Recent studies have identified thousands of regions in the genome associated with chromatin modifications, which may in turn be affecting gene expression. Existing works have used heuristic methods to investigate the relationships between genome, epigenome, and gene expression, but, to our knowledge, none have explicitly modeled the chain of causality whereby genetic variants impact chromatin, which impacts gene expression. In this work we introduce a new hierarchical fine-mapping framework that integrates information across all three levels of data to better identify the causal variant and chromatin mark that are concordantly influencing gene expression. In simulations we show that our method is more accurate than existing approaches at identifying the causal mark influencing expression. We analyze empirical genetic, chromatin, and gene expression data from 65 African-ancestry and 47 European-ancestry individuals and show that many of the paths prioritized by our method are consistent with the proposed causal model and often lie in likely functional regions. Genome-wide association studies (GWAS) have revealed that the majority of variants associated with complex disease lie in noncoding regulatory sequences. More recent studies have identified thousands of quantitative trait loci (QTLs) associated with chromatin modifications, which in turn are associated with changes in gene regulation. Thus, one proposed mechanism by which genetic variants act on trait is through chromatin, which may in turn have downstream effects on transcription. In this work, we propose a method that assumes a causal path from genetic variation to chromatin to expression and integrates information across all three levels of data in order to identify the causal variant and chromatin mark that are likely influencing gene expression. We demonstrate in simulations that our probabilistic approach produces well-calibrated posterior probabilities and outperforms existing methods with respect to SNP-, mark-, and overall path-mapping. Discerning the genetic and molecular basis of complex traits is a fundamental problem in biology. Genome-wide association studies have revealed that the majority of variants associated with disease lie in noncoding regulatory sequences , 2. IdenMethods to investigate the relationships between the genome, the epigenome, and expression have largely focused on quantifying the overlap between hQTLs and eQTLs , 14, 15.pathfinder, that explicitly models the hierarchical relationships between genome, chromatin, and gene expression to predict both the causal SNP and the causal mark within a gene region that are influencing expression of a given gene. Our framework assumes a causal model where a SNP impacts a chromatin which in turn alters gene expression. In our framework we refer to a “causal” SNP as any SNP that disrupts inter-individual variation of chromatin state either through a direct biological mechanism  or indirectly through an unobserved biological mechanism. Similarly, we refer to a “causal” chromatin mark as either a mark that biologically alters expression or that tags an underlying epigenetic regulatory mechanism of expression. Our framework takes as input the strength of association (as quantified through the standard Z-scores) between all SNP/mark pairs and all marks to expression as measured in a given set of individuals. To explicitly account for the correlation structure among SNPs and marks, we use a Matrix-variate Normal distribution to model all Z-scores jointly. By construction, this allows our probabilistic model to assign posterior probabilities for each SNP, mark, and path  to be causal in the region. A key advantage of our approach is that it produces well-calibrated posterior probabilities for causality. Thus, pathfinder can be used to prioritize variants and marks for validation experiments.Here we propose a fine-mapping framework, pathfinder outperforms alternative approaches with respect to both accuracy and calibration. This is largely because our comparators do not take into account mark-expression associations. In some cases, these additional associations may help distinguish between two potentially causal paths that have comparable evidence for causality. For example, in cases where a SNP is associated with expression of a local gene and is also associated with two local chromatin marks, knowledge of the impact of each mark on gene expression may help distinguish between two possible paths for causality. Finally, we analyze genotype, chromatin and expression data from 65 African-ancestry and 47 European-ancestry individuals. We show that the top causal SNPs proposed by pathfinder tend to lie in more functional regions and disturb more regulatory motifs than expected by chance. We also present evidence that most of the top paths reported by pathfinder demonstrate consistency with our proposed sequential model, thus strengthening the case for our method’s applicability to empirical biological data.In simulations we compare against several existing methods, demonstrating that Pathfinder enumerates over all possible causal paths, considering one causal SNP and one causal mark for each path, and outputs a posterior probability for each path to be causal, which can subsequently be used to prioritize SNPs and marks for validation. We compute marginal probabilities for individual SNPs (or marks) to be causal by summing the posterior probabilities over all paths that contain the SNP (or mark). For simplicity, in this work we refer to a “causal” mark as a mark that either causally drives inter-individual variation of gene expression or is correlated to an underlying causal mechanism (e.g. transcription factor binding), though it may not be biologically causal for expression.Here we introduce a hierarchical statistical method for fine-mapping of causal SNPs and chromatin marks  that may be concordantly influencing gene expression within a genomic region. We build upon previous insights that a vector of Z-scores is well-described by a Multivariate Normal (MVN) distribution parameterized by LD , 18, 19 g1 has a strong association with two local marks h1 and h2, as well as a significant association with gene expression. Using only SNP-mark and SNP-expression effects, we are unable to discern whether SNP g1 influences expression through mark h1 or h2. However, if we consider mark-expression effects, we see that mark h1 has a strong association with gene expression where mark h2 does not. This additional information helps support the hypothesis that there is a causal path from SNP g1 to mark h1 to gene expression.The advantage of our method over existing approaches is that it integrates mark-expression associations which may help distinguish between two paths with otherwise comparable evidence for causality. We illustrate a scenario in pathfinder’s performance against alternative methods with respect to SNP-, mark-, and path-finding efficiency as well as the calibration of its posterior probabilities. We generated genetic, chromatin, and gene expression data for 10,000 50kb regions, each centered around a single gene, over 100 individuals, using SNP LD and mark correlations derived from 65 Yoruban (YRI) individuals , a Bayesian network analysis , a naivepathfinder has consistently better performance than the other ranking approaches with respect to all three