domain: sdv info

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Keywords: crossbred performance, genetic correlation, genomic evaluation, pig, reliability, single-step method

INTRODUCTION

Crossbreding is predominant and intensively used in meat production systems [START_REF] Wei | Combined crossbred and purebred selection in animal breeding[END_REF], especially in swine and chicken. In two-way crossbreding schemes, selection of purebreds for their crossbred performance is the ultimate goal [START_REF] Wei | Combined crossbred and purebred selection in animal breeding[END_REF][START_REF] Bijma | Standard error of the genetic correlation: How much data do we need to estimate a purebred-crossbred genetic correlation?[END_REF]. Because there exist genetic differences between breeds and genotype × environment interaction effects, additive genetic effects estimated based on purebred performance cannot be used to perfectly predict the crossbred performance [START_REF] Lo | Genetic evaluation by BLUP in two-breed terminal crossbreding systems under dominance[END_REF]. Ideally, combined purebred and crossbred information is required to implement the genetic evaluation for crossbred performance [START_REF] Wei | Maximizing genetic response in crossbreds using both purebred and crossbred information[END_REF]. However, due to the difficulty and high cost of collection of data from crossbred animals [START_REF] Dekkers | Marker-assisted selection for commercial crossbred performance[END_REF], it is not common to have access to crossbred data.

Genomic selection has been successfully applied in purebreds based on data from purebred animals [START_REF] Loberg | Interbull survey on the use of genomic information[END_REF][START_REF] Fulton | Genomic selection for poultry breeding[END_REF], but it also offers opportunities for selecting purebreds for ABSTRACT: Crossbreding is predominant and intensively used in commercial meat production systems, especially in poultry and swine. Genomic evaluation has been successfully applied for breeding within purebreds but also offers opportunities of selecting purebreds for crossbred performance by combining information from purebreds with information from crossbreds. However, it generally requires that all relevant animals are genotyped, which is costly and presently does not seem to be feasible in practice. Recently, a novel single-step BLUP method for genomic evaluation of both purebred and crossbred performance has been developed that can incorporate marker genotypes into a traditional animal model. This new method has not been validated in real data sets. In this study, we applied this single-step method to analyze data for the maternal trait of total number of piglets born in Danish Landrace, Yorkshire, and two-way crossbred pigs in different scenarios. The genetic correlation between purebred and crossbred performances was investigated first, and then the impact of (crossbred) genomic information on prediction reliability for crossbred performance was explored. The results confirm the existence of a moderate genetic correlation, and it was seen that the standard errors on the estimates were reduced when including genomic information. Models with marker information, especially crossbred genomic information, improved model-based reliabilities for crossbred performance of purebred boars and also improved the predictive ability for crossbred animals and, to some extent, reduced the bias of prediction. We conclude that the new single-step BLUP method is a good tool in the genetic evaluation for crossbred performance in purebred animals. crossbred performance by using combined information from purebreds and crossbreds (Ibáñez-Escriche et al., 2009;[START_REF] Zeng | Genomic selection of purebred animals for crossbred performance in the presence of dominant gene action[END_REF] or by using only purebred data [START_REF] Esfandyari | Maximizing crossbred performance through purebred genomic selection[END_REF]. However, it generally requires that all relevant animals are genotyped. Recently, a novel single-step BLUP method [START_REF] Christensen | Genomic evaluation of both purebred and crossbred performances[END_REF] for genomic evaluation of both purebred and crossbred performance in a two-way crossbreding system was developed that is an extension of a single-step BLUP method [START_REF] Legarra | A relationship matrix including full pedigree and genomic information[END_REF][START_REF] Christensen | Genomic prediction when some animals are not genotyped[END_REF] from purebred performance to combined purebred and crossbred performances.

The aim of this study is to implement the new single-step BLUP method by using both purebred and crossbred data of total number of piglets born (TNB) in different scenarios, estimate the genetic correlation between purebred and crossbred performance, and then explore the impact of (crossbred) genomic information on prediction reliability for crossbred performance.

MATERIALS AND METHODS

Data

For this study, all data sets were provided by Danish Pig Research Centre. Three populations were simultaneously analyzed: Danish Landrace (LL), Danish Yorkshire (YY), and two-way crossbred Danish Landrace-Yorkshire. Crossbred animals that had a Landrace sire and a Yorkshire dam were termed "Landrace_Yorkshire" (LY), whereas "Yorkshire_Landrace" (YL) represented crossbreds with Yorkshire sires and Landrace dams. The TNB data in this study comprised the records of the first parity in all three populations. Altogether, TNB was recorded in 293,339 LL,180,112 YY,and 10,974 crossbred animals. This data set is termed the "full population" throughout the whole paper.

Among the crossbreds, 7,407 were LY and 3,567 were YL. All of the purebred animals had first farrowing dates between 2003 and 2013, and the crossbred animals first farrowed between 2010 and 2013. The pedigree for both purebred and crossbred animals was available, and all the crossbreds were traced back to their purebred ancestors until 1994 by the DMU Trace program (Madsen, 2012). Consequently, 332,929 LL, 210,554 YY, and 10,974 crossbreds were in the pedigree. Among those animals, 7,723 LL and 7,785 YY were genotyped with an Illumina PorcineSNP60 Genotyping BeadChip [START_REF] Ramos | Design of a high density SNP genotyping assay in the pig using SNPs identified and characterized by next generation sequencing technology[END_REF]. Two-thirds of purebred genotyped animals were boars. For the crossbreds, 5,203 animals (4,077 LY and 1,126 YL) were genotyped with a 8.5K GGP-Porcine Low Density Illumina Bead SNP Chip [START_REF] Geneseek | GeneSeek Genomic Profiler for Porcine LD[END_REF]. Single nucleotide polymorphism quality controls were applied on the same data set in a previous study [START_REF] Xiang | Imputation of genotypes in Danish purebred and twoway crossbred pigs using low-density panels[END_REF], where more details can be found. Finally, 41,009 SNP and 7,916 SNP in autosome chromosomes were accessible in purebreds and crossbreds, respectively. Imputation was implemented in crossbreds from 7,916 SNP to 41,009 SNP with software Beagle [START_REF] Browning | Missing data imputation and haplotype phase inference for genome-wide association studies[END_REF], which outputs phased SNP for both reference and imputed population, by using a joint reference panel of the two pure breeds [START_REF] Xiang | Imputation of genotypes in Danish purebred and twoway crossbred pigs using low-density panels[END_REF]. As a result, 41,009 phased genotyped SNP were available for the genotyped animals in both purebreds and crossbreds for the current study.

Single-Step BLUP Model for Purebred and Crossbred Performances

The new single-step BLUP method of evaluating both purebred and crossbred performance was developed by [START_REF] Christensen | Genomic evaluation of both purebred and crossbred performances[END_REF]. The model reformulates the "full" Wei and van der Werf (1994) A1 model and incorporates genomic information by using two breed-specific combined relationship matrices, which extend the marker-based relationship matrices to the non-genotyped animals.

The Wei and van der Werf model is a trivariate model: , respectively; a L and a Y contain breeding values for breed L and breed Y for their purebred performance (mating within each own breed); c LY stands for the additive genetic effects of crossbred LY animals; and Z L , Z Y , and Z LY are the respective incidence matrices. Note that the c LY animal additive genetic effects are actually formed as the sum of two additive gametic effects, one from L and another from Y. In other words, a crossbred diploid genome decomposes into two purebred haploid genomes.

y L = X L β L + Z L a L + e L , y Y = X Y β Y + Z Y a Y + e Y ,
The [START_REF] Christensen | Genomic evaluation of both purebred and crossbred performances[END_REF] method first assumes that effects of markers across the different origins (Yorkshire and Landrace, in this case) are unrelated. Under this assumption, the additive effect of the genome of an F 1 crossbred animal can be split into the sum of two additive gametic effects, one gamete from each breed, where the two gametic effects are uncorrelated by assumption of the model. Therefore, separate matrices of pedigree-based or genomicbased relationships can be set up within each breed and then be combined according to purebred theory for the single step [START_REF] Legarra | A relationship matrix including full pedigree and genomic information[END_REF][START_REF] Christensen | Genomic prediction when some animals are not genotyped[END_REF]. The analysis proceeds by estimating solutions to two different breed-specific random effects. The key to disentangle the breeds of origin for the genetic effect of the F 1 individuals is the ability to construct pedigree-based partial relationship matrices [START_REF] García-Cortés | Multibreed analysis by splitting the breeding values[END_REF] or separate (by origin) genomic matrices, which, in turn, requires ascertainment of breed origin of the marker genotypes. More specifically, there are three steps:

Step 1). Reformulate the Wei and van der Werf model by splitting additive genetic effects for crossbred animals (LY) into breed of origin specific genetic effects, that is, split the additive genetic value of the ith F1 crossbred in two additive genetic values, one from each origin (LL or YY):

c LYi = c L LYi + c Y LYi .
It has to be understood that neither of these is a breeding value sensu stricto; instead, they are additive effects in the statistical sense as "regression of value on gene dosage" as explained by [START_REF] Falconer | A note on Fisher's 'average effect' and 'average excess[END_REF], who clarifies the various definitions of average effect of genes in absence of random mating. Note that the new single-step model [START_REF] Christensen | Genomic evaluation of both purebred and crossbred performances[END_REF] is not the animal model used by [START_REF] Lo | Genetic evaluation by BLUP in two-breed terminal crossbreding systems under dominance[END_REF] and [START_REF] Lutaaya | Genetic parameter estimates from joint evaluation of purebreds and crossbreds in swine using the crossbred model[END_REF]. Actually, the new single-step model is a reformulation of the full model from Wei and van der Werf (1994; equation A1), whereas [START_REF] Lo | Genetic evaluation by BLUP in two-breed terminal crossbreding systems under dominance[END_REF] and [START_REF] Lutaaya | Genetic parameter estimates from joint evaluation of purebreds and crossbreds in swine using the crossbred model[END_REF] refer to the reduced animal model from [START_REF] Wei | Maximizing genetic response in crossbreds using both purebred and crossbred information[END_REF]equation A2). In the presence of only pedigree information, the full and the reduced animal model are equivalent, but in the presence of crossbred genomic information, this is no longer the case. In the papers of [START_REF] Lo | Genetic evaluation by BLUP in two-breed terminal crossbreding systems under dominance[END_REF] and [START_REF] Lutaaya | Genetic parameter estimates from joint evaluation of purebreds and crossbreds in swine using the crossbred model[END_REF], the additive genetic value of the ith

