APOB) and Adiponectin Receptor 1 (ADIPOR1) are related to the regulation of feed intake, fat metabolism and protein deposition and are candidate genes for genomic studies in birds. In this study, associations of two single nucleotide polymorphisms (SNPs) g.102A>T (APOB) and g.729C>T (ADIPOR1) with carcass, bone integrity and performance traits in broilers were investigated. Genotyping was performed on a paternal line of 1,454 broilers. The SNP detection was carried out by PCR-RFLP technique using the restriction enzymes HhaI for the SNP g.729C>T and MslI for the SNP g.102A>T. The association analyses of the two SNPs with 85 traits were performed using the restricted maximum likelihood (REML) and Generalized Quasi-Likelihood Score (GQLS) methods. For REML the model included the random additive genetic effect of animal and fixed effects of sex, hatch and SNP genotypes. In the GQLS method, a logistic regression was used to associate the genotypes with phenotypes adjusted for fixed effects of sex and hatch. The SNP g.729C>T in the ADIPOR1 gene was associated with thickness of the femur and breast skin yield. Thus, the ADIPOR1 gene seems implicated in the metabolism and/or fat deposition and bone integrity in broilers.Apolipoprotein B ( Gallus gallus) was one of the first domestic animals sequenced and its genome assembly was completed in 2004 [Gallus gallus genome has 38 pairs of autosome chromosomes  and one pair of sex chromosomes, Z and W [Gallus gallus an animal model for studying the genetic basis of phenotypic traits, capitalizing on the possibility of detecting more segregation than in other species.The chicken ( in 2004  by the WLoci that control quantitative traits (QTL) have been associated with a number of traits in chickens, including growth, body composition, egg production, antibody production, disease resistance, and behavior. However, determining causative genes of phenotypic variation is difficult, since each locus controls only a fraction of the phenotypic variance of a given trait [Many traits of economic interest are complex and determined by a number of unknown genes. en trait .Among the molecular markers, single nucleotide polymorphisms (SNPs) are promising for association studies. These markers are abundant and exhibit low mutation rates, which facilitates genotyping . Many stAPOB) gene may be related to early body growth rates and fat deposition [Studies on Apolipoprotein B have identified its role in lipid metabolism. This glycoprotein plays an important part in the absorption, assembly and secretion of lipids, including triglycerides and cholesterol. In chickens, Apolipoprotein B is the main component of IDL (intermediate density lipoprotein) and VLDL (very low density lipoproteins) present in plasma cholesterol . In ratsposition .ADIPO). Adiponectin exerts its action by binding to two specific receptors, ADIPOR1 and ADIPOR2. Both ADIPOR1 and ADIPOR2 are seven transmembrane receptors that are structurally and functionally distinct from G-protein-coupled receptors [ADIPOR1 gene, is a major adiponectin receptor that mediates the glucose and lipid metabolism-related effects of adiponectin on target cells [ADIPOR1 overexpression can increse the biological effects of adiponectin [Another candidate gene associated with fat metabolism in chickens is Adiponectin  under protocol number 011/201, following international guidelines for animal welfare.https://www.embrapa.br/en/home). The experimental research centre is located in the city of Concórdia (27° 14' 03" S—52° 01' 40" W), Santa Catarina state, Brazil. This line of broilers has been under development since 1992 and aims to: increase body weight and carcass yield; improve viability, fertility, hatchability, feed conversion, and reduce abdominal fat. The original population was randomly sampled and then 20 males and 100 females were mated to produce an initial population of approximately 1,500 animals.Phenotypic records were obtained from 1,454 animals from a paternal lineage of broilers. This line has been developed and is owned by the Poultry Genetic Improvement Program from EMBRAPA Swine and Poultry , grower from the 22nd to 34th day  and finisher from the 35st to 41st day .The birds were housed collectively until 35 days of age and then, in order to evaluate feed conversion, were moved to individual cages from 35 to 41 days. The birds were banded for identification, and fed a three-phase diet; starter from the 1The birds were slaughtered at 42 days of age following a 6 hour fasting. Cervical dislocation was manually applied before bleeding of the neck. Blood samples were taken for DNA extraction during bleeding. Approximately 2 mL of blood was collected in microtubes with 100 mL (10% v/v) of 0.5 M EDTA anticoagulant. The samples were immediately packed in ice and then stored in a freezer at -20°C.Eighty-five traits related to performance, carcass composition, organs and bone integrity were evaluated Tables  and 2. YAfter bleeding, the animals were scalded in a hot water bath (60°C for 45 s) and the feathers removed mechanically. The carcass weight was calculated by removing the feathers, blood, head, feet, and organs, except the lungs and kidneys. The carcass cuts  were individually weighed. Weight of muscle cuts, separated from skin and bones, and weight of skin with fat were also recorded.Tibia and femur length were measured between the distal and proximal ends, and thickness in the central region of the bones. Both traits were measured with manual caliper (0.01mm). The bones were kept at about 0°C for 48 hours and then left at room temperature for about an hour to determine flexural strength. The bending test was performed on a TA—XT Plus Texture Analyzer (Texture Technologies Corporation), using the probe TA-92 (Texture Technologies Corporation). The bones were placed in the same position, with the ends resting on two supports spaced 30 mm. The probe travelled 20 mm after touching the sample at 2.00 mm/s test and pretest speed, and 20.00 mm/s post-test speed. The probe touched the sample with programmed weight force of 500 mg. This force was applied to the central area (diaphysis) to determine the flexural strength and rupture modulus. After breaking, the bone fragments were placed in plastic bags, labeled with the sample number and stored at 0°C for 24 hours to determine dry matter and ash.To determine percent dry matter, the bone fragments were left at room temperature for about one hour. Subsequently, they were placed in pre-weighed porcelain crucible and kept in an oven at 105°C for 16 hours, and then, placed in a desiccator until they reach room temperature and weighed. The dry matter percentage was determined as the ratio of the dry weight to the wet weight of the samples.Ash was determined immediately after obtaining the dry matter, using the bone fragments. The samples were incinerated in a muffle for six hours. The initial temperature was 350°C for one hour and increased gradually to 450°C and 550°C for one hour and, finally, to 600°C for three hours. Then, the crucibles with the samples were left in the desiccator until reaching room temperature and then weighed. The ash percentage was determined by dividing ash weight by the dry weight of the sample.DNA extraction was performed using the DNAzol reagent (Invitrogen) according to manufacturer recommendations. After extration, the DNA was resuspended in 150 μL ultrapure water, incubated at 37°C for one hour in a water-bath and stored in a freezer at -20°C. A spectrophotometer (Eppendorf BioPhotometer) was used to quantitate the DNA and then, the samples were diluted with ultrapure water to the concentration of 25 ng/μL.APOB and ADIPOR1 genes were amplified with primers for the identification of SNPs. One pair of primers, including regions of exons and introns, was designed for each gene , and 50 ng of genomic DNA, and finally adjusting the volume to 25 μL by adding ultrapure water. A similar reaction was used for the primer ADIPOR1, but 1.6 mM MgCl2 was used instead. The PCR reactions were carried out in BigDye Terminator v3.1 Cycle Sequencing Kit  using a touchdown PCR protocol under the following condition: denaturing at 94°C for 5 minutes, followed by 35 cycles of denaturing at 94°C for 1 minute, annealing at 59°C for 1 minute and extension at 72°C for 1 minute. The final extension was performed at 72°C for 10 minutes. Electrophoresis using 1% agarose gel was done to visualize the final product.To amplify DNA fragment of MslI was selected for the evaluation of the SNPs in fragment amplified by the primers for APOB, whereas enzyme HhaI was selected for analysis of the SNP in the fragment amplified by the primers for ADIPOR1.After PCR, a cleavage reaction was performed with specific restriction enzymes. The NEBcutter program  was usedThe cleavage protocol for each enzyme was:MslI: Each reaction used 2 μL of the PCR product with 1 μL of 10X buffer; 1U enzyme MslI, and ultrapure water was added to reach 10 μL. The thermal cycler conditions were: one cycle at 65°C for 3.5 hours, followed by one cycle at 80°C for 20 minutes.HhaI: Each reaction used 2 μL of PCR product in 2 μL of 10X buffer; 1U enzyme HhaI, and ultrapure water added to a final volume of 10 μL. The reaction occurred in a water bath at a temperature of 37°C overnight.Finally, the digestion reaction was visualized using a 2% agarose gel stained with 0.01% ethidium bromide. The samples and molecular markers (100 bp DNA Ladder—Promega Corporation) were loaded on the gel and subjected to electric current at 100 V for about a half hour in 1X TBE (Tris-Borate-EDTA). A UV transilluminator was used to compare the sample band pattern to the 100 bp marker bands.Both phenotypic and genotypic data used in this study are available upon request to Dr. Mônica Côrreia Ledur .UNIVARIATE and GLM procedures in the Statistical Analysis