Florian Herry

David Picard Druet

Frédéric Hérault

Amandine Varenne

Thierry Burlot

Pascale Le Roy

Sophie Allais
email: sophie.allais@agrocampus-ouest.fr

IMPUTATION FOR GENOMIC EVALUATION IN LAYERS Interest of using imputation for genomic evaluation in layer chicken

Keywords: Genomic selection, layer chicken, low density panel, imputation accuracy, genomic evaluation accuracy 4

With the availability of the 600K Affymetrix® Axiom® high-density (HD) single nucleotide polymorphism (SNP) chip, genomic selection has been implemented in broiler and layer chicken. However, the cost of this SNP chip is too high to genotype all selection candidates. A solution is to develop low density SNP chip, at a lower price, and to impute all missing markers. But to routinely implement this solution, the impact of imputation on genomic evaluation accuracy must be studied. It is also interesting to study the consequences of the use of low density SNP chips on genomic evaluation accuracy. In this perspective, the interest of using imputation in genomic selection was studied in a pure layer line.

Two low density SNP chip design were compared: an equidistant (EQ) methodology and a methodology based on linkage disequilibrium (LD). Egg weight, egg shell color, egg shell strength and albumen height were evaluated with single-step GBLUP methodology. The impact of imputation errors or the absence of imputation on the ranking of the male selection candidates was assessed with a genomic evaluation based on ancestry. Thus, genomic estimated breeding values (GEBV), with imputed HD genotypes or low density genotypes, were compared to GEBV obtained with the HD SNP chip. The relative accuracy of GEBV was also investigated by considering as reference GEBV estimated on offspring.

A limited reordering of the breeders, selected on a multi-trait index, was observed.

Spearman correlations between GEBV on HD genotypes and GEBV on low density genotypes (with or without imputation) were always higher than 0.94 with more than 3K SNPs. For the genetically closer top 150 individuals for a specific trait, with imputation, the reordering was reduced with correlation higher than 0.94 with more than 3K SNPs. Without imputation the correlations remained below 0.85 with less than 3K and 16K SNPs for EQ and LD methodology, respectively. The differences in GEBV correlations between both methodologies never were significant. The conclusions were the same for all studied traits.

INTRODUCTION

The availability of single nucleotide polymorphisms (SNP) enabled the development of high-throughput genotyping technologies leading to the use of the 600K Affymetrix® Axiom® high density (HD) genotyping array, a high-density genotyping chip developed by [START_REF] Kranis | Development of a high density 600K SNP genotyping array for chicken[END_REF], in layer and broiler breeding. Genomic selection as described by [START_REF] Meuwissen | Prediction of total genetic value using genome-wide dense marker maps[END_REF] has then been implemented in many livestock species with different statistical methods like genomic best linear unbiased prediction methods (GBLUP) [START_REF] Legarra | A relationship matrix including full pedigree and genomic information[END_REF][START_REF] Goddard | Using the genomic relationship matrix to predict the accuracy of genomic selection[END_REF] or Bayesian methods [START_REF] Meuwissen | Prediction of total genetic value using genome-wide dense marker maps[END_REF][START_REF] Xu | Estimating polygenic effects using markers of the entire genome[END_REF][START_REF] Habier | Genomic selection using low-density marker panels[END_REF]. From a reference population with genotypes and phenotypes, it is possible to estimate the genomic value of the genotyped selection candidates with or without phenotype. The main objective is to choose among the selection candidates of generation N, the best breeders for one or more traits to produce the individuals of the generation N+1. In addition, compared to a genetic selection, genomic selection may increase the genetic gain through the decrease in generation interval, most particularly for species with high generation interval, through the increase in selection intensity by genotyping many selection candidates and through the increase in evaluation accuracy.

However, the high cost of such high density (HD) SNP chip is still a problem for all livestock species. To reduce the cost of genomic selection, low density SNP chips can be developed. The idea is to select a subset of markers from the HD SNP chip and to impute the genotypes at missing markers. Three main methods to select the marker panel have been developed: (1) selection of a subset of SNPs chosen at regular intervals along each chromosome taking into account or not the MAF of the selected SNPs [START_REF] Habier | Genomic selection using low-density marker panels[END_REF][START_REF] Weigel | Predictive ability of direct genomic values for lifetime net merit of Holstein sires using selected subsets of single nucleotide polymorphism markers[END_REF][START_REF] Zhang | Accuracy of genomic prediction using low-density marker panels[END_REF][START_REF] Cleveland | Practical implementation of cost-effective genomic selection in commercial pig breeding using imputation[END_REF][START_REF] Wang | Accuracy of genomic prediction using an evenly spaced, low-density single nucleotide polymorphism panel in broiler chickens[END_REF][START_REF] Herry | Design of low density SNP chips for genotype imputation in layer chicken[END_REF], (2) selection of a subset of SNPs having high effects on different traits of interest [START_REF] Weigel | Predictive ability of direct genomic values for lifetime net merit of Holstein sires using selected subsets of single nucleotide polymorphism markers[END_REF][START_REF] Zhang | Accuracy of genomic prediction using low-density marker panels[END_REF], or (3) selection of a subset of SNPs based on linkage disequilibrium (LD) between markers [START_REF] Herry | Design of low density SNP chips for genotype imputation in layer chicken[END_REF]. This latter method was studied because of the particularities of the Gallus gallus genome (International Chicken Genome Sequencing Consortium, 2004) and the particular structure of the avian linkage disequilibrium [START_REF] Megens | Comparison of linkage disequilibrium and haplotype diversity on macro-and microchromosomes in chicken[END_REF][START_REF] Qanbari | Linkage disequilibrium reveals different demographic history in egg laying chickens[END_REF][START_REF] Hérault | A linkage disequilibrium study in layer and broiler commercial chicken populations[END_REF].

Factors influencing imputation accuracy are well documented as well as the relation between imputation accuracy and genomic evaluation of the selection candidates.

Theoretically, due to imputation errors, genomic evaluation accuracy with imputed genotypes is expected to be lower than a genomic evaluation done with HD genotypes. The literature confirms it for very low density SNP chip (from few SNPs to 3K SNPs) with a decrease in genomic evaluation accuracy with a decrease, sometimes limited, in imputation accuracy [START_REF] Weigel | Predictive ability of direct genomic values for lifetime net merit of Holstein sires using selected subsets of single nucleotide polymorphism markers[END_REF][START_REF] Weigel | Accuracy of direct genomic values dervied from imputed single nucleotide polymorphism genotypes in Jersey cattle[END_REF][START_REF] Mulder | Imputation of genotypes with low-density chips and its effect on reliability of direct genomic values in Dutch Holstein cattle[END_REF][START_REF] Cleveland | Practical implementation of cost-effective genomic selection in commercial pig breeding using imputation[END_REF][START_REF] Raoul | Using a very low-density SNP panel for genomic selection in a breeding program for sheep[END_REF]. But concerning intermediate low density SNP chip (between 6K and 20K SNPs), other studies showed that the impact of imputation errors was very limited [START_REF] Weigel | Accuracy of direct genomic values dervied from imputed single nucleotide polymorphism genotypes in Jersey cattle[END_REF][START_REF] Vanraden | Genomic evaluation with many more genotypes[END_REF][START_REF] Vanraden | Genomic imputation and evaluation using high-density Holstein genotypes[END_REF][START_REF] Moghaddar | Accuracy of genotype imputation based on random and selected reference sets in purebred and crossbred sheep populations and its effect on accuracy of genomic prediction[END_REF][START_REF] Wang | Genotype imputation methods and their effects on genomic predictions in cattle[END_REF]. However, few studies about the impact of imputation on genomic evaluation have been led on chickens [START_REF] Wang | Accuracy of genomic prediction using an evenly spaced, low-density single nucleotide polymorphism panel in broiler chickens[END_REF]).

