Prunus species) maintained and phenotyped in five germplasm collections (four European and one Chinese) with the International Peach SNP Consortium 9K SNP peach array. The study of population structure revealed the subdivision of the panel in three main populations, one mainly made up of Occidental varieties from breeding programs (POP1OCB), one of Occidental landraces (POP2OCT) and the third of Oriental accessions (POP3OR). Analysis of linkage disequilibrium (LD) identified differential patterns of genome-wide LD blocks in each of the populations. Phenotypic data for seven monogenic traits were integrated in a genome-wide association study (GWAS). The significantly associated SNPs were always in the regions predicted by linkage analysis, forming haplotypes of markers. These diagnostic haplotypes could be used for marker-assisted selection (MAS) in modern breeding programs.Peach was domesticated in China more than four millennia ago and from there it spread world-wide. Since the middle of the last century, peach breeding programs have been very dynamic generating hundreds of new commercial varieties, however, in most cases such varieties derive from a limited collection of parental lines (founders). This is one reason for the observed low levels of variability of the commercial gene pool, implying that knowledge of the extent and distribution of genetic variability in peach is critical to allow the choice of adequate parents to confer enhanced productivity, adaptation and quality to improved varieties. With this aim we genotyped 1,580 peach accessions (including a few closely related  Prunus persica (L.) Batsch, has been shaped by three major forces [Prunus species that have an efficient gametophytic self-incompatibility system. Selfing, plus the directional selection of the domestication and the selective processes of plant breeding, have resulted in a dramatic loss of variability when compared to other cultivated species of the genus [Since its domestication in China more than 5,000 years ago, the genetic variability of cultivated peach, r forces . The firhe genus , 3. The he genus , 5. A lihe genus . The thihe genus ,7, and, he genus ,9.Prunus species are highly conserved [Prunus to be positioned on a single genetic map [Due to its economic value  and its self-pollinating behavior, peach has been one of the tree species most amenable to genetic studies , 11, beconserved , allowinetic map , using aetic map . Recentletic map , openingetic map , 15, 16 etic map  has beenetic map –22.The high abundance and relatively low cost of SNPs compared to SSRs allows for a complementary and deeper study of peach germplasm variability. In this paper, we used the IPSC 9K SNP array  to genotPrunus accessions  using the IPSC peach 9K SNP array v1 [We genotyped 1,580 array v1 . The avePrunus map .We compared all pairs of accessions to exclude known and unknown sport mutations or synonyms from the analysis, and detected 173 groups encompassing 473 cultivars with two or more cultivars with identical genotype . Consequently, 300 cultivars were removed, retaining only one accession for each unique genotype. The largest group of clones contained 11 genotypes, two had nine, eight, seven and six genotypes, respectively, and the rest were distributed in four groups of five, 17 of four, 28 of three and 115 of two genotypes. Most (76 out of 83) of the samples with the same name had the same genotype, including all those introduced as controls . Five pairs of accessions with the same name from two different germplasm collections , expected to be identical, were different. Additionally one replicate out of three of ‘Armking’ and one out of four of ‘Rubirich’ didn’t present the expected genotype.Some known sports were included in the panel. In all cases they grouped together. The largest group of identical genotypes included all known sports of ‘Springcrest’: ‘Maycrest’ , ‘Early Maycrest’, ‘Queencrest’, ‘Springbelle’, ‘Springold’ and ‘Springlady’, plus two cultivars of unknown origin, ‘Harken’ and ‘San Giovanni’. Their genotypes were identical for all markers analyzed. Another group included ‘O’Henry’ and its sports ‘John Henry’ and ‘Summer Lady’. In this case, differences were identified in 11 SNPs, with ‘O’Henry’ differing from ‘John Henry’ by nine SNPs and 11 from ‘Summer Lady’, with ‘John Henry’ and ‘Summer Lady’ only differing by one SNP. All these SNPs were located at a region of 5.4 Mb at the top of chromosome 6 (between markers SNP_IGA_604703 and SNP_IGA_621593), where there was a large amount of missing data for many other SNPs in the derived sports ‘John Henry’ (41 missing data out of 92 SNPs) and ‘Summer Lady’ (38 out of 92). A similar pattern was observed for the same genomic region in ‘Redskin’ and its sport ‘Redkist’  and in the two groups of cultivars classified as having the same genotype and of unknown relationship: ‘Vittorio Emanuele’ and ‘Paola Matteucci’ (differing by 8 SNPs in the same region and with ‘Paola Matteucci’ having 60 missing data out of 92 SNPs) and four Italian accessions with different names ‘Pieri 81’, ‘Bella Lucia’, ‘Vaccaro Roccalmunto’ and ‘Zingara Nera’, where the former three were identical and the latter differed by four SNPs in this region and had 46 out of 92 SNPs with missing data. In all cases, the differences in SNPs occurred at loci that were heterozygous in the complete accessions and homozygous in those having missing data. A similar case but involving a different region was observed between cultivars ‘Springtime’ and ‘Starcrest’. These were identical in all markers except for eight SNPs located in the upper part of chromosome 4, within a region of 17.9 Mb where there was also a large amount of missing data . As in the previous cases, all distinctive SNPs were homozygous in one variety (‘Starcrest’) and heterozygous in the other.o) per individual was 0.286, ranging from 0.003 in ‘Burrona di Rosano’ to 0.680 in ‘Zheng huang 4’. Ho per SNP ranged from 0.046 in SNP_IGA_762094 to 0.781 in SNP_IGA_550718. Chromosome 6 (Ho = 0.28) was the least heterozygous while chromosome 1 had more loci in heterozygosis (Ho = 0.33). The mean expected heterozygosity (He) was 0.39, ranging from 0.055 to 0.500. The mean average inbreeding coefficient (F = (Ho—He) / Ho) was 0.27, ranging from -0.705 to 0.9922. The vast majority of the markers (98.5%) showed significant (p < 0.001) deviation from the Hardy-Weinberg Equilibrium (HWE).A total of 1,240 unique accessions were genotyped with the 4,271 class A SNPs. The observed mean heterozygosity  for the complete set of SNPs, which separated the Oriental and Occidental accessions . The OccB, with 352 accessions), population 2, the traditional/non-breeding occidental accessions  and population 3, the oriental accessions . At K = 2, the division between the Oriental and Occidental cultivars was already evident. Increasing the number of subpopulations to four the Occidental breeding accessions were divided into two subgroups: the first comprising the vast majority of nectarines and the second the vast majority of peaches. Further increments of K maintained these four subpopulations, with additional ones empty or including only a few individuals with a membership coefficient ≥ 0.8. At all K values, more than half of the individuals (665) were not assigned to a single population and were considered as admixed (ADM)  . The posT and P3OR and between P1OCB and P3OR were moderate , while P1OCB and P2OCT showed a greater differentiation (Fst = 0.18)Distance between populations was calculated through the Fst statistic. Genetic differentiation between P2OC2, was calculated in the three subpopulations and in the admixed accessions separately, as well as in all 1,240 accessions together using a modified r² that corrects for population structure and relatedness [The LD, measured as rB, P2OCT and P3OR, respectively, failed the frequency test (MAF < 0.01) and/or the non-calling cut-off of 5% and were discarded. The average value of intra-chromosomal r2 was 0.096 in P1OCB and P2OCT, 0.133 in P3OR, and 0.082 in the admixed individuals. As expected, the average value of inter-chromosomal r2 was smaller than the intra-chromosomal and was 0.016 in P1OCB and admixed individuals, 0.018 in P2OCT, and 0.042 in P3OR. The LD decayed with distance between markers in all subpopulations. The average value of r2 dropped below 0.2 at 1.4, 1.0, 1.8, and 0.8 Mb in P1OCB, P2OCT, P3OR and ADM, respectively  was observed in LG1, starting at 8 Mb from the top of the chromosome and spanning 12 Mb. This block was followed in length by a 6 Mb block in the same chromosome at 32 Mb from the top. In P2OCT and P3OR, the SNPs at the bottom of chromosome 6, spanning 2 Mb, were in strong LD. The chromosomal heat-maps of the ADM population revealed only a few blocks of SNPs in high LD, most disappearing when the whole collection of 1,240 cultivars were analyzed with an LD statistic that corrects for population structure and relatedness, only the three common to all four subpopulations remaining.Chromosomal heat-maps showed the presence of several blocks with a high level of LD spread in almost all the chromosomes, generally spanning hundreds of kilobases –S6 Figs.G/g), fruit shape (S/s), fruit flesh color (Y/y), non-melting/melting flesh (M/m), titrable acidity (D/d), leaf gland type (E/e), and showy/non-showy flower type (Sh/sh) (see 10(p) > 5.63)  .The acid/sub-acid fruit trait has been mapped at the beginning of LG5 , where ty) while 499 were white (Y). The chromosomal heat-maps showed that the SNPs flanking this gene were in strong LD in a region 2 Mbp long (T and disappeared in P3OR (where most of the cultivars had white flesh) and ADM. Association analysis identified 21 SNPs located in a 5.2 Mb region in linkage group G1  associated to this trait  are responsible for the yellow flesh in peach, one produced by a mutation in a microsatellite at the first exon of the gene, one by an SNP in the second exon and one by the insertion of a transposable element . In our Mbp long –S5 Figs.is trait . The SNPG allele) and 290 glabrous . We found 19 SNPs associated with the trait, mapping in G5 . The average MAF of the associated SNPs was 0.31 and the heritability 0.73 (The recessive glabrous fruit trait (nectarine) has recently been associated to a retrotransposon insertion in an MYB gene  . Of the ity 0.73 . SNP_IGAB and admixed accessions. In all the subsets, a single peak of association at the end of chromosome six was evident . The number of associated SNPs varied between subsets, ranging from five to 22  and the association was due to an excess of the most frequent allele in homozygosis in the melting varieties. All four SNPs associated in LG4  were in high LD, the first two being in complete LD (r2 = 1) . The association was due to an excess of the less frequent allele in homozygosis in the non-melting fruits. Four and six haplotypes were imputed using the two and four associated SNPs of chromosomes 3 and 4, respectively. Two haplotypes had a frequency higher than 5% in both chromosomes. The average heterozygosity was 10.6% in chromosome 3 and 18.9% in chromosome 4.An endoPG gene, mapping at the end of LG4, has been reported to control melting flesh (M) in peach . In our Sh) maps in LG8 [The showy/non-showy flower locus , while 727 (9%) were false (monomorphic) and 2,037 (25%) failed. These data confirm the accuracy of the validation by Verde et al.  using a Such an array of polymorphic markers allows comparison of genotypes at the whole-genome level, identifying features not possible to discern with the low density marker analysis assays used to date , 34. OneB, containing most accessions from modern breeding programs, essentially the gene pool derived from that used by early US breeders; P2OCT, including the old European accessions, most of them non-melting peaches that were seed-propagated for a long time and as a consequence highly homozygous; and P3OR that encompasses the majority of Oriental accessions. Previous studies with SSRs [P. persica has a strong population stratification and identified the same groups. A recent study [T) are those derived from the migration of the Chinese accessions throughout central Asia towards Western Europe. Some of these materials were taken to America by European settlers and were the founders, with a few accessions coming directly from China, of the US breeding programs that started in the early 20th century using the concepts of modern genetics. These programs produced a new wave of successful cultivars that are currently the basis of European and North-American peach production, constituting subpopulation P1OCB.The UPGMA dendrogram separated Oriental accessions from those cultivated in Occidental countries. Breeding accessions were also clearly separated from those cultivated locally. The kinship analysis revealed that accession pairs were related on average at the level of grandparent-grandchild or half-sibs. This was in agreement with the results from PCA and the STRUCTURE software, identifying three subpopulations within the peach germplasm analyzed: P1OCith SSRs , 34, 38 nt study  identifiT and P1OCB subpopulations. In their study Cao et al. [The population structure identified in this study is also consistent with the genetic bottlenecks described by Verde et al.  using a o et al.  suggesteo et al. , 34 sepao = 0.28, a lower value than that usually reported with SSRs: Ho = 0.34 in a collection of Occidental cultivars [o = 0.47 for a larger collection of Oriental and Occidental materials [B, 54.2 for P2OCT and 61.3% for P3OR). This situation can be explained considering that the SNPs were identified in resequencing data of about 50 peach accessions, mostly from the P1OCB and the admixed groups [The mean observed heterozygosity was Haterials . This wad groups , and by B, P2OCT and P3OR or to a relatively low participation of these three populations in the admixture process  or mapped in a short interval. In all cases, the SNPs found to be associated with the traits were located in the known region. For the known genes ppa006109 for the Y locus, ppa023143 for G and ppa006839 for F, the most associated SNPs were 838, 875 and 849 Kb upstream of the 5’-UTR of the gene, which corresponds approximately to 1.9 cM (considering the Prunus genome of 519 cM and 230 Mb).Association mapping exploits natural diversity to search for functional variation. This method presents several advantages over linkage mapping but usually they are used together to validate or discard spurious associations. Here we applied association mapping to validate its use in peach. For some of the traits evaluated the genes have been cloned  is not straightforward and still needs to be validated. For most of the traits we found haplotypes that explain a large fraction of the phenotypes, however the association is not complete. For example three causal alleles have been reported for the yellow flesh trait . All thrA collection of 1,580 Occidental and Oriental peach accessions was analyzed with an SNP array covering the whole peach genome. Results showed that the levels and organization of variability are consistent with the known major events in the history of this species after domestication, and generally agree with published information obtained with SSR markers and SNPs.Given the high levels of LD conservation of the peach genome, whole-genome association analysis with a relatively small number of loci  showed that SNPs significantly associated with seven major genes were always in the regions predicted by classical linkage analysis. For the genes still not cloned, our data provide sets of tightly linked markers that can be the starting point to identify candidate genes and eventually used for their cloning and molecular characterization. The results also suggest that similar analysis with characters of complex inheritance and high value, such as those related with fruit organoleptic quality and extended post harvest life, will be able to identify genomic regions containing the most important genes responsible for variation of the traits.The information has other applications for peach breeding and germplasm management, as many commercial cultivars from private and public breeding programs have been characterized genetically to an unprecedented level of detail, enabling the study of the variability within each program and comparison of the variability between breeding programs on a whole-genome basis. This information is useful for parent selection and to search for variability at specific genomic regions not present in the set of parents used in a specific breeding program, but present in the global peach gene pool. Moreover, our results reveal that, for certain characters, the MAS strategy not only requires one or several markers sufficiently close to the gene of interest, but also identification of the different haplotypes for various markers in the region, selecting those that are diagnostic for the character based on the haplotypes of the set of parents used by each breeding program.Prunus were selected to be representative of five germplasm collections in four different countries. Of these accessions 1576 were P. persica and four were of other related Prunus. In detail, 420 accessions were from the peach germplasm collection of Centro di Ricerca per la Frutticoltura , 112 and 239 were provided by the National Institute of Agronomic Research (INRA) of Avignon (France) and Bordeaux , respectively, 367 were from the peach germplasm collection of IRTA at the experimental stations of La Tallada d’Empordà and Gimenells (Spain), 160 were obtained from the collection of University of Milan , and 282 from the peach collection of Zhejiang University . The complete list of the accessions is reported in A set of 1,580 accessions of Between 50 and 100 mg of young leaves from each accession were freeze-dried and used for genomic DNA extraction with the DNeasy 96 Plant Mini Kit  following the manufacturer’s protocol.The phenotype of a variable number of plants was available for seven monogenic traits: 1,071 for fruit pubescence (peach/nectarine), 1,060 for fruit shape (round/flat), 1,024 for fruit flesh color (yellow/white), 959 for fruit texture (melting/non-melting), 832 fruit acidity (acid/sub-acid), 940 for gland type (round/reniform), and 741 for flower type (showy/non-showy). All the traits were coded as qualitative.Prunus accessions were genotyped using the International Peach SNP Consortium (IPSC) peach 9K SNP array v1 [1,580 array v1 . SNP genThe SNPs were divided into five categories: A, B, C, D, and E. The first three included the polymorphic SNPs and the last two the non-polymorphic. The SNPs with GenTrain higher than 0.4 and GeneCall 10% higher than 0.2 and at least two genotypic classes were classified as polymorphic. The three classes of polymorphic SNPs were defined as follows:A. SNPs with less than 5% of No Call (failed genotyping) and all three possible genotypes  defined.B. SNPs with a potential null allele or preferential annealing. Between 5 and 50% of individuals showed a normalized signal intensity value R < 0.2 and the remaining individuals represented at least two of the three genotypic classes.C. Probable presence of duplicated sequences/genes. These SNPs were characterized by the absence of one homozygous cluster with an over-representation of heterozygous individuals, and a percentage of No Call <5%.The non-polymorphic SNPs were divided as:D. False SNPs. Characterized by a single genotypic class and a percentage of No Call < 5%.E. Failed SNPs. Having more than the 50% of No Call and/or with a GenTrain < 0.4 and/or a GeneCall 10% < 0.2.http://www.rosaceae.org/) under the accession number tfGDR1013 and in the FruitBreedomics database (http://bioinformatics.tecnoparco.org/fruitbreedomics/).All further analysis were performed using all Class A SNPs with a minor allele frequency (MAF) higher than 0.05. Genotypic data have been uploaded to GDR database  was assigned to the heterozygous state, the lower value (0) was assigned to the less frequent homozygous state, and the intermediate state (1) was assigned to the most frequent homozygous state.PCA analysis was performed using the “prcomp” R package, freely available at 2) where R2 is the multiple correlation coefficient for an SNP being regressed on all other SNPs simultaneously. The most probable number of populations was estimated using the method proposed by [Population structure was studied with the “prcomp” function of R and with the Structure v.2  softwareposed by . The fixposed by  using a The Hardy Weinberg equilibrium and the minor allele frequency were calculated for each SNP using PLINK . The SNP2 measure for population structure and relatedness. To measure LD, SNPs with a minor allele frequency (MAF) lower than 5% were discarded.Given that the phases between alleles at two heterozygous loci are unknown, the linkage disequilibrium (LD) was calculated using the squared correlation based on genotypic allele counts as implemented in PLINK . Additio2 values was computed using a polynomial fit as implemented in the “numpy” module (http://docs.scipy.org/doc/) of python v.2.7.3.The curve representing the observed rThe kinship matrix was computed using the VanRaden algorithm  as impleAssociation analysis was using the compressed mixed linear model  implemenThe SNPs associated to each trait were phased independently using fastPHASE . For eacS1 Fig(TIFF)Click here for additional data file.S2 FigThe diagonal black line indicates the physical position of the SNPs on the chromosome.(TIFF)Click here for additional data file.S3 FigThe diagonal black line indicates the physical position of the SNPs on the chromosome.(TIFF)Click here for additional data file.S4 FigThe diagonal black line indicates the physical position of the SNPs on the chromosome.(TIFF)Click here for additional data file.S5 FigThe diagonal black line indicates the physical position of the SNPs on the chromosome.(TIFF)Click here for additional data file.S6 FigThe diagonal black line indicates the physical position of the SNPs on the chromosome.(TIFF)Click here for additional data file.S1 Table(1) Asterisks indicate accessions from two different germplasm collections with the same name but with different genotype. (2) n/p = nectarine/peach; y/w = yellow/white flesh; f/nf = flat/non flat fruit shape; f/c/s = freestone/clingstone/semi-clingstone; m/n = melting/non melting; s/a = sub-acid/acid; r/g = reniform/globose leaf gland; s/ns = showy/non-showy flowers. (3) Numbers indicate the position of the accession in the (XLSX)Click here for additional data file.S2 Table(XLSX)Click here for additional data file.


