the following terms were used to describe the different kinds of genetic maps used in this study , as suggested by hudson et al .
( 2012 ) : 1 ) sex - averaged map : a consensus map for both parents of a pedigree ; 2 ) consensus map : an integrated map based on segregation data from individual component maps ; 3 ) composite map : an integrated map of different individual component maps built by a marker - merging method ; and 4 ) component map : each of the maps used in the construction of a composite map .
the graphics and the representations of genetic maps were produced with r 3.1.0 ( r core team 2014 ) .
we used 14 base maps generated from seven different crosses to generate a composite genetic linkage map for p. pinaster .
the first six maps were obtained from three controlled crosses ( pedigrees # 1 , # 2 , and # 3 in supplementary fig .
s1 , supplementary material online ) between three different genotypes : corsica  landes ( cl ) , morocco  landes ( ml ) , and corsica  morocco ( cm ) . in total , 106 , 117 , and 94 full - sibs
the regression mapping algorithm of joinmap 4.1 ( van ooijen 2011 ) was used to produce two maps for each parental genotype ( one per cross ) , according to a two - way pseudotestcross mapping strategy ( grattapaglia and sederoff 1994 ) , using testcross markers ( i.e. , segregating in a 1:1 mendelian ratio ) only .
the genetic maps were then combined into sex - averaged maps ( corsica , landes and morocco , see supplementary fig .
s1 , supplementary material online ) with the function  combine groups for map integration  of joinmap 4.1 .
more details on the construction of the maps can be found in lagraulet ( 2015 ) .
the first population was a three - generation inbred pedigree consisting of an f2 population ( # 4 in supplementary fig .
s1 , supplementary material online ) resulting from the selfing of an interprovenance tree ( landes  corsica ) .
the second population was a three - generation outbred pedigree ( g2 , # 5 ) resulting from a controlled cross of two intraprovenance hybrid trees ( landes  landes ) .
for the f2 population , two different sets of individuals were used to generate two maps ( f2_o and f2_n ) with the record algorithm ( van os et al .
for the g2 population , one map for each parent ( g2 m and g2f ) was produced with the regression mapping algorithm of joinmap 4.1 ( van ooijen , 2011 ) .
the f2_o , g2 m , and g2f maps included different marker types : aflp , single sequence repeat ( ssr ) , expressed sequence tag ( est ) , and snps from different arrays ( chancerel et al .
2011 , 2013 ) , whereas the f2_n map contained only snps from the 9k snp - array ( plomion et al .
we made use of the large number of common markers and the high level of collinearity between the two f2 maps to construct a composite map ( referred to as f2c by plomion et al .
the last four maps were generated from two different f1 crosses : c14c15 ( # 6 in supplementary fig .
s1 , supplementary material online ) and gal1056oria6 ( # 7 ) . from the initial parental maps of the c14c15 mapping population described by de miguel
( 2012 ) , we mapped an additional set of 980 snps from the 12k snp - array described by chancerel et al .
( 2011 ) and 273 snps from a 1,536 snp - array ( sez - laguna et al .
for both pedigrees , we used the maximum - likelihood mapping algorithm of joinmap 4.1 to generate individual genetic maps and sex - averaged maps .
the four maps from the c14c15 and gal1056oria6 crosses contained different types of molecular markers ( ssrs , ests , sampls , and snps ) .
for all maps , genetic distances in centimorgans ( cm ) were calculated with the kosambi mapping function ( kosambi 1943 ) .
we carried out a literature review to identify previously published high - density gene - based linkage maps for members of the pinaceae family , for which sequence information was publicly available .
eckert et al . ( 2010 ) provided a sex - averaged linkage map for a two - generation outbred pedigree based on snps for p. taeda ( accession number :
the white spruce pedigree was an f1 full - sib family , whereas the black spruce pedigree was a backcross representing the hybridization species complex pic .
lind et al . ( 2014 ) provided the most saturated and gene - rich map to date for pic .
this map was a sex - averaged map of the two parents of an f1 controlled cross and was also constructed with the regression algorithm of joinmap 3.0 .
a detailed list of mapping features for each component map included in this study is available in table 1 .
a high - density linkage map for c. japonica was incorporated into this study , as a representative species from the cupressaceae family ( moriguchi et al .
this map was constructed from an f1 full - sib family ( table 1 ) , with the regression mapping algorithm implemented in joinmap v 3.0 .
the 17 maps described above were mostly constructed with snp markers ( 100% of the markers for picea sp . , 98% for p. pinaster , and 90% for p. taeda ) .
the flanking sequences of each snp marker were compared with the most recent unigene sets available for each species to obtain the sequence of unigenes containing the mapped snps : canales et al .
this comparison was carried out with the blastn tool ( the blast 1 step in fig .
unigenes with a percentage identity exceeding 95% with mapped snp flanking sequences were retained for the next step .
