Md-PG1, a key gene belonging to the polygalacturonase gene family, was mapped on chromosome 10 and co-localized within the statistical interval of a major hot spot QTL associated to several fruit texture sub-phenotypes.Apple is a widely cultivated fruit crop for its quality properties and extended storability. Among the several quality factors, texture is the most important and appreciated, and within the apple variety panorama the cortex texture shows a broad range of variability. Anatomically these variations depend on degradation events occurring in both fruit primary cell wall and middle lamella. This physiological process is regulated by an enzymatic network generally encoded by large gene families, among which polygalacturonase is devoted to the depolymerization of pectin. In apple, Md-PG1 was validated and new functional alleles associated to the fruit texture properties in 77 apple cultivars were discovered. 38 SNPs genotyped by gene full length resequencing and 2 SSR markers ad hoc targeted in the gene metacontig were employed. Out of this SNP set, eleven were used to define three significant haplotypes statistically associated to several texture components. The impact of Md-PG1 in the fruit cell wall disassembly was further confirmed by the cortex structure electron microscope scanning in two apple varieties characterized by opposite texture performance, such as ‘Golden Delicious’ and ‘Granny Smith’.In this work, a QTL corresponding to the position of The results here presented step forward into the genetic dissection of fruit texture in apple. This new set of haplotypes, and microsatellite alleles, can represent a valuable toolbox for a more efficient parental selection as well as the identification of new apple accessions distinguished by superior fruit quality features. Fruit quality is defined by four main principal factors; appearance, flavour, texture and nutritional properties . Of thesBecause of the impact that such physiological changes have on the marketability of edible fruit, researchers have for many years attempted to unravel the genetic basis of this mechanism, with the final goal of elucidating the genes underlying this dynamic process and the development of molecular markers suitable for phenotype prediction ,20,21. QThe main purpose of this study was to validate a QTL identifying a new set of valuable alleles associated to apple fruit texture sub-phenotypes in 77 cultivars. The phenotype was measured using an extremely precise technique to improve association resolution , and theA panel of 77 apple varieties, including both modern and old apple cultivars Table , was choTotal genomic DNA was isolated from young leaf tissue, using the Qiagen DNeasy Plant mini kit (Qiagen) following the manufacturer’s protocol. DNA quantity and quality was measured spectrophotometrically with a Nanodrop ND-8000® .plus texture analyser coupled with an AED – acoustic envelop device . Sample preparation, instrument settings and parameter characterization are described in detail in Costa et al. [ad hoc compiled macro , which allowed the distinction of the texture sub-phenotypes using three main categories; mechanical, acoustic and force direction. General texture variability, expressed by the 14 parameters , was ilMd-PG1 gene was investigated. In particular, 2395 bp (representing the full length gene) and two regions of 800 bp each, located approximately 1 kb up and downstream from the start and stop codon respectively, were cloned and sequenced. The cloning was performed in order to design specific primers able to distinguish Md-PG1 (MDP0000326734) from its homoeologue Md-PG5 (MDP0000159240). Md-PG1 fragment amplification was performed for the 1 kb up and downstream regions in a final volume of 25 μl with 10 ng DNA, 10× buffer, 0.25 mM dNTPs, 0.1 μM reverse and forward primers and 0.625 U of Eppendorf ® Taq polymerase. Amplification was performed using the following conditions: 2 min at 94°C, 32 cycles of 30 sec at 94°C, 30 sec at 56°C, 1 min at 72°C and a final extension of 7 min at 72°C. The full length Md-PG1 gene was amplified in a final volume of 50 μl, with 10 ng DNA, 10× Advantage 2 PCR Buffer, 0.2 mM dNTPs, 0.4 μM reverse and forward primers and 50× Clontech® Advantage 2 Polymerase Mix. Amplification was performed following the manufacturer’s recommendations.In this study a region of approximately 6 kb containing the Md-PG1 region, in addition to the SNPs genotyped by re-sequencing, two microsatellites located in the assembled gene meta-contig were also used. The first was located 3 kb upstream of the Md-PG1 start codon and retrieved from Longhi et al. [SSR10kd  was positioned at 10 kb downstream the stop codon, and was identified de novo using the software Sputnik (http://espressosoftware.com/sputnik/index.html). PCR for SSR marker genotyping was performed as reported in Longhi et al. [Candidate gene SNP genotyping was performed by re-sequencing (Sanger technology) the regions described above from the 77 apple cultivars, using specific forward and reverse primers listed in Additional file i et al. . The seci et al.  (AdditioMd-PG1 meta-contig was mapped to the framework map of the ‘Fuji × Delearly’ population [http://primer3.sourceforge.net/). The marker was integrated employing the software JoinMap 4 [de novo using MapQTL 6 [SSR10kd as a co-factor in order to reduce the residual variance. A LOD threshold value of 3.0, established after running 1000 permutations, was chosen to consider a QTL significant. The linkage group was visualized using MapChart 2.1 [The novel microsatellite motif found in the pulation  by specioinMap 4 , using aMapQTL 6 , selectihart 2.1 .http://www.hidras.unimi.it). The population structure of the 77 apple cultivars was computed using a principal component analysis (PCA computed by Statistica software v7), which is a faster alternative to the MCMC model-based strategy, especially with large marker sets [To correct the analysis for population structure, the molecular profiles of 17 SSR markers  the marker pair-wise r2 values were plotted against their physical distance on the chromosome 10. To fit the data, a smoothed line, represented by the logarithmic trend, was also added. The distribution of 63190 pairs of unlinked markers (368 SNPs) was employed to compute the r2, and the 95th percentile was used as critical point to consider true the linkage between syntenic marker loci.The linkage disequilibrium level among markers (SNPs and SSRs) identified within the apple cultivar collection, was calculated and visualised using Haploview 4.2, a software package designed for linkage disequilibrium statistics and haplotype block inference from genotype data . This sohttp://pngu.mgh.harvard.edu/~purcell/plink/), was used to find associations between the marker set and the first two principal components (PC1 and PC2), derived by the PCA computation performed on the texture parameters. Genome-wide adjusted empirical P-values were then computed and corrected running 1000 permutations. In a second step, the same phenotypic and genotypic data sets were used to find association by implementing the MLM model of TASSEL, where P-values were corrected for false positives using the False Discovery Rate approach (FDR ≤ 0.05), performed by the QVALUE package implemented in R [P-value ≤ 0.05 was considered as the criterion for trait-phenotype association. MLM corrected by FDR was further used to exploit specific association between markers and each single texture dissected sub-phenotypes.Marker-phenotype association analysis was performed using markers with a MAF ≥ 0.05 , and employing both a fixed general linear model (GLM) and a mixed linear model (MLM) with random factors. Initially, the GLM algorithm, implemented in the software package PLINK release 1.07 . Samples were successively washed over night with 0.1 M phosphate buffer at pH 7 at 4°C. Dehydration was performed by incubating the slices for 15-20 min in solution with an increasing concentration of ethanol, and an Emscope 750  was used to identify the critical drying point. Samples were finally coated with a SC 500 gold sputter coater (Bio-Rad Micro-science division) and examined using a Cambridge Instruments Stereoscan 260 scanning electron microscope.To depict the different anatomic structure between a mealy (‘Golden Delicious’) and a crispy (‘Granny Smith’) apple fruit, a cortex portion from both cultivars was isolated and observed using a scanning electron microscope (SEM). Apple flesh slices were prepared by pulling apart the cortex portions which were then fixed for 2 hours at 4°C with 5% formaldehyde in a 0.1 M phosphate buffer  within the apple collection, with an average frequency of 1 SNP/108.9 bases. Among them, ten were located in exons  with a frequency of 1 SNP/138 bp, and 12 SNPs in introns  with a frequency of 1 SNP/84.5 bp. These frequencies are consistent with previous observation made for apple of 1 SNP/149 bp [Md-PG1 predicted gene  was retrieved from the data of Longhi et al. [SSR10kd, was de novo identified screening for microsatellite repetition over the Md-PG1 genomic contig .Out of the 38 SNPs genotyped over the P/149 bp , as wellP/149 bp , but lowP/149 bp ) and graP/149 bp ). From tP/149 bp . Within  Gardner ,56) the i et al. , while tSSR10kd microsatellite marker was further amplified and integrated into the ‘Fuji × Delearly’ genetic map, where several QTLs for apple fruit texture were previously mapped [Md-PG1 gene. The allele segregation allowed the mapping of this marker in the same position of the gene, at 22.5 cM from the top of the linkage group. This second version of the ‘Fuji x Delearly’ map was used to calculate an improved version of the QTL profile for fruit texture, implementing this new marker as co-factor for the multiple QTL detection . The QTL cluster was confirmed on chromosome 10 , the structure was taken into consideration as covariant. When phenotypic traits are correlated with population structure, loci that are not related to the trait under investigation may nonetheless be statistically associated [A collection of 77 apple cultivars was analyzed using 38 SNPs genotyped by re-sequencing and two SSR markers by PCR amplification, contained in a region of approximately 16 kb. Among them, 22 SNPs were associated with the  Sorells , which p Sorells  to find sociated . StatistMd-PG1 gene, and three are included in haploblocks. In particular, PG-full 10 is located in the 1st haploblock, and PG-full 19 and 20 in the 2nd haploblock. The remaining two SNPs, PG-full 1 and 12, were not present in any of the haploblocks defined here. This association additionally confirmed the role of one SNP in particular, here named PG-full 1. This SNP was in fact originally used to map Md-PG1 to linkage group 10 [SSR10kd, located in the 4th haploblock. In the MLM computation  this microsatellite was also associated with PC2 , principal factor expressing a lower quotient of phenotypic variability, but oriented towards the phenotypic dissection of the mechanical/acoustic components.The genetic relationship among the 77 apple cultivars was investigated by Principal Component Analysis. From the total number of PCs, ten were finally selected as covariates to represent the population structure, accounting for 32% of the total genetic variance. The traits employed in the association were represented by a phenotypic