Single nucleotide polymorphisms (SNPs) in genes derived from distinct pathways are associated with a breast cancer risk. Identifying possible SNP-SNP interactions in genome-wide case–control studies is an important task when investigating genetic factors that influence common complex traits; the effects of SNP-SNP interaction need to be characterized. Furthermore, observations of the complex interplay (interactions) between SNPs for high-dimensional combinations are still computationally and methodologically challenging. An improved branch and bound algorithm with feature selection (IBBFS) is introduced to identify SNP combinations with a maximal difference of allele frequencies between the case and control groups in breast cancer, i.e., the high/low risk combinations of SNPs.OR) as a quantitative measure to estimate the associated cancer risk of multiple SNP combinations to identify the complex biological relationships underlying the progression of breast cancer, i.e., the most likely SNP combinations. Experimental results show the estimated odds ratio of the best SNP combination with genotypes is significantly smaller than 1 (between 0.165 and 0.657) for specific SNP combinations of the tested SNPs in the low risk groups. In the high risk groups, predicted SNP combinations with genotypes are significantly greater than 1 (between 2.384 and 6.167) for specific SNP combinations of the tested SNPs.A total of 220 real case and 334 real control breast cancer data are used to test IBBFS and identify significant SNP combinations. We used the odds ratio (This study proposes an effective high-speed method to analyze SNP-SNP interactions in breast cancer association studies. A number of important SNPs are found to be significant for the high/low risk group. They can thus be considered a potential predictor for breast cancer association. At present, identifying SNP-SNP interactions in genome-wide case–control studies is computationally and methodologically challenging. To bettn dimensions to one dimension. However, the computational load can be excessive when dealing with more than 10 polymorphisms[Many methods have been developed to detect epistasis on the basis of a statistical definition to explore gene-gene interactions or SNP-SNP interactions (epistasis) in complex diseases; these include logic regression,6, Multiorphisms. PIA useOR) as a quantitative measure to assess combinations of SNPs in the case–control studies. The odds ratio is a commonly-used statistic that expresses the strength of association between exposure and disease[This study proposes a method based on statistical epistasis and an improved branch and bound algorithm combined with feature selection (IBBFS) to explore combinations of SNP-SNP interactions in a breast cancer association study. The proposed method can reduce the search time and accurately determine the difference between cases and controls in low and high risk groups. Finally, we use the odds ratio  with genotypes (2-1-1) and an OR of 2.384, SNPs  with genotypes (2-1-1-1) and an OR of 3.024, and SNPs  with genotypes (1-2-1-1-1) and an OR of 3.133. These two-to-five SNP combinations are statistically significant with a p-value smaller than 0.05. However, for combinations of six SNPs and combinations of seven SNPs, the p-value is greater than 0.05. In Table p-value smaller than 0.05. For all other combinations of SNPs the p-value was greater than 0.05. These experimental results prove that the proposed IBBFS method can handle combinations of multiple SNPs and determine the best combination of two to seven SNPs, both the in low and high risk categories.Finally, we used IBBFS to find the best-performing combinations of three or more SNPs, with results shown in Tables OR results are shown in Table OR value. Two SNPs are shown in a 3 × 3 table that represents nine state combinations. IBBFS shows that SNP  with genotype (2–3) with a maximal OR value of 4.342  has a maximal difference of 7 between the case and control groups. We observe that, for other combinations of SNP  with genotype (1–2), the OR value is at a minimum and the p-values are statistically significant . Hence, only two combinations of SNP  in the 3 × 3 table are statistically significant .First, we analyzed the high risk combination of SNP 4, 7) in breast cancer. Information related to the SNP  combination and the , 7 in brOR results shown in Table OR value. When the combination of SNP  with genotype (1–1) is chosen, the minimum OR value is 0.657 . Here, the maximum difference also is SNP  with genotype (1–1), with a difference of 68 between cases and controls. When considering combinations of more SNPs, the frequently occurring combinations of two SNPs in the following combinations are important.Secondly, we analyzed the low risk combinations of SNP , with the related information and CC, SN, SP, OR and 95% CI) of certain specific SNP combinations on the occurrence of breast cancer. These specific SNP combinations (two to seven SNPs) had a 0.657 to 0.165 risk of breast cancer (Table OR > 1) of breast cancer in Table OR value is larger than 1, the proportion of subjects with breast cancer is higher. On the other hand, when the proportion of subjects with breast cancer is smaller than 1, the OR values are lower than 1.Tables r Table . In addiIdentification of SNP-SNP interactions (epistasis) is an important task,21 when r = n-m + 1, where r is the number of features used, and n and m are the total number of SNPs and the number of selected SNPs, respectively. Examples of the ES and IBBFS calculations are respectively shown in Additional fileWe focus on understanding the breast cancer risk of functionally-relevant joint effects of combinatorial SNPs within and between different cancer pathways. We calculated the same data set by exhaustive search (ES) using two to seven SNP combinations to find SNP interactions which determine an optimal solution. These calculations were rather time-consuming and the ES method of calculating combinations of SNPs is thus impracticable for large data sets. From a practical standpoint, the main difference between the aforementioned methods is the computational time required to reach an improvement. The IBBFS method found optimal solutions faster for a high order of interaction combinations by cutting off unnecessary paths. IBBFS guarantees that each result contains an optimal solution through the use the integrated feature selection method. The selected number of features is OR) was used as a quantitative measure of the breast cancer risk. Experimental results indicate that the proposed IBBFS method can identify the complex interactions of the tested SNPs both in the low and high risk groups. In the future, the IBBFS method can potentially be applied to SNP-SNP interactions (epistasis) in other association studies.This study focused on the selection of SNP combinations that give a maximal difference between case and control groups. Evaluating a large number of SNPs associated with a disease requires a strategy for focusing on only a select number of complex interactions. IBBFS was used on complex SNP-SNP interactions and was demonstrated to provide the best SNP-SNP interactions for predicting breast cancer susceptibility. The odds ratio  is a divide-and-conquer approach used to solve global optimization issues. The conAs previously stated, traditional BB algorithms that search for all possible combinations are impractical since the number of combinations increases exponentially as the dimensionality increases. Hence, m) can assume.; LIST(m): List of the features that can be assumed at level m.; Φ: Empty set.B: Defined as 0.; R: Number of features used.; N: Total number of SNPs.; M: Total number of selected SNPs.; AVAIL: List of available feature values that LIST × (n-(m-1)), j is the jth node}Step 2: Generate branchm)={AVAIL | select top r node based on their bound value}LIST(m) =Φ, go to step 5.If LIST(Step 3: Select nodem), i.e., if nodemj=max(LIST(m)) remove theSelect the rightmost node in LIST(m)rightmost node in LIST >B, return nodemj to AVAIL and go to Step 5.If bound is empty, set m=m-1. If m=0, terminate the algorithm,If LIST is key to an efficient IBBFS. Backtracking is used until all successors or nodes and paths with bounds larger than the current bound value B have been searched. The computational savings in the IBBFS occur when the bound value at a node j at a higher level in the tree is the best value.IBBFS uses top-down and right-left search strategies together with backtracking. We define the update bound value as 0, which means that, if the number of cases and controls is 0, the node should be not explored. If the bound value at a node Statistical epistasis is a population phenomenon that depends on allele frequencies present in a specific population. This stN represents the number of samples in the control data, and C represents the number of samples in the case data. Check_controln (i) and Check_casec (i) are respectively checked as to whether or not the node i  matches the n sample in the control data and the c sample in the case data. If a match occurs, Check_controln (i)/ Check_casec (i) is set to 1, otherwise, it is set to 0.Check_controln (i) from 1 to N, andCheck_casec (i) from 1 to C. If the positive maximum bound value is selected as a feature in the next combination, then the respective OR value indicates a low cancer risk. On the other hand, if the negative maximum bound is selected as a feature in the next combination, then the respective OR value is associated with a high cancer risk. The supplementary example illustrates how the bound values are calculated.In Eq. (1), SNPs  with genotype (1–1) are the best SNP combination. SNP3 (rs2228014) has the three genotypes CC, CT, and TT, which can be respectively represented as 1, 2, and 3, and SNP4 (rs1801157) has the three genotypes GG, AG, and AA, which can also be respectively represented as 1, 2, and 3. We compute the number that matches the condition of the SNPs and genotypes for the case and control data. First, we calculate the control number for SNP3 with genotype 1 and SNP4 with genotype 1. The number of controls that independently match SNP3 with genotype 1 and SNP4 with genotype 1 are 254 and 175, respectively. The number of controls that match SNP  with genotype (1–1) is thus 137. Secondly, we calculate the number of cases independently matching SNP3 with genotype 1 and SNP4 with genotype 1 as 151 and 106, respectively. The number of cases that match SNP  with genotype (1–1) is thus 69. According to Eq. (1), the bound value is determined by subtracting 69 from 137, thus giving 68.For example, assume that CC), the sensitivity (SN), the specificity (SP) and the odds ratio (OR)[OR provides an estimate  for the relationship between two binary variables; 2) it allows us to examine the effects of other variables on that relationship via logistic regression; and 3) OR is very convenient for interpretation of case–control studies[http://statpages.org/ctab2x2.html.We used four common criteria to determine the best combinations of SNPs related to the cancer risk, namely the correctness (tio (OR). The oddtio (OR),37 due t studies. It corrTP represents the number of true positives, TN represents the number of true negatives, FN represents the number of false negatives, and FP represents the number of false positives.OR is used in the next step to evaluate each best SNP combination with regard to the susceptibility risk. A simple IBBFS calculation process is shown in the Additional fileThe proposed