Mycobacterium ulcerans. While proximity to stagnant or slow flowing water bodies is a risk factor for acquiring BU, the epidemiology and mode of M. ulcerans transmission is poorly understood. Here we have used high-throughput DNA sequencing and comparisons of the genomes of seven M. ulcerans isolates that appeared monomorphic by existing typing methods. We identified a limited number of single nucleotide polymorphisms (SNPs) and developed a real-time PCR SNP typing method based on these differences. We then investigated clinical isolates of M. ulcerans on which we had detailed information concerning patient location and time of diagnosis. Within the Densu river basin of Ghana we observed dominance of one clonal complex and local clustering of some of the variants belonging to this complex. These results reveal focal transmission and demonstrate, that micro-epidemiological analyses by SNP typing has great potential to help us understand how M. ulcerans is transmitted.Buruli ulcer (BU) is an emerging necrotizing disease of the skin and subcutaneous tissue caused by  Mycobacterium ulcerans causes a destructive skin disease known as Buruli ulcer (BU), which has been reported from more than 30 tropical or subtropical countries, with the highest prevalence in Western Africa. Due to the striking genetic monomorphism of African M. ulcerans populations, conventional genetic fingerprinting methods have largely failed to differentiate isolates coming from the same BU endemic area. Here we report a highly discriminatory fingerprinting method for M. ulcerans using a single nucleotide polymorphism-based genetic fine-typing technique. This method has enabled us for the first time to identify different M. ulcerans haplotypes within a BU endemic area. Linking the origins of M. ulcerans strains with the patients' residences unveiled the clustering of unique M. ulcerans haplotypes within the Densu river basin of Ghana. Results show, that haplotypes do not spread within a short time over the entire BU endemic region, but rather form independent focal transmission clusters. Mycobacterium ulcerans causes a chronic, necrotizing disease of the skin and the subcutaneous adipose tissue commonly known as Buruli ulcer M. ulcerans are still incompletely characterized M. ulcerans is an environmental mycobacterium and that micro-traumata of the skin may initiate infection M. ulcerans from an environmental source has been achieved only once so far, from an aquatic insect IS2404 has implicated insects such as biting aquatic hemiptera and mosquitoes in the transmission of M. ulceransM. ulcerans DNA in polymerase chain reaction (PCR) tests may be only an indicator for the presence of M. ulcerans or other genetically closely related mycobacteria in the environment. Although BU is known to develop in all age groups with a nearly equal gender distribution, children 15 years of age or younger make up at least 50% of all cases in Africa M. ulcerans may lead to disease only in a minority of exposed individuals Infection with M. ulcerans, including IS2404, IS2606 and IS2426 PCR IS2404 restriction fragment length polymorphism analysis (RFLP) IS2404-Mtb2 PCR Many genetic fingerprinting methods have been applied for M. ulcerans strains M. tuberculosisYersinia pestisSalmonella enterica TyphiM. ulcerans strains from a BU endemic area of Ghana. We selected two Ghanaian patient isolates representing two different Ghanaian VNTR types MLST, which has now been developed for more than 50 microbial taxa M. ulcerans isolates were obtained from BU diagnostic samples. Data were analyzed anonymously and bacterial isolates delinked from the patients from whom they originated. Ethical approval to use the diagnostic specimens for immunological and microbiological research was obtained from the ethical review board of the Noguchi Memorial Institute for Medical Research, University of Ghana, Legon, Ghana. Written informed consent was provided by all patients for standard surgical treatment and anaesthesia. In addition the ethical review board requested written informed consent for taking blood samples for immunological research on a special consent form, but not for potential later investigations of bacterial isolates generated during standard diagnostic procedures.In the present study, M. ulcerans patient isolates from a BU endemic area located in the Ga West, Ga East and Akuapim South Districts were included in the SNP typing analysis. Patients were aged 2–75 years, while 71% of the patients were younger than 15 years. In addition, M. ulcerans patient isolates from the Ashanti Region (Amansie West District) of Ghana and from other West-African countries  were enclosed for further SNP typing analyses , 5 ng genomic DNA and 0.3 µM forward and reverse primers each in a total volume of 25 µl. Reactions were carried out in a Step One Plus Real-time PCR system (Applied Biosystems) with a 96-well block. Thermal conditions were as follows and as described previously Real-time PCR hairpin primer (HP) assays http://www.bioinfo.rpi.edu/applications/mfold/old/dna/) to have a melting temperature of 67 to 70°C with a free energy of between −0.5 and −2.0. Primers are provided in The hairpin-shaped primers were designed as described previously 2 and 0.4 mM dNTPs (Sigma) in a total volume of 25 µl. PCR reactions were carried out in a Gene Amp PCR System 9700 PCR machine (Applied Biosystems). Thermal conditions for PCR amplification of M. ulcerans genomic DNA were as follows: initial denaturation step, 94°C for 5 min; 32 cycles: 94°C for 30 s (denaturation), 60°C for 30 s , 72°C for 1 min (elongation); final extension step, 72°C for 10 min. PCR products were analyzed on 1% agarose gels. PCR products were purified using the Nucleo Spin Extract II Kit (Macherey-Nagel). Sequencing of purified PCR products was done by Macrogen . Sequencing was conducted by the Sanger method using BigDye™ terminator cycling conditions using the Automatic Sequencer 3730xl (Applied Biosystems).Assays were validated on the published genome Agy99 as well as the reference strains NM20/02, NM31/04, NM14/01, NM43/02, NM49/02 and NM54/02 to confirm the presence of each allele and to verify the performance of SNP assays. Assays with genomic DNA samples from clinical isolates were considered reliable only if the cycle thresholds generated in the paired wells differed by three or more cycles and if the melting curves of paired wells were coherent. ARMS assays with low discriminatory power were not included into the standard set of 65 SNP typing assays. Sanger DNA sequencing of PCR products was used to validate a selected subset of SNPs. Primers used for PCR and sequencing were designed using Primer3 M. ulcerans isolates. Additionally, validation of pivotal SNPs loci revealing different haplotypes within the endemic area around the Ga District as well as differences between Amansie West District strains and isolates from other African countries was carried out. 100% of 36 randomly chosen SNP loci tested in reference strains Agy99, Nm20/02 and Nm31/04 were reconfirmed by Sanger sequencing. 100% of 70 significant SNP loci dividing Ga District strains into 5 haplotypes other than Agy99 and distinguishing Amansie West District as well as African strains were likewise verified by Sanger sequencing. In contrast, four real-time PCR typing results, which were validated because of unique allele occurrences in certain Ga District isolates diverged from reconfirmatory Sanger sequencing analysis. Subsequent repetition of real-time PCRs in duplicates revised the initial real-time PCR analyses and confirmed Sanger sequencing results.Several random SNP loci were validated by Sanger sequencing in order to verify the ARMS approach for a differentiation of MEGA software version 4.1 (beta) We created a map of West Africa by using the map creator tool of Epi Info version 3.5.1 in order to illustrate detected SNP patterns in different African countries.M. ulcerans patient isolates NM20/02 and NM31/04 originating from two different BU endemic areas of Ghana with the published genome sequence of the Ghanaian strain Agy99 M. ulcerans. Developed SNP assays were used to type a collection of strains from two different BU endemic areas of Ghana and other African countries  between these isolates . In compTyping of patient isolates from other BU endemic African countries at the 65 SNP loci yielded 2 of 15 strains with patterns similar to haplotypes found in Ghana . The onlM. ulcerans patient isolates collected between 2001 and 2007 from the BU endemic Densu river basin of Ghana, from which the sequenced strains Agy99 and NM20/02 originated. Within this group of 74 strains of common geographical origin, differences at 14 of the 65 SNP loci tested (22%) were observed (Using the 65 established ARMS assays we SNP-typed 74 observed . AltogetBased on detected haplotypes we re-sequenced 4 representative strains NM14/01 (haplotype 5), NM43/02 (haplotype 3), NM49/02 (haplotype 6) and NM54/02 (haplotype 4) in order to further differentiate strains from the same haplotype. We detected new unique SNPs in NM43/02 (26 SNPs), NM54/02 (11 SNPs), NM14/01 (9 SNPs) and NM49/02 (4 SNPs) and established 24 new assays, which enabled a segregation of each haplotype into two haplotypes . A phyloM. ulcerans complex in the Densu river basin as well as phylogenetic or sampling bias.In contrast to the generally rarer haplotypes 2–4; 6–8 and 10, haplotypes 5 and 9 were found within each time interval (one year) from 06/2001 to 06/2006 (data not shown). Haplotypes 1 and 2 were not found again in the whole strain collection. Possible explanations include the actual absence of these haplotypes from the residual clonal M. ulcerans isolates of identical haplotype (haplotype 4) were isolated from two patients coming from the same household, suggesting a common source of infection.For a phylogeographic analysis the homes of patients from whom the strains were isolated were marked in a map, depicting the distribution of haplotypes . HaplotyM. ulcerans by comparative genomic hybridization analysis enabled differentiation of a world-wide collection of strains into two main lineages and six continental haplotypes M. ulcerans populations and at low resolution. VNTR analysis of a Ghanaian strain collection based on the polymorphic loci ST1 and MIRU1 revealed the presence of three distinct allele combinations  in Ghana. All isolates from the Buruli ulcer endemic Densu river basin tested, except for Agy99, displayed combination BD/B M. ulcerans population as possible. Our assumption was reinforced by re-sequencing four M. ulcerans strains initially grouped into haplotypes 3–6. Only 16 additional SNPs could be detected by comparison of the four strains to reference strain Agy99, while 50 unique SNPs could be identified by comparing the four strains among themselves. We anticipated that detected SNPs will provide the first useful genetic markers for phylogeographic and transmission pathway analyses at least in the Densu river basin and other BU endemic areas of Ghana.Previous investigations on the genetic diversity of We have identified 10 different haplotypes in a relatively small BU endemic area within the Densu river basin. Haplotypes 1–10 are descendants of a founder haplotype that has spread over the district; the most common haplotype 5 may represent this founder haplotype. Analysis of this spatial distribution of haplotypes indicates that emerging new haplotypes do not readily spread over the entire endemic area, but form focal transmission clusters.Salmonella typhi, which report multiple strain