P values are permuted by rotation with respect to the genomic locations of the SNPs. Once these “simulated” P values are assigned, the joint gene P values are calculated using Fisher’s combination test, and the association of pathways is tested using the hypergeometric test. The circular genomic permutation approach was applied to a human genome-wide association dataset. The data consists of 719 individuals from the ORCADES study genotyped for ∼300,000 SNPs and measured for 51 traits ranging from physical to biochemical measurements. KEGG pathways (n = 225) were used as the sets of pathways to be tested. Our results demonstrate that the circular genomic permutations provide robust association P values. The non-permuted hypergeometric analysis generates ∼1400 pathway-trait combination results with an association P value more significant than P ≤ 0.05, whereas applying circular genomic permutation reduces the number of significant results to a more credible 40% of that value. The circular permutation software (“genomicper”) is available as an R package at http://cran.r-project.org/.Genome-wide association studies (GWAS) aim to detect single nucleotide polymorphisms (SNP) associated with trait variation. However, due to the large number of tests, standard analysis techniques impose highly stringent significance thresholds, leaving potentially associated SNPs undetected, and much of the trait genetic variation unexplained. Pathway- and network-based methodologies applied to GWAS aim to detect associations missed by standard single-marker approaches. The complex and non-random architecture of the genome makes it a challenge to derive an appropriate testing framework for such methodologies. We developed a rapid and simple permutation approach that uses GWAS SNP association results to establish the significance of pathway associations while accounting for the linkage disequilibrium structure of SNPs and the clustering of functionally related elements in the genome. All SNPs used in the GWAS are placed in a “circular genome” according to their location. Then the complete set of SNP association  Genome-wide association studies (GWAS) have successfully identified many loci associated with complex traits and diseases  to establish the significance of gene sets and pathway associations. The objective was to develop a method that would increase our understanding of the pathways involved in various traits and to detect the effects that would have been missed by traditional single-marker analysis without generating an excess of false-positive pathway associations. We explore the performance of pathway-based approaches applied to GWAS using this new “circular genomic permutation” approach. This approach is equivalent to the simulation of pathways with the same number of genes as the pathway under study and with a structure that faithfully reflects that found in the genome with regard to conservation of LD and relative location of features (SNPs and genes), but with the SNP effects on the phenotype randomized and no real pathway effects. This allows us to obtain appropriate distributions of the test statistics under the null hypothesis of no association for every pathway/trait combination surveyed. We compare our approach with that of applying the hypergeometric test alone, which assumes that the null distribution is hypergeometric, or of generating a null distribution by random permutations at the SNP and the gene levels, which does not take full account of genomic architecture. In addition, we attempt to measure the variation explained by the SNPs acting under a pathway.We have developed a permutation approach that uses GWAS results .The cohort consisted of 719 individuals measured for 51 traits ranging from physical to biochemical measurements. These traits include blood pressure, height, waist circumference measurements, and cortisol and cholesterol levels, among others . Individuals were genotyped using the Illumina HumanHap 300v2 array . The SNP association P values were calculated for each trait as described here. Each trait was adjusted for sex and age, and the residuals were transformed to ensure their normal distribution using quantile normalization. The mixed linear model mmscore function of the GenABEL package (http://www.R-project.org) was used to test association between SNP and trait under an additive model for the SNP fixed effect. This score test for family-based association takes into account pedigree structure and allows unbiased estimations of SNP allelic effects when relatedness is present between individuals in the population sample   were extracted from the NCBI Gene database . The SNP-to-gene annotation was performed by linking the SNP genomic location and the gene genomic locations. Two annotation datasets were created according to the physical distance of the SNPs to the genes. “Distance 0” refers to the dataset where the SNPs were annotated to a gene if they were located within the start and end of transcription of the gene . The second annotation annotated SNPs to genes if they were within a 20 kb distance of the gene(s), also from the start to end of transcription. SNPs from both datasets may be annotated to more than one gene, and many SNPs do not annotate to any gene.The genomic location of every SNP in the dataset was updated using the WGAViewer software annotation tool . To apply the hypergeometric test, we needed one P value per gene; however, the number of SNPs mapped to a specific gene varies across the whole dataset, and therefore, a variable number of SNP P values of association are assigned to each gene. The joint gene P value was calculated using the Fisher’s combination test as described by iP represents the SNP association P values, and K is the number of SNPs to be combined.KEGG pathways were used as the predefined gene sets. The significance of the association of a pathway with each trait was evaluated using the hypergeometric test. Under the assumption of independent variables, the hypergeometric test gives the probability of having P value is less than α ≤ 0.05. The hypergeometric test was calculated for the 51 traits and for each of the 225 pathways in the KEGG database  and are described below.We used circular genomic permutations, gene-level random permutations, and SNP-level random permutations to obtain the distributions of the hypergeometric test pathway-association P values are ordered according to their genomic position, first by chromosome and then by their location in the chromosome, including those SNPs genotyped in our data but not annotated to any gene. We consider the genome to be circular and ordered from chromosome 1 to chromosome X and restarting at chromosome 1 again. Then the complete set of SNP association P values are permuted by rotation with respect to their genomic locations, i.e., a random number between 1 and the total number of SNPs is drawn, and the P value associated with the first SNP in the genome rotates to that of the random number-th SNP and all other P values rotate to the same degree to the corresponding SNPs. SNPs thus retain the same position with respect to each other but, at each permutation, gain new random values of association with adjacent P values showing similar patterns of correlation as found in the original data. For each permutation, the joint gene P values are calculated using Fisher’s combination test followed by the hypergeometric test  correlations among them. Both random permutation procedures consisted of 10,000 runs on every trait for all the 225 pathways.To explore how circular permutations perform compared with random permutations, alternative permutation strategies were applied to the distance 0 dataset. These permutations randomly attributed the observed trait-association P values are randomly given to n SNPs, where n is the total number of SNPs per gene for each trait in the initial GWAS. This simulates a situation where SNPs within a gene are not correlated due either to LD or to their collocation within a gene. Random selection of SNP P values is followed by the Fisher’s combination test and the hypergeometric test.The difference between the SNP-level random permutation and circular permutation is that the association P values where calculated once for the original (non-permuted) dataset. This permutation consists of re-sampling genes according to the number of genes represented in our dataset per pathway with their P values. This simulates a situation where genes within a pathway are not correlated due to any spatial or functional clustering within the genome.For the gene-level random permutations, the gene P values were taken for further analyses. Approximately 42% of the SNPs were successfully annotated to a gene  at distance 0. The percentage of annotated SNPs rises to ∼61% for the 20 kb annotation dataset. According to the NCBI gene statistics, currently there are ∼42,059 unique human gene identifiers . The numbers of unique gene identifiers observed in the datasets were 17,806 and 30,580 for distances 0 and 20 kb, respectively. The analyzed pathways have a total of 5859 unique genes. We found 4403 genes mapping to pathways in the distance 0 dataset, whereas in the 20 kb dataset, the number goes up to 5595 genes.After quality control and genome-wide analysis of the data, 318,235 SNP association P ≤ 0.05 threshold, we would expect ∼574 significant results by chance alone. The hypergeometric results of the distance 0 dataset produced 1423 tests below the 0.05 threshold; therefore, more than twice the number of significant tests than expected by chance was observed. The number of significant tests below the 0.05 threshold on the 20 kb annotation increased to 1805.For each dataset, 11,475 hypergeometric tests were performed (from a combination of 225 pathways and 51 traits). At a Table S2). The range of the pathway significance thresholds observed across all the tests  was very variable . However, for most of the pathway-trait combinations, the hypergeometric-theoretical P value of 0.05 was too liberal for the empirical threshold of 0.05. For the empirical P value threshold of 0.05, the inter-quartile distribution  corresponded to P values from ∼0.02 to ∼0.03. As a result, more than 60% of the hypergeometric tests that were classified as significant when using the hypergeometric test alone do not remain statistically significant at α (P ≤ 0.05) if the empirical pathway significance thresholds are used. The opposite was also observed for some trait-pathway combinations; thus, five results with the hypergeometric-theoretical P value less significant than α , are deemed to be significant according to the permutations. These results correspond to four different pathways and five different traits.The results based on the empirical pathway significance threshold found 536 pathway-trait tests to be statistically significant (variable . For disTable S3). The pathway significance thresholds were smaller than those observed at distance 0. The inter-quartile threshold range was from ∼0.008 up to ∼0.02, and the maximum pathway significance threshold set was equal to 0.093 (P ≥ 0.05). However, these tests are not the same as the ones detected by the distance 0 analysis. The analyses at distances 0 and 20 kb share a total of 198 (∼34%) significant tests (Table S4).A total of 594 tests were found to be significant at the empirical threshold . The hypergeometric-empirical P values deviate notably from the estimated probability of the hypergeometric test; for most tests, the hypergeometric-empirical P values are larger  than those obtained by the hypergeometric test . For example, pathways containing from 1 to 25 genes would belong to category A, while pathways containing 26–50 genes would belong to category B. The pathways analyzed consisted approximately of 100 different pathway sizes according to the number of genes, ∼200 according to the SNPs, and ∼130 according to the SNP-gene ratio. To assign the pathways to their representative category, we used the k-means algorithm , implying that the number of SNPs in the pathways may influence the differences between the two methodologies. However, this was not surprising as the P values vary widely between the theoretical and the empirical approaches, especially for those extreme-sized pathways where more significant results  were observed by the theoretical-hypergeometric test, but when analyzed through permutations, these results obtain very large P values.To investigate further the causes of the difference between the hypergeometric-theoretical lgorithm  within R results . Howeveri.e., distribution of P values for each pathway-trait combination for both random permutations and the circular permutations). We explored the distribution of individual tests of the permuted hypergeometric results . The traits glucose and waist-to-height ratio were the only traits found to be significant for the ARVC pathway. Four other pathways were significant for the same two traits: cardiac muscle contraction, dilated cardiomyopathy (DCM), hypertrophic cardiomyopathy (HCM), and fc gamma R-mediated phagocytosis. e.g., actin-cytoskeleton reorganization, and phagosomal membrane remodeling)  pathway was selected to investigate how much variance was explained by the SNPs within the pathway. ARVC was selected because it was found to be highly significant in the distances 0 and 20 kb datasets for the trait glucose  .polygenic function of GenABEL was used to calculate the variance explained by the SNPs with an association P value more significant than α (P ≤ 0.05) for the trait glucose and all traits measured for the ARVC pathway. The analysis was performed using the distance 0 annotation. A total of ∼2311 SNPs were mapped to ARVC, from which 175 were below an association P value of 0.05 for the glucose trait. The percentage of the variance explained was given by the difference between the total estimated heritability and the estimated heritability with fixed effects, divided by the total estimated heritability. The estimated percentage of the variance of the trait glucose explained by the SNPs in the ARVC pathway with P value more significant than α ≤ 0.05 was ∼24%. In addition, only three other traits had some of the variance explained by the same SNPs: ∼20% variance of Hba1c ; ∼18% of CRP  (The mixed linear model implemented in the iseases) ; and ∼40In spite of the substantial developments in genetics and genomics, there are many unresolved questions, and the mechanisms driving complex diseases remain very poorly explained. Major advances have become available by sequencing genomes, characterizing their structure, and identifying the common elements across species . DespiteHere we propose a novel approach for testing association of gene sets taking genomic structure and non-independence among SNPs into account: the circular permutation method.