Nfe2l2-deficient mice develop tobacco-induced emphysema. We assessed the impact of Single Nucleotide Polymorphisms (SNPs) in both genes on the level and longitudinal course of Forced Expiratory Volume in 1 second (FEV1) in the general population.The metabolism of xenobiotics plays an essential role in smoking related lung function loss and development of Chronic Obstructive Pulmonary Disease. Nuclear Factor Erythroid 2-Like 2 (NFE2L2 or NRF2) and its cytosolic repressor Kelch-like ECH-associated protein-1 (KEAP1) regulate transcription of enzymes involved in cellular detoxification processes and NFE2L2 and three KEAP1 tagging SNPs were genotyped in the population-based Doetinchem cohort  and the independent Vlagtwedde-Vlaardingen cohort . On average 3 FEV1 measurements during 3 surveys, respectively 7 FEV1 measurements during 8 surveys were present. Linear Mixed Effect models were used to test cross-sectional and longitudinal genetic effects on repeated FEV1 measurements.Five KEAP1 was associated with a higher FEV1 level (p = 0.02 for an additive effect), and SNP rs2364723 in NFE2L2 was associated with a lower FEV1 level (p = 0.06). The associations were even more significant in the pooled cohort analysis. No significant association of KEAP1 or NFE2L2 SNPs with FEV1 decline was observed.In the Vlagtwedde-Vlaardingen cohort SNP rs11085735 in KEAP1 and NFE2L2 and lung function in a general population. It identified 2 SNPs in NFE2L2 and KEAP1 which affect the level of FEV1 in the general population. It additionally shows that NFE2L2 and KEAP1 variations are unlikely to play a role in the longitudinal course of FEV1 in the general population.This is the first genetic study on variations in key antioxidant transcriptional regulators  The mortality and morbidity of Chronic Obstructive Pulmonary Disease (COPD) has been increasing over the past decades and the disease is a fundamental medical and economical problem in Western societies . A genetNuclear Factor (Erythroid-derived 2)-Like 2 (NFE2L2 or NRF2) regulates the transcription of numerous antioxidant enzymes in response to oxidant injury, via direct binding to the antioxidant responsive element in the target gene -15. It tNfe2l2 protects mice against elastase-induced [KEAP1 and NFE2L2 is different in COPD patients as compared to healthy never- or former- smokers [NFE2L2, but these were not associated with COPD in a Japanese population [NFE2L2 expression in vitro and is associated with development of acute lung injury in a Caucasian population [NFE2L2 or KEAP1 polymorphisms in relation to the longitudinal course of lung function in the general population.Kelch-like ECH-associated protein-1 (KEAP1) is a cytosolic repressor of NFE2L2. Oxidative stress causes disruption of the KEAP1-NFE2L2 complex, translocation of NFE2L2 to the nucleus and subsequent induction of the expression of antioxidant genes . It has -induced  and toba-induced  emphysem smokers ,20 and t smokers . Three npulation . One stupulation . So far NFE2L2 or KEAP1 polymorphisms affect the level and longitudinal course of FEV1 (Forced Expiratory Volume in 1 second), both being important risks for COPD [Therefore, in the current study we investigated whether for COPD . In orde1 measurements during 3 surveys: surveys 1993–1997 , 1998–2002 , and 2003–2007 (n = 811)), table 1 was measured three times ) with 5-year intervals according to the European Respiratory Society (ERS) guidelines [Subjects from the Doetinchem cohort study , a prospidelines .1 measurements during 8 surveys, table 1 measurements ) every 3 years (following ERS guidelines) [An independent cohort  .The study protocols were approved by local medical ethics committees and all participants gave their written informed consent.NFE2L2 and KEAP1 with respectively five and three SNPs according to the HapMap CEU genotype data (23a) with an r2 threshold of 0.8 and Minor Allele Frequency (MAF)>5%. We additionally included three novel NFE2L2 polymorphisms [NFE2L2 TNR genotyping.We pairwise tagged orphisms  with MAF1 in both cohorts separately, using all available FEV1 measurements across all surveys. This analysis was adjusted for age , sex, packyears smoked, height and the correlation of FEV1 measurements within each subject (random effect assigned to the intercept).We used Linear Mixed Effect (LME) to study the effects of SNPs and haplotypes  on the level of FEV1 by introducing the interaction term of SNP × time (defined in relation to the first FEV1 measurement and with random effect assigned) into the primary analysis model described above  which were reported in case they showed significant effects in the pooled cohort analysis. Similarly we investigated whether there was a significant interaction between KEAP1 and NFE2L2 genotypes in relation to the level of FEV1, using two-way combinations of genetic effects with the highest statistical power i.e. dominant and additive.Finally, we pooled both cohorts, and performed analysis on the level and course of FEV1 was performed on the pooled cohorts using data from single surveys (i.e. second in the Doetinchem cohort and last in the Vlagtwedde-Vlaardingen cohort) in order to ensure the highest cumulative exposure to tobacco smoke and the highest number of subjects analyzed. The following interaction terms in two following regression models were analyzed:Gene by smoking interaction analysis in relation to the level of FEV1. SNP by ever/never smoking status in the total population with adjustment for ever-smoking status and genotypes and no adjustment for packyears smoked2. SNP by packyears smoked within ever smokers with adjustment for packyears smoked and genotypesP values < 0.05 were considered to be statistically significant (tested 2-sided).LME models were run using S-PLUS (version 7.0). Linkage Disequilibrium (LD) plots and Hardy-Weinberg Equilibrium (HWE) tests were performed with Haploview (version 4.1) . We idenKEAP1 rs1048290 SNP heterozygotes in the Vlagtwedde-Vlaardingen cohort, which caused a significant deviation (p = 0.01) from HWE . This SNP also showed a significant deviation from HWE  in the Vlagtwedde-Vlaardingen cohort.There was an excess of NFE2L2 TNR alleles, including three alleles not observed previously [Five eviously  i.e. 2, NFE2L2 G(-686)A (rs35652124) SNP, CCG TNR and rs2364723 SNP were in high LD as well as NFE2L2 C(-650)A (rs6721961) and rs4243387 SNPs  were unique, i.e. they were not tagged by a single allele of any SNP (table KEAP1 (haplotypes A and B) were unique  with a lower FEV1 level, and SNP rs11085735 in KEAP1 was significantly associated with a higher FEV1 level in the Vlagtwedde-Vlaardingen cohort  in relation to the level of FEV1 in the pooled cohort analysis (data not shown).There was no significant interaction between SNPs in NFE2L2 or KEAP1 in relation to the level of FEV1. Yet the effect of rs11085735 in KEAP1 was significant only in never smokers, while the effect of rs2364723 and haplotype C in NFE2L2 was significant only in ever smokers  SEINT = 0.9 p = 0.03) and haplotype B  SEINT = 0.9 p = 0.04). In the single cohort analysis these interaction terms were not significant (p > 0.10 for both cohorts).We observed no significant interaction between ever/never smoking status and variations in NFE2L2 and/or KEAP1 on the course of FEV1 in either of the cohorts nor in the pooled cohort analysis for any genetic model tested  that leads to a1-antitrypsin deficiency [Superoxide Dismutase 3 (frequency 1%–2% in Caucasians) [1 measurements in both cohorts, in order to achieve the highest possible statistical power. Moreover, we additionally performed analyses on the pooled cohorts including over 2,500 subjects with over 11,000 FEV1 measurements.It has been shown that lung function is genetically determined ,3, howevficiency -8 and thcasians) ,10, suggKEAP1 significantly associated with higher FEV1 levels in the pooled cohort as well as in both cohorts analyzed separately, yet using different genetic models. This SNP is located in the intron 3 of KEAP1, relatively close (73 bp) to the exon 3 of this gene, and thus it might have functional consequences e.g. via affecting KEAP1 mRNA splicing. Haplotype A in KEAP1 was associated with higher FEV1 level in the Doetinchem cohort and in the pooled cohort analysis using a recessive model only. Since this haplotype does not tag any SNP that was investigated in the current study, it may be in linkage disequilibrium with another functional SNP that is either not known yet or is located outside the region that was selected for tagging.In our opinion the most convincing association shown in the current study was that the rs11085735 SNP in NFE2L2 were associated with the level of FEV1 in the pooled cohort analysis, as caused by a similar though not significant trends present in both cohorts. SNP rs2364723 is in almost complete LD with the recently described promoter polymorphisms i.e. G(-686)A (rs35652124) and CCG Trinucleotide repeat A (rs6721961) tagged by us with rs4243387 SNP) [SNP rs2364723 and haplotype C in 1 between never and ever smokers, yet the effects provided by NFE2L2 rs2364723 SNP and haplotype C were more prominent in ever smokers while the effect of KEAP1 rs11085735 SNP was significant in never smokers exclusively. Interestingly another variation in KEAP1 (i.e. rs1048290 linked with haplotype B) showed a protective effect on the level on FEV1 in interaction with packyears smoked within ever smokers. The observed association of the level of FEV1 and the interaction between rs1048290 SNP and smoking can be somewhat weakened by a deviation from HWE observed for this SNP in one of the cohorts studied. Since the common cause of such deviation is a genotyping error, we have genotyped another, completely correlated, rs9676881 SNP, which also showed significant deviation from HWE. This suggests that genotyping error was not a cause of the observed deviation from HWE. Significant results obtained in the analysis stratified by smoking status (ever and never smokers), or in the gene by packyears interaction analysis did not reach significance in either of the cohorts analyzed separately. Since this could be due to insufficient power provided by single cohorts, subsequent studies are warranted.None of the analyzed genetic variations showed a significantly different effect on the level of FEV1 could be replicated in this independent population. The additive effects provided by. rs11085735 in KEAP1 and rs2364723 in NFE2L2 were not significant, p values being 0.11 and 0.59–0.70 (depending on the genotyping method) respectively. However, both associations were in the same direction as found in our two Dutch cohorts, i.e. positive for rs11085735 in KEAP1  = -12.6 – 118.0) and negative for rs2364723 in NFE2L2 . A subsequent meta-analysis of the Doetinchem, Vlagtwedde-Vlaardingen and British 1958 Birth cohorts showed a higher significant protective effect of the KEAP1 SNP on the level of FEV1 (p = 0.0008) as compared to the pooled analysis in the two Dutch cohorts (p = 0.003, table Using publicly available data on the British 1958 Birth cohort , we checKEAP1 and NFE2L2 affect the level, but not the longitudinal course of FEV1 in the general population. Therefore, it remains for future considerations whether these SNPs play a role in the development or growth of the lung. Given the importance of both genes in the regulation of oxidative stress in the lung, further studies focusing on the NFE2L2-KEAP1 pathway are warranted.Our study performed in two independent Dutch cohorts shows that genetic variations in MS has no conflict of interest to disclose. DSP has no conflict of interest to disclose. JMAB has no conflict of interest to disclose. GvdS has no conflict of interest to disclose. JPS has no conflict of interest to disclose. HAS has no conflict of interest to disclose. HMB has no conflict of interest to disclose.MS wrote the manuscript. MS, JPS, and HMB analyzed the data. HAS and JMAB designed the Doetinchem cohort study and managed the data. JPS designed the Vlagtwedde-Vlaardingen cohort study and managed the data. GvdS participated in the genotyping process. MS, JPS, MB, JMAB, HAS, and DS interpreted the data. All authors proposed corrections and approved the final version of the manuscript.SNP-selection and NFE2L2 genotypingClick here for file