IBBFS algorithm with incorporated feature selection selects the most promising solution and then evaluates only the features of the next SNP combinations of this branch. Furthermore, the algorithm is based on the expansion of two-SNP combinations, which means that the two-SNP combination results are used and expanded until the maximum combination (number of SNPs) is reached. For example, if the SNP 1, 2) with genotype (2–2) combinations constitutes the best result (feature), then combinations of three SNPs that contain SNP  with genotype (2–2) are found in the next step. The expanded results are SNP  with genotype (2-2-1), SNP  with genotype (2-2-2), and SNP  with genotype (2-2-3). A detailed example is shown in Additional file, 2 with In Additional filen is the total number of SNPs, and m is the number of selected SNP combinations.where C*32=54. Based on the aforementioned calculation process, the use of traditional BB algorithms or ES to explore combinations of three, four or more SNPs is impractical since the increased number of combinations exponentially increases the time complexity Simple ES, BB and IBBFS calculation processes are shown in the Additional fileWhen two SNPs are selected and each genotype has three possible state combinations, ES calculates the number of possible solutions as The authors declare that they have no competing financial interests.C-HY coordinated and oversaw this study, and modified the manuscript were appropriate. H-WC and L-YC provided the biochemistry background and introduced the bioinformatics needed. M-CL participated in the design of the algorithm, and wrote the program and the manuscript. All authors read and approve the final manuscript.Figure S2. Calculation process of the improved branch and bound feature selection (IBBFS) algorithms. Figure S3. Branch and bound search tree. Figure S4. Flowchart of a branch and bound algorithm. Figure S5. Flowchart of the improved branch and bound algorithm (IBBFS). Figure S6. Performance calculations. Figure S7. Extended SNP combinations. Supplementary example, include a example for calculation of the SNP-SNP interaction, Figure S8. Example of a search tree, Figure S9. Search tree of two-SNP combinations, Table S1. Example data set, Table S2. Results for two-SNP combinations, Table S3. Results for three-SNP combinations, Table S4. Results for four-SNP combinations, Table S5. Table of cases and controls, Table S6. Common criteria, Table S7. Performance calculation.Exhaustive search algorithm calculation process. Click here for file


Complex human diseases may be associated with many gene interactions. Gene interactions take several different forms and it is difficult to identify all of the interactions that are potentially associated with human diseases. One approach that may fill this knowledge gap is to infer previously unknown gene interactions via identification of non-physical linkages between different mutations  to avoid hitchhiking effect or lack of recombination. Strong non-physical SNP linkages are considered to be an indication of biological (gene) interactions. These interactions can be physical protein interactions, regulatory interactions, functional compensation/antagonization or many other forms of interactions. Previous studies have shown that mutations in different genes can be linked to the same disorders. Therefore, non-physical SNP linkages, coupled with knowledge of SNP-disease associations may shed more light on the role of gene interactions in human disorders. A user-friendly web resource that integrates information about non-physical SNP linkages, gene annotations, SNP information, and SNP-disease associations may thus be a good reference for biomedical research.Linkage-Disequilibrium-based Gene Interaction database (LDGIdb). The database stores 646,203 potential human gene interactions, which are potential interactions inferred from SNP pairs that are subject to long-range strong linkage disequilibrium (LD), or non-physical linkages. To minimize the possibility of hitchhiking, SNP pairs inferred to be non-physically linked were required to be located in different chromosomes or in different LD blocks of the same chromosomes. According to the genomic locations of the involved SNPs  and coding region (CDS)), the SNP linkages inferred were categorized into promoter-promoter, promoter-UTR, promoter-CDS, CDS-CDS, CDS-UTR and UTR-UTR linkages. For the CDS-related linkages, the coding SNPs were further classified into nonsynonymous and synonymous variations, which represent potential gene interactions at the protein and RNA level, respectively. The LDGIdb also incorporates human disease-association databases such as Genome-Wide Association Studies (GWAS) and Online Mendelian Inheritance in Man (OMIM), so that the user can search for potential disease-associated SNP linkages. The inferred SNP linkages are also classified in the context of population stratification to provide a resource for investigating potential population-specific gene interactions.Here we extracted the SNPs located within the promoter or exonic regions of protein-coding genes from the HapMap database to construct a database named the The LDGIdb is a user-friendly resource that integrates non-physical SNP linkages and SNP-disease associations for studies of gene interactions in human diseases. With the help of the LDGIdb, it is plausible to infer population-specific SNP linkages for more focused studies, an avenue that is potentially important for pharmacogenetics. Moreover, by referring to disease-association information such as the GWAS data, the LDGIdb may help identify previously uncharacterized disease-associated gene interactions and potentially lead to new discoveries in studies of human diseases.Gene interaction, SNP, Linkage disequilibrium, Systems biology, Bioinformatics Gene interactions are usually inferred from biological interactions such as protein-protein interactions (PPIs) -3, co-exr2 or D′[r2 ≥ 0.8) but are located in different LD blocks (or different chromosomes) to minimize the possibilities of accidentally linked SNPs or physical linkage, and thus increase the probability that the associations between the LRLD-linked SNPs/genes are functionally meaningful. To facilitate research based on these inferred SNP linkages , we constructed a user-friendly database, the LDGIdb, to store the information. The LDGIdb also contains information about disease-associated SNPs/genes, such as the associations identified in genome-wide association studies (GWAS) [Here we infer potential gene interactions on the basis of long-range linkage disequilibrium (LRLD) between SNPs. We term these potential interactions “linkage disequilibrium-based gene interactions” (LDGIs), where two genes are considered to be connected if the SNPs located in these two genes are subject to strong linkage disequilibrium . Theorer2 or D′. In thiss (GWAS)  and thoss (GWAS) . Users cr2 and D′[r2 ≥ 0.8); (2) to minimize the probability of hitchhiking or lack of recombination, the two SNPs had to be located in different chromosomes or be separated by at least one recombination hotspot retrieved from the International HapMap Project. The latter criterion may considerably decrease the probability that the identified LRLD-SNP pairs belong to the same “LD blocks”  even if they are located in the same chromosomes. Accordingly, we identified 801,340 LRLD-SNP pairs, which contained 94,876 SNPs  for allPA and PB be the major allele frequencies at SNP1 and SNP2, respectively. Define Pa and Pb as the minor allele frequencies at SNP1 and SNP2, respectively. Let PAB be the haplotype frequency of observing both A and B alleles at these two loci. Define D = PAB - PAPB. The LD scores, r2 and D′[1 and SNP2 can be computed byLet r2 and D′, betweenHapMap Phase II release 22) and III (release 2) haplotype data and the corresponding recombination hotspot information were retrieved from the International HapMap Project  and III , DIP 2424, MINT P value for PLINK population clustering (P < 0.01 or P < 0.001), and r2 value for linkage disequilibrium  , or genomic coordinates  by setting three adjustable parameters: HapMap data source (Phase II or III), r2 ≥ 0.8, ≥ 0.9, or 1). We suggest that this LRLD approach and the LDGIdb can be potentially applied to the following areas. First, LDGIs may represent potential uncharacterized gene interactions, in which the functional associations between the LDGI genes may not be explicitly indicated in other biological networks. Second, although we constructed the LDGIdb using SNP data in this study, the LRLD approach can actually be expanded to include other types of genomic variants such as copy number variation and insertion/deletion. Third, given enough haplotype information, population-specific LDGIs/LRLD-SNP pairs may be identified for more focused studies, particularly in the field of pharmacogenetics. Fourth, the correlation between the LDGIs/LRLD-SNP pairs and disease-associated SNPs such as those identified in GWAS studies can be explored. For example, SNP rs393152, which is associated with Parkinson’s disease [KCNJ11 is known to be associated with Type II diabetes [ABCC8. Mutations and deficiencies in the protein encoded by ABCC8 have been suggested to be associated with hyperinsulinemic hypoglycemia of infancy and non-insulin-dependent diabetes mellitus type II [Here we propose a new resource for studies of potential human gene interactions  based on haplotype data. In LDGIs, the linked genes are located in different chromosomes or LD blocks but are connected by one or more exonic/promoter SNP pairs that are subject to strong linkage disequilibrium  or synonymous SNPs  may be more subtle. These potential interactions may be associated with translational regulation. Specifically, 5′UTRs may contain multiple sequence features that are involved in translational regulation, including upstream open reading frames, secondary structures, internal ribosome entry sites, and iron regulatory protein binding sites . The disThe authors declare that they have no competing interests.TJC conceived and designed the study. FCC, YTH and TJC conducted the analyses. MCW and YZC built the web server. FCC and TJC wrote the manuscript. All authors read and approved the final manuscript.Project name: LDGIdb projecthttp://LDGIdb.genomics.sinica.edu.tw. Operating systems: Platform independentAvailability: LDGIdb is freely accessible at Programming language: Javascript, CSS, PHPOther requirements: None


