Salmo salar L.) populations using an Illumina SNP-chip.New sequencing technologies have tremendously increased the number of known molecular markers  in a variety of species. Concurrently, improvements to genotyping technology have now made it possible to efficiently genotype large numbers of genome-wide distributed SNPs enabling genome wide association studies (GWAS). However, genotyping significant numbers of individuals with large number of SNPs remains prohibitively expensive for many research groups. A possible solution to this problem is to determine allele frequencies from pooled DNA samples, such ‘allelotyping’ has been presented as a cost-effective alternative to individual genotyping and has become popular in human GWAS. In this article we have tested the effectiveness of DNA pooling to obtain accurate allele frequency estimates for Atlantic salmon (r > 0.99) between allele frequency estimates derived from individual genotyping and DNA pools. Our results also indicate that even relatively small DNA pools  can provide accurate allele frequency estimates for a given sample.In total, 56 Atlantic salmon DNA pools from 14 populations were analyzed on an Atlantic salmon SNP-chip containing probes for 5568 SNP markers, 3928 of which were bi-allelic. We developed an efficient quality control filter which enables exclusion of loci showing high error rate and minor allele frequency (MAF) close to zero. After applying multiple quality control filters we obtained allele frequency estimates for 3631 bi-allelic loci. We observed high concordance (Despite of higher level of variation associated with array replicates compared to pool construction, we suggest that both sources of variation should be taken into account. This study demonstrates that DNA pooling allows fast and high-throughput determination of allele frequencies in Atlantic salmon enabling cost-efficient identification of informative markers for discrimination of populations at various geographical scales, as well as identification of loci controlling ecologically and economically important traits. Technological advances in polymorphism detection and genotyping have made the single nucleotide polymorphisms (SNPs) the marker of choice for many high density genotyping studies ,2. High-Despite the recent technical advances, genotyping large numbers of individuals with thousands of SNPs remains prohibitively expensive for many research groups. Furthermore, many population genetic studies are based on population allele frequency rather than individual genotype data. Therefore, determination of allele frequencies from pooled DNA samples, i.e. ‘allelotyping’, has been suggested more than 30 years ago as a cost-effective alternative to individual genotyping . SeveraWhile the allelotyping of DNA pools can substantially reduce the costs compared to individual sample by sample genotyping, this approach is not without disadvantages. First, various sources of error occur during the allele frequency estimation from DNA pools. According to Earp et al. , variatiSalmo salar L.) Illumina SNP-chip to obtain accurate allele frequency estimates for multiple Atlantic salmon populations and evaluated the importance of different sources of errors arising from allelotyping. First, we assessed the effect of DNA pool construction and between-array variations on allele frequency estimates. Subsequently, the effect of cluster separation scores (parameter that summarizes the separation of three genotype classes in the theta dimension), two alternative sources of theta  and DNA pool size on allele frequency estimation were evaluated. Finally, two alternative quality control (QC) filters were tested to select optimal sets of SNP loci for subsequent population genetic analysis.Here, we tested the usefulness of DNA pooling for a first time using an Atlantic salmon   suggesting that small number of SNPs suffer from larger error irrespective of the source of reference values of theta while the majority of loci have relatively low error rates. Taken together, these results suggest that even relatively small number of individuals (~ 100) is sufficient to generate reliable reference values of theta. However, because all three genotype classes are needed for accurate estimation of allele frequencies, using relatively small number of individuals resulted in loss of SNPs as not all genotypes were observed in the reference datasets .The allele frequencies for 3631 SNPs that passed the quality control (see below) were estimated from DNA pools using reference values of theta provided by CIGENE and reference values of theta derived from the genotyping of 106 individuals used in pool construction. Comparison of the two sets of theta values revealed a small, but significant, difference in allele frequency estimates. Using individual genotypes from this study to derive reference values of theta provided slightly higher accuracy in allele frequency estimates compared to the larger (n = 300) but unrelated dataset provided by CIGENE  and cattle (Pearson’s r = 0.992 – 0.994) [We observed very high concordance between allele frequency estimates derived from DNA pools and from individual genotyping (Pearson’s – 0.994) ,33. The U test, both for array and pool replicates, P < 0.0001) . In addition, the estimated variation of theta was negatively correlated with the cluster separation score both for array  and pool construction  replicates  that use heterozygosity and variation in allele frequency estimates Figure . This reThis study tested the effectiveness of DNA pooling to obtain accurate allele frequency estimates for large number of Atlantic salmon populations using an Illumina SNP-chip. We demonstrated that pooled DNA approach provides a reliable, accurate and cost-effective means for obtaining genome-wide allele frequency estimates for multiple populations. We proposed a novel quality control filter based on spherical cut-off which enables efficient exclusion of loci showing high error rate and minor allele frequency close to zero. Our results indicate that even relatively small DNA pools  provide accurate allele frequency estimates for a given sample. Despite of higher levels of variation associated with array replicates compared to pool construction we suggest that both sources of variation should be taken into account. Taken together, this study demonstrates that DNA pooling allows fast and high-throughput determination of allele frequencies in Atlantic salmon enabling cost-efficient identification of informative markers for discrimination of salmon populations at various geographical scales, as well as identification of loci controlling ecologically and economically important traits. Moreover, the main findings of our study based on Atlantic salmon SNP-chip were in line with those observed for human SNP-chips, and thus the technical approaches described herein are encouraging for employing allelotyping approach in other species using Illumina SNP-chips or other SNP genotyping systems and arrays.In total, 927 Atlantic salmon individuals representing 19 populations from Northern Europe were used for individual genotyping and/or construction of DNA pools  were excluded from further analysis. Each extract was then tested for contamination  by screening individual samples using 18 microsatellite loci ; V. WennIn total, 56 DNA pools were constructed using individuals from 14 Atlantic salmon populations , only those samples with > 97% call rates were included when calculating ‘true’ allele frequencies. SNPs with call rates < 95%  were eliminated from the data set. Thresholds for quality control (QC) filtering were determined as in Murray et al.  and for In Illumina genotyping, the genotype is assigned after converting raw color signal data into a theta value which ranges from 0 to 1 and reflects the relative signal contribution for the 2 alternate alleles. In theory, an individual homozygous for the B allele would have a theta value close to 1, an individual homozygous for the A allele a value close to 0 and a value of 0.5 would indicate a heterozygous genotype. However, in reality a SNP’s theta for genotype clusters  may vary from 0, 0.5 and 1, therefore for estimation of allele frequency in a pooled sample, the theta value for each SNP is compared to the mean theta values for AA, AB and BB genotypes calculated by genotyping individual samples, i.e. the allele frequency of the DNA pool can be derived by applying correction algorithms from comparing pool-specific value of theta with the reference values of theta from individual genotyping data e.g. ,41.Bpool Sample position of each pool along the axis of normalized theta values were compared to the reference values of AA, AB and BB genotype cluster positions for each SNP  as in Janicki & Liu [To obtain the allele frequency estimate for allele B in the pool ki & Liu .The following equations were applied :θpool is the sample position and θAA, θAB, θBB are means of the cluster positions of the corresponding reference genotypes along the axis of normalized theta values. The frequency of allele A was calculated as Apool = 1–Bpool.Reference values for AA, AB and BB genotype positions along the axis of normalized theta values were obtained from individual genotyping of 300 Atlantic salmon specimens genotyped in previous studies by CIGENE. As this data did not include samples from all the populations used to construct the DNA pools, the mean cluster position values were also derived from the genotype classes of 106 individuals originating from 8 populations across the study area . For subsequent analyses, however, reference values of theta provided by CIGENE were used.The accuracy of allele frequency estimates was quantified as an absolute difference between allele frequencies derived from individual genotypes (referred to as ‘true’) and allele frequencies estimated from DNA pools from the River Kola population .To estimate the within-pool variation of theta, replicates of the same DNA pool were run on different arrays  Table . To asseVariation of theta within a SNP locus was estimated similar to Macgregor . The arrThe authors declare that they have no competing interests.M.O. constructed the DNA pools, performed the data analysis and lead in drafting the manuscript. J.