System (SAS) software [Preliminary analyses, including descriptive statistcs, normality test and testing of fixed effects to be included in the models, were done using the software . OutlierThe association of SNPs with the traits was investigated using two different methods: Restricted maximum likelihood via ASREML  and GeneThe ASREML software was used to fit the following univariate linear model for all the traits, which included additive and dominant effects of the SNP markers:yijk is observation for the trait on the ith animal,μ is the overall mean,sexj is the fixed effect of the jth sex ,inck is the fixed effect of the kth hatch ,x1l,x2l are the recoded genotypes  and  for the lth SNP, i.e. genotypes AA, AT and TT of the APOB gene and CC, CT and TT of the ADIPOR1 gene, respectively,βl1, βl2 are the linear regression coefficients  for the lth SNP,ai is the random additive genetic (polygenic) effect of the ith animal,eijk is the residual random effect for the ith animal.and An alternate model was also fit using ASREML to directly estimate the allele substitution effect of the SNPs:yijk is observation for the trait on the ith animal,μ is the overall mean,sexj is the fixed effect of the jth sex ,inck is the fixed effect of the kth hatch ,xl is is the number of copies of a given allele in the genotype of the lth SNP  for genotypes AA, AT and TT of the APOB gene and for genotypes CC, CT and TT of the ADIPOR1 gene, respectively,j is the linear regression coefficient  for the lth SNP,βai is the random additive genetic (polygenic) effect of the ith animal,eijk is the residual random effect for the ith animal.and All the available pedigree information  was used to build the numerator relationship matrix for the animal additive genetic effect.The Generalized Quasi-Likelihood Score (GQLS) method developed by Feng et al. , which uj) with the trait phenotypes (Xi):In the GQLS method a logistic regression is used to associate the expected frequency of a marker allele  for the SNP in the ADIPOR1 gene. Test for Hardy-Weinberg equilibrium showed an excess of heterozygotes (P<0.01), indicating that the population was not in Hardy-Weinberg equilibrium. For the APOB SNP, the T allele was more frequent than the A allele, while for the ADIPOR1 SNP, the C allele was more frequent than the T allele  and the traits LG_TIB, LNG_FEM, THK_FEM and TIB_ASH were not significanty affected by hatch (P>0.05). All other traits were significantly affected by sex and hatch (P<0.05).APOB and ADIPOR1 genes was found (P<0.10) at a comparison-wise level. These Tables also present results adjusted for multiple tests. After Bonferroni correction, only the SNP in ADIPOR1 gene was significantly associated (P<0.05) with two traits (YBTS and THK_FEM) and showed suggestive association (P<0.15) with WBTS and FEM_FORCE using the REML method , another bone integrity trait , weight and yield of dramstick skin (WDSS and YDSS) were observed, which corroborate with the significant associations found with yield of breast skin (YBTS) and thickness of the femur (THK_FEM) and suggestive associations with weight of breast skin (WBTS) and force required to break the femur (FEM_FORCE).Even though not significantly at an esperimetal-wise level associations of the SNP in the No significant SNP association with live weights at different ages and with weights of various cuts was found. However, Hendricks et al.  found thg.729C>T on abdominal fat, if at 42 days of age the adiponectin concentration is lower, then it cannot have an effect on the deposition and/or metabolism of abdominal adipose tissue.Pisto et al.  reportedAPOB and ADIPOR1 and the neural control mechanisms of satiety and hunger that regulate feed intake also influence those traits.Studies in other species have observed relationships between adiponectin, adipose tissue and bone . The exaADIPOR1 and abdominal fat deposition may be due to its expression occurs at an early age, as reported in the literature [ADIPOR1 with abdominal fat may be due to the fact that this gene influences the deposition of different fatty acids present in breast and drumstick fat. According Crespo and Garcia [The lack of association between terature , 20, 21 d Garcia , abdominADIPOR1 gene was significantly associated with bone integrity and fat deposition in the breast its role in the chicken’s metabolism needs to be further studied before it can be used for genetic screening. The role of the adiponectin gene on metabolism must be better understood, as the intense selection pressure for increased body weight affects this hormone. Excess of adipose tissue increases the production of many adipokines that influence several bodily functions in birds.While the results of this study provided essential information and found that the SNP in the ADIPOR1 gene is located in an intron region of the gene. According to Ninov et al. [The SNP in the v et al. , when a g.729C>T in the ADIPOR1 gene was found associated with thickness of the femur and breast skin yield. Thus, the ADIPOR1 gene seems implicated in the metabolism and/or fat deposition and bone integrity in broilers. Further studies are warranted to elucidate if this SNP could be used as molecular genetic markers in broiler selection.The SNP


More accurate genomic predictions are expected when the effects of QTL (quantitative trait loci) are predicted from markers in close physical proximity to the QTL. The objective of this study was to quantify to what extent whole-genome methods using 50 K or imputed 770 K SNPs (single nucleotide polymorphisms) could predict single or multiple QTL genotypes based on SNPs in close proximity to those QTL.Phenotypes with a heritability of 1 were simulated for 2677 Hereford animals genotyped with the BovineSNP50 BeadChip. Genotypes for the high-density 770 K SNP panel were imputed using Beagle software. Various Bayesian regression methods were used to predict single QTL or a trait influenced by 42 such QTL. We quantified to what extent these predictions were based on SNPs in close proximity to the QTL by comparing whole-genome predictions to local predictions based on estimates of the effects of variable numbers of SNPs i.e. ±1, ±2, ±5, ±10, ±50 or ±100 that flanked the QTL.Prediction accuracies based on local SNPs using whole-genome training for single QTL with the 50 K SNP panel and BayesC0 ranged from 0.49 (±1 SNP) to 0.75 (±100 SNPs). The minimum number of local SNPs for an accurate prediction is ±10 SNPs. Prediction accuracies that were based on local SNPs only were higher than those based on whole-genome SNPs for both 50 K and 770 K SNP panels. For the 770 K SNP panel, prediction accuracies were higher than 0.70 and varied little i.e. between 0.73 (±1 SNP) and 0.77 (±5 SNPs). For the summed 42 QTL, prediction accuracies were generally higher than for single QTL regardless of the number of local SNPs. For QTL with low minor allele frequency (MAF) compared to QTL with high MAF, prediction accuracies increased as the number of SNPs around the QTL increased.These results suggest that with both 50 K and imputed 770 K SNP genotypes the level of linkage disequilibrium is sufficient to predict single and multiple QTL. However, prediction accuracies are eroded through spuriously estimated effects of SNPs that are distant from the QTL. Prediction accuracies were higher with the 770 K than with the 50 K SNP panel. There has been considerable focus on the identification of quantitative trait loci (QTL) and on strategies for including genomic information in selection programs as the amount of data on the bovine genome has increased . Many rep > n problem, which is that the number of markers (p) is usually much larger than the number of phenotypic records (n). One approach to this problem is to use Bayesian inference that allows for an oversaturated model [v degrees of freedom and scale parameter S. Like BayesA, effects of SNPs are assumed to have different variances. In BayesC, the prior assumptions are similar to those of BayesB except that the effects of SNPs are assumed to have a common variance. It has been shown that BayesC is less sensitive to prior assumptions than BayesB [Genomic selection exploits genomic information and can be based on (1) direct genomic values (DGV), which are estimated breeding values, from marker effects and genotypes only, or (2) on genomic enhanced breeding values (GEBV) that also combine information from phenotypes. The construction of the prediction equation and subsequent estimation of DGV or GEBV is termed “genomic prediction” and is the first step in genomic selection . Althouged model . In a Baed model . This reed model . In Bayeed model . The pried model  include n BayesB . The defn BayesB .All these methods are expected to generate predictions that are highly correlated with the phenotype measured in the training population, particularly if the number of available SNPs greatly exceeds the number of animals and the number of QTL that contribute to the phenotype. Since BayesA and BayesC0 estimate the phenotype by using a linear combination that includes the effects of each SNP across the genome, there is no guarantee that SNPs that are physically close to the QTL will dominate the prediction even in models in which the phenotype is determined by a single QTL. Non-random association of alleles reflects LD and is important in fine-scale mapping of QTL . BayesB Committee approvals for animal care and use were not required for this study since the data came from existing industry databases. A total of 2677 purebred American Hereford animals were genotyped with the BovineSNP50 BeadChip  at GeneSeek  providing results on 54,555 SNPs. The DNA of registered Hereford cattle was obtained from cryo-preserved semen or from hair samples provided by artificial insemination organizations or individual breeders. None of the American Hereford