dataset containing 14 texture parameters, clustered in two main categories, mechanical and acoustic. These two groups, distinguished by the two principal components (computed on the phenotypic data set), captured 85% of the total phenotypic variance. Initially, the analysis considered the first two PCs as traits, and the association with markers (MAF ≥ 0.05) was computed by running both GLM and MLM modules. Six markers were commonly identified by both algorithms as statistically associated with PC1, consistent with the higher textural variation explained by this component compared to PC2  were associated with PG-full 1, 12, 13 and the allele 2 and 3 of the Md-PG1SSR10kd marker.To better exploit the association between the markers and fruit texture sub-traits, each SNP was further analyzed with each single texture parameter. Eight out of the fourteen texture components resulted statistically associated with the marker set employed in the analysis  is located in an un-conserved region and F is one of the most frequent residues, thus it is not expected to negatively influence PG activity  is closed to a highly conserved region, with the A as the predominant amino acid, while V being slightly bigger and more hydrophobic might decrease the PG activity. As the alleles leading to the three changes are homozygous in all the tested crispy varieties , we hypothesize that a less active polygalacturonase isoenzyme could be less effective in middle lamella depolymerization. This finding was moreover supported by the fact that apple cultivars having a homozygous presence of the haplotype H1 are characterized by an extremely low texture property , such as ‘Dalla Rosa’, ‘Early Gold’, ‘Limoncini’, ‘Napoleone’, ‘Permain Dorato’, ‘Rosmarina Bianca’ and ‘Tavola Bianca’ (a set represented for the most by old apple varieties).To estimate more accurately the SNP frequency markers assembled into haplotypes were tested for association with the texture sub-traits. From the total number of SNPs significantly associated to the texture components, eleven, with a MAF ≥ 0.05 and located in the SSR10kd which was highly associated to the set of texture sub-traits. This microsatellite was targeted in the Md-PG1 meta-contig, in strong LD with SNPs located within the gene. The allelic state configuration of this marker within the apple cultivars  lack the allele “3” of this microsatellite marker, which showed a dosage effect associated to fruit texture decay. The haplotype survey carried out on the 77 apple cultivars, and the validation of their association with the texture components, highlighted that cultivars showing the two haplotypes H2 and H3, or lacking the allele “3” of the microsatellite marker, are distinguished by a favourable fruit texture behaviour. These varieties (Table These SNPs and haplotypes can be considered as a novel toolbox to improve the phenotype prediction efficiency of breeding programs towards the programmed identification of the most suitable parents (MAPS-marker assisted parent selection) and the subsequent selection of novel accessions (MASS-marker assisted seedling selection) with improved fruit texture quality. It is also worth emphasizing, as markers useful for breeding, the microsatellite Md-PG1rs Table . Apple cMd-PG1 gene on fruit development and ripening was also investigated by SEM (scanning electron microscopy). The mealy/crispy texture behaviour of the two cultivars was assessed using the texture analyser  is generally recognised as the main priority in the creation of new elite accessions.The results of this work validated the impact of a QTL associated to fruit texture in apple presenting a new set of The authors declare that they have no competing interests.SL performed the molecular work and drafted the manuscript; MTH supported the association analysis and edited the manuscript; LT contributed to SEM and PG1 sequence analysis; CPP participated to the genotyping; RV provided access to the apple genome; FC designed the experiment, supervised the work and finished the manuscript. All authors read and approved the final manuscript.List of primers used for gene cloning and sequencing as well as sequences of the two microsatellite markers identified within theMd-PG1region, and located at 3 kb upstream (Md-PG1SSR) and 10 kb downstream (Md-PG1SSR10kd) from the start and stop codon, respectively.Click here for fileList of 17 SSRs used to compute the population structure. For each marker the forward and reverse primer sequences and linkage group are provided. For the list of SNPs see Longhi et al. [i et al. .Click here for filePCA plot (first two dimensions) distribution of the varieties assessed with the novel texture phenomics strategy at postharvest stage for year 1 (A) and 2 (C) respectively. On the variables projection (panel B and D) “a” and “f” means acoustic and force parameters respectively, while “d” is for force direction. Panels E and F show the eigenvalue for the two years of assessment. Numbers: variety codes as reported in Table 1.Click here for fileSNP organization withinMd-PG1gene. For each SNP used in the association analysis the location with respect to the gene structure (location), the type of aminoacid substitution  and the change of aminoacid and physical position (in bp after the start codon ATG) are reported.Click here for fileMd-PG1 amino acid sequence derived by the predicted Md-PG1 sequence. The synonymous substitutions are shown in blue, while the non-synonymous substitutions are shown in yellow. (PPT 179 kb)Click here for fileSignificant marker – texture sub-traits association computed with MLM algorithm and corrected by FDR < 0.05. Only significant values  are reported. Ac is for Acoustic parameter, while F is for Force-mechanical parameters.Click here for fileAmino acid sequence alignment of the different PG enzymes derived from Md-PG1 (MDP0000326734) alleles with other plant PGs. The consensus Md-PG1 sequence is here indicated as PG1p-gen and is aligned with the amino acid sequence of the three haplotypes H1, H2 and H3  and the Md-PG1 sequence already present in public databases (Acc. no.: P48978). Other sequences are indicated with the species initials followed by the Acc. nos (e.g.: Pc_BAC22688.1 stands for Pyrus communis BAC22688.1). Apple, , peach  and Arabidopsis  sequences have been downloaded from Phytozome (http://www.phytozome.net), while the others have been downloaded from NCBI (http://www.ncbi.nlm.nih.gov/entrez). Species used in the alignment are: Malus xdomestica (Md), Pyrus communis (Pc), P. pyrifolia (Ppy), Actinidia chinensis (Ac), A. deliciosa (Ad), Prunus persica (Pp), Ricinus communis (Rc), Diospyros kaki (Dk), Vitis vinifera (Vv), Populus trichocarpa (Pt), Glycine max (Gm), Solanum lycopersicum (Sl), Arabidopsis lyrata (Al), Persea americana (Pa), Carica papaya (Cp), Cucumis melo (Cm), Capsicum annuum (Ca), Brachypodium distachyon (Bd) and Oryza sativa (Os). The sequence alignment was carried out using the ClustalW module present in the DNASTAR Lasergene software package (Version 10) with default settings (Gonnet protein weight matrix). Amino acid positions corresponding to SNPs associated with texture parameters are marked with red rectangles while the amino acid changed in H2 and H3 are highlighted in yellow. Residues that match the consensus sequence exactly are indicated as “.”, while insertions introduced to optimize the alignment are indicated as “-“.Click here for fileMd-PG1SSR10kd allelism. The microsatellite allelic profile in two apple varieties, representing the three alleles detected within the assembled apple collection and here named Md-PG1SSR10kd_”1” 289 bp), Md-PG1SSR10kd-”2” (292 bp) and Md-PG1SSR10kd-”3” (298 bp).Click here for file


Fast growth is an important and highly desired trait, which affects the profitability of food animal production, with feed costs accounting for the largest proportion of production costs. Traditional phenotype-based selection is typically used to select for growth traits; however, genetic improvement is slow over generations. Single nucleotide polymorphisms (SNPs) explain 90% of the genetic differences between individuals; therefore, they are most suitable for genetic evaluation and strategies that employ molecular genetics for selective breeding. SNPs found within or near a coding sequence are of particular interest because they are more likely to alter the biological function of a protein. We aimed to use SNPs to identify markers and genes associated with genetic variation in growth. RNA-Seq whole-transcriptome analysis of pooled cDNA samples from a population of rainbow trout selected for improved growth versus unselected genetic cohorts (10 fish from 1 full-sib family each) identified SNP markers associated with growth-rate. The allelic imbalances  were considered at scores >5.0 as an amplification and <0.2 as loss of heterozygosity. A subset of SNPs (n = 54) were validated and evaluated for association with growth traits in 778 individuals of a three-generation parent/offspring panel representing 40 families. Twenty-two SNP markers and one mitochondrial haplotype were significantly associated with growth traits. Polymorphism of 48 of the markers was confirmed in other commercially important aquaculture stocks. Many markers were clustered into genes of metabolic energy production pathways and are suitable candidates for genetic selection. The study demonstrates that RNA-Seq at low sequence coverage of divergent populations is a fast and effective means of identifying SNPs, with allelic imbalances between phenotypes. This technique is suitable for marker development in non-model species lacking complete and well-annotated genome reference sequences. Traits associated with fast and efficient growth have a major influence on the profitability of food animal production including aquaculture species. In addition, optimizing genetic×diet interactions to improve feed efficiency has the potential to reduce aquaculture effluents leading to more environmentally sustainable production. Successful selection for optimal growth rate or body weight is a key objective in aquaculture breeding programs. Traditional phenotype-based selection is typically used to select for growth traits, however, it requires several generations to optimize genetic improvement. In addition, insight into the genetic bases of growth can be used to make better selection decisions. Molecular genetics have been used to identify several genes and markers associated with quantitative traits including genetic variation explaining phenotypic differences in growth Genetic maps characterizing the inheritance patterns of traits and markers have been developed and used for a wide range of species, including fish. Genetic maps are used to target discovery of allelic variation affecting traits with the ultimate goal of identifying DNA sequences underlying phenotypes Recent developments of the next-generation sequencing technologies have allowed whole transcriptomes sequencing (RNA-Seq) and SNPs discovery at fast, accurate and affordable scale. This study tests the hypotheses if RNA-Seq can be used in a non-model species to discover true SNPs with allelic imbalances between two phenotypes in a selection population, and identify genetic markers and candidate genes suitable for association studies.Rainbow trout is one of the most intensively studied fishes in