Knowledge of linkage disequilibrium (LD) levels among different populations can be used to detect genetic diversity and to investigate the historical changes in population sizes. Availability of large numbers of SNP through new sequencing technologies has provided opportunities for extensive researches in quantifying LD patterns in cattle breeds. The aim of this study was to compare the extent of linkage disequilibrium among Iranian cattle breeds using high density SNP genotyping data.2 criterion were computed by grouping all syntenic SNP pairwises for inter-marker distances from 0 Kb up to 1 Mb using three distance sets. Average r2 above 0.3 was observed at distances less than 30 Kb for Sistani and Kermani, 20 Kb for Najdi, Taleshi, Kurdi and Sarabi, and 10 Kb for Mazandarani. The LD levels were considerably different among the Iranian cattle breeds and the difference in LD extent was more detectable between the studied breeds at longer distances. Lower level of LD was observed for Mazandarani breed as compared to other breeds indicating larger ancestral population size in this breed. Kermani breed continued to have more slowly LD decay than all of the other breeds after 3 Kb distances. More slowly LD decay was observed in Kurdi and Sarabi breeds at larger distances (>100 Kb) showing that population decline has been more intense in more recent generations for these populations.A total of 70 samples, representing seven Iranian indigenous cattle breeds, were genotyped for 777962 SNPs. The average values of LD based on the rA wide genetic diversity and different historical background were well reflected in the LD levels among Iranian cattle breeds. More LD fluctuation was observed in the shorter distances (less than 10 Kb) in different cattle populations. Despite of the sample size effects, High LD levels found in this study were in accordance with the presence of inbreeding and population decline in Iranian cattle breeds. Linkage disequilibrium (LD) is defined as the non-random association of alleles at different loci within a population. Pattern of LD within a population can be affected by several factors including selection, mutation rate, migration, genetic drift, population structure and recombination rates . DetectiDepending on the population and the threshold used to measure LD, the average extent of LD is highly variable in different studies. Compared to human studies, likely due to smaller effective population size and intensive selection pressure, higher levels of LD have been found in livestock species –11. AlthCattle domestication and raising have an historical origin in Iran. Iranian indigenous cattle have been keeping in different geographical regions of the country and have been adapted with various environment conditions. Some important traits including resistance to local diseases and parasites , adaptation to low quality feed resources and heat tolerance were attributed to these breeds. In recent years, high dense SNP data has been widely applying as the standard tools in LD analysis of livestock populations. However, the level of LD has not been previously investigated in the Iranian indigenous cattle. Therefore, the objective of this study was to compare the LD levels among different Iranian cattle populations based on a high density SNP data set. Knowledge of difference in LD levels of these populations can help to detect diversity between cattle breeds and to investigate the historical changes in population sizes. Moreover, the applicability of the modern genomic technologies such as genomic selection and genome-wide association studies can be compared between different populations using the LD data.For this study, ten samples per each breed were collected from 70 individuals representing seven Iranian indigenous cattle breeds. Unrelated individuals were selected where possible based either on pedigree or farmers information. Animals used in this study were included the three main cattle types: Bos taurus breeds (Sarabi and Kurdi), Bos indicus (Sistani) and composite cattle . Samples were genotyped using the BovineHD SNP chip  designed to genotype 777,962 SNPs.p-value < 10−7 were removed from data set. MAF was calculated using PLINK for all autosomal SNPs and the distribution of the allelic frequencies was graphed as the proportion of the SNPs represented in 6 different categories of MAF: <0.05, ≥0.05 to <0.1, ≥0.1 to <0.2, ≥0.2 to <0.3, ≥0.3 to <0.4 and ≥0.4 to ≤0.5.Quality control (QC) were performed using PLINK 1.07 software . The SNP2    pointed  studies , 30. DesMuch more LD fluctuations were observed in the distances less than 10 Kb among different breeds. After 10 Kb apart between markers, LD decay had the similar trend in most of the breeds. Kermani breed continued to have more slowly LD decay than all of the other breeds after 3 Kb distances. It appears that population decline has been initiated earlier in Kermnai than other breeds. More rapid LD decay was observed in Kurdi breed for distances less than 100 Kb than other breeds. However, LD decay has been slower in Kurdi breed for the larger distances indicating the rising trend of population decline in more recent generations for this population. It appears that Sarabi breed has also had more intense population decline in recent times. Among the studied breeds, Mazandarani had the lowest LD level in different intervals of genome which could be an indicator of larger ancestral population in this breed. Based on both phenotype characteristics and genetic structure analysis, Sistani breed has more indicine genetic background . Resultshttp://www.animalgenome.org/cgi-bin/QTLdb/BT/index. and http://bovinegenome.org/bovineqtl_v2/login.jsp) . Also, it appears that several traits such as milk fat, somatic cell score and udder attachment have been affected by stronger selection in Kermani breed (chromosome 19). Mazandarani breed has some prominent traits such as growth efficiency, carcass weight, tick resistance and high milk fat and protein that can be largely explained by QTLs located on chromosome 14. In agreement with higher average r2 found for chromosome 26 in Kurdi breed, more QTLs associated with prominent traits of Kurdi breed  were also observed for this chromosome. Similar trend can be reported for QTLs on chromosome 11 that were related to clinical mastitis, SCC and milk fat in Taleshi breed. More selections on calving ease, calf size, dairy capacity and milk protein in Sarabi breed may have led to higher average r2 found for genomic regions located on chromosome 25. Furthermore, chromosome 28 and 8 have included QTLs affecting traits highly attributed to Najdi cattle such as easy calving, Immunoglobuin G level, heat tolerance and high milk protein. In harmony with these findings, lower LD levels were also observed for chromosomes containing lower numbers of QTLs identified for outstanding traits of Iranian cattle  undergoing selection , 35. Nump) Table . This inp) Table . In accoIn this study, large number of SNPs were excluded from the data set due to lack of enough quality. This may be attributed to DNA quality, however the GC-score, which provides information on the genotyping quality, did not show any abnormal deviations (16 % of markers had a GC score <0.5), and therefore we don’t believe that there have been issues with DNA quality. SNP ascertainment bias effect must be acknowledged in this study. Iranian indigenous cattle have not been included in the bovine genome sequencing projects and the SNPs on the chip have been mainly selected based on information from European taurine cattle , this can be somewhat affected genotyping quality. However, the number of remained SNPs was enough to perform analysis and could provide more information, for instance, compared to 50 K chips. Despite of removing a large part of SNP data due to low quality, these results confirmed that the SNP densities used in this work can provide enough accuracy for the future genomic selection programs and GWAS in Iranian cattle.A wide genetic diversity and different historical background were well reflected in LD levels among Iranian cattle breeds. LD fluctuations were more detectable in the shorter distances (less than 10 Kb) among different breeds. Despite of the sample size effects, High LD levels obtained in this study confirmed the small size of Iranian cattle populations that were exposed to serious extinction risk in recent years.


The milk fat profile of the Danish Holstein (DH) and Danish Jersey (DJ) show clear differences. Identification of the genomic regions, genes and biological pathways underlying the milk fat biosynthesis will improve the understanding of the biology underlying bovine milk fat production and may provide new possibilities to change the milk fat composition by selective breeding. In this study a genome wide association scan (GWAS) in the DH and DJ was performed for a detailed milk fatty acid (FA) profile using the HD bovine SNP array and subsequently a biological pathway analysis based on the SNP data was performed.DGAT and SCD. In addition we suggest ACSS3 to be a good candidate gene for the QTL on BTA5 for C10:0 and C15:0. In addition, genetic correlations between the FA traits within breed showed large similarity across breeds. Furthermore, the biological pathway analysis revealed that fat digestion and absorption (KEGG04975) plays a role for the traits FP, C14:1, C16 index and C16:1.The GWAS identified in total 1,233 SNPs (FDR < 0.10) spread over 18 chromosomes for nine different FA traits for the DH breed and 1,122 SNPs (FDR < 0.10) spread over 26 chromosomes for 13 different FA traits were detected for the DJ breed. Of these significant SNPs, 108 SNP markers were significant in both DH and DJ , BTA14). This was supported by an enrichment test. The QTL on BTA14 and BTA26 represented the known candidate genes There was a clear similarity between the underlying genetics of FA in the milk between DH and DJ. This was supported by the fact that there was substantial overlap between SNPs for FP, C14 index, C14:1, C16 index and C16:1. In addition genetic correlations between FA showed a similar pattern across DH and DJ. Furthermore the biological pathway analysis suggested that fat digestion and absorption KEGG04975 is important for the traits FP, C14:1, C16 index and C16:1.The online version of this article (doi:10.1186/1471-2164-15-1112) contains supplementary material, which is available to authorized users. In contrast, long chain FA are derived from blood lipids, which are originating from the diet and endogenously produced lipids. These long chain FA have, however, low to moderate heritabilities  [The calculation of the genomic relationship matrix has been described in detail by Buitenhuis et al. . In shori(1-pi)] .The analysis was done using the following linear model for each breed separately:ijklm is the phenotype of individual k in herd i and lactation j, μ is the fixed mean effect, herdi is a fixed effect , parityj is a fixed effect , b1 is the regression coefficient for DIMk, DIMk is a covariate of days in milk , b2 is the allele substitution effect, SNPl is a covariate indicating the if a SNP is homozygote  or heterozygote (1), and animal is the random additive genetic effect based on G of animal k [0: b = 0. The analyses were performed using REML in the R interface of DMU [http://dmu.agrsci.dk). Significance thresholds were determined using a false discovery rate (FDR) correction using the R package “qvalue” version 1.34.0. A FDR of P < 0.10 was considered significant.Where Ye of DMU   Randomly pick an element (ej) from this vector. Let this jth test statistic be the first element in the permuted vector and the remaining elements ordered ej+1, ej+2, …, eN, e1, e2, …, ej-1 accordingly to their original numbering. Thus, all elements from the original vector are now shifted to a new position starting with ej. The mapping of genes is however kept fixed as to the original mapping. 