Pyrus communis L.) cultivars and subsequently developed a subset of 1096 pear SNPs into high throughput markers by combining them with the set of 7692 apple SNPs on the IRSC apple Infinium® II 8K array. We then evaluated this apple and pear Infinium® II 9K SNP array for large-scale genotyping in pear across several species, using both pear and apple SNPs. The segregating populations employed for array validation included a segregating population of European pear (‘Old Home’×‘Louise Bon Jersey’) and four interspecific breeding families derived from Asian (P. pyrifolia Nakai and P. bretschneideri Rehd.) and European pear pedigrees. In total, we mapped 857 polymorphic pear markers to construct the first SNP-based genetic maps for pear, comprising 78% of the total pear SNPs included in the array. In addition, 1031 SNP markers derived from apple  were polymorphic and were mapped in one or more of the pear populations. These results are the first to demonstrate SNP transferability across the genera Malus and Pyrus. Our construction of high density SNP-based and gene-based genetic maps in pear represents an important step towards the identification of chromosomal regions associated with a range of horticultural characters, such as pest and disease resistance, orchard yield and fruit quality.We have used new generation sequencing (NGS) technologies to identify single nucleotide polymorphism (SNP) markers from three European pear ( The recent technological revolution initiated by new generation sequencing (NGS) has enabled the sequencing of the entire genome of complex organisms, including the higher plants grape Single nucleotide polymorphisms (SNPs) are single base variations in DNA sequences that are abundant in plant genomes and are useful for identifying differences within individuals or populations as well as identifying genetic loci associated with phenotypic variation. Within coding regions, SNPs may be defined as non-synonymous or synonymous (resulting in an amino acid change or not) and are also found in gene-regulating regions . Once polymorphisms have been detected by NGS, the next challenge is to screen large genetic populations with multiple markers simultaneously. While re-sequencing can be used for both SNP discovery and genotyping of the entire set of polymorphisms of a species www.rosbreed.org) Prunus species and interspecific hybrids) Citrus, 54 accessions and 52 interspecific hybrids between pummelo and Clementine were genotyped using a 1457 GoldenGate® SNPs assay developed from clementine BAC-end sequencing. Out of 622 SNPs showing consistent results, 80.5% were demonstrated to be transferable to the whole Citrus gene pool High throughput SNP arrays have been recently developed for a range of fruit tree species. In Rosaceae, an apple SNP array was developed by the International RosBREED SNP consortium (IRSC) (Pyrus includes both European (Pyrus communis) and Asian pears . To date, only a few genetic maps have been developed for Pyrus and none of these contains SNP markers. The first map was constructed using random amplified polymorphic DNA (RAPD) markers in a P. pyrifolia cross between ‘Kinchaku’ and ‘Kosui’ P. communis) and ‘Hosui’ (P. pyrifolia). As the ‘Bartlett’×‘Hosui’ map contained SSRs derived from both pear and apple, this study enabled the assessment of genome synteny between pear and apple and suggested that these species have co-linear genomes. Apple and pear markers had also been used earlier to generate maps for the two European pear cultivars ‘Passe Crassane’ and ‘Harrow Sweet’ P. communis cultivars ‘Bartlett’ and ‘La France’, along with two apple rootstocks. Lu et al. P. pyrifolia×P. communis)×‘Jinhua’ (P. bretschneideri) with apple SSRs and were able to construct a genetic map. However, the number of markers used in all these studies was limited to few hundreds. Recently, NGS was used to develop a genetic map of ‘Bayuehong’ (P. bretschneideri×P. communis)×‘Dangshansuli’ (P. bretschneideri) to anchor the Chinese pear genome; however, these SNPs were not evaluated for the screening of large segregating populations The genus Pyrus, but also between the genera Malus and Pyrus.In this study, we used NGS to detect SNPs in the pear genome, to enable the design of a medium throughput SNP assay. These new pear SNPs were evaluated for genetic map construction using five segregating populations of European and Asian pear origin. Our incorporation of the new pear SNPs into the IRSC apple Infinium® II 8 K array P. communis) cultivars was chosen for low coverage whole-genome sequencing. The individuals were ‘Bartlett’ (a.k.a. ‘Williams Bon Chrétien’), ‘Old Home’ (OH) and ‘Louise Bon Jersey’ (LBJ). These accessions were chosen as ‘Bartlett’ is a founder of most breeding programmes worldwide, and OH and LBJ are the parents of a segregating population developed at Plant & Food Research (PFR). Each accession was sequenced using one lane of Illumina GA II with 75 cycles per read and small insert paired-end sequencing, as described in A SNP detection panel consisting of three European pear (http://www.vertis-biotech.com/). One run of 454 sequencing on a Roche/454 GS FLX Sequencer was performed.Two pear unnormalized cDNA libraries were prepared by vertis Biotechnologie AG for the European pear cultivar ‘Max Red Bartlett’ following VERTIS customized protocol (de novo assembly was performed for the ‘Bartlett’ sequencing data using AbySS 1.2.1 (k = 43). Contigs of 600 bp or larger were used as a reference genome set. The sequencing data from OH and LBJ were mapped to the reference genome set of ‘Bartlett’ using Soap2.20 (-p 8 -M 4 -v 5 -c 52 -s 12 -n 5 -r 2 -m 50 -x 600). Soap output files were split into a single file per contig and each contig file sorted by location of the mapped reads. SoapSNP was used for SNP detection and filtering with the same parameters as described in Malus×domesticagmap with command line options -K 3000–L 50000.A M.×domestica genome and only unique alignments were considered to avoid parology issues. SNPs were predicted using a customized bioinformatics pipeline and selected to be well spread over the 17 apple chromosomes.454 cDNA reads were assembled using CAP3 The Illumina Infinium® assay design tool (ADT) was used on the detected SNPs with a threshold of 0.7. These pear SNPs were synthesized as probes and located on the same array as the IRSC apple Infinium® II 8 K array P. communis intraspecific family and four interspecific  pear populations: OH×LBJ, of 297 F1 individuals and both parents; P128R068T003×‘Moonglow’ (T003×M), of 220 F1 individuals and both parents; P019R045T042×P037R048T081 (T042×T081), of 142 F1 individuals and both parents; P202R137T052×P128R068T003 (T052×T003), of 91 F1 individuals and T003 parent only; and P202R137T052×P266R225T064 (T052×T065), of 123 F1 individuals and T064 parent only, since parent T052 has been lost. Five pear segregating populations were screened using the apple and pear Infinium® II 9K SNP array. No permission was required to collect plant material and pear is not an endangered or protected species. These were one GenCall Threshold of 0.15. The software automatically determines the cluster positions of the AA/AB/BB genotypes for each SNP and displays them in normalized graphs  and scanned with the Illumina HiScan. Data were analyzed using Illumina’s GenomeStudio v 1.0 software Genotyping Module, setting a d graphs . A systeMd-Exp 7, from the work of Costa et al. 2, 0.2 mM of each dNTP, 0.4 uM of each forward and reverse primer and 0.75 U of AmpliTaq Gold® DNA polymerase (Applied Biosystems® by Life Technologies™). All SSR amplifications were performed in a Biometra T gradient Thermocycler  or in a Bio-Rad C-1000 thermocycler  at FEM  and INRA, Angers (France) under the following conditions: an initial denaturation at 95°C for 5 min, followed by 36 cycles of 95°C for 30 sec, TA  for 30 sec, 72°C for 1 min, finishing with a final extension at 72°C for 7 min. Fragment analysis was performed with an ABI PRISM_3730 capillary sequencer (Applied Biosystems® by Life Technologies™) in a final mix of 0.5 uL of PCR product, 9.97 uL formamide and 0.03 uL of 500-LIZ dye, denaturated for 3 min at 95°C. Fragment sizing was performed with GeneMapper software v. 4.0 (Applied Biosystems® by Life Technologies™).The T003×M population was genotyped with apple and pear microsatellite markers as well as SNPs. Fifty-four SSRs were selected based on the ‘Bartlett’ consensus map developed by Celton et al. The genetic maps of both parents of all five populations were constructed using JoinMap v3.0 and v4.0 software The pear SNPs included in the array were aligned to the apple genome assembly de novo assembly genome set of ‘Bartlett’ consisted of 78,748 contigs of 600 bp or greater in length containing a total of 79,067,993 bases, with a maximum contig length of 15,094 bases, N50 of 1004 bases, N90 of 658 bases, and an average contig length of 1004 bases. A total of 73,214 SNPs were predicted by SoapSNP when reads of OH and LBJ were aligned to the genome of ‘Bartlett’ using the Soap aligner, corresponding to one SNP per 1079 bases. In total, 1,456 SNPs passed the filtering criteria and were then subjected to the Illumina ADT. This yielded 1107 SNPs, of which 1064 were included in the final SNP array.In total, 34,082,435, 35,687,533 and 25,167,853 paired-end reads were generated for ‘Bartlett’, OH and LBJ, respectively. The de novo assembled using CAP3. The average depth of assembly for all samples was ∼2.5. A total of 1751 cDNA SNPs were predicted using a customized bioinformatics pipeline and 69 experimentally validated by M. Troggio (unpublished data) that passed the Illumina ADT design, were selected for inclusion in the SNP array.A total of 144,816 high quality 454 sequence reads were generated. Total sequence output was 32,418,987 bases, with an average read length of 224 bases. Quality filtered sequences were In total, 1133 pear SNPs were incorporated in the final array, making a grand total of 9000 attempted apple and pear SNPs .Of the 1133 attempted pear SNPs, 1096 (96.7%) were successful bead types on the IRSC Infinium® II (Illumina Inc.) array. When the 1096 pear and 7692 apple bead types were evaluated using five segregating populations, twelve and three individuals from the T003×M and T052×T003 populations, respectively, did not hybridize well to the BeadChip and were excluded from the clustering, which resulted in 873 F1 individuals that were used for evaluating the SNP array. All the 1096 pear SNPs hybridized well, resulting to be either polymorphic or monomorphic in at