-P.V. and A.V. designed the study and significantly contributed to the data analysis and the writing of the manuscript. V.W. took part in designing the study, provided microsatellite data and together with E.N. and S.P. collected biological samples. M.K. performed SNP array screening and initial data analysis. All authors read and approved the final manuscript.U test, P < 0.0001). Horizontal line, grey square, whiskers, open circles, and stars indicate median, 25th and 75th quartiles, non-outlier range, outliers and extreme outliers, respectively. Figure S3. A significant negative correlation between (a) array- and (b) pool-construction  variation and cluster separation scores. Figure S4. Proportion of loci remained in each allele frequency class after application of (a) uniform and (b) spherical filter.Example of SNP loci failed to pass QC: a) call rate < 95%; b) cluster separation < 0.40; and SNP loci met QC requirements: c) cluster separation = 0.41 and d) call rate 100%, cluster separation = 1.00. Figure S2. Box-plot showing estimated variation of theta in two sets of SNPs with cluster separation score < 0.4 and > 0.4 for (a) array and (b) pool construction replicates (both tests, Mann–Whitney Click here for file


Arabidopsis thaliana. We show that the heuristic can be used to maximise the number of true polymorphisms returned, and with a proof-of-principle experiment show that Bubbleparse is very effective on data from unsequenced wild relatives of potato and enabled us to identify disease resistance linked genes quickly and easily.Single Nucleotide Polymorphisms are invaluable markers for tracing the genetic basis of inheritable traits and the ability to create marker libraries quickly is vital for timely identification of target genes. Next-generation sequencing makes it possible to sample a genome rapidly, but polymorphism detection relies on having a reference genome to which reads can be aligned and variants detected. We present Bubbleparse, a method for detecting variants directly from next-generation reads without a reference sequence. Bubbleparse uses the de Bruijn graph implementation in the Cortex framework as a basis and allows the user to identify bubbles in these graphs that represent polymorphisms, quickly, easily and sensitively. We show that the Bubbleparse algorithm is sensitive and can detect many polymorphisms quickly and that it performs well when compared with polymorphism detection methods based on alignment to a reference in  Genetic polymorphisms between genomes of individuals in a population, such as Single Nucleotide Polymorphisms (SNPs), are invaluable markers for tracing the genetic basis of inheritable traits or diseases. Rapid detection of polymorphisms and creation of large libraries of SNPs is vital for timely investigation and identification of genes associated with medically and agronomically important phenotypes. Next-generation sequencing (NGS) can sample genomes comprehensively in only hours, but making use of the typically short reads remains a challenge. Detection of SNPs and short Indels is typically achieved by aligning reads to a reference genome and identifying where the consensus from the aligned reads differs from the reference sequence. Factors such as the need for a reference sequence and the implicit assumption of a monomorphic sample mean that the consensus approach is limited in organisms for which we lack a reference genome, outbred diploid samples, bulked population data or analysis of metagenomes. The ability to identify genetic differences between two (or more) sequenced but not assembled, and genetically divergent genomes would be of great benefit to biotechnologists wishing to create libraries of genetic markers for breeding and disease programs in as short a time as possible. The major obstacles to identifying genetic differences directly from NGS reads are the volume of sequence data that must be assessed and the accurate detection of variants in reads that individually are often prone to error. But even with perfect reads the alignment-based methods will struggle with complex genomic regions or in the presence of indels.kmers, being of arbitrary length k)  . They caet al.Methods based on the de Bruijn graph have been implemented recently that can efficiently identify differences in sequence read sets Phytophthora infestans resistance in crosses of resistant and susceptible accessions that are relatives of potato.We present here an extension of the method in Iqbal et al. Our software for identifying SNPs, Bubbleparse, uses the efficient de Bruijn graph representation provided by Cortex kmer, which is a nucleotide sequence of length k that differs from connected kmers by a single nucleotide.A de Bruijn graph is a labeled directed graph represented by a pair A path through the graph can be represented by Two paths, d, determines the number of levels of bifurcation that will be allowed when exploring potential bubble paths. A value of 1 means that 1 level of bifurcation will be explored before the algorithm abandons further exploration of the path. Thus, the maximum number of paths that will be explored is equal to 41+d.In the first step of bubble finding, we examine each node in the graph looking for one of two kinds of possible branch point.: ‘Y’ nodes feature one path in one direction and 2  or more  in the opposite direction; and ‘X’ nodes feature 2 or more paths in both directions. Branch points are places where the path through the graph diverges and if these paths later re-converge, a bubble is formed. Therefore, the next step is to explore each node flagged in the first step and see if the paths originating from it later converge on a common node. This is achieved by building a list of all paths from a given branch node, up to a maximum number of bifurcations traversed, which is a depth-first search. These paths are then compared step-by-step, looking for a common node of convergence. A parameter, When a point of convergence is identified, we have the start and end nodes of a bubble and can easily produce a set of nucleotide sequences representing the paths through the bubble. For practical use, such as the design of primers, we need to find flanking sequences at either side of the bubble. These are obtained by walking the graph as far as possible from the start and end nodes, until we reach another branch node.This bubble detection algorithm has been implemented as a new module within Cortex, independent of and separate from the standard variant calling options. The primary advantage of our new algorithm is that it allows the reference-free detection of more complicated bubble structures than Cortex does , but also that it allows us control over the information that is available to the Bubbleparse ranking algorithm. The output of this new module is a pair of files that can be read by the Bubbleparse ranking tool. Once the collection of bubbles is created, Bubbleparse gives each a type classification determined by the number of paths through the bubble and the number of colours that follow each path . BubblepArabidopsis thaliana released by the 1001 Genomes Project and available from the Sequence Read Archive at EMBL (ENA SRA Experiments SRX000702 to SRX000704), against A. thaliana Col-0 reads. The Bur-0 reads provided approximately 50× coverage, the Tsu-1 reads 38× coverage, while for Ler-1 we present results for a range of coverage values from 40× to 340×.The reference free exploration of the graph is expected to generate high numbers of false positives induced by repeats in the underlying genome and numerous artefacts in the sequence reads that can cause bubble structures that are not due to polymorphisms. In order to quantify this error level and to examine the efficiency with which we could detect true SNPs in bubble collections from experimentally generated reads, we compared our SNP calls with those from a commonly used SNP calling pipeline based on reference alignment, as well as with a curated high-quality set of SNPs from an external genetic variation project. We used reads from the Bur-0, Tsu-1 (31–40 nt) and Ler-1 (40–80 nt) ecotypes of Alignment against the reference was performed using BWA In Bubbleparse, reads were assembled into the de Bruijn graph, removing paths of nodes with coverage 2 or below and tips less than 100 nucleotides in length. Bubble detection was run using depth from 0 to 3 and k from 15–31. The contigs representing bubbles were BLASTed Bubbleparse identified many bubbles in each comparison , maximu, but decComparing the canonical SNP positions, obtained by pileup, with the Bubbleparse matches allowed us to estimate sensitivity and compare this with the proportion of real SNPs retained in our bubble collections. The number of real SNPs found in our bubble collections increased with depth of search to a maximum of 86.82% of SNPs identified in Tsu-1  for deptArabidopsis ecotype Ler-1. For this, we used the 1001 Genomes projects own list of SNPs, generated as described by Schneeberger et al.To test the effects of high read coverage and to compare