# High-density SNP assay development for genetic analysis in maritime pine (Pinus pinaster)

Christophe Plomion, Jérôme Bartholomé, Isabelle Lesur Kupin Lesur, Christophe C. Boury, Isabel Rodríguez-Quilón, Hélène Lagraulet, François Ehrenmann, Laurent Bouffier, Jean-Marc J.-M. Gion, D. Grivet, et al.

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## To cite this version:

Christophe Plomion, Jérôme Bartholomé, Isabelle Lesur Kupin Lesur, Christophe C. Boury, Isabel Rodríguez-Quilón, et al.. High-density SNP assay development for genetic analysis in maritime pine (Pinus pinaster). Molecular Ecology Resources, 2016, 16 (2), pp.574-587. 10.1111/1755-0998.12464 . hal-02640393v2

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<figure><img src="image_2.png" /></figure>
High-density SNP assay development for genetic analysis in
maritime pine (Pinus pinaster)

<table><tr><td>Journal:</td><td>Molecular Ecology Resources</td></tr><tr><td>Manuscript ID:</td><td>Draft</td></tr><tr><td>Manuscript Type:</td><td>Resource Article</td></tr><tr><td>Date Submitted by the Author:</td><td>n/a</td></tr><tr><td>Complete List of Authors:</td><td>Plomion, Christophe; INRA, Bartholomé, Jérôme; INRA, Lesur, Isabelle; INRA, Boury, Christophe; INRA, Rodríguez-Quilón, Isabelle; INIA, Lagraulet, Hélène; INRA, Ehrenmann, François; INRA, Bouffier, Laurent; INRA, Gion, Jean-Marc; CIRAD, Grivet, Delphine; INIA, de Miguel, Marina; INRA, de Maria, Nuria; INIA, Cervera, María Teresa; Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA). Centro de Investigación Forestal (CIFOR), Ecología y Genética Forestal Bagnoli, Francesca; CNR, Isik, Fikret; NCSU, Vendramin, Giovanni; Institute of Biosciences and Bioresources, CNR, González-Martínez, Santiago; CIFOR-INIA, Forest Ecology and Genetics</td></tr><tr><td>Keywords:</td><td>Adaptation, Bioinfomatics/Phyloinfomatics, Gymnosperms, Molecular Evolution, Natural Selection and Contemporary Evolution, Population Genetics - Empirical</td></tr></table>
1 Molecular Ecology Resources – Permanent Genetic Resources

2

3 High-density SNP assay development

4 for genetic analysis in maritime pine (Pinus pinaster)

5

6 Plomion C<sup>1,2†</sup>, Bartholomé J<sup>1,2</sup>, Lesur I<sup>1,3</sup>, Boury C<sup>1,2</sup>, Rodríguez-Quilón I<sup>4</sup>, Lagraulet H<sup>1,2</sup>,

7 Ehrenmann F<sup>1,2</sup>, Bouffier L<sup>1,2</sup>, Gion JM<sup>1,5</sup>, Grivet D<sup>4</sup>, de Miguel M<sup>1,2</sup>, de María N<sup>4</sup>, Cervera

8 MT<sup>4</sup>, Bagnoli F<sup>6</sup>, Isik F<sup>7</sup>, Vendramin GG<sup>6</sup>, González-Martínez SC<sup>4</sup>

9

10 <sup>1</sup>INRA, UMR1202 BIOGECO, F-33610 Cestas, France

11 <sup>2</sup>Univ. Bordeaux, BIOGECO, UMR 1202, F-33400 Talence, France

12 <sup>3</sup>HelixVenture, F-33700 Mérignac, France

13 <sup>4</sup>INIA, Forest Research Centre, E-28040 Madrid, Spain

14 <sup>5</sup>CIRAD, UMR AGAP, F-33612 Cestas, France

15 <sup>6</sup>Institute of Biosciences and Bioresources, National Research Council, Sesto Fiorentino (FI), Italy

16 <sup>7</sup>North Carolina State University, Department of Forestry and Environmental Resources, Raleigh, NC, USA

19

20 **Keywords**: Maritime pine, SNP, linkage mapping, population genetics

21 †Corresponding author: Christophe PLOMION

22 INRA, UMR1202 BIOGECO, F-33610 Cestas, France

23 e-mail: plomion@pierroton.inra.fr

24

25
26 **Running title:** SNP discovery and validation in maritime pine

27

28
29 **Abstract**

30

31 Maritime pine provides essential ecosystem services in the south-western Mediterranean
32 basin, where it covers around 4 million ha. Its scattered distribution over a range of
33 environmental conditions makes it an ideal forest tree species for studies of local adaptation
34 and adaptive responses to climatic change. Highly-multiplexed single-nucleotide
35 polymorphism (SNP) genotyping arrays are increasingly used to study genetic variation in
36 living organisms and for practical applications in plant and animal breeding and genetic
37 resource conservation. We developed a 9k Illumina Infinium SNP array and genotyped
38 maritime pine trees from i) a three-generation inbred (F2) pedigree, ii) the French breeding
39 population, and iii) natural populations from Portugal and the French Atlantic coast. A large
40 proportion of the exploitable SNPs (2,052 / 8,410, i.e. 24.4%) segregated in the mapping
41 population and could be mapped, providing the densest ever gene-based linkage map for this
42 species. Population genetics and structure analyses based on 3,981 SNP markers common to
43 the Portuguese and French gene pools revealed high levels of differentiation, leading to the
44 identification of a set of highly differentiated SNPs that could be used for seed provenance
45 certification. Finally, we discuss how the validated SNPs could facilitate the identification of
46 ecologically and economically relevant genetic factors in this species, improving our
47 understanding of the demography and selective forces shaping its natural genetic diversity,
48 and providing support for new breeding strategies.

49

50
## 51 Introduction

Maritime pine (*Pinus pinaster* Aiton, Pinaceae) is a long-lived wind-pollinated forest tree species native to the western part of the Mediterranean Basin. Its natural range extends from northern Morocco in the south to French Brittany in the north, and from Portugal in the west to Italy in the east (http://www.euforgen.org/distribution-maps/). It is found in various ecological situations, from sea level to an altitude of 2,100 m in the High Atlas (Morocco), from regions characterised by heavy annual rainfall in a maritime climate to dry regions in the semi-arid Mediterranean climate, and from calcareous to acidic soils (Alía and Martín, 2003).

Its scattered distribution has resulted in local adaptations and high levels of genetic differentiation for adaptive traits across its natural distribution range (González-Martínez *et al.* 2002, Santos-del-Blanco *et al.* 2012, Lamy *et al.* 2011, 2014). Considerable genetic differentiation between ecotypes has been reported for various neutral molecular markers, providing clear evidence for a geographic structure of genetic diversity (Bucci *et al.* 2007, Santos-del-Blanco *et al.* 2012, Jaramillo-Correa *et al.* 2010, 2015).