Meleagris gallopavo) is an important agricultural species and the second largest contributor to the world’s poultry meat production. Genetic improvement is attributed largely to selective breeding programs that rely on highly heritable phenotypic traits, such as body size and breast muscle development. Commercial breeding with small effective population sizes and epistasis can result in loss of genetic diversity, which in turn can lead to reduced individual fitness and reduced response to selection. The presence of genomic diversity in domestic livestock species therefore, is of great importance and a prerequisite for rapid and accurate genetic improvement of selected breeds in various environments, as well as to facilitate rapid adaptation to potential changes in breeding goals. Genomic selection requires a large number of genetic markers such as e.g. single nucleotide polymorphisms (SNPs) the most abundant source of genetic variation within the genome.The turkey (Alignment of next generation sequencing data of 32 individual turkeys from different populations was used for the discovery of 5.49 million SNPs, which subsequently were used for the analysis of genetic diversity among the different populations. All of the commercial lines branched from a single node relative to the heritage varieties and the South Mexican turkey population. Heterozygosity of all individuals from the different turkey populations ranged from 0.17-2.73 SNPs/Kb, while heterozygosity of populations ranged from 0.73-1.64 SNPs/Kb. The average frequency of heterozygous SNPs in individual turkeys was 1.07 SNPs/Kb. Five genomic regions with very low nucleotide variation were identified in domestic turkeys that showed state of fixation towards alleles different than wild alleles.The turkey genome is much less diverse with a relatively low frequency of heterozygous SNPs as compared to other livestock species like chicken and pig. The whole genome SNP discovery study in turkey resulted in the detection of 5.49 million putative SNPs compared to the reference genome. All commercial lines appear to share a common origin. Presence of different alleles/haplotypes in the SM population highlights that specific haplotypes have been selected in the modern domesticated turkey. Meleagris gallopavo gallopavo) indigenous to Mexico, first domesticated in 800 BC[All commercial turkey lines descend from the South Mexican turkey  were used for whole genome sequencing. The seven commercial lines, L1 through L7, were obtained from two different primary breeding companies. The three heritage varieties were the Beltsville Small White (BvSW), the Royal Palm (RP) and the Narragansett (Nset)-47. Tissb, ND2;[Considering mature erythrocytes in poultry are nucleated, genomic DNA was extracted from whole blood of the commercial and heritage lines with the QIAamp DNA blood Midi Kit ; the procedure included a proteinase K digestion followed by column purification. Integrity of high molecular weight DNA following the extraction was confirmed by agarose gel analysis. Genomic DNA was sheared using the Covaris S2 to yield an average fragment size of 450 bp, as determined with the Agilent Bioanalyzer 2100 . The DNA from the three historic SM samples was extracted from the toe-pads in the ancient DNA laboratory at the Smithsonian Institution’s Center for Conservation and Evolutionary Genetics, that is fully equipped to avoid contamination with modern DNA. DNA extraction followed a standard protocol of proteinase k and DTT digestion followed by phenol-chloroform extraction and centrifugal dialysis with Centricon concentrators (following methods provided in). An extb, ND2;) followeGenomic libraries were prepared with the Paired-end Sequencing Sample Preparation Kit  with 5 μg of genomic DNA for commercial and heritage lines according to the manufacturer’s instructions; for the SM samples 0.54 μg was used to construct the libraries. All genomic DNA libraries were validated with the Agilent Bioanalyzer (model 2100). The automated cBot Cluster Generation System (Illumina) was used to generate clusters on the flow cell. Each individual was sequenced  in a single lane of a flow cell using the Illumina GAIIx. The DNA extracted from museum samples for the SM turkeys was highly degraded, and thus single-end reads of 40 bp were generated from these samples.Sequence reads of each individual from the domesticated populations  were filtered on base quality; reads were trimmed if three consecutive bases had an average Phred-like quality score of less than 13. Both sequences in a pair needed to exceed 40 bp in length after trimming to be retained for analyses. Sequence reads from the individuals of the SM population were not quality-trimmed before further analyses since they were sequenced to a length of 40 bp only. Sequence reads were aligned against the turkey reference genome (UMD 2.01) using the MOSAIK aligner. MappingThe mpileup function of SamTools version 0.1.12a was usedTo estimate heterozygosity (heterozygous SNPs/kb), mpileup genotyping analysis (described above) was used and the number of heterozygous SNPs was calculated at the reference bases covered from 5 to 10 fold. For each individual in a population, heterozygosity was estimated by dividing the total number of discovered heterozygous SNPs by the total genome sequence covered from 5 to 10 fold. Population heterozygosity was estimated by averaging the heterozygosity of all individuals within a population.The gene-based analysis of ANNOVAR software was usedGenome wide mapping density, or read depth distribution, and the nucleotide diversity across the whole genome were assessed for each individual of the 11 turkey populations. Read depth distribution was used to calculate average sequence coverage across the whole genome. To get genotypes of each individual without imputation, pileup function of SamTools version 0.1.12a was usedPHYLIP software, version 3.69 was usedThe genome of each individual was screened, using the nucleotide diversity analysis described above, for the occurrence of non-reference allelic states. Determining the ancestral allelic state of SNPs was not possible because species with appropriate evolutionary distance are not available. Chicken is considered a closely related domesticated agricultural species to turkey but the evolutionary distance to the last common ancestor of these two species is around 30 million years. To quanThe difference in non-reference allele homozygosity between domesticated and the SM turkey populations was calculated for each bin. This difference was then divided by the average homozygous non-reference allele SNP density for the bin to yield a relative measure that can be compared between bins with different levels of variation.The ratio of non-reference homozygosity in wild SM vs. domesticated populations was calculated within bin sizes of 300 Kb. A high ratio points to non-reference alleles being lost, or decreased in frequency during domestication and selection. A high ratio of non-reference homozygosity, in combination with low heterozygosity in the domesticated populations, is interpreted as a reduction of allelic variation from wild to domesticated populations, or “fixation of the reference alleles”. A bin was considered “fixed for the reference allelic state” in domesticated populations when two conditions were met. First, bins were considered “fixed” when heterozygosity was equal or lower than 0.0002 on average across all domesticated populations. This threshold was chosen because only 5% of the bins had a heterozysity equal or lower than 0.0002 (1 heterozygous position/5000 bp). Second, bins that were considered “fixed” had to have a ratio of non-reference allele homozygosity above or equal to 1.73, which means that the non-reference allele homozygosity of the wild population must be at least 73% higher than the domesticated populations. This threshold was chosen because only 5% of all the bins in the genome had a ratio equal or higher than 1.73.Although animals were used in this study, no direct experiments were performed on them. Blood sample collection was carried out by highly skilled and experienced personnel from the breeding companies. No approval from the ethics committee was necessary according to local legislation.The obtained sequence from the DNA samples of the domestic populations  varied from 2.30-13.21 Gbp (Giga basepairs) per individual. After quality trimming and alignment of the short reads, the percentage of bases in the reference genome covered by at least 1 and a maximum of 25 reads varied from 47.48% to 86.13% for the animals analyzed .The number of heterozygous genotypes detected within the individuals from the ten domesticated populations  varied from 0.08 to 0.80 million with an average of 0.55 million heterozygous genotypes per individual. Individuals from the SM population showed relatively low numbers of heterozygous SNPs; between 0.01 and 0.07 million. Heterozygosity (heterozygous SNPs/kb) of all individuals from the different turkey populations ranged from 0.17-2.73 while heterozygosity of populations ranged from 0.73-1.64  showed differences between the SM and the domesticated populations with respect to the occurrence of no-reference wild type and the reference allelic states  relies in part on Hardy-Weinberg Equilibrium (HWE) for imputation of genotypes.By using a NGS (Illumina GAIIx) approach, we discovered millions of high quality SNPs in the turkey. Next generation sequencing approaches are considered highly reliable for genome-wide discovery of sequence variation, when us4 false discovered heterozygous positions per turkey genome (size of 1.1 x 109 base pairs). The SNPs FDR rate for the same 10 animals from distinct turkey populations was estimated after correcting for the coverage and using estimates of FDR per nucleotide position. The SNPs FDR was found to be 2.6%, a number that is similar in magnitude as found previously in the human 1000 Genome Project. In addition to the low FDR, we found a transition/transversion (Ti/Tv) ratio within the expected range. The expected Ti/Tv ratio of true novel variants can vary with the targeted region , species and also can vary greatly by the CpG and GC content of the region[The high quality of the SNPs discovery reported here is reflected by the low FDR of 0.00002 per nucleotide in the genome. This FDR suggests around 2.1 x 10e region-61. In te region. Observee region, but sliWe report the number of segregating as well as total number of SNPs with their functional annotation. The 23,795 nonsynonymous variants that were observed can potentially change the structure of proteins, possibly resulting in altered phenotypes. Out of -1 in the turkey is substantially lower than in chicken, which was previously reported as 4.28 and 2.24 heterozygous SNPs Kb-1 in two different studies[The estimated average frequency of 1.07 heterozygous SNPs Kb studies. In our Modern commercial turkey lines are derived from historic turkey populations that displayed low variation as a result of small effective population size,71. HeriMost birds have a characteristic division in chromosome size, with 5 or 6 large chromosomes, around 5 intermediate size chromosomes, and 25 to 30 very small chromosome pairs. In our study, we observed higher nucleotide diversity on smaller chromosomes compared to the larger turkey chromosomes which is in agreement with the previous study. Since tThe presence of different allelic states in the wild SM and the domesticated populations is a demonstration of their divergence during the course of domestication event. Domesticated turkey lines were selected  for non-wild type alleles. Domestication has involved the selection on a desired trait(s), and preThe genetic diversity analysis among the 11 different turkey lines showed that the heritage varieties and the commercial populations are derived from the wild South Mexican population. All of the heritage varieties  are closely related which is in agreement with previously published data,44. The The turkey genome is much less diverse with a relatively low frequency of heterozygous SNPs as compared to other livestock species like chicken and pig. The whole genome SNP discovery study in turkey resulted in the detection of 5.49 million putative SNPs compared to the reference genome. All commercial lines appear to share a common origin. Presence of different alleles/haplotypes in the SM population highlights that specific haplotypes have been selected in the modern domesticated turkey.The authors declare that they have no competing interests.MLA, JWMB, MGE and HJM analysed the data. LAB assisted and trained laboratory personnel in genomic DNA isolation from blood and preparation of all libraries. RCF isolated the museum specimen DNA. CPVT assisted with project design and coordination. TSS helped develop sequencing strategy and guided library preparation of ancient DNA samples. SGS was responsible for sequencing processing. JAL conceived and developed the project, organized blood sample collection and DNA sequencing, and was the Principal Investigator (Agriculture and Food Research Initiative Competitive Grant no. 2010-65205-20428). MLA wrote the paper and all other authors gave suggestions and comments for the improvement of paper. All authors read and approved the final manuscript. Overall coordination of the project was by JAL, JWMB, RPMAC and MAMG. All authors read and approved the final manuscript. Positions of turkey genomic regions with their mapping positions and underling QTL in chicken genome. This file contains the start and the end positions of turkey genomic regions that showed fixed haplotype for the reference alleles in domesticated populations. This file also contains information about the start and the end positions of these turkey genomic regions in chicken genome and the chicken QTL reported within these regions.Click here for file


