Yet Another Meta Analysis Software), however, enables cross-GWAS conclusions prior to finished and polished imputation runs, which eventually are time-consuming.Meta-analysis (MA) is widely used to pool genome-wide association studies (GWASes) in order to a) increase the power to detect strong or weak genotype effects or b) as a result verification method. As a consequence of differing SNP panels among genotyping chips, imputation is the method of choice within GWAS consortia to avoid losing too many SNPs in a MA. YAMAS (ad hoc results, possibly providing an incentive for follow-up studies. We propose our method as a quick screening step prior to imputation-based MA, as well as an additional main approach for studies without available reference data matching the ethnicities of study participants. As a proof of principle, we analyzed six dbGaP Type II Diabetes GWAS and found that the proxy algorithm clearly outperforms naïve MA on the p-value level: for 17 out of 23 we observe an improvement on the p-value level by a factor of more than two, and a maximum improvement by a factor of 2127.Here we present a fast method to avoid forfeiting SNPs present in only a subset of studies, without relying on imputation. This is accomplished by using reference linkage disequilibrium data from 1,000 Genomes/HapMap projects to find proxy-SNPs together with in-phase alleles for SNPs missing in at least one study. MA is conducted by combining association effect estimates of a SNP and those of its proxy-SNPs. Our algorithm is implemented in the MA software YAMAS. Association results from GWAS analysis applications can be used as input files for MA, tremendously speeding up MA compared to the conventional imputation approach. We show that our proxy algorithm is well-powered and yields valuable YAMAS is an efficient and fast meta-analysis program which offers various methods, including conventional MA as well as inserting proxy-SNPs for missing markers to avoid unnecessary power loss. MA with YAMAS can be readily conducted as YAMAS provides a generic parser for heterogeneous tabulated file formats within the GWAS field and avoids cumbersome setups. In this way, it supplements the meta-analysis process. Yet Another Meta Analysis Software) software. In particular, it is possible to carry out a first analysis without the need to impute. The idea of our algorithm is that for SNPs that are present in one study but not in another, substitute proxy SNPs are defined using reference data from the HAPMAP [The ongoing GWAS era has led to 1,449 association findings for 237 complex traits by 6/2011  or intere HAPMAP  or 1,000e HAPMAP . In thisM. In general, we simulated series of new data sets Mi by random re-assignment of cases-control status, so that each Mi consisted of 144 cases and 144 controls. For each data set, we conducted a single-marker analysis on Mi and identified the smallest p-value Mi. In case −6, we kept the simulated data set for further analysis. We stopped the simulation process when 500 data sets with Mi was split into two “studies” Ai and Bi, each involving only 144 individuals. In addition, for Ai SNP information was kept only for SNPs from the Illumina® Human660WQuad v1 panel, and for Bi, SNP information was kept only for Affymetrix® 6.0 chip content. Meta-analysis of Ai and Bi was then performed either using only the SNPs available in both panels (MA-intersection), or using the proxy algorithm (MA-proxy), or based on Ai and Bi imputed with the IMPUTE [minP values: we counted the fraction of data sets for which minP was smaller than α ∈{0.01,0.001,..,1×10−6}. For comparison, we will show also the power values that would be obtained when both studies A and B had been genotyped for all SNPs of the 1,000 Genomes panel (LIMIT). That reflects the upper limit of what the meta-analysis approaches could have reached. Adjustment for the number of SNPs within the 100 kb window was not performed. In particular, minPMA−proxy and minPMA−impute were treated equally, even though more SNPs were considered with imputation-based MA. Thus, MA-imputed was favored in a way by our power definition. However, in practice a significance level of 5×10−8 is the consensus to establish genome-wide significance, irrespective of the number of SNPs actually tested. Therefore, it seems to be appropriate not to adjust for the varying amount of SNPs tested when comparing different MA strategies.We conducted a power study based on 1,000 Genomes  data  and was included to investigate the robustness against poorly fitting reference panels.time command, real time) from analysis with YAMAS are listed. Analysis of the Type II Diabetes data . In Table h/92m5s=98).We wish to contrast the running time of the proxy-algorithm of 92m5s (6 studies) to that of imputation. Imputation was carried out in chunks using IMPUTEv2 . We usedα = 0.0 or α= 0.01, irrespective of the reference file that is chosen. In particular, there is no evidence for inflated type I error with the new proxy algorithm. This was also true when the r2-limit for a SNP and its proxy was relaxed from 0.80 to 0.50 (data not shown). We conclude that the proxy approach is a valid method. Of notice, all methods are too conservative when a random effects model is used. This is in concordance with a recent publication [In Table lication  in whichα-levels on a logarithmic scale, moving from higher to lower levels. The y-axis displays power levels.The results from our power study are depicted in Figures When an ideal reference panel is used Figure , MA withad hoc results at an early analysis stage.In the presumably most realistic scenario . The same arrays were used for the second project, the “Northwestern NUgene Project”. Here, 1,239 individuals with 495,588 from the Human660W-Quad v1 array were available after QC, and, 267 individuals with 908,692 markers were available for Human1M-Duo v3. Finally the third project “GENEVA Diabetes Study”, comprised two further studies, the Nurses Health Study (NHS) and the Health Professionals Follow-up Study (HPFS). After provided quality control (QC), 3,435 individuals and 764,679 SNPs were available for NHS, and 2,606 individuals and 787,213 SNPs were available for HPFS. Both studies were performed on the Affymetrix® Human SNP Array 6.0. In total, data from three different platforms with different marker content were used in six GWAS studies.There are two projects of the Northwestern NUgene Project Type 2 Diabetes from the National Human Genome Research Institute (NHGRI), each of which contributed 2 studies to our analysis. The “Project Health Research - Vanderbilt University” project provided two studies from different platforms. Data generated with the IlluminaThe dbGaP data was analyzed with naïve MA (MA-intersection), i.e., conventional MA restricted to the joint marker panel, with the proxy algorithm (MA-proxy), and based on data imputed with IMPUTEv2 , using 1r2 greater than 0.8 with the substituted SNP.Only 141,105 SNPs were available in all six studies and 561,282 SNPs were available in at least four studies. The proxy approach enabled the analysis of 1,427,514 SNPs and SNP/SNP–proxy combinations, whereof 1,0464,482 were available in all six studies and 1,279,702 were available in at least four studies. More than 85% of the proxies had an −8. For each of these gene regions, we investigated the 100 kb up- and downstream region of the SNP reported to be most significant and computed minPMA−impute and minPMA−proxy for the six studies available for us. Of note, a considerable part of the 33 genes was identified by meta-analysis efforts and shows only moderate odds ratios [Our philosophy was to compare the performance of the methods on Type II Diabetes genes that the community considers to be “undoubtedly” confirmed. To this purpose, we used the catalog of published GWAS results provided by the American National Human Genome Research Institute . The cats ratios ,25. As aminP improves from 3.2×10−19 to 1.5×10−22, a change by a factor of 2127. In total, there are 6 gene regions with a p-value improvement of at least a factor of 10, including FTO, IRS1, JAZF1, KCNJ11, and KCNQ1. For KCNQ1, we observe p = 0.021 with MA-intersection and p = 0.00027 with MA-proxy,In Table an increase in significance by a factor of 78.6. For another 11 genes we observe an improvement with the proxy algorithm by a factor ranging from 2.18 (TSPAN8/LGR5) to 8.46 (JAZF1). There are also 5 gene regions for which no difference between MA-intersection and MA-proxy can be observed. In these cases, the most significant SNP is available in all 6 studies and, therefore, MA-intersection and MA-proxy coincide. Finally, for TSPAN8/LGR5, significance slightly decreases from 0.012 to 0.008. In summary, MA-proxy outperforms MA-intersection in the majority of cases and we observe an average (median) improvement of the level of significance of 107.1 (3.33), demonstrating the usefulness of proxy-SNPs.Imputation-based MA outperforms MA-proxy in 19 out of 22 cases and we observe an average (median) improvement of the level of significance of 19.1 (2.79). In two cases, imputing outperforms the proxy-algorithm by a factor of more than 100, SLC30A8 (193) and HMGA2 (149.8), and in another case by a factor of 21.7 (SPRY2) which demonstrates the usefulness of long-range LD for association analysis. For 16 genes, the loss of significance with the proxy-algorithm is moderate with a factor of less than 10, of which for 11 genes we observe a factor of less than 4. For three genes, FTO, KCNQ1 and RBSM1/ITGB6, the proxy-algorithm even performs slightly better than MA-impute. In summary, one can say that the proxy algorithm yields good approximations of the actual level of significance in the majority of cases and that it is a potentially useful screening algorithm.