The decay of linkage disequilibrium was slow with r2 = 0.95 in 50 kb distance. The last 7-200 generations ago, Ne was at 10-13. An increase of Ne was noted in the very recent generations with a peak value of 19 for Ne at generation 4. The FROH estimated for 50-, 65- and 358-SNP windows were 0.87, 087 and 0.81, respectively. The most likely estimates for FROH after removing identical-by-state segments due to linkage disequilibria were at 0.80-0.81. The extreme loss of heterozygosity has been accumulated through continued inbreeding over 200 generations within a probably closed population with a small effective population size. The mean inbreeding coefficient based on pedigree data for the last 11 generations (FPed = 0.10) was strongly biased downwards due to the unknown coancestry of the founders in this pedigree data. The long-range haplotype test identified regions with genes involved in processes of immunity, olfaction, woundhealing and neuronal development as potential targets of selection. The genes QSOX2, BMPR1B and PRRX2 as well as MYOM1 are candidates for selection on the Lundehund characteristics small body size, increased number of digits per paw and extraordinary mobility, respectively.The Lundehund is an old dog breed with remarkable anatomical features including polydactyly in all four limbs and extraordinary flexibility of the spine. We genotyped 28 Lundehund using the canine Illumina high density beadchip to estimate the effective population size (N Presumably, intensive selection was necessary to retain the Lundehund phenotype. This includes a polydactyly with six or more toes per each limb and additional foot pads for sure-footedness, closable ears, an extraordinary mobility of the forelimbs and a height at withers of 32 to 38 cm (http://www.fci.be/en/nomenclature/NORWEGIAN-LUNDEHUND-265.html).Recently, an extraordinary low genetic variability could be demonstrated for the Lundehund using both single nucleotide polymorphisms (SNPs) and microsatellite markers –3. Two bTo preserve this rare Nordic dog breed with special phenotypic characteristics is a demanding task for breeding associations. Understanding the genetic basis of this dog breed would help to establish a breeding program conserving desirable characteristics and controlling hereditary defects.e) from data on linkage disequilibria (LD). We identified runs of homozygosity (ROH) as regions with a local loss of genetic variation. Inbreeding coefficients were calculated on the basis of pedigree data (FPed) and genotype information  to compare the results of these different methods for this population. We applied the long-range haplotype test to search for potential selective sweeps.The objectives of the present study were to genotype 28 Lundehund using the canine Illumina high density beadchip  to estimate the effective population size . The minimum length of a ROH in SNPs was set to 50 or 65 according to the recommendations of a simulation study in man with a high density SNP-array and approximately 0.65 of all 669,219 SNPs with a MAF >0.01 [ROH_358SNP for all Lundehund was 0.81 (0.53 to 0.86) and both, the FROH_50SNP- and FROH_65SNP-values, were 0.87 (0.83 to 0.90).We expected a high level of inbreeding in this small population when calculating the inbreeding coefficients FAF >0.01 . In ordeAF >0.01 . The 50-ROH_358SNP>1–10 Mb showed correlations at 1.00–0.98 (P<0.001) with FROH_358SNP (ROH_358SNP >1–10 Mb were at 0.70–0.81.Setting minimum ROH lengths for the 358-SNP-threshold to 1–10 Mb (representing the latest 50 to 5 generations), short ROHs due to LD and probably most non-IBD segments have been discarded. The values for FH_358SNP . The valROH_50SNP and FROH_65SNP were obtained for the same restrictions to the size of ROHs. Correlations for both, FROH_50SNP>1–10 Mb and FROH_65SNP>1–10 Mb were at 0.64–0.65 (>10 Mb) to 1.0 (>1 Mb) and also the values for both, FROH_50SNP>1–10 Mb and FROH_65SNP>1–10 Mb were at 0.70 (>10 Mb) to 0.87 (>1 Mb).Similar results for FIS-value was -0.07 and had a significant correlation of 0.88 (P<0.001) with both FROH_50SNP and FROH65_SNP. The correlation of FIS with FROH_358SNP was at 0.29 (P = 0.12) because all ROHs <358 SNPs were not regarded when calculating FROH_358SNP. The mean pedigree inbreeding coefficient FPed calculated for the 28 Lundehund for the last 11 generations with completeness of pedigrees of 97.3% was 0.10 (ΔF = 0.01 per generation) and 0.01 for 5 generations with 100% completeness. We found a negative correlation of -0.47 between FIS and FPed (P<0.05) and a slight positive correlation (0.11) between FROH_358SNP and FPed.The mean Fuiescin Q6 sulfhydryl oxidase 2 (QSOX2) at 49,213,295–49,245,232 bp and LIM homeobox 3 (LHX3) at 49,248,643–49,254,118 bp on CFA9 as two potential candidates for selection on the small body size in the Lundehund . The genes bone morphogenetic protein receptor, type IB (BMPR1B) at 17,819,265–17,978,113 bp on CFA32 and paired related homeobox 2 (PRRX2) at 54,091,700–54,224,620 bp on CFA9 in two regions containing long-range homozygous haplotypes were previously reported as candidate genes for polydactyly in mice. A potential candidate gene for the selection on the extraordinary mobility of the Lundehund is myomesin1 (MYOM1) at 69,701,355–69,834,023 bp on CFA7.The analysis for extended haplotype homozygosity (EHH) revealed 15 core regions on ten chromosomes with significant values for the relative extended haplotype homozygosity (REHH) and a haplotype frequency of at least 0.50 . On cani10P-values (P) for the REHH and number of genes annotated within the core haplotype region are given. The nearest potentially selected gene and its associated function or phenotype in man or mice is indicated. The regions identified in the present study were compared to the results of previous studies regarding positive selection in dogs. Indicated is the position (CanFam2.0) of the SNPs previously supposed to be selected.The canine autosome (CFA), the chromosomal positions in base pairs (bp) for the canine assembly CanFam3.1, the frequencies of the candidate selected core haplotype, the—log2 = 0.95) for SNPs in 50 kb distance in comparison to previous reports [2 fell below 0.25 in the Lundehund (6.3 Mb) was longer in comparison to the Golden Retriever (200–500 kb), the Rottweiler (500–1000 kb) and the Newfoundland dog (100–200 kb) [e for the last 7–200 generations indicated that the Lundehund already was a genetically small and closed population at a very early stage. This seems to be likely since the dogs were bred for a long time as valuable working dogs and were restricted to the coast area of northern Norway because of this task. The most likely explanation for the increasing Ne and the decreasing ΔF in the recent 4–6 generations was crossbreeding as reported by Lundehund breeders (http://www.lundehund.com/the%20lundehund.htm). However, the use of a few crossbred Lundehund did not increase Ne for more than 2–3 generations and accordingly a substantial and longer lasting improvement of the genetic variability was not achieved for the Lundehund. Particularly, repeated backcrossings and selection for the Lundehund phenotype might have quickly eroded the newly introduced genetic variability.The Lundehund featured a clearly higher mean LD  . The lowPed-values. The FPed is a measure of IBD status for the individuals of a pedigree assuming unrelated founders. There were low correlations between FPed and FROH as well as FIS indicating large variation of FROH and FIS for individuals with the same FPed. We assume strong relationships among the founders of the pedigree data due to the small Ne over 200 generations. Similar observations were made for FPed and FROH in cattle [Ped and FROH did not diverge in cattle at such an extent like in Lundehund and FROH in cattle was much lower than in the Lundehund investigated here [The inbreeding coefficients based on 11 generations of pedigree information were biased downwards because founders seemed strongly related. Restricting the pedigree information to 5 generations considerably decreased the inbreeding load registered by the estimated Fn cattle ,26. Howeted here –27. The ROH was reported as the most effective method to distinguish between IBD and IBS [ROH were 100, 64 and 12 generations back. This considerable restriction in generations considered for calculating FROH still resulted in a mean FROH_358SNP similar (ΔFROH = 0.06) to the mean FROH_50SNP and FROH_65SNP. Nevertheless, the correlation between FROH and FIS for the 50 and 65 SNPs thresholds decreased from 0.88 to 0.29 for the 358-SNP-threshold. This demonstrates that the 358-SNP-threshold may be less influenced by chance or single homozygous SNPs. We therefore assumed that the extreme level of homozygosity in the Lundehund with 87% of the genome covered by ROHs made it impossible to completely differentiate between IBS- and IBD-segments. The level of inbreeding might be slightly overestimated using FROH_50SNP and FROH_65SNP thresholds. Restricting minimum length of ROH to >10 Mb, gave still estimates for FROH at 0.70, independent of the SNP-threshold. The genetic variability of more polymorphic microsatellite markers was also found to be consistently low in the Lundehund in previous studies [ROH values of the individuals were suggested as a measure of inbreeding in conservation programs [e over 200 generations due to geographical isolation and historical bottleneck inevitably led to more consanguineous matings and to an increase of the lengths of IBD-segments through common ancestors with increasing inbreeding. This might be the reason why restrictions of ROHs from >1 Mb up to >10 Mb (representing 5 generations back) gave very similar estimates for genomic inbreeding coefficients.Of the inbreeding coefficients based on SNP data, the F and IBS . A high  and IBS , but red and IBS . We usedThe long and frequent ROHs made it difficult to identify signatures of selection by the reduction of local genetic variability, since most consensus ROHs had possibly arisen due to bottleneck or inbreeding effects and not due to selective pressure. This assumption was confirmed by the results of a study detecting ROHs in pigs . We therThe potential selected regions on CFA3, 5 and 18 overlapped with candidate selected regions identified by previous studies ,11,20. T10P-value. QSOX2 on CFA9 at 49.2 Mb is the canine homologue of a human gene associated with adult height [QSOX2 has been linked to canine body size. Another candidate gene influencing the height at withers is LHX3 on CFA9 at 49.2 Mb. In German shepherd dogs, a 7-bp deletion of this gene causes a combined pituitary hormone deficiency including pituitary dwarfism [LHX3 might lead to a reduction of height without associated health problems. Body size is a selection objective in nearly all breeding standards. It seems to be especially important and thus a potential target of selection in the Lundehund, which is bred for moving in spatially limited breeding cavities of puffins [spermatogenesis and oogenesis specific basic helix-loop-helix 1 (SOHLH1 on CFA9 at 49.6 Mb) near the second core region encodes a transcription factor essential for spermatogenesis that is a known target of selection [caspase recruitment domain family, member 9 [BMPR1B on CFA32 at 17.9 Mb led to a praeaxial polydactyly in mice [paired related homeobox genes PRRX1 and PRXX2 (PRRX2 on CFA9 at 54.1–54.2 Mb) also displayed postaxial polydactyly [aristaless-like 4 and limb development membrane protein 1 known for polydactyly in dogs [MYOM1 on CFA7 that acts as an elastic ribbon in the central M-band of muscle sarcomeres [As a consequence of the long-range haplotype test in the present study, these haplotypes occurred in a high frequency, but were not fixed in the Lundehund. Genes contained in this region may still be under selection for Lundehund characteristics up to date. Assuming previous crossbreeding, this is especially true for genes encoding breed defining traits that have lost their original fixation. We identified one candidate gene 44 kb from the core haplotype on CFA9 at 49.3 Mb with the highest—logt height . Accordidwarfism . Other melection ,36,39. Imember 9 . A high member 9 . Selecti in mice . Double ydactyly . The gen in dogs  were notrcomeres .ROH reflected the extreme loss of genetic variability in this breed. We identified 15 regions with long-range haplotypes that were potentially under positive selection in the Lundehund. New potential candidate genes for polydactyly and body size in dogs including PRRX2, BMPR1B and QSOX2 were identified. Selection for body size or male fertility might have predisposed the Lundehund to gastroenteropathies.The Lundehund very likely descended from a small founder population with an effective population size <20 for the last 200 generations. The high FAnimal welfare committee approval was not obtained according to the German Animal Welfare Law  because all data was extracted from an existing database and EDTA-blood samples from the bio-bank of a diagnostic lab. The blood samples were collected and sent in by accredited veterinarians according to the national and international guidelines for animal welfare.Blood samples of 28 Lundehund were taken from the bio-bank of the Institute for Animal Breeding and Genetics at the University for Veterinary Medicine Hannover. Twelve of the dogs had been used for a previous analysis of genetic variability in this breed . The pedhttp://www.ncbi.nlm.nih.gov/geo/) using accession number GSE66677.Raw and processed data is available for the 28 Lundehund with genotypes and can be obtained through the GEO website  [2–values for SNP pairs with 1 kb to 33.3 Mb distance between each other were grouped into distance bins of 0.1 Mb. For each bin the mean r2-values were calculated and the effective population size was estimated as eN = (1- 2r)/(24cr) with c = recombination rate in Morgan units [e) [We calculated rl/plink) . The r2–an units . Regardian units . The incnits [e) .l = logSPN x ni))( α /(n / log(hom), with an error rate ɑ of 0.05, a mean number of SNPs per individual nSNP = 168,467, a mean homozygosity (hom) = 0.95 and the number of individuals ni = 28 [The sliding windows for ROH detection with the program PLINK contained 50 or 65 SNPs. A ROH in one individual was called if a homozygous stretch contained 50 or 65 or more SNPs and extended over 1000 kb . We did ROH for each dog was estimated as the length of all ROHs in the respective individual divided by the total length of all autosomes covered by SNPs [IS-values for each individual i as FIS,i = (Oi-Ei)/, with Ei = number of expected homozygous SNPs, Oi = number of observed homozygous SNPs and nSNP,i = number of all SNPs genotyped in the respective individual [Ped) for the 28 Lundehund were estimated using OPTI-MATE, version 3.88 (http://www.tiho-hannover.de/?id=3442) [The inbreeding coefficient F by SNPs . We useddividual . The pedid=3442)  consider10P-values >3 and were considered to be significant.Since it was difficult to differentiate if the consensus ROHs had their reduced genetic variability due to positive selection or inbreeding following the bottleneck, we used another method to identify potential selective sweeps in the Lundehund. The long-range haplotype test detects haplotypes that are long and frequent because they have risen to high frequency without time for recombination . We calchttp://david.abcc.ncifcrf.gov/home.jsp) [http://may2012.archive.ensembl.org/Canis_familiaris/Info/Index) were considered for the analysis. Annotation terms represented in the functional annotation chart for the canine genes were tested for enrichment in a modified Fisher Exact test provided by DAVID. We took a closer look at the canine genes of the regions with long-range haplotypes using the databases Gene and OMIM (http://www.ncbi.nlm.nih.gov). All identified regions were checked against with the locations of canine homologues of genes known to be associated with polydactyly in humans [The Database for Annotation, Visualization and Integrated Discovery (DAVID 6.7) (ome.jsp)  was usedn humans .S1 Fige) was estimated from the mean r2 for the 38 canine autosomes.The effective population size (N(TIF)Click here for additional data file.S2 Fig(TIF)Click here for additional data file.S3 FigThe black graph shows the decay of linkage disequilibria without restriction due to MAF and the dark yellow graph represents the decay of linkage disequilibria after restriction due to a MAF<0.01.(TIF)Click here for additional data file.S4 FigThe black graph shows the decay of linkage disequilibria without restriction due to MAF and the dark yellow graph represents the decay of linkage disequilibria after restriction due to a MAF<0.05.(TIF)Click here for additional data file.S5 Fige) was estimated from the mean r2 for the 38 canine autosomes and after filtering for a minor allele frequency (MAF) <0.01 in a multibreed dog panel. The black graph shows Ne without restriction due to MAF and the red graph represents Ne after restriction due to a MAF<0.01.The effective population size (N(TIF)Click here for additional data file.S6 Fige) was estimated from the mean r2 for the 38 canine autosomes and after filtering for a minor allele frequency (MAF) <0.05 in a multibreed dog panel. The black graph shows Ne without restriction due to MAF and the red graph represents Ne after restriction due to a MAF<0.05.The effective population size (N(TIF)Click here for additional data file.S1 Tablehttp://www.ncbi.nlm.nih.gov/gene/) is given. Note that no minimum threshold was set for the length of a consensus ROH (Exel data sheet).For each consensus ROH the canine chromosome, the position of the start and end SNP in CanFam2.0, the number of SNPs included and the genes included to the region according to the Gene database ((XLSX)Click here for additional data file.S2 TableROH_50SNP, FROH_65SNP and FROH_358SNP with their standard deviations (SD) and Pearson correlation coefficients with the respective FROH without restrictions for minimum length.Given are the minimum length of ROHs, their equivalence in number of generations back (GEN), the inbreeding coefficients F(DOCX)Click here for additional data file.S3 TableIS) and the mean number of observed and expected homozygous SNPs are given.The mean minor allele frequency (MAF) over all 157,423 autosomal SNPs in the Lundehund, the proportion and mean number of informative SNPs (MAF >0.0), the fixation index (F(DOCX)Click here for additional data file.


Parastagonospora nodorum is an important pathogen of one of the world’s most economically important cereal crops, wheat (Triticum aestivum L.). P. nodorum produces necrotrophic protein effectors that mediate host cell death, providing nutrients for continuation of the infection process. The recent discovery of pathogen effectors has revolutionized disease resistance breeding for necrotrophic diseases in crop species, allowing often complex genetic resistance mechanisms to be broken down into constituent parts. To date, three effectors have been identified in P. nodorum. Here we use the effector, SnTox1, to screen 642 progeny from an eight-parent multiparent advanced generation inter-cross  population, genotyped with a 90,000-feature single-nucleotide polymorphism array. The MAGIC founders showed a range of sensitivity to SnTox1, with transgressive segregation evident in the progeny. SnTox1 sensitivity showed high heritability, with quantitative trait locus analyses fine-mapping the Snn1 locus to the short arm of chromosome 1B. In addition, a previously undescribed SnTox1 sensitivity locus was identified on the long arm of chromosome 5A, termed here QSnn.niab-5A.1. The peak single-nucleotide polymorphism for the Snn1 locus was converted to the KASP genotyping platform, providing breeders and researchers a simple and cheap diagnostic marker for allelic state at Snn1.The necrotrophic fungus  Parastagonospora  nodorum causes the disease Septoria nodorum blotch (SNB), responsible for significant wheat (Triticum aestivum L.) yield losses in Europe, Australasia, United States, North America, and North Africa , and leucine-rich repeat (LRR) domains  the corresponding host sensitivity loci. To date, only the Tsn1 host sensitivity locus has been cloned, with Snn1  interaction  were screened for SnTox1 sensitivity. Genotype data were generated using the wheat Illumina 90,000 SNP array  mapping (listed in Table S2). Genotype calling for selected SNPs was checked manually with GenomeStudio. Additionally, for SNPs not included in the initial analysis due to failure to automatically call genotypes, but predicted to map to the short arms of the group 1 chromosomes from BLASTn analysis to flow sorted wheat chromosomes  to the peak marker, were included in a second QTL scan.The eight-parent MAGIC population described by SnTox1 was heterologously expressed in Pichia pastoris, as previously described (escribed .http://www.niab.com/magic/. Marker-trait associations were considered significant above the Bonferroni corrected P = 0.05 threshold (-log10P = 5.49). SNPs were ordered in the resulting Manhattan plots according to the consensus genetic map generated with the wheat 90k SNP chip (Oryza sativa L. ssp. japonica cv. Nipponbare) coding regions (CDS) , brachypodium (Brachypodium distachyon accession BD21) gene models , and hexaploid wheat cv. ‘Chinese Spring’ genome survey sequence (GSS) from flow-sorted chromosome arms . Line means estimated as fixed effects were used in subsequent QTL analyses. Heritability among MAGIC line means was estimated as (variance among MAGIC lines estimated as random effects) / (variance among MAGIC lines + average variance of differences among MAGIC lines). QTL analyses were undertaken as described in http://wheat-urgi.versailles.inra.fr/). To identify putative homeologous copies on the A, B, and D wheat subgenomes, the highest matching sequence contigs were used as inputs for gene predictions using FGENESH (http://www.softberry.com/) using the ‘Triticum aestivum’ model. Predicted gene models for each homeologous set were aligned with ClustalW (http://www.nrbsc.org/gfx/genedoc/). Homeologue-specific nucleotides flanking each targeted SNP were highlighted for incorporation into KASP primer design. Genomic DNA from the eight MAGIC parents and a subset of the progeny (see Table S1) was extracted using a modified Tanksley protocol (http://lgcgenomics.com/), and compared with the corresponding SNP calls from the 90,000 SNP array.Selected SNPs were converted to the Kompetitive Allele-Specific PCR (KASP) genotyping platform (LGC Genomics), based on single-plex technology that employs a universal fluorescent reporting system in conjunction with allele-specific primers. SNP flanking sequences  SNPs (File S1). Of these, 29 genetically mapped to chromosome 1B, 11 to 5A, and 2 were unassigned. By far the strongest QTL was located on chromosome 1B, with the highest marker-trait associations identified for SNPs Excalibur_c21898_1423 (-log10P = 55.29) and BS00093078_51 (-log10P = 54.12), which cosegregated at 8.36 cM. The peak marker(s) explained 25.9% of the variation in line means for SnTox1 sensitivity, with box-plots of genotype at Excalibur_c21898_1423 vs. host sensitivity clearly indicating phenotypic partitioning according to genotype (P = 7.25) on the long arm of chromosome 5A, termed here QSnn.niab-5A.1 following Graingenes annotation (www.graingenes.org/), and explained 4.1% of the variation. Modeled together, the two peak SNPs at Snn1 and QSnn.niab-5A.1 explained 28.7% of the variation in line means. Of the six significant SNPs that lacked a genetic map position in the consensus map, all but BS00022296_51 could be tentatively assigned to chromosome arms on the basis of high linkage disequilibrium with the peak Snn1 marker (D′ > 0.94) or by BLASTn hits to wheat genome survey sequence generated from flow-sorted chromosome arms (2 >0.6) with the peak markers  were recovered: BobWhite_c4303_524, BS00011824_51, BS00015608_51, BS00022296_51, BS00026180_51a, BS00030768_51, BS00071333_51, BS00076192_51b, BS00111170_51b, Excalibur_c35316_388a, Jagger_c5878_119, and Kukri_c37738_417. Inclusion of these additional SNPs within subsequent QTL analyses  found them to be highly significant (-log10P ≥ 42.26), although not more significant than the two best markers identified from the automated 90k SNP chip calls.Using 15,514 SNPs across 642 MAGIC lines, QTL mapping of SnTox1 sensitivity in the MAGIC population identified 42 significant , indicating only two major