This fast-growing tree has been widely planted in systematic reforestation programs since the 19th century, to secure coastal (along the Atlantic) and inland (Castilian plateau) sand dune areas, to drain marshes and to create new forests for resin production. Over the last 50 years, this species has been commercially exploited as a timber resource for the forestry industry (sawmills, wood panels, pulp and paper). In recent years, it has also been used as a source of chemicals for the bio-industry (Rohdewald 2002, Touriño *et al.* 2005, Jorge *et al.* 2002), as bioactive phenolic compounds can be extracted from its bark. There are now 4.2 million ha under maritime pine within its natural range and 200,000 ha outside this range (mostly in Australia) (Bouffier *et al.* 2013). The breeding of maritime pine began in the 1960s in
76 southwestern France, after several species and provenance trials had shown that the local
77 ecotype was the best adapted and fastest growing tree in the Aquitaine soil and climatic
78 conditions (Illy 1966, Harfouche 1995). This programme has now reached its third generation,
79 and is one of the most advanced conifer breeding programmes in the world (Mullin et al.
80 2011).

81

82 The maritime pine has also been adopted by the forest tree genetics research community as a
83 key model species for investigations of the genetic variation or linked mutations underlying
84 phenotypic variability, particularly those selected by the environment and involved in local
85 adaptation (reviewed by González-Martínez et al. 2011). It is now hoped that the discovery of
86 polymorphisms causing changes in gene expression and/or amino-acid sequences will lead to
87 innovations in genetic resource management, for both breeding (Isik 2014) and conservation
88 strategies (Ouborg et al. 2010). Such discoveries should also lead to changes in silviculture
89 practices to take into account the evolutionary processes inferred from neutral and selected
90 markers (Lefèvre et al. 2014). Major efforts have been devoted to the sequencing and
91 assembly of the maritime pine transcriptome (Canales et al. 2014), for studies of the
92 molecular basis of the phenotypic response to biotic and abiotic constraints (e.g. Le Provost et
93 al. 2013). Moreover, since the pioneering work of Lepoittevin et al. (2010), describing the
94 design of the first multiplex single-nucleotide polymorphism (SNP) genotyping assay in
95 maritime pine, medium-scale SNP-arrays have been developed (Supplemental Table 1). These
96 assays have made it possible to characterise hundreds of trees, at hundreds of loci, for various
97 applications: nucleotide diversity analysis (Plomion et al. 2014), QTL detection (de Miguel et
98 al. 2014), association mapping (Lepoittevin et al. 2012, Budde et al. 2014), analyses of the
99 correlations between genotype frequencies and environmental gradients (Jaramillo-Correa et
100 al. 2015) and linkage map construction (Chancerel et al. 2013).
101

102

Given the high throughput and reliability of the Infinium platform from Illumina (e.g.

103

Bartholomé et al. 2015a for eucalyptus, Pavy et al. 2013 for spruce), we used this platform to

104

design a customised genotyping array for maritime pine, including about 5,000 SNPs from

105

the studies cited above and about 4,000 SNPs newly identified from RNA-seq data and new

106

amplicon resequencing. The resulting 9k SNP array is the largest genotyping chip ever

107

produced for this species. We assessed the suitability of this array for genetic linkage mapping

108

and genetic differentiation analysis between two gene pools (Portugal and French Atlantic

109

coast). Finally, we discuss the potential utility of this SNP array for exploring genetic

110

diversity and its contribution to phenotypic variation, genetic inferences about historical

111

demographic events, the past action of natural selection and adaptive evolution, and the

112

implementation of novel tree breeding strategies.

113

114

## Materials and methods

115

116

### *Design of an Illumina Infinium array for maritime pine*

117

118

We designed a 9k Illumina Infinium SNP array for maritime pine, including the two subsets

119

of SNPs described below.

120

### *Previously available SNPs*

121

Over the past five years, several studies have reported the development of SNP markers in

122

maritime pine (see Supplemental Table 1). We selected a total of 4,997 SNPs from assays

123

based on VeraCode, GoldenGate or Infinium Illumina technologies (Illumina, San Diego, CA,

124

USA) and incorporated them into the same Infinium genotyping array. Some SNPs had been

125

used on a number of occasions (270 SNPs were common to at least two studies), whereas
others were developed specifically for one study, as shown in Figure 1. SNP redundancy was checked by aligning flanking sequences with the maritime pine UniGene (Canales et al. 2014: http://www.scbi.uma.es/sustainpinedb/home_page). Alignments were performed by carrying out BlastN searches in the Blastall V2.2.26 suite (e-value = 10⁻⁵). We retained the best aligned contig for each sequence and the position of the SNP was retrieved from that contig. When a redundant SNP was associated with different dbSNP accessions, we retained the accession with the longest flanking sequence. Overall, we retained 4,712 different SNPs in this first subset.

## Newly developed SNPs

This second subset comprised SNPs obtained by the random screening of EST data or specifically detected in candidate gene sequences.

## Newly developed SNPs from 454 sequence reads

A flowchart describing the steps involved in the identification of SNPs from 454 data is shown in Figure 2. Three genotypes involved as progenitors of interprovenance hybrids in the framework of the maritime pine breeding programme (accessions 110-4019-1 from Corsica (C), 0284-2 from Landes (L) and 112-4-1 (M) from Morocco) were used. A composite cDNA library was constructed with the SMART PCR cDNA synthesis kit (Clontech, Laboratories Inc., Mountain View, CA, USA) for each tree. The C and M libraries contained equal proportions of cDNAs from differentiating xylem, swelling buds and young needles, whereas the L library consisted of equal proportions of cDNAs from differentiating xylem, swelling and quiescent buds, and young and mature needles. Pyrosequencing (454 Titanium, Roche, Branford, CT, USA) was performed with the Roche-454 Genome Sequencing platform (FLX Titanium technology). Sequences (available under accession numbers SRX031589,
SRX208012 and SRX031592 from the NCBI short-read archive) were cleaned with the Pyrocleaner tool (Mariette et al. 2011), which removes particularly short (<150 bp) and long reads (>600 bp), reads with a percentage of Ns (ambiguous base calls) greater than 2%, low-complexity regions, and duplicated reads. For each library, the SNPs were identified by aligning each set of sequences against the 198,425 contigs of the second maritime pine UniGene established by Chancerel et al. (2013). Alignments were performed with the CLC Genomics Workbench Reference Mapping function of CLC Genomics Workbench V6.0 (CLC Bio, Aarhus, Denmark), with the default parameters. SNPs were then detected with the neighbourhood quality standard algorithm (NQS) and the following parameters: minimum coverage = 6, minimum central base quality = 20, minimum neighbourhood quality over a window length of 11 nucleotides = 15, maximum gap and mismatch count = 2 and minimum allele frequency = 20%.

