Evolutionary Applications. 2019;12:519–534.	 	 	 | 	519wileyonlinelibrary.com/journal/eva
 
Received:	12	September	2018  |  Revised:	2	November	2018  |  Accepted:	5	November	2018
DOI: 10.1111/eva.12736
O R I G I N A L  A R T I C L E
Detailed insights into pan‐European population structure and 
inbreeding in wild and hatchery Pacific oysters (Crassostrea 
gigas) revealed by genome‐wide SNP data
David L. J. Vendrami1  | Ross D. Houston2 | Karim Gharbi3 | Luca Telesca4,5 |  
Alejandro P. Gutierrez2 | Helen Gurney‐Smith6 | Natsuki Hasegawa7 | Pierre Boudry8  |  
Joseph I. Hoffman1,5
This	is	an	open	access	article	under	the	terms	of	the	Creative	Commons	Attribution	License,	which	permits	use,	distribution	and	reproduction	in	any	medium,	
provided	the	original	work	is	properly	cited.
©	2018	The	Authors.	Evolutionary Applications	published	by	John	Wiley	&	Sons	Ltd
1Department	of	Animal	Behavior,	Bielefeld	
University,	Bielefeld,	Germany
2The	Roslin	Institute	and	Royal	(Dick)	
School	of	Veterinary	Studies,	University	of	
Edinburgh,	Midlothian,	UK
3Edinburgh	Genomics,	Ashworth	
Laboratories,	University	of	Edinburgh,	
Edinburgh,	UK
4Department	of	Earth	Sciences,	University	
of	Cambridge,	Cambridge,	UK
5British	Antarctic	Survey,	High	Cross,	
Cambridge,	UK
6Department	of	Fisheries	and	
Aquaculture,	Vancouver	Island	University,	
Nanaimo,	British	Columbia,	Canada
7National	Research	Institute	of	
Aquaculture,	Japan	Fisheries	Research	
Agency,	Minami‐Ise,	Japan
8Ifremer,	Laboratoire	des	Sciences	de	
l’Environnement	Marin	(UBO/CNRS/IRD/
Ifremer),	Plouzané,	France
Correspondence
David	Vendrami,	Department	of	Animal	
Behavior,	Bielefeld	University,	Bielefeld,	
Germany.
Email:	david.vendrami@student.unife.it
Funding information
Biotechnology	and	Biological	Sciences	
Research	Council,	Grant/Award	Number:	
BB/M026140/1;	Natural	Environment	
Research	Council,	Grant/Award	Number:	
NE/P010695/1;	European	Union	Marie	
Curie	Seventh	Framework	Programme,	
Grant/Award	Number:	605051;	BBSRC	
Institute	Strategic	Program	Grants,	Grant/
Award	Number:	BBS/E/D/20002172	and	
BBS/E/D/30002275
Abstract
Cultivated	bivalves	are	important	not	only	because	of	their	economic	value,	but	also	
due	to	their	impacts	on	natural	ecosystems.	The	Pacific	oyster	(Crassostrea gigas)	is	
the	world's	most	heavily	cultivated	shellfish	species	and	has	been	introduced	to	all	
continents	except	Antarctica	for	aquaculture.	We	therefore	used	a	medium‐density	
single	nucleotide	polymorphism	(SNP)	array	to	 investigate	the	genetic	structure	of	
this	species	in	Europe,	where	it	was	introduced	during	the	1960s	and	has	since	be‐
come	 a	 prolific	 invader	 of	 coastal	 ecosystems	 across	 the	 continent.	We	 analyzed	
21,499	polymorphic	SNPs	in	232	individuals	from	23	localities	spanning	a	latitudinal	
cline	 from	Portugal	 to	Norway	and	 including	 the	source	populations	of	 Japan	and	
Canada.	We	confirmed	the	results	of	previous	studies	by	finding	clear	support	for	a	
southern	 and	 a	 northern	 group,	with	 the	 former	 being	 indistinguishable	 from	 the	
source	populations	indicating	the	absence	of	a	pronounced	founder	effect.	We	fur‐
thermore	conducted	a	large‐scale	comparison	of	oysters	sampled	from	the	wild	and	
from	 hatcheries	 to	 reveal	 substantial	 genetic	 differences	 including	 significantly	
higher	levels	of	inbreeding	in	some	but	not	all	of	the	sampled	hatchery	cohorts.	These	
findings	were	confirmed	by	a	smaller	but	representative	SNP	dataset	generated	using	
restriction	 site‐associated	DNA	 sequencing.	We	 therefore	 conclude	 that	 genomic	
approaches	can	generate	increasingly	detailed	insights	into	the	genetics	of	wild	and	
hatchery	produced	Pacific	oysters.
K E Y W O R D S
aquaculture,	Crassostrea gigas,	genetic	structure,	high‐density	genotyping	array,	inbreeding,	
Pacific	oyster,	restriction	site‐associated	DNA	(RAD)	sequencing,	single	nucleotide	
polymorphism	(SNP)
520  |     VENDRAMI Et Al.
1  | INTRODUC TION
Oysters	 are	 among	 the	most	 economically	 important	 aquaculture	
species,	 with	 worldwide	 annual	 production	 exceeding	 600,000	
tonnes	(FAO,	http://www.fao.org).	In	particular,	the	Pacific	cupped	
oyster	(Crassostrea gigas),	which	is	native	to	the	Pacific	coast	of	east‐
ern	Asia,	was	 introduced	 into	many	countries	worldwide	 for	 com‐
mercial	 cultivation.	 Starting	 in	 the	 1960s,	C. gigas	 was	 introduced	
into	Europe	to	support	oyster	farming	after	severe	declines	of	the	
two	 previously	 cultivated	 oyster	 species–the	 Portuguese	 oyster	
(C. angulata)	and	the	flat	oyster	(Ostrea edulis,	Grizel	&	Héral,	1991,	
Nehring,	1999,	Wolff	&	Reise,	2002).	Large	quantities	of	seed	as	well	
as	adult	oysters	were	brought	to	France	and	the	Netherlands	from	
the	Miyagi	prefecture	in	Japan	and	from	British	Columbia	in	Canada,	
where C. gigas	was	also	introduced	from	Japan	in	the	1920s	(Quayle,	
1988)	and	became	quickly	established	in	the	wild.	Concurrently,	sev‐
eral	small	importations	of	less	than	a	hundred	individuals	at	a	time	
also	took	place	 into	the	United	Kingdom	for	hatchery	propagation	
(Walne	&	Helm,	1979).
Subsequently,	 Pacific	 oysters	 produced	 in	UK	hatcheries	were	
farmed	 in	 the	 German	 Wadden	 Sea	 (Reise,	 1998)	 as	 well	 as	 in	
Denmark	(Nehring,	2006),	while	oysters	produced	 in	French	farms	
were	 transferred	 to	 various	 locations	 in	 the	 Mediterranean	 Sea	
(Grizel	 &	 Héral,	 1991;	 Šegvić‐Bubić	 et	 al.,	 2016)	 including	 south‐
ern	 Portugal,	 where	 hybridization	 with	 C. angulata	 is	 known	 to	
occur	 (Batista,	 Fonseca,	 Ruano,	 &	 Boudry,	 2017;	 Huvet,	 Fabioux,	
McCombie,	Lapegue,	&	Boudry,	2004).	More	recently,	C. gigas	also	
reached	the	southern	coasts	of	Sweden	and	Norway	(Troost,	2010),	
where	 it	 arrived	 as	 a	 consequence	 of	 both	 natural	 dispersal	 from	
Denmark	and	human‐mediated	translocation	from	British	hatcheries	
(d'Auriac	 et	 al.,	 2017).	Consequently,	 Pacific	 oysters	 have	become	
widespread	across	the	Atlantic	and	Mediterranean	coasts	of	Europe,	
where	they	are	responsible	for	major	changes	to	coastal	ecosystems	
(Troost,	2010)	and	are	considered	an	invasive	species	(Goulletquer,	
Bachelet,	Sauriau,	&	Noel,	2002).
Several	 studies	 have	 used	 genetic	 markers	 such	 as	 mitochon‐
drial	DNA	and	microsatellites	to	investigate	the	population	structure	
of	 the	Pacific	oyster	 in	Europe,	mainly	with	 a	 view	 toward	under‐
standing	 the	history	of	 invasion	 (d'Auriac	et	 al.,	2017;	Faust	et	 al.,	
2017;	Lallias	et	al.,	2015;	Meistertzheim,	Arnaud‐Haond,	Boudry,	&	
Thébault,	2013;	Rohfritsch	et	al.,	2013)	as	well	as	interrelationships	
between	 wild	 populations	 and	 hatcheries	 (Kochmann,	 Carlsson,	
Crowe,	&	Mariani,	2012;	Lallias	et	al.,	2015;	Moehler,	Wegner,	Reise,	
&	Jacobsen,	2011).	Many	of	 these	studies	uncovered	evidence	for	
two	distinct	genetic	clusters:	one	in	southern	Europe	(subsequently	
referred	to	as	the	“southern	group”)	that	includes	populations	from	
the	Mediterranean,	Spain,	France,	the	Netherlands,	and	the	south‐
western	coast	of	England,	and	one	in	northern	Europe	(subsequently	
referred	 to	 as	 the	 “northern	 group”)	 that	 consists	 of	 the	 remain‐
ing	 British,	 German,	 and	 Scandinavian	 populations	 (Lallias	 et	 al.,	
2015;	Meistertzheim	et	al.,	2013;	Moehler	et	al.,	2011;	Rohfritsch	
et	 al.,	 2013).	 Furthermore,	 no	 genetic	 differences	were	 found	be‐
tween	the	southern	group	and	the	source	populations	of	Japan	and	
British	 Columbia,	 suggesting	 that	 the	 original	 mass	 introduction	
may	not	have	resulted	in	a	founder	effect	(Rohfritsch	et	al.,	2013).	