In addition, several studies showed that for traits affected by few large QTL, genomic evaluations are more sensitive to imputation errors. This was shown by [START_REF] Habier | Genomic selection using low-density marker panels[END_REF] and [START_REF] Zhang | Accuracy of genomic prediction using low-density marker panels[END_REF] in simulation studies and confirmed by [START_REF] Chen | Impact of genotype imputation on the performance of GBLUP and Bayesian methods for genomic prediction[END_REF] on real data. They showed, in Holstein bulls, that the accuracy of direct genomic value (DGV) for milk fat percentage, a trait affected by few large QTL, decreased by 34% via GBLUP using imputed genotypes. Conversely, they showed that the accuracy of DGV for the somatic cell score, a trait affected by many small QTL, decreased only by 15%. In layer chickens, most of studied traits are affected by many small QTL. This could indicate that genomic evaluation would not be severely impacted by imputation errors.

Finally, most studies investigated the impact of imputation on genomic evaluation accuracy, but only few studies focused on the impact of the use of medium density SNP chip [START_REF] Su | Comparison of genomic predictions using medium-density (~54,000) and high-density (~777,000) single nucleotide polymorphism marker panels in Nordic Holstein and Red Dairy Cattle populations[END_REF][START_REF] Moghaddar | Accuracy of genotype imputation based on random and selected reference sets in purebred and crossbred sheep populations and its effect on accuracy of genomic prediction[END_REF] or low density SNP chip [START_REF] Weigel | Predictive ability of direct genomic values for lifetime net merit of Holstein sires using selected subsets of single nucleotide polymorphism markers[END_REF][START_REF] Harris | The impact of high density SNP chips on genomic evaluation in dairy cattle[END_REF] without imputation on genomic evaluation.

The main objective of a company is to select their breeders and to describe the consequences on the loss of selection response and on genetic progress by investigating if the ranking of their best candidates would be modified with the use of low density SNP chip.

Thus, focusing on four generations of a pure line of laying hens, the first objective of this study was to investigate the impact of imputation errors on genomic evaluation with an evaluation based on ancestry of the candidates of the second generation with true HD genotyping or imputed HD genotyping. The second objective was to study the impact of a direct use of low density SNP chips, without imputation, on genomic evaluation. To do so, a comparison was done between the same previous genomic evaluation of the candidates based on ancestry with true HD genotyping or with low density genotyping without imputation.

Then, to get closer to the true breeding values of the candidates, their genomic estimated breeding values (GEBV) was estimated with a genomic evaluation with optimal information (phenotypes on descendants). Thus, the third objective was to assess the relative accuracy of genomic evaluation by comparing the GEBV of the candidates of the second generation with optimal information (phenotypes on their descendants of the third and fourth generations) and their GEBV based on ancestry with imputed HD genotyping. Finally, imputed HD genotyping of the candidates were replaced by their low density genotyping without imputation.

Therefore, the fourth objective was to assess the relative accuracy of genomic evaluation of the candidates without imputation.

MATERIAL AND METHODS

Ethics Statement

All blood samples were carried out as part of the commercial and selection activities of Novogen. These animals studied and the scientific investigations described herein are therefore not to be considered as experimental animals per se, as defined in EU directive 2010/63 and subsequent national application texts. As a consequence, we did not seek ethical review and approval of this study as one inclusing the use of experimental animals. All animals were reared in compliance with national regulations pertaining to livestock production and according to procedures approved by the French Veterinary Services.

Animals

All animals studied were detailed in [START_REF] Herry | Design of low density SNP chips for genotype imputation in layer chicken[END_REF]. They consisted in a commercial pure line of Rhode Island (RI) laying hens. This line was created and selected by Novogen (Plédran, France). The population studied was comprised of 21,475 chickens split in four generations. Each generation was divided in three batches and a new batch was bred every six months from 2010 to 2015 (Figure 1).

Concerning the laying hens, phenotypic data were recorded from 60 to 90 weeks of age, when birds where bred in individual cages. Each data collected was associated with a laying hen.

There were 75,121 measures recorded for 7983 birds. Finally, the sires were bred in individual cages.

Genomic selection was implemented in 2015 on males of this line. However, females were still selected based on pedigree and performances, and not with genomic selection. Thus, this study concerned male selection candidates. In addition, among the different parameters studied and detailed in a next section, the relative accuracy of genomic selection was investigated. To calculate this relative accuracy, it is necessary to have a set of male selection candidates with information on their offspring. These male selection candidates were the 67 male breeders of the generation G1.

Genotyping

Genotyping are briefly described because detailed in [START_REF] Herry | Design of low density SNP chips for genotype imputation in layer chicken[END_REF]. 2370 animals were genotyped for 580,961 SNPs using the 600K Affymetrix ® Axiom ® HD genotyping array [START_REF] Kranis | Development of a high density 600K SNP genotyping array for chicken[END_REF].

Based on the fifth annotation release of Gallus gallus genome (Warren et al., 2017), these SNPs were distributed on macro-chromosomes (1 to 5), intermediate chromosomes (6 to 10), micro-chromosomes (11 to 28 and 33), one linkage group (LGE64), two sexual chromosomes Z and W, as well as a group of 3,724 SNPs with unknown location.

Genotypes were filtered through six successive steps (Table 1) including individual call rate (<95%), MAF (<0.05), SNP call rate (<95%) and Hardy-Weinberg equilibrium (P < 10 -4 ). SNPs with unknown location or located on sexual chromosome W were removed, as well as the animals showing pedigree incompatibilities. Most of the SNPs had to be removed because they showed zero MAF. Finally, 300,351 SNPs and 2362 individuals remained available for the analyses.

Low Density SNP Chips Design

Several low density SNP chips were previously designed in silico by selecting a subset of SNPs [START_REF] Herry | Design of low density SNP chips for genotype imputation in layer chicken[END_REF] from the HD SNP chip.

An equidistant (EQ) methodology was studied by selecting SNPs at regular physical intervals (in pb) along each chromosome. In addition, for each interval, the SNP with the densities: 1K, 2K, 3K, 4K, 5K, 7.5K, 10K, 15K, 20K, 30K, 40K and 50K SNPs.

A linkage disequilibrium (LD) methodology was studied considering the particular structure of the chicken linkage disequilibrium [START_REF] Robert | A linkage disequilibrium study in a layer chicken population[END_REF]. Low density SNP chips were designed using the SS4I software [START_REF] Hérault | SS4I: select SNP susbset for imputation[END_REF]. This software enabled to obtain clusters of SNPs according to a chosen LD threshold. For each cluster, the SNP with the highest MAF was selected and used as representative of this cluster. 9 low density "LD" SNP chips were designed with different LD thresholds: 0.05, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7 and 0.8.

Imputation Accuracy

In our study, the selection candidates were the 580 sires of the second generation (G1) with simulated low density genotyping. The selection candidates were imputed from the high density genotyping of the 447 sires of the first generation (G0). These 447 individuals were the fathers or the fathers' half-brothers of the selection candidates. Thus, the selection candidates were directly related to them.