least one population. Of the apple SNPs, 7562 out of the total 7692 bead typed (98.3%) were either polymorphic or monomorphic in at least one population, while only 130 showed low quality hybridization. All 1096 pear SNPs hybridized pear DNA and were either monmorphic or polymorphic.P. communis parents had higher polymorphism rate  than Asian×European hybrid parents . The number of polymorphic apple SNPs per pear population ranged from 115 to 381 out of 7692 beadtypes (1.5 to 5.0% polymorphic SNPs per population). When the transfer rate of the new pear SNPs was evaluated in the apple ‘Royal Gala’×‘Granny Smith’ segregating population, it was similar to the apple SNP to pear transfer rate, with 13 (1.2%) polymorphic pear SNPs.In total, 1528 unique pear and apple-derived SNPs (872 pear SNPs and 656 apple SNPs) were polymorphic in at least one segregating population, with 713, 508, 437, 442 and 711 polymorphic SNPs for the OH×LBJ, T003×M, T042×T081, T052×T003 and T052×T064 populations, respectively . For theThe analysis of SNP polymorphism in segregating populations highlighted the presence of SNP markers with potential null alleles. By default, the standard SNP calling algorithms of GenomeStudio clustered heterozygous A0 and B0 genotypes together with homozygous AA and BB genotypes, and called homozygous null genotypes (00) as missing genotypic calls. However, some SNPs containing null alleles do not follow the expected Mendelian segregation based on the parental genotypes. Therefore, manual editing of clusters for all the SNPs with strong deviation from Mendelian ratio or around 25% or 50% of no calls was performed and the SNPs which displayed a clear clustering and for which genotypes could be unequivocally determined as containing potential null alleles, were selected for further linkage analysis . The folThe total number of unique polymorphic markers, including both apple and pear-derived SNPs and SNPs with null alleles, was 2400 for all five populations. For the pear SNPs, 918 (83.8%) were polymorphic in at least one segregating population, and 623 (56.8%) were polymorphic in OH×LBJ, 384 (35%) in T052×T064, 337 (30.7%) in T042×T081, 337 (30.7%) in T003×M, and 295 (26.9%) in T052×T003.Parental genetic maps were constructed for five segregating populations using the 2400 unique polymorphic SNPs. All maps contained 17 linkage groups except T003, T042 and T081. For theThe markers in common among the five segregating populations enabled the alignment of parental genetic maps as shown in Of the 54 SSR markers derived from the published ‘Bartlett’ consensus map ×LBJ consensus map as an example, 433 (42.9%) of the pear SNPs were anchored to apple and enabled the comparison of this genetic map with the GD genome assembly. On average, 20 markers per LG were in common between the OH×LBJ map and the GD genome  were successfully anchored to the GD genome using bioinformatics analysis. Using the OHD genome , with LGPyrus, the number of available SNPs was marginal. We developed more than 1,000 SNPs from the re-sequencing of P. communis cultivars and for the first time we included them in an array, making them easily available for further studies. These SNPs were selected based on their location within candidate genes, to ensure their usefulness for marker-trait association and for future breeding programmes.SNPs are considered to be the most efficient tools for comprehensive genetic studies We used the apple and pear Infinium® II 9K SNP array for the genotyping of five segregating pear populations, for a grand total of 873 individuals. The clustering of the SNPs using the GenomeStudio software depends on the minor allele frequency of the SNPs: the lower the minor allele frequency, the more samples are required to achieve accurate representation of all clusters. Illumina recommends a population of 100 or more. In our case, all the populations had largely more than 100 individuals , and this large dataset of 873 individuals ensured an accurate clustering of array SNPs. Moreover, the threshold of 15% for the MAF is relatively high, in comparison with other studies using the same technique P. communis, P. pyrifolia and P. bretschneideri. These maps are the first dense SNP-based genetic maps for pear of any species. The previously developed maps in Pyrus, including those of Yamamoto et al. and Celton et al. P. bretschneideri genome sequence, these SNPs are not available as a genotyping array, as they were obtained using genotyping by sequencing. In addition to the new P. communis pear SNPs developed in this study, we found that 1482 SNP markers derived from apple  were polymorphic in pear, and 1031 of them were positioned on the pear genetic maps. The apple SNPs considerably improved the density of all maps, in some cases, e.g. T052×T003 and T052×T064, even doubling the number of mapped markers. In fact, because of the lower polymorphism of pear SNPs in the interspecific hybrid parents compared with the P. communis parents, the apple SNPs were necessary to saturate these maps.A large proportion (83.8%) of the 1096 pear SNPs used to construct the first pear genotyping array were polymorphic in at least one segregating population, and 857 of these unique polymorphic pear markers (93.4%) were demonstrated to be useful for construction of genetic maps, using five populations of a range of genetic backgrounds across P. communis accessions. However, the number of pear SNPs that were polymorphic in the interspecific parents was more variable, and reflects both the number of SNPs that are conserved between European and Asian pear and those that were introgressed from the European parent into the interspecific hybrid parents. The transferability of SNPs between species of the same genus has been reported previously in a few studies. These include the plant genera VitisCitrusEucalyptusBubalusPyrus.The higher number of polymorphic pear markers identified in the European pear cross OH×LBJ compared with the four populations with an Asian pear background is because sequence data from OH and LBJ were used to design the pear SNPs, which also validates the bioinformatic SNP detection method used. In the T003×M population, the number of polymorphic pear SNPs in the European parent (‘Moonglow’) was significantly higher than in the hybrid (T003), again because the SNPs were derived from sequencing of Malus and Pyrus, making it the first cross-genera SNP array created. It therefore enables, for one of the first time, the assessment of SNP marker transferability between genera. Most of the numerous studies on genetic marker transferability in recent years have focused on SSR markers, including those concerning apple and pear M.×domestica to P. communis and P. pyrifolia using 237 apple SNPs. In the present study, we observed that 7562 apple SNPs (98.3%) were either monomorphic or polymorphic in at least one pear population, while only 130 did not hybridize well in all of them. The high percentage of hybridization of pear genomic DNA to apple SNPs and vice versa obtained in the present study are not surprising, given that Malus and Pyrus are closely related genera and might be expected to share high sequence similarity. Furthermore, both the pear and apple SNPs included in the array were selected to be located in coding genes, with the consequence that the flanking sequences are more likely to be conserved between species. Although many of the apple SNPs were monomorphic (but still hybridized to pear DNA) and were not useful for genetic mapping in the five pear populations, we were able to map 99 apple markers in the OH×LBJ population, 255 in T003xMoonglow, 199 in T042×T081, 365 in T052×T003, and 631 in T052×T064.The distinguishing feature of the apple and pear Infinium® II 9K SNP array is its combination of SNPs from both P. communis population. This was expected, as the frequency of null alleles increases with genetic distance between the samples genotyped and the discovery panel Malus versus Pyrus). We found that the within-species frequency of null alleles was similar in apple and pear SNPs. As heterozygous null alleles are useful for genetic mapping, we used them to increase map density in interspecific populations. It must be noted, however, that null alleles are a potential source of increased false positives in marker-trait association studies The existence of null or unexpected alleles has been already demonstrated in several other SNP genotyping studies. Such alleles can be explained as deletions spanning a polymorphic site, secondary polymorphisms, or tri-allelic sites at the primary polymorphism In total, 92% of the pear SNPs included in the Infinium® II array were successfully anchored to the ‘Golden Delicious’ genome P. communis, as well as those of a mixed genetic background that includes P. communis, P. pyrifolia and P. bretschneideri. Furthermore, we attested that the arrayed SNPs are transferable not only across these species, but also between the two closely related genera Malus and Pyrus.We have thoroughly validated the apple and pear Infinium® II 9K SNP array, and demonstrated its usefulness for high throughput genotyping in breeding populations of P. communis, P. pyrifolia and P. bretschneideri. The OH×LBJ population was a repeat of a cross P. communis) scions for the purpose of a QTL analysis of rootstock induced dwarfing in pear. The T003×M population was developed to study the genetic basis of resistance to pear scab (Venturia pirina), fire blight (Erwinia amylovora), pear psylla (Cacopsylla pyri) and pear sawfly . T003  is not host to V. pirinaC. pyri and C. cerasiThe construction of high density gene-based genetic maps using our SNP array represents an important step for the discovery of chromosomal regions associated with commercially important horticultural traits, such as pest and disease resistance, orchard productivity and fruit quality www.rosaceae.org). SNPs are available in dbSNP (http://www.ncbi.nlm.nih.gov/projects/SNP/) under accessions ss527787751 to ss527789916.The pear SNPs detected by sequencing, the pear SNPs chosen for the apple and pear Infinium® II 9K SNP array, and the GenomeStudio cluster file developed are deposited in the Genome Database for Rosaceae Click here for additional data file.Table S2List of 1096 pear SNPs on the pear Infinium® II 9K SNP array. The NCBI dbSNP accession, location on the ‘Golden Delicious’ genome assembly is indicated.(XLSX)Click here for additional data file.Table S3Genetic linkage maps of five populations used to validate the apple and pear 9K SNP array.(XLSX)Click here for additional data file.