CW-3 in Japanese Black cattle. The risk allele was on the same chromosome as the Q allele that increases carcass weight. Phenotypic characterization revealed that the risk allele causes disproportional tall stature and bone size that increases carcass weight in heterozygous individuals but causes disproportionately narrow chest width in homozygotes. A non-synonymous variant of FGD3 was identified as a positional candidate quantitative trait nucleotide (QTN) and the corresponding mutant protein showed reduced activity as a guanine nucleotide exchange factor for Cdc42. FGD3 is expressed in the growth plate cartilage of femurs from bovine and mouse. Thus, loss of FDG3 activity may lead to subsequent loss of Cdc42 function. This would be consistent with the columnar disorganization of proliferating chondrocytes in chondrocyte-specific inactivated Cdc42 mutant mice. This is the first report showing association of FGD3 with skeletal dysplasia.Recessive skeletal dysplasia, characterized by joint- and/or hip bone-enlargement, was mapped within the critical region for a major quantitative trait locus (QTL) influencing carcass weight; previously named  CW-3, was previously identified as one of three major QTL in Japanese Black cattle, and it was found to originate from a specific line that had been maintained in a regional subpopulation. Recent efforts to maintain genetic diversity of the Japanese Black breed have resulted in the widespread use of this line throughout Japan. Half-sib QTL analyses of the elite sires repeatedly detected the CW-3 QTL, while skeletal dysplasia has been found in the descendants. Genomic analyses revealed that skeletal dysplasia is inseparably linked with CW-3 and a functional variant of FGD3 was identified as a positional candidate QTN. Further studies such as creating a genetically modified mouse model will be useful to understand a molecular mechanism of FGD3 to modulate bone development.Livestock are typically subjected to intensive artificial selection for traits of economic value to producers. In spite of this strong selection, some major quantitative trait loci (QTLs) for an economically important trait never reach fixation in the population. Several studies have revealed that such QTLs are accompanied with an unfavorable effect on other traits of economic importance, including heritable disease phenotypes. The carcass weight QTL, named  CW-1 and CW-2, correspond to PLAG1 [NCAPG-LCORL [CW-3, showed the largest allele substitution effect (+35.0 kg) among the three loci, while the Q allele frequency was the lowest (11.5%) and detected in a specific line of Japanese Black cattle [Carcass weight, as a measure of meat yield, is an economically important trait in livestock. Due to its economic significance, several quantitative trait locus (QTL) mapping and genome-wide association studies (GWAS) have been conducted with the objective of identifying genes for improving meat production. These studies reveal that body length or stature is often times directly related to meat yield –3. In huto PLAG1  and NCAPPG-LCORL –10 and aPG-LCORL ). The thk cattle .DGAT1 increases milk yield but reduces milk fat content in dairy cattle [MRC2 increases muscle mass in carriers but causes the recessive Crooked Tail Syndrome in Belgian Blue cattle [CW-3, skeletal dysplasia is observed more frequently following line breeding within the founding genetic line. The disease is characterized by joint- and/or hip bone-enlargement but the conditions are various.A QTL allele with a large effect size that is not near fixation, may be associated with an unfavorable trait. Examples include the Lys-232-Ala substitution in y cattle , and a fe cattle . In the CW-3 is linked inseparably with skeletal dysplasia and a non-synonymous variant of FGD3 causing a reduced activity of the encoding protein is a positional candidate QTN. The risk allele for skeletal dysplasia increased carcass weight in heterozygotes, most likely because of their taller stature and increased bone mass, while the homozygotes showed disproportionately narrow chest width causing an economic loss. The results explain the low CW-3 Q allele frequency despite its large effect on carcass weight.Here we show that CW-3 QTL on bovine chromosome (BTA) 8 has been detected in seven Japanese Black paternal half-sib families of Sires I through VII  and DIK2402 (95.0 Mb) (Q haplotype between IDVGA-52 (76.5 Mb) and MS067 (90.2 Mb)  and MS091 (87.5 Mb); thus, the telomeric region distal to MS091 was identical to the Q chromosomes of Sires I and II (MS091 (87.5 Mb). On the other hand, the centromeric end of the QTL region was tentatively and conservatively determined at IDVGA-52 (76.5 Mb) to include not only one  (IDVGA-52 and DIK2402) [IDVGA-52 (76.5 Mb) to MS091 (87.5 Mb), which was covered by a shared Q haplotype  . Further95.0 Mb) , and sha90.2 Mb) . The inh evident . InteresI and II . These r weight)  but alsoIDVGA-52 6.5 Mb anaplotype .CW-3 [p < 3.1 × 10–15), of which the genotype of BovineHD0800025437 was experimentally validated to show 99.2% concordance . Inclusion of BovineHD0800025437 genotype in the statistical model as a covariate resulted in the loss of all significant associations on BTA 8, indicating that the SNP is in strong linkage disequilibrium with the causative variation for CW-3   shared a homozygous 2.36-Mb risk haplotype between ARS-BFGL-NGS-108358 (84.18 Mb) and BTA-52694-no-rs (86.54 Mb) (p > 0.7) and ASSIST programs (p > 0.11). These seven animals may be erroneously diagnosed, because the disease phenotype is highly variable and the conditions are quantitative rather than qualitative as described below  , and the on BTA8  was simi on BTA8 . The ris6.54 Mb) . Of the ed below .Q haplotype of CW-3, indicating they are closely linked (IARS gene (85.3 Mb), whose missense mutation has been identified as the cause of hereditary perinatal weak calf syndrome in Japanese Black cattle [The 2.36-Mb risk haplotype for skeletal dysplasia was encompassed by the 3.3-Mb y linked . Althougk cattle , is locaCW-3, three sires segregating the CW-3 QTL , three non-Q homozygous sires , and a Q-homozygous steer (Off-23) were subjected to targeted resequencing as described previously [CW-3 region contained 910 candidate QTNs (858 SNPs and 52 indels), including four non-synonymous and five synonymous SNPs. Coding regions that were not covered by targeted resequencing were read by Sanger sequencing, where four synonymous SNPs were detected  (85.8 Mb) and PTPDC1 (protein tyrosine phosphatase domain-containing protein 1) (86.8 Mb), and synonymous SNPs were located in BICD2  (85.7 Mb) and FGD3  between the non-synonymous SNP in PTPDC1 and those in FGD3 was 0.907 in the GWAS population.To search for candidate causative variations for skeletal dysplasia and/or eviously . The 3.3detected . Non-synand FGD3 . All nonand FGD3 . The thrPTPDC1 as a CW-3 marker, growth curves were compared between heterozygous (n = 98) and homozygous q-steers (n = 165), indicating a highly significant effect of the Q allele on withers height (p < 1 × 10–4) and body weight (p < 0.003) during all test periods, while the effect on chest circumference was significant only at day 382 (p = 0.039) and day 438 (p = 0.020)  . The reseers n = 5, indica-1 [CW-2 .BICD2 has been recently reported as the causative gene for spinal muscular atrophy in human [CW-3  and other three genes (82.6–84.6 Mb).n height , while ind PTCH1 . In boviPTPDC1 as a responsible gene for CW-3, we first searched for the sires that have a recombination between FGD3 and PTPDC1. Sire B [PTPDC1 but had homozygous q alleles for the non-synonymous SNPs in FGD3 (Q/non-Q between MS087 (86.0 Mb) and MS091 (87.5 Mb) of the CW-3 QTL interval (CW-3 maps centromeric to MS087 (Ptpdc1-deficient mice did not show any differences in body weight (at 4–12 weeks of age) or body length (at 12 weeks of age) .To examine a possibility of . Sire B  was foun in FGD3 . An IBD interval , while ainterval . These dto MS087 . Consistn testes , homozygPTPDC1 than non-synonymous SNPs in FGD3 that are located centromeric to MS087 . In contrast, the three non-synonymous SNPs in FDG3 caused loss of the start codon (ATG to ACG) and an amino acid substitution from His-171 (CAC) to Cys (TGC). Since the Kozak consensus sequence is present at the second Met of the 17th amino acid residue  and heavier (p < 0.01) than the non-carrier calves, but the chest circumferences were not different between these two groups (p = 0.34) (p < 0.05) but had smaller chest circumference (p < 0.01) than the non-carrier calves , suggesting that the risk allele increases a ratio of bone to carcass weight. Carcass data of the risk allele homozygous animals could be traced for six of the nine animals. They were worse than those from heterozygous and non-carrier steers  . The dat = 0.34) , suggestr calves , indicatr calves . The smar calves . The carr steers .CW-3 is a result of an increase in height with unchanged width, while the homozygotes display skeletal dysplasia including poor development and disproportionately narrow chest width.These results indicate that the risk allele causes disproportional tall stature in an additive manner. CW-3, is closely linked with recessive skeletal dysplasia. Skeletal measurements of calves revealed that the risk allele  causes disproportional tall stature in an allele dosage-dependent manner, suggesting that skeletal dysplasia and CW-3 are attributed to the same mutation. Only FGD3 has a non-synonymous coding variant within the critical region for skeletal dysplasia and the mutant protein showed reduced GEF