genetic loci controlling SnTox1 sensitivity were segregating in the MAGIC population. It is of course possible that additional loci with smaller effects were segregating, but were not detected.To attempt to identify additional genetic loci controlling SnTox1 sensitivity, QTL analyses were repeated with the peak 1B marker  as a covariate. These analyses failed to identify any additional QTL , predicted to contain four genes, including the cloned resistance gene Lr21 . No NBS-LRR genes were present in this rice interval nor were any genes homologous to the cloned wheat SnToxA effector sensitivity locus, Tsn1. (2) Brachypodium: 16 orthologous brachypodium genes in the colinear region of Bradi2g, spanning a region from Bradi2g38970 to Bradi2g40120 . This delimited a physical interval of 884 kb in brachypodium, predicted to contain 118 genes. No genes homologous to Tsn1 were identified in this region. Although two NBS-LRR genes were found , the use of CDS from these gene as a queries for BLASTn searches of the wheat genome identified best hits on chromosomes 1D (IWGSC_GSS_1899471) and 7A (IWGSC_GSS_4256951), respectively. No NBS-LRR genes were found in brachypodium within the region colinear with the highest associated wheat SNPs  . However, manual inspection of SNP calls showed that BS00093078_51 to possess a cluster of heterozygotes, expected in this MAGIC population, as some heterozygosity will remain, depending on how advanced recombinant inbred line development was at the time on genotyping .Markers BS00093078_51  and Excalibur_c21898_1423 (codominant) were found to be highly significant , complicating fine-scale genetic analyses. However, numerous genomic tools are now emerging in wheat, including genome survey sequence and dense SNP chips, as used in this study. In addition, recent developments in genetic mapping population design widen the opportunities for genetic analyses in crops. MAGIC is one of a number of multi-parent population designs now emerging in plant sciences . The benefit of such populations is that they allow the use of linkage and association genetic methodologies for genetic analysis, while avoiding the difficulties imposed by highly structured association mapping populations , to 2.36 (Xi19). Genotypic scores at the three most significant SNPs discriminate between Xi19 and Soissions on the one hand, and the other six founders on the other (Table S5). We also identified SNPs uniquely tagging Soissions  and uniquely tagging Xi19 (Kukri_c37738_417), which map within a 1.3-cM interval. Both of these are highly significant (-log10P ≥ 8.11), with their joint effects accounting for a similar proportion of the total sum of squares (SS) compared with the peak SNP, Excalibur_c21898_1423 (29% compared to 26%). Therefore, SnTox1 sensitivity is introduced in the population via Xi19 and Soissons. The Xi19 QTL is the larger effect, accounting for 20% of the SS. Given the previous reports of a single major genetic locus, Snn1, controlling SnTox1 sensitivity at this location . However, conservation of gene content and order between members of the Poaceae has meant that colinearity is commonly used as a tool for inferring possible gene content in wheat and other large genome cereal species , 62% of rice genes were conserved in brachypodium, whereas 68% of brachypodium genes were conserved in rice. Additionally, a small local inversion between these two species was observed, predicted to involve the wheat region carrying the peak SNP. We note that the rice region involved is within 60 kb of the chromosome Os05 short arm telomere, indicating this rearrangement could have occurred as a result of the macrochromosomal rearrangements that resulted in different chromosome numbers between rice and brachypodium. Thus, while colinearity with sequenced grass species will clearly be of use in map-based cloning of Snn1, differences in gene content mean that sequenced physical wheat maps of the region for cultivars showing SnTox1 sensitivity will be essential. This was found to be the case in the cloning of Tsn1, which is encoded by a gene absent from the colinear region of rice and other sequenced cereal species (10P = 38.60) originates from the same wheat contig as a marker (BE498831) previously shown to cosegregate with Snn1 , this illustrates the additional power available in the MAGIC population, despite it not having been constructed specifically to investigate SnTox1 sensitivity.Although genome survey sequence is available, wheat does not contain a completed physical map. This is largely due to the extremely large size of the wheat genome (16 Gb) in comparison to other sequenced cereal species  coamplification occurred from one or more of the nontarget homeologues in the MAGIC lines; (2) allele bin creation was constructed using a training set composed of a large and diverse set of germplasm, including varieties from around the world, as well as synthetic, tetraploid and diploid wheats. Thus, where nontarget homeologues were not amplified in the training set, a SNP allele calling bin would have been created . In such instances, the heterozygote bin would include heterozygotes (AB), as well as homozygotes + nontarget homeologue (BBBB) and heterozygotes + nontarget homeologue (ABBB). Thus, allele calling would be confounded, as was observed here. This observation has important implications for all those using the array, as it will increase the number and accuracy of markers available for genetic and genomic studies. Indeed, marker enrichment around the e.g., Snn1 SNPs successfully converted to the KASP platform, due to a combination of the need to incorporate homeologue-specific nucleotides in the primers, and the constraints of effective primer design. The successfully converted KASP marker was shown to be co-dominant, allowing discrimination of heterozygotes from homozygotes, an important feature for effective use in wheat breeding and research. The development of co-dominant KASP markers closely linked to Snn1 provides a cheap, flexible and robust way for selecting for SnTox1 insensitivity in wheat germplasm.KASP has become the platform of choice for wheat breeding companies, and is in common use in the cereal research community (


Iron (Fe) deficiency in plants is the result of low Fe soil availability affecting 30% of cultivated soils worldwide. To improve our understanding on Fe-efficiency this study aimed to (i) evaluate the influence of two different Fe regimes on morphological and physiological trait formation, (ii) identify polymorphisms statistically associated with morphological and physiological traits, and (iii) dissect the correlation between morphological and physiological traits using an association mapping population.The fine-mapping analyses on quantitative trait loci (QTL) confidence intervals of the intermated B73 × Mo17 (IBM) population provided a total of 13 and 2 single nucleotide polymorphisms (SNPs) under limited and adequate Fe regimes, respectively, which were significantly (FDR = 0.05) associated with cytochrome P450 94A1, invertase beta-fructofuranosidase insoluble isoenzyme 6, and a low-temperature-induced 65 kDa protein. The genome-wide association (GWA) analyses under limited and adequate Fe regimes provided in total 18 and 17 significant SNPs, respectively.2O), and SPAD value of leaf 3 (SP3) were located in genes or recognition sites of transcriptional regulators, which indicates a direct impact on the phenotype. SNPs which were significantly associated on a genome-wide level under both Fe regimes with the traits NEC, RW, SDW, H 2O, and SP3 might be attractive targets for marker assisted selection as well as interesting objects for future functional analyses.Significantly associated SNPs on a genome-wide level under both Fe regimes for the traits leaf necrosis (NEC), root weight (RW), shoot dry weight (SDW), water (H The online version of this article (doi:10.1186/s12863-014-0153-0) contains supplementary material, which is available to authorized users. Iron (Fe) deficiency in plants is the result of a low Fe availability which might be induced by lime-chlorosis that affects 30% of cultivated soils worldwide . As an aFor the crop plants which are graminaceous plant species such as barley, rice, and maize, Fe is acquired using the so-called strategy II . CharactTo improve our understanding of the mechanisms which are responsible for Fe-efficiency in maize, two different methods have been applied so far. The RNA-Sequencing approach used by  focused The objectives of our study were to (i) evaluate the influence of different Fe regimes on morphological and physiological trait formation, (ii) identify polymorphisms statistically associated with morphological and physiological traits, and (iii) dissect the correlation between morphological and physiological traits using an association mapping population.H2) of the examined traits ranged for the whole set of phenotyped inbred lines from 0.53 (H 2O) to 0.72  under the Fe-deficient regime  and 0.87 (SP4).The repeatability (me Table . H2 of tα= 0.05) difference between both Fe regimes was found. The remaining morphological and physiological traits except H 2O showed a significant (α= 0.05) lower trait value under the Fe-deficient regime in comparison to the Fe-sufficient regime. For H 2O the opposite trend was observed.The adjusted entry means (AEM) were calculated for all physiological and morphological traits under consideration of the block effects for each Fe regime Figure . No varir = 0.17 observed between H 2O and LAT under the Fe-deficient regime  and the lowest between RL and RW (r = 0.23).The lowest pairwise correlation coefficient was with In the ASMP, the population structure explained on average 2.02% of the phenotypic variation with a minimum of 0.08% (SL) and a maximum of 5.32% (RL) under the Fe-deficient regime  analyses resulted in total in 13 significant (FDR = 0.05) SNPs detected in QTL confidence intervals of the IBM population where NEC QTL1 comprised the highest amount (4) under the Fe-deficient regime  SNPs for SP4 QTL1 Table . The maxThe genome-wide association (GWA) analyses of the traits examined in the Fe-deficient regime provided in total 18 significant SNPs (FDR = 0.05) where NEC showed with 12 SNPs the highest number (Table 2O (9) included the highest number (Table 2O (21.21%). In a simultaneous fit of all significant (FDR = 0.05) SNPs, proportion of the phenotypic variance maximally explained was 57.47% (H 2O) and the minimum was 10.99% (SP3).The GWA analyses under the Fe-sufficient regime revealed in total 17 significant FDR = 0.05) SNPs where H  SNPs wheUnder consideration of the global extent of LD, 18 and 9 unique genes were linked to the significantly (FDR = 0.05) associated SNPs under the Fe-deficient and Fe-sufficient regime, respectively Tables  and 3. NEnvironmental factors such as pH variation in the soil, temperature, water stress, and mineral concentration effects have a strong influence on Fe availability for plants . To reveWe observed for all traits moderate to high repeatabilities under both Fe regimes Table . This fiWe observed, under the Fe-deficient regime, variation for the trait BTR Figure . Long etThe whole set of traits evaluated in one Fe regime showed mostly moderate to high pairwise correlations Figure . This fiUsing the ASMP we were able to validate 13% and 3% of detected QTLs from our former study  for Fe-dWe observed under the Fe-deficient regime several genes to be associated with NEC Figure  and RW tWe did not observe a clear clustering of genotypes with high NEC values in the individual subgroups. Furthermore, when examing the subgroups individually  or recognition sites (non-coding) of transcriptional regulators, which indicates a direct impact on the phenotype. Beside being attractive targets for marker assisted selection, these loci are interesting objects for future functional analyses.The QTL confidence intervals of the traits NEC, RW, SDW/SL, SP3, SP4, and SP6, from a previous study contained hundreds of genes and millions of base pairs. A dissection of these QTL confidence intervals using association mapping methods allowed a confirmation of the previously detected QTLs as well as the fine-mapping. In addition, our study described SNPs which were