Additionally,	 the	northern	group	was	 found	 to	have	 lower	genetic	
diversity,	 suggesting	 that	 it	 probably	 arose	 locally	 in	 Europe	 and	
more	specifically	in	the	UK	as	a	consequence	of	repeated	small	in‐
troduction	events	that	may	have	acted	as	bottlenecks	due	to	hatch‐
ery	propagation	followed	by	genetic	drift	(Faust	et	al.,	2017;	Lallias	
et	al.,	2015).
Although	previous	studies	have	provided	important	insights	into	
the	population	structure	of	Pacific	oysters	in	Europe,	many	focused	
on	 local	scales	and,	even	though	Rohfritsch	et	al.	 (2013),	Lallias	et	
al.	 (2015),	 and	 Faust	 et	 al.	 (2017)	 sampled	 extensively	 along	 the	
western	Atlantic	seaboard,	there	is	still	a	need	for	more	comprehen‐
sive	 studies	 encompassing	 the	 full	 latitudinal	 range	of	 the	 species	
in	 Europe	 and	 including	 hatcheries	 from	 both	 Britain	 and	 France.	
Furthermore,	classical	approaches	such	as	mitochondrial	sequencing	
and	microsatellite	genotyping	have	limited	power	to	detect	popula‐
tion	structure,	especially	over	fine	geographic	scales	where	genetic	
differences	may	be	too	subtle	to	be	captured	with	a	handful	of	mark‐
ers	 (Vendrami	et	al.,	2017).	By	contrast,	new	genomic	approaches	
capable	of	genotyping	tens	of	thousands	of	single	nucleotide	poly‐
morphisms	 (SNPs)	have	been	proven	 to	have	 far	greater	power	 to	
resolve	 genetic	 differences	 among	 populations	 (Morin,	 Luikart,	 &	
Wayne,	2004;	Rašić,	Filipović,	Weeks,	&	Hoffmann,	2014)	and	there‐
fore	allow	more	in‐depth	studies	of	population	genetic	structure.
One	of	the	most	commonly	used	approaches	for	generating	large	
SNP	datasets	 for	nonmodel	organisms	 is	 to	use	genotyping	by	se‐
quencing	 methods	 such	 as	 restriction	 site‐associated	 DNA	 (RAD)	
sequencing	(Baird	et	al.,	2008),	which	allows	concurrent	SNP	identi‐
fication	and	genotyping	via	high‐throughput	sequencing	of	flanking	
regions	of	restriction	enzyme	digestion	sites	dispersed	throughout	
the	genome.	These	methods	have	democratized	the	study	of	popu‐
lation	genomics	but	are	not	without	their	disadvantages	(da	Fonseca	
et	al.,	2016)	such	as	the	need	for	extensive	bioinformatic	processing,	
high	rates	of	missing	data,	and	the	issue	of	uncertainty	in	genotype	
calling,	which	can	affect	downstream	analyses	(Shafer	et	al.,	2017).	
A	convenient	alternative	where	available	 is	therefore	to	use	a	me‐
dium‐	or	high‐density	SNP	array,	 in	which	the	probe	sequences	of	
many	tens	or	hundreds	of	 thousands	of	SNPs	are	 “printed”	onto	a	
slide	against	which	the	genomic	DNA	is	hybridized.	SNP	arrays	typ‐
ically	 generate	 very	 high‐quality	 data	 with	 relatively	 few	 missing	
genotypes,	but	they	also	have	some	downsides.	Arguably,	the	most	
important	of	these	is	ascertainment	bias,	which	occurs	when	not	all	
of	the	genetic	diversity	present	in	a	population	can	be	captured	by	
the	array	due	to	the	use	of	a	limited	pool	of	individuals	in	the	original	
SNP	discovery	phase	(Lachance	&	Tishkoff,	2013).
Another	drawback	of	small	panels	of	nuclear	markers	like	micro‐
satellites	is	that	their	sampling	variance	is	usually	too	large	to	accu‐
rately	 quantify	 variation	 in	 inbreeding	 (Balloux,	Amos,	&	Coulson,	
2004).	 This	may	 be	 relevant	 to	 aquaculture	 because	moderate	 to	
high	levels	of	 inbreeding	have	been	shown	to	have	detrimental	ef‐
fects	on	a	variety	of	commercially	important	traits,	such	as	harvest	
body	size	and	larval	growth,	in	several	species,	including	turbot	(Lyu,	
     |  521VENDRAMI Et Al.
Wu,	Hu,	&	Wang,	2018),	Pacific	white	shrimp	(Moss,	Arce,	Otoshi,	
Doyle,	&	Moss,	2007),	 coho	salmon	 (Gallardo,	Garcıa,	 Lhorente,	&	
Neira,	2004),	and	Pacific	abalone	(Deng,	Liu,	Zhang,	&	Guo,	2005)	as	
well	as	in	flat	oysters	(Lallias,	Boudry,	Lapegue,	King,	&	Beaumont,	
2010)	and	Pacific	oysters	(Evans,	Matson,	Brake,	&	Langdon,	2004;	
Launey	&	Hedgecock,	2001;	Plough	&	Hedgecock,	2011).	Hence,	in‐
breeding	depression	could	conceivably	be	a	problem	if	aquaculture	
practices,	such	as	the	use	of	restricted	numbers	of	parents	as	brood‐
stock	 and/or	 the	 crossing	 of	 related	 individuals,	 lead	 to	 increased	
inbreeding	in	cultured	populations	(Norris,	Bradley,	&	Cunningham,	
1999;	Taris,	Batista,	&	Boudry,	2007).
Given	the	 limited	power	of	microsatellites	to	quantify	 inbreed‐
ing,	the	method	of	choice	until	recently	has	been	to	derive	individual	
inbreeding	coefficients	(f)	from	deep	pedigrees	(Pemberton,	2008).	
However,	pedigrees	can	be	costly	and	time‐consuming	to	construct	
and	may	also	be	unworkable	for	many	aquaculture	species	due	to	their	
high	 fecundity	 and	 broadcast	 spawning	 life‐histories.	 Fortunately,	
recent	simulation	(Kardos,	Luikart,	&	Allendorf,	2015;	Wang,	2016)	
and	empirical	(Hoffman	et	al.,	2014;	Huisman,	Kruuk,	Ellis,	Clutton‐
Brock,	 &	 Pemberton,	 2016)	 studies	 suggest	 that	 inbreeding	 can	
now	be	directly	and	accurately	quantified	from	genomic	data,	with	
around	 ten	 thousand	 or	 more	 SNPs	 being	 preferable	 under	 most	
circumstances	 even	 to	 a	 high‐quality	 pedigree.	 Consequently,	 the	
increasing	availability	of	SNP	arrays	for	non‐model	species	provides	
an	exciting	new	opportunity	to	elucidate	how	different	aquaculture	
practices	influence	inbreeding,	as	well	as	to	identify	suitable	sources	
of	individuals	for	use	as	broodstock	to	establish	effective	manage‐
ment	and	breeding	protocols.
Recently,	 Gutierrez	 et	 al.	 (2017)	 developed	 a	 medium‐density	
combined	species	SNP	array	for	Pacific	and	flat	oysters	(Ostrea edu‐
lis).	Whole	genome	sequencing	of	pooled	genomic	samples	from	eight	
European C. gigas	populations	led	to	the	discovery	of	1.2	million	pu‐
tative	SNPs,	of	which	40,625	were	printed	on	the	array	and	27,697	
were	validated	as	being	polymorphic	and	of	high	quality.	This	array	
represents	an	 important	 resource	 for	 selective	breeding	programs	
as	well	as	more	generally	for	population	genetic	studies	of	oysters.	
We	 therefore	 used	 it	 to	 investigate	 population	 genetic	 structure	
and inbreeding in C. gigas	sampled	from	wild	European	populations	
and	hatcheries.	Specifically,	we	genotyped	192	individuals	from	13	
populations	spanning	a	European	 latitudinal	cline	 from	Portugal	 in	
the	 south	 to	Norway	 in	 the	north.	We	 then	 incorporated	 existing	
data	 from	Gutierrez	 et	 al.	 (2017)	 to	 generate	 a	 combined	 dataset	
of	273	individuals	sampled	from	23	populations,	of	which	just	over	
half	were	wild.	Our	 results	may	be	useful	 for	designing	exchanges	
TABLE  1 Table	of	sampling	locations	including	coordinates,	origin	classified	as	either	wild	or	farmed,	and	the	number	of	samples	that	
were	retained	for	analysis	after	quality	control.
Population ID Location Latitude Longitude Origin
Sample size 
passing QC
POR Faro,	Portugal 37.002 −7.583 Wild 9
ITA Ravenna,	Italy 44.402 12.122 Wild 10
SPA Santoña,	Spain 43.426 −3.543 Wild 8
FRA Brest,	France 48.215 −4.462 Wild 10
ENG Plymouth,	UK 50.373 −3.441 Wild 9
NE1 Oosterschelde,	Netherlands 51.608 3.91 Wild 12
NE2 Texel,	Netherlands 53.001 4.474 Wild 10
IFR Ifremer,	France 48.351 −4.551 Hatchery 12
FH1 Hatchery	1,	France na na Hatchery 10
FH2 Hatchery	2,	France na na Hatchery 10
FH3 Hatchery	3,	France na na Hatchery 10
FH4 Hatchery	4,	France na na Hatchery 10
GUE Guernsey,	UK 49.497 −2.502 Hatchery 10
SCO Oban,	UK 55.534 −5.244 Hatchery 8
SES Sea	Salter,	UK 51.378 1.212 Hatchery 9
MAL Maldon,	UK 51.724 0.71 Hatchery 9
WAL Bangor,	UK 53.098 −4.15 Hatchery 6
GER Sylt,	Germany 55.152 8.253 Wild 12
DEN Limfjorden,	Denmark 56.833 8.906 Wild 11
SWE Kristineberg,	Sweden 58.134 58.134 Wild 12
NOR Arendal,	Norway 58.428 8.793 Wild 11
JAP Matsushima,	Japan 38.367 141.066 Wild 12
CAN Vancouver,	Canada 50.164 −124.432 Wild 12
All ‐ ‐ ‐ ‐ 232
522  |     VENDRAMI Et Al.
among	hatcheries,	identifying	potential	sources	of	broodstock,	and	
for	the	elaboration	of	other	effective	management	strategies	aimed	
at	minimizing	inbreeding	within	hatchery	propagated	Pacific	oysters.	