In parallel, the sequence of the grapevine cultivar Pinot Noir is also providing thousands of polymorphisms present in this highly heterozygous genome. Still the general application of those SNPs requires further validation since their use could be restricted to those specific genotypes.Single-nucleotide polymorphisms (SNPs) are the most abundant type of DNA sequence polymorphisms. Their higher availability and stability when compared to simple sequence repeats (SSRs) provide enhanced possibilities for genetic and breeding applications such as cultivar identification, construction of genetic maps, the assessment of genetic diversity, the detection of genotype/phenotype associations, or marker-assisted breeding. In addition, the efficiency of these activities can be improved thanks to the ease with which SNP genotyping can be automated. Expressed sequence tags (EST) sequencing projects in grapevine are allowing for the In order to develop a large SNP set of wide application in grapevine we followed a systematic re-sequencing approach in a group of 11 grape genotypes corresponding to ancient unrelated cultivars as well as wild plants. Using this approach, we have sequenced 230 gene fragments, what represents the analysis of over 1 Mb of grape DNA sequence. This analysis has allowed the discovery of 1573 SNPs with an average of one SNP every 64 bp (one SNP every 47 bp in non-coding regions and every 69 bp in coding regions). Nucleotide diversity in grape (π = 0.0051) was found to be similar to values observed in highly polymorphic plant species such as maize. The average number of haplotypes per gene sequence was estimated as six, with three haplotypes representing over 83% of the analyzed sequences. Short-range linkage disequilibrium (LD) studies within the analyzed sequences indicate the existence of a rapid decay of LD within the selected grapevine genotypes. To validate the use of the detected polymorphisms in genetic mapping, cultivar identification and genetic diversity studies we have used the SNPlex™ genotyping technology in a sample of grapevine genotypes and segregating progenies.These results provide accurate values for nucleotide diversity in coding sequences and a first estimate of short-range LD in grapevine. Using SNPlex™ genotyping we have shown the application of a set of discovered SNPs as molecular markers for cultivar identification, linkage mapping and genetic diversity studies. Thus, the combination a highly efficient re-sequencing approach and the SNPlex™ high throughput genotyping technology provide a powerful tool for grapevine genetic analysis. Single nucleotide polymorphisms (SNP) and insertions/deletions (INDELs) are the most abundant type of DNA sequence polymorphisms and can be theoretically found within every genomic sequence ,2. They SNPs are a useful tool to quantify LD. The structure of LD along each particular genome or genomic region affects the resolution of association studies ,8. For gin silico SNP discovery approach [In plants, systematic analyses of nucleotide polymorphism have only been approached in a few, well-studied model species such as Arabidopsis, barley, maize -15 and aapproach -19 or deapproach ,15,20-22approach  to 5–10-approach ), dependapproach , while sapproach ), soybeaapproach ), Arabidapproach ) or wheaapproach  and 0.56approach  and slowapproach .Vitis vinifera L.) has prompted a considerable effort in EST sequencing and more than 336789 EST entries are currently found at the National Center for Biotechnology Information (NCBI [The economic relevance of grapevine (on (NCBI ). Recenton . From them, 81.4 kb corresponded to coding regions and 19.1 kb to non-coding regions, mostly belonging to intron sequences present in the genome sequences amplified with the EST-based designed primers. The nucleotide variation observed through the analysis of these sequences is summarized in Table The SNP variation corresponded to an average of one SNP every 64 bp. Most of the SNPs were bi-allelic, with only four (0.25%) showing three alleles. Among the detailed nucleotide polymorphisms, 59.3% were due to transitions and 40.7% to transversions. This observed transition/transversion ratio (1.46) is similar to the previously reported for grape  and potRegarding the 52 INDELs identified, one third 17) could be classified as mono-, di-, tri- and tetranucleotide variants whereas the two other thirds (35) represented variable size INDELs ranging from one to 38 bp. The frequency of detected INDELs (one every 1932 bp) is an underestimation. If we consider the intron-bearing sequences that did not yield readable data, we would expect the frequency of INDELs to be at least five times higher in introns. This underestimation was even higher in a previous report , where o7 could bvinifera species in their study (i.e. Vitis riparia and a complex genotype Freiburg 99360 derived from multiple crosses involving wild species such as V. rupestris and V. lincecumii) [The overall SNP frequency observed (1 every 64 bp) was lower than that described by Salmaso et al. (1 every 47 bp), being the difference attributable to the inclusion of non-cecumii) . Surpriscecumii)  in displcecumii) ,23 and hcecumii) , soybeancecumii) , wheat [cecumii) ,35 or Arcecumii) ,32. Conscecumii) ) and ~5-cecumii) ) or humaDiversity values (expected heterozygosity) for SNP are generally low due to their bi-allelic nature. In grapevine, SNP diversity values ranged from 0.00 to 0.66 with a mean value of 0.30 Table  which isP < 0.01) which could suggest the possible existence of balancing selection operating in this locus [The allele distribution in the set of cultivars selected for this study was analyzed by calculating the Tajima D statistic, designed to test the neutrality of mutations . Sequencis locus . In any et al. [The number of haplotypes per locus was estimated using the EM algorithm . The aveet al. . A similet al.  and barlet al. . The expet al. ,43. The 2 parameters for SNP loci within each sequence. Decay of D' and r2 was observed between 100–200 bp  for 4.4% of the within gene pairs of SNP loci. Similar patterns of rapid LD decay were observed in other outcrossing species such maize and woody perennial species like the Norway spruce  displayed very low levels of short-range LD [G. soja  generally decays rapidly in the absence of selection, which when existing produces locus-specific bottlenecks . To estia abies, ) or the tremula, ). Howevetremula, . On the range LD , probablca. 13%, ,45). Onlrapevine ,47, has rapevine . In thisly decay . This rely decay , a discrly decay .-4.The final goal of the SNP discovery project was to develop molecular markers that could be combined with a high throughput genotyping technology such as SNPlex™ to address different genetic applications ,2. The rV. vinifera accessions including a large set of wine and table grape cultivars and wild populations, showed that 80% of them displayed MAF ≥ 0.10, with an average MAF of 0.24 (Table 2 = 0.61) with the MAF values observed in the original sample of 11 genotypes used in the re-sequencing strategy  for 41%, 24% and 28% of the SNPs respectively  value which affects the information provided by the marker in different genetic applications such as linkage and association studies. In general, SNPs with MAF values ≥ 0.05 or 0.10 can be considered as common SNP that are useful in most applications. An analysis of MAF values for the 80 genotypes SNPs in a sample of ~300 -9. However, in spite of all the effort dedicated to SSR genotyping and standardization of allele sizes and genotypes [Cultivar identification is an important issue in grapevine where the estimation is that there are over 10000 vegetatively propagated genotypes, frequently confused due to the existence of multiple synonyms and homonyms . Currentenotypes , there aenotypes . In this1 populations and maps are constructed for each of the parental genotypes using a pseudo test cross strategy [Linkage mapping has become a common approach to determine the genetic basis of qualitative and quantitative traits in grapevine. The heterozygous nature of grape cultivars, makes linkage mapping to be performed in Fstrategy -59. For in silico SNP discovery approaches based in EST libraries, such as the public PlantMarkers database [Vitis vinifera UniGene Build #4; [The highly polymorphic nature of the grape genome represents a challenge for the efficient implementation of in-silico SNP discovery approaches, even those based in whole genome sequencing projects  or in ESdatabase , since guild #4; ). In a suild #4;   genotypes used for SNP discovery were selected to included wine cultivars , table grape cultivars (Sultanina and Ahmeur Bou Ahmeur) and wild accessions (two genotypes of Vitis vinifera ssp. sylvestris) from populations sampled in the Iberian Peninsula . Additionally, 368 accessions were used for genotyping analysis (see below). These accessions are mostly maintained at the germplasm collection of "El Encín" . These accessions included cultivated and wild accessions as well as 53 F1 hybrids from four different mapping populations . Their name, collection code and main use  are listed in Additional file et al. [Grapevine , 1× reaction buffer, 1.5 mM MgClAmplified PCR products (5 μl) were treated with 0.2 μl of ExoSAP-IT reagent (USB Corporation) in a 10 μl final volume. Treated PCR products were sequenced at the Genomic Unit of the Parque Científico de Madrid using Universal M13 forward and M13 reverse primers in an ABI Prism 3730 (Applied Biosystems) DNA sequencer. Base calling, quality trimming and alignment of ABI chromatograms was performed using SeqScape Software v2.5 (Applied Biosystems). Sequence polymorphisms were verified manually with the help of BioEdit v7.0.5.3 software . IdentifNucleotide diversity π, the average number of nucleotide differences per site between two sequences [Number of segregating sites θ [Pij is the frequency of the jth allele for ith locus, was calculated by means of PowerMarker V3.25 software [Estimates of nucleotide polymorphism  were ob sites θ  Gene div sites θ  was calcsoftware . Tajima'software , was usesoftware .For each target locus the haplotype number and frequency and the expected haplotype heterozygosity were calculated using the EM algorithm  implemen2) between two loci in the genome and the exact test for multi-locus association were calculated as described by Zaykin et al. [Decay of LD with distance in base pairs (bp) between sites within each locus was evaluated by nonlinear regression . Linkagen et al.  using Pon et al. .Probability of identity (PI) for SSR and SNP markers was calculated by means of the Multilocus option of the GenAlEx6 software .Selected SNPs and INDELs from the SNP discovery process were considered for the genotyping analysis when present in at least 2 of the "original genotypes" and both strand chromatograms confirmed the polymorphism. Genotyping of the 368 accessions described in Additional file DL designed the study, performed sequence and statistical analysis, coordinated the study and drafted the manuscript. JAC performed the PCR primer designs, participated in the design of the study, organized the DNA samples for the genotyping analysis and contributed to the coordination of the study. AI and VR carried out the DNA isolations, PCR amplifications and sample treatments prior to DNA sequencing. JMMZ conceived the study, participated in its design and drafted the manuscript. All authors read and approved the final manuscript.DNA fragments sequenced in this work. PDF file containing the sequences ID, primers used for their amplification, amplicon size (bp) and associated UniGene or locus identifier.Click here for fileSNPs genotyped using SNPlex™. PDF file containing the SNP IDs, gene diversity, heterozygosity and MAF of the 80 validated SNPs used for the genotyping analysis, together with the MAF observed in the original re-sequenced accessions.Click here for fileGrapevine accessions used in this work. PDF file containing the code, name, main use and repository institution of the grape accessions utilized in the present study.Click here for fileRegression plot of MAF in genotyped accession vs. MAF in original accessions. PDF file displaying the linear regression between the MAF values for the 80 SNPs observed in the sample of ~300 genotyped accessions with the MAF values observed in the original sample of 11 genotypes use in the re-sequencing strategy.Click here for fileGlobal and class specific SNP minor allele frequencies. PDF file comparing the global minor allele frequency versus those observed in wild, table and wine classes for the 80 validated SNPs tested in the genotyping analysis.Click here for file