activity, strongly suggesting that a mutation in FGD3 is causative.This study revealed that a QTL for carcass weight, FGD1 cause faciogenital dysplasia affecting multiple skeletal structures including short stature [PAX8-PPARγ1 rearrangement and localized at the lateral membrane but not at the basal or apical membranes. Because FGD3 is expressed in the growth plate cartilage  mice [fl/fl; Col2-CreCdc42) mice [fl/fl; Prx1-CreCdc42 mice die within a few days [fl/fl; Col2-CreCdc42 mice die within 1 day after birth [Fgd1, is also expressed in growth plate chondrocytes [fl/fl; Prx1-CreCdc42 and fl/fl; Col2-CreCdc42 neonate mice [Fgd3-deficient mice may be crucial to verify the causality of Fgd3 for skeletal dysplasia.The mammalian FGD gene family consists of six members. Mutations in human  stature . FGD6 wa stature . FGD3, s stature , however stature  reportedartilage , reducedre) mice  and chonre) mice . These cfew days  and nearer birth . Furtherdrocytes , which mate mice ,30. TherNpr2 causes severe disproportionate dwarfism [NPR2 producing excessive cGMP causes human overgrowth disorder [Disproportional tall stature is caused by activation of the C-type natriuretic peptide (NPPC)/NPR2 pathway. C-type natriuretic peptide binds to NPR2 in growth plate chondrocytes, which functions as a guanylyl cyclase to increase intracellular cGMP level . The incdwarfism , while adisorder . The tradisorder . NPR3 fudisorder . The divMRC2 causing the recessive Crooked Tail Syndrome gives desired characteristics such as increased muscularity in the carrier status in Belgian Blue cattle. Likewise, CW-3 Q allele increases carcass weight in the heterozygous state. Because CW-3 heterozygous bulls tend to be preferentially selected due to enhanced longitudinal growth, marker-assisted breeding will be useful to avoid an increased frequency of skeletal dysplasia.Fasquelle et al.  showed tThis research was approved by the National Livestock Breeding Center for Animal Research (H26-5) and conducted in accordance with the Institutional Animal Care and Use Committee Guidelines from National Livestock Breeding Center.QTL analyses were performed with the interval mapping method using a linear regression model for half-sib families, as described previously . Slaughthttp://bioinfo.ut.ee/primer3/) [Genomic sequences were examined to identify microsatellites. Primers targeting microsatellites or SNPs were designed using Primer 3 (rimer3/) . The UMDBovineHD0800025437, was genotyped for the 1156 steers by direct sequencing of the PCR products  but was normally distributed ,500 but . The Bovproducts .CW-3 (Sires I-VII), their common ancestor (Sire X), and an offspring from Sire VI (Off-6) were genotyped with BovineHD Beadchips (Illumina), followed by imputation as described above. Since imputed genotypes were obtained as phased haplotypes, the CW-3 Q haplotype consisting of BovineHD SNPs was obtained as a shared haplotype among the sires and confirmed by the Off-6 genotype that possessed homozygous Q alleles between 77.8 and 100.9 Mb.The sires segregating Q or non-Q haplotypes. Association of Q haplotypes with carcass weight was examined with a sliding window of 10 SNPs using EMMAX software [For a haplotype-based association analysis, one out of every nine SNPs was taken from the phased BovineHD genotypes of the GWAS population to reduce the number of SNPs that constitute haplotypes. The average interval of the SNPs was 50 kb. Haplotypes consisting of 20 contiguous SNPs, whose length was estimated to approximately 1 Mb, were divided into software  as descrThe initial mapping was done with 14 affected and 34 control animals from three families: 5 affected and 10 control offspring from Sire II, 5 affected and 10 control offspring from Sire P, and 4 affected and 12 control offspring from Sire Q. Sires P and Q were also used as control animals. Both affected and control animals included males and females. They were genotyped with BovineSNP50 Beadchips and submitted to homozygosity and autozygosity mapping using ASSHOM and ASSIST programs, respectively [non-Q-homozygous sires  [Q-homozygous offspring from Sire VI (Off-23) were subjected to sequence capture (NimbleGen custom array), followed by resequencing . This experiment was performed in parallel with the targeted resequencing of the CW-1 region [Non-Q-homozygous sires were defined as those that harbored homozygous q alleles at BovineHD0800025437 and did not segregate a carcass weight QTL on BTA 8 in the half-sib analyses using more than 236 offspring per family.Three sires segregating the QTL , three , and J) , and a Q1 region . Non-Q-hCW-3 region from 76.5 to 87.5 Mb on UMD3.0. Obtained putative sequence variations were filtered by: (1) heterozygous in the three sires segregating CW-3 QTL, (2) homozygous in the three non-Q- and Q-homozygous animals, and (3) opposite alleles between the Q- and non-Q-homozygous animals.Targeted region included an 11-Mb interval of the Coverage of the coding regions in the 3.3 Mb-interval between 83.7 and 87.0 Mb was checked in each animal. The regions with fewer than four reads in either of the animals were subjected to Sanger sequencing .PTPDC1  and was excluded from the analysis. The measurements of withers height, body weight, and chest circumference were interpolated by cubic spline at 4-week-intervals starting at 270 days of age. Significance of the difference between heterozygous and homozygous q-steers was tested using Student’s t-test.Two hundred and sixty-four steers from 35 sires, which were used for progeny tests at Shimane Prefectural Livestock Technology Center from 2002 to 2011, were genotyped for non-synonymous SNP in FGD3_e2_HC in Skeletal measurements of calves, withers height, body weight, and chest and abdominal circumferences, were collected at the stock market in Miyagi Prefecture from October in 2008 to March in 2009. Hair roots were collected for DNA samples. Of those, 333 offspring steers from Sire R were genotyped for the SNPs encoding His-171-Cys in FGD3 (Ptpdc1-deficient mice (TF0596) were obtained from Taconic Knockout Mouse Repository . The targeted allele was deleted from exon 4 to exon 7 of the Ptpdc1 gene and replaced by LacZ and Neo genes. The heterozygous males were backcrossed into C57BL/6J females  four times and the resultant heterozygous males and females were crossed to obtain littermates with respective genotypes. Genotyping was performed by PCR analysis. The PCR reaction was done using TAKARA LA Taq and GC buffer I  in a single reaction tube containing the following three primers: Neo_new, 5’-TCGCCTTCTTGACGAGTTCT-3’; TF0596-wild, 5’-CCCTGTAGCCCTCTGAACTG-3’; TF0596-31, 5’-GGGCAGGTTCTGTTTCTCTG-3’. The primer set Neo_new and TF0596-31 amplifies 247 bp of the targeted allele, while the primer set TF0596-wild and TF0596-31 amplifies 450 bp of the wild allele. Body weight of calves at the age of 4 to 12 weeks and body length at 12 weeks were compared among genotypes by ANOVA.Heterozygous cDNAs were amplified by PCR using PrimeSTAR GXL polymerase (Takara). The PCR primers contained a 15-bp sequence of the 5’-end of the cloning vector or of another primer sequence. The primer sequences are shown in Briefly, a cDNA encoding full-length bovine FGD3 was amplified by PCR from the bovine fetus kidney cDNA library  and inseNIH3T3 cells were transfected with bFGD3SA-HA/pIRES2-Cdc42V5His or bFGD3SA-2ndMCys-HA/pIRES2-Cdc42V5His using Lipofectamine 2000 (Life Technologies) according to the manufacturer’s instructions, followed by the incubation for 48h. The cell lysis and pull-down assays were performed as described previously . HA and The Japan Wagyu Register Association  providedThe animals with at least two measurements of less than 2σ (S.D.) were recognized as poorly developed. One exception (ID = 47), which had one measurement of less than 2σ, was defined as poorly developed because it was diagnosed as such at a livestock hygiene service center. An imbalance between chest depth and width or thurl width and pin-bone width was considered only if the difference between the two measurements was more than one σ.S1 FigSummary of the quantitative trait locus (QTL) analyses is shown in (TIF)Click here for additional data file.S2 FigPtpdc1+/- and wild-type littermates at 5-weeks of age. (B) Quantification of gene expression by real-time PCR. RNA was extracted from tibias and a testis of C57BL/6 mice. Real-time PCR was performed using TaqMan Gene Expression Assays (Applied Biosystems): Ptpdc1, Mm01327051_m1; Hprt, Mm01324427_m1. Hprt was used as an endogenous control. Data represent mean ± standard deviation (S.D.) in triplicate. Bone-1 and Bone-2 were from different individuals.(A) X-gal staining of testes from (TIF)Click here for additional data file.S3 FigRNA was extracted from testes of three heterozygous calves using RNeasy (QIAGEN). RT-PCR was performed to amplify adjacent exons of a synonymous SNP. PCR primers are shown in (TIF)Click here for additional data file.S4 Fig(A) The Kozak consensus sequence is present at the first and second methionine residues of bovine FGD3, shown by underlines. (B) The other two experiments of the GST–CRIB pull-down assay.(TIF)Click here for additional data file.S1 Table(XLSX)Click here for additional data file.S2 Table(XLSX)Click here for additional data file.S3 Table(XLSX)Click here for additional data file.S4 Table(XLSX)Click here for additional data file.S5 Table(XLSX)Click here for additional data file.S6 Table(XLSX)Click here for additional data file.S7 Table(XLSX)Click here for additional data file.S8 Table(XLSX)Click here for additional data file.