types circulating within a specific location M. ulcerans isolates will lead to a further subdivision and differentiation of African strains. Phylogenetic discovery bias is implicit to SNP typing and will continue to exist as long as not every single sample will be sequenced. It will become smaller though with every additional re-sequenced genome. SNP typing based on a wider range of SNPs may therefore yield evidence for genetic divergence of strains from Benin, DRC, and strain two from the Ivory Coast. Hence, phylogeographic analyses in other African BU endemic areas will require whole genome comparison of strains from that area to develop a local set of informative SNPs. This is supported by our recent identification of seven insertion sequence element-related SNP types within Africa Our data are comparable to studies of other genetically monomorphic organisms like M. ulcerans has evolved from the aquatic environmental M. marinum and seems to have adapted to a more stable ecological niche M. ulcerans, whereas relatively few develop clinical disease M. ulcerans. Based on numerous reports demonstrating an association of BU with slow flowing or stagnant water bodies, it is therefore commonly assumed that infection takes place through trauma of the skin or insect bites via an environmental reservoir in the aquatic ecosystems. Both biotic components, such as biofilms and aquatic invertebrate species are being considered as potential vectors and/or reservoirs M. ulceransM. ulcerans, a similar mammalian reservoir has not been identified in BU endemic regions of Africa. If such a reservoir plays a role in transmission, spread of M. ulcerans from chronic, ulcerated lesions to insect vectors or another currently unknown environmental reservoir should be considered. Subsequent infection of individuals living in the same settlements may be responsible for the focal transmission patterns, such as of haplotype 10, which was found only within one small village.M. ulcerans bacteria would be carried freely by the flow of water of the Densu river and its tributaries, haplotypes present in upper parts of the river system should be represented in the lower parts. However, among patient isolates coming from the lower part of the basin close to the Weija Lake the rare haplotypes 4 and 7 dominated. M. ulcerans bacteria that are swept downstream in the river water thus do not seem to play a major role in transmission.The Densu river basin represents one of the coastal drainage systems of Ghana. Its water is collected in the Weija Lake, which developed after construction of a dam at the river's mouth in the 1970s. If M. ulcerans.Results of this retrospective pilot study indicate that future longitudinal micro-epidemiological studies involving SNP typing of isolates may give deeper insight into transmission pathways and relevant reservoirs of Figure S1M. ulcerans isolates from the Ga district of Ghana. M. ulcerans isolates from the Densu river basin of Ghana were analyzed at 89 SNP loci (Primer IDs) by real-time PCRs. SNPs were registered as 1 (grey). Agy = 1; Nm98/03 = 2; Nm38/02 = 3; Nm83/03 = 4; Nm103/03 = 5; Nm43/02 = 6; Nm44/02 = 7; Nm100/03 = 8; Nm74/03 = 9; Nm97/03 = 10; Nm102/03 = 11; Nm32/02 = 12; Nm54/02 = 13; Nm19/02 = 14; Nm21/02 = 15; Nm27/02 = 16; Nm30/02 = 17; Nm31/02 = 18; Nm33/02 = 19; Nm37/02 = 20; Nm40/02 = 21; Nm41/02 = 22; Nm45/02 = 23; Nm46/02 = 24; Nm47/02 = 25; Nm51/02 = 26; Nm52/02 = 27; Nm53/02 = 28; Nm56/02 = 29; Nm59/02 = 30; Nm60/02 = 31; Nm62/02 = 32; Nm63/02 = 33; Nm68/02 = 34; Nm69/02 = 35; Nm72/02 = 36; Nm76/03 = 37; Nm77/03 = 38; Nm79/03 = 39; Nm82/03 = 40; Nm90/03 = 41; Nm91/03 = 42; Nm94/03 = 43; Nm95/03 = 44; Nm06/03 = 45; Nm34/04 = 46; Nm50/05 = 47; Nm53_1/05 = 48; Nm53_2/05 = 49; Nm59/05 = 50; Nm65/05 = 51; Nm77/06 = 52; Nm82/07 = 53; Nm14/01 = 54; Nm18/02 = 55; Nm23/02 = 56; Nm34/02 = 57; Nm13/04 = 58; Nm15/04 = 59; Nm57/05 = 60; Nm63b/05 = 61; Nm20/02 = 62; Nm22/02 = 63; Nm28/02 = 64; Nm50/02 = 65; Nm89/03 = 66; Nm01/03 = 67; Nm12/04 = 68; Nm36/04 = 69; Nm60a/05 = 70; Nm54/05 = 71; Nm42/02 = 72; Nm48/02 = 73; Nm49/02 = 74; Nm78/03 = 75.SNP typing analysis of (2.97 MB TIF)Click here for additional data file.Table S1Real-time PCR primers. Primers and hairpin primers used for real-time PCR amplification refractory mutation assays.(0.28 MB DOC)Click here for additional data file.


The anticoagulant drug warfarin is widely prescribed for reducing the risk of stroke, thrombosis, pulmonary embolism, and coronary malfunction. However, Caucasians vary widely (20-fold) in the dose needed for therapeutic anticoagulation, and hence prescribed doses may be too low (risking serious illness) or too high (risking severe bleeding). Prior work established that ∼30% of the dose variance is explained by single nucleotide polymorphisms (SNPs) in the warfarin drug target VKORC1 and another ∼12% by two non-synonymous SNPs  in the cytochrome P450 warfarin-metabolizing gene CYP2C9. We initially tested each of 325,997 GWAS SNPs for association with warfarin dose by univariate regression and found the strongest statistical signals (p<10−78) at SNPs clustering near VKORC1 and the second lowest p-values (p<10−31) emanating from CYP2C9. No other SNPs approached genome-wide significance. To enhance detection of weaker effects, we conducted multiple regression adjusting for known influences on warfarin dose  and identified a single SNP (rs2108622) with genome-wide significance (p = 8.3×10−10) that alters protein coding of the CYP4F2 gene. We confirmed this result in 588 additional Swedish patients (p<0.0029) and, during our investigation, a second group provided independent confirmation from a scan of warfarin-metabolizing genes. We also thoroughly investigated copy number variations, haplotypes, and imputed SNPs, but found no additional highly significant warfarin associations. We present power analysis of our GWAS that is generalizable to other studies, and conclude we had 80% power to detect genome-wide significance for common causative variants or markers explaining at least 1.5% of dose variance. These GWAS results provide further impetus for conducting large-scale trials assessing patient benefit from genotype-based forecasting of warfarin dose.We report the first genome-wide association study (GWAS) whose sample size  is sufficiently powered to detect genome-wide significance  already known to cause ∼40% of the variability in warfarin dose and discovered a new gene (CYP4F2) contributing 1%–2% of the variability. Since our GWAS searched the entire genome, additional genes having a major influence on warfarin dose might not exist or be found in the near-term. Hence, clinical trials assessing patient benefit from individualized dose forecasting based on a patient's genetic makeup at VKORC1, CYP2C9 and possibly CYP4F2 could provide state-of-the-art clinical benchmarks for warfarin use during the foreseeable future.Recently, geneticists have begun assaying hundreds of thousands of genetic markers covering the entire human genome to systematically search for and identify genes that cause disease. We have extended this “genome-wide association study” (GWAS) method by assaying ∼326,000 markers in 1,053 Swedish patients in order to identify genes that alter response to the anticoagulant drug warfarin. Warfarin is widely prescribed to reduce blood clotting in order to protect high-risk patients from stroke, thrombosis, and heart attack. But patients vary widely (20-fold) in the warfarin dose needed for proper blood thinning, which means that initial doses in some patients are too high (risking severe bleeding) or too low (risking serious illness). Our GWAS detected two genes ( Warfarin is the most widely prescribed anticoagulant for reducing thromboembolic events that often give rise to stroke, deep vein thrombosis, pulmonary embolism or serious coronary malfunctions VKORC1  contains common polymorphisms that account for a major portion (∼30%) of the variance in required warfarin dose VKORC1 and in the warfarin-metabolizing gene CYP2C9  predict ∼40% of dose variance while non-genetic factors  jointly account for another ∼15%. The robust and now widely replicated associations of warfarin dose with VKORC1 and CYP2C9 have provided one of the most successful applications of pharmacogenetics to date We and others have shown that the warfarin drug target VKORC1 and with one of two non-synonymous CYP2C9 SNPs (*3) known to influence warfarin dose (CYP2C9 SNP (*2) with known but weaker influence on warfarin dose was not detected by univariate regression, but *2 was statistically significant in multivariate regression adjusted for the other known genetic and non-genetic predictors of dose  enables a systematic search of the entire genome for genetic factors that cause any inherited trait. This method has successfully identified susceptibility loci for common diseases VKORC1 (P = 5.4×10−78). In prior fine-mapping of the VKORC1 locus VKORC1 that exhibit almost perfectly concordant genotypes yielding pairwise linkage disequilibrium (LD) r2≈1 and which define the warfarin-sensitive A-T-T haplotype at rs9923231-rs9934438-rs2359612  and completely accounted for the dose variance explained by all other fine-mapping SNPs near VKORC1CYP2C9 which contains two non-synonymous exonic SNPs whose minor alleles  impair warfarin metabolism and are well known to be associated with warfarin dose. In our previous work CYP2C9 allele formed by combining *2 and *3 into a single allele. Indeed, the GWAS results (1053 subjects) confirmed that LD is nearly perfect (pairwise r2≈1.0) between rs4917639 and the composite of *2 and *3. Thus, the highly significant univariate result for rs4917639  reflects the combined effect of CYP2C9*2 rs1799853  and CYP2C9*3 rs1057910 . *2 and *3.VKORC1  with thesee also . These h−7) was a non-synonymous SNP (rs2108622) in exon 2 of CYP4F2  introducing a Val to Met amino acid change at position 433 (V433M). SNP rs2108622 predicts additional dose variance (∼1.1%) that is independent of the variance already explained by VKORC1 and CYP2C9. As noted in the Introduction, our early studies with a small sample of 201 Swedish patients failed to detect the weak CYP2C9*2 effect on dose by univariate regression but *2 was significant in multiple regression CYP4F2 show the same phenomenon with a p-value of 1.6×10−5 in univariate regression (line 1) but progressively lower p-values as known predictors are added to the multivariate model so that for the full model a p-value of 8.3×10−10 is achieved which is far below genome-wide significance (p<1.5×10−7). The CYP4F2 association was further confirmed by testing an independent replication panel of 588 Swedish warfarin patients who gave a multivariate p-value of 0.0029 and a total overall p-value of 3.3×10−10 when combined with the GWAS subjects  to the model as a predictor and conducted further analyses. First, we retested the GWAS SNPs, but no new SNPs reached genome-wide significance and there was also no apparent excess of SNPs at lower significance thresholds  detectable by the HumanCNV370 array might influence warfarin dose, we used rigorous quality control and retained 879 samples calling 2530 CNVs (see −4 which exceeds 0.05/2530≈2.0×10−5). We note that probe density in many of the detected CNVs is not optimal for conducting association analyses and these results should therefore be viewed as preliminary.To increase the power of our multivariate regression model and possibly detect additional weak effects, we added resholds . We alsoCNVs see . None ofVKORC1, CYP2C9 and CYP4F2, we identified 40 other loci containing one or more GWAS SNPs with p-values below 2.0×10−4 and we genotyped 40 SNPs representing these loci in a follow-up sample of 588 Swedish warfarin patients. However none of the 40 loci replicated for association with warfarin dose, the lowest p-value being 0.04 which is not significant after correction for 40 tests . None of the pairs exhibited statistically significant interaction after p-values were corrected for the 15 interaction tests  at several SNPs in and around VKORC1 including rs9923231 (P = 8.9×10−9), but no other SNPs achieved genome-wide significance including CYP2C9*3 (p<4.0×10−5), CYP2C9*2 (p = 0.93), or the “composite” *2*3 SNP rs4917639 (p<0.007) (VKORC1 rs9923231 (P = 5.7×10−11) and CYP2C9*3 (P = 1.5×10−9) VKORC1 and CYP2C9*3 have a significant effect independent of dose (P<0.05)  which we previously found was associated with p<0.007) . However(P<0.05) .