F 1 crossbred is u LYi = (u L LYp(i, L) + Φ Li ) + (u Y LYp(i, Y) + Φ Yi ).
Here , u L LYp(i, L) and u Y LYp(i, Y) are half the additive genetic values of the purebred parents p(i, Y) and p(i, L), which are common to all the offspring of the same sire or dam, and Φ Li and Φ Yi are the respective Mendelian samplings, which are different for each offspring. In the reduced animal model, both Mendelian sampling terms are included in the residual effect of the crossbred animals, and only u L LYp(i, L) and u Y LYp(i, Y) are estimated. This is for two reasons: first, with only pedigree information, this term cannot be estimated; second, setting up matrices of additive relationships (and their inverse) for crossbred animals at the animal model is not straightforward [START_REF] Lo | Covariance between relatives in multibreed populations: Additive model[END_REF][START_REF] García-Cortés | Multibreed analysis by splitting the breeding values[END_REF]. Therefore, in the works of [START_REF] Lo | Genetic evaluation by BLUP in two-breed terminal crossbreding systems under dominance[END_REF] and [START_REF] Lutaaya | Genetic parameter estimates from joint evaluation of purebreds and crossbreds in swine using the crossbred model[END_REF], the additive genetic value of the ith F 1 crossbred u LYi is replaced by u L LYp(i, L) + u Y LYp(i, Y) . With genomic relationships and in the model of [START_REF] Christensen | Genomic evaluation of both purebred and crossbred performances[END_REF], these Mendelian sampling terms are embedded into a genomic relationship matrix (relationships across animals for purebreds and gametes for crossbreds) and they are no longer uncorrelated. Therefore, the absorption of this term into the residual error term is not suitable. In the current study, [START_REF] Lo | Genetic evaluation by BLUP in two-breed terminal crossbreding systems under dominance[END_REF] and [START_REF] Lutaaya | Genetic parameter estimates from joint evaluation of purebreds and crossbreds in swine using the crossbred model[END_REF]. Therefore, our model (which is a gametic model at the level of crossbreds) is not a single-step model equivalent of [START_REF] Lo | Genetic evaluation by BLUP in two-breed terminal crossbreding systems under dominance[END_REF] and [START_REF] Lutaaya | Genetic parameter estimates from joint evaluation of purebreds and crossbreds in swine using the crossbred model[END_REF], which, at the level of crossbreds, are reduced animal models.

c L LYi = u L LYp(i, L) + Φ L LYi and c Y LYi = u Y LYp(i, Y) + Φ L LYi . The additive genetic value of the ith F 1 crossbred c LYi is not identical to u L LYp(i, L) + u Y LYp(i, Y) in
Step 2). Construct breed-specific partial relationship matrices for each breed of origin genetic effects. Considering pedigree relationships, the variance and covariance between additive genetic purebred (a) and crossbred (c) effects of breed LL is described as

( ) L L L L L L 2 , L 2 , var s s s s a a c c a c = ⊗               a H c
. This is a two-trait representation. For better understanding, the genetic effects can be split into animal effects belonging to purebred animals (a L and c L ) and gametic effects belonging to crossbred animals ( a LY L ( )

and c LY L ( ) ): ( ) ( ) ( ) ( ) ( ) ( ) L L L L L L L L L L L L L 2 L , LY L 2 L , L LY 2 L , L,L L,LY L L 2 LY ,L LY ,LY , var s s s s s s s s a a c c a c a a c c a c = ⊗ = ⊗                                   a a H c c H H H H
, in which matrix H (L) is a matrix of partial relationships that contains 4 blocks, 1 for within purebred animals (H L, L ), 2 for purebred with crossbred animals ( H L LY L , ( ) ) and vice versa ( H LY L L , ( ) ), and 1 for within crossbred animals ( H LY LY L , ( ) ). If there are nL pure Landrace animals and nLY crossbred animals, the size of H (L) is (nL + nLY) × (nL + nLY). The nL purebred animals have additive effects, which are breeding values, a L (when mated within breed) and c L (when mated to the other breed). The nLY purebred gametes of crossbred animals have additive effects c LY L ( ) (within the cross itself). The covariance structure includes, for ease of representation, a LY L ( ) , which are effects of crossbred gametes in purebred performance; these effects are merely conceptual but they simplify the representation and computation. The covariance structure for breed YY is similar:

( ) ( ) ( ) ( ) ( ) ( ) Y Y Y Y Y Y Y Y Y Y Y Y Y 2 Y , LY Y 2 Y , Y LY 2 Y , Y ,Y Y ,LY Y Y 2 LY ,Y LY ,LY , var s s s s s s s s a a c c a c a a c c a c = ⊗ = ⊗                                   a a H c c H H H H , with the size of H (Y) equal to (n Y + n LY ) × (n Y + n LY ),
and both structures are assumed independent, that is, there is no covariance between LL effects and YY effects.

As in [START_REF] Wei | Maximizing genetic response in crossbreds using both purebred and crossbred information[END_REF], there are six genetic variance or covariance components, three for each breed.

Matrix H (L) can be constructed based on available information (pedigree and markers) as follows. The pedigree-based and marker-based breed LL partial relationship matrices are

( ) ( ) ( ) ( ) L L,L L,LY L L L LY,L LY,LY é ù ê ú = ê ú ê ú ë û A A A A A and ( ) ( ) ( ) ( ) L L,L L,LY L L L LY,L LY,LY é ù ê ú = ê ú ê ú ë û G G G G G
, respectively, in which the partition divides purebred animals from purebred gametes in crossbred animal. Because of the split into breed-specific gametes, the pedigree-based partial relationship matrices A (L) and A (Y) must be computed as in [START_REF] García-Cortés | Multibreed analysis by splitting the breeding values[END_REF].

Construction of the breed-specific marker-based relationship matrices assumes that the breed of origin of phased alleles in crossbred animals is known. In other words, it is known which phased allele in a crossbred animal LY is from breed LL and which one is from breed YY. Then, the marker-based partial relationship matrix contains cross products of centered genotypes:

G L, L = (m L -2p L 1 ′ )(m L -2p L 1 ′ )′, G L LY L , ( ) = (m L -2p L 1 ′ )(q LY -p L 1 ′ )′,
and

G LY LY L , ( ) = (q LY -p L 1 ′ )(q LY -p L 1 ′ )′,
in which m L and q LY contain breed-specific allele contents of the second allele for purebred LL (coded as 0, 1, or 2) and crossbred animal LY (coded as 0 or 1), respectively, and vector p L are breed LL specific allele frequencies based on marker genotypes for purebred and crossbred animals.

Later, matrix G (L) is adjusted to be compatible with A (L) :

G (L) a = G (L) β + Kα, in which K = / 2 / 2 / 4 é ù ê ú ê ú ë û J J J J
and J denotes a matrix of one's partitioned as G (L) . Scalars α and β are estimated through solving the 2 following equations:

( ) ( ) L L 22 b a = + A G K and ( ) ( ) L L 22 b a = + dA dG dK ,
for example, equating the averages of the full matrices and equating the averages of the diagonals of pedigree and genomic relationships for genotyped individuals [START_REF] Christensen | Single-step methods for genomic evaluation in pigs[END_REF]. Matrix A 22 L ( ) contains pedigree relationships for genotyped LL individuals. The procedure is identical for breed YY.

Step 3). Combine the pedigree-based and adjusted marker-based partial relationship matrices to a combined partial relationship matrix H (L) , which is similar to the H matrix used in the single-step method for purebred animals [START_REF] Legarra | A relationship matrix including full pedigree and genomic information[END_REF][START_REF] Christensen | Genomic prediction when some animals are not genotyped[END_REF]. The inverse of H (L) is

( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) 1 1 1 1 22 L L L L w - - - - = + -         0 0 H A 0 G A , in which G w L ( ) = (1 -ω) A 22 L ( ) + ω A 22 L ( ) . Parameter ω
is the relative weight on the residual polygenic effect. Many other studies have investigated the weighting factors between the pedigree-based and markerbased relationship matrices [START_REF] Christensen | Genomic prediction when some animals are not genotyped[END_REF][START_REF] Christensen | Single-step methods for genomic evaluation in pigs[END_REF][START_REF] Gao | Comparison on genomic predictions using three GBLUP methods and two single-step blending methods in the Nordic Holstein population[END_REF][START_REF] Su | Genomic prediction for Nordic Red cattle using one-step and selection index blending[END_REF][START_REF] Guo | Improving genetic evaluation of litter size and piglet mortality for both genotyped and non-genotyped individuals using a single-step method[END_REF] and, commonly, they put forward that the weighting factors should be determined by the specific trait and the data set analyzed.

We investigated weighting factors from 0.1 to 0.5. Preliminary analysis (results not shown) for different weighing factors showed that ω = 0.4 was appropriate, in terms of balance between predictive abilities and biases for crossbred animals. The procedure is identical for breed YY. The sparse inverse partial relationship matrices (H (L) ) -1 and (H (Y) ) -1 are used as input to solve the mixed model equations of the model.

Step 4). Therefore, the complete representation of the final model for genetic evaluation is This is a three observed trait model (performance in LL, YY, and LY) but with two genetic effects (LL and YY), each with two genetic traits: purebred and crossbred performance. Estimation of genetic parameters by REML and BLUP predictions were done using the DMU software [START_REF] Madsen | A user's guide to DMU[END_REF].

y L = X L β L + Z L a L + e L , y Y = X Y β Y + Z Y a Y + e Y , y LY = X LY β LY + c LY L ( ) + c LY Y ( ) + e LY , ( ) ( ) ( )
L L L L L L L 2 L , LY L 2 L , L LY var s s s s a a c c a c = ⊗                     a a H c c , ( ) ( ) ( ) Y Y Y Y Y Y Y 2 Y , LY Y 2 Y , Y LY var s s s s a a c c a c = ⊗                    

Crossbred Allele Tracing

Software Beagle, which was used to impute and phase genotypes in crossbred animals, does not give breed allele origins as an output. Therefore, to infer the allele origins in crossbred animals, we proceeded as follows. The allele tracing was processed separately on each chromosome per individual.