For each low density SNP chip designed, imputation accuracy of the selection candidates was previously assessed as the mean correlation between true and imputed genotypes [START_REF] Herry | Design of low density SNP chips for genotype imputation in layer chicken[END_REF]. Correlations were calculated one SNP at a time for all the candidates, as suggested in Pearson's method. The mean correlation was then estimated on 300,351 correlations. The mean correlations obtained were subsequently compared for the different low density SNP chips and/or scenarios, using Student tests with type 1 error rate of 0.1%.

Measurement of Traits

Four distinct traits were studied in this paper. They are named according to Animal Trait Ontology for Livestock (Atol Ontology, 2012). From 60 to 75 weeks, egg production was recorded each day for all individuals. There were individual data. 75,121 eggs concerning 7983 birds were measured from (G0) to (G3).

One egg was collected per layer and per week, between 60 and 75 weeks, for all layers. These eggs were then transferred at Zootests (Ploufragan, France) to study egg quality traits. The first step was to measure Egg Weight (EW, in g). Then, three traits concerning egg shell color were estimated with a Minolta Chroma Meter: redness (a*), yellowness (b*) and lightness (L*) of egg shell. Egg Shell Color (ESC) was then calculated as = 100 -( * - * - * ). The next step consisted in measuring Egg Shell Strength (ESS, in N) by using a compression machine to evaluate the shell static stiffness. ESS corresponded to the maximum force recorded before fracturing the shell. Finally, each egg was broken and Albumen Height (AH) was measured using a tripod.

Genomic Evaluation Strategies

EW, ESC, ESS and AH were evaluated with single-step GBLUP methodology [START_REF] Legarra | A relationship matrix including full pedigree and genomic information[END_REF] using BLUPF90 programs [START_REF] Misztal | BLUPF90 and related programs[END_REF].

The first part aimed to investigate the impact of imputation errors on genomic evaluations (Figure 2a). To do so, a genomic evaluation based on ancestry "Anc_HD" was done using true HD genotyping of the 447 G0 sires and selection candidates (G1), and phenotypes of the first generation (G0). A second genomic evaluation based on ancestry "Anc_Imputed" was done using the same data for the 447 G0 sires and imputed HD genotyping of the selection candidates (G1) from simulated low density SNP chips previously designed. For each low density SNP chip and for each trait, Spearman correlations, that enabled to estimate the reordering of the selection candidates, were calculated between true "Anc_HD" Genomic Estimated Breeding Value (GEBV) and "Anc_Imputed" GEBV.

Spearman correlations were calculated for the top 150 individuals from G1 according to each trait. Spearman correlations were limited to the top 150 males to better describe the consequences of imputation errors on the reordering of these individuals, and thus to better describe the consequences on the loss of selection response and on genetic progress. The objective was to identify the good candidates and to successfully rank them among themselves. We did not focus on the ranking of the less good candidates. There were also calculated for the 67 breeders from G1 having at least 10 offspring in G2.

Then, concerning the second objective, imputed HD genotyping of the candidates were replaced by their low density genotyping without imputation, allowing to simulate the impact of the direct use of the different low density SNP chips without imputation (Figure 2b). This part also implied the use of low density genotyping without imputation for the reference population. For each low density SNP chip and for each trait, Spearman correlations were calculated between the same previous true "Anc_HD" GEBV and "Anc_Not_Imputed"

GEBV obtained with low density genotyping (without imputation). These correlations were calculated for the same 67 breeders of G1 and the top 150 individuals from G1 according to each trait.

The third objective was to study the attainable relative accuracy with imputation (Figure 2c). To calculate this relative accuracy, it is necessary to have a set of male selection candidates with information on their offspring. On one hand, males don't have own phenotypes and only a few of them have daughter records. Thus, information from them is limited. On the other hand, Generation 2 had 662 genotyped females with own performances and some of them with progeny records. They would provide a more reliable validation set

with GEBVs using all available information fairly close to the true breeding values. However, females were still selected based on pedigree and performances, and not with genomic selection. Thus, this study focused on male selection candidates. To get closer to the true breeding values for the males, a genomic evaluation "Full_HD" of the G1 candidates was done with all available information (phenotypes and genotypes) from (G0) to (G3). These "Full_HD" GEBV leaded to closer to the true breeding values of the G1 candidates which cannot be calculated. These "Full_HD" GEBV represented the maximum of relative accuracy attainable regarding this genomic evaluation with all information and were calculated only for the 67 G1 breeders which had at least 10 offspring in G2. Then, these "Full_HD" GEBV were compared by Pearson correlations with the previous GEBV based on ancestry "Anc_Imputed" with imputed HD genotyping of the breeders, for each simulated low density SNP chip.

Finally, imputed HD genotyping of the candidates were replaced again by their low density genotyping without imputation. The "Full_HD" GEBV of the 67 G1 breeders were compared by Pearson correlations with their GEBV obtained with low density genotyping without imputation ("Anc_Not_Imputed" GEBV). The fourth objective was thus to investigate the impact of a direct use of low density SNP chips without imputation on relative accuracy of genomic evaluation (Figure 2d).

The four traits were jointly estimated according to a classical multi-trait animal model:

= 1μ + + +
. is a vector of the four traits of each individual, μ is the vector of means of each trait, is a vector of fixed effects including batches, battery and position in the battery, is a vector of genomic breeding values and is a vector of random residual effects.

and are design matrixes relating respectively phenotypes to fixed effects and phenotypes to genomic breeding values ( ). It is assumed that ~ (0, ⊗ ) where is the genetic relationship matrix combining SNP information and pedigree data [START_REF] Legarra | A relationship matrix including full pedigree and genomic information[END_REF] and W is the matrix of variance and covariance of the genomic breeding values of the four traits.

Finally, ~ (0, ⊗ ) where is the identity matrix and is the matrix of residual variance and covariance of the four traits.

Software

FImpute V2.2 [START_REF] Sargolzaei | A new approach for efficient genotype imputation using information from relatives[END_REF] was used to impute the selection candidates with low density genotyping to high density genotyping from the individuals of G0 with high density genotyping.

The scenario with all available information (Full_HD) was used to estimate the genetic parameters of the model. Remlf90 [START_REF] Misztal | BLUPF90 and related programs[END_REF] was used to estimate the genetic and residual variance components. Once fixed, all different genomic evaluations based on ancestry were performed with Blupf90. The variance components were compared to components estimated with a pedigree based model using all phenotypes. They were highly correlated [START_REF] Picard Druet | Properties of genomic evaluation for egg quality traits in layers[END_REF].

RESULTS AND DISCUSSION

Imputation Accuracy

All the results concerning imputation accuracy were presented in [START_REF] Herry | Design of low density SNP chips for genotype imputation in layer chicken[END_REF] but the evolution of the mean correlations between true and imputed genotypes for the two different methodologies were recalled in Figure 3. For both methodologies, there was an increase in mean correlation with an increase in the number of SNPs on the different low density SNP chips. Better imputation accuracies were obtained with the LD methodology at an equivalent SNP density. The differences observed in mean correlation between the two methodologies were all significant. In addition, for the EQ methodology at a very low density of 1K SNPs, the mean correlation was 0.7098 indicating a quite deteriorated imputation accuracy. This corresponded to a genotyping imputation error rate of 18.5%.