our data with a heavily examined and well-curated canonical set of SNPs we carried out a similar analysis with the Arabidopsis reads as input to the latest version of Cortex_var (1.0.5.13) and verified the bubbles it output using the same method as for Bubbleparse. At optimal kmer size for each tool, Cortex_var called a lower percentage of the canonical SNPs than Bubbleparse for all values of the Bubbleparse depth parameter  arameter . For Burarameter .E. coli genome containing 100,000 randomly positioned SNPs. Using SimSeq, a tool created for the Assemblathon project E. coli and the mutant at approximately 20× coverage and using SimSeq's supplied Illumina GAIIx error profile. Both sets of reads were used as input to Bubbleparse and for comparison, we used BWA/SamTools to call SNPs based on the mutant reads aligned to the original E. coli reference. The BWA/SamTools combination was able to locate 86,275 of the SNPs, with only 1 false positive. Bubbleparse located 93,446 of the SNPs, with 12,917 additional false positives , and attributes of the structure of the bubbles: the length of the path through the bubble  and total difference .We examined the bubble collections for the Ler-1 50× data at k = 21, search depth = 1, sorted the bubble table by each attribute in turn and calculated the proportion of real SNPs in sets of 100 bubbles, based on comparison with the 1001 genomes list. Arbitrary ranking of the table by the internal index of each bubble (bubble number) provided a baseline for detection of real SNPs throughout the bubble collection, the proportion of real SNPs in this ranking was constant through the bubble collection  indicatiThe type classification of a bubble – determined by the number of colours on each path through the bubble  of each path through the bubble. If no quality scores are available, this step is omitted.Combined coverage – the combined coverage is obtained by summing the mean coverage values of each path through the bubble.Accompanying pseudocode for this heuristic can be found in Supporting It was not possible to plot a ROC curve for the performance of the heuristic, as we do not make a binary distinction between bubbles due to SNPs and those due to other artefacts - which means that calculating true and false negatives has no meaning. Instead, the top of the ranked list represents bubbles for which there is high confidence that they are SNPs and are linked to the expected heterozygosity, while those at the bottom of the list represent bubbles with a low likelihood of being a real linked SNP.To provide further confirmation of the tool's performance, we used Sanger sequencing to test the top 48 Bubbleparse ranked SNPs, as well as 3 sets of 16 SNPs that were placed at the 25%, 50% and 75% boundaries of all SNPs which were in contigs of 200 nt or greater (this length chosen for effective oligo design). We looked to see if the SNPs were in the 1001 genomes list by aligning contigs to the TAIR9 reference. Of the top 48, we were able to locate 39 SNPs in the 1001 genomes list; however, the sequencing successfully confirmed 46 of the SNPs, at the predicted position and with the predicted alleles . The remWe then examined the SNPs that were confirmed by sequencing but which we were not able to find in the curated list. Of these 23 SNPs, we were able to match 14 by relaxing the alignment criteria . This left 3 SNPs that we were unable to align to the reference and 6 of the 91 confirmed SNPs, which, though easily aligning to the reference, were not in the curated list. This result leads us to believe that Bubbleparse provides even higher accuracy than that indicated in Phytophthora infestans resistant Solanum berthaultii and susceptible Solanum stenotomum. These plants were manually scored for resistance and found to have 50% resistance in the bulk resistant population, thus we expected the resistant genotype to be heterozygous and present at roughly 50%. We sequenced each normalized library at approximately 40× coverage . We ran Bubbleparse at a kmer size of 31 and with cleaning options set to remove tips of 100 nodes or less and any node of coverage of only 1. Bubbleparse identified 201,000 SNPs of different patterns of heterozygosity directly from reads  with non-proofreading DNA polymerase. 2 µl of the clean PCR reaction (∼20 ng) was used in the Sanger sequencing with either a forward or reverse sequence oligo. The chromatograms were analysed using the program DNASTAR Lasergene version 8.The validation of the Bubbleparse predicted SNPs was performed using PCR amplification and Sanger sequencing. We designed oligos  to matchTo compare these SNPs with the curated list, we aligned the contigs to the TAIR9 reference genome using BLAST, looking for matches of 98% identity and 98% length. For the subsequent investigation into SNPs that were confirmed, but could not be found in the curated list, we relaxed the alignment criteria to 93% identity and 93% length in order to find the additional matches. Further relaxation of alignment criteria did not produce further matches.Solanum berthaulltii (PI1477731) et al., 2006) and a susceptible Solanum stenotomum (CGN19035).We used 30 resistant and 30 susceptible plants from a cross between diploid parents, a resistant SfiI (New England Biolabs) digestion, we selected fragments (>500 nt) with Chroma Spin 1000 columns. In order to prepare the samples for Illumina sequencing, we acoustically sheared the cDNA to fragments of average 200 bp  and ligated adapters to the fragments . Subsequently, we size selected fragments (5% polyacrylamide gel) of 300 bp that were sequenced on the Illumina machine . All methods were according the manufacturers protocols.Total RNA was extracted from two young leaves of each resistant and each susceptible plant by using TRIzol reagent (Invitrogen) from which 2.5 µg total RNA of each individual was bulked up to generate a bulked resistant (BR) and a bulked susceptible (BS) sample. PolyA+ RNA was extracted (Invitrogen) and double stranded cDNA (ds cDNA) was generated by using a SMART cDNA library kit (Clonetech). Next, we normalized the cDNA  and after To validate the outcome of the Bubbleparse SNP prediction, we designed oligos to match locations where a SNP was predicted . 1 ng ofTable S1Speed of execution. The table provides typical execution times for assembly of de Bruijn graph, identification of potential SNPs and output of contigs for the Col-0 vs. Tsu-1 experiment. Times are for illustration only, as assemblies were carried out on a range of individual machines within a workload managed cluster. Increased numbers of kmers result in larger graphs, requiring more processing time to walk them. Increased levels of depth mean the bubble finding algorithm explores a much larger portion of the graph around a bubble, also resulting in increased execution time.(DOC)Click here for additional data file.Table S2Sequenced SNPs not in the 1001 genomes list. For SNPs that were confirmed by Sanger sequencing, but not in the 1001 genomes list, the table shows the position of alignment to the TAIR9 reference, as well as loci from the TAIR browser at www.arabidopsis.org.(DOC)Click here for additional data file.Table S3Arabidopsis thaliana experiment oligos.(DOC)Click here for additional data file.Table S4Solanum berthaultii experiment oligos.(DOC)Click here for additional data file.Figure S1Kmer counts for different values of k in the Bur-0 and Tsu-1 datasets.(TIFF)Click here for additional data file.Figure S2Chromatograms for Solanum berthaultii experiment. Typical chromatograms from Sanger sequencing confirmation of SNPs. In each case, the top graph represents the resistant bulk cDNA, the bottom graph the susceptible bulk cDNA and dotted lines indicate the predicted SNP location. (A) An example of a linked heterozygous SNP, with the resistant showing both a C and a G at the SNP position, while the susceptible shows only a G. (B) An example of an unlinked heterozygous SNP, where both resistant and susceptible show a C and a G at the SNP position. (C) An example homozygous SNP, with the resistant and susceptible each showing a single, different, nucleotide. (D) An example of an unconfirmed SNP, where there is no apparent nucleotide difference at the predicted SNP position.(TIFF)Click here for additional data file.Figure S3SNP finding compared in Cortex_var and Bubbleparse. Graphs showing percentage of canonical SNPs found and the percentage of predicted SNPs confirmed for Bubbleparse  at a range of search depths  and Cortex_var . (A) Bur-0 results for minimum BWA/SamTools pileup of 10 (left hand column) and 20 (right hand column). (B) Tsu-1 results for minimum pileup of 10 (left hand column) and 20 (right hand column). (C) For Ler-1, a curated SNP list was available, so only one column.(TIFF)Click here for additional data file.Figure S4Bubbleparse SNP recall for synthetic E. coli. Graph showing percentage of SNPs found by bubbleparse, percentage of true positive SNPs output by bubbleparse and percentage of false positive SNPs output for a simulated E.coli dataset. Input was a set of simulated reads from the E. coli genome and a second set of simulated reads from an E. coli genome containing 100,000 simulated SNPs. Both sets of reads were designed to mimic 76 nt Illumina reads of approximately 20× coverage. Bubbleparse was run with minimal cleaning (removing paths of coverage 1 or less and tips of 100 nodes or less) and search depth 1.(TIFF)Click here for additional data file.Methods S1Implementation of bubbleparse heuristic.(DOC)Click here for additional data file.Methods S2Variation of coverage for Ler-1 experiments.(DOC)Click here for additional data file.