Consequently,	we	believe	this	study	provides	important	information	
for	breeding	programs	as	well	as	a	baseline	for	future	studies.
2  | MATERIAL S AND METHODS
2.1 | Sample collection and DNA extraction
Pacific	oyster	samples	were	collected	between	November	2014	and	
March	2016	from	12	different	sites	along	the	Atlantic	seaboard	of	
mainland	Europe	as	well	as	from	one	location	in	the	Mediterranean	
(Table	 1	 and	 Figure	 1).	 Samples	 from	 Scotland	 (SCO)	 and	 Wales	
(WAL)	 were	 from	 hatcheries,	 while	 the	 remaining	 11	 populations	
were	wild.	Specimens	from	Portugal	(POR)	originated	from	an	area	
where	 hybridization	 between	C. angulata and C. gigas	 is	 known	 to	
take	place	(Batista	et	al.,	2017;	Huvet	et	al.,	2004)	and	could	there‐
fore	 represent	C. gigas	 samples	 introgressed	with	C. angulata.	 For	
comparison,	we	also	included	samples	from	the	Miyagi	Prefecture	in	
Japan	(JAP)	and	from	British	Columbia	in	Canada	(CAN).
2.2 | DNA extraction and SNP genotyping
Adductor	muscle	tissue	was	taken	from	each	adult	oyster	and	stored	
in	95%	ethanol	at	−20°C.	Whole	genomic	DNA	was	then	extracted	
following	 an	 adapted	 phenol–chloroform	 protocol	 (Sambrook,	
Fritsch,	 &	 Maniatis,	 1989)	 and	 sent	 to	 Edinburgh	 Genomics	 for	
genotyping	 at	 40,625	 SNPs	 on	 the	 custom	Affymetrix	 SNP	Array	
(Gutierrez	 et	 al.,	 2017).	 Out	 of	 a	 total	 of	 204	DNA	 extracts,	 192	
(94%)	passed	quality	checks	and	were	therefore	selected	for	geno‐
typing	using	the	protocol	described	by	Gutierrez	et	al.	(2017).
2.3 | Incorporation of existing data
We	also	incorporated	data	into	our	study	from	81	oysters	that	were	
previously	 genotyped	 on	 the	 same	 array	 (Gutierrez	 et	 al.,	 2017).	
These	samples	were	initially	included	in	the	discovery	panel	used	to	
develop	and	validate	the	SNP	array	and	originated	from	eight	hatch‐
eries,	three	from	the	UK	(MAL,	SES,	and	GUE)	and	five	from	France	
(FH1–4	and	IFR,	Table	1	and	Figure	1).	In	general,	most	companies	
use	 breeding	 practices	 based	 on	 rotational	 crosses	 among	 yearly	
cohorts	 (P.	Harray,	personal	 communication,	November	21,	2018).	
F IGURE  1 Map	showing	Pacific	oyster	sampling	locations.	The	two	source	populations	from	the	Miyagi	Prefecture	in	Japan	and	
British	Columbia	in	Canada	are	indicated	with	yellow	circles.	Wild	and	hatchery	populations	within	the	southern	group	are	indicated	in	
red	and	orange,	respectively,	while	wild	populations	and	hatcheries	within	the	northern	group	are	represented	by	blue	and	purple	circles,	
respectively.	Finally,	the	population	from	Portugal,	where	hybridization	between	C. gigas and C. angulata is	known	to	occur,	is	indicated	by	a	
green circle
     |  523VENDRAMI Et Al.
Consequently,	the	genetic	variability	of	a	given	cohort	may	not	be	
representative	of	the	population	as	a	whole.
After	the	inclusion	of	these	additional	samples,	our	dataset	con‐
sisted	of	(a)	six	wild	populations	from	the	southern	group	(red	circles	
in	Figure	1);	(b)	five	hatcheries	from	the	southern	group	(orange	cir‐
cles	in	Figure	1);	(c)	four	wild	populations	from	the	northern	group	
(blue	circles	in	Figure	1);	(d)	five	hatcheries	from	the	northern	group	
(purple	circles	in	Figure	1);	(e)	the	source	populations	of	Japan	and	
Canada	(yellow	circles	in	Figure	1);	and	(f)	a	single	population	from	
Portugal,	 where	 hybridization	 between	 C. angulata and C. gigas	 is	
known	to	occur	(green	circle	in	Figure	1).
2.4 | SNP calling
We	 imported	 raw	output	data	 for	 all	 273	 samples	 into	 the	Axiom	
Analysis	Suite	(version	3.1,	Affymetrix)	for	quality	control	and	geno‐
type	calling.	All	 thresholds	 for	quality	assessment	were	 set	 to	 the	
values	 recommended	 in	 the	 Affymetrix	 best	 practice	 workflow	
(Supporting	information	Table	S1)	and	allowed	for	the	categorization	
of	each	SNP	 into	one	of	six	possible	classes:	 (a)	 “polymorphic	high	
resolution”	where	the	SNP	passed	all	quality	controls;	(b)	“no	minor	
homozygote”	where	the	SNP	passed	quality	checks	but	no	homozy‐
gotes	for	the	minor	allele	were	found;	(c)	“off‐target	variant”	where,	
in	 addition	 to	 the	 heterozygote	 and	 the	 two	 alternative	 homozy‐
gotes,	a	fourth	genotype	cluster	was	also	observed;	(d)	“monomor‐
phic	high	resolution”	where	the	SNP	passed	quality	checks	but	was	
uninformative;	 (e)	 “call	 rate	 below	 threshold”	where	 the	 genotype	
call	 rate	was	below	the	specified	threshold	of	97%;	and	 (f)	 “other”	
where	the	SNP	failed	to	pass	any	other	quality	threshold.	Following	
Affymetrix	 recommendations,	 SNPs	 from	 the	 first	 two	 categories	
were	retained	for	 further	analysis,	 in	addition	to	a	subset	of	SNPs	
from	the	third	category	that	were	selected	after	applying	the	“off‐
target	caller”	tool	that	allows	for	off‐target	variant	recalibration.	The	
resulting	dataset	was	 then	 filtered	 to	 retain	only	SNPs	genotyped	
in	 at	 least	90%	of	 individuals	 and	only	 samples	with	 less	 than	5%	
missing	data.	Finally,	the	software	PLINK	(version	1.9,	Purcell	et	al.,	
2007)	was	used	to	prune	out	linked	loci	using	an	r2 threshold	of	0.5.	
The	final	dataset	therefore	comprised	232	individuals	genotyped	at	
21,499	polymorphic,	unlinked	SNPs.
2.5 | 2.5 RAD	sequencing
To	provide	a	comparison	with	the	SNP	array	data,	we	also	RAD	se‐
quenced	a	representative	subset	of	40	individuals	from	eight	popu‐
lations	 (Supporting	 information	Table	S2).	Specifically,	we	 included	
the	source	population	of	Japan	 (JAP),	 the	potential	hybrid	popula‐
tion	 from	Portugal	 (POR),	 two	geographically	 distant	wild	popula‐
tions	from	the	southern	group	(SPA	and	NE2),	two	wild	populations	
from	the	northern	group	(DEN	and	NOR),	the	Mediterranean	popu‐
lation	from	Italy	(ITA),	and	a	hatchery	from	Scotland	(SCO).	Only	one	
hatchery	could	be	 included	because	DNA	from	the	other	hatcher‐
ies	was	either	not	of	high	enough	quality	to	pass	thresholds	for	 li‐
brary	construction,	or	it	was	not	available	due	to	the	sample	having	
been	genotyped	as	part	of	a	previous	study	(Gutierrez	et	al.,	2017).	
Whole	genomic	DNA	was	shipped	to	the	Beijing	Genomics	Institute	
(BGI)	 for	 library	 preparation	 and	 sequencing.	 The	 libraries	 were	
constructed	using	the	restriction	enzyme	PstI	and	sequenced	on	an	
Illumina	X	Ten	platform	to	generate	a	 total	of	869,113,776	100	bp	
paired‐end	 sequence	 reads.	 Already	 demultiplexed	 sequence	 data	
were	 received	 from	BGI	 and	 further	 sequence	quality	 assessment	
was	 performed	 using	 the	 software	 FastQC	 (http://www.bioinfor‐
matics.babraham.ac.uk/projects/fastqc/).	We	then	conducted	a	de	
novo	assembly	of	the	data	and	called	genotypes	using	the	Stacks	2.1	
pipeline	 (Catchen,	Hohenlohe,	Bassham,	Amores,	&	Cresko,	2013).	