−51 are reported [r2 of 0.945 with rs7903146 and which is located only 2.3 kb downstream. Moreover, the A-T haplotype has a frequency of 0.668, while under linkage equilibrium a frequency of only 0.454 would be expected, based on the allele frequencies of 0.68 for rs7903146-A and 0.668 for rs4506565-T. Thus, rs7903146-A and rs4506565-T are “in-phase” alleles and proxy meta-analysis can combine the respective effect estimates and standard errors. Since in all 6 studies the effect estimates for rs7903146-A or rs4506565-T, respectively, are positive, joint meta-analysis becomes highly significant with a p-value of 1.5×10−22. Of course, rs7903146 also is indicated by conventional meta-analysis with a p-value of 3.3×10−19, but in this case only 4 GWAS studies can be used and the additional evidence coming from studies 5 and 6 is lost. We note that the proxy-SNPs that YAMAS uses may differ from study to study. In the example, the same proxy SNP was used for both study 5 and 6. In general, however, the proxy algorithm identifies for each study independently the SNP with the highest r2 with the SNP that shall be substituted.Table reported . In our reported  data, haVia real data analysis we were able to show that the proxy algorithm is not only fast and quickly employed, but also powerful. It clearly outperformed naïve SNP-by-SNP meta-analysis on real genotype data, when applied to a set of established Type 2 Diabetes regions. Moreover, our simulation study indicates that the proxy algorithm is very robust in terms of power with respect to a ethnically poorly matched reference panel. Thus, it is worth considering it as an alternative MA approach for studies on ethnical groups that are not directly represented in the 1,000 Genomes, for instance studies carried out in population isolates.It is a known phenomenon that the catalogue of confirmed GWAS findings  is strikAnother notable feature of YAMAS is that it can be combined with imputed data. In practice, it may happen that studies are imputed with different reference panels. Moreover, particular SNPs might pass imputing-QC in some studies but not in others. In these situations, the analysis panel can be completed with the proxy-algorithm. In this context we wish to emphasize that our goal is not to compete with imputing as the standard approach for meta-analysis. Indeed, our own simulation study demonstrates a power advantage of imputation-based MA in a standard setting. Our aim is rather to speed up and give impetus to meta-analysis efforts. Even though the required analysis time for genome-wide imputing is meanwhile limited to a few weeks for experienced and well-equipped groups, joint projects are frequently long-lasting. Everyone who has worked in a meta-analysis project has either experienced or can imagine that it can easily take one year until all participating groups have provided their imputing results, either because some of the participating groups are less experienced in imputing analysis than others or either because they are involved in projects which they assign higher priority to. In particular, the priorities the participants have will sometimes be heterogeneous, causing delay for those who have the primary interest in the joint effort. Therefore, we believe that a method that facilitates MA and yields ad hoc, but still interpretable and meaningful results, is highly warranted. The proxy algorithm we have introduced fulfills these criteria: it directly operates on GWAS analysis results and can be run in a few hours even when the meta-analysis comprises many groups, and has descent power since it can analyze all SNPs that were genotyped in at least one of the participating studies.ORs) or estimates of the allelic effects (“betas”) as obtained in regression models. In case of ORs, the effect becomes E=ln(OR), else it stays un-transformed. Effects are merged across studies with the weighted average, YAMAS either combines allelic odds ratios (k is the number of studies and wi is a weight given by the standard error (SEi) for the ith study: Ē is computed as  , where Q-value [ and the combined two-tailed p-value becomes mplified  and is amplified ,28. Taki[Q-value , w with w∗ = (SE2 + τ2)−1 and use this weight to reflect heterogeneity, τ2 being the between-study variance:  , as an indicator for the total between-study variance, we are able to replace Q−(k−1) > 0, else τ2 = 0. Q itself follows a χ2-distribution with k − 1 degrees of freedom [ if  freedom . We note freedom  that thereal genotype data of each participating study. In order to enable MA on the complete marker panel missing markers can be filled with “proxy markers”. For this purpose, a sample reference file based on 1,000 Genomes [r2 as a linkage disequilibrium indicator [r2 ≥ 0.5 are listed. For the X chromosome and the MHC region, we choose a distance limit of 5 Mb. If the algorithm would encounter a situation where one marker is present in one of the studies, but missing in one or more of the other studies, it will try to find a proxy marker in those studies, compare Figure r2 (higher r2 ranks higher). This sorted list of markers is tried for the presence in the data set. The first present SNP, i.e., the SNP with the highest LD with the missing SNP, is chosen to be the proxy-SNP and will subsequently be used for MA, see also Figure We assume that association results (effect estimates and standard errors) are available for the  Genomes  SNP cont Genomes  with the Genomes . The refndicator  and a bor2 of 0.50. from the previous section. In contrast to “standard” meta-analysis the effect estimates that are combined do not always refer to the same SNP (rsNumber) in each study, but to a SNP from one study and its proxy-SNPs in other studies. In order to select a SNP as a proxy, we require a minimum http://yamas.meb.uni-bonn.de/ The OpenMP API specification for parallel programming, http://openmp.org/wp.YAMAS, Yet Another Meta-Analysis Software; The authors declare that they have no competing interests.CM and TB developed the proxy algorithm and are responsible for the software design. CM programmed the major part of YAMAS. ML and TB prepared the manuscript. ML is responsible for the programming of additional features and software maintenance. MA performed parts of the T2D analysis. CH, MM, DD and AL provided significant input to the work and the manuscript. All authors read and approved the final manuscript.


Structural Maintenance of Chromosomes 2 (SMC2) on Chromosome 8 at position 95,410,507 (UMD3.1). This polymorphism changes amino acid 1135 from phenylalanine to serine and causes a non-neutral, non-tolerated, and evolutionarily unlikely substitution within the NTPase domain of the encoded protein. Because only exome capture sequencing was used, we could not rule out the possibility that the true causative mutation for HH3 might lie in a non-exonic genomic location. Given the essential role of SMC2 in DNA repair, chromosome condensation and segregation during cell division, our findings strongly support the non-synonymous SNP (T/C) in SMC2 as the likely causative mutation. The absence of concordant variations for HH2 or BH1 suggests either the underlying causative mutations lie within a non-exomic region or in exome regions not covered by the capture array.The recent discovery of bovine haplotypes with negative effects on fertility in the Brown Swiss, Holstein, and Jersey breeds has allowed producers to identify carrier animals using commercial single nucleotide polymorphism (SNP) genotyping assays. This study was devised to identify the causative mutations underlying defective bovine embryo development contained within three of these haplotypes (Brown Swiss haplotype 1 and Holstein haplotypes 2 and 3) by combining exome capture with next generation sequencing. Of the 68,476,640 sequence variations (SV) identified, only 1,311 genome-wide SNP were concordant with the haplotype status of 21 sequenced carriers. Validation genotyping of 36 candidate SNP identified only 1 variant that was concordant to Holstein haplotype 3 (HH3), while no variants located within the refined intervals for HH2 or BH1 were concordant. The variant strictly associated with HH3 is a non-synonymous SNP (T/C) within exon 24 of the  APAF1 gene for HH1 CWC15 gene for JH1 FANCI gene While a number of recessive disease loci and a few causative disease mutations have been discovered in cattle http://www.1000bullgenomes.com/), but this finding was not supported by SNP testing to validate co-segregation in designated HH3 carriers Fertility effects for the remaining three haplotypes, known as Brown Swiss haplotype 1 (BH1), Holstein haplotype 2 (HH2), and HH3; were confirmed significant by comparing normal conception rates (27% for Brown Swiss and 31% for Holsteins) to rates when mating heterozygous males to daughters of heterozygous males Based on these previous findings, the main goal of this present study was to find and validate causative mutations for BH1, HH2, and HH3; and thereby, allow development of single marker diagnostic SNP tests that could be added to commercial SNP chips already in use by the dairy industry for generating genomic evaluations. Such tests could provide producers with the ability to manage the risk of decreased fertility from these haplotypes with 100% accuracy without additional cost. We chose to attempt candidate SNP discovery using exome capture rather than whole genome re-sequencing based on the previous discovery of stop codon mutations underlying HH1 and JH1. At the start of this investigation, no study had validated the causative mutation for HH3 or attempted SNP discovery and validation for HH2 or BH1.Most DNA used in this study was derived from a previous breed-based genome association study by Cole and colleagues The search for BH1, HH2, and HH3 causative mutations followed the steps of Sonstegard et al. Bos taurus UMD 3.1 genome reference assembly Three μg of genomic DNA from each animal (N = 21) was sent to Cofactor Genomics  to generate Illumina next generation sequence reads derived from Cofactor's custom bovine exome capture assay libraries. Pre-capture libraries were prepared using Tru-Seq DNA Library kit sample preparation guidelines . Genomic DNA was sheared to a size of 300 to 500 bp using the Covaris S2 instrument  prior to adaptor ligation, size selection (350 to 400 bp) on 2% E-gels , and PCR amplification. Pre-capture library quality and size was