Vitis vinifera L. cultivar Pinot Noir (clone ENTAV 115) genome gave rise to several thousand polymorphisms and offers a good model to study the transferability of its degree of polymorphism to other individuals of the same species and within the genus.Efforts to sequence the genomes of different organisms continue to increase. The DNA sequence is usually decoded for one individual and its application is for the whole species. The recent sequencing of the highly heterozygous V. vinifera, of 18.8% across the wild forms of V. vinifera, of 2.3% among non-vinifera Vitis species, and of 0% with Muscadinia rotundifolia was found. In addition, mean allele frequencies were used to evaluate SNP informativeness and develop useful subsets of markers.This study was performed by genotyping 137 SNPs through the SNPlex™ Genotyping System (Applied Biosystems Inc.) and by comparing the SNPlex sequencing results across 35 (of the 137) regions from 69 grape accessions. A heterozygous state transferability of 31.5% across the unrelated cultivars of V. vinifera (including cultivars and wild forms), but had a limited application for non-vinifera Vitis species where a resequencing strategy may be preferred, knowing that homology at priming sites is sufficient. This work will allow future applications such as mapping and diversity studies, accession identification and genomic-research assisted breeding within V. vinifera.Using SNPlex application and corroboration from the sequencing analysis, the informativeness of SNP markers from the heterozygous grape cultivar Pinot Noir was validated in  Arabidopsis thaliana, . The filinkage disequilibrium analyses  incl incl21]]analyses ,28. Moreanalyses . Differeanalyses  and highanalyses  methods.ultivars  and to aultivars . In addiV. vinifera and within the genera Vitis and Muscadinia to validate the use of Pinot Noir genome-based SNP markers for future grape improvement programs using diverse genetic resources. This study was performed by genotyping 137 SNPs through the SNPlex™ Genotyping System (Applied Biosystems Inc.) and by confirming the SNPlex results sequencing 35 (of the 137) regions on 69 accessions.The primary goal of this study was to assess the use and transferability of SNPs discovered from the heterozygous Pinot Noir  within tGenomic DNA (gDNA) of 71 genotypes was isolated from 50 to 100 mg of young leaves. After freeze-drying, leaf material was ground using the MM 300 Mixer Mill  and DNA extraction was performed using the DNeasy 96 Plant Mini Kit  according to the manufacturer's protocol.V. vinifera cultivars housed at IASMA , including the sequenced Pinot Noir clone ENTAV 115; this set included 7 varieties related to Pinot Noir at the 1st degree level (Pinot Noir is a parent), 4 varieties related to Pinot Noir at the 2nd degree level (Pinot Noir is a grand-parent) and 12 varieties unrelated to Pinot Noir; ii) 10 accessions of V. vinifera subsp. sylvestris housed at Vassal INRA-Montpellier (France); and iii) 37 non-vinifera accessions housed at UC Davis , representing 25 Vitis species and 2 accessions of M. rotundifolia , ,42, 41,4 species . In the in silico against the near-homozygous PN40024 genome sequence [Additional transferability tests of these 137 SNP markers, developed based on the highly heterozygous Pinot Noir clone ENTAV 115 ,30, wereet al. and Ward et al., 3rd SNPlex user meeting 2008). One possible factor could be the multiple taxonomic levels compared in this study  while other studies have used more simplified study sets.The SNPlex genotyping system is a comprehensive solution for medium to large-scale genotyping studies, such as fine scale mapping, association analysis and marker-assisted breeding. Based on oligonucleotide ligation/polymerase chain reaction, it uses capillary electrophoresis to separate selectively amplified gene regions, reports easy-to-survey cluster plots, and manages large numbers of datasets . Althouget al. [Vitis species. The remaining cases (1.7%) were of the greatest interest, because they represented inconsistencies due to SNPlex failure, and limitations of this genotyping system. Possible explanations of the observed discrepancies may be: a preferential ligation of one SNPlex probe, given an additional SNP close to the target SNP ; a ligaFinally, no significant positive relationship was found when correlating SNPlex/sequencing inconsistencies  to the average SNP and In/del frequency in each resequenced region (data not shown) and the reliability of the SNPlex assay in a genetically diversified sample was confirmed.V. vinifera have one SNP for every 117 bp in coding and every 91 bp in non-coding regions. The results in coding regions match reports for other V. vinifera cultivars . Other /115 bp, ), but th1/73 bp, ), but noctively, ).Vitis groups. No substantial difference in In/del occurrence was detected between coding and non-coding regions of each group, although this result is highly influenced by the chosen regions. There has not yet been a comprehensive study of In/del distribution in Vitis to which the results of this study can be compared. The Pinot Noir heterozygous genome has an average frequency of one In/del every 450 bp [Vitis groups.In most organisms studied to date, SNPs are more prevalent in the non-coding regions of the genome, in fact the frequency of SNP distribution has been shown to vary not only among species, but also within each genome . The SNPy 450 bp , which cV. vinifera, has a more limited application for wild forms of this species, and has no direct application for non-vinifera Vitis species The SNPlex technology was again validated as a robust method for rapid analysis of a limited number of SNPs on a large number of plants. Although additional SNPs could be used, the SNPs developed in this study will be very useful for accession identification and genomic-research assisted breeding at the V. vinifera level.In conclusion, using this SNPlex application and corroboration from the sequencing analysis, the informativeness (MAF information) of SNP markers from the heterozygous grape cultivar Pinot Noir is validated in SV carried out the genomic DNA extraction and the WGA, performed the sequencing data analysis, contributed to the SNPlex data analysis and drafted the manuscript. DM provided support in the SNPlex assay, carried out the sequencing data analysis and contributed to the manuscript writing. SR carried out the genomic DNA extraction and the SNPlex assay, contributed to the WGA and the SNPlex data analysis and helped in the discussion of the results. MP provided support in the SNPlex assay, performed the SNPlex data analysis and helped in the sequencing data analysis. RV contributed to reviewing the manuscript. PT provided important plant material and critically contributed to the discussion of the results. MAW provided basic plant material, helped in the introduction draft and in the discussion of the results, and contributed to reviewing the manuscript. MT performed the sequencing data analysis and critically contributed to the discussion of the results. RV conceptualized the project and contributed to the discussion of the results.All authors read and approved the final manuscript.Table S1. List of the studied 71 grape accessions.Click here for fileTable S2. Linkage group, gene location and primer sequence of the 35 resequenced SNP regions.Click here for fileTable S3. Linkage group, SNPlex results and allele frequencies of the 137 successful SNPs in all 71 grape accessions.Click here for fileTable S4. BLAST-N results of the 137 SNP-regions – the 25 bp targeted by SNPlex – of Pinot Noir (clone ENTAV 115) on the PN40024 grape genome.Click here for fileSupplementary Text. Details of the BLAST-N analysis of the 137 SNP regions on the PN40024 genome sequence.Click here for file


The last decade has seen rapid improvements in high-throughput single nucleotide polymorphism (SNP) genotyping technologies that have consequently made genome-wide association studies (GWAS) possible. With tens to hundreds of thousands of SNP markers being tested simultaneously in GWAS, it is imperative to appropriately pre-process, or filter out, those SNPs that may lead to false associations. This paper explores the relationships between various SNP genotype and phenotype attributes and their effects on false associations. We show that (i) uniformly distributed ordinal data as well as binary data are more easily influenced, though not necessarily negatively, by differences in various SNP attributes compared with normally distributed data; (ii) filtering SNPs on minor allele frequency (MAF) and extent of Hardy–Weinberg equilibrium (HWE) deviation has little effect on the overall false positive rate; (iii) in some cases, filtering on MAF only serves to exclude SNPs from the analysis without reduction of the overall proportion of false associations; and (iv) HWE, MAF and heterozygosity are all dependent on minor genotype frequency, a newly proposed measure for genotype integrity. Genome-wide association studies (GWAS) using single nucleotide polymorphism (SNP) markers have become increasingly popular for dissecting the genetics of complex traits (reviewed in Hobs), minor allele frequency (MAF), and extent of Hardy–Weinberg equilibrium (HWE) deviations. In this paper, we also propose minor genotype frequency (MGF) as a filtering criterion and explore its value as a quality control measure.A filtering process, defined by a set of rules, is generally applied to remove markers from an analysis. The deduction of these rules may be arbitrary e.g. , and thiHobs = 0) SNPs may impact on statistical power because of loss of information. SNPs with excessively high Hobs may reflect contamination and poor genotyping integrity  progenies/bull (max 47 progenies/bull) and 478 dams averaging one (±0.2 SD) progeny/dam (max three progenies/dam). Kinship coefficients were estimated using pedigree information of 9082 animals spanning up to seven generations and the  package .Five trait-types were simulated according to the following distributions reflecting the majority of real data structures:2 = 1).Continuous data with normal distribution, Normal.Ordered categorical data with normal distribution,Binomial , where X∼ Uniform .Ordered categorical data with discrete distribution,Binomial .Ordered categorical data with uniform distribution,Uniform .Binary data with binomial distribution, Binomial and hypergeometric distributions respectively as per the pchisq and fisher.test functions in r/stats  association per SNP across the 1000 replicates.Association between each trait at each polymorphic SNP was assessed using linear regression, where the simulated trait values across the 565 individuals were regressed onto the numeric code of each SNP genotype ; this tested the null hypothesis of the additive allelic effect on the trait. Regression analyses were performed using P-values (one from each polymorphic SNP) against the null distribution . P-values were obtained using the ks.test function in r/stats. The median P-values from the 1000 KS tests were 0.14 ± 0.30 SD for continuous normal traits, 0.12 ± 0.24 SD for categorical normal traits, 0.12 ± 0.27 SD for categorical discrete traits, 0.12 ± 0.27 SD for categorical uniform traits and 0.02 ± 0.14 SD for binary traits.To test whether association is independent of SNP attributes, we compared, using the Kolmogorov–Smirnov (KS) test, the observed distribution of the 8623 P < 0.05 (two-sided test against the null that ρ = 0). As per the cor.test