animals were genotyped with the 770 K panel, but we had access to SNP genotypes for 364 Irish Hereford cattle genotyped with Illumina BovineHD BeadChip. Marker genotypes for the 2677 American Hereford cattle were imputed using BEAGLE software  based onSimulated phenotypes with a heritability of 1 based on SNP genotypes  were created for these animals and used as response variables. Phenotypes used to simulate a single QTL simply represented the vector of SNP genotypes at the locus chosen to represent the QTL since multiplying by a simulated QTL effect would simply scale the vector by a constant. For multiple QTL, the phenotype of each animal was simply defined as the sum of the 42 genome-wide QTL, which means that all the QTL had the same effect and that the contribution of each QTL to the genetic variance of the polygenic trait depended on its allele frequency relative to that of the other loci.In this study, every 1000th ordered SNP from the 50 K panel was chosen to represent a QTL. Among those, the 42 polymorphic autosomal SNPs that were located in mapped regions were used in the analysis. Two prediction strategies were followed: (1) estimating the effects of only the SNPs that flanked the QTL with variable numbers of SNPs i.e. ±1, ±2, ±5, ±10, ±50 or ±100 and predicting the QTL based on the effects of these SNPs only ; (2) training on the whole genome using all SNPs except those assumed to be QTL and then predicting the QTL based on the effects of only the SNPs that flanked the true QTL location with variable numbers of SNPs i.e. ±1, ±2, ±5, ±10, ±50 and ±100 .yi is the simulated phenotype on animal i, μ is the overall mean of y, zij is equal to 0, 1 or 2 depending on the SNP genotype at marker locus j in individual i, βj is the allele substitution effect associated with marker j, δj is a 1 or 0 indicator variable for inclusion or exclusion of marker j in the model, and ei is a residual. Parameter π was assumed for BayesB and BayesC to be 0 , 0.95  or 0.996  for the 50 K analyses, corresponding to fitting non-zero effects for 50 K, 2500 or 200 SNPs per iteration, whereas π was equal to 0 (BayesC0) or 0.9997 , corresponding to fitting 770 K or about 200 SNPs per iteration for 770 K SNP panels. High values of π were chosen to provide variable selection by assuming that most loci had zero effect while still ensuring that the number of SNPs fitted in each iteration was larger than the actual number of QTL (1 or 42) that contributed to the trait. In real data analyses, the number of QTL is not known, and BayesCπ in which π is estimated from the data is a practical alternative that was also used in this study. Markov chain Monte Carlo (MCMC) methods with 41,000 or 21,000 iterations for the 50 or 770 K SNP panels, respectively, were used to provide posterior mean estimates of effects of SNPs after discarding the first 1000 samples for burn-in. A smaller number of iterations was chosen for the 770 K analyses to reduce computing effort since preliminary analyses demonstrated rapid convergence of these models for both the simulated monogenic or polygenic phenotypic traits with a heritability of 1. Relationships between minor allele frequency (MAF) and prediction accuracies were also studied. All analyses were performed using GenSel software [Bayesian methods including BayesA, BayesB , BayesC0software .Animals were clustered into six groups using K-means clustering on pedigree estimates of additive genetic relationships between animals in order to increase within-group and decrease between-group relationships . All sixA frequent criticism of MCMC-based Bayesian regression models is that convergence may not be achieved with a given number of iterations in the Markov chain. It was assumed that the single-QTL analyses with a heritability of 1 would converge faster than the analyses using the summed 42 QTL to represent a polygenic trait. A preliminary analysis using the simulated polygenic trait showed convergence of the chains with far less iterations than were performed in our study. Post-burn-in samples of SNP effects were averaged to obtain posterior means of SNP effects as the length of the chain increased. Using BayesC with π = 0.996 for 50 K SNPs, the correlation between phenotype and whole-genome prediction exceeded 0.97 at burn-in, 0.980 after 280 post-burn-in iterations, 0.983 after 6280 post-burn-in iterations, and then it failed to increase further. For a trait with a heritability of 1, this correlation represents the correlation between true and estimated genetic merit. Using BayesC with π = 0.9997 for 770 K SNPs, the correlation exceeded 0.99 at burn-in, and asymptoted to 0.996 within a further 300 iterations.y and DGV) averaged over 42 QTL for local  training and predictions with the 50 or 770 K SNP panels using BayesC0. Prediction accuracies ranged from 0.50 (±1 SNP) to 0.97 (±100 SNPs) and 0.75 (±1 SNP) to 0.99 (±100 SNPs) for the 50 and 770 K SNP panels, respectively. Accuracies increased as the numbers of SNPs flanking the QTL increased for both 50 and 770 K SNP panels. Regardless of the number of SNPs flanking the QTL, accuracies that were estimated with the 770 K panel were higher than with the 50 K panel but differences between these accuracies decreased as the number of SNPs flanking the QTL increased.If the QTL locations are known (e.g. from genome-wide association studies or from the location of candidate genes), higher accuracy might be achieved by using increased SNP density in those regions and eliminating SNPs in other unassociated regions. To quantify this effect, local training and predictions were performed. Figure y and DGV) averaged over 42 QTL for whole-genome training and local predictions that were estimated with the 50 and 770 K SNP panels using BayesC. Accuracies obtained with the 50 K SNP panel and BayesC0 were lowest and highest for ±1 SNP (0.49) and ±100 SNPs (0.75) flanking the QTL, respectively, with minor variations in accuracy for numbers of flanking SNPs between ±5 and ±100 SNPs. With the same 50 K SNP panel and BayesC0.996, accuracies increased steadily as the number of SNPs flanking the QTL increased from 0.38 (±1 SNP) and to 0.91 (±100 SNPs). Comparison of results obtained with BayesC0 and BayesC0.996 indicated that except for ±1, ±2 and, ±5 flanking SNPs, accuracies were higher for BayesC0.996. With the 770 K SNP panel and BayesC0, accuracies were higher than 0.70 for all numbers of flanking SNPs, with minor variations that ranged from 0.73 (±1 SNP) to 0.77 (±5 SNPs). With the 770 K SNP panel and BayesC0.9997, accuracies were lowest and highest for ±2 (0.67) and ±100 flanking SNPs (0.88), respectively. In this case, at least ±10 flanking SNPs are required for the accuracy to be higher than 0.80. With BayesC0, accuracies were higher with the 770 K than with the 50 K SNP panel regardless of the number of SNPs flanking the QTL but if π was not equal to 0, accuracies were lower with the 770 K than with the 50 K panel for ±50 and ±100 flanking SNPs.Figure Accuracies of whole-genome prediction for both SNP panels regardless of the number of flanking SNPs did not exceed 0.76. In contrast, accuracies for local prediction with ±100 flanking SNPs were close to 1. Estimating higher accuracies for local training than for whole-genome training may be due to the spurious interference of more distant SNPs in whole-genome training. Comparison of results from local and whole-genome training Figs.  indicateIn general, the amount of LD between any two SNPs decreases as the physical distance between them increases. However, forces such as selection can cause markers that are far apart physically (or even on different chromosomes) to be in high LD with one another . GeneralSince most complex quantitative traits are associated with multiple QTL rather than a single QTL, the same 42 QTL used for single-QTL prediction were summed to represent a polygenic trait and whole-genome training and local  predictions were done with the 50 or 770 K SNP panels using BayesC Fig. . SimilarFigure MAF for the 42 QTL ranged from 0.0001 to 0.499 with an average of 0.27. Figure The genotypes used in this simulation represented actual genotypes for a real cattle population, but it is likely that the distribution of the allele frequencies of those genotypes will not reflect the distribution of allele frequencies of real QTN, because SNPs that are selected for genotyping arrays are chosen to be particularly informative and therefore tend to include more SNPs with a higher allelic frequency than those included in exonic variants . FurtherThis study applied single- and multiple-QTL predictions using SNPs that flank the QTL to American Hereford beef cattle genotypes and simulated phenotypes. Overall, our results suggest that for a dataset with less than 3000 animals, the LD of the 50 K SNP panel is sufficient to predict single or multiple QTL. Training on SNPs that are located close to the QTL rather than whole-genome SNPs resulted in higher prediction accuracies than whole-genome analyses, which indicates that predictive accuracy can be improved if prior knowledge of the QTL locations or of the likely informative SNPs was available. In our simulation, we assumed a complex trait that was influenced by only 42 QTL with a heritability of 1, which suggests that for traits with more QTL or lower heritabilities, predictions would be less accurate. As far as marker density is concerned, the 770 K SNP panel performed slightly better than the 50 K SNP panel for prediction of single and multiple QTL. Analysis of the QTL MAF shows that in order to get high prediction accuracies for rare QTL, a larger number of SNPs flanking the QTL (±50 or ±100 SNPs) is required. Bayesian regression methods that involve variable selection were better able to detect and localize QTL than methods that fitted all whole-genome SNPs simultaneously.