assessed using the Biorad Experion DNA 1K chip . Each pre-capture library (1 ug) was hybridized to Cofactor Genomics' bovine capture probes at 47°C for 66 hours. The exome capture probes were primarily designed against exons and annotated genomic regions of the http://samtools.sourceforge.net/). Depth of coverage, mismatches, and micro-indels between sequence reads of each animal and the reference animal were tabulated across genome coordinate positions using the Cofactor Genomics variant caller. Minimum mapping quality of 20 was required to retain a read, and a minimum coverage of 4 reads was required at each location to report a candidate SNP or micro-indel. This approach was able to detect micro-indels up to 6 bases in length. Exome sequence variation (SV) for all 21 carrier animals and L1D were uploaded to Activesite, Cofactor Genomic's web-based analysis software (http://www.cofactorgenomics.com/activesite), and were only reported if each animal had >8-fold coverage per location. Disease candidate SV were identified by selecting those polymorphisms present in all carrier animals and absent in non-carrier animals. For example, BH1 candidate SV were present in all 7 BH1 carriers, but absent in the other 14 animals (HH2 and 3 carriers and L1D).Filtered sequences were aligned to the UMD 3.1 genome assembly with Novoalign 2.08 , and PCR duplicates were removed with samtools 1.05  within the three refined haplotypes were selected for validation genotyping. Due to the low number of candidate SV, a third SV outside the refined HH2 interval but within 10 Kb was also tested. Two multiplex genotyping panels were designed and assayed using the MassARRAY analyzer  at Neogen/GeneSeek . One panel validated HH2 and HH3 candidate SV (HH2_HH3), and the other was for BH1 SV. DNA from carrier Holsteins and all available Brown Swiss samples was sourced from the BFGL archive and selected based on having adequate DNA quantity for genotyping. Non-carrier Holsteins were selected to maximize screening of other haplotypes found in the breed at the HH2 and HH3 loci. Animals scored across the HH2_HH3 panel included: 119 HH2 carriers, 315 HH3 carriers, 4 HH2/HH3 carriers, 2 animals homozygous for sub-segments of the HH2 haplotype, 4 animals homozygous for sub-segments of the HH3 haplotype, 304 non-carrier Holsteins, and 5 Brown Swiss. Animals scored across the BH1 panel included: 127 BH1 carriers, 615 non-carrier Brown Swiss, 5 Italian Brown Swiss with no recorded BH1 haplotype status, and 6 Holsteins. Five random purebred samples were selected from each of three unrelated breeds  as putative negative controls for both validation panels. SV marker genotypes were checked for concordance to disease status based on locus haplotype in the AIPL database. In the case of HH3 SV marker genotypes, if non-concordance to HH3 status and the most likely causative mutation reported by Hayes and colleagues A Copy Number Variation (CNV) analysis was performed using all available Illumina BovineHD genotypes de novo mutation affecting fertility. No animals were found to be homozygous for all of the HH3 or any portion of the BH1 haplotype.The initial search for unbalanced Mendelian inheritance SMC2 at 95,410,507 on Chr 8, was concordant with HH3 carrier status for all but seven animals  was due to a sample retrieval error from the DNA archive, where re-genotyping with BovineSNP50 followed by parentage analysis identified the misplaced sample as a HH3 carrier (HO1264). This result now matched the initially observed heterozygous SV genotype at Chr8: 95,410,507T>C (de novo mutation event (T>C) at Chr8: 95,410,507. The observation of such a recombinant haplotype in our Holstein test population left open the possibility of detecting false positive results for homozygosity in other animals of the breed, if sub-sections of the HH3 haplotype were used as a diagnostic test by the dairy industry.We then re-examined these seven outlier samples to rule out potential laboratory errors as the cause of the discordance between HH3 status and the non-synonymous SNP (T/C) in 0,507T>C . Similar0,507T>C . FinallyBecause no candidate SV were concordant with BH1 status, we also investigated the possibility that the fertility haplotypes could be the result of some larger deletion event categorized as CNV. During initial haplotype analysis of Brown Swiss BovineSNP50 genotypes, there was an observation of homozygosity between two adjacent markers, ARS-BFGL-NGS-11889 and BTB-02092796 , within the BH1 haplotype in some animals. A CNV survey of 54 animals detected 6 large deletion events within this haplotype . One CNVStructural Maintenance of Chromosomes 2 (SMC2), while all HH2 candidate SV were intergenic or within exons of NCEH1. Candidate SV for BH1 encompassed 7 genes  and included intergenic SV. The larger pool of candidate SV for BH1 was due in part to the size of the refined haplotype (4.5 Mbp) in comparison to the sizes for HH2 and HH3 .This manuscript represents the first application of a commercial bovine exome capture array for the discovery of causative mutations underlying a disease trait. All of the HH3 candidate SV were either intergenic or within the gene boundaries of NCEH1 looked promising; however, the comparative discordance between HH2 status and SV genotypes generated from the HH2_HH3 validation assay was too widespread to be warrant further investigation. Combining these results with the lack of detectable CNV within the refined HH2 locus, it seems probable that the causative mutation(s) for this disease is located in a region not designed to be interrogated by the exome capture array; which includes intergenic regions, introns, and unannotated genes. It is also possible the mutation could also be a relatively small indel within the exome , lie within a ROI region not well represented with respect to sequence depth, or a larger insertion not present in the UMD 3.1 genome assembly. The relatively high average depth of read coverage provided by exome sequencing (about 45-fold) across the three disease loci supports any one of these possibilities. To further add complexity, a few HH2 homozygous animals have recently been identified by AIPL  and Fritz et al., The SNP discovery results within SMC2 was 100% concordant with the HH3 haplotype status and consistent with the SNP discovery report of Hayes and colleagues SMC2 (25 exons) The HH3 candidate SV Chr8: 95,410,507 T>C in SMC2 sequence data, gene structure, and protein studies from other species that support our findings. First, the non-synonymous SNP in exon 24 of SMC2 changes amino acid 1135 from a phenylalanine to a serine (GenBank #: XP_002689921.2) within the P-loop-nucleoside triphosphate hydrolase (NTPase) domain (http://www.ncbi.nlm.nih.gov/gene?cmd=retrieve&dopt=full_report&list_uids=539217#reference-sequences). P-loop NTPase enzymes most commonly hydrolyze the beta-gamma phosphate bond of a bound nucleoside triphosphate. An orthologous F1135S mutation could not be found in a search of publicly available SNP databases SMC2 reveal that the encoded phenylalanine residue position is conserved across 13 eutherian mammals, except for mouse and rats which have a leucine (data not shown). Whereas phenylalanine and leucine are both non-polar and have positive hydropathy index values, serine is polar with a negative hydropathy index value. Furthermore, the F1135S mutation is predicted to alter protein function and structure based on the following in silico evidence: 1) a non-neutral change by SNAP However, there are numerous pieces of evidence that can be gleaned from SMC2 support this hypothesis. SMC2 belongs to a family member of genes involved in both the structural maintenance of chromosomes and DNA repair in mammals. Mainly, SMC2 is critical for functional formation of condensing complexes to convert interphase chromatin into mitotic-like condensed chromosomes. This condensin complex containing SMC2 introduces positive supercoils into relaxed DNA in the presence of type I topoisomerases, and converts nicked DNA into positive-knotted forms in the presence of type II topoisomerases SMC2 are unable to form functional condensin (I and II) complexes SMC2 knockout has yet to be created (www.mousephenotype.org), a conditional knockout has been made in chicken DT40 cells. This SMC2 KO resulted in delayed chromosome condensation, compromised chromosome structure, and aberrant localization of non-histone proteins Maintaining functional integrity of the NTPase domain of SMC2 would seem to be vital for proper transmission of the duplicated genome during cell proliferation. Studies on the protein encoded by Although further research must be done to validate a functional effect of the bovine F1135S mutation on SMC2 activity, we hypothesize that an individual homozygous for this genetic lesion has a severely reduced ability to hydrolyze ATP resulting in compromised condensin activity that eventually leads to a chromosome structural abnormality through formation of dysfunctional condensin complexes While not all Mendelian diseases are caused by mutations in exon regions, those mutations that do reside in the exome can be quickly identified with exome capture methods. Causative mutations for human genetic diseases have been identified previously using whole exome sequencing methods Table S1Information on exome sequenced animals.(DOCX)Click here for additional data file.Table S2Original and refined BH1 derived from BovineSNP50 haplotypes.(XLSX)Click here for additional data file.Table S3Original and refined HH2 derived from BovineSNP50 haplotypes.(XLSX)Click here for additional data file.Table S4Original and refined HH3 derived from BovineSNP50 haplotypes.(XLSX)Click here for additional data file.Table S5Count of total SV annotations by chromosome after filtering exome sequence from 21 animals.(DOCX)Click here for additional data file.Table S6SV re-genotyping results for 5 individuals with discordance between their chr8:95,410,507T>C genotype and HH3 status.(XLSX)Click here for additional data file.Table S7CNVs located in BH1 locus region.(XLSX)Click here for additional data file.


