, thus allowing forstandardized allele nomenclature based on a single reference sample of individuals.For the future, it is envisaged that additional informative markers will be added to theBTMP. Work is on-going to provide an online resource to store information regard-ing allele bins for locus typing and marker information for efficient customizationof the BTMP. Finally, it is argued that while there is a trend to move towards newtechnologies, which potentially allow for the screening of a very large number ofmarkers (e.g . single nucleotide polymorphisms), their application may not always beappropriate to address specific questions in S. trutta population genetic research. It istherefore anticipated that the microsatellite panel described here will have utility (e.g .low cost, well-developed statistical framework, accessibility and high throughput),in such applications, at least in the medium term (e.g . 5–10 years).

K.K. was supported by a PhD studentship from the Beaufort Marine Research Award inFish Population Genetics funded by the Irish Government under the Sea Change programme.P.A.P, T.F.C, W.W.C., P.McG. and C.R.B were also supported by this award. J.J.M wassupported by a PhD studentship funded by the Environmental Protection Agency Ireland.The authors are grateful to the staff of the Marine Institute, Ireland, and the Agri-Foodand Biosciences Institute for help with sample collection. The authors would also like toexpress their gratitude to the Associate Editor and two anonymous reviewers for constructivecomments that helped to substantially improve this manuscript.

Supporting Information

Supporting Information may be found in the online version of this paper:Table SI Details of locus diversity statistics estimated per population sample, includ-ing number of individuals typed per locus per population sample (N ), number ofobserved alleles (N a), population sample proportion of the number of total alle-les observed across all samples for a particular locus (P/T), allelic richness (AR),observed (H O) and expected (H E) heterozygosities; significance (P -value) of testsfor departures from Hardy–Weinberg equilibrium (ns, non-significant).Table SII Marker information for the two anadromous Salmo trutta MicroPlexpanels assembled in this study, including primer sequences (with ABI-labelledprimer). Loci names prefixed with ‘m’ have been modified in this study from


1802 K . K E E NA N E T A L .

their original sources for use in S. trutta (see main text for additional details). Allunlabelled primers are pigtailed (i.e. prefixed with ‘gttt’)APPENDIX SI. Brown trout MicroPlex marker loci scoring notes

References

Bailey, G. S., Poulter, R. & Stockwell, P. A. (1978). Gene duplication in tetraploid fish: modelfor gene silencing at unlinked duplicated loci. Proceedings of the National Academy ofSciences 75, 5575.

Brownstein, M. J., Carpten, J. D. & Smith, J. R. (1996). Modulation of non-templatednucleotide addition by Taq DNA polymerase: primer modifications that facilitate geno-typing. BioTechniques 20, 1004–1010.

Buschiazzo, E. & Gemmell, N. J. (2006). The rise, fall and renaissance of microsatellites ineukaryotic genomes. BioEssays 28, 1040–1050.

Cairney, M., Taggart, J. B. & Høyheim, B. (2000). Characterization of microsatellite andminisatellite loci in Atlantic salmon (Salmo salar L.) and cross-species amplificationin other salmonids. Molecular Ecology 9, 2175–2178.

Carlsson, J., Olsen, K. H. H., Nilsson, J., Øverli, Ø. & Stabell, O. B. O. (1999). Microsatellitesreveal fine-scale genetic structure in stream-living brown trout. Journal of Fish Biology55, 1290–1303.

Carlsson, J., Carlsson, J. E. L., Olsen, K. H., Hansen, M. M., Eriksson, T. & Nilsson, J. (2004).Kin-biased distribution in brown trout: an effect of redd location or kin recognition?Heredity 92, 53–60.

Coughlan, J., McGinnity, P., O’Farrell, B., Dillane, E., Diserud, O., de Eyto, E., Farrell, K.,Whelan, K., Stet, R. J. M. & Cross, T. F. (2006). Temporal variation in an immuneresponse gene (MHC I) in anadromous Salmo trutta in an Irish river before and duringaquaculture activities. ICES Journal of Marine Science 63, 1248–1255.