Xanthomonas axonopodis pv. phaseoli (Xap), is a major yield-limiting factor of common bean (Phaseolus vulgaris L.) production around the world. Host resistance is practically the most effective and environmentally-sound approach to control CBB. Unlike conventional QTL discovery strategies, in which bi-parental populations  need to be developed, association mapping-based strategies can use plant breeding populations to synchronize QTL discovery and cultivar development.Common bacterial blight (CBB), incited by A population of 469 dry bean lines of different market classes representing plant materials routinely developed in a bean breeding program were used. Of them, 395 lines were evaluated for CBB resistance at 14 and 21 DAI (Days After Inoculation) in the summer of 2009 in an artificially inoculated CBB nursery in south-western Ontario. All lines were genotyped using 132 SNPs (Single Nucleotide Polymorphisms) evenly distributed across the genome. Of the 132 SNPs, 26 SNPs had more than 20% missing data, 12 SNPs were monomorphic, and 17 SNPs had a MAF (Minor Allelic Frequency) of less than 0.20, therefore only 75 SNPs were used for association study, based on one SNP per locus. The best possible population structure was to assign 36% and 64% of the lines into Andean and Mesoamerican subgroups, respectively. Kinship analysis also revealed complex familial relationships among all lines, which corresponds with the known pedigree history. MLM (Mixed Linear Model) analysis, including population structure and kinship, was used to discover marker-trait associations. Eighteen and 22 markers were significantly associated with CBB rating at 14 and 21 DAI, respectively. Fourteen markers were significant for both dates and the markers UBC420, SU91, g321, g471, and g796 were highly significant (p ≤ 0.001). Furthermore, 12 significant SNP markers were co-localized with or close to the CBB-QTLs identified previously in bi-parental QTL mapping studies.This study demonstrated that association mapping using a reasonable number of markers, distributed across the genome and with application of plant materials that are routinely developed in a plant breeding program can detect significant QTLs for traits of interest. Phaseolus vulgaris L.) is a diploid (2n = 2x = 22) annual species, and is predominantly self-pollinating [http://www.pulsecanada.com/).Common bean (linating . It is tlinating . In 2006Xanthomonas axonopodis pv. phaseoli (Xap), is a serious seed-borne disease in both temperate and tropical bean production zones [P. vulgaris and its close relatives in intra- and inter-specific crosses to improve CBB resistance in beans. These sources include the common bean cultivar Montana No. 5 and introduction line PI207262, tepary bean (Phaseolus acutifolius L.) introduction lines (PI319443 and PI440795), and scarlet runner bean (Phaseolus coccineus L.) [Common bacterial blight (CBB), incited by on zones . Yield lon zones . Controlon zones . In pracon zones . Over thneus L.) ,9 and OAneus L.) . The twoneus L.) .Traditionally, QTL mapping approaches have been based on the analysis of populations derived from bi-parental crosses that segregated for trait(s) of interest. To date, at least 24 different CBB resistance QTLs have been reported across all eleven linkage groups of common bean . HoweverGlycine max L. Merr.) two markers, Satt114 and Satt239, were found to be associated with iron deficiency chlorosis loci using advance breeding lines [Oryza sativa L.), microsatellite markers associated with yield and its components were identified in a variety trial, and many of them were located in regions where QTL had previously been identified [2 r= 0.1) was found in common bean [2 r= 0.18) compared to wild populations  [Association mapping is increasingly being utilized to detect marker-QTL linkage associations using plant materials routinely developed in breeding programs. Compared with conventional QTL mapping approaches, association mapping using breeding populations may be a more practical approach for cultivar development, considering that markers linked to major QTL can immediately be utilized in MAS, once new QTLs are identified. For instance, in soybean (ng lines . In ricemon bean . Much hi = 0.08) . In the  = 0.08) .Zea mays L.) [In association mapping, where unlike conventional QTL mapping, populations of un-structurally related individuals are employed, it is important to consider population structure and kinship among individuals, because false associations may be detected due to the confounding effects of population admixture . This mamays L.) , rice [1mays L.) . Anothermays L.) .http://www.luminexcorp.com) which was used to confirm SNP calls in DNA from 10 common bean genotypes, finding 2.5% of SNPs were miscalled and 1% had no signal as compared with direct sequencing [CEL I mismatch digestions to analyze and map SNP-based, EST-derived markers, finding that the method worked well with SNPs located in the middle of amplification fragments and that digestion products could be visualized on agarose gels. Single strand conformation polymorphism (SSCP) technology was employed to develop and map EST based markers, which resulted in identification of a total of 118 new marker loci in DOR364 × G19833 mapping population [http://www.illumina.com). 79%  SNPs produced a working GoldenGate assay [Poaceae Saccharum L.) [Triticum spp.) [Hordeum vulgare L.) [Single Nucleotide Polymorphic (SNP) markers are currently known as valuable markers for genotyping because of their abundance, stability, and simplicity. The total number of SNPs in cultivated bean is estimated to be in the range of 3-4 millions, based on the rate of 237 SNPs observed in 38.2 kbp of sequence in 6 diverse genotypes . So far,quencing . In an equencing  used CELpulation . Latest te assay . Anotherarum L.) , high-tharum L.)  and wheaum spp.) , and varm). 79% 8 of 1,050The objectives of our study were to 1) apply unified MLM association mapping approach to identify CBB resistance loci in Ontario bean breeding materials and 2) evaluate whether association mapping can be used effectively to discover CBB resistance QTLs using SNP genotyping of plant materials, routinely developed in a bean breeding program.CBB resistance in common bean is a complex trait, known to be controlled by both major and minor genetic factors . Each liOver 99% of data points were identically scored in the 14 repeated samples evenly distributed over all 96-well plates. Only one type of genotyping error was found in three SNP assays, where a SNP was called in one plate but uncalled in the repeated sample in another plate. Thus, the reproducibility and reliability of SNP assay were high and comparable with other SNP assays in plant species.Of the 132 bean SNPs used in the SNP assay, 106 SNPs (80.3%) were successfully called in the 469 lines with less than 20% missing data points. Of them, 12 SNPs were monomorphic in all 469 lines and 94 SNPs were polymorphic. The 94 polymorphic SNPs were retained for the next stage of screening.Bean cultivars, and advanced breeding lines are generally homozygous and highly homogeneous. However, complete homozygosity is practically unattainable, and slight levels of heterogeneity may be present for small number of loci. In the present study, the number of genotypes heterogeneous for the two alleles at a SNP locus ranged from 1 to 300 at 69 of the 94 SNP loci. Although heterozygosity ranged from 0 to 0.62 with one SNP having diversity values in excess of 0.3, the heterozygosity average was 0.02, well within the expected ranges for residual heterozygosity found in bean cultivars.All SNPs were well distributed across the 11 bean chromosomes with a genome coverage ranging from 6 SNPs on chromosome 6 to 11 SNPs on chromosome 10 (Table The software STUCTURE was run for K (number of fixed subgroups or clusters) ranging from 1 to 10 on the entire set of breeding lines using all SNPs scored as biallelic markers. The likelihood value of this analysis is shown in Figure Molecular markers can be used to estimate the relative kinship between pairs of individuals in a study, which provides useful information for quantitative inheritance studies. The relative kinship reflects the approximate identity between two given individuals over the average probability of identity between two random individuals . In thisP-value determines whether a QTL is associated with the marker. The 2 Rstatistic is commonly used in QTL mapping studies to measure the proportion of phenotypic variation explained by molecular markers. However, unlike fixed linear regression models, linear mixed models have no well-established 2 Rstatistic for assessing goodness-of-fit and prediction power [2R_marker only measures the contribution of the marker to sum square after accounting for all other effects in the model [BA), g680(CBBBJ), g321(CBBBH), g2581(CBBBH), g2538(CBBPX), g2531(CBBPX), g1119(CBBXC), g696(CBBPX), g796(CBBPX), g1286(CBBBJ), g1215(CBBXC), and g1415(CBBXC)  and CBB rating were determined by Q + K MLM method. Very high LD was observed with 95.9% comparisons between loci significant at P < 0.01. Because CBB ratings varied between disease observation dates , while association mappings rely on historical differential decay of LD between pairs of loci in natural and domesticated populations [In theory, both association mapping and linkage mapping depend on the LD between phenotypic causative and linked molecular variants . Traditiulations . Therefoulations . In geneulations . If therulations . Germplaulations . In convulations . This reulations . In contIn our study, fifteen SNP markers Figure  co-localHowever, many of the initial associations detected have not been consistently replicated and may well have been spurious, particularly because the tests could not take sufficient account of the effect of population structural problems such as admixture . In orde6 and 3.6 × 106 sequence reads for each genotype, respectively. Afterwards, about 500 SNPs were identified between parental lines [http://www.jgi.doe.gov/). Ultimately the availability of high-throughput and cost-effective genotyping platforms, combined with automation in phenotyping methodologies, will increase the uptake of genomic tools into breeding programs, and thus usher in an era of genomics-enabled bean breeding [In comparison with soybean, common bean has poorly developed genomic infrastructure . In order to accelerate association studies in bean, large-scale SNP discovery is required beforehand. Next generation sequencing is playing an increasingly significant role to speed up SNP discovery in less-characterized legumes. For instance in chickpea, Solexa 1 Gbp technology was used to sequence root cDNAs from parents of a mapping population segregating for drought tolerance . One-halal lines . In commal lines . Moreovebreeding .2, RIL, or DH) need to be developed, association mapping-based strategies can use existing plant breeding populations with wide coverage of the existing genetic diversity. This may address some of the concerns with conventional QTL mapping that the bi-parental mapping populations rarely give rise to new cultivars, the identified QTLs may not be effective in multiple genetic backgrounds and that the QTL-linked markers are not immediately available for MAS.This study demonstrated that association mapping using a reasonable number of markers, distributed across the genome and with application of plant materials that are routinely developed in a plant breeding program can detect significant QTLs for traits of