Acacia auriculiformis and Acacia mangium. Like other non-model species, SNP detection and genotyping in Acacia are challenging due to lack of genome sequences. The main objective of this study is to develop the first high-throughput SNP genotyping assay for linkage map construction of A. auriculiformis x A. mangium hybrids.Next Generation Sequencing has provided comprehensive, affordable and high-throughput DNA sequences for Single Nucleotide Polymorphism (SNP) discovery in Acacia hybrid mapping populations using Bowtie against 7,839 de novo transcriptome contigs. Given a set of 10 validated SNPs from two lignin genes, our in silico SNP detection approach is highly accurate (100%) compared to the traditional in vitro approach (44%). Further validation of 96 SNPs using Illumina GoldenGate Assay gave an overall assay success rate of 89.6% and conversion rate of 37.5%. We explored possible factors lowering assay success rate by predicting exon-intron boundaries and paralogous genes of Acacia contigs using Medicago truncatula genome as reference. This assessment revealed that presence of exon-intron boundary is the main cause (50%) of assay failure. Subsequent SNPs filtering and improved assay design resulted in assay success and conversion rate of 92.4% and 57.4%, respectively based on 768 SNPs genotyping. Analysis of clustering patterns revealed that 27.6% of the assays were not reproducible and flanking sequence might play a role in determining cluster compression. In addition, we identified a total of 258 and 319 polymorphic SNPs in A. auriculiformis and A. mangium natural germplasms, respectively.We identified a total of 37,786 putative SNPs by aligning short read transcriptome data from four parents of two A. auriculiformis x A. mangium hybrids using next generation transcriptome sequencing. By using a reference genome from the most closely related species, we converted most SNPs to successful assays. We also demonstrated that Illumina GoldenGate genotyping together with manual clustering can provide high quality genotypes for a non-model species like Acacia. These SNPs markers are not only important for linkage map construction, but will be very useful for hybrid discrimination and genetic diversity assessment of natural germplasms in the future.We have successfully discovered a large number of SNP markers in  Acacia auriculiformis x A. mangium hybrid is emerging as an important forest tree for pulpwood production in South East Asia. Marker-assisted breeding is a promising approach for selection of superior trees with improved wood and pulp properties for the establishment of forest plantations. Previous efforts to develop molecular markers such as Cleaved Amplified Polymorphic Sequence (CAPS)  using NCBI Blastn Blast2sequences to identify shared regions. The flanking sequences of validated in vitro SNPs were aligned against the genomic sequence of C4H and CAD [Genbank: JN204274.1 and JN227538.1] and any SNP flanking region located outside the exonic region was removed. The 120 bp flanking sequences of the validated SNPs in these two genes  were aligned against the shared coding regions. All the SNPs located in the shared region were identified from the modified pileup file for each parent. The successfully genotyped, polymorphic SNPs and monomorphic SNPs from the validated SNPs were compared to the SNPs identified from in vitro and in silico approach. Based on these findings, the stringency of the SNP detection approach was increased by removing SNPs with SNP score less than 50 and MAF less than 8%. parents . These irmplasms . The seqA. auriculiformis parents (AA6 and AA3), 12 SNPs shared by both A. mangium parents (AM20 and AM22) and 18 SNPs for each parent were selected. A total of 30 SNPs were predicted to be polymorphic for each parent. Only testcross SNPs that were heterozygous in either one parent and segregating in the mapping population in ratio 1:1 were used in SNP genotyping. The SNPs validation was performed on 96 DNA samples using Illumina GoldenGate Assay on an Illumina BeadXpress platform according to manufacturer’s protocol. The samples consisted of four parents, 24 progenies for each mapping population, 21-22 individuals each for A. auriculiformis and A. mangium natural germplasms  was considered a monomorphic SNP in this study. A SNP was polymorphic if at least one parent was heterozygous and the SNP was segregating in the mapping population following testcross (AB × AA → AB:AA = 1:1 or AB x BB → AB:BB = 1:1) or intercross (AB × AB → AA:AB:BB = 1:2:1) configuration. Mendelian inheritance in the mapping population and reproducibility within parent replicates were checked using Heritability and Reproducibility Error function.The assay success rate was calculated based on the number of successful assays divided by total number of assays while the conversion rate was calculated based on number of polymorphic SNPs divided by total number of assays. Validation rate of the SNPs was calculated using the number of polymorphic SNPs divided by the number of successful assays. We also checked the significance and correlation between: 1) SNP design score and assay success rate; 2) validation rate and SNP score, using t-test and correlation test in Microsoft Excel. For comparison of SNPs detected from Bowtie and BWA alignments, the successfully validated SNPs were compared to putative SNPs detected by BWA. The validation rate was calculated based on the number of true SNPs also detected by BWA divided by the total number of SNPs also detected by BWA. The sensitivity of combined Bowtie and BWA approach was estimated by dividing the number of true SNPs detected from BWA alignments over the total number of true SNPs while the false negative rate is 1 minus sensitivity.Medicago truncatula version 3.5 (Release dated 25 August 2010) were downloaded from M. truncatula genome project website (http://www.jcvi.org/cgi-bin/medicago/download.cgi). For exon-intron boundary prediction, the start and stop position of each exon for each gene in M. truncatula genome was first extracted from IMGAG file and the length of each exon was calculated using an AWK script. The Acacia contigs were searched against M. truncatula full length transcripts using local NCBI Blast-2.2.25+ blastn algorithm  and the results were output in both tab-delimited and XML formats. The XML Blastn results were simplified by extracting only useful columns with a custom Python script  Obtain exon-intron boundary positions of the best hit M. truncatula transcript from extracted IMGAG file; b) predict the exon-intron boundary positions in Acacia contigs using alignments from extracted XML file; c) report SNPs that contain predicted exon-intron boundaries in 35 bp upstream and downstream of SNP site. The reported SNPs were removed from filtered SNP dataset of AA6 and AM20 using an AWK one-liner script. Known paralogous genes were identified by searching the contigs against other contigs in the transcriptomes using local NCBI Blast-2.2.25+ blastn algorithm . Any SNP from a gene that shared more than 90% similarity in nucleotide level with another gene was removed. In addition, we also predicted paralogous genes by searching the contigs against the M. truncatula genes using the same method. Two or more contigs that map to the same M. truncatula genes were predicted as paralogous genes or fragmented contigs. Any contig that map to multiple M. truncatula gene transcripts are potential paralogous genes.A custom Python script  were excluded. A flowchart summarizing the SNP detection approach for AA6 and AM20 is shown in Figure caffeoyl CoA 3-O-methyltransferase (CCoAOMT) gene were included. SNPs from CCoAOMT were detected using the same methods except the gene sequence of CCoAOMT1 [Genbank: JL053016] which was not included in AArefseq was used as reference. The SNPs were designed in two 384-plex Oligonucleotide Pooled Assays (OPAs), namely aaOPA and amOPA. Any two SNPs from the same gene or located less than 50 bp away were placed in separate OPA. All the SNPs in amOPA were markers from AM20 while aaOPA contained mainly SNPs from AA6 and a small proportion SNPs from AM20 .Click here for fileA SNP summary of 96 SNPs validation and 768 SNPs genotyping. This Excel file contains three sheets, namely “96plex”, “aaOPA3” and “amOPA3”. Each sheet contains SNP information of dbSNP id, SNP contig and position, design score, SNP score, homologous genes in Mt3.5, assay success, polymorphism, mapping population, minor allele frequency in A. auriculiformis and A. mangium natural germplasms. Description of each column can be found on the right in sheet “96plex”. Click here for file