Dieringer, D. & Schlotterer, C. (2003). Microsatellite analyser (MSA): a platform independentanalysis tool for large microsatellite data sets. Molecular Ecology Notes 3, 167–169.

Ellis, J. S., Gilbey, J., Armstrong, A., Balstad, T., Cauwelier, E., Cherbonnel, C., Consuegra,S., Coughlan, J., Cross, T. F., Crozier, W., Dillane, E., Ensing, D., García de Leaniz,C., García-Vazquez, E., Griffiths, A. M., Hindar, K., Hjorleifsdottir, S., Knox, D.,Machado-Schiaffino, G., McGinnity, P., Meldrup, D., Nielsen, E. E., Olafsson, K.,Primmer, C. R., Prodohl, P. A., Stradmeyer, L., Vaha, J.-P., Verspoor, E., Wennevik,V. & Stevens, J. R. (2011). Microsatellite standardization and evaluation of genotypingerror in a large multi-partner research programme for conservation of Atlantic salmon(Salmo salar L.). Genetica 139, 1–15.

Excoffier, L., Laval, G. & Schneider, S. (2005). Arlequin (version 3.0): an integrated softwarepackage for population genetics data analysis. Evolutionary Bioinformatics 1, 47–50.

Ferguson, A. (1989). Genetic differences among brown trout, Salmo trutta , stocks and theirimportance for the conservation and management of the species. Freshwater Biology21, 35–46.

Gharbi, K., Gautier, A., Danzmann, R. G., Gharbi, S., Sakamoto, T., Høyheim, B., Taggart,J. B, Cairney, M., Powell, R., Krieg, F., Okamoto, N., Ferguson, M. M., Holm, L.-E.& Guyomard, R. (2006). A linkage map for brown trout (Salmo trutta): chromosomehomeologies and comparative genome organization with other salmonid fish. Genetics172, 2405.

Grimholt, U., Drabløs, F., Jørgensen, S., Høyheim, B. & Stet, R. J. M. (2002). The majorhistocompatibility class I locus in Atlantic salmon (Salmo salar L.): polymorphism,linkage analysis and protein modelling. Immunogenetics 54, 570–581.

Hansen, M. M. & Mensberg, K.-L. D. (1996). Founder effects and genetic population structureof brown trout (Salmo trutta) in a Danish river system. Canadian Journal of Fisheriesand Aquatic Sciences 53, 2229–2237.

Hansen, M. M., Ruzzante, D. E., Nielsen, E. E. & Mensberg, K.-L. D. (2000). Microsatelliteand mitochondrial DNA polymorphism reveals life-history dependent interbreedingbetween hatchery and wild brown trout (Salmo trutta L.). Molecular Ecology 9,583–594.



Hansen, M. M., Meier, K. & Mensberg, K.-L. D. (2010). Identifying footprints of selection instocked brown trout populations: a spatio-temporal approach. Molecular Ecology 19,1787–1800.

Hendry, A. P. & Stearns, S. C. (2004). Evolution Illuminated: Salmon and their Relatives .Oxford: Oxford University Press.

Hynes, R. A., Ferguson, A. & McCann, M. A. (1996). Variation in mitochondrial DNA andpost-glacial colonization of North Western Europe by brown trout. Journal of FishBiology 48, 54–67.

Jacob, A., Nussle, S., Britschgi, A., Evanno, G., Muller, R. & Wedekind, C. (2007). Maledominance linked to size and age, but not to “good genes” in brown trout (Salmotrutta). BMC Evolutionary Biology 7, 207–216.

Jensen, L. F., Hansen, M. M., Pertoldi, C., Holdensgaard, G., Mensberg, K. L. D. &Loeschcke, V. (2008). Local adaptation in brown trout early life-history traits:implications for climate change adaptability. Proceedings of the Royal Society B 275,2859–2868.