all research areas and is the most cultivated cold freshwater fish in the US The molecular and genetic basis of growth traits is inadequately understood in fish Institutional Animal Care and Use Committee of The United States Department of Agriculture, National Center for Cool and Cold Water Aquaculture  specifically reviewed and approved all husbandry practices used in this study .This study was carried out using fish from a growth-selected line at NCCCWA breeding program 3. Neomales were developed from a subset of alevins from the 2008 year class by feeding 2 mg/kg of 17α-methyltestosterone  for 60 d post swim-up, and the masculinized females were used as sires for the following generation. At about 5-months old, fish were anesthetized using 100 mg/L of tricaine methanesulfonate  and uniquely tagged by inserting a passive integrated transponder  into the dorsal musculature, and tagged fish were combined and reared in 1,000-L communal tanks using flow-through spring water (ambient temperature ∼12.5–13.7°C). Fish were fed a commercial fishmeal-based diet  using automatic feeders . Initially, young fish were fed at a daily feeding rate ∼ 2.5% of body weight (BW), which later was gradually reduced to approximately 0.75% of BW.Each generation, full-sib families were produced from single-sire×single-dam matings. Eggs were reared in spring water, and water temperatures were manipulated between approximately 7 and 13°C to synchronize hatch times. Each family was stocked separately in 200-L tanks at a density of approximately 600 alevins/tank and fish were hand-fed a commercial fishmeal-based diet  beginning at swim-up. Fish were randomly culled every month to maintain stocking densities <50 kg/mIndividual BW were measured at four ages, approximately 6 (Weight1), 7 (Weight2), 9 (Weight3) and 12 (Weight4) months post-hatching, each generation using a Biomark tagging station . Fish from three generations were included in this study; breeding years, 2006, 2008 and 2010. An index of 10-mo BW estimated breeding value (EBV) and thermal growth coefficient TGC EBV was used as selection criterion in the 2008 and 2010 generations, whereas the 10-mo BW EBV was used as selection criterion in the 2006 generation using MTDFREML RNA-Seq and SNP discovery were carried out using muscle tissues collected from 10 fast growing  and 10 slow growing  female rainbow trout. Each group represents a full-sib family from the 2008 hatching year of the above-mentioned growth-selected line from the NCCCWA breeding program. Tissues were flash frozen in liquid nitrogen, shipped on dry ice to WVU, then stored at −80°C until total RNA isolation. Total RNA was isolated from each sample using TRIzol™ . Equal masses of total RNA from the samples of each group were pooled and used for RNA-Seq sequencing.cDNA libraries were prepared and sequenced on an Illumina Genome Analyzer  at the National Center for Genome Resources  as previously described Putative SNPs identified by RNA-Seq as presumably associated with growth traits were initially validated by individually genotyping fish of the discovery panel  using the Sequenom iPLEX Genotyping platform. Association of markers with growth traits were further estimated by genotyping 778 individuals of the growth-selected line at NCCCWA; 661 fish from hatch year 2010 belonging to 40 families  in addition to fish parents (78 fish from hatch year 2008) and grandparents (39 fish from hatch year 2006).Body weight was recorded on each animal at approximately 6, 7, 9 and 12 months post-hatching months post-hatching. The specific growth rate (SGR) was estimated as:Before performing association analysis, variables with significant contribution to the predictive power of growth trait models (Weight1-4), were identified by performing multivariable regression analysis using two linear models that included random family, fixed effect tank, and continuous covariates  using STEPWISE model selection with SAS procedure REG Growth traits (Weight1-4) were tested for departure from multivariate normal (MVN) distribution and estimated basic statistical measures for the response variables using SAS procedure UNIVARIATE From the YC2010 progeny of 40 growth selected families with their corresponding parents and grandparents , a subset of offspring (∼17 siblings/family) were random selected  to genotype with the validated nuclear SNPs. The marker genotype data from the validated SNPs that had a genotype completion rate <0.70 and had monomorphic alleles were filtered out. After filtering out poor quality marker genotype data, we had a marker genotype dataset with 30 validated nuclear SNPs, which was used in association analysis with growth traits.t-statistic for regression of phenotype on allele count (y.xb), and the asymptotic P-value for the t-statistic were estimated; and the empirical P-value was estimated using 20,000 permutations. The association results from PLINK should be taken with caution because variables  with significant contribution to the predictive power of growth trait models were not accounted in the association analysis. The current version of PLINK has a limitation of not allowing the inclusion of fixed effects and covariates in the linear model of family-based association analysis when using continuous quantitative traits (“qfam” function).Three different algorithms of family-based association analysis were used in the association analysis of the validated nuclear SNPs with growth traits. The rate of false positive claims of association was reduced by following up the initial analysis with different methods of association analysis. Family-based association analysis methods were used to detect association signals that are robust to population stratification P-value for the test statistic distributed as χ2 with 1 and 2 DF for dominant and general model, respectively, and the proportion of phenotypic variance explained by each SNP Second, we performed a family-based association analysis using the R package genome-wide association analysis with family data (GWAF) The same sample of n = 877 animals used above in the association analysis of nuclear SNPs was genotyped with the validated mitochondrial SNPs. After filtering out, as indicated above, mitochondrial SNPs with poor quality marker genotype data, we had a marker genotype dataset of 24 validated mitochondrial SNPs genotyped on n = 877 animals (40 families each with ∼17 siblings). From this marker genotype dataset, a sibling was random sampled from each family to generate a population-based sample of n = 40 unrelated individuals; the random sampling was repeated to develop three sets of unrelated individuals. These three sets of unrelated individuals were used in association analysis of mitochondrial SNPs with growth traits.t-statistic for regression of phenotype on allele count (y.xb); the square of the multiple correlation coefficient (2R) which measures the proportion of total variation explained by the regression y.xb; and the asymptotic P-value for the t-statistic. The empirical P-value was estimated using 20,000 and 10,000 permutations when using marker genotype and haplotype data in association analysis, respectively. The false discovery rate (FDR-BH) for the association analysis with marker genotype data was estimated according to Benjamini and Hochberg Population-based association analysis was performed using the validated mitochondrial SNPs marker genotype and haplotype data with PLINK version 1.07 Genomic DNA used in genotyping samples was purified from fin clips using a modified salt-out extraction protocol. Fin clips were placed in microtubes with 500 µL of lysis buffer , containing 0.2% SDS and 6 µL of 10 mg/mL of proteinase K and 5 µL DTT (1 M). Tubes were incubated immediately in a water bath at 56°C for 3 h or 37°C overnight, shaking occasionally. Samples were spun for 2 min at 14,000 rpm. Supernatant was transferred to a new 1.5 mL tube. Then, 200 µL of 5 M NaCl was added to each sample before being centrifuged for 10 min at 14000 rpm. The supernatant was transferred to new microtubes where the DNA was precipitated with 1 mL of cold absolute ethanol and incubated at -20°C for 10 min. The DNA samples were centrifuged, washed with 700-µL 70% ethanol and re-suspended in 60 µl of nuclease-free water. Fish were genotyped using the Sequenom iPLEX service at Partners HealthCare Center for Personalized Genetic Medicine (PCPGM), Cambridge, MA as previously described Four full-sib NCCCWA mapping families each with 46 progeny were genotyped with the 30 validated nuclear SNP markers and 214 microsatellite markers selected from the rainbow trout reference genetic maps Mitochondrial markers were positioned on the rainbow trout mitochondrial genome by aligning the flanking sequences of SNP markers against the rainbow trout mitochondrial reference sequence (GenBank: L29771.1) Markers’ polymorphism was assessed in unrelated fish collected from three aquaculture breeding programs: two broodstocks from the USDA-ARS Hagerman Fish Culture Experiment Station, Hagerman, ID (2009 and 2010 odd and even brood-year), two stocks from Clear Springs Foods, Buhl, ID (2009 and 2010 odd and even brood-year) and 8 stocks from Troutlodge Inc., Sumner, WA (4 spawning time broodstocks x 2 year cycles). A total of 96 unrelated fish (8 fish/broodstock) were genotyped. These samples represent populations from the main rainbow trout breeders in the US.Single-end short read (36-bp) RNA-Seq technology was used to identify putative SNPs in cDNAs from fast growing versus slow growing rainbow trout fish. Sequencing one lane of an Illumina Genome Analyzer flow-cell from a pool of 10 samples from the fast growing fish yielded 8,275,289 reads; from which 6,077,479 (73%) were mapped to a transcriptome reference sequence with 1,624,444 (20%) uniquely mapped to transcripts. Similarly, sequencing a cDNA library from a pool of 10 slow growing fish on a separate flow-cell lane yielded 6,340,991 reads; from which 3,981,922 (63%) were mapped to the reference transcriptome with 1,190,804 (19%) uniquely mapped to transcripts. To validate heterozygosity and allelic imbalance scores of the putative SNPs, a Sequenom iPLEX platform was used to individually genotype each of the 20 fish of the SNP discovery panel. Ninety-nine SNPs failed the Sequenom iPLEX Genotyping assay design, mostly because SNP flanking sequences were less than the 100-bp long requirement for iPLEX assay design. The remaining 262 SNPs were submitted to Sequenom genotyping, of which 211 SNPs were successfully genotyped and 51 SNPs failed due to technical multiplexing errors (∼81% SNP conversion rate). Sequenom genotyping showed that 149 SNPs are heterozygous and 62 are monomorphic . SeventyThese results indicate about 70% success rate of the pooled sequencing strategy in detecting true SNPs (147 true SNPs out 211 successful assays). Pooled sequencing is a cost-effective but challenging strategy for detection of variants, especially rare ones. Detection of SNPs from pooled sequencing requires high stringency methods and deep sequence coverage compared to individual genotyping strategy. To overcome the next generation technical sequencing error that causes false SNP discovery, individual genomes are typically sequenced to 20–30X depth of coverage In this study, individual BW was measured at four ages, 6, 7, 9 and 12 months