Verticillium dahliae is a plant-pathogenic, ascomycete fungus with no known sexual stage, even though related genera have well-described sexual reproduction. V. dahliae reproduces mitotically and its population structure is highly clonal. However, previously described discrepancies in phylogenetic relationships among clonal lineages may be explained more parsimoniously by recombination than mutation; therefore, we looked for evidence of recombination within and between clonal lineages. Genotyping by sequencing was performed on 141 V. dahliae isolates from diverse geographic and host origins, resulting in 26,748 single-nucleotide polymorphisms (SNPs). We found a strongly clonal population structure with the same lineages as described previously by vegetative compatibility groups (VCGs) and molecular markers. We detected 443 recombination events, evenly distributed throughout the genome. Most recombination events detected were between clonal lineages, with relatively few recombinant haplotypes detected within lineages. The only three isolates with mating type MAT1-1 had recombinant SNP haplotypes; all other isolates had mating type MAT1-2. We found homologs of eight meiosis-specific genes in the V. dahliae genome, all with conserved or partially conserved protein domains. The extent of recombination and molecular signs of sex in (mating-type and meiosis-specific genes) suggest that V. dahliae clonal lineages arose by recombination, even though the current population structure is markedly clonal. Moreover, the detection of new lineages may be evidence that sexual reproduction has occurred recently and may potentially occur under some circumstances. We speculate that the current clonal population structure, despite the sexual origin of lineages, has arisen, in part, as a consequence of agriculture and selection for adaptation to agricultural cropping systems.Most asexual species of fungi have either lost sexuality recently, or they experience recombination by cryptic sexual reproduction.  Asexual reproduction has evolved from sexually reproducing ancestors in almost all major groups of eukaryotes, except birds and mammals, and confers several short-term advantages Verticillium dahliae is a plant-pathogenic fungus in the Sordariomycetes of the Ascomycota (www.mycobank.org). Although the Sordariomycetes are associated with well-described sexual reproductive stages, e.g., Neurospora crassa, V. dahliae appears to reproduce only by mitotically-produced spores and persistent soil-borne dormant structures (microsclerotia). Not surprisingly, population structure is highly clonal V. dahliae causes vascular wilt in a broad range of dicotyledonous host plants V. dahliae is known to disperse by the movement of soil, irrigation water, and with seed and vegetative propagation V. dahliae has a clonal population structure with little or no evidence of recombination. Before the widespread availability of molecular markers, isolates of V. dahliae were classified into vegetative compatibility groups (VCGs). In ascomycetes, VCGs represent a multilocus genotype defined by the alleles at multiple heterokaryon incompatibility (het) loci; individuals with the same alleles at all het loci are vegetatively compatible and can form stable heterokaryons V. dahliae, three VCGs  are divided into subgroups based on the strength of complementation in heterokaryon compatibility assays, resulting in VCG subgroups 1A, 1B, 2A, 2B, 4A, and 4B V. dahliae represents a distinct clonal lineage V. dahliae have the same mating type (MAT1-2) MAT1-1 are rare V. dahliae appears to be low, assuming that mating can only occur between individuals of opposite mating type. Moreover, a considerable number of chromosome rearrangements has been observed among isolates of V. dahliae, which are thought to be involved in adaptation to different hosts V. dahliae, some population-genetic evidence suggests that recombination may occur or has occurred in the past. First, high diversity of multilocus genotypes and the presence of all four dilocus genotypes (the four-gamete test) for some pairs of microsatellite loci were interpreted as evidence of recombination in V. dahliae334 and 2B824het genotype (which defines a VCG) arising independently in different lineages by mutation cannot be estimated for V. dahliae because the underlying genetics of VCGs and mutation rates at het loci are not known. In another discrepancy, subgroups of VCGs 2 and 4 (namely A and B) occur in distinct phylogenetic lineages defined by molecular markers 824 is more closely related to VCG4A than it is to VCG2A (or to VCG2B334)  . This paAspergillus species MATA-HMG genes V. dahliae, mating-type idiomorphs have been identified (see above) and evidence of repeat-induced point (RIP) mutations was found in a small subset of transposable elements in V. dahliae.Analyses of fungal genomes have revealed molecular “signs of sex” V. dahliae arose by recombination. In addition to lineages VCG2B334 and VCG2B824, and subgroups A and B of VCGs 2 and 4 discussed above, we looked for evidence of recombination within and between most of the known VCGs and lineages. A second objective was to determine the mating types of V. dahliae isolates in the major lineages. A third objective was to search for homologs of meiosis-specific genes in the V. dahliae genome. We used 26,748 SNPs obtained from 141 isolates of V. dahliae by genotyping by sequencing V. dahliae genome with known locations on chromosomes in a reference genome, allowing us to infer the number and locations of recombination events.The primary objective of this study was to test the hypothesis that clonal lineages of V. dahliae isolates from diverse geographic and host origins, representative of all but one of the known V. dahliae VCGs . DNA was extracted from ground mycelia using the Kit i-Genomic Plant DNA Extraction Mini Kit . DNA purity and concentration were determined using a Nanodrop spectrophotometer ND-1000  and by 1% agarose gel electrophoresis.Each isolate was cultured for DNA extraction as follows. Isolates were grown on modified sodium polypectate agar (SPA) selective for http://www.igd.cornell.edu/index.cfm/page/GBS.htm) for genotyping by sequencing (GBS) as described by Elshire et al. http://www.broadinstitute.org. Only sequences that aligned to the reference genome could yield SNPs; all other sequences were discarded. SNPs were called based on differences among the isolates genotyped, not relative to the VdLs.17 genome sequence. Data were filtered by IGD for 80% coverage in all isolates per nucleotide site and a minimum minor allele frequency of 1%. Additional filtering was done using Tassel 4.0 (http://www.maizegenetics.net/), by recoding heterozygous sites  as missing data, and removing isolates with data at <90% of the scored nucleotide sites. Exploratory analysis was done by constructing a neighbor-joining tree Purified DNA was submitted to the Cornell University Institute for Genomic Diversity (IGD) (We used Recombination Detection Program v.3.44 (RDP3) to identify unique recombination events Recombination analyses use only polymorphic nucleotide sites in sequence alignments, effectively ignoring all monomorphic sites. Therefore, we used SNP haplotype data with the rationale that SNPs ordered according to their positions on supercontigs in the VdLs.17 sequenced reference genome (hereafter referred to as contigs) are a proxy for actual sequence data. We used all the nucleotide sites called in the initial genotyping dataset for recombination analyses, which upon closer analysis included many monomorphic sites see . We retaY = 2 ln4X/L, where Y is the minimum pairwise distance between haplotypes, X is the number of haplotypes and L is the length of the sequence . Recombination analyses were also done using a reduced set of isolates for each contig separately and for contigs on each of the eight chromosomes in the V. dahliae genome concatenated.For analysis of recombination among lineages, we used a subset of haplotypes to avoid duplication of nearly identical haplotypes. Analysis of highly similar haplotypes results in detecting the same recombination events multiple times, but does not increase the likelihood of detecting additional unique recombination events . We used a minimum pairwise distance between haplotypes of 0.00017, . This criterion was calculated from the formula in the RDP3 Instruction Manual: V. dahliae isolate was determined by multiplex PCR using primers pairs VdMAT1-1a/VdMAT1-1b and VdMAT1-2a/VdMAT1-2b MAT1-1 and MAT1-2 idiomorphs, respectively. PCR reaction mixtures (20 µl) included 20 ng of fungal DNA template, 100 pmol of each primer and 10 µl of Premix Ex Taq Hot Start Version (Takara Bio). Amplifications were performed in a PTC-100 thermocycler  using the following conditions: denaturation at 94°C for 3 min followed by 30 cycles of 30 s at 94°C, 30 s at 65°C, 1 min at 72°C, and a final step of 72°C for 3 min. The PCR products were separated on 1% agarose gels stained with ethidium bromide and visualized under UV light. A set of reactions always included negative controls (no DNA), and positive controls using template DNA from V. dahliae isolates known to carry MAT1-1 (isolate W83) or MAT1-2 (isolate 461) idiomorphs The mating type idiomorph in each V. dahliae genome of isolate VdLs.17 Aspergillus fumigatus or Saccharomyces cerevisiae as homologs with known meiosis-specific function A. fumigatus genome of isolate Af293 were obtained from GenBank HOP2 in the A. fumigatus genome, we used a sequence for this gene from S. cerevisiae. V. dahliae candidate genes were identified by blastp analysis through the Broad Institute website (http://www.broadinstitute.org). Candidate homologs were determined using reciprocal best hit analysis. Resulting proteins were then analyzed using the Conserved Domains tool on NCBI.We searched for homologs of meiosis-specific genes in the V. dahliae. After filtering out polymorphic sites caused by missing or ambiguous data, and isolates with too much missing data, we were left with 26,748 SNPs in 141 isolates  that had SNP haplotypes in lineage 2A. VCG assays and GBS were repeated to confirm these discrepancies.Neighbor-joining analysis on 26,748 SNPs resulted in a tree  with almR1  compared to other haplotypes in the same lineage were also tested for recombination. In total, 26 haplotypes were used in recombination analyses among all lineages.Observation of haplotypes in intermediate positions between well-defined clades in the neighbor-joining tree suggests that they could be recombinants. Most prominently, we found an intermediate clade with haplotypes of two isolates in VCG2B between clades I and II; we named this lineage 2BR1 . Other pr = 0.95) and contig length .From these analyses we conclude that recombination occurred between clades. Because of the phylogenetic incompatibility inherent in recombination, a bifurcating tree, such as the one generated from neighbor-joining analysis , is not S) and nucleotide diversity (π) within each lineage were determined using Tassel 4.0 for each of the nonrecombining clades   . Estimativersity . The lowMAT1-2. Both isolates in lineage 6 and isolate 395 in lineage 2A have mating-type MAT1-1. Interestingly, these MAT1-1 isolates were shown to be the result of recombination , as found previously, where MAT1-1 is rare V. dahliae suggests that meiosis may be impaired during sexual reproduction V. dahliae populations experience frequent recombination between lineages at the present time. Nonetheless, new VCGs/lineages are occasionally found, e.g., lineage 6/VCG6, as described above. Overall, we conclude that recombination may not occur frequently in V. dahliae, but it has the potential to give rise to new clonal lineages.Despite the evidence of recombination and the inference that lineages of V. dahliae. Although there is no known sexual stage for V. dahliae, the presence of both mating-type idiomorphs and homologs of meiosis-specific genes strongly suggest that V. dahliae has either lost sexuality recently or maintained the potential to reproduce sexually. This type of evidence has been used previously to infer the potential for sex in other fungi, most notably in the Glomeromycota V. dahliae in this study have mating type MAT-2. The three individuals with MAT1-1 were identified independently as recombinants. Mating type segregates in 1∶1 ratios during sexual reproduction in heterothallic ascomycetes. By chance, three recombinants have the less common mating type, MAT1-1, whereas other recombinants haplotypes have MAT1-2.A crucial question that remains to be answered is the mechanism of recombination in V. dahliae isolate VdLs.17. All of these homologs had conserved or partially conserved protein structures as those found in A. fumigatus and S. cerevisiae  were not found in VdLs.17. However, these four genes are not found in the genomes of N. crassa, Sordaria macrospora or Podospora anserina in the Sordariomycetes (like V. dahliae), or in Ustilago maydis (Basidiomycota) We also found homologs of eight meiosis-specific genes in the reference genome of revisiae . Such coV. dahliae because of extensive genomic rearrangements V. dahliaeSome authors have speculated that meiosis is unlikely to occur in V. dahliae334, 2B824 and 2BR1) occurred within chromosomes, with at least one event on each of the eight chromosomes. In lineage 2A, 22 recombination events were found within seven of the eight chromosomes. All recombination within lineage 2A was attributable to three recombinant haplotypes , all of which were also associated with recombination between lineages 2A and 4B, mostly likely by sexual reproduction between VCGs. Within-chromosome recombination by mitotic crossing over is rare in parasexuality V. dahliaeParasexuality is an alternative, non-meiotic mechanism of recombination that has been demonstrated in laboratory studies of many ascomycetes V. dahliae is primarily clonal, the entire genome is effectively linked and affected by hitchhiking selection. Therefore, reduced diversity within lineages could be caused either by hitchhiking selection or by a recent origin after which few neutral mutations have occurred.We cannot determine the time since the origin of clonal lineages with any confidence using methods of molecular evolution. Many SNPs are shared between lineages because of recombination, whereas dating techniques are typically based on fixed differences between taxa. Comparisons of nucleotide diversity in nonrecombining lineages  could potentially show their relative ages. However, estimates of nucleotide diversity are confounded by differences in sampling among lineages. Sample sizes and their geographic and host distributions varied among lineages, which are likely to affect estimates of diversity. Dating techniques depend on estimates of mutation rates–which are not known for most fungi. More importantly, molecular clocks are based on assumptions of neutrality. The small number of clonal lineages with widespread geographic distributions, together with limited diversity within lineages, is consistent with a history of selective sweeps. Because V. dahliae determined by 26,748 SNPs is highly clonal, with the exception of a small number of recombinant haplotypes Click here for additional data file.Table S2SNP data in hapmap format. Data for 26,748 SNPs are provided in hapmap format which can be read into Tassel 4.0 (http://www.maizegenetics.net/) to find the locations relative to the reference genome of isolate VdLs.17.(TXT)Click here for additional data file.Table S3SNP data in Phylip format. Data for 26,748 SNPs are provided in Phylip interleaved format for analyses standard software.(PHY)Click here for additional data file.Table S4Number of SNPs and recombination events per contig. Number of SNPs and recombination events for each supercontig relative to the Verticillium dahliae reference genome for isolate VdLs.17.(DOCX)Click here for additional data file.