2) A summary statistic is computed for each SNP set based on the original SNP set position in the original vector of test statistics. Hereby the link between SNPs and genes are broken while retaining the correlation structure among test statistics. Step 1 and 2 are repeated 10,000 times and from this empirical distribution of summary test statistics for each SNP set a P-value can be obtained. This empirical P-value corresponds to a one-side test of the proportion of randomly sampled summary statistics that are larger than the observed summary statistic. The arbitrary significance level was set to 0.01.In genome-wide association studies, a large number of correlated tests are routinely performed to detect genetic variants (e.g. SNPs) associated to complex trait phenotypes. In order to control the probability of false discoveries, it is necessary to use multiple-comparison methods such as Bonferroni or FDR to adjust for the large number of test. These corrections greatly reduce the power to detect true associations, which means that genetic variants with small or intermediate effects remain undiscovered. To alleviate this major problem, we applied a biological pathway method that reduces the number of tests from more than 400 k (number of SNPs) to about 200 (number of pathways) using a priori biological information. We do this by grouping SNPs according to their physical location within annotated genes and how these genes are connected in biological pathways (KEGG). For each set we construct an appropriate summary statistics that characterizes the set. We used a summary statistic to identify genes that can explain the phenotypic variation within the traits . It is bThe overlap of the associated SNPs in the two breeds was compared within each trait. This was done by constructing for each breed an incidence matrix with n = 494,984 rows corresponding to the number of SNPs and m = 25 columns corresponding to the number of traits. For each breed if the test statistic for a SNP was above the significance threshold then the corresponding element in the incidence matrix was set to one and otherwise zero. The observed overlap in the associated SNPs in the two breeds for each trait was computed based on the matrix product between the two incidence matrices. This overlap was compared to an empirical distribution of the overlap as follows. For a total of 1,000 times the element within each column (i.e. trait) was permutated using the same procedure as described above, and the overlap among columns was recorded. The probability of the found overlap was estimated under the null hypothesis of independence of association among traits. We determined the empirical P-value of a one-sided test as the fraction of all random permutations that was larger or equal than the observed overlap among columns (i.e. traits).http://res.illumina.com/documents/products/datasheets/datasheet_bovinehd.pdf. Names and location of these SNPs can be found at: http://support.illumina.com/downloads.html. Assigning the SNPs to genes was based on the publicly available bovine genome assembly: ftp://ftp.cbcb.umd.edu/pub/data/assembly/Bos_taurus/Bos_taurus_UMD_3.1/annotation/UMD3.1.gff.gz .No new SNPs were discovered in this manuscript. The SNPs used in this manuscript are from the Illumina Bovine HD SNP array: Additional file 1: Table S1: Significant SNP associated to Fatty Acid traits in the Danish Holstein. A genome wide association scan was performed on the Danish Holstein cattle for detailed fatty acid composition. SNPs were considered significant at FDR < 0.10. (TXT 190 KB)Additional file 2: Table S2: Significant SNP associated to Fatty Acid traits in the Danish Jersey. A genome wide association scan was performed on the Danish Jersey cattle for detailed fatty acid composition. SNPs were considered significant at FDR < 0.10. (TXT 174 KB)Additional file 3: Table S3: Results for the biological KEGG pathway analysis for the individual fatty acids and groups of fatty acids for both the Danish Holstein and Danish Jersey breed. In the table the P-value is given of the one-sided test for overlapping SNPs after 1,000 permutations. (TXT 58 KB)Additional file 4: Table S4: Significant (P < 0.05) results for the biological KEGG pathway analysis for fat percentage for both the Danish Holstein and Danish Jersey breed. (TXT 3 KB)Additional file 5: Table S5: Significant (P < 0.05) results for the biological KEGG pathway analysis for C14 index for both the Danish Holstein and Danish Jersey breed. (TXT 3 KB)Additional file 6: Table S6: Significant (P < 0.05) results for the biological KEGG pathway analysis for C14:1 for both the Danish Holstein and Danish Jersey breed. (TXT 3 KB)Additional file 7: Table S7: Significant (P < 0.05) results for the biological KEGG pathway analysis for C16 index for both the Danish Holstein and Danish Jersey breed. (TXT 4 KB)Additional file 8: Table S8: Significant (P < 0.05) results for the biological KEGG pathway analysis for C16:1 for both the Danish Holstein and Danish Jersey breed. (TXT 3 KB)Additional file 9: Table S9: Mean and standard deviation for the fatty acids for both the Danish Holstein and Danish Jersey breed . (TXT 7


FST values or significant association with phenotypic traits (bitter versus sweet fruits). These candidate regions encompassed 420 genes involved in a variety of functions and metabolic pathways, including several colocalizations with QTLs for polyphenol compounds.Unraveling the genomic processes at play during variety diversification is of fundamental interest for understanding evolution, but also of applied interest in crop science. It can indeed provide knowledge on the genetic bases of traits for crop improvement and germplasm diversity management. Apple is one of the most important fruit crops in temperate regions, having both great economic and cultural values. Sweet dessert apples are used for direct consumption, while bitter cider apples are used to produce cider. Several important traits are known to differentiate the two variety types, in particular fruit size, biennial versus annual fruit bearing, and bitterness, caused by a higher content in polyphenols. Here, we used an Illumina 8k SNP chip on two core collections, of 48 dessert and 48 cider apples, respectively, for identifying genomic regions responsible for the differences between cider and dessert apples. The genome-wide level of genetic differentiation between cider and dessert apples was low, although 17 candidate regions showed signatures of divergent selection, displaying either outlier  Domestication and variety diversification have been models for studying the mechanisms underlying adaptation since Darwin , being tMalus domestica Borkh.) is one of the most important fruit crops in temperate regions, with great economic and cultural values , and 3704 SNP markers. First, we analyzed the population genetic structure in our sample to assess the differentiation between dessert and cider apple varieties using a much higher number of markers than in a previous study  cider versus dessert variety types or (ii) high versus low bitterness cultivars. Altogether, these analyses aimed at localizing the genomic regions that have been under divergent selection and responsible for the phenotypic differences between cider and dessert apples. We then examined in these regions the putative functions of genes to find candidates that have potentially undergone differential changes during the divergence between cider and dessert apples. Recent selection programs on cider apples aim at improving yield, regularity of production, resistance to pests and pathogens, while maintaining their specific technologic characteristics . The identification of the genomic regions responsible for the differences between dessert and cider variety types could therefore be of great use for instance in a marker-assisted selection approach trying to select new cider varieties combining a higher content in polyphenols with the agronomic performances of dessert apples such as regular annual bearing, higher yield, and fruit size.In this study, we set out to identify genomic regions under divergent selection between cider and dessert apples using two core collections, one of each variety type  using the ‘Maximum Length Subtree’ option of the DARwin software , 2.5 volumes of cold 100% ethanol, and 1 μL of glycogen were added, the tubes were centrifuged at 13 000 g for 30 min, the supernatant was discarded, 200 μL of 70% ethanol was added, the tubes were centrifuged at 13 000 g for 10 min, the supernatant was discarded, the tubes were air-dried overnight, and the DNA was resuspended in the appropriate volume of water. DNA samples were then checked for quality using Nanodrop 1000 , quantified using PicoGreen® , and processed onto the International RosBREED SNP Consortium (IRSC) apple 8k SNP array v1 . Because apple leaves are full of polysaccharides and phenols that contaminate the extracted DNA and may prevent hybridization on the array, DNA samples were purified as follows: 0.1 volume of sodium acetate  of the Illumina® GenomeStudio software . A visual inspection of each SNP was performed, and SNPs exhibiting a good genotypic clustering in distinct spots were kept. Paralogous SNPs were removed by performing BLAST onto the apple genome and removing probes having two equally good best hits onto the reference genome. This step was necessary for avoiding potential paralogy, due to the whole-genome duplication having occurred in the apple evolutionary history  and calculates estimates for these genotypes. Each genotype is then predicted, and the software calculates a prediction error across all masked genotypes. The Q matrix, that is, the posterior probabilities for each individual to belong to a given cluster, outputted by ADMIXTURE 1.23 was used for the genotype–phenotype association analysis.The ADMIXTURE 1.23 software  calculated in GEMMA and the Q matrix from ADMIXTURE. A first analysis was performed on the two core collections by giving cider cultivars a score of 1 and dessert cultivars a score of 0. However, because not all cider cultivars are bitter, a second analysis was performed, this time not considering the cider versus dessert cultivars classification, but instead the bitterness of the cider apple cultivars, as recorded in the literature  was run using the univariate linear mixed model (LMM) implemented in GEMMA  functional annotation, the KEGG tools were used to visualize the corresponding metabolic pathways. A BLASTN was run, and its results were used as inputs in Blast2GO 3.0 to retrieve GO annotations for the entire gene set of the apple genome. The regions of interest were then tested for enrichment of particular gene functions.The online apple genome browser hosted on After visually screening the 7867 SNPs of the IRSC apple 8K SNP array v1 on GenomeStudio, a set of 4234 polymorphic SNPs evenly spread across the apple genome was obtained; after removing potential paralogous SNPs, the number of markers was reduced down to 3704. The number of markers per linkage group was approximately proportional to their length. The average distance between two adjacent SNPs was 140 kb, with the maximum distance separating markers ranging from 1.26 Mb on LG17 to 4.25 Mb on LG15. The distribution of the SNP minor allele frequencies (MAF) was quite uniform across the different possible MAF values  with the physical distance showed that the squared allele correlation parameter FST between cider and dessert apples ranged from 0 to 0.24, with a mean value of 0.014, confirming the weak differentiation between the two core collections. ADMIXTURE analyses revealed a minimum value of the cross-validation error for K = 2. Only a quarter of the individuals actually showed a clear assignment (membership probability >0.9) to any cluster, supporting the lack of further structure in the dataset. The Q matrix for K = 2 . These markers were located as follows: one SNP on LG05 at 19.23 Mb, two SNPs on LG08 at 13.05 Mb and 19.85 Mb, one SNP on LG09 at 29.70 Mb, one SNP on LG12 at 1.03 Mb, and one SNP on LG15 at 23.86 Mb  amino acid metabolism and starch and sugar metabolism for the FST outliers, (ii) nucleotide metabolism and glycerolipid metabolism for the variety type associated regions, and (iii) purine metabolism and thiamine metabolism for the bitterness associated regions. The enrichment test made using the entire predicted gene set as reference did not yield any significant result.We looked at the gene predictions available on the first version of the