Values	for	the	three	main	parameters	–m,	–M,	and	–n	were	chosen	
following	 the	 optimization	 procedure	 described	 by	 Rochette	 and	
Catchen	(2017).	Briefly,	–m	was	set	to	three,	and	increasing	values	
for	–M and –n	were	tested.	The	combination	of	these	parameters	for	
which	the	number	of	polymorphic	loci	present	in	at	least	80%	of	the	
individuals	reached	a	plateau	was	defined	as	optimal.	Two	different	
strategies	were	employed:	–n	was	either	set	as	equal	to	–M or one 
unit	greater,	to	account	for	the	potential	presence	of	fixed	C. angu‐
lata	polymorphisms	(Paris,	Stevens,	&	Catchen,	2017).	The	combina‐
tion	yielding	the	highest	plateau	(m	=	3,	M	=	5,	and	n = 6; Supporting 
information	Figure	S1)	was	selected	for	analyzing	the	entire	dataset,	
from	which	PCR	duplicates	were	then	removed.	The	raw	genotypes	
were	subsequently	quality	filtered	to	retain	only	biallelic	SNPs	with	
both	genotype	quality	and	depth	of	coverage	greater	than	10	using	
VCFTools	 (Danecek	 et	 al.,	 2011).	 Subsequently,	 all	 SNPs	 and	 indi‐
viduals	with	 genotyping	 rates	below	10%	were	 removed	 and	only	
variants	with	minor	allele	frequency	(MAF)	greater	than	0.05	were	
retained.	 Finally,	 the	 software	 PLINK	was	 employed	 to	 prune	 out	
linked	loci	using	an	r2 threshold	of	0.2.
2.6 | Analysis of population genetic structure
Three	 complimentary	 approaches	 were	 used	 to	 characterize	 the	
strength	and	pattern	of	population	genetic	structure.	First,	we	cal‐
culated	pairwise	Fst	values	among	populations	and	determined	their	
statistical	significance	based	on	1,000	permutations	of	the	dataset	
using	Arlequin	version	3.5.2.2	(Excoffier	&	Lischer,	2010).	We	then	
performed	 an	 analysis	 of	molecular	 variance	 (AMOVA)	 employing	
the	R	package	“poppr”	version	2.8.0	(Kamvar,	Brooks,	&	Grünwald,	
2015;	Kamvar,	Tabima,	&	Grünwald,	2014)	 to	evaluate	the	propor‐
tion	of	genomic	variation	explained	by	different	hierarchical	 levels	
of	population	structure.	Specifically,	we	started	by	dividing	our	data	
into	 four	 regions	 corresponding	 to	 the	 northern	 group,	 southern	
group,	Portugal,	and	source	populations.	Each	was	then	subdivided	
into	wild	and	hatchery	samples,	which	were	further	split	based	on	
the	population	of	origin.	Variation	among	samples	within	each	popu‐
lation	 and	within	 individuals	was	 also	 evaluated.	 A	 randomization	
test	with	1,000	repetitions	was	used	to	determine	statistical	signifi‐
cance.	Subsequently,	we	used	Mantel	 tests	to	evaluate	the	signifi‐
cance	of	isolation‐by‐distance	patterns	for	the	full	dataset	as	well	as	
for	 the	wild	and	hatchery	oysters	 separately	 for	 the	northern	and	
southern	 groups.	 For	 this	 analysis,	 geographic	 distances	 between	
524  |     VENDRAMI Et Al.
populations	 were	 calculated	 as	 shortest	 coastline	 distances	 using	
“FreeMapTools	 (https://www.freemaptools.com/measure‐distance.
htm).”	 In	the	case	of	 locations	not	connected	by	land,	the	shortest	
sailing	distance	between	coasts	was	used.	Second,	we	used	 the	R	
package	Adegenet	version	2.1.1	(Jombart,	2008;	Jombart	&	Ahmed,	
2011)	to	conduct	a	principal	component	analysis	(PCA)	of	the	SNP	
dataset.	Third,	we	utilized	the	software	package	fineRADstructure	
(Malinsky,	 Trucchi,	 Lawson,	 &	 Falush,	 2018)	 to	 infer	 population	
structure	 using	 a	 model‐based	 Bayesian	 clustering	 approach	 that	
groups	together	individuals	with	high	levels	of	shared	coancestry.	A	
“coancestry	matrix,”	defined	as	a	summary	of	nearest	neighbor	hap‐
lotype	 relationships,	 is	 required	 as	 input	 and	was	generated	using	
the	“RADpainter”	module	of	fineRADstructure.	We	used	the	default	
parameters	of	100,000	Markov	 chain	Monte	Carlo	 (MCMC)	 itera‐
tions	with	 a	burn‐in	of	100,000	 iterations	 and	 sampling	occurring	
every	1,000	iterations.	A	tree	was	then	constructed	with	10,000	hill‐
climbing	iterations,	and	the	results	were	visualized	using	the	scripts	
FINERADSTRUCTUREPLOT.R	 and	 FINESTRUCTURELIBRARY.R,	
which	 are	 available	 via	 http://cichlid.gurdon.cam.ac.uk/fineRAD‐
structure.html.
2.7 | Genomic inbreeding coefficients
We	calculated	̂FI,	a	genomic	inbreeding	estimator	based	on	the	vari‐
ance	of	additive	genotype	values	 (Yang,	Lee,	Goddard,	&	Visscher,	
2011),	for	each	individual	in	our	dataset	based	on	the	SNP	data.	To	
test	for	an	association	between	levels	of	relatedness	and	inbreeding,	
we	calculated	mean	pairwise	relatedness	among	 individuals	within	
populations	from	the	SNP	data	using	GCTA	(Yang	et	al.,	2011)	and	
correlated	this	with	mean	̂FI	values.	Genomic	inbreeding	coefficients	
were	compared	between	populations	and	groups	using	a	Kruskal–
Wallis	 test	 followed	 by	 post hoc	 pairwise	 Mann–Whitney	 tests,	
whose	p‐values	were	adjusted	according	to	Benjamini	and	Hochberg	
(1995),	to	formally	test	for	significant	pairwise	comparisons.	As	vari‐
ation	 in	 inbreeding	 causes	 heterozygosity	 to	 be	 correlated	 across	
loci,	we	also	estimated	the	extent	of	identity	disequilibrium	(ID,	Weir	
&	 Cockerham,	 1973)	 by	 calculating	 the	 two‐locus	 heterozygosity	
disequilibrium,	 g2	 (David,	 Pujol,	 Viard,	 Castella,	 &	 Goudet,	 2007)	
within	the	R	package	inbreedR	(Stoffel	et	al,	2016).	The	same	pack‐
age	was	also	used	to	calculate	the	95%	confidence	interval	of	g2 by 
bootstrapping	the	data	1,000	times	over	individuals,	as	described	by	
Stoffel	et	al.	(2016).
3  | RESULTS
To	 provide	 detailed	 insights	 into	 the	 pan‐European	 population	
structure	 of	C. gigas	 and	 facilitate	 comparisons	 between	wild	 and	
hatchery	oysters,	we	analyzed	medium‐density	SNP	array	data	for	
a	total	of	273	individuals	sampled	from	23	locations.	Data	from	192	
individuals	were	 newly	 generated,	 while	 the	 remaining	 data	were	
incorporated	from	Gutierrez	et	al.	(2017).	Sampling	sites	were	puta‐
tively	assigned	to	either	the	northern	or	the	southern	group	on	the	
basis	of	previous	genetic	studies	(Lallias	et	al,	2015;	Rohfritsch	et	al,	
2013).	Application	of	the	filtering	criteria	described	in	the	Materials	
and	methods	 resulted	 in	 the	exclusion	of	an	17,411	SNPs	 that	did	
not	meet	Affymetrix	 recommendations	and	of	an	additional	1,715	
SNPs	due	to	low	genotyping	rates	or	linkage	disequilibrium.	On	aver‐
age,	10	individuals	were	genotyped	for	each	location,	and	the	final	
dataset	consisted	of	232	samples	 (see	Table	1	for	a	breakdown	by	
population)	genotyped	at	21,499	SNPs.
3.1 | Population genetic structure
To	 investigate	 broad‐scale	 patterns	 of	 genetic	 differentiation,	 we	
used	AMOVA	to	quantify	the	proportion	of	genomic	variation	attrib‐
utable	to	each	of	five	hierarchical	levels	of	population	substructure.	
As	expected,	over	95%	of	the	total	variation	was	partitioned	within	
individuals.	 The	 remaining	variance	was	mainly	partitioned	among	
the	 northern	 and	 southern	 groups,	 the	 source	 populations,	 and	
Source of variation df Sum of squares % variation Φ p‐value
Among	regions 3 48,727.66 1.7 0.017 0.024
Between	origins	
within	regions
2 17,734.93 0.39 0.004 0.005
Among	sampling	
locations	within	
origins
17 1,16,998.80 2 0.02 0.001
Among	samples	
within	sampling	
locations
209 10,14,805.34 0.22 0.002 0.406
Within	samples 232 11,21,225.26 95.69 0.043 0.001
Total 463 23,19,491.99 100 ‐ ‐
Notes.	Five	different	hierarchical	levels	were	evaluated.	First,	the	dataset	was	divided	into	four	“re‐
gions”	 corresponding	 to	 the	 southern	 group,	 the	 northern	 group,	 the	 source	 populations,	 and	
Portugal.	Next,	each	region	was	divided	into	“origins”	depending	whether	the	samples	were	from	
wild	populations	or	hatcheries.	Finally,	the	remaining	variance	was	partitioned	among	sampling	loca‐
tions,	individuals	within	sampling	locations,	and	within	individuals.
TABLE  2 Results	of	the	hierarchical	
analysis	of	molecular	variance	(AMOVA)
     |  525VENDRAMI Et Al.