These results were consistent with those found in the literature [START_REF] Dassonneville | Short communication: Imputation performances of 3 low density marker panels in beef and dairy cattle[END_REF][START_REF] Carvalheiro | Accuracy of genotype imputation in Nelore cattle[END_REF] where an increase in the number of SNPs on low density SNP chip led to better imputations.

Impact of Imputation Errors

The impact of imputation errors was investigated by comparing the results of a genomic evaluation based on ancestry, with true HD genotyping or with imputed HD genotyping. Only the results for Egg Weight (EW) were shown to simplify the reading and because of the similarity of the results for the other traits.

Results For The Top 150 Individuals.

For both methodologies (Figure 4a), there was an increase in Spearman correlations between "Anc_HD" GEBV and "Anc_imputed" GEBV with an increase in SNP density.

Indeed, for the LD0.05 and LD0.8 SNP chips, the mean correlations were respectively 0.8661 and 0.9931. For the 3Kequi and 20Kequi SNP chips, there were respectively 0.9045 and 0.9885. These results are in agreement with imputation accuracies obtained with the different low density SNP chips. There was an increase in mean correlation concerning the evaluations with an increase in imputation accuracy which is consistent with the literature. [START_REF] Moghaddar | Accuracy of genotype imputation based on random and selected reference sets in purebred and crossbred sheep populations and its effect on accuracy of genomic prediction[END_REF] showed, for Merino sheep, that the mean correlations between GEBV based on true genotypes (50K) and GEBV based on imputed genotypes (50K imputed from 12K) increased with imputation accuracies.

It was noticed that for both methodologies, with more than 5K SNPs, the mean correlations were above 0.90 indicating a re-ranking rather reduced of the best individuals for EW. However, for the 1Kequi SNP chip, the mean correlation was 0.7833 indicating a reordering quite important of the best individuals for egg weight.

Finally, at equivalent SNP density of 3K SNPs, the EQ methodology seemed to present higher results than the LD methodology with mean GEBV correlations of respectively 0.9045 and 0.8661 for the 3Kequi and LD0.05. But the differences were not significant since the standard errors were ±0.04 for both SNP chips. At a density of 20K SNPs, both methodologies were equivalent with mean GEBV correlations of respectively 0.9885 and 0.9931 for the 20Kequi and LD0.8. However, as seen previously, the LD methodology appeared to be better to get good imputation accuracies. Thus, higher imputation accuracies with the LD methodology were not synonymous of better mean correlations between GEBV compared to the EQ methodology. This could be due to the methodology itself. Indeed, Harris and Johnson (2010) and [START_REF] Weigel | Accuracy of direct genomic values dervied from imputed single nucleotide polymorphism genotypes in Jersey cattle[END_REF] said that an equidistant methodology was better to get good genomic evaluation results for traits controlled by many small QTL, which is the case for the four traits studied. On the contrary, genomic evaluations concerning traits controlled by few large QTL were more sensitive to equidistant methodology which was consequently not the most appropriated methodology. Moreover, ssGBLUP methodology considers a same variance for each SNP [START_REF] Legarra | A relationship matrix including full pedigree and genomic information[END_REF] and consequently would favor the EQ methodology. Finally, another reason could be due to the errors done with imputation.

Some imputation errors from LD SNP chips could degrade more the GEBV estimation than imputation errors from equidistant SNP chips. The EQ methodology would be more robust than the LD methodology in case of imputation errors.

Results For The Breeders.

Spearman correlations between "Anc_HD" GEBV and "Anc_Imputed" GEBV were also calculated for the 67 G1 breeders having at least 10 offspring in the next generation G2.

For both methodologies (Figure 4b), there was an increase in Spearman correlations with an increase in SNP density. Indeed, for the LD0.05 and LD0.08 SNP chips, the mean GEBV correlations were respectively 0.9777 and 0.9979. For the 3Kequi and the 20Kequi SNP chips, the results were respectively 0.9771 and 0.9972. Thus, the results were higher compared to the results for the top 150 individuals. This is due to the distribution of the 67 breeders which were not the best breeders of G1 for EW, but the best for a set of selection criteria. This was confirmed by plotting the normal distribution of HD GEBV estimated on ancestry with true HD genotyping for all G1 candidates (Figure 5). The 67 breeders (in red on the plot) were well distributed among the 580 individuals of G1 which reduced the reordering of the individuals.

The results also showed that even with a SNP density superior to 2K SNPs, good mean correlations (superior to 0.95) could be obtained indicating a very reduced re-ranking of the individuals. With only 5K SNPs imputed to the HD SNP chips, mean correlations above 0.98 could be reached.

However, with the 1Kequi SNP chip, the mean GEBV correlation was under 0.95.

This decrease in correlation was also illustrated by [START_REF] Cleveland | Practical implementation of cost-effective genomic selection in commercial pig breeding using imputation[END_REF] in pig. They used only 450 SNPs imputed to the Illumina PorcineSNP60 BeadChip which resulted in a decrease in correlation to 0.866 (for an imputation accuracy of 0.914). Thus, by decreasing to much the SNP density, the reduced imputation accuracies can have negative consequences on genomic evaluations.

Finally, our results did not show any difference between EQ and LD methodologies.

Impact of the Absence of Imputation

Given the good results of genomic evaluations with imputed genotyping, the impact of the absence of imputation was studied. Only the results for Egg Weight (EW) were shown to simplify the reading and because of the similarity of the results for the other traits.

Results For The Top 150 Individuals.

For the top 150 individuals for both methodologies (Figure 6a), there was an increase in Spearman correlation between "Anc_HD" GEBV and "Anc_Not_Imputed" GEBV with an increase in SNP density. Indeed, the mean correlations for the 3Kequi and the 20Kequi SNP chips were respectively 0.8507 and 0.9379. For the LD0.05 and the LD0.8 SNP chips there were respectively 0.7816 and 0.8658. [START_REF] Zhang | Accuracy of genomic prediction using low-density marker panels[END_REF] showed in simulation studies that compared to the results of a genomic evaluation done with HD SNP chip, the results of genomic evaluations done with low density SNP chips without imputation also decreased.

With an effective population size of 100, heritability of 0.5, 241 QTL, and a SNP chip of 10K markers, the relative accuracy of the GBLUP evaluation decreased from 0.88 with 5K markers to 0.69 with only 200 markers.

For both methodologies, there was a consequent decrease in mean correlations compared to the results of the genomic evaluations done with imputed HD genotyping. For the 1Kequi and the 50Kequi SNP chips, both imputed, the results were respectively 0.7833 and 0.9964. Without imputation, the results were respectively 0.6261 and 0.9503. Likewise, for the LD0.05 and the LD0.8 SNP chips with imputation, the results were respectively 0.8661 and 0.9931. Without imputation, the results decreased respectively to 0.7816 and 0.8658. Furthermore, from 20K SNPs, the results for the EQ methodology seemed to reach a mean correlation threshold of 0.95 whereas with imputation the mean correlations were above 0.99. Thus, imputations enabled to increase significantly the mean correlations, mainly for very low density SNP chips. In addition, these results indicate that the ranking of the best 150 individuals of G1 for EW obtained without imputation was quite different from the ranking obtained with HD genotyping. The lower results obtained for very low SNP density indicated that using few SNPs could not be sufficient to accurately rank individuals having very close genomes.