genome of apple within 200 kb around the seventeen SNPs detected above as putatively under diversifying selection. In the regions containing the five FST outliers and GWAS methods should be robust to such biases, and even conservative. Indeed, core collections balance the initial extreme allelic frequencies, so that association should be valid across even more diverse genotypes to be significant in core collections. The use of core collections instead of random sampling may in addition have led to an underestimation of linkage disequilibrium. Indeed, the increased distances between accessions within a core collection reflect an increased number of generations from the most recent common ancestor and thus a higher number of crossing-overs between linked loci  also supports the lack of genome-wide differentiation and is consistent with the mean FST value of 0.02 found by Cornille et al.  and could lead the causative factors for our outliers to be located outside of the windows examined.Regarding the results of the association between the genotypic information and the variety type, the two SNPs located on LG08 colocalized with QTLs linked to biennial bearing and yield  was identified underlying this QTL hotspot and is thought to be the gene responsible for the numerous QTLs in this area  located at 1.53 Mb, which makes our result highly consistent with this particular QTL hotspot and the LAR candidate gene. Altogether, the six SNPs associated to the bitter/sweet phenotype were located very close (less than 1 Mb on average) to the markers exhibiting the highest LOD score in the QTL analyses.All the six genomic regions identified when testing the association between the genotypes and the bitter/sweet phenotype were found to colocalize with QTLs responsible for the content of several polyphenolic compounds, either measured in the flesh or measured in the peel of the fruits  for breeding cider varieties carrying both traits already present in cider varieties (such as high polyphenol content) and traits mainly present in dessert apple varieties . Our markers can indeed guide both the choice of the cider apple progenitors and the selection of seedlings from crosses between dessert and cider varieties and thus segregating for the favorable haplotypes. By genotyping the seedlings of a cross between a cider and a dessert variety type at the loci we identified as linked with traits of interest, one could choose the individuals bearing the favorable alleles and keep the individuals combining traits from the two variety types. Another application of the information we described here could be the inventory of the several traits and genomic regions responsible for them to better manage germplasm diversity in the cultivated apple Prada .http://www.divseek.org/). Recently, it has been suggested that an international consortium for the sustainable management of apple genetic diversity in particular is timely (Volk et al. M. sylvestris (Cornille et al. Unraveling the genomic bases of quantitative trait variation is essential for understanding evolution and for accelerating plant breeding (Alonso-Blanco and Méndez-Vigo


Camelina sativa, a largely relict crop, has recently returned to interest due to its potential as an industrial oilseed. Molecular markers are key tools that will allow C. sativa to benefit from modern breeding approaches. Two complementary methodologies, capture of 3′ cDNA tags and genomic reduced-representation libraries, both of which exploited second generation sequencing platforms, were used to develop a low density (768) Illumina GoldenGate single nucleotide polymorphism (SNP) array. The array allowed 533 SNP loci to be genetically mapped in a recombinant inbred population of C. sativa. Alignment of the SNP loci to the C. sativa genome identified the underlying sequenced regions that would delimit potential candidate genes in any mapping project. In addition, the SNP array was used to assess genetic variation among a collection of 175 accessions of C. sativa, identifying two sub-populations, yet low overall gene diversity. The SNP loci will provide useful tools for future crop improvement of C. sativa.The online version of this article (doi:10.1007/s11032-015-0224-6) contains supplementary material, which is available to authorized users. Camelina sativa (L. Crantz) is a species from the highly diverse Brassicaceae family, which contains a number of economically important oilseed crops  (Supplementary Table 1). DNA was extracted from freeze dried tissue of young leaves using a cetyltrimethylammonium bromide (CTAB) based method  for 1 h at 37 °C. Small fragments were removed by hybridizing the digested cDNA to AMPure beads  for 5 min at room temperature, washing with 70 % ethanol and eluting the cDNA in 10 mM Tris Buffer. The adaptor was prepared by annealing 100 pmol of oligo A1 (CCATCTCATCCCTGCGTGTCTCCCACTCAGCAT) and 100 pmol of oligo A2 (CGATGCTGAGTCGGAGACACGCAGGGATGA) in annealing buffer  at 55 °C for 5 min and then allowing the mixture to return to room temperature. Purified cDNA was hybridized with 25 μL M-270 streptavidin beads  for 20 min at room temperature. The adaptor (5 pmol) was ligated to the immobilized library for 20 min at room temperature, followed by a fill-in reaction using Large Fragment Bst Polymerase  for 20 min at 42 °C. The single-stranded library was eluted from the beads using 0.1 N NaOH and neutralized in Qiagen PBI buffer containing NaOAc pH5.2. The neutralized, single-stranded library was cleaned using a Qiagen MinElute Kit according to the manufacturer’s protocol. The libraries were sequenced on a Roche 454 GS FLX sequencer in the DNA Technologies Laboratory at National Research Council, Saskatoon.3′ biased 454 Roche libraries were generated from cDNA digested with EcoRI (4 U/μg) for 12 h at 37 °C. The digested DNA was separated in 0.7 % agarose (1× TAE) for 2 h at 110 V, fragments between 2 and 4 Kb in length were excised from the gel and eluted from the agarose using QIAquick gel extraction kit. The eluted DNA was then used to generate a reduced representation library with the Illumina paired-end sample preparation kit according to the manufacturer’s protocol, with a final insert size of approximately 300 bp . The libraries for each line were sequenced for 101 cycles from each end of the insert on an Illumina Genome Analyser IIx in the DNA Technologies Laboratory at National Research Council, Saskatoon.Ten microgram of genomic DNA from Lindo and Licalla were digested to completion with The 454 transcriptome data was processed and analysed using SeqMan NGen v2.1.0. The raw data were trimmed for quality and adapter sequence. Sequences from line 33708-06 were assembled de novo using the following parameters: match Size 19, match Spacing 75, minimum match percentage 95, match score 10, mismatch penalty 25, gap penalty to generate 25, maximum gap 15 and expected coverage 100. The filtered sequences from line 31471-03 were reference mapped to the assembled contigs using the same parameters as for the de novo assembly except that the minimum match percentage was increased to 98. Nucleotide variation was identified in NGen using default parameters. The resultant list of SNPs was filtered as described in "C. sativa was an ancient hexaploid , with only unique matches being considered. SNP variants were called with a minimum variant frequency of 35 % and a predicted genome ploidy level of three, since it had been previously suggested that ® Assay Design Tool (ADT) to generate an ADT score; those SNPs falling below 0.6 were rejected. The final selection of 768 SNP loci was submitted to Illumina to generate the custom pooled oligo set (OPA).The sequences containing potential SNPs along with 100 bp of flanking DNA were submitted to the IlluminaC. sativa Illumina GoldenGate array according to the manufacturer’s instructions. Subsequently the arrays were scanned using an Illumina HiScan. The SNP data were analysed and the genotypes for each line called using the Genotyping module of the GenomeStudio software. The genetic linkage map was generated using Mapmaker v3 with a LOD score of 3.0  a 100,000 burn-in period was used, followed by 100,000 iterations using a model allowing for admixture and correlated allele frequencies with no a priori location or population information. At least 10 independent runs of STRUCTURE were performed by setting K from 1 to 10, with 10 replicates for each K. The ∆K was calculated for each value of K using Structure Harvester  was higher than a standard threshold value of 70 %. For the chosen optima value of K, membership coefficient matrices of replicates from STRUCTURE were integrated to generate a Q matrix using the software CLUMPP . Bootstrap support for this tree was determined by resampling loci 1000 times.Statistics including gene diversity, PIC value and allele frequency for each locus were calculated using Powermarker v3.25 , were adopted to identify SNPs in the C. sativa, a de novo assembly was generated for line 33708-06, which resulted in 582,229 reads (60.9 %) being assembled into 47,313 contigs with an average length of 425 bp. Seventy-four percent  of the reads from line 31471-03 were reference mapped to the assembled contigs with a fivefold average coverage. Nucleotide variation was identified using a depth cut-off of 3 and a variant percentage of 30, which identified 8,037 SNPs  and 21,537 insertion/deletions . Due to the anticipated polyploid nature of the genome and the desire to generate locus-specific SNPs, further filtering required both the reference and the alternate base to be represented in 100 % of the reads. This significantly reduced the potential number of useful SNPs to 426 (5 % of the observed variation). Screening for SNPs with sufficient flanking sequence that also passed Illumina’s quality check for probe design (ADT score >0.6) identified 252 SNP loci, which were submitted for Illumina GoldenGate array design.The 3′ biased cDNA libraries were sequenced using Roche 454 and 956,538 high quality sequences were generated from line 33708-06 and 586,982 for line 31471-03. Since no reference genome sequence was available for Arabidopsis thaliana was exploited. The sequence of each contig with potentially useful SNPs was aligned to the A. thaliana genome using BLASTN . Approximately 50 % of the contigs  were homologous to 1,878 annotated A. thaliana genes. A subset of SNPs were selected for the array design from contigs that potentially covered the expanse of the A. thaliana genome. This represented 288 SNPs that were positioned in contigs with homology to 64, 58, 48, 47 and 61 A. thaliana genes on chromosomes one to five, respectively. Since genic SNPs can be less robust due to the influence of unidentified homologues, 228 SNPs were chosen randomly from those assumed to be intergenic. Including SNPs designed from the 3′ cDNA analyses a total of 768 SNPs were submitted for Illumina GoldenGate array design (Supplementary Table 3).The reduced representation genomic libraries were sequenced on the Illumina GAIIx platform and the resultant data for each line are shown in Supplementary Table 2. Eighty-two percent of the Lindo reads  were de novo assembled using CLCBio Genomics Workbench to generate 288,946 contigs (≥200 bp), with an average length of 511 bp covering 147.7 Mb of genome sequence. The data from Licalla was referenced mapped to the Lindo contigs, resulting in alignment of 46,922,482 reads to 260,431 contigs. SNP detection using CLCBio identified 234,838 SNP positions with a single variant base in Licalla at a depth of at least 8 reads and a variant percentage greater than 35 %. In order to reduce the impact of duplicate loci only SNPs where the reference and alternate base showed no variation were further processed. This reduced the number of available SNP positions to 48,421 (20.6 % of possible variation). In addition, SNPs were further restricted by selecting those with 100 bp of flanking sequence and which contained no additional SNPs, reducing the available SNPs to 6,686 in 4,919 contigs. These SNPs were submitted to Illumina’s Assay Design Tool and only those with a score of >0.6 were considered further. In an attempt to select SNPs across the genome, inferred synteny with C. sativa. The