features—SNP-, mark-, and path-mapping within a region . Because anti-correlated marks (e.g. activating and repressing marks) often tend to act in the same region, we also assess pathfinder’s behavior specifically when two marks have opposite effects on expression. As expected, pathfinder’s performance does not decline in the presence of anti-correlated peaks . Under violation 6, where a single causal SNP affects gene expression directly, which in turn affects a single mark, pathfinder actually captures a higher proportion of the affected marks and overall paths. For example, in order to capture 90% of the causal paths, pathfinder must select on average only 3% of the top-ranked paths (compared with 15% under standard simulations). In violation 7, where the SNP has independent effects on the mark and the gene expression, we show that pathfinder’s accuracy in finding the causal mark and path is significantly reduced. Note that in this case, the “path” is not truly a path but a SNP/mark pair, as effects of the SNP on mark and gene expression are independent. Our power in distinguishing between these two models depends on the prior variance explained parameter. Under violation 7, the variance explained in gene expression by the causal mark is much smaller than expected, thus reducing our confidence in the true causal configuration. We conclude that under the SNP→expression→mark violation, pathfinder will identify causal paths very confidently even if they do not follow the assumed SNP→mark→expression model. Therefore a high posterior probability for a path may not be sufficient evidence for causality. On the other hand, when SNP effects on mark and expression are independent, pathfinder is less likely to produce false positives. For these reasons, we recommend a pre- or post-filtering step to retain only those regions that show some prior evidence for the SNP→mark→expression model using a conditional analysis or partial correlation approach  were obtained from [ minutes .pathfinder on real data. We compare against basic eQTL mapping, where we fine-map SNPs to gene expression ignoring chromatin data. We show that the credible set sizes are significantly narrower when running pathfinder with all three levels of data, consistent with our findings in simulations. For example, eQTL mapping requires an average of 45.3 SNPs in order to capture 90% of the posterior probability for SNP causality, whereas pathfinder only requires 28.4 SNPs. If we define a gene to be fine-mapped if 99% of the posterior probability mass for SNP causality is contained within the top 10 SNPs or fewer, then standard eQTL mapping fine-maps 46 of the genes in our data, whereas pathfinder fine-maps 73 of the genes. Notably, pathfinder also requires only 1.8 marks on average in order to capture 90% of the posterior probability for causal marks. In 82% of the regions where the top two marks capture more than 90% of the posterior probability, these two marks are two distinct peaks of the same mark type.In pathfinder, across all conforming regions, were 0.38 (s.e. 0.01) for the SNP-mark effect and 0.20 (s.e. 0.01) for the mark-expression effect  partial correlation between SNP and gene expression after controlling for chromatin. However, 193 paths had a significant partial correlation between SNP and chromatin after controlling for gene expression. This finding suggests that the top paths are more consistent with the SNP→mark→expression model than with a SNP→expression→mark model.As our pre-filtering step was designed to preserve only regions in which SNP effects on gene expression are mediated by chromatin, we expected a large majority of the analyzed regions to show evidence for this mechanism. To confirm this, we investigated whether the top paths prioritized by our method demonstrate consistency with this causal model. We defined a set of top paths as those which were ranked first in a region and whose posterior probabilities for causality were assigned by pathfinder is identifying many pathways that are likely to be following its causal model.Next we examined the relationship between the product of the effect sizes between SNP-mark and mark-expression against the overall SNP-expression association . We expepathfinder across all real data regions. Most SNPs implicated in these paths are known to alter several regulatory motifs and often lie in an enhancer region or a promoter region of the genes whose expression they affect. 59% (s.e. 2%) of the SNPs implicated in the top paths fall into active ChromHMM states (1–7) in LCLs, including active TSS, flanking active TSS, transcription at gene 5’ and 3’, strong transcription, weak transcription, genic enhancers, and enhancers. Only 47% (s.e. 2%) of random paths fall into these active states (p = 0.001834). Moreover, on average, SNPs in the top paths disturbed 5.35 (s.e. 0.26) regulatory motifs, whereas random SNPs chosen at the same regions only disturbed 4.40 (s.e. 0.20) motifs on average (p < 0.001). We did not, however, observe a similar change in transcription factor binding affinity at these motifs ). As an example, in pathfinder, including average mark signals for DHS, H3K4me1, H3K4me3, and H3K27ac, stratified by genotype, in a 4kb region centered around the TSS of the NDUFA12 gene. The implicated SNP lies within the NDUFA12 TSS. pathfinder, spanning a 50kb region centered around the NDUFA12 TSS.In pathfinder . The average distance from mark to TSS in selected paths was approximately 8.6kb, compared to 9.7kb in randomly chosen paths (p = 0.026). SNP to TSS distances were not significantly different in top versus random paths (p = 0.108), with top SNPs lying on average 11.7kb away from the TSS and random SNPs lying 12.4kb away. 5% of top SNPs lied within 2kb of the TSS while 15% lied within 2kb of the corresponding peak. 23% of peaks in the top paths lied within 2kb of the gene TSS. Next we examined the spatial relationships between the SNP, mark, and TSS implicated in the top paths reported by thfinder . SNP to pathfinder, we determined the extent to which SNPs in these paths overlap with eQTLs that have been identified in LCLs using the larger scale Geuvadis data set [pathfinder. We suspect this discrepancy is due either to imperfect locus ascertainment  or the fact that the Geuvadis eQTLs were also selected using standard fine-mapping approaches and we may thus expect a stronger agreement between the two resulting eQTL sets.To further validate the top paths chosen by data set . 