−8 . The small number of individuals genotyped in this study results in low power to detect associations reaching the stringent genome-wide threshold. This in turn results in true associations remaining undetected. The aim of the gene-set analysis using circular genomic permutation is to detect associations missed by standard GWAS single-marker association analyses by identifying SNPs with joint effects on a phenotype. Although our study provides a large number of phenotypic data, a larger study would have the potential to yield results that give us a better biological insight of the gene sets.The circular genomic permutation was applied to a small study . Single-marker GWAS analysis detected 11 SNPs across three genomic regions and three traits more significant than 5 × 10P values that are used in our gene-set analysis is recommended.The methodology was applied to a study genotyped on a single platform. In instances where studies use a variety of platforms, imputation of genotypes at loci that are not present in all platforms, prior to the association analysis that will produce the association We included the X chromosome in the analysis (and permutations). LD in chromosome X is higher than for the autosomes . We do ni.e., gene-level and SNP-level random permutations). The hypergeometric-empirical P values of the gene-level random permutations agreed largely with the hypergeometric-theoretical P values because the number of significant genes is constant across all tests and the only source of variation is derived from how many of the re-sampled genes fall in pathways below the α threshold. Not all correlations of the initial SNP association P values are broken in this type of permutation, as the gene P values were derived from consecutive SNPs that were annotated to the same gene. In addition, the simulated pathways would mainly contain non-correlated gene P values, and therefore, the assumption of the hypergeometric test is not violated in this scenario. For the SNP-level random permutations, the SNPs are taken randomly from the genome and a new gene P value calculated from non-correlated SNPs, leading to different number of significant genes in the pathways and also in the universe (complete dataset), consequently generating more variation in the permuted hypergeometric results. In contrast, the circular genomic permutation, which is also performed at the SNP-level where the number of significant genes in the pathway and in the universe are not fixed (similarly to the SNP-level random permutations), seems to capture the genomic structure and shows how likely an outcome is expected according to this structure  and 594 (20 kb) were observed as significant in both analyses, suggesting that the annotation has a substantial contribution to the pathway association results. Some instances in which the distances 0 and 20 kb analyses agreed included two tests of the five in distance 0 found significant through permutations but with a theoretical hypergeometric P > 0.05. They were both confirmed as significant in the 20 kb analyses with a hypergeometric test P value of P = 0.04 and P = 0.001, and both also significant through the circular permutations of 20 kb.Finding a consensus between pathway-based approaches represents a challenge. Several methodologies demand a single association value per gene. Many of the methodologies select the strongest SNP signal of the gene, but doing so will overestimate the association for pathways with long genes. We chose to calculate a single gene P values with the hypergeometric-theoretical P values is very strong. However, the strong correlation only shows the degree of similarity between the ranked order between the empirical P values and the theoretical P values. As shown in P values tend to be larger than those obtained only through the hypergeometric test, which means that the use of the hypergeometric test alone will lead to false-positive results.In both analyses (distances 0 and 20 kb), the correlation of the hypergeometric-empirical P values below 0.05 but not empirically significant at the same level included pathways of different sizes in terms of number of genes, and this pattern was also observed when the tests results were clustered by SNPs .We also looked at the impact of the number of SNPs and the number of genes of the pathway. As shown in P values of association were below 0.05, there is an expected degree of variance explained by the subset chosen for the analysis. Subsequently, the present analysis is likely to overestimate the variance explained by the SNPs, and a better methodology needs to be developed. Still, because of the small sample size of the study (n = 719), we do not claim to detect the link between the ARVC pathway and the trait glucose; to confirm this result, replication in another cohort would be required.We presented as an example the significant pathways and traits linked to one of the most significant pathway-trait associations found through this methodological study. The ARVC pathway groups genes involved in one type of cardiomyopathy (characterized by replacement of the myocardium by fibrofatty tissue). Cardiomyopathy is a dysfunction of the heart muscle highly associated with sudden death, especially in the young . Our resP ≤ 0.05; however, empirical results suggest that ∼60% of them could arise just by chance. In this study, we applied Fisher’s combination test and a hypergeometric test to test for pathway significance; however, this permutation approach can be applied to any SNP-to-gene or SNP-to-pathway P value estimation methodology and to any other gene sets or pathway resources.The rigorous evaluation of pathway methodologies is very challenging, but the application of permutations can help in their assessment. Our results demonstrate that the circular genomic permutations provide robust associations. A thoughtful consideration of the advantages and disadvantages on the window-size annotation should be done according to the objective of the study. Due to the various possible sources of bias, keeping the annotation window size shorter than 20 kb is recommended. Our new methodology permits the spurious associations that are detected when not accounting for genomic structure to be discarded. According to the hypergeometric-theoretical results, ∼1,400 pathway trait results have a


Fst values against genome location. The highest Fst peaks were obtained on BTA6 and BTA13 where some candidate genes are located. We identified selection signatures peculiar of each breed which suggest selection for genes involved in milk or meat traits. The genetic structure was investigated by using a multidimensional scaling of the genetic distance matrix and a Bayesian approach implemented in the STRUCTURE software. The genotyping data showed a clear partitioning of the cattle genetic diversity into distinct breeds if a number of clusters equal to the number of populations were given. Assuming a lower number of clusters beef breeds group together. Both methods showed all five breeds separated in well defined clusters and the Bayesian approach assigned individuals to the breed of origin. The work is of interest not only because it enriches the knowledge on the process of evolution but also because the results generated could have implications for selective breeding programs.In this study we used a medium density panel of SNP markers to perform population genetic analysis in five Italian cattle breeds. The BovineSNP50 BeadChip was used to genotype a total of 2,935 bulls of Piedmontese, Marchigiana, Italian Holstein, Italian Brown and Italian Pezzata Rossa breeds. To determine a genome-wide pattern of positive selection we mapped the The online version of this article (doi:10.1007/s11033-013-2940-5) contains supplementary material, which is available to authorized users. Present day cattle breeds are the result of years of human selection, adaptation to different environments and demographic effects as domestication, migration and selection, all contributing to the actual patterns of genetic diversity , 2. DuriRecently, the availability of high density SNP panels has given the possibility of performing population genetic studies in cattle populations using thousands of markers distributed across the entire genome. Medium density SNP panels have been used for example to analyze the genetic structure of cattle populations –8 to stuWe considered a total of 2,935 bulls belonging to two dairy , two beef (Piedmontese and Marchigiana) and one double purpose  breeds. The Italian Holstein derives from Dutch and North America Holstein breeds imported in Italy in the late XX century and it is currently the most common dairy breed. Piedmontese is mainly located in Northern Italy and it was in the past a dual purpose breed, while today it is selected for beef traits mainly exploiting a private myostatin mutation . MarchigThe aim of this study was to identify genomic regions potentially under selection in the above five Italian cattle breeds using a 54K medium-density SNP panel. Our results could have implications for selective breeding programs by identifying signatures of artificial selection in gene involved in milk, meat or functional traits. We analyzed also the genetic structure of the breeds by classical multidimensional scaling and by Bayesian inference methods.www.geneseek.com). The 50K SNP array contains 54,001 SNPs distributed across the entire genome, with an average SNP spacing of 51 Kb and a proportion of known chromosome positions of about 97 %; SNP positions within each chromosome were based on the Bos taurus genome assembly Btau_4.0 [The initial sample was formed by 2,935 bulls: 761 Italian Brown, 899 Italian Holstein, 323 Piedmontese, 464 Marchigiana and 488 Italian Pezzata Rossa bulls, respectively. The sample represents almost all the bulls available in Italy for all breeds but Holstein, where the bulls analyzed correspond to slightly less than a half of the available ones. Genomic DNA was extracted from semen using the NucleoSpin Tissue kit (Macherey-Nagel) according to manufacturer’s instruction. DNA was checked for quality on agarose gel and quantified using a DTX microplate reader (Beckman Coulter) after staining with Picogreen (Invitrogen). Samples were genotyped using BovineSNP50 Genotyping BeadChips . Genotyping was outsourced to Geneseek . Only autosomal markers were used and SNP with complete map information were used. Sires and markers with a call rate under 99 % were discarded as well as SNPs having a minor allele frequency (MAF) <5 % according to the currently employed thresholds [P < 0.01 in the filtered data set [Data were initially filtered using the GenABEL R package .To determine a genome-wide pattern of positive selection, the lculated . The locigh Fst  thus we  Chesser  using indij = (0.5 − kij) where d is the distance and k is genetic kinship coefficient for sires i and j and then applying classical multidimensional scaling to the complete matrix. STRUCTURE software v. 2.3.2.1 [L(K) represents the Kth LnP(D), m is to the mean of 10 runs and s their standard deviation. We used the method of Evanno et al. [Genetic distance between breeds and sires was estimated calculating the matrix  2.3.2.1  was used 2.3.2.1 , althoug 2.3.2.1  reportedo et al.  to estimo et al. .Elimination of markers on the X chromosome or with incomplete map information left 51,515 SNPs out of the 54,001. A total of 138 sires and 12,388 markers showing a call rate <99 % were excluded. Additional 8,874 SNPs with a MAF <5 % were discarded. Then, a total of 1,443 markers were discarded because they were out of HWE in at least one breed. No sires or markers were excluded after the second check on call rates and allele frequencies. The final complete dataset was thus formed by 2,797 sires  and 29,848 SNPs.Fst values calculated for all autosomal SNPs and averaged over a 8-wide SNP window within each chromosome. Results including all five populations, only dairy breeds and only beef breeds are displayed in panels A, B and C, respectively. Fixation index values containing the 98 % of point are also indicated with green lines. In panel A, Fst ranges from 0.04 to 0.30, with an average value of 0.077 ± 0.058, with the exception of a single Fst= 0 signal on BTA 19. SNP clusters with Fst values above 0.0175 (the 99 % threshold) were located on chromosomes 3–6, 13, 14, 16, 18, 26 and 29. In most cases Fst peaks are formed by only one or two groups of 8 markers, therefore spanning about 4 Mb. More consistent signals are present on BTA13 and on BTA6, where peaks are formed by 11 and 4 groups of markers above the 99 % quantile value. A similar pattern is shown in Panels B and C, where Fst values are calculated for dairy and beef breeds separately; the strongest signals are still observed on BTA6. Fst obtained in dairy breeds are higher than those observed for beef breeds. The peak identified in dairy breeds spans from 72.35 to 72.47 Mb on BTA6  can be observed at ~38 Mb.Figure Fst values for BTA6 comparing each breed against the remaining four  and CTNNBL1  may be related to muscle formation and to body weight, respectively.The eds Fig. b shows aeds Fig. c. Of theFigure To estimate the number of genetic clusters within the 2,797 cattle samples and 29,848 SNPs, a parametric genetic mixture analysis implemented in the STRUCTURE software was performed. Between 2 and 5 clusters  were tested using the admixture model, considering allele frequencies correlated. Consistent results across runs were obtained and a clear clustering of breeds was observed for any K tested Below is the link to the electronic supplementary material.