interest. Unlike conventional QTL discovery strategies, in which bi-parental populations . A 0-5 scale was used for disease severity ratings based on a visual estimate of the percentage of CBB symptoms on total leaf area, where 0 = no symptoms, 1 = less than 10%, 2 = 11-30%, 3 = 31-50%, 4 = 51-80%, and 5 = more than 80% of inoculated areas showing symptoms. CBB resistant (HR45) and susceptible (Dresden) checks were included in each block. Excel Macros programmed by QI Macros (http://www.qimacros.com/) was used to conduct Kolmogorov-Smirnov test of normality.A total of 395 bean lines, the advanced breeding lines in Category c, were evaluated in the field in 2009 in the common bacterial blight nursery in Harrow, Ontario in Canada. The experimental design was a randomized complete block with two replications. Each experimental unit consisted of a single 0.5 feet long row with 2 feet row-spacing. Artificial inoculation was carried out using fresh bacterial inoculum, prepared by mixing equal amount of two fuscans isolates 12 and 118, and two no-fuscans isolates 18 and 98 with spores at the concentration of 10Young leaf samples (100 mg) were frozen in liquid nitrogen and ground using an AutoGrinder 48 . After incubation with plant lysis buffer (AutoGen AG00121) at 65°C for 30 min, DNA was automatically extracted using an AutoGen 850 alpha DNA automatic system following the manufacturer's manual (AutoGen Inc.).http://www.comparative-legumes.org/index.php/Home) [http://www.ncbi.nlm.nih.gov/) and uploaded into AlignX module of Vector NTI Advance 11  for sequence alignment. Only one SNP per alignment was chosen and the preference was given to the SNP found in central region of the alignment.According to McClean (NDSU) 2007 genetic map at Legume Information System (hp/Home) , originaGenotyping was performed using the Sequenom iPLEX Gold Assay  in Genome Quebec . Locus-specific PCR primers and allele-specific detection primers were designed using MassARRAY Assay Design 3.1 software. DNA was amplified in a multiplex PCR and labelled using a locus-specific single base extension reaction. The products were desalted and transferred to a 96-element SpectroCHIP array. Allele detection was performed using Matrix-Assisted Laser Desorption/Ionization Time-of-Flight Mass Spectrometry (compact MALDI-TOF MS). Mass spectrograms and clusters were analyzed by the TYPER 3.4 software package that was described in details by Ehrich et al. . All DNA® Safe staining .Two previously-characterized SCAR markers SU91 and BC420, known to be associated with CBB resistance  were inchttp://www.maizegenetics.net/index.php?option=com_content&task=view&id=89&Itemid=119. The MLM analyses were performed using a kinship K matrix and population structure Q matrix. The K matrix was generated based on 75 SNPs using kinship matrix function in TASSEL. Population structure consisted of a Q matrix that describes the percent subpopulation parentage for each line in the analysis. These percentages were calculated by STRUCTURE 2.3.3 software, available at http://pritch.bsd.uchicago.edu/structure.html. We set k (the number of subpopulations) from 1 to 10 and performed 10 runs for each k value. For each run, a burn in of 5,000 iterations was followed by an additional 5,000 iterations. Since the likelihood for model parameter k = 2 was much higher than k = 1 and comparable with k = 3 or higher, we chose k = 2 and generated a Q matrix from 75 SNPs.Association mapping analyses were carried out with TASSEL 2.1 software, available at http://www.comparative-legumes.org/index.php/Home) [http://www.biometris.wur.nl/uk/Software/MapChart/).The mapping information of SNP markers was extracted from McClean (NDSU) 2007 genetic map at Legume Information System (hp/Home) . The disCS conceived of the study, designed and carried out the lab experiments and data analysis, and drafted the manuscript. AN conceived of the study, designed and carried out the field experiments, and helped to draft the manuscript. KY conceived of the study, coordinated the study, and helped to draft the manuscript. All authors read and approved the final manuscript.Loci, LG (Linkage Group), MAF (Minor Allelic Frequency), SNP alleles, PCR primers, and Sequenom probe sequences of 75 selected SNPs used for association mappingClick here for file


Zea mays VTE4 (ZmVTE4), and a single nucleotide polymorphism (SNP) located ∼85 kb upstream of ZmVTE4 were identified to be significantly associated with α-tocopherol levels in maize kernels by conducting an association study with a panel of ∼500 diverse inbred lines. Linkage analysis in three populations that segregated at either one of these three polymorphisms but not at the other two suggested that the three polymorphisms could affect α-tocopherol content independently. Furthermore, we found that haplotypes of the two InDels could explain ∼33% of α-tocopherol variation in the association panel, suggesting ZmVTE4 is a major gene involved in natural phenotypic variation of α-tocopherol. One of the two InDels is located within the promoter region and associates with ZmVTE4 transcript level. This information can not only help in understanding the underlying mechanism of natural tocopherol variations in maize kernels, but also provide valuable markers for marker-assisted breeding of α-tocopherol content in maize kernels, which will then facilitate the improvement of maize as a better source of daily vitamin E nutrition.Tocopherols are a class of four natural compounds that can provide nutrition and function as antioxidant in both plants and animals. Maize kernels have low α-tocopherol content, the compound with the highest vitamin E activity, thus, raising the risk of vitamin E deficiency in human populations relying on maize as their primary vitamin E source. In this study, two insertion/deletions (InDels) within a gene encoding γ-tocopherol methyltransferase,  Tocopherols are lipid-soluble antioxidants that occur in four natural forms: α-tocopherol (αT), γ-tocopherol (γT), δ-tocopherol (δT) and β-tocopherol (βT) Arabidopsis and other model organisms . Tocopherol levels in maize kernels vary widely, however, αT only constitutes less than 20% of total tocopherols in maize kernels Thus, in this study, we used genome-wide association mapping with high-resolution SNP density in highly diverse maize germplasm to identify natural allelic variations that contribute to tocopherol levels in maize kernels. Three polymorphisms with independent effects were identified. The identification of these polymorphisms can not only further our understanding of mechanisms controlling natural tocopherol variation, but also provide markers for high vitamin E maize breeding.An association panel comprising of 543 inbred lines P value (1.02×10−6) was used as cutoff, thirteen, three, one and one SNPs were still significantly associated with αT, δT, γT and TT, respectively  of ZmVTE4, and it had two more prevalent alleles in CAM478: a 0-bp insertion  and a 7-bp insertion , whereas a rare allele  having a 4-bp insertion was also present in only four high-oil lines   and strongly associated with the ratio of αT to γT (denoted αT/γT) , but no association was observed with TT   . Additioctively) , suggestctively) . This paZmVTE4 but co-located within the 2.4-Mb region  . When we included InDel7, InDel118, SNP25801 and SNP25815 as covariates, no significant associations for other SNPs were detected  . All the , 5% (γT) and 26% (αT/γT) of the phenotypic variation  and one SNP (SNP25815) that are causative for the associations within this region. Linkage populations segregating at only one of these polymorphisms also confirmed their independent effect on αT  adds evidence to the influence of allelic series on natural phenotypic variation, as what had been observed for flowering time and carotenoid content ZmVTE4 contributed to a large proportion of natural tocopherol variation , 234 lines from China and 67 lines from the USA. Most of the CIMMYT lines were of tropical or subtropical origin, whereas most lines from the USA and China were of temperate origin. Detailed information on 525 of these lines can be found in a previous study All maize lines used in the association panel were inbred, and hence should be homozygous for most of the SNPs. Because the two homozygous states, namely AA and BB, were randomly labeled with fluorescence, an AA to BB ratio of approximately one would be expected. However, 21 lines had a ratio of 2.42–136.91. Most of these lines were located near each other on the chips, and principal component analysis showed that they formed a separate cluster; therefore, they were excluded from further analysis. We also excluded nine lines with a high missing genotyping rate (>50%) or a high heterozygous rate (>33%). Therefore, only 513 lines with high-quality genotypic data were used for the final association analysis.To estimate population structure and kinship coefficients ZmVTE4) was retrieved from NCBI database (www.ncbi.nlm.nih.gov) by blasting Arabidopsis protein sequences against maize high-throughput genomic sequences. Primers were designed using Primer Premier 5 and Primer 3 (ZmVTE4 InDels (InDel7 and InDel118) in CAM478 was performed with PCR based markers .Embryos and endosperms from developing seeds were collected 20 days after pollination . Root anThe association panel was phenotyped in multi-environments, therefore, we fitted a model to obtain an unbiased estimation of each tocopherol compound in each line:ijk = µ+Environmenti+Replicate (Environment)ij+Linek+(Environment × Line)ik+εijk.where Yijk is the observed phenotype for the kth line in the jth replicate of the ith environment, µ is the grand mean, Environmenti is the random effect of the ith environment, Replicate (Environment)ij is the random effect of the jth replicate in the ith environment, Linek is the random effect of the kth line, (Environment × Line)ik is the random interaction effect of the ith environment and the kth line, and εijk is the error term, which follows an independent, identically distributed N  distribution.YThe MIXED procedure in SAS (SAS Institute Inc.) was used to get the best linear unbiased estimate (BLUP) of the line effect, which was then added to the estimate of the grand mean. The resulting values were used as phenotypes for the association analysis. The GLM procedure in SAS (SAS Institute Inc.) was used to calculate the mean squares. These were used to estimate the heritability and 95% confidence interval of each tocopherol-related trait according to the method of Knapp et al. We used the “Q + K” model Figure S1Summary of population structure (A) and individual relatedness (B) in the association panel of 513 inbred lines. (A) Three ancestral populations were detected . Each vertical line represents an inbred line, and the membership percentages of each inbred line belonging to each of the three ancestral populations were depicted by the height of the colored vertical line. The three ancestral populations were re-named as: SS, stiff stalk lines; NSS, non-stiff stalk lines; TST, tropical or subtropical lines. (B) Histogram depicting the percentage distribution of pairwise kinship coefficients. A total of 131,328  kinship coefficients were used. Only kinship coefficients <0.50 are shown; 0.24% of kinship coefficients were >0.50.(TIF)Click here for additional data file.Figure S2Manhattan plots for δ-tocopherol (A), γ-tocopherol (B), α-tocopherol (C) and total tocopherol (D). These plots are based on the association results in 513 lines using 48,962 SNPs. Raw P values were adjusted using false discovery rate and named q. Each dot represents a SNP. The dashed line represents threshold of significance level ( = -lg0.05); SNPs that met this level were enlarged.