A variance component based approach to account for population stratification uncovered twelve QTL regions on seven chromosomes. The identified QTL point to MCM6, PAX3, ERBB3, KITLG, LEF1, DKK2, KIT, CRIM1, ATRN, GSDMC, MITF and NBEAL2 as underlying genes for eye area pigmentation in cattle. The twelve QTL regions explain 44.96% of the phenotypic variance of the proportion of daughters with ACOP. The chromosomes harbouring significantly associated SNPs account for 54.13% of the phenotypic variance, while another 19.51% of the phenotypic variance is attributable to chromosomes without identified QTL. Thus, the missing heritability amounts to 7% only. Our results support a polygenic inheritance pattern of ACOP in cattle and provide the basis for efficient genomic selection of animals that are less susceptible to serious eye diseases.Pigmentation patterns allow for the differentiation of cattle breeds. A dominantly inherited white head is characteristic for animals of the Fleckvieh (FV) breed. However, a minority of the FV animals exhibits peculiar pigmentation surrounding the eyes . In areas where animals are exposed to increased solar ultraviolet radiation, ACOP is associated with a reduced susceptibility to bovine ocular squamous cell carcinoma . Eye cancer is the most prevalent malignant tumour affecting cattle. Selection for animals with ACOP rapidly reduces the incidence of BOSCC. To identify quantitative trait loci (QTL) underlying ACOP, we performed a genome-wide association study using 658,385 single nucleotide polymorphisms (SNPs). The study population consisted of 3579 bulls of the FV breed with a total of 320,186 progeny with phenotypes for ACOP. The proportion of progeny with ACOP was used as a quantitative trait with high heritability (h Fleckvieh and Hereford, are particularly susceptible to BOSCC i.e. Fleckvieh) cattle is up to 53% Excessive exposure to UV radiation and a lack of ambilateral circumocular pigmentation (ACOP) are two predisposing factors to bovine ocular squamous cell carcinoma  The aim of the present study was to gain insights into the genetic architecture of a special aspect of skin pigmentation and to provide the basis for more efficient selection for animals that are less susceptible to serious eye diseases. Recording the pigmentation status in large progeny groups of artificial insemination bulls provided a highly heritable phenotype for a genome-wide association study. Using densely spaced SNPs, the association study identified twelve QTL regions.. The resulting trait is a highly heritable progeny-derived phenotype for the bulls. Using the numerator relationship matrix among the 3579 animals built based on in-depth pedigree information in a random effect model, the heritability was estimated to be 0.79 (±0.04).The proportion of daughters with ambilateral circumocular pigmentation (ACOP) Figure 1S13. An additional analysis conditional on the most significantly associated SNP indicates the presence of a second independent QTL on BTA6 and BTA22, respectively .The genome-wide association study based on a variance component based approach to account for population stratification identified twelve QTL regions on seven chromosomes Figure 2KIT (BTA6), KITLG, ERBB3 (BTA5) and MITF (BTA22), four genes which play central roles in the migration of melanoblast cells and melanocyte development PAX3 and MCM6 as candidate genes for ACOP in cattle. PAX3 is a transcription factor known to be involved in melanogenesis MCM6 is up regulated during pheomelanogenesis DKK2 and LEF1. DKK2 plays an essential role during eye development LEF1 interacts with MITF via Wnt signalling CRIM1, which is up regulated in developing ocular tissues ATRN, a candidate gene for the BTA13 QTL, was shown to influence coat colour in mice GSDMC  was observed. MLZE is up regulated in growing metastatic melanomas and is supposed to be important for melanoma progression NBEAL2 as candidate gene for ACOP. NBEAL2 shows homology to LYST, which is responsible for pigmentation defects in humans and mice The gene content of the associated regions was analysed based on the University of Maryland UMD3.1 assembly of the bovine genome  & Alleles raising the proportion of daughters with ACOP were determined for the most significantly associated SNP for each QTL  & Table 2i.e. 93.22% of the heritability) while the medium-dense map explains 69.97% (i.e. 88.56% of the heritability) .The genomic relationship matrix, which was also applied for the genome-wide association study, was fitted in a mixed linear model to estimate the proportion of phenotypic variation accounted for by all 658,385 SNPs. To quantify the benefit of a high-density SNP map, the genomic relationship based on only 43,029 SNPs, which were genotyped in all animals, was estimated additionally. The very dense SNP map explains 73.64% of the phenotypic variation (2 = 0.06) .We next built genomic relationship matrices among the 3579 animals for each chromosome separately in order to partitioning the phenotypic variation onto different chromosomes. The proportion of phenotypic variation attributable to a particular chromosome was then estimated with the effects of all chromosomes fitted simultaneously. The contribution of particular chromosomes varies strongly Figure 4i.e. 56.91% of the heritability). A major fraction of the phenotypic variance is attributable to the QTL regions encompassing KITLG (7.87%), KIT (14.56%) and MITF (11.33%) on chromosomes 5, 6 and 22, respectively  for the proportion of daughters with ACOP was estimated as 0.79, which is considerably higher than previous estimates of the heritability for eye-area pigmentation in cattle Coat colour phenotypes are routinely recorded for a large number of females, while genotyping is routinely performed in males in the FV breed. To benefit from the large number of cows with phenotypes and the substantial number of bulls with genotypes, the proportion of daughters with ACOP was assessed for a total of 320,186 cows sired by 3579 genotyped artificial insemination bulls. The resulting phenotypes can be considered as breeding values for the bulls. Using pedigree information, the heritability  as candidate gene for ACOP in cattle. There are no clues for a direct contribution of MLZE to mammalian pigmentation traits or embryonal eye development. However, expression of MLZE in growing metastatic melanomas implies a contribution of MLZE to melanoma progression Eight of the twelve identified QTL point to genes, e.g. during melanocyte development and melanocyte migration LEF1, PAX3) encode transcription factors regulating the promoter for the BTA22 candidate gene MITFKIT encodes a transmembrane receptor for the mast cell growth factor encoded by the BTA5 candidate gene KITLGKIT alleles acting in a dominant fashion completely inhibit pigmentation in pigs e.g.The identified candidate genes for ACOP interact in a complex fashion, Pigmentation around the eyes is highly correlated with eye-lid and corneoscleral pigmentation http://www.lfl.bayern.de). The number of daughters per sire ranged from 20 to 3949 with a median of 59 daughters. An approximately normally distributed phenotype was obtained by square root transformation of the proportion of daughters with ACOP . Genetic parameters were estimated using the random effect model The proportion of daughters with ACOP Figure 1v1) and 54,609  SNPs, respectively. Additionally, 810 FV bulls were genotyped with the Illumina BovineHD Bead chip® interrogating 777,962 SNPs (777K). 521 bulls of the 777K data set were also genotyped with the 54Kv1 genotyping array (S3). The chromosomal position of the SNPs was determined according to the University of Maryland UMD3.1 assembly of the bovine genome sequence PLINKTable S1.Genotyping was performed with three different genotyping arrays. 3387 FV bulls were genotyped with the Illumina BovineSNP 50K Bead chip® interrogating 54,001 .Imputation accuracy was evaluated within 802 animals of the high-density dataset to assess the quality of the imputed genotypes. We randomly selected 400 animals as reference population with full genotype information. Genotypes were masked for the remaining 402 animals for all SNPs except for 40,062 SNPs interrogated by the 54KEMMAX−8 .A genome-wide association study was performed using a variance components based approach to account for population stratification and to eliminate the resulting inflation of false positive associations. We used 2) of the trait variance can be estimated as The required sample size (N) for a GWAS to identify a QTL explaining a given fraction  for each of the 30 chromosomes separately. We used Figure S1Distribution of the applied phenotype. The boxplot (A) and histogram (B) display the distribution of the square root transformed proportion of daughters with ambilateral circumocular pigmentation. The deviation from the expected Gaussian normal distribution is only marginal (C). 66.67%, 95.87% and 99.89% of the values are within one two and three standard deviations, respectively.(PDF)Click here for additional data file.Figure S2Detailed overview of the identified QTL region on BTA2 . The gene content was assessed based on the University of Maryland (UMD3.1) assembly of the bovine genome. The putative functional candidate gene is highlighted with red colour. The diamond represents the most significantly associated SNP while different colours represent the linkage disequilibrium (r2) between the most significantly associated SNP and all other SNPs within the displayed region. The heatmap displays the pairwise linkage disequilibrium.(PDF)Click here for additional data file.Figure S3Detailed overview of the identified QTL region on BTA2 . The gene content was assessed based on the University of Maryland (UMD3.1) assembly of the bovine genome. The putative functional candidate gene is highlighted with red colour. The diamond represents the most significantly associated SNP while different colours represent the linkage disequilibrium (r2) between the most significantly associated SNP and all other SNPs within the displayed region. The heatmap displays the pairwise linkage disequilibrium.(PDF)Click here for additional data file.Figure S4Detailed overview of the identified QTL region on BTA5 . The gene content was assessed based on the University of Maryland (UMD3.1) assembly of the bovine genome. The putative functional candidate gene is highlighted with red colour. The diamond represents the most significantly associated SNP while different colours represent the linkage disequilibrium (r2) between the most significantly associated SNP and all other SNPs within the displayed region. The heatmap displays the pairwise linkage disequilibrium.(PDF)Click here for additional data file.Figure S5Detailed overview of the identified QTL region on BTA5 . The gene content was assessed based on the University of Maryland (UMD3.1) assembly of the bovine genome. The putative functional candidate gene is highlighted with red colour. The diamond represents the most significantly associated SNP while different colours represent the linkage disequilibrium (r2) between the most significantly associated SNP and all other SNPs within the displayed region. The heatmap displays the pairwise linkage disequilibrium.(PDF)Click here for additional data file.Figure S6Detailed overview of the identified QTL region on BTA6 . The gene content was assessed based on the University of Maryland (UMD3.1) assembly of the bovine genome. The putative functional candidate gene is highlighted with red colour. The diamond represents the most significantly associated SNP while different colours represent the linkage disequilibrium (r2) between the most significantly associated SNP and all other SNPs within the displayed region. The heatmap displays the pairwise linkage disequilibrium.(PDF)Click here for additional data file.Figure S7Detailed overview of the identified QTL region on BTA6 . The gene content was assessed based on the University of Maryland (UMD3.1) assembly of the bovine genome. The putative functional candidate gene is highlighted with red colour. The diamond represents the most significantly associated SNP while different colours represent the linkage disequilibrium (r2) between the most significantly associated SNP and all other SNPs within the displayed region. The heatmap displays the pairwise linkage disequilibrium.(PDF)Click here for additional data file.Figure S8Detailed overview of the identified QTL region on BTA11 . The gene content was assessed based on the University of Maryland (UMD3.1) assembly of the bovine genome. The putative functional candidate gene is highlighted with red colour. The diamond represents the most significantly associated SNP while different colours represent the linkage disequilibrium (r2) between the most significantly associated SNP and all other SNPs within the displayed region. The heatmap displays the pairwise linkage disequilibrium.