Plasmodium falciparum) genome is crucial for understanding its evolution; however the 81% (A+T) genome poses challenges to high-throughput sequencing technologies. We explore the viability of the Roche 454 Genome Sequencer FLX (GS FLX) high throughput sequencing technology for both whole genome sequencing and fine-resolution characterization of genetic exchange in malaria parasites.Knowledge of the origins, distribution, and inheritance of variation in the malaria parasite  and many non crossover (NCO) gene conversions throughout the genome.We present a scheme to survey recombination in the haploid stage genomes of two sibling parasite clones, using whole genome pyrosequencing that includes a sliding window approach to predict recombination breakpoints. Whole genome shotgun (WGS) sequencing generated approximately 2 million reads, with an average read length of approximately 300 bp. By sequencing the 23 Mb genomes of two haploid progeny clones derived from a genetic cross at more than 30× coverage, we captured high resolution information on COs, NCOs and genetic variation within the progeny genomes. This study is the first to resequence progeny clones to examine fine structure of COs and NCOs in malaria parasites. Arabidopsis [Plasmodium falciparum [P. falciparum - 80.6% (A + T) composition [Advances in genotyping technology led to an explosion of studies to identify genes of interest using classical genetic approaches . Such stbidopsis ,4, rice bidopsis -7, and nlciparum -11; howeposition , resultiAlternatively, massively parallel DNA sequencing technologies have revolutionized single nucleotide polymorphism (SNP) discovery and the study of genome variation of diverse categories . 454 LifHigh-resolution genome views provided by new sequencing technologies can be especially informative when applied to progeny clones derived from genetic crosses. Homologous recombination plays an essential role in ensuring correct chromosomal segregation during meiosis  and incr454 sequencing has been used to discover SNPs in a variety of organisms including plants -19, RhesP. falciparum progeny strains derived from a well-studied genetic cross between a multi-drug resistant and generally drug sensitive parent [P. falciparum.Here, we use the 454 Genome Sequencer FLX (GS FLX) platform for whole genome shotgun sequencing (WGS) to characterize the genomes of two e parent ,25. In aP. falciparum. By sequencing the 23 Mb genomes of two haploid progeny clones derived from a genetic cross at more than 30× coverage  biased genome, we compared ng Table . We demode novo assembly, the improved no-PCR method enabled de novo assembly [de novo assembly statistics obtained using 454 technology, at approximately 36× coverage with considerably fewer contigs than that with the Illumina modified no-PCR method. Furthermore, we aligned contigs larger than 1 kb to the 3D7 assembly  to searTo establish the platform for calling parental alleles inherited in the progeny clones, we developed a four step procedure: (1) map parental reads to the reference genome ; (2) idin vitro culture and are considered further below. For the purpose of marker selection, we excluded these positions with secondary alleles from further analysis.To identify SNPs between parents, we began by re-analyzing the trace reads of the parental genomes HB3 and Dd2 . In stepIn step 2, we established a set of 24,599 high quality SNP markers by requiring uniquely mapping reads, with no mixed base calls at any SNP position, an average quality score of ≥ 30, and at least 2 reads supporting the base call identity  was genotyped at the candidate SNP loci that distinguished the parents with the added requirement of at least 10 reads supporting the base call. Although these requirements reduced the sensitivity in detecting SNPs, especially in low coverage regions, it increased the specificity of true SNP detection by lowering the likelihood of including false variants that arise due to sequencing and/or mapping errors. We analyzed only SNPs and excluded all indels and variants involving more than one nucleotide. In parallel to the work presented here, our lab developed a gene chip to resequence 45,000 SNPs cataloged in PlasmoDB   was used for allele typing. Note that the numbers are lower than for each individual parent because only ~7 Mb met the stringent coverage and quality thresholds. Parental SNP identity was used to call the genotype at each allelic position in each progeny. A total of 8,201 SNPs were genotyped in 7C126, while 8,441 were genotyped in SC05   Figure . The dis all COs . FurtherP. falciparum genome provides a unique opportunity to gain insight into systematic biases that may be introduced by 454 technology in an (A+T) rich genome. As a predominantly haploid organism, single nucleotide base variants differing in base composition from either of the parental sequence or heterozygous alleles for a given genome position is not expected. Two types of variant base calls were detected in the progeny; de novo SNPs and alternate SNP positions .The ability to distinguish true sequence variants from sequencing errors is a fundamental challenge in the discovery of SNP variants and genotyping efforts; thus it is important to understand the types and probabilities of error in base calls . CharactDe novo SNPs are defined here as bases in the progeny that are different from those of either parent. Eighty (0.0011%) and 128 (0.0017%) de novo SNPs were detected for 7C126 and SC05 respectively in high quality mapped base positions . The number of de novo SNPs detected is considerably lower than expected sequencing errors  with a phred quality score of 30 [re of 30 .de novo SNPs in the progeny (89% in 7C126 and 93% in SC05) occurred at positions that are identical in the parents. Eight loci were chosen and sequenced by traditional Sanger (capillary) methods in Dd2, HB3, SC05 and 7C126 , indicating that these were not necessarily associated with homopolymer tracks; a majority (9/12) of the de novo SNPs detected in the homopolymer tracts were G (5/9) or C; suggesting these are unlikely to be sequencing errors arising due to homopolymer bias [Local sequence context affects genome sequence coverage as well as base call errors in NGS platforms . To inver tracts ,38 of 5 r tracts . None ofmer bias .de novo SNPs, the proportion of transversion nucleotide substitutions (7C126 = 48% and SC05 = 56%) was greater than the proportion of transitions (7C126 = 46% and SC05 = 41%) in both progeny genomes. We observed a bias in the base changes  and the two progeny genomes exhibited alternate SNP positions, i.e. multiple base calls at a single position. Of all positions uniquely mapped, the progeny clones sequenced in this study exhibited few positions 7C126 (0.1%) and SC05 (0.09%) with multiple base calls compared to the parental genomes. We observed no specific location bias for the genome-wide distribution of the alternate SNP positions in either parents or progeny genomes.in vitro culture. The primary base call of the alternate SNP positions was compared with the parental base calls at the position. The majority of the primary base calls were parental SNPs , while the majority of the secondary base calls were novel i.e. non parental SNPs . The novel primary base call position in 7C126 was primarily transitions, while it was primarily transversions in SC05. The uniformity of the distribution of alternate SNP positions and the type of substitutions observed in 7C126 suggested base conversion bias: C > T (shown in SC05 as well), G > A transitions were the most frequent followed by transitions, T > A, A > G, and T > C  was used to detect the genome-wide distribution of large (> 1 kb) CNV regions in the two progeny clones. Five known CNVs that exist between parental strains were detected in the progeny  used in linkage analysis. By sequencing the 23 Mb genomes of two haploid progeny clones derived from a genetic cross at more than 30× coverage, this investigation captured high resolution information on COs, NCOs and genetic variation within the progeny genomes. Our approach for surveying recombination in this predominantly haploid genetic system allow for not only genome wide detection and fine scale analysis of recombination products but also reveal potential details on CO interference and double strand break resolution.Comparison of sequence data from the 454 GS FLX platform with genome sequence generated by conventional dideoxy-based sequencing demonstrates that the GS FLX data is favorably comparable to standard dideoxy-based sequencing for Plasmodium falciparum strains 3D7, Dd2, HB3, 7C126 and SCO5 were thawed from genotyped source stocks and cultured at parasitemia suitable for DNA extraction. Parasites were grown at 37°C and 5% hematocrit in O+ human red blood cells using RPMI 1640  supplemented with 0.5% Albumax I (Invitrogen), 0.25% sodium bicarbonate  and 0.01 mg ml-1 gentamicin (Invitrogen) under an atmosphere of 90% nitrogen, 5% oxygen, and 5% carbon dioxide. Cultures were gassed every day, the media was changed every 2 days and parasitemia was maintained below a level of 5%. Total genomic DNA was isolated from frozen culture using standard phenol-chloroform extraction. Each parasite DNA was genotyped for a set of 8 microsatellite markers to ensure clonality and to confirm parasite identities.P. falciparum genome, the concentration of thymidine in the sequencing reaction was increased to 1.4 times the recommended amount, and 150 cycles of sequencing were performed instead of the standard 100 cycles. Two GS FLX Titanium paired-end libraries (3 kb) were constructed and sequenced at 454 Life Sciences according to the manufacturer's specifications .GS FLX Titanium shotgun libraries were made from genomic DNA according to the manufacturer's specifications at 454 Life Sciences . Briefly, sequencing was performed according to GS FLX standard protocols with the following modifications: due to the high (A+T) content of the This SNP analysis scheme began with a comprehensive re-analysis of the trace reads of HB3 and Dd2 from the database  before aThe 454 reads from the 7C126 and SC05 strains were aligned to the 3D7 reference assembly using SSAHA2 (-tags 1 -output cigar -diff 0 -identity 90.0 -best 1). The alignments were filtered using ssaha_cigar with default parameters. The primary and secondary base calls and quality scores were summarized into VMA in a similar manner as the parental strains.Parental SNP identification: The base calls and quality values of the sequence from the Dd2 and HB3 strains were considered at each position of the reference genome. We required each parent to have two or more reads with an average quality score of at least 30. Additionally, we required both parents to uniformly exhibit a single base at a position . Positions that met all of these criteria were considered candidate positions for progeny genotyping, i.e. the Dd2 base call differs from the HB3 base call. The base calls and quality values of the 7C126 and SC05 strains were considered at each position determined above. We required a strain to have 10 or more read depth coverage with average quality score of at least 30. Additionally, to call an allele for a progeny clone, we required it to uniformly exhibit a single