Pseudotsuga menziesii), one of the most economically and ecologically important tree species in the world, also has one of the largest tree breeding programs. Although the coastal and interior varieties of Douglas-fir (vars. menziesii and glauca) are native to North America, the coastal variety is also widely planted for timber production in Europe, New Zealand, Australia, and Chile. Our main goal was to develop a SNP resource large enough to facilitate genomic selection in Douglas-fir breeding programs. To accomplish this, we developed a 454-based reference transcriptome for coastal Douglas-fir, annotated and evaluated the quality of the reference, identified putative SNPs, and then validated a sample of those SNPs using the Illumina Infinium genotyping platform.Douglas-fir  and 102,623 singletons from 2.76 million 454 and Sanger cDNA sequences from coastal Douglas-fir. We identified 278,979 unique SNPs by mapping the 454 and Sanger sequences to the reference, and by mapping four datasets of Illumina cDNA sequences from multiple seed sources, genotypes, and tissues. The Illumina datasets represented coastal Douglas-fir (64.00 and 13.41 million reads), interior Douglas-fir (80.45 million reads), and a Yakima population similar to interior Douglas-fir (8.99 million reads). We assayed 8067 SNPs on 260 trees using an Illumina Infinium SNP genotyping array. Of these SNPs, 5847 (72.5%) were called successfully and were polymorphic.Based on our validation efficiency, our SNP database may contain as many as ~200,000 true SNPs, and as many as ~69,000 SNPs that could be genotyped at ~20,000 gene loci using an Infinium II array—more SNPs than are needed to use genomic selection in tree breeding programs. Ultimately, these genomic resources will enhance Douglas-fir breeding and allow us to better understand landscape-scale patterns of genetic variation and potential responses to climate change. The availability of high-throughput sequencing methods has led to the discovery of thousands to millions of single nucleotide polymorphisms (SNPs) in diverse organisms, particularly humans, model experimental organisms, and agriculturally important plants and animals. Combined with high-throughput genotyping platforms, SNP markers are having substantial impacts on human medicine as well as plant and animal breeding -3. They Pseudotsuga menziesii (Mirb.) Franco) that could be used to enhance and accelerate Douglas-fir breeding via genomic selection. Tree breeders typically make breeding decisions based on an individual’s breeding value, which is the average performance of an individual’s offspring. Currently, breeding values are estimated from measurements made in progeny tests containing thousands to tens of thousands of trees. Genomic selection, or whole-genome marker-assisted selection . Non-matches  were subsequently screened using BLASTN and two local databases: the NCBI nucleotide collection (nr/nt) and NCBI non-human, non-mouse ESTs (est-others). Assembled isotigs and singletons were filtered if the best hit had a bit-score > 50 and an E-value < 10-10, and the corresponding genus name was found in a custom database of 162,679 bacterial and 59,139 fungal names downloaded from the NCBI Taxonomy database (http://www.ncbi.nlm.nih.gov/Taxonomy)  to assemble the reads in the MG1SANG, MG2454, and SG454 datasets into a single reference transcriptome consisting of isogroups (unigene models), isotigs (presumed transcript variants), and singletons ≥ 100 nt  option, minimum read length (-minlen) of 40 nt, isotig length threshold (-icl) of 40 nt, a large contig threshold (-l) of 100 nt, plus a factorial arrangement of the following parameters: minimum overlap lengths of 35 and 45 nt; alignment difference scores of -2 and -6; and minimum overlap identities of 82% to 98%. The 20 resulting assemblies were evaluated based on the total numbers of isogroups, number of isogroups represented by a single isotig (I1 subset), and the number of isogroups represented by multiple isotigs (IM subset). We also evaluated the assemblies by comparing the assembled isogroups to white spruce unigenes using the approach described below. Based on these evaluations, we performed the final Newbler assembly using a minimum overlap length of 45 nt, alignment difference score of -6, and a minimum overlap identity of 96%. We clustered the resulting isotigs using Vmatch  to form a non-redundant set of sequences, and then calculated the assembly statistics shown in Table We used Newbler v2.3  between Douglas-fir isotigs and white spruce unigenes, and then assigned the isotigs to these classes based on three criteria .We conducted the same SCARF analysis using loblolly pine as the reference, but these analyses were completed after the SNP array was constructed and tested. These analyses were conducted using 35,550 contigs that comprise the first release of the PineDB transcriptome assembly , which assigns GO terms [http://www.chem.qmul.ac.uk/iubmb/), and KEGG pathways [http://www.uniprot.org/). We also assigned GO-slim terms to the isogroups and singletons using the results from the TAIR10 tBLASTX search. We extracted GO-slim terms for the matching Arabidopsis accessions from the TAIR10 database, and then compared the distributions of GO-slim terms for the I1 isogroups, IM isogroups, and singletons versus the distribution of GO-slim terms for all 35,386 Arabidopsis accessions in the TAIR10 database (http://ftp.arabidopsis.org/home/tair/Ontologies/Gene_Ontology/). Finally, we assigned taxonomic affiliations to the isogroups and singletons using the results from the UniRef50 tBLASTX search described above. We extracted the taxonomic assignment for each best-hit, and then summarized them according to the categories shown in Table We annotated the isogroups using a local tBLASTX  search a5 Figure , Step 8.GO terms , EC numbpathways  to proteIllumina short read sequences were mapped to the transcriptome reference to identify SNPs. Some of the short-read sequences contained strings of nucleotides with a quality score of 2 , which Illumina uses to indicate that these calls should not be used for downstream analysis. Therefore, we changed these positions to ‘N’s before read mapping and SNP detection Figure , Step 7.IL and YKIL libraries to infer the orientation of the isotigs and singletons. We used Bowtie v 0.12.7  and cusSANG, MG2454, and SG454) into a single dataset, and then aligned them to the reference using the BWA-SW program with default parameters [IL, YKIL, and INTIL), we used the Novoalign short-read aligner with default parameters . We used SAMTools [PF) of 0.10 in any single dataset or pooled across datasets , and Pi is the SNP probability for each of the five datasets [The first step toward identifying likely SNPs and designing SNP assays was to identify flanking variants (SNPs and indels) using permissive criteria Figure , Step 9.rameters . For theSAMTools  to outpuSAMTools , using adatasets . We thenPS) of 10-2, 10-3, and 10-4  of 10-1, 10-2, 10-3, and 10-4 , genotyping was performed by the UC Davis Genome Center according to protocols from Illumina, and the resulting data were analyzed using Illumina GenomeStudio software v2011.1 .-5 . Finally, we used SAS Proc Logistic and stepwise model selection to determine whether the high-quality SNPs could be predicted from 12 SNP bioinformatic characteristics.We assessed the quality of the resulting SNP loci based on the Illumina GenTrain scores, GenCall scores, SNP call frequencies, MAFs, and probabilities of deviation from HWE Figure , Step 13BAM: Binary version of a SAM (Sequence Alignment/Map) file; CBIL: Coos Bay sequence database produced by Illumina sequencing; cM: centiMorgan; EC: Enzyme Commission; EMBL: European Molecular Biology Laboratory; EST: Expressed sequence tag; GO: Gene Ontology; HWE: Hardy-Weinberg equilibrium; INTIL: Interior Douglas-fir sequence database produced by Illumina sequencing; IUPAC: International Union of Pure and Applied Chemistry; KEGG: Kyoto Encyclopedia of Genes and Genomes; MAF: Minor allele frequency; Mb: Megabase; MG1SANG: Coastal Douglas-fir multi-genotype sequence database #1 produced by Sanger sequencing; MG2454: Coastal Douglas-fir multi-genotype sequence database #2 produced by Roche 454 sequencing; NCBI: National Center for Biotechnology Information; nt: Nucleotide; QTL: Quantitative trait locus; SG454: Coastal Douglas-fir single genotype sequence database produced by Roche 454 sequencing; SNP: Single nucleotide polymorphism; SSR: Simple sequence repeat; YKIL: Yakima sequence database produced by Illumina sequencingThe authors declare that they have no competing interests.IL and YKIL libraries, archived the short-read datasets, provided technical advice, and helped write the manuscript. RC conceived and guided the preparation and sequencing of the CBIL and YKIL libraries and provided strategic advice. SK helped design the plant materials protocol, maintained and harvested plants, and isolated RNA. PD conducted the bioinformatics and statistical analyses used to orient the transcriptome assembly. WWL isolated RNA and worked with JGI to prepare the SG454 dataset. JFDD conceived and guided the preparation of the SG454 dataset. All authors read, edited, and approved the final manuscript.GTH conceived and coordinated the project, helped with the bioinformatics of transcriptome evaluation and SNP detection, designed the Infinium array, analyzed the Infinium results, and wrote the final manuscript with written contributions from JY and BK. JY had primary responsibility for the design and implementation all aspects of transcriptome assembly and annotation, helped with the bioinformatics of transcriptome evaluation and SNP detection, collected plant tissues, isolated RNA, and helped write the manuscript. BK helped prepare and sequence the CBCharacteristics of the reference transcriptome isotigs and isogroups. Information on the isotigs and their associated isogroups is found on the ‘Isotig data (S1)’ worksheet and variable descriptions are found on the ‘Variable descriptions (S2)’ worksheet.Click here for fileCharacteristics of the reference transcriptome singletons. Information on the singletons is found on the ‘Singleton data (S3)’ worksheet and variable descriptions are found on the ‘Variable descriptions (S4)’ worksheet.Click here for fileCharacteristics of the 278,979 putative SNPs summarized in Table 6. Information on the SNPs and their associated isogroups is found on the ‘Target SNP data (S5)’ worksheet and variable descriptions are found on the ‘Variable descriptions (S6)’ worksheet.Click here for fileThe locus summary report from Illumina’s GenomeStudio v2011.1 software and additional derived variables are found on the ‘SNP Infinium results (S7)’ worksheet. Descriptions of GenomeStudio variables  and Click here for file