Finally, at equivalent SNP density, a tendency to get higher results with the EQ methodology was observed. Indeed, at 3K SNPs, the difference in mean correlation between 3Kequi and LD0.05 SNP chips was equal to 0.07. The same difference was obtained between 20Kequi and LD0.8 SNP chips. Such differences were higher than with imputation but were not significant. However, we can note that the correlations remained always below 0.90 for the top 150 individuals whatever the SNP density with the LD methodology without imputation. The differences between methodologies are consistent with the genetic determinism of the four traits as explained in the previous part [START_REF] Harris | The impact of high density SNP chips on genomic evaluation in dairy cattle[END_REF][START_REF] Weigel | Accuracy of direct genomic values dervied from imputed single nucleotide polymorphism genotypes in Jersey cattle[END_REF]. In addition, the EQ methodology enabled a covering of all chromosomes more optimal than the LD methodology [START_REF] Herry | Design of low density SNP chips for genotype imputation in layer chicken[END_REF]. With the LD methodology, there were some gaps on chromosomes without SNPs selected on low density SNP chips. With the EQ methodology, the number of gaps was decreased, or at least their size was lower.

Results For The Breeders.

Spearman correlations between "Anc_HD" GEBV and "Anc_Not_Imputed" GEBV were also calculated for the 67 breeders (Figure 6b). For both methodologies, there was an increase in Spearman correlations with an increase in SNP density. At equivalent SNP density, the results for the 3Kequi and 20Kequi SNP chips were respectively 0.9484 and 0.9802. For the LD0.05 and LD0.8 the results were respectively 0.9349 and 0.9665.

Compared to the results for the top 150 individuals, the results were better for the 67 breeders as shown previously in the scenario with imputation. Finally, for a SNP density higher than 3K, the mean correlations were above 0.94 for both methodologies, indicating a reordering rather reduced of the 67 breeders. In bovine, [START_REF] Weigel | Predictive ability of direct genomic values for lifetime net merit of Holstein sires using selected subsets of single nucleotide polymorphism markers[END_REF] showed that compared to the top 500 bulls selected from progeny testing, 306 were truly selected with 32K SNPs chosen from the Illumina BovineSNP50 Bead Chip. With 2K equally spaced SNPs, 292 bulls were chosen. With only 500 equally spaced SNPs, 247 bulls were chosen. This illustrates that compared to the HD SNP chip, the re-ranking was limited and that even with few SNPs, the reordering of the individuals was limited.

Compared to the results obtained with imputation, there was a slight decrease in correlations with "Anc_HD" GEBV. Indeed, for the 1Kequi and 50Kequi SNP chips, the results were respectively 0.9316 (±0.0451) and 0.9983 (±0.0072) with imputation, and 0.8718 (±0.0608) and 0.9815 (±0.0238) without imputation. Likewise, for the LD0.05 and the LD0.8, the results were respectively 0.9777 (±0.0261) and 0.9979 (±0.0080) with imputation, and 0.9349 (±0.0440) and 0.9665 (±0.0318) without imputation. Thus, the differences observed for both methodologies were not significant and the results were still high whatever the SNP chip used. These results were rather different from those obtained by [START_REF] Aliloo | The feasibility of using low-density marker panels for genotype imputation and genomic prediction of crossbred dairy cattle of East Africa[END_REF].

They showed in bovine, for 1034 individuals, that correlations between HD GEBV (on 777K genotypes) and GEBV based on imputed HD genotyping were significantly higher than without imputation. Indeed, according to their MAFI (Minor Allele Frequency within Interval) method which was the closest to our EQ methodology, using 4013 and 25,410 SNPs imputed to 777K SNPs resulted respectively in correlations of 0.9398 and 0.9927. These results decreased dramatically without imputation with correlations of respectively 0.6485 and 0.8598. Such a large decrease was not observed in our study.

Finally, the differences observed between the two methodologies were also not significant. Consequently, the simpler EQ methodology seems to be sufficient to get good genomic evaluation results for traits controlled by many small QTL, which is the case for the four traits studied.

Impact Of Imputation On Relative Accuracy Of Genomic Evaluation 20

The impact of imputation on the attainable relative accuracy of genomic evaluations was studied by comparing a genomic evaluation "Full_HD" of the 67 G1 breeders using all available information (phenotypes and genotypes) from generation (G0) to (G3) and GEBV of the G1 breeders based on ancestry with imputed HD genotyping ("Anc_Imputed" GEBV), for each low density SNP chip. Only the results for Egg Weight (EW) were shown to simplify the reading and because of the similarity of the results for the other traits.

It was noticed (Figure 7) for the EQ methodology a slight increase in Pearson correlations from very low density SNP chips to 20K SNPs. Indeed, for the 1Kequi and the 20Kequi SNP chips, the mean correlations were respectively 0.4472 and 0.4854. But for the LD methodology, the results were rather stable with mean correlations of respectively 0.4917 and 0.4875 for the LD0.05 and LD0.8 SNP chips. For both methodologies, the results varied slightly up to 20K SNPs. They became steady for the EQ methodology from 20K to higher SNP densities. Finally, for both methodologies, the correlations of "Anc_Imputed" GEBV with "Full_HD" GEBV were not significantly different from those obtained by comparison between true HD GEBV on ancestry and "Full_HD" GEBV. The mean correlation was 0.4848 and corresponded to a theoretical maximum value attainable. The standard error for each low density SNP chip was ± 0.11 indicating that there was no difference with the theoretical maximum value. For information purposes, the mean correlations for ESC, ESS and AH were 0.2618 ± 0.12, 0.4027 ± 0.11 and 0.4802 ± 0.11. This is consistent with the previous results showing a very slight impact of imputations errors on GEBV estimations of the 67 breeders on ascendance. For both methodologies, from a density of 5K SNPs imputed to the HD SNP chip, the mean correlations were above 0.98 between "Anc_HD" GEBV and "Anc_Imputed" GEBV. These results are also in agreement with the literature. Indeed, [START_REF] Harris | The impact of high density SNP chips on genomic evaluation in dairy cattle[END_REF] showed that in bovine, from 5K to 1000K SNPs, the increase in correlations between true phenotypes and predicted phenotypes was very limited (0.62 to 0.65). [START_REF] Vanraden | Genomic imputation and evaluation using high-density Holstein genotypes[END_REF] showed that, for 28 traits tested in bovine, in average, the estimated genomic reliability was 61.1% with 300K SNPs and decreased to only 60.7% when they used 45K SNPs. In the study of Wellman et al. (2013), 768 SNPs imputed to the Illumina PorcineSNP60 BeadChip (60K SNPs) led to a negligible loss in genomic evaluation accuracy.

Likewise, [START_REF] Chen | Impact of genotype imputation on the performance of GBLUP and Bayesian methods for genomic prediction[END_REF] estimated in bovine that the accuracy of genomic prediction with observed 50K or imputed 50K (from 6K) genotypes was 0.61 for milk yield and 0.62 for somatic cell score (SCS).

However, a decrease in relative accuracy was observed with the 1Kequi SNP chip with a mean correlation of 0.4472. The highest decrease was observed for albumen height (AH)

where the mean correlation for the 1Kequi SNP chip was 0.4045 (±0.11) and the theoretical maximum value was 0.4802. One cannot conclude about the significance of this difference but this decrease was also expected because the results regarding the impact of imputation accuracies showed a mean correlation of 0.9316 for the 1Kequi SNP chip. Other studies showed that decreasing to much the SNP density has consequences on genomic evaluation accuracies. [START_REF] Raoul | Using a very low-density SNP panel for genomic selection in a breeding program for sheep[END_REF] Consequently, we can conclude that the effects of imputation errors on GEBV relative accuracies were very limited even if slightly more important for very low densities.