Newly developed SNPs from candidate genes

Grivet et al. (2015) obtained 105 *in vitro* SNPs from two full-length candidate genes with functions relating to phenology (*coll* and *gia*) and 64 amplicons sequenced as part of the CRIEC (Comparative Re-sequencing in European Conifers) project, an EvolTree (http://www.evoltree.eu) initiative. An additional set of *in silico* SNPs was then obtained from 281 candidate genes with functions relating to abiotic and biotic (plant defence) stress responses in forest trees selected from the following sources: i) 66 genes from published (Perdiguero et al. 2013) and unpublished maritime pine sequences available from GenBank, ii) 149 genes from sequencing studies in other conifers (Wachowiak et al. 2009, Kujala and Savolainen 2012), iii) 53 transcripts displaying differential expression in the presence and absence of pine wood nematode infection (Santos et al. 2013), iv) 10 genes associated with adventitious shoot induction and plant development in pines (Alonso et al. 2007, Ordás et al.
176 unpublished for *knox* genes), and v) 3 genes potentially involved in cavitation resistance in
177 beech (Lalagüe *et al.* 2014). These sequences were blasted (BlastN, e-value = 10⁻⁵) against the
178 maritime pine UniGene to retrieve the best matching contig and the sequences surrounding
179 the SNP from a catalogue of 55,607 available SNPs (Figure 3). Redundant SNPs were
180 removed.

181

182 *Annotation of synonymous and non-synonymous substitutions*

183

184 The respective positions of SNPs were defined in the contigs of the maritime pine UniGene
185 from Canales *et al.* (2014). Within the coding sequences (when characterised), non-
186 synonymous and synonymous SNPs were annotated by comparing the amino acids translated
187 from the reference codon to the codon containing the SNP. The functional annotation was
188 retrieved from the study by Canales *et al.* (2014). All these items of information are available
189 in Supplemental Table 3.

190

191 *The populations studied and genetic analysis*

192

193 The SNP assays were tested and validated on the basis of Mendelian segregation in mapping
194 pedigrees and genetic diversity analysis in an elite breeding population from France and
195 natural populations of different origins (French Atlantic coast and Portugal).

196

197 *Mapping population and linkage analysis*

198

199 The mapping population consisted of a three-generation inbred pedigree (F2) obtained by the
200 self-pollination of an inter-provenance "Landes x Corsica" hybrid (accession H12 resulting
from the control cross between L146 and C10 genotypes). In total, 638 F2 seeds were planted in a nursery in June 1998 and 626 saplings were transplanted into the field in March 1999 (4 m × 2 m, 0.51 ha, Lacanau de Mios, France). After 15 years, 565 F2 plants were still available for genetic analysis. We used 92 F2 plants to test the Mendelian segregation of the SNP markers and to associate them with a particular genetic position. The F2 plants with the most recombinant genotypes were selected with MapPop software (http://www.bio.unc.edu/faculty/vision/lab/mappop/, Vision et al. 2000) and a linkage map was established by Lavigne et al. (manuscript in preparation), by genotyping 477 F2 plants for 248 SNPs distributed over 12 linkage groups (LG), the haploid chromosome number in pines.

The R package onemap v2.0-3 (Margarido et al. 2007, Mollinari et al. 2009) was used for linkage mapping. Only SNPs and individuals for which there was less than 5% and 1% missing data, respectively, were considered in the analysis. SNPs were clustered into LGs on the basis of a LOD score greater than 10. The LG names were defined on the basis of previously mapped loci (Chancerel et al. 2013). The RECORD algorithm (Os et al. 2005) was used to order markers within LGs, with the following parameters: LOD=3 and max.rf=0.4. Recombination rates were converted into genetic distances (cM) with the Kosambi mapping function (Kosambi 1943). The goodness-of-fit of SNP segregations to the expected Mendelian segregation ratio (i.e. 1:2:1 for an F2 population) was assessed in chi-squared tests, with adjustment of the significance threshold for simultaneous multiple tests (Benjamini and Yekutieli 2001) within each LG. The same procedure was also applied to a previous SNP dataset genotyped in the same F2 family, but with different genotypes and mapping software (Chancerel et al. 2013). The two genetic maps were then combined into a composite linkage map with the R package LPmerge (Endelman and Plomion, 2014).
226

227 *Populations of unrelated individuals and genetic diversity estimation*

228 The French Atlantic coast gene pool was represented by two subsets of individuals: i) 50 trees
229 from two natural populations, Hourtin and Petrocq, sampled from a clonal collection
230 (CLONAPIN) established directly from the source populations (as described by Rodríguez-
231 Quilón *et al.* 2015). Hourtin (45°11′N, 1°09′E) and Petrocq (44°04′N, 1°18′E) are coastal
232 populations growing at low altitude (<30 m a.s.l.) and under typical maritime climate (annual
233 precipitation of 980-1248 mm and mean annual temperature of 12-13°C); and ii) 50 elite trees
234 from a larger set of about 600 genotypes mass-selected in natural forests of the Aquitaine
235 region in the early 1960s to constitute the first generation of the maritime pine breeding
236 program (Illy 1966). These 50 genotypes constitute the founders of a three-generation
237 pedigreed population used to develop proof-of-concept for genomic selection in maritime
238 pine.

239

240 The Portuguese population was also represented by two subsets of genotypes: i) 19 trees
241 sampled from two provenances in a provenance trial carried out at Mimizan (France:
242 44°20′N, 1°28′W). These provenances were described by Illy (1966): 6 trees were from seeds
243 collected at "Pinhal de Leiria" (an 11 ha coastal forest located at 39°79′N, 8°98′W; 0-50 m
244 a.s.l.; annual precipitation of 700-900 mm) and 13 trees were from seeds collected at "Trás-
245 os-Montes" (a mountain forest located at 41°57′N, 7°50′W; 1150 m a.s.l., annual
246 precipitation of 1200-1400 mm); and ii) 23 additional trees from "Pinhal de Leiria" collected
247 from the CLONAPIN maritime pine collection.

248

249 We first tested departure from Hardy-Weinberg equilibrium, in standard chi-squared tests
250 with a nominal significance threshold of 1.05-1.15 × 10⁻⁵, corresponding to an experiment-
wise type I error of 5 % (Bonferroni correction to account for multiple testing). We then
estimated three genetic diversity parameters for each SNP: minor allele frequency (MAF),
observed (Ho) and expected heterozygosity (He, Nei's index of genetic diversity corrected for
sample size, Nei 1987). These three parameters were highly correlated (>0.95). We therefore
present data for MAF and He only. Genetic differentiation ($F_{st}$) was assessed for pairs of
populations, with a set of 3,981 polymorphic SNPs common to both populations, with GDA
software (Lewis and Zaykin 2001). Finally, Bayesian clustering analysis (Structure software,
Pritchard et al. 2000, Falush et al. 2003) was performed to identify different gene pools in
maritime pine, as described by Jaramillo-Correa et al. (2015).