Among the 5,203 genotyped crossbred animals, sires of 4,520 crossbreds were genotyped, whereas neither parent of the other 683 crossbreds was genotyped. When the sire was genotyped, total differences between the two sets of phased imputed alleles of a crossed animal and two sets of phased alleles of its corresponding purebred sire were compared. Comparisons between crossbred and purebred phased alleles were made on each SNP along the chromosome. For a specific comparison, if a crossbred allele was different from the corresponding purebred allele, that SNP was counted as one difference. Along the chromosome, if the sum of differences between one set of crossbred phased alleles and one set of specific purebred phased alleles was lowest among the 4 comparisons, then this set of specific crossbred phased alleles was considered as originating from the breed of the sire. Logically, the other set of crossbred phased alleles was assigned to the other breed.

When neither parent was genotyped, one of the two sets of phased imputed crossbred alleles was studied segment by segment. Each crossbred phased chromosome was split into several small segments, which consisted of 50 consecutive SNP markers. These were compared with the corresponding collection of segments from phased chromosomes of two purebred reference populations LL and YY, which were used for imputing crossbred genotypes. Each small segment in the crossbred animals should exactly match at least one segment in the reference panel, because each crossbred segment was imputed by the purebred reference population. Copies of that specific segment being detected in the reference population of LL and YY were counted separately and were divided by the total number of segments in the same position in the reference panel of LL and YY to get proportions of the matched segment. If the proportion was higher in one breed, the crossbred segment was considered to originate from this breed. Throughout all the segments within a crossbred phased chromosome, if the vast majority of segments were considered as originating from one specific breed, then the crossbred phased chromosome was assigned to that breed. Consequently, 5,203 crossbred phased alleles were traced to either breed LL or YY.

Statistical Model

For Landrace and Yorkshire, the statistical model was as follows:

y ijklmn = μ + hys i + month j + hybrid k + b 1 × age ijklmn + b 2 × age 2 ijklmn + a m + sb n + e ijlkmn ,
in which the dependent variable, y ijklmn , represented TNB in the first parity in breed LL or YY; μ was the general mean; hys i , month j , and hybrid k represented fixed effects of herd-year-season, month at farrowing, and hybrid indicator of service sire (same or different breed as sow); age ijklmn and age 2 ijklmn were covariates for the age of farrowing and its squared value, with regression coefficient b 1 and b 2 , respectively; a m was the random additive genetic effect of sow; sb n was a random service sire effect; and e ijklmn was the random residual effect. Random effects were assumed to be independently normally distributed, a ~ N(0,

H (L) L 2 s a ) or a ~ N(0, H (Y) Y 2
s a ), depending on pure breed; sb ~ N(0, Iσ 2 sb ) and e ~ N(0, Iσ 2 e ), in which H (L) and H (Y) were previously defined; I was the identify matrix; L 2 s a and Y 2 s a were additive genetic variances for breed LL and YY for purebred performances, respectively; and σ 2 sb and σ 2 e were the variance of service boar effect and the variance of the residual effect, respectively.

The model for crossbred records was

y ijlm = μ + hys i + month j + b 1 × age ijlm + b 2 × age 2 ijlm + c (L) m + c (Y) m + e ijlm ,
in which the dependent variable, y ijlm , represented TNB in the first parity in crossbred animals; μ, hys i , month j , age ijlm , and e ilmn represented the same effects as in the model for purebred records; and c (L) m and c (Y) m were breed LL and YY origin additive genetic effects, respectively. The two additive genetic effects were assumed to be independently normally distributed, c (L) ~ N(0,

H (L) L 2 s c ) and c (Y) ~ N(0, H (Y) Y 2 s c ),
in which H (L) and H (Y) were breed LL or YY specific partial additive genetic relationships and

Scenarios

Variance components, heritabilities, and genetic correlations between purebred and crossbred performances (r pc ) were first investigated in the full population. Heritability for purebred performance was defined as the ratio of additive genetic variances for purebred performance (σ2 a ) to phenotypic variances (σ 2 p = σ 2 a + σ 2 sb + σ 2 e ), whereas heritability for crossbred animals was defined as the ratio of total additive genetic variance of crossbred performance for two breed-specific gametes (0.5(

L 2 s c + Y 2 s c )) to phenotypic variances (0.5( L 2 s c + Y 2 s c ) + LY 2 s e ).
To explore the effect of different genotyping strategies on genetic evaluation for crossbred performance, the breed-specific partial relationship matrices were constructed based on three different scenarios (SC): 1) Nogen_SC, only pedigree information, which represented the traditional BLUP method; 2) Genpure_SC, pedigree information and purebred genotypes (7,723 LL and 7,785 YY), representing genotyping only purebreds; and 3) Genall_SC, pedigree information and all purebred and crossbred genotypes (7,723 LL,7,785 YY,and 5,203 crossbreds). The purposes of studying Genall_SC were to check the necessity of including crossbred genomic information, which is normally not available, and to study the improvement of genomic prediction of purebred animals for crossbred performance. Information on each scenario is shown in Table 1. To make the results comparable across all studies, specific relationship matrices for breed LL and YY were calculated using allelic frequencies estimated from "old" purebred population (born before January 1, 2011), which were 2,210 LL and 2,161 YY, respectively. For each scenario, the variance components for purebred and crossbred performances were estimated and the genetic correlation between them was obtained.

Second, model-based reliabilities of crossbred performance for purebred boars were calculated in the 3 different scenarios mentioned. According to pedigree, 7,407 LY and 3,567 YL were offspring of 765 LL and 465 YY sires, respectively. These sires were divided into 2 subgroups of genotyped and non-genotyped animals and mean model-based reliabilities were computed in each subgroup. Mean model-based reliability was calculated as [START_REF] Mrode | Linear models for the prediction of animal breeding values[END_REF]:

( ) 2 2 2 1 1 SEP / / n i c i r n s = = - ∑
, in which SEP i was the SE of prediction for animal i, σ c 2 was the variance of additive genetic effect for crossbred performance, and n was the number of purebred boars that were studied. In addition, the proportion of animals that have higher model-based reliabilities in one scenario compared with another scenario in each subgroup was also investigated.

Finally, the predictive ability for crossbred animals in the validation population (4,195 crossbreds) was investigated in different scenarios. The farrowing date of January 1, 2012, was used as the cut-off date to divide recorded sows in the full population into training and validation populations. For purebred genotyped boars, only birth dates were accessible, not days of farrowing. Therefore, for genotyped animals, the birth date of January 1, 2011, was, instead, used as the cut-off date. As a result, 240,543 LL, 139,868 YY, and 6,779 crossbreds were contained in training population, with 2,210 genotyped LL, 2,161 genotyped YY, and 2,357 genotyped crossbreds being included as well. The validation population for crossbred performance included 4,195 crossbreds, among which 2,846 were genotyped. Phenotypes of crossbred animals in the validation population were corrected for fixed and random effects other than additive genetic effect (Y c = c (L) m + c (Y) m + e). Y c were obtained by using full population data, with partial relationship matrices constructed in Genall_SC.

Breed-specific partial relationship matrices were constructed based on scenarios, concerning genotypes of animals in the training population: Nogen_T is the scenario in which relationship matrices H contained only pedigree information (i.e., H (L) = A (L) and H (Y) = A (Y) ); ) in each scenario, in which ĉ were the estimated additive genetic effects for crossbreds ( ĉ = c (L) m + c (Y) m ) in the validation population from different scenarios. For Genall_T, the validation population was divided into 2 subgroups of genotyped and non-genotyped animals and the validation correlations were made in the subgroup as well as in the whole validation population. A Hotelling-Williams t test at a 5% confidence level was applied to evaluate the significance for the differences of validation correlations in each scenario. Moreover, to detect the possible inflation or deflation of predictions, the regression coefficients of Y c on ĉ were explored to check whether they were close to 1. In addition, to measure uncertainty associated with results, bootstrap sampling [START_REF] Mäntysaari | GEBV validation test revisited[END_REF][START_REF] Cuyabano | Bootstrap study of genome-enabled prediction reliabilities using haplotype blocks across Nordic Red cattle breeds[END_REF] was used in the test population to estimate means and SE of correlations. Results were similar to the Hotelling-Williams test above and are not shown.

To check the possible impact of different genotyping scenarios on the ranking and selection of purebred animals for their crossbred performance, Spearman's rank correlations [START_REF] Spearman | The proof and measurement of association between two things[END_REF] between breeding values of purebred sires (765 LL and 465 YY) for crossbred performance were calculated across different scenarios. In addition, the breeding values for cross-bred performance were ranked from highest to lowest in different scenarios, and then the consistency of the purebred boars in the top 5% highest breeding values was checked across different scenarios. Furthermore, to investigate re-rankings in a situation closer to the way selection for crossbred performance could be implemented in practice for such a sow trait, the Spearman's rank correlation and the top 5% studies were also made on the "young" sows that were included in the validation population (52,796 LL and 40,244 YY), that is, purebred animals without their own records. Among these young sows, 1,103 LL and 1,085 YY were genotyped. These two studies were separately processed on the genotyped and non-genotyped young sows.

The new single-step BLUP method for crossbreds is complex, and therefore, we tried a simpler singletrait single-step BLUP method [START_REF] Legarra | A relationship matrix including full pedigree and genomic information[END_REF][START_REF] Christensen | Genomic prediction when some animals are not genotyped[END_REF]. This method assumed that all animals belonged to a single population, using a single relationship matrix, where the compatibility adjustment of G to A 22 was done as in [START_REF] Christensen | Single-step methods for genomic evaluation in pigs[END_REF]. Predictive abilities for crossbred animals in the validation population were also measured as cor( ĉ , Y c ).

RESULTS

Variance Components, Heritabilities, and Genetic Correlations

Estimates of variance components and genetic correlations between purebred and crossbred performances for Landrace and Yorkshire in each scenario are shown in Table 3 together with calculated heritabilities. For each scenario, both pure breeds showed higher additive genetic variances for purebred performance (σ a 2 ) than for crossbred performance (σ c 2 ). Residual variances for purebred animals (σ e 2 ) were larger than those for crossbred animals ( LY 2 s e ). For all scenarios, the estimated heritabilities for purebred performance (h 2 ) were always 0.11 and 0.09 for Landrace and Yorkshire, respectively. Heritabilities for crossbred animals (h 2 LY ) were around .12 (Nogen_SC) to 0.1 (Genall_SC) for both breeds. Slight differences of the estimated genetic correlation were observed between the two breeds. The Landrace breed showed slightly higher genetic correlation between purebred and crossbred performance than that for the Yorkshire breed.