Mosaic somatic alterations are present in all multi-cellular organisms, but the physiological effects of low-level mosaicism are largely unknown. Most mosaic alterations remain undetectable with current analytical approaches, although the presence of such alterations is increasingly implicated as causative for disease.Here, we present the Parent-of-Origin-based Detection (POD) method for chromosomal abnormality detection in trio-based SNP microarray data. Our software implementation, triPOD, was benchmarked using a simulated dataset, outperformed comparable software for sensitivity of abnormality detection, and displayed substantial improvement in the detection of low-level mosaicism while maintaining comparable specificity. Examples of low-level mosaic abnormalities from a large autism dataset demonstrate the benefits of the increased sensitivity provided by triPOD. The triPOD analyses showed robustness across multiple types of Illumina microarray chips. Two large, clinically-relevant datasets were characterized and compared.Our method and software provide a significant advancement in the ability to detect low-level mosaic abnormalities, thereby opening new avenues for research into the implications of mosaicism in pathogenic and non-pathogenic processes. Chromosomal abnormalities, including deletions, amplifications, and uniparental disomy (UPD) events, are a significant cause of Mendelian and complex disorders, as well as a source of benign variation within a population. Technological advancements such as SNP microarrays and next-generation sequencing have dramatically enhanced disease research and diagnosis by improving the ability to detect genomic variation. Along with technological advances, new algorithms for data analysis improve our ability to identify biological aberrations within large datasets. Many algorithms are designed to detect abnormalities that result in copy number variation (CNV), but frequently neglect regions of UPD and somatic mosaicism. Within a population of cells originating from a single zygote, any somatic change results in mosaicism, in which a subset of cells harbors a unique genetic variant. Our understanding of the prevalence and consequences of mosaic abnormalities remains limited, due to the difficulty of detecting alterations in a small subpopulation of cells. Mosaic abnormalities have been implicated in a multitude of disorders, including Alzheimer’s disease, schizophrenia, autism, neurofibromatosis, McCune-Albright syndrome, Duchenne muscular dystrophy, Proteus syndrome, heart, kidney, neuromuscular, and dysmorphic syndromes, as well as cancer -5. LymphThere are many useful algorithms for abnormality detection in SNP array data. Implementations of segmentation-based approaches applied to B allele frequency (BAF) and log R ratio (LRR) values, including BAFsegmentation  and MAD Parent-of-Origin-based Detection in trios), which includes additional parental contribution-based approaches for abnormality detection. triPOD outperforms current state of the art detection methods, shows greatly improved sensitivity for detection of low-level mosaicism, and uniquely provides the parental origin for each detected abnormality. triPOD software is made available as a command line program and as a web application.Here we present an algorithm for highly sensitive and specific detection of mosaic and non-mosaic abnormalities in offspring by employing the Parent of Origin-based Detection (POD) method on SNP array data from a parent–child trio. We also describe an implementation of this algorithm in triPOD  potentially reveals abnormal parental contribution for the surrounding region Table . AbnormatriPOD is a fast, efficient, multi-threaded software program for chromosomal abnormality detection in offspring using SNP array data from parent–child trios. It is implemented as a Perl script and makes use of the R software environment for graphical output. It can be used for single trios or batches. It is designed to distribute analyses of individual chromosome arms to a user-supplied number of processors and perform parallelized single processor analyses of samples in batch mode. For the current implementation, average run times for analyses of a single trio with ~600,000 SNPs were recorded on a Linux server with 2.67 GHz Xeon x5650 processors and range from ~90 seconds on a single CPU to ~35 seconds employing 10 CPU cores (--cores=10). In batch mode, a number of samples equal to the number of CPU cores can be analyzed in parallel in the same amount of time required for a single sample on a single CPU core. The user can specify the use of the four detection algorithms described below. The output includes detailed annotation of detected abnormal regions in tabular format, optional graphical output of chromosomes harboring abnormalities, a file in BED format for use with genome browsers, an optional file reporting calculated parameters and thresholds, and a log file for error reporting.triPOD includes an implementation of the POD method, in addition to streak-based approaches to homozygous deletion (HD)(PODhd), single Mendelian error (MI1)-based (PODmi1), and cryptic (low information content) region detection (PODcr). The sliding window approach employed combines high resolution and an adequate sampling of the surrounding region. The following is a summary of the triPOD workflow:1. Sample-specific parameter specifications and probability estimations are calculated.2. POD method. SNPs are analyzed for information content based on genotype combinations and progeny BAF values.3. POD region detection. Informative SNPs are analyzed using overlapping windows. The ratios of parental abnormal SNP contributions are evaluated using a two-tailed binomial test along each chromosome. Overlapping abnormal windows are combined and expanded until evidence of a change in contribution is encountered. Remaining normal segments are evaluated for low-level mosaicism.4. Streak-based region detection. The PODhd, PODmi1, and PODcr algorithms detect abnormalities by identifying statistically significant streaks of abnormal SNPs.5. The boundaries of detected abnormal regions are refined using an optimized bidirectional cumulative sums (CUSUM) approach applied to the LRR values or mirrored BAF (mBAF) values .6. Steps 2 – 5 are then repeated, following a refinement of the BAF and LRR parameters, which are generally derived from the normal regions of the local chromosome arm.7. Overlapping detected regions are combined or spliced and annotated for parent-of-origin, type of abnormality, and inheritance pattern.8. The abnormal regions can be viewed as tabular and/or graphical output Figure .n informative SNPs, the number k that indicate paternal contribution can be viewed as the outcome of the binomial random variable Xn ~ B. Thus, a two-tailed binomial test can be applied to identify statistically significant deviations from the expected distribution over a region of the genome, indicating the presence of a chromosomal abnormality.Genetic abnormalities can be detected using a parental contribution model derived from the principles of inheritance. Normal biparental inheritance results in progeny who share approximately 50% of their genome with each parent. Inheritance of a parental chromosome harboring a mutation (a germline mutation) or the acquisition of a somatic mutation alters the expected 1:1 parental contribution ratio for that region. The triPOD implementation of the POD method is based on a statistical model in which the parental contributions revealed by informative SNPs in regions of biparental inheritance can be viewed as a sequence of Bernoulli trials where the outcomes occur with equal probability. So in a set of k SDs of the mean, defined as Informative SNPs are detected using a combination of genotypes and progeny BAF values exceeding a threshold. To specify the BAF thresholds, the mean BAF values of heterozygous and homozygous SNPs in normal autosomal chromosomes for each sample are calculated, and thresholds are defined as a specified number of standard deviations (SDs) of the mean. The default number of SDs for heterozygous SNPs is Xn is calculated. The probability mass function for the binomial distribution is defined to beThe informative SNPs are first evaluated using overlapping windows (default = 100 SNPs) in single SNP increments along each chromosome arm. For each window the binomial test is applied and the p-value of the observed outcome of the random variable n is the number of trials, k is the number of successes, and p is the probability of success. The formula for calculating the p-value for the two-tailed binomial test iswhere p = 0.5 and k is the number of SNP positions with abnormal paternal contribution out of n informative SNPs in a window. The null hypothesis, that the window lies in a region of biparental inheritance, is rejected if the p-value falls below the significance threshold, indicating a chromosomal abnormality within or including the current window.where, in our case, E(Wk) containing k informative SNPs given byEach overlapping window which contains a new set of informative SNPs and is not inside of an abnormal region constitutes a new binomial test. Due to the unusual dependency structure created by tests applied to overlapping windows, the most appropriate method of controlling the familywise error rate (FWER) is derived from the field of scan statistics, which focuses on the clustering of events observed while scanning time or space. We first compute the expected number of windows M is the total number of SNPs, N is the total number of informative SNPs, and w is the window size [β, the probability of making a type I error is given bywhere dow size  (adaptatwhereβ such that PFWER(β) is below the α-value. Since our set of possible p-values is discrete and relatively small, this can be done efficiently, and in our implementation we test the values βk = P for increasing k. The first βk satisfying PFWER(βk) < α becomes our significance threshold for the individual binomial tests.Thus we can control the FWER by finding a When an abnormal window is identified, it becomes the seed of an abnormal region, which is expanded until there is evidence of a change-point in the distribution of parental contributions. The boundary is then retracted to the most recent significantly abnormal