21% of pathfinder’s top SNPs overlap with eQTLs that have been experimentally validated through differential expression in an LCL dataset [pathfinder’s top SNPs overlap with this validated set, where choosing the SNP with the highest association with gene expression in each region resulted in an overlap of 2.3% .We also investigated the extent to which  dataset . Here, wpathfinder could be found within GWAS hit regions for various autoimmune diseases, as our data were collected from LCLs. These autoimmune diseases included Celiac disease, Crohn’s disease, PBC (Primary Biliary Cirrhosis), SLE (Systemic Lupus Erythematosus), MS (Multiple Sclerosis), RA (Rheumatoid Arthritis), IBD (Irritable Bowel Disease), and UC (Ulcerative Colitis). We restricted to GWAS hits with variants associated to the trait with p < 5 × 10−8. We found that 19 of our 480 top paths were contained in a GWAS-implicated region. In Finally, we investigated whether any of the top paths reported by pathfinder produces well-calibrated posterior probabilities, and is thus a reliable method for the prioritization of SNPs and marks for functional validation.In this work we proposed a hierarchical fine-mapping framework that integrates three levels of data—genetic, chromatin, and gene expression—to pinpoint SNPs and chromatin marks that may be concordantly influencing gene expression. A key contribution of our approach is the ability to model the correlation structure in the association statistics using a Matrix-variate Normal distribution. Our approach is superior to existing methods, demonstrating the advantage of using a probabilistic approach that takes into account the full sequential model. Moreover, pathfinder may identify causal paths very confidently, leading to false positives under the proposed model. When a SNP is in fact independently influencing a mark and gene expression, pathfinder is less likely to produce false positives. However, the risk of mis-appropriating our method in this way can be reduced by requiring genomic regions to show evidence for our causal model. We recommend a pre-filtering step before running pathfinder on real data that we outline in Methods. In our empirical data analyses, we demonstrate that this two-step regression robustly filters out non-conforming regions. We also acknowledge that, though there are multiple lines of evidence for SNPs influencing expression through local hQTLs, recent works have also emphasized the importance of interactions with distal hQTLs. Thus, developing a systematic way to incorporate data in distal regions with evidence for interactions with a local eQTL would be a fruitful direction. Moreover, pathfinder assumes that the true causal SNP and mark within a region are present in the data, which may not always be the case. In this scenario, pathfinder will instead place its confidence in the SNP or mark that best correlates with the missing causal SNP or mark in question. Similarly, many epigenetic marks are not themselves causal for gene expression, but are simply correlated to a causal event . It is also often the case that multiple marks at promoter and enhancer regions are concordantly acting to impact gene expression. In these cases, individual marks are not necessarily causal in themselves, but may be viewed as a cause for inter-individual variation or simply correlated to a causal factor. In this light, pathfinder aims to identify the epigenetically modifying region so that it can be tested experimentally and/or characterized functionally . We also note that pathfinder currently uses an approximation whereby the observed Z-score at the causal SNP is used to estimate the true NCP at the causal SNP ) is set to be uniform. As a parameter of the model, we set a prior variance explained ϵ of 0.0001 along the diagonal of ΣC,h to ensure positive semidefiniteness. Thus, the mark-expression association statistics can be expressed as:As we do not know the causal effect size arameter , and chojth SNP on the kth mark through the Wald statistic:As before, we estimate SNP to mark effects with linear regression to quantify the strength of association of the Σg and all pairwise mark correlations into Σh, and use the Matrix-variate Normal distribution to jointly approximate the association statistics for all SNPs on all marks as:Here, M is an s × t matrix representing association means between all s SNPs and all t marks, where each entry j on mark k is just the NCP for causal SNP c on causal mark d, attenuated by the correlation between SNPs j and c, as well as the correlation between marks k and d. Here, rather than integrating out the causal NCPs as we did with the mark-expression associations, we use the observed Z-score for the causal SNP-mark pair to approximate the λj,k terms, as the integration is not straightforward in the matrix-variate setting.Here, P(Zh|Ch) from P from P by summing over the individual likelihoods for all possible causal paths. Here our method assumes a single causal SNP and mark per region, as we restrict our enumeration to only pairwise causal SNP-mark combinations.The posterior probability for causality for a given path can be expressed asausality , 21, or We simulated data for 100 individuals over 10,000 50KB regions, using genotypes and LD from 65 YRI individuals obtained through 1000 Genomes . SNP andHind = HcΣh,c, represent the induced values on non-causal marks due to correlations with the causal mark. The mean mark values for the causal mark were generated for each of the 100 individuals as Hc = βgGc, where Gc is the genotype of the individual at the causal SNP, the effect size βg was drawn from a normal distribution, ϵg set to E = βhHc + ϵh, where Hc is the causal mark value as computed from the MVN, the effect size βh was set to the desired variance explained from mark to expression In order to simulate correlations between histone marks as observed in our empirical data, we drew mark values from an MVN as m or p, the number of causal SNPs or marks, from a binomial distribution where the expected number of causals per region was set to 1. However, we only included simulations with two or more causals. For multi-causal-SNP simulations, we then randomly selected m causal SNPs in the region and simulated chromatin marks and gene expression as described previously, but drew the effect sizes of each SNP as p causal marks in the region and simulated chromatin marks by defining the means, Hc, of each causal mark independently as described for the single-causal simulations. We then computed gene expression by drawing the effect size, βh, of each causal mark from For simulations in which there were multiple causal SNPs or marks, we randomly drew We benchmark our method against five alternative approaches. Firstly, we compare against the standard overlap analysis whereby hQTLs and eQTLs are independently identified within a region centered around a gene. We follow the protocol outlined in . In thisSecondly, we compared against the approach of independently fine-mapping the two levels of data (SNP-mark and mark-expression), and multiplying together pairs of posterior probabilities to produce probabilities of causality for paths. For these independent fine-mapping experiments, we used a simple approach that assumes a single causal variant, approximating posterior probabilities for causality directly from Z-scores .In addition, we compared against a basic ranking approach, where we independently computed SNP-mark, mark-expression, and SNP-expression associations for every SNP and mark within a region. For SNP and mark prioritization, we simply produced a ranking of the SNP-expression and mark-expression posterior probabilities for causality, respectively. For path prioritization, we produced a ranking of the product of SNP-mark and SNP-expression posterior probabilities.We next compared against a bayesian network model which computes directed association strengths between all possible pairs of nodes in a given network . The metFinally, we compared against Coloc, which is designed to identify SNPs that are likely to be causal for multiple traits at once. Specifically, Coloc outputs a posterior probability that a SNP is causal for two arbitrary traits simultaneously. We adapted Coloc for our purposes by running the method