Alu and other transposed elements. Finally, we identified cases of primate-specific Alu elements that depend on RNA editing for their exonization. These results shed light on TE fixation and the exonization process within human and mouse genes.Insertion of transposed elements within mammalian genes is thought to be an important contributor to mammalian evolution and speciation. Insertion of transposed elements into introns can lead to their activation as alternatively spliced cassette exons, an event called exonization. Elucidation of the evolutionary constraints that have shaped fixation of transposed elements within human and mouse protein coding genes and subsequent exonization is important for understanding of how the exonization process has affected transcriptome and proteome complexities. Here we show that exonization of transposed elements is biased towards the beginning of the coding sequence in both human and mouse genes. Analysis of single nucleotide polymorphisms (SNPs) revealed that exonization of transposed elements can be population-specific, implying that exonizations may enhance divergence and lead to speciation. SNP density analysis revealed differences between  The draft sequences of the human and mouse genomes confirmed that transposed elements (TEs) have played a major role in shaping mammalian genomes Alu. Alu elements are the most abundant repetitive elements in the human genome; there are upwards of 1.1 million copies, accounting for more than 10% of the human genome Alu elements are derived from the 7SL RNA Alu retroposition took place 50–60 million years ago and has since dropped to a frequency of one new retroposition for every 20–125 births Alu-mediated mutagenesis, mostly through nucleotide insertions, has been estimated to be involved in close to 1% of Mendelian genetic disorders Alu sequences has been discussed Exonization is the creation of a new exon as a result of mutations in intronic sequences Alu elements within mature mRNA in human Alu elements are usually alternatively spliced; these exons comprise ∼5% of alternatively spliced exons in the human transcriptome Alu exonization has been well characterized. A typical Alu is around 300 nt and contains two similar monomer segments joined by an A-rich linker and a poly(A) tail-like region. Alus insert into introns of primate genes by retrotransposition, usually in the antisense orientation. Eighty-five percent of exonizations have occurred from the right arm in the antisense orientation Alus that reside in antisense orientation relative to the coding sequences to yield a consensus 3′ splice site Alus are very similar to those of the 3′ splice sites of mammalian interspersed repeat (MIR) exons Makalowski and coworkers were the first to describe Recent studies indicate that the pattern of splicing of exonized TEs differs among human tissues Alu exons enriches the human transcriptome with new mRNAs without eliminating the original, functionally important transcripts, which are generated via exon skipping The exonization process is subject to many evolutionary constraints: New exons are generally alternatively spliced Here we set to find additional characteristics of TE exonization events within human and mouse. We looked at the location of the exonizations within genes and the SNP densities, and evaluated SNPs that change canonical splice sites. We found that exonizations occur preferentially in the beginning of protein coding sequences. Moreover, we show that exonizations can be population specific. Our findings reveal a possible contribution of TE exonizations to population divergence within human and mouse.p = 1.2244e–027 and p = 1.2322e–006 for human and mouse, respectively). These results imply that most TE exonizations tend to occur within the first introns of genes. In human non-TE alternatively spliced exons, 1353 out of 17,642 are the second exon, whereas in TE-derived exons 233 out of 927 are found in the first intron and if spiced become the second exon; this difference is statistically significant . The first intron is substantially longer, with respect to the other introns, in most human and mouse genes and shows higher rate of TE insertion Non-symmetrical, conserved, alternatively spliced exons are more often located at the beginning of the CDS than elsewhere in transcripts p = 0.0428) but not between the second group and the third group or the first group and the second . This observation strengthens the hypothesis that the 5′ position bias of TE exonization has a connection with the NMD machinery. We previously showed that non-symmetrical exons (not related to TEs) that are alternatively spliced in both human and mouse  tend to be located near the 5′ end of the CDS, whereas conserved symmetrical alternative exons are located throughout the CDS In order to analyze whether the location bias results from potential involvement of purifying selection, we separated our data to three groups: exonizations that contain an in-frame stop codon (599 exons), exonizations that are non-symmetrical and do not contain an in-frame stop codon (216 exons), and symmetrical exons that do not contain stop codons (137 exons). The median locations within the normalized CDS of these three groups are 0.3062, 0.3795, and 0.4199, respectively. The Wilcoxon Rank Sum test showed that there is a statistically significant difference between the first and the third group . An exception was observed in the CR1 (LINE-3) elements; exonized CR1 elements have a higher than average SNP density. However, only four CR1 elements were exonized so the sample size is very small. In mouse, for all transposed element families, the density of SNPs in exonized TEs was significantly higher than the overall density in all TEs . In mouse, exonization seems to occur preferentially in areas with higher SNP density.We then examined the SNP density in exonized TEs . The SNPAlu elements, two in splice sites of exonized L2 elements, and one in the splice site of an exonized LTR element. To ensure that we identified the sequence without the SNP correctly, we examined the sequences of the orthologous TEs in chimp (http://sssnptarget.org/) In order to investigate the possibility that exonization of TEs creates transcriptomic diversity within the human population, we searched for SNPs that eliminate or create canonical splice site in a TE. Specifically, we looked either for changes in the invariant AG dinucleotide at the 3′ splice site or the canonical GT or GC at the 5′ splice site. Although there are other positions that might alter recognition by the splicing machinery, only the four positions must be fully conserved to ensure selection by the spliceosome. To enhance the fraction of bona fide exonization events we searched for exonized TEs that are supported by at least two ESTs. Our analysis revealed 10 SNPs in canonical splice sites of TE-derived exons in the human genome ; these Sin chimp . AdditioWe searched the NCBI Database of Single Nucleotide Polymorphisms for population frequency data. Data were only available for two of the 10 SNPs observed in the human genome . One of We have also built a dataset of TEs with non-canonical splice sites that appear to be active based on evidence of exonization from ESTs or cDNAs. We searched the SNP database for SNPs that might change the non-canonical splice sites into canonical ones. In the human genome, we found 45 SNPs that changed a non-canonical splice site into a canonical site . Moreover, transcripts containing this exon have three additional A-to-I editing sites within the Alu-derived exon. Several potential editing sites are usually observed within a region that contains two Alu elements located in opposite orientation due to the formation of a long double-stranded RNA structure between the elements Alu to that exonized in the NR_024561 gene is in the downstream intron . The normalized location was the quotient of the actual location of the exon start point within the CDS divided by N.In order to create a dataset of cassette exons that had not originated from TE exonization, we downloaded the altSplice table from the UCSC genome browser http://www.ncbi.nlm.nih.gov/projects/SNP/) were obtained from the UCSC Genome Browser Database baselines. The positions of all TEs in the genome and locations of SNPs within each TE were determined using the SNP data set from UCSC Genome Browser Database. The same was done for the surrounding 50-bp non-overlapping windows (up to distance of 3 kb) for determination of the baseline density of SNPs. The SNP densities were averaged over all TEs and normalized to SNP frequency per 100 bp by dividing the average number of SNPs within the TE by the average length of the TEs divided by 100. Averaging the SNP frequencies in all 50-bp windows flanking the TE yielded the baseline SNP frequency, similar to the calculation described in SNP locations  Alu exonizations was searched for Alu elements with the non-canonical AA 3′ splice sites or the AT non-canonical 5′ splice site. These Alus were filtered according to the following criteria: (1) no SNPs were detected within these slice sites, (2) at least one A to G transition was detected between the DNA sequence and the mRNA, and (3) another Alu sequence in reverse orientation is located within a distance of 2000 bp.The dataset of Table S1SNPs in non-canonical splice sites of exonized transposed elements in the human genome as well as in the mouse genome resulting in a canonical splice site. Given are the gene id, the chromosome and strand on which the SNP is located, the start and end of the exon which derived from the transposed element, the transposed element's family, the SNP id and the alleles of the SNP and the position at which the SNP is located .(0.05 MB DOC)Click here for additional data file.Table S2Population frequency data for the SNPs which changed a non-canonical splice site into a canonical one while the other splice site was already canonical. Given is the SNP id along with the alleles and the position where this SNP occurred as well as the frequency data. Here, the homozygosity for the first allele, the heterozygosity, the homozygosity for the second allele, the Hardy-Weinberg proportions as well as the frequencies for each of the alleles are given. CEPH-European, HISP-Hispanic, AD-African American, CEU-European, HCB-Asian, JPT-Asian, YRI-Sub-Saharan African, HWP-Hardy-Weinberg proportions.(0.10 MB DOC)Click here for additional data file.