(PDF)Click here for additional data file.Figure S9Detailed overview of the identified QTL region on BTA13 . The gene content was assessed based on the University of Maryland (UMD3.1) assembly of the bovine genome. The putative functional candidate gene is highlighted with red colour. The diamond represents the most significantly associated SNP while different colours represent the linkage disequilibrium (r2) between the most significantly associated SNP and all other SNPs within the displayed region. The heatmap displays the pairwise linkage disequilibrium.(PDF)Click here for additional data file.Figure S10Detailed overview of the identified QTL region on BTA14 . The gene content was assessed based on the University of Maryland (UMD3.1) assembly of the bovine genome. The putative functional candidate gene is highlighted with red colour. The diamond represents the most significantly associated SNP while different colours represent the linkage disequilibrium (r2) between the most significantly associated SNP and all other SNPs within the displayed region. The heatmap displays the pairwise linkage disequilibrium.(PDF)Click here for additional data file.Figure S11Detailed overview of the identified QTL region on BTA22 . The gene content was assessed based on the University of Maryland (UMD3.1) assembly of the bovine genome. The putative functional candidate gene is highlighted with red colour. The diamond represents the most significantly associated SNP while different colours represent the linkage disequilibrium (r2) between the most significantly associated SNP and all other SNPs within the displayed region. The heatmap displays the pairwise linkage disequilibrium.(PDF)Click here for additional data file.Figure S12Detailed overview of the identified QTL region on BTA22 . The gene content was assessed based on the University of Maryland (UMD3.1) assembly of the bovine genome. The putative functional candidate gene is highlighted with red colour. The diamond represents the most significantly associated SNP while different colours represent the linkage disequilibrium (r2) between the most significantly associated SNP and all other SNPs within the displayed region. The heatmap displays the pairwise linkage disequilibrium.(PDF)Click here for additional data file.Figure S13Detailed overview of the identified QTL region on BTA22 . The gene content was assessed based on the University of Maryland (UMD3.1) assembly of the bovine genome. The putative functional candidate gene is highlighted with red colour. The diamond represents the most significantly associated SNP while different colours represent the linkage disequilibrium (r2) between the most significantly associated SNP and all other SNPs within the displayed region. The heatmap displays the pairwise linkage disequilibrium.(PDF)Click here for additional data file.Figure S14Association of 30,985 SNPs on Chromosome 6 with ambilateral circumocular pigmentation in 3579 animals of the Fleckvieh population. Results for the initial analysis (A) and for the analysis conditional on the BTB-00263209 SNP (B). Orange dots represent significantly associated SNPs on a chromosome-wide level, red dots represent significantly associated SNPs on a genome-wide level.(PDF)Click here for additional data file.Figure S15Association of 16,722 SNPs on Chromosome 22 with ambilateral circumocular pigmentation in 3579 animals of the Fleckvieh population. Results for the initial analysis (A) and for the analysis conditional on the BovineHD2200009208 SNP (B). Orange dots represent significantly associated SNPs on a chromosome-wide scale, red dots represent significantly associated SNPs on a genome-wide scale.(PDF)Click here for additional data file.Figure S16Effect of different marker densities on the chromosomal partitioning of the genetic variance. The grey and blue bars indicate the fraction of phenotypic variance attributed to a particular chromosome using the genomic relationship matrices built based on medium-density and high-density SNP information, respectively.(PDF)Click here for additional data file.Figure S17Correlation between chromosome length and the fraction of phenotypic variance explained. The estimate of the proportion of phenotypic variance explained by a particular chromosome is displayed as a function of the physical chromosome length (in Mb units). Red numbers indicate chromosomes with identified QTL. The blue line is a linear regression line with slope 3.2×10−4 (r2 = 0.06).(PDF)Click here for additional data file.Figure S18Accuracy of the imputed genotypes. Imputation accuracy was assessed based on genotypes of 613,232 chromosome-wide distributed SNPs of 402 animals. The proportion of correctly imputed alleles is displayed as a function of the allele frequency. The boxplots show the results for allele frequency bins of 2.5%. The concordance between imputed and true allele was poor (41.06%) for rare alleles , while imputation of frequent alleles  resulted in an allelic concordance >99%.(PDF)Click here for additional data file.Table S1Number of SNPs not passing the quality control parameters for the medium-density  and the high-density (777K) datasets. The number of SNPs and animals not passing the applied quality parameters as well as the final number of SNPs and animals is given for the two medium-density  and for the high-density (777K) dataset, respectively .(PDF)Click here for additional data file.Table S2Number of SNPs for each of the three datasets after quality control. Numbers along the diagonal represent the final number of SNPs for the two medium-density  and for the high-density (777K) dataset, respectively. Off-diagonal numbers indicate the intersection.(PDF)Click here for additional data file.Table S3Number of animals for each of the three datasets after quality control. Numbers along the diagonal represent the final number of SNPs for the two medium-density  and for the high-density (777K) dataset, respectively. Off-diagonal numbers indicate the intersection.(PDF)Click here for additional data file.Table S4Number of SNPs used for the evaluation of the imputation accuracy.(PDF)Click here for additional data file.


This study evaluated the effect of marine n-3 PUFA on sCD36 levels in overweight subjects. Fifty overweight subjects were randomized to 1.1 g of n-3 PUFA or 2 g of olive oil daily for six weeks. Neutrophils were isolated at baseline and after six weeks of treatment while an adipose tissue biopsy was obtained at baseline. The content of n-3 PUFA in adipose tissue and neutrophils was analyzed by gas chromatography, while plasma levels of sCD36 were determined using an enzyme-linked immunosorbent assay (ELISA). After six weeks of supplement plasma sCD36 did not differ between supplements (P = 0.18). There was no significant correlation between plasma sCD36 levels and n-3 PUFA in neutrophils at baseline , after six weeks supplement  or in adipose tissue . This study therefore does not provide evidence for a cardioprotective effect of n-3 PUFA acting through a CD36-dependent mechanism. CD36 is a scavenger receptor involved in lipid uptake and inflammation. Recently, non-cell-bound CD36 (sCD36) was identified in plasma and suggested to be a marker of lipid accumulation in the vessel wall. Marine  Several mechanisms including antithrombotic, antiarrhythmic, triglyceride lowering and antihypertensive effects of marine n-3 PUFA have been suggested as explanation for these effects. Recently, much focus has been on anti-inflammatory effects of n-3 PUFA [Epidemiological studies have shown an inverse association between fish consumption and morbidity or mortality from cardiovascular disease (CVD) ,2,3,4. Tntribute ,3,4,5,6.l trials ,8,9,10 hn-3 PUFA  as inflan-3 PUFA . CD36 is a membrane protein containing two transmembrane areas, two short intracellular domains and a large extra-cellular loop . In relaObesity is associated with an increased risk of CVD, at least partly related to an associated increased occurrence of risk factors for CVD like diabetes mellitus, hypertension and dyslipidaemia, but probably also low-grade chronic inflammation ,19. n-3 PUFA intake and levels of CD36. Studies have been performed in LDL receptor null mice and human dermal microvascular endothelial cells but not in humans [Little is known about the association between n humans ,21.n-3 PUFA in a low—but generally recommended dose for prevention of CVD—of approximately 1 g per day decreased plasma levels of sCD36 in overweight subjects. Secondary aims were to assess whether short-term and long-term biomarkers of n-3 PUFA intake were correlated to sCD36.The aim of the present study therefore was to investigate whether a supplement with marine Fifty overweight subjects were included in the study, but one subject, randomized to olive oil, dropped out due to gastrointestinal discomfort. No changes in medications or diets in either group during the 6-week period of supplementation were reported. n-3 PUFA group  and β-blockers in the FA group . The subn-3 PUFA (eicosapentaonoic acid (EPA), docosahexaenoic acid (DHA) and docosapentaenoic acid (DPA)) increased significantly in the n-3 PUFA group when compared to controls (P < 0.01). There was no alteration in the content of saturated fatty acids neither within nor between the n-3 group and the control group . Despite a small increase in sCD36 in the n-3 PUFA group and a small decrease in the control group, there was no significant difference between treatments after 6 weeks (P = 0.18) .At baseline, sCD36 levels were significantly higher in the control group than in the  = 0.18) . The mear = 0.01, P = 0.92), DPA  and DHA  or total n-3 PUFA  in adipose tissue at baseline, and this result was not affected by adjustment for possible confounding effects of age, gender and BMI.Plasma levels of sCD36 showed no significant association with the content of EPA , and there was no significant association between sCD36 and n-3 PUFA neither at baseline  nor after 6 weeks’ supplement . After adjusting for confounding effects of age, gender and BMI, the results remained insignificant.Changes in sCD36 from baseline to day 42 showed no correlation with changes in n-3 PUFA is associated with cardiovascular protection, and we hypothesized a reduction in CD36 after supplementation with marine n-3 PUFA. In the present study, we measured plasma levels of sCD36 at baseline and after six weeks of treatment with n-3 PUFA or a control oil (olive oil). We measured n-3 PUFA in both neutrophils and adipose tissue. The composition of adipose tissue reflects long-term (months/years) dietary fatty acid intake [n-3 PUFA and no correlation between long or short-term intake of n-3 PUFA and plasma levels of sCD36, and therefore the cardioprotective effect of n-3 PUFA seems not to be mediated through a modulation of CD36 levels.Several studies have indicated that sCD36 may reflect tissue CD36 levels, and sCD36 has been proposed to be a marker of CVD. Supplementation with marine d intake , whereasn-3 PUFA and CD36 has been studied by others. Thus, in an animal study [n-3 PUFA for 32 weeks. Membrane-bound CD36 from macrophages was extracted, and there was no significant association between intake of n-3 PUFA and CD36, an observation that is in line with our results. Madonna et al. [n-3 PUFA on CD36 using human dermal microvascular endothelial cells. After stimulation with insulin, an increased surface expression of CD36 was observed; this is in line with the consistent finding of elevated sCD36 in conditions with hyperinsulinemia, such as obesity, type 2 diabetes mellitus and polycystic ovary syndrome [The effect of marine al study  LDL recea et al.  studied syndrome . Interessyndrome . This efn-3 PUFA and olive oil was published [n-3 PUFA/day , 2 g of n-3 