−78 to 10−13) at SNPs in and near VKORC1 and CYP2C9, two genes already known to explain ∼30% and ∼12% of warfarin dose variance, respectively. By applying multivariate regression adjusting for known genetic and non-genetic predictors of dose (−10 at CYP4F2 (rs2108622) that accounted for approximately 1.5% of dose variance. The increased power of multivariate regression to detect this modest effect is nicely illustrated in CYP4F2 (p<1.6×10−5) but progressively lower multivariate p-values as known predictors of dose are added to the regression model. We confirmed the CYP4F2 association in a second large sample set and the association was also reported by another group CYP4F2 . Although multivariate regression has not been widely used to increase power in other GWAS analyses because known genetic variants usually explain little phenotypic variance, the potential for power increase is perhaps obvious if known predictors do explain substantial variance. Thus multiple regression has, for example, been previously advocated for linkage analyses of line crosses We conducted the first GWAS sufficiently powered to detect DNA variants with a modest influence on the warfarin dose needed to achieve therapeutic anticoagulation. In univariate analysis of GWAS SNPs , we iden of dose , we also−7) is essentially 100% for VKORC1 rs9923231 , we used Equation 1 see  to calcudose see . The tabCYP2C9*2 and *3 which are each known to alter warfarin metabolism CYP4F2 rs2108622 which, like *2 and *3, changes protein coding sequence. However, to explore whether other dose-altering variants might be undetected due to insufficient LD with genotyped GWAS SNPs, we determined the relationship between the variance observed at a marker  above 10% in our warfarin subjects implying that a “rare” dose-altering variant (MAF≈1%–5%) would be covered at a likely maximum r2 of only ∼0.1 to ∼0.5. This low r2 coverage implies that rare variants could have r2 values in most instances  is a third gene that influences warfarin dose, but our GWAS and statistical analysis also implies that additional common SNP variants that influence dose may not exist in Caucasian populations. However, Caucasians might carry common variants with effects smaller than CYP4F2 or rare variants whose effects are substantially larger than the ∼1% of dose variance explained by CYP4F2. Furthermore, other unidentified genes may influence warfarin dose in other ethnicities such as Asians or Africans, and some rare dose-altering variants in known genes such as VKORC1 may exist in only a subset of populations of European descent As noted in the Introduction, the widely replicated warfarin dose associations with VKORC1 and CYP2C9 together with non-genetic predictors of dose. Indeed, in August 2007, the US Food and Drug Administration (FDA) updated warfarin labeling to recommend initiating lower warfarin dose in some patients based on VKORC1 and CYP2C9 genotypes. However this recommendation is not a requirement due to a lack of large trials demonstrating warfarin patient benefit from dose forecasting  are likely to provide state-of-the-art clinical benchmarks for warfarin use during the foreseeable future.In a recent study http://www.druggene.org/). This is a multi-centre study of warfarin bleeding complications and response to warfarin treatment The study subjects were 1053 Swedish patients collected for the WARG study −6) were also excluded. Subjects with genotyping call rate below 95% were also eliminated. Using iPLEX (Sequenom), subject identity (and associated phenotypic data) was cross-checked by genotyping four gender markers and 47 SNPs also carried on the HumanCNV370 array, enabling us to exclude ∼136 misidentified subjects. Sample quality (contamination) was further assessed by plotting each subject's genome-wide heterozygosity and eliminating outliers (with heterozygosity above or below the range of 0.312–0.372). After these quality control steps, a total of 1053 warfarin patients and 325,997 GWAS SNPs were retained for analysis. The GWAS SNPs included two SNPs not on the HumanCNV370 array but which are highly predictive of warfarin dose [rs9923231 (VKORC1) and rs1799853 (CYP2C9*2)] which we genotyped by TaqMan assay (Applied Biosystems).From approximately 1500 WARG samples Although we retained 325,997 GWAS SNPs for association testing of SNPs, it should be noted that all ∼370,000 probes on the Human CNV370 array were used to define CNVs. Log R ratio values of probes were output from the BeadStudio software In order to define regions for association testing, merging of CNV across samples was performed. This was achieved by merging two putative CNV into a region if there was greater than 40% reciprocal overlap. This procedure defined 2530 CNV regions in total. Of these, most were singletons (54%) or low frequency, <3% (93%), while 820 (70%) of the non-singleton regions overlapped CNVs from the Database of Genomic Variants http://pngu.mgh.harvard.edu/~purcell/plink/) or in R software (http://www.r-project.org/). We used the same regression analysis to test association with all HapMap SNPs not on the HumanCNV370 array by imputing ∼2.2 million SNPs using Beagle software At each SNP, genotypes were coded 0, 1 or 2 and the SNP was tested for association with the square root of warfarin dose CYP4F2) was considered a quantitative trait value and tested for association with haplotypes defined across the genome in sliding windows of 2, 3 or 4 consecutive SNPs as implemented by PLINK software; (2) by scanning GWAS genotypes, Beagle software groups genetically related haplotypes into clusters which it then resolves into diallelic (SNP-like) “pseudo-markers” optimized for detecting phenotypic association. To test haplotypes, we evaluated the pseudo-marker genotypes of warfarin patients at 1.97 million pseudo-markers covering the genome by testing each pseudo-marker in the same multivariate regression framework used to test individual SNPs (as described in the preceding paragraph).We also tested haplotypes for association with warfarin dose by two approaches: (1) each subject's warfarin dose residual  using multivariate regression and R software as described above. An interaction term formed by multiplying the pair of predictor variables was added to the multivariate regression equation which contained only main effects of the 6 predictors, and standard ANOVA compared this main-effect model with the enhanced interaction model by testing for a statistically significant increase in explained dose variance. Interaction test p-values were considered statistically significant if below the Bonferroni cutpoint determined by correcting for the 15 interaction tests (i.e. p<0.0033≈0.05/15).We tested for statistical interaction in modulating warfarin dose for each pair of established dose predictors (To test for association with over-anticoagulation (INR>4.0) during treatment days 1–35, we performed Cox proportional hazard regression on survival time (day of over-anticoagulation) using the survival library of R software. The GWAS data set of 1053 WARG subjects contained 215 subjects whose INR exceeded 4.0 during days 1–35 while the entire dataset of 1489 WARG subjects contained 312 such subjects.For each CNV locus, association was tested with square root of warfarin dose by multivariate regression analysis in which subject copy number intensity was the CNV predictor of dose. This analysis differs from association testing with SNP genotypes since the two CNV alleles on homologous chromosomes generate one copy number intensity rather than a separate allele for each chromosome. As a QC strategy, we determined each subject's rank in the dataset for copy number intensity at each CNV on chromosome 17. This enabled us to differentiate the majority of subjects  from 174 obvious outliers due to poor quality DNA (whose ranking distributions were “U-shaped” since their intensities strongly clustered at both high and low ranks). These 174 subjects were excluded from the primary CNV association analysis  SNP call rates). However, we also crosschecked the primary CNV analysis by conducting association testing on the dataset without excluding the 174 subjects and found no statistically significant association with warfarin dose at any CNV whether the dataset excluded or included the subjects. Association testing of the CNVs was executed using R software CYP4F2 rs2108622, we genotyped a panel of 588 warfarin patients consisting of 410 subjects from the WARG cohort For the replication of VKORC1, CYP2C9, CYP4F2 and identified 40 other GWAS loci for follow-up genotyping exhibiting multivariate regression p-values below 0.0002, and selected 40 SNPs representing these loci for genotyping. Only genotyped (not imputed) SNPs were chosen for follow-up. We genotyped the same 558 patients as in the CYP4F2 replication using the iPLEX MassARRAY.To possibly identify SNPs with genuine but weak associations to warfarin dose, we excluded N total samples by testing a SNP with coefficient of determination  R2test after adjustment for known predictors whose total of coefficient of determination is R2knw. The probability (power) to detect the tested SNP at a significance level α equals:F′ is the probability density function for an F distribution with 1, N-2 degrees of freedom and non-centrality parameter θ2  is the probability density function for a F-distribution of degree of freedom one and N–2.Suppose multiple regression analysis is conducted in r2 with a marker SNP, then the coefficient of determination for the marker SNP (Association with a quantitative trait (QT) becomes weaker for a marker SNP in LD with a SNP that alters the QT, and hence the association becomes more difficult to detect at the marker than at the QT-altering SNP. Here we quantify the LD attenuation for a QT when testing for association by linear regression (which includes the Cochran-Armitage trend test for dichotomous traits), and we obtain a result analogous to the LD attenuation for the Pearson Chi-square test for allelic association to dichotomous traits as in cases and controls r2.In other words, when testing a marker, the proportion of explained variance decreases by a factor of q”, and let “m” and “x” be SNP genotypes  representing the marker and causative  SNP, respectively. The coefficients of determination m or x with q:To begin the proof of Equation 3, let the QT be represented by the random variable “genotypes at the marker and causative SNP is given by another correlation coefficient:Also note that correlation between partial correlation coefficient of m and q conditioned on x is  and thus the numerator of Equation 7 would be zero implying that:However, conditional on genotype at the causative SNP, marker r2 are approximately equal if the population is in Hardy-Weinberg equilibrium. Therefore, substituting r2 for Based on prior work Figure S1VKORC1, CYP2C9*2 and *3, CYP4F2. The QQ plot omits SNPs in loci already known to be associated with warfarin dose . The excess of SNPs with small p-values is minor: whereas 65.4 SNPs with p<0.0002 are expected, 70 were observed (1.069 times inflated).QQ plot for association of each GWAS SNP with warfarin dose. SNPs were tested for association with warfarin by regression analysis that adjusted for age, sex, and genotype at (0.20 MB PDF)Click here for additional data file.Figure S2p<1.5×10−7) at SNPs in the VKORC1 locus such as rs9923231 (p = 8.9×10−9). However, no other loci achieved genome-wide significance. See main text for more details.Manhattan plot of GWAS results of testing for association with warfarin-induced over-anticoagulation. Horizontal axis is the genomic position, and vertical axis is minus log of p-value. Red dots above the gray line indicate association of genome-wide significance ((0.07 MB PDF)Click here for additional data file.Table S1CYP4F2, none of these 40 SNPs exhibited statistical significance after correction for multiple testing. See main text for more details.Multivariate regression results for 40 SNPs followed-up after GWAS. Unlike rs2108622 of (0.03 MB XLS)Click here for additional data file.Table S2Testing for statistical interaction between predictors of warfarin dose. After correcting for the 15 interaction tests, no pair of predictors exhibited statistically significant interaction. Data is for the combined panel of subjects (N = 1641). See main text for more details.