function in r/stats, P-values were computed using the AS 89 algorithm.To ascertain the relationship between a SNP attribute and the number of false positives (FPs), Spearman’s correlation coefficients (ρ) were calculated. Significant correlation was only asserted if |ρ| ≥ 0.1 at P-values obtained from 10 000 permutations using chisq.test in r/stats.For each trait-type, we tested the null hypothesis that the numbers of SNPs across eight FP bins  are the same between the ‘good’ and ‘bad’ SNP sets. Pearson’s chi-squared test was used for this purpose, with P-values for the chi-squared test were determined from 10 000 simulations using chisq.test and those for the Mann–Whitney tests were approximated from a Gaussian distribution using wilcox.test in r/stats.Two tests were used for comparing the distributions of the same SNP attribute between FP-free (FP = 0) and FP-prone (FP ≥ 4) SNPs: (i) Pearson’s chi-squared test for LOH; and (ii) Mann–Whitney test for all other (non-binary) SNP attributes. Hobs = 0.21 ± 0.17, of which 0.4% (33/8623) have LOH (Hobs = 0).Each SNP has a median call-rate of 99.8% (85–100%), a median of one (range: 0–90) missing genotype, and 5% of SNPs are monomorphic. Excluding monomorphic SNPs, In this paper, we introduce and examine the effects of MGF on GWAS. The necessity to include MGF in addition to MAF is justified because SNPs with low MGF do not always imply low MAF . An extrP < 0.0001 for autosomal SNPs with MAF ≥ 0.05 as in P < 0.05 for autosomal SNPs with at least five expected samples per genotypic group as in Minor allele frequency is 0.10 ± 0.14 SD across all SNPs and MGF is 0.05 ± 0.07 SD, with the former figure increasing to 0.16 ± 0.14 SD following the exclusion of monomorphic markers, whilst the latter figure for MGF remains unchanged. Depending on the test statistic and associated criteria, between 13.6% [Fisher’s Exact test at We examined the effects of SNP attributes on type I errors in GWAS in consideration of five types of phenotypic traits. As we are interested in the extent of false associations, we chose to simulate these traits under the null hypothesis of no association: traits were purely simulated under the specified distribution independent of the animals and their genotypes, i.e. no genetic structure was simulated.P-value distributions should be uniform for each GWAS (i.e. each simulated trait); and (ii) an average of one FP should be observed for each SNP. Here, FP is the number of 1000 simulated traits passing the significance threshold of P < 0.001, and thus each SNP is expected to falsely associate with one of the 1000 simulated traits by chance alone (FP = 1).Under our null hypothesis, two observations are expected: (i) P-values that are significantly non-uniform (median P = 0.02 for tests of uniformity), signifying an increased sensitivity of binary traits to various SNP attributes. The second expectation is satisfied by all but categorical-discrete traits  and binary (ρ ≈ 0.3) traits. Because of the relationships between MAF, MGF and Hobs, FPs are also significantly influenced by MAF and MGF with 0.16 ≤ ρ ≤ 0.28 for categorical-uniform and binary traits.The extent of false associations is not affected by call-rate, missing data, or LOH. It is, however, significantly affected by Significant correlations between FP and various SNP attributes suggest that FP should decrease if problematic or ‘bad’ SNPs are eliminated prior to association. Here we assess this by comparing the extents of FPs from ‘good’ and ‘bad’ SNPs. As our objective was to investigate the impact of various SNP attributes on false associations, our null hypothesis here was that the extent of FPs are equal between the set of ‘good’ and ‘bad’ SNPs. In GWAS, SNPs are commonly excluded based on several criteria that generally reflect their informativeness and level of variation. These criteria are variable in the literature, and for the purpose of this study, ‘good’ SNPs are defined as those passing the following set of criteria derived from recent literature:Call-rate ≥ 95%  for all but continuous-normal traits. In particular, >59% of ‘bad’ SNPs have FP = 0 for categorical-uniform traits and <40% of the ‘good’ SNPs have FP = 0 for binary traits.The extent of false associations between ‘good’ and ‘bad’ SNPs is not significantly different  and FP-prone (FP ≥ 4) and found  FP-False positive-prone SNPs deviate from HWE more often than FP-free SNPs, but this difference disappears when 49% SNPs with at least one of the three genotypes represented by less than five (or <1%) individuals are excluded. We infer from this that deviation from HWE alone does not affect false associations, rather FP is dependent on low MGF-induced HWE deviation. Again, continuous-normal traits appear unaffected by this.Finally, we explored the trade-off between the number of FP we can reduce and the number of useful SNPs we can retain. In particular, we examined the effects of all three-way combinations of MAF, MGF and HWE threshold at:MGF ≥ 0, MGF > 0, MGF > 0.005, MGF > 0.01, MGF > 0.05, MGF > 0.1.MAF > 0, MAF > 0.005, MAF > 0.01, MAF > 0.05, MAF > 0.1.P≥ 0, P> 10−6, P > 10−5, P > 10−4, P > 10−3, P > 10−2, P > 0.05.HWE: x% of the SNPs removes ∼x% of FP. This is particularly true for continuous-normal traits, reaffirming that the loss (and gain) of FP is random and thus proportional to the number of SNPs excluded from analysis.For most traits, the rate of FP reduction is proportional to the rate of SNP loss , i.e. reP ≤ 0.05 (largest circle).However, for binary, categorical-discrete and categorical-uniform traits, some combinations of SNP filtration criteria result in more rapid SNP loss than FP loss. Specifically, an increase in MAF stringency only serves to increase the number of excluded SNPs but does not reduce the extent of false associations. .Hobs and deviation from HWE can also protect against false associations; this is especially true for categorical-uniform and binary traits. Again, this is because SNPs with these attributes are susceptible to false negatives. In the case of deviation from HWE, and possibly for low Hobs and MAF, its effect is only manifested when the corresponding SNP also has near zero MGF. In fact, we failed to establish any connection between deviation from HWE and false associations with any trait-type for SNPs with MGF < 0.009 . This finding is of particular importance in GWAS, because deviation from HWE is a widely used SNP quality control measure.In addition (and in some cases as a consequence of) low to zero MGF, low MAF, low Hobs = 0) markers (with sufficiently low MAF to escape detection from HWE deviation) have been shown to cause false associations in transmission-disequilibrium tests  populations. For example, although this population demonstrated a relatively low MAF across all SNPs (32% polymorphic SNPs with MAF < 0.05), the only difference compared with a population with a higher average MAF is the extent of FP. The nature of the effect of low MAF and the fact that the effect would be more prominent for categorical-uniform and binary traits is indisputable. Clearly, in order to make inference on statistical power and type II error, one would have to model family structure into the phenotype data and then account for it in the association test (e.g. Secondly, several studies have claimed that genotyping error can confound association studies because of distortion of allele frequencies (e.g. Hobs, MAF and MGF, as well as the characteristic and distribution of the trait which the SNP is to be tested against. Furthermore, the fact that an SNP is FP-free does not necessarily imply that it will be more efficient in a test of association, because the FP-free nature may simply be a reflection of the SNP’s inherent lack of statistical power for such a purpose.In conclusion, we emphasize that whether an SNP is FP-free or FP-prone is highly dependent on


We use high-density single nucleotide polymorphism (SNP) genotyping microarrays to demonstrate the ability to accurately and robustly determine whether individuals are in a complex genomic DNA mixture. We first develop a theoretical framework for detecting an individual's presence within a mixture, then show, through simulations, the limits associated with our method, and finally demonstrate experimentally the identification of the presence of genomic DNA of specific individuals within a series of highly complex genomic mixtures, including mixtures where an individual contributes less than 0.1% of the total genomic DNA. These findings shift the perceived utility of SNPs for identifying individual trace contributors within a forensics mixture, and suggest future research efforts into assessing the viability of previously sub-optimal DNA sources due to sample contamination. These findings also suggest that composite statistics across cohorts, such as allele frequency or genotype counts, do not mask identity within genome-wide association studies. The implications of these findings are discussed. In this report we describe a framework for accurately and robustly resolving whether individuals are in a complex genomic DNA mixture using high-density single nucleotide polymorphism (SNP) genotyping microarrays. We develop a theoretical framework for detecting an individual's presence within a mixture, show its limits through simulation, and finally demonstrate experimentally the identification of the presence of genomic DNA of individuals within a series of highly complex genomic mixtures. Our approaches demonstrate straightforward identification of trace amounts (<1%) of DNA from an individual contributor within a complex mixture. We show how probe-intensity analysis of high-density SNP data can be used, even given the experimental noise of a microarray. We discuss the implications of these findings in two fields: forensics and genome-wide association (GWA) genetic studies. Within forensics, resolving whether an individual is contributing trace amounts of genomic DNA to a complex mixture is a tremendous challenge. Within GWA studies, there is a considerable push to make experimental data publicly available so that the data can be combined with other studies. Our findings show that such an approach does not completely conceal identity, since it is straightforward to assess the probability that a person or relative participated in a GWA study. Resolving whether an individual's genomic DNA is present at trace amounts within a complex mixture containing DNA from numerous individuals is of interest to multiple fields. Within forensics, determining whether a person contributed their DNA to a mixture is typically a manual process that requires extensive experience and careful training. Furthermore, different laboratories can often come to different conclusions due to differences in methodology or lab intervariability. In large part, forensically identifying whether a person is contributing less than 10% of the total genomic DNA to a mixture is not easily done, is difficult to automate, and is highly confounded with the inclusion of more individuals. Within the field of forensics, as well as the field of human genetics, there is a base assumption that it is not possible to identify individuals using pooled data  from SNP data. In this paper we investigate the accuracy of such assumptions.Numerous methods examining DNA mixtures currently exist, most of these addressing mixtures with smaller numbers of individuals within forensics studies In this study, we assess the feasibility of using hundreds of thousands of SNPs assayed on a high-density microarray as a means to resolve trace contributions of DNA to a complex mixture. High-density SNP genotyping arrays have predominately been developed as tools for geneticists to identify common genetic variants that predispose an individual to disease. In the context of forensic mixtures, SNPs are traditionally analyzed by genotype  and thought to be non-ideal for resolving mixtures. In fact, it is argued that their poor performance in the analysis of mixed DNA samples is one of the primary reasons SNP genotyping arrays have not become adopted by the forensics community We demonstrate an approach for rapidly