significantly associated on a genome-wide level under both Fe regimes with the traits NEC, RW, SDW, H A set of 302 maize inbred lines representing world-wide maize diversity  was used4 solution for germination in the dark at room temperature. After 6 days, the germinated seeds were transplanted to a continuously aerated nutrient solution with nutrient concentrations as described by [μM Fe(III)-EDTA for 7 days. From day 14 to 28, plants were cultured at 10 (Fe-deficient) and 300 (Fe-sufficient) μM iron regimes. The nutrient solution was exchanged every third day. Plants were cultivated from day 7 to day 28 in a growth chamber at a relative humidity of 60%, light intensity of 170 μmol m −2 s −1 in the leaf canopy, and a day-night temperature regime of 16 h/24°C and 8 h/22°C, respectively.Maize seeds were sterilized with 60°C hot water for 30 minutes. Afterwards, seeds were placed between two filter paper sheets moistened with saturated CaSO ribed by . The plab= 3 times for the Fe-deficient and sufficient regime, respectively.Each genotype was grown in one shaded pot of 600 milliliter volume. All pots of one Fe regime were arranged in an alpha lattice design with 13 incomplete blocks. The entire experiment was replicated 2O) as well as the ratio between SDW and SL (SDW/SL) was according to [Under both Fe regimes, the following traits were evaluated: the relative chlorophyll content of the 3rd, 4th, 5th, and 6th leaf (SP) measured with a SPAD meter (Minolta SPAD 502). Branching at the terminal 5 cm of the root (BTR) was evaluated with 1 for strong presence and 9 for absence of terminal root branching. Leaf necrosis (NEC) was recorded as a visual score on a scale from 1 for high trait expression and 9 for low trait expression. The lateral root formation (LAT) was recorded on a scale from 1 for absence to 9 for high trait expression. Furthermore, root length (RL), root weight (RW), shoot length (SL), shoot dry weight (SDW), water content  and edited with BioLign (http://en.bio-soft.net/dna/BioLign.html) manually. The SNPs were filtered as described above and the remaining 562 SNPs were added to the above mentioned set of genome-wide distributed SNPs.A set of 16 candidate genes for mobilization, uptake, storage, and transport of Fe as well as regulatory function on these processes was selected for sequence analyses to detect additional polymorphisms compared to the above mentioned SNP data set  for all inbreds at each of two Fe regimes, we considered g as fixed as well as r and b as random. Furthermore, we considered g, r, and b as random to estimate the genotypic  = 2Kmatrix Q , and eipresidual . The varthe ASMP , and \do package .Physical map positions of QTL confidence intervals detected in the linkage mapping study of  were useMultiple testing was considered by applying the  correctiFor each SNP of the marker set, the information about the physical map position was available. The extent of linkage disequilibrium in the maize ASMP which was estimated by  was usedIf not stated differently, all analyses were performed using statistical software R .


Over the last few years, continuous development of high-throughput sequencing platforms and sequence analysis tools has facilitated reliable identification and characterization of genetic variants in many cattle breeds. Deep sequencing of entire genomes within a cattle breed that has not been thoroughly investigated would be imagined to discover functional variants that are underlying phenotypic differences. Here, we sequenced to a high coverage the Danish Holstein cattle breed to detect and characterize single nucleotide polymorphisms (SNPs), insertion/deletions (Indels), and loss-of-function (LoF) variants in protein-coding genes in order to provide a comprehensive resource for subsequent detection of causal variants for recessive traits.We sequenced four genetically unrelated Danish Holstein cows with a mean coverage of 27X using an Illumina Hiseq 2000. Multi-sample SNP calling identified 10,796,794 SNPs and 1,295,036 indels whereof 482,835 (4.5 %) SNPs and 231,359 (17.9 %) indels were novel. A comparison between sequencing-derived SNPs and genotyping from the BovineHD BeadChip revealed a concordance rate of 99.6–99.8 % for homozygous SNPs and 93.3–96.5 % for heterozygous SNPs. Annotation of the SNPs discovered 74,886 SNPs and 1937 indels affecting coding sequences with 2145 being LoF mutations. The frequency of LoF variants differed greatly across the genome, a hot spot with a strikingly high density was observed in a 6 Mb region on BTA18. LoF affected genes were enriched for functional categories related to olfactory reception and underrepresented for genes related to key cellular constituents and cellular and biological process regulation. Filtering using sequence derived genotype data for 288 Holstein animals from the 1000 bull genomes project removing variants containing homozygous individuals retained 345 of the LoF variants as putatively deleterious. A substantial number of the putative deleterious LoF variants had a minor allele frequency >0.05 in the 1000 bull genomes data set.Deep sequencing of Danish Holstein genomes enabled us to identify 12.1 million variants. An investigation into LoF variants discovered a set of variants predicted to disrupt protein-coding genes. This catalog of variants will be a resource for future studies to understand variation underlying important phenotypes, particularly recessively inherited lethal phenotypes.The online version of this article (doi:10.1186/s12864-015-2249-y) contains supplementary material, which is available to authorized users. Identification of genetic variants underlying phenotypic traits is one of the major tasks associated with contemporary cattle genomic research. The traditional genome-wide association studies (GWAS) based on SNP genotyping technology is likely to have reduced power in detecting causal variants because of incomplete linkage disequilibrium with the genotyped SNPs . This inOver the last few years, a number of bovine whole-genome sequencing studies have been carried out providing a substantial number of genetic variants in the form of single nucleotide polymorphism (SNP) and insertion/deletion (indel). These variants are catalogued in dbSNP  with inpGenetic variants detected using whole-genome sequencing has been used successfully to identify causal variants and to map complex traits in domestic cattle , 17. In In this study, we describe the results of whole-genome sequencing (at 27X coverage) of four unrelated (at least back to grandparents) Danish Holstein-Friesian cows. Multi-sample variant calling facilitated the detection of 12.1 million variants. The concordance of sequencing derived SNPs ranged from 93.3 to 99.8 % compared with the high-density chip data. We then generated a catalog of filtered LoF variants that will provide a resource for future functional studies on economically important traits thereby contributing to the contemporary cattle genomics research.Bos taurus reference assembly UMD 3.1 [Sequencing generated 3,035,569,908 reads of 100 bp for four Holstein cow genomes. Reads were mapped to the  UMD 3.1  using th UMD 3.1 . The per UMD 3.1 , 12. Per UMD 3.1  as longeMulti-sample variant calling using the UnifiedGenotyper from the Genome Analysis Toolkit (GATK)  identifiSamples used for sequencing were also genotyped using the BovineHD BeadChip  to evaluate the accuracy of SNP detection and genotype calling from sequencing data. Mitochondrial SNPs and SNPs with ambiguous chromosomal positions on the UMD 3.1 assembly  were filTo validate the indel calls, PCR primers and probes were designed for 10 indels. PCR products were used to genotype 90 Danish Holstein breeding bulls. Genotyping was performed by size determination on a 3730XL DNA analyzer and the data was analyzed using Genemapper v.3.7 . Of the 10 indels genotyped, 9 indels were found to be true indels.Annotation using NGS-SNP  assignedALDH16A1) and uncharacterized proteins . A comparison with annotated variants in the Danish Jutland breed [We identified 2145 LoF variants in 1453 protein-coding genes in total including 395 stop gains in 345 genes, 448 splice site variants (splice donor and splice acceptor sites) in 392 genes and 1302 frame-shift indel variants in 931 genes Table . There wnd breed  revealedP < 0.05) underrepresented in the set of LoF affected genes  and 1-acylglycerol-3-phosphate O-acyltransferase 2 (AGPAT2) between our data and the data from MacArthur et al. [ABCA12 vs human ABCA13); kallikrein-related peptidase 9 and 12 (cattle KLK9 vs human KLK12); tigger transposable element derived 6 and 7 (cattle TIGD6 vs human TIGD7); uridine diphosphoglucuronosyltransferase 2, poly peptide B15 and 17 (cattle UGT2B15 vs human UGT2B17); and zinc finger protein 648 and 671 (cattle ZNF648 vs human ZNF681). These observations give an indication that a similar LoF tolerance exists for members of these gene families implicating the potential for rescuing LoF mutations by other genes within the gene family both in cattle and human.Gene ontology (GO) enrichment analysis using the PANTHER version 10.0  revealedr et al. . The twoRecent studies reported an increased rate of false-positive calls among LoF variants , 45; theHeterozygous LoF variants segregating in the absence of homozygotes in a population could be imagined to encompass deleterious variants including causal variants for embryonic/fetal death. We got access to the data on genotypic variation from whole-genome resequencing data of 288 Holstein animals from the 1000 bull genomes project after completion of run four of this project . These aWe then tested the 345 filtered LoF variants for concordance of the class of genetic variants called in both the 1000 bull genome and our data set and found that 268 variants were concordantly called. However, 77 positions had an inconsistent call between the two data sets; particularly 76 were called as indels in our data but were called as a SNP in the 1000 bulls data  distribution of the 268 concordantly called LoF variants Fig.  using thThe list of putative deleterious LoF variants can be used to mine candidate regions from association studies for potential deleterious candidate variants. However, the candidates from the list should be validated experimentally by Sanger resequencing to confirm the actual presence of the variant and the gene models used for predicting the annotation should be investigated to confirm the annotation. Likewise, gene expression studies should be conducted to validate the consequences of the splice site variants. The list of putative deleterious LoF variants will despite the necessity of experimental validation be valuable information for future studies on detecting causal variants for recessive lethal or deleterious phenotypes in Holstein cattle also because of the similarity with results in human .The re-sequencing of four Danish Holstein cow genomes with a mean depth of 27X coverage followed by multi-sample variant calling identified a total of 12.1 million variants. The SNP detection rate and genotype concordance compared with high density chip data indicate high-confidence of the SNP calls. Functional annotation of identified SNPs showed that the proportion of non-synonymous substitutions was lower than those identified in genetically distant cattle breeds. A comparison of the results obtained in this study with the results from our previous study  using thAn investigation into LoF variants revealed the highest density of putative deleterious LoF variants on BTA18, particularly in genes within the region spanning 57–64 Mb. This higher density might be associated with structural variants segregating in this region. Genes affected by putative deleterious LoF were strongly enriched for functional categories related to olfactory reception and depleted for genes related to key cellular constituents, cellular and biological process regulation. A comparison between our data and data from MacArthur et al. [We selected four unrelated (at least back to grandparents) Danish Holstein cows representing the genetic diversity of the Danish Holstein-Friesian breed based on pedigree records from Danish breeding animals. Genomic DNA was extracted from ear tissue of selected cows using