on all SNPs independently. For each SNP, the two given traits were (1) gene expression, and (2) a mark value. Thus, we ran Coloc independently for all SNP-mark combinations. This produced a set of posterior probabilities indicating, for each SNP-mark combination, the likelihood that the SNP is causal for both the mark value and gene expression simultaneously. For path prioritization, we ranked these probabilities over all SNP and mark combinations. For SNP and mark prioritization, we marginalized over all marks and SNPs, respectively, producing posterior probabilities for each SNP and mark to be causal independently.p < 0.05/n.snps) to the proportion of gene expression explained by chromatin data, we kept this region for our real data analysis. After this filtering procedure, we retained 1,317 regions.The real data analyses were done on 65 YRI individuals whose genotypes were obtained through 1000 Genomes and standardized. PEER-normalized  H3K4me1,We obtained motif annotations from HaploReg  and Chrop < 5 × 10−8. Associations were obtained from recent literature for eight autoimmune phenotypes [pathfinder’s top reported paths, we determined whether the corresponding SNP was contained within any of the GWAS regions in our dataset. In order establish a null distribution for this statistic, we ran the same analysis for random regions in the genome not overlapping with the GWAS regions in our dataset. Specifically, for each GWAS region, we randomly selected a SNP in the same chromosome matched for MAF (ϵ = 0.01) and LD score (ϵ = 0.001) with the GWAS tag SNP. We established a window around this matched SNP corresponding to the window size of the GWAS region. Finally, we determined the number of top paths that fell within these random regions. We repeated this experiment 100 times to establish the null distribution of this measurement and calculated a p-value using a Z-test.For GWAS analyses, we explored regions whose tag SNP was associated to an autoimmune trait with enotypes –36. For S1 Tablepathfinder to basic eQTL mapping with respect to the size of their credible sets, averaged across all regions. Standard errors are included next to each measurement.We compare (TIF)Click here for additional data file.S2 Tablepathfinder’s performance on simulations using SNP LD from YRI versus from CEU, with respect to the size of its credible sets, averaged across all regions. Standard errors are included next to each measurement.We compare (TIF)Click here for additional data file.S3 Tablepathfinder on empirical data. We display both the raw probability mass and the average mass contribution per peak location for each mark type.We compare the total probability amassed at all peaks for each mark type after running (TIF)Click here for additional data file.S4 Tablepathfinder’s performance on PEER-corrected data and raw data, with respect to the size of its credible sets, averaged across all regions. Standard errors are included next to each measurement.We compare (TIF)Click here for additional data file.S1 Figpathfinder to the technique of independently fine-mapping the two levels of data, with respect to the calibration of their credible sets  and the size of their credible sets .We compare (TIF)Click here for additional data file.S2 Figpathfinder’s behavior in simulations with respect to SNP-, mark-, and path-mapping (A-C) when an additional peak in the region has an effect on expression that is opposite from the mediating peak in question, compared with regions in which the effect of the additional peak has a matching sign.We assess (TIF)Click here for additional data file.S3 Figpathfinder’s response to violations of the causal model against the behavior of other ranking approaches. Causal models are illustrated to the left of the figure.  display SNP-mapping accuracy.  display mark-mapping accuracy.  display path-mapping accuracy.We compare (TIF)Click here for additional data file.S4 Figpathfinder’s runtimes on empirical data with respect to the number of SNPs, marks, and paths within a region (A-C). We plot each simulation as a point and fit a line to all points.(TIF)Click here for additional data file.S5 Figpathfinder.We report the distribution of SNP-mark (A), mark-expression (B), and SNP-expression (C) (TIF)Click here for additional data file.S6 Figpathfinder. The implicated SNP, rs835044, lies 6bp downstream of the NDUFA12 TSS.(A) Mark-expression Z-scores are reported for all marks. (B) SNP-mark Z-scores are reported for the top mark chosen by (TIF)Click here for additional data file.S7 Fig(A-C) DHS. (D-F) H3K4me1. (G-I) H3K4me3. (J-L) H3K27ac.(TIF)Click here for additional data file.


The China, Oxford and Virginia Commonwealth University Experimental Research on Genetic Epidemiology (CONVERGE) project on Major Depressive Disorder (MDD) sequenced 11,670 female Han Chinese at low-coverage (1.7X), providing the first large-scale whole genome sequencing resource representative of the largest ethnic group in the world. Samples are collected from 58 hospitals from 23 provinces around China. We are able to call 22 million high quality single nucleotide polymorphisms (SNP) from the nuclear genome, representing the largest SNP call set from an East Asian population to date. We use these variants for imputation of genotypes across all samples, and this has allowed us to perform a successful genome wide association study (GWAS) on MDD. The utility of these data can be extended to studies of genetic ancestry in the Han Chinese and evolutionary genetics when integrated with data from other populations. Molecular phenotypes, such as copy number variations and structural variations can be detected, quantified and analysed in similar ways. It is also the most comprehensive collection of sequence variants from any non-Caucasian population.CONVERGE2–4, Hapmap project5, the Human Genome Diversity Project6 (HGDP) and most recently assembly of many population based cohorts in the Haplotype Reference Consortium have enabled the study of genetic diversity in human populations and the building of population reference panels for checking any new cohorts against as well as genotype imputation. Despite these efforts, most genetic studies have mostly been conducted in European and African populations. Examples of relatively large GWAS on East Asian populations on schizophrenia7, IgA nephrology8, prostate cancer9 and systemic lupus erythematosus10 have discovery sample sizes below 2,000 cases. In comparison, more diseases are studied using the GWAS method on populations of European descent, more cohorts were collected by independent groups for each disease, many of them are aggregated in large consortiums for joint analysis, and as such have sample sizes at least a scale of magnitude larger. Some examples include the Wellcome Trust Case Control Consortium Phase 1 , the CardioGRAM Consortium13 for coronary artery disease , the CHARGE Consortium14  and most recently, the UKBiobank and 23andMe datasets each containing hundreds of thousands of samples and numerous quantitative and qualitative traits reflective of human physiology and lifestyle.The 1000 Genomes Project Phase 1   maps, and particular disease related gene annotations17–19 being developed with and drawing information mostly from European populations, and population specific ones are increasing in numbers but still exceptions20 rather than the norm. CONVERGE