Complex diseases or phenotypes may involve multiple genetic variants and interactions between genetic, environmental and other factors. Current genome-wide association studies (GWAS) mostly used single-locus analysis and had identified genetic effects with multiple confirmations. Such confirmed single-nucleotide polymorphism (SNP) effects were likely to be true genetic effects and ignoring this information in testing new effects of the same phenotype results in decreased statistical power due to increased residual variance that has a component of the omitted effects. In this study, a multi-locus association test (MLT) was proposed for GWAS analysis conditional on SNPs with confirmed effects to improve statistical power. Analytical formulae for statistical power were derived and were verified by simulation for MLT accounting for confirmed SNPs and for single-locus test (SLT) without accounting for confirmed SNPs. Statistical power of the two methods was compared by case studies with simulated and the Framingham Heart Study (FHS) GWAS data. Results showed that the MLT method had increased statistical power over SLT. In the GWAS case study on four cholesterol phenotypes and serum metabolites, the MLT method improved statistical power by 5% to 38% depending on the number and effect sizes of the conditional SNPs. For the analysis of HDL cholesterol (HDL-C) and total cholesterol (TC) of the FHS data, the MLT method conditional on confirmed SNPs from GWAS catalog and NCBI had considerably more significant results than SLT. Genome-wide association studies (GWAS) have identified genetic variants associated with a number of complex diseases or phenotypes μ = the population mean of the phenotypic values, Zi = 1×p vector of the p covariates for subject i  to account for environment, population stratification, and other factors; βZ = p×1 vector of the partial regression coefficients of the covariates Zi; Gi =  = 1×(S−1) vector of the S−1 SNP genotypes that were confirmed to be associated with the phenotype for subject i, with i,jG taking values of 0, 1 or 2 according to the number of copies of the minor allele for subject i at SNP j; βG = (S−1)×1 vector of partial regression coefficients of S−1 conditional SNPs; i,sG = the genotype value of the candidate SNP; sβ = the partial regression coefficient of the candidate SNP, and ie = random residual that is assumed to follow N normal distribution. The residual variance of Equation 1 isThe multiple linear regression model for the MLT method can be expressed as:0: sβ = 0, where sβ is the regression coefficient of the candidate SNP and is the Mth element of β =  and M = 1+p+S. One-sided statistical power was derived in this study for simplicity . Using a multiple linear regression framework, the power of the one-sided t-test can be formulated as:αt denotes the α% quantile value for t distribution and f denotes the non-central t-distribution with N−M degrees of freedom and non-centrality parameter:Mth row and Mth column of variance-covariance matrix M×M variance-covariance matrix of X is the design matrix in Equation 1.A standard t-test can be used for testing the significance of the candidate SNP based on testing the following hypotheses, Hσ2 and has the following mathematical expression:Gi) is calculated based on the Gi values defined in Equation 1. Equation 6 shows that the residual variance of the SLT model is inflated over the MLT model of Equation 1 due to the fact that confirmed SNP effects are now in the residual term of Equation 6. Using this inflated estimated residual variance, the variance of c is the element at the (p+2)th row and (p+2)th column of matrix X is the design matrix for the regression model of SLT. Therefore, the t-test statistic for SLT does not have a t-distribution but a t-distribution divided by a constant For SLT, the statistical model is the same as Equation 1 except that the residual term is now a summation of confirmed effects and random residuals. The residual variance for the SLT model is no longer αt denotes the α% quantile value for t-distribution with N−p−2 degrees of freedom and f denotes the non-central t-distribution with N−p−2 degrees of freedom and non-central parameter λ.Similar to Equations 3, the power of the one-sided t-test can be formulated as:The analytical formulae for statistical power for MLT accounting for confirmed effects and for SLT without accounting for confirmed effects (Equation 3 and Equation 8) were validated by simulated data of 2000 subjects for various effect sizes of the candidate SNP and confirmed SNPs with 10,000 repeats. The phenotypic values were simulated by the summation of a population mean, three additive SNP effects and a random error which followed a standard normal distribution. The three SNPs were simulated under Hardy-Weinberg equilibrium and linkage equilibrium with allele frequencies, 0.3, 0.4 and 0.2. The first two SNPs were assumed to have confirmed effects and the last SNP was assumed to be the candidate SNP. The candidate SNP was tested by the MLT and SLT methods in each simulation. Empirical power was calculated as the proportion of significant results from all 10,000 simulation results. We fixed the effects of the two confirmed SNPs as 0.3 and 0.2 standard deviation of residuals (SD) and varied the effect of the candidate SNP from 0.04 to 0.2 SD. Simulated statistical power were nearly identical to the predicted power for MLT and SLT based on different candidate SNP effect sizes  and on dWe further evaluated predicted statistical power using reported effect sizes for some confirmed SNP effects. We collected all reported SNP effects for HDL cholesterol (HDL-C), LDL cholesterol (LDL-C), triglycerides (TG), total cholesterol (TC), and serum metabolites (SM) from the GWAS catalog http://www.ncbi.nlm.nih.gov), we selected six SNP markers  that had 6575 individuals with SNP genotypes of the 500k SNP panel from dbGAP  markers  with mul markers . The firIn our analysis, we did not impute genotypic data to fill in missing genotypes so that the MLT test had smaller sample size than SLT. The observed significance should have been larger than observed in Table S1Power comparison between analytic formulas and simulation for MLT (Power I) and SLT (Power II) with varied candidate SNP effect sizes, constant confirmed effect sizes (0.2 and 0.3 SDs for two confirmed SNPs) and constant sample size (2000). (DOC)Click here for additional data file.Table S2Power comparison between analytic formulas and simulation for MLT (Power I) and SLT (Power II) with varied sample sizes, constant candidate SNP effect size (0.1 SD) and confirmed effect sizes (0.2 and 0.3 SDs for two confirmed SNPs). (DOC)Click here for additional data file.


When combined into a set, the 10 SNPs account for 2.9%  of the phenotypic variance. The association is linear across the distribution consistent with a quantitative trait locus (QTL) hypothesis; the third of children in our sample who harbour 10 or more of the 20 risk alleles identified are nearly twice as likely  to be in the lowest performing 15% of the distribution. Our results correspond with those of quantitative genetic research in indicating that mathematical ability and disability are influenced by many genes generating small effects across the entire spectrum of ability, implying that more highly powered studies will be needed to detect and replicate these QTL associations.Numeracy is as important as literacy and exhibits a similar frequency of disability. Although its etiology is relatively poorly understood, quantitative genetic research has demonstrated mathematical ability to be moderately heritable. In this first genome-wide association study (GWAS) of mathematical ability and disability, 10 out of 43 single nucleotide polymorphism (SNP) associations nominated from two high- vs. low-ability ( Mathematics is fundamental to many fields such as science, engineering, economics and medicine, and the understanding of basic numeracy and related skills is a crucial component of normal brain function. Despite widespread appreciation of the increasing importance of numeracy in modern society, research has revealed poor average performance in many countries, with extremely low enrolment in mathematical subjects after age 16 . DefininQuantitative genetic research indicates a genetic component to individual variation in mathematical ability, yielding heritability estimates of 0.2–0.9  in a two-stage GWAS of mathematical ability and disability. The top-performing 46 SNPs nominated in these two scanning stages were individually genotyped in a sample of 2356 individuals spanning the entire distribution of ability, to test not only the association with low vs. high mathematical performance, but also the QTL hypothesis that most SNPs associated with mathematical ability at the extremes are also associated with the entire range of mathematical ability.Here, we use pooled DNA from 10-year-olds of high vs. low mathematical ability (Participants were part of the Twins Early Development Study (TEDS), a longitudinal study involving a representative sample of over 11,000 sets of twins born in England and Wales between 1994 and 1996 . We created a mathematics composite mean score by summing standardized scores for the three ratings because our multivariate genetic analyses reveal that the ratings are highly correlated genetically  and quantified in triplicate using PicoGreen® dsDNA quantification reagent . Upon obtaining reliable quantification triplicate readings (±0.5 ng/µl), 100 ng of DNA for each individual was added to their respective pool. To avoid compromise because of pipetting errors 1 µl was deemed the minimum volume that could be reliably added to a pool. DNA samples found to be at a concentration greater than 100 ng/µl were diluted before being added to a pool. The range of final concentrations across the 20 pools for high and low mathematical ability was 14.77–16.13 ng/µl and 15.33–17.03 ng/µl, respectively.In order to maximize the power of this DNA pooling study to detect associations of the small effect sizes expected here, a high- vs. low-ability design was employed  in accordance with the standard protocol for individual DNA samples documented in the GeneChip® Mapping 500K Assay Manual. This platform has been previously validated for use with DNA pooling techniques . Here, pTo validate the pooling results and to extend the analysis of the high and low extremes to a normally distributed population sample, the 46 top-ranked SNPs from the second stage were individually genotyped: 41 using the Sequenom MassARRAY iPlex Gold® system  and 5 using Applied Biosystems' TaqMan® assay . The medium-throughput Sequenom MassARRAY iPlex Gold® system processes ‘plexes’ of up to 40 SNP-assays simultaneously. Only compatible assays may be combined into a single plex. Because of this, and to economize on cost and man-hours, the 41 SNPs we investigated here using the Sequenom iPlex Gold® system were coupled with SNP-assays from other studies and spread across three plexes of 26, 33 and 36 SNPs. The sample for the individual genotyping stage comprised 2356 individuals (one member of each twin pair) drawn from a normal distribution of mathematics scores. 