Jonsson, B. & Jonsson, N. (2006). Life-history effects of migratory costs in anadromousbrown trout. Journal of Fish Biology 69, 860–869.

Kalinowski, S. T. (2002). How many alleles per locus should be used to estimate geneticdistances? Heredity 88, 62–65.

King, T. L., Eackles, M. S. & Letcher, B. H. (2005). Microsatellite DNA markers for thestudy of Atlantic salmon (Salmo salar) kinship, population structure, and mixed-fisheryanalyses. Molecular Ecology Notes 5, 130–132.

Klemetsen, A. & Amundsen, P. A. (2003). Atlantic salmon Salmo salar L., brown troutSalmo trutta L. and Arctic charr Salvelinus alpinus (L.): a review of aspects of theirlife histories. Ecology of Freshwater Fish 12, 1–59.

Matthews, M. A., Poole, R., Thompson, C. E., McKillen, J., Ferguson, A., Hindar, K. &Wheelan, K. F. (2000). Incidence of hybridization between Atlantic salmon, Salmosalar L., and brown trout, Salmo trutta L., in Ireland. Fisheries Management andEcology 7, 337–347.

McKeown, N. J., Hynes, R. A., Duguid, R. A., Ferguson, A. & Prodohl, P. A. (2010).Phylogeographic structure of brown trout Salmo trutta in Britain and Ireland: glacialrefugia, postglacial colonization and origins of sympatric populations. Journal of FishBiology 76, 319–347.

Meier, K., Hansen, M. M., Bekkevold, D., Skaala, O. & Mensberg, K.-L. D. (2011). Anassessment of the spatial scale of local adaptation in brown trout (Salmo trutta L.):footprints of selection at microsatellite DNA loci. Heredity 106, 488–499.

Nei, M. & Chesser, R. K. (1983). Estimation of fixation indices and gene diversities. Annalsof Human Genetics 47, 253–259.

Olsen, E. M. & Vøllestad, L. A. (2001). Within-stream variation in early life-history traits inbrown trout. Journal of Fish Biology 59, 1579–1588.

O’Farrell, B., Benzie, J. A. H., McGinnity, P., Carlsson, J., Eyto, E. d., Dillane, E., Coughlan,J. & Cross, T. (2011). MHC-mediated spatial distribution in brown trout (Salmo trutta)fry. Heredity 108, 403–409.

Olsen, J. B., Wilson, S. L., Kretschmer, E. J., Jones, K. C. & Seeb, J. E. (2000). Characteriza-tion of 14 tetranucleotide microsatellite loci derived from sockeye salmon. MolecularEcology 9, 2155–2234.

O’Reilly, P. T., Hamilton, L. C., McConnell, S. K. & Wright, J. M. (1996). Rapid analysis ofgenetic variation in Atlantic salmon (Salmo salar) by PCR multiplexing of dinucleotideand tetranucleotide microsatellites. Canadian Journal of Fisheries and Aquatic Sciences53, 2292–2298.

Paterson, S., Piertney, S. B., Knox, D., Gilbey, J. & Verspoor, E. (2004). Characterizationand PCR multiplexing of novel highly variable tetranucleotide Atlantic salmon (Salmosalar L.) microsatellites. Molecular Ecology Notes 4, 160–162.

Prodohl, P. A., Walker, A. F., Hynes, R. A., Taggart, J. B. & Ferguson, A. (1997). Geneticallymonomorphic brown trout (Salmo trutta L.) populations as revealed by mitochon-drial DNA, multilocus and single-locus minisatellite (VNTR) analyses. Heredity 79,208–213.


1804 K . K E E NA N E T A L .

Renshaw, M., Saillant, E., Bradfield, S. & Gold, J. (2006). Microsatellite multiplex panelsfor genetic studies of three species of marine fishes: red drum (Sciaenops ocellatus),red snapper (Lutjanus campechanus), and cobia (Rachycentron canadum). Aquaculture253, 731–735.