Model-Based Reliability

Table 4 compares the mean model-based reliabilities for purebred sires for their crossbred performance in different scenarios across all boars and for genotyped and non-genotyped subgroups. The genotyped subgroup always had higher model-based reliabilities than the nongenotyped group, and for the group of all boars, modelbased reliabilities were in between those of the subgroups of genotyped and non-genotyped animals in each scenario. Model-based reliabilities increased from about 0.28 to 0.39 for the Landrace breed and from about 0.22 to 0.37 for the Yorkshire breed from Nogen_SC to Genall_SC. From Nogen_SC to Genall_SC, model-based reliabilities kept increasing in all three groups. Overall, methods with marker information (Genpure_SC and Genall_SC) presented higher model-based reliabilities than the pedigree-based scenario (Nogen_SC). In addition, proportions of purebred boars that have larger model-based reliabilities between pairwise scenarios were also studied. Result shows that 100% of LL and YY boars had larger model-based reliabilities in the Genall_SC compared with the Nogen_SC and Genpure_SC (results not shown).

Concerning the single-trait, single-step BLUP model, model-based reliabilities for purebred LL and YY in Genall_SC were 0.70 ± 0.12 and 0.69 ± 0.12, respectively. Although these values are much higher than results shown in Table 4, they cannot be directly compared because they represent the reliability of animals drawn from a breed that would be a mixture of YY and LL, which is not the case. In fact, this single-trait model has lower predictive abilities than Christensen's model, as will be shown next.

Predictive Abilities

Predictive abilities for crossbred pigs in the validation group for different scenarios are shown in Table 5. The Pearson correlation between the corrected phenotypes and the EBV (cor( ĉ , Y c )) range from 0.084 in Nogen_T to 0.120 in Genall_T, as shown in the second row of Table 5. No statistically significant differences between Genpure_T and Nogen_T were found, but Genpc_T and Genall_T were statistically significantly more accurate than those two scenarios. For the Genall_T, the subgroup of 2,846 genotyped crossbred pigs reveals larger correlation coefficients than that in the subgroup of non-genotyped pigs. Furthermore, the subgroup of non-genotyped pigs in Genall_T shows larger correlation coefficients than those in other scenarios.

Regression coefficients of corrected phenotypes on the EBV are shown in Table 5. In general, regression coefficients were a little bit larger than 1 for all the scenarios. Regression coefficients for scenarios with marker information (Genpure_T, Genpc_T, and Genall_T) were closer to 1 than that for pedigreebased scenario (Nogen_T). Among scenarios with marker information, in terms of unbiasedness, there was no clear trend showing which scenario performed better, but none was clearly biased. For the Genall_T, Table 3. Variance components, 1 heritabilities for purebred performance, 2 genetic correlation between purebred and crossbred performance for Landrace and Yorkshire, 3 and heritabilities for crossbred animals s e = residual vari- ance for crossbred animals.

2 h 2 = heritability for purebred performance (σ 2 a /(σ 2 a + σ 2 sb + σ 2 e ). 3 r pc = genetic correlation between purebred and crossbred performance. the subgroup of genotyped animals had less bias than the subgroup of non-genotyped animals.

Single-Trait Single-Step BLUP Predictive Abilities

Predictive abilities by a single-trait single-step BLUP method for crossbred animals in the validation population are shown in last 2 rows in Table 5. They increase from 0.082 in Nogen_T to 0.106 in Genall_T. It can also be seen that the predictive abilities calculated based on the single-trait model show trends similar to those calculated from the three-trait model but are smaller than in each corresponding scenario. Regression coefficients increase slightly from 0.61 in Nogen_T to 0.71 in Genall_T but are further from 1 when compared with regression coefficients calculated based on the three-trait model. For Genall_T, the genotyped subgroup also had higher predictive abilities than that in non-genotyped subgroup.

Re-ranking of Purebred Animals across Scenarios

The Spearman's rank correlations between estimated crossbred breeding values of purebred boars (765 LL and 465 YY) in pairwise scenarios are shown in Table 6. For both breeds, it can be seen that the pairwise correlations are always smaller than 1. In terms of the "top 5%" study, from 60% to 82% of purebred boars (either LL or YY) were shared from one scenario to another in the top 5% highest breeding values (results not shown). Similar results were observed in young purebred sows (results not shown).

DISCUSSION

This study implemented the single-step BLUP method of [START_REF] Christensen | Genomic evaluation of both purebred and crossbred performances[END_REF] by using both purebred and crossbred data from LL and Yorkshire in several scenarios with regard to different amounts of genomic information. Results indicated that the model was applicable. The genetic correlation between purebred and crossbred performance for TNB was successfully estimated. Methods with marker information were powerful for genetic evaluation for crossbred performance with regard to the predictive ability and unbiasedness. In addition, this study demonstrated that, to implement genetic evaluation for crossbred performance, crossbred genomic information is useful in addition to purebred genotypes.

In the model, a key assumption was that breed origins of phased marker genotypes for crossbred animals were known. In this study, 60K crossbred genotypes were imputed from a 8K crossbred panel. Although [START_REF] Xiang | Imputation of genotypes in Danish purebred and twoway crossbred pigs using low-density panels[END_REF] concluded that the imputation accuracies would be larger than 99% in terms of allele correct rates and 95% in terms of correlation coefficients between imputed genotypes and true genotypes, the uncertainty of crossbred genotypes cannot be totally eliminated. The algorithm of tracing alleles in the current study was considered to be working efficiently, because the differences between two purebred reference panels were considerably large in several sampled chromosomes. However, errors of tracing alleles probably still appeared if the similarity of two phased crossbred segments were high. All in all, a hidden risk of using incorrect alleles may still exist when building the breedspecific partial relationship matrix. This needs further research.

The additive genetic variances for purebred performance (σ2 a ) were larger than those for crossbred performance (σ 2 c ), implying that the phenotypes of purebred animals could be more diverse than for the crossbred animals, which was in line with the phenotypic variances for purebred animals (15.12 and 14.14 for LL and YY, respectively) being larger than those for crossbred animals (9.49). The heritabilities for crossbred animals (h 2 LY ) were not dramatically different from heritabilities for purebred performance (h 2 ), which was opposite to results in [START_REF] Wei | Genetic correlation and heritabilities for purebred and crossbred performance in poultry egg production traits[END_REF] and is due to the fact that in the current study, variances of environmental effects for crossbreds ( LY 2 s e ) were only two-thirds of those for purebreds (σ 2 e ), which could be a consequence of heterosis and phenotypic plasticity (better fitness) to the multiple herds for crossbreds than for purebreds [START_REF] Misztal | Environmental physiology of livestock[END_REF] or could, alternatively, be due to fact that only three different herds were used for crossbreds. Crossbreding capitalizes on heterosis effects and complementarity between breeds and results in an increased performance of crossbreds compared with purebreds [START_REF] Dekkers | Marker-assisted selection for commercial crossbred performance[END_REF].

When selection is based on purebred performance, the genetic correlation between purebred and crossbred performance (r pc ) is a key genetic parameter in crossbreding schemes [START_REF] Bell | Selection for heterosis -Results with laboratory and domestic animals[END_REF][START_REF] Bijma | Standard error of the genetic correlation: How much data do we need to estimate a purebred-crossbred genetic correlation?[END_REF]. The genetic correlations between purebred and crossbred performance for TNB were around 0.75 and 0.63 for Landrace and Yorkshire, respectively, which confirmed the existence of a moderate correlation. The r pc is smaller than 1, which is due to different environments for purebreds and crossbreds [START_REF] Lutaaya | Genetic parameter estimates from joint evaluation of purebreds and crossbreds in swine using the crossbred model[END_REF] and the presence of dominant gene action combined with different allele frequencies in the two breeds [START_REF] Lo | Genetic evaluation by BLUP in two-breed terminal crossbreding systems under dominance[END_REF][START_REF] Christensen | Genomic evaluation of both purebred and crossbred performances[END_REF]. This result was in line with [START_REF] Wong | Purebred versus crossbred performance as a basis of selection in swine[END_REF], who reported that the r pc for litter size was 0.74. However, [START_REF] Wei | Combined crossbred and purebred selection in animal breeding[END_REF] reviewed some other studies that reported low or even negative genetic correlations between purebred and crossbred performance for litter size. A change of r pc over time was reported to be caused by long-term purebred selection [START_REF] Pirchner | Genetic parameters of cross-and purebred poultry[END_REF], and therefore, it needs to be estimated regularly. The SE on the estimated genetic correlations were generally large in the current study, which implies that the sample size was not large enough, especially for crossbreds. Taking the SE into account, the estimated correlations in different scenarios were not very different. Nevertheless, the slight decrease of SE with an increased amount of genomic information indicated that genotypes, especially crossbred genotypes, would reduce the uncertainty of r pc . The decreasing SE demonstrated the better performance of the new single-step model incorporating crossbred marker information compared with the pedigree-based selection of purebred animals for crossbred performance. [START_REF] Bijma | Standard error of the genetic correlation: How much data do we need to estimate a purebred-crossbred genetic correlation?[END_REF] showed that when using pedigree relationships, the SE of r pc was determined by number of sire families and reliabilities of EBV and suggested that the SE should not exceed 0.05. In the current study, the TNB was a low heritable trait (around 0.1) and only 1,018 sires of the 1,230 sires of 10,974 crossbred animals were genotyped, which was also low. Therefore, large SE were expected. Results in the current study showed that the r pc for Landrace was slightly larger than that for Yorkshire, although the SE were large. Genetic correlations (r pc ) also consistently increase with number of genotypes used. One possible explanation could be that there are still some discordances between the definition of base populations in genomic and pedigree relationships. Concerning heritabilities for purebred performance, our estimates confirmed the results of [START_REF] Guo | Improving genetic evaluation of litter size and piglet mortality for both genotyped and non-genotyped individuals using a single-step method[END_REF], who estimated heritabilities of 0.11 and 0.09 for TNB in Landrace and Yorkshire, respectively.