Impact of the Direct Use of Low Density SNP Chips Without Imputation on Relative Accuracy of Genomic Evaluation

The impact of the direct use of low density SNP chips on relative accuracy of genomic evaluation was studied by comparing the "Full_HD" GEBV of the G1 and GEBV of the G1 breeders on ancestry with low density genotyping without imputation ("Anc_Not_Imputed" GEBV), for each low density SNP chip. For both methodologies, only the results for Egg Weight (EW) were shown to simplify the reading and because of the similarity of the results

for the other traits.

Both methodologies were rather stable with slight variations in Pearson correlations up to 20K SNPs (Figure 8). The results for the 3Kequi and 20Kequi SNP chips were respectively 0.4471 and 0.4675. For the LD0.05 and LD0.8 the correlations were respectively 0.4583 and 0.4888. However, the standard errors associated to these results were ±0.11 and the correlation between the "Full HD" GEBV and the HD GEBV based on ancestry was 0.4848.

This indicates that the differences observed between each low density SNP chip, and consequently between the two methodologies, were not significant. These results are in agreement with the previous results showing a very slight impact of the absence of imputation on GEBV estimation of the 67 breeders on ascendance. However, the results for the 1Kequi was 0.4018 (±0.11). This lower but non-significant result was also expected because the correlation between "Anc_HD" GEBV and "Anc_Not_Imputed" GEBV was lower (0.8718 ±0.0608) than those obtained with higher SNP densities. This was the case for all traits studied.

The results found in the literature are contrasted. [START_REF] Moghaddar | Accuracy of genotype imputation based on random and selected reference sets in purebred and crossbred sheep populations and its effect on accuracy of genomic prediction[END_REF] showed in Merino sheep, that the accuracy of genomic prediction based on observed 50K genotypes was 0.446 for post-weaning weight (PWW) and 0.219 for post-weaning eye muscle depth (PW_EMD). Based on genotypes imputed from 12K to 50K genotypes, with imputation accuracy comprised between 0.88 and 0.99, the accuracy of genomic prediction was 0.443 for PWW and 0.219 for PW_EMD. Based on observed 12K genotypes, the accuracy was 0.412 for PWW and 0.205 for PW_EMD. Thus, the results were slightly better with imputation compared to a direct use of the 12K without imputation, but in both cases, there was not a dramatic decrease in genomic prediction accuracy despite a significant gap of SNP density between HD and low density chips. [START_REF] Weigel | Predictive ability of direct genomic values for lifetime net merit of Holstein sires using selected subsets of single nucleotide polymorphism markers[END_REF] had a gap of SNP density closer to our but the results were rather different. The correlation between the results from progeny testing and the genomic result with a HD SNP chip of 32K was 0.612. With 300, 1K and 2K equally spaced SNPs, the results were respectively 0.253, 0.422 and 0.539. Contrary to the results of [START_REF] Moghaddar | Accuracy of genotype imputation based on random and selected reference sets in purebred and crossbred sheep populations and its effect on accuracy of genomic prediction[END_REF], there was a significant decrease in their results with the use of low density SNP chips without imputation. In 2010, they showed that their results were better with imputation.

Finally, for a SNP density higher than 3K, using low density SNP chips without imputation leaded to results as good as those obtained with the HD SNP chip itself.

CONCLUSIONS

This study showed a very limited reordering of the breeders, selected on a multi-traits index, with low density genotyping (with or without imputation) instead of HD genotyping.

Indeed, Spearman correlations between GEBV on HD genotyping and GEBV on low density genotyping were always higher than 0.94 with more than 3K SNP. For the top 150 individuals, who are genetically closer than the breeders, the reordering was a bit more important. Thus, the correlations between GEBV with HD genotyping and GEBV with low density genotyping remained below 0.85 with less than 3K SNP with the EQ methodology and less than 16K SNP (LD0.6) with the LD methodology. The differences in GEBV correlations between the two methodologies were never significant but seemed to indicate that the simpler EQ methodology was sufficient to obtain similar results.

Thus, using directly low density SNP chips designed with the EQ methodology with more than 5K SNPs could enable to get good results of genomic evaluation and could be a cost effective solution for genomic selection. However, only four traits were studied. These four traits were controlled by many small QTL, which explained why the equidistant methodology was more appropriated to realize genomic evaluation with ssGBLUP than the LD methodology, whereas the results on imputation accuracies were inverted. Further investigations on other traits with different genetic architectures should be conducted.

Finally, as shown by [START_REF] Habier | Genomic selection using low-density marker panels[END_REF], there could be a decrease in genomic evaluation accuracy over the generations with low density genotyping. This would require to genotype at higher density birds selected at each generation to avoid a decrease in genomic evaluation accuracy which could be prejudicial for genomic selection. In addition, in our study, only the males were genotyped but having both parents genotyped could lead to higher genomic evaluation accuracies. Results are shown for egg weight and for the 67 G1 breeders according to the number of SNPs on low density SNP chip for both methodologies.

Figure 2 .Figure 3 .

 23 Figure 2. Summary of all different genomic evaluation strategies studied

Figure 4 .

 4 Figure 4. Spearman correlations between GEBV based on ancestry obtained with true HD

Figure 5 .

 5 Figure 5. Normal distribution of all G1 selection candidates according to their HD GEBV of

Figure 6 .

 6 Figure 6. Spearman correlations between GEBV based on ancestry obtained with true HD

Figure 7 .

 7 Figure 7. Pearson correlations between "Full_HD" GEBV based on offspring with true HD

Figure 8 .

 8 Figure 8. Pearson correlations between "Full_HD" GEBV based on offspring with true HD

Table 1 .

 1 Summary of the different steps of quality control Population structure of the RI line

	Genotypes filtration	RI Line
	Individual Call Rate (<95%)	8
	MAF (=0)	204,122
	MAF (<0.05)	54,650
	SNP Call Rate (<95%)	7541
	Hardy-Weinberg equilibrium (P<10 -4 )	12,538
	SNP with unknown location or on chromosome W	1759
	Pedigree Incompatibility	0
	SNP retained for analyses	300,351
	Animals retained for analyses	2362

ACKNOWLEDGMENTS

This research project was partly supported by the French national research agency "ANR" within the framework of project ANR-10-GENOM_BTV-015 UtOpIGe. FlH is a PhD fellow supported by the poultry breeding company Novogen as part of a CIFRE thesis between PEGASE INRA's unit, Agrocampus Ouest and Novogen.