Robertsen, G., Kvingedal, E. & Einum, S. (2011). Is there genetic variation in the responseto competition intensity in juvenile brown trout Salmo trutta? Journal of Fish Biology78, 635–646.

Rogers, S. M., Marchand, M. H. & Bernatchez, L. (2004). Isolation, characterization andcross-salmonid amplification of 31 microsatellite loci in the lake whitefish (Coregonusclupeaformis , Mitchill). Molecular Ecology Notes 4, 89–92.

Rosenberg, N. A., Li, L. M., Ward, R. & Pritchard, J. K. (2003). Informativeness ofgenetic markers for inference of ancestry. American Journal of Human Genetics 73,1402–1422.

Rousset, F. (2008). GENEPOP’007: a complete re-implementation of the genepop softwarefor Windows and Linux. Molecular Ecology Resources 8, 103–106.

Ryman, N. & Jorde, P. E. (2001). Statistical power when testing for genetic differentiation.Molecular Ecology 10, 2361–2373.

Ryman, N. & Palm, S. (2006). POWSIM: a computer program for assessing statistical powerwhen testing for genetic differentiation. Molecular Ecology Notes 6, 600–602.

Ryman, N., Palm, S., Andre, C., Carvalho, G. R., Dahlgren, T. G., Jorde, P. E., Laikre,L., Larsoon, J. C., Palme, A. & Ruzzante, D. E. (2006). Power for detecting geneticdivergence: differences between statistical methods and marker loci. Molecular Ecology15, 2031–2045.

Scribner, K. T., Gust, J. R. & Fields, R. L. (1996). Isolation and characterization of novelsalmon microsatellite loci: cross-species amplification and population genetic applica-tions. Canadian Journal of Fisheries and Aquatic Sciences 53, 833–841.

Smith, C. T., Koop, B. F. & Nelson, R. J. (1998). Isolation and characterization of coho salmon(Oncorhynchus kisutch) microsatellites and their use in other salmonids. MolecularEcology 7, 1614.

Swatdipong, A., Vasemagi, A., Niva, T., Koljonen, M. L. & Primmer, C. R. (2010). Highlevel of population genetic structuring in lake-run brown trout, Salmo trutta , of theInari Basin, northern Finland. Journal of Fish Biology 77, 2048–2071.

Tiira, K., Laurila, A., Enberg, K., Piironen, J., Aikio, S., Ranta, E. & Primmer, C. R. (2005).Do dominants have higher heterozygosity? Social status and genetic variation in browntrout, Salmo trutta . Behavioral Ecology and Sociobiology 59, 657–665.

Vasemagi, A., Gross, R., Paaver, T., Koljonen, M. L., Saisa, M. & Nilsson, J. (2005a). Anal-ysis of gene associated tandem repeat markers in Atlantic salmon (Salmo salar L.)populations: implications for restoration and conservation in the Baltic Sea. Conserva-tion Genetics 6, 385–397.

Vasemagi, A., Nilsson, J. & Primmer, C. R. (2005b). Seventy-five EST-linked Atlantic salmon(Salmo salar L.) microsatellite markers and their cross-amplification in five salmonidspecies. Molecular Ecology Notes 5, 282–288.

Wang, J. (2010). Effects of genotyping errors on parentage exclusion analysis. MolecularEcology 19, 5061–5078.

Youngson, A. F., Jordan, W. C., Verspoor, E., McGinnity, P., Cross, T. F. & Ferguson,A. (2003). Management of salmonid fisheries in the British Isles: towards a practicalapproach based on population genetics. Fisheries Research 62, 193–209.


Beaufort trout MicroPlex: a high‐throughput multiplex platform comprising 38 informative...

Documents

Transcript of Beaufort trout MicroPlex: a high‐throughput multiplex platform comprising 38 informative...