newly developed GoldenGate array was hybridized with DNA from the two parental lines and 180 RI lines. Eighteen of the probes on the array gave poor signals with normalized R values <0.2 for each sample. Two hundred and seven probes on the array showed no polymorphism between the parental lines. The majority of these monomorphic loci (189) were designed from the 3′ cDNA data, and only 18 of these loci had been designed to specifically target SNP variation between Lindo and Licalla. The cluster distribution for the remaining probes on the array varied in pattern and ease of scoring  population derived from a cross between Lindo and Licalla was used to develop a genetic map for C. sativa (Table p < 0.01) imbalanced .After manual editing of the GenomeStudio cluster file it was possible to score and map 533 SNP loci. These were arranged over twenty linkage groups, representing the haploid chromosome number of va Table ; Fig. 2.C. sativa genome sequence using BLAT  (Supplementary Table 4). Similarly the EST sequences used to design the SSR primer sequences were compared to the two genomes. There was a strong correlation between the genetic and physical maps of C. sativa ; however, in regions of reduced recombination there were minor discrepancies between the marker order of the genetic map and the genome sequence. On average the markers were distributed 1 locus per 1 Mb of genome sequence, the regions with increased recombination or the larger gaps in the map corresponded to a paucity of loci selected for the particular genomic segment with physical distances ranging from 3.7 to 6.2 Mb between the loci. Some of the centromeric regions also displayed a low density of SNP loci, which was not reflected in the genetic distance (Supplementary Table 4).The 100 bp sequences flanking the SNP loci were aligned to the LAT Kent  with defA. thaliana genes or adjacent genome sequence identified the Brassicaceae ancestral blocks (A–X) defined by Schranz et al. , gene diversity (0.006–0.5) and major allele frequency (0.5–0.99) for each SNP locus is provided in Supplementary Table 6. The gene diversity for the entire collection was 0.26, which is lower than a similar analysis of elite maize germplasm . A very high inbreeding coefficient  of 0.96 was calculated from the C. sativa lines that can be explained by the inbreeding nature of the species whereas the overall fixation index  of 0.276, which provides a measure of population differentiation, indicates a similar level of differentiation among sub-populations as that found among winter and spring types of B. napus . Using the program Structure Harvester . Using a minimum value of 70 % ancestry, 152 accessions were assigned to one of the two sub-populations, 61 accessions to Population I and 91 accessions to Population II . The population clusters did not group according to the available geographical information. A similar pattern was observed for the relationship as determined by the unweighted Neighbour-Joining method, which clustered accessions into two major groups. In Fig. Population structure analysis was completed using STRUCTURE  that equated to relatively large physical distances indicating a paucity of markers in these regions. The linkage groups ranged from 63.8 cM (Cas20) to 145.8 cM (Cas11) and together covered 1,808.7 cM. This was somewhat larger than the previously published map  for the same mapping population  described by Delourme et al.  as observed for B. napus, there were no significant differences found in mean PIC value either between the triplicated sub-genomes or when independently analysing the sub-populations, whereas differences were observed both between sub-genomes and across morphotypes in B. napus Supplementary material 2 (PDF 491 kb)Below is the link to the electronic supplementary material.


In Table S2, column #13 (‘BETA') is incorrect. A corrected version of Table S2 can be viewed below.Table S2eQTL SNP replication table. Summary of eQTL findings and replication across all included studies. eQTL SNPs were included in the table if, from a given study, they were the most highly associated cis-linked SNP within an LD block and if their 1. REF STUDY. eQTL discovery study.2. GENE. RefSeq gene identifier.3. CHR. Chromosome of the eQTL SNP and associated gene expression trait.4. STRAND. Annotated strand of the associated transcript.5. TSS. Genomic coordinate of the transcription start site.6. TES. Genomic coordinate of the transcription end site.7. RSID. Identity of the eQTL SNP.8. HS INDEX. Identity of the LD block containing the SNP.9. RSCOORD. Genomic coordinate of the SNP.10. TIER. Tier of the SNP with respect the associated gene.11. UBF. Univariate 12. MBF. Multivariate 13. BETA. Estimate of the effect size per minor allele of the SNP.14. GEX. Mean gene expression level across all samples in the discovery study.15. PROBE MAF. Maximum minor allele frequency of SNPs overlapping the coordinates of the gene expression probe.16. NALN. Number of high quality alignments between the gene expression probe and hg18.17. STL, MBR, CLI, CPL, HVC, GCT, GCF, HCE, HPC, MLI, GCL. Each subsequent column denotes the measure of univariate replication between the SNP and gene expression trait in the study set indicated by the TLA, where:Click here for additional data file.


Identifying gene-gene interactions is essential to understand disease susceptibility and to detect genetic architectures underlying complex diseases. Here, we aimed at developing a permutation-based methodology relying on a machine learning method, random forest (RF), to detect gene-gene interactions. Our approach called permuted random forest (pRF) which identified the top interacting single nucleotide polymorphism (SNP) pairs by estimating how much the power of a random forest classification model is influenced by removing pairwise interactions.We systematically tested our approach on a simulation study with datasets possessing various genetic constraints including heritability, number of SNPs, sample size, etc. Our methodology showed high success rates for detecting the interaction SNP pair. We also applied our approach to two bladder cancer datasets, which showed consistent results with well-studied methodologies, such as multifactor dimensionality reduction (MDR) and statistical epistasis network (SEN). Furthermore, we built permuted random forest networks (PRFN), in which we used nodes to represent SNPs and edges to indicate interactions.We successfully developed a scale-invariant methodology to detect pure gene-gene interactions based on permutation strategies and the machine learning method random forest. This methodology showed great potential to be used for detecting gene-gene interactions to study underlying genetic architectures in a scale-free way, which could be benefit to uncover the complex disease mechanisms. Genome-wide association studies (GWASs) have revolutionized the strategy for identification effects of single nucleotide polymorphisms (SNPs) on disease susceptibility and detecting genetic architectures underlying complex diseases from large-scale genotyping data, such as type II diabetes, obesity and cancer –5. GWASsAs an alternative to commonly used linear models and other classical methods as above, we applied data mining and machine learning methods, such as multifactor dimensionality reduction (MDR), artificial neural network (ANN) and statistical epistasis network (SEN), etc., to detect interactions between different genes, and between genes and environmental exposures during modeling. The concept here is that these methods perform better to capture the non-linear mapping from genotypes to phenotypes , 8–11. MOne class of widely used algorithms in machine learning are tree-based methods, such as decision trees (DTs) and random forests (RFs), which belong to the supervised machine learning methods that are used for variable selection, classification and outcome prediction . A singlRF can capture interactions between SNP predictors based on DT modeling on non-linear associations. RF also yields variable importance measures (VIMs) that can be used to rank SNPs as a screening and filtering method. Using RF for studying gene-gene and gene-environmental interactions considering both marginal and interaction effects is appealing . RF was Although RF implicitly considers interactions, further work is required to separate main effects from interactions in RF since VIMs as estimated in RF reflect both main and interaction effects , 25. UnfIn this study, we proposed an approach called permuted random forest (pRF) to detect pure interactions between SNPs, which included four steps: training, permutation, testing and ranking. Random forest was trained using original dataset and for each pairwise of SNPs, dataset was later permuted using two methods: one permutation method kept the main effects of the chosen pair of SNPs, the other method kept the main effects and interaction of the chosen pair of SNPs. The subtraction of the two schemes was defined here as the interaction signal between the chosen SNP pair, and therefore measures how different RF outcomes were due to how much the interaction signal contributes to the prediction models. We hypothesized that if two SNPs are highly interacting with each other, the success rate for RF to classify the samples correctly would be affected greatly when removing the interaction between the two SNPs. The stronger interactive SNPs could then be identified by ranking the different classification errors from the above two permutation schemes. We tested our hypothesis systematically on simulated datasets obtained from Genetic Architecture Model Emulator for Testing and Evaluating Software (GAMETES) with different genetic constraints including heritability, number of SNPs, sample size, etc, and achieved good success rates on detecting the interacting SNP pairs even under very low heritabilities. We also applied our approach on two real bladder cancer datasets: one was a 7-SNP dataset with a single pair of SNPs had interaction, the other was a 39-SNP dataset with multiple pairs of SNPs had interaction. We were able to replicate previously identified interacted SNPs by two well-studied methods, MDR and SEN, and also made evidently new discoveries on SNPs with interactions. Finally, we introduced the idea of permuted random forest networks (PRFN), in which we used nodes to represent SNPs and edges to indicate interactions.We proposed a method called permuted random forest (pRF) to address these two questions: First, given a SNP dataset, how can we detect the SNP-SNP interactions accurately? Second, how can we analyze all SNPs together into a model to incorporate multi-SNP interactions instead of only analyzing the interactions using the data from the pair of SNPs? In our approach, we quantified the interaction signal by estimating how much the signal contributes to the model prediction power. Our conjecture was that if the interaction between a pair of SNPs contributes greatly to the phenotype prediction, meaning that deleting the interaction causes an increase in prediction error rate, then there was probably a strong interaction between this pair of SNPs. To test if a pair of SNPs have strong interactions, the prediction error rates were obtained from the two testing datasets that were generated from two permutation schemes as shown below. For each pair of SNPs, one testing dataset had its interaction deleted, while the other testing dataset kept the interaction between this pair of SNPs. Thus, the difference of the two testing datasets was purely the interaction, and the different prediction error rate was caused by the existing interaction between this pair of SNPs.