post-hatching; Weight1, Weight2, Weight3 and Weight4, respectively. There was substantial variation in BW means measured at each age  among thP≤0.0001, P<0.01, P<0.01, Design variables, fixed effects and covariates contribution to the predictive power of growth trait models were assessed with stepwise model selection, and we found that family as independent variable has significant effects on Weight1 when using model A (P>0.01). All SGR measurements showed deviations from MVN distribution (data not shown). Therefore, only BW data from Weight2, Weight3 and Weight4 were included in association analysis.Multivariate normal distribution for residuals diagnostic tests (including Skewness and Kurtosis) revealed significant departures from normality for growth response traits Weight1 and Weight4 . HoweverA family-based marker association analysis of 30 validated nuclear SNPs with growth traits on 778 fish  was performed with PLINK program P = 1.5E−19 to 9.2E−63, P = 2.0E−5 to 8.1E−09). Each SNPs, except nuSNP 12, explained <1% of phenotype variation The family-based association analysis performed with the R-package (GWAF) P<0.05, Family-based association analyses were conducted using three methods with SOLAR P = 0.001, 0.0017, respectively).Family-based Bayesian quantitative trait nucleotide (BQTN) analysis was performed with software SOLAR P-value test of Hardy-Weinberg (HW) proportion for multiple alleles were simulated using PLINK program and compared to values predicted based on HW equilibrium P<0.01). SNPs exhibiting HW deviation are often excluded from association studies, but we maintained them because the HW deviation may be due to directional selection bias in population or partial tetraploidy genome of the rainbow trout.P<0.01 and seven extra nuSNPs were significant at P<0.05  caused amino acid changes . The lisNine nuSNPs were identified in genes related to structural and functional muscular proteins. nuSNP 8 in ATP2A1  showed association with growth. This enzyme hydrolyzes ATP to catalyze translocation of calcium from the cytosol to the sarcoplasmic reticulum. ATP2A1 is important in muscular contraction and perhaps has a role in the growth of the developing muscle Growth association of genotypes from 24 validated mitochondrial SNPs was examined using the same set of 778 fish as explained in nuSNPs. Population-based association analysis was performed with PLINK program 2R correlation coefficient values indicating that a substantial proportion of total phenotypic variation is explained by mtSNPs . Noteworthy, Hap2 and Hap3 share identical sequences except for mtSNP 8 and mtSNP 21. mtSNP 8 and mtSNP 21 were the most significantly associated mtSNPs and the only mtSNPs associated with BW at all 3 ages when mtSNPs were analyzed individually  . The OPPTwelve mtSNPs were found in 4 genes/subunits of the NADH dehydrogenase (complex I) “entry enzyme” of the OPP pathway; 3 SNPs in subunit 1 (mtSNP 1–3), 4 SNPs in subunit 2 (mtSNP 4–7), 1 SNP in subunit 4 (mtSNP 16) and 4 SNPs in subunit 5 (mtSNPs 17–20) . mtSNP 1In addition, 4 SNPs were found in the Cytochrome b gene (mtSNPs 21–24); the only subunit encoded by a mitochondrial gene of Cytochrome bc1 (complex III) . A numbeFurthermore, 4 SNPs were found in the ATP synthase F0 (complex V), the final enzyme in the OPP pathway; 2 SNPs in subunit 6 (mtSNP 14&15) and 2 SNPs in subunit 8 (SNP 12 &13) . These rMarkers’ polymorphism was assessed in other U.S. aquaculture broodstocks to evaluate the markers potential usefulness for marker assisted selective breeding in rainbow trout. Ninety-six individuals were genotyped from 12 rainbow trout broodstocks (8 fish/stock) representing 3 aquaculture populations. Forty-eight out of the fifty-four markers were polymorphic among individuals of the aquaculture populations, 3 markers were monomorphic  and 3 genotyping assays failed  because of technical errors . The aveAmong the 30 nuSNPs used to genotype the four NCCCWA mapping families, 23 markers were polymorphic . Based oMitochondrial markers were placed on the mitochondrial genome by BLASTing sequences flanking markers against the rainbow trout mitochondrial reference sequence (GenBank: L29771.1) RNA-Seq approach was used in a global allele-specific expression approach to identify a set of markers associated with growth traits in rainbow trout. The study provides a proof of concept demonstrated, perhaps for the first time, in a non-model species that RNA-Seq can be used as a discovery tool; first to identify SNPs with allelic imbalances between two phenotypes, second develop genetic markers for association studies, and third to identify candidate genes explaining variations in phenotypes. In this study, only muscle tissues from two families were used as the RNA source. Other tissues, more families and life stages should be used to explore more SNPs in the population and detect tissue- or life stage-specific SNPs that are associated with growth and development. Although many of SNPs identified in this study are located outside of the open reading frames or caused synonymous mutations, marker loci may be linked to nearby other causative mutations in the genome. Further studies are needed to identify causative mutations. In addition, synonymous mutations may still have functional effects by altering the mRNA folding/stability and or by translation suppression Table S1Summary of 54 SNPs  considered for the growth traits association study and their annotations. SNPs were submitted to NCBI dbSNP database (DOCX)Click here for additional data file.Table S22.Variables with significant contribution to the predictive power of growth trait1 models using stepwise model selection(DOCX)Click here for additional data file.Table S31 using family-based quantitative trait linkage disequilibrium (QTLD) analysis2.Association of nuclear SNPs with weight(DOCX)Click here for additional data file.Table S41 using population-based association analysis2.Association of mitochondrial SNP haplotypes with growth traits(DOCX)Click here for additional data file.Table S5Markers’ heterozygosity and minor allele frequency (MAF) in outbred populations, 96 individuals were genotyped from 12 rainbow trout families (8 fish/family) representing 3 outbred populations from the Hagerman Fish Culture Experiment Station (HF), Clear Springs Foods (CS) and Troutlodge Inc (TL).(XLSX)Click here for additional data file.Table S630 nuSNPs were genotyped on mapping families from the NCCCWA. Linkage groups were determined and nuSNPs were added to the a recently published version of the NCCCWA genetic map (LOD ≥ 7.0). Segregating data of nuSNPs genotypes were used to identify the closest markers from the published map using two-point linkage analyses. Linkage analysis placed 19 loci onto 8 chromosomes.(XLSX)Click here for additional data file.


Interactions among genomic loci  have been suggested as one of the potential sources of missing heritability in single locus analysis of genome-wide association studies (GWAS). The computational burden of searching for interactions is compounded by the extremely low threshold for identifying significant p-values due to multiple hypothesis testing corrections. Utilizing prior biological knowledge to restrict the set of candidate SNP pairs to be tested can alleviate this problem, but systematic studies that investigate the relative merits of integrating different biological frameworks and GWAS data have not been conducted.We developed four biologically based frameworks to identify pairwise interactions among candidate SNP pairs as follows: (1) for each human protein-coding gene, a set of SNPs associated with that gene was constructed providing a gene-based interaction model, (2) for each known biological pathway, a set of SNPs associated with the genes in the pathway was constructed providing a pathway-based interaction model, (3) a set of SNPs associated with genes in a disease-related subnetwork provides a network-based interaction model, and (4) a framework is based on the function of SNPs. The last approach uses expression SNPs (eSNPs or eQTLs), which are SNPs or loci that have defined effects on the abundance of transcripts of other genes. We constructed pairs of eSNPs and SNPs located in the target genes whose expression is regulated by eSNPs. For all four frameworks the SNP sets were exhaustively tested for pairwise interactions within the sets using a traditional logistic regression model after excluding genes that were previously identified to associate with the trait. Using previously published GWAS data for type 2 diabetes (T2D) and the biologically based pair-wise interaction modeling, we identify twelve genes not seen in the previous single locus analysis.We present four approaches to detect interactions associated with complex diseases. The results show our approaches outperform the traditional single locus approaches in detecting genes that previously did not reach significance; the results also provide novel drug targets and biomarkers relevant to the underlying mechanisms of disease. The typical analytic framework for the genome-wide association studies (GWAS) of complex diseases or traits considers the additive contribution of common variants  to genetic risk one at a time, based on an assumption of an underlying simplified genetic architecture -4. In th-13 to be significant for an exhaustive search in GWAS data and, very few interactions can pass such stringent thresholds [One of the challenges in detecting gene-gene interactions is the computational burden due to the size of the search space for exhaustive pairwise testing . Consequresholds . On the resholds  and thisThe progress of biomedical research over the last several decades has resulted in the accumulation of vast amounts of biological information stored in public databases, including gene/genome annotations, pathway, and network information. The analysis of GWAS data can benefit from the application of such rich resources. Various biological frameworks have been integrated with GWAS to detect biomarkers or pathways associated with complex diseases -23. ReceA recent study described a method to search for what could be interactions within the local SNP "neighborhood", but was proposed rather as a simple way to detect associated haplotypes, and demonstrated that this approach is efficient in detecting new disease associations . By applcis and/or trans mechanisms, and the loci identified in this manner are referred to as expression quantitative trait loci, or eQTLs [Many searches for interactions based on biological information and annotation have focused on the SNPs located in coding gene regions. However, the vast majority of SNPs are located in intergenic regions, many of them are likely to have unannotated biological functions, and their potential interactions have been neglected in previous studies. Recent studies showed that many intergenic genomic loci can modulate gene expression by both or eQTLs ,29. Highor eQTLs -32. Furtor eQTLs . HoweverWe have applied the approach of using several biological frameworks  to reduce the relevant search space and make exhaustive pairwise searches tractable. Using these frameworks, which can be generalized to all complex diseases, we probe for pairwise SNP statistical interactions to provide novel candidate genes associated with type 2 diabetes (T2D) using the Wellcome Trust Case Control Consortium (WTCCC) data . The reshttps://www.wtccc.org.uk/info/access_to_data_samples.shtml, [-7 in controls; (ii) allele frequency difference test based on 1 degree of freedom (df) trend test P value < 5 × 10-7 between the two control groups; (iii) minor allele frequencies < 1%. We were most interested to see if a focus on interactions could promote SNPs that were non-significant to significant status. After filtering and taking out the SNPs in genes found by single SNP analysis, the number of SNPs analyzed decreased from 500,568 to 418,097.We obtained permission to access the WTCCC dataset for T2D from the consortium websites . The deSNP pairs were generated based on gene annotation, pathway, and network knowledge, respectively. Briefly, a set of all SNP pairs in the same gene was constructed based on their genomic coordinates. Similarly, for each pathway, a set of all SNP pairs was created comprising SNPs located in different genes involved in the pathway. In the case of the network-based approach, a disease-associated network was first extracted from a human interactome database; then, SNP pairs with each member of the pair positioned in a different gene of the network were generated. The overall procedure is illustrated in Figure http://pngu.mgh.harvard.edu/~purcell/plink/res.shtml, [We downloaded the genomic coordinates of 18,657 genes from the plink website , which 6 SNP pairs were tested. To correct for the multiple hypothesis-testing problem, a Bonferroni correction was used with a p-value cutoff of 1.85 × 10-8 for a significance level of 5%.To reduce the computational burden, only genes with less than 100 SNPs were analyzed. This constraint could be relaxed in future studies by considering the Linkage Disequilibrium (LD) structure, where SNPs located in the same LD blocks are highly correlated, and the genotyping information becomes redundant. The final analyzed set has 15,953 genes, which include 205,402 SNPs in total. For each of the 15,953 genes, SNP pairs were generated exhaustively based on SNPs located in the same gene, and tested for interactions. In total, more than 2.7 × 10http://www.broadinstitute.org/gsea/msigdb/index.jsp, c2.cp.v3.0). The initial data contain 880 canonical pathways. Some pathways have very general functions, and contain large numbers of genes, e.g., the gene expression pathway from the Reactome has 425 genes, and the pathways in cancer from KEGG have 328 genes. To focus on pathways with more specific functions and to increase computational efficiency, only pathways with less than 50 genes were analyzed in this study. The final set has 655 pathways, and 1.9 × 105 SNPs in total. For each pathway, the interactions among SNPs located in different genes were tested, which led to 2.7 × 107 tests in total.Canonical pathway data were downloaded from the Molecular Signatures Database (The subnetwork associated with T2D was constructed in three steps: 1) first, a human protein-protein interactome was downloaded from a public database; 2) then, genes associated with T2D (T2D genes) were also obtained from a database curated by literature mining; 3) finally, the T2D genes were used as seeds to extract a subnetwork from the interactome by applying the Steiner tree algorithm. The details for each of these steps are as follows.http://string-db.org/, [The human interactome was downloaded from the STRING database maintained by EMBL . Note thttp://t2db.khu.ac.kr:8080/, [Genes associated with T2D (T2D genes) were downloaded from a public database , deriver:8080/, -39. Brie7 tests.The SNPs located in the 453 genes of the subnetwork were collected, and the SNP pairs were exhaustively generated from all SNPs. SNP pairs from the same genes were removed. The final SNP pairs were tested for interactions, which results in 4.7 × 10To detect the interactions involved in SNPs located in intergenic regions, we analyzed the interactions of SNP pairs between eSNPs and SNPs positioned in the genes whose expressions are affected by the eSNPs.http://www.sph.umich.edu/csg/liang/asthma/, [http://www.scandb.org, [-5) was used to filter out the unreliable associations between eSNPs and the expression of genes. In total, association was established between 151,571 eSNPs and 11,558 genes. SNPs located in 11,558 genes were mapped to genes as described above. Overall 3.5 × 106 eSNP-SNP pairs were generated.The association data between eSNPs and genes was downloaded from a previous study  and pubndb.org, ). The p-The following logistic regression model as implemented in Plink was applied to test the interactions among SNPs using the option -epistasis :0 is the intercept, b1, b2, and b3 are coefficient terms, and rs1 and rs2 are the additive codes for the two SNPs . The biological meaning of these terms are as follows: b0, the baseline odds of disease; b1 and b2 are odds of disease due to the two SNPs respectively; b3, the odds of disease due to interaction between two SNPs. They are calculated by traditional logistic regression analysis. Then, a two-sided test of the null hypothesis b3 = 0 is performed assuming the test statistic follows its asymptotic distribution. The Bonferroni method was used to correct for multiple hypothesis-testing separately for each of the gene, pathway, network, and eSNP levels. Corrected p-values < 0.05 were considered as significant. The associations of single SNPs with T2D were also compared to interaction p-values.where P is the probability of being affected, bThe initial analysis of this data by the WTCCC identified three SNPs significantly associated with T2D [-7[-5). In the case of multiple SNP pairs for one gene, one SNP was shared between pairs, which was the one with a nominal (but not genome-wide) significant p-value. Notably, the p-values for these interaction SNPs are improved by several orders of magnitude  compared to the p-values from individual SNPs. The average distance between the SNPs pairs is 47 kb (ranging from 0.3 to 129), and based on their correlations it is likely that they locate in different LD blocks . ZFAT is not well studied, but likely binds DNA and functions as a transcriptional regulator related to apoptosis and cell survival [One significant SNP pair is detected for ZFAT (zinc finger and AT hook domain); while the individual SNPs have non-significant p-values, the correlation between them is less than 0.1, and the p-value for interaction is the most significant one  and the marginally significant p-values 1.3 × 10-7 (rs4253764). rs4253764 is located in an intron of PPARA , while rs1130199 is in an exon near the 3' region of the CDC6 (cell division cycle) gene. PPARA and CDC6 are not present in the same pathway; however, our SNP to gene assignment associated this SNP with two genes because we included 20 kb of sequence adjacent to the gene, thus including the RARA  gene. RARA is present in two pathways with PPARA: nuclear receptors in a lipid metabolism and toxicity pathway, and a nuclear receptor transcription pathway. Although CDC6 and RARA do not belong to a single LD block, segments of the genes have strong associations, as shown in Figure The interactions among SNPs located in different genes of 655 pathways were exhaustively tested for each pathway individually. In total, 2.67 × 10PPARA is a nuclear transcription factor, which also mediates peroxisome proliferators and affects the expression of target genes involved in cell proliferation, in cell differentiation and in immune and inflammation responses. PPARA is believed to associate with diabetic "microvascular" complications , and is considered as a potential treatment target for such complications . Further7 SNP-SNP tests, and the cutoff p-value after correction is 1.1 × 10-9.The subnetwork associated with T2D was generated by 446 T2D genes and the Steiner tree algorithm and is shown in Figure Two pairs of SNPs were found with significant p-values Table , and theThe protein encoded by ATF6 is a transcription factor. During endoplasmic reticulum (ER) stress, ATF6 activates target genes for the unfolded protein response. There are reports that its polymorphisms are associated with diabetes in various populations, such as Dutch Caucasians and Indians ,49. OLFM-11, and p-values for the other two pairs are non-significant. Interestingly, four out of six possible SNP pairs in Figure The pathway and network analysis led to the detection of three interactions among four SNPs Figure . One SNP6 tests were performed for the search of interaction between expression altered genes and eSNPs. One SNP pair was detected with significant p-value after correction (Table -5. The interaction between rs12517663 and rs3751726 (located in the neighborhood of KLHDC4) is significantly associated with T2D . The function of KLHDC4 is unknown. However, Kelch proteins are commonly seen to associate with actin tails.The above analysis focused on testing of interactions among SNPs based on gene-gene based frameworks. We also attempted to explore the interactions of SNPs through an examination of eSNPs and associated genes whose expression is altered by the eSNPs. In total, 3.5 × 10on Table . rs12517Two major assumptions of the analysis are: 1) the numbers of individuals in the nine genotype cells of the contingency table for two SNPs, for both cases and controls, are large enough to assume an asymptotic null distribution for the test statistic, and 2) our results are not confounded by population structure. To be sure that the first assumptions does not invalidate our results we made sure that each of the pairs of SNPs showed a significant difference between cases and controls using Fisher's exact test, as implemented in the R function fisher.test. This was an overall test for the effect of the two SNPs, not just for the interaction term in our logistic regression model. The results showed the p-values for all detected SNP pairs are still significant after correction. Regarding the second assumption, we calculated the 10 top principal components using ancestry informative marker SNPs with Plink , and theIn conclusion, this study presented several approaches to search for disease-associated gene-gene interactions from GWAS data based on prior biological knowledge and discrete biological frameworks. This is in stark contrast to the single locus approach and the results provide many interesting genes and interactions with significant p-values. While some of the identified SNPs are linked to genes that are well known for their association with T2D, such as PPARA, others are novel, and potentially provide new avenues for further research. The original analysis based on single locus models revealed only three genes associated with T2D: PPARG, KCNJ11 and TCF7L2 , may outGWAS: genome wide association study; SNP: single nucleotide polymorphism; eSNP: expression single nucleotide polymorphism; eQTL: expression quantitative trait loci; T2D: type two diabetes; LD: linkage disequilibrium; WTCCC: wellcome trust case control consortium.The authors declare that they have no competing interests.YL designed the study, collected data, carried out the analysis and drafted the manuscript; SM programmed the pipeline; TF, XZ and RCE helped on the statistical testing; MK collected and managed data, and help on the study design; MRC conceived of the study and participated in its design and coordination. All authors have read and approved the final manuscript.