Portugal	 (Φ	=	0.017,	p	=	0.024,	Table	2),	 between	wild	populations	
and	hatcheries	(Φ	=	0.004,	p	=	0.005,	Table	2)	and	among	sampling	
locations	 (Φ	=	0.02,	p	=	0.001,	 Table	 2).	 Furthermore,	 the	majority	
of	pairwise	Fst	values	between	populations	were	highly	significant,	
even	 after	 correction	 for	 multiple	 tests	 (Supporting	 information	
Table	 S3),	 although	 a	 significant	 isolation‐by‐distance	 pattern	was	
only	 detected	 among	wild	 populations	 belonging	 to	 the	 southern	
group	(Mantel's	r = 0.971; p	=	0.022).
To	evaluate	population	structure	at	the	individual	level,	we	per‐
formed	a	principal	 component	 analysis	 (PCA).	Consistent	with	 the	
AMOVA,	 a	 number	 of	 clear	 differences	 were	 apparent.	 First,	 the	
northern	and	southern	groups	clearly	separated	apart	from	one	an‐
other,	as	did	oysters	from	Portugal,	although	no	genetic	differences	
were	 apparent	 between	 the	 southern	 group	 and	 the	 two	 source	
populations	 (Figure	2).	Furthermore,	within	both	the	northern	and	
southern	 groups,	 hatcheries	 showed	 consistently	 greater	 scatter	
than	wild	 populations,	 indicating	 that	 they	may	 have	 experienced	
stronger	genetic	drift.
We	next	used	a	model‐based	Bayesian	clustering	approach	imple‐
mented	in	fineRADstructure	to	infer	population	structure	via	shared	
ancestry.	The	resulting	cladogram	and	coancestry	matrix	shown	in	
Figure	3	confirmed	the	results	of	the	AMOVA	and	PCA	while	also	
uncovering	 the	 presence	 of	 more	 subtle	 structuring.	 Specifically,	
two	major	clades	were	identified.	The	first	of	these,	shown	on	the	
left	of	the	cladogram	and	represented	by	a	cluster	of	individuals	in	
the	bottom	left	of	the	coancestry	matrix,	comprised	individuals	from	
the	northern	group.	This	was	 further	 subdivided	 into	 two	distinct	
clusters,	the	first	comprising	mainly	 individuals	from	the	Seasalter,	
Maldon,	and	Bangor	hatcheries	in	the	UK	and	the	second	compris‐
ing	 mostly	 wild	 individuals	 from	 Germany	 and	 Scandinavia.	 The	
remaining	 individuals	were	 grouped	 together	 into	 a	 second	major	
clade	shown	on	the	right	of	the	cladogram,	which	in	turn	was	sub‐
divided	into	three	main	clusters	comprising	the	southern	group	and	
source	populations,	oysters	from	the	Guernsey,	Seasalter,	and	two	
of	the	French	hatcheries,	and	Portugal.	The	fact	that	samples	from	
Seasalter	were	distributed	across	two	different	clusters	is	consistent	
with	the	fact	that	oysters	have	been	exchanged	between	Seasalter	
and	 Guernsey	 (M.	 Dravers,	 personal	 communication,	 November	
21,	 2018).	 Most	 of	 the	 individuals	 from	 the	 remaining	 French	
hatcheries	 could	 also	 be	 clearly	 distinguished	within	 the	 southern	
group.	Samples	from	Scotland	were	in	a	different	part	of	the	clado‐
gram,	but	always	clustered	together	with	other	samples	from	hatch‐
eries.	Furthermore,	 levels	of	 shared	coancestry	varied	appreciably	
across	the	dataset,	with	oysters	from	the	Guernsey	hatchery	having	
the	highest	levels,	the	remaining	hatchery	samples	as	well	as	oysters	
from	Portugal	having	intermediate	levels,	and	wild	individuals	having	
the	lowest	levels.
3.2 | Variation in inbreeding
To	 explore	 patterns	 of	 inbreeding	 in	 wild	 populations	 and	 hatch‐
eries,	we	 calculated	 genomic	 inbreeding	 coefficients	 for	 all	 of	 the	
individuals	 in	 our	 dataset.	 Mean	 genomic	 inbreeding	 coefficients	
were	 strongly	 positively	 correlated	with	 average	 pairwise	 related‐
ness	values	within	populations	(linear	regression,	b = 1.39,	r2	=	0.79,	
p	<	0.001,	Figure	4),	which	in	turn	were	tightly	correlated	with	mean	
shared	 coancestry	 values	 extracted	 from	 the	 fineRADstructure	
analysis	(linear	regression,	b = 48.99,	r2	=	0.99,	p	<	0.001).	This	indi‐
cates	that,	as	expected,	genomic	inbreeding	coefficients	tend	to	be	
higher	in	populations	with	elevated	levels	of	relatedness	and	shared	
coancestry.
Pooling	individuals	according	to	the	six	main	groups	described	in	
the	first	paragraph	of	the	results	revealed	highly	significant	differ‐
ences	(Figure	5a,	Kruskal–Wallis	test:	χ2	=	105.17,	df	=	4,	p	<	0.001).	
First	 of	 all,	 regardless	 of	 whether	 wild	 populations	 or	 hatcheries	
were	 considered,	 a	 significant	 tendency	 was	 found	 for	 genomic	
inbreeding	 coefficients	 to	 be	 higher	 in	 the	 northern	 than	 in	 the	
southern	 group	 (post hoc	 pairwise	Mann–Whitney	 tests:	wild:	 ad‐
justed	 p < 0.001,	 hatchery:	 adjusted	 p = 0.005).	 This	 is	 consistent	
with	previous	studies	showing	that	genetic	diversity	is	lower	in	the	
northern	group	(see	Discussion).	Second,	genomic	inbreeding	coeffi‐
cients	were	significantly	higher	in	hatcheries	versus	wild	populations	
(post hoc	 pairwise	Mann–Whitney	 tests:	 northern	 group:	 adjusted	
p	<	0.001,	southern	group:	adjusted	p	<	0.001).	In	line	with	this,	iden‐
tity	disequilibrium	was	higher	in	individuals	sampled	from	hatchery	
(g2	=	0.0065,	bootstrap	95%	confidence	interval	=	0.0044–0.0089)	
than	from	wild	populations	(g2	=	0.0022,	bootstrap	95%	confidence	
interval	=	0.0014–0.0029),	 indicating	 that	 hatchery‐reared	 oysters	
F IGURE  2 Scatterplot	showing	
individual variation in principal component 
(PC)	scores	derived	from	a	principal	
component	analysis	(PCA)	of	the	genomic	
data.	Panel	(a)	shows	PC1	plotted	against	
PC2,	and	panel	(b)	shows	PC1	plotted	
against	PC3.	The	amounts	of	variation	
explained	by	each	PC	are	given	as	
percentages.	Samples	are	color	coded	as	
described	in	the	legend	of	Figure	1
526  |     VENDRAMI Et Al.
have	greater	overall	variance	in	inbreeding	(Figure	5b).	This	reflects	
a	general	tendency	for	the	variance	in	inbreeding	to	be	higher	both	
within	and	among	hatcheries	relative	to	wild	populations	(Figure	5c).	
The	highest	genomic	inbreeding	coefficients	were	found	in	oysters	
from	Guernsey,	while	 in	France	and	England,	hatcheries	with	both	
intermediate	(e.g.,	Fh1,	FH3,	SES,	and	MAL)	and	relatively	low	(e.g.,	
IFR,	FH2,	FH4,	WAL,	and	SCO)	levels	of	inbreeding	were	present.
3.3 | RAD sequencing
Although	we	 did	 not	 expect	 our	 results	 to	 be	 strongly	 affected	
by	 ascertainment	 bias	 because	 oysters	 from	 both	 the	 northern	
and	 southern	 groups	 were	 used	 in	 the	 discovery	 panel	 for	 the	
SNP	array	(Gutierrez	et	al.,	2017),	we	nevertheless	generated	for	
comparison	a	parallel	genetic	dataset	comprising	RAD	sequencing	
data	for	40	individuals	from	eight	populations	(see	Materials	and	
methods	for	details).	This	resulted	in	a	total	of	869,113,776	high‐
quality	paired‐end	reads	that	were	assembled	 into	697,354	RAD	
loci.	 From	 these,	 we	 called	 a	 total	 of	 7,322,935	 SNPs,	 of	which	
115,087	 were	 retained	 for	 further	 analyses	 after	 filtering.	 We	
found	 a	 virtually	 identical	 pattern	 of	 population	 structure,	 with	
the	PCA	clearly	discriminating	the	northern	group	from	the	south‐
ern	 group,	 the	 wild	 populations	 from	 the	 hatcheries,	 and	 oys‐
ters	from	Portugal	 (Figure	6a	and	b).	Similarly,	 fineRADstructure	
identified	 four	 main	 groups	 comprising	 the	 southern	 group	 and	
source	population,	the	northern	group,	Portugal,	and	the	Scottish	
hatchery	 (Figure	 6c).	 Genomic	 inbreeding	 coefficients	 based	 on	
the	RAD	data	showed	a	similar	pattern	 to	 those	calculated	 from	
F IGURE  3 Output	of	the	fineRADstructure	analysis	of	the	genomic	data.	In	the	cladogram,	branches	are	color	coded	according	to	
sampling	origin	as	shown	in	Figure	1	and	nodes	with	greater	than	95%	bootstrap	support	are	marked	by	black	points.	The	heatmap	indicates	
pairwise	coancestry	between	individuals,	with	blue	and	purple	representing	the	highest	levels,	red	and	orange	indicating	intermediate	levels,	
and	yellow	representing	the	lowest	levels	of	shared	coancestry
     |  527VENDRAMI Et Al.
the	SNP	array	(Figure	6d),	although	inbreeding	levels	appeared	to	
be	 somewhat	higher	 for	 the	Danish	and	Norwegian	populations.	