260

261 *Folded site frequency spectrum (SFS) for the French and Portuguese groups*

262

263 Sites with missing data were excluded from the samples, to ensure general consistency across
264 loci in terms of the number of sequences analysed for each site. Furthermore, we retained the
265 same SNPs for the French and Portuguese groups, 3,513 SNPs in total, corresponding to 88%
266 of the original dataset (3,981 SNPs). Finally, two individuals were discarded from the French
267 group as they presented 19% (INIA_PET11) and 10% (INIA_PET12) missing data across the
268 3,981 SNPs. The estimate of folded SFS was therefore based on 3,513 common SNPs with no
269 missing data, genotyped in 94 French and 42 Portuguese individuals.

270

271 SNPs were analysed as unpolarized (i.e. no ancestral state inferred), with the least frequent
272 nucleotide at a given SNP site considered to correspond to the minor allele and thus used for
273 the calculation of minor allele frequency (MAF). MAF was calculated for the French and
274 Portuguese groups separately. The folded SFS was then plotted from the observed MAF and
275 from the expected SFS. We used equation 6 from the paper by Ganapathy and Uyenoyama
276 (2009) to determine the expected SFS. This equation is widely used to describe the expected
277 SFS for a genomic sample of SNPs, each of which is assumed to correspond to a mutation on
278 an independent gene genealogy.

279

280 DNA extraction and genotyping

281

282 Developing needles were collected after bud burst for each genotype of the F2, natural and
283 breeding populations. They were stored at -80°C or dried with silica gel before DNA
284 extraction. About 30 - 40 mg of frozen/dried needles were crushed with a mixer mill (Retsch
285 MM300, Haan, Germany). Genomic DNA was isolated with the Invisorb DNA plants 96 kit
286 from Invitek (GmbH, Berlin, Germany), according to the manufacturer's instructions. All
287 concentrations were determined with a NanoDrop spectrophotometer (NanoDrop
288 Technologies, Wilmington, DE, USA) and a fluorescence assay (Quant-IT kit, Invitrogen,
289 Carlsbad, CA, USA). Samples with DNA concentrations greater than 50 ng/µl (based on
290 fluorescence measurements) were used for the Infinium assay. Genotyping was carried out at
291 the Genes Diffusion Facility (Douai, France).

292

293 **Results**

294

295 SNP detection

296

297 The SNP genotyping array included 8,949 SNPs, 4,712 of which (i.e 52.65%) were already
298 available (166 from Lepoittevin et al. 2010, 835 SNPs from Chancerel et al. 2011, 3,378
299 SNPs from Chancerel et al. 2013, 434 SNPs from de Miguel et al. 2014, and 184 SNPs from
300 Lavigne *et al.* (manuscript in preparation) (Figure 1). The other 4,237 (i.e 47.35%) corresponded to newly developed SNPs.

302

303 *Newly developed SNPs*

304

305 *In silico* SNPs from 454 sequence reads (Figure 2)

306 Pyrosequencing of the C (Corsica), M (Morocco) and L (Landes) libraries provided 188,494, 307 611,673 and 458,882 reads, respectively. After cleaning, we retained 107,089, 484,348 and 308 225,842 reads for C, L and M, respectively. We identified 2,039 SNPs in the C dataset, 4,937 309 in the L dataset and 4,752 in the M dataset. We then selected SNPs in a test-cross 310 configuration (1:1 segregation), i.e. heterozygous in one parent and homozygous in the other, 311 because, in a full-sib family with biallelic codominant markers, pairs of markers presenting 312 this configuration provide the best estimate of recombination rates, particularly if compared 313 with pairs having a test-cross and intercross (1:2:1) configuration (Plomion *et al.* 1997). Thus, 314 we selected 2,830, 1,695 and 2,957 SNPs for the C × M, L × C and L × M crosses, 315 respectively. In total 784, 2,046 and 911 SNPs were informative for the C, M and L parents, 316 respectively. This procedure resulted in the retention of 3,741 unique SNPs as particularly 317 suitable for linkage mapping.

318

319 *In vitro* and *in silico* SNPs from candidate genes (Figure 3)

320 Of the 281 candidate genes, 166 were associated with at least one contig of the maritime pine 321 UniGene. We retrieved 888 SNPs from these 166 contigs. Finally, after removing duplicated 322 sequences and SNPs located less than 100 bp apart, we retained 391 SNPs for SNP array 323 development. In addition, 105 *in vitro* SNPs from 66 resequenced candidate genes were 324 identified and incorporated into the SNP array.
325

326

Annotation of synonymous and non-synonymous substitutions

327

328

329

330

331

332

333

334

335

336

337

338

339

Linkage mapping

340

341

342

343

344

345

346

347

348

349

In total, 4,257 (47.6%) SNPs were found in contigs for which a full-length protein was predicted, with 1,557 (36.6%) of these SNPs located in non-coding regions. SNPs in coding sequences (2,700 i.e. 63.4%) were characterised in terms of their synonymous (S) or non-synonymous (NS) nature and codon responsible for the NS change (Figure 4A and Supplemental Table 3). We identified 567 (21%) synonymous SNPs, and found that the point mutation affected the third position in the codon in 87.5% of cases. There were 2,133 (79%) non-synonymous SNPs with the mutation affecting the first codon position in 21.5% and the second codon position in 70.6%. For NS mutations, the change in the protein is indicated in Supplemental Table 3. Most of the SNPs were transitions (59.3%). The most common transition was C↔T. The most frequent transversion was G↔T (Figure 4B).

Of the 8,410 SNPs used to genotype the 92 trees of the F2 mapping population, 4,634 (55.1%) were monomorphic, 2,052 (from 1,672 different contigs) were polymorphic (24.4%) and the remaining 1,724 (20.5%) corresponding to failed assays. All polymorphic SNPs passed the quality threshold and were therefore used in linkage analysis. The total map length was 1,993 cM, spread over 12 LGs corresponding to the haploid number of chromosomes for the maritime pine genome. LG length ranged from 129.2 (LG11) to 198.2 cM (LG5) and the mean number of SNPs per LG was 171 cM (F2_N in Table 1). The mean SNP density was 0.98, but more than half of the markers were grouped into clusters (i.e. groups of markers displaying no recombination). This resulted in 901 unique positions, separated by a mean
distance of 2.25 cM. Two factors could account for the clustering of SNPs: i) the small size of the mapping population (92 genotypes) and ii) the presence of more than one SNP in 260 of the 1,672 mapped contigs. Distorted SNPs were retained in the linkage analysis and accounted for 3.6% of the mapped markers. They were grouped together in five regions located on three LGs (segregation distortion regions, SDRs): two linked SDRs on LG#3 with 10 and 5 markers, one SDR on LG#6 (26 markers) and three SDRs on LG#10 (14, 12 and 2 markers) (Figure 5). The order of SNPs was similar (Spearman p=0.98) to that obtained from the linkage map reconstructed with the dataset of Chancerel *et al.* (2013) based on different genotypes of the same progeny (F2_O in Table 1). Only 56 of the 1,180 markers common to the two genetic maps differed in order between the two maps (Figure 5). These differences in marker order occurred between tightly linked markers (<5 cM), mostly on LG#1 and LG#10, for which 10 and 15 markers were involved, respectively. Finally, based on the highly conserved marker order between the two maps, a composite map was established (F2_C in Table 1) with LPmerge software. It contained 2,353 SNPs (including 1,121 SNPs in the same contigs). This composite map included 1,661 different loci, and is therefore the densest yet gene-based linkage map for this species. The total map length was 1,711.7 cM. There were 955 unique positions, separated by a mean distance of 1.82 cM.