The model-based reliabilities for purebred boars for their crossbred performance were generally low in the current study. The magnitude of these reliabilities is a direct function of the prediction error variances, which, in this case, are mostly determined by the numbers of offspring per boar [START_REF] Dufrasne | Using test station and on-farm data for the genetic evaluation of Pietrain boars used on Landrace sows for growth performance[END_REF]. In the current study, the numbers of crossbred offspring for each boar ranged from 1 to 11, with an average of 5, which were low and led to a high uncertainty of prediction. According to Table 4, model-based reliabilities tended to increase as the amount of genomic information increased for both breeds. The scenarios with marker information presented larger model-based reliabilities than the pedigree-based scenario, which may be due to 1 Nogen_T = the scenario in which relationship matrices H contained only pedigree information; Genpure_T = the scenario in which relationship matrices H contained pedigree information and purebred genotypes of 2,210 LL and 2,161 YY; Genpc_T = the scenario in which relationship matrices H contained pedigree information and genotypes of the 2,210 LL, 2,161 YY, and 2,357 crossbreds that were involved in the training data set; Genall_T = the scenario in which relationship matrices H comprised all information in Genpc_T plus extra genomic information (but not the phenotypic information) of the 2,846 crossbreds in the validation population.

2 cor( ĉ , Y c ) is correlation coefficients between corrected phenotypes and EBV.

3 Regression coefficients of corrected phenotypes on EBV.

4 Single-trait cor( ĉ , Y c ) is correlation coefficients between corrected phenotypes and EBV based on the single trait single-step BLUP method.

5 Single-trait regression coefficients is regression coefficients of corrected phenotypes on EBV based on the single trait single-step BLUP method.

the additional marker information. Reliabilities for the subgroup of genotyped animals were larger than that for the subgroup of non-genotyped animals in each scenario, but non-genotyped animals also benefitted from the genomic information of genotyped animals, as the reliabilities for the non-genotyped subgroup kept increasing from Nogen_SC to Genall_SC. These results are in line with [START_REF] Lourenco | Accuracy of estimated breeding values with genomic information on males, females, or both: An example on broiler chicken[END_REF]. Reliabilities for non-genotyped animals in Genall_SC were even larger than those in Nogen_SC and Genpure_SC for genotyped animals, implying the benefit of genotyping crossbred animals. In addition, 100% of purebred boars had larger model-based reliabilities in Genall_SC than that in the other two scenarios, which also provided evidence that the model incorporating crossbred marker information was useful for genetic evaluation for crossbred performance in purebred boars. We concluded that crossbred genomic information plays a role in improving reliabilities for crossbred performance in purebred boars. Nevertheless, it has been reported that the model-based reliabilities overestimated the true reliabilities [START_REF] Vanraden | Invited review: Reliability of genomic predictions for North American Holstein bulls[END_REF], because the markers may overfit the data set [START_REF] Su | Genomic prediction for Nordic Red cattle using one-step and selection index blending[END_REF]. Therefore, further investigation on true reliabilities is needed, potentially by a simulation study.

Correlation coefficients between corrected phenotypes and EBV for TNB in crossbred animals were lower than results for daily gain and feed conversion ratio in [START_REF] Christensen | Single-step methods for genomic evaluation in pigs[END_REF]. This may be related to the fact that the heritability was higher for the traits of daily gain and feed conversion ratio than for the TNB in the current study. Moreover, the additive genetic effects for crossbred animals required estimating 2 breed of origin genetic effects c (L) and c (Y) , which may lead to more uncertainty for crossbred animals than studies for purebred animals in [START_REF] Christensen | Single-step methods for genomic evaluation in pigs[END_REF]. The cor( ĉ , Y c ) in different scenarios confirmed that the methods with marker information would enhance the predictive ability. The crossbred genomic information was useful to improve the prediction, because scenarios with only purebred genotypes did not show significant improvement compared with the pedigree-based sce-nario but significantly improved when crossbred genomic information was also involved. Results showed that genotyped animals had larger cor( ĉ , Y c ) than nongenotyped animals, which was opposite to studies by [START_REF] Guo | Improving genetic evaluation of litter size and piglet mortality for both genotyped and non-genotyped individuals using a single-step method[END_REF]. This could be because in the current study, the validation group consisted of crossbred animals among which the genotyped subset was a random sample, without biases for prediction [START_REF] Su | Genomic prediction for Nordic Red cattle using one-step and selection index blending[END_REF], whereas in [START_REF] Guo | Improving genetic evaluation of litter size and piglet mortality for both genotyped and non-genotyped individuals using a single-step method[END_REF], the validation group consisted of purebred animals among which the genotyped subset was a preselected group. Pre-selection reduces accuracies of EBV [START_REF] Bijma | Accuracies of estimated breeding values from ordinary genetic evaluations do not reflect the correlation between true and estimated breeding values in selected populations[END_REF][START_REF] Lourenco | Accuracy of estimated breeding values with genomic information on males, females, or both: An example on broiler chicken[END_REF]. The non-genotyped subgroup of crossbred animals in Genall_T had larger accuracies than those in other scenarios, indicating that non-genotyped validated animals benefited from crossbred genotyped animals in the validation population. Therefore, we suggest genotyping crossbred animals as well as purebred animals when implementing genomic selection for crossbred animals.

Regression coefficients of corrected phenotypes on EBV did not show a clear preference for a specific scenario, but coefficients in all scenarios with marker information were closer to 1 than in the pedigree-based scenario. All the regression coefficients were larger than 1, suggesting the underestimation (deflation) of variation of the estimated genomic breeding values [START_REF] Gao | Comparison on genomic predictions using three GBLUP methods and two single-step blending methods in the Nordic Holstein population[END_REF].

Both the values of Spearman's rank correlations lower than 1 and the top 5% study indicated that rankings of purebred animals' breeding values for crossbred performance were not consistent across different scenarios. The selected purebred candidates for crossbred performance will be different with the availability of (crossbred) genomic information.

In terms of the predictive abilities and bias, the single-trait model was less robust than the three-trait model, although it was easier to implement. With crossbred genomic information, the three-trait model showed up to 13% higher predictive abilities than the single-trait model, which seems an interesting gain for this low heritable trait.

Conclusion

The new single-step model works well for genetic evaluation for crossbred performance in pigs. A moderate, positive genetic correlation between purebred and crossbred performance (r pc ranged from 0.57 to 0.78) for TNB in purebred Landrace and Yorkshire is confirmed. Crossbred genomic information reduces the SE on the estimate of this genetic correlation. Models with marker information, especially crossbred genomic information, improve model-based reliabilities for crossbred performance of purebred boars and also improve the predictive ability for validated crossbred animals and somehow reduce the bias of prediction. The single-

  Application of single-step genomic evaluation for crossbred performance in pig 1 T. Xiang,* † 2 B. Nielsen, ‡ G. Su,* A. Legarra, † and O. F. Christensen* *Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Aarhus University, DK-8830 Tjele, Denmark; †INRA, UR1388 GenPhyse, CS-52627, F-31326 Castanet-Tolosan, France; and ‡SEGES, Pig Research Centre, DK-1609 Copenhagen, Denmark

  additive genetic variances for crossbred performances of breed LL and YY, respectively.

Table 1 .

 1 Scenarios for model-based reliability

	Scenario 1	Genotypes 2	Phenotypes
	Nogen_SC	No genotypes	Full data:
	Genpure_SC	7,723 LL and 7,785 YY	293,339 LL, 180,112 YY,
	Genall_SC	7,723 LL, 7,785 YY, and 5,203 crossbreds	and 10,974 crossbred animals

ĉ , Y c

Table 2 .

 2 Scenarios for predictive ability

	Scenario 1	Genotypes 2	Phenotypes
	Nogen_T	No genotypes	Training: 240,543 LL, 139,868 YY,
	Genpure_T	2,210 LL and 2,161 YY	and 6,779 crossbred animals
	Genpc_T Genall_T	2,210 LL, 2,161 YY, and 2.357 crossbreds 2,210 LL, 2,161 YY, and 5,203 crossbreds	Validation: 52,796 LL, 40,244 YY, and 4,195 crossbred animals

Table 4 .

 4 Mean model-based reliabilities of purebred boars for their crossbred performance

			All 2			Genotyped 3			Non-genotyped 4
	Breed 1	Nogen_SC Genpure_SC Genall_SC	Nogen_SC Genpure_SC Genall_SC	Nogen_SC Genpure_SC Genall_SC
	LL	0.303	0.332	0.385	0.307	0.341	0.391	0.280	0.279	0.346
	YY	0.262	0.284	0.365	0.264	0.288	0.369	0.218	0.223	0.301

Table 5 .

 5 Predictive abilities for crossbred animals in the validation population in different scenarios Different superscripts of small letters among scenarios indicate significant differences (P < 0.05) by a Hotelling-Williams t test.

						Genall_T 1
	Perdiction	Nogen_T 1	Genpure_T 1	Genpc_T 1	All	Genotyped	Non-genotyped
	cor( ĉ , Y c ) 2 Regression coefficients 3	0.084 a 1.179	0.088 a 1.049	0.097 b 1.081	0.120 c 1.067	0.126 1.048	0.106 1.105
	Single-trait cor( ĉ , Y c ) 4 Single-trait regression coefficients 5	0.079 0.588	0.084 0.644	0.088 0.644	0.106 0.698	0.109 0.875	0.103 0.647
	a-c

Table 6 .

 6 Spearman's rank correlations between crossbred breeding values for 765 Landrace boars (above the diagonal) and 465 Yorkshire boars (below the diagonal) of crossbred animals in pairwise scenarios

		Nogen_SC 1	Genpure_SC 1	Genall_SC 1
	Nogen_SC	1.00	0.92	0.90
	Genpure_SC	0.93	1.00	0.98
	Genall_SC	0.87	0.95	1.00

Nogen_SC = only pedigree information; Genpure_SC = pedigree information and purebred genotypes; Genall_SC = pedigree information and all purebred and crossbred genotypes.

LL = Danish Landrace; YY = Danish Yorkshire.

Nogen_T = the scenario in which relationship matrices H contained only pedigree information; Genpure_T = the scenario in which relationship matrices H contained pedigree information and purebred genotypes

of 2,210 LL and 2,161 YY; Genpc_T = the scenario in which relationship matrices H contained pedigree information and genotypes of the 2,210 LL, 2,161 YY, and 2,357 crossbreds that were involved in the training data set; Genall_T = the scenario in which relationship matrices H comprised all information in Genpc_T plus extra genomic information (but not the phenotypic information) of the 2,846 crossbreds in the validation population.2 LL = Danish Landrace; YY = Danish Yorkshire.

Nogen_SC = only pedigree information; Genpure_SC = pedigree information and purebred genotypes; Genall_SC = pedigree information and all purebred and crossbred genotypes.

LL = Danish Landrace; YY = Danish Yorkshire.