Next generation sequencing and advances in genomic enrichment technologies have enabled the discovery of the full spectrum of variants from common to rare alleles in the human population. The application of such technologies can be limited by the amount of DNA available. Whole genome amplification (WGA) can overcome such limitations. Here we investigate applicability of using WGA by comparing SNP and INDEL variant calls from a single genomic/WGA sample pair from two capture separate experiments: a 50 Mbp whole exome capture and a custom capture array of 4 Mbp region on chr12.Our results comparing variant calls derived from genomic and WGA DNA show that the majority of variant SNP and INDEL calls are common to both callsets, both at the site and genotype level and suggest that allele bias plays a minimal role when using WGA DNA in re-sequencing studies.Although the results of this study are based on a limited sample size, they suggest that using WGA DNA allows the discovery of the vast majority of variants, and achieves high concordance metrics, when comparing to genomic DNA calls. There has been considerable focus in human genetics on characterizing rare variation in the human population, and the role these variants play in human diseases to account for the “missing heritability” in genome-wide association studies using common variants ,2. UntilWhile the discovery of genetic variation is no longer a rate-limiting step for human genetic analysis, the application of NGS and sequence capture technologies can be limited by the amount of DNA available . In partin situ on laser capture micro-dissection cancer cells for the discovery of structural variants in a tumor genome using Illumina mate-pair sequencing. Tao et. al. [Previous studies have shown that WGA DNA performs well on high-density SNP genotyping arrays -16. Thre et. al.  showed t et. al.  investigWe analyzed capture sequencing metrics of genomic and WGA sample pairs for two capture experiments, a chr12 custom array and a whole exome capture array. Additional file Since the WGA capture experiments had a larger sequencing library compared to the genomic, a random subset of reads were selected from the starting fastq files to match the number of PF reads of the genomic sequencing library . In addition to downsampling the reads from the aligned BAM file, a subset of fastq reads were chosen at random to match the starting number PF reads in the genomic library for both experiments  and non-reference discrepancy (NRD) ,23, showThe NRS and NRD values for SNPs and INDELs for each capture experiment are shown in Table Next, genotype concordance for each bootstrap downsampled chr12 capture BAM was calculated by comparing its calls to the ones made from the original genomic BAM file. NRS and NRD values were summarized by calculating their median value across all 100 downsampled BAMs. In addition, NRS and NRD of the subsetted WGA BAM was calculated by comparing its genotypes to the original genomic BAM. Figure The NRS and NRD values and the genotype concordance matrix from which they were calculated for the whole-exome capture experiment are also shown in Table Targets with higher amounts of GC% have lower amounts of median target coverage for both capture experiments and both types of DNA, as described above. Figure To investigate whether there is any evidence of allele bias in SNP variant calls, all calls from the original chr12 and whole-exome WGA datasets were divided into four groups: concordant genotypes, unique genomic calls (these are sites that contribute to NRS), discordant genotypes (these are sites that contribute to NRD), and WGA unique. In each group, the percentage of each six possible reference/alternate allele combinations was calculated. The results are shown in Figure Sequencing derived SNP variant calls were validated by comparing genotypes to Affymetrix 6.0 Human SNP array genotypes for the same sample. The 6.0 array has over 900,000 variants covering the whole genome, hence only those array genotypes that overlapped a capture target interval were examined. For the whole-exome capture array there were a total of 11831 overlapping SNPs and for the custom chr12 capture array there were a total of 1435 overlapping SNPs. See the Methods section for more details. The NRS and NRD metrics of the WGA and genomic sequencing based SNP genotypes when compared to Affymetrix 6.0 SNP array genotypes for both capture experiments is shown in Table The concordance matrix for the WGA whole-exome comparison to capture array genotypes is shown in the top panel and the genomic concordance matrix is shown in the bottom panel in Additional file The NRS and NRD values when comparing the WGA, chr12 capture sequencing SNP genotypes to the SNP array genotypes are 82.6% and 22.3%. The NRS for the genomic DNA, chr12 capture SNP genotypes when compared to SNP array genotypes is 83% and the NRD is 22.6%. The concordance metrics for the chr12 custom array SNP genotypes when compared to the SNP array derived genotypes is shown in Additional file To investigate if there were any biases in the comparisons of the sequencing derived genotypes to the Affymetrix array based genotypes the percentage of each six possible reference/alternate allele combinations was calculated in sites that contributed to concordant, NRS, and NRD categories. The results are shown in Figure The study described here provides an in-depth assessment of the suitability of WGA DNA for targeted resequencing and variant discovery using next generation sequencing. We evaluated whole exome as well as targeted genomic enrichment using Agilent SureSelect technology, and compared findings from WGA samples to results obtained with genomic DNA from the same individual, as well as validated a subset of SNP variant calls with Affymetrix SNP array genotypes. Overall, Venn analysis showed that the numbers of SNPs and INDELs called in the whole exome and chr12 capture callsets using WGA or genomic DNA is very similar, with the vast majority of variant sites shared between datasets. The concordance metric NRS demonstrates that using WGA DNA has high sensitivity for SNP sites with values of 98.28% and 99.63% for the whole exome and chr12 sequence capture callsets, respectively. The NRS for INDELs is lower at 91.17% and 94.07%. SNP NRD values for the whole-exome and chr12 callset were both less than 1%, but were an order of magnitude higher for INDEL calls. The lower values of these metrics may be due to slight differences in alignment of reads between genomic and WGA DNA in regions that contain INDEL variants. The majority of discrepant genotypes between WGA and genomic DNA involve heterozygous genotypes and statistical analysis suggests that these are enriched for GC alleles, at least in the whole-exome capture data. Validating a subset of the SNP made with genomic and WGA DNA that overlap sites on the Affymetrix 6.0 SNP array showed high sensitivity and high genotype accuracy for the whole exome capture callset. The sensitivity and genotype concordance numbers for the chr12 capture array were not as high, but the loss of sensitivity can be explained by lack of evidence of the alternate allele in the read pileup or poor zero mapping quality values spanning the SNP position. Downsampling and subsetting of reads to achieve lower coverage in WGA callsets (or match the starting number reads in the genomic sequencing experiment) consistently resulted in lower genotype concordance and sensitivity metrics for the whole exome capture experiment, in contrast to the chr12 capture experiment. This difference may be due to statistical fluctuations of read sampling in the downsampling process, combined with the much smaller size of the chr12 capture region. Coverage and concordance metrics correlated with GC% of target intervals, with target intervals above the 3rd quartile of each respective capture array having less coverage and poorer concordance metrics. Our work complements the study of ElSharawy  who usedTwo sample sets were analyzed in this study. One sample was from a family cohort  that wasWe used two different Agilent SureSelect kits to perform sequence capture on the samples used in this study. The first was a custom array designed to capture a 3.87 Mbp region on chromosome 12. The second was an Agilent SureSelect All Exon kit designed to capture at total of 49.4 Mbp of exonic sequence spanning the whole genome. The standard Agilent SureSelect protocol for Illumina paired-end sequencing was used which requires 3 of micrograms of starting genomic DNA.Samples were paired-end sequenced on an Illumina GAII machine with read lengths of 101 bp. with insert size for the genomic and WGA whole exome capture samples being each 370 bp, respectively. Insert sizes for the genomic and WGA chr12 capture samples were both 320 bp, respectively. Both sets of genomic DNA samples were multiplexed with other samples not part of this study, while each of the corresponding WGA DNA samples were sequenced in an individual flow cell lane. Fastq files were generated via the Illumina CASAVA pipeline v1.8. The starting number of passed filter reads is shown in Additional file We applied the same bioinformatics pipeline to WGA and genomic DNA samples as shown in Additional file INDEL variant calls were filtered with the following: Where MQ0 = Number of reads with mapping quality zero, DP = depth of coverage, QUAL = Phred scaled quality score, HRun = Largest contiguous homopolymer run of variant allele in either direction, QD = Variant Confidence/Quality by Depth, and SB = Strand Bias.To investigate the relationship between sequence coverage and number of variants discovered, aligned reads from both WGA BAM files were downsampled to different levels average target coverage using the Picard v1.50 program DownsampleSam. Since UnifedGenotyper restricted its variant calling to target capture interval regions, only aligned reads that had a minimum 1-bp overlap with a target interval were considered in the downsampling process by removing off target alignments by using the pairToBed program in BEDTools package  For the In addition to downsampling the reads from the WGA BAMs for both capture experiments, an exact number of read pairs were randomly sampled from the initial WGA fastq files to match the starting number of genomic DNA fastq read pairs. This was accomplished by writing a Python script that randomly selects a specified number of read pairs from a fastq file. Once the subset of fastq read pairs were selected they were put through the same bioinformatics pipeline applied to the original data.We compared the variant calls from genomic and WGA using three types of metrics. The first was site level intersection to see if the same genomic position was called variant in both callsets. The other two types of metrics were non-reference sensitivity (NRS), and non-reference discrepancy (NRD), shown in Additional file The SNP variant calls for WGA and genomic DNA for both capture sequencing experiments were compared to Affymetrix 6.0 Human SNP array derived genotypes for the same samples. SNP array genotypes were called with Birdseed v2. The 6.0 Human SNP array contains a genomewide collection of more than 900,000 sites. For a SNP array variant to be included in the validation analysis it must overlap a target region on the capture array and have a confidence score of at least 0.05. Only those variants that met these two conditions were considered. Based on these criteria there were a total of 11831 SNPs on the 6.0 array that overlapped the whole exome capture targets and 1435 SNPs that overlapped the custom chr12 capture targets. Similar to the comparison of WGA calls to genomic DNA calls, the VCFs of sequencing and array derived genotypes were merged using the GATK program CombineVariants. The sequencing derived genotypes were evaluated by comparing them to the array based genotypes and the NRS and NRD concordance metrics were calculated. Only sites that have PASS in the filter column of the individual VCFs were included when calculating NRS and NRD from the CombineVariants derived VCF.