Using Illumina sequence data from a set of 24 re-sequenced soybean lines, we performed SNP calling with these pipelines and compared the GBS SNP calls with the re-sequencing data to assess their accuracy. The number of SNPs called without a reference genome was lower (13k to 24k) than with a reference genome (25k to 54k SNPs) while accuracy was high (92.3 to 98.7%) for all but one pipeline . Among pipelines offering a high accuracy (>95%), Fast-GBS called the greatest number of polymorphisms  and yielded the highest accuracy (98.7%). Using Ion Torrent sequence data for the same 24 lines, we compared the performance of Fast-GBS with that of TASSEL-GBSv2. It again called more polymorphisms (25.8K vs 22.9K) and these proved more accurate (95.2 vs 91.1%). Typically, SNP catalogues called from the same sequencing data using different pipelines resulted in highly overlapping SNP catalogues (79–92% overlap). In contrast, overlap between SNP catalogues obtained using the same pipeline but different sequencing technologies was less extensive (~50–70%).Next-generation sequencing (NGS) has revolutionized plant and animal research in many ways including new methods of high throughput genotyping. Genotyping-by-sequencing (GBS) has been demonstrated to be a robust and cost-effective genotyping method capable of producing thousands to millions of SNPs across a wide range of species. Undoubtedly, the greatest barrier to its broader use is the challenge of data analysis. Herein we describe a comprehensive comparison of seven GBS bioinformatics pipelines developed to process raw GBS sequence data into SNP genotypes. We compared five pipelines requiring a reference genome  and two  Generally, pipelines for handling GBS data are categorized in two groups; de novo-based and reference-based. When a reference genome is available, the reads from reduced-representation sequencing can be mapped to the reference genome and SNPs can be called as for whole-genome resequencing projects [de novo-based approach are UNEAK and Stacks [Next-generation sequencing (NGS) has facilitated greatly the development of methods to genotype very large numbers of molecular markers such as single nucleotide polymorphisms (SNPs). NGS offers several approaches that are capable of simultaneously performing genome-wide SNP discovery and genotyping in a single step, even in species for which little or no genetic information is available . This reprojects –12. Up tprojects , 13–15. d Stacks .Finally, different NGS sequencing platforms are currently available and offer different advantages. For example, whereas the Illumina technology offers very high throughput and read quality, this usually comes at the expense of speed as close to two weeks are required to complete a run. In contrast, the Ion Torrent technology  offers gIn this study, we comprehensively compared existing GBS analysis pipelines on the basis of the number of SNPs called, the accuracy of the resulting genotypes as well as the speed and ease of use of these pipelines. We also compared the results obtained using Illumina and Ion Torrent reads. Finally, we examined the amount of overlap in the SNP loci that were called using different pipelines.Glycine max L.) is a diploid species with 20 pairs of chromosomes and it has a medium-sized genome (1.1 Gb). Because it is an autogamous species, soybean lines/cultivars breed true and are highly homozygous. A set of 23 Canadian soybean lines and one plant introduction (PI) was subjected to GBS analysis. These same lines were resequenced as previously described by Torkamaneh and Belzile [ApeKI digestion: one for Illumina sequencing  and thee et al. ). Singlede novo variant callers and five reference-based pipelines  to callThe Fast-GBS analysis pipeline has been developed by integrating public packages with internally developed tools. The core functions include: (1) demultiplexing and cleaning of raw sequence reads; (2) read quality assessment and mapping; (3) filtering of mapped reads and estimation of library complexity; (4) re-alignment and local haplotype construction; (5) fit population frequencies and individual haplotypes; (5) raw variant calling; (6) variant and individual-level filtering; (7) identification of highly consistent variants. Since researchers may not always have immediate access to cluster resources, this pipeline allows either parallel processing of a large number of samples in a cluster or serial processing of multiple samples on a single machine.A pipeline implemented in Perl programming language was developed for the processing of Illumina sequence read data. The steps involved in the pipeline were executed in separate shell scripts. This pipeline uses different publicly available software tools  as well as some in-house tools , 21, 22.TASSEL-GBS pipelines are implemented in Java programming language. Currently, two versions are available: TASSEL-GBS v1 (TASSEL 3.0)  and TASSThe general design of UNEAK is as follows: 1) reads are trimmed to 64 bp; 2) identical 64-bp reads are collapsed into tags; 3) pairwise alignment identifies tag pairs having a single base pair mismatch. These single base pair mismatches are candidate SNPs. A “network filter” is employed to discard repeats, paralogs and sequencing errors, resulting in a collection of reciprocal tag pairs, or SNPs.de novo or via alignment against a reference genome [de novo, or aligned against a reference genome, many subsequent steps in Stacks are shared. The pipeline can be described as follows: (1) Raw sequence reads are demultiplexed and cleaned (process_radtags). (2) Data from each individual are grouped into loci, and polymorphic nucleotide sites are identified . (3) Loci are grouped together across individuals and a catalogue is written (cstacks). (4) Loci from each individual are matched against the catalogue to determine the allelic state at each locus in each individual (sstacks). (5) Allelic states are either converted into a set of mappable genotypes (for a genetic map) using genotypes or subjected to population genetic statistics via populations, with the results being written in one or several output files.The raw input data to Stacks are sequenced DNA fragments from any restriction enzyme–based GBS protocol. Stacks can handle raw sequencing data to identify loci e genome . Regardlde novo pipelines we aligned the tags supporting SNPs against reference genome to find the physical position and then we compared them with WGS dataset.For the estimation of the accuracy of genotype calls, we used an in-house script to compare the genotypes called using GBS with the genotypes called at the same loci following WGS. The sequencing and calling of SNPs in this collection of 24 soybean lines was previously described in Torkamaneh and Belzile . Brieflyde novo variant callers: UNEAK and Stacks. We used the same number of reads for all analyses (42M reads) and attempted to select parameters that would be as similar as possible for all the pipelines . The number of SNPs called by UNEAK was not too far below the mean number of SNPs called by reference-based pipelines . Among reference-based pipelines, the number of SNPs called varied between 18,941 (Stacks) and 54,412 (TASSEL-GBS v1), a 2.8-fold difference. The other three reference-based pipelines were much closer to the mean, calling between roughly 25k and 35k SNPs. In addition to calling SNPs, IGST and Fast-GBS were also able to call indels. In both cases, these contributed an extra 12–13% to the tally of variants.To assess the performance of different GBS analysis pipelines, we analyzed publicly available GBS data (100-bp Illumina reads) from a set of 24 previously studied soybean lines. We compared five reference-based analysis pipelines: TASSEL-GBS v1 and v2, Stacks, IGST, and Fast-GBS. We also compared two widely used de novo pipelines, UNEAK was almost three times faster than Stacks (1h11 vs 3h07) and proved the fastest of all pipelines. In terms of memory required, here also, very large differences were observed. Among de novo pipelines, UNEAK required almost three times as much disk space compared to Stacks (20 Gb vs 7 Gb). Among the reference-based pipelines, the differences were even greater as IGST required 17.1-fold more memory (240 Gb) than Stacks (14 Gb).Fast-GBS and TASSEL-GBS v1 proved to be the fastest running among the reference-based pipelines (~1h45), whereas IGST proved the slowest, requiring almost 13h to complete the analysis. Among de novo pipelines, the proportion of missing data was less variable, ranging from 39.4% (Stacks) to 41.3% (UNEAK).To examine the quality of the SNP data obtained using reference-based pipelines, we first measured the amount of missing data and then estimated genotype accuracy by comparing the GBS-derived genotypes with the true genotypes uncovered through whole-genome resequencing of the same lines. Assessments of the accuracy of GBS-called SNPs were performed on all SNPs for all pipelines at the same levels of tolerance for missing data (≤80%) and minor allele frequency (≥0.05). As can be seen in de novo pipelines, the accuracy of genotype calls was only slightly lower  than that obtained with the reference-based pipelines other than TASSEL-GBS v1 .When we compared the genotypes obtained using each pipeline with the genotypes derived from resequencing, we found that 98.7% of SNP genotypes called using the Fast-GBS pipeline matched the true genotypes. Similar levels of accuracy were found for SNPs called with IGST (98.4%). With a single exception, all reference-based pipelines achieved levels of accuracy >92%. TASSEL-GBS v1 proved the least accurate of these pipelines, as only 76.1% of the genotypes it called were identical to the resequencing data. Among de novo pipelines called a similar proportion of heterozygous genotypes , and did not retain any loci with a large proportion of heterozygotes. Among reference-based pipelines, Fast-GBS and TASSEL-GBS v1 called the fewest and the most heterozygous genotypes . Additionally, TASSEL-GBS v1 called the largest number of loci with a large proportion of heterozygous genotypes (1125), while Stacks only called 65 loci with more than 50% heterozygotes.Among plants, recent or ancient polyploidization events can generate paralogs that can be mistaken to represent alleles of a single locus based on short sequence reads. We therefore examined both the overall number of heterozygous genotype calls and the number of loci containing a large proportion (>50%) of heterozygous calls. As can be seen in de novo