(0.02 MB XLS)Click here for additional data file.Table S3Survival time analysis for incidence of over-anticoagulation within the first 5 weeks of treatment in the whole WARG cohort (N = 1489). See main text for more details.(0.02 MB XLS)Click here for additional data file.Table S4Multiple regression analysis of warfarin dose in WARG replication samples, WARG GWAS plus replication, or Uppsala replication samples. This table shows the same results displayed in (0.02 MB XLS)Click here for additional data file.Text S12 and Genotypic R2 are approximately equal.For populations in Hardy-Weinberg Equilibrium, Linkage Disequilibrium r(0.03 MB DOC)Click here for additional data file.


It has now become clear that gene-gene interactions and gene-environment interactions are ubiquitous and fundamental mechanisms for the development of complex diseases. Though a considerable effort has been put into developing statistical models and algorithmic strategies for identifying such interactions, the accurate identification of those genetic interactions has been proven to be very challenging.double fault is designed to quantify the degree of complementarity.In this paper, we propose a new approach for identifying such gene-gene and gene-environment interactions underlying complex diseases. This is a hybrid algorithm and it combines genetic algorithm (GA) and an ensemble of classifiers . Using this approach, the original problem of SNP interaction identification is converted into a data mining problem of combinatorial feature selection. By collecting various single nucleotide polymorphisms (SNP) subsets as well as environmental factors generated in multiple GA runs, patterns of gene-gene and gene-environment interactions can be extracted using a simple combinatorial ranking method. Also considered in this study is the idea of combining identification results obtained from multiple algorithms. A novel formula based on pairwise Our simulation study demonstrates that the proposed genetic ensemble algorithm has comparable identification power to Multifactor Dimensionality Reduction (MDR) and is slightly better than Polymorphism Interaction Analysis (PIA), which are the two most popular methods for gene-gene interaction identification. More importantly, the identification results generated by using our genetic ensemble algorithm are highly complementary to those obtained by PIA and MDR. Experimental results from our simulation studies and real world data application also confirm the effectiveness of the proposed genetic ensemble algorithm, as well as the potential benefits of combining identification results from different algorithms. With thsparsity . Furthersparsity ,8, or insparsity . All thesparsity .χ2-statistic and logistic regression [p-values after testing for a very large number of possibly non-independent hypotheses. Second, complex diseases are usually caused by the action of multiple genes in a nonadditive fashion. The standard analytical approaches in GWA studies often proceed by identifying only a very small number of SNPs  that exhibit strong statistical association with the target phenotype. In other words, only SNPs that independently have a strong discriminating ability are selected, while other SNPs that individually have weaker association are not discovered [Most of the earliest studies focused on identifying a set of SNPs in which individual SNP has a strong association with the phenotype by applying statistical measures such as gression ,12. Howescovered . HoweverTo cope with these problems, it is desirable to develop new methods which can consider multiple loci jointly. A number of such methods have been developed recently. Among these methods, nonparametric methods like Polymorphism Interaction Analysis (PIA) , Multifaet al. [et al. found that combining the set of SNP-SNP interactions into a graph can yield interesting insights about the underlying biological processes. It is anticipated that similar network-based analysis approaches can be used as a down-stream analysis for any gene-gene interaction identification algorithms.A recent study by McKinney et al.  proposedsi={g1, g2, ..., ng},  where each SNP, i g, is a categorical variable which can take the value of 0, 1, and 2 for genotypes of aa, Aa, or AA at this locus, and m is the number of samples in the dataset. The dataset can, therefore, be described as an m × n matrix Dmn={, , ..., }, where i yis the class label of the ith sample. One can evaluate the discrimination ability of a set of SNPs jointly by applying the following two steps:In this study, we attempt to address the problem from an alternative perspective by converting the issue into a combinatorial feature selection problem. From the data mining perspective, a sample from a SNP dataset of an association study is described as a SNP feature set of the form • Generating a reduced SNP feature set h using an inductive algorithm, and evaluating the quality of the trained classification model using criteria such as accuracy, sensitivity, and/or specificity with an independent test set.• Creating classification hypothesis s to denote applying a SNP subset to restrain the SNP dataset Dmn. If a SNP combination s yields a lower misclassification rate than others, we shall consider that it possibly containing SNPs with main effects or SNP-SNP interactions with major implications. We now have two challenging problems for the SNP interaction identification. The first challenge is to generate SNP combinations efficiently since the number of SNP combinations grows exponentially with the number of SNPs and it is infeasible to evaluate all possible combinations exhaustively. The second challenge is to determine which inductive algorithm should be applied for the goodness test of SNP combinations. To tackle the first problem, we shall apply genetic algorithm (GA) since it has been demonstrated to be one of the most successful wrapper algorithms in feature selection from high-dimensional data [Without lose of generality, we use notation nal data ,21.Furthermore, its intrinsic ability in capturing nonlinear relationships  is valuai generates a hypothesis space ℋi for sample classification. If the number of training samples m is large enough to characterize the real hypothesis f  and the data are noise-free, the hypothesis space generated by i should be able to converge to f through training. However, since the amount of the training samples is often far too small compared to the size of the hypothesis space which increases exponentially with the size of the features (SNPs), the number of hypotheses a classifier can fit the available data is often very large. One effective way to constrain the hypothesis space is to apply multiple classifiers each with a different hypothesis generating mechanism. If each classifier fulfills the criteria of being accurate and diverse [f by combining them with an appropriate integration strategy [The rationale of using an ensemble of classifiers can be described as follows: suppose that a given classifier  diverse , it can strategy .genetic ensemble (GE) algorithm for gene-gene interaction identification. The proposed algorithm has the following advantages:By combining GA and the ensemble of classifiers, we obtain a • It is a nonparametric and model-free approach. Unlike traditional parametric methods (e.g. linear regression etc.), there is no need to specify and assume the number of parameters and the interaction models. As a consequence, the proposed method generalizes well and can capture a range of interaction relationship such as additive and dominant effects.• It accommodates the detection of both linear and nonlinear relationship of gene-gene interactions. As aforementioned, the ensemble could be formed by classifiers with nonlinear separation abilities. Both linear and nonlinear gene-gene interaction relationships are common in complex disease , and cou• Unlike many other methods which often study different sizes of multi-loci interactions separately and repeatedly, our algorithm identifies different sizes of interactions in parallel. This feature makes the proposed algorithm particularly attractive in identifying higher-order gene-gene and gene-environment interactions.