YWHAZ, KRTCAP3, TSPEAR, PIK3R4, KIF16B, PTPN3, GPRC5A, DDX47, TCF9, TPTE2, EPHA5 and NBEA genes. Our results not only confirm the results of previous reports, but also provide a suite of novel SNP markers and candidate genes associated with wool traits. Our findings will be useful for exploring the genetic control of wool traits in sheep.Genome-wide association studies (GWAS) provide a powerful approach for identifying quantitative trait loci without prior knowledge of location or function. To identify loci associated with wool production traits, we performed a genome-wide association study on a total of 765 Chinese Merino sheep (JunKen type) genotyped with 50 K single nucleotide polymorphisms (SNPs). In the present study, five wool production traits were examined: fiber diameter, fiber diameter coefficient of variation, fineness dispersion, staple length and crimp. We detected 28 genome-wide significant SNPs for fiber diameter, fiber diameter coefficient of variation, fineness dispersion, and crimp trait in the Chinese Merino sheep. About 43% of the significant SNP markers were located within known or predicted genes, including  Ovis aries) are one of the earliest domesticated animals. Sheep provide humans with a source of meat, milk, wool, and skins, and play a vital role in the global agricultural economy. The Merino sheep is an economically influential breed of sheep prized for its wool. Merino wool is regarded as the finest and softest wool of any sheep. Wool quality traits, such as fiber diameter, length and strength, are important goals in Merino breeding programs. Most countries have high-quality performance measurement programs and well-developed tools for index selection using best linear unbiased prediction (BLUP). However, in the field of sheep breeding for high wool quality, it is difficult to accurately evaluate the genetic component for wool quality traits since the phenotypes of these traits are difficult to be measured.Sheep  KRTAP6 gene (keratin-associated protein 6) PROP1 gene (PROP paired-like homeobox 1) ADRB3 (beta3-adrenergic receptor) Over recent decades, advances in DNA-based marker technology have made it possible to identify genomic regions or quantitative trait loci (QTLs) underlying complex traits, such as fiber diameter, in Merino sheep. Instead of traditional animal breeding programs solely relying on phenotype and pedigree information, the incorporation of detected QTLs into genetic evaluation has the potential to enhance selection accuracy, thereby expediting the genetic improvement of animal productivity. A number of papers have been published concerning the detection of QTLs for wool traits. To date, 31 QTLs for wool traits have been reported via genome scan based on marker-QTL linkage analyses (With the advent of genome-wide panels of single nucleotide polymorphisms (SNPs), it has become possible to identify and localize QTLs for complex traits in many livestock species Chondrodysplasia  DMP1 gene was identified to be associated with inherited rickets of Corriedale sheep by a GWAS To date, only a small number of GWASs in sheep have been conducted because of limited information available for the sheep genome. These studies mainly focused on diseases RXFP2. García-Gámez et al.. LALBA gene influencing milk protein percentage in dairy sheep. Zhang et al.. MEF2B, RFXANK, CAMKMT, TRHDE, and RIPK2. Nevertheless, no GWAS for sheep wool traits has been performed.Johnston et al.. The main objective of this study was to detect significant SNP loci for wool traits on a genome-wide scale using the Illumina sheep SNP50 BeadChip, and to explore potential causal genetic variants and major candidate genes for wool traits.All animal work was conducted according to the guidelines for the care and use of experimental animals established by the Ministry of Science and Technology of the People's Republic of China  and was approved by the Laboratory Animal Management Committee of Northeast Agricultural University.A total of 765 Chinese Merino sheep (JunKen type) used in this study came from the Xinjiang Academy of Agricultural and Reclamation Science. These sheep were from six strains of Chinese Merino sheep (strain A (n = 159), strain B (n = 103), strain U (n = 35), strain D (n = 145), strain DR (n = 135) and strain X (n = 188)). All of these sheep were located in the same farm. We measured and recorded five wool production traits: fiber diameter, fiber diameter coefficient of variation, fineness dispersion, staple length and crimp. The descriptive statistics of the phenotypic measurements of the sheep used for the GWAS are given in Genomic DNA extraction from sheep ear tissue was performed by the phenol-chloroform method. The DNA was stored at −20°C. Genotyping of the Sheep 50 K SNP chips from Illumina Inc. was performed by DNA LandMarks Inc.  using 75 µL of approximately 50 ng/µL genomic DNA. BeadStudio software with the genotyping module  was used to determine the genotypes of the individuals used in the current study.Following quality control, 47,286 SNPs with minor allele frequencies (MAF) of 5% or greater and call rates of 95% or greater were selected for use in this study. These SNPs were distributed across 26 autosomes and the X chromosome, with the number of SNPs per chromosome ranging from 640 to 5161, and with a mean distance between adjacent SNPs ranging from 53.4 to 100.7 kb  P-value was <0.05/N, here N is the number of SNP loci tested in the analyses. In this study, for each trait, the threshold P-value for declaring genome-wide significance was (0.05)/47,286 = 1.06×10−6 = 10−5.97 .The Bonferroni method was used to adjust for multiple testing from the number of SNP loci detected. We declared a significant SNP at the genome-wide significance level if the raw Sheep transcripts and annotations were downloaded from the sheep UCSC database (build 3.0). In addition, the human-sheep comparative map and the syntenic regions annotations were also downloaded from the UCSC database.The global view of P-values ) for all SNP markers of each trait are represented by a Manhattan plot shown in As shown in TSPEAR (Thrombospondin-type laminin G domain and EAR repeat), PIK3R4 , KRTCAP3 (Keratinocyte-associated protein 3) and YWHAZ (of the 14-3-3 family of proteins) genes. The others were located 6.6 to 396.1 kb away from the nearest known gene.Four of the 9 significant SNP markers were located within known or predicted genes including the KRTCAP3 gene is expressed in skin keratinocytes KRTCAP3 gene.The YWHAZ gene product belongs to the 14-3-3 family of proteins, which mediate signal transduction by binding to phosphoserine-containing proteins. The encoded protein interacts with the IRS1 protein, suggesting a role in regulating insulin sensitivity. The human YWHAZ gene, located on chromosome 8q22.3, is expressed in HNSCC (head and neck squamous cell carcinomas) cases. The YWHAZ mRNA is frequently upregulated in tumor tissues. Furthermore, the YWHAZ gene-specific RNAi significantly suppressed the growth rate of HNSCC cell lines, and overexpression of YWHAZ in HaCaT immortalized human skin keratinocytes promoted overgrowth, as well as morphological changes YWHAZ increased the proportion of cells in G1/G0-phase, and decreased the proportion in S-phase and the rate of DNA synthesis. Consequently, Lin et al.. suggested that YWHAZ is a candidate proto-oncogene YWHAZ gene.The CCNY gene controls cell division cycles and regulates cyclin-dependent kinases. Franke et al.. found that SNP marker rs3936503 in the CCNY gene on chromosome 10p11.2 was related to Crohn's disease, based on a sample of 1850 German Crohn's disease patients and 1,817 controls CCNY gene from 6.6 kb.The The functions of all of the above genes are directly or indirectly related to skin development. Hair follicles are skin appendages and produce hair; therefore, we hypothesize that these genes control hair follicle development and fiber diameter trait.On chromosomes OAR1, OAR5, OAR6, OAR13 and OAR25, 7 QTLs were reported to be related to fiber diameter KIF16B gene; the others were not located within known or predicted genes. SNP marker OAR1_293501261.1 on OAR1 is 141 bp downstream from the 3′ end of the closest gene, Mir563. Target gene search using TargetScan Mir563 has 36 target genes in human, including FBN1 (fibrillin 1), KIAA0831 .As shown in No significant SNPs were detected for staple length trait. However, several papers have been published concerning detection of QTLs for the staple length trait, and seven were detected on chromosomes 3, 7, 14, 15, 18 and 25. Ponz et al.. typed 40 microsatellites covering 20 autosomes on the synthetic breed INRA401 population, which is a composite Romanov (prolific breed) and Berrichon du Cher (meat breed), and detected three QTLs on chromosomes 3, 7 and 25 related to staple length PTPN3 (Tyrosine-protein phosphatase non-receptor type 3), TCF9 (GC-rich sequence DNA-binding factor 2), GPRC5A , DDX47 (DEAD (Asp-Glu-Ala-Asp) box polypeptide 47), EPHA5 (EPH receptor A5), TPTE2 (transmembrane phosphoinositide 3-phosphatase and tensin homolog) and NBEA (neurobeachin), and the other SNPs were located 26.2 kb to 379.1 kb away from the nearest known genes.Fifteen significant SNPs were detected . The encoded protein SH3BP5L interacts with YWHAZ protein YWHAZ gene harbors the marker OAR9_80743202.1, which was significantly associated with sheep fiber diameter in this study.The As mentioned above, none of these genes plays an obvious role in the crimp trait. But, the functions of all of the above genes are directly or indirectly related to epithelial cells or skin development. So we presume these genes may be involved in determining the crimp trait. A future study will investigate the biological functions of these genes.In the present study, we detected 28 genome-wide significant SNPs for fiber diameter, fiber diameter coefficient of variation, fineness dispersion and crimp trait in a Chinese Merino sheep (JunKen type) populations. Some significant SNP markers were located within previously reported candidate genes. However, most of the candidate genes and SNP markers, for the first time, were reported as related to wool production traits. Our findings lay the basis for follow-up replication studies, which will reveal the causal mutations underlying wool production traits in Merino sheep.Table S1Distributions of SNPs after quality control and the average distances between adjacent SNPs on each chromosome.(DOC)Click here for additional data file.Table S2All targeted genes by Mir563 in human.(DOC)Click here for additional data file.