Syncerus caffer, is one of the most abundant and ecologically important species of megafauna in the savannah ecosystem. It is an important prey species, as well as a host for a vast array of nematodes, pathogens and infectious diseases, such as bovine tuberculosis and corridor disease. Large-scale SNP discovery in this species would greatly facilitate further research into the area of host genetics and disease susceptibility, as well as provide a wealth of sequence information for other conservation and genomics studies. We sequenced pools of Cape buffalo DNA from a total of 9 animals, on an ABI SOLiD4 sequencer. The resulting short reads were mapped to the UMD3.1 Bos taurus genome assembly using both BWA and Bowtie software packages. A mean depth of 2.7× coverage over the mapped regions was obtained. Btau4 gene annotation was added to all SNPs identified within gene regions. Bowtie and BWA identified a maximum of 2,222,665 and 276,847 SNPs within the buffalo respectively, depending on analysis method. A panel of 173 SNPs was validated by fluorescent genotyping in 87 individuals. 27 SNPs failed to amplify, and of the remaining 146 SNPs, 43–54% of the Bowtie SNPs and 57–58% of the BWA SNPs were confirmed as polymorphic. dN/dS ratios found no evidence of positive selection, and although there were genes that appeared to be under negative selection, these were more likely to be slowly evolving house-keeping genes.The African buffalo,  Investigating genetic variation between individuals, populations or species provides the basis for understanding the heritability of traits and phenotypes, and provides researchers with the opportunity to study complex issues in conservation, disease susceptibility, molecular ecology and many other disciplines The development of next-generation sequencing technologies, such as the ABI SOLiD, Illumina GA and HiSeq and Roche 454 platforms, has enabled a faster and more cost-effective approach to generating sequence data and SNP discovery. Each of these platforms has its own particular chemistry and combination of template preparation, sequencing and data analysis, and thus each has its own advantages and disadvantages Syncerus caffer, is one of the most abundant and ecologically important species of megafauna in the savannah ecosystem. There are three recognised subspecies of African buffalo – the Cape buffalo (Syncerus caffer caffer), the West African buffalo (Syncerus caffer brachyceros) and the Forest buffalo (Syncerus caffer nanus). A fourth subspecies, the Central African Savannah buffalo , has also been proposed. All individuals used in this study were Syncerus caffer caffer. African buffalo are an important prey species, as well as a host for a vast array of nematodes, pathogens and infectious diseases The African buffalo, Our goals in this study were to perform next-generation sequencing and mapping of the African buffalo genome using two different software packages, implement large-scale identification of novel SNPs within the African buffalo genome, and determine and compare the SNP validation rates using fluorescent genotyping. This data can then inform future studies.The Stellenbosch University Animal Care and Use Committee (SU ACU) deemed it unnecessary to obtain ethical clearance for this study as the blood samples used for DNA extraction were collected under the directive of SANParks and KwaZulu Natal Wildlife for other purposes, and their use in the present study is incidental.EDTA blood samples from nine Cape buffalo from Hluhluwe iMfolozi Park, in the KwaZulu Natal province of South Africa, were obtained during an annual BTB test and cull operation. Genomic DNA was extracted from the blood samples using the Qiagen DNeasy Blood and Tissue Kit, according to the manufacturer's instructions. Equimolar amounts of DNA from four and five animals were made into two barcoded paired end libraries. Fragment libraries were constructed according to manufacturer's instructions using the SOLiD™ Fragment Library Construction Kit, and sequenced on a single plate of an ABI SOLiD4 sequencer (Lifetech) to generate 50 bp reads.Bos taurus UMD3.1 genome sequence, produced by the Centre for Bioinformatics and Computational Biology (CBCB) at the University of Maryland, was used as the reference genome in this study Bos taurus genome in colour space using two separate packages, BWA Bos taurus reference genome in this study enabled the identification of two classes of SNPs - those that occur between the cow and buffalo, and those that occur within the buffalo population.Due to the absence of an assembled African buffalo reference genome, the Gene annotation was added to the SNPs identified within gene regions, which were defined as from gene start to gene end, including introns. A local Perl script was then used to submit the SNPs within gene regions to the Ensembl v66 Application Programme Interface (API) to determine functional consequence Bos taurus genome build UMD3.1), and Cape buffalo SNPs were substituted into the gene coding sequences at these positions before submission to a local copy of PAML to obtain the dN/dS ratios. P values were obtained by taking twice the absolute difference between the log likelihood of the observed dN/dS ratio and the ratio obtained with dN/dS set to 1 and comparing it to χ2 with one degree of freedom. The Database for Annotation, Visualization, and Integrated Discovery (DAVID) was used to identify whether there were pathways that were overrepresented in the gene lists The ratio of non-synonymous to synonymous substitutions (dN/dS) was calculated for all buffalo genes using PAML www.kbioscience.co.uk), using their competitive allele-specific KASP SNP genotyping platform. Assays were designed for each of the SNPs by KBioscience. The KASP assay system is a competitive allele-specific PCR incorporating a FRET quencher cassette (www.kbioscience.co.uk). All but six of the SNPs validated as polymorphic within Cape buffalo were also validated in the original 9 individuals from the sequencing pool; the six SNPs not genotyped in the original samples were omitted due to technical reasons. KBioscience assay IDs are shown in A selection of 173 SNPs was made from the collective BWA pileup and Bowtie pileup SNP pool based on the predicted consequence and/or gene location of the SNP, in order to use the validation data in a subsequent case-control association study. SNPs were fluorescently genotyped in 87 Cape buffalo by KBioscience  in the Bowtie parameters; this is the proportion of positions that are expected to be polymorphic. With snpfrac at 0.01, 23% of the reads mapped to the reference genome but the validation rate of the SNPs was relatively low . We therefore repeated the mapping with snfrac = 0.001, which reduced the percentage mapping to 21% and increased the validation rate to 54%. The mapped Bowtie reads were all 47 bases in length and covered 1.58GB, which is equivalent to 56% of the bovine genome being covered by at least one read, and represents 2.7× coverage of aligned data. Even fewer reads mapped with BWA (19%), of which 9.7% were PCR duplicates. The BWA reads were trimmed based on the base quality; 61% of reads were 48 bases and the mean length was 44.5 bases. The percentage of 35 bp reverse reads that mapped was very low with both alignment programmes (<2%) and these data were not used. The percentage of exon sequence covered by at least one read was slightly higher (64%) than that of the genome as a whole, which is consistent with exons occurring in more highly conserved regions of the genome.The number of reads generated by the SOLiD sequencing was 442,984,249 in total. Sequence reads have been submitted to the sequence read archive at NCBI (accession number SRA051668.1). Given the relatively large genetic distance between In order to be identified as SNPs, nucleotide variants called by SAMtools had to be supported by at least two reads, and have a base quality of at least 20, indicating 99% accuracy in the call. SNPs identified by GATK had to be classified as ‘PASS’. A flow diagram of the analysis is shown in 173 SNPs were sent for validation on 87 Cape buffalo DNA samples, and 146 were amplified successfully. Of these 146 SNPs, 71 appeared to be monomorphic and 75 were polymorphic. The different mapping and SNP calling methods produced different numbers of validated SNPs . The difhttp://www.genomics.liv.ac.uk/tryps/resources.html) The predicted consequences of the SNPs were obtained from Ensembl and a summary of the numbers of the different types of consequences obtained using the BWA and GATK pipeline is shown in 2 p<0.05). DAVID analysis of the 205 genes with no synonymous SNPs suggested that C-type lectins might be overrepresented in this set (Genes were screened for particularly high or low non-synonymous/synonymous substitution ratios (dN/dS) that might be indicative of positive or negative selection, respectively . No genethis set . In contthis set .Parus major, to the reference genome of the zebra finch, Taeniopygia guttataMeleagris gallopavo, to the sequenced genome of the chicken, and approximately 8000 SNPs were identified in the turkey genome The African buffalo has become a species of interest in recent years due to its role as a wildlife maintenance host for a variety of infectious and zoonotic diseases, such as corridor disease, foot-and-mouth disease and bovine tuberculosis Bos taurus. The most recent common ancestor of the domestic cow and the African buffalo is estimated to have existed approximately 5–10 million years ago (MYA), at the time of the divergence of the subtribes Bubalina, which consists of the Syncerus and Bubalus genera, and Bovina, which is comprised of the Bos and Bison genera et al. et al. de novo assembly (>80× coverage), and mapping to a related species dramatically simplifies the SNP discovery and annotation compared to that required for a de novo assembly.In this study, the Cape buffalo sequence reads were mapped to the reference genome of the domestic cow, Significantly different results were found in this study when using different mapping and SNP calling methods . From thThe non-synonymous/synonymous substitution ratios provided very little evidence for genes being under positive selection, although a large number appeared to be under purifying selection. This may be because this approach was developed for comparing distantly related taxa, and the relatively small number of SNPs between the Cape buffalo and the cow (mean 4.2 per gene) means that the power to detect positive selection is very limited This investigation of the Cape buffalo, a species without an assembled genome, has yielded a wealth of data that will provide useful tools for further study of this species and related aspects.Table S1All data based on BWA mapping.List of the 69 validated SNPs, their Btau4 and UMD3 positions and KBioscience assay IDs, ValidatedSNPs.txt.(TXT)Click here for additional data file.Table S2Complete list of BWA/GATK SNPs with consequences and annotated gene names, BwaSNPwithConsequences.txt.(ZIP)Click here for additional data file.Table S3List of genes that had at least one synonymous or non-synonymous SNP, with dN/dS ratios and p values calculated with PAML, Annotated_dNdSratios.txt.(TXT)Click here for additional data file.Table S4David annotation of genes with dN/dS ratios of 99, which are mainly those with no synonymous SNP, DAVIDAnalysisDnDS = 99.(TXT)Click here for additional data file.Table S5David annotation of genes that had dN/dS ratios significantly less than 1, DAVIDAnalysisDnDS_P0.05.(TXT)Click here for additional data file.