• The system is flexible. Different inductive algorithms as well as integration methods can be readily added in for further improvements.double fault [One other motivation for developing alternative methods for SNP-SNP interaction identification is in the hope that different algorithms may complement each other to increase the overall chance in identifying true interaction relationships. Therefore, to evaluate the degree of complementarity of multiple algorithms for SNP-SNP interaction identification is also an important part of this study. Specifically, based on the notion of le fault , we desiIn our previous study, a multi-objective GA system is implemented for high dimensional data analysis . Here, wblocking and voting, and a diversity promoting method called double fault statistic are employed to guide the optimization process. When the evaluation of a SNP subset is completed, the feedbacks of this SNP subset are combined through a given set of weights and sent back to GA as the overall fitness of this SNP subset. After the entire population of GA is evaluated, selection, crossover and mutation are applied and the next generation begins. At the last generation of GA, the chromosome with the highest fitness is selected, and the SNP subset it represents is said to be the best SNP subset genrated by GA. The entire GA procedure is repeated  n times (n = 30 in our experiments) to generate n best SNP subsets. These SNP subsets are then analyzed to identify frequently occurring SNP-pairs, SNP-triplets, and higher-order SNP combinations.As illustrated in Figure n selected SNP subsets. Each possible SNP combination is then given an identification frequency score . For example, if the SNP combination {snp1, snp2} appears in 25 out of 30 iterations, then its identification frequency score is 25/30 = 0.833. Two alterative criteria can be used to decide whether a SNP combination should be called or not. The first criterion is to set a frequency score cut-off, say 0.8, and call all SNP combinations with frequency score higher than this cut-off as functional SNP combinations. The second criterion is to set a cut-off rank, and call all SNP combinations with equal or higher to that rank as functional SNP combinations. As will be demonstrated in subsequent sections, the choice between this two criteria is likely a balance between detection power and false discovery rate.For SNP interaction identification, a combinatorial ranking is applied to the d, where d is the "chromosome" size of GA. The size of the GA chromosome has two implications. Firstly, it controls the number of factors we can identify. For example, if the size of d = 15 is used, we can identify from 2-factor up to 15-factor interactions in parallel. Secondly, d also influences on the size of the combinatorial space to be explored. It is a trade-off between the computational time and the combinatorial space to be searched. Therefore, for different SNP sizes , we shall use different sizes of d accordingly. Similar to the size of GA chromosome, the population size p and the generation of GA g are also specified according to the SNP size in the dataset. In our implementation of the GE algorithm, the parameters d, p, and g can be specified by users. The default values of these parameters are chosen empirically such they work well in a range of datasets.The number of SNPs considered by the genetic ensemble algorithm for potential interaction detection ranges from the lower bound of 2 to the upper bound of t, is dependent on the size of the population, varying from 3 to 7. The measure for determining the winner is as follows:For GA selection operation, we employ the tournament selection method as it gives the control of convergence speed. Specifically, the tournament selection size, denoted as iR(.) is the random selection function which randomly selects gene subset s from the GA population p, t is the tournament size, and fitness(.) determines the overall fitness of the randomly selected gene subset. Single point crossover is adopted with the probability of 0.7. In order to allow pair mutations, we implemented a multi-mutation strategy; that is, when a single mutation occurs (configured with the probability of 0.1) on a chromosome, another single point mutation may occur on the same chromosome with a probability of 0.25 and so on. The chromosome coding scheme is to assign an id to each SNP in the dataset, and to represent the chromosome as a string of SNP ids which specify a selected SNP subset. For each position on a chromosome, it could be a SNP id or a "0" which specifying an empty position.where Therefore, different sizes of SNP combinations are explored in a single GA population in parallel. Table The fitness of GA is defined as follows:s denotes a SNP combination under evaluation. The functions Bfitness(s), V fitness(s) and Dfitness(s) denote the fitness of a SNP combination s as evaluated by the blocking, voting and double fault diversity measures, respectively. A complexity regularization procedure is implemented in the GE algorithm to favor shorter SNP combination if two SNP combinations have the same fitness value. The computation details of each component of the fitness function are described in the next section.where Blocking is a statistical strategy that creates similar conditions to compare random configurations in order to discriminate the real differences from differences caused by fluctuation and noise [M classification algorithms, each having a different hypothesis denoted as i = 1, ..., M), are used to classify the data using a SNP subset s. The fitness function determined by blocking integration strategy is as follows:nd noise . Supposey is the class label vector of the test dataset D, function p(.) predicts/classifies samples in D as t using BC(.) is the balanced classification accuracy devised to deal with the dataset with an imbalanced class distribution. In the binary classification, it is the area under ROC curve (AUC) [where ve (AUC) , which cSe is the sensitivity value calculated as the percentage of the number of true positive classification (TP N) divided by the number of cases (caseN). Sp is the specificity value calculated as the percentage of the number of true negative classification (TNN) divided by the number of controls (controlN). Such a balanced classification accuracy measure can accommodate the situation in which the dataset contains an imbalanced class distribution of cases and controls [where controls .o, increasing the chance of correctly identifying functional SNPs and SNP-SNP interactions.The idea of applying this strategy for classifier integration in SNP selection is that by using more classifiers to validate a SNP subset, we are able to constrain the hypothesis space to the overlap region ℋmajority voting [M classifiers, consensus is made by k classifiers where:The second classifier integration strategy applied in our GE system is y voting . Majority voting . With a M classifiers, each having a different hypothesis denoted as i = 1, ..., M), are used to classify the data using SNP subset s, the fitness function derived from majority voting is as follows:Again, suppose a total of y is the class label vector of the test dataset D, kV(.) is the decision function of majority voting, and t' is the voting prediction. Here the balanced classification accuracy BC(.) is calculated with voting results.where The reason for using the majority voting integration is to improve sample classification accuracy while also promoting the diversity among individual classifiers implicitly .double fault statistic. This statistic is commonly used to measure the diversity of ensemble classifiers [The third objective function is an explicit diversity promoting strategy called ssifiers .ac, b c∈ {F, S} in which F denotes the sample being misclassified by a classifier while S denotes the sample being correctly classified. We define a cand b crespectively. Using this notation, we can obtain the term:Let N samples in test dataset D, using SNP subset s.which is the estimation statistic of coincident errors of a pair of classification models M classifiers over subset s (denoted as Dfitness(s)) derived from the double fault statistic are defined as follows:The fitness with regard to the diversity measurement of The value of this fitness function varies from 0 to 1. The value equals 0 when all classifiers misclassified every sample. It equals 1 when no sample is misclassified or there is a systematic diversity, leading to no sample been misclassified by any pair of classifiers.The motivation of applying nonlinear classifiers is based on the assumption that nonlinear and nonadditive relationships are commonly presented in gene-gene interaction . This isWith above considerations, an initial set of experiments is conducted to select candidate classifiers for ensemble construction. The classifier selection details are presented in Results Section.et al. [In this study, we use the simulated datasets generated by gene-gene interaction models described by Moore et al. . In eachFor the datasets with balanced class distribution, the class ratio is 1:1 with 100 case samples and 100 control samples. The datasets are simulated under three different genetic heritability models , and two SNP sizes (SNP size of 20 and 100). This gives six sets of datasets and each set contains 100 replicates each generated with a different random seed . The proAge-related macular degeneration (AMD) is the major cause of uncorrectable blindness of the elderly in many countries. As a typical complex disease, AMD is influenced by complex interactions among multiple genes and environmental factors, making it ideal for testing gene-gene interaction identification methods. In our experiment, the proposed GE algorithm, PIA, and MDR are applied to the dataset generated from a GWA study of AMD . This daPIA-2.0) and MDR (version: mdr-2.0_beta_6). In the previous studies of MDR [We compare the detection power of the proposed GE algorithm with PIA , and S Nis the number of successful identification.where For GE in particular, we are also interested in estimating the distribution of false discovery rate (FDR) and true positive rate (TPR) since, in the worst case, if there is no SNP-SNP interaction in the dataset, a top-ranked interaction list only contains false positive identifications. Formally, we estimate FDR as:FDR(c) is the FDR at the cut-off of c, FP N(c) is the number of accepted false positive identifications at the cut-off of c, and N(c) is the number of accepted identifications at the cut-off of c.where Similarly, TPR can is calculated as:TPR(c) is the TPR at the cut-off of c. TP N(c) and FN N(c) are the number of accepted true positives and the number of false negatives at the cut-off of c.where Both the rank and the identification frequency score of each SNP combination can be used as the cut-off to calculate FDR and TPR at different confidence levels. We consider both approaches and using the 100 replicate datasets of each heritability model, we obtain the average FDR and TPR at different cut-offs for each heritability model.One major motivation for developing a genetic ensemble algorithm for gene-gene interaction identification is to harness the complementary strength of different classifiers such that a more robust and predictive SNP subset can be obtained. To extend this idea further, we propose to combine the inferred SNP-SNP interaction from different algorithms (such as MDR and PIA), in the hope that more robust results can be obtained. However, such benefits may come only when the results yielded by different SNP-SNP interaction identification algorithms are complementary to each other, which is analogous to the idea of the ensemble diversity.By modifying the equation of double fault, we design the following terms to quantify the degree of complementarity (CD) of a pair of algorithms in SNP-SNP interaction identification:XY Nis the number of datasets with certain identification status using algorithms X and Y , and X, Y ∈ {F, S} in which F denotes an algorithm fails to identify the functional SNP pair while S denotes it succeeds to identify the functional SNP pair. SF  (single fault) is the number of times algorithms X and Y give inconsistent identification result, which is the situation that one algorithm succeeds while the other one fails. DF  (double fault) is the number of times both X and Y fail. The pairwise degree of complementarity of the algorithms X and Y is determined by CD.where X and Y achieve 100% successful identification  varies between 0 and 1. When the results produced by X and Y are completely complementary to each other, the value of DF  decreases to 0, and the value of CD reaches 1.Excluding the case in which both CD decreases with the decrease of the degree of complementarity between algorithms X and Y , and reaches 0 when no degree of complementarity is found.On the contrary, the value of Our premise is that combining algorithms with higher degree of complementarity will yield higher identification power. In this study, we estimate the joint power of two or three algorithms as:TF  is the "triple fault" which gives the coincident errors of three identification algorithms, and J Power and J Power are the