Though numerous polymorphisms have been associated with risk of developing lymphoma, how these variants function to promote tumorigenesis is poorly understood. Here, we report that lymphoma risk SNPs, especially in the non-Hodgkin’s lymphoma subtype chronic lymphocytic leukemia, are significantly enriched for co-localization with epigenetic marks of active gene regulation. These enrichments were seen in a lymphoid-specific manner for numerous ENCODE datasets, including DNase-hypersensitivity as well as multiple segmentation-defined enhancer regions. Furthermore, we identify putatively functional SNPs that are both in regulatory elements in lymphocytes and are associated with gene expression changes in blood. We developed an algorithm, UES, that uses a Monte Carlo simulation approach to calculate the enrichment of previously identified risk SNPs in various functional elements. This multiscale approach integrating multiple datasets helps disentangle the underlying biology of lymphoma, and more broadly, is generally applicable to GWAS results from other diseases as well. Lymphoma, including the non-Hodgkin’s lymphoma subtype chronic lymphocytic leukemia (CLL), was responsible for more than 130,000 new cases of cancer and 44,000 deaths in 2014 . CollectWe have previously observed that single nucleotide polymorphisms (SNPs) found in evolutionary conserved regions and in regions epigenetically marked for transcriptional regulation are more likely to be under negative selection in humans, suggesting biological function . Others Encyclopedia of DNA Elements) [Uncovering Enrichment through Simulation), that uses a Monte Carlo approach to test whether a set of SNPs is significantly enriched for a particular functional genomic annotation of the genome, taking linkage disequilibrium (LD) patterns into account. We demonstrate a significant enrichment of these lymphoma risk SNPs in regulatory marks specific for lymphoid tissue.To test the hypothesis that lymphoma risk SNPs, or their LD partners, tend to alter regulatory elements, we interrogated the functional genomics data from ENCODE (lements) , 23 and lements) consortialements) , 26. To st qu = 0.244, 3rd qu = 0.274, max = 0.347). We first looked at the Deoxyribonuclease I (DNase I) hypersensitivity sites (DHSs) for GM12878, since genomic regions open to DNase digestion have been shown to be accurate markers of regulatory DNA[We first asked if lymphoma risk SNPs are enriched in regions annotated as putatively regulatory in GM12878 using our novel method, UES . Using tAs some physical regions of the genome harbor more than one independent risk SNP, we were concerned this could lead to oversampling of a given region and false positives. To test this, we location pruned our input SNPs, ensuring that none of the SNPs tested were within one megabase of one another, reducing the input set from 36 initial SNPs down to 30 SNPs. We observed nearly identical results between the location pruned dataset and the original dataset  and concWe next asked if enrichment could be observed in regulatory elements predicted by genome segmentation of integrated functional genomics data. Rather than querying individual histone modifications, we chose to examine the results from two different segmentation algorithms, ChromHMM and Segw2 > 0.8 since this was the threshold during the design of the GWAS chips. In order to test whether our cutoff was appropriate, we performed the enrichment analysis using additional r2 thresholds of r2>0.2, r2>0.4, r2>0.6, and r2 = 1. The results showed nearly identical enrichment of the same cell lines and tissue specificity for both DNase hypersensitivity marks and segmentation data . Using the same SNPs, we interrogated the set of ENCODE ChIP-Seq data for GM12878 (January 2011 data freeze). We created a master dataset consisting of the union of 75 GM12878 transcription factor ChIP-Seq data and observed a significant enrichment of the lymphoma SNPs in the peaks when compared to the random controls . Our hypothesis is that since the diseases do not share a tissue of origin, there should be no enrichment of the breast cancer SNPs in the GM12878 annotations. We did not observe statistical enrichment  of the breast cancer risk loci in DNase hypersensitivity, ChromHMM enhancer, Segway enhancer data, or TF union data for GM12878 . We expaWe next asked if the observed enrichments were specific to cells of the lymphoid lineage. First, we interrogated the other 124 unified DNase tracks from ENCODE with the same GWAS and random SNP sets used for the GM12878 analysis and observed enrichment of 8 additional cell lines that achieved Bonferoni-corrected significance with p<0.0004. Interestingly, all of the lines that showed enrichment at that level were of the lymphoid lineage . When weWe next performed a similar analysis on the chromatin segmentation data. We analyzed eight additional cell lines with ChromHMM segmentation data and five additional lines with Segway segmentation data for enhancer classes. For both the ChromHMM and Segway strong enhancer classifications, GM12878 was the only cell line that showed strong, significant enrichment with p = 0.0002 and p<0.0001 for each dataset, respectively . When loIt has been previously noted that, under some models in which the functional variants underlying GWAS are not regulatory, enrichment of GWAS-identified SNPs in regulatory regions could occur if proper controls are not used. While our method controls for the major factors that need to be controlled for (LD patterns and distance from transcription start), we nevertheless asked if a similar enrichment could be observed with an alternative approach, GoShifter, that shifts annotations at the associated loci to test the significance of enrichment. This approach identified 5 cell lines that showed enrichment for the risk SNPs. Notably, 4 of these 5 lines that showed enrichment at p < 0.05– GM19238, Th0, Cd20, and GM06990 –are from the lymphoid lineage .2 = 0.978, D’ = 0.996), was found in a DNase hypersensitivity site of GM12878, which was categorized as falling in an “Active Promoter” by ChromHMM or a “Transcription Start Site” by Segway. Even as both the original SNP, rs7097, and the tag, rs694609, showed evidence of being cis-eQTLs for the same genes , our pipeline would suggest that the tagged SNP is the more likely candidate to be the functionally relevant SNP as it is found in open chromatin and in functional marks  using a recently published set of eQTLs in blood. We obseal marks .We have shown significant enrichment of previously reported lymphoma and CLL risk SNPs in regulatory elements in lymphoblastoid cell lines, pointing to the tissue specific manner through which these genetic loci may confer increased risk for lymphomagenesis. Looking specifically at the analyses in GM12878, we saw this enrichment in DNase hypersensitivity loci as well as numerous enhancer sites; we did not observe enrichment when we looked at risk loci for other cancer types. We have also identified candidate functional SNPs that co-localize with these genomic marks and have also been shown to be eQTLs in published blood datasets.We were able to perform these analyses because there are significant amounts of functional genomic data available for the cell line GM12878. Taking a deeper at those results, we note a similar number of loci for which a candidate functional SNP can be found in DNase regions, ChrommHMM-Strong Enhancers, and Segway-Strong Enhancers . While none of these 3 datasets were complete subsets of each other, there is significant overlap . HoweverRUNX3 is a gene which is highly expressed in LCLs [TBLR1 (p = 0.0005); disruptions at the TBLR1 locus in diffuse large B-cell lymphoma have been seen through a deletion of the locus [TP63, a paralogue of TP53 [We believe that these enrichment studies can provide valuable insight into the potential etiology of the disease of interest. For example, looking at the enrichment of lymphoma SNPs in ChIP-Seq data, we see an enrichment of risk SNPs in RUNX3 binding sites(p<0.0001).  