base at the position . Positions that met all of these criteria were considered valid.To further confirm the SNP calls, we compared the SNP calls on an independent platform. In parallel to the work presented here, our lab developed a gene chip to resequence 45,000 SNPs cataloged in PlasmoDB   using standard dideoxy-based sequencing on an ABI 3730xl DNA Analyzer. Sequencing chromatograms were analyzed with Contig Express .At each valid position that we identified as a parental SNP, we classified each strain as inheriting the Dd2 or HB3 allele, or alternatively, a A sliding window approach was used for the prediction of recombination breakpoints . The filThe distance between events was calculated as the distance between the beginning of the previous breakpoint window of an event and the beginning of the consecutive breakpoint window of the next event. The chromosomal alignments at CO and NCO regions were visualized using Integrative Genomics Viewer . Regionsde novo SNPs and alternate allelic positions. De novo SNPs were defined as called bases in the progeny that are different from those of either of the parents. All de novo SNPs were checked for association with homopolymer tracts of > 5 bps  and 3 to 5 bps (NNDN or NDNN). Alternate allelic positions were defined for parental genomes as well as the progeny genomes. The base with the most reads was considered the primary allele, while the alternate base was considered the secondary allele at that position. Two different sets of read cutoffs were used to differentiate the secondary allele in parent (at least 2 supporting reads) and progeny (at least 5 supporting reads). Both de novo SNPs and the primary allele in progeny were analyzed for base substitution changes in comparison with the parental base using custom perl scripts.Custom perl scripts were used to analyze single nucleotide base variants including P. falciparum 3D7 reference genome (PlasmoDB [http://www.nimblegen.com/products/lit/cgh_userguide_v6p0.pdf). The normalized values were used for calculation of log2 ratio values and used for CNV detection using a segmentation model based on a Gaussian framework [A custom 385 k NimbleGen array designed for the PlasmoDB , 2006) uPlasmoDB  was usedPlasmoDB . The arrPlasmoDB . 7C126 aramework .Five characterized copy number differences were used to test structural variation detection with 454 shotgun read library in the (A+T) biased genome. Read locations along chromosomes were derived from CIGAR alignments (see section iii Read Mapping) used for SNP discovery; reads were assigned to non-overlapping 2.5 kb intervals if at least 85% of its length aligned to that interval. To compute CNVs, we used a simple 2 × 2 Chi square test. We compared the proportion of reads in each non-overlapping 2.5 kb interval relative to all reads that mapped to all other intervals on the chromosome; and compared each window between each progeny. The resulting statistic was converted to a p-value based on a Chi square distribution with two degrees of freedom, but not corrected for multiple comparisons. This computational approach is more similar to array-based detection and digital expression  than moGS FLX: Genome Sequencer FLX; WGS: whole genome shotgun; CO: crossover; NCO: non-crossover; NGS: next generation sequencing; DSB: double strand break; SNP: single nucleotide polymorphism; QS: quality score; indel: insertion or deletion; CGH: comparative genomic hybridization; CNV: copy number variant; VMA: vertical multiple alignment; NCBI: National Center for Biotechnology Information.de novo genome assemblies. SU, AR, BD, JT, SE and MTF wrote the manuscript. All authors have read and approved the final manuscript.SU, BD, JT and MTF conceived the project. SU, AR, AT, SE and MTF conducted the read mapping and data analysis. SU grew parasites, extracted, genotyped DNA and carried out the CGH hybridizations. SU and BC performed ABI sequencing for SNP verification. Shot gun and paired end library preparation and 454 sequencing were carried out by BD along with library quality analysis and (A+T) content of WGS reads in uniquely mapped regions of parents and progenyClick here for fileSNPs between HB3 and Dd2Click here for fileRefined views of previously unknown COs shown in Figure P. falciparum [lciparum . Each liClick here for fileVisual inspection of mapped WGS sequence at CO and NCO gene conversion breakpoints shown in Figure Click here for filede novo SNP positionsResequencing results of Click here for filede novo SNPsDistance between consecutive . The distance between consecutive de novo SNPs were calculated to detect SNP clustering characteristic of sequencing errors or mis-mapping errors. 35% of the de novo SNPs were clustered in distances of less than 5 bps.Click here for filede novo SNPsBase conversion trends in . The type of base conversion was investigated for positions at which the parental base calls were identical. More transversions were detected for SC05 compared to 7C126 (A), but did not show a predominant base conversion bias from 7C126 (B).Click here for fileAlternate SNP positions. The alternate SNP positions were assessed for their primary and secondary positions base call identity. Most primary base calls reflected the parental base call. The secondary base call position varied in the 2 progeny genomes in base call identity. Majority of the secondary base calls were parental in 7C126, whereas majority of the secondary base calls were non-parental in SC05 (A). Majority of the primary base calls were transitions in 7C126, while they were transversions in SC05 .Click here for fileSelected CNVs in 7C126 and SC05. Comparative genomic hybridization (CGH) was used to detect large (> 1 kb) CNV regions in 7C126 (A) and SC05 (B). Five known CNVs that exist between parental strains were detected in the progeny in Chr 2, 5, 9 and 12.Click here for file


Dense genotype data can be used to detect chromosome fragments inherited from a common ancestor in apparently unrelated individuals. A disease-causing mutation inherited from a common founder may thus be detected by searching for a common haplotype signature in a sample population of patients. We present here FounderTracker, a computational method for the genome-wide detection of founder mutations in cancer using dense tumor SNP profiles. Our method is based on two assumptions. First, the wild-type allele frequently undergoes loss of heterozygosity (LOH) in the tumors of germline mutation carriers. Second, the overlap between the ancestral chromosome fragments inherited from a common founder will define a minimal haplotype conserved in each patient carrying the founder mutation. Our approach thus relies on the detection of haplotypes with significant identity by descent (IBD) sharing within recurrent regions of LOH to highlight genomic loci likely to harbor a founder mutation. We validated this approach by analyzing two real cancer data sets in which we successfully identified founder mutations of well-characterized tumor suppressor genes. We then used simulated data to evaluate the ability of our method to detect IBD tracts as a function of their size and frequency. We show that FounderTracker can detect haplotypes of low prevalence with high power and specificity, significantly outperforming existing methods. FounderTracker is thus a powerful tool for discovering unknown founder mutations that may explain part of the “missing” heritability in cancer. This method is freely available and can be used online at the FounderTracker website. DASH Associates Shared Haplotypes) With the advent of SNP arrays and next-generation sequencing, it is now possible to genotype millions of SNPs in a single experiment, at low cost. This has made it feasible to detect DNA regions inherited from a common ancestor in two apparently unrelated individuals. Two stretches of DNA are said identical by descent if they are identical due to common ancestry. The detection of identity by descent (IBD) in populations is considered a highly promising approach to the linkage mapping of disease genes. Indeed, linkage analyses are usually carried out in small pedigrees containing closely related individuals. Consequently, the IBD segments identified tend to be too long to delineate focal regions of interest with a restricted number of candidate genes. By contrast, tracts of IBD in unrelated individuals rarely span more than a few centimorgans (cM). Population-based linkage studies are thus very useful for the fine delineation of candidate loci Our method is specifically dedicated to the mapping of cancer genes. One key feature of cancers is that tumor cells accumulate chromosome aberrations providing a growth advantage during cancer progression, such that the tumor genome eventually becomes a highly rearranged version of the constitutive genome of the patient. Besides, the location of somatic alterations is partly driven by the presence of risk alleles in the germline genome TP53 founder mutation. In the second dataset, comprising 30 pheochromocytomas and paragangliomas, we detect a novel founder mutation of the SDHD gene, present in only two samples. Finally, we show with simulated data that FounderTracker detects conserved haplotypes of low prevalence with high power and specificity, significantly outperforming existing methods.Our method is designed for detecting cancer-associated mutations inherited from a common founder, using recurrent IBD in minimal regions of LOH . BuildinOur strategy for the population-based linkage mapping of founder mutations in cancer is illustrated in With one probe targeting each of the two alleles of each SNP, SNP arrays are an excellent tool for the genome-wide detection of LOH, and several algorithms have been described for this purpose −5).We then reconstruct the haplotype of the retained allele in each tumor . As onlyFinally, we search for significantly recurrent IBD in the set of reconstructed tumor haplotypes. The analytical pipeline for this step is represented in TP53  TP53 mutation. We used the Genome Alteration Print method to detect LOH. This method identified two regions displaying LOH in all samples: a 4.4 Mb region on 11p15, and the entire chromosome 17. We then used FounderTracker to detect significantly recurrent IBD in these two regions. No significant region of IBD was detected in the 11p15 region (–16). In particular, the peak IBD score defined a 520 kb region around the TP53 gene . These rare cancers (0.3–0.4 cases per year per million children under the age of 15 years) are exceptionally prevalent in southern Brazil (3.4–4.2 cases per million) 5 region , but higP53 gene . DetaileP53 gene . This reTP53 was 5.4 cM  Morgans after n generations (hence 2n meiosis) TP53 mutation occurred around nine generations ago.The mean length of pairwise IBD tracts around RET, NF1, VHL, TMEM127, MAX, and genes encoding proteins from the succinate dehydrogenase complex . Here, we applied our methodology for founder mutation discovery to a set of 30 pheochromocytomas/paragangliomas from unrelated patients. Samples were fully characterized in terms of germline mutations, and analyzed on Illumina 610K SNP arrays. LOH analysis revealed ten regions with LOH frequency above 20%, at 1p, 3p, 3q, 6q, 11p, 11q, 17p, 17q, 21q, and 22q . To