α=.05 was chosen. Additional file For both the whole-exome and chr12 capture experiments, genomic and WGA SNP call sets were merged, and then placed into 4 categories: concordant, uniquely called genomic, differing genotypes (NRD contributing), and WGA uniquely called SNPs. The counts of each of the 6 possible allele combinations in each category were tallied. To test the null hypothesis that the proportion of SNPs are equal across all 4 categories, the pairwise.fisher.test using the Bonferroni correction method was applied in succession to each of the 6 possible allele combinations in R . The paiA similar analysis was performed when comparing the sequencing derived SNP calls to Affymetrix array derived genotypes for genomic and WGA capture experiments. The sequencing and Affy callsets were merged (only SNPs on the Affymetrix array that overlapped a target capture region were included) and placed into concordant, NRS, or NRD contributing categories. Additional file All samples and protocols for this study have been reviewed and approved by the IRB of the Medical College of Wisconsin. In accordance with the approved protocols, all participants provided written informed consent to participate in the study. Only adult individuals were included in the study.WGA: Whole genome amplification; NRS: Non-reference sensitivity.Authors declare no competing interests.MO conceived the experiment, RC generated the data, CLR provided DNA samples and financial resources, ARI analyzed the data and wrote the paper with edits from MO and GTM. All authors read and approved the final manuscript.Excel table containing information about each of the capture experiments. Excel table containing information about each of the capture experiments as summarized from Picard CalculateHsMetrics.Click here for fileFigure S1. Boxplot of GC%. Boxplots summarizing GC% of whole-exome and chr12 capture targets as well as overall GC% of the whole genome and chr12 for comparison. For whole genome and whole chromosome 12, GC% was calculated in 10 kbp windows with a 5 kbp overlap.Click here for fileFigure S2. Calculating NRS and NRD genotype concordance. Figure shows how concordance metrics of non-reference discrepancy (NRD) and non-reference sensitivity (NRS) are calculated.Click here for fileFigure S3. Genotype concordance matrices. Figure shows genotype concordance matrices for chr12 and and whole-exome SNP and INDEL callsets from which concordance metrics of NRS and NRD were calculated from.Click here for fileExcel file with results of allele bias statistical analysis. Excel worksheet containing the computed p-values for the allele bias statistical analysis.Click here for fileFigure S5. Affymetrix genotype concordance matrices whole exome. Genotype concordance matrices of WGA and genomic DNA SNP calls to Affymetrix genotypes for the whole exome capture experiment.Click here for fileFigure S6. Affymetrix genotype concordance matrices chr12. Genotype concordance matrices of WGA and genomic DNA SNP calls to Affymetrix genotypes for the chr12 capture experiment.Click here for fileFigure S4. Bioinformatics pipeline. The bioinformatics pipeline applied to each of the genomic and WGA DNA samples for each of the capture experiments analyzed in this study.Click here for file


The results obtained from genome-wide association studies (GWAS) often show pronounced disagreements. Validation of association studies is therefore desired before marker information is incorporated in selection decisions. A reliable way to confirm a discovered association between genetic markers and phenotypes is to validate the results in different populations. Therefore, the objective of this study was to validate single nucleotide polymorphism (SNP) marker associations to female fertility traits identified in the Nordic Holstein (NH) cattle population in the Nordic Red (NR) and Jersey (JER) cattle breeds. In the present study, we used data from 3,475 NH sires which were genotyped with the BovineSNP50 Beadchip to discover associations between SNP markers and eight female fertility-related traits. The significant SNP markers were then tested in NR and JER cattle.A total of 4,474 significant associations between SNP markers and eight female fertility traits were detected in NH cattle. These significant associations were then validated in the NR  and JER  dairy cattle populations. We were able to validate 836 of the SNPs discovered in NH cattle in the NR population, as well as 686 SNPs in the JER population. 152 SNPs could be confirmed in both the NR and JER populations.The present study presents strong evidence for association of SNPs with fertility traits across three cattle breeds. We provide strong evidence that SNPs for many fertility traits are concentrated at certain areas on the genome , and these areas would be highly suitable for further study in order to identify candidate genes for female fertility traits in dairy cattle. There is often pronounced disagreement between different populations in the results obtained from genome-wide association studies (GWAS). Validation of association studies is therefore desired before marker information is incorporated in selection decisions, or before large sums are invested into identification of causal factors. The probability of observing spurious associations between a particular trait and SNPs in multiple populations by chance is small, particularly if significant associations are confirmed in two or more validation populations or breeds ,2.As a result of the widespread use of artificial insemination (AI), effective dairy cattle population sizes are relatively small. This has had an effect on patterns of linkage disequilibrium (LD) in dairy cattle breeds. For example, The Bovine Hapmap Consortium  reportedCattle breeding organizations in Denmark, Sweden and Finland participate in a joint breeding value estimation system, known as the Nordic Cattle Breeding Evaluation (NAV). Trait definitions are standardized across the countries and collected data is housed in a centralized database. The increased total number of records in pooled datasets allows for reliable detection of associations for traits with low heritability. NAV undertakes breeding value estimation for three breeds: Nordic Holstein (NH), Nordic Red (NR) and Jersey (JER). The standardized phenotype recording for female fertility traits across these three breeds provides unique data for discovering and validating female fertility associations. The objective of this study was to detect associations for female fertility traits in the NH population and to validate the discovered associations in the NR and JER breeds.DNA was extracted from semen samples from sires for genotyping in a previous project, so no ethical approval was required for this study.http://www.nordicebv.info), and were adjusted for the same systematic environmental effects as in official routine evaluations. The traits evaluated included: number of inseminations per conception (AIS), 56-day non-return rate (NRR), days from first to last insemination (IFL), and the length in days of the interval from calving to first insemination (ICF). The Nordic female fertility index (FTI) is a compound index, based on data collected from Denmark (since 1985), Sweden (since 1982) and Finland (since 1994). The traits included in the FTI were: AIS in cows and heifers; ICF in cows; IFL in cows and heifers; NRR in cows and heifers; and heat strength (HS) in cows and heifers. FTI is meant to reflect the ease with which a cow or heifer was able to conceive. The economic value of HS and NRR were set to zero and therefore had no weight in the FTI. With the exception of ICF, STBVs from the national evaluation were available for both heifers, suffixed H, and cows, suffixed C. For details regarding the phenotypes recorded and models used in routine breeding value prediction, see http://www.nordicebv.info.Data from three Nordic cattle breeds was used in this study. A total of 3,475 NH sires from Denmark, Sweden and Finland with official breeding values for female fertility traits were used to discover associations. Data from NR and JER cattle was used for population validation. There were 4,998 and 1,225 individuals with official breeding values in the NR and JER breeds, respectively. The numbers of animals with official breeding values for each of the eight fertility-related traits for the three breeds studied are presented in Table Bos taurus genome UMD3.1 assembly [All animal samples were genotyped using the BovineSNP50 beadchip , which assayed 54,001 SNP markers with a median interval of 37 kb between SNPs . For theassembly . After fA SNP-by-SNP analysis was carried out in which each SNP was tested in turn for association with phenotypes. The following linear mixed model was used to estimate SNP effects in the NH population:ij was the single trait estimated breeding value of individual j, belonging to the half-sib (sire) family i, μ was the general mean, b is the allelic substitution effect, xij was the number of copies of an allele (with an arbitrary labeling) of the SNP count in the individual j , and si was the random effect of the i-th half-sib family and assumed to have covariances according to the relationship among sires. For example, s = {si} is normally distributed As is the additive relationship matrix among the sires derived from the pedigree, and eij was a random residual of individual i, assumed to follow a normal distribution with mean zero and unknown variance. Testing was done using a t-test against a null hypothesis of H0: b = 0. The significance threshold was determined using a Bonferroni correction . The genome-wide significance threshold was 1.3×10-6, and was calculated by dividing the nominal significance threshold of 0.05 by the total number of SNPs included in the analysis. The analysis were carried out using DMU software (http://www.dmu.agrsci.dk)Where y2 = 0.0975. Finally, the risk of simultaneous falsely validating in two populations is (0.05)2 = 0.0025.The genome-wide significant SNPs were selected from the NH discovery population. These SNPs were then tested in the NR and JER populations using the same model as that described above for NH. For the NR population, an additional fixed effect of breed was fitted as individuals in this population are derived from Danish Red, Swedish Red and White, and Finnish Ayrshire breeds. A stringent threshold for validation study will result in a few validated associations. As we have used a stringent criterion in the discovery population, a threshold of P < 0.05 was used in the validation populations. Therefore, if a SNP showed a genome-wide significant association for a trait in the NH population, and also showed a significant association (P < 0.05) for the same trait in the NR and JER populations, the association was considered validated. Using one validation population, the risk of falsely validating of an effect is 0.05. The risk of falsely validating in exactly one of the two validation populations is 2*0.05 – (0.05)The present association study identified a total of 4,474 genome-wide significant SNPs associated with eight female fertility traits in the NH population  and do not necessarily reflect the same physical location on the genome as the results derived from different genome assemblies. There are also differences in trait definitions across countries, which makes comparison difficult.Many significant SNP markers were detected for female fertility