380 individuals within this sample overlapped with the 600 individuals in the second pooling stage. Of these 380 individuals, 66 had monozygotic twins and 78 had dizygotic twins within sample 1. The remainder of sample 3 also contained 142 monozygotic and 243 dizygotic twins of individuals from sample 1, and 82 monozygotic and 105 dizygotic twins of individuals in sample 2.P < 0.05) SNPs in sample 3 were then combined together to form a SNP-set score between 0 and 20 for each of the individuals in sample 3. This score equalled the number of mathematics-increasing alleles each individual possessed, and was analysed using linear models in R to gauge the effect of the SNP set on mathematics. Sample 3 was then dichotomized, and logistic regression used to test association of the 10 SNPs and the SNP-set score with low mathematical performance.Individuals calling on fewer than 70% of the SNPs within each ‘plex’, and also within the TaqMan®-genotyped samples, were retyped, as were SNPs with a call rate lower than 95%. 22 individuals with persistently low call rates were removed entirely from the analysis leaving a final sample of 2334 individuals. However, on a ‘within-plex’ basis, 175, 172, 244 and 185 individuals were removed from the analysis of SNPs within the 26-plex, 33-plex, 36-plex and Taqman-genotyped SNPs, respectively. Three SNPs with persistently low call rates were also removed. The 43 remaining SNPs were assessed for Hardy—Weinberg equilibrium and analysed using linear models in R, fitting an additive model to test for association with mathematics, then testing for evidence of non-additivity by likelihood ratio test comparison of nested models . The 10 P < 0.05 level to detect a causal  association with an allelic variant of 20% frequency and 1% and 1.25% effect size, respectively; with a marker in linkage disequilibrium (D′ = 0.8) with a causal variant of 20% frequency and 1.55% and 2% effect size, respectively. It is worth noting that P-values were used solely as a means of ranking SNP performance in the pooling stages. A threshold of P < 0.05 has been used simply to provide an estimate of the power of samples 1 and 2. With mathematical ability assessed as a quantitative trait under the additive model, at the P < 0.05 level the individual genotyping sample had 80% power to detect a causal variant of 20% frequency and 0.41% effect size; with a marker in linkage disequilibrium (D′ = 0.8) with a causal variant of 20% frequency and 0.65% effect size. The genotypic model provides the same power to detect non-additive effects, but has less power to detect additive effects. When dichotomized for case (defined as the lowest performing 15%) vs. control analyses, under the additive model at the P < 0.05 level the individual genotyping sample had 80% power to detect risk alleles for mathematical disability of 20% frequency and 0.75% effect size, and a marker in linkage disequilibrium (D′ = 0.8) with a causal variant of 20% frequency and 1.2% effect size.Power was estimated using the Genetic Power Calculator  identified by the WTCCC as revealing strong geographical differentiation across the United Kingdom (Wellcome Trust Case Control Consortium 2007) did not associate with mathematical ability in either sample 1 or sample 2 see , and nonP-values from samples 1 and 2. Marked in black are the top-performing 46 SNPs from the 3000 SNPs tested in sample 2. For financial reasons, 46 was the maximum number of SNPs we could afford to carry forward to an individual genotyping stage. P> 0.01 level.These 46 SNPs were tested for QTL association via individual genotyping in a third sample of 2356 individuals spanning the entire distribution of mathematical ability. Although SNPs with call rates below 95% were retyped, 3 of the 46 SNPs yielded persistently low call rates and were excluded from further analyses. 22 individuals with persistently low call rates were also completely excluded at this stage, leaving a sample of 2334 individuals. In our sample there was no significant effect of sex on either mathematical ability or its association with any of the remaining 43 SNPs; for this reason and to increase power the sexes were analysed together. All 43 SNPs were in Hardy—Weinberg equilibrium at the P-values obtained for the 43 individually genotyped SNPs in sample 3, which demonstrates that more associations were observed than would be expected under the null hypothesis. The observed distribution deviates from the expected distribution very early on, suggesting that more associations of small effect size might be detected in a larger sample with more power. P < 0.05) with individual differences in mathematical ability. Moreover, the first three associations in MMP7, GRIK1 and DNAH5, respectively  of the phenotypic variance. This method takes into account the relative effect sizes of each SNP in our sample and weights them in the model accordingly. As our sample may not have the power to accurately distinguish the exact magnitude of such small effect sizes, we also created a ‘SNP-set score’ for each individual in sample 3 by summing the math-increasing alleles they possess. This SNP-set score, in which each SNP is weighted equally, accounts for 2.9%  and 2.1% of the variance in web-test performance . When the same method was used to calculate a 43-SNP-set score from all SNPs tested in sample 3, we found it accounted for 3.2%  of the variance in mathematics composite score in our sample. The effect sizes of these 10 SNPs are small—with the largest at rs11225308 accounting for 0.58% of the variance in ability in the sample and the smallest at rs1215603 accounting for only 0.13%. However, when all 10 SNPs are combined in an additive model to form a ‘SNP set', together they account for 3.4% , and individuals within our sample with 10 or more of the 20 risk alleles were nearly twice as likely as those with 9 or fewer risk alleles to fall within the low performance group . P-value = 6.477e–07). The proportion of cases falls as the SNP-set score  rises. The sensitivity and specificity of the SNP set to identify a child as low performing is 0.46 and 0.88, respectively, which translates to a positive predictive value (PPV) of 0.46 and a negative predictive value (NPV) of 0.70 within our sample.In addition to the analysis of mathematical ability as a continuous trait, sample 3 was dichotomized to assess the effect of these 10 SNPs on the lowest performing 15% of the sample. Although power is reduced in this approach, 4 of the 10 SNPs revealed significant associations with low performance . The 10-P < 0.05, data not shown). A SNP-set score combining these four SNPs accounts for 1.2% of the variance in mathematical ability in this smaller sample . Individuals with four or more of the eight risk alleles are nearly 1.5 times as likely to fall within the lowest performing 15% of the sample .Finally, we analysed sample 3 with the overlapping 380 (16%) individuals from sample 2 removed. Of the 10 SNP associations reported, those of rs11225308, rs363449, rs12199332 and rs17278234 remain nominally significant after this exclusion  with all previously associated NRCAM-tagging SNPs (r2 = 0.83) with rs2300045. Common haplotypes estimated from HapMap data indicate an association between the rs2300052 allele conferring lower mathematical ability and the rs2300045 allele conferring autism risk. This is in keeping with the observation that although some autistic savants exhibit high mathematical ability, autism is generally associated with lower IQ, and even within high-functioning individuals with Asperger's syndrome, mathematical ability is significantly lower  family. MMPs are involved in the breakdown of extracellular matrix during normal physiological processes such as embryonic development, growth and tissue repair   , there m


We applied a subset of these sex-specific quality control filters to eight chromosomes in the Framingham Heart Study samples genotyped by Affymetrix 500 k SNP arrays, and identified another two single-nucleotide polymorphisms that failed to be picked up by the above global filters.We explored five sex-specific quality control filters in North American Rheumatoid Arthritis Consortium's Illumina 550 k datasets. Three X chromosome and three autosomal single-nucleotide polymorphisms flagged by sex quality control filters were missed by filters of call rate at 95% and Hardy-Weinberg equilibrium at 10 SNP filters such as call frequency, minor allele frequency (MAF), Hardy-Weinberg equilibrium (HWE), replicate concordance, and mendelian errors have been widely implemented to identify problematic SNPs. For example, the SNP thresholds used by Plenge et al.  for the Such SNPs, where data is differentially missing, miscalled, or both by sex may be of particular concern for diseases with differences in risk by sex, such as RA. In this analysis, we report on sex-specific differences in SNP call rates and genotype frequencies in the NARAC and Framingham Heart Study (FHS) data as released in Genetic Analysis Workshop 16 (GAW16).® Human Mapping 500 k Array Set. We used the 6,808 of FHS samples with a specified sex .Data were extracted from NARAC and FHS data files for GAW16 and analyzed using PLINK 1.04 . In the The overall sample and SNP statistics including observed proportion of heterozygotes (PropHet), call rate, and MAF were calculated using PLINK. We explored three approaches to identifying X chromosome SNPs that may be called differently by sex. The simplest were the proportion of male heterozygote calls and the absolute value of the difference in the call rates for males and females. The other, more statistical approach was to code all samples as female , use sex as the phenotype, and test whether the proportion of missing data was associated with sex (--test-missing option in PLINK ). We usep-values less than 10-7. This method flags SNPs with smaller differences between males and females when the overall call rate for the SNP is higher. Figure p-values less than 10-7.The call rates for males and females are shown in Figure p-values among controls greater than 10-6, there are 108, 90, and 3 of the SNPs that passed these tests had one, two, or three of the sex-difference flags. The three SNPs flagged by all three methods are shown in Table There are 151, 248, and 21 SNPs flagged exclusively by one, two, or three of these flags. After applying other QC filters of an overall call rate higher than 95% and Hardy-Weinberg -7. By utilizing the surrounding sequence for these three SNPs as provided by dbSNP, these three show significant alignment to both chromosomes 1 and Y, resulting in very similar BLASTn E-values and alignment scores [p-value less than 10-7 for the test of missingness by sex. There are 96 SNPs flagged by one or more of these thresholds . Table -6, there are three SNPs with two sex-difference flags and three with one flag.Figure t scores . Twenty-n = 51) with sex differences in call rate (>5%) and/or PropHet (>10%) on the chromosomes we examined . We flagged two such SNPs in the eight FHS autosomes we examined. Applied with care, sex-specific filters may be useful to identify and filter SNPs before the analysis of GWAS data.In the NARAC dataset, we flagged three SNPs on the X chromosome and three on autosomes with sex-specific calling errors that would not have been detected with general QC filters (95% call rate and 10EIRA: Epidemiologic Investigation of Rheumatoid Arthritis; FHS: Framingham Heart Study; GAW16: Genetic Analysis Workshop 16; GWAS: Genome-wide association study; HWE: Hardy-Weinberg equilibrium; MAF: Minor allele frequency; NARAC: North American Rheumatoid Arthritis Consortium; PropHet: Proportion of heterozygotes; RA: Rheumatoid arthritis; QC: Quality control; SNP: Single-nucleotide polymorphism.The authors declare that they have no competing interests.JEBW analyzed the FHS data. EWP proposed the study, drafted and critically revised the manuscript and suggested further analysis. HL analyzed the NARAC data and drafted the manuscript. KH reviewed results and suggested further analyses. All authors read and approved the final manuscript.