pipelines showed similar levels of overlap with Fast-GBS (Stacks = 89.1% and UNEAK = 87.5%). In an additional analysis , with Fast-GBS included in both panels . What stGiven the observed variation in the number of called SNPs and their accuracy, we chose to investigate the causes of erroneous calls. To conduct this investigation, we followed a systematic approach illustrated in The results of this analysis are shown in de novo and reference-based versions of Stacks had called the fewest SNPs. We investigated the efficacy of the demultiplexing step as this had already been described as problematic. In our analyses, we found that 19.7% of Illumina reads failed to be assigned to a specific barcode file, a number that is much higher than that seen with the other pipelines. To measure the impact of such a decrease in the number of reads available to call SNPs, we used an alternative demultiplexing tool (Sabre), instead of the one provided in Stacks. The proportion of missing reads decreased to ~2% and the number of SNPs called using this more extensive set of reads increased by 12 and 24%  for Stacks reference-based and Stacks de novo, respectively. We conclude that the poor performance of the Stacks demultiplexing tool is an important contributor to the decreased number of SNPs called by Stacks.Another result that begged investigation was the relatively low number of SNPs called by Stacks, as both To compare SNP calling using different sequencing technologies, we performed GBS on the same 24 soybean samples on an Ion Torrent platform. In contrast to Illumina reads that are all exactly the same length (100 bp), Ion Torrent reads varied in length from 50 to 135 bp. In this analysis, we used only two reference-based pipelines that had performed best in the tests described above (Fast-GBS and TASSEL-GBS v2) using 38 million Ion Torrent reads. As seen in In a second analysis, we measured the amount of missing data and estimated the accuracy of genotypes both by comparing GBS-called genotypes to the ones obtained through resequencing and by assessing the amount of heterozygosity in these lines that are presumed homozygous. As can be seen in Finally, we compared the overlap among SNP catalogues obtained using the two sequencing platforms (Illumina vs Ion Torrent). As illustrated in In conclusion, the amount of overlap across sequencing platforms was similar using both pipelines but much lower than the overlap seen across pipelines using the same sequencing platform.The flexibility and low cost of genotyping methods relying on NGS make these excellent tools for many applications and research questions in genetics, breeding, and biodiversity , 23–25. de novo vs reference-based methods. In the absence of a reference genome, there is little choice but to use one of the two currently widespread tools, UNEAK and Stacks. Although they use different algorithms to do so, these two pipelines are conceptually similar in that they seek to first establish catalogues of identical reads and then to search for highly related reads that are potentially alleles at the same locus. Under the conditions used in this work, UNEAK greatly outperformed Stacks in that it generated 82% more SNPs (~25k vs ~13k). From a qualitative perspective, both de novo pipelines performed similarly well in terms of missing data (~40%) and genotypic accuracy (~94%). This is comparable to the results reported by Lu et al. (2013) in maize where it was estimated that 92% of genotype calls were accurate and that this proportion could be increased to 96.2% by filtering for SNPs with a MAF > 0.3 in a segregating biparental population [de novo pipelines can be run quite quickly and are relatively conservative in their SNP calls resulting in a dataset of high quality. Thus, for the vast majority of species for which no reference genome is available currently or in the foreseeable future, the de novo SNP calling tools perform extremely well in terms of accuracy, but UNEAK will yield almost two-fold more SNPs.The first question that arises concerns the use of pulation . Both dede novo pipelines in this comparison may be too rosy, however. Indeed, for the sake of uniformity, we used the same filtering options  for both de novo and reference-based pipelines. But this high tolerance towards missing data may not be realistic in the case of de novo pipelines. We have shown previously that missing data imputation is very efficient and accurate on a dense set of SNPs obtained using a reference-based pipeline [de novo pipelines, in the absence of positional information on the different SNPs and the haplotype structure, imputation is much more challenging. For this reason, most users of de novo pipelines will set a lower ceiling for the maximal amount of missing data, typically between 20% and 50% at most [de novo pipelines . Under these more realistic conditions (in view of the necessary imputation of missing data), we find that reference-based pipelines yielded about 5- to 7-fold more high-quality SNP markers (~5k vs 25k to 35k markers).The picture painted of the performance of pipeline . In the  at most , 19, 27.Given the increasing availability of reference genomes in economically important crops and animals, we then need to ask which of the available reference-based pipelines produces the best catalogue of SNPs both in terms of abundance of markers and their accuracy. Among the five reference-based pipelines, Fast-GBS can be run quickly, resulted in the highest genotyping accuracy for a very large number of SNP loci  in addition to almost 4,000 indels. Based on these considerations, it seems to be the pipeline of choice, at least in the case of soybean and likely also for other species with similar genomic and reproductive characteristics.Of the pipelines tested, TASSEL-GBSv1 stood out from the rest of the group in terms of the number of SNP loci called (50–100% more than the others), but this came at the cost of accuracy as it was the only pipeline whose genotypic calls were accurate in less than 90% of cases 76.1%). As it is not easy to distinguish true from false genotypes, we would argue that TASSEL-GBSv1 is insufficiently accurate to be used on its own. In previous work, the large resulting catalogue of SNPs was often “filtered” by discarding markers that did not behave as expected in a segregating population [.1%. As iThe reference-based version of Stacks is the other pipeline that stood out in that it called much fewer SNPs than the others. In investigating the different steps needed to go from sequences to SNPs, we found that Stacks lost ~20% of reads at the demultiplexing step, i.e. some barcoded reads were not attributed to a sample and were simply discarded from the ensuing steps. This obviously resulted in a concomitant decrease in the number of SNPs called (~19k vs ~25k). This poor performance of the Stacks demultiplexing step has been previously reported by Herten et al .In our view, the genome-wide measurement of the accuracy of GBS datasets derived from different bioinformatics pipelines represents an important and key contribution of this work. It was assessed by comparing directly to whole genome resequencing data. In many previous studies, estimates of genotypic accuracy were often achieved by indirect measurement  or perfoAnother important consideration is whether the SNP catalogues produced using different pipelines and different sequencing technologies are concordant. When using a single sequencing technology (Illumina), we found that ~80% or more of SNPs called by most pipelines were also present in the SNP catalogue derived from Fast-GBS. Thus, these pipelines largely agree on the loci that are polymorphic within a given set of germplasm. The only exception was TASSEL-GBS v1, as, only a quarter of the SNPs present in the resulting catalogue was also present in the set derived using Fast-GBS. This is likely due to the shorter sequences used (only 64 bp) and a large number of “false” SNPs as this pipeline proved the least accurate of all. When using the same pipeline to analyze data derived from two sequencing technologies (Illumina and Ion Torrent), we typically found that the overlap between SNP catalogues varied between roughly 50 and 70%. Thus, the choice of sequencing technology used resulted in a greater variability in the catalogue of SNPs produced than did the choice of pipeline used on a single set of reads. At first glance, this would seem to contradict the conclusions drawn by Mascher et al. (2013) who found that the SNP catalogues produced using two pipelines (TASSEL-GBS v1 and SAMtools) differed more than the catalogues obtained using different sequencing technologies (Illumina and Ion Torrent) . In our The conclusions drawn from this work are likely to extend to other organisms sharing similar genomic features . It can be anticipated that species having experienced recent whole genome duplication events will represent a greater challenge as the risk of confounding alleles at the same locus and paralogs will likely increase in such cases. In species where such events occurred in the more distant past, there will have been more opportunity for paralogs to diverge, thus facilitating the correct mapping of reads.As such, it is impossible to devise a single pipeline that will be equally suited to every situation. This is where it becomes important for users to be able to change various parameters in the SNP calling process. Unfortunately, not all pipelines are equally “transparent” in this regard and offer the same opportunity to be altered. At one end of the spectrum, UNEAK and TASSEL-GBS offer very good performance, but rely on some purpose-built tools or algorithms that a user cannot easily alter . Also, the intermediate data files are not always easily accessible and this makes it more difficult to investigate specific problems. At the other end of the spectrum, IGST and Fast-GBS string together a set of existing tools for which the user can alter parameters/options at will, and the intermediate files are easily accessible. In this spectrum, in our view, Stacks offers an intermediate level of transparency.Finally, although whole-genome sequencing of entire populations is rapidly approaching, we believe that the methods described here are likely to remain invaluable for years to come in population genomics, breeding, mapping studies and reference genome sequence assembly, particularly for non-model organisms.S1 Table(DOCX)Click here for additional data file.S1 Text(DOCX)Click here for additional data file.