a modified salting out method . Paired-All procedures were approved by the National Guidelines for Animal Experimentation and the Danish Animal Experimental Ethics Committee, and all sampling was restricted to routine on-farm procedures that did not cause any inconvenience or stress to the animals and hence no specific permissions was required.http://www.bioinformatics.babraham.ac.uk/projects/fastqc/). The primary processing and filtering of the sequence data was performed using the FASTX-Toolkit version 0.0.13 [Bos taurus genome assembly UMD 3.1 [http://broadinstitute.github.io/picard/) with options “USE_THREADING = true, VALIDATION_STRINGENCY = LENIENT, SO = coordinate, ASSUME_SORTED = true, and CREATE_INDEX = true”. The Picard command CollectMultipleMetrics was employed to each of the merged bam files to provide information on alignment summary metrics, insert size metrics and quality by cycle metrics. Duplicates in BAM files were marked by applying MarkDuplicates from Picard. Genome coverage of each merged BAM file was estimated using BEDtools version 2.15.0 [The quality of the sequence data was assessed using FastQC version 0.10.0  containing 777,962 SNPs. Chromosomal positions of the SNPs was determined according to the  UMD 3.1 . MitochoTo validate novel functional indels, we designed PCR primers for 10 indels. Primers are listed in Additional file Homo sapiens as the model species for sequence conservation during annotation because most of the eukaryotic genes are well characterized in human.We used NGS-SNP  for funcBos taurus) in the PANTHER database was used as the reference list in our analysis. The analysis set comprised the Ensembl IDs for 1453 genes of which 1308 were present in the reference list while the remaining 108 genes were not found. Therefore, our enrichment analysis represents results for 1308 genes predicted to be affected by LoF variants. Annotation data sets used were the complete GO biological process, GO cellular component and GO molecular function. In our analysis, we used default settings with Bonferroni correction for multiple testing. GO analysis for 345 putative deleterious variants was performed in a similar setup using the corresponding gene list for 345 variants.The list of genes predicted to be affected by putative deleterious LoF variants was subjected to gene ontology enrichment analysis using the PANTHER Overrepresentation Test (release 20150430) from the PANTHER version 10.0 . All genCustom Taqman SNP genotyping assay  was used to test the false positive rate (FPR) of novel functional SNPs. We used a subset of 50 novel SNPs identified by sequencing for genotyping using DNA samples from 90 Danish Holstein breeding bulls. Genotyping was performed on a ViiA7 Real-Time PCR System . To test FPR for frame-shift indels, 90 bulls were genotyped for 24 indels using size determination of PCR products. Primers and probes are listed in Additional file TM Aligner-Software  version 3.7.1. Primers are listed in Additional file To validate the nature of the genetic variant 15 inconsistent calls between our data and the 1000 bull genomes project data were tested by sequencing of PCR products using a 3730XL DNA analyzer  and data were analyzed using the CodonCodeWhole-genome sequencing data from the four cows have been deposited at NCBI’s Short Read Archive (SRA) under the accession numbers SRX1370285-6, SRX1369736, and SRX1364945. The identified SNPs have been deposited at the Database of Short Genetic Variations (dbSNP) with accession numbers ss1947222024-1958018817 and indels with accession numbers ss1958018838-1960972587. Genotype data used in this study are from the 1000 Bull Genome Project .


Gene expression microarrays measure the levels of messenger ribonucleic acid (mRNA) in a sample using probe sequences that hybridize with transcribed regions. These probe sequences are designed using a reference genome for the relevant species. However, most model organisms and all humans have genomes that deviate from their reference. These variations, which include single nucleotide polymorphisms, insertions of additional nucleotides, and nucleotide deletions, can affect the microarray’s performance. Genetic experiments comparing individuals bearing different population-associated single nucleotide polymorphisms that intersect microarray probes are therefore subject to systemic bias, as the reduction in binding efficiency due to a technical artifact is confounded with genetic differences between parental strains. This problem has been recognized for some time, and earlier methods of compensation have attempted to identify probes affected by genome variants using statistical models. These methods may require replicate microarray measurement of gene expression in the relevant tissue in inbred parental samples, which are not always available in model organisms and are never available in humans.a priori. However, there is no published software tool that makes it easy to eliminate these probes from an annotation. I present equalizer, a software package that uses genome variant data to modify annotation files for the commonly used Affymetrix IVT and Gene/Exon platforms. These files can be used by any microarray normalization method for subsequent analysis. I demonstrate how use of equalizer on experiments mapping germline influence on gene expression in a genetic cross between two divergent mouse species and in human samples significantly reduces probe hybridization-induced bias, reducing false positive and false negative findings.By using sequence information for the genomes of organisms under investigation, potentially problematic probes can now be identified equalizer package reduces probe hybridization bias from experiments performed on the Affymetrix microarray platform, allowing accurate assessment of germline influence on gene expression.The  Several groups have noted that Single Nucleotide Polymorphisms (SNPs) can interfere with hybridization of cDNA to the microarray probes that span the SNP [cis-acting eQTL.Naturally occurring germline DNA variants in human populations and distinct strains of model organisms affect many phenotypes, including basal levels of gene expression –4. When  the SNP –8. This et al. suggested a protocol that removed microarray probes bearing SNPs, but did not provide software tools to automate this approach. Previously published packages predict the presence of SNPs that affect microarray hybridization using statistical models [equalizer package provides a general solution to this problem for the commonly used Affymetrix gene expression microarrays. equalizer can be applied to any experiment performed on the Affymetrix Gene or IVT platforms where founder sequences are known, including humans. In this manuscript I compare equalizer’s results to the performance of a recently published method for identifying probe bias by statistical measurements [equalizer to microarray data generated for an eQTL study of mouse skin and mammary gene expression, as well as human lymphoblastoid cell lines.A recent publication highlighted this serious problem which has caused incorrect findings to be reported and replicated widely in the genetics literature . Ramasaml models , 9, 10. equalizer package makes several contributions. First, as it uses known genomic variants to identify probes affected by SNPs, it does not require expression measurements in replicate parental samples. Such replicates are not available in human populations and may not be available in individual studies of model organisms. Second, it can remove any probe that intersects a feature specified in a Variant Call Format (VCF) file, and is not restricted to genes expressed in a particular tissue used to identify potentially affected probes. It is straightforward to prepare custom microarray annotations for any genome where variant information is available. Third, while equalizer is fully compatible with Bioconductor analysis tools, equalizer generates a new copy of the Affymetrix array definition files and can be used by any analysis pipeline.The equalizer improves the signal quality in the presence of SNPs by selectively removing probes that intersect the SNPs from their probeset assignments , including a BED-formatted file describing all probe locations. The user also provides one or more VCF files specifying the location of SNPs or other genomic features that may interfere with probe hybridization. equalizer then uses the bedtools package [equalizer also produces a report indicating how many probes were removed from each probeset. If every probe in a probeset is removed by equalizer, the probeset itself is also removed from the new annotation files. The resulting files are suitable for use by any downstream normalization pipeline, including R’s oligo package [equalizer can be invoked either from an R package  or by calling a Python script from the command line. If called from the command line, equalizer produces an R script that can be used to create an R Platform Design Information package.Affymetrix microarrays report a probeset expression value summarized from an ensemble of probes. The location and probeset assignments are controlled during the normalization procedure by files available from the manufacturer. nts Fig. . To crea package  to ident package  or the PMus spretus is separated by approximately two million years of evolution from Mus musculus strains such as C57BL/6, the reference mouse genome strain. We have previously performed eQTL experiments using backcrosses of the Mus spretus strain (SPRET/Ei) and a Mus musculus strain (FVB/N) [Mus spretus SPRET/Ei animals with inbred Mus musculus FVB/N animals to make an F1 generation, and then breeding F1 progeny with the FVB/N line. Genotypes at any given locus on backcross chromosomes one through nineteen are therefore expected to be 50 % heterozygous  and 50 % homozygous .Genetic crosses between mouse strains with differing susceptibility to cancer are a useful tool for identifying genes which play a role in cancer biology. The goal of these studies is to map loci associated with gene expression and other phenotypes. The SNP bias problem is particularly obvious in genetic studies of model organisms where the founder strains are not evolutionarily equidistant from the reference strain against which the microarray probes were designed. The inbred mouse strain  (FVB/N) , 13. Bacequalizer indicated that of the 833,910 probes on the Affymetrix Gene 1.1 ST array, 176,191 intersected a SPRET/Ei SNP while only 24,196 intersected a FVB/N SNP. The imbalance between SPRET/Ei and FVB/N SNPs was more extreme on the older mouse M430 2.0 chip; of its 496,469 probes, 104,772 intersected a SPRET/Ei SNP while 10,296 intersected a FVB/N SNP. Probe-intersecting SNPs could therefore affect eQTL results for either parental genome, but were a priori more likely appear on the SPRET/Ei genome at a ratio of more than 7:1 for Gene ST array or 10:1 for the M430 array. An unusually large number of cis-eQTL with lower expression associated with the SPRET/Ei allele would strongly suggest technical bias due to probe hybridization artifacts.Analysis of published genome sequences for the strains FVB/N and SPRET/Ei , 15 by eequalizer I obtained 71 Affymetrix M430 2.0 microarrays measuring gene expression in mouse skin and 115 Affymetrix Gene ST 1.1 microarrays measuring gene expression of normal mouse mammary tissue [Mus spretus and Mus musculus strains of mice [a priori that these macroscopic differences would be consistently associated either higher or lower expression of any particular gene when that gene is derived from a SPRET/Ei allele. We therefore expect higher expression to be associated with the heterozygous eQTL allele 50 % of the time.To test y tissue . eQTL an of mice , but theeqtl) that `performs linear regression for eQTL experiments (see Methods). For each of the 16,588 probesets the eqtl program tested the association between probeset expression and genotype variation at each locus, reporting the statistically strongest locus as a candidate eQTL. In the skin dataset, mice bearing a SPRET/Ei allele at these loci had lower average expression of the eQTL probeset 76 % of the time  decreased in statistical significance. However, considering only those