Lucia Rugeri
email: lucia.rugeri@chu-lyon.fr

Christophe Martinaud

Philippe Beurrier

Yvonne Borg

Herve Chambost

Mirela Chirila-Hetsch

Dominique Desprez

Annie Harroche

Hervé Chambost

Vanessa Milien

Brigitte Pan-Petesch

Sandrine Meunier

Gynecological and obstetric outcome in the French cohort of women with factor XIII deficiency

Keywords: factor XIII deficiency, menorrhagia, miscarriages, pregnancy outcome

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

Introduction

Congenital factor XIII (FXIII) deficiency is a very rare life-threatening autosomal recessive bleeding disorder. FXIII is a plasmatic protein circulating as a tetramer comprising two Achains and two B-chains. After activation, FXIII increases the resistance of clots and possibly plays many other roles, notably in angiogenesis, placental attachment and it is essential for maintaining pregnancy [START_REF] Muszbek | Factor XIII: a coagulation factor with multiple plasmatic and cellular functions[END_REF][START_REF] Tahlan | Factor XIII: congenital deficiency factor XIII, acquired deficiency, factor XIII A-subunit, and factor XIII B-subunit[END_REF]. Two separate genes (F13A1 and F13B) located on 2 different chromosomes are responsible of the production of FXIII [START_REF] Kohler | Factor XIII And Fibrinogen SSC Subcommittee Of The ISTH. Diagnosis and classification of factor XIII deficiencies[END_REF].

The prevalence of FXIII deficiency varies around the world. One in every 1-2 million live births is affected, with higher prevalence in consanguineous families [START_REF] Hsieh | Factor XIII deficiency. Haemoph Off[END_REF][START_REF] Odame | Factor XIII deficiency management: a review of the literature[END_REF]. In Europe, a register based on 13 centers identified 42 patients, corresponding to 7% of rare bleeding disorders (sRBD) [START_REF] Peyvandi | Coagulation factor activity and clinical bleeding severity in rare bleeding disorders: results from the European Network of Rare Bleeding Disorders[END_REF] In France, among the 476 patients with rare bleeding disorders registered in the FranceCoag Network, 33 patients (6%) present a severe FXIII deficiency (< 10 IU dL -1 ) [START_REF] Francecoag | Données démographiques : autres Déficits Héréditaires en Protéines Coagulantes[END_REF]. FXIII-A deficiency is the most severe form which leads to neonatal umbilical haemorrhage in 80% of cases, and spontaneous intracranial haemorrhage in 30% of cases [START_REF] Hsieh | Factor XIII deficiency. Haemoph Off[END_REF][START_REF] Schroeder | New developments in the area of factor XIII[END_REF]. Factor XIII deficiencies can also be revealed by induced bleeding, delayed wound healing, and repeated miscarriages [START_REF] Sharief | Congenital factor XIII deficiency in women: a systematic review of literature[END_REF]. The minimum FXIII activity level required to prevent major bleeding remains controversial in the absence of controlled prospective data [START_REF] Menegatti | Minimal factor XIII activity level to prevent major spontaneous bleeds[END_REF][START_REF] Dorgalaleh | Minimal factor XIII activity level to prevent major spontaneous bleeds: comment[END_REF][START_REF] Mumford | Guideline for the diagnosis and management of the rare coagulation disorders: a United Kingdom Haemophilia Centre Doctors' Organization guideline on behalf of the British Committee for Standards in Haematology[END_REF].

During the reproductive period of life, menstruation and pregnancy are 2 circumstances in which women with severe FXIII deficiency may experience an increased risk of bleeding. Yet little data in the literature are available as to how to manage these situations in women affected by FXIII deficiency due to the rarity of the disease. Because FXIII deficiency leads to frequent abortion in pregnant women, the knowledge of the gynecological issues and/or obstetric complications in affected women could be helpful to propose prophylactic treatment during pregnancy. A recent review of the literature highlighted the high frequency of pregnancy loss in these women and that treatment by FXIII concentrates is required for pregnancy success [START_REF] Sharief | Congenital factor XIII deficiency in women: a systematic review of literature[END_REF].

In addition, Naderi et al. reported data from 17 cases in Iran [START_REF] Naderi | Evaluation of the FXIII deficiency prophylaxis intervals in large number of FXIII deficiency patients from Iran[END_REF], but these authors revealed the lack of consistent data in Western countries.

The aim of the present study was to describe clinical characteristics of women presenting severe congenital FXIII deficiency in France, to focus on gynecological and obstetrical events, and to report the management of these rare situations.

Materials

A retrospective study based on medical files of patients followed in one of the French Hemophilia Comprehensive Care and Clinical Hemostasis Centers was conducted. Inclusion criteria were: follow-up in one of these centers, age between 15 and 65 years, and to have provided informed consent. Laboratory assessment of FXIII deficiency was performed using the Berichrom ® FXIII activity assay (Siemens Healthcare Diagnostics, Tarrytown, NY, US).

Because the diagnostic accuracy of FXIII measurements remains uncertain, in the present study the limit of quantification of the method to assess the FXIII activity level varied from < 1 IU dL -1 or < 10 IU dL -1 according to the analyzer and the laboratory. Data on medical history including demographic characteristics, age, and circumstance of diagnosis, and prophylactic treatment were recorded. Gynecological and obstetrical events including abortions, pregnancies, and modalities of treatment during pregnancy and delivery were particularly investigated. The study was approved by the medical ethics committee of the National Reference Center in Lyon.

Results

Eleven women from 31 centers were included. The median age at diagnosis was 1.5 years (range: 0-35), and at inclusion it was 30 years (range: 15-63). Consanguinity was reported in 2 patients. Nine patients presented a FXIII-A deficiency, while genotype was unavailable in 2 patients. Eight patients had FXIII levels less than 1%, and 3 0 had < 10% of FXIII. In 7 patients, umbilical cord or cerebral hemorrhage was the primary clinical manifestation. The 2 remaining patients were diagnosed following fetal miscarriage with massive bleedings (Table 1).

At inclusion, prophylactic regimen was reported in 9 women, it was based on plasma-derived FXIII concentrates (pdFXIII; Fibrogammin, CSL Behring, Germany) administered at a variable dose per infusion (range: 500-2500 IU) and frequency of infusion outside pregnancy (range: every 3-8 weeks).

Menorrhagia was reported in 2 patients receiving prophylaxis with pdFXIII (Table 2). These patients were also treated with tranexamic acid, and one received combined oral contraceptives, and iron supplementation. Three patients did not report menorrhagia in the absence of prophylaxis.

Nine women experienced pregnancies. One patient had an abortion and 1 patient presented 12 miscarriages but no full-term pregnancy. Twelve deliveries were reported in 7 women: 4 patients experienced pregnancies without miscarriages, 3 had both pregnancies and miscarriages. Two preterm deliveries occurred, at 26 and 33 gestational weeks, respectively due to incompetent cervix or to premature rupture of membrane (Table 2). In the particular case of patient 8, a premature delivery occurred in a context of incompetent cervix and disseminated intra-vascular coagulation with a fetal death due to prematurity. In this woman, the diagnosis of inherited FXIII deficiency was made after this first bleeding complication.

Among the 30 miscarriages reported in 4 patients: 97% occurred during the first trimester. Six miscarriages occurred despite prophylactic regimen; for 3 of these very early miscarriages occurred under pre-gestational prophylaxis (1 infusion every 2 months) (Table 3).

All full term-pregnancies (n =11) were observed in women with prophylactic regimen. The doses and the frequency of infusion varied among included patients. One infusion at least per month was administered; the frequency of infusion ranged from 1 every 2 weeks to 1 every 4 weeks. There was no intensification of treatment during 8 pregnancies in 5 women. In 2 pregnancies the same dose was infused more frequently (1/3 weeks to 1/2 weeks, then 1/10 days) and patient 5 received a higher frequency of infusion only during the second trimester (Table 4). FXIII measurements were not systematically performed, leading to a systematic prescription at the same regimen. Only 2 centers were able to collect these data: the median of FXIIII levels before an infusion was 17 IU dL -1 (range: 7-30).