( 2014 ) , pine v3 , was then used as the reference for the identification of homologous unigenes within pinaceae species . a second sequence comparison ( r - blast 2 in fig .
1a ) was performed , between the mapped unigenes of each species and the unigene sequences of pine v3 . for this interspecific comparison ,
only sequences with a reciprocal best hit with a percentage identity exceeding 85% , an e value below e , and an alignment of more than 200 bp were retained as homologous unigenes .
homologous unigenes between different species were considered as orthologs if they were positioned in the same lg ( i.e. , syntenic unigenes ) .
identified orthologous unigenes were used as anchor markers to construct a composite linkage map for each genus ( pinus and picea ) , as a preliminary step in the construction of a composite map for the pinaceae family including both genera .
we used the r package lpmerge ( endelman and plomion 2014 ) to integrate component linkage maps into a composite map without the use of segregation data .
lpmerge assessed the goodness of fit of the composite map by calculating a root mean squared error ( rmse ) per lg , by comparing the position ( in cm ) of all markers on the composite map with that on the component maps .
we calculated this metric for different maximum interval sizes ( parameter k in the algorithm ) , ranging from 1 to 10 .
the value of k minimizing the mean rmse per lg was selected for construction of the composite map .
this method was used for the construction of all the composite species maps reported here .
further details about the production of each composite map are described below . before integrating the 14 base genetic linkage maps into a single composite map , we established consensus maps ( supplementary fig . s1 , supplementary material online ) based on markers common to different accessions across pedigrees ( corsica , landes , morocco genotypes for pedigrees # 1 , # 2 , and # 3 , respectively ) , or accessions within pedigrees ( coca and gxo for pedigrees # 6 and # 7 , respectively ) , or based on the merging of different data sets of the same pedigree ( f2 for pedigree # 4 ) .
this process , designed to increase the number of markers common to component maps , was facilitated by the use of the same 12k ( chancerel et al .
2013 ) and 9k ( plomion et al . 2015 ) snp - arrays for some pedigrees .
the snp marker i d of each component map was replaced by the corresponding maritime pine unigene i d from canales et al .
this step , which was essential for the use of lpmerge ( i.e. , same marker name for orthologous markers ) , also made it possible to check the collinearity between maps .
thus , nonsyntenic unigenes between different p. pinaster linkage maps were removed from the analysis with the exception of those validated for at least two other component maps ( supplementary table s2 , supplementary material online ) .
finally , lpmerge was used to create the composite map for p. pinaster . given that similar numbers of genotypes were used to obtain the base maps and the high degree of synteny between base maps , each component map was assigned the same weight in lpmerge .
the snp marker i d of each species component map was replaced by the corresponding homologous unigene i d of p. pinaster ( canales et al .
we established composite maps at genus level before integrating the four genetic maps for each species into a single composite map for pinaceae .
this process was designed to increase the number of markers common to the component maps for each genus ( pinus and picea ) .
lpmerge was used to build these two composite maps , following the same procedure as described for p. pinaster .
we discarded nonsyntenic unigenes , except for nonsyntenic unigenes validated by at least two component maps in the p. pinaster composite map , from the construction of composite linkage maps .
noncollinear unigenes with inversions of more than 15 cm were also excluded from the construction of the composite linkage map .
abies ( lind et al . 2014 ) , when the map for this species was compared with that for pic .
abies lg7 ( renamed lg2 after comparison with the p. pinaster reference map ) was reconstructed from genotyping data provided as supplementary material by lind et al .
( 2014 ) , using the same parameters described in lind s article and the same mapping software ( joinmap v4.1 ) .
two markers with a log10 ( p ) > 1 that produced a large number of double recombinants were excluded from this lg map .
we were thus able to map 16 additional markers , and a much higher degree of synteny and collinearity was found between the homologous lgs of pic .
the available map for c. japonica consisted of 77% of snp markers ( moriguchi et al .
sequences of unigenes containing the mapped snps were retrieved from the unigene set developed by ueno et al .