All = all the sires of crossbred animals, consisting of 765 Landrace and 465 Yorkshire. Nogen_SC = only pedigree information; Genpure_SC = pedigree information and purebred genotypes; Genall_SC = pedigree information and all purebred and crossbred genotypes.

Genotyped = genotyped sires of crossbred animals, consisting of 656 Landrace and

Yorkshire. 4 Non-genotyped = Non-genotyped sires of crossbred animals, consisting of 109 Landrace and 22 Yorkshire.

Nogen_SC = only pedigree information; Genpure_SC = pedigree information and purebred genotypes; Genall_SC = pedigree information and all purebred and crossbred genotypes.

The project was funded through the Green Development and Demonstration Programme (grant number 34009-12-0540) by the Danish Ministry of Food, Agriculture and Fisheries, the Pig Research Centre, and Aarhus University. The first author benefited from a joint grant from the European Commission and Aarhus University, within the framework of the Erasmus-Mundus joint doctorate "EGS-ABG." A.L. thanks financing from INRA SelGen metaprogram projects X-Gen and SelDir. The authors also thank for the valuable suggestions given by Per Madsen from Center for Quantitative Genetics and Genomics,


Florence Ayral
email: florence.ayral@vetagro-sup.fr

Anne-Laure Zilber

Dominique J Bicout

Angeli Kodjo

Marc Artois

Zoheira Djelouadji

Distribution of Leptospira interrogans by Multispacer Sequence Typing in Urban Norway Rats (Rattus norvegicus): A Survey in France in 2011-2013

ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Introduction

It has been estimated that more than one million severe cases of leptospirosis occur per year worldwide [START_REF] Abela-Ridder | Estimating the burden of human leptospirosis[END_REF], and leptospirosis is considered to be an emerging disease in many countries [START_REF] Hartskeerl | Emergence, control and re-emerging leptospirosis: dynamics of infection in the changing world[END_REF].

The infection can cause fever, renal failure and pulmonary hemorrhage and is potentially fatal in 5% to 15% of cases [3][START_REF] Bharti | Leptospirosis: a zoonotic disease of global importance[END_REF][START_REF] Meerburg | Rodent-borne diseases and their risks for public health[END_REF]. It is a bacterial zoonosis caused by pathogenic Leptospira, resulting from direct or indirect contact with the urine or tissues of infected animals [6]. Although many animals can be carriers, rats are the most common source of human infection, particularly in urban environments, as rats are a synanthropic species (adapted to coexisting with the human population) [START_REF] Vinetz | Sporadic urban leptospirosis[END_REF][START_REF] Ko | Leptospira: the dawn of the molecular genetics era for an emerging zoonotic pathogen[END_REF][START_REF] Evangelista | Leptospira as an emerging pathogen: a review of its biology, pathogenesis and host immune responses[END_REF]. Environmental factors also play a role in leptospirosis, leading to a higher incidence in tropical countries; however, because rats are ubiquitous, leptospirosis remains a public health issue even in cities of developing countries [START_REF] Ko | Leptospira: the dawn of the molecular genetics era for an emerging zoonotic pathogen[END_REF][START_REF] Evangelista | Leptospira as an emerging pathogen: a review of its biology, pathogenesis and host immune responses[END_REF][START_REF] Socolovschi | Strikes, flooding, rats, and leptospirosis in Marseille, France[END_REF]. Pathogenic species include L. interrogans, L. kirschneri, L. noguchii, L. borgpetersenii, L. weilii, L. santarosai, L. alexanderi, L. alstonii and L. mayottensis [START_REF] Adler | Leptospira and leptospirosis[END_REF][START_REF] Cerqueira | A century of Leptospira strain typing[END_REF][START_REF] Bourhy | Leptospira mayottensis sp. nov., a pathogenic species of the genus Leptospira isolated from humans[END_REF]. Leptospira can also be classified into approximately 300 serovars, based on the lipopolysaccharide (LPS) structure. Antigenically related serovars have been grouped into 25 serogroups [START_REF] Adler | Leptospira and leptospirosis[END_REF]. Because the species identification is not related to the LPS-associated genes (i.e., O-antigen gene cluster), the molecular and the serology-based taxonomies do not entirely overlap [START_REF] Kmety | Classification of the Species Leptospira interrogans and the History of Its Serovars[END_REF]. Different serovars are adapted to different mammalian hosts, which can either act as a reservoir of a co-adapted Leptospira serovar or carry and disseminate other serovars [6]. It is therefore difficult to track strains with molecular identities or serotypes that change with the hosts and the environments they inhabit or pass through.

Rats are colonial and territorial [START_REF] Barnett | The rat: A study in behavior[END_REF] animals that promote intra-colony interactions. In the urban environment, limited available space leads to the formation of colonies with a small home range, and inter-colony contacts may occur. Therefore, the urban rat population more likely functions as a meta-population, as described by Viana et al. [START_REF] Viana | Assembling evidence for identifying reservoirs of infection[END_REF], in which the sub-unit is the rat colony. Determining rat meta-population behavior with regard to Leptospira strain distribution would provide evidence of rat interactions resulting in rat infections. These investigations are of public health importance according to the One Health principle, as they could aid in understanding of the spatial distribution of infected rats, leading to potential disease transmission to humans. A molecular tool that can identify and monitor the isolates circulating in urban rat colonies is required for the investigation of potential routes of contamination (intra vs. inter colony) and their relative importance.

To successfully establish the epidemiological relationship between any two isolates, it is necessary to type them at a sufficient level of resolution [START_REF] Efsa | Scientific Opinion on the evaluation of molecular typing methods for major food-borne microbiological hazards and their use for attribution modelling, outbreak investigation and scanning surveillance: Part 1 (evaluation of methods and applications[END_REF]. Rats have been reported as the primary carriers of Leptospira serovars Icterohaemorrhagiae and Copenhageni (i.e., the Icterohaemorrhagiae serogroup) [8;18]. Therefore, the molecular data on the leptospires circulating in urban rat populations at the serovar level are not sufficient, and opportunities to distinguish at the isolate level could provide new perspectives for epidemiological analysis and for source tracking, in particular. Other than serological methods, molecular tools that have been employed to detect and genotype leptospiral agents in rat surveys include pulse field gel electrophoresis (PFGE), analyses of variable number tandem repeats (VNTR), and multi-locus sequence typing (MLST) [START_REF] Li | Source tracking of human leptospirosis: serotyping and genotyping of Leptospira isolated from rodents in the epidemic area of Guizhou province, China[END_REF][START_REF] Bourhy | Serovar diversity of pathogenic Leptospira circulating in the French West Indies[END_REF][START_REF] Villanueva | Leptospira-rat-human relationship in Luzon, Philippines[END_REF][START_REF] Cosson | Epidemiology of Leptospira transmitted by rodents in southeast Asia[END_REF]. However, these methods suffer from low discriminatory power and can only distinguish between Leptospira species, serogroups or serovars. O-genotyping could be a relevant method for identifying isolates; however, because of a lack of sequences of O-antigen gene clusters from various serogroups, this method has not been developed in Leptospira. Recently, multispacer sequence typing (MST) technology has been used and validated to distinguish between L. interrogans at the strain level in both human and animal samples. Because it is based on sequencing, MST has particularly high discriminatory power for the Icterohaemorrhagiae serogroup compared with VNTR [START_REF] Zilber | High-resolution typing of Leptospira interrogans strains by multispacer sequence typing[END_REF]. The method can generate sequence data that are suitable for epidemiological and rat population studies. To our knowledge, no previous studies have applied MST for the purpose of a survey of wild rats.

The objectives of the study were as follows: (1) to identify the MST profile of L. interrogans isolates circulating in rat colonies in central Lyon, France, and in suburbs of Lyon; and (2) to characterize the distribution of MST profiles in rat colonies over space. Based on the results of this study, the use of MST for the surveillance of Leptospira in animal hosts should be considered, and we offer a public health perspective on the potential for its use.

Materials and Methods

Ethics statement

The authors assert that all procedures conducted in this study complied with the ethical standards of the European regulation governing the care and use of laboratory animals, following the agreement n°69-020931, delivered by Rhône Préfecture (S1 File).

Study area

The study was conducted in Lyon (N45°45'/E4°50') and included seven trapping locations within the city center and one in a suburb. The eight urban trapping locations encompassed five dwelling areas: "Mermoz-B" N45°43'43.16"/E4°53'09.63", "Mermoz-F" N45°43'38.47"/E4°5 3'05.35", and "Mermoz-R" N45°43'44.96/E4°52'57.56" were from an inner-city neighborhood; "Guillotière" N45°45'08.45"/E4°50'08.45" included ancient buildings; and "Chapelle" N45°45'52.58/E4°47'24.28" included more recent buildings, a public garden ("Parc de la Tête d'Or" N45°46'26.93"/E4°44'04.06"), the waste incineration plant ("UIOM" N45°43'26.95"/E4°5 0'16.75") and a grassland in the suburb ("Francheville" N45°44'28.87/E 4°44'04.06"). The dwellings and public areas were selected based on the presence of high rat concentrations, as reported by the "Service d'Hygiène de Lyon" (i.e., the department of public health in Lyon). This study formed the basis of a larger project characterizing the distribution of zoonotic pathogens, including Leptospira, in rat populations conducted in the Rhône département (i.e., administrative sub-unit of France) [START_REF] Ayral | The relationship between socioeconomic indices and potentially zoonotic pathogens carried by wild Norway rats: a survey in Rhône, France (2010-2012)[END_REF]. For the purposes of this project, trapping locations were chosen based on population density to represent a range of urbanization levels. Only eight locations where Leptospira was detected were included in this study because the bacterial load in rats' kidneys was found to be appropriate for further molecular investigations.

Trapping occurred on both public and private properties, and permission to trap was obtained from the City and the property managers, respectively.