## Genetic diversity and population structure analysis

Only a few markers displayed significant departure from Hardy-Weinberg equilibrium in each population: 6 (0.10%) for the French Atlantic-based breeding population, 16 (0.26%) for the natural French Atlantic population and 18 (0.35%) for the Portuguese population. These markers, together with those for which >10% of the data were missing in any of the three populations, were removed from further analyses, resulting in a set of 5,016 loci (including
24% monomorphic loci) successfully scored in all populations. We found no differences in genetic diversity, assessed with this common SNP set, between the natural and breeding populations in the French Atlantic region (*He* of 0.336 vs. 0.332, based on polymorphic loci only; Table 2), suggesting that the mass selection in natural forests for constitution of the base breeding population for this species was broad enough to collect most of the standing genetic variation of this ecotype. This is further supported by the absence of significant genetic differentiation between the natural and mass-selected populations (*F*<sub>st</sub> = 0.0005, 95% CI: -0.00002, 0.00107). Genetic diversity was slightly lower for the Portuguese population (*He* of 0.319) than for both the breeding and natural populations in France. High levels of genetic differentiation were observed between the French Atlantic and Portuguese populations (*F*<sub>st</sub> = 0.0847, 95% CI: 0.08087, 0.08864), with 263 SNPs having *F*<sub>st</sub> values above 0.25. These SNP markers are good candidates to replace the biochemical assay (based on terpene content analysis, Baradat and Marpeau-Bezard, 1988) currently used to determine the putative origin of adult forest stands in Aquitaine before the collection of seeds and their distribution for commercial purposes in France. Indeed, seed-lots from Portugal were introduced into Aquitaine in the 1950s and the stands they formed suffered frost damage after the exceptionally cold winter of 1985 (Ribeiro *et al.* 2002). Therefore, from 1986 onwards, candidate stands for seed collection in Aquitaine had to be certified as of French origin. The Bayesian clustering pattern observed, with the identification of only two gene pools (*K*=2), with the French natural and breeding populations grouped together into a single gene pool significantly separated from the Portuguese population (Figure 6), is an strong asset in this respect. This result will guide the development of highly informative genotyping multiplexes based on the Sequenom MassARRAY technology that has already been developed for this species (Chancerel *et al.* 2013).

399
As expected, the SFS of both the French and Portuguese groups showed a deficit of the observed low-frequency variants (Supplemental Figure 1). This deficit is probably due to a combined effect of the approach developed to select most of the SNPs (i.e. *in silico* analysis of ESTs obtained from small numbers of genotypes, precluding the capture of rare variants of the SFS), and the sampling scheme (a small sample size for each group), hindering the detection of rare variants even if present on the SNP array.

406

## Discussion

408

In this section, we discuss how the substantial number of polymorphisms detected and validated in this study will help researchers working on maritime pine to address scientific questions in population genomics and quantitative genetics with large samples of individuals from natural and pedigreed populations that can now be surveyed for genetic variation at a few thousand expressed genes across the genome.

414

### Linkage and QTL mapping

416

High-throughput SNP genotyping has been successfully implemented in several conifers (Pavy et al. 2008; Eckert et al. 2009a; Chancerel et al. 2011, 2013). This approach has generated thousands of markers for genetic mapping, significantly improving our understanding of large, complex conifer genomes (Jermstad et al. 2011, Pavy et al. 2012, Martínez-García et al. 2013, Neves et al. 2014). We show here that a combination of highly multiplexed SNP genotyping and a selective mapping strategy is useful for the mapping of hundreds of genes at low cost. The genetic map obtained with this strategy was highly reliable and accurate, as revealed by comparison with a previously developed map based on different
425 genotypes from the same progeny (Chancerel *et al.* 2013). The use of markers common to the
426 two sets of offspring made it possible to construct a composite genetic linkage map
427 confirming the positions of most of the previously mapped markers. Highly saturated gene-
428 based maps are very useful for the identification of orthologous genes, facilitating the study of
429 genome structure and evolution in conifers (Mackay *et al.* 2012, Ritland *et al.* 2011). Conifers
430 display a high degree of genome macrostructure conservation (Chagné *et al.* 2003, Pavy *et al.*
431 2012, Krutovsky *et al.* 2004, Jermstad *et al.* 2011). Comparative mapping is therefore a useful
432 tool to transfer information between species. SNP-based genetic maps have been used to
433 anchor large genome scaffolds to support conifer sequencing projects (Martínez-García *et al.*
434 2013, Neale *et al.* 2014). In addition, high-density gene-based maps are essential for the
435 identification of genes controlling targeted traits through their colocalization with QTLs
436 (Monclus *et al.* 2012, de Miguel *et al.* 2014, Lind *et al.* 2014, Li *et al.* 2014, Bartholomé *et al.*
437 2015b). Thus, the development and application of high-throughput genotyping technologies to
438 non-model species without a reference genome sequence, such as maritime pine, represents a
439 major advance towards the identification of genes controlling traits of interests.