2014 ) mapped in the pinaceae composite map using tblastx ( blast 3 , fig .
different e - value cutoff for homologous unigenes identification between pinaceae and c. japonica was tested : lower than 1e and 1e .
selected homologs from the pinaceae and c. japonica linkage maps were used for comparative mapping .
we established orthologous blocks within lgs where several homologous unigenes were shared between both families .
different thresholds were also tested to consider an orthologous block within an lg : blocks with at least four and six shared unigenes .
the most parsimonious evolutionary model between pinaceae and cupressaceae considering the existence of the identified cars was proposed .
circular genetic maps used in interfamily comparative mapping were drawn with circos software ( krzywinski et al .
the following terms were used to describe the different kinds of genetic maps used in this study , as suggested by hudson et al .
( 2012 ) : 1 ) sex - averaged map : a consensus map for both parents of a pedigree ; 2 ) consensus map : an integrated map based on segregation data from individual component maps ; 3 ) composite map : an integrated map of different individual component maps built by a marker - merging method ; and 4 ) component map : each of the maps used in the construction of a composite map .
the graphics and the representations of genetic maps were produced with r 3.1.0 ( r core team 2014 ) .
we used 14 base maps generated from seven different crosses to generate a composite genetic linkage map for p. pinaster .
the first six maps were obtained from three controlled crosses ( pedigrees # 1 , # 2 , and # 3 in supplementary fig .
s1 , supplementary material online ) between three different genotypes : corsica  landes ( cl ) , morocco  landes ( ml ) , and corsica  morocco ( cm ) . in total , 106 , 117 , and 94 full - sibs
the regression mapping algorithm of joinmap 4.1 ( van ooijen 2011 ) was used to produce two maps for each parental genotype ( one per cross ) , according to a two - way pseudotestcross mapping strategy ( grattapaglia and sederoff 1994 ) , using testcross markers ( i.e. , segregating in a 1:1 mendelian ratio ) only .
the genetic maps were then combined into sex - averaged maps ( corsica , landes and morocco , see supplementary fig .
s1 , supplementary material online ) with the function  combine groups for map integration  of joinmap 4.1 .
more details on the construction of the maps can be found in lagraulet ( 2015 ) .
the first population was a three - generation inbred pedigree consisting of an f2 population ( # 4 in supplementary fig .
s1 , supplementary material online ) resulting from the selfing of an interprovenance tree ( landes  corsica ) .
the second population was a three - generation outbred pedigree ( g2 , # 5 ) resulting from a controlled cross of two intraprovenance hybrid trees ( landes  landes ) .
for the f2 population , two different sets of individuals were used to generate two maps ( f2_o and f2_n ) with the record algorithm ( van os et al .
for the g2 population , one map for each parent ( g2 m and g2f ) was produced with the regression mapping algorithm of joinmap 4.1 ( van ooijen , 2011 ) .
the f2_o , g2 m , and g2f maps included different marker types : aflp , single sequence repeat ( ssr ) , expressed sequence tag ( est ) , and snps from different arrays ( chancerel et al .
2011 , 2013 ) , whereas the f2_n map contained only snps from the 9k snp - array ( plomion et al .
we made use of the large number of common markers and the high level of collinearity between the two f2 maps to construct a composite map ( referred to as f2c by plomion et al .
the last four maps were generated from two different f1 crosses : c14c15 ( # 6 in supplementary fig .
s1 , supplementary material online ) and gal1056oria6 ( # 7 ) . from the initial parental maps of the c14c15 mapping population described by de miguel
( 2012 ) , we mapped an additional set of 980 snps from the 12k snp - array described by chancerel et al .
( 2011 ) and 273 snps from a 1,536 snp - array ( sez - laguna et al .
for both pedigrees , we used the maximum - likelihood mapping algorithm of joinmap 4.1 to generate individual genetic maps and sex - averaged maps .
the four maps from the c14c15 and gal1056oria6 crosses contained different types of molecular markers ( ssrs , ests , sampls , and snps ) .
for all maps , genetic distances in centimorgans ( cm ) were calculated with the kosambi mapping function ( kosambi 1943 ) .
we carried out a literature review to identify previously published high - density gene - based linkage maps for members of the pinaceae family , for which sequence information was publicly available .
eckert et al . ( 2010 ) provided a sex - averaged linkage map for a two - generation outbred pedigree based on snps for p. taeda ( accession number :
the white spruce pedigree was an f1 full - sib family , whereas the black spruce pedigree was a backcross representing the hybridization species complex pic .