Gasterosteus aculeatus) has become an important model species for studying both contemporary and parallel evolution. In particular, differential adaptation to freshwater and marine environments has led to high differentiation between freshwater and marine stickleback populations at the phenotypic trait of lateral plate morphology and the underlying candidate gene Ectodysplacin (EDA). Many studies have focused on this trait and candidate gene, although other genes involved in marine-freshwater adaptation may be equally important. In order to develop a resource for rapid and cost efficient analysis of genetic divergence between freshwater and marine sticklebacks, we generated a low-density SNP (Single Nucleotide Polymorphism) array encompassing markers of chromosome regions under putative directional selection, along with neutral markers for background.The threespine stickleback  sequencing of sixty individuals representing two freshwater and one marine population led to the identification of 33,993 SNP markers. Ninety-six of these were chosen for the low-density SNP array, among which 70 represented SNPs under putatively directional selection in freshwater We have developed a cost-efficient low-density SNP array that allows for rapid screening of polymorphisms in threespine stickleback. The array provides a valuable tool for analyzing adaptive divergence between freshwater and marine stickleback populations beyond the well-established candidate gene Ectodysplacin (EDA).The online version of this article (doi:10.1186/1471-2164-15-867) contains supplementary material, which is available to authorized users. Gasterosteus aculateus) is distributed throughout the northern Hemisphere and shows extensive morphological and ecological variation [It is becoming increasingly evident that evolution is not just a long-term process on the scale of millennia; contemporary evolution can take place over just a few generations , 2. Simiariation . Numerouariation , 9 and pariation –16. Adapariation , 16, 17.ariation –13, and In some geographical regions, notably Northern Europe, the patterns of divergence between marine and freshwater populations of threespine sticklebacks appear less distinct than in other regions, possibly reflecting gene flow overcoming selection , 19. HowWe identified SNPs based on RAD sequencing of one marine and two isolated freshwater populations. Based on these data we chose 96 SNPs for inclusion in the array. In order to validate the array we also analyzed a sample of threespine sticklebacks from a Danish river that represents a mixture of marine and freshwater morphs.Sampling of sticklebacks took place in accordance with Danish law and regulations. Threespine stickleback is not included in the Directive “Bekendtgørelse om fredning af visse dyre- og plantearter mv., indfangning af og handel med vildt og pleje af tilskadekommet vildt”  by the Danish Ministry of the Environment. Catch of sticklebacks is therefore permitted unless it involves so high numbers of individuals that it would significantly affect the ecosystem, which was clearly not the case in this study. The fish were euthanized using an overdose of benzocaine and were subsequently stored in 96% ethanol.2 freshwater lake, 2) a small unnamed freshwater pond (ca. 0.01 km2) near the town of Hadsten and 3) the Mariager Fjord, a marine environment  Lake Hald, a 3.3 kmA. anguilla) by Pujolar et al.[Genomic DNA was extracted from muscle tissue using standard phenol-chloroform extraction. RAD sequencing was conducted by Beijing Genomics Institute . The procedures for construction of libraries and Illumina HiSeq paired-end sequencing followed those described for European eel . Final read lengths were trimmed to 75 nucleotides to avoid an increase of sequencing errors in the tail ends [Only the first reads (with the restriction site) were used in subsequent analyses due to low coverage of the second reads (not containing the restriction site). The sequence reads were sorted according to their unique barcode tag and filtered and trimmed using the FASTX Toolkit  SNPs outside regions of elevated differentiation, presumably reflecting neutral markers. We used the threespine stickleback genome sequence to extend the flanking sequence to at least 100 bp to allow for optimal primer design. We also searched for possible candidate loci marked by the SNPs using the stickleback genome browser (http://sticklebrowser.stanford.edu), in which many genes are already annotated by name and putative orthology. The best Blast hit was used to assess the putative orthologous gene. The putative orthology relationships of the remaining genes, i.e. those that have not yet been annotated, were further analyzed by a Blast comparison of their predicted protein sequence against the NCBI protein database. The function of the candidate genes was assessed using two searchable databases: The AmiGO 2 GO browser and an integrated database of human genes that also provides putative orthology with other vertebrates (http://www.genecards.org/).Based on the outcome of the analysis of RAD data we selected 96 SNPs for inclusion in the low density SNP array. We selected SNPs 1) exhibiting high genetic differentiation between the two freshwater and marine populations, both at the individual SNPs and based on smoothed FGenalex 6.5 [The selected 96 SNPs were genotyped in 96 individuals from the Odder River population on 96.96 Dynamic Arrays , using the Fluidigm EP1 instrumentation according to the manufacturer’s recommendations. The Fluidigm system uses nano-fluidic circuitry to simultaneously genotype up to 96 individuals at 96 loci  and Mariager Fjord (marine) populations and 0.111 between Hadsten (freshwater) and the Mariager Fjord populations. Sliding window analysis of FST revealed high peaks of differentiation, potentially marking chromosome regions under differential selection in marine and freshwater. Twenty-one peaks distributed across 15 different chromosomes were thus identified in the Hadsten – Mariager Fjord comparison, whereas 15 peaks across 9 different chromosomes were revealed in the case of Hald – Mariager Fjord  are listed in Table e < 0.05), whereas mean He across all loci was 0.226  and American eel (A. rostrata) to develop a 96 SNP chip encompassing markers diagnostic for the two species. This resource was subsequently used for tracing hybridization between the two species several generations back in time.We are currently witnessing a transition from population genetics to population genomics, particularly mediated by the development of Next Generation Sequencing –31. Wheret al. used RADThe SNP chip developed in the current study similarly distills information derived from RAD sequencing. The 96 SNPs encompass markers of chromosomal regions that exhibit elevated differentiation in comparisons involving a marine population and two independent freshwater stickleback populations, possibly reflecting diversifying selection. It therefore provides a useful resource for analyzing differential adaptive responses in freshwater and marine sticklebacks and the extent to which this reflects parallel evolution. Nevertheless, it also involves some important caveats. First, although there is evidence for geographically widespread parallel evolution and gene reuse when marine sticklebacks colonize freshwater environments , 13, 16,et al.[et al. [Other marker resources have been developed for three-spine stickleback, including a 3,072 SNP chip , a resouet al. and a ma[et al.  are specSimilar to previous studies undertaking genome-wide scans of threespine sticklebacks , 16, 17,The identified outlier chromosomal regions harbor a number of candidate genes with functional relationships that are already known to be important for adaptation between freshwater and marine habitats, such as genes affecting bone development, kidney function and osmoregulation .Additional file 1: Table S1: Nucleotide sequences for each SNP position. Fifty nucleotides before and after the targeted position are reported in this table. The two nucleotides corresponding to SNP alleles are presented in brackets. (XLSX 15 KB)Additional file 2: Table S2: Diversity indices estimated for each locus over 96 individuals from Odder river system. Sample Size (N), observed heterozygosity (Ho), expected heterozygosity (He) and outcomes of tests for Hardy-Weinberg equilibrium (HWE test). *significant at 5% level, **significant at 0.01 level, ***significant at 0.001 level. (XLSX 12 KB)SNP genotypes for 96 SNPs in 96 sticklebacks. SNP genotype data for 96 SNPs in 96 sticklebacks from the Odder River, Denmark. The data are provided in Genalex 6.5 [Additional file 3:alex 6.5  format.


Combining genotype data across cohorts increases power to estimate the heritability due to common single nucleotide polymorphisms (SNPs), based on analyzing a Genetic Relationship Matrix (GRM). However, the combination of SNP data across multiple cohorts may lead to stratification, when for example, different genotyping platforms are used. In the current study, we address issues of combining SNP data from different cohorts, the Netherlands Twin Register (NTR) and the Generation R (GENR) study. Both cohorts include children of Northern European Dutch background  who were genotyped on different platforms. We explore imputation and phasing as a tool and compare three GRM-building strategies, when data from two cohorts are (1) just combined, (2) pre-combined and cross-platform imputed and (3) cross-platform imputed and post-combined. We test these three strategies with data on childhood height for unrelated individuals  to explore their effect on SNP-heritability estimates and compare results to those obtained from the independent studies. All combination strategies result in SNP-heritability estimates with a standard error smaller than those of the independent studies. We did not observe significant difference in estimates of SNP-heritability based on various cross-platform imputed GRMs. SNP-heritability of childhood height was on average estimated as 0.50 (SE = 0.10). Introducing cohort as a covariate resulted in ≈2 % drop. Principal components (PCs) adjustment resulted in SNP-heritability estimates of about 0.39 (SE = 0.11). Strikingly, we did not find significant difference between cross-platform imputed and combined GRMs. All estimates were significant regardless the use of PCs adjustment. Based on these analyses we conclude that imputation with a reference set helps to increase power to estimate SNP-heritability by combining cohorts of the same ethnicity genotyped on different platforms. However, important factors should be taken into account such as remaining cohort stratification after imputation and/or phenotypic heterogeneity between and within cohorts. Whether one should use imputation, or just combine the genotype data, depends on the number of overlapping SNPs in relation to the total number of genotyped SNPs for both cohorts, and their ability to tag all the genetic variance related to the specific trait of interest. Before embarking on Genome Wide Association (GWA) projects, the heritability of complex traits is often assessed in twin and family studies, or, more recently, assessed based on common single nucleotide polymorphisms (SNPs). Such SNP-based heritability can be estimated when genetic similarities between distantly related individuals are summarized in a genetic relatedness matrix, which then is used to predict their phenotype similarity  will likely improve. However, it requires sharing and pooling of raw phenotype and genotype data from multiple cohorts. For genotype data this likely means that data of multiple genotyping platforms need to be combined and this might lead to biased results due to “platform stratification”, when relationships between individuals of different cohorts are estimated based on overlapping SNPs only. In case of GWA meta-analyses, each individual cohort performs its own imputation using a reference set (e.g. HapMap or 1000 Genome) and statistical analysis prior to the combination of results. In this way the confounding effects of genotyping platforms are avoided. SNPs showing platform stratification effects will be detected with heterogeneity testing and meta-analysis Quality Control (QC). With GREML analyses, the genotyped data of cohorts need to be combined at the SNP level. If different platforms have been used for genotyping, a cross-platform imputation is required in order to combine genotypes from several cohorts and assure that all individuals have the same SNP information to estimate relationships between them.In this paper, we compare approaches that combine autosomal genotype data from different cohorts and genotyping platforms into a single GRM. We aim to address and resolve problems of stratification when cohorts differ in genotyping strategies and phenotype characteristics. Therefore this study has two aims: 1) to allow the combination of genetic data from