and sensitively determining whether a trace amount (<1%) of genomic DNA from an individual is present within a complex DNA mixture. We focus on solving the problem forensically, whereby the problem is much more difficult due to the multiple sources of experimental noise that would further mask identification. Our method can be interpreted as a cumulative analysis of shifts in allele probe intensities in the direction of the individual's genotype. Similarly, we can also interpret our method as measuring the difference of two distances: the distance of the individual from a reference population and the distance of an individual from the mixture. Our method does not require knowledge of the number of individuals in the mixture and we demonstrate robustness for discriminating mixtures composed of over a thousand individuals. We first give a theoretical justification for our method with modifications for known factors including homogeneity of the mixture and accuracy of our reference populations. We then proceed to simulate the effects of three combinations of variables when using SNP microarrays, including probe measurement noise, fraction of the person of interest's DNA in the mixture, and the number of SNPs probed. Finally in a series of proof-of-principle experiments using both Affymetrix and Illumina microarrays, we demonstrate resolving whether an individual is within a series of complex mixtures  when the individual contributes trace levels (at and below 1%) of the total genomic DNA. We finally discuss the implications of these results in the context of forensics and population genetics.A total of 8 complex mixtures were constructed See . All DNAShown in Two CEU males were combined in a single mixture so that one individual (NA12752) contributed 90% (675 ng) of the DNA in the mixture, while the other individual (NA07048) contributed only 10% (75 ng) DNA into the mixture by concentration.Two CEU individuals, a female and a male, were combined in a single mixture so that one individual (NA10839) contributed 90% (675 ng) of the DNA in the mixture, while the other individual (NA07048) contributed only 10% (75 ng) DNA into the mixture by concentration.Two CEU males were combined in a single mixture so that one individual (NA12752) contributed 99% (742.5 ng) of the DNA in the mixture, while the other individual (NA07048) contributed only 1% (7.5 ng) DNA into the mixture by concentration.Two CEU individuals, a female and a male, were combined in a single mixture so that one individual (NA10839) contributed 99% (742.5 ng) of the DNA in the mixture, while the other individual (NA07048) contributed only 1% (7.5 ng) DNA into the mixture by concentration.Two mixtures were combined into a single mixture so that each of the original mixtures contributed the same amount of genomic DNA by volume into the final mixture. CAU2 mixture contained 184 Caucasian control individuals obtained from the Coriell Cell Repository. Mixture A1 was constructed as above and contained 41 CEU individuals.Two mixtures were combined into a single mixture so that each mixture contributed the same amount of genomic DNA by volume into the final mixture. CAU3 mixture contained 184 Caucasian control individuals obtained from the Coriell Cell Repository. Mixture B2 was constructed as above.Two mixtures were combined into a single mixture with Mixture A2 comprising of 5% of the mixture and the CAU3 comprising of 95% of the mixture. CAU3 mixture contained 184 Caucasian control individuals obtained from the Coriell Cell Repository. Mixture A2 was constructed as above.Two mixtures were combined into a single mixture with Mixture B1 comprising of 5% of the mixture and the CAU2 comprising of 95% of the mixture. CAU2 mixture contained 184 Caucasian control individuals obtained from the Coriell Cell Repository. Mixture B1 was constructed as above.Four cohorts were assayed on the Illumina  HumanHap550 Genotyping BeadChip v3, one cohort was assayed on the Illumina (San Diego) HumanHap450S Duo, and three cohorts were assayed on the Affymetrix  Genome-Wide Human SNP 5.0 array, with each cohort being assayed on a single chip. Probe intensity values were extracted for analysis from the file folders generated by the BeadScan software for the Illumina platform, and from Affymetrix GTYPE 4.008 software for the Affymetrix data, as described in previous studies We recognize there are multiple approaches to derive a test-statistic to evaluate the hypotheses that a person is within a mixture, and these are discussed further in later sections. In this primary approach we take a frequentist rather than a Bayesian approach, recognizing that both are possible and each has unique advantages.An overview of our approach is described in AA, AB, BB, or NoCall where A and B symbolically represent the two alleles for a biallelic SNP. However, as evident from copy number, calling algorithm, and pooling-based GWA studies A” and “B” alleles hybridized to a specific probe (or termed features) on a microarray A allele and B allele respectively, whereas Illumina arrays have a random number of probes averaging approximately 18 probes per allele. With 500,000+ SNPs, there are millions of probes (or features) on a SNP genotyping array. One should note that there are considerably different sample preparation chemistries prior to hybridization between SNP genotyping platforms and thus probes behave differently on the respective platforms.Current genotyping microarray technology can assay millions of SNPs. Genotypes are expected to result from an assay and data is categorical in nature, e.g. iY = iA/(iA+iBik), where iA is the probe intensity for the A allele and B is the probe intensity for the B allele for the jth SNP. Multiple papers have shown that jY transformation approximates allele frequency, where jk is the SNP specific correction factor accounting for experimental bias and is easily calculated from individual genotyping data iY is an estimate of allele frequency (termed Ap) for each SNP. Since most individuals contain two copies of the genome for autosomal SNPs, values for the A allele frequency (Ap) in a single individual may be 0%, 50%, or 100% for the A allele at AA, AB, or BB, respectively. Equivocally iY will be approximately 0, 0.5, or 1, varying from these values due to measurement noise. By example and assuming jk = 1, probe intensity measurements of jA = 450 and jB = 550 yield jY = 0.45 and this SNP would be likely called AB. For a single individual, we thus expect to see a trimodal distribution for Y across all SNPs since only AA, AB, or BB genotype calls are expected. However, in a mixture of multiple individuals, the assumptions of the genotype-calling algorithm are invalid, since only AA, AB, BB, or NoCall are given regardless of the number of pooled chromosomes.Before we discuss resolving mixtures, we summarize ‘genotype calling’ in the context of data from a single individual at a single SNP. SNP genotyping algorithms typically begin by transforming normalized data into a ratio or polar coordinates. For simplicity, we will utilize a ratio transformation M, where iM = iA/(iA+iBik)) to estimates of the mean allele frequencies of a reference population. The selection of the reference population is important and will be discussed later. For now, we assume that the reference population has a similar ancestral make-up as that of the mixture. We refer to having similar population substructure, ethnicity, or ancestral components interchangeably, and define similar ancestral components for an individual or mixture as having similar allele frequencies across all SNPs. We let i,jY be the allele frequency estimate for the individual i and SNP j, where i,jY∈{0,0.5,1}, from a SNP genotyping array. We then compare absolute values for two differences. The first difference i,j−Mj||Y measures how the allele frequency of the mixture jM at SNP j differs from the allele frequency of the individual i,jY for SNP j. The second difference i,j−Popj||Y measures how the reference population's allele frequency jPop differs from the allele frequency of the individual i,jY for each SNP j. The values for jPop can be determined from an array of equimolar pooled samples or from databases containing genotype data of various populations. Taking the difference between these two differences, we obtain the distance measure used for individual iY:However, this does not prevent us from extracting information and meaning from the relative probe intensity data. In our approach, we compare allele frequency estimates from our mixture D>0D<0DDDDD)SDDTT we assume Different populations will have different mean SNP allele frequencies based on ancestry, admixture, and population bottlenecks. An obvious assumption of this type of analysis is that the reference population must be either (a.) accurately matched in terms of ancestral composition to the mixture and person of interest or (b.) limited to analysis of SNPs with minimal (or known) bias towards ancestry. It is first important to recognize that any single SNP will have only a small effect on the overall test-statistic. Moreover, it is realistic that ancestry of the reference population could be determined by analysis of a small subset of SNPs, followed by analysis of a person's contribution to the mixture with a separate set of SNPs . In the absence of SNP-specific ancestral information towards allele frequency as was assumed in our study, we can also use normalization methods that leverage the fact that we have assayed hundreds of thousands of SNPs and consequentially have largely sampled the distribution of the test-statistic. In essence, we fit the test-statistic to a second reference population matched to the individual of interest to account for ancestry differences that do not effect the overall distribution of allele frequencies. Thus under the assumption of similar test-statistic distributions, normalizing SNP data from the mixture to a reference population reduces the effect of systematic biases on allele frequency from the microarray or, to an extent, towards ancestry at a cost of power.stF) or at have a common distribution of allele frequencies would be preferable. Identifying such a set of SNPs and more appropriately considering ancestral biases are reserved for future database studies whereby genotype data of an ancestrally diverse set of individuals is available.While not necessary in this study, the effect of ancestry on allele frequency could be more directly managed by SNP selection combined with extensive allele frequency data across multiple ancestrally diverse populations. Ideally, one would use a subset of SNPs to identify ancestry of the individual and then match them to a reference population. Moreover, SNPs that are stable for allele frequency across populations . Finally, multiple test statistics and distance calculations are implemented including our original test statistic, Pearson correlation, Spearman rank correlation and Wilcoxon sign test.Pre-compiled UNIX binaries are available for a software implementation of our method and can be found at http://www.HapMap.org. To overcome the differences in distribution of signal intensity between CEL files, we matched the distribution of the signal intensities to the distribution of the mixture's CEL file. This was achieved by ordering allele frequencies on a given chip . We then substituted the ith allele frequencies from the mixture of interest for the ith allele frequencies for the given chip. Without this adjustment, there was difficulty resolving any individual in any mixture due to the fact that we did not account for off-target cross-hybridization. This type of adjustment is the preferred type of normalization method when raw data is available for the mixture, person of interest, and reference population.With the Affymetrix platform we were able to use genotypes for each individual and found similar results with the Illumina platform. Additionally, we were able to use the raw CEL files from the HapMap dataset iY means for AA, AB, and BB to map genotype calls to expected iY values to each SNP for individually genotyped samples, but this was not necessary for our analysis.For