Notably,	oysters	from	Portugal	had	the	highest	levels	of	inbreed‐
ing	in	our	RAD	analysis,	which	would	not	be	expected	if	our	sam‐
ple	contained	hybrids.	Nevertheless,	due	to	the	high	pairwise	FST 
values	obtained	in	all	comparisons	involving	oysters	from	Portugal	
(Supporting	information	Table	S3),	we	cannot	exclude	the	possibil‐
ity	that	these	samples	may	actually	be	pure	C. angulata rather than 
pure C. gigas. (see Discussion)
4  | DISCUSSION
We	used	a	medium‐density	SNP	array	 to	 characterize	 the	genetic	
structure	of	C. gigas	populations	across	Europe	as	well	as	to	evalu‐
ate	levels	of	inbreeding	in	wild	and	hatchery	oysters.	Our	compre‐
hensive	sampling	design	coupled	with	high‐resolution	genomic	data	
allowed	us	to	resolve	patterns	of	genetic	differentiation	over	both	
broad	and	fine	geographic	scales.	Specifically,	we	found	clear	sup‐
port	 for	 a	 northern	 and	 southern	European	group,	with	 the	 latter	
being	virtually	identical	to	the	Japanese	and	Canadian	source	popula‐
tions,	consistently	with	previous	studies	(Huvet,	Lapegue,	Magoulas,	
&	Boudry,	2000;	Moehler	et	al.,	2011;	Rohfritsch	et	al.,	2013).	We	
furthermore	resolved	substantial	genetic	differences	between	wild	
populations	and	hatcheries	and	compared	genomic	inbreeding	coef‐
ficients	 to	 show	 that	 some	of	 the	 sampled	hatchery	 cohorts	have	
higher	levels	of	inbreeding	than	wild	populations.	Given	that	C. gigas 
carries	a	high	genetic	load	that	has	been	proposed	to	be	responsible	
for	substantial	early	mortality	(Launey	&	Hedgecock,	2001;	Plough	&	
Hedgecock,	2011;	Taris	et	al.,	2007)	as	well	as	variation	in	commer‐
cially	important	adult	traits	(Evans	et	al.,	2004),	we	believe	that	our	
findings	could	have	important	implications	for	aquaculture.
4.1 | Population genetic structure
Several	studies	have	investigated	the	population	genetic	structure	of	
Pacific	oysters	in	Europe	and	interpreted	their	findings	in	the	light	of	
the	known	and	rather	complex	history	of	multiple	introductions	and	
invasions.	Our	research	compliments	and	builds	upon	these	studies	
in	a	number	of	ways.	First,	we	were	able	to	confirm	previous	find‐
ings	based	on	mitochondrial	DNA	as	well	as	small	panels	of	nuclear	
markers	(Huvet	et	al.,	2000;	Lallias	et	al.,	2015;	Meistertzheim	et	al.,	
2013;	Moehler	et	al.,	2011;	Rohfritsch	et	al.,	2013)	 that	European	
Pacific	oyster	populations	are	broadly	structured	into	northern	and	
southern	groups.	Although	this	is	not	necessarily	surprising,	studies	
based	on	one	or	a	few	markers	can	suffer	from	biases	related	to	sto‐
chastic	processes	(Rokas	&	Carroll,	2005).	Consequently,	our	study	
lends	further	weight	to	the	conclusion	that	the	north–south	divide	is	
a	genome‐wide	phenomenon	that	is	robust	to	different	methodolo‐
gies	and	repeatable	across	studies.
We	 were	 also	 able	 to	 confirm	 previous	 studies	 (Huvet	 et	 al.,	
2000;	Moehler	et	al.,	2011;	Rohfritsch	et	al.,	2013)	reporting	negligi‐
ble	genetic	differentiation	between	the	source	population	of	Japan	
and	the	southern	European	group.	Despite	having	analyzed	samples	
from	both	Japan	and	British	Columbia,	which	was	a	secondary	site	
of	introduction	into	Europe	(Wolff	&	Reise,	2002),	and	having	sev‐
eral	 orders	 of	 magnitude	 higher	 genetic	 resolution	 than	 previous	
studies,	both	PCA	and	fineRADstructure	failed	to	detect	any	clear	
differences	 between	 the	 southern	 group	 and	 source	 populations.	
Furthermore,	 although	 a	 number	 of	 comparisons	 involving	 Japan	
and	British	Columbia	 yielded	 significant	Fst	 values,	 the	magnitude	
of	 these	 estimates	was	 low.	Our	 results	 therefore	 lend	 additional	
weight	to	the	notion	that	Pacific	oysters	did	not	experience	a	pro‐
nounced	founder	effect	when	they	were	 introduced	 into	southern	
Europe.	This	is	consistent	with	the	observation	that	many	thousands	
tonnes	of	spat	were	transferred	into	northern	France	from	Japan	as	
well	 as	many	 hundreds	 of	 tonnes	 of	 adults	 from	British	Columbia	
(Grizel	&	Héral,	1991;	Nehring,	1999;	Wolff	&	Reise,	2002).
In	addition	to	confirming	previous	findings,	our	genomic	data	also	
allowed	us	 to	 resolve	 fine‐scale	patterns	 that	could	not	be	detected	
in	previous	studies.	In	particular,	Huvet	et	al.	(2004),	Meistertzheim	et	
al.	(2013),	and	Rohfritsch	et	al.	(2013)	did	not	find	any	significant	ge‐
netic	differences	among	populations	of	the	southern	group,	regardless	
of	whether	mitochondrial	or	nuclear	markers	were	used.	This	apparent	
homogeneity	was	attributed	to	the	prodigious	reproductive	potential	
of	this	species	coupled	with	the	possession	of	long‐lived	pelagic	larvae	
and	frequent	 transfers	of	 farmed	stocks	 (Meistertzheim	et	al.,	2013;	
Rohfritsch	et	al.,	2013).	By	contrast,	we	not	only	found	that	a	number	
of	pairwise	population	comparisons	within	the	southern	group	yielded	
significant Fst	values	(Supporting	information	Table	S3)	but	also	uncov‐
ered	a	significant	isolation‐by‐distance	pattern	among	the	wild	popula‐
tions	from	southern	Europe.	The	highest	Fst	values	in	southern	Europe	
were	obtained	for	comparisons	involving	Italy,	presumably	due	to	the	
fact	that	C. gigas	was	 introduced	 into	this	part	of	the	Mediterranean	
during	the	late	1960	s	(Šegvić‐Bubić	et	al.,	2016).	We	also	found	some	
evidence	for	the	presence	of	fine‐scale	population	structure	within	the	
F IGURE  4 Relationship	between	average	pairwise	relatedness	
and	average	genomic	inbreeding	coefficient	within	populations	
(linear	regression,	b = 1.39,	r2	=	0.79,	p	<	0.001)
528  |     VENDRAMI Et Al.
northern	cluster,	although	this	was	more	equivocal.	Specifically,	most	
but	not	all	of	the	individuals	from	Scandinavia	and	Germany	clustered	
apart	from	the	British	hatcheries	in	the	fineRADstructure	analysis,	al‐
though	this	distinction	was	not	readily	apparent	in	the	PCA.	Such	a	pat‐
tern	is	consistent	with	Pacific	oysters	having	been	imported	repeatedly	
from	UK	hatcheries	to	Germany	and	Scandinavia	(d'Auriac	et	al.,	2017).
4.2 | Comparison of wild populations and hatcheries
Two	innovations	of	our	study	were	first	to	sample	wild	and	hatch‐
ery	oysters	extensively	enough	to	facilitate	a	meaningful	and	broad‐
scale	comparison,	and	second	to	quantify	 inbreeding	directly	from	
genomic	 data.	 Repeated	 introductions	 of	 genetic	 material	 from	
different	aquaculture	broodstocks	are	commonplace	and	should	in	
principle	contribute	toward	the	genetic	homogenization	of	wild	pop‐
ulations	and	hatcheries	 (Moehler	et	al.,	2011).	Moreover,	a	certain	
degree	of	genetic	exchange	between	wild	populations	and	hatcher‐
ies	can	be	expected,	at	 least	 in	France	where	oyster	production	in	
some	hatcheries	 is	partially	based	on	wild‐caught	spat	and	natural	
reproduction	of	farmed	oysters	occurs	 (Pouvreau	et	al.,	2016).	Set	
against	 this,	 however,	 temporal	 sweepstake	effects	 (Hedgecock	&	
Pudovkin,	2011)	and	far	smaller	numbers	of	breeding	individuals	in	
aquaculture	populations	 (Kochmann	et	 al.,	 2012)	 could	potentially	
increase	 genetic	 drift	 and	 drive	 genetic	 differentiation	 from	 wild	
F IGURE  5 Levels	of	inbreeding	in	wild	populations	and	hatcheries	inferred	from	genome‐wide	SNP	data.	Panel	(a)	shows	differences	
between	wild	and	hatchery	samples	from	the	northern	and	southern	groups	separately	as	well	as	for	the	source	populations.	Raw	data	
points	are	shown	together	with	standard	Tukey	box	plots.	Panel	(b)	shows	bootstrapped	g2	values	for	individuals	sampled	from	wild	
populations	(dark	gray)	versus	hatcheries	(light	gray).	The	empirical	g2	values	and	their	corresponding	95%	confidence	intervals	are	depicted	
by	dashed	vertical	lines	and	horizontal	bars,	respectively.	Panel	(c)	shows	population‐specific	variation	in	inbreeding.	In	panels	(a)	and	(c),	the	
populations	are	color	coded	as	described	in	the	legend	of	Figure	1
     |  529VENDRAMI Et Al.
populations.	 Our	 data	 lend	 support	 to	 the	 latter	 scenario	 as	 we	
found	that	hatcheries	showed	a	clear	tendency	to	cluster	apart	from	
wild	populations	and	were	also	characterized	by	elevated	 levels	of	
shared	coancestry	and	inbreeding.