in LCLs  and has  in LCLs . We also in LCLs . Lymphomhe locus  and the  of TP53 .It should be no surprise that we observed an enrichment of lymphoma risk-SNPs in the DNase hypersensitivity sites for multiple B-cells lines, as a majority of the reported SNPs are from studies on CLL. However, we also see an enrichment in lymphoid but non-B cell types such as adult CD4+ Th0 cells (p < 0.0001). If we slightly relax the stringent definition of significance and look to those lines that just failed to meet statistical significance, such as p < 0.001, the vast majority of those cells are also T-cells. These data makes sense since the B-cell and T-cells lines share a common lineage. This hypothesis is further supported as a hematopoietic progenitor cell line, CD34+ Mobilized cells, was approaching significance in the DNase hypersensitivity enrichment analysis (p = 0.016). This type of analysis could also reveal novel insight into how different cell-types work in concert to lead towards the progression of a disease. For example, in our own results we saw an enrichment of lymphoma risk-SNPs in the DNase hypersensitivity track of human renal epithelial cells (DNase: p = 0.0004). A literature search revealed that there is a known, rare disease–primary renal lymphoma–with 31 reported cases through 1991. Little We acknowledge that there are pipelines similar to our UES algorithm that are already deployed and perform in similar ways. However, the UES pipeline makes improvements on those methods through the controlling for the input SNPs based on the initial SNP chip the study was done on, the distance a SNP is from a TSS, and the number of LD partners to compute an empirical p-value. Though Maurano et al. were ablMore recently, Trynka et al.  discuss Overall, the data presented support the hypothesis that regulatory variants that influence transcription in cells of the lymphoid lineage contribute to inherited risk of lymphoma and chronic lymphocytic leukemia. These results validate our computational approach that, moving forward, could provide novel insight into disease etiology when applied to other diseases.2 thresholds: r2>0.2, r2>0.4, r2>0.6, r2>0.8, and r2 = 1. Furthermore, LD was calculated between both SNPs and indels in the 1000genomes database. We excluded 79364 SNPs or indels that had duplicate start positions, and all the remaining variants were used in the simulations. Each of the initial SNPs is then categorized by its distance from the nearest transcription start site (TSS) and its number of LD partners. Quartiles for both the TSS distance and LD partner count are calculated separately, and the initial SNPs are binned accordingly. The number of each of the initial SNPs contained in each bin (characterized by distance from TSS and LD partner count) is recorded and used for subsequent random SNP set selection. Upon completion of this step, all of the SNPs from the appropriate genotyping platform are loaded (excluding the HLA region) and binned according to the initial SNP criteria. Since it has been shown that disease-associated SNPs have a higher MAF than expected by chance[2>0.8). All the data have been pre-calculated and are retrieved using Tabix[rloci = the number of instances when the frequency of co-localization of the random SNP sets with the feature > = the number of loci that co-localize with the feature for the initial input set of SNPs, and n = the number of random-SNP sets chosen. Using the Bonferoni method of multiple test correction, the p-value was considered significant if p < 0.05/(number of tracks tested for enrichment). The current pipeline and subsequent versions are available for download from the Klein lab’s website http://research.mssm.edu/kleinlab/ues/We developed a computational pipeline entitled “Uncovering Enrichment through Simulation” (UES) to test if GWAS-identified SNPs are enriched in particular functional annotations through use of Monte Carlo simulations. The pipeline  is writtby chance, we filting Tabix. The scring Tabix in order2 > 0.8), the SNP with the lower reported p-value was kept. Initially, 56 CLL & lymphoma SNPs were entered into the pipeline, and once the HLA region was excluded, there were 36 SNPs used for the remainder of the analysis . The ChromHMM track was also downloaded from ENCODE (http://hgdownload-test.cse.ucsc.edu/goldenPath/hg19/encodeDCC/wgEncodeAwgSegmentation/), after which a Perl script was used to extract the active promoter, strong enhancer, and weak enhancer regions, or combine the active promoter and strong enhancer regions into a combination track. The Segway segmentation was downloaded directly from the Noble lab’s website and was modified in the same way as described for the ChromHMM data (http://noble.gs.washington.edu/proj/segway/).The ENCODE datasets were obtained directly from the ENCODE Consortium’s website. The DNase hypersensitivity analysis was performed using the ENCODE Consortium’s “unified DNase hypersensitivity” tracks (ftp://ftp.ncbi.nlm.nih.gov/pub/geo/DATA/roadmapepigenomics/). The segmentation data was processed in the same manner as described for the ENCODE segmentation data.The Roadmap Epigenomics data were downloaded from the consortium’s FTP website (2>0.8). Locus boundaries were defined by the most downstream and upstream LD SNP and extended by two times the median size of a tested annotation. We then circularized the locus and allowed annotations to randomly shift in 10,000 iterations. For each of the shifting iterations we quantified the number of loci at which a variant overlapped with an annotation. The reported p-value corresponds to the number of iterations where enrichment exceeded the observed value[We also used the new software GoShifter to test for enrichment. Using European samples from the 1000 Genomes dataset (v3) we first iterated over each locus to identify all the variants in tight LD Click here for additional data file.S2 Table2>0.8 threshold, as reported in the body of the paper. The “location pruned pValue” is the reported p-value for the rerun of the analysis using the input data where SNPs were removed within one megabase of one another.  were obtained directed from ENCODE’s description of the cells used. The “OrigLoci” column gives the number of loci (once the SNPs are collapsed into loci based on LD partners) for the input lymphoma & CLL SNPs that overlapped with the specific mark. The “Rand> = Orig” column is the number of times a random SNP file had greater than or equal to the number of loci co-localizing with the particular mark. The “Random_Avg” column is the average of the 10,000 random generated SNP sets and the loci that overlap with the mark. The “pValue”is calculated by taking the number of random SNP sets that were greater than or equal to the input SNPs divided by n, in this case, 10,000. The table is sorted according to p-value at the rher. See . The las(XLSX)Click here for additional data file.S3 Table2>0.8.Sorted according to p-value for r(XLSX)Click here for additional data file.S4 TableWe ran the UES pipeline for each of the segmentation tracks, both ChromHMM and Segway, for GM12878. These data are calculated and represented in the same way as (XLSX)Click here for additional data file.S5 TableThe UES pipeline was run to calculate the enrichment of the lymphoma risk SNPs with with transcription factor binding data in GM12878. The “Gm12878 ChipSeq” Union track was created by merging all of the ChipSeq tracks for GM12878. That track was subsequently merged with the DNase hypersensitivity track of GM12878 to create the “Gm12878 DNase & Gm12878 ChipSeq Union”. The “All LCL Union” track was constructed in a similar manner by merging all of the DNase hypersensitivity tracks for the 10 LCLs in the ENCODE database. The “All LCL Union & Gm12878 ChipSeq Union” track was created by merging both of those union tracks. These data are calculated and represented in the same way as (XLSX)Click here for additional data file.S6 TableThese data are calculated and represented in the same way as (XLSX)Click here for additional data file.S7 Table“Strong Enhancer” tracks (combination tracks made of strong enhancer states 4 & 5) for all the ChromHMM cell types. These data are calculated and represented in the same way as (XLSX)Click here for additional data file.S8 Table2>0.8.Sorted according to p-value for r(XLSX)Click here for additional data file.S9 Table2>0.8.Sorted according to p-value for r(XLSX)Click here for additional data file.S10 Table(XLSX)Click here for additional data file.S11 TableSummary of all original lymphoma & CLL SNPs and their LD partners, along with information on whether or not any of these SNPs were found to be in DNase hypersensitivity sites, active promoters (for both ChromHMM and Segway), or strong enhancers (ChromHMM and Segway). All of the tracks are for GM12878. The final column describes the eQTL status of the as seen in Westra et al.(XLSX)Click here for additional data file.