‘randomForestSRC’ package in R with the settings nsplit = 0, ntree = 100 and the rest as default, which was a well-established package for carrying out random forest analysis for survival, regression and classification [Our approach consisted of four steps of 1) training, (2) permutation, (3) testing and (4) ranking using machine learning algorithm RF. In the first step training, RF was trained using the whole original SNPs dataset. We used fication . The RF  training1 and SNP 2, indicated inside red rectangles by the blue-orange pattern, was consistent before and after our second permutation strategy. For each pair of SNPs, the above two permutation frames were repeatedly applied to generate two testing datasets. It is also worth mentioning that the interactions among other non-selected SNPs were preserved in both of the permutation frameworks since all the SNPs were considered for the model. This is an advantage of our method since other interactions may have direct or indirect effects on the selected SNPs.In the second step of permutation, for each pair of SNPs independently, we carried out two permutation strategies to generate two testing datasets. The difference between the two testing datasets was merely the preservation or deletion of the interaction between the pair of SNPs. Previously, Greene et al. designed an explicit test of epistasis to remove SNP interactions, which was based on a permutation method . In thei1E1, in which the testing dataset did not maintain the interaction between the two selected SNPs. We named the average classification error from the second permutation framework, E2, in which two selected SNPs maintained both main effects and interaction. Therefore, the subtraction would be the interaction signal that left between the two permutation schemes.In the third step of testing, for each pair of SNPs independently, the prediction error rates from the two testing datasets were calculated using the method below. For each pair of SNPs, both of the permuted datasets (contained both of the permuted pair of SNPs and the rest of the non-permuted SNPs) were tested using the retained RF structure (from training) to detect classification errors. RF determined class membership of each sample by majority voting from all trees. Classification was correct if the voted class was the same as the original dataset. Classification error was calculated by averaging the classification error from all samples. Permutation was repeated 10 times and the average classification error was calculated from all permutations. We named the average classification error from the first permutation framework, ΔE = E1 − E2) was calculated for each pair of SNPs. The ΔE was used to define the strength of the interaction exists, since omitting a strong interaction could have a strong affect on classification power. The larger the ΔE was, the stronger interaction signal was indicated for that pair of SNPs. The ΔEs were ranked and the pair of SNPs with the largest ΔE having the strongest interaction among all SNPs, or we could identify the top interactive SNPs given a particular threshold. For simulation studies, the same process was repeated on the 100 replicate datasets in order to calculate the overall success rate of interaction detection using our approach.In the last step, after each pair of SNPs independently was permuted using the above two permutation schemes and tested to get the prediction errors, the error rate difference  is a very popular method that can accurately identifies gene-gene and gene-environmental interactions, which is nonparametric and model-free , 28. To Statistical Epistasis Network (SEN) uses network science to globally study the interactive gene-gene interactions exist in the large GWAS dataset . Each noGenome-scale Integrated Analysis of gene Networks in Tissues (GIANT) is a user friendly interface which provides the interactive visualization of the tissue specific networks . The genn-locus models. Pure models are defined as no single locus displays a marginal effect and strict models refer to no subset of the n-locus are predictive of phenotype information [Genetic Architecture Model Emulator for Testing and Evaluating Software (GAMETES) is a user-friendly software designed by Urbanowicz et al. for simulation studies . GAMETESormation . Such a ormation . Data anormation . In thisormation . In addi“RandomForestSRC” is a R package developed for doing survival, regression and classification using machine learning method random forest. Survival forests can grown for right-censored survival data, while regression and classification forests can grown based on either categorical or numeric response . SplittiM0P0, M1P1, and the rest of SNPs were named Nx. We calculated the success detection rate of our method pRF by observing how often the datasets among the 100 datasets lead to detection of the interacting SNP pair, M0P0 and M1P1.In order to systematically evaluate our method for detecting SNP-SNP interactions, simulated datasets were first considered. Such simulated data provides better control and understanding of the interaction detection through a systematic evaluation process. Hence for this purpose, epistatic 2-locus SNP-disease models and an archive of datasets for each given model were generated using the GAMETES. Specifically, GAMETES was used to generate 100,000 random, strict and pure genetic models for each of 8 different combinations of genetic constraints that are differed by number of locus (SNPs) of 2, heritabilities of 0.001, 0.005, 0.01, 0.05, 0.1, 0.2, 0.3 or 0.4, minor allele frequency (MAF) of 0.2 and population prevalence that is allowed to vary. For each of the 8 genetic constraints combinations, 100,000 models were ranked by EDMs and the models with the highest and lowest EDMs were selected as the two models for data simulation . For eacQiagen genomic DNA extraction kits (QIAGEN Inc.) from peripheral circulating blood lymphocyte specimens, and genotyping was performed using SNP mass-tagging system from Qiagen Genomics and PCR-RFLP. Dataset was preprocessed based on the sufficient DNA concentration and successful genotyping, and the missing phenotypes/genotypes were imputed using the corresponded most frequent phenotypes/genotypes. In this study, we selected a subset of 7 SNPs that had been previously identified to include a highly interacted SNP pair from this data. This dataset includes 560 controls and 354 bladder cancer cases after pre-processing. This dataset had one pair of SNPs had interaction.The cases in the dataset were selected from people who were diagnosed with bladder cancer among those between 25–74 years old and from July 1994 through June 1998 in the New Hampshire State Cancer Registry. Controls were chosen from population lists in New Hampshire Department of Transportation (age ≥ 65), population lists in Centers for Medicare & Medicaid Services (CMS) of New Hampshire (age < 65), shared controls from a non-melanoma skin cancer study with diagnostic period of July 1993 to June 1995, and with additional controls assigned to match the cases on age and gender . InformeThe cases in the dataset were selected from people at the same age range who were diagnosed with bladder cancer among from the same start time through longer enrollment period until June 2001 in the New Hampshire State Cancer Registry. Controls were chosen the same way, and shared controls from a non-melanoma skin cancer study with longer diagnostic period of July 1993 through June 1995 and June 1997 through March 2000, and with additional controls assigned as above described . DNA wasM0P0, M1P1, and with the rest of SNPs named Nx. The success rates for identifying the interacting SNPs, M0P0 and M1P1, were calculated for each dataset, and averaged from 100 replicates; see Table A total of 160 different datasets, each with 100 replicates were simulated using GAMETES based on the different combination of genetic constraints, that included number of interaction locus (2), heritability , minor allele frequencies (0.2), number of SNPs , sample size (2000 or 4000), extreme EDM (highest or lowest) and population prevalence that is allowed to vary. The datasets were generated from random, pure and strict epistasis models using GAMETES, with one pair of highly interacted SNPs named XRCC3 (rs861539), APE1 (rs3136820), XPD_751 (rs13181), XRCC1_399 (rs25487), XPD_312 (rs1799793), XRCC1_194 (rs1799782), XPC_PAT (rs2228001). We applied our permuted Random Forest (pRF) on this dataset and successfully identified the SNP pair, XPD_751 and XPD_312, with the highest different error rates using our two permutation schemes. RF was applied on the whole dataset using R package ‘randomForestSRC’ with the default setting except nsplit = 0, ntree = 100. The same two permutation strategies were used and repeated 10 times to obtain the average error rate difference ΔE for each pair of SNP. As shown in Table ΔE was 7.23 % for this SNP pair, while the rest ranged from −0.84–0.99 %. Different numbers of permutations, tree numbers and splitting rules had been applied in our method; however, the interaction pair could always be identified (data not shown). To compare our method with other method, MDR was also used on the same dataset to identify the top 2-way models, which indicates the highly interacted SNPs. The last column showed how the MDR ranks the top 2-way models based on the likelihood of having interactions among the SNPs. We found for the most interacted pair shown by our approach, MDR was showing the consistent results by ranking it as the most interactive pairs of SNPs as well.The subset of bladder cancer dataset contained 7 SNPs, 1 rs313680, XPD_75Cytoscape as shown in Fig. 22‘ramdomForestSRC’ with the default setting except nsplit = 0, ntree = 100 using the same two permutation strategies as previously mentioned. Permutations were done 10 times to obtain the average error rate difference ΔE. The ΔEs were ranked from largest to smallest and the top candidates were selected as the most interactive SNPs. In order to achieve a fair comparison with previous results obtained by SEN, we used the same number of the edges in each cluster from the original 39-SNP connected components identified by SEN in Fig. 22ΔE. The SNP pairs with the higher difference indicated a strong interaction from the two permutation schemes shown in Fig. 2Previously, Hu et al designed a new methodology to detect SNP-SNP interactions using statistical epistasis network (SEN), in which pairwise interactions were calculated based on information theory . In theiCCL5 and PARP4 in panel A, MBD2 and GSTM in panel B, BCL6 and XPC in panel C were characterized using GIANT with network filters set as minimum relationship confidence equals to 0.8 and maximum number of genes equal to 5. By comparing our method to SEN, we concluded the difference was reasonable.We further characterized the newly identified interactions using Genome-scale Integrated Analysis of gene Networks in Tissues (GIANT). As shown in Fig. GWAS provides a powerful approach to discover disease associated genetic variants and a lot of diseases associated SNPs have been discovered via GWAS, yet, that knowledge is still not enough to explain complex diseases XPD was found to possess DNA repair capacity (DRC) and studies have found two XPD polymorphisms, XPD Asp312Asn and Lys751Gln, had a modulating effect on DRC and there existed possible association between XPD Asp312Asn and Lys751Gln polymorphisms in lung cancer To discuss our result for the simulation study, we found the success rates of our method to detect highly interacted SNP pairs were decreased as the number of SNPs in the dataset increased. For instance, we observed the success rates to be 70 and 30 % under the lowest EDM model at the number of SNP 20 and 25. However, when the sample size was increased from 2000 to 4000, it compensated for the difficulty of detection using datasets with lowest EDM models or the larger SNP size, which improved the ability to detect the interaction pair of SNPs. This was due to the nature of RF when the feature space gets expanded, and it was more generally of the problem of high dimensional data with sparse signal. Also, because this was a permutation-based method, when as the SNP size increased, it took much longer time to sort through all pairwise combinations. However, using parallel mode and running on clusters could help resolve this issue. On the 7-SNP bladder cancer dataset which we compared our result with MDR, we found the most interacted pair shown by our approach was the same as MDR as shown in the “MBD2_02 (rs1145315) and GSTM3_01, MBD2_02 and AXIN2_02. To further check the biological functions, MBD2 was methl-CpG-binding domain protein 2, which belonged to methyl-CpG-binding domain (MBD) and had been previously identified possessing the function of activating certain promoters by de-methylation, particularly in cancer GSTM3 had been found to play a role in detoxification of carcinogens and modulating cancer susceptibility 2PIM1_01 (rs10507), TPS313_03 (rs2303287) and AXIN2_02 in the top cluster and RERG_10 and RERG_31 in the bottom cluster. To get an idea of false positive detection, we also tested some of the gene pairs that were not identified with strong interactions using our method, such as CCL5 and PIM1_03 (rs262933), XPC_01 (rs2228001) and MYBL2_31 (rs826950), BIRC3_02 (rs3758841) and AHRR_10. None of them were detected with interactions using the same threshold by GIANT (results not shown). To characterize the newly identified interactions we found using our method on this 39-SNP dataset, we used GIANT to further look into this. It is shown from the current databases that those genes have indirect interactions via only one neighbor. Although from the results of GIANT, we did not observe direct interactions between the genes identified using our approach, it is possible that those genes had interactions yet to be demonstrated. Building PRFN on each of the clusters independently might have led to different results compared to building SEN across the whole dataset to get the result of a cluster of 39 SNPs. It is also worth to mention that RF could be used to impute the missing data, which could be better to use over the traditional method of using the average across the samples. However, we wanted to keep the consistency of the same dataset that previously methods used in order to better compare our results. Thus, we did not use the RF to impute the missing data.Followed by the analysis, we started to wonder how did our method work on a dataset with multiple interactions. To do that, we obtained a dataset from a 39-SNP bladder cancer dataset that was previously identified using SEN from a 1500-SNP dataset to be the largest cluster which had strong interactive functions. The 39-SNP were highly interactive as a network. To compare our result with the previous result, we found there were 28.57 %, 84.62 %, 70.00 % overlapping interacting SNP pairs detected using our method in three clusters in Fig. 2‘randomForestSRC’ is well designed with clear instructions. This R package could run in both serial and parallel modes, which greatly increase our efficiency by running the jobs on computational clusters. (3) It captures gene-gene interactions by incorporating all SNP predictors into the model. When RF is grown in the training stage, all SNPs are considered in the RF run and RF itself chooses the best SNP at each node. When testing the classification error, the whole dataset with two permuted SNPs information, was dropped on to the trees. Most other methods consider combinations of SNPs instead of involving all SNPs together in the model, thus our method has the advantage of considering interactions that may exist among all other SNPs. (4) RF naturally captures feature interactions based on the DT structures, thus making it a suitable machine learning tool in our approach. (5) One of the best parts of our method is that it does not need a p-value threshold to detect the interactions. Unlike most genome-wide analyses, our approach does not need to perform multi-testing correction since the candidates are identified by sorting and selecting the top candidates. (6) Our approach is highly extendable. The permutation based strategy is not only suitable for application by RF, but could also be used with other machine learning algorithms, such as artificial neutral networks (ANNs) in the deep learning field, which models high-level abstractions from genetic data by the complex architectures. (7) We also think our approach could used on both the categorical data and continuously data due to the fact that RF could be grown using the categorical and continuously data.We think our approach has several advantages: (1) It is scale-invariant when detecting the SNP-SNP interactions. Random forest itself is non-parametric and our method does not rely on data belonging to any particular distribution. Most of the biological networks are very complicated and have not been studied well to have its mechanism uncovered, therefor we think using a scale-free model could be more reasonable and accurate than using a pre-scaled model. (2) It is based on the idea of permutations and random forest, which are comprehensible and accessible. Both of the permutation strategies are simple and easy to apply to the datasets, and the R package We also think our approach does have some drawbacks. (1) Most machine learning methods, including RF and MDR, are not well suited for unbalanced numbers of cases and controls , 41. ThuIn conclusion, we presented an approach called permuted random forest (pRF), which identified top interacting SNP pairs by estimating how much power the pairwise interactions influence a random forest classification model. Our approach was based on permutation strategies and relying on machine learning method, random forest. Results were shown that our methodology achieved high success rates for interacting SNP pairs detection on an archive of simulated datasets by GAMETES. Our approach was also applied on two bladder cancer datasets and consistent results with MDR, SEN and logistic regression were seen , 28. Fur


Bacillus anthracis. Here we report the use of next-generation sequencing technology to investigate the historical, geographic and genetic diversity of Bacillus anthracis in France. 122 strains isolated over a 60-years period throughout the country were whole-genome sequenced and comparative analyses were carried out with a focus on SNPs discovery to discriminate regional sub-groups of strains.Single nucleotide polymorphisms (SNPs) are ideal signatures for subtyping monomorphic pathogens such as department were closely related. Identification of SNPs from whole-genome sequences facilitates high-resolution strain tracking and provides the level of discrimination required for outbreak investigations. Eight diagnostic SNPs, representative of the main French-specific phylogeographic clusters, were therefore selected and developed into high-resolution melting SNP discriminative assays.A total of 1581 chromosomal SNPs precisely establish the phylogenetic relationships existing between the French strains. Phylogeography patterns within the three canSNP sub-lineages present in France  were observed. One of the more remarkable findings was the identification of a variety of genotypes within the A.Br.011/009 sub-group that are persisting in the different regions of France. The 560 SNPs defining the A.Br.011/009- affiliated French strains split the Trans-Eurasian sub-group into six distinct branches without any intermediate nodes. Distinct sub-branches, with some geographic clustering, were resolved. The 345 SNPs defining the major B.Br CNEVA sub-lineage clustered three main phylogeographic clades, the Alps, the Pyrenees, and the Massif Central, with a small Saône-et-Loire sub-cluster nested within the latter group. The French strains affiliated to the minor A.Br.001/002 group were characterized by 226 SNPs. All recent isolates collected from the Doubs B. anthracis population structure in a country. An extensive next-generation sequencing (NGS) dataset of 122 French strains have been created that allowed the identification of novel diagnostic SNPs useful to rapidly determine the geographic origin of any strain found in France.This work has established one of the most accurate phylogenetic reconstruction of  Bacillus anthracis, the causative agent of anthrax, is a spore-forming bacterium found throughout the world. The bacterium mainly affects wild and domesticated herbivores, causing serious, often fatal disease. Spores which are the infectant form of the bacteria can remain in soils for decades before being ingested by grazing animals and later on contaminated browse by blow flies  was used as the reference genome for assembly. Ames Ancestor plasmid pXO1 [GenBank:AE017336.2] and pXO2 [GenBank:AE017335.3] were used as references for plasmids assembly. Short reads data sets were exported on the FastQ format and mapped to the Ames Ancestor genome and both pXO1 and pXO2 plasmidic sequences using BioNumerics version 6.6  and Power assembler module asking for a similarity of at least 90%. A set of SNPs was deduced for each genome sequence data using BioNumerics Chromosome Comparisons module. Individual lists were compiled by using an in-house Python script, and data filtered to remove SNP positions at which one or more isolate displayed an ambiguous residue call or missing data. Ribosomal operons and VNTR loci were also excluded from the analysis, so as contiguous SNPs (in a 10pb window). The list of canSNP positions is provided in Table B. anthracis strains CNEVA-9066  [NCBI: NZ_AAEN00000000.1], A0465  [NCBI: NZ_ ABLH00000000.1], Ames ancestor (A.Br.Ames) [NCBI: NC_ 007530.2], Sterne (A.Br.001/002) [NCBI: NC_005945.1] and B. cereus AH820 (outgroup) [NCBI: NC_011773.1] can be found in the NCBI microbial genome website at http://www.ncbi.nlm.nih.gov/.The draft genome sequences of one representative French strains affiliated to the A.Br.001/002  [NCBI: JHCB00000000], A.Br.011/009  [NCBI: JHDR00000000] and B.Br.CNEVA  [NCBI: JHDS00000000] have been deposited in the NCBI database. The whole genome sequences for B. cereus AH820 strain as outgroup. B. cereus AH820 resides very close to the B. anthracis cluster using multilocus sequence typing [A minimum spanning tree was drawn in BioNumerics by using the filtered whole genome sequencing SNP data as input. The tree was rooted by using the e typing . Nodes w+ software [We designed High Resolution Melting (HRM) assays for eight French-specific SNPs using Primer 3software . The posAmplification was performed on the ViiA7™ Real-Time PCR System (Life Technologies) using the LightCycler® 480 High Resolution Melting Master Mix (Roche Diagnostics). The reaction mixture consisted of 0.2 μM of each primer, 1 × LightCycler® 480 HRM master mix and 2.5 mM MgCl2 in a 10-μl final volume. The following parameters were used: 10 min at 95°C were followed by 40 cycles consisting of 10 s at 95°C, 10 s at 58°C and 20 s at 72°C. Samples were next heated to 95°C for 30 s, cooled down to 65°C for 1 min and heated from 65°C to 88°C at a rate of 1°C/s with 25 acquisitions/°C. HRM data were analyzed by the ViiA7™ Software (version 1.2.1).The ANSES Genome Shotgun project has been deposited at DDBJ/EMBL/GenBank under the BioProject number PRJNA242332, with accession numbers JHCB00000000 , JHDR00000000  and JHDS00000000 .http://datadryad.org/) with the following identifier: http://doi.org/10.5061/dryad.rc6m9.Phylogenetic trees data are available from the Dryad Digital Repository (DNA: Deoxyribonucleic acid; SNP: Single nucleotide polymorphism; canSNP: Canonical single nucleotide polymorphism; VNTR: Variable-number tandem repeat; MLVA: Multiple-locus variable-number tandem repeat analysis; WGS: Whole-genome sequencing; NGS: Next generation sequencing; PCR: Polymerase chain reaction; HRM: High resolution melting; CC: Clonal complex; NCBI: National center for biotechnology information; TEA: Trans-Eurasian.The authors declare that they have no competing interests.GV and SD conceived the study. GG and SD performed the experiments, analyzed the data and wrote the paper. YB developed scripts used for whole-genome SNP analysis. All authors read and approved the final manuscript.Strains used in this study and NGS data.Click here for file