further evaluate the performance of our method, we generated simulated data into which we artificially introduced conserved haplotypes of different lengths and frequencies. We benchmarked FounderTracker by analyzing the same simulated data with the DASH algorithm, recently developed to detect clusters of individuals sharing an identical haplotype Childhood ACT is an ideal case study for population-based linkage mapping. As this disease is extremely rare in absence of the p.R337H −3 and 5.4×10−4 for FounderTracker, and DASH).All haplotypes conserved in ≥50% samples were successfully detected by either method . Both FoThe simulated data presented here correspond to the SNP content of the Illumina 1M-Duov3 beadchip . To assess the impact of SNP density on the performance of the method, we analyzed the same simulated data, restricted to the sole 373,397 SNPs of the Illumina HumanCNV370-Quad chip. Although there was a slight degradation in the detection of short haplotypes (0.5 cM) with Illumina CNV370 array, we obtained a comparable level of performance with both marker densities . It has The running time of FounderTracker is linearly associated with the number of samples and SNP markers analyzed. For example, the processing of the childhood ACT dataset  took 4 min 07 seconds, and the longest run in our simulations, for a dataset of 100 chromosome 1 haplotypes , took 2 hours 48 minutes on a single core of an Intel Xeon X5470 Quad-Core processor running at 3.33 GHz. However, as noted above, little increase in power would be expected from the analysis of denser SNP data, so the amount of data analyzed can be constrained, in terms of markers, to the current SNP content of high-definition arrays.Most of the founder effects detected to date were revealed by targeted studies of polymorphic markers around well known cancer-related mutations. We introduce here the first systematic approach for detecting founder mutations on a genome-wide basis, from dense SNP profiles of tumor samples.BRCA genes SNP arrays are widely used in cancer research for two main purposes: the discovery of germline risk variants in linkage or association studies, and the identification of recurrent chromosome rearrangements. Researchers typically study exclusively the germline genome for association studies, or the tumor genome for the analysis of chromosome rearrangements, but a combined analysis of risk alleles and chromosome aberrations has been shown to be fruitful TP53 mutation in Brazilian ACT) will be detected with a power of 0.92 with FounderTracker versus 0 with DASH .In terms of frequency, FounderTracker can detect, with high power, haplotypes conserved in more than 5% of samples, depending on the length of the IBD segments. The proportion of tumors harboring a founder mutation in a random population sample depends principally on the genetic diversity of the population and the prevalence of the disease. Rare cancers, such as childhood ACT, with a higher prevalence in a specific geographic area are ideal for IBD mapping, because almost all cases are due to the founder mutation. In common diseases, such as breast cancer, high-frequency founder mutations were initially described in geographically or culturally isolated populations. For example, the TP53 mutation analysis was performed as previously described Thirteen ACT patients from a Brazilian cohort NF1 diagnosis and genetic testing for RET, SDHA, SDHB, SDHC, SDHD and VHL was performed as previously described Tumor samples from thirty patients with pheochromocytoma or paraganglioma from the French COMETE network, collected by the Georges Pompidou European Hospital, were included in this study. Ethical approval for the study was obtained from the institutional review board . All patients provided written informed consent for the collection of samples and subsequent analyses. Tumor DNA was extracted as previously described The 13 adrenocortical tumor samples were analyzed with Illumina HumanCNV370-Duo v1.0 chips, containing 370,404 probes, and the six matched normal samples were analyzed with Illumina HumanCNV370-Quad v3.0 chips, containing 373,397 probes. The 30 pheochromocytomas/paragangliomas were analyzed with Illumina Human610-Quad v1.0 chips, containing 620,901 probes. Hybridization was performed by IntegraGen , according to the instructions provided by the array manufacturer. Raw fluorescent signals were imported into Illumina BeadStudio software and normalized, as previously described Genome-wide copy-number changes in tumors were determined as previously described In a genomic region of LOH, the BAF profile is either close to 0  or close to 1 . We used fixed thresholds to reconstruct tumor haplotypes in regions of LOH. SNPs with a BAF>0.65 were assigned the genotype B, whereas SNPs with a BAF<0.35 were assigned the genotype A. We considered SNPs with a BAF between these two thresholds in at least one of the tumors to be unreliable, and removed them from the analysis. We estimated the error rate for haplotype inference by this method, by analyzing previously published data for two tumors from a single patient that displayed LOH for the same chromosomes Once recurrent regions of LOH have been established and tumor haplotypes within these regions have been reconstructed, the FounderTracker pipeline is used to detect haplotypes that are significantly conserved in each candidate region. FounderTracker first assigns an IBD score to each SNP in the region. The significance of each IBD score is then assessed by comparison with a null distribution obtained from a reference set of haplotypes.http://www1.cs.columbia.edu/~gusev/germline/), with default settings, except that: (i) the minimum length for a match (-min_m) was set to 0.4 cM, to allow the detection of short IBD segments, (ii) the number of mismatching markers allowed (-err_hom) was set to 0, because the error rate for the inference of haplotypes from tumor samples was shown to be negligible, (iii) the size of each slice used for exact matching seeds (-bits) was set to 5, to increase the accuracy of IBD boundaries.The first step in our scoring approach is the identification of pairwise IBD in the set of tumor haplotypes. The GERMLINE algorithm has been shown to be a robust tool for detecting pairwise IBD from phased haplotype data H a haplotype of N SNPs identical in two samples of the dataset. H is a vector iH represents the allele of the thi SNP (A or B) in the conserved haplotype. The score S associated with the pairwise IBD segment of haplotype H is given by the formula below, where iBfreq is the allelic frequency of the B allele for the thi SNP:The GERMLINE algorithm provides output in the form of a list of pairwise IBD segments with, for each segment, the indices of the first and last probe delineating the segment, and the identifiers of the two samples sharing identical haplotypes. We added a score to each segment to characterize the significance of pairwise IBD. As a series of common alleles is more likely to be identical in two samples by chance, our score takes into account both the length of the segment and the allelic frequencies of the probes defining the common haplotype. Let us denote by This score corresponds to the log-transformed probability of observing these two identical haplotypes by chance if all SNPs were independent.i, by summing the scores of all the pairwise IBD segments containing that SNP:An IBD score is then calculated for each SNP The IBD score described above characterizes the recurrence of IBD at a given SNP location. It takes into account the length of IBD segments and the allelic frequencies of SNPs, but not the linkage disequilibrium between SNPs. This bias is corrected by comparing the IBD score of each SNP with the distribution of IBD scores for the same SNP under the null hypothesis that no haplotype shows significant IBD in the set of samples. This distribution is established by generating 100 reference haplotype subsets, of the same size as the tumor set, by random sampling in a reference data set of phased haplotypes  algorithm The DASH  was used as reference data set, and the other (EUR2) was used to generate simulated tumor data sets.The performance of each method was evaluated by analyzing simulated datasets with IBD segments of different sizes  conserved in various proportions of the samples (2 to 50%). We included, in the simulated data, all the SNPs present on the Illumina 1M-Duov3 beadchip, to model the performance that could be expected with the use of a commercial array containing ∼10TP53 founder mutation in adrenocortical tumors.For each simulation, a chromosome was chosen at random, and 100 haplotypes of this chromosome were picked up in the EUR2 haplotype set, to constitute a simulated data set of 100 tumors. A genomic location was then randomly selected on the chromosome, and the haplotype of the first sample for this region was repeated in the desired proportion of samples. In a real data set of tumors harboring a founder mutation, all patients have inherited a different chromosome segment from the founder. These segments all comprise the mutation, but they have different boundaries. To reproduce this variability, the size of the conserved haplotype in each sample was randomly picked up from a Gumbel distribution of location and scale equal to the desired mean haplotype length. Such distribution was found to reproduce well the natural variation in IBD lengths observed around the FounderTracker and DASH were then applied to the same simulated data, and the rates of true positives (significant SNPs within the conserved haplotype) and false positives (significant SNPs outside the conserved haplotype) were determined at a series of q-value thresholds, with the PresenceAbsence R package http://cit2.ligue-cancer.net/FounderTracker). The SNP data from the two tumor datasets analyzed in this study have been deposited in NCBI's Gene Expression Omnibus FounderTracker can be used online through a web application, and the source code can be downloaded from our web page  displays some peaks, but these peaks correspond to normal variations due to the pattern of linkage disequilibrium across the region and are also encountered in the null distribution of IBD scores (middle). Thus, no significant IBD is detected in this region (bottom).(PDF)Click here for additional data file.Figure S2Length of IBD segments between the different pairs of childhood adrenocortical tumors. This image, exported from the Integrative Genomics Viewer (PDF)Click here for additional data file.Figure S3Application of FounderTracker (top) and DASH (bottom) to the analysis of chromosome 17 in the childhood adrenocortical tumor dataset. Both methods detect the conserved haplotype around the TP53 gene.(PDF)Click here for additional data file.Figure S4Power analyses of simulated data for the SNP content of Illumina 370K (in blue) or 1M (in red) SNP arrays.(PDF)Click here for additional data file.Figure S5Application of FounderTracker (top) and DASH (bottom) to the detection of significantly recurrent IBD on the 11q arm of 11 pheochomocytomas and paragangliomas.(PDF)Click here for additional data file.Table S1Clinical data for the 30 pheochromocytoma and paraganglioma samples.(XLS)Click here for additional data file.