traits in this study . For all of these traits, SNPs were confirmed in both the NR and JER populations  gene. The fact that the SNPs of these four different traits overlap indicates that these traits can be regulated by the same gene(s). Leptin , which is also located on BTA4, is also associated with fertility [leptin gene, SNP markers within the leptin gene [BTA4 harbored several SNPs with association to five fertility traits , which were confirmed in both the NR and JER populations . The validated SNP markers occurred around 40 Mb and 90 Mb. These findings strongly suggest that this area of the genome influences female fertility in dairy cattle. A previous study . HoweverSNPs associated with eight fertility traits  were detected on BTA9. SNPs for IFLC, NRRC and FTI were validated for all three breeds in the area of 90 Mb to 100 Mb. Furthermore, SNPs for AISC, IFLC, FTI, and NRRC were validated in the range of 55 Mb to 70 Mb, whereas confirmed SNPs associated with ICF were located at 25 Mb and 30 Mb. A previous study  detectedPPM1B and SLC1A4 (63.3 Mb)genes, respectively.Validated SNPs associated with all eight fertility traits  were detected on BTA11. Markers for IFLH, IFLC and FTI centered in the area of 80 Mb to 85 Mb, while markers for NRRC and ICF were located in the area of 60 Mb. SNPs were also validated in the area between 0 Mb and 33 Mb for NRRC, NRRH, and FTI. Interestingly, the NRRC and ICF traits showed overlap on two different regions of the genome with strong association in the NH population. However, these markers could not be validated in either the NR or JER populations. Even though BTA11 shows many significant SNP markers for multiple traits, only a few of them could be validated in the NR and JER breeds (ICF(1), FTI(2), IFLC (1)). A previous study  detectedWe identified 117 significant SNP markers for ICF on BTA13. Of the 117 significant SNP markers, 5 could be validated in both the NR and JER populations. Four SNPs were validated in the area from 38 Mb-45 Mb, while one SNP was validated around 65 Mb. In a previous study, a QTL for ICF was detected between markers BL1071 (71.9 Mb) and AGLA232 (77.6 Mb) by using linkage analysis . In addiThe genetic correlation between cow and heifer fertility traits varies in the literature but is generally low ,25. ThisComplex familial relationships are the primary confounding factor in dairy cattle GWAS studies . When faWe observed that the distribution of P-values had an intermediate to large genome-wide inflation. The lambda-values that reflect the genome-wide inflation of the test statistics  ranged fValidation of association studies is important before marker information is incorporated in selection decisions or before large sums is invested into identification of causal factors. The present study presents strong evidence for association of SNPs with fertility traits across three cattle breeds. We provide strong evidence that SNPs for many fertility traits are concentrated at certain areas on the genome , and these areas would be highly suitable for further study in order to identify candidate genes for female fertility traits in dairy cattle.ftp://ftp.ensembl.org/pub/release-73/fasta/bos_taurus/dna).No new SNPs were discovered in this manuscript. All DNA sequences used were taken from a publicly available assembly. The assembly is available for download (The authors declare that they have no competing interests.Conceived and designed the experiment: JKH, GS, BG, MSL. Analysed the data: JKH, GS. Contributed reagents/materials/analysis tools: MSL, BG, GS. Wrote the paper: JKH. All authors read and approved the final manuscript.Significant SNPs for female fertility traits in Danish Holstein cattle.Click here for fileSignificant SNPs for female fertility traits in Danish Holstein cattle which are confirmed in Danish Jersey and Nordic red cattle.Click here for file


LIM domain-binding factor 2 (LDB2) gene in this region had the strongest association with body weight for weeks 7–12 and with average daily gain for weeks 6–12. This GGA4 region was previously reported to contain body weight QTL. GGA1 and GGA18 had three SNP effects on body weight with genome-wide significance. Some of the SNP effects with the significance of “suggestive linkage” overlapped with previously reported results.Chicken body weight is an economically important trait and great genetic progress has been accomplished in genetic selective for body weight. To identify genes and chromosome regions associated with body weight, we performed a genome-wide association study using the chicken 60 k SNP panel in a chicken F2 resource population derived from the cross between Silky Fowl and White Plymouth Rock. A total of 26 SNP effects involving 9 different SNP markers reached 5% Bonferroni genome-wide significance. A chicken chromosome 4 (GGA4) region approximately 8.6 Mb in length (71.6–80.2 Mb) had a large number of significant SNP effects for late growth during weeks 7–12. The  Body weight is an economically important trait for broiler chickens. The identification of DNA polymorphisms and causative genes affecting body weight provides necessary molecular information for marker assisted selection and gene based selection to improve quantitative traits Blood samples of chickens were collected from the brachial vein by standard venipuncture procedure #XK622, approved by the Animal Welfare Committee of China Agricultural University.The study population was the China Agricultural University chicken F2 resource population that was produced from reciprocal crosses of Silky Fowl and White Plymouth Rock which consisted of four half-sibling pedigrees. In this study, 278 individuals of three generations were included. Body weights of the 229 F2 animals were measured weekly from birth to 12 weeks of age, average daily weight gains (ADG) were calculated from birth to 6 weeks of age (ADG6) and from 6 weeks to 12 weeks of age (ADG12). Basic statistics of phenotype data are displayed in Genomic DNA extraction from blood was performed with phenol/chloroform method, and DNA concentration was diluted to 50 ng/ul. The quality and concentration of genomic DNA fulfilled the requirements for the Illumina Infinium SNP genotyping platform. Genotyping using the Illumina 60 K Chicken SNP Beadchip was carried out at the Illumina-certified service provider, DNA LandMarks Inc., Canada. Quality control was assessed in GenomeStudio v2008.1 Pairwise linkage disequilibrium (LD) measured by r2 values for the F2 population and the parental breeds  were calculated for each chromosome using PLINK (v1.07) We assessed the F2 population structure using MDS analysis available from the PLINK software. All autosomal SNPs were pruned using the indep-pairwise option, with a window size of 25 SNPs, a step of 5 SNPs, and r2 threshold of 0.2 Genome-wide association analyses were carried out in PLINK. Linear regression analyses for body weights were performed with the first MDS component, sex, batch, and birth weight as covariates. While the statistical model for ADGs included the first MDS component, sex, and batch as covariates. Measures of SNP effects were calculated by the EPISNP2 package (v3.4) 2 values exceeding 0.40. Using this approach, the estimated number of independent SNP markers and LD blocks was 25,941, so that the threshold P-value of the 5% Bonferroni genome-wide significance was 1.92×10−6 (0.05/25941). The threshold P-value for the significance of “suggestive linkage” that allows one false positive effect in a genome-wide test −5 (1/25941). Empirical genome-wide P-values were obtained from 25,000 permutations for each SNP using the maxT function in PLINK.The threshold P-value of the 5% Bonferroni genome-wide significance was calculated based on the estimated number of independent markers and LD blocks for autosome markers 2<0.2 using the first two MDS components  showed that a chicken  chromosome 4 (GGA4) region was strongly associated with body weight for weeks 7–12 and with average daily gain for weeks 6–12. A total of 26 SNP effects involving 9 different SNP markers reached 5% Bonferroni genome-wide significance under the LD conditions , and 19 of these 26 SNP effects reached 5% empirical genome-wide significance from permutation tests  gene had the strongest association with late growth (body weights from 7 to 12 weeks of age and ADG12 from 6 to 12 weeks of age). LDB2 is capable of binding to a variety of transcription factors, and is of vital importance during brain development and blood vessel formation TBC1D1 gene was highly significant for body weight at 12 weeks of age. TBC1D1 was reported to be a candidate gene for obesity in humans TBC1D1 haplotype has been under selection during domestication in broiler chickens LOC769270 gene had strong association with late growth (body weights from 11 to 12 weeks of age and ADG12). LOC769270 is a hypothetical protein coding which was bioinformatically predicted in chicken only.The oculocutaneous albinism II (OCA2) gene had highly significant effects on body weight in weeks 11–12. The association between OCA2 and body weight in chicken was the first report in this study but the SNP effect in OCA2 overlapped with a reported body weight QTL region detected in intercrossed lines involving White Plymouth Rock background OCA2 gene is associated with body weight and body size in mouse OCA2 gene could be relevant to growth traits.One SNP on GGA1 in the For early growth traits, only one SNP  on GGA18 had significant association with body weight at 2 weeks of age. The lack of SNP effects on early growth traits could be due to epistatic interaction that may explain more of the genetic variance of early growth than single gene effects −5)  . These eglypican 6 (GPC6) gene, glypican 5 (GPC5) gene, and gga-mir-17-92 cluster, and is located within the QTL for bodyweight identified in previous studies using the same F2 population as in this study Two SNPs located at 151 and 152.3 Mb on GGA1 had effects on body weight in weeks 11–12 and ADG12. This region harbors Popeye domain-containing protein 1 (POPD1) gene on GGA3 had effects on body weight in weeks 10–12 and ADG12, and a polymorphism (Gga_rs15178951) 31 kb downstream of BMP7 gene on GGA20 had effects on body weight in weeks 11–12. They overlapped with QTL regions reported by two studies A SNP (Gga_rs14373757) within the A previous study DYNC1I1 located at 23.9 Mb on GGA2 was associated with body weight in week 6 and ADG6, and two SNPs both located within the Opioid-binding protein/cell adhesion molecule-like (OPCML) gene on GGA24 were found to be associated with body weight in week 12 and ADG12. A SNP (Gga_rs14269721) within the gene Cbfa2t2 on GGA20 was in association with body weight in week 12 and ADG12. This is a new QTL identified in this study only.Other previous studies In summary, our GWAS detected 26 SNPs with genome-wise significance and 128 SNPs with the significance of suggestive linkage. Most of these SNPs were reported for the first time. Many of the SNP effects overlapped with previously reported QTL regions, providing evidence towards confirmation of QTL effects. The results are also helpful for identifying the exact QTL locations because of the much improved map resolution of the 60 k SNP panel over the map resolution of microsatellite markers used by most of previous reports on chicken QTL effects.Figure S1Manhattan plot of genome-wide association analysis for body weight traits. The dashed line indicates genome-wise significance of suggestive association , and the solid line declares genome-wise 5% significance with a p-value threshold of 1.92×10−6.(PDF)Click here for additional data file.Table S1Associated SNP with genome-wise significance of suggestive association for body weight traits.(XLS)Click here for additional data file.