Sample collection

Previous research has indicated that urban rats form tight colonies with a small home range, suggesting that a colony could be limited to a block [START_REF] Feng | The secret life of the city rats: a review of ecology of urban Norway and black rats (Rattus norvegicus and Rattus rattus) Urban ecosyst[END_REF][START_REF] Davis | Studies on Home Range in the Brown Rat[END_REF]. Thus, we defined colonies to consist of rats inside geographical barriers, such as rivers, or urban infrastructures, including railways or blocks. Using this definition, the trapping locations were chosen to encompass not more than one colony. The survey was subdivided into a main trapping period, consisting of one 6-month period (October 2011 to March 2012), and a follow-up period, consisting of a 1-week period in January 2013 in which trapping was limited to the "Parc de la Tête d'Or" location. This extra week of trapping was performed to verify the consistency of the former results over time, and the location was chosen based on public health relevance (public garden) and high rat abundance. For the purposes of both pest control and this study, trapping/removal methods were used (snap trapping and live capture), with live capture being the preferred method to prevent post-mortem kidney contamination. Most rats were captured with small (28 cm x 9 cm x 9 cm) or large (50 cm x 15 cm x 15 cm) single-catch rat traps. The captured rats were transported to the laboratory, immediately anesthetized using isoflurane and sacrificed by cervical dislocation. For each animal, the weight, size, sex and level of sexual maturity (i.e., the presence of seminal vesicles in males and a developed uterus in females) were recorded. The rats that died during capture were frozen at -20°C and thawed on the day of dissection.

One kidney was removed and samples were taken for bacteriological culture immediately following collection, and one fourth of the other kidney, including the cortex and medulla, was stored at -80°C until further analysis.

Molecular investigations

The flow chart of the molecular investigations is presented in Fig 1 and is detailed as follows.

Leptospira isolation. Half of one crushed kidney was aseptically transferred to tubes containing Ellinghausen McCullough Johnson and Harris (EMJH) culture media (Indicia, St Génis, France). Three dilution tubes were incubated at 29°C, according to a previously published protocol for the isolation of pathogenic Leptospira [START_REF] Zuerner | Laboratory maintenance of pathogenic Leptospira[END_REF]. The tubes were examined weekly by dark-field microscopy for two months [START_REF] Picardeau | Diagnosis and epidemiology of leptospirosis[END_REF]. An additional month of culture examination was performed when PCR analysis yielded positive results (TaqMan real-time PCR kit performed in parallel and detailed below) to increase the probability of detecting Leptospira growth.

DNA was extracted from 200 μl of each Leptospira culture using a QIAamp DNA Mini Kit (Qiagen, Courtaboeuf, France) following the manufacturer's instructions. All DNA samples were stored at -20°C.

MST profile determination. DNA extraction and Leptospira detection in kidney tissue and urine. One fourth of each kidney was homogenized aseptically using a syringe. A small amount of this crushed kidney (ca. 25 mg) or urine when available was incubated with 180 μl T1 Buffer and 25 μl proteinase K for 3 hours. After this protein digestion, DNA was extracted from 200 μl of lysed tissue using a Nucleospin Tissue kit (Macherey Nagel, Hoerd, France) following the manufacturer's instructions. All DNA samples were stored at -20°C.

The presence of Leptospira colonization of the kidney was assessed using a specific pathogenic Leptospira TaqMan real-time PCR kit (TaqVet PathoLept Kit, LSI, Lissieu, France) following the manufacturer's instructions. A final volume of 25 μl, consisting of 20 μl Mix Lepto (LSI) and 5 μl target DNA, was amplified using a Rotor-Gene Q (Qiagen) according to the following program: 10 min of enzyme activation at 95°C, followed by 45 cycles of 95°C for 15 s and 60°C for 1 min. An appropriate negative control, consisting of the PCR mix without the target DNA, was included. The specimens with a cycle threshold (Ct) less than 45 cycles were considered positive.

When available, the DNA extraction and PCR technique was performed on urine samples as well.

Molecular typing. The genotyping of Leptospira DNA was conducted using extracts from the isolate cultures and from the kidney samples. As the MST that we used was developed to discriminate among strains of the species Leptospira interrogans, this species was verified first as follows: the rpoB gene was amplified by PCR using HotStarTaq DNA Polymerase (Qiagen) under standard conditions and the primers previously described [START_REF] La Scola | Partial rpoB gene sequencing for identification of Leptospira species[END_REF]. A final volume of 50 μl, consisting of 35 μl H 2 O, 5 μl of 10× buffer (Qiagen), 2 μl of 25 mM MgCl 2 , 1 μl of 10 mM dNTPs (Qiagen), 1 μl of forward primer (10 μM), 1 μl of reverse primer (10 μM), 0.3 μl of HotStarTaq DNA Polymerase (Qiagen) and 5 μl of target DNA, was amplified according to the following program: 15 min of enzyme activation at 95°C, followed by 40 cycles consisting of 95°C for 30 s, 56°C for 30 s, and 72°C for 1 min and a final elongation step of 10 min at 72°C. The PCR products were sequenced using the BigDye Terminator sequencing kit and a 3730XL DNA analyzer (Applied Biosystems, Saint Aubin, France). The Leptospira species in the samples were identified by analyzing these sequences using the NCBI nucleotide BLAST software (http://blast.ncbi.nlm.nih.gov).

The samples in which the L. interrogans species was identified were screened using the MST method as reported previously [START_REF] Zilber | High-resolution typing of Leptospira interrogans strains by multispacer sequence typing[END_REF]. PCR amplifications were performed in a final volume of 50 μl containing 35 μl of H 2 O, 5 μl of 10× buffer (Qiagen), 2 μl of 25 mM MgCl 2 , 1 μl of 10 mM dNTPs (Qiagen), 1 μl of forward primer (10 μM), 1 μl of reverse primer (10 μM), 0.3 μl of Hot-StarTaq DNA Polymerase (Qiagen) and 5 μl of target DNA. For samples with a low quantity of Leptospira DNA, the volume of the target DNA was increased to 8 μl. The mix was amplified according to the following program: 15 min of enzyme activation at 95°C; followed by 40 cycles consisting of 95°C for 30 s, 50°C or 52°C, depending on the primer, for 30 s, and 72°C for 1 min; and a final elongation step of 10 min at 72°C. PCR mixes without the target DNA and with DNA from Leptospira interrogans serovar Copenhageni were used as appropriate negative and positive controls, respectively. The PCR products were sequenced using the BigDye Terminator sequencing kit with an Applied Biosystems 3730XL DNA analyzer (Applied Biosystems, Saint Aubin, France). The Leptopira serovars and strains were deduced from a comparison of the sequences obtained with the MST reference sequences using NCBI nucleotide BLAST software (http://blast.ncbi.nlm.nih.gov) and a previously published database [START_REF] Zilber | High-resolution typing of Leptospira interrogans strains by multispacer sequence typing[END_REF].

Spatial analysis

Variations in the locations, infection status and strains of rats trapped across the study area were visualized spatially using the ArcGIS software, version 9.3 (ESRI, Redlands, CA, USA). The mean distance between colonies in which a given strain was identified was compared with the mean distance between colonies in which different strains were identified using Student's T test; an alpha value of 0.05 was used to identify significant differences. The statistical analyses were conducted using the R software, version 3.0.1 (R Development Core Team (2013), R Foundation for Statistical Computing, Vienna, Austria).

Results

Leptospira spp. detection

A total of 84 rats were trapped in the eight locations, and all were Norway rats. The distributions of the rats trapped at each site and of PCR-positive samples are displayed in Table 1.

L. interrogans was isolated from 24 rats, and L. interrogans DNA was extracted from 36 rats' kidneys. For one rat, a positive culture result was observed together with a negative PCR result. For another rat, L. interrogans DNA was detected in the urine, whereas it was not in the kidney tissue.

Leptospira MST profiles

All 37 rats in which L. interrogans was detected were typed by MST, and MST profiles were determined in 35 samples; two MST profiles could not be identified due to low DNA concentrations. Four different MST profiles were identified and related to the serogroup Icterohaemorrhagiae and the serovars Icterohaemorrhagiae and Copenhageni (strains are indicated in brackets). The most commonly observed MST-profile-related strain was Copenhageni [Wijinberg or M20] (n = 26), followed by Icterohaemorrhagiae [CHU Réunion] (n = 7), Icterohaemorrhagiae [R1] (n = 1) and Copenhageni [Shibaura 9] (n = 1). All of the MST profiles of the Leptospira isolates were similar to the MST profiles of the Leptospira DNA from the corresponding kidneys or urine.

The characteristics of the infected-rat populations are displayed in Table 2. All the morphometric characteristics and the laboratory results of the 81 rats collected from October 2011 to March 2012 are available (S1 Table ). in each colony. The colonies infected with Leptospira serovar Copenhageni [Wijinberg or M20] were most common (n = 5); each of the three other strains was found in only one colony. Among the five colonies infected by Copenhageni [Wijinberg or M20], three locations ("Mermoz-B", "Mermoz-F" and "Mermoz-R") were separated by less than 0.5 km. Two other colonies infected by Leptospira serovar Copenhageni [Wijinberg or M20] ("Francheville" and "Guillotière") were farther from the former locations, at 11.5 km and 4.5 km away, respectively. The colony where Icterohaemorrhagiae [CHU Réunion] circulated was located in "Parc de la Tête d'or" and was separated by 2.6 km to 10 km from the other locations. The mean distance between colonies where Leptospira Copenhageni [Wijinberg or M20] circulated was 5.5±4.82 km and was not significantly different from the mean distance between colonies that contained different strains, which was 6.88±1.84 km (t = -0.8, df = 10, p = 0.44).

Spatial patterns observed

In the two infected rats trapped in the second period in the "Parc de la Tête d'Or", the serovar Icterohaemorrhagiae [CHU Réunion] was identified from the MST profiles. The serovar detected in this period was the same as the serovar detected in the same location one year previously.

Discussion

This study has demonstrated that urban rats carry a variety of L. interrogans strains, all with MST profiles that are concordant with reference strains from the serogroup Icterohaemorrhagiae, which has been previously identified in humans. More specifically, four distinct MST profiles were identified in the rat population in this study, and a single MST profile was attributed to each rat colony. These outcomes raised public health, methodological and epidemiological issues.

Implications for public health

This study distinguished among four MST profiles related to four strains from the serogroup Icterohaemorrhagiae. As an MST profile is only a partial typing of the entire leptospiral genome, two identical MST profiles are not necessarily evidence of two identical strains; however, two identical MST profiles strongly suggest that the strains are closely related. The MST profiles found in the rats in our survey were identical to those of the reference strains that were isolated from humans in various areas. This finding indicates that the strains circulating in rats and humans are closely related. Assuming that a human patient would be infected in Lyon city center with one of the four MST profiles described in our study, it would be possible to speculate that the transmission occurred via the rat population. Although rats are the main source of Leptospira in cities, many reservoir hosts of leptospirosis exist, and to determine whether rats are the source of human disease, an inventory of strains among the varied hosts species and humans is required. In this context, the MST approach could be used for the purpose of source tracing.