440

441 *Association mapping*

442

443 In long-lived outbreeding organisms, such as forest trees, association mapping is an attractive
444 alternative to QTL mapping, as it can be used to identify molecular variation underlying
445 phenotypes from multiple genetic backgrounds without the need to produce segregating
446 progenies (Neale and Savolainen 2004). Under certain circumstances (e.g. moderate-high
447 heritability), association mapping can also be applied to natural populations. This approach is
448 useful for studies of adaptive traits in the precise environments in which they evolved (see
449 Parchman *et al.* 2012; Budde *et al.* 2014 for forest trees). Recent years have seen an explosion
in the number of association mapping studies in model organisms, such as humans and *Arabidopsis* (see reviews in Stranger *et al.* 2011; Weigel 2012). However, only a few studies, mostly focusing on technological characters associated with wood quality, have been carried out on forest trees (e.g. González-Martínez *et al.* 2011 and references therein; Cappa *et al.* 2013; Guerra *et al.* 2013). Progress has been particularly slow in maritime pine, partly due to the lack of a reliable and cost-efficient genotyping platform capable of dealing with the large sample sizes required to detect small- and medium-sized allelic effects. However, promising results were obtained in the first genetic association studies in maritime pine, based on only a few hundred markers. Lepoittevin *et al.* (2012) found two polymorphisms significantly associated with growth (in a HD-Zip III transcription factor) and wood cellulose content (in a fasciclin-like arabinogalactan protein), respectively. Cabezas *et al.* (2015) discovered a strong association between several (linked) polymorphisms in *korrigan*, a gene encoding a membrane-bound endo-1,4-β-d-glucanase involved in cellulose biosynthesis, and various growth traits, including height and polycyclism. Our SNP array includes all SNPs reported to display significant genotype:phenotype associations in previous studies. It is also enriched in SNPs from candidate genes, based on all the available information (published or unpublished) concerning genes displaying signatures of natural selection (e.g. Eveno *et al.* 2008; Grivet *et al.* 2011), involved in environmental associations (e.g. Jaramillo-Correa *et al.* 2015) or displaying differential expression (e.g. for pine nematode resistance, Santos *et al.* 2012; or drought response, Perdiguero *et al.* 2013) in this species. We therefore expected the use of this SNP genotyping array to generate highly informative data, increasing our understanding of the ecological drivers of adaptation and of use for breeding applications, including genomic selection (see below). For example, Westbrook *et al.* (2013) found that a small number of significantly associated SNPs (~20-30) had the same predictive power as the full dataset (4,854 SNPs) in SNP-based models for oleoresin flow in *Pinus taeda* L. Finally, this SNP
array will allow unprecedented explorations of the molecular basis of polygenic quantitative
traits, through the implementation of multilocus association models (e.g. piMASS, Guan and
Stephen 2011), for which high-density genotyping is required. Most of the traits relevant for
adaptation or breeding are polygenic. The development of high-density SNP genotyping
assays for forest trees thus constitutes a first step towards the use of molecular information in
operational forestry.

481

482 *Genomic selection*

483

484 High-throughput genotyping platforms, such as the new SNP array developed here, can guide
485 breeding and selection decisions (Eggen 2011). Genomic selection (GS) is a paradigm shift
486 first introduced into animal breeding in 2008 for the selection of superior individuals in many
487 countries (Goddard 2009). The major difference between GS and marker-assisted selection
488 (MAS) is the number of markers used. GS makes use of a much larger number of markers to
489 trace all the QTLs with small or large effect (Hayes and Goddard 2010). SNP-based
500 genotyping platforms are reliable and repeatable for the genotyping of large numbers of
491 individuals. There are few missing genotypes, and these genotypes can be handled (dropped
492 or imputed) without markedly decreasing data quality. Thus, SNP arrays have become the
493 choice genotyping platform for animal breeding and human genetics studies, despite advances
494 in the efficiency of DNA sequencing technologies. Whole-genome sequences have been
495 obtained for several major forest trees subjected to breeding programmes in recent years, and
496 draft assemblies of whole genomes are now available even for conifers, which have
497 particularly large genomes (Birol *et al.* 2013, Nystedt *et al.* 2013, Neale *et al.* 2014). These
498 resources should facilitate the development of efficient genotyping by sequencing platforms
499 in the near future. GS is expected to revolutionise forest tree breeding, by decreasing the need
for expensive and time-consuming progeny-testing practices. If successful, GS could halve
the long breeding cycles (>15 years) of forest trees and double the genetic gain per unit time
(Isik 2014). One probable application of GS application in forest trees would involve the use
of data from different sources, such as progeny tests and genotyping centres, in a single-step
approach to predicting the genetic merit of individuals (Legarra et al. 2009). The predicted
model for one cycle can then be refined as new data become available. As breeding cycles
progress and genotyping/sequencing costs fall, progeny testing will have a lesser effect on
selection decisions. Nevertheless, the lack of a reliable and cost-efficient genotyping platform
remains the major bottleneck for the routine application of GS in forest trees. The new high-
density SNP array presented here constitutes a first step towards the implementation of GS in
maritime pine.

511

512 *Population genomics*

513

514 The high-density SNP array developed in this study provides a powerful tool for the genome-
515 wide genotyping of a large number of populations across the full distribution range of the
516 species. The genotyping of hundreds of individuals across the entire range of maritime pine,
517 with SNPs located both in coding and noncoding regions, would increase our understanding
518 of the role of the evolutionary, demographic and adaptive mechanisms acting on natural
519 populations. The available molecular markers have shown that maritime pine populations are
520 spatially structured into regional gene pools connected by gene flow, particularly in the
521 Iberian Peninsula (Burban and Petit 2003, Bucci et al. 2007, Jaramillo-Correa et al. 2015).
522 However, the timing of the historical events leading to this spatial separation, and the degree
523 of connectivity between gene pools, remain to be determined. In addition, maritime pine
524 grows in diverse environmental conditions, resulting in the local adaptation of populations
over the range of this species (González-Martínez et al. 2002, Jaramillo-Correa et al. 2015, Serra-Varela et al. 2015), but the molecular mechanisms underlying this adaptation are poorly understood. Most ecologically and economically important traits are quantitative in nature and controlled by many loci (Rockman 2012). Extensive genome coverage is therefore required to disentangle selection from other factors that have shaped genomic variation across populations (e.g. genetic drift). This SNP array provides a means of identifying functional variation and the molecular bases of adaptation, through various methods based on the differentiation of allele frequencies between populations ($F_{ST}$-based methods; see e.g. Prunier et al. 2011, Chen et al. 2012), site-frequency spectrum statistics (e.g., Eckert et al. 2009b), correlations with environmental variables (e.g., Eckert et al. 2010, Jaramillo-Correa et al. 2015), or combinations of these approaches. ‘Reverse’ ecology approaches connecting genomic data with environmental parameters have also proved useful for identifying the major ecological drivers of adaptation (Levy and Borenstein 2012). This SNP array is therefore highly promising for studies aiming to interpret the signatures left in the genome by different evolutionary forces, making inferences about complex population processes, or investigating geographic patterns of genome-wide genetic variation.

541

## Conclusion

543

In this study we report the discovery of new SNP markers in maritime pine, from RNA-seq and amplicon resequencing data, and the establishment of an Infinium genotyping array including SNPs that have already been validated. The SNP array presented here represents a major step forward for population and conservation genetics and for breeding in maritime pine. However, this array is subject to a number of limitations. Ascertainment biases, resulting from the small panel of individuals from which SNPs were obtained (ascertainment width)

549
and from the stringent criteria in terms of minimum allele frequency and read coverage used to retain the SNPs (ascertainment depth), must be taken into account when interpreting the results of future studies of natural populations. A systematic bias would be expected for estimates of nucleotide diversity, inferences about population structure, evolutionary processes based on natural selection and/or historical demographic models, particularly if based on the site frequency spectrum (reviewed by Helyar *et al.* 2011). Furthermore, most of the SNPs were obtained from independent contigs of the UniGene, precluding the use of linkage disequilibrium and haplotype diversity as metrics for population genetic inferences. In the near future, we will develop genotyping-by-sequencing approaches, which should make it possible to decrease the problem of ascertainment bias, because the individuals of interest are sequenced directly. Considering allele drop out of genotyping-by-sequencing approaches which reduces genome complexity through restriction digest (e.g. restriction-site-associated DNA sequencing, Baird *et al.* 2008) as a major drawbacks for genetic inferences in highly heterozygous species such as maritime pine (Davey *et al.* 2013, Gautier *et al.* 2013, Puritz *et al.* 2014, Mastretta-Yanes *et al.* 2015), future development of marker technology in this species will be based on sequence capture and direct sequencing (Gnirke *et al.* 2009).