Mycoplasma genitalium. The value of these methods has never been comparatively assessed. The aims of this study were: 1) to identify new potential genetic markers based on an analysis of short tandem repeat (STR) sequences in the published M. genitalium genome sequence; 2) to apply previously and newly identified markers to a panel of clinical strains in order to determine the optimal combination for an efficient multi-locus genotyping system; 3) to further confirm sexual transmission of M. genitalium using the newly developed system.Several methods have been reported for strain typing of M. genitalium type strain G37 and identified 18 loci containing STRs. In addition to one previously studied locus, MG309, we chose two others, MG307 and MG338, for further study. Based on an analysis of 74 unrelated patient specimens from New Orleans and Scandinavia, the discriminatory indices (DIs) for these three markers were 0.9153, 0.7381 and 0.8730, respectively. Two other previously described markers, including single nucleotide polymorphisms (SNPs) in the rRNA genes (rRNA-SNPs) and SNPs in the MG191 gene (MG191-SNPs) were found to have DIs of 0.5820 and 0.9392, respectively. A combination of MG309-STRs and MG191-SNPs yielded almost perfect discrimination (DI = 0.9894). An additional finding was that the rRNA-SNPs distribution pattern differed significantly between Scandinavia and New Orleans. Finally we applied multi-locus typing to further confirm sexual transmission using specimens from 74 unrelated patients and 31 concurrently infected couples. Analysis of multi-locus genotype profiles using the five variable loci described above revealed 27 of the couples had concordant genotype profiles compared to only four examples of concordance among the 74 unrelated randomly selected patients.We performed a comprehensive analysis of STRs in the genome of the M. genitalium infection. The multi-locus typing analysis of 74 unrelated M. genitalium-infected individuals and 31 infected couples adds to the evidence that M. genitalium is sexually transmitted.We propose that a combination of the MG309-STRs and MG191-SNPs is efficient for general epidemiological studies and addition of MG307-STRs and MG338-STRs is potentially useful for sexual network studies of  M. genitalium has been recognized as an important cause of nongonococcal urethritis (NGU) in men and is likely to be associated with genital tract inflammatory diseases in women, such as cervicitis , indicating that these STRs are unsuitable for strain typing. Because our earlier study showed that the STR in the MG309 PLP gene was highly variable among clinical strains [Previous studies have found that the STRs in MG191, MG192 and MgPars undergo extensive intrastrain variation due to DNA recombination and/or other mechanisms ,21, Ma e, Ma e21, strains , we targP < 0.0001) while this correlation was not significant for the MG309-STRs , which showed no evidence of sequence mixtures, the number of MG309 repeats varied from 7 to 17. Two to four different AGT/AAT distribution patterns were observed in specimens having the same number of STRs with the exception of the five specimens with 9 repeats, which were all identical. When both the repeat number and distribution pattern variations were taken into account, there were 29 unique MG309 sequence types among these 57 patients. Figure P < 0.0004 by Fisher's exact test). Thus far types 4 and 5 are seen only in New Orleans. Of interest is that none of the other typing loci studied here differ in prevalence between the two locations.Overall rRNA-SNPs types 2 and 3 are most common . Types 4 (5.4%) and 5 (1.4%) are relatively uncommon. As in our previous study, Type 1 is not present. This type is uniformly found in the currently available ATCC strains but apparently nowhere else. Stratifying the data geographically revealed a significant difference between rRNA-SNP types in New Orleans and Scandinavia Figure . In New To increase efficiency of rRNA-SNP locus typing we developed a simple and rapid method based on single-strand conformational polymorphism (SSCP) analysis to detect the rRNA-SNPs. As shown in Figure et al. [EU131379 to EU131382.This typing method is based on sequence analysis of the polymorphic sites in an approximately 280-bp fragment of the MG191 conserved AB region . Previouet al. . Among 1Two sequential specimens were obtained from each of 10 New Orleans patients at an interval of 10 to 36 days Table . None ofThe discriminatory index (DI) for each marker was calculated using the 28 specimens having a single allele at all five loci studied  in comparison with the 74 unrelated patients. Figure To study the sexual transmission of M. genitalium type strain G37 and found two new genetic markers that are potentially useful for molecular typing of this organism. We evaluated the discriminatory power of the new STR markers (MG307 and MG338) and three previously described markers, including the MG309-STRs [M. genitalium is sexually transmitted [In this study, we carried out a comprehensive analysis of the STRs in the genome of the 309-STRs , rRNA-SN309-STRs  and MG19309-STRs , using ansmitted .M. genitalium. Kokotovic et al., were the first to describe a typing method for M. genitalium, which is based on whole-genome fingerprinting involving selective amplification of restriction fragments [mgpB) gene [mgpC) gene [et al. [M. genitalium genome. We and others have found that this region is undergoing rapid sequence shifts over time in vitro and in vivo due to homologous recombination with MgPar repeats [M. genitalium genotyping. The other three typing methods, including MG309-STRs, rRNA-SNPs [Molecular typing has proven to be an important tool for understanding the epidemiology and pathogenesis of bacterial infections. During the past few years, multiple attempts have been made to develop strain typing methods for ragments . This meragments , SNPs ofragments  and the pB) gene , and RFLpC) gene . The tar [et al.  involves repeats ,21, sugg repeats  is not sRNA-SNPs  and MG19RNA-SNPs , appear M. genitalium based on data derived from the same collection of specimens. We found that MG309-STRs and MG191-SNPs had greater discriminatory power than other markers. The major advantage of STR typing is that it gives the results as numbers allowing for easy interpretation and exchange of data between different laboratories. A disadvantage of STRs typing is the presence of mixed repeat patterns in approximately 20–34% of clinical strains, which may complicate the interpretation of the results for laboratory personnel inexperienced in reading sequence chromatograms. Investigation of the possibility of separating the mixed sequences and quantifying the copy number by other approaches such as denaturing high-resolution gel electrophoresis [M. genitalium infection. The addition of the other three loci results in a small increase in discriminatory power but requires increased effort and expense.The present study is the first to compare the discriminatory power of different genotyping markers for phoresis  or fluorphoresis  would bephoresis . Of the M. genitalium. It would be of considerable interest to determine the rRNA-SNPs distribution in diverse populations from around the world. Moreover, it would be worth determining whether or not rRNA-SNPs associations correlate with environmental factors and/or bacterial virulence.It is of interest that the distribution of the rRNA-SNPs differs strongly between Scandinavia and New Orleans Figure  whereas While the PLP STRs are stable over a period of up to 5 weeks Table , high frM. genitalium has been suggested by a number of epidemiological studies which have found either high infection concordance rates among sexual partnerships [M. genitalium infection. We have previously addressed this issue by MG191-SNP analysis of 19 concurrently-infected couples [Neisseria gonorrhoeae infection from sexual contacts, which was determined by multi-antigen sequence typing [M. genitalium. It is noteworthy that, despite the presence of mixed alleles in at least one of the three variable STR loci in 18 couples, mismatching MG338 and MG309 STR copy numbers were observed between the members of only two couples (12 and 97), while all the remaining couples showed matched repeat copy numbers . This suggests that the clonal variant populations are continuously transmitted between regular sexual partners through repeated exposure. The stability of these highly discriminatory genotypes suggests that STR analysis might be useful for sexual network studies of M. genitalium infection.Sexual transmission of nerships -8,29 or nerships ,31. Howe couples . For alle typing . These fM. genitalium and found that the MG191-SNPs and the MG309-STRs had comparable high degrees of discriminatory power. A combination of MG309-STRs and MG191-SNPs achieved almost perfect discrimination. The ability to find concordant genotypes within concurrently infected couples and different genotypes among unrelated patients suggests that these typing methods have excellent epidemiological concordance. We propose that a combination of the MG309-STRs and MG191-SNPs is efficient for general epidemiological studies and addition of MG307-STRs and MG338-STRs is potentially useful for sexual network studies of M. genitalium infection.We evaluated the performance of several genetic markers for typing of M. genitalium type strain G37 [In an attempt to increase the repertoire of previously established STR markers , we screrain G37  for tandrain G37 . The proM. genitalium positive specimens were used in this study  and sequenced directly. In direct sequencing of samples containing two or more trinucleotide repeat populations, the minority populations are three bases out of alignment with the majority population, which can be directly observed on the sequence chromatograms Figure . The seqM. genitalium genotypes based on sequence analysis of two fragments of the rRNA operon, including a fragment of approximately 800 bp covering parts of the 16S and 23S rRNA