samples1 consist of cases of recurrent MD collected from 58 provincial mental health centres and psychiatric departments of general medical hospitals in 45 cities and 23 provinces of China. A similar number of controls are recruited from patients undergoing minor surgical procedures at general hospitals (37%) or from local community centres (63%). While these data are collected for the investigation of genetics of severe recurrent MDD in the Han Chinese population, sequence data from this dataset are a resource for population genetics studies on the Han Chinese population. In particular, these data can be used as a reference panel for future design of genotyping arrays specific to the Han Chinese population, and as comparison with other populations in the investigation of human evolution and migration. LD maps, recombination maps and allele frequency spectrums can be generated from this dataset for use as reference for Han Chinese or other related East Asian populations.Few methods22 can be used to trace the maternal lineage of the Han Chinese population. We have previously reported mitochondrial DNA copy number is under genetic control22; there are potentially other molecular traits detectable from whole-genome sequencing data under genetic control.In addition, the presence of mitochondrial sequence data at an average coverage of 102X in all CONVERGE samplesCONVERGE is the largest genetic resource derived from next generation whole genome sequencing (WGS) and it showcases the utility of low-coverage WGS in providing high quality genotypes for GWAS at common variant sites. We outline the tested practices in sequence data processing, variant calling, variant filtering, imputation and downstream quality control, setting a benchmark for future studies using low coverage WGS for GWAS and other genetic analyses.23 with the first episode occurring between 14 and 50 years of age, and had not abused drugs or alcohol before their first depressive episode. All subjects were interviewed using a computerized assessment system. Interviewers were postgraduate medical students, junior psychiatrists or senior nurses who had gone through at least a week of training by the CONVERGE team. The diagnosis of MDD was established with the Composite International Diagnostic Interview (CIDI) , which utilized DSM-IV criteria. The interview was translated into Mandarin by a team of psychiatrists in Shanghai Mental Health Centre, with the translation reviewed and modified by members of the CONVERGE team.CONVERGE collected cases of recurrent MDD from 58 provincial mental health centres and psychiatric departments of general medical hospitals in 45 cities and 23 provinces of China. Controls were recruited from patients undergoing minor surgical procedures at general hospitals (37%) or from local community centres (63%). Cases were excluded if they had a pre-existing history of bipolar disorder, psychosis or mental retardation. Cases were aged between 30 and 60 and had two or more episodes of MDD meeting DSM-IV criteria24 using default parameters, after filtering out reads containing adaptor sequencing or consisting of more than 50% poor quality  bases. Samtools (v0.1.18)25 was used to index the alignments in BAM format25 and Picardtools (v1.62) was used to mark PCR duplicates for downstream filtering. The Genome Analysis Toolkit’s 26 Base quality score recalibration (BQSR) was then applied to the mapped sequencing reads using BaseRecalibrator in Genome Analysis Toolkit 26 with the known insertion and deletion (INDEL) variations in 1000 Genomes Projects Phase 1 and known single nucleotide polymorphisms (SNPs) from dbSNP  excluded from the empirical error rate calculation. GATKlite (v2.2.15)26 was then used to output sequencing reads with the recalibrated base quality scores while removing reads without the ‘proper pair’ flag bit set by Stampy (1–5% of reads per sample) using the --read_filter ProperPair option .DNA was extracted from saliva samples using the Oragene protocol. A barcoded library was constructed for each sample. Sequencing reads obtained from Illumina Hiseq machines were aligned to Genome Reference Consortium Human Build 37 patch release 5 (GRCh37.p5) with Stampy (v1.0.17)3 was performed simultaneously using post-BQSR sequencing reads from all samples using the GATK’s UnifiedGenotyper (version 2.7-2-g6bda569) using option --genotype_likelihood_model BOTH and default annotation outputs for variant calls. dbSNP v137 rsids were used to fill in the variant ID column of the result variant call format (VCF) files using the --dbSNP option. Variant quality score recalibration (VQSR) was then performed with GATK’s VariantRecalibrator (v2.7-4-g6f46d11) in SNP variant calls using the SNPs in 1000 Genomes Phase 1 ASN Panel3 as the known, truth and training sets with a prior of 15.0, and the following default annotations: -an QD -an HaplotypeScore -an MQRankSum -an ReadPosRankSum -an FS -an MQonly. A sensitivity threshold of 90% to SNPs in the 1000G Phase1 ASN panel was applied for SNP selection for imputation after optimizing for Transition to Transversion (TiTv) ratios in SNPs called. Genotype likelihoods (GLs) were calculated at selected sites using a sample-specific binomial mixture model implemented in SNPtools (version 1.0)27, and imputation was performed at those sites without a reference panel using BEAGLE (version 3.3.2)28. A second round of imputation was performed with BEAGLE on the same GLs, but only at biallelic SNPs polymorphic in the 1000G Phase 1 ASN panel using the 1000G Phase 1 ASN haplotypes as a reference panel. A final set of allele dosages and genotype probabilities was generated from these two datasets by replacing the results in the former with those in the latter at all sites imputed in the latter. We then applied a conservative set of inclusion threshold for SNPs for genome-wide association study (GWAS): (a) P-value for violation HWE > 10−6, (b) Information score > 0.9, (c) MAF in CONVERGE > 0.5% to arrive at the final set of 6,242,619 SNPs for individual genotype analsyses.Variant discovery and genotyping (for both SNPs and INDELs) at all polymorphic SNPs in 1000G Phase1 East Asian (ASN) reference panel29. Genetic maps were obtained from the Impute2  Impute2  website.The study protocol was approved centrally by the Ethical Review Board of Oxford University  and the ethics committees of all participating hospitals in China. All interviewers were mental health professionals who are well able to judge decisional capacity. The study posed minimal risk . All participants provided their written informed consent.Sequencing data of 11,670 samples in paired FASTQ files are available from the European Nucleotide Archive (ENA) at Study and Short Read Archive Accession PRJNA289433 (Data Citation 1). Data totals about 100 terabytes (TB); after download, alignment of raw sequence data in paired FASTQ format to Genome Reference Consortium Human Build 37 patch release 5 (GRCh37.p5) with BWA takes not more than 2 h per FASTQ file (4 h per pair) and less than 5 gigabytes (GB) of RAM per process. Once in BAM format, preprocessing steps can be parallelized per sample using less than 12GB of RAM and 2 h on a single processor.1 with major and minor alleles and allele frequencies in both the CONVERGE cohort and 1000 Genomes Phase 1 ASN samples in variant call format (VCF) is provided through