window and further retracted to the first and last informative SNPs with contribution from the appropriate parent. The region boundaries are then refined using the bidirectional CUSUM approach (see Boundary refinement).e . As before, we can apply a binomial test to assess deviations from the expected distribution which would indicate that the abnormal region has ended and initiate boundary retraction. In this case the applicable test is one-tailed, and the p-value is equal to In an abnormal region, all informative SNPs should show abnormal contribution from the same parent unless a genotyping error has occurred. Most detectable errors in an abnormal region will show up as informative for the opposite parent. Therefore we can view each SNP in the window as a Bernoulli trial for which the probability of success (in this case success is defined as the SNP being informative for the opposite parent) is the estimated error rate Chromosomal regions without detected abnormalities undergo further analysis using a larger overlapping window size . This analysis is specifically designed to enhance the detection of large low-level mosaic abnormalities and illustrates the utility of employing multiple window sizes within the POD method. A large window analysis has higher sensitivity and lower specificity than a small window analysis. By implementing an initial analysis with smaller windows, a large majority of abnormalities will be detected with highly specific boundaries before any remaining low-level mosaic abnormalities would be detected during the subsequent large window analysis. This allows for an elevation of overall sensitivity with a minimal reduction in overall specificity.m for which the p-value of the occurrence of m adjacent informative SNPs falls below the user defined α-value. A highly accurate approximation of the p-value of a streak of size m is given byWhen analyzing trios, there are many additional ways to evaluate parental contribution data. To complement the POD approach, triPOD employs a streak-based algorithm for detection of HD- and MI1-based abnormalities, as well as abnormal regions lacking sufficient parental information content. A streak is comprised of adjacent informative SNPs. Informative SNPs are defined differently depending on the algorithm used for streak detection and autosomal rates are calculated . The minimum number of adjacent informative SNPs which can be considered a statistically significant abnormality is calculated by computing the minimum wheren is the total number of SNPs, and p is an autosomal rate of informative SNPs [ive SNPs  . For each trio member, the minimum size of a significant HD streak is computed as above with n defined as the total number of polymorphic SNPs and p as the autosomal rate of MI1 SNPs. PODmi1 detection is very useful for identifying small non-mosaic or high-level mosaic abnormalities frequently below the detection limit of the POD method. It is also useful for resolving instances in which the POD method detects a small abnormality but is unable to completely refine the boundaries (see Overlap and annotation).In the PODmi1 algorithm, informative MI1 SNPs are defined as all genotype combinations from which a single inheritance error can be inferred, including all informative SNP combinations with an abnormal BAF value which exceeds 5SD of the normal heterozygous BAF mean. The minimum PODmi1 region size and region detection is performed as above with n defined as the total number of polymorphic SNPs and p as the autosomal rate of outlier BAF values. Windows with a significant rate of outlier BAF values may be due to a local asymmetry in BAF [n is the total number of outlier BAF values and k is the minimum number of the upper or lower values. Windows for which the null hypothesis is rejected, given the α-value corrected for number of detected cryptic regions, are evaluated and undergo boundary refinement with unknown parental origin. Parent of origin is assigned, if possible, during the annotation stage.We define cryptic regions as having a statistically significant presence of outlier BAF values. The autosomal rate of non-homozygous SNPs with BAF values which exceed 2SD of the normal autosomal mean is calculated. The minimum size of a streak is computed as above with y in BAF , and areThe detection of abnormal regions prior to boundary determination requires a unique application of change-point analysis for detection of single change-points bidirectionally from within an abnormality. The cumulative sums (CUSUM) approach is a standard method of change-point analysis developed to detect small changes hidden in a continuous process . A basicSn, x is a dataset with n members, and k is commonly the in-control sample mean or target value. We adapt this equation for boundary detection as follows: Sn is a region boundary defined by a change-point between the normal and abnormal distributions, optimized k = /2, and x is a sequence of LRR or non-homozygous mBAF values. Our assignment of k maximizes the value of the change-point by creating approximately equal slopes on either side. This estimation of k is only optimal when variation between distributions is similar. Since the HD variation is much larger than the other distributions, k ≤ 1.5 is considered to be more appropriate. From within an abnormal region the abnormal values will generally be >k, creating a positive slope which will peak at the change-point, after which the values in the normal region will generally be <k, creating a negative slope . If a region does not meet the LRR requirements, it will undergo BAF-based CUSUM boundary refinement if the BAF median meets the BAF threshold requirements . For the application of a one-sided CUSUM approach, the LRR values for regions with a median < 0 are reflected about 0 and a maximum change-point is detected. The members of i = number of SNPs in the abnormal region, s0 = x0 and sn = xn for the start boundary calculation, e0 = x0 and en = xn for the end boundary calculation, start = the initial start position of the abnormal region, end = the initial end position of the abnormal region, min = minimum POD region size, min inf = a min number of informative SNPs, crit = the position of the maximum LRR value in the region which is not equal to start or end, and cen = the position of the center informative SNP for BAF CUSUM. The variables s0 and e0 are SNP positions located inside the abnormal region and sn and en, respectively, are upstream and downstream baseline SNP positions located outside of the region. The sn and en values are determined by iterative moving median calculations of the data immediately adjacent to the abnormal region boundaries. Median values are calculated for 5i ≤ 25 SNPs in overlapping single SNP increments. When the median value is <k, it is determined that the window extends into the adjacent baseline data and sn or en = the most distant SNP evaluated. Since a detected region likely includes false positive SNPs in addition to the true abnormality, when 3 ≤ i ≤ 3min, s0 = e0 = the position of the maximum LRR value in the abnormal region to ensure that CUSUM begins within the true abnormality. For BAF-based CUSUM of similar regions, CUSUM begins from the center informative SNP, since extreme values are less informative. In order to reduce the chance that CUSUM finds an incorrect local maximum when evaluating a large region, the analysis begins min inf SNPs from start and end when i > 3min.where A series of rules are designed to control for unusual situations. If a max peak is not detected, the boundary defaults to the initial value. If the median of the adjacent data is twice as large as or larger than the median of the abnormal region, it is deemed likely that there is an adjacent unique abnormality and the initial boundary remains, unless a CUSUM minimum was detected. Random HD LRR outliers in a hemizygous deletion region are ignored. Random BAF outliers > the region median + 0.1 are ignored.For occasions when regions called by different triPOD detection methods overlap, rules have been created to prioritize, combine, and splice such regions. PODhd regions are predominantly small regions and are reported as detected without fragmenting larger overlapping regions. Non-mosaic or high-level mosaic abnormalities can be detected by multiple methods. Since the PODmi1 algorithm is more adept at defining small regions than the POD algorithm, in certain cases we assume that both algorithms are detecting the same small abnormalities and give the PODmi1 region priority. When a POD region overlaps no more than two PODmi1 regions and the information content within non-overlapping POD segments is not larger than the minimum acceptable region size, the POD region is discarded in favor of the PODmi1 region(s). Otherwise, overlapping regions are combined or spliced based upon various factors, including size of region, number of informative SNPs, parental contribution, and type of abnormality.Detected regions are annotated by parent-of-origin, type of abnormality and inheritance. The type of abnormality is assigned using a threshold-based approach applied to the normalized median LRR value. This value is compared to a threshold normalized to the local baseline LRR median as follows: amplification (AMP) ≥ 0.1, deletion (DEL) > −1.5 and ≤ −0.1, and HD ≤ −1.5. Detected regions with normalized median LRR values > −0.1 and < 0.1 may be any of the following: a UPD region, a region of low-level mosaicism of any type , or a region containing a small abnormality along with many normal SNPs. Within this group, uniparental heterodisomy (UPhD) is assigned to otherwise unannotated regions if the child’s genotypes exactly match the genotypes of the parent of origin, taking into account the estimated error rate. Uniparental isodisomy (UPiD) is assigned to unannotated regions if ≥ 90% of the child’s genotypes are homozygous or if the normalized median mBAF value is > 0.55 and LRR values are > −0.05 and < 0.05. Noisy LRR values may affect proper annotation of small regions. When the inheritance pattern indicates that an abnormality was likely inherited, an annotation of inherited (INH) or inherited with a unique CN state (INH-CN) is provided. The INH state is defined as follows: an AMP is inherited if the contributing parent also has an AMP; a DEL is inherited if the contributing parent also has a DEL; an HD region is inherited if the contributing parent(s) have any heritable combination of HD and DEL regions not indicative of a unique CN state. The INH-CN state is defined as follows: an INH-CN DEL exists when the contributing parent has an HD region and the opposite parent is normal; an INH-CN HD region exists when both parents have a single-copy DEL. We make no judgment as to the inheritance state of any region which does not fall within the above stated parameters.triPOD provides graphical Figure  and annoet al. [et al., we assumed that the simulated abnormalities were the only aberrant regions in this dataset.For benchmarking purposes, we adapted a simulated tumor dilution dataset made available by Staaf et al. , which het al. ,9,12,22 et al.  samples et al. [The dataset was analyzed using triPOD’s POD method and five leading software programs for chromosomal abnormality detection: paired BAFsegmentation , genoCNA (a 9 state HMM for CN aberration detection), MAD , PennCNV joint , and PSCN  ,9,11-13.et al.   