566

567 Acknowledgements

568

This study was carried out with financial support from the European Union Sixth and Seventh Framework Programmes: Evoltree (no. 016322), Procogen (no. 289841), Noveltree (no. 211868), Forger (no. KBBE-289119), TipTree (BiodivERsA-ERANET); the French National Research Agency: ANR-FLAG (ANR-12ADAP-007-01); the Italian Ministry of Education, University and Scientific Research: Biodiversitalia (RBAP10A2T4); the Spanish Ministry of Science and Innovation: AdapCon (CGL2011-30182-C02-01). We thank C García-Barriga for
575 DNA extraction. D Grivet was supported by a "Ramón y Cajal" Fellowship from the Spanish
576 Ministry of Science and Innovation, I Lesur by ANR-FLAG and C Boury by Procogen. H
577 Lagraulet received a PhD fellowship from INRA (EFPA division and ACCAF
578 metaprogramme) and Région Aquitaine (no. 20111203004). J Bartholomé was supported by a
579 postdoctoral fellowship from "Conseil Général des Landes". I Rodríguez-Quilón
580 acknowledges a PhD scholarship (FPI-INIA) from the Spanish National Institute for
581 Agricultural and Food Research and Technology and the Spanish Ministry of Science and
582 Innovation.

583

584 Data accessibility

585

586 Roche 454 raw data have been deposited in the short-read archive of NCBI (accessions:
587 SRX031589 for genotype 110-4019-1, SRX208012 for genotype 0284-2 and SRX031592 for
588 genotype 112-4-1). The assembled contigs of the second maritime pine UniGene are available
589 from:
590 http://genotoul-contigbrowser.toulouse.inra.fr:9092/Pinus pinaster2/index.html.

591

592 Authors' contributions

593

594 IL and HL: *in silico* SNP detection in mapping populations; IL: SNP array design and SNP
595 annotation; CB, LB, JMG, FB, GGV, DG, IRQ, and SCGM: logistical aspects of genotyping;
596 CB, JB IRQ, and DG: SNP scoring; CP, IL, MdM, MTC, NdM and FH: database compilation
597 from published and unpublished data; JB: linkage mapping; SCGM and DG: population
598 genetics analysis; CP: design of the study and overall coordination. All the authors
599 participated in the writing of the paper, and have read and approved the manuscript submitted.
600

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966
967 **Figure and table legends**
968
**Figure 1** Number of SNPs developed over the last five years for maritime pine and interdependence of studies (for more details see Supplemental Table 2). Studies from de Miguel *et al.* (2014) in orange, Chancerel *et al.* (2013) in blue, Chancerel *et al.* (2011) in green, Lepoittevin *et al.* (2010) in red, and Lavigne *et al.* (manuscript in preparation) in black.

973

**Figure 2** Strategy for developing SNPs segregating in three full-sib progenies (CxM, LxC, LxM), from high-throughput sequencing of the parental lines (C, L and M).

976

**Figure 3** Strategy for developing new SNPs in candidate genes.

978

**Figure 4** Synonymous and non-synonymous substitutions. (A) Synonymous (S) and non-synonymous (NS) mutations at the three codon positions. (B) Proportion of transitions and transversions.

982

**Figure 5** Comparison between the two F2 genetic linkage maps (F2_N in blue and F2_O in white). Segregation distortion regions are indicated in orange.

985

**Figure 6** Bayesian clustering analysis for *K*=2, showing the French Atlantic (red) and Portuguese (green) maritime pine gene pools. The red and green portions of each bar (individual tree) indicate the probability of genetic ancestry (y-axis) for each cluster.

989

**Table 1** Characteristics of the F2 genetic linkage maps. F2_N: map obtained from the 9 k-array (this study), F2_O: map constructed with data from Chancerel *et al.* (2013) and F2_C: composite map generated from the F2_N and F2_O maps.

993
994 **Table 2** Genetic diversity estimates for French Atlantic and Portuguese maritime pine populations.

996

997 **Supporting Information**

998

999 **Supplemental Table 1** List of SNP arrays already developed for maritime pine and associated research questions.

1000

1001

1002 **Supplemental Table 2** List of SNP markers already developed for maritime pine

1003

1004 **Supplemental Table 3** List of SNP markers with sequence, dbSNP accession, type of SNP, corresponding contig ID in PineV3 and functional annotation, and linkage group assignment on the F2, CL, ML, CM linkage maps.

1005

1006

1007

1008 **Supplemental Figure 1** Distribution of minor allele frequency (MAF from France [A] and Portugal [B]) and folded site frequency spectrum (SFS from France [C] and Portugal [D]).
1009 The expected distribution of minor allele variants is skewed towards an excess of rare variants. According to Hartl and Clark (1998), more than one third of all polymorphisms would be expected to have a frequency below 5%, reflecting purifying selection.

1009

1010

1011

1012
<figure><img src="image_3.png" /><figcaption>Figure 1</figcaption></figure>

view Only
Corsican genotype
110-4019-1 (C)

Landes genotype
0284-2 (L)

Moroccan genotype
112-4-1 (M)

Roche 454 sequencing

<figure><img src="image_4.png" /></figure>

188,494 reads

<figure><img src="image_5.png" /></figure>

611,673 reads

<figure><img src="image_6.png" /></figure>

458,882 reads

pyrocleaner

<figure><img src="image_7.png" /></figure>

107,089 reads

<figure><img src="image_8.png" /></figure>

484,348 reads

<figure><img src="image_9.png" /></figure>

225,842 reads

Mapping on PineV2 (198,425 contigs)

CLC Genomics Workbench V6.0

CLC Genomics Workbench Reference Mapping function

<figure><img src="image_10.png" /></figure>

104,043 mapped reads
(97.16%)

<figure><img src="image_11.png" /></figure>

350,917 mapped reads
(72.45%)

<figure><img src="image_12.png" /></figure>

219,054 mapped reads
(96.99%)

SNP detection

CLC Genomics Workbench V6.0

Quality-based variants detection based on the neighborhood quality standard algorithm (NQS)

minimum coverage = 6

minimum central base quality = 20

minimum neighborhood quality over a window length of 11 nucleotides = 15

maximum gap and mismatch count = 2

minimum allele frequency = 20%

<figure><img src="image_13.png" /></figure>

2,039 SNPs (C)

<figure><img src="image_14.png" /></figure>

4,937 SNPs (L)

<figure><img src="image_15.png" /></figure>

4,752 SNPs (M)

Set of 3,741 SNPs distributed as follows:

C x M

2,830 SNPs

<figure><img src="image_16.png" /></figure>

784

<figure><img src="image_17.png" /></figure>

L x C

1,695 SNPs

<figure><img src="image_18.png" /></figure>

<figure><img src="image_19.png" /></figure>

911

<figure><img src="image_20.png" /></figure>

L x M

2,957 SNPs

<figure><img src="image_21.png" /></figure>

<figure><img src="image_22.png" /></figure>
<figure><img src="image_23.png" /><figcaption>Figure 3</figcaption></figure>
A

<figure><img src="image_24.png" /></figure>

B

<figure><img src="image_25.png" /></figure>

Synonymous and non-synonymous substitutions in the polymorphic SNPs.