Despite the dramatic reduction in the cost of high-density genotyping that has occurred over the last decade, it remains one of the limiting factors for obtaining the large datasets required for genomic studies of disease in the horse. In this study, we investigated the potential for low-density genotyping and subsequent imputation to address this problem.Using the haplotype phasing and imputation program, BEAGLE, it is possible to impute genotypes from low- to high-density (50K) in the Thoroughbred horse with reasonable to high accuracy. Analysis of the sources of variation in imputation accuracy revealed dependence both on the minor allele frequency of the single nucleotide polymorphisms (SNPs) being imputed and on the underlying linkage disequilibrium structure. Whereas equidistant spacing of the SNPs on the low-density panel worked well, optimising SNP selection to increase their minor allele frequency was advantageous, even when the panel was subsequently used in a population of different geographical origin. Replacing base pair position with linkage disequilibrium map distance reduced the variation in imputation accuracy across SNPs. Whereas a 1K SNP panel was generally sufficient to ensure that more than 80% of genotypes were correctly imputed, other studies suggest that a 2K to 3K panel is more efficient to minimize the subsequent loss of accuracy in genomic prediction analyses. The relationship between accuracy and genotyping costs for the different low-density panels, suggests that a 2K SNP panel would represent good value for money.Low-density genotyping with a 2K SNP panel followed by imputation provides a compromise between cost and accuracy that could promote more widespread genotyping, and hence the use of genomic information in horses. In addition to offering a low cost alternative to high-density genotyping, imputation provides a means to combine datasets from different genotyping platforms, which is becoming necessary since researchers are starting to use the recently developed equine 70K SNP chip. However, more work is needed to evaluate the impact of between-breed differences on imputation accuracy. The introduction of high-throughput, single nucleotide polymorphism (SNP) chips that permit the analysis of large numbers of SNPs in parallel has enabled large-scale studies of human and livestock populations. A common feature of genome-wide association studies (GWAS) is that large sample sizes are needed to ensure sufficient power to detect what are hypothesised to be quantitative trait loci (QTL) with relatively small effects. To validate any detected QTL, both a substantial number of samples for the initial analysis and a second independent sample are required. Furthermore, any underlying data structure, such as that caused by different ancestries, e.g. different breeds in the case of livestock, and the presence of environmental factors, has the potential to reduce power for a given sample size.ad hoc significance thresholds, since authors attempt to balance the risk of Type I and Type II errors [In the equine setting, the accumulation of large numbers of samples represents a significant challenge. Since the introduction of the first equine SNP chip by Illumina in 2007, several GWAS of monogenic diseases have been successful in identifying associated regions of the genome and in several cases, causal mutations -3. HowevI errors ,5. The aOne opportunity to reduce genotyping costs is the development of low-density genotyping. If a reference population of individuals genotyped at high-density is available, individuals from a test population or selection candidates can be genotyped for a subset of these loci on a low-density panel (LDP), followed by imputation to fill in the ‘missing’ SNP genotypes . ProvideEfforts to develop improved imputation algorithms have resulted in a wide range of software programs, most of which have evolved from programs written to infer haplotype phase from large-scale genotype data. Commonly used programs include fastPHASE , MACH 1, IMPUTE Because the imputation method relies on LD between SNPs on the LDP and the remaining SNPs on the high-density panel, the choice of SNPs for the LDP also affects the accuracy of imputation. A significant effort has been devoted to optimising LDP SNP selection and several algorithms have been developed along this vein. Many programs use LD between pairs or groups of markers to select LDP SNPs in a so-called block-free approach, e.g. Tagger  or LDSelhttp://www.illumina.com/documents/products/datasheets/datasheet_equine_snp50.pdf) were used to investigate the accuracy of imputation that can be achieved in Thoroughbred horses, without pedigree information, and using a typical imputation program (BEAGLE). Three methods of LDP SNP selection were tested across six LDP sizes in order to evaluate the impact of various SNP selection criteria that involve both information content and LD of SNPs. The effect of geographical substructure on the accuracy of imputation was also investigated.In this study, genotypes from the Illumina Equine SNP50 BeadChip (The data for this study consisted of 853 Thoroughbred horses originating from the United Kingdom (UK dataset), and 348 Thoroughbred horses from the United States (US dataset). The UK dataset had been the subject of two GWAS, and the US dataset had been the subject of a further GWAS, and each GWAS was structured as a case–control study for one of three diseases. None of the GWAS identified any major QTL for their target disease  and so fIn the UK, blood samples were collected by the Animal Health Trust between 2006 and 2008, from Thoroughbred horses competing in both flat- and jump-racing . Horses were from a wide geographical area and are expected to be relatively unrelated. Samples in the UK dataset were randomly assigned to one of three subsets: Set A, containing 200 samples, which was used to select LDP SNPs; Set B, containing 490 (75%) of the remaining samples, which was used as the training reference population; and Set C, containing the remaining 163 samples (25%), which was used as the test population, and which were assumed to be genotyped with the LDP. Genotypes for the LDP were obtained by masking SNPs that were not selected to be in the LDP being tested. A graphical representation of this data flow is in Figure In the US, blood samples were collected over two years (2007 and 2008) from 348 Thoroughbreds  admitted to the Rood and Riddle Equine Hospital, Lexington (Kentucky). Horses originated from one of 19 surrounding horse farms, with the number of horses per farm ranging from two to 89. Since sampling was anonymous, pedigree details for the horses were not available but the data set was expected to consist of a mixture of half-sibs (by sire and by dam since data was collected across two years) and more distantly related horses. Two analyses were performed using this dataset along with the UK dataset to investigate imputation across populations. In the first analysis, the training information obtained from Set B in the UK dataset was used for imputation of the entire US dataset, which was assumed to be genotyped with the LDP. In the second analysis, samples in the US dataset were randomly assigned to one of two subsets: Set D, containing 261 samples (75%), which was used as the training reference population, or Set E, containing the remaining 87 US samples, which was used as the test population and which was assumed to be genotyped with the LDP. A graphical representation of this data flow is in Figure http://www.cgs.path.cam.ac.uk/services/genotyping/) for genotyping using the Illumina Equine SNP50 Genotyping BeadChip, which comprises 54 602 SNPs across all autosomes and the X chromosome. These SNPs were selected from a database of over one million SNPs (http://www.broadinstitute.org/ftp/distribution/horse_snp_release/v2/) generated during the sequencing of the horse genome [All blood samples were collected in EDTA, sent to the Animal Health Trust for further processing, and to Tepnel for DNA extraction (Tepnel has since been bought by Gen-Probe). An aliquot of each sample was diluted to 70 ng/μl and sent to Cambridge Genomic Services  chromosomes: ECA1, ECA10, ECA20 and ECA26; these were chosen to represent the shortest (ECA26), longest (ECA1), and median length (ECA20) chromosomes  below 0.01 were excluded. The analyses within the UK dataset focused on four e et al. ), and toe (effective population size) SNPs per Morgan, as described in Solberg et al. [e/Morgan, assuming Ne = 150 and a total genome of 27.72 Morgan [nchr) was proportional to the ratio of the length of the chromosome (lenchr) to the whole genome (lengenome) in terms of physical distance in base pairs (nchr = LDPsize*lenchr/lengenome).The three methods were used to select LDP SNPs, as detailed below. Genotype data from the 200 UK samples assigned to Set A were used to generate (i) the MAF of the SNPs for the algorithm used in Methods 2 and 3 and (ii) the LD map used in Method 3 (see below). In the within-population analysis of the UK dataset, the methods were tested at six different densities, representing genome-wide panels with 384, 768, 1K, 2K, 3K and 6K SNPs. The equivalent densities, expressed in terms of Ng et al. , were 0.2 Morgan . In the nchr) minus 1. The closest SNP to each segment boundary was then chosen to be a SNP in the LDP, irrespective of its MAF.SNPs for the LDP were selected such that their spacing along the chromosome was approximately equidistant in base pairs. This was achieved by dividing the total base pair length of the chromosome into equally sized segments, the number of segments being equal to the desired number of LDP SNPs for the given LDP density  - d)2 (2), where S is the base pair position of the SNP. The set of nchr SNPs was then derived by iteratively minimizing the following function over all SNPs:SNPs for the LDP were selected so that their spacing along the chromosome was approximately equidistant in base pairs and their MAF was high. In order to meet both objectives, SNP selection was performed separately for each chromosome using a custom python program that applied a genetic algorithm. The cost function to be minimized included two components: the first component aimed at driving the MAF of the selected SNPs towards 0.5 by applying a penalty equal to In order to ensure good coverage at the telomeres, where recombination events are more frequent and hence accuracy of imputation is expected to be lower, the SNPs from the high-density panel that were closest to the ends of each chromosome were included in the LDP.In the absence of a detailed recombination map for the horse, an alternative measure of distance was used as a proxy, such that the assumption of uniformity of LD and recombination along the length of a chromosome could be removed. The proxy used was linkage disequilibrium units (LDU), as calculated using the LDMAP program described and developed by Maniatis et al. . The the-6) before entering the SNP locations in the LDP SNP selection algorithm such that SNP order remained consistent with the physical map.SNPs for the LDPs were then selected according to the same algorithm used in Method 2, but with SNP locations given in LDU instead of base pair positions. In cases for which SNPs were allocated to the same position in the LD map, a small addition was made to subsequent locus positions (10The software program BEAGLE (v 3.3.1)  was usedImputation accuracy was evaluated for the three LDP SNP selection methods, the six LDP densities and four chromosomes, and was summarised per SNP and per individual. For each imputed SNP, imputation accuracy was assessed using two measures: (i) the proportion of genotypes for the SNP that were correctly imputed