probesets where the uncorrected eQTL result was significant at a FDR ≤ 0.05, 68 % of results decreased in significance after correction. Genes with higher uncorrected statistical significance were significantly more likely to have reduced significance after correction, indicating that many of the strongest results were inflated by probe bias . However, a large number of loci also increased in eQTL significance after correction , which increased in statistical significance after correction from P = 0.17 to P = 2 x 10−17  and African-derived (AFR) genome sequences obtained by the 1000 Genomes Project [Many individual strains of model organisms have been sequenced, making it possible to have complete knowledge of the possible genotypes of their progeny. However, many human eQTL studies are conducted without exome or whole genome sequences of the participants. To test the applicability of  Project . This id Project . Of the cis-eQTL, and spurious SNP-associated effects could be detected. HLA-DPA1 is a member of the major histocompatibility complex, a highly polymorphic family of genes crucial for immune cell recognition and antigen recognition [HLA-DPA1 in African-derived samples . These probes would usually be eliminated from downstream analysis due to low signal quality. However, a significant minority of probes expressed above background levels and bearing SNPs would not be excluded by maskBAD. Figure maskBAD quality score. Of the 378,858 probes in probesets that were expressed above background levels, 298,724 (79 %) do not intersect a SNP. 269,113 probes (71 %) had a maskBAD quality score above 0.2. At this stringency level maskBAD would remove 70,312 (19 %) probes that lack a known SNP and retain 40,701 (11 %) probes that that bear a known SNP.The lleagues  attemptsSNP Fig.  and b. AEqualizer significantly reduced the effect of SNP bias, although the correction was not complete. It is plausible that the remaining deviation from the expected value of 50 % is due in part to the fact that this analysis corrected only for the presence of SNPs and did not account for other variants such as insertions, deletions, or germline copy number variations which could also affect gene expression levels. There are also large gene families such as the hair follicle keratins which may be expressed systemically at higher or lower levels in one species, and this would affect the predicted effect of a single FVB/N allele for those genes.MaskBAD showed strong specificity and sensitivity if the correct quality score cut-off was chosen, and for situations where the required data are available to train its model, maskBAD is a valuable tool. Choosing a stringent maskBAD quality cut-off would result in high sensitivity and specificity, but the best value for this parameter is hard to assess without the parental genome sequences. The effect of SNPs on probe hybridization is complex, and some SNPs which intersect a probe will have no detectable effect on probe hybridization. This is more likely to be true when SNPs are located near the end of a probe rather than near the middle. Particularly in the case of genes with multiple isoforms, the effect of a given probe on the reported gene expression levels can vary between different tissues. A conservative approach which removes potentially compromised probes even if they are not obviously a problem in a particular tissue is therefore a reasonable choice. A future development step for equalizer will be to generate a version of the software entirely as an R package to avoid the requirement for calling a command-line script.equalizer package makes it straightforward to remove individual probes from Affymetrix annotations by reading directly from VCF files describing genomic features such as SNPs which may affect cDNA hybridization. This reduces prevalence of false-positive findings in eQTL mapping studies. Importantly, this correction also increased the statistical significance of some eQTL, indicating that equalizer both reduced false positive signals and allowed results previously obscured by SNP bias to be detected. equalizer provides a complementary approach to statistical methods such as maskBAD, and it can be applied in situations such as human experiments where complete parental gene expression measurements are unavailable.The http://www.sanger.ac.uk/resources/mouse/genomes. The backcross design ensured that all SNPs were present at or near a 50 % minor allele frequency. VCF files listing polymorphic loci in Caucasian- and African-derived populations using the HG19 annotation generated by the 1000 Genomes Project were downloaded from the NCBI Trace Archive May 2 2013 release, ftp-trace.ncbi.nih.gov [http://davidquigley.com/reproduce.html. Microarray data were normalized using the oligo package in R and the Platform Design Information packages available from bioconductor.org [equalizer as described in the text. Microarray batch effects were removed using ComBat [http://www.affymetrix.com.A VCF file for FVB/N and SPRET/Ei mouse genomes using the MM10 annotation was downloaded from the Sanger Mouse Genomes project, .nih.gov . Human vctor.org , 21. Theg ComBat . AffymetR [eqtl program, a software package written in C++ that performs linear regression of gene expression compared to genotype. In analysis of human samples, cis-eQTL were calculated using a window of one megabase around the transcription start site for each gene. The eqtl package can perform cis-only or genome-wide eQTL analysis and can run as a multi-threaded program to exploit multiple-core computational clusters. Binary packages for eqtl are available at davidquigley.com, and eqtl and equalizer source code is freely available at https://github.com/DavidQuigley/QuantitativeGenetics. Source code to reproduce the analysis presented in this manuscript is available at http://davidquigley.com/reproduce.html.Statistical analysis was performed with R  and the Project name:equalizerProject home page:http://github.com/DavidQuigley/QuantitativeGenetics/tree/master/equalizerOperating system: platform independentProgramming language: Python, ROther requirements: Bedtools 2.15 or higherLicense: Apache 2.0Any restrictions to use by non-academics: no restrictions


Motivation: Single nucleotide polymorphism (SNP) discovery is an important preliminary for understanding genetic variation. With current sequencing methods, we can sample genomes comprehensively. SNPs are found by aligning sequence reads against longer assembled references. De Bruijn graphs are efficient data structures that can deal with the vast amount of data from modern technologies. Recent work has shown that the topology of these graphs captures enough information to allow the detection and characterization of genetic variants, offering an alternative to alignment-based methods. Such methods rely on depth-first walks of the graph to identify closing bifurcations. These methods are conservative or generate many false-positive results, particularly when traversing highly inter-connected (complex) regions of the graph or in regions of very high coverage.Results: We devised an algorithm that calls SNPs in converted De Bruijn graphs by enumerating 2k + 2 cycles. We evaluated the accuracy of predicted SNPs by comparison with SNP lists from alignment-based methods. We tested accuracy of the SNP calling using sequence data from 16 ecotypes of Arabidopsis thaliana and found that accuracy was high. We found that SNP calling was even across the genome and genomic feature types. Using sequence-based attributes of the graph to train a decision tree allowed us to increase accuracy of SNP calls further. Together these results indicate that our algorithm is capable of finding SNPs accurately in complex sub-graphs and potentially comprehensively from whole genome graphs.Availability and implementation: The source code for a C++ implementation of our algorithm is available under the GNU Public Licence v3 at: https://github.com/danmaclean/2kplus2. The datasets used in this study are available at the European Nucleotide Archive, reference ERP00565, http://www.ebi.ac.uk/ena/data/view/ERP000565Contact:dan.maclean@tsl.ac.ukSupplementary information:Supplementary data are available at Bioinformatics online. These k-mer graphs have been implemented into efficient data structures for large collections of k-mers and have proven to be of great utility as the underlying data model over which numerous de novo genome assembly algorithms have been implemented  between genomes of individuals are valuable markers for tracing the genetic basis of inheritable traits or diseases. Rapid detection and creation of large libraries of SNPs is vital for timely investigation and identification of genes associated with important phenotypes. Contemporary sequencing technology can sample genomes comprehensively in only hours, with these data SNP detection is typically achieved by aligning reads to a reference genome and identifying SNPs as a difference between consensus and the reference . Factorsk + 2 with two equidistant branching vertices in which the edge paths on each branch are a different colour. Internal nodes may be branching. These constraints allow us to simplify the search for cycles by only checking the locality of the starting branching vertex. Once the algorithm terminates, a simple walk of the vertices in the bubble, collecting the labels, gives the sequence. The two coloured k + 1 walks result in separate nucleotide sequences of length k + 1 in which the first nucleotide differs between the samples. Our method’s very precise description of a bubble means that it has the potential to be extremely specific and generate highly accurate lists of SNPs from graph structure alone.De Bruijn graphs of sequence reads are typically very large with the vast majority of vertices having only a single in and out edge, as such no complex bubbles can exist in these areas and much of the graph need not be searched. Thus, we first build a sub-graph from a branching vertex that has at least two out edges and search sub-graphs for cycles. We retain only cycles of length 2A.thaliana with the A.thaliana ecotype Col-0 genome as the common reference  tractable genome with only a relatively small repeat content and well-catalogued genetic diversity, making it an ideal test organism.We used thirteen sets of Illumina sequence reads from different ecotypes of the model plant eference . For eack + 2  do c is empty string   s that have attribute colour   Traverse edges of sequence to c   Append last character from edge attribute string end for  end forCOutput: Set of contigs We used Cortex  study. The contigs generated by the 2k + 2 algorithm were used as query sequence in a BLASTn search with default settings , specificity  and accuracy (proportion of predicted SNPs that were included in the known SNP list). Specificity in this analysis is always very close to 100% because of the very large number of non-SNP genome sites. As our algorithm searches only a subset of the graph defined in parameter T . Each bubble in a graph was classified as a Real SNP or a false positive according to its presence in the SNP list and were divided into a training set and testing set. Two-third of the dataset is used for training the classifier and one third for testing. To train, we used a decision tree found in the freely available WEKA package , but recalling a smaller number of the known SNPs than seen in We ran the 2k + 2 structure with those from the canonical lists for Col-0 and Tsu-1. Analysis of the proportion of published SNPs found by 2k + 2 in windows of 20 kbp across the genome showed consistently high recovery of SNPs. Average recall in the windows was 73%  regardless of the number of branching vertices. We would expect the total number of SNPs predicted to increase as graph complexity increases, since SNPs nearby in the genome (in particular within k of each other) would increase graph complexity. However, this is not observed, for sub-graphs with more than 15 branching vertices, we predict only one SNP in any ecotype (Supplementary Data). The result indicates that the algorithm is most successful at retrieving SNPs in sub-graphs with only two branching vertices, though can handle moderately complex graphs and successfully identify SNPs within those sub-graphs. We have not been able to confirm whether SNP density does truly increase with graph complexity, and therefore, cannot rule out whether SNP prediction drop off in more complex regions is a true property of the graph or a failure of the 2k + 2 algorithm to detect in these regions.To establish the performance of the algorithm in complex regions of the graph, we compared the total number of branching vertices in a 100 vertex sub-graph with the number of SNPs that were predicted when each sub-graph was searched exhaustively. We looked at 54 616, 35 043 and 36 483 randomly sampled sub-graphs with more than two branching vertices for ecotypes Can-0, Bur-0 and Po-0, respectively. The majority of sub-graphs have just a few branching vertices though there are a few very heavily branched sub-graphs with more than 20 out of 100 vertices with branches A. The nu ecotype . IntuitiA.thaliana data k + 2 algorithm and classifier outperforms that approach significantly returning better than 90% accurate SNP calls for very large SNP sets.We trained a decision tree to distinguish between known SNPs and non-SNP bubbles and applied it to the predicted bubbles from 16 ecotypes to successfully classify bubbles that represented real SNPs with higher accuracy whilst maintaining similar sensitivity and specificity than using bubble structure alone . The spek + 2 algorithm takes a graph theoretical approach to identifying topological structures, namely 2k + 2 cycles in undirected graphs that can represent SNPs in sequences input to a donor De Bruijn graph. We have shown that 2k + 2 can be used to generate sets of SNPs in graphs at high accuracy in the A.thaliana genome which is increased by application of the decision tree classifier. 