(TIF)Click here for additional data file.Figure S3Linkage mapping results on chromosome 5 in a RIL population derived from By804 and B73. The x-axis is the genetic map position, and the y-axis is the LOD score. The dashed grey line is the empirical significance threshold (LOD  = 2.5). A QTL for αT, γT and αT/γT was identified, and no significant QTL was mapped for total tocopherol. Also, ZmVTE4 was mapped within the QTL interval for αT, γT and αT/γT. This result was based on previous results (TIF)Click here for additional data file.Figure S4Pairwise LD between 11 significant polymorphisms for αT that were within a 2.4-Mb interval on chromosome 5 including ZmVTE4. These polymorphisms are shown according to their relative positions on chromosome 5. 2r values were shown below the diagonal and 2r>0.1 are highlighted in pink. P values are shown above the diagonal. Five of the 11 polymorphisms, InDel118, InDel7, SNP3462, SNP25820 and SNP25821, were from ZmVTE4.(TIF)Click here for additional data file.Figure S5PCR assays for InDel7 (A) and InDel118 (B) of ZmVTE4. For each polymorphism, the segregation of the PCR product (left panel) and a schematic diagram of the PCR amplicon (right panel) are shown. The primer sequences and detailed PCR reaction procedures are given in (TIF)Click here for additional data file.Figure S6ZmVTE4 expression analysis in the endosperm, leaf and root.  Plots of the results from correlation analyses between α-tocopherol content and ZmVTE4 expression level in the endosperm (A), leaf (B) and root (C). (D) Box plot of ZmVTE4 expression levels at each observed InDel118 allele across the three tissues. The number before and after the slash are the number of lines for allele118 (118-bp insertion) and allele0 (0-bp insertion), respectively. The maximum, 75% quartile, median, 25% quartile and minimum expression levels are given. The P values are based on two-sided Student’s t-tests.(TIF)Click here for additional data file.Figure S7RNA secondary structure in the 5′ UTR of ZmVTE4 aligned to the standard motif identified by Schmitz-Linneweber et al.. The 7-bp motif and the 11-bp motif (blue boxes) were separated by 51 nucleotides (51 nt). InDel7 from ZmVTE4 is in the red box. 0, 0-bp insertion; 4, 4-bp insertion; 7, 7-bp insertion; n, any of the four nucleotides.(TIF)Click here for additional data file.Table S1Phenotypic variation, heritability and correlation analysis in the association panel containing 513 lines.aANOVA, analysis of variance, showing the mean square and degrees of freedom (in parentheses). The F-test was applied to determine the significance level. Both the environments and lines were fitted in the model as random effects. b95% confidence interval for broad-sense heritability. cThe number above and below the diagonal is the genetic and phenotypic correlation coefficients, respectively. **, P<0.01; s.d., standard deviation.(DOCX)Click here for additional data file.Table S2Summary of association results based on false discovery rate.aThe SNP code from Illumina MaizeSNP50 BeadChip. The corresponding name and source sequences of the SNPs can be obtained from the Illumina website (Illumina). SNPs with a false discovery rate less than 0.05 in the association panel of 513 lines were reported in this table. bThe favorable allele is underlined. cCandidate genes were based on (DOCX)Click here for additional data file.Table S3Summary of association results before and after excluding high-oil lines.aThe SNP code from Illumina MaizeSNP50 BeadChip. The corresponding name and source sequences of the SNPs can be obtained from the Illumina website (Illumina). SNPs with a significance level less than 1.02×10−6 in the association panel of 513 lines were reported in this table. bThe favorable allele is underlined. c SNPs that were significant at P<1.02×10−6 in CAM478 are in bold red font. NA, not available because the minor allele frequency was less than 0.05.(DOCX)Click here for additional data file.Table S4Effect of oil content on tocopherol content. These results are based on the linear unbiased estimate of the 513 lines across three locations. a Significance level from a one-way ANOVA model, where the given compound content was the response variable and high-oil lines versus normal lines was the factor. s.d., standard deviation.(DOCX)Click here for additional data file.Table S5Association results for two polymorphisms from ZmVTE4 with α-tocopherol in a subpanel of 155 lines.aThe favorable allele is underlined. InDel7 had three alleles, 0/4/7, with allele4 present in only four lines, allele4 and allele7 were combined because both alleles were unfavorable. 0, 0-bp insertion; 4, 4-bp insertion; 7, 7-bp insertion; 118, 118-bp insertion. bThe best linear unbiased prediction (BLUP) of each line across the two environments (Beijing 2006 and Beijing 2007) was calculated using a model in which the compound content was the responsive variable and the line was a random effect Click here for additional data file.Table S6Association of four polymorphisms with tocopherol-related traits across three environments in CAM478. The P values are from a “Q + K” model fitted in TASSEL. HN, Hainan; SC, Sichuan; YN, Yunnan; n.s., not significant.(DOCX)Click here for additional data file.Table S7Evaluation of the genetic effect of four polymorphisms on tocopherol traits in F2∶3 populations.an is the number of F2 individuals. A one-way ANOVA model with the compound as the response variable and the segregating polymorphism as the factor was used to get the significance level and phenotypic variation explained (R2). αT, α-tocopherol; γT, γ-tocopherol; TT, total tocopherol; ND, not detected; N.P., not polymorphic; n.s., not significant. The favorable allele is underlined in each population.(DOCX)Click here for additional data file.Table S8Inbred lines used for expression analysis. The phenotypic data for α-tocopherol is based on the field experiment with the 155 lines in 2006 and 2007. Presence (√) and absence  are indicated for ZmVTE4 expression data. 2008SZ, Shangzhuang 2008; 2009CP, Changping 2009.(DOCX)Click here for additional data file.Table S9Summary of correlation between expression levels of five genes from tocopherol biosynthesis pathway and tocopherol content. The embryos collected at 20 days after pollination (n = 24) were used to perform quantitative RT-PCR of each gene. The tocopherol content was averaged over two years . The CORR procedure in SAS was used to obtain the Pearson correlation coefficients (r). “–” represents negative correlation.(DOCX)Click here for additional data file.Table S10Primers used in the study.(DOCX)Click here for additional data file.Table S11PCR reaction system and protocol for InDel7 and InDel118 of ZmVTE4.(DOCX)Click here for additional data file.


Drosophila melanogaster, a trait that is highly variable in natural populations and highly heritable in the laboratory. We examined abdominal pigmentation phenotypes in approximately 8000 female European D. melanogaster, isolating 1000 individuals with extreme phenotypes. We then used whole-genome Illumina sequencing to identify single nucleotide polymorphisms (SNPs) segregating in our sample, and tested these for associations with pigmentation by contrasting allele frequencies between replicate pools of light and dark individuals. We identify two small regions near the pigmentation genes tan and bric-à-brac 1, both corresponding to known cis-regulatory regions, which contain SNPs showing significant associations with pigmentation variation. While the Pool-GWAS approach suffers some limitations, its cost advantage facilitates replication and it can be applied to any non-model system with an available reference genome.Various approaches can be applied to uncover the genetic basis of natural phenotypic variation, each with their specific strengths and limitations. Here, we use a replicated genome-wide association approach (Pool-GWAS) to fine-scale map genomic regions contributing to natural variation in female abdominal pigmentation in  Drosophila, a trait with a long history of study in Drosophila genetics and evolution. We conducted the first genome-wide scan for polymorphism associated with pigmentation variation in a large natural sample of D. melanogaster, and found SNPs near two genes, tan and bric-à-brac 1, affecting the trait. The SNPs associated with pigmentation variation in these genes appear to act by affecting the regulation of the pigmentation genes, rather than their protein coding sequence.Phenotypic variation is abundant in natural populations, but its genetic basis is not always well-understood. Here, we examine the genetic basis of body pigmentation in  Phenotypic variation is abundant in natural populations, but usually its genetic basis is unknown. That is, even when the biochemical pathways that underlie a trait are well-understood, the identities of the alleles or genes which contribute to variation in natural populations are often not known. This situation, however, is likely to soon change. Statistical genetic approaches originally developed for mapping complex genetic diseases have proved to be successful at mapping phenotypic differences segregating in natural populations, as long as the alleles underlying them are not rare and have reasonably large effects. As a result, these approaches are particularly successful at uncovering the genetic basis of traits that differ due to local adaptation e.g.,  or artifDrosophila cuticle pigmentation. In genetics, pigmentation has been a classically studied trait— indeed, many of the first markers identified in Drosophila melanogaster had pigmentation phenotypes D. melanogaster, pigmentation is highly variable in natural populations due to both genetic polymorphism and phenotypic plasticity Drosophila genus D. melanogaster, in particular, pigmentation shows patterns of spatial variation that suggest that local selection pressures might vary Drosophila.Here, we examine the genetic basis of phenotypic variation in DrosophilababAbd-BD. melanogaster has been studied via either studies of individual genes Currently, there are at least 9 genes known to be directly involved in pigment synthesis pathways in D. melanogaster—significant LD declines over just 200 bp in most regions of the genome cis-regulatory elements controlling pigmentation genes.To accomplish this, we use a modified GWAS approach, Pool-GWAS, in which individuals with extreme phenotypes are pooled by phenotype, and the pools genotyped and analyzed for differences in allele frequencies  and Vienna (Austria). We divided these flies into five independent replicates (two from Vienna and three from Bolzano), cultured a single generation of offspring in a common laboratory environment, and examined the abdominal pigmentation pattern of approximately 8000 of the female offspring . We then selected from each replicate 100 of the darkest and lightest flies, as measured by the pigmentation area of the A7 tergite  affected primarily by bab1 activity bab1 maps to the “dimorphic” regulatory element in the first intron of this gene, which upregulates bab1 in females, and represses it in males , and a third region near the promoter region of bab1—though they have not been validated with functional genetic studies. Finally, the highest ranked SNP outside of the tan and bab regions lies in the male repressor module of ebony, which affects pigmentation