among samples; and (ii) the correlation between the true and imputed genotypic allele counts across all samples . The very small number of loci where true or imputed SNP genotypes were monomorphic among samples, were excluded from the correlation calculations. Summary statistics were then calculated across all SNPs. For each individual, imputation accuracy was calculated using the same two measures: (i) the proportion of all of the horses’ genotypes that were correctly imputed; and (ii) the correlation between the true and imputed genotypes across all the horses’ SNPs when coded as above. As before, monomorphic SNPs were excluded from the correlation. Summary statistics were then calculated across all horses.accuracy is the proportion of correctly imputed genotypes achieved for the SNP and random_accuracy is the expected proportion using random imputation. The expected proportion is given by p4 + 4p2q2 + q4[p and q are the frequencies of the major and minor alleles of the SNP in the reference population. This statistic adjusts for the fact that SNPs with a low MAF are likely to be imputed with high accuracy by chance alone.Finally, an adjustment to the proportion of correctly imputed genotypes by the expected proportion that would be correct from random sampling of alleles was calculated as: _accuracy, where ap2q2 + q4, where pr2 measure of LD when mating is at random, i.e. assuming genotypic frequencies are in Hardy-Weinberg equilibrium [r2, the term In order to explore possible causes of differences in imputation accuracy across SNPs, PLINK ,37 was uilibrium . Howevere/Morgan, to 0.97 at a density of 1.44Ne/Morgan. The increase was greatest at the lower densities and showed diminishing returns with further increases in density. A large range in the proportion of genotypes correctly imputed was observed between animals, particularly when the density of the LDP was lowest, when the proportion ranged from an average of 0.54 to an average of 0.93 across the three methods for ECA1. Although the difference in accuracy between the three LDP SNP selection methods was small or absent, Methods 2 and 3 reduced the variation in imputation accuracy across SNPs  Figure . The hypThe correlation between true and predicted genotypes was also calculated as an alternative measure of imputation accuracy. While accuracies were generally lower when expressed as correlations, considerable differences between horses and between SNPs remained Table a. AdjustWhen Set B (the UK reference population) was used as the reference population for imputation in the US dataset, there was very little change in the mean proportion of correctly imputed genotypes relative to the within-population results Table . With raWhen Sets D and E were used as reference and test populations, respectively, imputation accuracy was slightly increased compared to that obtained for within-UK imputation, for both imputation methods (random and BEAGLE) and for all three LDP SNP sets Table . This inIn this study, the efficacy of imputation from low- to high-density in Thoroughbred horses was investigated and three methods for selecting the LDP SNP were compared. Two measures were used to assess imputation accuracy, the proportion of correctly imputed genotypes and the correlation between true and imputed genotypes. While these two measures were correlated, the proportion of correctly imputed genotypes was related to the MAF of the imputed SNPs. Adjusting the proportion of correctly imputed genotypes by the expected accuracy using random imputation (as in ) weakenee, results can be compared to those from other studies and species. Using a 2K low-density panel in Border Leicester sheep, equivalent to a SNP density of 0.23Ne/Morgan (assuming Ne = 242 [e/Morgan bpMAF SNP panel (the 1K panel) compare favourably, with accuracies that ranged from 0.74 to 0.84 across chromosomes. A study on Jersey cattle that used equivalent SNP densities also obtained accuracies in the range of 0.7 to 0.8 [Increasing the SNP density of the LDP serves to reduce the considerable range in imputation accuracy between SNPs and between horses. For example, at the lowest density, some SNPs were imputed correctly for all horses, whereas other SNPs were correct in as few as 24% of horses (ECA10 bpEQ results). Increasing the density of the LDP led to an increase in the minimum accuracy across SNPs, so for ECA10 the 6K LDP gave a minimum accuracy of 83%. A similar effect was observed across horses. By calculating marker densities normalised by NNe = 242  and a toNe = 242 ), Hayes Ne = 242  achieved7 to 0.8 .Random imputation resulted in a direct and predictable relationship between the MAF of SNPs and the accuracy with which they were imputed. This relationship was less evident when using BEAGLE for imputation, except at the lowest LDP SNP densities, for which the amount of information available from LD was presumably low. Using a genetic algorithm to preferentially select LDP SNPs that are more informative, i.e. having a high MAF, while simultaneously ensuring consistent coverage across the chromosome (bpMAF), achieved a small but consistent increase in the proportion of correctly imputed genotypes, with increases ranging from 0.6% to 5.3% for the 1K SNP panel. Further small improvements might be obtained by differential weighting of the two parts of the objective function used, so making MAF of greater or lesser importance. Using method bpMAF to select LDP SNPs also resulted in a decrease in the variation in imputation accuracy across SNPs compared to the bpEQ method.A major source of the variation in imputation accuracy across SNPs was the extent of LD, with variation both between and within chromosomes. There was a tendency for imputation accuracies to be higher for the longer chromosomes (ECA1 and ECA10) and this coincides with the higher average LD of these chromosomes, shown by Corbin et al. . Within D′ rather than r2 in the Malecot model. Constructing an LDP including consideration of r2 may give better results, although r2 is already low in Thoroughbreds at the densities used for the LDP [Using lduMAF to select LDP SNPs increased imputation accuracy for SNPs that were in low LD regions due to a greater concentration of SNPs selected in these regions, but the decrease in accuracy for SNPs in high LD regions was relatively greater, resulting in a trend for the mean accuracy to be reduced. The relatively poor performance of the lduMAF may be due in part to the use of  the LDP . An addiThe results of analyses presented here suggest that there is some ambiguity over which properties of imputation are most important when assessing efficacy. Does the utility for imputation argue for choosing LDP SNPs to maximise the mean imputation accuracy, or to maximise a minimum (or low percentile of) imputation accuracy; as judged by SNPs or by horses? Concern over lower percentiles will place more value on reducing the variance of imputation errors. While the answer lies in the intended use of the imputed genotypes, it would be useful to have some generic assessment of imputation performance. One solution might be to use a utility function such as the area under the curve obtained from plotting SNP correlation against SNP position, as in Figure Transferability across breeds and across countries within breeds is an important consideration when designing a LDP. Here, data from a cohort of US Thoroughbreds was used to evaluate the impact of geographical origin on the efficacy of imputation. While comparisons of within-UK, within-US and UK to US imputations involved reference and test populations of different sizes, studies have shown that the size of the reference population does not have a major impact on imputation accuracy for the sizes used here ,10. In tReplacing both the UK reference and test populations with horses from the US resulted in an increase in imputation accuracy for the US test population compared to using UK horses as the reference population. This was despite the fact that the LDP SNPs were selected using a UK population sample. One explanation for this increase in accuracy is the higher average relationships in the US dataset, which has been shown to improve imputation accuracy . HoweverThe value of imputed genotypes depends on both their accuracy and their purpose. Daetwyler et al.  observedAny loss of accuracy in imputation that occurs as a result of using lower density SNP panels must also be considered alongside the cost savings that would be achieved. Part of the accuracy lost might be recovered if the pedigrees of genotyped individuals were available . HoweverThe results of this study show that it is possible to impute genotypes from low- to high- density in Thoroughbred horses with reasonable to high accuracy. An investigation of the source of differences in imputation accuracy revealed dependence on the MAF of the SNPs being imputed, and on the underlying LD structure. While equidistant LDP SNPs worked well, optimising LDP SNP selection to increase their MAF was advantageous leading to increased imputation accuracy, even when LDPs were subsequently used in a population of different geographical origin. By using LD map distance instead of physical distance to select LDP SNPs, differences in imputation accuracy between SNPs were reduced. Whereas a 1K SNP panel was generally sufficient to ensure that more than 80% of genotypes were correctly imputed, inference from other studies suggests that a 2 to 3K SNP panel would ensure that the subsequent loss in accuracy for, for example, genomic prediction was minimal ,44. FurtThe authors declare that they have no competing interests.LJC conceived the study, carried out the analyses  and wrote the manuscript; JAW, SCB (The Roslin Institute) and AK provided advice on the study design and analyses, and contributed to the manuscript; SCB , JES and MV coordinated the collection of all the samples, provided the genotype data, and collated information on genotyping costs. All authors read and approved the final manuscript.Quality control criteria implemented on genotype data and number of SNPs discarded at each step. This table contains details of SNP exclusions made on the basis of quality control carried out using the Illumina GenomeStudio genotyping module.Click here for fileCalculation of LD maps. This document contains further details of the method used to calculate SNP positions in LDU for LDP SNP selection Method 3, including settings used to run the LDMAP program.Click here for fileLD maps. This document contains figures showing the relationship between physical map distance (Mb) and map distance in LDU for chromosomes 1, 10, 20 and 26.Click here for fileThe mean proportion of correctly imputed genotypes, as calculated in the within-population analysis of the UK dataset. The data provided represent the results of imputation from LDPs with SNPs selected by Methods 1 to 3, expressed as the mean proportion of correctly imputed genotypes, both per individual and per SNP. Results are shown for chromosomes 1, 10, 20 and 26.Click here for fileThe mean proportion of correctly imputed genotypes and its variance across SNPs, as calculated in the within-population analysis of the UK dataset. The figures provided show the results of imputation from LDPs with SNPs selected by Methods 1 to 3, expressed as the mean proportion of correctly imputed genotypes per SNP and plotted against the total number of SNPs on a genome-wide LDP of equivalent density. Results are shown for chromosomes 1, 10, 20 and 26.Click here for fileProperties of low density panel SNPs, as calculated in the within-population analysis of the UK dataset. The data represent properties of LDP SNPs as selected by Methods 1 to 3. Results are shown for chromosomes 1, 10, 20 and 26.Click here for fileThe mean correlation between true and predicted genotypes, as calculated in the within-population analysis of the UK dataset. The data provided represent the results of imputation from LDPs with SNPs selected by Methods 1 to 3, expressed as the mean correlation between true and predicted genotypes, both per individual and per SNP. Results are shown for chromosomes 1, 10, 20 and 26.Click here for file