Consortium . Twelve HapMap CEU and YRI datasets processed on four different Illumina microarray chips and available in the NCBI GEO database  were anaThe ability to detect newly emerging mosaic abnormalities is illustrated in Figures The cleft lip/palate dataset is a part of the Gene Environment Association Studies initiative (GENEVA) and described in . It consThe AGRE trios were previously run on the Illumina HumanHap550 microarray, which does not contain CNV-specific probes. The DNA for these samples was derived from cultured lymphoblastoid cell lines. The autosomal data of 1587 trios was successfully analyzed by triPOD (NC ≤ 3%).The total numbers and ratios of detected abnormalities by type ) are presented in Table -8).For both the cleft and AGRE datasets, the large, highly informative abnormalities were identified. These regions with ≥ 250 informative SNPs were visually investigated and multiple reports for single abnormalities were combined . The resulting regions are presented in Tables et al. [A large study on the presence of mosaic abnormalities (≥ 50 kb) in GENEVA datasets was recently reported by Laurie et al. . Since tThe ability to accurately detect all types of chromosomal abnormalities is vital to advance the understanding of normal and disease processes. Sensitive and specific detection is the first step in uncovering the effects of low-level mosaic alterations on human health. In addition to the role of mosaicism in disease, several groups have hypothesized that the prevalence of large mosaic abnormalities in the brain and liver suggests a role in normal physiological function, possibly associated with a favorable increase in genetic diversity and unique neuronal signaling processes -32. ReceThe implementation of the POD method in triPOD greatly increases the ability to detect mosaic abnormalities in SNP array data. Benchmarking with simulated and real mosaic abnormalities reveals the superior sensitivity of the triPOD software. The analysis of large mosaic abnormalities within the AGRE autism dataset reveals that triPOD allows for identification of previously undetected mosaic abnormalities. Since the AGRE samples are derived from transformed cell lines, it is impossible to distinguish large regions of low-level mosaicism originating in the patient from cell line artifacts. However, the ability to detect such regions is crucial since low-level aneuploidy has been proposed as a genetic risk factor for idiopathic autism, as detected using extensive cytogenetic analyses in cultured peripheral blood cells . Since tDifferences in abnormality detection are expected across microarray chips due to differences in the number/density of probes, the number of CNV-specific probes, DNA preparation and quality, flux in the levels of mosaicism during expansion or passage of individual samples, and the frequent appearance of new mosaic abnormalities in transformed cell lines . In spitin vivo sources will be helpful to identify global patterns resulting from the source of DNA and those resulting from disease-associated processes. triPOD’s findings were highly concordant with all of the previously reported [et al. approach.A comparison of two large datasets reveals that the presence of CNV-specific probes results in a great increase in detected abnormalities. It was discovered that the AGRE dataset harbored significantly more large informative abnormalities than the cleft lip/palate dataset. While there may be multiple reasons for this difference, the source of DNA offers a likely explanation. Large abnormalities may occur with increased frequency or with neutral or advantageous results in cell culture, resulting in frequent mosaic events due to the process of clonal selection. Additional datasets from cultured and reported  large moet al. [Although validation of the newly discovered very low-level mosaic abnormalities by additional experimental approaches would be ideal, the correlation between alterations of the heterozygous BAF band and underlying mosaic abnormalities has been rigorously validated by multiple groups ,18,34, aet al. . The curWe have several recommendations for triPOD usage. triPOD may be used concurrently with a CNV-specific algorithm, in order to benefit from both types of specialized detection capabilities. Care should be taken when interpreting large abnormalities in commonly variable regions due to a tendency to combine small adjacent abnormalities of the same type. Interesting findings should be graphically investigated until the user has gained expertise with the strengths and weaknesses of the various detection and annotation methods employed by triPOD. Only in very rare instances of consanguineous relationships  in which a significant portion of the parental genomes are identical, would there be a large reduction in the number of informative SNPs for analysis. To identify such cases we recommend examining the relationship status of trios using a specialized software program such as kcoeff .We believe that many of our algorithmic approaches, including applications of scan statistics, abnormal chromosome detection using k-means clustering and the jump method, CUSUM applied to boundary refinement following region detection, and error rate estimation, may be novel applications for abnormality detection in SNP array data. We expect that such methods can be generalized and of benefit for alternative analyses of genomic data or similar clustering of observed events over time.Application of the POD method to trio-based SNP array data provides a highly sensitive and specific means for detecting chromosomal abnormalities, especially low-level mosaicism. Our software implementation, triPOD, outperformed comparable programs when benchmarked with simulated mosaic data. Examples from the AGRE autism dataset in which a progeny chromosome harbored a large low-level mosaic abnormality highlighted the superior performance of triPOD for sensitive detection of mosaic events. triPOD analyses were shown to be robust across multiple types of microarray chips. Significant differences in the abundance of large abnormalities between two large datasets were revealed, likely due to the DNA source. triPOD makes significant advances in reducing the mosaicism detection barrier and increasing the accessibility of mosaicism-directed research.et al. [A simulated tumor dilution dataset made available by Staaf et al.  was adapDatasets containing HapMap samples run on Illumina microarray chips were obtained from the NCBI GEO database (see Tables The cleft dataset is available in dbGaP (accession phs000094.v1.p1).Benchmarking analyses were performed on the autosomes of the simulated dataset using the default parameters with the following programs: PennCNV joint version 2011Jun16, paired sample analysis with BAFsegmentation version 1.2.0, PSCN version 1.0 (*.longlist.update1.txt output), and genoCN version 1.0.0. MAD, as part of R-GADA version 0.9-5, was performed with example parameters from the User’s Guide  due to an absence of default parameters. triPOD analyses were performed using only the POD detection algorithm with default parameters. The other optional detection methods were excluded using the following flags: --nohd --nomi1 --nopodcr.et al. [et al. and publications with similar plots [Sensitivity and specificity calculations were performed as indicated in Staaf et al. , with onar plots ,9,12,22.k parameter was calculated as described above (see Boundary refinement). CUSUM was applied to each of the 12 chromosomes harboring low-level mosaic abnormalities and was able to detect an appropriate change-point for each abnormal region boundary.Boundary estimation, used for sensitivity calculations, was obtained as local minima or maxima provided by a CUSUM-based approach. CUSUM was applied to a subset of BAF values  and < 0.7). The Err) and expressed as the ratio,Data quality detection and adjustments have an integral role in maintaining high resolution. The number of undetectable single errors was estimated by mutating all genotype combinations of normal inheritance to all possible single error combinations (Err to the detectable errors (MI1). Assuming that the frequency of occurrence of MI1 combinations is an adequate representation of the error frequency, the overall error rate e is estimated as 3.75 times the MI1 rate. This is a conservative rate estimate which does not distinguish between technical and biological causes of MI1 calls in normal regions and includes MI1 inference based on BAF .which relates Acceptable levels of quality-related variables were determined based on empirical observation of samples with relationship and DNA quality issues. The analysis of a trio with an estimated MI1 rate ≥ 2% (default) or an autosomal NC rate ≥ 3% (default) for any member is halted, due to a high likelihood of a relationship annotation error or DNA quality issues, respectively, which could invalidate the applicability of the statistical model.BAF and LRR thresholds are calculated both globally and locally for each sample, adjusting for quality-related variation between samples and chromosomes.scan statistic for each arm. The scan statistic, Sw, is defined as the largest number of events (informative SNPs) in a window of size w, given N number of points (SNPs) [scan statistic