the Illumina platform we used the genotypes from the HapMap dataset s, the fraction f of the total DNA mixture contributed by our person of interest iY, and the variance or noise inherent to assay probes pv. In the simulations, theoretical mixtures were composed by randomly sampling individuals from the 58C Wellcome Trust Case-Control Consortium (WTCCC) dataset N individuals to be equally represented in our mixture and then computed the mean allele frequency (iY) of our mixture for each SNP. SNPs j with an observed ijY below 0.05 or above 0.95 in the reference population were removed due to their potential for having false positives and low inherent information content.Simulation was used to test the efficacy of using high-density SNP genotyping data for resolving mixtures. The key variables of the simulation are: the number of SNPs j we added to the ijY of each probe a Gaussian noise based off the previously measured probe variance. When fixed, we set probe variance to 0.006 when simulating Affymetrix 5.0 arrays, and to 0.001 for both Illumina 550 K and Illumina 450S Duo arrays. The allele frequency of the mixture was then calculated to be the mean of these probe values. A mixture size of N is equivalent to saying that an individual's DNA represents f = th1/N of the total DNA in the mixture. We tested equimolar mixtures ranging from 10 individuals to 1,000 individuals. Using this design, we tested each individual for their presence where they contributed between 10% and 0.1% genomic DNA to the total mixture. To obtain significance levels  for testing the null hypothesis, we sampled from the normal distribution. We note that we do not have enough samples to test the tail of our distribution and therefore our p-values are not completely accurate (e.g. below 10−6). Nonetheless, p-values are expected to be sufficiently accurate to qualitatively assess the limits of our method.We then simulated a microarray that would contain a mean of 16 probes for simplicity, approximating the mean number of probes found on the Illumina 550 K, Illumina 450S Duo and Affymetrix 5.0 platforms . For each SNP To examine empirically the efficacy of our method we formed various known mixtures of DNA from HapMap individuals and genotyped the mixtures on three different platforms. Listed in Using the theoretical framework established in the methods, we evaluated the feasibility of using high-density SNP genotyping data to resolve complex mixtures. First, we constructed a series of simulations to evaluate the theoretical limits of resolving an individual within a mixture using the described analytical framework and given characteristics of current generation SNP genotyping microarrays. Second, we experimentally tested the feasibility of detecting if an individual is contributing trace amounts of DNA to highly complex mixtures. Within these simulations and experimental tests, particular focus was given on complex mixtures–those containing hundreds or thousands of individuals. While these mixtures are more complex than those of previous studies, they can be used to evaluate the theoretical bounds of current technology and to justify the use of reduced platforms for practical application. Conceptually, such approaches may have utility in resolving a mixture of DNA from common surfaces where many individuals have left DNA.We performed simulations to examine the trade-off between the number of SNPs considered, the fraction of the DNA mixture belonging to our person of interest, and the probe variance or noise of the microarray.pv) for the estimated allele frequency of each probe of 0.001, which follows closely our observed variance (0.00158) of the Illumina 550 K platform across multiple arrays in other genotyping studies. s = 10 to s = 500,000 and f = 0.1 to f = 0.001, where the Z-axis is the p-value. We see that with only 10,000 to 25,000 SNPs we were able to resolve mixtures where the person of interest was less than 1% of the total mixture at a p-value of less than 10−6. To resolve mixtures where the person of interest is less than 1% of the total mixture, conservatively 25,000 SNPs are required to achieve a p-value of less than 10−6. At the extreme, if we use all the available SNPs, we can easily resolve mixtures where our person of interest is less than 0.1% of the total mixture to achieve a p-value of less than 10−6.We first examined the trade-off between the numbers of SNPs considered versus the fraction of the DNA mixture belonging to our person of interest. Clearly, we expect greater ability to resolve individuals from a mixture when more SNPs are used in the calculation, though the absolute limits of detection are ultimately determined by the genetic variation of the population. We assumed a variance . While conceivably we could use a much greater number of SNPs, the lower number of SNPs would be more realistic in a setting where preference has been given to SNPs whose allele frequencies minimally vary across different populations. pv = 0.0001 to pv = 0.01 and f = 0.1 to f = 0.001. We see that within a small amount of probe variance we are able resolve an individual who comprises of one-thousandth of a mixture. If the probe variance is below 0.001 we are able to easily resolve an individual whose DNA comprises 10% to 0.1% of the mixture. Even with increasing noise, we are still able to resolve mixtures where the person of interest contributes less than 2.5% with a p-value of less than 10−6. We also observe that the probe variance does not have a large impact on the p-value, and in this case the fraction of the mixture is the important factor when the number of SNPs is fixed.In these simulations, we assume that we have 50,000 SNPs on each microarray (f = 0.01). s = 10 to s = 500,000 and pv = 0.0001 to pv = 0.01. The probe variance has little effect on the significance of the test. Consequently, it would be sufficient to use 50,000 SNPs, even with very high levels of noise to resolve mixtures of sizes up to 100. We note that within simulations the number of probes is fixed to be 16, and thus the noise does not affect the allele frequency estimate, as would be the case with arrays using 4 probes.Finally we examined the trade-off between the number of SNPs and the probe variance. We assume that our person of interest contributes 1% to the mixture /N of the mixture and individual two would make up 1/N of the mixture. When the three simulations were performed we observed an increase in significance . This gives further utility to the method when there are a small number of total contributors with the person of interest making up only a small fraction of the mixture.We performed the same three simulation designs for mixtures that only included two individuals. Instead of Our simulations demonstrate that 10,000 to 50,000 SNPs can resolve mixtures where the genomic DNA of the person of interest composes 10% to 0.1% of the DNA within the total mixture. Noise plays an important but secondary role since microarray technologies such as the Illumina 550 K and Illumina 450S Duo platforms have a sufficiently large number of replicate probes compared to population sampling variance. Another consideration is that the choice of SNPs was not made with any specific intent and therefore we could potentially reduce the number of SNPs significantly if we choose the most informative SNPs, for example by choosing a set of SNPs that do not vary across differing populations.To examine empirically the efficacy of our method we formed various known mixtures of DNA from HapMap individuals and genotyped the mixtures on three different platforms.i,j)S is in the mixture; the yellow and orange marked individuals being observed as significant in In In Within this study, we develop a theoretical framework for resolving mixtures using high-density SNP array data, use simulation to test the limitations of these approaches, and experimentally demonstrate rapid and robust determination of whether individuals are within an assayed mixture. Our results show a remarkable ability to identify trace amounts of an individual's DNA within highly complex mixtures. These results further suggest novel forensic applications where the existence of DNA from numerous other individuals currently hampers the ability to identify the presence of any single individual.Whereas few conclusions can be drawn by a SNP measurement that is slightly biased (less than 1%) towards an individual's genotype, considerable confidence is gained by statistical analysis of the cumulative aggregate of all measurements across millions of SNPs. While in hindsight this conclusion seems obvious, it represents a fundamental paradigm shift in thinking about the utility of SNPs at resolving mixtures. The approach described here uses the ratio of intensity measures from common biallelic SNPs. As a result, one expects more robust scaling in DNA quantity or quality at any given SNP. We assume neither a known number of individuals present in the mixture nor the presence of equal amounts of DNA from each individual within the mixture. Described in simplistic terms, we determine whether a person is in a mixture by comparing a statistically describable distance measure between the individual and the mixture versus the individual and the reference population.The analytical framework presented within this study builds upon pioneering approaches for assessing and quantifiably calculating whether a person is within a mixture. These methods have frequently employed match probability estimation after inferring genotypes using STRs, where the probability of two unrelated individuals sharing a combination of markers is calculated One can also consider using other statistical approaches. For example, likelihood ratios are also commonly used when testing which hypothesis is favored by DNA evidence Given the results of this study, it is possible to speculate on future research assessing the viability of using commonly handled surfaces as a forensics source. In the context of degraded samples, further research will be needed to choose which SNPs (of millions assayed SNPs) provide sufficient amplifiable DNA or show less allelic bias at low concentrations. Further, the theoretical principles described here will apply to mitochondrial variants. Regardless of the artifacts encountered, the large number of assayed SNPs may allow for partitioning sets of SNPs for different analyses, such that a small subset of SNPs becomes reserved for detecting specific artifacts, such as biases in allele amplification or ancestry. Additional areas of future research include conversion tables using haplotype or imputation frameworks to convert between SNPs and microsatellite markers.Finally, it is important to consider these findings in light of GWA studies. Indeed, the push to develop high-density SNP genotyping arrays is largely driven by the desire to identify common variants predisposing to a disease. For many GWA studies, the overall cost of genotyping thousands of individuals is substantial. However since genotype data is transferable and can be combined with data from other studies, there is a considerable effort to make experimental data publicly available. As part of this effort, many studies provide pooled allele frequency data in the form of summary statistics , in part to mask individual-level genotype data. Though counter-intuitive, our findings show a clear path for identifying whether specific individuals are within a study based on summary-level statistics. Such approaches may have specific utility for identifying redundant individuals when new individual-level genotype data is combined with previous studies sharing only summary statistics.Considering privacy issues with genetic data, it is now clear that further research is needed to determine how to best share data while fully masking identity of individual participants. However, since sharing only summary data does not completely mask identity, greater emphasis is needed for providing mechanisms to confidentially share and combine individual genotype data across studies, allowing for more robust meta-analysis such as for gene-environment and gene-gene interactions.