Motivation: Over the past few years several pathway analysis methods have been proposed for exploring and enhancing the analysis of genome-wide association data. Hierarchical models have been advocated as a way to integrate SNP and pathway effects in the same model, but their computational complexity has prevented them being applied on a genome-wide scale to date.Methods: We present two novel methods for identifying associated pathways. In the proposed hierarchical model, the SNP effects are analytically integrated out of the analysis, allowing computationally tractable model fitting to genome-wide data. The first method uses Bayes factors for calculating the effect of the pathways, whereas the second method uses a machine learning algorithm and adaptive lasso for finding a sparse solution of associated pathways.Results: The performance of the proposed methods was explored on both simulated and real data. The results of the simulation study showed that the methods outperformed some well-established association methods: the commonly used Fisher’s method for combining P-values and also the recently published BGSA. The methods were applied to two genome-wide association study datasets that aimed to find the genetic structure of platelet function and body mass index, respectively. The results of the analyses replicated the results of previously published pathway analysis of these phenotypes but also identified novel pathways that are potentially involved.Availability: An R package is under preparation. In the meantime, the scripts of the methods are available on request from the authors.Contact: marina.evangelou@cimr.cam.ac.ukSupplementary Information: Supplementary data are available at Bioinformatics online. It is increasingly recognized that pathway analysis can support exploration of genome-wide association study (GWAS) data by incorporating the available biological knowledge of genes and pathways. Pathway analysis has gained great popularity over the past few years and several methods have been proposed . GSEA was originally proposed for pathway analysis of gene expression microarray data and later adjusted by P-values. In addition, P-values. The number of the smallest P-values multiplied is found through a permutation procedure.Other pathway analysis methods include Fisher’s method (FM): its test statistic depends on the product of the pathway gene (SNP) In addition, a number of online bioinformatics tools have made their appearance over the recent years that test whether the pathway in question contains more significant genes than expected by chance, using either the hypergeometric test or Fisher’s exact test.Most of the current pathway analysis methods are frequentist methods that depend on the results of single SNP analysis. In this article, two novel methods are proposed that are based on a hierarchical framework that models both pathway and SNP level effects. As discussed by P-value of association between the tested pathway and the phenotype is computed using Markov chain Monte Carlo.Few pathway analysis methods have been published that use hierarchical modelling for identifying associated pathways. Two are the BGSA method suggested by In contrast to the methods proposed by Furthermore, our methods do not depend on Markov chain Monte Carlo techniques for identifying associated pathways. The two methods differ in the prior distribution assumed for the pathway parameters. The first method assumes a normal distribution for the pathway parameters with mean zero and diagonal variance–covariance matrix with equal entries. It uses Bayes factors to test whether the pathways are associated with the phenotype. The second method assumes a normal distribution with mean zero and a diagonal variance–covariance matrix whose entries are not necessarily equal. This method adapts an iterative algorithm proposed by et al.  were downloaded and tested . The Plaet al. , 2009. FThe EPIC-Norfolk GWAS involves 3559 individuals whose BMI was measured. The BMI ranges between 16 and 48, with a mean of 28.5. Individuals with a BMI >30 are defined to be obese. In the study, there are 2035 controls and 1514 obese cases. The BMI of three individuals was missing. The individuals were genotyped using the Affymetrix500 chip. By applying standard quality control filters on the SNPs, 382 037 SNPs were retained for further analysis.For both studies, the SNPs of each study were assigned to genes according to physical distance: a SNP was mapped to every gene whose coding sequence had an overlap with a 10 kb range around the SNP.L tested SNPs is modelled through a linear regression such thatN is the number of individuals in the study. The vector s of size L represents the SNP coefficients. The matrix G is the genotype matrix of size ε is assumed to follow a normal distribution with mean zero and variance In contrast to single SNP analysis in which each SNP is tested independently from the others, in a multi-SNP analysis a selection of SNPs is tested for association. If the phenotype is a continuous random variable, the association between the phenotype and the s, which represent the pathway memberships of the tested SNPs. The SNP parameters s are assumed to follow the linear modelP is a pathway matrix of size M is the total number of pathways tested. The pathway matrix has entries 1 and 0 indicating whether a SNP belongs to a pathway. If a SNP was mapped to multiple genes, then this SNP was considered to be a member of all the pathways that the genes were members of. In addition, if a SNP was mapped to multiple genes that belonged to the same pathway, then no extra weight was given to these SNPs. The β vector of size M represents the pathway coefficients. The error u is assumed to follow a normal distribution with mean zero and variance A is a diagonal matrix with equal entries along the diagonal, such that a is a positive constant. This matrix assumes that all SNP effects come from the same distribution and that they are independent. Similarly to y, and they do not necessarily follow the same correlation structure as that of the SNP genotypes.The hierarchical model adds a second level of linear regressions for the SNP parameters X equals the genotype matrix (Z = G) multiplied by the pathway matrix (P). The random variables ε and u follow By combining u represents the SNP parameters, which are treated as nuisance parameters and integrated out from the analysis. By conditioning on the pathway parameters β, the prior predictive distribution of The vector y follows a normal distribution with mean In other words, the phenotype Two prior distributions are considered for the pathway coefficients β. Each prior distribution is linked to a different procedure for identifying the associated pathways. We propose a separate inference method for each of the two prior distributions. The first method is based on Bayes factors and the second on the iterative algorithm of B is a diagonal matrix with equal entries along the diagonal, such that b is a positive constant. The diagonal matrix assumes that pathway effects are independent and come from the same distribution.The first prior distribution considered is a normal distribution with mean zero and variance By assuming a scaled-inverse-y conditioned on the variance components is a normal distribution with mean zero and covariance matrixy is a multivariate t-distribution with mean 0, degrees of freedom The predictive likelihood of y is used for testing the null hypothesis H0: the thq pathway is not associated. This null hypothesis is tested using BFs, the ratio of the probability of the data under a model without the pathway in question over the probability of the full model including all pathways. The logarithm of the BF for pathway q is given byX matrix with the contribution of the thq pathway removed. The tp denotes the probability density function of a random variable with a multivariate t-distribution with H0 rejected.The marginal distribution of The second prior distribution assumes that the pathway effects do not necessarily have the same variance and it allows some of variances to be zero. The prior distribution is a normal distribution with mean zero and variance Γ, where Γ is a diagonal matrix with positive or zero entries along the diagonal. The associated pathways are found using the iterative algorithm proposed by The diagonal entries of Γ can be estimated from the data by first marginalizing over the coefficients β and then performing a type II maximum likelihood, which is equivalent to minimizingIn each iteration of the algorithm, an adaptive lasso problem is solved. An adaptive lasso problem is similar to the original lasso problem with the difference that individual adaptive weights are assigned to each parameter. The weights of the adaptive lasso problem are updated in each step of the algorithm of V equals The probability density function presented in The iterative algorithm proposed by The error variance P-values within each pathway. Its significance is calculated using a The proposed methods were compared in a simulation study with FM and BGSA  phenotype in the Platelets GWAS, as described later in text. The variance of ε is set to the residual error of the linear regression given in The simulated phenotypes were computed asP-values of association with the PC phenotype as the subset of K selected SNPs. The P-values of these SNPs ranged from We chose the first 100 SNPs with the smallest individual J associated pathways were selected using the Normal/Bayes factors method (NBF) method. Two different hyper-parameter combinations were tested. The hyper-parameter combinations used are In the first scenario of the simulation study, the J associated pathways were found using the Sparse normal/Adaptive lasso (SNAL) method. In the first case of the second scenario, the hyper-parameter matrix factor a was set to a was In the second scenario of the simulation study, the P-values <0.05 and < Two more scenarios were simulated. In the third scenario, two cases were simulated in which the selected pathways had FM P-values <0.05 and in the second case the selected pathways are 8 pathways with BGSA P-values < Similarly, in the fourth scenario, two cases were simulated. In the first case, the selected pathways are the 201 pathways that had BGSA u. This is in contrast to A scaled-inverse-P-values. This enabled assessment of the overall power of the methods without assuming specific thresholds. Sensitivity is the true positive rate calculated as the ratio of pathways found to be associated divided by the number of true associated pathways. Specificity is one minus the false positive rate (FPR), which is the ratio of false positive pathways over the number of true non-associated pathways. The AUC represents the probability that