provides sensitive identification of arms harboring small abnormalities, which are ignored as the BAF thresholds, autosomal rates, and probability calculations are performed using values from normal arms and imputed values for abnormal ones.Normal chromosome arms serve as the basis of several parameter and probability calculations. A two-step process is employed to refine global calculations. The SDs of the autosomal heterozygous BAF values are analyzed as described below to remove outlier chromosome arms before calculating the initial BAF thresholds for informative SNP detection. Informative SNPs are then identified, allowing for the calculation of the s (SNPs) . This noscan statistic of each arm. Cluster results are then evaluated using the jump method [In order to distinguish the normal chromosome arms from those harboring an abnormality, k-means clustering (k = 2) is applied to both the heterozygous BAF SDs and to the p method . The jumK* is the optimal number of clusters, JK are the jumps in the transformed distortion curve, K centers to a dataset (dK), and Y is the transformation factor. The recommended value of Y is ≤ p/2, where p is the dimensionality of the dataset. The Y value can be modified to prevent over-clustering. We employ the jump method iteratively to identify a single cluster containing the normal values. Initially, the dataset is clustered (k = 2) and transformed using Y = 0.5. If K* = 1, the members of the dataset are considered to be normal. If K* ≠ 1, the cluster with the lowest mean is assumed to contain the normal values and is iteratively reclustered and evaluated with Y = 0.475 until the K* = 1. The iterative approach allows for filtering of abnormalities with dissimilar sizes and levels of mosaicism. Lowering Y increases the likelihood that members of the normal distribution will remain in a single cluster. Outliers 4SDs from the mean of the normal cluster are then removed.where Several probability estimations are dependent upon autosomal rate calculations. For such calculations, we consider adjacent abnormal SNPs to be more likely of biological origin than error or chance and treat non-adjacent abnormal SNPs identically regardless of biological or technical origin. We assume that the non-adjacent abnormal SNPs in normal regions can provide an adequate estimate of the number of abnormal SNPs expected by chance.n = the number of polymorphic SNPs in the region, k = the number of heterozygous MI1calls, and p = the autosomal heterozygous MI1 rate. Regions for which the p-value falls below a corrected threshold are indicative of a parental abnormality and are removed from the output. Although the current implementation of triPOD reports only abnormalities in the child, the POD algorithm is adaptable to any member of the trio.Since the POD method is modeled upon normal inheritance patterns, somatic abnormalities in parental data, such as cell line artifacts, may be identified as a region of abnormal contribution. Heterozygous MI1 calls , indicate a novel allele in the child and can be due to a genotyping error, a single nucleotide variant in the child, or a parental somatic alteration. The rate of heterozygous MI1 calls should not be elevated in a detected region unless the detected abnormal inheritance was due to a somatic change in a parent. Once again we can view each polymorphic SNP in the region as a Bernoulli trial where success is defined as a heterozygous MI1 call. We apply a one-tailed binomial test to check for deviations from the expected distribution, where triPOD is available for download as a command-line version for use on a Unix-like operating system and as a web application . triPOD AMP: Amplification; BAF: B allele frequency; CNV: Copy number variation; CUSUM: Cumulative sums; DEL: Deletion; FWER: Familywise error rate; HD: Homozygous deletion; HMM: Hidden Markov model; LRR: Log R Ratio; mBAF: Mirrored B allele frequency; POD: Parent-of-Origin-based Detection; PPV: Positive predictive value; SNP: Single nucleotide polymorphism; SD: Standard deviation; UPD: Uniparental disomy; UPhD: Uniparental heterodisomy; UPiD: Uniparental isodisomy.The authors declare that they have no competing interests.JDB conceived of the POD method, conceived of and implemented the triPOD software program, performed software testing, benchmarking, and data analysis, and drafted the manuscript. BDB assisted with the strategic design and implementation of triPOD, statistical modeling, and algorithm refinement, and helped to draft the manuscript. MDS participated by facilitating the triPOD web application. JP participated in the acquisition of necessary data and helped to draft the manuscript. All authors read and approved the final manuscript.triPOD software.Click here for file


Although whole human genome sequencing can be done with readily available technical and financial resources, the need for detailed analyses of genomes of certain populations still exists. Here we present, for the first time, sequencing and analysis of a Turkish human genome. We have performed 35x coverage using paired-end sequencing, where over 95% of sequencing reads are mapped to the reference genome covering more than 99% of the bases. The assembly of unmapped reads rendered 11,654 contigs, 2,168 of which did not reveal any homology to known sequences, resulting in ∼1 Mbp of unmapped sequence. Single nucleotide polymorphism (SNP) discovery resulted in 3,537,794 SNP calls with 29,184 SNPs identified in coding regions, where 106 were nonsense and 259 were categorized as having a high-impact effect. The homo/hetero zygosity  and transition/transversion ratios  were within expected limits. Of the identified SNPs, 480,396 were potentially novel with 2,925 in coding regions, including 48 nonsense and 95 high-impact SNPs. Functional analysis of novel high-impact SNPs revealed various interaction networks, notably involving hereditary and neurological disorders or diseases. Assembly results indicated 713,640 indels (1∶1.09 insertion/deletion ratio), ranging from −52 bp to 34 bp in length and causing about 180 codon insertion/deletions and 246 frame shifts. Using paired-end- and read-depth-based methods, we discovered 9,109 structural variants and compared our variant findings with other populations. Our results suggest that whole genome sequencing is a valuable tool for understanding variations in the human genome across different populations. Detailed analyses of genomes of diverse origins greatly benefits research in genetics and medicine and should be conducted on a larger scale. Following the publication of two draft sequences Efforts (other than WGS) that target discovery of human genome variations also exist, such as the HapMap project Despite the increasing use of human WGS for both research and clinical purposes, there remain two areas that require further attention: i) there are populations for which WGS or SNP discovery efforts have not been done; ii) very few of the human WGS performed so far provide high-coverage sequencing results with detailed analysis. Out of the 185 individuals for whom WGS has been performed in the 1000 Genomes Project's Pilot Phase, only six were subjected to high-coverage sequencing (∼42x) while the remaining individuals were subjected to low-coverage sequencing (2–6x). All of the individuals studied in Phase I of the project were analyzed using low-coverage sequencing (2–6x).There have been various efforts to perform high coverage WGS of different populations with detailed analysis Turkey, the most populous well-defined region inhabited by Turks, is an interesting geographical region as it lies at the crossroads between Europe and Asia. Historically, migration from Central Asia and ancestral contribution to regions surrounding Anatolia, such as the Balkans, Middle East, Caucasian and Caspian regions, has positioned the Turkish population as an interesting genetic resource that requires further detailed analysis. Certain important diseases, such as hemoglobinopathies (e.g. sickle-cell disease), thalassemias, and Behcet's disease, are highly prevalent in the Turkish population; and diseases exist where the Turkish population does not exhibit the variant believed to be the cause This study is approved by the Committee on Ethics in Research on Humans of Istanbul Bilgi University.The genomic DNA (gDNA) used in this study came from a healthy male individual, who was anonymous and was reported to come from Turkish ethnicity for at least three generations. Informed consent was obtained prior to the collection of the blood sample from which gDNA was isolated using a QIAamp DNA blood kit (Qiagen®). The individual gave written consent to the publication of his genome sequence. A quality control inspection and rough quantitation of the gDNA sample was performed by agarose gel electrophoresis and UV-induced ethidium bromide fluorescence . The quaThe genomic DNA sample was used to generate a paired-end library suitable for the HiSeq sequencing platform (Illumina®) prepared using the TrueSeq DNA Sample Preparation kit, following the manufacturer's instructions. Quality control analysis of the library using an Agilent 2100 Bioanalyzer indicated that the library was of acceptable quality, containing the expected fragment size and yield, for continued sample processing . The libSanger sequencing was used for validation of 20 small indels identified by computational analysis of the whole genome sequence data. DNA was isolated using a Dual DNA isolation kit . Twenty regions were amplified from 50 ng genomic DNA with 10 pmol of forward and reverse primer pairs. Polymerase chain reaction (PCR) was performed using the following cycling profile: initial denaturation at 95°C for 5 min. followed by 10 cycles of 95°C for 30 s, 63°C for 30 s, and 72°C for 30 s; 25 cycles of 95°C for 30 s, 56°C for 30 s, and 72°C for 30 s; and a final extension step at 12°C for 5 min. Amplicons were purified using GenedireX PCR Clean-Up kit  and quantified. ABI 3100 DNA analyzer and ABI BigDye Terminator cycle sequencing was used for Sanger sequencing.bwasw algorithm to index the database and generated suffix array indices for two ends in a paired-end read separately. We then combined the two results with the sampe algorithm to produce the final sequence alignment/map (SAM) file. BWA analysis results were investigated with