To enhance capabilities for genomic analyses in rainbow trout, such as genomic selection, a large suite of polymorphic markers that are amenable to high-throughput genotyping protocols must be identified. Expressed Sequence Tags (ESTs) have been used for single nucleotide polymorphism (SNP) discovery in salmonids. In those strategies, the salmonid semi-tetraploid genomes often led to assemblies of paralogous sequences and therefore resulted in a high rate of false positive SNP identification. Sequencing genomic DNA using primers identified from ESTs proved to be an effective but time consuming methodology of SNP identification in rainbow trout, therefore not suitable for high throughput SNP discovery. In this study, we employed a high-throughput strategy that used pyrosequencing technology to generate data from a reduced representation library constructed with genomic DNA pooled from 96 unrelated rainbow trout that represent the National Center for Cool and Cold Water Aquaculture (NCCCWA) broodstock population.HaeIII; sequencing produced 2,000,000 reads providing an average 6 fold coverage of the estimated 150,000 unique genomic restriction fragments . Three independent data analyses identified 22,022 to 47,128 putative SNPs on 13,140 to 24,627 independent contigs. A set of 384 putative SNPs, randomly selected from the sets produced by the three analyses were genotyped on individual fish to determine the validation rate of putative SNPs among analyses, distinguish apparent SNPs that actually represent paralogous loci in the tetraploid genome, examine Mendelian segregation, and place the validated SNPs on the rainbow trout linkage map. Approximately 48% (183) of the putative SNPs were validated; 167 markers were successfully incorporated into the rainbow trout linkage map. In addition, 2% of the sequences from the validated markers were associated with rainbow trout transcripts.The reduced representation library consisted of 440 bp fragments resulting from complete digestion with the restriction enzyme The use of reduced representation libraries and pyrosequencing technology proved to be an effective strategy for the discovery of a high number of putative SNPs in rainbow trout; however, modifications to the technique to decrease the false discovery rate resulting from the evolutionary recent genome duplication would be desirable. Single Nucleotide Polymorphisms (SNPs) are highly abundant markers which are evenly distributed throughout the genome and can be functionally relevant. They aret al. [Since 2002, the National Center for Cool and Cold Water Aquaculture (NCCCWA) has selectively bred rainbow trout broodstock for improvement of aquaculture production traits. The primary objectives have been to reduce the negative impacts of specifc bacterial pathogens on rainbow trout culture and to improve growth performance -15. Curret al.  found sihttp://www.ncbi.nlm.nih.gov/sites/entrez?db=unigene presents over 200,000 Rainbow trout EST sequences. We first used custom bioinformatic pipelines to assemble the available ESTs and discover SNPs in rainbow trout. The occurrence of the genome duplication led to the assembly of paralogous sequences and resulted in a high number of false positive putative SNPs. Next, we sequenced genomic DNA based on EST sequences which proved to be a more efficient method for SNP discovery in rainbow trout, genomic DNA sequencing would allow the representation of the entire genome, and not just the transcriptomes, as that is the case with ESTs. However, this procedure was also extremely time consuming and not suitable for high throughput SNP discovery and it became clear that an alternative approach would be necessary to achieve the goal of a dense set of markers spanning the entire rainbow trout genome. In this study, we present the use of a high-throughput methodology to discover SNPs in rainbow trout based on pyrosequencing technology to generate data from a reduced representation library (RRL) constructed with genomic DNA pooled from 96 unrelated rainbow trout individuals, and evaluate the efficiency of this approach.Expressed Sequence Tags (ESTs) have been used for SNP discovery in several non-model organisms, including salmonids -27. HoweHpyCH4V and BstUI did not entirely digest the rainbow trout DNA, therefore, they were not considered for further procedures. Trout DNA of an individual animal was completely digested with AluI, BstUI and HaeIII. In contrast with bovine DNA[HaeIII . An aliquot of the recovered fragments was cloned and Sanger sequenced before proceeding with the library construction protocols: 89% of 1,269 sequenced fragments were unique and 33% of the analyzed bases masked to the cGRASP salmon repeat masker database http://lucy.ceh.uvic.ca/repeatmasker/cbr_repeatmasker.py.After digesting the pooled DNA from 96 unrelated individuals with The Genome Sequencer FLX system produces about 400,000 reads (250 bp) per run, therefore, to cover 1% of the trout genome and to achieve a 6× average coverage, 4.5 runs were carried out, which resulted in 2,044,864 reads that contained a total of 459,825,391 bases for an average of 217 bp/read. The distribution of read lengths is rather asymmetric with a thick tail on the shorter side of the most common read length, which is 242 bp. After repeat masking, culling reads with less than 50 consecutive base pairs of non-repetitive content, and trimming repetitive ends, the result is 1,128,655 reads with an average length of 206 bp. Less than 15% of these reads contained internal repetitive content. The Newbler assembly of the 2 million unmasked reads resulted in 149,096 contigs with an average length of 259 bp and an average depth of coverage of 5.9. One-quarter of the contigs covered the length of the restriction fragments by connecting the reads from each end that overlap sufficiently in the middle. The rest of the contigs had lengths consistent with the read lengths. The estimate of the number of loci given in table In order to discover SNPs present only in non-repetitive regions, the set of masked reads were mapped onto the two assemblies, with 75% mapping to the Newbler assembly and 63% mapping to the Velvet assembly. Both assemblies had similar distributions of coverage depth: ~40% less than 4×, ~10% greater than 10× and ~50% 4-10×. The average read depth at SNPs was skewed to the high end, ~9×. Despite the similarities, the two methods discussed thus far produced widely different predictions for the number of SNPs: 187,916 for Newbler and 52,942 for Velvet. The distribution of the SNPs across the contigs reveals that the vast majority of SNPs in the Newbler set came from contigs with 5 or more predicted SNPs. Filtering out such SNPs from both sets leaves 47,128 for Newbler and 33.624 for Velvet. Previous experience with SNP discovery in swine using a reduced representation library showed a much more reasonable distribution of putative SNPs across the assembled contigs [A comparison of the SNPs from the three methods was done on a contig by contig basis, allowing for imperfect matches between velvet contigs and newbler contigs, as well as the occasionally varying numbers of predicted SNPs for the contigs. While compiling the lists of testable SNPs, some moderate filtering was performed to ensure sufficient quality and quantity of flanking sequence. As stated above, the two assemblies were more alike than different, yet the largest subgroup of SNPs is V1, indicating that the contigs unique to the Velvet assembly contain either a rich source of false positives or the best source of true SNPs. The next largest subgroup is N1, despite the fact that most of its potential SNPs were filtered out due to their presence in contigs with 5+ SNPs. The false positives seem to be infiltrating the unfiltered contigs as well. The third largest subgroup contains the SNPs predicted by all three methods, giving some assurance that the three approaches are similar enough to warrant comparison of validation rates as a means to incrementally improve the computational methods for SNP discovery. Even though the reads mapped in N2 were a subset (48%) of those mapped in N1, N2 contained SNPs not predicted in N1. These were from contigs that had 5 or more SNPs predicted by N1 but fewer than 5 predicted in N2. SNPs from each of the subgroups were tested and the validation results are discussed in the next section.GoldenGate genotyping assays resulted in the validation of 183 (48%) of the tested putative SNPs (384); 46 12%) were homozygous; 4 (1%) showed paralogous sequences without allelic variation . It was not possible to obtain satisfactory genotyping results from the remaining sequences (39%), due to either unacceptable genotyping quality or the presence of paralogous sequences with allelic variation, therefore not conclusive to contemporary approach. Table % were hoCall frequencies; minor allele frequencies and expected/observed heterozygosis of the validated SNPs are reported in Additional file BLAST analysis of the potential SNP sequences revealed that 1201  are associated with 790 ESTs/TCs in the rainbow trout transcriptome . The validated SNP sequences have 4 sequences associated with rainbow trout transcripts including proteins involved in lipocalin-type prostaglandin metabolism and immune system-Toll-like receptors Table .Validated markers were also genotyped using the NCCCWA mapping families and 167 markers were polymorphic and mapped to the rainbow trout second generation map. In AddiIn our first attempts to find SNPs in rainbow trout we evaluated multiple bioinformatic pipelines for their ability to detect SNPs from EST data (unpublished). The occurrence of the genome duplication resulted in many assemblies of paralogous sequences, which led to the identification of a large proportion of false positives (data not shown). In addition, a high number of the putative markers were not validated due to PCR amplification problems caused by incorrect assemblies of sequences; presence of introns in the amplicons and unspecific binding of primers (multiple loci). Similar results were observed in other salmonid species ,26,27 anUsing a high throughput technology, over 20,000 putative SNPs were identified, 384 were tested and 183 validated; 90% of the validated markers were placed and widely distributed in the rainbow trout linkage map. A BLAST analysis was performed to identify coding regions in validated SNP sequences and 2% of the validated markers were associated with relevant genes. SNPs found within or near a coding sequence, are of particular interest because they are more likely to alter the biological function of a protein. This class of SNPs is especially important for species without a genome sequence such as aquaculture species. Gene-associated SNPs serve as suitable markers for mapping in comparative genome studies and in MAS of economically important traits ,30.Recent reports of using highly parallel sequencing methodologies in combination with reduced representation libraries (RRL) for SNP discovery in cattle and swine reported validation rates above 92% . These sThe use of reduced representation libraries and pyrosequencing technology proved to be an effective strategy for the discovery of a high number of SNPs in rainbow trout. Over 20,000 putative SNPs were discovered and 384 were tested, resulting in a 48% validation rate. A hundred and eighty three markers were validated and 167 (43.5%) of those markers were polymorphic and placed in the rainbow trout linkage map. According to the validation results, it could be implied that at least 10,000 putative SNPs from this data set would be validated and useful in future genomic studies. However, it is still necessary to dramatically decrease the false discovery rate.AluI; BstUI; HaeIII; HpyCH4V and RsaI) having four-base recognition target sequences and producing fragments with blunt ends were tested for the library construction. Digestions of 1 mg of DNA from a single fish were performed for each enzyme as suggested by the manufacturer (New England Bio Labs). The digested DNA was separated on 3 mm thick 5% nondenaturing polyacrylamide gels. DNA fragments between 400 and 500 bp were excised from the gels (slices were cut every 2 mm or approximately 20 bp), fragments were eluted using a modified [Five different restriction enzymes (modified  "crush aHaeIII. Complete digestions of 1 mg of DNA from the pool (3 U enzyme/μg) were performed overnight at 37°C. DNA fragments were separated and eluted from the gel following the procedures previously described. Excised fragments approximately 440 bp in length were selected to construct the library; 