in males . Here, the decline in power is even more apparent; in these analyses, the number of significant associations ranged from 0 to 3 (mean = 0.8 across the 5 replicates at an FDR level of 0.05). This contrast clearly shows the utility of replication, as analyses of single replicates often did not identify even those SNPs with strong effects after accounting for multiple testing.Thus far, we have considered all replicates from the two populations jointly. To gain insight into the strength and robustness of these associations, we also invested the consistency of the significant SNPs across populations. We therefore analyzed the Viennese and Bolzano samples separately. The Drosophila populations are quite similar genetically tan, bab, or ebony regions for evidence of population-specificity, as population specificity may explain why some SNPs were highly ranked, but non-significant, in the combined analysis. There were some indications of other population-specific associations in the bab and ebony regions; e.g., SNPs in the bab1 promoter region discussed above tend to be highly ranked in only one of the populations  mapping of female abdominal pigmentation identified only the bab region as the major contributor to variation in the trait tan alleles. Pool-GWAS, however, unlike QTL mapping, surveys a large sample of natural variation, making it less likely that alleles contributing to the trait are excluded from the analysis. A follow-up association study did use a large sample of strains, but focused only on bab, and failed to identify individual SNPs strongly associated with pigmentation variation tan locus confounding attempts to detect associations at bab. Pool-GWAS avoids these pitfalls by simultaneously surveying variation in many strains and genome-wide, revealing that both tan and bab influence pigmentation.As proof of concept, this study demonstrates the power of a Pool-GWAS approach for investigating the genetic basis of natural variation. Comparison to previous studies analyzing the same or similar phenotypes highlights some of the advantages of Pool-GWAS. Our results are consistent with those of Drosophila pigmentation is a well-understood genetic trait, and the associated SNPs map to pigmentation genes, in principle, associated SNPs could have been detected anywhere in the genome, allowing for discovery of loci that affect a trait, but which have not been previously described.Pool-GWAS is appealing to biologists working on non-model organisms for various reasons. First, it is low cost relative to many other GWAS approaches—sequencing pools of individuals is far less expensive than individual sequencing Approaches similar to this one have been attempted in the past. Pooling of samples, of course, has long been a cost saving measure, and has been used in bulk segregant analysis e.g., , and in tan and bab1). Like other GWAS methods, Pool-GWAS is expected to have little power to analyze traits with very complex genetic bases, and the power to detect the contribution of rare alleles is limited. Consistent with this, the SNPs with the strongest signals in this study were at high frequencies in the control sample compared to unassociated SNPs [comparison of 100 random SNPs with 17 SNPs with FDR<0.05 : Wilcoxin rank sum test, p = 8.1e-05 (Vienna) and p = 0.000117 (Bolzano), D. melanogaster genome, and with similar levels of variability and recombination rates  in lateral view. A7 pigmentation reflects overall posterior pigmentation We scored approximately 3,500 adult F1 females from Vienna and almost 5,000 adult F1 females from Bolzano for abdominal pigmentation, measured Genomic DNA was extracted from each pool of female flies by chloroform extraction and ethanol precipitation, and used for paired-end libraries preparation with standard kits and protocols, to obtain fragments of ∼350 bp after gel-purification . Libraries were amplified using Phusion DNA polymerase  using a 65°C annealing temperature and 10 cycles of amplification, and then purified and quantified using the Qubit HS Assay Kit . Libraries were sequenced using the 2×101 bp or 2×151 bp paired-end protocol on either a Genome Analyzer IIx or a HiSeq2000 (see D. melanogaster reference genome (v5.18), Wolbachia pipientis wMel strain (NC_002978.6) and phiX174 (NC_001422.1) using BWA [v0.5.8c Following  v0.5.8c  with theD. melanogaster and D. simulans and the difficulty of distinguishing females of these species, we suspected some D. simulans contamination among the sequenced flies, which may yield false positive associations as the species can differ in pigmentation D. simulans contamination of around 1% in both collections. We thus filtered the mapped reads for D. simulans contamination, by simultaneously remapping the above reads to five D. melanogaster genomes  and five D. simulans genomes (unpublished data), using GSNAP version 2011-12-28 D. melanogaster if they mapped at least as well to D. melanogaster as to D. simulans, as measured by mapping quality. The effectiveness of this filtering procedure was assessed by inspecting the filtered D. melanogaster specific reads for a target set of D. simulans specific variants , with one replicate-phenotype combination per column.Due to the overlap in ranges of variants ; we conswww.repeatmasker.org) with crossmatch version 0.990329 (http://www.phrap.org/phredphrapconsed.html) as the search engine; simple repeats were not masked (Repeatmasker option -nolow). After undergoing all filtering procedures, an average of 112-fold coverage per replicate was obtained  and the two major allele variants at each SNP as the independent nominal variables, and the counts of each allele in each phenotypic category as dependent variables. The CMH test tests independence of the nominal variables across replicates. We analyzed only SNPs that met the following requirements: (i) the SNP must contain two alleles that occur at least five times each across all samples, (ii) the site must have a coverage of at least 10 in each sample, and (iii) to avoid including differences between paralogs and other artifacts due to copy number variation, the site must have a coverage lower than a maximum cutoff set independently for each sample. This maximum cutoff excludes sites with coverage in the upper 2% tail of coverage for all sites. To correct for multiple tests, we used a FDR control  test, a meta-analysis method for repeated measures of independence, as implemented in PoPoolation2 revision 176 trol see . Indels D. melanogaster annotation v5.40 to assign candidate SNPs to genomic features. SNPs not further than 200 bases from the 5′or 3′UTR of a gene were considered upstream or downstream, SNPs further than were considered intergenic. Overlapping or alternative spliced genes were treated separately.We used SnpEff v2.0.3 http://www.ebi.ac.uk/ena/data/view/ERP001827).The FASTQ files are available from the European Sequence Read Archive Click here for additional data file.Figure S210P-values are plotted against the position on each chromosome. The horizontal dashed line indicates the genome-wide significance threshold at an FDR of 0.05. The red bars indicate candidate genes previously shown to affect pigmentation.Manhattan plot for abdominal pigmentation in the Bolzano sample. The –log(PDF)Click here for additional data file.Figure S3tan (left), bab (middle), and ebony (right) regions. The −log p-values in this plot were obtained from the joint analysis, combining data from both populations, and not from the analysis of the two populations separately. The horizontal dashed lines indicate the genome-wide significance threshold at an FDR level of 0.05 for the combined analysis. Results from the separate analysis of the populations are shown by the color of the SNPs. SNPs that are highly ranked in both analyses are highlighted in green, SNPs highly ranked only in Bolzano are shown in blue, and SNPs highly ranked only in Vienna are shown in orange.Pigmentation loci with differences between Bolzano and Viennese populations highlighted. Shown are SNPs in the (PDF)Click here for additional data file.Figure S4Comparison of effect estimates from the CMH test and logistic regression on simulated SNPs. Shown are the results of 500 simulations of selection for extreme phenotypes for a quantitative trait. For each simulation, individual and pooled genotypes were collected for 20 SNPs, each with a frequency between 0.4 and 0.6 and a positive or negative effect on the trait mean, drawn from a uniform distribution . An environmental contribution to the trait was simulated by drawing a random normal deviate with a variance equal to that of the genetic variance in the trait. The plotted effect estimates are, for the top panel, the absolute value of the log of the pooled odds ratio estimate from the CMH test, and for the bottom panel, the absolute value of the logistic regression coefficient . The magnitude of this effect is a function of the probability of an allele at that SNP causing the individual carrying it to fall into the light or dark extreme. For comparison, the absolute value of the effect on the trait mean is shown on each plot in blue. Note that this is a different quantity than the estimated effects from the statistical tests, so the magnitudes of the two quantities are uninformative, and these are plotted only to show which SNPs have very similar or very different effects on the trait means.(PDF)Click here for additional data file.Figure S5Receiver operator curves (ROC) for simulations with 10 causal loci. The true and false positive rates are shown on the y- and x- axes, respectively, for the case in which there are 10 causal loci and different genetic penetrance (g), with 25% (top row), 50% (middle row) of 100% of the phenotypic value of an individual due to its genotype. Cases where alleles have uniform effect are denoted with Eff = U, and where alleles have exponentially distributed effects with Eff = E. Causal SNPs were either drawn randomly (mf = 0), or required to have a minor allele frequency of 0.2 (mf = 0.2). The black lines show the cases where extreme phenotypes were selected, and the grey lines show where one extreme phenotype was compared to a random sample (as in a case-control experiment). The solid lines show the same results, except that here regions of zero recombination were excluded from the analysis. Each curve was estimated from 50 simulations. See (PDF)Click here for additional data file.Figure S6Receiver operator curves (ROC) for simulations with 20 causal loci. Plots are as before, but summarize simulations in which there were 20 causal loci.(PDF)Click here for additional data file.Figure S7Receiver operator curves (ROC) for simulations with 100 causal loci. Plots are as before, but summarize simulations in which there were 100 causal loci.