PUFA/day or 1 g of olive oil/day. All subjects were treated with aspirin and statins. After two weeks of treatment, the subjects who received 1 or 2 g of n-3 PUFA had a reduction in platelet reactivity, but there was no significant change in surface expression of platelet-CD36. Recently another randomized placebo-controlled study with ublished . Thirty n-3 PUFA, and we cannot exclude that the dose was too low to exert any effect on sCD36. However, we used the dose of approximately 1 g of marine n-3 PUFA recommended by the European Society of Cardiology and the American Heart Association for prevention of CVD [n-3 PUFA group than in the control group at baseline which might potentially render it more difficult to show any effect of n-3 PUFA. Another important limitation of our study was that 42 of the participants were treated with lipid-lowering drugs and 36 were treated with other anti-hypertensive drugs. Furthermore, randomization was not successful with respect to use of aspirin and β-blockers which was higher in the n-3 PUFA group than in controls. Medications may be a confounder to a potential n-3 PUFA-induced alteration in CD36 as a previous study reported that statins reduce CD36 [n-3 PUFA group had a sCD36 value of 3.12 at day 42 which is extremely high. To make sure it was not an analytical error, the measurement of sCD36 was repeated and confirmed. We chose not to exclude the subject though the sCD36 is more than 6 × SD from the mean sCD36. The study cohort comprised 50 subjects, and it could be argued that a larger cohort might have given significant and clear-cut results. Also, it could be questioned whether olive oil is actually inert or has an effect on CD36. Indeed, a study of eleven healthy subjects who ingested 50 mL olive oil after 1 h showed an upregulation in CD36 in peripheral blood mononuclear cells [In our study we used a low dose of n of CVD ,26. For uce CD36 , and typuce CD36 . Finallyar cells . Resultsn-3 PUFA from both neutrophils (short-term intake) and from adipose tissue (long-term intake) from which we could further explore the association between dietary intake of n-3 PUFA and plasma sCD36. The study design also had some strengths. It was a randomized controlled study with two parallel groups in a relevant group of overweight patients with an increased risk of vascular disease. We measured Fifty overweight subjects, aged between 30 and 75, were recruited from the patients of the Department of Cardiology and the medical staff at Aalborg Hospital, Denmark . The incn-3 PUFA (640 mg EPA and 480 mg DHA) or 2.0 g of olive oil daily for six weeks. The study was approved by the local ethical committee, and all participants gave informed consent. The trial was registered at Clinicaltrials.gov (NCT00885053).The study design has been described in detail elsewhere . Briefly3-EDTA and frozen at −80 °C. To eliminate inter-assay variability, samples from each participant were analyzed in the same analytical run for all parameters. Adipose tissue samples were taken at baseline from subcutaneous fat of the buttocks as previously described [Blood samples were collected at baseline and after six weeks of supplement from a cubital vein after the participants had been fasting for at least 10 h. Blood samples were anticoagulated with Kescribed .2O were added to the samples and centrifuged. Adipose tissue was dissolved in 1.0 mL heptane, methylated for 2 min. at 50 °C using 50 µL 2 M potassium hydroxide in methanol and subsequently centrifuged. Fatty acid composition of neutrophils and adipose tissue was analyzed by gas chromatography using a Varian 3900 GC supplied with a CP-8400 auto sampler and a flame ionisation detector . To identify the individual fatty acids, commercially available standards  were used.The extraction of neutrophils from blood has previously been described in detail , and totsCD36 was measured by an in-house enzyme-linked immunosorbent assay (ELISA) using plasma anticoagulated with EDTA as previously described . A pool A food questionnaire was used to collect information of the subjects’ daily intake of fish at baseline, distinguishing between fatty and lean fish as previously described .t-test, with Welch correction when variances differed between groups. Associations between biomarkers of short-term (neutrophils) and long-term (adipose tissue) dietary intake of n-3 PUFA and plasma sCD36 were assessed by Pearson correlations. As an explanatory analysis, we also assessed these associations with a multiple linear regression model in which selected potential confounders  were included. Logistic transformation was applied when appropriate, and model assumptions were subsequently found to be adequate, using relevant graphical plots.Differences between treatment groups in changes in sCD36 over time were analyzed by unpaired P < 0.05 was considered significant. Stata version 11.2  was used.Statistical tests were two-tailed, and n-3 PUFA for six weeks had no effect on plasma levels of CD36. Furthermore, there was no correlation between sCD36 and n-3 PUFA content in neutrophils and adipose tissue. The study therefore does not provide evidence for a cardioprotective effect of marine n-3 PUFA acting through a CD36-dependent mechanism. Although it cannot be excluded that other doses of marine n-3 PUFA could have an effect on plasma sCD36 levels.Supplementation with 1.1 g of


I), and the fraction of potential adults excluded from parentage (PE) were 1.1×10(−39) and 0.999987, respectively, for the 109 SNPs combined. The availability of a well-characterized set of 163 parentage SNPs facilitates the development of high-throughput genetic technologies for implementing accurate and economical parentage testing and traceability in many of the world’s sheep breeds.DNA-based parentage determination accelerates genetic improvement in sheep by increasing pedigree accuracy. Single nucleotide polymorphism (SNP) markers can be used for determining parentage and to provide unique molecular identifiers for tracing sheep products to their source. However, the utility of a particular “parentage SNP” varies by breed depending on its minor allele frequency (MAF) and its sequence context. Our aims were to identify parentage SNPs with exceptional qualities for use in globally diverse breeds and to develop a subset for use in North American sheep. Starting with genotypes from 2,915 sheep and 74 breed groups provided by the International Sheep Genomics Consortium (ISGC), we analyzed 47,693 autosomal SNPs by multiple criteria and selected 163 with desirable properties for parentage testing. On average, each of the 163 SNPs was highly informative (MAF≥0.3) in 48±5 breed groups. Nearby polymorphisms that could otherwise confound genetic testing were identified by whole genome and Sanger sequencing of 166 sheep from 54 breed groups. A genetic test with 109 of the 163 parentage SNPs was developed for matrix-assisted laser desorption/ionization–time-of-flight mass spectrometry. The scoring rates and accuracies for these 109 SNPs were greater than 99% in a panel of North American sheep. In a blinded set of 96 families , each parent of every lamb was identified without using the other parent’s genotype. In 74 ISGC breed groups, the median estimates for probability of a coincidental match between two animals (P Significant gains in efficiency are realized in production systems that use teams of rams for breeding ewes. These advantages include fewer enclosures and equipment, reduced labor, and increased mating efficiency. However, the success of genetic evaluations systems is directly affected by the accuracy of pedigrees. Misidentification of parents reduces the genetic gain and is economically disadvantageous E) is the probability that an alleged parent would be excluded from parentage There are numerous theoretical approaches for DNA-based parentage assignment. These range from simple exclusion, to categorical and fractional allocation, to genotype reconstruction E value in as many breeds as possible. SNPs with high PE values also tend to have a low probability of identity (PI) values, i.e., the probability that two animals selected at random from the same population would have identical genotypes An important and challenging use of SNP parentage testing is the assignment of one parent without knowledge of the other parent’s genotype. For this application, a candidate parent with a homozygous genotype is excluded when the offspring has the opposing homozygous genotype. Achieving accurate parentage assignment without the other parent’s genotype, while also keeping the number of SNPs  to a minimum, requires that each “parentage SNP” has a high PFour sets of parentage SNPs have been used with Australian and New Zealand sheep since the Ovine SNP50k BeadArray was reported by the International Sheep Genomics Consortium (ISGC) The present article describes the identification and characterization of 163 parentage SNPs with the exceptional overall qualities for use in diverse breeds of sheep, and a subset of 109 parentage SNPs developed for use in North American sheep. Of these 109 parentage SNPs, 34, 44, 55, and 56 were also contained within the four international parentage sets, respectively, and thus provide reference markers for standardization between sets. The set of 109 parentage SNPs also contained 22 that had not previously been identified or used in any parentage SNP set. The combined power of the 109 SNPs for determining parentage and tracing animals appeared to be suitable for use in many breeds throughout the world.Starting with 47,693 autosomal SNPs on the Ovine SNP50 Bead Array, markers were evaluated in a step-wise fashion by multiple criteria to identify those with desired properties . A SNP wThe 163 parentage SNPs were, on average, highly informative in 48 of the ISGC breed groups (±5.1). The average MAF for 163 SNPs across all 74 breed groups was 0.33±0.04. The names, MAFs, GenBank accession numbers, and other features of the 163 parentage SNPs are provided in Multiplex assays of 57 and 52 SNPs were developed starting from the 163 parentage SNPs, as described in the Materials and Methods. In the first round of development, 119 parentage SNPs were selected for testing and 117 markers produced quality genotypes in a U.S. panel of 96 rams (assay conversion rate 98.3%). Comparison of these genotypes with those derived from Sanger Sequencing and the OvineSNP50 Bead Array indicated that eight of the 117 SNPs did not meet the cutoffs for 97% scoring rate  and 99% accuracy (data not shown). These eight MALDI-TOF MS assays were omitted from subsequent rounds of MALDI-TOF MS multiplex assay development. The remaining MALDI-TOF MS assays for 109 parentage SNPs (multiplexes of 57 and 52 SNPs) were used to genotype 95 tetrad families . ThirteeIn the U.S. panel of 96 rams, there were 10,464 SNP genotypes possible from the 109 SNPs and 10,452 of them were reported . When the 10,452 MALDI-TOF MS genotypes were compared to those from Sanger sequencing and the Ovine SNP50k Bead Array, 10,431 were in agreement and 21 were discordant owing to MALDI-TOF MS genotyping errors (99.80% SNP accuracy). In the set of 380 sheep from the 95 tetrad families, there were 41,420 genotypes possible from the 109 SNPs and 41,037 of them were reported . The SNP call rate for complete tetrad families was also relevant for parentage analysis . In the 95 tetrad families, genotypes from all four family members were reported in 10,061 of the 10,355 possible cases . In these 10,061 sets of tetrad genotypes, there were 