Branta leucopsis, a model species for the study of bird migration. We used the genome of a different waterfowl species, Mallard Anas platyrhynchos, as a reference to align Barnacle Goose second generation sequence reads from an RRL library and detected 2188 SNPs genome wide. Furthermore, we used chimeric flanking sequences, merged from both Mallard and Barnacle Goose DNA sequence information, to create primers for validation by genotyping. Validation with a 384 SNP genotyping set resulted in 374 (97%) successfully typed SNPs in the assay, of which 358 (96%) were polymorphic. Additionally, we validated our SNPs on relatively old (30 years) museum samples, which resulted in a success rate of at least 80%. This shows that museum samples could be used in standard SNP genotyping assays. Our study also shows that the genome of a related species can be used as reference to detect genome wide SNPs in birds, because genomes of birds are highly conserved. This is illustrated by the use of chimeric flanking sequences, which showed that the incorporation of flanking nucleotides from Mallard into Barnacle Goose sequences lead to equal genotyping performance when compared to flanking sequences solely composed of Barnacle Goose sequence.Migratory birds are of particular interest for population genetics because of the high connectivity between habitats and populations. A high degree of connectivity requires using many genetic markers to achieve the required statistical power, and a genome wide SNP set can fit this purpose. Here we present the development of a genome wide SNP set for the Barnacle Goose  However, because of the high connectivity between migratory populations high discriminating power is needed to disentangle population structure, especially when insight in recent migratory changes is desired. The detection and development of Single Nucleotide Polymorphisms (SNPs) could fill this knowledge gap for migratory species since the statistical power of SNPs, of which hundreds can nowadays be easily applied in a single study, is considerably higher than of microsatellites Anas platyrhynchos), which is a partial migrant, SNPs have been described genome wide Anser anser. Although barnacle geese are not used in agricultural production, the detection of SNPs in Barnacle Goose may provide potential SNPs for related species and their domesticated forms.Migration of animals is one of the most visible natural phenomena and as such has attracted much scientific attention. Because migrants connect habitats, migratory species can play a key role in understanding how local environmental changes affect populations and habitats at a larger scale et al. (2009) et al (2010) Meleagris gallopavo and Great Tit Parus major respectively). These studies created an incomplete genome from short sequences stemming from next generation Illumina sequencing and used that as a reference genome for SNP detection. The goal of our study was to detect SNPs in Barnacle Goose by using a reference genome from a different bird species, the Mallard , knowing that geese and ducks diverged approximately 30 million years ago Kerstens AluI and HaeIII. The RRL size ranged from 100 to 150 bp. We pooled equal amounts of the two RRLs and submitted them for sequencing on the Illumina GAII  using the Illumina Sample Preparation protocols The SNP discovery panel consisted of ethanol preserved whole blood samples from 16 individuals from Spitsbergen, The Netherlands and Russia  . Sequences not starting with ‘C’ were therefore discarded from the dataset. We trimmed all reads beyond position 62, where the average phred quality score per base position Quality filtering of raw reads was carried out by Perl scripts. Due to the use of the restriction enzymes et al. in prep) as a reference. The divergence time between mallard and the genus Branta is 28.1 Mya We aligned the resulting  reads to the reference genome with default parameters in MAQ chimeric flanking sequence from both Mallard and Barnacle Goose. We retrieved all flanking sequences using ad hoc R-scripts From the aligned Barnacle Goose reads we made a consensus file in MAQ to retrieve 50 bp flanking sequences of the SNPs on both sides. Whenever there were no flanking sequences available from the Barnacle Goose consensus, we used the flanking sequences obtained from the Mallard genome, resulting in a Gallus gallusWe mapped the detected SNPs against the Chicken genome et al. chimeric flanking sequences, i.e., as many as possible bases in the flank sequences originated from the Barnacle Goose and where not enough were available Mallard sequence was used. We performed the allele calling (clustering) with the program Genome Studio (Illumina). We calculated the observed MAF for each SNP with CoAncestry For validation by genotyping we used all 16 individuals of the discovery panel, which were genotyped for 384 SNPs with the Illumina Golden Gate® genotyping assay on an Illumina® BeadXpress with VeraCode™ technology as described in Kraus et al. in prep) which resulted in 363,014 candidate SNPs  as inferred by MAQ, of which 2188 SNPs (0.6%) passed all quality criteria. These SNPs have been deposited in the NCBI dbSNP database under accession numbers ss295471227 through ss295473414 for internal SNP identifiers Ble_1 - Ble_2188. We obtained 377 SNPs with at least 30 bp of goose consensus sequences on both sides of the SNP, 647 with 20–29 bp on both sides and 586 with 10–19 bp on both sides. The amount of SNPs detected per position on the reads was uniformly distributed  using paired-end sequencing on two lanes of an Illumina GAII, representing approximately 5% of the genome with an estimated sequence depth of 38× . The raw2χ = 59.1, d.f. = 63, p = 0.62; flanking origin: 2χ = 4.16, d.f. = 3, p = 0.25).The validation by genotyping, for which we used all 16 discovery panel individuals and the ten museum samples, showed that 374 (97%) of the 384 assayed SNPs gave reliable genotypes in the assay and 358 (96% of the 374) were polymorphic. The quality of the historical samples was initially thought to be insufficient for SNP detection due to high fragmentation of the DNA. Of the initial 26 historical samples we used ten samples, despite the agarose gel showing high degradation, for genotyping and our worst performing sample of these then still had a success rate of 80% for the 374 SNPs. The lowest call rate among our discovery individuals was 91%. The heterozygosity of the genotyped discovery individuals was 0.34 and the measured mean observed MAF was 0.29. There was no effect of sequencing position in the read or origin of flanking sequence (proportion stemming from Barnacle Goose) on the technical failure of SNPs , our distribution of MAF shows that also relatively low-frequency SNPs could be detected, which may be especially useful for discriminating populations. The TS/TV of 2.7 for the detected SNPs is comparable to the TS/TV ratios described in other studies The museum samples that we genotyped performed with a minimum success rate of 80%. This provides opportunities for using relatively old highly degraded museum samples for SNP genotyping with the Illumina Golden Gate® genotyping assay, provided that sufficient quantities of DNA are available. Caution should be taken however, as we selected those samples that we expected to have the largest chance of successful genotyping. We did not genotype all museum samples as it was not the main priority of our genotyping assays. Studies using only such museum samples should take potential loss of samples into account in the design. Still, earlier SNP genotyping of highly degraded DNA samples was tedious and only possible on low automation and throughput Anas and Branta, both belonging to the family Anatidae, corroborates earlier findings of highly conservative genome evolution in birds Approximately 16% of our reads  aligned to the Mallard genome. Because we obtained our SNPs from these reads, it is not surprising that also the nearby sequences from Mallard provided good flanking sequences for genotyping, because we apparently have a bias for SNPs in the better conserved regions of the genome. This extreme sequence conservation between the genera chimeric flanking sequences are used successfully. We show that an RRL can be used to obtain SNPs and flanking sequences by aligning to a related species of the focal species in birds.Our results show that our method, in which we used the genome of the Mallard, provides excellently performing SNPs. We show that there is no effect on the performance of the SNP assay of the origin of flanking sequences in the assay design between these two species. Both SNPs with a high percentage of flanking sequences of Barnacle Goose and SNPs with a high percentage of flanking sequences of Mallard worked very well, and we observed no difference in their overall performance during genotyping. To our knowledge this is the first study in which de novo assembly for the focal species With the current developments, sequencing costs are rapidly decreasing, which will make the use of RRLs redundant. However, in this study with an RRL approach we are able to demonstrate that our method could work equally well when scaled up to whole genome sequencing of a discovery panel of individuals using a reference genome of a related (bird) species. This makes the complicated steps of a chimeric flanking sequences for genotyping our SNPs is in line with earlier findings and expectations for bird genome evolutionary patterns. Additionally, our study shows that the detection of thousands of assayable SNPs is now within reach for many more species than there is detailed genomic information for.This genome wide SNP development of the Barnacle Goose provides us with a tool to study the genetic effects of population, and possibly migration, changes within a species that is renowned for its flexibility in migration