genes and the internal transcribed spacer between them (16S-ITS1-23S) and a fragment of approximately 450 bp covering parts of the 23S and 5S rRNA genes and the ITS between them (23S-ITS2-5S) [We previously identified 6 ITS2-5S) . Types 1ITS2-5S) .M. genitalium, we developed a SSCP method to permit rapid detection of the 3 most common types. The SSCP method was developed using the specimens from the 31 unrelated New Orleans patient isolates for which the 16S-ITS1-23S sequences had been determined previously as noted above [Because we found that rRNA-SNPs typing may be useful for the study of the geographic distribution of ed above . SSCP coed above  and ITS1PCR amplification and sequencing of the MG191 conserved region AB (approximately 280 bp) were performed as described previously .Sequence analysis was done using the Chromas 1.45  and CLC Combined Workbench 1.0.2 . Discriminatory index was calculated according to the method described previously . To evalDQ470670 and DQ470674 for MG307-STRs, DQ470672 and DQ470673 for MG338-STRs, and DQ470671 and DQ470675 for MG185 sequences, and EU131379 to EU131382 for the sequence types 57 to 60 of the MG191 conserved AB region.Representative nucleotide sequences obtained in this study have been deposited in the GenBank under accession numbers LM contributed to experimental design, bioinformatics analysis, and manuscript writing. SNT and RL supervised the collection of the New Orleans patient specimens and clinical data and participated in data analysis. JSJ provided Scandinavian patient specimens and data about most of the MG191-SNP typing, and was involved in data analysis and manuscript editing. LM (Tulane) performed the statistical analysis. DHM participated in coordination and helped to edit the manuscript. All authors read and approved the final manuscript.Relationship between repeat copy number and prevalence of mixed STR alleles.Click here for fileM. genitalium specimens.Analysis of plasmid clones from selected Click here for fileM. genitalium.Discriminatory power of various genetic markers for Click here for file


The risk of common diseases is likely determined by the complex interplay between environmental and genetic factors, including single nucleotide polymorphisms (SNPs). Traditional methods of data analysis are poorly suited for detecting complex interactions due to sparseness of data in high dimensions, which often occurs when data are available for a large number of SNPs for a relatively small number of samples. Validation of associations observed using multiple methods should be implemented to minimize likelihood of false-positive associations. Moreover, high-throughput genotyping methods allow investigators to genotype thousands of SNPs at one time. Investigating associations for each individual SNP or interactions between SNPs using traditional approaches is inefficient and prone to false positives.We developed the Polymorphism Interaction Analysis tool (PIA version 2.0) to include different approaches for ranking and scoring SNP combinations, to account for imbalances between case and control ratios, stratify on particular factors, and examine associations of user-defined pathways (based on SNP or gene) with case status. PIA v. 2.0 detected 2-SNP interactions as the highest ranking model 77% of the time, using simulated data sets of genetic models of interaction  generated previously . Interacting SNPs were detected in both balanced (20 SNPs) and imbalanced data  in the context of non-interacting SNPs.PIA v. 2.0 is a useful tool for exploring gene*gene or gene*environment interactions and identifying a small number of putative associations which may be investigated further using other statistical methods and in replication study populations. In the absence of highly penetrant, rare, genetic mutations, the risk of common diseases, such as cancer, is likely determined by a complex interplay between several genetic and environmental factors. Common genetic variation, in the form of single nucleotide polymorphisms (SNPs), are believed to modulate cancer susceptibility ,2. HowevWith the advent of high-density SNP arrays and genome-wide association studies, the amount of genetic data available gives researchers unprecedented opportunities to explore complexity of common diseases. Using traditional methods of data analysis, such as logistic regression modeling, it is difficult to detect complex interactions due to the sparseness of data in high dimensions ,10. MoreA recent review examined several different approaches for studying complex genetic interactions . The autPIA v. 2.0 was developed as a method of feature selection. In a data set of a large number of SNPs or features, PIA v.2.0 can be implemented to sift through the large number of interactions to aid in the selection of SNPs for future study. We theorized that no single test or informatic approach is optimal for detecting complex interactions in all situations. Therefore, PIA v. 2.0 was intended to provide several opportunities for internal validation of observed genetic interactions by examining SNP data using multiple approaches. The program is designed to simultaneously use up to seven scoring metrics for each SNP combination , make each metric as independent as possible, account for imbalances in the number of cases and controls, and present results for the top 100 SNP combinations for each scoring function and for an overall scaled summation of all scores. Other options included in the PIA program include the ability to incorporate pathway assignments for SNPs to explore associations of particular pathways with a phenotype and an option to allow stratified analysis of SNP associations. PIA also explores the number of times particular SNPs or SNP-pairs are observed in larger combinations of SNPs (triplets or quartets).st–4th order) of categorical exposure variables  are examined. Since SNPs represent the majority of the features examined, for the remainder of this discussion, all categorical variables, or features, to be analyzed for interactions with PIA will be denoted as SNPs.PIA uses a case-based exclusion for missing SNP data, i.e. only those subjects that have all SNPs (in a particular combination) identified are used in the analysis. PIA v. 2.0 is a non-parametric combinatorial method , meaningFor a given SNP combination, the scoring first involves assigning the phenotypes (i.e. case vs. control status) for each of the possible genotypes or genotype combinations. In the case of pair-wise SNP interactions, this involves placing a sample into the appropriate cell of a 9 × 2 table (9 genotypes and 2 phenotypes) as shown in the genotype-phenotype table in Figure For scoring functions 1–5, an N-fold cross validation (user defined N) is implemented and this can be run multiple times. In each run, the order of the samples is scrambled before division into training and testing sets. The genotype-phenotype table for the training set is used for the assignments of genotypes. Genotype combinations with a larger number of cases than controls are considered case-genotype combinations, while combinations with more controls than cases are considered control phenotypes. When there are an equal number of cases and controls for a particular genotype combination, the combination is considered an undetermined phenotype.After assignment of genotypes in the training samples, scoring for the first five metrics requires the formation of a 2 × 2 contingency table Figure  where thAn alternative option in PIA v. 2.0 is to allow the training data to be included in the formation of the contingency table for scoring. This is done by performing a Jackknife  or maximum likelihood analysis of the training genotype-phenotype table. Allowing the user to include the training data was implemented as an option to increase the power for datasets with a small number of samples, as used in other programs such as MDR ,13.Scoring functions 6 and 7 are based on the distribution of all participants in the cells of the genotype-phenotype table. Therefore, using these functions, a cross-validation procedure is not implemented and the entire population is used. If the number of SNPs is very large, running multiple cycles of multi-fold cross-validation may be computationally prohibitive. Therefore, PIA v. 2.0 also allows a single examination of all data to be used to construct the contingency table. Therefore, a single pass through all of the data can be performed for all scoring functions.Often in case-control studies, the case/control ratio is imbalanced, or the study consists of more controls than cases. PIA v. 2.0 includes options for accounting for imbalances in populations with particular phenotypes, or differences in case to control ratios. Using the default PIA v. 2.0 options, if the number of samples in one phenotype is significantly larger than the number of samples in the other, it is likely that all genotypes will be assigned to the more populous phenotype, resulting in biased estimates. For example, in studies with case to control ratios of 1:4, SNP combinations will be weighted towards control associations, because most genotype combinations would have a higher number of controls than cases. To circumvent this, PIA v. 2.0 allows for fractional occupations, or percents of cases/controls associated with each genotype combination, to be used in the genotype-phenotype table to determine the phenotype of each genotype. For example, if there are 90 cases and 180 controls in the training data and a genotype combination contained 12 cases and 20 controls, the fractional occupations would be 13.3% (12/90) and 11.1% (20/180), respectively, which results in a case-phenotype. The training contingency table then has the property that (NTP+NFN) = (NFP+NTN) = 1.0. The resulting contingency table from the testing data in an N-fold cross-validation has (NTP+NFN) = (NFP+NTN) = 1/(N-1), so that the training contingency table is still (N-1) times larger than the testing contingency table.k samples are have genotype k, the Gini