lind et al . ( 2014 ) provided the most saturated and gene - rich map to date for pic .
this map was a sex - averaged map of the two parents of an f1 controlled cross and was also constructed with the regression algorithm of joinmap 3.0 .
a detailed list of mapping features for each component map included in this study is available in table 1 .
a high - density linkage map for c. japonica was incorporated into this study , as a representative species from the cupressaceae family ( moriguchi et al .
this map was constructed from an f1 full - sib family ( table 1 ) , with the regression mapping algorithm implemented in joinmap v 3.0 .
we used 14 base maps generated from seven different crosses to generate a composite genetic linkage map for p. pinaster .
the first six maps were obtained from three controlled crosses ( pedigrees # 1 , # 2 , and # 3 in supplementary fig .
s1 , supplementary material online ) between three different genotypes : corsica  landes ( cl ) , morocco  landes ( ml ) , and corsica  morocco ( cm ) . in total , 106 , 117 , and 94 full - sibs
the regression mapping algorithm of joinmap 4.1 ( van ooijen 2011 ) was used to produce two maps for each parental genotype ( one per cross ) , according to a two - way pseudotestcross mapping strategy ( grattapaglia and sederoff 1994 ) , using testcross markers ( i.e. , segregating in a 1:1 mendelian ratio ) only .
the genetic maps were then combined into sex - averaged maps ( corsica , landes and morocco , see supplementary fig .
s1 , supplementary material online ) with the function  combine groups for map integration  of joinmap 4.1 .
more details on the construction of the maps can be found in lagraulet ( 2015 ) .
the first population was a three - generation inbred pedigree consisting of an f2 population ( # 4 in supplementary fig .
s1 , supplementary material online ) resulting from the selfing of an interprovenance tree ( landes  corsica ) .
the second population was a three - generation outbred pedigree ( g2 , # 5 ) resulting from a controlled cross of two intraprovenance hybrid trees ( landes  landes ) .
for the f2 population , two different sets of individuals were used to generate two maps ( f2_o and f2_n ) with the record algorithm ( van os et al .
for the g2 population , one map for each parent ( g2 m and g2f ) was produced with the regression mapping algorithm of joinmap 4.1 ( van ooijen , 2011 ) .
the f2_o , g2 m , and g2f maps included different marker types : aflp , single sequence repeat ( ssr ) , expressed sequence tag ( est ) , and snps from different arrays ( chancerel et al .
2011 , 2013 ) , whereas the f2_n map contained only snps from the 9k snp - array ( plomion et al .
we made use of the large number of common markers and the high level of collinearity between the two f2 maps to construct a composite map ( referred to as f2c by plomion et al .
the last four maps were generated from two different f1 crosses : c14c15 ( # 6 in supplementary fig .
s1 , supplementary material online ) and gal1056oria6 ( # 7 ) . from the initial parental maps of the c14c15 mapping population described by de miguel
( 2012 ) , we mapped an additional set of 980 snps from the 12k snp - array described by chancerel et al .
( 2011 ) and 273 snps from a 1,536 snp - array ( sez - laguna et al .
for both pedigrees , we used the maximum - likelihood mapping algorithm of joinmap 4.1 to generate individual genetic maps and sex - averaged maps .
the four maps from the c14c15 and gal1056oria6 crosses contained different types of molecular markers ( ssrs , ests , sampls , and snps ) .
for all maps , genetic distances in centimorgans ( cm ) were calculated with the kosambi mapping function ( kosambi 1943 ) .
we carried out a literature review to identify previously published high - density gene - based linkage maps for members of the pinaceae family , for which sequence information was publicly available .
eckert et al . ( 2010 ) provided a sex - averaged linkage map for a two - generation outbred pedigree based on snps for p. taeda ( accession number : tg091 , http://dendrome.ucdavis.edu/cmap/ , last accessed may 21 , 2015 ) . the map provided by pavy et al .
the white spruce pedigree was an f1 full - sib family , whereas the black spruce pedigree was a backcross representing the hybridization species complex pic .
lind et al . ( 2014 ) provided the most saturated and gene - rich map to date for pic .
this map was a sex - averaged map of the two parents of an f1 controlled cross and was also constructed with the regression algorithm of joinmap 3.0 .
a detailed list of mapping features for each component map included in this study is available in table 1 .
a high - density linkage map for c. japonica was incorporated into this study , as a representative species from the cupressaceae family ( moriguchi et al .
this map was constructed from an f1 full - sib family ( table 1 ) , with the regression mapping algorithm implemented in joinmap v 3.0 .
the 17 maps described above were mostly constructed with snp markers ( 100% of the markers for picea
the flanking sequences of each snp marker were compared with the most recent unigene sets available for each species to obtain the sequence of unigenes containing the mapped snps : canales et al .
this comparison was carried out with the blastn tool ( the blast 1 step in fig .
unigenes with a percentage identity exceeding 95% with mapped snp flanking sequences were retained for the next step .