2k + 2 found SNPs across all portions of the A.thaliana genome without bias towards feature type or region. We did not find that all SNPs seen in alignment-based methods using a reference could be detected with this algorithm and in all our experiments, we found that around 70% of SNPs seen by alignment could be predicted. The 2k + 2 strategy is, therefore, a useful algorithm that should find general use in reference-free SNP calling strategies in the future.The 2


N = 300,000 single nucleotide polymorphisms (SNPs) with minor allele frequency (MAF) ≥ 0.15, for the dichotomous outcomes of allergic rhinitis, asthma, cardiac disease, depression, dermatophytosis, type 2 diabetes, dyslipidemia, hemorrhoids, hypertensive disease, and osteoarthritis. A total of N = 45,171 subjects were included after quality control steps were applied. These data were divided into discovery and replication subsets; the discovery subset had > 80% power, under selected models, to detect genome-wide significant interactions (P < 10−12). Interactions were also evaluated for enrichment in particular SNP features, including functionality, prior disease relevancy, and marginal effects. No interaction in any disease was significant in both the discovery and replication subsets. Enrichment analysis suggested that, for some outcomes, interactions involving SNPs with marginal effects were more likely to be nominally replicated, compared to interactions without marginal effects. If SNP-SNP interactions play a role in the etiology of the studied conditions, they likely have weak effect sizes, involve lower-frequency variants, and/or involve complex models of interaction that are not captured well by the methods that were utilized.The identification of statistical SNP-SNP interactions may help explain the genetic etiology of many human diseases, but exhaustive genome-wide searches for these interactions have been difficult, due to a lack of power in most datasets. We aimed to use data from the Resource for Genetic Epidemiology Research on Adult Health and Aging (GERA) study to search for SNP-SNP interactions associated with 10 common diseases. FastEpistasis and BOOST were used to evaluate all pairwise interactions among approximately  Gene-gene interaction, also known as epistasis, is the phenomenon where the phenotypic effect of variation at one genetic locus depends on variation at other loci . Epistasi.e., to identify SNP-SNP interactions), using data from the GERA study  who were genotyped using genome-wide SNP arrays, and who had health outcome data available for a number of electronic medical records-derived medical conditions. In addition, we sought to identify SNP characteristics that were enriched among replicated interactions, which could inform strategies to reduce the search space in smaller datasets.We aimed to conduct well-powered, genome-wide searches for epistasis at the pairwise SNP level  (http://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/GetPdf.cgi?id=phd004309). In the present study, we included all subjects who were genotyped on the EUR (“Non-Hispanic White”) chip .Authorized access to data from the GERA study was obtained (dbGaP Study Accession: phs000674.v1.p1), and use of these data was approved by the Yale Human Investigation Committee. This study is described in detail elsewhere . We excluded from potential analysis the conditions “Cancer” and “Psychiatric,” as it was judged that these conditions were too broadly defined for the objectives of the present study. We also excluded any conditions for which we had less than 50% power to detect an interaction . After these exclusions, the conditions “asthma,” “allergic rhinitis,” “cardiac disease,” “depression,” “dermatophytosis,” “diabetes, type 2,” “dyslipidemia,” “hypertensive disease,” “hemorrhoids,” and “osteoarthritis” (N = 10 conditions) were included as outcomes for analysis.Health outcomes were binary-coded (yes or no) variables representing various medical conditions, as derived from Kaiser Permanente Electronic Medical Records. These conditions were defined based on a subject having at least two medical diagnoses within an ICD-9-based disease category. These conditions and the ICD-9 codes that were used to define them are listed here: http://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/GetPdf.cgi?id=phd004309. Birth year category was defined as one of 14 possible time periods . We defined “minimum age” as the difference in years between the time of survey (2007) and the last year of the birth year category. For example, the minimum age for a subject with a birth year category of 2 was defined to be 2007 − 1928 = 79 years.Additional variables included from the dataset included birth year category, sex, and genetic structure principal components . The principal components were previously calculated and provided by the original GERA study investigators; details of this are reported here: N = 358,590 SNPs). Pi-hat was calculated for every possible pair of subjects; for pairs with pi-hat ≥ 0.20, one subject of the pair was randomly excluded. The numbers of subjects excluded at each QC step are listed in Supplemental Material, Table S1.Subjects were excluded if they (i) had an ambiguous SNP-estimated sex; (ii) had a subject call rate < 95%; (iii) were selected for exclusion due to relatedness with other subjects; (iv) had a reported race/ethnicity other than “white” or did not have principal components available for the EUR chip; and (v) were a PCA outlier, defined as having a value > 6 standard deviations on any one of the 10 principal components. Ambiguous SNP-estimated sex was defined as having an X-chromosome homozygosity estimate (F) between 0.2–0.8. There were 17 subjects for whom the unambiguous SNP-estimated sex differed from the reported sex; in such cases, the SNP-estimated sex was considered to be the true sex. To assess subject relatedness, pi-hat  was estimated using the dataset after being pruned for linkage disequilibrium (LD)  were excluded. This was performed in order to exclude controls that were not of a sufficient age to be of comparable risk for the condition. No exclusions based on age were made for asthma, allergic rhinitis, and dermatophytosis, since these conditions are common in children. The number of controls excluded based on age are shown in Table S2. In the final analyses, across the different conditions, the numbers of cases ranged from 5549–24,047, and the numbers of controls ranged from 20,018–39,154.Cases and controls were defined after sample quality control procedures were completed. For each of the 10 outcomes included for analysis, cases were subjects who were coded as having the respective condition, while controls were subjects without it. Controls were excluded if their birth year category exceeded the 99.5 percentile birth year category of cases . For example, for osteoarthritis cases, the 99.5 percentile birth year category was 10 (minimum age of this category: 39 years); therefore, all controls with a birth year category of 11 or higher  could not be mapped to the hg19 reference genome; (ii) had a SNP call rate < 98% overall or in cases or controls separately; (iii) had a minor allele frequency (MAF) < 0.15 (this was specified because there was little power to detect an interaction involving SNPs that were less common than this); (iv) were nonautosomal; or (v) had a test for deviation from Hardy-Weinberg equilibrium with Table S2.After sample and SNP QC, each outcome-specific dataset was randomly divided into a discovery and a replication dataset. These divisions were made such that the targeted size of each replication dataset was 1000 cases and 3000 controls. These numbers were chosen because they provided > 80% power for nominal replication . The final numbers of subjects in each discovery and replication dataset are shown in Table S4). For cardiac disease and dermatophytosis, this inflation was largely removed after adjusting for the first two principal components. For the other conditions, adjustment for the first two components either had no effect (type 2 diabetes) or reduced but did not remove the inflation [dyslipidemia (λ = 1.07) and hypertensive disease (λ=1.10)]. Adjustment for the full 10 principal components yielded no further improvement in inflation (data not shown). All principal components were those that were provided with the GERA dataset. Since subjects were genotyped using one of two different kits, we also sought to determine whether the type of kit had any effect on test statistic inflation. In our final analyses , 1.5% of all subjects were genotyped on the less commonly used kit; when these subjects were excluded, the observed values of λ decreased by 0.02 or less, which was determined to be a negligible amount (data not shown). As a result, no exclusions based on kit type were made in the final analyses.At the level of marginal  effects, test statistic inflation due to population stratification or other sources was assessed via calculations of the genomic inflation factor λ. Values of λ greater than 1.05 were considered evidence of test statistic inflation. Marginal test statistics were derived using genome-wide single-SNP logistic regression analyses performed within all 10 sets of discovery and replication datasets. The cardiac disease (λ = 1.14), dermatophytosis (λ = 1.23), type 2 diabetes (λ = 1.08), dyslipidemia (λ = 1.11), and hypertensive disease (λ = 1.30) data showed evidence of inflation (7 pairs) were tested for interaction using the FastEpistasis analytical approach (described below). Observed vs. expected-under-the-null −log P-values were plotted, and no evidence of inflation was observed .Interaction test statistic inflation was visually evaluated using quantile-quantile plots. To do this, a random selection of approximately 10,000 SNPs was obtained from each condition-specific dataset, and all possible pairs of SNPs , while the nonreferent allele  was the minor allele. To assess marginal effects, logistic regression models were used, with SNPs coded additively . Unadjusted and adjusted  analyses were performed for all condition-specific datasets. For ranking of SNPs by marginal effect P-value < 10−7 were selected for follow-up. For interactions selecting from FastEpistasis analyses, the follow-up consisted of reanalysis of the selected interactions using logistic regression modeling, in both the discovery and replication datasets. Unadjusted and adjusted  logistic regression analyses were