BIRNM), off the coast of St. Croix, recently deployed a 52 passive acoustic receiver array. We targeted 19 array‐representative receivers for range‐testing by submersing fixed delay interval range‐testing tags at various distance intervals in each cardinal direction from a receiver for a minimum of an hour. Using a generalized linear mixed model (GLMM), we estimated the probability of detection across the array and assessed the effect of water depth, habitat, wind, temperature, and time of day on the probability of detection. The predicted probability of detection across the entire array at 100 m distance from a receiver was 58.2% (95% CI: 44.0–73.0%) and dropped to 26.0% (95% CI: 11.4–39.3%) 200 m from a receiver indicating a somewhat constrained effective detection range. Detection probability varied across habitat classes with the greatest effective detection range occurring in homogenous sand substrate and the smallest in high rugosity reef. Predicted probability of detection across BIRNM highlights potential gaps in coverage using the current array as well as limitations of passive acoustic technology within a complex coral reef environment.Submerged passive acoustic technology allows researchers to investigate spatial and temporal movement patterns of many marine and freshwater species. The technology uses receivers to detect and record acoustic transmissions emitted from tags attached to an individual. Acoustic signal strength naturally attenuates over distance, but numerous environmental variables also affect the probability a tag is detected. Knowledge of receiver range is crucial for designing acoustic arrays and analyzing telemetry data. Here, we present a method for testing a relatively large‐scale receiver array in a dynamic Caribbean coastal environment intended for long‐term monitoring of multiple species. The U.S. Geological Survey and several academic institutions in collaboration with resource management at Buck Island Reef National Monument ( Researching the movement patterns and behavior of highly mobile marine vertebrates is challenging  recorded, along with the date and time the detection occurred. Several companies manufacture passive acoustic equipment; for this study, we used Vemco VR2W receivers and V16 and V13 range‐testing tags Vemco .e.g., Klimley & Nelson, e.g., battery miniaturization), receivers and transmitters are now being deployed in a variety of different contexts  even within a relatively small array . Environmental variables at receiver locations can cause heterogeneity in effective receiver detection range , a federally protected marine area, have maintained a passive acoustic array since 2012 to better understand the spatial ecology of several keystone species that utilize habitat within the park. However, no formal range‐testing had been previously conducted, and therefore, variation in detection probability between receivers in different habitats remains unknown. Our objectives were to (1) estimate the probability of detection as a function of distance between the receiver and the tag throughout the current acoustic array at BIRNM and (2) determine how habitat, transmitter depth, wind speed, time of day, and sea surface temperature affect detection probability. We predicted that receivers in areas with less complex physical structure in deeper waters would have increased probability of detection and greater effective detection ranges.2 of almost entirely submerged lands in addition to the 0.7 km2 uninhabited Buck Island  that rise to the surface from a depth of 9–15 m called “haystacks” . One of the V16‐4Ls and the V13‐1L had 20 sec fixed delay intervals, whereas the two additional V16‐4L tags had 10 sec fixed delay intervals not including 3.5 sec to transmit the entire eight ping acoustic sequence . Power output for the V13‐1L tag was 147 dB and for the V16‐4L tags was 152 dB. To preliminarily assess temporal differences in detection, we deployed the two 20‐sec delay interval tags near four receivers in distinct areas of the array. Tag start times were staggered 7‐sec apart to reduce the likelihood of signal collision and deployed for 48‐h periods at each of the four receivers. Tags were placed 25 and 40 m from receiver #12, 50 and 70 m from receiver #15, 50 and 75 m from receiver #03, and 100 and 150 m from receiver #05.We conducted range‐testing in the fall of 2013 from September 6 to September 10 and from October 28 to November 14 and in the spring of 2014 from February 25 to March 5 and from April 28 to May 2. We focused on the first 19 receivers deployed as they were evenly distributed across the habitat classes covered by the array Figs.  and MaljAt the beginning of each event, a range‐testing tag was secured to a ~10 m polypropylene line 1 m above a cinder block anchor Fig. . We secuTo analyze the preliminary 48‐h deployments, we separated tag deployment periods into 2‐min intervals and recorded detection  or nondetection . We then aggregated the number of successful detections by each hour of the day. Two‐minute time intervals were used to account for any variability in range‐testing tag transmission rate as well as mimic the typical delay intervals of tags attached to species of interest. We performed Fisher's exact test to identify significant differences between night and day in the number of successfully recorded detections at each of the four receivers.To analyze the one‐hour range‐testing events, we uniquely labeled each event with a chronological identifier and the receiver station where the detection occurred. This was carried out because a single event could have been recorded by multiple receivers. If a range‐testing event was not recorded on any receiver, we used the nearest receiver to the event location. Similar to the preliminary analysis, we created 2‐min time intervals for each range‐testing event and recorded detection/nondetection within each interval.We categorized 5 × 5 m habitat and depth cells in ArcGIS 10.0 . The random effect in our models was the receiver where detections were recorded. We scaled distance between the receiver and the tag by subtracting the mean of all the distances and dividing by the standard deviation to achieve model convergence. We built the full model first with all of our variables included additively and then created subsequent models with reduced complexity by removing covariates. Pseudo‐R2 values were calculated for each model using the techniques outlined by Nakagawa and Schielzeth  with binomial distribution implemented in RStudio with R version 3.2.2 (R Core Team 2015) and the package lme4 . At 200 m from a receiver, predicted detection probability decreased to 26.0% (95% CI: 11.4–39.3). Predicted detection probability dropped to 0.2% (95% CI: 0.03–0.3%) 500 m away from a receiver.Model comparison indicated the full model best fit the data with the lowest AICc and 0.84 model weight Table . Given tP < 0.001) in the number of detections recorded during the day as opposed to the night based on Fisher's exact test. However, detection data from receiver #03 showed the opposite trend and receivers #05 and #12 had no significant difference in the number of detections between the night and day  as opposed to receiver habitat variable (0.22). Association plots for both receiver and transmitter habitat showed range‐testing was performed less frequently in low rugosity hard bottom habitat at deeper depths, but more in the shallowest depth class. Habitat variables were also correlated with one another and had a Cramer's V of 0.38. However, the variance inflation factor was low enough (<3) to indicate that multicollinearity does not drastically influence our predictions. No significant correlation was found among the other independent variables.Time of day, wind speed, and sea surface temperature variables were included to help control for multi‐season range‐testing. The methods used provide a snapshot of the spatial extent of a passive acoustic array, but it is critical to note they do not account for temporal variations. Areas where overlap of detection ranges between receivers occurs, such as the line of four perpendicular receivers south of Buck, may not provide the same coverage consistently throughout the year. Long‐term control tags are necessary for discerning behavioral shifts from temporal fluctuations in receiver detection range  may have an increased probability of detection further away from a receiver due to a greater number of transmissions than a more mobile species that is in the same area for a limited amount of time. Conversely, constrained detection ranges may not be problematic for highly mobile species such as some species of fish and marine turtles that potentially cover a large area in a relatively short time span. Decreased detection ranges allow for a more accurate interpretation of location as well. However, species with limited movement patterns will have a decreased chance of detection if they primarily inhabit areas where coverage is sparse such as north/northeast of Buck Island. Finally, tag type should be considered when performing range‐testing. We used two of the larger high powered tags offered by Vemco, and due to time constraints, we deployed tags based on proximity of location from the previous range‐testing event. Our predicted effective detection ranges therefore may be constrained for higher powered tags such as V16s and larger than expected for tags with lower output as used by some researchers in the BIRNM array. However, How and de Lestang  will ultimately affect the probability of detection. We used two‐minute time intervals to control for variability in transmission rate among range‐testing tags as well as provide coherent interpretation for tags deployed on marine turtles with 30‐90 sec transmission delay intervals. Less mobile species such as queen conch ( Lestang  show tha Lestang .The maps showing predicted probability of detection throughout BIRNM highlight areas where additional receivers might offer the best coverage. However, areas where receiver detection range is constrained, in the haystacks for instance, should not be abandoned given the coarse categorization of habitats. Overall, receivers placed in the haystacks may have constrained effective detection ranges, but can still provide necessary coverage in that area by being placed in locations where line of sight to the receiver can be maximized. Besides informing additional receiver locations, our results can also help more accurately determine residency time for tagged individuals by weighting detections based on the receiver's effective detection range where they occurred.As passive acoustic technology continues to improve, the cost of existing equipment will likely decrease, making it more readily available to researchers and resource managers and enabling larger arrays. Large, long‐term arrays can provide important movement data and residency information on a number of different species, but it is important to understand how receivers function in their environment in order to meet or revise research objectives. The Caribbean coastal environment is dynamic with numerous variables that can potentially affect receiver detection range and should be taken into consideration when configuring an array.Here, we range‐tested a relatively low‐cost passive acoustic array system, discerned factors influencing effective detection range, and developed a method for estimating array coverage at BIRNM. Our results indicate that deep habitat with minimally rugose substrate provides the greatest effective detection range and increased probability of detection. Our study revealed that array coverage north/northeast of Buck Island is poor at best; additional receiver stations in this part of BIRNM would be needed to provide increased coverage of that area. Furthermore, our study identifies variables affecting detection range that should be monitored throughout the course of the study with long‐term control tags. Understanding how each variable affects probability of detection or effective range of detection may not be necessary for every study design, but being aware of potential variations in detection range or lack of coverage is crucial for designing and implementing ecological studies based on an acoustic array.Postdeployment range‐testing is obviously constrained by the existing receiver station locations. However, range‐testing should not be abandoned once receivers have already been deployed, especially when a study is long term. Our results not only provide important information for future placement of additional receivers at BIRNM, but also highlight some of the constraints in using passive acoustic technology in a Caribbean coastal environment.Data and R scripts will be held at 3321 College Ave. Davie, FL 33314 and will be uploaded to a USGS server if manuscript is accepted for publication.None declared.