joint power of combining two and three identification algorithms respectively.where et al. [One of the most important steps in forming an ensemble of classifiers is base classifier selection. As described above, characteristics such as nonlinear separation capability, computational efficiency, high accuracy and diversity should be taken into account. With those considerations, a classifier selection and ensemble construction experiment was carried out. Specifically, we tested the merits of each candidate classifier using datasets with model number of 10, 11, 12, 13 and 14 from Moore et al. , all of et al. , Random et al. , 1-Neareet al. , entropyet al.  and 5-Nek-Nearest Neighbor hybrid - CT1NN, CT3NN, and CT5NN.The identification power of each classifier was estimated using the simulated datasets. Among the twelve classifiers, six of them successfully identified the functional SNP pair more than 50% of the time. Five of them were selected to form the ensemble , the TPR increases, and therefore, more functional SNP pairs can be successfully identified. However, this is achieved by accepting increasingly more false identifications (higher FDR). The simulation results indicate that FDR calculated by using the identification frequency cut-off is very steady regardless the change of heritability, SNP size, or class ratio. In most cases, an FDR close to 0 is achieved with a cut-off greater than 0.78.As illustrated in Table We carried out a pairwise evaluation using Equations (13) and (14). Tables The last columns of Tables As an example of real-world data application, we applied the GE algorithm, PIA and MDR, to analyze the complex disease of AMD. To reduce the combinatorial search space, we followed the two-step analysis approach  and used• Excluding SNPs which have more than 20% missing genotype values of total samples;χ2-statistics of each remaining SNP and keeping SNPs which have a p-value smaller than 0.05 while discarding others. A number of 3583 SNPs passed filtering.• Calculating allelic id of rs380390 and rs10272438 are selected.• Utilizing RTREE program  to selec• Utilizing Haploview program  to constAfter the above processing steps, we obtained 17 SNPs from the two LD blocks. They are rs2019727, rs10489456, rs3753396, rs380390, rs2284664, and rs1329428 from the first block, and rs4723261, rs764127, rs10486519, rs964707, rs10254116, rs10486521, rs10272438, rs10486523, 10486524, rs10486525, and rs1420150 from the second block. Based on the previous investigation of AMD [BBS9 located in 7p14, while the second SNP is a C/G variant located in intron 15 of CFH located in 1q32. The second frequently identified gene-gene interaction is characterized by the SNP-SNP interaction pair of rs10490924×rs10272438. The first SNP in this interaction pair is a nonsynonymous coding SNP of Ser69Ala alteration located in exon 1 of ARMS2 located in 10q26. And the second SNP is again the A/G variant located in intron 5 of BBS9 located in 7p14. As to the gene-environment interaction pair, it is characterized by rs10272438×Sex. This pair indicates that SNP factor of the A/G variant located in intron 5 of BBS9 in location of 7p14 is likely to associate with the disease differently between male and female.Tables We also test the association of Age factor with AMD by using Gaussian discretization to partition the age value of each sample into three categories as follows:μ is the average age value and σ is the standard deviation of age values.where BBS9 located in 7p14.After including the Age factor to the dataset, all three algorithms identified the gene-environment interaction of rs1420150×Age as the interaction with major implication, indicating Age factor is, expectedly, strongly associated with the development of AMD. The SNP that interacted with the Age factor is a C/G variant located in intron 9 of BBS9 (Bardet-Biedl syndrome 9), CFH (complement factor H), and ARMS2 (age-related maculopathy susceptibility 2) with the external factors of Age and Sex, and the interactions among them are strongly associated with the development of AMD. This is essentially consistent with current knowledge of AMD development in the literature [Table terature -47.How multiple genes contribute to the development of complex diseases is an essential question for complex disease study. This is because a single gene often does not have the power to discriminate the status of the complex disease, and it is likely that multiple genes each with a weak or moderate effect together contribute to the development of complex disease. Although great effort has been devoted to characterizing such gene-gene interactions in complex disease analysis, the results remain unsatisfactory.The advance of high-throughput genotyping technologies provides the opportunity to elucidate the mechanism of gene-gene and gene-environment interaction via SNP markers. However, current algorithms have limited power in terms of identifying true SNP-SNP interactions. Moreover, the simulation results indicate that the factors such as heritability, candidate SNP size, and the presence of imbalanced class distribution all have profound impact on a given algorithm's power in identifying functional SNP interactions. One practical way to improve the chance of identifying SNP-SNP interactions is to combine different methods where each addresses the same problem from a different perspective. The rationale is that the consensus may increase the confidence of identifications and complementary results may improve the power of identification.Due to these considerations, we proposed a hybrid algorithm using genetic ensemble approach. Using this approach, the problem of SNP-SNP interaction is converted to a combinatorial feature selection problem. Our simulation study indicates that the proposed GE algorithm is comparable to PIA and MDR in terms of identifying gene-gene interaction for complex disease analysis. Furthermore, the experimental results demonstrate that the proposed algorithm has a high degree of complementarity to PIA and MDR, suggesting the combination of GE with PIA and MDR will likely lead to higher identification power.For the practical application of the GE algorithm, the experimental results from the simulation datasets suggest that taking the top-ranked result generally gives a higher sensitivity of identifying SNP-SNP interactions than using a frequency score cut-off. However, if the delectability of the SNP-SNP interaction is low or no such interaction is present in the dataset, the top-ranked result is likely to be a false positive identification. A more conservative approach is to use an identification frequency cut-off of 0.75-0.8 which in our simulation study gives identification results with an FDR close to 0. For any identified SNP pair with an identification frequency higher than 0.8, the confidence is very high.p-value of its coefficient in order to quantify the strength of the interaction. In particular, to test additive and dominant effects, we can fit the reported SNP combinations using the model described by Cordell [As a down-stream analysis, we can fit the identified SNP pairs using logistic model with interaction terms and calculate the  Cordell  and analCurrent GWA studies commonly produce several hundreds of thousands of SNPs, yet the gene-gene interaction identification algorithms like MDR, PIA and the proposed GE algorithm can only cope with a relatively small number of SNPs in a combinatorial manner. Therefore, a filtering procedure is required to reduce the number of SNPs to a "workable" amount before those combinatorial methods can be applied to datasets generated by GWA studies ,49. MoreIn conclusion, we proposed a GE algorithm for gene-gene and gene-environment interaction identification. It is comparable to two other state-of-the-art algorithms (PIA and MDR) in terms of SNP-SNP interaction identification. The experimental results also demonstrated the effectiveness and the necessity of applying multiple methods each with different strengths to the gene-gene and gene-environment interaction identification for complex disease analysis.PY conceived the study, designed, and implemented the algorithms, and performed the experiments. PY and JWKH interpreted the results and drafted the manuscript. BZ and AYZ revised the manuscript critically and supervised research. All authors read and approved the final manuscript.http://www.cs.usyd.edu.au/~yangpy/software/GEsnpx.htmlThe GE algorithm (GEsnpx) is implemented in Java. It is freely available from the supplementary website at


STF within and across the three breeds for each SNP under a pure drift model. We further considered two different strategies to evaluate the effect of selection at the genome level. First, smoothing STF values over each chromosome with a local variable bandwidth kernel estimator allowed identifying 13 highly significant regions subjected to strong and/or recent positive selection. Some of them contained genes within which causal variants with strong effect on milk production traits (GHR) or coloration (MC1R) have already been reported. To go further in the interpretation of the observed signatures of selection we subsequently concentrated on the annotation of differentiated genes defined according to the STF value of SNPs localized close or within them. To that end we performed a comprehensive network analysis which suggested a central role of somatotropic and gonadotropic axes in the response to selection. Altogether, these observations shed light on the antagonism, at the genome level, between milk production and reproduction traits in highly producing dairy cows.Dairy cattle breeds have been subjected over the last fifty years to intense artificial selection towards improvement of milk production traits. In this study, we performed a whole genome scan for differentiation using 42,486 SNPs in the three major French dairy cattle breeds  to identify the main physiological pathways and regions which were affected by this selection. After analyzing the population structure, we estimated  As for other domestic animals, both natural and artificial selection have resulted over a short period of time in a broad phenotypic variety and in genetic differentiation of numerous different cattle breeds. This recent history provides a unique opportunity for the identification of loci subjected to adaptive selection. Following domestication, about ∼10,000 years ago, early breeders might have imposed a so-called “unconscious” selection “which results from every one trying to possess and breed from the best individual animals” Currently, more than 95% of the cows milked in France belong to Holstein (HOL), Normande (NOR) or Montbéliarde (MON) breeds. The herd-book of these three different breeds were created in 1922, 1883 and 1872 respectively using individuals originated from distant areas . Since the middle of the twentieth century, these three breeds have been subjected to strong artificial selection mainly oriented towards an improvement of dairy abilities. Nonetheless, because of varying local breeder objectives and herding systems, these breeds displayed some differences in most of their milk production traits  and on other morphological characteristics  as broadly summarized in et al.The advent of high throughput and cost-effective genotyping techniques allows evaluating the response to these various selective pressures at the genome level. For instance, comparing allele frequencies or differentiation among different breeds is straightforward to identify footprints of selection which are characterized by