performed. In these models, SNPs were coded additively, and the nonreferent allele was the minor allele in the discovery dataset. Models included main effects for each SNP and an interaction term for the SNPs; statistical significance was based on an explicit test of the interaction term. For interactions selected from BOOST analyses, the follow-up consisted of analysis of the selected interactions in the replication dataset, also using BOOST. For final ranking of interactions by P-value, the adjusted analyses of the logistic regression modeling, or the BOOST analyses, from the discovery datasets were used.To assess interactions on a genome-wide scale, all pairwise interactions among all postQC SNPs were assessed in each condition-specific discovery dataset using two different approaches: FastEpistasis and BOOST (as implemented in the version of Plink listed below). To avoid spurious evidence of interaction due to linkage disequilibrium between SNPs, an interaction was excluded if its two SNPs were located within 1 Mb of each other. From these genome-wide analyses, interactions with an interaction P < 0.05. Bonferroni correction was used to determine strict statistical significance. For marginal effects, genome-wide significance was defined as P-value < 10−7 . Marginal effect significance was declared based on P-values from the analyses that were adjusted for birth year category, sex, and the first two principal components. For interactions, genome-wide significance was defined as interaction P-value < 10−12 . Interaction significance was declared based on P-values from the logistic regression (adjusted) or BOOST analyses. For the enrichment analyses, significance was defined as P-value < 0.0004 .Nominal significance was defined as i.e., interactions that were followed-up after the FastEpistasis analyses), an effect was considered replicated if it had both a P-value < 0.05 and a consistent direction of effect  in the replication dataset. For interactions assessed using BOOST, an interaction was considered replicated only if it had a P-value < 0.05 in the replication dataset .For marginal effect analyses and interactions assessed with logistic regression (https://www.cog-genomics.org/plink2) . Logistic regression tests for epistasis were performed using CASSI Genome-Wide Interaction Analysis Software v2.50 (https://www.staff.ncl.ac.uk/richard.howey/cassi/).Quality control procedures, marginal effects testing, and genome-wide interaction tests (FastEpistasis and BOOST) were performed using Plink 1.90 β . File S2 provides additional methodological detail. Figure S1 depicts quantile-quantile plots for interactions in the discovery datasets. Figure S2 provides a comparison of epistasis analysis results derived from Plink 1.07 and Plink 1.90. Table S1 contains subject quality control data. Table S2 contains disease-specific subject counts. Table S3 contains SNP quality control data. Table S4 contains genomic inflation data for marginal effects. Table S5 contains database search terms for identifying disease-related genes. Table S6 contains SNP annotation counts. Table S7 contains power analyses for the discovery datasets. Table S8 contains power analyses for the replication datasets. Table S9 contains the penetrance table for epiSIM power simulations. Table S10 contains estimates of phenotypic variance explained by additive genetic variance of the included SNPs. Table S11, Table S12, Table S13, Table S14, Table S15, Table S16, Table S17, Table S18, Table S19, and Table S20 describe the top 10 marginal effects for each studied condition. Table S21, Table S22, Table S23, Table S24, Table S25, Table S26, Table S27, Table S28, Table S29, and Table S30 describe the enrichment analyses for each condition. Table S31 describes the BioGrid interactions.N = 300,000 SNPs, or N = 4.5 × 1010 interactions), assuming an interactions odds ratio of ≥ 1.50 for SNPs with MAF of ≥ 0.15 (Table S7). The exceptions to this are the data for depression, dermatophytosis, and type 2 diabetes, which only had greater than 50% power. All replication datasets had greater than 80% power to detect the same kind of interaction at a nominal significance of P < 0.05 (Table S8). Estimates of the proportion of phenotypic variance explained by additive genetic variance of the included SNPs ranged from 3.6% (dermatophytosis) to 24.6%  (Table S10).The 10 conditions included as outcomes for analysis, and case/control characteristics for each condition, are shown in i.e., the effects of SNPs considered individually) were conducted across all 10 conditions . Among the most significant SNPs for each condition, five were genome-wide significant , three of which were replicated (IL1RL1 (rs2160203) in allergic rhinitis  in asthma (TCF7L2 (rs4506565) in type 2 diabetes (APOB (rs1367117) in dyslipidemia (KCNK3 (rs1275985) in hypertensive disease . This was the interaction between rs4456135 and rs12162346 for the outcome of dermatophytosis ; however, this was nonsignificant and in opposite direction in the replication dataset  are described in File S1.The discovery datasets of all 10 conditions were subjected to exhaustive genome-wide searches for pairwise interactions, using both the FastEpistasis (with follow-up via logistic regression) and BOOST analytical approaches. Only one interaction met the criteria for being declared genome-wide significant . Both cardiac disease and hypertensive disease showed strictly significant (P < 0.0004) evidence for this type of enrichment, while dyslipidemia also showed nominally significant evidence (P < 0.05) for the same. For example, in cardiac disease , among interactions where both SNPs had the “marginal” annotation, 20.0% were nominally replicated; while among interactions where neither of the SNPs had this annotation, only 2.5% were nominally replicated.We next attempted to determine if there was an enrichment of nominally replicated interactions with particular SNP annotations , among all followed-up interactions. These results are shown in Table S21, Table S22, Table S23, Table S24, Table S25, Table S26, Table S27, Table S28, Table S29, and Table S30, comparing entries in the columns “Min Int P” with prefix “R”  and “Thresh” . For example, in cardiac disease, there were N = 16,284 SNPs with the “marginal” annotation (Table S6). An exhaustive pairwise analysis of just these SNPs would result in 1.3 × 108 evaluated interactions and a significance threshold of P < 3.77 × 10−10. The most significant replicated interaction among all of these had an actual P-value of 1.09 × 10−7 (Table S23), and thus would not have been considered significant. Therefore, although we can detect a possible enrichment among interactions with marginal effects, it may not be possible to use this filtering approach alone to identify which of the interactions are actually be genuine; effect sizes may be too small to detect.However, no annotation strategy could have been used to successfully narrow the search space in order to find a replicated, significant interaction. This can be seen in Table S31). However, none of these interactions were nominally replicated.Finally, we sought to determine whether any interaction that met the criteria for follow-up involved genes (or their products) that have been previously reported to physically or genetically interact, as curated in the BioGRID database. Across all analyzed conditions, there were three BioGRID interactions among the followed-up interactions for the FastEpistasis analyses, and two for the BOOST analyses .To our knowledge, this study presents one of the largest (by subject count) searches for SNP-SNP interactions yet conducted for human disease. Despite this, and the use of two different analytical methods, we failed to detect a significant, replicable interaction after exhaustively searching through 45 billion possible interactions in each of 10 different complex diseases. One possible explanation for this is that no interactions exist for these conditions involving SNPs with a MAF ≥ 0.15 operating under the interaction models specified in the power analyses. If SNP-SNP interactions do contribute to the conditions that were analyzed, then they are likely of weak effect and/or involve lower-frequency SNPs, which will be very challenging to study. Statistical power drops off rapidly as the interaction odds ratio goes from 1.50–1.25; even at a modest significance level of In addition, SNP annotation by functionality  and gene assignment (disease-relevant gene and any gene) failed to result in any appreciable enrichment in replicated interactions, suggesting that these methods alone may not be useful in trimming the epistasis search space. Only SNPs with marginal significance showed evidence for enrichment, for some outcomes. Interestingly, this is consistent with a recent suggestion that “epistatic effects that seem to be statistically robust have large marginal effects” (http://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/GetPdf.cgi?id=phd004308). Moreover, it may be possible that associations will differ depending on age of onset, which we did not have information on. The etiology of some of the conditions  may simply have a weaker genetic basis than others, which could also explain a lack of association. For half of the conditions , strong marginal effects involving well-known disease-relevant genes were found, which supports the validity of the analytical approach for these conditions. However, no strictly significant marginal effects were found for the other half , which could be attributable to the aforementioned problems. This is also suggested by the observation that the additive genetic variance captured by the included SNPs was low for some conditions (Table S10). In addition, the datasets for depression, dermatophytosis, and type 2 diabetes had lower power in general than the other conditions (Table S7), which could also explain a lack of interactions for these conditions in particular. Furthermore, we excluded interactions involving SNPs located within 1 Mb of one another, to prevent the analysis of SNPs in linkage disequilibrium; however, this may have also excluded genuine interactions involving close-proximity SNPs. Additionally, power was estimated assuming a limited number of interaction models and no noise in the data; as such, it is likely that the analytical approaches  had low power to detect interaction models that are not captured well by these methods. Finally, epistasis may involve higher orders of interaction that may not be apparent in the second-order (pairwise) interactions that were examined in this study.Apart from the nonexistence of strong (large effect size) interactions, there are a number of possible alternative explanations for our observed lack of detection. The first is that some of the included conditions may have been too broadly defined to capture a strong association signal, since they were defined based on having two or more of several possible ICD-9 codes within a diagnostic class, and it was not possible to ascertain narrower phenotypes. This might particularly have been a problem for the outcome of “cardiac disease,” which was constructed out of a large of number of disparate codes  or a disease gene (± 5 kb). Although these choices were made in order to limit the number of annotated SNPs, they could potentially miss SNPs that were in linkage disequilibrium with these features but located a farther distance away. In addition, the disease gene annotations relied on literature curation databases that may have different levels of completeness or validity for different conditions. It is also possible that other annotation methods not considered here (such as the use of Biofilter (Given the development of statistical methodologies that enable the computationally efficient evaluation of vast numbers of interactions, and the increasing availability of very large genetic association databases, it is becoming increasingly feasible to search for SNP-SNP interactions on a genome-wide scale. However, based on the experiences found in the present study, these interactions are likely to have small effect sizes, involve low-frequency variants, and/or involve complex models of interaction, which may require alternative methods of detection.