Figshare (Data Citation 2).A list of high quality SNP variant siteset al.1 (Data Citation 2). After applying stringent quality controls, we obtained 10,640 samples  for inclusion in GWAS of MDD1. We have submitted all imputed genotypes at 20,539,441 SNPs of these 10,640 samples passing quality control in VCF format to the European Variations Archive (EVA), Accession PRJNA289433 ; access will be brokered through the EVA with standard application procedures. SNPs with imputation information score below 0.95  are marked ‘INFO0.95’ instead of ‘PASS’. Minor allele frequency, P value for violation of Hardy-Weinberg equilibrium and imputation information score are also present in the INFO column of the VCFs. As CONVERGE is part of Phase 2 of the Haplotype Reference Consortium31 (HRC), these data will also be available in the near future through the HRC.We imputed genotype probabilities and obtained alternative allele dosages at 22,847,544 SNPs from all autosomes and chromosome X including 20,539,441 SNPs in the CONVERGE cohort and all polymorphic sites in the 1000 Genomes ASN Panel on all 11,670 CONVERGE samples, as detailed in Cai 22; summary statistics for GWAS on MDD is available from the Psychiatric Genomics Consortium (https://www.med.unc.edu/pgc/files/resultfiles/converge.MDD.summary_stats.2Sep2015.tbl.gz) and on Figshare (Data Citation 4) and that of mitochondrial DNA copy number for all common SNPs (>5% MAF) is available on Figshare (Data Citation 5). All phenotypes are currently under analysis, interested research groups and individuals are welcome to contact us directly for collaborations and access.GWAS on MDD and mitochondrial DNA copy number quantified from sequencing data are publishedet al.1.These methods are expanded versions of descriptions in our related work Cai The coverage of sequencing is the number of times, on average, a single site in the genome is covered by sequencing reads. For each sample in CONVERGE, the average sequencing coverage over the nuclear genome is 1.7x, and that on the mitochondrial genome is 102x .Sets or ‘tranches’ of SNPs with increasing VQSLOD scores (increasing stringency in exclusion of outliers and concomitant decreasing sensitivity to known SNPs) from VQSR (Methods) were generated at the specified sensitivities to known SNPs using the –tranche option: -tranche 100.0 -tranche 99.9 -tranche 99.0 -tranche 98.0 -tranche 95.0 -tranche 90.0 -tranche 89.0 -tranche 87.0 -tranche 85.0 -tranche 80.0 -tranche 79.0. Transition-to-transversion (TiTv) ratios were calculated for each tranche. This gave a total of 21,356,798 , biallelic SNPs identified from all chromosomes and unassembled contigs. 20,539,441 SNPs from the autosomes and chromosome X were put forth for imputation of genotype probabilities and downstream analyses. All known sites in 1000 Genomes Phase 1 ASN panel were included in all further analyses regardless of whether they were identified as polymorphic in CONVERGE, and their VQSLOD scores if they were identified as polymorphic in CONVERGE. Numbers of SNPs imputed in each chromosome and the percentage of SNPs in each category are shown in Nine samples in CONVERGE were sequenced to an average of 10x coverage using the same Illumina Hiseq platform used for the low-coverage sequencing samples. Mapping and processing of the 10x sequencing reads were performed in an identical fashion as the low-coverage sequences. Variant calling (for both SNPs and INDELs) was performed on the high coverage dataset using sequencing reads from all nine samples with UnifiedGenotyper in GATK (v2.7-2-g6bda569) using option --genotype_likelihood_model BOTH and default annotation outputs for variant calls. dbSNP v137 rsids are used to fill in the variant ID column of the result variant call format (VCF) files using the --dbSNP option.2) and concordance with imputed allele dosages and hard-called genotypes of the same samples respectively.Variant quality score recalibration (VQSR) was performed using two different training sets. The first VQSR was performed using SNP variant calls using the SNPs in 1000 Genomes Phase 1 ASN Panel as the known, truth and training sets with a prior of 15.0 and the default annotations just like it was done for the raw variant calls from the low coverage data, but with two maximum Gaussians for building the positive model instead of one, with the --maxGaussian option. The second VQSR was performed using SNPs from the HumanOmniZhongHua-8 (v1.0) BeadChip. Sets or ‘tranches’ of SNPs with increasing VQSLOD scores (increasing stringency in exclusion of outliers and concomitant decreasing sensitivity to known SNPs) were generated at the specified sensitivities to known SNPs using the –tranche option: -tranche 100.0 -tranche 99.9 -tranche 99.0 -tranche 98.0 -tranche 95.0 -tranche 90.0 -tranche 89.0 -tranche 87.0 -tranche 85.0 -tranche 80.0 -tranche 79.0. Transition to Transversion (TiTv) Ratio was calculated for each tranche. A sensitivity to known SNPs in the Zhonghua chip of 90.0% was chosen for the inclusion of novel SNPs in downstream analyses as it balances optimization of TiTv ratio in novel SNPs while retaining as many potential novel SNPs as possible to not lose variant information. This gave a total of 5,914,716  biallelic SNPs identified in high coverage sequencing data with which we are able to check for squared Pearson correlation coefficient  and genotypes called from the 10x coverage sequencing data at all imputed variant sites for each of the nine samples. The mean concordance per sample across all sites was 98.1% (s.e.=0.12%) and the percentage concordance calculated for each alternative allele frequencies was shown in 2 between imputed allele dosages and genotypes from the 10x coverage sequencing data at all imputed variant sites for each of the nine samples. The mean r2 per sample across all sites was 0.938 (s.e.=0.003). The r2 calculated for each alternative allele frequencies is shown in We also calculated ret al.1 combines all these information and shows the mean percentage concordance and Pearson r2 among 9 samples for each genotype class  for each alternative allele frequency bin.72 samples from CONVERGE were genotyped on the Illumina HumanOmniZhongHua-8 (v1.0B) BeadChip which contains 898,122 SNPs, optimized for the Chinese population using tag SNPs from all three HapMap phases and the 1000 Genomes Project. Sample genotypes with GenCall score of 0.15 or lower were considered no-calls.2 (2 was 0.974 (s.e.=0.0005).857,766 out of the 895,623 SNPs on the array were included in our imputation (95.77%). Upon examining, why 37,857 SNPs on the array were not imputed, we found that SNPs that were on the array, but not imputed, were not polymorphic in either CONVERGE samples or 1000 Genomes Phase 1 ASN panel. None of the 72 samples genotyped were polymorphic at these 37,857 sites. 