an unexpectedly high level of divergence, relatively to the neutral hypothesis The goal of this study was to perform a genome scan for SNP differentiation, by considering 42,846 SNPs genotyped in HOL, MON and NOR dairy cattle breeds, to identify the main regions affected by the strong and recent artificial selection they have been subjected to. To that end we applied and extended previously proposed approaches STF under the pure-drift model proposed by Nicholson et al.STF are computed relatively to the ancestral population and their inverse might thus be interpreted as an effective size of bottleneck . Overall, the average STF across breeds was equal to 0.0709 (from 0.0576 for BTA27 to 0.0840 for BTA05) and population-specific STF were all close to this average value . The slightly higher HOL average STF might reflect the more distant geographic origin (Northern Europe) of animals from which this breed originates, compared to NOR (North-Western France) and MON ones (Eastern France).As expected from the recent breed history, unsupervised clustering of the 2803 bulls belonging to HOL, MON and NOR breeds  highlighSTF across populations was substantially lower than the one (0.0710 against 0.103) computed when using the Weir and Cockerham estimator STF being nevertheless highly correlated (r = 0.961) between the two methods. This suggested that the resulting classification of SNPs is rather insensitive to bias introduced by our estimation procedure, the main advantage of this latter being the simple computation of population-specific estimates. Therefore, providing populations are well but not too much differentiated (low admixture), this procedure might be thought of as a straightforward way to compare allele frequencies across several populations.As mentioned above, such levels of population differentiation would be expected from a common ancestral population with a bottleneck starting 25 generations ago (∼150 years ago if we assume a generation time of about 6 years in dairy cattle) and a constant (haploid) effective population size varying from 340 (in HOL) to 360 (in NOR). These effective population size estimates appeared somewhat upwardly biased when compared to those derived from the extent of LD STF across and within the three breeds are given in STF<0.1), the mode of the empirical (and simulated) distributions was very close to zero, hindering the identification of SNPs under balancing selection (with low STF). Yet, very highly differentiated SNPs (STF across breeds >0.5) were overrepresented in the real data set . Finally, the interval #11 on BTA18 contained MC1R (13.776–13.778 on BTA18) which is localized within a ∼500 kb gap between two consecutive SNPs (the peak corresponding to the beginning of the gap). MC1R represents an obvious candidate since it determines the ratio of eumelanin and pheomelanin and corresponds to the locus Extension involved in coat color in cattle. As reported by Seo et al.DE, +E and e. The wild-type allele +E is responsible for combination of red and reddish brown color. Individuals carrying the dominant DE are black and the recessive e allele results in a red color. It has previously been shown that +E, DE and e are respectively fixed in NOR, HOL and MON For most of the 13 regions identified, we were able to propose candidate genes on the basis of the gene content in the vicinity of the peak location . InteresSTF of their representative SNP(s) both across and within breeds. In addition, significance of the departure of these scores from the value expected under the neutral hypothesis was further evaluated by considering simulated distributions. For each RefSeq, p-values of the different scores adjusted for multiple testing STF, across breeds only one additional RefSeq score was found significant while more than half of the 91 previously identified ones were also significant. The 43 annotated genes underlying these different RefSeq are given in For most of the regions identified above, it remained difficult to propose candidate genes explaining the observed pattern of region differentiation. Moreover, focusing on large regions might only capture the strongest and/or most recent selection events. As previously mentioned, unexpectedly high (low) level of SNP differentiation might be interpreted as positive  selection of the underlying genes. We thus computed scores for each annotated RefSeq see  based onIn order to characterize the main physiological pathways underlying genes harboring footprints of positive selection we carried out a network analysis under a systems biology framework see . Indeed,As expected, GN contained several genes involved in the somatotropic axis. In addition to GH1, other important molecules participating to the different GH signal transduction pathways Besides, several GN molecules are involved in the gonadotropic axis, in particular through the β-estradiol which is a key driver of reproduction. Hence β-estradiol could be connected to 21 GN molecules , four ofSome of the genes under positive selection belonging to GN could also be related to functions not directly associated with metabolism or reproduction. In particular, CD163 localized under the peak of the previously identified region #5 , is invoOverall, most of the genes under selection were found to be involved in the gonadotropic system, a key driver of reproduction, and somatotropic system which affects in particular milk metabolism. The antagonistic relationship between milk production and reproductive performances has been largely reported in highly producing dairy cows DNA needed for the study was previously extracted from commercial AI bull semen straws. No ethics statement is thus required.http://www.illumina.com). As detailed in A total of 2,803 AI bulls  were genotyped on the Illumina BovineSNP50 chip assay http://cran.r-project.org/web/packages/qvalue/index.html). A total of 829 SNPs exhibiting q-value<0.01 in at least one breed were then discarded from further analysis.A total of 728 SNPs mapping to the X chromosome and 1,672 other SNPs (∼3%) which were genotyped on less than 90% of the individuals in at least one breed were not considered in the analysis. An exact test for Hardy-Weinberg Equilibrium (HWE) Although the number of families and individuals considered within each breed was large, we took into account the half-sib pedigree structure in estimating population allele frequencies by considering only maternally inherited allele. Indeed, dams were more representative of the population and less related than the bulls . Within Assessment of population structure was performed by the standard unsupervised Bayesian clustering approach implemented in the software STRUCTURE 2.2 STF were based on the pure drift model proposed by Nicholson et al.ij of a given reference allele at SNP i within population j is modeled as a truncated Gaussian: αij ∼ N) where iπ can be interpreted as the frequency of the allele in the population ancestral to the three breeds considered and jc represents a differentiation parameter  analogous to a STF coefficient for low level of differentiation jc as I represents the total number of SNPs and J the total number of populations. Note that et al.jc performed well providing levels of differentiation are similar across populations. We further simply estimated the i within population j as Estimation of SNP and population-specific http://www.hgsc.bcm.tmc.edu/projects/bovine/), the others 1,069 belonged to unassigned contigs. As shown in th (95th) percentile of the inter-marker distance distribution was equal to 278 kb (159 kb). Conversely, few short gaps between successive SNPs were observed: the 1st (5th) percentile of the inter-marker distance distribution being equal to 20.1 kb (21.7 kb).Among the 42,846 SNPs, 41,777 mapped to a bovine autosome on the latest bovine genome assembly Btau_4.0 (−8 per bp). To reflect breed formation the demographic scenario consisted in three completely isolated populations separated t = 25 generations ago from an initial common population with an effective population of Ne = 5,000. Note that under the pure drift model described previously, the population specific differentiation parameter j = t/Njc where jN represents the (haploid) effective population size of population jSTF both computed within and across populations. Effective chromosome size (twice the effective population size) retained for simulations was respectively 215, 220 and 180 for MON, NOR and HOL. Finally, for each chromosome we fixed the number of mutations to 4,000. The resulting 126,000 SNPs were subjected to the same ascertainment scheme as the one adopted for real data (MAF>0.001 in the three populations) leading to a simulated data set containing 55,591 SNPs. Marker coverage was close to the real data set one (on average one SNP every 51.8 kb) with a similar proportion of large gaps (only 3.04% above 200 kb). The estimated STF across the three simulated populations was 0.0707 and respectively 0.0693, 0.0683 and 0.0745 for the simulated MON, NOR and HOL populations, almost identical to the one computed on the real data sets (see Results).Simulations were carried out using the coalescent program GENOME STF values were first smoothed over each chromosome with a local variable bandwidth kernel estimator http://cran.r-project.org/web/packages/qvalue/index.html) to identify significant outlier regions .In order to identify regions under selection (with an unexpectedly high proportion of SNPs subjected to selection), we followed the locally adaptive procedure which allows to account for variations in distance between the different tested positions http://genome.ucsc.edu/). For closer evolutionary distances, the alignments are created using syntenically filtered BLASTZ alignment chains, resulting in a prediction of the orthologous genes in cow. In total, 46,598 different RefSeq identifiers were anchored in the latest bovine genome assembly (http://genome.ucsc.edu/). Considering that most consecutive SNPs on the map were separated by more than 20 kb and the high correlations between the http://www.ingenuity.com/). Among the 18,986 different TransMap RefSeq identifiers (see above), 18,944 identifiers (99.8%) were represented in the Ingenuity Pathway Knowledge Base (IPKB) and corresponded to 7,935 different genes further considered as the reference set. Although because of RefSeq redundancy, most SNPs were representative of several RefSeq, only 402 SNPs  were representative of more than one gene.Because the annotation of the bovine genome is still sparse, the gene content information was derived from the TransMap cross-species alignments available in the UCSC Genome Browser  which were assigned to 18,986 “simulated” RefSeq by exactly mimicking the observed SNP RefSeq content. A p-value was then computed for each RefSeq (both across and within each population) by counting the number of times the observed score was above or below the simulated ones. To deal with multiple testing issues, we further applied a Benjamini and Hochberg correction http://cran.r-project.org/web/packages/qvalue/index.html). Note that our strategy considered SNPs are independent from each others which might be reasonable under the null hypothesis of neutrality given our marker density and background LD within the different populations (see Results).A gene could be regarded as being under selection if it contained an unexpectedly high proportion of highly (or lowly) differentiated SNPs. First, each RefSeq was given a score corresponding to the IPA was used to organize genes showing evidence of selection into