Index for the distribution of the table is given by the GINIsplit formula. A better separation of cases and controls by a genotype combination results in a reduction of the value of GINIsplit. In order to maximize the formula, scoring is based on GINIparent-GINIsplit, where GINIparent is the un-separated state with all subjects in the same cell. Scoring function 7 is the sum of the absolute difference in probabilities of finding the cases and controls across all genotypes. Since both scoring functions 6 and 7 do not require N-fold cross-validation or formation of the contingency table, these are well suited for studies with a large number of SNPs or features.As described, scoring functions 6 (Gini Index) and 7 (Absolute Probability Difference) are based on the distribution of all samples in the genotype-phenotype table. The scoring for metric 6 uses the Gini Index formula, which is used in CART decision trees . The GinIt should be noted that if the number of cases is equal to the number of controls, scoring metrics 1 (% Correct) and 2 (Sensitivity + Specificity) produce identical results. Therefore, if the number of samples, as opposed to fractional occupations, in the genotype-phenotype table is used for the assignment of phenotypes, all seven metrics are used. If fractional occupations are used, the first quality metric is dropped and only metrics 2 through 7 are used since metrics 1 and 2 will produce identical results.PIA v. 2.0 stores the top 100 SNP combinations for each scoring metric used. The strength of examining multiple SNP combinations, as observed previously , is thatThe original version of PIA (v. 1.0) contained five different scoring functions, but only two were used in practice . The current version has incorporated several additional functions, but it is possible to select functions used in the original version. In addition to more methods of ranking SNP combinations, handling of ties (genotype combinations assigned to undetermined phenotypes), missing data, and combining testing and training data is modified in PIA v. 2.0. PIA v. 1.0 required that only one scoring function be used in a given run, while the current version ranks the SNP combinations for each of the seven scoring functions in a single run. In addition, overall ranks of SNP combinations are determined using the combined score across these seven functions by storing the top 100 SNP combinations for each scoring function and then linearly scaling their scores so that the best combination has a score of 50.0. The overall score for a given SNP combination is then the sum of the scores it receives across the seven functions.PIA v. 2.0 lists the number of times SNPs and SNP-pairs  appear in the top 100 combinations for each scoring metric and overall, both as counts and using the scaled scores, as long as they appear more times than expected on average. When studying the complex 3-SNP and 4-SNP interactions, investigating the SNP-pairs that appear frequently in higher order combinations may provide some additional clues about combinations of genes with a role in disease risk. Moreover, by studying pairs, it is possible to examine interactions with a particular gene of interest, i.e. explore what additional genes or SNPs are often observed in combination with the gene/SNP of interest.Finally, PIA v. 2.0 determines the number of times each gene pathway appears in the top 100 combinations if the user-defined pathway information for each gene is present in the allele file. Scores for each pathway are derived from the individual scores for each combination. Expected scores are based on random distribution of pathways among SNP combinations, accounting for the number of SNPs in each pathway within the data set. Observed and expected scores for each pathway are tabulated.Simulated data sets that model genetic interaction were created as described previously  and obtaTo demonstrate the ability of PIA to detect interactions, we examined SNP-pairs and determined the number of times the interacting alleles were observed as the highest or second highest scoring model across the 100 data sets. The percent of times the interacting alleles were correctly observed represents the power of PIA to detect interactions . This approach was used previously to estimate power of MDR . Our anaPIA v. 2.0 was used to evaluate SNP combinations and interactions in a case-control study of colon cancer in a population 216 male cases and 255 male controls from the greater Baltimore area as described previously ,16.st or the 2nd model in 82% of the data sets using the total scoring. Results were similar when the training data also contributed to the contingency table using either a leave-one out procedure or maximum likelihood procedure for the balanced data sets  in 76% of the balanced data sets  and 68% (1:4) of the data sets (Table st or 2nd rank of 2-SNP models in 88% (1:2) or 77% (1:4) of the data sets. Results were similar when using both the training and testing data in scoring by using either a leave-one out procedure or maximum likelihood procedure for the imbalanced data sets  and 46 times (2nd rank) in the 2-SNP models.To deal with imbalanced data, PIA v. 2.0 includes the option of using fractional occupations for scoring functions 1–5. PIA v. 2.0 was applied to imbalanced data sets with case:control ratios of 1:2 and 1:4 and 10 total SNPs. When using the option of fractional occupations (IFRACT = 1), scoring functions % Correct (1) and Sensitivity + Specificity (2) reduce to the same function. Therefore, for the analysis of the imbalanced data only the Sensitivity + Specificity function was included. Using fractional occupations, PIA v. 2.0 was able to detect the interacting SNP as the 1When comparing the performance of the different scoring functions at predicting the interacting SNPs in the imbalanced data sets, as with the balanced data, the Gini Index was the most powerful function at scoring interactions and the total scoring function performed better than all the other functions Table . The freCASP8_03 plus GSTT1_02. This combination was the highest scoring combination for 3/7 of the scoring functions. The association of this combination with colon cancer using several different methods of scoring provides further evidence for the importance of this combination of SNPs in colon cancer risk. IL1B_01 plus IL1B_03 was the highest-ranking 2-SNP combination using only the Gini Index scoring function.The top scoring SNP combinations for all combinations of containing 1–4 SNPs associated with colon cancer are shown Table . The modGSTT1_02 with CASP8_03  only in a particular subgroup of individuals. Finally, PIA v. 2.0 allows examinations of associations of user-defined pathways (based on SNP or gene) with a particular phenotype.Molecular epidemiology is entering a new era owing to advancements in genotyping technology and annotation of variation in the human genome. Few established statistical and bioinformatics tools exist for studying complex interactions underlying common diseases such as cancer and cardiovascular diseases. There is no In this study, we applied PIA v. 2.0 to simulated data sets with 2 interacting SNPs in the context of non-interacting SNPs for a total of 10  or 20  SNPs and examined the power of PIA v. 2.0 to detect the interacting SNPs. The genetic model for these data sets assumed a modest genetic association with phenotype (MAF of 0.2 and heritability of 0.01). PIA v. 2.0 detected the interacting SNPs as the highest-ranking model in 77% of the data sets, and using some models, detected the SNPs in over 90% of the data sets. These results indicate that PIA is a powerful tool for detecting interactions.st model using PIA v. 2.0 more frequently than when using MDR [CASP8_03 plus GSTT1_02 was the highest scoring 2-SNP combination overall, while IL1B_01 plus IL1B_03 was the highest-ranking 2-SNP combination using only the Gini Index scoring function, indicating that each function may have strengths in different contexts or datasets. Of note, IL1B_01 and IL1B_03 are in linkage disequilibrium [A variety of approaches for studying complex interactions exist ,10,17-19sing MDR . As showilibrium .The interacting SNPs in the simulated data sets were most frequently detected using the Gini Index scoring function, compared with the total score. The reason for this is unclear, but may be due to the fact that the Gini Index uses the entire data set in scoring, instead of dividing the population into testing and training data. It is possible, with the balanced data the scoring is biased based on the counting of the same function twice. In the balanced data sets, the number of cases and controls were the same. In this situation, the % Correct and Sensitivity scoring functions reduce to the same formula. However, even with the imbalanced data, where fractional occupations are used and the % Correct function is excluded for the scoring, the Gini Index performed better than the total score.stranking and 2nd ranking SNP models may be modest. Only studying the highest scoring model may result in missing relevant associations [IL1B_01 plus IL1B_03 (1st scoring 2-SNP model) and the GSTT1_02 plus CASP8_03  interactions are similar, both were high scoring models using multiple scoring functions. In this report, using the simulated genetic data, the interacting SNPs were frequently observed in the 2nd ranking SNP models (~10% of data sets).In addition to examining the highest scoring SNP models, to increase sensitivity, we suggest that several of the top scoring models should be investigated . The difciations . For exaIn addition to exploring the 1–4 SNP combinations most strongly associated with outcome, PIA v. 2.0 allows the user to examine the most commonly occurring SNP pairs among the top scoring 3-SNP models. The interacting SNPs were frequently detected as pairs when using PIA to examine the 3-SNP models using the simulated genetic data. Therefore, using PIA v. 2.0, there are multiple approaches to exploring 2-SNP interactions. It is unclear