( 2014 ) , pine v3 , was then used as the reference for the identification of homologous unigenes within pinaceae species . a second sequence comparison ( r - blast 2 in fig .
1a ) was performed , between the mapped unigenes of each species and the unigene sequences of pine v3 . for this interspecific comparison ,
only sequences with a reciprocal best hit with a percentage identity exceeding 85% , an e value below e , and an alignment of more than 200 bp were retained as homologous unigenes .
homologous unigenes between different species were considered as orthologs if they were positioned in the same lg ( i.e. , syntenic unigenes ) .
identified orthologous unigenes were used as anchor markers to construct a composite linkage map for each genus ( pinus and picea ) , as a preliminary step in the construction of a composite map for the pinaceae family including both genera .
we used the r package lpmerge ( endelman and plomion 2014 ) to integrate component linkage maps into a composite map without the use of segregation data .
lpmerge assessed the goodness of fit of the composite map by calculating a root mean squared error ( rmse ) per lg , by comparing the position ( in cm ) of all markers on the composite map with that on the component maps .
we calculated this metric for different maximum interval sizes ( parameter k in the algorithm ) , ranging from 1 to 10 .
the value of k minimizing the mean rmse per lg was selected for construction of the composite map .
this method was used for the construction of all the composite species maps reported here .
further details about the production of each composite map are described below . before integrating the 14 base genetic linkage maps into a single composite map , we established consensus maps ( supplementary fig .
s1 , supplementary material online ) based on markers common to different accessions across pedigrees ( corsica , landes , morocco genotypes for pedigrees # 1 , # 2 , and # 3 , respectively ) , or accessions within pedigrees ( coca and gxo for pedigrees # 6 and # 7 , respectively ) , or based on the merging of different data sets of the same pedigree ( f2 for pedigree # 4 ) .
this process , designed to increase the number of markers common to component maps , was facilitated by the use of the same 12k ( chancerel et al .
2013 ) and 9k ( plomion et al . 2015 ) snp - arrays for some pedigrees .
the snp marker i d of each component map was replaced by the corresponding maritime pine unigene i d from canales et al .
this step , which was essential for the use of lpmerge ( i.e. , same marker name for orthologous markers ) , also made it possible to check the collinearity between maps .
thus , nonsyntenic unigenes between different p. pinaster linkage maps were removed from the analysis with the exception of those validated for at least two other component maps ( supplementary table s2 , supplementary material online ) .
given that similar numbers of genotypes were used to obtain the base maps and the high degree of synteny between base maps , each component map was assigned the same weight in lpmerge .
the snp marker i d of each species component map was replaced by the corresponding homologous unigene i d of p. pinaster ( canales et al .
we established composite maps at genus level before integrating the four genetic maps for each species into a single composite map for pinaceae .
this process was designed to increase the number of markers common to the component maps for each genus ( pinus and picea ) .
lpmerge was used to build these two composite maps , following the same procedure as described for p. pinaster .
we discarded nonsyntenic unigenes , except for nonsyntenic unigenes validated by at least two component maps in the p. pinaster composite map , from the construction of composite linkage maps .
noncollinear unigenes with inversions of more than 15 cm were also excluded from the construction of the composite linkage map .
abies ( lind et al . 2014 ) , when the map for this species was compared with that for pic .
abies lg7 ( renamed lg2 after comparison with the p. pinaster reference map ) was reconstructed from genotyping data provided as supplementary material by lind et al .