There were 5 vaginal deliveries and 6 cesarean sections. A bolus of a least 1250 IU of pdFXIII was administered just before the delivery in all patients and was sufficient to prevent hemorrhagic complications during delivery and post-partum. No epidural analgesia was performed. All newborns were healthy and no neonatal haemorrhagic complication associated with the birth process was reported. No bleeding complication was observed in the patient who had an abortion under infusion pdFXIII prior to the procedure.

Discussion

The present report also found that the main feature of pregnancies in women with FXIII deficiency is the high risk of miscarriage; 30 miscarriages were reported in 4 patients, including 12 in one patient who did not receive FXIII substitution before or during pregnancies. Six miscarriages occurred despite a substitution: one patient experienced 3 miscarriages due to incompetent cervix but one patient experienced 3 miscarriages probably due to a too low dose of pdFXIII. In a recent review Sharief et al. estimated that in women with severe FXIII deficiency the risk of miscarriages occurs in 2 out of 3 pregnancies. Without FXIII substitution, the achievement of pregnancy with birth of a healthy infant was estimated to be 10%, the miscarriages occurring mainly during the 1 st trimester [START_REF] Sharief | Congenital factor XIII deficiency in women: a systematic review of literature[END_REF]. The role of FXIII is well-known in coagulation but also in the pregnancy achievement. But the role of FXIII in maintaining of pregnancy is not completely understood. Although, FXIII is not essential to become pregnant, it has been reported that FXIII knockout mice can initiate pregnancies but died prematurely due to vaginal bleedings concomitant with massive placental hemorrhage. Embryos exhibit no abnormalities, suggesting a maternal origin to these miscarriages [START_REF] Koseki-Kuno | Factor XIII A subunit-deficient mice developed severe uterine bleeding events and subsequent spontaneous miscarriages[END_REF]. Fibrin and fibronectin are 2 substrates of FXIII and are major components of the layer between zona compacta and zona spongiosa, also known as Nitabuch's layer, which constitutes the separating line at the time of delivery. Co-localization of FXIII, fibrin and fibronectin has been described in this layer: its activity would participate in the maintenance of the integrity of this layer. Moreover, Nitabuch's layer would be involved in immune tolerance and FXIII deficiency could disrupt this function and participate in fetal losses [START_REF] Muszbek | Factor XIII: a coagulation factor with multiple plasmatic and cellular functions[END_REF].

Eleven pregnancies conducted under prophylaxis had a good outcome. During the study period, there were no guidelines available with regard to substitution regimen during pregnancy and a large variation of doses and frequency were reported by each center. Herein, 9 pregnancies were conducted and the pdFXIII doses ranged from 400 to 800 UI/week throughout the pregnancy.

Increased doses were reported in only 3 pregnancies. These results suggest that at least one infusion of 400 UI/week could lead up to healthy child birth. The level of substitution remains debated but a target between 10 and 20 IU dL -1 of FXIII activity is [START_REF] Dorgalaleh | Minimal factor XIII activity level to prevent major spontaneous bleeds: comment[END_REF]. The United Kingdom Doctor's Haemophilia Organisation, recommend a monthly injection from the diagnosis of pregnancy, with a FXIII level above 3 IU dL -1 [START_REF] Bolton-Maggs | The rare coagulation disorders--review with guidelines for management from the United Kingdom Haemophilia Centre Doctors' Organisation[END_REF]. Some authors advocate for the use of 250 FXIII UI/week from the beginning of the pregnancy until the 6 th month, followed by 500 IU/week and an additional dose of 1000IU at the time of the delivery [START_REF] Asahina | Congenital blood coagulation factor XIII deficiency and successful deliveries: a review of the literature[END_REF][START_REF] Inbal | Coagulation factor deficiencies and pregnancy loss[END_REF]. Furthermore, herein the administration of an additional dose before delivery prevented post-partum hemorrhage that has been described in previous studies; these series reported more than 25% of post-partum hemorrhage in this population, which represents a 5-fold increased risk as compared to healthy population in France [START_REF] Deneux-Tharaux | Epidemiology of post-partum haemorrhage[END_REF].

In addition, a quarter of women with FXIII deficiency (n=3) did not experience menorrhagia before introduction of prophylaxis and among women receiving prophylaxis, 2 patients reported menorrhagia; no life-threatening peritoneal bleeding was observed. In the literature, however, a high rate of peritoneal bleeding by ruptured ovarian cyst and of menorrhagia (26 to 64%) were described in women without prophylaxis [START_REF] Sharief | Congenital factor XIII deficiency in women: a systematic review of literature[END_REF][START_REF] Lak | Pattern of symptoms in 93 Iranian patients with severe factor XIII deficiency[END_REF][START_REF] Burrows | Bleeding risk and reproductive capacity among patients with factor XIII deficiency: a case presentation and review of the literature[END_REF], The low rate of gynecological bleeding in the series reported herein could be explained by prophylaxis treatment received by the patients.

One of the limitations of this study is the lack of measurement of FXIII levels to guide the frequency of infusions. The observational nature and the absence of control group represent other potential limitations of the study. But this is the biggest series reporting obstetrical outcome in pregnant women with severe FXIII deficiency in Western countries.

Taken together, despite the small number of patients due to the rarity of this bleeding disorder, the results confirm the high prevalence of miscarriages and the effectiveness of FXIII prophylaxis to achievement of pregnancy. An injection per month of at least 400 UI of FXIII as soon as possible, personalized individual care through collaboration between obstetricians, hematologists should allow successful outcome of each pregnancy.

Tables

Table 1 :

 1 Demographic, clinical, and prophylaxis regimen in 11 women with FXIII deficiency.

		Age at		FXIII
			Age at		First bleeding	Prophylactic regimen
	Patient	inclusion,		level,
			diagnosis, years		symptom	(pdFXIII), IU/weeks
		years		IU dL-1
					central nervous	1250/4
	1	15	1
				<10	system
	2	22	0	<10	umbilical cord	500/3
	3	28	0	<1	umbilical cord	2500/4
	4	33	3	<1	hematoma	1500/4
					central nervous	1250/4
	5	47	1
				<1	system
	6	25	2	<1	umbilical cord	1250/4
	7	26	0	<1	umbilical cord	2500/4
	8	42	31	<10	fetal miscarriages	none
	9	40	3	<1	hemarthrosis	1750/4
	10	41	18	<1	fetal miscarriages	1250/8
	11	63	35	<1	umbilical cord	none

Table 4 :

 4 Data of prophylactic regimen during pregnancies and mode of deliveries (n=11) in 7

	women.
			Frequency (infusion/weeks or days)
		pdFXIII
	Patient		First	Second	Third	Mode of delivery
		(IU)/infusion
			trimester	trimester	trimester
	3	2500	1/3w		1/2w	Vaginal
	4	1750		1/4w		Cesarean section
	5	1250	1/2w	1/3w	1/2w	Cesarean section
		2500		1/4w		Vaginal
	7
		2500		1/4w		Vaginal
		1250		1/3w		Cesarean section
	8	1250	1/3w	1/2w	1/10d	Cesarean section
		1250		1/3w		Cesarean section
	9	1750		1/2w		Cesarean section
		1250		1/3w		Vaginal
	10
		1250		1/3w		Vaginal

w: weeks, d: days.

Acknowledgments

We thank Philip Robinson (DRCI, Hospices Civils de Lyon) for their advice.