SNP–ureide associations within and across the four environments were assessed using 33,957 SNPs with a MAF ≥0.03. In total, 53 putative loci on 18 chromosomes were identified as associated with ureide concentration. Two of the putative loci were located near previously reported QTL associated with ureide concentration and 30 loci were located near genes associated with ureide metabolism. The remaining putative loci were not near chromosomal regions previously associated with shoot ureide concentration and may mark new genes involved in ureide metabolism. Ultimately, confirmation of these putative loci will provide new sources of variation for use in soybean breeding programs.Ureides are the N-rich products of N-fixation that are transported from soybean nodules to the shoot. Ureides are known to accumulate in leaves in response to water-deficit stress, and this has been used to identify genotypes with reduced N-fixation sensitivity to drought. Our objectives in this research were to determine shoot ureide concentrations in 374 Maturity Group IV soybean accessions and to identify genomic regions associated with shoot ureide concentration. The accessions were grown at two locations  in 2 yr (2009 and 2010) and characterized for ureide concentration at beginning flowering to full bloom. Average shoot ureide concentrations across all four environments (two locations and two years) and 374 accessions ranged from 12.4 to 33.1 µmol g A genome-wide association study (GWAS) is an alternative to QTL mapping of biparental populations, is widely used in human genetics, and is increasingly used in plant studies. The GWAS approach typically requires a high density of single-nucleotide polymorphisms (SNP) across the genome of a large number of individuals, as well as phenotyping of all individuals in the study. Significant statistical associations are then determined between SNP alleles and the trait phenotype. Soybean traits analyzed by GWAS include mineral deficiency , seed traits , disease resistance (cyst nematode and soybean mosaic virus), environmental responses , as well as seed yield and yield components  associated with the traits of interest. However, it is expected that these regions will require independent validation. Nonetheless, reducing the genome to fewer regions of interest for further study is a significant accomplishment. In a recent GWAS study on soybean protein and oil (Phaseolus vulgaris L.), there was a greater concentration of ureides in shoots of drought-sensitive genotypes than drought-tolerant genotypes, which is similar to results for soybean  are the N-rich products of N-fixation that are transported from soybean nodules to the shoot. Considerable research has focused on the metabolism of ureides, especially with regard to drought. Ureides have long been thought to play a role in the sensitivity of N-fixation to drought that may involve a feedback inhibition resulting from accumulation of ureides in leaves and nodules during water-deficit stress  and the University of Arkansas (http://www.uaex.edu/publications/pdf/mp197/chapter5.pdf). Furrow irrigation was applied to experiments S09 and S10 as needed and experiments C09 and C10 were grown without irrigation .Field experiments were conducted at two locations  over 2 yr (2009 and 2010). In Columbia, the experiments were conducted at the Bradford Research and Extension Center  and in Stuttgart, they were conducted at the Rice Research Experiment Station . For the purpose of analysis and discussion, each year and location was treated as a separate environment and designated as C09, C10, S09, and S10 for Columbia and Stuttgart in 2009 and 2010. Experimental details were described by Glycine max (L.) Merr.] MG IV accessions were sown in a randomized complete block design with three replications in all four environments . The accessions evaluated were obtained from the USDA-ARS Germplasm collection based on GRIN  data and with the assistance of the collection curator, Dr. Randall Nelson. The selected accessions were all MG IV genotypes with seed yield >1.7 Mg ha−1 and good agronomic traits . Because SNP data on the germplasm collection were not yet available at the time when entries were selected, genetic diversity was estimated by considering country and province of origin in proportion to the number of entries from that source in the germplasm collection. For the ureide trait described in this analysis, data on 374 of the 385 accessions are reported and analyzed.As reported by g for 5 min. Fifty to 100 µL of the supernatant was analyzed for ureides using the colorimetric procedure of The above-ground portions of five plants chosen at random from each plot were harvested between beginning bloom (R1) to full bloom (R2) [stages according to Supporting Information, File S1.In each environment , the experimental design was a randomized complete block. Ureide values were log-transformed to equalize variance among environments. ANOVA was conducted on the log-transformed values within and across environments using a general linear mixed model with environments and accessions treated as random effects. Additionally, accession mean ureide concentrations were obtained using BLUP (best linear unbiased prediction) predictors by Proc GLIMMIX of SAS . Ureide phenotypic means are shown in www.soybase.org). Brief instructions for obtaining the SNP data are shown in File S2. For the 374 accessions, 33,957 SNPs had a minor allele frequency (MAF) of ≥3%, which was the threshold for inclusion in the analysis reported herein. This threshold was chosen to facilitate the identification of rare genotypes. BLUP predictors of ureide accession means derived for each individual environment, and also across all environments, were used for genome-wide association analysis. Linkage disequilibrium (LD) was calculated using all SNPs with a MAF ≥3% among the 374 soybean accessions and distributed over the 20 soybean chromosomes. Calculation of pairwise LD (r2) among SNPs was based on SNPs within a 1-Mb window using PLINK  improved the description of variance between genotypes in all environments as compared to a model without adjustment for genetic relatedness. Both matrices were generated using all 33,957 SNPs with a MAF ≥3%. These matrices were used with the Q-K Mixed model procedure (PROC GLIMMIX) to test for association between ureide concentration and SNP while simultaneously adjusting for population structure and genetic relatedness  was used to perform the genome-wide association analysis and to generate covariate matrices to account for population structure (Q-matrix) and genetic relatedness (K-matrix). First, the K-matrix was generated using allele sharing similarity, and from this the Q-matrix with eight dimensions  to identP ≤ 0.0001, which is comparable or more stringent than that reported in other soybean GWAS studies    at a thrFile S1 contains the ureide means of the 374 plant introductions for the four environments. File S2 describes how to obtain the SNP data from www.soybase.org. File S3 contains the by environment probabilities, 2-environment joint probabilities and FDR adjusted 2-environment joint probabilities for all 33,397 SNPs tested for significant associations with ureide content. Table S1 shows the SNP information for all SNPs identified as significantly associated with ureide concentration.Although environmental conditions were generally more similar between years in Columbia and in Stuttgart than between the locations, considerable variation in environmental conditions was observed among all four environments . Solar rUreide concentration data were obtained on a total of 374 accessions in each of the four environments. Overall, the accessions represented 11 different national sources and a total of at least 37 different provinces within those countries . Based o2) declined to 0.2 within approximately 185 kbp, which is approximately half that reported by Using all SNPs with a MAF ≥0.03 and all 374 genotypes, LD was separately calculated for euchromatic and heterochromatic chromosomal regions. In euchromatic regions the mean LD  as random effects, ANOVA showed that the variability due to accessions  was 33%, 32%, 23%, and 38% for the C09, C10, S09, and S10 environments, respectively. Combined across environments, the total variability was 32% (14% for accessions plus 18% for environment × accession interactions). In all cases, the random effects were significant based on the 95% CIs of the variance component estimates. ANOVA across environments and treating accessions as fixed effects revealed significant differences among accessions  and a significant accession × environment interaction . Regression of accession means between locations within each year indicated little correspondence (r2 = 0.12 and P < 0.0001 in 2009 and r2 = 0.02 and P = 0.0058 in 2010). Better correspondence between years was observed for the Columbia  than the Stuttgart  location.By treating accessions .Potential marker associations with ureide concentration were evaluated by comparing the BLUP mean Log(ureide) concentrations of the two homozygous marker alleles for each of the 33,957 SNP markers with a minor allele frequency ≥0.03 across all 374 accessions. Log values were used to equalize variances among environments and BLUP means were used to help reduce the effect of extreme values see . For eacP ≤ 0.0001 identified 40 SNPs with significant associations with ureide concentration in at least one of the four environments  as well as 15 significant SNP associations with the overall mean. Of these SNPs, seven were significant both in one environment and for the overall mean. Thus, a total of 48 unique SNPs were identified as significant (P ≤ 0.0001) in at least one environment, with the overall mean, or both. A list of these SNPs and their details are provided in Table S1.Adjusting for population structure and genetic relatedness and using a stringent probability threshold of P ≤ 0.01. No SNPs in any of the three-environment combinations met the P ≤ 0.01 threshold. However, 141 SNPs met the threshold in at least one of the two-environment combinations. A list of these SNPs and their details are provided in Table S1.The above results considered associations within each individual environment and for the mean ureide concentration across the four environments. To further take advantage of the multiple environments used in this study, we also calculated the joint probability  of SNP–tTable S1. The number of SNPs tagging each putative locus ranged from 1 to 26  unique SNPs were identified in individual environments, by the mean overall environments, or by considering two-environment combinations. The relative genomic locations of these 146 SNPs are shown in Of the 53 total putative loci identified, 19 were identified by at least one SNP with a significant association in one or more individual environments  previously identified by i.e., uricase), the catabolism of ureides , or in a biochemical pathway related to ureide metabolism .In addition to reported ureide QTL, a search was conducted in SoyBase for genes that might be related to ureide metabolism and that were located within 3 Mbp of the 53 putative loci shown in Even though a relatively large number of SNPs  were evaluated in this study, gaps of various lengths in the coverage of almost every chromosome  are visible in P ≤ 0.0001). Additionally, we examined associations over multiple environments using joint probabilities adjusted for multiple testing . The stringent conditions used in the analysis provides confidence that these loci warrant more detailed investigation.To help control false positives we used BLUP means  and accounted for both population structure (Q-matrix) and genetic relatedness (K-matrix) , significant SNP associations were detected within 0.4 MB (chromosome 13) and 0.6 MB (chromosome 15) for both pairs of genes. Thus, the identification of these SNPs associated with well-characterized ureide-related genes again indicates that very stringent criteria may mask true associations. This emphasizes the necessity for approaches that balance the need to identify true associations with the need to eliminate false positives, because at lower stringencies many more likely false-positive SNP associations can be identified. Without independent information  near the loci identified in the accessions comprising the minor SNP frequency may reveal novel insights into the regulation of ureide synthesis or catabolism. Ultimately, confirmation of the putative loci identified in this study will provide new sources of variation for use in breeding programs developing improved soybean cultivars.In this study, 53 putative loci associated with ureide concentration were identified. Two of the putative loci were located near previously reported QTL associated with ureide concentration and 30 loci were located near genes associated with ureide metabolism. Potentially, these results indicate variation in known genes that require further investigation. The remaining loci may represent new genes affecting ureide concentration  and also warrant further in-depth investigation. Confirmation of these loci could be accomplished through quantifying segregation in appropriately constructed biparental mapping populations. Further investigations such as expression analyses and sequencing of important known genes (