2  between et al.1 shows the mean percentage concordance and Pearson R2 among 72 samples for each genotype class  for each alternative allele frequency bin. At all alternative allele frequency bins between 1 to 99%, the mean percentage concordances were above 96% and the mean R2s were above 0.87.11,669 samples out of all CONVERGE samples were genotyped at 21 random sites in the genome using a custom Sequenom SpectroCHIP . Mass spectra were collected using the MassARRAY system mass spectrometer and TYPER4.0 was used to assess the reliability of genotype calls generated by SpectroREAD from the mass spectra. Default genotype call inclusion criteria were used.2 between hard-called genotypes and allele dosages from imputation with genotype calls from the Sequenom for each of the 21 sites that were genotyped. An r2 of 0.988 and concordance of 98.19% were obtained for the 21 sites. Per site r2 (mean=0.985) and percentage concordance (mean=98.16%) are shown et al.1) along with the alleles and MAF of each site.Just as we had compared imputed results with genotypes called directly from 10x coverage sequencing and the Illumina HumanOmniZhongHua Beadchip, we calculated the percentage concordance and rWe performed a range of quality control filters on all 11,670 samples and selected only those samples with high-quality sequencing results and imputed genotype probabilities for downstream analysis.We first looked into both the nuclear genome and mitochondrial genome for an excess of variants called, since this would indicate cross-sample contamination due to technical issues during sequencing. We quantified the number of singleton variants called in the genic regions of the nuclear genome (Methods) and found a mean of 71.55 singletons variants in exon regions across the whole genome supported by more than 2 sequencing reads passing sequencing quality controls per sample. While most samples fall in a normal distribution with mean of 71.55 singletons, others had excess singletons, which could be indicative of poor DNA quality that compromised sequencing and mapping qualities. We excluded 117 samples with a number of singletons greater than the 99th percentile (237.62 singletons).We then asked if there were any samples with an excess of heteroplasmic mutations in the mitochondrial DNA per sample. As the DNA for all individuals recruited in CONVERGE were extracted from saliva samples, which would also contain the oral microbiome, external DNA contamination levels could be told from number of mismatches in mapped reads at regions of the genomes where the human genome was similar to bacterial genomes. The mitochondrial DNA in human genome, a 16 kb circular DNA, is one of such instances. Mitochondrial DNA is inherited only from the mother and there is a strict bottleneck in the female germline for mitochondrial DNA, usually only one clonal copy of mitochondrial DNA is inherited at a time. Heteroplasmic variations in the mitochondrial genome  are rare and usually occurring at low levels. Mismatches that occur in some sequencing reads mapping to the mitochondrial DNA region of the human genome reference in one sample would therefore indicate either a rare event heteroplasmy, or a contamination from similar DNA sequences of bacterial origins. Samples with excessive numbers of heteroplasmic sites can therefore be suspected as having a higher than normal level of bacterial contamination, and the conservative approach would be to filter them out to ensure the quality of sequencing, and all downstream analyses.Coverage of the mitochondrial genome is on average 102X, making it possible to obtain high quality sequence for this part of the genome. We called a mean of 15.70 heteroplasmic sites per sample, and 116 samples were found to have greater than the 99th percentile of number of heteroplasmic sites. Of these 116 samples, 26 were already discarded for having excess nuclear genome singletons; we excluded the additional 90 from further analysis.P-values and even false positive results in GWAS, as alleles inherited together due to high relatedness but not related to etiology of disease could appear so in association testing. To exclude duplicates and first degree relatives from our sample for GWAS, we calculated the proportion IBD +0.5*P(IBD=1)) for every pair of samples in CONVERGE using LD pruned SNPs (LD<0.5). We excluded all pairs of samples  whose PIHAT exceeded 0.5 and P(IBD=1) exceeded 0.75 from further analyses.Varying levels of relatedness could cause inflated Finally, we also excluded 29 samples with fewer than 90% of imputed sites with maximum genotype probabilities>0.9, and 402 samples with incomplete phenotype information. We used the remaining set of 10,640 samples  for GWAS on MDD and mitochondrial DNA levels (Data Citation 4 and Data Citation 5).CONVERGE is the largest well-curated, high quality low-coverage WGS dataset from a single study and a single population where all samples are from the same ethnic group. Although sequencing was performed at low coverage, we have shown good quality genotypes can be obtained through recalibration of sequencing base qualities, filtering of variants based on sensitivity to known variants, imputation using a two-step approach, and finally strict quality control over imputed genotypes. We have also shown robust ways of identifying and removing potentially contaminated samples using sequencing data, particularly where the source of DNA was saliva. We have ensured there are no related individuals in this dataset for downstream genetic analysis, and we recommend using genotypes of SNPs with high imputation information scores, available in the VCF we provide through EVA (Data Citation 3) in future analyses. We encourage all users of CONVERGE data to use and expand upon on these quality control measures especially when performing analyses on rare, copy number and structural variants, for which we do not currently have gold standards in low coverage WGS. Development of methods and best practices for these purposes can enable more economical sequencing of large cohorts, and the usage of existing data to the fullest.The CONVERGE project has the aim of studying MDD in a homogenous population where genetic ancestry and cultural diversity minimally confound genetic effects. Hospital staff, trained to use computer-assisted interviews, obtained highly detailed information on clinical symptoms, comorbidities, personality traits, social support, socio-economic status, life style, and major stressful life events. All cases of MDD collected in CONVERGE had severe and recurrent MDD not concurrent with other major illnesses . The use of screened controls limited the possibility of including undiagnosed cases of MDD in the study. These, together with strict inclusion criteria for ancestry, age (between 30 to 60) and sex  of all samples in CONVERGE, made CONVERGE the most homogenous and extensively phenotyped cohort of severe, recurrent MDD and matched controls to date.32  compounded with heterogeneity in disease identification and characteristics. The estimated number of samples needed for uncovering a genetic locus associated with this heterogenous disease was 50,000 cases and a similar number of controls. In CONVERGE we have shown that using stringent criteria and deep phenotyping to minimise misdiagnosis and biases in self-reporting, we were able to find and replicate two genetic associations to MDD using a tenth of the estimated sample size. Thus, in some cases, dissection of disease etiology through studying clearly defined and rigorously collected phenotypes can lead to a substantial increase in power to detect genetic effects. We note the contrast in strategy with a recent study using data from 23andMe that reported 15 genetic associations with 30 times our sample size and a yet bigger departure from our definition of MDD33. It will take further investigations to know if and how these new genetic associations from a large but heterogenous cohort may contribute to genetic heterogeneity in MDD.MDD is a highly heterogeneous disease with different ages of onset and physiological symptoms, prevalence between sexes, definitions across cultural contexts and diagnostic instruments. In addition to heterogeneity in the disease presentation, misdiagnoses and biases in self-reporting further contribute to heterogeneity in studies with less detailed enquiry, documentation and classification of MDD phenotypes. Until recently, GWAS has not been able to uncover genetic loci associated with MDD due to the complex genetic architectureHow to cite this article: Cai, N et al. 11,670 whole-genome sequences representative of the Han Chinese population from the CONVERGE project. Sci. Data 4:170011 doi: 10.1038/sdata.2017.11 (2017).Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.