Although	small	panels	of	genetic	markers	like	microsatellites	are	
capable	of	resolving	population	structure,	under	most	circumstances	
they	provide	poor	estimates	of	inbreeding	(Balloux	et	al,	2004).	This	
has	 hindered	 the	 study	 of	 inbreeding	 in	 wild	 populations	 lacking	
pedigrees	(Pemberton,	2008).	Consequently,	we	used	our	SNP	data	
to	calculate	genomic	inbreeding	coefficients	for	the	first	time	to	our	
knowledge	 for	 a	 marine	 invertebrate	 in	 order	 to	 investigate	 how	
aquaculture	 practices	may	 have	 influenced	 levels	 of	 inbreeding	 in	
oyster	hatcheries.	We	uncovered	a	clear	tendency	for	both	the	mag‐
nitude	of	inbreeding	and	its	variance	to	be	higher	within	the	sampled	
hatchery	 cohorts.	 This	 might	 be	 considered	 surprising	 given	 that	
C. gigas	is	produced	in	vast	numbers	and	is	capable	of	long‐distance	
F IGURE  6 Results	of	repeated	analyses	of	population	genetic	structure	and	inbreeding	based	on	a	subset	of	RAD	sequenced	individuals	
from	eight	populations	(see	Methods	for	details).	Panels	(a)	and	(b)	show	individual	variation	in	principal	component	(PC)	scores	derived	from	
a	principal	component	analysis	(PCA).	Panel	(a)	shows	PC1	plotted	against	PC2,	while	panel	(b)	shows	PC1	plotted	against	PC3.	The	amounts	
of	variation	explained	by	each	PC	are	given	as	percentages.	Panel	(c)	presents	the	output	of	the	fineRADstructure	analysis,	including	both	
the	cladogram	and	the	heatmap	representing	pairwise	coancestry	values	between	individuals.	Panel	(d)	shows	population‐specific	variation	
in	inbreeding.	Samples	are	color	coded	as	described	in	the	legend	of	Figure	1
530  |     VENDRAMI Et Al.
dispersal	 mediated	 by	 free‐swimming	 planktotrophic	 larvae	 (FAO,	
http://www.fao.org/fishery/culturedspecies/Crassostrea_gigas).	
However,	Pacific	oysters	also	have	one	of	the	smallest	documented	
effective	 to	 census	 population	 size	 ratios	 (10−6,	 Frankham,	 1995)	
reflecting	 a	 general	 tendency	 in	 marine	 invertebrates	 for	 highly	
variable	sweepstakes	reproductive	success	resulting	from	a	combi‐
nation	of	high	fecundity	and	 low	larval	survivorship	 (Hedgecock	&	
Pudovkin,	2011).	Concretely,	a	single	oyster	can	produce	several	tens	
of	millions	eggs	in	a	single	season	(FAO,	http://www.fao.org/fishery/
culturedspecies/Crassostrea_gigas),	but	mortality	rates	within	com‐
mercial	oyster	hatchery	cultures	can	be	as	high	as	98%	between	fer‐
tilization	and	the	spat	stage	(Plough	&	Hedgecock,	2011),	which	may	
lead	to	high	variance	in	the	reproductive	success	of	hatchery	brood‐
stock	(Boudry,	Collet,	Cornette,	Hervouet,	&	Bonhomme,	2002).
Our	findings	are	in	line	with	a	previous	study	documenting	lower	
microsatellite	allelic	diversity	in	hatchery‐reared	relative	to	wild	indi‐
viduals	within	Loch	Foyle	in	Northern	Ireland	(Kochmann	et	al.,	2012),	
although	a	similar	study	did	not	find	any	differences	between	wild	
populations	and	hatcheries	in	the	Wadden	Sea	(Moehler	et	al,	2011).	
Moreover,	 heterozygote	 deficiency	 has	 been	 observed	 in	 several	
previous	studies	of	oysters	(Lallias	et	al.,	2015,	2010;	Meistertzheim	
et	al.,	2013;	Rohfritsch	et	al.,	2013),	which	has	been	interpreted	as	
being	 suggestive	of	 inbreeding	 (Faust	et	 al.,	 2017).	 Finally,	 experi‐
mental	 studies	 have	 observed	 massive	 distortions	 in	 marker	 seg‐
regation	ratios	 in	F2	oyster	families,	consistent	with	a	high	genetic	
load	comprising	multiple	recessive	mutations	under	strong	viability	
selection	(Launey	&	Hedgecock,	2001;	Plough	&	Hedgecock,	2011).	
Hence,	our	study	contributes	toward	a	growing	body	of	evidence	in	
support	of	Launey	and	Hedgecock's	(2001)	argument	that	inbreed‐
ing may be a biologically and economically important phenomenon 
in	oysters	as	well	as	possibly	in	other	marine	invertebrates.
It	 is	 important	 to	 recognize	 that	not	all	of	 the	hatchery‐reared	
oysters	 in	our	 study	 showed	higher	 levels	of	 inbreeding	 than	wild	
populations.	By	implication,	inbreeding	is	not	associated	with	hatch‐
ery propagation per se	 but	may	 rather	 arise	 due	 to	 differences	 in	
management	practices	among	facilities,	which	in	many	cases	will	re‐
flect	differing	priorities.	For	example,	many	hatcheries	minimize	the	
risk	of	 inbreeding	by	enhancing	their	broodstock	with	oysters	col‐
lected	from	the	wild	(E.	Vernier,	personal communication,	November	
21,	 2018)	 or	 by	maximizing	 numbers	 of	 effective	 breeders,	 while	
others	actively	avoid	these	practices	(M.	Montergous,	personal com‐
munication,	June	6,	2018),	presumably	to	minimize	the	risk	of	disease	
transmission.	In	other	cases,	it	may	be	desirable	to	maintain	partic‐
ular	families	or	 lineages	that	have	been	selected	based	on	specific	
characteristics,	even	if	this	results	in	somewhat	higher	levels	of	con‐
sanguinity	(M.	Dravers,	personal communication,	September	6,	2018).
4.3 | Practical implications for oyster aquaculture
Moderate	to	high	levels	of	 inbreeding	are	known	to	negatively	im‐
pact	a	multitude	of	commercially	 relevant	 fitness	 traits,	 from	 indi‐
vidual	 growth	 rate	 through	harvest	 body	 size	 to	 survival,	 in	many	
aquaculture	organisms	(Deng	et	al.,	2005;	Gallardo	et	al.,	2004;	Lyu	
et	al.,	2018;	Moss	et	al.,	2007).	More	specifically,	previous	studies	of	
oysters	have	found	strong	inbreeding	depression	for	early	viability	
(Plough	&	Hedgecock,	2011)	as	well	 as	 for	yield,	growth	 rate,	 and	
survival	to	harvest	 in	adults	 (Evans	et	al.,	2004).	Consequently,	el‐
evated	inbreeding	levels	in	certain	hatcheries	are	worthy	of	further	
exploration	and	it	may	be	worth	considering	intervention	strategies	
aimed	at	increasing	genetic	diversity.
With	respect	to	the	need	for	further	exploration,	it	is	worth	bear‐
ing	in	mind	that	although	our	total	sample	size	of	oysters	was	reason‐
ably	 large	given	the	number	of	markers	deployed,	only	around	ten	
samples	were	analyzed	on	average	from	each	population.	While	this	
is	unlikely	to	have	appreciably	affected	our	inference	of	population	
structure	 (Willing,	 Dreyer,	 &	 Oosterhout,	 2012),	 the	 inference	 of	
inbreeding	levels	within	populations	could	be	sensitive	to	the	inad‐
vertent	sampling	of	highly	related	individuals	when	sample	sizes	are	
small,	and	this	may	be	particularly	true	for	hatcheries.	Furthermore,	
high	variance	in	reproductive	success	within	and	across	generations	
could	potentially	lead	to	different	cohorts	from	the	same	hatcheries	
varying	substantially	 in	 their	 levels	of	 inbreeding.	Consequently,	 it	
would	 be	worthwhile	 enlarging	 sample	 sizes	within	 hatcheries,	 as	
well	 as	 collecting	 and	 analyzing	 samples	 from	 the	 same	 locations	
over	multiple	 years	 in	order	 to	provide	more	 robust	 inferences	of	
average	 inbreeding	 levels	and	allow	these	 to	be	 interpreted	 in	 the	
light	of	temporal	variation.	This	would	further	benefit	from	the	de‐
velopment	of	a	larger	SNP	array	and	a	more	contiguous	C. gigas	ref‐
erence	genome,	which	would	allow	inbreeding	to	be	evaluated	with	
even	greater	precision	through	the	use	of	mapped	genetic	markers	
to	quantify	runs	of	homozygosity.
Having	 done	 so,	 a	 useful	 next	 step	 would	 be	 to	 evaluate	 in	
greater	detail	the	effects	of	different	 levels	of	 inbreeding	on	com‐
mercially	 important	 traits	 within	 hatcheries.	 So	 far,	 only	 a	 single	
study	 has	 evaluated	 the	 effects	 of	 inbreeding	 on	 adult	 traits	 in	 a	
commercial	growing	environment	and	this	was	based	on	a	crossing	
design	that	maximized	variation	in	inbreeding	among	families	(Evans	
et	 al.,	 2004).	 By	 contrast,	 our	 approach	 of	 quantifying	 inbreeding	
directly	 from	genomic	data	could	 in	principle	circumvent	the	need	
for	 experimental	 crosses,	 thereby	 allowing	 inbreeding	 depression	
to	be	directly	quantified	in	real	hatchery	populations	when	pheno‐
typic	data	are	available.	This	could	help	to	inform	hatchery	managers	
about	the	potential	costs	of	inbreeding	and	the	possible	benefits	of	
intervention	strategies.