networks of interacting genes and to identify pathways containing functionally related genes. More precisely, network analysis consists in searching for direct and indirect interactions  between candidate genes and all other molecules  contained in IPKB. The complete list of RefSeq identifiers with their respective scores (across and within breeds) were uploaded into IPA and each were mapped to their corresponding IPKB gene object (see above). Candidate genes are eligible for network generation if there is at least one wild type IPKB interacting molecule. Based on the information available for eligible candidate genes (focus genes), IPA further constructs networks by maximizing the number of focus genes and their inter-connectivity in the limit of 35 molecules per network. Note that additional highly connected non focus molecules are also included. Finally, for each network, a right-tailed Fisher exact test is implemented to evaluate how likely the focus genes it contains might be found together by chance. Only those networks with a score ) greater than 3 were considered as significant. In addition, networks might be inter-connected (sharing at least one molecule) which strengthen the importance for the underlying biological functions. Networks are graphically represented by nodes with various shapes (according to the molecule type) and edges .Table S1Sample description(0.00 MB PDF)Click here for additional data file.Table S2Description of the results for the 18,986 RefSeq represented in the analysis. For each RefSeq, we report the position on the genome, the underlying gene (based on IPA annotation), the score derived from the FST values of the SNP localized within it and the and p-values corrected for multiple testing for positive and balancing selection tests both across and within each breed. The interval number  is repor(1.87 MB ZIP)Click here for additional data file.Table S3Genome coverage and SNP density.(0.01 MB PDF)Click here for additional data file.Figure S1Observed FST (across and within the three breeds) for each SNP as a function of chromosome position (one page per chromosome). The red (blue) dashed line corresponds to the 99% (97.5%) threshold on the corresponding empirical distributions.(1.39 MB ZIP)Click here for additional data file.Figure S2Correlation of FST (across and within each of the three breeds) for pairs of markers as a function of physical distances in the real (upper panel) and simulated (lower panel) data sets.(0.02 MB PDF)Click here for additional data file.Figure S3Whole genome map of regions under selection based on the FST within MON. For each of the 29 bovine autosomes, the smoothed FST is plotted against the chromosomal position (green line). For significant positions , non smoothed SNP FST are indicated by a red star.(0.22 MB ZIP)Click here for additional data file.Figure S4Whole genome map of regions under selection based on the FST within NOR. For each of the 29 bovine autosomes, the smoothed FST is plotted against the chromosomal position (green line). For significant positions , non smoothed SNP FST are indicated by a red star.(0.22 MB ZIP)Click here for additional data file.Figure S5Whole genome map of regions under selection based on the FST within HOL. For each of the 29 bovine autosomes, the smoothed FST is plotted against the chromosomal position (green line). For significant positions , non smoothed SNP FST are indicated by a red star.(0.23 MB ZIP)Click here for additional data file.


Relatively little information is available for sequence variation in the pig. We previously used a combination of short read (25 base pair) high-throughput sequencing and reduced genomic representation to discover > 60,000 single nucleotide polymorphisms (SNP) in cattle, but the current lack of complete genome sequence limits this approach in swine. Longer-read pyrosequencing-based technologies have the potential to overcome this limitation by providing sufficient flanking sequence information for assay design. Swine SNP were discovered in the present study using a reduced representation of 450 base pair (bp) porcine genomic fragments (approximately 4% of the swine genome) prepared from a pool of 26 animals relevant to current pork production, and a GS-FLX instrument producing 240 bp reads.Approximately 5 million sequence reads were collected and assembled into contigs having an overall observed depth of 7.65-fold coverage. The approximate minor allele frequency was estimated from the number of observations of the alternate alleles. The average coverage at the SNPs was 12.6-fold. This approach identified 115,572 SNPs in 47,830 contigs. Comparison to partial swine genome draft sequence indicated 49,879 SNP (43%) and 22,045 contigs (46%) mapped to a position on a sequenced pig chromosome and the distribution was essentially random. A sample of 176 putative SNPs was examined and 168 (95.5%) were confirmed to have segregating alleles; the correlation of the observed minor allele frequency (MAF) to that predicted from the sequence data was 0.58.The process was an efficient means to identify a large number of porcine SNP having high validation rate to be used in an ongoing international collaboration to produce a highly parallel genotyping assay for swine. By using a conservative approach, a robust group of SNPs were detected with greater confidence and relatively high MAF that should be suitable for genotyping in a wide variety of commercial populations. The identification of genes and mutations that lead to genetic variation in complex, economically important traits in livestock has been hindered by the lack of genomic sequence, adequate map density and effective platforms for high density genotyping. It is estimated that linkage disequilibrium extends for hundreds of kilobases in the pig  and thatReduced representation was first used to construct an SNP map in human to scan the genome for haplotypes associated with disease . RecentlBstUI library contained the least repetitive DNA detected by RepeatMasker, but after aligning individual contigs to pig genomic sequence it was found to contain a high proportion (25%) of centromeric repeats. Because of the probability of a high number of unmapped SNPs locating to centromeric regions, this library was not pursued. The HaeIII library was chosen for pyrosequencing because of the large number of predicted fragments and the relatively lower amount of repetitive DNA. Also, in silico digestion of the draft sequence of pig chromosome 1 indicated there was not a concentration of repetitive bands present in the targeted region around 450 base pair , indicating that digestion was complete. About 32.6% of the sequence was found to be repetitive DNA by RepeatMasker, consistent with the results obtained from the Sanger sequenced HaeIII fragments , with 87% of bases having a quality score of 20 or more. Most of the reads (96.4%) started with "CC", as expected for these restriction enzyme digested fragments. Less than 1% of reads had an internal BV729586 to BV999999, GF000001 to GF089508 and GF089703 to GF091743]. The SNPs are available in dbSNP [GenBank: ss107796326 to ss107911925].The Newbler assembler, version 1.1.03, assembled 73% of the unmasked reads into 421,060 contigs, which were used to define the reference sequence for SNP discovery. Attempts to increase the number of reads in the assembly resulted in fatal software errors. Although 27% of the original reads were not used in the assembly, they were included in the mapping and SNP detection steps. Figure Prior to the SNP discovery step, the repetitive content of the reads was masked by RepeatMasker and only those reads containing a 50 bp length of non-repetitive sequence were retained. Repetitive sequence near the ends of the reads was trimmed. Using the Newbler unmasked assembly  as the reference, the ssaha2 software mapped 2,189,534 (72.0%) of the 3,041,168 repeat-masked and trimmed reads to the reference contigs, placing a total of 468,787,385 bp onto the reference sequence. The repetitive regions of the reference assembly are not expected to have coverage from the repeat-masked reads, and, in fact, 27% of the reference had no reads mapped onto it. For the positions that did have coverage, the average depth was 5.84×. We used ssaha_pileup to detect variation between the mapped reads and the reference, and among the mapped reads.Looking only at the SNP in the masked reads, we filtered out those that had a gap, an N or a third allele. We also required that both alleles were observed at least twice. We did not filter based on quality scores or on the presence of nearby homopolymers, as has been done with 454 data ,11. This with an average spacing of 44.2 kb and a median spacing of 29.3 kb  mapped to the finished pig genomic sequence Multiplex assays targeting 192 putative SNP randomly selected from the set of SNP mapping to finished porcine genomic sequence were attempted in order to estimate the conversion rate and compare the predicted MAF to an empirical MAF derived from genotyping a sample of 192 animals representing 12 breeds. Nine SNP failed as a result of the assay design (unrecognized SNPs under probe or primers), and seven failed to produce an amplification product when combined in the multiplex, leaving 176 SNP for analysis. From this reduced set, eight were monomorphic indicating an overall conversion rate of 95.5% (168 of 176). Of the eight SNP assays that were monomorphic, four contained homopolymers of 4–9 nucleotides flanking the polymorphic site and one contained a short dinucleotide repeat. Two monomorphic SNPs were in contigs that contained only four reads. Fifteen successful assays contained homopolymers of 5 or more nucleotides flanking the SNP. The SNP position in the contig and predicted minor allele frequency did not seem to affect the validity of the SNP (data not shown). The MAF for those SNP genotyped on the Sequenom platform averaged 0.28 and the average heterozygosity was 0.27. The MAF and heterozygosity for individual breeds in the panel are shown in Table HaeIII RRL library to get deeper coverage would likely uncover more SNPs, but would bias toward those with lower minor allele frequencies. From this resource, assays could be designed from the greater than 50,000 non-repetitive loci (contigs) for high-density genotyping providing a reasonable distribution of markers across the genome. Although map positions will only be known for those contigs that fall within the sequenced regions of the pig genome, the remaining contig positions should be available in the near future. Alternatively, the contigs could be mapped by linkage or with a high resolution radiation hybrid panel . The SNPs are submitted under the handle MARC in batch number 2008-11-06 [GenBank: ss107796326 to ss107911925].The sequence and nucleotide variation has been submitted to GenBank dbSTS and dbSNP databases. The Accession Numbers are: [GenBank: Additional data is included in the form of three figures (Additional files Polyacrylamide gel of restriction digests of bovine and porcine DNA. Two micrograms of genomic DNA was digested with the indicated enzyme and electrophoreses in 5% polyacrylamide gel. Repetitive elements are clearly seen as bands in the gel. The numbers to the left of the fragment size markers have units of bp.Click here for filein silico HaeIII digest of pig chromosome 1.Size distribution of restriction fragments from an Click here for filein silico digest of chromosome 1.Total content of DNA by fragment size in the  Repetitive elements are seen as spikes in the histogram.Click here for file