Genetically, SNP that are in complete linkage disequilibrium with the causative SNP cannot be distinguished from the causative SNP. The Complete Linkage Disequilibrium (CLD) test presented here tests whether a SNP is in complete LD with the causative mutation or not. The performance of the CLD test is evaluated in 1000 simulated datasets.th, 10th and 50th highest TCLD value from 1000 replicated analyses were used to control the type-I-error rate of the test at p = 0.005, p = 0.01 and p = 0.05, respectively.The CLD test consists of two steps i.e. analysis I and analysis II. Analysis I consists of an association analysis of the investigated region. The log-likelihood values from analysis I are next ranked in descending order and in analysis II the CLD test evaluates differences in log-likelihood ratios between the best and second best markers. Under the null-hypothesis distribution, the best SNP is in greater LD with the QTL than the second best, while under the alternative-CLD-hypothesis, the best SNP is alike-in-state with the QTL. To find a significance threshold, the test was also performed on data excluding the causative SNP. The 5In a situation where the QTL explained 48% of the phenotypic variance analysis I detected a QTL in 994 replicates (p = 0.001), where 972 were positioned in the correct QTL position. When the causative SNP was excluded from the analysis, 714 replicates detected evidence of a QTL (p = 0.001). In analysis II, the CLD test confirmed 280 causative SNP from 1000 simulations (p = 0.05), i.e. power was 28%. When the effect of the QTL was reduced by doubling the error variance, the power of the test reduced relatively little to 23%. When sequence data were used, the power of the test reduced to 16%. All SNP that were confirmed by the CLD test were positioned in the correct QTL position.The CLD test can provide evidence for a causative SNP, but its power may be low in situations with closely linked markers. In such situations, also functional evidence will be needed to definitely conclude whether the SNP is causative or not. QTL mapping efforts often result in the detection of genomic regions that explain quantitative trait variation, but seldom in the detection of the causative mutation underlying the trait variation. Recently, methods developed to genotype high numbers of SNP have permitted to reduce the size of the genomic regions detected. High density SNP genotyping enables the detection of QTL regions of up to 2 cM in size. Availability of genome sequences and/or comparative maps make it possible to set up a shortlist of positional candidate genes. These candidate genes can be sequenced by second-generation sequencing technologies, leading to the detection of many potentially causative SNP that probably include the causative mutation. However, genetic approaches cannot distinguish between SNP in complete linkage disequilibrium (CLD) with the QTL and the QTL itself and at best, they can test whether a SNP is in complete LD with the QTL or not. Because false discovery rate and power are tightly connected when dealing with complex traits , the chaRecently, we investigated the effect of precision and power obtained by including the causative mutation among the markers in a QTL mapping experiment . Both po-8 per bp (106 bp per cM was assumed). The size of the effective population did not exceed that of the sample, which is usually the case in livestock and which makes the continuous time approximation of the coalescence process somewhat unrealistic. In spite of this, we expected the resulting genealogies to resemble those in QTL mapping experiments involving unrelated individuals, such as Genome Wide Association Studies (GWAS). In addition to the 100 replications previously analysed by Uleberg and Meuwissen [th SNP was selected as the causative SNP and the effect of the QTL genotype was 0, 1 or 2. Phenotypic records were obtained by summing the QTL genotype effect and an environmental effect, which was sampled from N. The average genetic variance (from the first 100 replicates) was 0.48, leading to a heritability of 0.54. Two datasets were selected for each of the 1000 simulations i.e. one containing 20 markers but not the causative SNP and one containing 21 markers including the causative SNP as the 11th marker. Figure 2 [The simulated data used in this study have been previously described in Uleberg and Meuwissen . Brieflyeuwissen , 900 newThe analysis consisted of two steps: analysis I and analysis II.In analysis I, a QTL analysis of the region was performed using a statistical model that regressed directly on marker effects, as in association mapping, calculating the log-likelihood of effects of the different markers. The model assumed additive inheritance and was:1 is a vector of ones, m is a vector of two random SNP allelic effects and e is a vector of random sampling errors; Z is a design matrix indicating which marker alleles are carried by the animals. The correlation matrix of m is the identity matrix, I. The variance of the random effects m and e and the log-likelihood of the model were estimated using the ASREML package [where μ is an overall mean,  package . A modelIn Analysis II, the two SNP that gave the highest log-likelihood values in analysis I were compared by the CLD test. The idea is that, if the maximum-likelihood-SNP is in complete LD with the QTL, it will not only have a high Identity-By-Descent (IBD) probability with the QTL but also be alike-in-state (AIS) and thus will explain substantially more variance than a SNP that is only in partial LD with the QTL, such as the second highest log-likelihood SNP. The test statistic is thus:(i)) is the log-likelihood of the model including the i-th ranking marker. The TCLD values are a measure of the relative importance of the best SNP compared to the second best SNP. Since the best SNP is expected to explain more variance than the second best SNP, the null-hypothesis distribution differed from the usual one, i.e. the best SNP was expected to explain more variance. Thus, under the null-hypothesis distribution, the best SNP is in somewhat more LD with the QTL than the second best SNP, whereas under the alternative-hypothesis the best SNP is in complete LD with the causative mutation and thus also alike-in-state with the QTL. In order to establish a significance threshold for the CLD test, the test was also performed on data where the causative SNP was excluded. The 5th, 10th and 50th highest TCLD value out of 1000 replicated analyses excluding the causative SNP were taken as the p = 0.005, p = 0.01 and p = 0.05 significance threshold, respectively.where LogLik for the causative SNP of 0.38 and 0.24, respectively, indicating that a higher MAF value favours a significant test result, although this effect is relatively small.We investigated the relationship between the TThe proposed CLD test confirmed 280 out of 1000 causal SNP at a p-value of 0.05 (231 when the QTL effect size was reduced). The power of the CLD test is thus 23-28% and is much lower than when the SNP are used to detect QTL-SNP associations. This relatively low power reflects the fact that proving that a SNP is in complete LD is more difficult than showing that it is merely associated with the QTL. Thus, as previously reported [An alternative approach to find the causative SNP is the concordance test  in whichThe test-statistic of the CLD test is based on the assumption that, under the null-hypothesis distribution, the best SNP explains more variance of the phenotype than the second best, whereas under the alternative hypothesis the best SNP is also alike-in-state with the QTL and explains much more of the phenotypic variance. Based on the average log-likelihood values for all 1000 simulations, the difference between the best and second best SNP in these data is ~17 log-likelihood units when the causative SNP is included and only ~0.5 log-likelihood units when the causative SNP is excluded from the analysis. However, the variance between replicates is large, leading to a relatively low power when all replicates are evaluated.In GWAS, isolated significant SNP are often distrusted, because none of the neighboring SNP confirms the presence of a QTL. In such a case of an isolated significant SNP, the CLD test would provide a positive result since its signal is so much higher than that of neighboring SNP. Here, we assumed that the previous QTL mapping study unequivocally detected a QTL in the studied region, so that regions with spurious significant SNP will not be subjected to the test.QTL mapping cannot distinguish between a causative SNP and a SNP that is in perfect LD with the causative SNP . Thus, iCLD-statistics were found for SNP with a low r2 with their nearest marker, we investigated the effect of SNP density on the power of the test. Here, we considered the highest possible density, namely sequence data, which is becoming increasingly available. We reran 1000 "ms"-simulations as described in the Methods section, but retained all the SNP that resulted from the simulated mutations. This resulted in an average of 470 SNP in the 2 cM segment, with an average r2 between adjacent markers of 0.12. The average r2 was rather low due to the often low MAF, but for 6% of the marker pairs r2 was equal to 1. The SNP closest to the middle of the 2 cM segment was designated as the QTL and an environmental effect sampled from N was added to obtain phenotypes. Out of 1000 replicates, 545 had a single most significant QTL, and 402 of these had the QTL correctly identified. Out of these 402 replicates, 63 had a significant TCLD statistic (P < 0.05), resulting in a power of 16% (= 63/402). Thus, the power was substantially reduced if the marker density was increased to that of sequence data, but some level of power remained. Again none of the misplaced QTL positions passed the CLD test.Since higher TCLD statistic will be artificially increased if the second best SNP is removed and, e.g., replaced by the i-th best.The fact that high marker densities, such as in sequence data, results in a reduction of the power of the test, may suggest that removing some SNP from the data (obviously not the putative causative SNP) will improve power. However this invalidates the CLD test, since the test assumes that some SNP from the QTL region were obtained through a SNP discovery process that is not related to the phenotypic data. Moreover, this artificial reduction of SNP density can result in false positive test results, because the TCLD values and resulted in the stringent significance threshold that was used here.In 59 replicates, analysis I found two or more SNP with equal log-likelihood values for the most likely SNP. This was typically the causative SNP and a SNP located 1 to 3 positions away from the causative SNP. Evaluating five of these replicates showed equal haplotype combinations for every animal for the two most likely SNP, thus the two SNP were in perfect LD. Other replicates produced similar results, with the causative SNP and one close SNP returning log-likelihood values at a higher level than the rest of the SNP, although not equal. In these replicates, the analysis excluding the causative SNP returned large TAs explained by Goddard and Hayes , causatiWhen SNP are evaluated, a number of these will be coding SNP that change amino acids . The numIncluding the causative SNP as a marker increased the average log-likelihood values about four times in these simulations Figure . AlthougCLD statistic ). Then, records are simulated assuming that the SNP detected by the QTL analysis is causative, with simulated QTL variances equal to the estimates obtained from the real data analysis, where every SNP i will in turn be assigned as causative, and will be masked when analysing the data. This simulates replicated data under the null-hypothesis with an LD structure as found in the QTL region. Analysing these null-hypothesis data without including the assumed causative SNP will provide a significance threshold for the analysed data. A significance level can be obtained by counting how many of the null-hypothesis TCLD values exceed the real data TCLD -value. For example, if 100 out of 1000 null-hypothesis datasets have TCLD values exceeding TCLD, the p-value of the real data CST is 0.1 (= 100/1000).To apply the CLD test to real data, the significance threshold must be estimated from the real data. The basic approach that is proposed is to perform a QTL analysis , and to calculate the TCLD (as described in the previous paragraph) will in most situations be quite high. Ron and Weller [The relatively low power of the CLD test does not imply that it should not be used, since it is not very costly to perform and, depending on its p-value, it may provide substantial statistical evidence for a causative SNP. However, because of the low power of the test, the p-value of the real data Td Weller  suggesteThe authors declare that they have no competing interests.EU carried out data analysis and drafted the manuscript. THEM participated in the design of the study and statistical analysis and helped draft the manuscript.Both authors have read and approved the final manuscript.