at present, which approach is the more sensitive approach. However, it should be noted that the data sets used in this paper, while appropriate for testing the ability of PIA v. 2.0 to detect interacting SNPs, might not accurately represent the situation in complex diseases. In complex diseases, multiple genes interact in complex pathways, as opposed to only 2 interacting SNPs in the context of non-interacting SNPs – i.e. in real data there are likely many competing interactions and alternative pathways. In this context, if an investigator is interested in 2-SNP interactions, it may be more appropriate to study the commonly occurring pairs in the 3-SNP models. Until the better approach is determined, we suggest that both looking at the 2-SNP models and the pairs observed in the 3-SNP models is the optimal approach.st ranking 2-SNP models. In contrast, if cell counts were used for imbalanced data, PIA v. 2.0 performed poorly. As a result, using the option of fractional occupations, PIA v. 2.0 may be applied to imbalanced case-control data to explore interactions.Dealing with imbalances in case:control ratios is a challenge using multi-locus approaches for examining gene*gene interactions . Using fOverall, PIA v. 2.0 is a robust method for detecting SNP-SNP interactions, as exemplified by the ability of PIA to detect the two interacting SNPs in the context of non-interacting SNPs using the simulated genetic data. PIA v. 2.0 detected the interacting SNPs more frequently than MDR using both the balanced and imbalanced data sets . MoreoveSeveral studies examined association of genetic variation with disease using large-scale multi-locus approaches. Previously, PIA was implemented in a study of colon cancer to examine complex interactions using 94 SNPs in 67 genes . CART dePIA v. 2.0 incorporates some aspects of the more common approaches implemented in other studies of complex genetic interaction, CART decision trees and MDR. The Gini Index, one of the PIA v. 2.0 scoring functions, is used for splitting branches in CART decision trees . PIA v. With the advent of genome wide association studies (GWAS), it is possible to genotype over 500,000 SNPs on a single individual. In complex diseases, there are likely many genes that interact in pathways that are related to disease susceptibility. As a result, in GWAS, there is an interest in exploring complex gene*gene interactions. Investigating complex gene*gene interactions is a challenge due to the computational time required with such a large amount of genotyping data. We observed, using PIA v. 2.0, that a single run of cross-validation was powerful at detecting the 2-SNP interactions similar to 10 rounds of 10-fold cross-validation. Further, the Gini Index and the Absolute Probability Difference functions, which both only implement a single run of the data, were robust at detecting the 2-SNP interactions. PIA also allows for the incorporation of user-defined pathways in the analysis of SNP interactions, which may be used to explore the association of global pathways, or gene ontologies with disease outcome. Therefore, while PIA v. 2.0 currently can only be used for up to 1400 SNPs, using a single run of the data or scoring functions 5 and 6, are a possible approach to be implemented to reduce computational time and may eventually be applied to analysis of GWAS.In this paper, we describe a new method for exploring genetic interactions, but some of the limitations should be considered. In classifying genotype combinations associated with disease, PIA, as other dimensionality reduction methods, effectively dichotomizes exposure as "low" or "high" risk. Such a simplification of genotype combinations results in a loss of information, because in reality each SNP combination may be associated with levels of risk. In addition, PIA v. 2.0 is not equipped for continuous variables, such as age or years of smoking exposure. These types of variables may only be analyzed using PIA if split into a maximum of five categories. While PIA v. 2.0 is more powerful that traditional methods, when studying higher order interactions, associations become less stable due to the reduced number of individuals in each cell. Therefore, PIA does not eliminate the need to conduct studies of large sample sizes and to confirm findings with more traditional statistical methodologies.In conclusion, when evaluating a large number of genetic factors associated with disease, a strategy for focusing on only select complex interactions is more efficient and results in fewer comparisons. PIA v. 2.0 is a useful tool for exploring these interactions, generating hypotheses for gene*gene or gene*environment interaction, which may be investigated further using other statistical methods and in replication study populations..PIA v. 2.0 must be implemented on a Windows PC using the command window. The following documents and programs for use of PIA v. 2.0 in the zip file PIA2-distribution.zip: PIA2_Guide.doc, the user's manual; PIA2_examples.doc, step by step instructions with example data; test.csv, example data; csv2pia2.exe, converts csv files to pia files; csv2pia2.list, program that controls the conversion of the csv file to run csv2pia2.exe; pia2.exe, executable PIA v 2.0 program; pia2.list, control file to direct pia2.exe; pia2html.exe, program to generate PIA output in html format; pia2html.list, program to run pia2html.exe. PIA is available for download from the following website th order interactions (or combinations of factors associated with phenotype).PIA v. 2.0 was written in Fortran and is available for implementation on PC platforms from the following website  with detLM and JG contributed to the design of the method and interpretation of data; BL developed method, wrote software for PIA and aided in interpretation; SC developed and conducted genotyping assays; CH conceived of the study. All authors read and approved the final manuscript.Supplemental Tables. Simulation results applying different options in PIA 2.0 and parameter descriptions.Click here for file


With improvements in genotyping technologies, genome-wide association studies with hundreds of thousands of SNPs allow the identification of candidate genetic loci for multifactorial diseases in different populations. However, genotyping errors caused by genotyping platforms or genotype calling algorithms may lead to inflation of false associations between markers and phenotypes. In addition, the number of SNPs available for genome-wide association studies in the Japanese population has been investigated using only 45 samples in the HapMap project, which could lead to an inaccurate estimation of the number of SNPs with low minor allele frequencies. We genotyped 400 Japanese samples in order to estimate the number of SNPs available for genome-wide association studies in the Japanese population and to examine the performance of the current SNP Array 6.0 platform and the genotype calling algorithm "Birdseed".About 20% of the 909,622 SNP markers on the array were revealed to be monomorphic in the Japanese population. Consequently, 661,599 SNPs were available for genome-wide association studies in the Japanese population, after excluding the poorly behaving SNPs. The Birdseed algorithm accurately determined the genotype calls of each sample with a high overall call rate of over 99.5% and a high concordance rate of over 99.8% using more than 48 samples after removing low-quality samples by adjusting QC criteria.Our results confirmed that the SNP Array 6.0 platform reached the level reported by the manufacturer, and thus genome-wide association studies using the SNP Array 6.0 platform have considerable potential to identify candidate susceptibility or resistance genetic factors for multifactorial diseases in the Japanese population, as well as in other populations. Together with technology developments on large-scale single nucleotide polymorphism (SNP) genotyping ,2, thereThe SNP Array 6.0 platform offers the genotype calling algorithm "Birdseed" to determine the genotypes of 909,622 SNPs . Some ofThe SNP Array 6.0 platform has three check points prior to hybridization on GeneChip arrays in order to exclude experimental errors; PCR amplicon size check by electropherograms, DNase I digested fragment size check by electropherograms and quantity check of the purified PCR products. The platform also includes Quality Control (QC) probes for 3,022 SNPs to assess the overall quality for a sample based on the Dynamic Model (DM) algorithm. There are assay criteria to exclude experimental errors and low-quality samples; however, we empirically know that some samples, which pass these criteria, have low-quality genotyping results.In this study, we genotyped 400 non-HapMap Japanese samples using the SNP Array 6.0 platform in order to evaluate the number of SNPs available for GWAS in the Japanese population, to examine an appropriate approach for acquiring accurate genotype calls using the Birdseed genotype calling algorithm, and to evaluate the assay criteria for preventing low-quality genotyping data.st set of 200 samples was 54.8 ng/μl and that for the 2nd set of 200 samples was 52.7 ng/μl. One of the critical points for the SNP Array 6.0 platform to acquire high quality genotyping data is to prepare a uniform quantity of 250 ng genomic DNA for Nsp I and Sty I digestion steps. When an almost 10-fold excess amount of genomic DNA was used, the average overall call rate drastically decreased to about 80% for both Nsp I and Sty I digestion steps with the Mapping 500 K Array (data not shown).We collected 2 sets of 200 Japanese samples for genotyping using the SNP Array 6.0 platform. The average concentration of genomic DNA for the 1st set and 9 samples for the 2nd set) showed low QC call rates below the default 86% QC criteria  for the 2nd set  and 497.3 ng/μl (range 256.6 to 939.8 ng/μl), respectively Figure . In totaa Figure . The gen% Figure . For the% Figure . When get Figure .st set of 200 samples and 59 samples for the 2nd set of 200 samples. When