MST has previously helped to define phylogeographical lineages of mycobacteria [START_REF] Djelouadji | Multispacer sequence typing for Mycobacterium tuberculosis genotyping[END_REF]. As the present study suggests that only one strain may be assigned to a colony home range, the areas in which individual strains are distributed are expected to be well delimited without overlapping. Among the numerous colonies that exist in Lyon, the eight colonies in this study provide a restricted overview of currently circulating strains; with more sampling, it would be possible to determine whether additional strains circulate and to provide a more complete picture of their spatial distributions. Maps of the spatial distribution of Leptospira strains would be of great interest in epidemiologic investigations. This knowledge would help to locate the potential sources of infection. Together with surveillance of MST outcomes in rats, an updated map of strain distributions would permit early detection of emerging strains. Strain emergence or re-emergence in a given area is likely to occur due to changing environments, host population fluctuations and vaccine pressure. Detecting such a change would facilitate appropriate leptospirosis preparedness efforts and would be useful in preparing for natural disasters, such as storms, hurricanes and particularly flooding, all of which are usually associated with an increased incidence of human and animal leptospirosis [31][32].

Finally, our finding that all of the strains belonged to the serogroup Icterohaemorrhagiae supported the use of the current vaccine available for humans in France, which includes the serovar Icterohaemorrhagiae (Spirolept 1 ). By distinguishing Leptospira strains, MST could identify further strains of interest for public health and could aid in the designing of future vaccines.

Use of MST to distinguish among Leptospira strains in rat surveys MST allows for the direct detection of Leptospira in rat tissues; therefore, it is useful in field surveys in which appropriate sampling for bacteriological cultures is limited. The robustness of MST was highlighted by the consistency with which it could identify strains in various samples (e.g., isolates or Leptospira DNA from the kidney or urine) from the same rat. This finding suggested that Leptospira DNA extracted from rat samples could be sufficient for MST use.

The MST profiles were not fully obtained for two rats that tested positive for L. interrogans because the DNA load, characterized by the Ct, was lower in these samples (LYO0845, Ct = 38.67 and LYO0881, Ct = 38.72), compared to other samples (Ct35). Because the MST method is less sensitive than real-time PCR, the MST method cannot be used alone for screening purposes. The use of serial testing, consisting of culture and/or qPCR combined with MST, can be expected to provide a reliable reflection of Leptospira infection and of the Leptospira strains circulating in rat colonies.

The recovery of bacteria from an individual kidney by bacteriological culture was considered to be definitively diagnostic of L. interrogans infection and was considered to be appropriate for early detection because kidney colonization occurs during the first week of Leptospira infection [START_REF] Athanazio | Rattus norvegicus as a model for persistent renal colonisation by pathogenic Leptospira interrogans[END_REF]. However, this culturing method is slow and insensitive, and the frequency with which bacteria are isolated is typically low. Thus, qPCR was also performed because it is reportedly the most sensitive method of detection for human Leptospira [START_REF] Bourhy | Comparison of real-time PCR assays for detection of pathogenic Leptospira spp. in blood and identification of variations in target sequences[END_REF]. However, false negatives can still be obtained if PCR is used to identify uncharacterized pathogenic strains [START_REF] Bourhy | Comparison of real-time PCR assays for detection of pathogenic Leptospira spp. in blood and identification of variations in target sequences[END_REF] or if the bacteria have aggregated in an unsampled part of the kidney. Thus, a combination of culturing and qPCR analyses on a variety of sample types was performed in an attempt to increase detection sensitivity. For instance, one rat that was PCR-positive in the urine and PCR-negative in the kidney would have been missed without this combination of tests.

Implications for the epidemiology of Leptospira

Spatial analysis suggests that a single strain was found in each colony. It cannot be determined whether this specific geographical pattern resulted from the dispersal of individual rats and rat-to-rat transmission or from exposure to a non-rat source, such as the environment within a given colony, or both. As colonies infected with the same strain are not significantly closer to each other than are colonies infected with different strains, a dispersal of individuals resulting in admixed strains would have been expected; however, such a pattern was not observed in this study. Based on the assumption that a single strain was associated with each colony, it is likely that few contacts occur between colonies, which is consistent with previous studies of the genetic characteristics of urban rats [START_REF] Gardner-Santana | Commensal ecology, urban landscapes, and their influence on the genetic characteristics of city-dwelling Norway rats (Rattus norvegicus)[END_REF]. Moreover, high degrees of habitat fragmentation in urban areas should also limit individual rat movement between areas and result in the geographical isolation of urban rat populations [START_REF] Feng | The secret life of the city rats: a review of ecology of urban Norway and black rats (Rattus norvegicus and Rattus rattus) Urban ecosyst[END_REF]. The absence of cross-contamination between colonies strongly supports this hypothesis. Thus, Copenhageni [Wijinberg or M20] infections in rats from remote colonies may be related to rare and ancient rat movements between colonies, as has been described previously in other cities by assessing population genetics [START_REF] Kajdacsi | Urban population genetics of slum-dwelling rats (Rattus norvegicus) in Salvador, Brazil[END_REF]. Interestingly, the environment associated with the 5 sites varied considerably (low-income dwelling, old dwellings, public garden in the suburb), suggesting that a variety of habitats may be associated with the same strain.

Although follow-up observations were limited to one location, the fact that the same strain was isolated in the same location one year later suggests that the isolated Leptospira strain persist over time within the same colony and supports the hypothesis that there is limited interaction between colonies. Therefore, the strain Copenhageni [Wijinberg or M20] encountered in remote locations may have been there for a substantial amount of time.

One potential method for testing the distributions of strains over space and time would be to tag rats and monitor their movements. Such protocols have been used before in rural or island habitats, although they are less appropriate for urban wild rats, mainly due to the adverse effects of human activities on capture success (e.g., pest control measures and trap deterioration). Thus, our original findings aid in the estimation of temporal stability and spatial connectivity of populations, increasing our knowledge of the epidemiology of this disease, which will be crucial for the development of prevention methods.

Conclusions

Our study demonstrates the relevance of MST for the accurate characterization of Leptospira interrogans extracted-DNA from rat tissues. Improved typing methods are required to investigate the molecular epidemiology of leptospirosis, and MST could aid in achievement of the level of definition that is needed for proper interpretation, thereby offering novel perspectives for epidemiological surveillance and investigations.

In the future, large-scale Leptospira genotyping using MST could provide substantial knowledge of leptospirosis in various host populations and habitats. This approach could generate valuable information that could be used to generate a phylogeographical database to improve epidemiological investigations and to track the sources of this disease in a particular epidemiological catchment area. Early detection of emergent strains or changes in the strain distribution using MST could drive future public health efforts.

  Distribution of Leptospira interrogans by multispacer sequence typing in urban norway rats (Rattus norvegicus): A survey in France in 2011-2013 Florence Ayral, Anne-Laure Zilber, Dominique J. Bicout, Angeli Kodjo, Marc Artois, Zouheira Djelouadji To cite this version: Florence Ayral, Anne-Laure Zilber, Dominique J. Bicout, Angeli Kodjo, Marc Artois, et al.. Distribution of Leptospira interrogans by multispacer sequence typing in urban norway rats (Rattus norvegicus): A survey in France in 2011-2013. PLoS ONE, 2015, 10 (10), pp.1-14.10.1371/journal.pone.0139604. hal-02640150 Funding: This project was supported by USC 1233 and by WildTech Project WP6, Epidemiology and Risk Assessment, which is supported by the European Commission under the Food, Agriculture and Fisheries and Biotechnology theme of the Seventh Framework Programme for Research and Technological Development (grant number 222633).

Fig 1 .

 1 Fig 1. Flow chart of the molecular methods performed to determine Leptospira strains. doi:10.1371/journal.pone.0139604.g001

Fig 2

 2 Fig 2 illustrates the distribution of rats infected with L. interrogans and shows the strains and locations of larger clusters of rats carrying a given strain. Interestingly, one strain was isolated

Table 2 .Fig 2 .

 22 Fig 2. Geographic distribution of L. interrogans. L. interrogans strains deduced from MST profiles detected in the kidneys of wild Norway rats from downtown Lyon and a suburb, trapped between October 2011 and March 2012 (contains information from OpenStreetMap 1 , which is made available here under the CC BY 3.0 License, the original images were modified for representative purposes). doi:10.1371/journal.pone.0139604.g002

Table 1 .

 1 Distribution of rats testing positive among the trapping locations and nature (species, serogroup [Sg], serovar [Sv] and strain) of Leptospira based on multispacer sequence typing profiles.

		No of rats	No of	No of PCR a -	No of	Total	L.interrogans	No of	L.strains deduced
		captured	PCR a -	positive urines	culture-	infected	species (by	complete	from the MST-profile
			positive	(no performed)	positive	rats	sequencing)	MST-profiles
			kidneys		kidneys
									Sg Sv	strain
	Francheville	9	1		1	1	1	1	IH cop	M20 or
											Wijinberg
	Guillotière	3	2		0	2	2	2	IH cop	M20 or
											Wijinberg
	la chapelle	1	0		1	1	1	1	IH	ict	R1
	Mermoz	13	9	2 (3)	9	9	9	9	IH cop	M20 or
	sud_B										Wijinberg
	Mermoz	17	12		6	12	12	11	IH cop	M20 or
	sud_F										Wijinberg
	Mermoz	7	2	1(2)	2	3	3	3	IH cop	M20 or
	sud_R										Wijinberg
	PTO	8	5	1(1)	3	5	5	5	IH	ict	CHU
											Réunion
	PTO (2)*	3	2		2	2	2	2	IH	ict	CHU
											Réunion
	UIOM	23	2	0(1)	0	2	1	1	IH cop Shibaura 9
	Total	84	36	4(7)	24	37	36	35

a PCR using rpoB primer *second trapping period for the strain followed up in the Parc de la Tête d'Or IH, Icterohaemorrhagiae; cop, Copenhageni; ict, icterohaemorrhagiae; PTO, "Parc de la Tête d'Or" doi:10.1371/journal.pone.0139604.t001

PLOS ONE | DOI:10.1371/journal.pone.0139604 October 8, 2015

Acknowledgments

We thank the city of Lyon and its public health department (Dr. Gilbert Gault), the waste plant UIOM de Lyon (Mr. Joël Baud) and the Parc de la Tête d'Or (Drs. David Gomis and Guillaume Douay, managers) for assisting with rat and census data collection. We thank Philippe Berny and Joffrey Goulois for assisting with rat collections.