(A) Synonymous (S) and non-synonymous (NS) mutations at the three codon positions.

(B) Proportion of transitions and transversions.
<figure><img src="image_26.png" /><figcaption>Figure 5</figcaption></figure>
For Review Only
<figure><img src="image_27.png" /><figcaption>Figure 6</figcaption></figure>
<table><caption>Table 1</caption><thead><tr><th></th><th>1</th><th>2</th><th>3</th><th>4</th><th>5</th><th>6</th><th>7</th><th>8</th><th>9</th><th>10</th><th>11</th><th>12</th><th>Total</th></tr></thead><tbody><tr><td>LG</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Size (cM)</td><td>157.7</td><td>197.2</td><td>178.5</td><td>180.6</td><td>198.2</td><td>134.8</td><td>161.2</td><td>166.9</td><td>151.17</td><td>184.4</td><td>126.2</td><td>156</td><td>1,992.9</td></tr><tr><td>Number of SNPs</td><td>157</td><td>152</td><td>190</td><td>176</td><td>199</td><td>163</td><td>181</td><td>155</td><td>184</td><td>148</td><td>156</td><td>191</td><td>2,052</td></tr><tr><td>Number of unique positions</td><td>65</td><td>81</td><td>88</td><td>77</td><td>80</td><td>80</td><td>76</td><td>79</td><td>74</td><td>66</td><td>68</td><td>67</td><td>901</td></tr><tr><td>Distance between SNPs (cM)</td><td>1.01</td><td>1.31</td><td>0.94</td><td>1.03</td><td>1</td><td>0.83</td><td>0.9</td><td>1.08</td><td>0.83</td><td>1.25</td><td>0.81</td><td>0.82</td><td>0.98</td></tr><tr><td>Distance between unique positions (cM)</td><td>2.46</td><td>2.47</td><td>2.05</td><td>2.38</td><td>2.51</td><td>1.71</td><td>2.15</td><td>2.14</td><td>2.07</td><td>2.84</td><td>1.88</td><td>2.36</td><td>2.25</td></tr><tr><td>Distorted SNPs (%)</td><td>0</td><td>0</td><td>7.9</td><td>0</td><td>0</td><td>16</td><td>0</td><td>0</td><td>0</td><td>18.9</td><td>0</td><td>0</td><td>3.6</td></tr><tr><td>Size (cM)</td><td>121.8</td><td>160.1</td><td>158.4</td><td>141.5</td><td>160.5</td><td>129.7</td><td>144.6</td><td>139.7</td><td>130.6</td><td>149.9</td><td>113.2</td><td>137.6</td><td>1,687.6</td></tr><tr><td>Number of SNPs</td><td>106</td><td>117</td><td>147</td><td>138</td><td>126</td><td>113</td><td>124</td><td>93</td><td>134</td><td>107</td><td>130</td><td>146</td><td>1,481</td></tr><tr><td>Number of unique positions</td><td>41</td><td>57</td><td>60</td><td>54</td><td>56</td><td>58</td><td>58</td><td>47</td><td>45</td><td>54</td><td>49</td><td>50</td><td>629</td></tr><tr><td>Distance between SNPs (cM)</td><td>1.16</td><td>1.38</td><td>1.09</td><td>1.03</td><td>1.28</td><td>1.16</td><td>1.18</td><td>1.5</td><td>0.98</td><td>1.41</td><td>0.88</td><td>0.95</td><td>1.17</td></tr><tr><td>Distance between unique positions (cM)</td><td>3.04</td><td>2.86</td><td>2.69</td><td>2.67</td><td>2.92</td><td>2.28</td><td>2.54</td><td>3.04</td><td>2.97</td><td>2.83</td><td>2.36</td><td>2.81</td><td>2.75</td></tr><tr><td>Distorted SNPs (%)</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>33</td><td>0</td><td>0</td><td>2.75</td></tr><tr><td>Size (cM)</td><td>124.1</td><td>160.1</td><td>159.3</td><td>142.2</td><td>160.5</td><td>131.3</td><td>144.6</td><td>139.7</td><td>132.2</td><td>155.4</td><td>123.7</td><td>138.8</td><td>1,711.7</td></tr><tr><td>Number of SNPs</td><td>179</td><td>177</td><td>221</td><td>191</td><td>219</td><td>188</td><td>201</td><td>174</td><td>207</td><td>173</td><td>194</td><td>229</td><td>2,353</td></tr><tr><td>Number of unique positions</td><td>64</td><td>81</td><td>95</td><td>75</td><td>88</td><td>88</td><td>83</td><td>81</td><td>75</td><td>76</td><td>74</td><td>75</td><td>955</td></tr><tr><td>Distance between SNPs (cM)</td><td>0.7</td><td>0.91</td><td>0.72</td><td>0.75</td><td>0.74</td><td>0.7</td><td>0.72</td><td>0.81</td><td>0.64</td><td>0.9</td><td>0.64</td><td>0.61</td><td>0.74</td></tr><tr><td>Distance between unique positions (cM)</td><td>1.97</td><td>2</td><td>1.69</td><td>1.92</td><td>1.84</td><td>1.51</td><td>1.76</td><td>1.75</td><td>1.79</td><td>2.07</td><td>1.69</td><td>1.88</td><td>1.82</td></tr></tbody></table>
For Review Only
<table><caption>Table 2</caption><thead><tr><th rowspan="2">Population</th><th rowspan="2"></th><th rowspan="2">N</th><th rowspan="2">% mono</th><th colspan="3">All loci</th><th colspan="3">Common polymorphic loci</th></tr><tr><th>Loci #</th><th>MAF</th><th>He</th><th>Loci #</th><th>MAF</th><th>He</th></tr></thead><tbody><tr><td rowspan="2">French Atlantic</td><td>Breeding population</td><td>46</td><td>0.242</td><td>5,016</td><td>0.201</td><td>0.266</td><td>3,981</td><td>0.254</td><td>0.336</td></tr><tr><td>Natural population</td><td>50</td><td>0.241</td><td>5,016</td><td>0.198</td><td>0.263</td><td>3,981</td><td>0.250</td><td>0.332</td></tr><tr><td>Portugal</td><td></td><td>42</td><td>0.227</td><td>5,016</td><td>0.187</td><td>0.253</td><td>3,981</td><td>0.236</td><td>0.319</td></tr></tbody></table>
For Review Only