genotype calls were determined for every 48 samples simultaneously analyzed in the same batch, the average overall call rate of 48 samples for batch #1 from the 2nd set was 97.21%, which was almost 2% lower than other batches  and performed the genotype call comparison for each set of 12 samples. Figure Our results showed that the average overall call rate of the 12 samples was almost constant when the genotype calls were determined with fewer than 48 samples; however, it gradually decreased as the sample number increased from 48 to 198, which showed a negative correlation with a P value of 0.0053. In contrast, the concordance rate gradually increased as the sample number increased, which showed a positive correlation with a P value of 0.0115.Our results showed that the average overall call rate gradually decreased as the sample number increased, presumably due to low-quality samples included in the genotype calling with the Birdseed algorithm. Indeed, there was one sample which had an overall call rate lower than 97% among the 198 samples with over 86% QC call rate. Therefore, we applied more stringent QC criteria to remove the low-quality samples, because a linear relationship was observed between QC call rate and overall call rate Figure . When weThe genotype calls of 909,622 SNPs were determined with 184 samples after sample filtering with adjusted QC criteria. However, these genotype calls still included inaccurate SNPs, which could lead to inflation of false positives, presumably due to systematically miss-called SNPs. Therefore, SNP filtering was considered to be important for a reliable and accurate set of genotype calls that avoid false association signals and false negative signals, allowing rapid identification of disease susceptibility genetic factors. We reported that the poorly behaving SNPs were effectively eliminated with the SNP filtering parameters; MAF > 5% or 1%, HWE p-value > 0.001 and SNP call rate > 95% . Here, SAmong a total of 909,622 SNPs genotyped using 184 samples, 590,248 SNPs passed the three SNP filtering criteria with MAF > 5%, HWE p-value > 0.001 and SNP call rate > 95%, while 661,559 SNPs passed with MAF > 1%, HWE p-value > 0.001, and SNP call rate > 95%. A total of 180,859 SNPs were observed to be monomorphic in the Japanese population.The emerging SNP typing technologies have enabled genome-wide association studies to be conducted with hundreds of thousands of genotyped SNPs. According to Affymetrix, the SNP Array 6.0 platform can genotype over 900 K SNP markers across the human genome with an overall call rate of at least 97%, over 99.7% concordant with the HapMap genotypes, and the Mendelian inheritance consistency for 10 Trios of greater than 99.9% when performing analysis under the default 86% QC criteria. To evaluate the SNP 6.0 Array platform and the Birdseed genotype calling algorithm, we genotyped two sets of 200 non-HapMap Japanese samples using the SNP Array 6.0 platform.st set of 200 samples were excluded and the average overall call rate was 99.58%. There was one sample with an overall call rate of lower than 97% among the 198 samples. Here, we found a linear relationship between QC call rate and overall call rate. Therefore, we applied stringent QC criteria of over 95% in order to remove the low-quality samples and found that the average overall call rate for 189 samples passing the stringent QC criteria improved to 99.65%. Among the 189 samples, 187 samples had higher overall call rates than those determined under the default QC criteria; however, the remaining two samples showed lower overall call rates (by 0.76% and 0.12%). When we repeated the removal of samples until none had a lower overall call rate than the one determined under the default criteria, none of the remaining 184 samples with an overall call rate lower than 97%. The average overall call rate of 184 samples was thus improved to 99.71%. The decay of average overall call rate may be caused by some samples that pass the QC criteria, but still have a low overall call rate. We can thus improve overall call rate by removing these samples and adjusting the QC criteria.When we applied the default 86% QC criteria, 2 samples out of the 1One of the crucial points for the SNP array 6.0 platform is to prepare the exact amount of intact genomic DNA. A 10-fold excess amount of genomic DNA decreased the overall call rate of each sample to by about 80% and another study revealed that samples with less than 50 ng/μl genomic DNA show low overall call rates . TherefoThe genotype calling accuracy of the Birdseed algorithm was assessed by comparing the 909,622 genotype calls of 12 samples from among198 samples with over 86% QC criteria, to those of 12 samples determined with six different sample sizes; 12 samples, 24 samples, 36 samples, 48 samples, 72 samples and 96 samples. The concordance rate gradually increased as the number of samples increased. The average concordance rate was almost constant over 99.8%, when the genotype calls were determined with over 48 samples using the Birdseed algorithm. However, the average overall call rate of the 12 samples gradually decreased as the sample number increased from 48 to 198. We could explain the reasons why the overall call rate decreases, and why the concordance rate increases for these 12 samples in a grouping of samples greater than 48 by means of characteristic properties of the Birdseed algorithm and minor allele frequency of each SNP. When the sample number was smaller than 48, all of three clusters designating AA, AB and BB genotypes were rarely observed for the SNPs with low MAF. In such cases, the Birdseed algorithm would determine the genotype as a single cluster, however, would ambiguously genotype as AA, BB and AB (tend to miss-genotype). Therefore, high call rate and low concordance were observed with the sample number smaller than 48. In contrast, when the sample number was greater than 48, two or three clusters would be observed for many SNPs. For these SNPs, the Birdseed algorithm could determine the outlying samples from each cluster as "No Calls", leading to low call rate and high concordance.We can accurately determine the genotype calls with high overall call rates by determining the genotype calls with more than 48 samples, after removing low-quality samples by adjusting the QC criteria. Our results showed that the SNP Array 6.0 platform reached the expected level reported by the manufacturer, with an average overall call rate of over 99.5% and an average concordance rate of over 99.8%. However, about 20% of a total of 909,622 SNPs were found to be monomorphic in the Japanese population, which is due to SNP selection methods. The SNPs assayed on the SNP Array 6.0 platform were mainly selected as observed with high MAF in the Caucasian population. Among a total of 909,622 SNPs genotyped using the SNP Array 6.0 platform with 184 Japanese samples, 590,248 SNPs passed three SNP filtering criteria; MAF > 5%, HWE p-value > 0.001 and SNP call rate > 95%. Although the exact number of SNPs within the human genome remains under discussion, it has been reported that the genome coverage of the JPT + CHB population in the Phase II HapMap data was 66% using the Mapping 500 K Array set . The genThe current Affymetrix SNP Array 6.0 platform enables the genotyping of over 900 K SNPs with high overall call rate (over 99.5%) and high concordance rate (over 99.8%). The number of SNPs available for GWAS in the Japanese population was revealed to be over 660 K SNPs, all of which passed the three SNP filtering criteria; MAF > 1%, HWE p-value > 0.001 and SNP call rate > 95%. GWAS using the SNP Array 6.0 platform has considerable potential in identifying candidate susceptibility or resistance genetic loci for multifactorial diseases in the Japanese population, as well as in other populations.Finally, the genotyping data of 400 Japanese samples using the SNP array 6.0 platform will be deposited in a public database to share with the research community .Blood samples were obtained from two sets of 200 Japanese individuals in two institutes. Genomic DNA was extracted from peripheral blood leukocytes using the QIAamp Blood Mini Kit (Qiagen) according to the manufacturer's instructions. All genomic DNA was resuspended with Reduced EDTA TE Buffer (TEKnova) at 50 ng/μl. This study was approved by the Research Ethics Committee of the Faculty of Medicine, The University of Tokyo and Tokai University. Informed consent was obtained from all participants.st set of 200 samples, five samples had low genomic DNA concentrations with an average of 41.1 ng/μl ranging from 38.2 to 44.5 ng/μl, and the remaining 195 samples had an average of 54.8 ng/μl, ranging from 45.0 to 57.8 ng/μl. For the 2nd set of 200 samples, one sample had 39.1 ng/μl and the remaining 199 samples had an average of 52.7 ng/μl, ranging from 45.0 to 55.9 ng/μl. For each individual assayed, 250 ng of genomic DNA was digested with Sty I and Nsp I (New England BioLabs) by adding 6 μl for the 6 samples with low concentration (five samples for 1st set and one sample for 2nd set) and 5 μl for the remaining samples. For two sets of 200 samples, every 48 samples were simultaneously processed in a single 96-well plate. After the reaction with restriction enzymes, we followed the manufacturer's instructions for the Affymetrix Genome-wide Human SNP array 6.0. The concentration of PCR products after purification with magnetic beads  was measured using a spectrophotometer (NanoDrop ND-1000). Purified PCR products were diluted 10-fold with TE buffer (pH 8.0) (WAKO) in order to have a suitable concentration for the spectrophotometer to measure. The genotype calls of each individual were determined by the Birdseed version 1 genotype calling algorithm, embedded in the software Affymetrix Genotyping Console 2.0 (Affymetrix). The number of samples used to determine the genotype calls varied depending on the examination.The concentration of genomic DNA for all individuals was measured using a spectrophotometer . For the 1