10,047 inheritance patterns that were consistent with Mendelian expectations (99.86%).The 95 tetrad families were used to evaluate the use of the 109 parentage SNP MALDI-TOF MS assays for assigning exactly two parents to each offspring without using genotypes from the other parent. These families had germplasm derived from 11 breeds of sheep . A notewI and PE for each SNP was estimated from its genotype and allele frequencies as described in the Materials and Methods  and 9.3×10−45 (Gulf Coast Native), respectively. Thus, for the purposes of traceability, it would be unlikely that two samples with matching genotypes would be from different animals.For each of the 74 ISGC breed groups, the P Methods . AlthougAfshari, . The witE was estimated under stringent conditions where genotype information from only one parent was used . Among unrelated candidate parents, the median within-breed PE for the combined 109 parentage SNPs was estimated to be 0.999987  to the Namaqua Africaner (0.992), there was no apparent geographic bias. Assuming unrelated parents, breed groups within 0.000005 of the median PE were from Australia, Bangladesh, Brazil, China, Germany, Indonesia, Iran, Jamaica, Spain, Sumatra, Switzerland, and the United Kingdom. As expected, close relationships between candidate parents reduced the PE by two to six orders of magnitude, depending on the breed and the relationship  to the predicted frequency of opposing homozygotes derived from the average genotype frequencies . In this analysis, no correction was made for any family relationships among the parents. The average measured PE was 0.113±0.037 for comparisons of 190 adults with the 190 offspring in the 95 tetrad families. The average predicted PE calculated for 109 SNPs in the same 380 members of the 95 tetrad family panel (i.e. a random adult and a random offspring) was 0.129±0.021. The average difference between the measured PE and the predicted PE of a SNP was 0.016±0.033. Thus, the predicted PE indicated that 1.6% more parents would be excluded by each SNP than were actually excluded. This likely reflects the impact of family relationships between some of the ewes.The accuracy of the PThis report describes the identification of 163 SNPs with exceptional qualities for use in parentage testing and traceability in globally diverse breeds of sheep. The application of stringent selection criteria identified SNPs that have a high degree of informativity and are amenable to accurate scoring by a variety of genotyping technologies. These SNPs are relatively unencumbered with negative attributes such as indels, repetitive structures, and unknown flanking SNPs, and thus more likely to perform well when interrogated by present and future genotyping technologies. A subset of 109 SNPs was also developed for a MALDI-TOF MS platform and used with success in “one-parent” parentage testing in U.S. sheep breeds. All 163 SNPs and the multiplex MALDI-TOF MS assays for the subset of 109 SNPs are available for world-wide use without restriction. Alternatively, other subsets from the 163 SNPs could be tailored to specific breeds and still have substantial overlap with existing SNP sets. If needed, more SNPs with similar properties could be developed and added. However, genetic linkage between SNP alleles increases as their distance decreases. The current average distance between the 163 SNP markers (15.3 Mb) is already small enough that a significant degree of haplotype sharing is expected between breeds Until recently, commercial and research laboratory parentage testing in sheep was accomplished with sets of eight to 24 multi-allelic simple tandem repeat markers  E estimates, poor quality assay designs, inefficient genotyping platforms, or degraded DNA samples from the field could result in parentage tests without sufficient discriminatory power. Ultimately, the usefulness of any set of parentage SNPs in a given population is determined locally by those who use it. The present report describes a commercial test for U.S. sheep that shares significant overlap with other contemporaneous international tests and provides a starting point and a rationale for designing other sets customized for local breeds.Several factors may reduce the chances of success when applying the present results to other breeds and real world production settings. Inaccurate PPrior to their implementation, all animal procedures were reviewed and approved by the care and use committees at the United States Department of Agriculture (USDA), Agricultural Research Service (ARS) Meat Animal Research Center (USMARC) in Clay Center, Nebraska.The ISGC collected and genotyped samples from 2,819 sheep from 74 breeds as part of a large study into genetic diversity and the impact of selection after domestication Ovis aries. Additional animals were recruited that either had previously been used in the construction of genomic resources for the sheep genome Ovis Canadensis) and Thinhorn  populations of North America. Each genome was sequenced to a depth of approximately 10-fold mapped read coverage with Illumina GAII (unpublished). Prepublication access to the .bam files was provided under the Toronto guidelines for data users The ISGC had selected 75 animals for WGS to extend its investigation of genetic diversity and selection in the world’s sheep breeds The USMARC Sheep Diversity Panel version 2.4 (MSDPv2.4) consists of 96 rams from Dorper, White Dorper, Dorset, Finnsheep, Katahdin, Rambouillet, Romanov, Suffolk, Texel, USMARC composite , has been reclassified as a non-family member because the genotypes from multiple disperse loci indicate it is not the sire of the twin offspring. The corresponding composite ewe (200023372) and her twin lambs (200440264 and 200440265) have also been removed. The remaining 95 families . The frequency of minor homozygotes in a population is critical for parentage exclusion based on opposing homozygous genotypes. For SNPs with a MAF≥0.3, the minor allele nucleotide is often different among breeds. A simple average of the MAF among all animals leads to an inflated estimate. Thus, to correctly calculate the average MAF among breeds, the MAF must first be estimated within breed, regardless of which nucleotide is the minor allele. The minor allele for each of the 163 parentage SNPs is identified in For each of the 356 candidate SNPs , set D, A multi-phase iterative strategy was used to validate assay development and check concordance of diplotypes derived from MALDI-TOF MS with those derived from the Ovine SNP50k Bead Array and Sanger sequencing. Prior to the development, the cutoffs for call rate and accuracy were set at 97% and 99%, respectively. Although, these cutoffs are relatively high, they are well within the capability of today’s DNA testing technology and lend substantial efficiency to testing when met. In each phase of development, the samples were blinded, scored, and decoded. Adjustments in assay conditions were made between phases of development. Genotyping was performed at GeneSeek  with the Sequenom MassARRAY platform and iPLEX GOLD chemistry according to the manufacturer’s instructions . In the first phase, two multiplex assays were attempted with approximately 60 of the 163 parentage SNPs in each multiplex. The expectation was that some SNPs would not advance to subsequent rounds. Within each multiplex design, software settings were adjusted and markers grouped to maximize the number of autosomes represented, spacing between markers, and overlap with parentage SNPs from the other sources. Multiplex information and primer sequences are provided in I for locus A with SNP alleles A1 and A2, was the sum of the squares of the three genotype frequencies: PI =  (χ11)2+ (χ12)2+ (χ22)2, where χ11, χ12, and χ22 were the relative genotype frequencies of A1A1, A1A2, and A2A2, respectively I for multiple SNP markers was the product of the PI for each individual marker. The underlying assumption was that the marker spacing was sufficient for meiotic recombination to cause alleles to be randomly associated with one another. However, as parentage SNP density increases, the validity of this assumption is decreased. Thus, it is recognized that the combined PI for 163 parentage SNPs is an underestimate of the probability of a coincidental match between random animals from the population owing to linkage disequilibrium between SNPs on the same chromosomes.The PE were estimated without the use of the other parent’s genotype information and, thus, exclusion was based only on the frequency of the opposing homozygous SNP genotypes in the offspring and the purported parent. Briefly, the probability of opposing SNP homozygotes (POH) between a random offspring and a random eligible adult at SNP locus A with alleles A1 and A2, was calculated as follows: POH = (χ11offspring)(χ22adult)+(χ22offspring)(χ11adult), where χ11 and χ22 were the relative genotype frequencies of A1A1 and A2A2, respectively for the adults or offspring groups. The frequencies of homozygous SNP genotypes were assumed to be the same within a breed group regardless of age. Thus, for a single biallelic SNP, PE = POH = 2(χ11)(χ22) when one of the parent’s genotypes are unavailable. This represents the fraction of eligible adults that would be excluded from parentage at one locus, averaged over all comparisons between offspring and adults. Without using the other parent’s genotype information, the combined PE for multiple SNPs was as follows: PE(SNPn) = PE(SNP1)+R1PE(SNP2)+R2PE(SNP3) …+Rn-1PE(SNPn), where PE(SNP1) represents the fraction of eligible adults excluded by the first SNP and R1 is the remaining fraction of unexcluded adults. R2 to Rn-1 are remaining fractions of unexcluded adults after each round of subsequent testing with n parentage SNPs. Thus, for 163 parentage SNPs, the combined PE for unrelated parents is given by: PE(163) = PE(1) + R1PE(2) + R2PE(3) …+ R162PE(163). As was the case with combined PI, the combined PE for 163 parentage SNPs is an underestimate of the probability that a random alleged parent would be excluded from parentage owing to linkage disequilibrium between SNPs on the same chromosomes. For related parents, the PE for each SNP was multiplied by a coefficient of relatedness (r), where r = 0.125, 0.250, or 0.500 E(163) for related parents = (rPE(1)+rR1PE(2)+rR2PE(3) …+rR162PE(163)).In this report, all PFigure S1Distribution of SNP informativity in ISGC breed groups. The MAF was calculated for 47,963 autosomal SNPs in the Ovine SNP50k Bead Array for each of the 74 ISGC breed groups. SNPs with a MAF greater than or equal to 0.3 in an ISGC breed group were defined as highly informative in that group.(TIF)Click here for additional data file.Table S1Features of 163 sheep parentage SNPs.(XLSX)Click here for additional data file.Table S2I, and PE for each 163 sheep parentage SNPs in breeds from around the world.Statistics for allele frequency, P(XLSX)Click here for additional data file.Table S3OvineSNP50k Bead Array genotypes for 2,819 ISGC sheep and 163 parentage SNPs.(XLSX)Click here for additional data file.Table S4List of 587 SNPs from four parentage sets.(XLSX)Click here for additional data file.Table S5Consensus reference genotypes for 96 U.S. sheep (MSDPv2.4) and 163 parentage SNPs.(XLSX)Click here for additional data file.Table S6Oligonucleotide sequences for multiplex MALDI-TOF MS assays of 109 parentage SNPs.(XLSX)Click here for additional data file.Table S7MALDI-TOF MS genotypes for 380 U.S. sheep  and 109 parentage SNPs.(XLSX)Click here for additional data file.