600 ng of DNA obtained from the preparative gel was used for construction of the library and ligated to the sequencing adapters provided in the GS FLX library construction kit (Roche Applied Science). From this point, the library was prepared and sequenced on the GS FLX as per the manufacturer's protocols.Approximately equal amounts of DNA from 96 unrelated individuals from the NCCCWA 2005 and 2006 brood years  was poolhttp://lucy.ceh.uvic.ca/repeatmasker/cbr_repeatmasker.py and sequences with at least 50 consecutive non-repetitive bases were mapped onto the assembly using ssaha2[Three independent SNP discovery approaches were used. The first two, which were called N1 and N2, were initiated by creating a reference sequence set by using the Newbler algorithm  to assemble all the unmasked sequence data into contigs. The individual reads were then screened for known repetitive elements and repeat masked using the cGRASP RepeatMasker database ng ssaha2. N2 diffng ssaha2 for the ng ssaha2 to detecIllumina GoldenGate assays  were useGenomic DNA from 85 of the discovery panel fish and 98 samples from five NCCCWA mapping families  were useThe BeadStudio software version 3.2 (Illumina) was used to analyze the Goldengate assays SNP data. Genotyping data quality was assessed following the guidelines suggested by Illumina . The no-http://www.mbio.ncsu.edu/BioEdit/BioEdit.html) was performed to identify coding regions.A local BLAST search using the sequences of the validated SNPs against the rainbow trout transcriptome data from TIGR (BioEdit software at default settings Segregating data from the NCCCWA mapping families was used to anchor the polymorphic validated markers into the second generation rainbow trout linkage map . GenotypCCS contributed to project design, was responsible for construction of the reduced representation library and Illumina genotyping for SNP validation, and drafted the manuscript. TS shared in conception and design of the project, assisted with library preparation, and led the sequencing and data pipeline aspects. RW was responsible for project design relating to SNP identification, performed the sequence assembly and developed the list of putative SNP. RV performed the linkage analysis. MS annotated the SNP sequences. JY contributed to overall project design. CR shared in conception and design of the project, led linkage analysis, coordinated the project and shared responsibility for manuscript preparation. All authors reviewed the manuscript.Additional Table S1. Validated SNPs information.Click here for fileAdditional Table S2. Mapped SNPs.Click here for file


The interactions of multiple single nucleotide polymorphisms (SNPs) are highly hypothesized to affect an individual's susceptibility to complex diseases. Although many works have been done to identify and quantify the importance of multi-SNP interactions, few of them could handle the genome wide data due to the combinatorial explosive search space and the difficulty to statistically evaluate the high-order interactions given limited samples.Three comparative experiments are designed to evaluate the performance of MegaSNPHunter. The first experiment uses synthetic data generated on the basis of epistasis models. The second one uses a genome wide study on Parkinson disease (data acquired by using Illumina HumanHap300 SNP chips). The third one chooses the rheumatoid arthritis study from Wellcome Trust Case Control Consortium (WTCCC) using Affymetrix GeneChip 500K Mapping Array Set. MegaSNPHunter outperforms the best solution in this area and reports many potential interactions for the two real studies.The experimental results on both synthetic data and two real data sets demonstrate that our proposed approach outperforms the best solution that is currently available in handling large-scale SNP data both in terms of speed and in terms of detection of potential interactions that were not identified before. To our knowledge, MegaSNPHunter is the first approach that is capable of identifying the disease-associated SNP interactions from WTCCC studies and is promising for practical disease prognosis. Single nucleotide polymorphisms (SNPs) are single nucleotide variations of DNA base pairs. Researchers often use SNPs as genetic markers in disease studies. It has been well established in the field that SNP profiles characterize a variety of diseases. By investigating SNP profiles associated with a disease trait, researchers would be able to reveal relevant genes. However, in many complex diseases, SNPs have shown little penetrance individually; on the other hand, their interactions are suspected to possess stronger associations with complex diseases. Some SNPs, which have no direct impact on health, may be linked to nearby genes which do have effects. Researchers hypothesize that many common diseases in humans are not caused by one genetic variation within a single gene, but are determined by complex interactions among multiple genes. Since the sheer volume of data generated by SNP studies is difficult to be manually analyzed, an efficient computational model is required to detect or indicate which pattern is most likely associated with the disease. Then, it will just be a matter of time before physicians can screen individuals for susceptibility to a disease by analyzing their DNA samples for specific SNP patterns, and further design some experiments to target the genes that implicate the disease.Recently, many methods have been proposed to identify SNP interaction patterns associated with diseases. To name a few studies, BEAM  designedBEAM has successfully demonstrated its capability of handling large data sets using synthetic data. When the authors applied BEAM to an AMD (aged-related macular degeneration) study , howeverGiven a genome wide association study with thousands of SNPs and a limited number of samples, it is difficult to detect and evaluate the multi-SNP interactions in a traditional statistic manner. The feasible solution is to first find a small set of relatively more relevant SNPs and then evaluate the interactions within it. This procedure was applied in HapForest  to inferHowever, the typical feature selection models, which use univariate ranking on feature importance and arbitrary threshold to select relevant features, cannot be applied because they will filter out those SNPs that have weak marginal effects, while their joint behavior may significantly contribute to disease traits. In this paper, we introduce an alternative learning approach (MegaSNPHunter) to hierarchically rank the multi-SNP interactions from local genomic regions to global genome. MegaSNPHunter takes case-control genotype data as input and produces a ranked list of multi-SNP interactions. In particular, the whole genome is first partitioned into multiple short subgenomes and each subgenome covers the genomic area of possible haplotype effects in practical. For each subgenome, MegaSNPHunter builds a boosting tree classifier based on multi-SNP interactions and measures the importance of SNPs one the basis of their contributions in the classifier. The method keeps relatively more important SNPs from all subgenomes and let them compete with each other in the same way at the next level. The competition terminates when the number of selected SNPs is less than the size of a subgenome. At the last step, MegaSNPHunter extracts and reports the valuable multi-SNP interactions.The performance of MegaSNPHunter is evaluated through comparative studies with existing work. The goal of MegaSNPHunter is to discover the multi-SNP interactions from genome wide studies. Among many recently proposed methods, BEAM is the best one which could handle the large scale data set and finish in a reasonable time. Therefore, we mainly compare our method with BEAM in this paper using synthetic data generated on the basis of epistasis models and the data sets from two real studies on complex diseases. In the experiments on two real studies, one uses a genome wide study on Parkinson disease (data acquired by using Illumina HumanHap300 SNP chips ). The otSimulation studies are developed to validate the performance of our approach in correctly determining the associated SNPs defined by an epistatic model. To make the fair comparison, we use the simulation program provided in BEAM package and follow the same procedure in  to generIdeally, the results on the genome wide simulation would be more convincing but such a simulation is computationally expensive. In general, the goal of simulation study is to provide the evidence for validity of our approach. In practice, the real data is very complex and the SNP interactions in the real data may not match any epistatic model. Therefore, our approach does not assume any epistatic model. We believe the most effective criterion for judging the epistatic interaction is that the joint effect is much more significant than the marginal effects of individual SNPs. The next two experiments would show the effectiveness of our approach on the real data.Parkinson disease is a chronic neurodegenerative disease with a cumulative prevalence of greater than 0.1 percent. The primary symptoms of Parkinson's disease include tremors, rigidity, slow movement, poor balance, and difficulty walking. In this experiment, we choose the study in  which prMegaSNPHunter is first run on each chromosome with 10 fold cross validation. Cross validation is a model evaluation method that estimates how well the model built from some training data is going to perform on unseen data. The 10 fold cross validation is conducted every time when the boosting tree classifier is built in the whole hierarchical procedure. In our test, the samples are randomly sampled into 10 subsets and each validation uses 9 subsets to train the model and the left one to test the performance. The output from every validation is a classifier and a list of ranked SNPs.χ2 P values reach a critical value (default is 0.05), and those SNPs whose interaction's genotype association χ2 P values are above a critical value (default is 0.0025). The top ranked SNPs among the selected 302 SNPs are reported in Table χ2 P values. The selected interactions with genotype association χ2P values are reported in Table After 10 validations are finished, a post process is invoked to isolate those SNPs whose genotype association rs13032261, rs7924316 and rs2235616) have noticeable marginal effects in Table rs7172832 and rs906428) and the genotype distribution under the interaction. Figure rs1505376 and rs3861561. These figures clearly illustrate how the two weak SNPs significantly affect disease traits .We observe that among 12 SNPs involved in the selected interactions in Table K genotypes in 3503 samples (1999 cases and 1504 controls). We use the same procedure mentioned above to conduct the experiment. The top ranked SNPs among the selected 213 SNPs are reported in Table χ2 P values. The selected interactions with genotype association χ2 P values are reported in Table rs4418931 and rs4523817. Its genotype association χ2 P value is 6.83 * 10-15. The genotype distribution of cases and controls for these two SNPs and the distribution under their interaction are plotted in Figure The Wellcome Trust Case Control Consortium (WTCCC) is a collaboration of many British research groups. To date, the WTCCC has examined the genetic signals of seven common human diseases: rheumatoid arthritis, hypertension, Crohn's disease, coronary artery disease, bipolar disorder, and type 1 and type 2 diabetes. The rheumatoid arthritis study  containsrs4418931 and rs4523817 are located on the gene GPC6, which is a member of the glypican gene family and encodes a product structurally related to GPC4  with class label.   S:SNP Data [while numberOfSNPs(S) > W do   SelectedSNPs ← ∅      S into S0, S1,...,Sm where sizeof(Si) = W (i <m), sizeof(Si ∩ Sj) = O and sizeof(Sm) <W      Separate for i = 0 to m do      Fi ← TreeBoostingClassfier         for SNPj ∈ Fi do         SelectedSNPs ← SelectedSNPs + {SNPj}            end for         end for      S ← S(SelectedSNPs)      end while   F ← TreeBoostingClassfier.   Pi from F.   Extract all path H(Pi) and Rank Pi.   Compute H-statistic Function T reeBoostingClassfier   F ← 0   for t = 0 to T do   ei = yi -i ∈       D depth regression tree       Fit a       F ← F + η∑λl1(x ∈ Rl)      end for   return F   X.W. and C.Y. designed the models and simulation studies together. N.T. and W.Y. initialized the study and proposed the framework of multi-level learning. Q.Y. and H.X. direct the evaluation of methodologies. All authors contributed to the writing of the manuscript.