(PDF)Click here for additional data file.Figure S8p-values causal SNPs with equal effects. To investigate the extent to which we expect our results to be repeatable given the same causal loci, effects and allele frequencies in the population before selection is applied, we performed some additional simulations. To this end, we used a similar approach as above, but fixed a random seed such that the causal loci and their effects were identical among replicates. Some stochastic effects remain: the division of flies into replicates and change in individual phenotypes due to the environment varied between simulations (the proportion of the phenotype due to the environment was set to 0.5). We then ran 20 simulations and assessed the consistency of results between runs. Different numbers of causal loci were assigned to regions of the simulated genomes corresponding to the tan and bab regions, with L = 1, 5, 10, or 20 per locus. As the strongest candidates from the analysis of the data are at intermediate frequencies in the unselected reference populations, we required the causal loci to have a MAF between 0.2 and 0.5. As an indication of repeatability of the simulation results, we use the range of p-values from the CMH test . The range of p-values is plotted against the minor allele frequency (MAF) of the causal SNPs. Causal SNPs are shown in the plots as points, with SNPs with high minimum p-values shown in gray , those with intermediate minimum p-values shown in red . Note that with loci having equal effects, a higher number of causal SNPs necessarily results in smaller phenotypic effect per locus. The plots show that those simulations with few loci have highly repeatable results, while the effect of the MAF in this restricted range is not very pronounced.Replicability of (PDF)Click here for additional data file.Figure S9p-values causal SNPs with exponential effects. Simulations were done as described in the legend for Replicability of (PDF)Click here for additional data file.Figure S10p-value.Allele frequencies in the control samples from Vienna and Bolzano. Shown are frequencies of minor allele frequencies in the control samples from both populations for random vs. highly ranked SNPs. Random SNPs are a randomly drawn subset of the ∼3.3 million SNPs that met our filtering criteria in the combined analysis of the Bolzano and Vienna populations, but which did not occur among the top 200 SNPs ranked by (PDF)Click here for additional data file.Figure S11D. simulans contamination. Samples were treated to filter D. simulans contamination as described in the Result of treatment for (PDF)Click here for additional data file.Figure S12p-values from the joint analysis and the simulated distribution obtained under the null . The plot shows the–log10p-values.QQ-plot for combined analysis. Quantile-quantile plot for observed (PNG)Click here for additional data file.Table S1p-value from our analysis. The colors indicate which, if any, pigmentation gene the SNP is near .Characteristics of highly ranked SNPs in the joint analysis of the Viennese and Bolzano samples. For the top 100 ranked SNPs, the table shows the rank, the location (chromosome and position), the reference and alternative nucleotide, the gene(s) in which they lie (gene ID and gene name), the effect on the gene , the change in amino acid and codon (if in coding sequence) and the (PDF)Click here for additional data file.Table S2Characteristics of highly ranked SNPs in the analysis of the Viennese sample analyzed alone. Values given are as in (PDF)Click here for additional data file.Table S3Characteristics of highly ranked SNPs in the analysis of the Bolzano sample analyzed alone. Values given are as in (PDF)Click here for additional data file.Table S4Differences in indel frequencies in the regions near the tan, bab1 and ebony loci. These regions are defined, as before, as 20 kb up- and down- stream of the center of the coding sequence of each gene (within 20 kb of the middle of the coding sequence of these genes); indels are found as described in p-values. Similarly, indels with adjacent positions are also merged into a single entry, and the same information is reported for these.(PDF)Click here for additional data file.Table S5tan, bab1 and ebony. These regions are defined, as before, as 20 kb up- and down- stream of the center of the coding sequence of each gene. Transposable element insertions found in these regions using the method described in Transposable element insertion frequencies in the pigmentation genes (PDF)Click here for additional data file.Table S6p-values and odds ratios come from the CMH test used for the main analysis, and ranks are based on the magnitude of the log of the odds ratio. We use the magnitude of the log of the odds ratio to estimate the rank of the effect, as is standard, for the following reason: For a SNP of no effect, the odds ratio should be 1 . The size of the estimated effect increases as the odds ratio deviates in either direction from 1 (and the log odds from 0). But, as odds ratios are restricted between 0 and infinity, this deviation is a non-linear function of the size of the real effect. The log odds, in contrast, is symmetric about 0, and the size of this deviation in either direction is expected to be proportional to the size of the effect. Though the effects here are systematically overestimated, the rankings appear to be robust as discussed in Estimated effects of SNPs with FDR<0.05. The effect of each SNP is ranked by its log odds ratio, as discussed in the text. The (PDF)Click here for additional data file.Text S1D. simulans contamination, statistical analysis for association mapping and FDR correction. Also includes detailed methods on the indel and TE insertion analysis and results.Supplementary methods and results. Additional detail on the sequencing method, removal of (PDF)Click here for additional data file.Text S2Description of highest ranked SNPs. Describes the characteristics, allele frequencies before selection, and the estimation of effects for the highly ranked SNPs.(PDF)Click here for additional data file.Text S3Results of separate analysis of Bolzano and Vienna populations. Describes additional results obtained for association mapping of pigmentation SNPs done separately for the Bolzano and Vienna samples.(PDF)Click here for additional data file.Text S4Simulation methods. Describes detailed methods for the simulations for examining the effects of different initial allele frequencies, and different genetic architectures of the trait .(PDF)Click here for additional data file.


DGAT1 was included as a fixed effect. As there were no obvious common SNPs between the traits analyzed independently in this study, a network analysis was carried out to identify unforeseen relationships that may link production and fertility traits.A genome wide scan was performed on a total of 2093 Italian Holstein proven bulls genotyped with 50K single nucleotide polymorphisms (SNPs), with the objective of identifying loci associated with fertility related traits and to test their effects on milk production traits. The analysis was carried out using estimated breeding values for the aggregate fertility index and for each trait contributing to the index: angularity, calving interval, non-return rate at 56 days, days to first service, and 305 day first parity lactation. In addition, two production traits not included in the aggregate fertility index were analysed: fat yield and protein yield. Analyses were carried out using all SNPs treated separately, further the most significant marker on BTA14 associated to milk quality located in the DGAT1 region was treated as fixed effect. Genome wide association analysis identified 61 significant SNPs and 75 significant marker-trait associations. Eight additional SNP associations were detected when SNP located near  Herd fertility is important for the economic sustainability of dairy cattle farming. However, the fertility of Holstein cattle is steadily decreasing , and is APP2) on chromosome 16 [CAST), which are expressed in reproductive organs [Linkage mapping studies have localised genetic loci putatively associated with reproductive and production traits in dairy cattle ,4 and thosome 16  and calpe organs . Furthere organs . Genomic estimation of breeding values of bulls using SNPs and phenotypic data collected on their progeny are being used in selection programmes. The data used for the genomic estimates can also be used for genome wide association studies to identify genetic loci with major effects on fertility and other traits. Recently, Genome Wide Association Studies (GWAS) of female fertility traits have been carried out using 50K SNP genotypes in Holstein-Friesian, Finnish Ayrshire, and Jersey breeds ,11,12,13The GWAS reported here used data from Italian Holstein proven bulls genotyped with 50K SNPs, with the objective of identifying loci associated with fertility related traits and to test their effects on milk production traits. The analysis was carried out using EBVs for the aggregate fertility index traditionally used in selection for Italian Holstein cows  and for A total of 2139 Holstein bulls progeny tested in Italy were chosen based on the: i) availability of biological material or DNA samples; ii) reliability of at least 75% for the Production, Functionality, Type (PFT) national selection index. PFT takes into account both production and functional traits in the proportion 49% : 51% ; iii) largest number of families and balanced number of members per family, to maximize the representativeness of the population. In detail the dataset contained 204 son-father pairs. Semen samples (straws) were provided by Italian certified artificial insemination centers (EU Directive 88/407/CEE), thus ethics committee approval for this study is not required. Bulls were genotyped with the BovineSNP50 BeadChip . Genotypes were quality controlled and markers excluded for missing data (≥ 2.5%), minor allele frequency (≤ 5%), mendelian errors identified for father-son pairs (≥ 2.5%), and deviation from Hardy-Weinberg equilibrium . Bulls with more than 5% missing data were also removed. A mendelian transmission check was performed on father-son pairs. Sons of father-son pairs with unexpectedly high (≥100) mendelian errors were excluded. Classical Multi Dimension Scaling (MDS) was performed within the R statistical environment using the GenABEL package  to exploOfficial EBVs for eight fertility and production traits were provided by the Italian Holstein breeder association ANAFI; . FertiliThe number of animals included in the GWAS, and the mean and standard deviations of EBVs for all the traits analyzed are shown in GRAMMAR-GC (Genome wide Association using Mixed Model and Regression - Genomic Control) approach [GRAMMAR test, based on the estimation of the lambda factor, which is the median of all genome-wide observed test statistics divided by the expected median of the test statistic under the null hypothesis of no association, assuming that the number of true associations is very small compared to the number of tests that are actually performed.Uncorrected p-values of ≤ 5 x e-05were considered as significant associations (Wellcome Trust Case Control Consortium 2007). Genome-wide association analysis was performed with the GenABEL package in R using a three step approach ,17, usinapproach . To accoDGAT1 gene (diacylglycerol O-acyltransferase 1) on production traits. The K232A mutation in the DGAT1 gene that affects milk traits is known to segregate in the Italian Holstein population . After testing all SNPs together, a second GWAS analysis was carried out including the SNP ARS-BFGL-NGS-4939 on BTA14 at position 1801116 bp, as a fixed effect, to account for the known large effect of the nearby http://www.ensembl.org). Canonical transcripts were blasted against the Human RefSeq and the Uniprot databases to search annotations for unannotated cow genes by retrieving homologous human transcript IDs. The Bovine Ensemble protein IDs were analyzed using Ingenuity Pathways Analysis software . Each identifier was then mapped to its corresponding gene object in the Ingenuity Pathways Knowledge Base. Finally, networks of genes were generated on the basis of their connectivity. IPA assignes a score to each network  representing the likelihood