SAMStat v1.08 −10.The reference genome used in this study was NCBI human reference genome build 37.1 (GRCh37/hg19 assembly). We adopted two workflows for read mapping. First, we used the vendor supplied Eland and Casava (v1.8) pipeline using recommended settings with a variants covariance cut-off value of 3. Alternatively, we used the Burrows-Wheeler Alignment tool (BWA v0.6.2) for mapping reads to the reference genome SNP identification was done using the vendor-supplied Eland-Casava pipeline with the recommended settings and The Genome Analysis Toolkit (GATK v2.2) We used Ingenuity Software Knowledge Base (IKB),  for the functional interaction analysis of the genes affected by high-impact, novel SNPs. IKB uses interactions between genes and/or gene products based on manual curation of scientific literature providing a robust interaction database. Once a gene list of interest is identified, IKB uses known interactions between these genes to build an interaction network. The final network includes genes that are not in the input list but are highly connected to the genes in the input list. This feature enables the investigation of new modules of interactions that are not covered by existing canonical pathways. The results are also analyzed in terms of drugs, small metabolites, functions, and diseases that are overlaid on the resulting network. To this end, an annotation of the resulting interaction network is achieved where functional entities involved in the network are underlined.Prior to the sequencing step, we compared the microarray genotyping results of the individual used in this project, who was an anonymous, healthy male claiming to have come from Turkish ancestry for at least three generations, with those obtained from the HapMap project DNA sequencing generated 1,238,722,496 paired-end reads corresponding to ∼125,111 M bases of data yielding ∼35x coverage. Quality and length-based trimming and filtering dropped 4.44% of the reads eliminating a total 5.03% of total base pairs. The remaining ∼1.18 billion reads  proved to be of high quality , with sufficient length (mode is ∼100 bp), and included no Ns . Of the Casava and GATK workflows identified 3,642,449 and 4,301,769 SNPs, respectively. In order to increase the reliability of our findings, all downstream analysis was performed with SNPs identified by both methods, which resulted in 3,537,794 variants. Of these concordant SNP calls, 97.8% were in agreement with the SNPs called by the genotyping performed on the array, showing high reproducibility. The transitions  transversions , Ts/Tv, ratio was 2.06, and the homozygosity  and heterozygosity  proportions were 40% and 60%, respectively, with both ratios and percentages resembling expected figures in similar studies www.genecards.org) SNPs, where (i) the variant hits a splice acceptor/donor site, (ii) a start codon is changed into a nonstart codon, or (iii) a stop codon is gained or lost due to the variant are categorized as having a “high-impact” effect. There were a total of 259 SNPs with a high-impact effect and 167 of these were found to be on gene sequences. Out of the ∼3.5 million SNPs identified, there were 480,396 potentially novel SNPs that did not exist in dbSNP. Of these potentially novel SNPs, 49% and 41% were in intergenic and intronic regions, respectively. Over 8% were upstream or downstream of a gene, and 2,925 (or 0.5%) of the SNPs were found to be in coding regions. There were 48 nonsense SNPs and a total of 95 SNPs with a high impact. These high-impact SNPs affected 47 genes (45 well characterized). In We identified 713,640 indels, which consisted of 341,382 insertions and 372,258 deletions. Of these indels, 159,593 (or 22%) were found to be novel. The length distribution ranged from -52 bp to 34 bp  and detailed analysis of the whole genome sequence of a Turkish individual. Although whole genome human sequencing is almost routinely done, very few of these efforts provide high coverage and analysis; and various populations are not included in large consortium efforts. Therefore, we believe the current study provides a reference data set in understanding human genome variation on a large scale and a population-dependent context and is an initial step in exploring Turkish genomic features. Despite its importance as the first Turkish whole genome analysis, we acknowledge that further studies are required to validate and generalize our findings to the population scale. Nevertheless, we believe the data presented here will provide a cornerstone for such studies and enrich the analysis of human genomic variation across diverse populations.Our genotyping results from chip and sequence data, and the comparative analysis of these with HapMap and other chip genotyping results suggest that the sequenced individual represents a typical member of the Turkish population. The sequencing analysis proves that the data generated is of high quality as only 5% of the total sequence is filtered out, and the remaining reads almost completely cover the reference human genome. High N50 values indicate successful assembly of unmapped reads, which also resulted in ∼1 Mbp of unmapped human genome sequence that did not reveal any homology in the RefSeq database. SNP identification rendered high reproducibility with ∼98% consistency between the sequencing and microarray genotyping results and validated about 86% of identified SNPs in the dbSNP database. We believe the remaining 480,396 potentially novel SNPs contribute to understanding the human genomic variation and the relationship between genotypes and phenotypes.The length distribution of the 9,109 identified CNV/SV events showed peaks at the 300 bp and 6,000 bp marks potentially representing short and long interspersed elements, respectively. A similar trend is seen in the 1,629 high confidence calls . The relIn order to position our results in a better population genetics context, we compared the SNPs identified in the sequenced Turkish individual to the SNPs found in Utah, USA inhabitants with ancestry from Europe (CEU) and to the SNPs found in Han Chinese in Beijing, China (CHB). Both CEU and CHB populations were included in the HapMap project, which constitute the source of the identified SNPs in these populations used in our analysis. From a historical perspective, precursors of the Turks originated in Central Asia and Turks are known to have been inhabitants of regions that are parts of modern day China. Turks' migration toward the West ended up mostly in Anatolia with minor settlements in Europe. Turks' presence in Europe was later expanded throughout the Ottoman era. We, therefore, performed our comparative analysis with CEU and CHB, which are the two most closely related populations to the Turkish population with available large-scale genetic data. We found 665,032 SNPs commonly shared by the three populations, which corresponds to 47% of all of the CEU SNPs and 50% of all the CHB SNPs . SNPs exNovel SNPs predicted by our results have the potential to explain genetic features specific to the Turkish population. The 23 well-characterized genes that were affected by the novel nonsense SNPs identified in this study were found to affect 25 different phenotypes listed in OMIM, potentially leading to additional genetic disease mechanisms see . We usedIKB analysis revealed three interaction networks that included 15, 13, and 3 of the 47 genes. In The whole genome sequencing reads have been deposited in the NCBI Short Read Archive (SRA) database under the accession number SRA056442. SNPs and indels have been deposited in the NCBI dbSNP database with the handle ID “BILGI_BIOE”. CNV/SV calls have been deposited in the NCBI dbVar database with project ID PRJNA171612.Figure S1QC gel image (0.8% agarose) of gDNA sample (1) compared to molecular weight marker (M). Sample quality was considered to be acceptable if the gDNA supplied a single visible band while lacking any significant degradation products .(TIF)Click here for additional data file.Figure S2Library quality. The electropherogram for the generated library displaying expected yield and size.(TIF)Click here for additional data file.Figure S3Quality statistics for the forward reads only  following trimming and filtering. a) Average base quality with respect to the position of the base in the read; b) Histogram of the sequence lengths; c) histogram of the average quality scores of the reads; d) Ns seen in the reads with respect to the position of the base in the read.(TIF)Click here for additional data file.Figure S4Length distribution of the identified 713, 640 indels.(TIF)Click here for additional data file.Figure S5Distribution of 9,109 identified CNV/SV calls across chromosomes. A) Number of CNV/SV events; B) Length normalized (per million base pairs) CNV/SV events.(TIF)Click here for additional data file.Figure S6Length distribution of 9,109 identified, 3870 novel, and 1629 high confidence CNV/SV calls. Note that the bin size for SVs less than 10 Kbp is 200 bp while the bin size for SVs more than 10 Kbp is 2 Kbp. The last bar in the graphs on the right-hand column represents SVs more than 250 Kbp.(TIF)Click here for additional data file.Figure S7Overlap of SNPs identified in the Turkish individual used in the manuscript (TUR); Utah, USA inhabitants with ancestry from Europe (CEU); and Han Chinese in Beijing, China (CHB).(TIF)Click here for additional data file.Figure S8IKB Network analysis of 45 genes affected by a high impact novel SNP. Genes indicated by red are affected by a nonsense SNP and genes indicated by green are affected by a SNP targeting a splice site donor/acceptor region. Hereditary and Neurological Disorders/Diseases are indicated where applicable.(TIF)Click here for additional data file.Table S145 well characterized genes that were affected by a high-impact SNP. Effect Types: 1: Stop gained; 2: Splice site acceptor; 3: Splice site donor; 4: Stop lost; 5: Start lost.(PDF)Click here for additional data file.Table S220 predicted indels used for validation by Sanger Sequencing .(PDF)Click here for additional data file.Table S3Forward and reverse primers used in Sanger Sequencing.(PDF)Click here for additional data file.Table S4Biological Function categories known to involve 45 well characterized genes that were affected by a high-impact SNP.(PDF)Click here for additional data file.