( 2014 ) , using the same parameters described in lind s article and the same mapping software ( joinmap v4.1 ) .
two markers with a log10 ( p ) > 1 that produced a large number of double recombinants were excluded from this lg map .
we were thus able to map 16 additional markers , and a much higher degree of synteny and collinearity was found between the homologous lgs of pic .
before integrating the 14 base genetic linkage maps into a single composite map , we established consensus maps ( supplementary fig .
s1 , supplementary material online ) based on markers common to different accessions across pedigrees ( corsica , landes , morocco genotypes for pedigrees # 1 , # 2 , and # 3 , respectively ) , or accessions within pedigrees ( coca and gxo for pedigrees # 6 and # 7 , respectively ) , or based on the merging of different data sets of the same pedigree ( f2 for pedigree # 4 ) .
this process , designed to increase the number of markers common to component maps , was facilitated by the use of the same 12k ( chancerel et al .
2013 ) and 9k ( plomion et al . 2015 ) snp - arrays for some pedigrees .
the snp marker i d of each component map was replaced by the corresponding maritime pine unigene i d from canales et al .
this step , which was essential for the use of lpmerge ( i.e. , same marker name for orthologous markers ) , also made it possible to check the collinearity between maps .
thus , nonsyntenic unigenes between different p. pinaster linkage maps were removed from the analysis with the exception of those validated for at least two other component maps ( supplementary table s2 , supplementary material online ) .
given that similar numbers of genotypes were used to obtain the base maps and the high degree of synteny between base maps , each component map was assigned the same weight in lpmerge .
the snp marker i d of each species component map was replaced by the corresponding homologous unigene i d of p. pinaster ( canales et al .
we established composite maps at genus level before integrating the four genetic maps for each species into a single composite map for pinaceae .
this process was designed to increase the number of markers common to the component maps for each genus ( pinus and picea ) .
lpmerge was used to build these two composite maps , following the same procedure as described for p. pinaster .
we discarded nonsyntenic unigenes , except for nonsyntenic unigenes validated by at least two component maps in the p. pinaster composite map , from the construction of composite linkage maps .
noncollinear unigenes with inversions of more than 15 cm were also excluded from the construction of the composite linkage map .
abies ( lind et al . 2014 ) , when the map for this species was compared with that for pic .
abies lg7 ( renamed lg2 after comparison with the p. pinaster reference map ) was reconstructed from genotyping data provided as supplementary material by lind et al .
( 2014 ) , using the same parameters described in lind s article and the same mapping software ( joinmap v4.1 ) .
two markers with a log10 ( p ) > 1 that produced a large number of double recombinants were excluded from this lg map .
we were thus able to map 16 additional markers , and a much higher degree of synteny and collinearity was found between the homologous lgs of pic .
the available map for c. japonica consisted of 77% of snp markers ( moriguchi et al .
2012 ) . sequences of unigenes containing the mapped snps were retrieved from the unigene set developed by ueno et al .
2014 ) mapped in the pinaceae composite map using tblastx ( blast 3 , fig .
different e - value cutoff for homologous unigenes identification between pinaceae and c. japonica was tested : lower than 1e and 1e . selected homologs from the pinaceae and c. japonica linkage maps were used for comparative mapping .
we established orthologous blocks within lgs where several homologous unigenes were shared between both families .
different thresholds were also tested to consider an orthologous block within an lg : blocks with at least four and six shared unigenes .
the most parsimonious evolutionary model between pinaceae and cupressaceae considering the existence of the identified cars was proposed .
circular genetic maps used in interfamily comparative mapping were drawn with circos software ( krzywinski et al .
all the linkage maps described here are available from the pinus portal ( a european genetic and genomic resource for pinus ) through the pinusmap application ( https://w3.pierroton.inra.fr/pinusportal/index.php ) .
accession numbers for marker sequences used in this study are available in supplementary table s3 , supplementary material online .
supplementary figures s1s5 and tables s1s3 are available at genome biology and evolution online ( http://www.gbe.oxfordjournals.org/ ) .
 although recent advances have been gained on genome evolution in angiosperm lineages , virtually nothing is known about karyotype evolution in the other group of seed plants , the gymnosperms . here , we used high - density gene - based linkage mapping to compare the karyotype structure of two families of conifers ( the most abundant group of gymnosperms ) separated around 290 ma : pinaceae and cupressaceae . 
 we propose for the first time a model based on the fusion of 20 ancestral chromosomal blocks that may have shaped the modern karyotpes of pinaceae ( with n = 12 ) and cupressaceae ( with n = 11 ) . 
 the considerable difference in modern genome organization between these two lineages contrasts strongly with the remarkable level of synteny already reported within the pinaceae . 
 it also suggests a convergent evolutionary mechanism of chromosomal block shuffling that has shaped the genomes of the spermatophytes . 