two cohorts, where participants are genotyped on different platforms with little overlap, (2) to explore the effect of three different strategies of combining such data on SNP-heritability estimates, when two cohorts are either cross-platform imputed (post- or pre-combined) or just combined  the combined data as a single dataset. We phase combined data without- and with a reference imputation set using MaCH , identical by descent (IBD) and gender mismatch were checked and samples not fitting the expected relations and/or gender were removed.The 3107 subjects in the NTR cohort were genotyped for 692,694 SNPs on Affymetrix 6.0 chip . Pairwise comparison between the SNPs overlapping in NTR and GoNL, in GENR and GoNL and in NTR and GENR combined identified 4001 SNPs, which were significantly different in allele frequency . All SNPs differing in allele frequency were removed. The resulting set of SNPs was either present on both platforms and in the reference set, or in a single platform and in the reference set. In order to minimize the amount of imputation stratification between samples, we selected the SNPs from the GoNL reference set that were present either on one or both genotyping platforms  using VCFtools . SNPs that did not match by strand were flipped to the reference set strand. SNPs with discordant alleles or that were not present in the reference set were excluded. Genotyped data from the NTR and GENR cohorts have 120,568 overlapping autosomal SNPs, of which 255 (0.2 %) SNPs were significantly different in frequency across cohorts  and 2826 GENR  individuals left. These individuals were genotyped for 641,554 and 468,259 SNPs in NTR and GENR respectively. The two data sets were merged in Plink for pre-combined imputation.First explorations of pre-combined cross-platform imputation approaches were done for chromosome 22. Genotype data comprising 13,712 SNPs were extracted, phased and imputed using the three methods described below, aiming to determine the one to apply to the autosomal genome. The first approach uses MaCH phasing (selected because GCTA can read MaCH dosage files) and, inherently, also imputation of the missing genotypes. No reference set is involved. The second approach uses MaCH phasing but this time with the GoNL reference set. Here the haplotypes are predicted and genotypes are imputed based on the GoNL reference set, which contains the full SNP haplotypes representing the Dutch population regardless of the platform. The third approach uses MaCH-Admix instead of MaCH. Here, a new piecewise reference selection method is employed  calculated by the MaCH tools. The R2 measures imputation quality and ranges between 0 and 1 with higher value indicating better imputation accuracy, hence better genotype prediction. We used R2 to inspect whether filtering on this measure helps to reduce platform stratification. Subsequently, a case–control analysis of the imputed sample with cohort as phenotype was done using the Mach2dat software  filtering with R2 > 0.8 and MAF > 0.01, additionally excluding alleles with low minor allele frequency. To estimate the effects of stratification by SNP platforms after imputation we examined the GRMs using PCA in GCTA tool. We performed PCA on data from unrelated individuals. As PCs can be confounded by inversions of long linkage disequilibrium (LD) regions of chromosomes, which are observed in the Dutch population  a GRM with MAF > 0.01 and R2 > 0.8 filters from NTR and GENR cohorts imputed independently and (3) a GRM with a MAF > 0.01 from QC-ed NTR and GENR genotypes combined, merged in Plink. Additionally, to check the effect of QC we built the GRM with MAF > 0.01 and R2 > 0.8 filters from the total cross-platform imputed data set excluding SNPs significantly different in frequency between cohorts after imputation. To distinguish between combination approaches throughout the paper we will refer to these GRMs as “imputed”, “imputed independently”, “combined” and “imputed clean”, respectively. Finally, SNP-heritability of height was estimated in NTR and GENR after building two separate GRMs with MAF > 0.01 filter from QC-ed NTR and GENR samples. We performed PCA for each of the autosomal GRM based on GoNL pruned set of SNPs and included these PCs in the analysis of height.Genetic relationship matrices (GRMs) were built from pre-combined cross-platform imputed dosages of the three approaches for chromosome 22 using GCTA. Different SNP filter criteria can be used to build these GRMs, which might affect the results. Therefore, we employed the criteria from three filters to estimate the matrices resulting in 9 GRMs. These criteria were: (1) without any filtering options on SNPs, (2) filtering on the imputation quality of RUsing GCTA, we estimated SNP-heritability of height using GRMs based on the autosomal genome. Imputation, SNP quality control as well as employing the different imputation approaches all determine the GRM relatedness of individuals. Therefore, for fair comparison between different ways of combing the genotype data in a GRM, we used the same unrelated individuals for each analysis. These were selected using the relatedness cut-off of 0.025 for individuals with height measurements from the combined and imputed GRMs (N = 3124). The difference in relatedness selection between the combined and imputed GRM was 22 individuals, which were excluded from the analyses. For the independent study analyses, however, we selected unrelated individuals, as one would have based on the GRM of the single study alone, using the same GRM cut-off of 0.025. Hence, if there are samples with family relations between NTR and GENR studies, they are still included in these separate study analyses.In the SNP-heritability analyses, age and sex were included as covariates. To test whether there is still a platform effect present after imputation we included cohort as an extra covariate in addition to sex and age and compared results of both analyses. To detect and account for possible genetic stratification in relation to height . The R2 measure also demonstrated different imputation quality: mean = 0.83 and median = 0.86 for the first, mean = 0.93 and median = 0.98 for the second and mean = 0.95 and median = 0.99 for the third method.Three imputation approaches aimed to pre-combine and cross-platform impute two cohorts were tested on chromosome 22: the first was MaCH without a reference set , the second was MaCH with the GoNL reference set and the third was MaCH-Admix with the GoNL reference set. The comparison of the post-imputation quality control measures for these approaches is shown in Figs. 2 > 0.8 and MAF > 0.01 filters are shown. As expected given the median quality of SNPs, filtering on R2 and MAF  did not affect the outcome of the imputation results  and second (PC2) principal components for each imputed GRM matrix in R Team RC . In Fig.When examining imputation differences for individual SNPs by comparing the allele frequencies between cohorts, we identified some significantly different SNPs, as was noted above. We computed squared LD correlations between each significant SNP that resulted from post-imputation QC analysis of the chromosome 22 imputation with MaCH-Admix and all neighboring SNPs within a 1 Mb region in Plink. The majority of these estimates were low , indicating regions with weak LD around significant SNPs. Therefore we can hypothesize that these SNP differences may arise from imperfect phasing and imputation for these SNPs with low LD.2 distribution for imputed , imputed clean , NTR imputed independently  and GENR imputed independently  samples. The quality of imputation in NTR seems slightly better than in GENR, which showed 203 monomorphic SNPs after imputation. These SNPs were excluded from calculation of mean and median of R2 for GENR. They also did not contribute to further analysis, as they have MAF = 0 and were filtered out with MAF > 0.01 option. As shown in Fig. Repeating the same MaCH-Admix imputation procedure of chromosome 22, (1) the NTR and GENR pre-combined sample was cross-platform imputed for all autosomal chromosomes and subsequently an “imputed” GRM was made; (2) the NTR and GENR samples were imputed independently for all autosomal chromosomes, post-combined and an “imputed independently” GRM was built. Figures The pooled data set comprised a total of 3124 distantly related individuals, where 1526 were males and 1598 were females. Childhood mean height in the pooled data set was 123.2 cm (SD = 8.8) at mean age of 6.7 years (SD = 1.2)  just combined, (2) pre-combined and cross-platform imputed and (3) cross-platform imputed and post-combined (Fig.  just com2 did not largely seem to influence the cohort stratification, mainly because the quality of the imputed SNPs was generally high. Imputation of the autosomal genome followed by PC analysis showed that to some extend there is still platform stratification present after imputation (Fig. Based on the chromosome 22 analyses of pre-combined cross-platform imputation approaches, we showed that phasing and imputation of missing genotypes with a reference dataset that contains all SNPs and LD information between these SNPs does not substantially increase cohort stratification due to genotyping platform within the GRM, while phasing without a reference set, lacking this essential LD information, does. Using only the SNPs that are overlapping between genotyping platforms as an imputation backbone is insufficient which was evident from the subsequent PC analyses. Given that one could consider two cohorts with different platforms as a stratified population, the use of MaCH-Admix additionally seems to have helped to improve the imputation quality. However, this effect was much weaker in comparison to the use of a reference set. The analysis based on PCs, also showed that post imputation filtering on MAF and Rion Fig. . InteresThe analysis of childhood height yielded relatively the same estimates of SNP-heritability for cross-platform imputed GRMs, suggesting a slight increase of the estimate in comparison to the combined GRM. Adjusting for 10 PCs with or without study as covariate results in ≈11 % reduction of SNP-heritability for all GRMs, including the combined one. Whereas there was only ≈2 % reduction in SNP-heritability when study was used as a covariate for imputed GRMs and not for the combined one. PC adjustment of independent cohorts results in a SNP-heritability drop of ≈13 % for NTR and ≈7 % for GENR. Drop in NTR SNP-heritability estimate in contrast to GENR is more pronounced, as individuals in NTR spread across the Netherlands resulting in a more diverse cohort. Given that λ estimates obtained from association analysis are not inflated it is possible that PCs may capture true variation of height along with platform stratification and may overcorrect the estimates. On the other hand, PCs may help to capture and correct for other sources of stratification within cohorts. Interestingly, SNP-heritability estimates resulting from GRM imputed and GRM imputed independently are approximately the same for all conditions. Moreover, SNP-heritability estimates from the combined GRM are just slightly lower in comparison to the imputed GRMs, which may support the conclusion that relationships between individuals across cohorts, estimated from SNPs overlap of ≈120 K, is enough to explain substantial proportion of variation in childhood height.In this study we estimated SNP-heritability of childhood height using different GRM building strategies. These GRMs yielded significant estimates of SNP-heritability in range from 0.33 to 0.52 depending on various correction options. Height is a highly heritable trait with heritability estimates ranging from 0.89 to 0.93 in adults (Silventoinen et al. Strategies aiming to detect and correct for platform stratification after cross-platform imputation were considered in this study for cohorts with the same ethnicity. However, when combining cohorts with a different ethnicity this approach is unlikely going to be appropriate for several reasons (de Candia et al. In conclusion, using the complete information of a reference set for phasing and imputation of all SNPs on two different genotyping platforms, allows the combination of cohort data genotyped on both of these platforms. When combining genotype data across platform or cohort thorough pre- and post QC is required, which can be tested with association and principal component analyses. For our approach we assume that the cohorts have a similar ethnicity/genetic background. To account for platform stratification or phenotypic differences in the dataset, cohort should always be included as a covariate. Whether one should use imputation, or just combine the genotype data, depends on the number of overlapping SNPs in relation to the total number of genotyped SNPs for both cohorts, and their ability to tag all the genetic variance related to the specific trait of interest.