that the focus genes within the network are found therein by random chance. A high number of focus genes within a dataset leads to a higher network score. Genes located within 1 Mbp upstream and downstream of significant SNPs were retrieved from the Ensembl database. Location and gene annotation were based on the UMD 3.1 genome assembly and on Ensembl release 65 , low minor allele frequency (10643 SNPs), high mendelian error rate (177 SNPs) and because they were out of HWE (79 SNPs) were excluded. Markers without position information and on sex chromosomes were also removed . Thirty-eight samples were removed because of low genotyping rate, and 8 because of excessive mendelian errors. No outlier was identified by Classical Multi Dimension Scaling . The working dataset contained 2,093 samples and 38,535 SNPs. In each GWAS run, samples missing phenotype data for the specific trait were eliminated. DGAT1 was included as a fixed effect. Among the significant 69 SNPs, 58 were associated with a single trait, 8 with two traits and 3 with three traits. The three SNPs associated with three traits were all in the proximal 1Mbp of BTA14 , associated with MILK, FAT and PROT. Five of the 8 SNPs associated with two traits mapped in the same 1Mbp BTA14 region , all associated with MILK and FAT. The 3 other SNPs were associated with CI and FRT, on the distal region of BTA2  and on BTA28  and with ANG and MILK on BTA26 . GWAS identified 61 significant SNPs and 75 significant marker-trait associations with the single marker model. The genome-wide Manhattan Plots and details on these marker-trait associations are shown in Four regions contained more than one significant SNP. The proximal region of BTA14 between 1.2 and 2.8Mbp contained 21 SNPs , associaThe number of significant associations per trait ranged from a minimum of 2 for ANG to a maximum of 33 for FAT . The numSignificant associations were detected on 20 of the 29 bovine autosomes . The greIn total 118 genes located within 1Mbp of significant SNPs were used in the IPA network analysis. Five main networks were identified. The first had an IPA score of 37 and includes genes involved in Cell Death, Cellular Development and Cell Signaling. The second network was comprised of genes involved in Gene Expression, DNA Replication, Recombination, Repair, and Lipid Metabolism, and had a score of 34 . Three oThe genome wide association studies described here detected in total 69 significant markers and 83 marker/trait associations with individual fertility traits, the aggregated fertility index and the three production traits (P ≤ 5 x e-05). A larger number of significant associations were identified for production (54) than for fertility traits (29). In addition, the associations with production traits showed higher significance levels and lower false discovery rates . This maConsidering significant markers closer than two Mbp as defining a same QTL region, 22 regions were associated with fertility traits and 14 with production traits. In general, regions associated with the fertility traits were marked by fewer SNPs than the production traits. This is due to the weaker association of SNPs associated to fertility traits, compared to production traits, and hence for fertility traits often only a single marker in a QTL peak exceeded the significance threshold.DGAT1 on BTA14 was included as a fixed effect, only 43 marker-trait associations were significant, which were not completely overlapping with the 69 identified when SNP close to DGAT1 was included in the analysis. These 43 significant SNP were located at 20 independent chromosome locations.. Interestingly, some were common with the ones identified when the DGAT1 SNP was not included in the model, while 8 SNPs became significant only when considering the effect of the marker nearby DGAT1 in the model. These new significant associations were found on chromosomes 5, 19, 26 and 28 and were associated with FAT, MILK and FRT. This suggests that in populations of limited size undergoing strong selection, major QTL for a trait (DGAT1 in this case) may mask loci with a lower effect on the same or correlated traits (FAT and MILK in this study). There is extensive evidence that DGAT1 gene influences production traits [When the SNP close to n traits ,19, and A total of 38 SNPs were significantly associated with traits included in FRT index . Only 3 of these SNPs were significantly associated with the FRT composite index. The three SNPs in common were on BTA2 and BTA28 for CI and on BTA19 for NR56, which are the two traits with highest relative weight in the index . Low SNP effects, epistatic interactions, and/or the relative low weight in FRT of the other individual traits may have reduced the chance of detecting the association of the other 35 markers with the composite index. Interestingly, FRT was associated with two regions not detected for the individual traits, on BTA17 and BTA28. Pleiotropy or epistasis may explain this finding. No QTL was found in common between the individual fertility traits included in the FRT index . A recent study based on the Finnish Ayrshire breed identified SNPs on chromosomes 1, 2, 4, 12, 13, 20, 24 and 27, associated with non-return rate, time from first to last insemination, number of inseminations and time from calving to first insemination [Other recently published genome wide studies in dairy cattle have reported markers associated with fertility traits ,12,11. IA literature review based on recently published association or expression studies  identifiCPT 1B) maps on BTA5 approximately 2Mbp from ARS-BFGL-NGS-7979 which was associated with NR56. CPT 1B is involved in lipid metabolism and transport, and has been shown to be up-regulated in dairy cattle endometrium at day 17 of pregnancy [The carnitine palmitoyl transferase 1B gene (regnancy . At implIGF1) is also located on BTA5, close to two SNPs associated with DFS  in a study of fertility, milk production and body condition in Holstein-Friesian dairy cows [The Insulin-like growth factor 1 gene (iry cows .AMHR2) is located on BTA5 under the peak centered on BTA-72978-no-rs, associated with CI  and the apolipoprotein H (APOH) genes are under the peaks identified by BTA-40382-no-rs on BTA 16 and ARS-BFGL-NGS-39839 on BTA 19, respectively associated to DFS and NR56 (The transcription growth factor b2 (and NR56 . Both TGIGLL1) is located near to ARS-BFGL-NGS-74168 on BTA17 which was associated with FRT. The IGLL1 gene has been found to be up-regulated during the peripartum period in the endometrium in lactating vs. non-lactating dairy cattle [The immunoglobulin lambda-like polypeptide 1 precursor (y cattle . This suPTEN), which is involved in regulating the cell cycle and in endometrial cancer in humans [LIPJ), a gene with testis specific expression that belongs to the family of mammalian acid lipases and superfamily of AB hydrolases that catalyse the hydrolysis of triglycerides in the body. Neither of these genes has an obvious role in the quantitative traits associated with this chromosomal region. Only one SNP was associated with more than one trait: ARS-BFGL-NGS-4670 on BTA26 was associated with MILK and ANG. It is not known if this SNP marks the same QTL which affects both of these traits, or if there is a fortuitous co-location of two different QTL. There are two genes located within 1 Mbp of this SNP. Phosphatase and tensin homolog (n humans  and LipaThe relationship between fertility and production was investigated using two strategies: i) SNPs significant for a trait were examined to identify if they may be associated with another trait which fell slightly below the p-value threshold set for significance and ii) a network analysis was performed taking into account all genes identified under the main peaks, to identify candidate genes that may affect both production and fertility traits through intermetabolic network connections The SNP BTA-27242-no-rs on BTA5 was significantly associated with PROT and CI. Using a lower threshold for significance  this SNP was also associated with FRT, ANG and DFS. The SNP had opposite effects on fertility and production traits, in so-far-as the positive allele for PROT had a negative effect on FRT, CI , DFS and ANG. Five genes are located within 1Mbp of this SNP , none of An IPA network analysis of 118 genes located within 1Mbp of the 83 significant SNPs  identifiDGAT1 and APOH described above, and ATPase, Ca++ transporting plasma membrane 1 (ATP2B1) which has been shown to be involved in milk production traits [anterior pituitary gland, and variations in the concentration of the LH trigger ovulation and development of the corpusluteum in females, while in males LH is involved in the production of testosterone . LH acts synergistically with FSH to regulate follicle development, embryonic growth, pubertal maturation, and general reproductive processes [The second network had a score of 34 and contains 18 genes among the 118 close to the significant SNPs . This nen traits  and in arocesses  .DGAT1. These are the platelet-derived growth factor beta polypeptide (PDGFBB), transforming growth factor beta 1 (TGFB1), and transforming growth factor beta 2 (TGFB2) genes. Three genes  are known to be involved in ovarian follicle development [The second network also includes genes connecting LH and FSH with elopment . In addiThe third network had a score of 30 and includes 16 of the genes close to significant SNP. This network, which includes amino acid metabolism, small molecule signalling and molecular transport, is more closely associated with production traits than with fertility. Although the details of the mechanism controlling variation in fertility traits and production traits in cattle still needed to be untangled. The chromosomal regions with significant loci contain genes within pathways described here identify unforeseen relationships that would not be obvious from classical GWA studies and merit further investigation.The results presented here support the polygenic nature of the traits analysed. All these traits have low heritability, particularly the fertility traits, which are correlated predictors rather than direct biological measurements. These factors reduce the probability of identifying associations in a genome wide associations in study of this size. Few markers were found in common among the traits: one marker was associated with production (MILK) and ANG on BTA26, and 2 markers on BTA5 were associated with FRT and CI. Only one association, on BTA5, was identified as having effects on both production and fertility traits when a slightly lower significance threshold was applied. Marker ARS-BFGL-NGS-4939 on BTA14, which is close to the DGAT1 gene, was found to have an effect on all the production traits analysed. This genetic locus had little or no direct effect on fertility traits, however, network analysis indicated an interconnection between DGAT1 and genes influencing fertility. The complex balance between production and fertility deserves the combination of GWAS and network analysis to provide novel insights into the reproductive biology of dairy cows and the possible connections between fertility and production, justifying the further development of novel and interdisciplinary approaches, as used here.Table S1List of Candidate genes liked with fertility obtained from literature review and their relative genomic position.(DOCX)Click here for additional data file.Table S2Reference list used to identify functional candidate genes for fertility.(DOCX)Click here for additional data file.