a random true associated pathway has a lower score  than a random non-associated pathway. An AUC close to 1 indicates an optimal method, whereas an AUC of 0.5 just random performance. The AUC is calculated using the convex hull of the ROC curve (The performance of the methods was assessed by plotting receiver operator characteristic (ROC) curves and calculating the area under the ROC curves (AUC) . An ROC OC curve . Under ei.e. more pathways are found to be associated. Further, for both FM and BGSA, the P-value cut-offs ranged between P-values were of size 50.For NBF, a BF cut-off value has to be applied for finding the pathways that are associated with the response; a set of values between a and b of the variance matrices A and B tested were A number of different hyper-parameters were tested for the two proposed methods. The values of the hyper-parameter values t-tests, the four methods were compared. FM is statistically superior to BGSA with a P-value of order P< P-value <0.05 (P = 0.0043). The mean and median AUCs of the four methods for each one of the tested scenarios (and their subcases) are given in the Supplementary Tables S3–S6.The performance of the methods was tested on the simulated responses created under the four tested scenarios. As discussed in the previous section, the hyper-parameters with the highest AUCs of the methods were recorded. The hyper-parameters that achieved the maximum AUC most frequently across all 50 simulations of phenotypes for each of the four tested scenarios are For a BF cut-off of 1, the FPR of the NBF method is ∼0.30, whereas its true positive rate is ∼0.90. For an FPR ∼0.05, the sensitivity of NBF is slightly higher than that of SNAL.Differences across the selected pathways chosen by the FM, SNAL and NBF methods were also observed. The average number of SNPs within a KEGG pathway is equal to 723 SNPs, whereas the average number of SNPs within a pathway from the ones that FM selected was 1252 (for the first case of scenario 3) and 1854 (for the second case of scenario 3). This phenomenon was not observed for either NBF or SNAL, where the average number of SNPs in the selected pathways was ∼500 SNPs.J pathways were random pathways that either had <300 SNPs or >700 SNPs. t-tests were used to test the differences between the four methods for each one of the two cases. FM is statistically superior to all three other methods for the second case with P-values <0.05. On the other hand, for the first case, both NBF and SNAL are significantly different from the other two methods with P< t-tests were used to compare the differences of each method for the two cases. Only the differences of SNAL were not found significant, whereas for the other three methods differences between the two cases were statistically significant.Therefore, the performance of the four methods was further tested for another scenario. The selected SNAL generally outperforms the other methods, followed by NBF. In addition, SNAL was not affected by the sizes of the pathways, whereas the other three methods were found to be affected by pathway size. FM had the greatest power in identifying pathways that were considered to be large compared with pathways that were smaller in size.et al. . Amongst them 17 of these pathways were identified by both methods. Some of the pathways found to be associated with BMI are Cell cycle, Steroid biosynthesis, SNARE interactions in vesicular transport, Viral myocarditis, Fc epsilon RI signalling pathway, TGF-β pathway, Haematopoietic cell lineage, Glioma, Melanogenesis and Jak-STAT signalling pathway. Pathways related with glycolysis were also identified as, for example, Glycolysis/Gluconeogenesis, Other glycan degradation, Glycosaminoglycan biosynthesis and Glycerolipid metabolism.NBF identified 18 pathways as associated with BMI, whereas SNAL identified 55 pathways  removed. The P) with the contribution of its thi SNP (row) removed. The difference between a SNP BF and a pathway BF is that now the effect of the SNP is removed from both the genotype and pathway matrices.The two proposed methods can be used to identify the associated SNPs. For example, the SNP BFs of NBF can be computed asNBF was applied to identify SNPs associated with platelet function. The results of the analysis are in agreement with previously published results that identified the GP6 gene as strongly associated with platelet function . No otheNBF was also applied to the EPIC-Norfolk GWAS for identifying associated SNPs. The five SNPs with the smallest BFs are rs12488868, rs2952271, rs3393, rs8076465 and rs10887578. SNP rs12488868 lies close to ROBO1 gene on chromosome 3, the second and fourth SNPs lie on chromosome 17 close to gene PRKAR1A and SNP rs10887578 lies close to GRID1 gene (chromosome 10). All of the aforementioned genes have been reported as associated with BMI. SNP rs3393 lies close to ADORA3 gene (chromosome 1) and that has not been reported previously as associated with BMI. Gene ADORA3 is an adenosine A3 receptor involved in a number of intracellular signalling pathways and physiological functions. It is related with both neuroprotective and neurodegenerative effects as well as with cell proliferation and death.In this article, a Bayesian hierarchical framework is proposed that incorporates pathway membership of SNPs through a two-level hierarchical model for identifying pathways associated with a phenotype. The phenotype of an individual is modelled to depend on both, its genotype and on a common contribution from alleles in each pathway. The SNP effects, over the common contribution from pathways they are members of, are assumed to be distributed identically and independently. These SNP effects are considered nuisance parameters and integrated out of the analysis so that the pathways associated with the disease can be identified. Intuitively, as is typical for random effects models, integration over SNP effect implies a correlation structure in the noise that is dependent on genotypes: individuals with similar genotypes show similar deviations from expected values of the regression model.Within this hierarchical setting, we proposed two methods for selecting pathways as predictor variables for the phenotype. The NBF and the SNAL methods test the predictive power of a pathway, and hence its association with the phenotype. NBF uses the BF of the model that drops the pathway of interest over the full model that includes all pathways see . An altel0 and l1 norm regularization [see l1 norm regularization , but is not as computationally demanding as l0 norm regularization, which requires a combinatorial search through all sets of predictors. Therefore, SNAL provides the best compromise between the ideal of a systematic combinatorial search for the best set of pathways and computational feasibility.On the other hand, SNAL balances sparsity of predictors with explanatory power. The iterative algorithm proposed by The simulation study showed that both SNAL and NBF are able to identify pathways associated with the phenotype. Further, the performance of NBF improves with increasing pathway size, whereas that of SNAL is unaffected by the size of the pathways. This can be explained by the fact that some of the smaller pathways are subsets of larger pathways. Therefore, the predictors of the model are correlated, and NBF finds sparser solutions with good predictive behaviour by dropping some of these pathways.l1 norm regularization problem. We used the least angle regression algorithm in this study. Depending on the number of predictors, other algorithms, for example, based on coordinate descents (NBF has the advantage over SNAL to be easily parallelized. The computing time of SNAL crucially depends on the algorithm used for a subroutine solving an The two methods were applied to the data of two GWAS on the genetic structure of platelets function and body mass index. Through simulation, suitable hyper-parameters of NBF and SNAL were found, as well as the appropriate BF cut-off and tuning parameter The results of the simulations showed that the methods outperform both FM and BGSA. BGSA is one of the recently published methods for pathway analysis that uses hierarchical modelling for identifying associated pathways. In addition, we have recently published a comparison of current pathway analysis methods (P. This is comparable with the work of Although the proposed methods are formulated in a Bayesian framework, an advantage is their implementation without time-consuming Monte Carlo simulation and their ability to deal with both large numbers of tested SNPs and pathways. They are also easily adapted to include other functional information on the SNPs in the design matrix We have considered a two-level model with SNPs assigned to pathways, but further levels of grouping SNPs, for example in genes, can be easily included potentially improving power by borrowing more information. This approach might be useful when dealing with rare variants that individually contribute little information. Further extensions of our approach are possible to allow for structure within the pathway database, such as the hierarchical organization of the Gene Ontology . Finally


Allergic reactions to sapodilla ingestion are rare; a 21 kD protein was recognized as an allergen in sapodilla extracts by IgE-immunoblot [A case of oral allergy syndrome to sapodilla and custard apple was investigated following approval by Institutional Ethics Committee. Sapodilla allergy was confirmed by diagnostic tests . Sapodilla proteins were separated on SP-Sepharose by adsorption at pH 4 followed by step elution at pH 5 (SP1), and with increasing NaCl – 0.1 M (SP2) and 0.2 M (SP3). Forward primers and nested reverse primers specific to the SP1 component were designed based on its N-terminal sequence and conserved regions of homologous plant TLPs. PCR was performed using sapodilla  leaf genomic DNA as template.SPT and allergen-specific IgE were positive. ELISA revealed that IgE from allergic serum recognized two 21 kD proteins – one in the SP1 pool and the other in the SP2 pool; the 21 kD protein in SP2 was identified as basic TLP. The N-terminal sequence of SP1 component was found to be ATFDVVNQCTFTVWAGASPGGGKQL- which was identified as an additional TLP. Sequence analysis of overlapping PCR products revealed an almost full-length gene  corresponding to acidic TLP . Phylogenetic analysis shows that sapodilla acidic TLP is evolutionarily related to the allergenic TLPs from olive and kiwi fruits, all belonging to the order Ericales.A partial gene coding for sapodilla acidic TLP representing 97% of the mature sequence has been cloned. Sapodilla acidic TLP has weak β-1,3-glucanase activity and is an important allergen belonging to the TLP family of pollen and fruit allergens .