Finally,	 a	 number	 of	 potential	 intervention	 strategies	 aimed	 at	
reducing	inbreeding	and	increasing	genetic	diversity	could	be	envis‐
aged.	The	first	obvious	approach	would	be	to	incorporate	individuals	
from	wild	populations	into	hatchery	broodstocks,	as	also	discussed	
by	Lallias	et	al.	(2010)	in	the	context	of	flat	oysters.	However,	caution	
is	warranted	as	selective	breeding	in	captivity	may	lead	to	adaptive	
changes	 that	 are	 absent	 from	wild	 populations	 (Lachambre	 et	 al.,	
2017)	so	the	fitness	consequences	of	such	crosses	remain	unclear.	A	
second	possibility	would	be	exchange	individuals	more	extensively	
among	hatcheries.	Within	Europe,	 the	practice	of	exchanging	oys‐
ter	stocks	between	different	countries	is	becoming	more	common,	
but	we	are	not	aware	of	 any	 such	exchanges	between	 the	United	
     |  531VENDRAMI Et Al.
Kingdom	and	the	European	mainland,	probably	due	to	the	perceived	
risk	of	disease	transmission.	A	third	possibility	would	be	to	mitigate	
the	risk	of	inbreeding	by	implementing	oyster	rearing	based	on	mo‐
lecular	pedigree	assignments	 (Boudry,	2009;	Lapegue	et	al.,	2014)	
as	is	common	practice	in	fish	farming	(Vandeputte	&	Haffray,	2014).	
Clearly,	 hatchery	managers	 need	 to	 balance	 the	 pros	 and	 cons	 of	
selective	breeding	and	maximizing	genetic	diversity,	but	either	way	
genomic	tools	such	as	SNP	arrays	provide	a	means	of	evaluating	the	
genetic	consequences	of	chosen	management	practices.
4.4 | Caveats
SNP	 arrays	 provide	 a	 cost‐effective	 and	 convenient	 route	 to	 ge‐
nome‐wide	 investigations	 but	 can	be	prone	 to	 ascertainment	 bias	
when	 the	 samples	 used	 in	 the	 initial	 SNP	 discovery	 phase	 differ	
from	 those	 being	 interrogated	 on	 the	 array	 (Lachance	&	 Tishkoff,	
2013).	However,	we	believe	this	is	unlikely	to	substantially	affect	our	
main	conclusions	for	two	reasons.	First,	the	discovery	panel	of	indi‐
viduals	used	to	construct	the	array	was	unusually	large,	comprising	
over	200	 individuals	 from	eight	different	 localities.	By	 implication,	
much	of	the	genetic	diversity	of	the	species	in	Europe	should	have	
been	captured,	including	rare	alleles	that	may	easily	be	missed	with	
smaller	discovery	panels	but	which	could	potentially	be	present	at	
higher	frequencies	in	unsampled	populations.	Second,	although	the	
discovery	panel	comprised	primarily	individuals	from	hatcheries,	the	
northern	and	southern	groups	were	roughly	equally	represented	and	
we	 therefore	 have	 no	 reason	 to	 expect	 any	 broad‐scale	 biases	 to	
be	present.	Two	further	points	should	also	be	recognized.	First,	our	
analyses	of	population	structure	will	if	anything	be	conservative,	as	
ascertainment	 bias	 should	 lead	 to	 the	 underestimation	 of	 genetic	
differentiation	when	peripheral	populations	carry	previously	unde‐
tected	alleles.	Second,	ascertainment	bias	cannot	explain	higher	lev‐
els	of	inbreeding	nor	variation	in	inbreeding	among	hatcheries	in	the	
UK	and	France.	This	 is	because	all	of	these	populations	were	used	
to	generate	the	array,	so	ascertainment	bias	if	present	would	be	ex‐
pected	to	generate	the	opposite	pattern	of	increased	homozygosity	
in	wild	populations.
Nevertheless,	we	 conservatively	 took	 into	 account	 the	 possibility	
that	 ascertainment	 bias	 could	 be	 responsible	 for	 the	 ostensibly	 high	
level	of	 inbreeding	 in	the	putatively	hybrid	Portuguese	population.	To	
test	this	possibility	as	well	as	to	confirm	our	broader	findings,	we	RAD	
sequenced	a	subset	of	individuals	and	repeated	all	of	our	analyses.	Our	
previous	 results	were	 largely	 confirmed,	with	 very	 similar	 patterns	of	
population	genetic	structure	and	inbreeding	being	obtained,	lending	fur‐
ther	weight	to	our	main	conclusions.	Furthermore,	oysters	from	Portugal	
were	again	found	to	have	relatively	high	genomic	inbreeding	coefficients	
based	on	 the	RAD	data.	As	we	would	expect	hybrids	 to	be	 relatively	
outbred,	this	finding	points	toward	hybridization	between	C. gigas and 
C. angulata being	negligible	in	our	sample.	Consequently,	it	appears	that	
the	Portuguese	oysters	could	represent	and	inbred	and	isolated	C. gigas 
population.	However,	we	cannot	discount	the	further	possibility	that	we	
inadvertently	sampled	C. angulata	from	this	location,	as	the	two	species	
are	morphologically	 indistinguishable,	 the	 SNP	 array	may	 include	 loci	
that	cross	amplify	in	C. angulata	(Gagnaire	et	al.,	2018),	and	Fst	compari‐
sons	involving	our	Portuguese	sample	were	consistently	high.
5  | CONCLUSION
We	harnessed	some	of	the	latest	developments	in	genomics	to	shed	
new	 light	 on	 the	 population	 structure	 of	 Pacific	 oysters	 along	 a	
European	latitudinal	cline	as	well	as	to	compare	levels	of	inbreeding	
between	wild	and	hatchery	samples.	The	several	orders	of	magni‐
tude	higher	genetic	resolution	provided	by	the	medium‐density	SNP	
array	allowed	us	not	only	to	confirm	previous	findings	(Faust	et	al.,	
2017;	Lallias	et	al.,	2015;	Meistertzheim	et	al.,	2013;	Rohfritsch	et	
al.,	2013)	but	also	to	detect	fine‐scale	patterns	including	genetic	dif‐
ferences	between	wild	populations	and	hatcheries.	We	furthermore	
uncovered	evidence	for	higher	levels	of	inbreeding	in	sampled	hatch‐
ery	 cohorts,	which	merits	 further	 investigation.	 Finally,	 our	 study	
contributes	to	a	growing	consensus	that	inbreeding	could	be	more	
prevalent	in	animal	populations	than	previously	envisaged	(Keller	&	
Waller,	2002),	even	in	highly	fecund	species	with	high	dispersal.
ACKNOWLEDG EMENTS
We	thank	Carsten	Fomsgaard,	Jonathan	King,	Robert	Mescam,	Stein	
Mortensen,	 Kirti	 Ramesh,	 Yoshiko	 Shimahara,	 and	 Karin	 Troost	 for	
providing	Pacific	oyster	samples	or	DNA	extracts	as	well	as	SYSAAF	
for	 coordinating	 the	 provision	 of	 the	 French	 hatchery	 samples	 from	
Marinove,	Satmar,	France	Naissain	and	Novostrea.	We	are	also	grate‐
ful	to	Melody	Clark,	Dennis	Hedgecock	and	Pierrick	Haffray	as	well	as	
the	 participants	 of	 the	 7th	 International	Oyster	 Symposium	 (Bangor,	
Wales	 2017)	 and	 the	 Wild	 Genomics	 Meeting	 (Bielefeld,	 Germany	
2018)	for	useful	discussions.	Martin	Stoffel	provided	helpful	advice	on	
data	analysis,	while	Stéphane	Duval,	Mathieu	Montergous,	Emmanuel	
Vernier,	Mark	Dravers,	and	Gordon	Goldsworthy	shared	 information	
on	 Pacific	 oyster	 breeding	 practices.	 The	 research	 leading	 to	 these	
results	 has	 received	 funding	 from	 the	 European	 Union	Marie	 Curie	
Seventh	Framework	Programme	under	grant	agreement	no.	605051.	
We	also	gratefully	acknowledge	funding	from	BBSRC	and	NERC	(BB/
M026140/1,	NE/P010695/1)	in	addition	to	BBSRC	Institute	Strategic	
Program	 Grants	 (BBS/E/D/20002172	 and	 BBS/E/D/30002275).	
Finally,	we	acknowledge	the	financial	support	of	the	German	Research	
Foundation	(DFG)	and	the	Open	Access	Publication	Fund	of	Bielefeld	
University	for	the	article	processing	charge.
CONFLIC T OF INTERE S T
None	Declared.
DATA ACCE SSIBILIT Y
Individual	genotype	files	obtained	from	the	SNP	array	and	from	the	
analyses	of	the	RAD	sequencing	data	are	available	from	the	Dryad	
Digital	Repository:	https://doi.org/10.5061/dryad.6d778b6
532  |     VENDRAMI Et Al.
ORCID
David L. J. Vendrami  https://orcid.org/0000‐0001‐9409‐4084 
Pierre Boudry  https://orcid.org/0000‐0002‐5150‐2276 
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SUPPORTING INFORMATION
Additional	 supporting	 information	 may	 be	 found	 online	 in	 the	
Supporting	Information	section	at	the	end	of	the	article.				
How to cite this article:	Vendrami	DLJ,	Houston	RD,	Gharbi	
K,	et	al.	Detailed	insights	into	pan‐European	population	
structure	and	inbreeding	in	wild	and	hatchery	Pacific	oysters	
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