There has been limited research on genome-wide association with physical activity (PA). This study ascertained genetic associations between PA and 344,893 single nucleotide polymorphism (SNP) markers in 8842 Korean samples. PA data were obtained from a validated questionnaire that included information on PA intensity and duration. Metabolic equivalent of tasks were calculated to estimate the total daily PA level for each individual. In addition to single- and multiple-SNP association tests, a pathway enrichment analysis was performed to identify the biological significance of SNP markers. Although no significant SNP was found at genome-wide significance level via single-SNP association tests, 59 genetic variants mapped to 76 genes were identified via a multiple SNP approach using a bootstrap selection stability measure. Pathway analysis for these 59 variants showed that maturity onset diabetes of the young (MODY) was enriched. Joint identification of SNPs could enable the identification of multiple SNPs with good predictive power for PA and a pathway enriched for PA. Physical activity (PA) is any bodily movement produced by skeletal muscles that requires energy expenditure . It inclThe behavior of PA may be determined by genetic factors ,5,6. In In reporting genetic variants associated with a trait or disease, most traditional GWA studies adopted a single-marker approach that identifies single genetic factors one by one. However, this method is inefficient in predicting joint effects of multiple genetic variants on the common complex trait ,10. A muet al. [To identify multiple genetic variants for common complex traits or diseases, an elastic-net (EN) regularization method had been proposed by Cho et al. , along wet al. ,13 and pet al. ,15 when The present study aimed to find genetic variants influencing PA in the Korean population. Single-SNP association tests were initially conducted to assess genetic associations between daily PA and various SNP markers. We then performed multiple SNP analysis with EN regularization to determine genetic variants associated with PA. The SNPs identified provide novel biological evidence to understand the genetics of PA through pathway enrichment analysis.−1) appeared to be slightly more active than women . The mean PA level of the Ansung cohort  was higher than that of the Ansan cohort , suggesting that people in the rural community tended to be more active than their city counterparts. Overall, the average daily PA level was 1332 (SD 871) MET·min for the Korean participants. Men . This pathway includes GCK and HES1 genes and has a p-value of 0.076.The multi-stage procedure was applied to identify multiple causal SNPs. After performing single-SNP association tests in the first stage, we chose top 1000, top 2000, top 3000, and top 4000 SNPs that exhibit the strongest individual associations with PA. During the next stage, 639, 1248, 1760, and 2239 from the respective top SNP groups were jointly identified as PA-related genetic variants by the EN regularization method. The elastic-net allows correlation among predictors in variable selection, so there can be different selection results according to pre-screened datasets . At the R2 value. R2 values versus number of SNPs between the multiple SNP analysis and the single-marker approach. It is clear that the predictive power increases with the number SNP for both approaches, but the multi-stage approach always performs better than the single-marker approach for prediction purpose. This shows that multi-stage approach using a BSS cut-off value provides a better explanation of phenotype than the single marker approach.The predictive power of the 2239 SNPs identified from the top 4000 SNPs was next investigated. SNPs were ranked in order form smallest to largest and selected using a given BSS cut-off value, 0.95. Then, a multiple regression model with the selected SNPs was fitted to compute the corresponding adjusted The present study investigated genetic factors associated with PA for the Korean population, by performing large-scale GWA through single-SNP analysis and multiple SNP analysis via the EN regularization method. Single-SNP association tests are appropriate to determine individual associations between each SNP and the trait or phenotype. However, if the purpose is to predict the phenotype, then the joint identification of genetic factors would be powerful and provide a better prediction of the trait when multiple genetic factors exist for a common complex trait. In the presence of multicollinearity due to linkage disequilibrium among SNPs, EN regularization with BSS offers more accurate identification of multiple SNPs than ordinary multiple regression analysis.p-value = 4.67 × 10−6), 41 SNPs did exhibit some evidence of association with PA at a less stringent significance level . Among the 27 genes identified, CDKAL1, CDK5 regulatory subunit associated protein 1-like 1, is one of the tailfloated protein family and associated with a type 2 diabetes susceptibility gene responsible for tRNALys modification [CDKAL1 explores the TCR40/Get3 assisted pathway for insertion of its C-terminal transmembrane domain into the endoplasmic reticulum [Our single-marker analysis results showed that, although the most significant SNP did not attain the genome-wide significance level , is a member of the neuroxines protein family that acts in the vertebrate nervous system as cell adhesion molecules and receptors [NRXN3 are related to alcohol and nicotine dependence in patients who suffer from schizophrenia [Through the multi-stage approach, subsets of multiple SNPs were jointly identified through the EN regularization method. Among the 457 common SNPs found, 59 SNPs with BSS values exceeding 0.95 were mapped to 76 genes. Of these genes, y member . ADAM12 diseases . CCNI gen cortex . CCNI isgression . Moreoveisorders . The bas changes . Anothereceptors . The mutophrenia ,30.GCK gene and the HES1 gene. The gene GCK, glucokinase, is one of the frequently causing MODY genes and accounts for approximately 35% of cases [HES1, Hairy enhancer of split 1, is one of the highly conversed family of Hairy related basic helix-loop-helix (bHLH) proteins [HES1 is also known to be involved specifically in Notch1 signaling in neural cells and in bone narrow [Pathway analysis of the 59 SNPs led to the identification of MODY. Its inherence is responsible for non-insulin-dependent diabetes typically diagnosed among young people, especially under 25 years of age ,32. MODYof cases . HES1, HThe main limitation of our study concerns the self-reporting nature of PA and the questions used were not sufficiently detailed. In addition to possible recall error by the participants, the phenotypes of total PA level may not be well defined. PA was classified by five categories based on MET intensities with average MET assigned to each category. The estimated total PA level may dilute MET intensities and lead to a low power for the genetic association study. Furthermore, it is not feasible to compare our results with previous studies in the absence of similar genotyped SNPs. Consequently, we adopted the bootstrap sampling scheme to obtain replication data sets with appropriate selection stability measure to confirm the findings .Study subjects were selected from an ongoing population-based cohort, as part of the Korean Genome and Epidemiology Study (KoGES). Participants were recruited from residents in two cities (Ansung and Ansan) in Gyeonggi-do, Korea. We enrolled 10,038 males and females between 2001–2002 for a baseline study, whose demographics have been reported . Table 3Information on intensity and duration of daily PA was obtained from each participant using a structured questionnaire that included five components on PA: stable (lying down except sleeping), sitting , low intensity , medium intensity , high intensity . For each type of PA, its duration was measured in minutes. Since each question contained multiple PAs with varying MET intensities, the average MET intensity was assigned. The total amount of daily PA was then calculated by summing across the products of the average MET and the corresponding durations .n = 401), high heterozygosity , gender inconsistencies (n = 41), and those obtained from individuals who had developed any form of cancer (n = 101), were excluded from subsequent analyses, along with related or identical individuals whose computed average pairwise identity-by-state value was higher than that estimated from first-degree relatives of Korean sib-pair samples . Samples whose genotype-inferred sex disagreed with clinical records were re-tested for sex confirmation using the SNaPshot Multiplex System . Markers with high missing gene call rate (>5%), low Minor Allele Frequency (MAF) (<0.01) and significant deviation from Hardy-Weinberg equilibrium  were excluded, leaving a total of 352,228 markers to be examined among 8842 individuals. For multiple SNP analysis, reduced information was common due to missing values. To get complete genotype data, we imputed missing genotypes using the Fastphase software  [Genomic DNA samples were isolated from peripheral blood drawn from the participants. The majority of genomic DNAs were genotyped on the Affymetrix Genome-Wide Human SNP array 5.0 containing 500,568 SNPs. From the total of 10,038 participants, 10,004 available samples were genotyped  by impleet al. [Single-SNP association analysis was first performed for each of the 344,893 SNPs using linear regression analysis with adjustments for sex, age, area, and body mass index. A multi-stage approach  was nextet al. .p-values less than 0.1 defined as significantly regulated pathway. The single-marker association tests and adopted multi-stage approach were conducted using the PLINK [To investigate the biological significance of PA-related genetic variants, we mapped the identified SNPs to an exon/intron and performed pathway enrichment analysis. All pathways related to the identified genes were investigated via the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. Pathways were evaluated by the over-representation statistic and the Expression Analysis Systematic Explorer , Frederick, MD, USA) score . EASE cahe PLINK  and R [4he PLINK  softwareIn this study, we demonstrated that the joint identification of SNPs could enable the identification of multiple SNPs with good predictive power for PA and a pathway enriched for PA. Previous genetic studies have focused on the relationship between PA and health (or fitness) ,42,43,44