However, synthesis of these datasets into effective summary metrics to characterize the functional non-coding genome remains a challenge. Here, we present GenoSkyline-Plus, an extension of our previous work through integration of an expanded set of epigenomic and transcriptomic annotations to produce high-resolution, single tissue annotations. After validating our annotations with a catalog of tissue-specific non-coding elements previously identified in the literature, we apply our method using data from 127 different cell and tissue types to present an atlas of heritability enrichment across 45 different GWAS traits. We show that broader organ system categories (e.g. immune system) increase statistical power in identifying biologically relevant tissue types for complex diseases while annotations of individual cell types (e.g. monocytes or B-cells) provide deeper insights into disease etiology. Additionally, we use our GenoSkyline-Plus annotations in an in-depth case study of late-onset Alzheimer’s disease (LOAD). Our analyses suggest a strong connection between LOAD heritability and genetic variants contained in regions of the genome functional in monocytes. Furthermore, we show that LOAD shares a similar localization of SNPs to monocyte-functional regions with Parkinson’s disease. Overall, we demonstrate that integrated genome annotations at the single tissue level provide a valuable tool for understanding the etiology of complex human diseases. Our GenoSkyline-Plus annotations are freely available at  After years of community efforts, many experimental and computational approaches have been developed and applied for functional annotation of the human genome, yet proper annotation still remains challenging, especially in non-coding regions. As complex disease research rapidly advances, increasing evidence suggests that non-coding regulatory DNA elements may be the primary regions harboring risk variants in human complex diseases. In this paper, we introduce GenoSkyline-Plus, a principled annotation framework to identify tissue and cell type-specific functional regions in the human genome through integration of diverse high-throughput epigenomic and transcriptomic data. Through validation of known non-coding tissue-specific regulatory regions, enrichment analyses on 45 complex traits, and an in-depth case study of neurodegenerative diseases, we demonstrate the ability of GenoSkyline-Plus to accurately identify tissue-specific functionality in the human genome and provide unbiased, genome-wide insights into the genetic basis of human complex diseases. Large consortia such as ENCODE  and Epigtotal≈3.8M) and identified biologically relevant tissues for a broad spectrum of complex traits. We next performed an in-depth, annotation-driven investigation of Alzheimer’s disease (AD), a neurodegenerative disease characterized by deposition of amyloid-β (Aβ) plaques and neurofibrillary tangles in the brain. Late-onset AD (LOAD) includes patients with onset after 65 years of age and has a complex mode of inheritance [To demonstrate the ability of GenoSkyline-Plus to systematically provide novel insights into complex disease etiology, we jointly analyzed summary statistics from 45 genome-wide association studies , which are known to regulate a variety of cellular processes through the translational repression and degradation signaling of transcripts . Recent We next examined long non-coding RNAs (lncRNA), another non-coding element known for its tissue-specific regulatory action . Using aWe also assessed enhancers, non-coding elements that can remotely regulate transcription of an associated promoter elsewhere on the genome with important roles in cell-type specificity . We extrIL17A-IL17F locus control region (LCR) in humans, a ~200kb regulatory region surrounding the IL17A gene locus. IL17A encodes the primary secreted cytokine effector molecule IL-17 of T helper 17 (Th17) cells [+ T-cells, differentiated Th17 cells, and differentiated Th1/Th2 cell populations, we identified highly Th17-specific functionality in the conserved regions of the human genome corresponding to known murine CNS regions  cells . The LCR7) cells , 24. Exp7) cells . Compari regions . CNS sitWe jointly analyzed three tiers of annotation tracks that respectively represent the overall functional genome, 7 broad tissue clusters, and 66 tissue and cell types , with suThe immune annotation track was significantly enriched for 7 immune diseases, namely celiac disease (CEL), Crohn’s disease (CD), ulcerative colitis (UC), primary biliary cirrhosis (PBC), rheumatoid arthritis (RA), systemic lupus erythematosus (SLE), and multiple sclerosis (MS). Using tracks for cell types, we identified several significant enrichments, including monocytes for CD (p = 2.9e-11) and B cells for PBC (p = 2.3e-6), RA (p = 1.2e-5), and MS (p = 2.2e-6). Inflammatory bowel diseases showed significant enrichment in the gastrointestinal (GI) annotation track . Another autoimmune disease with a well-established GI component, CEL, also showed nominal enrichment in the GI annotation track (p = 3.7e-4).Several brain annotation tracks were significantly enriched for associations of schizophrenia (SCZ), education years (EDU), and cognitive performance (IQ). Bipolar disorder (BIP), neuroticism (NEU), and chronotype (CHT) all showed nominally significant enrichment in the anterior caudate annotation track. Body mass index (BMI) and age at menarche (AAM) were significantly enriched in multiple brain annotation tracks. Compared to other brain regions, the substantia nigra annotation track showed weaker enrichment for these brain-based traits, which is consistent with its primary function of controlling movement.Hundreds of height-associated loci have been identified in GWAS . Such a Our results demonstrated that annotations with refined specificity could provide insights into disease etiology while broader annotations have greater statistical power. Age-related macular degeneration (AMD) was significantly enriched in broadly defined annotation tracks including immune, brain, CV, and GI, despite the non-significant enrichment results using tier-3 annotation tracks. Analyses based on all three tiers of annotations could systematically provide the most interpretable results for most traits. Importantly, we note that greater GWAS sample sizes will effectively increase statistical power in the enrichment analysis while leaving the overall enrichment pattern stable . TherefoNext, we performed an integrative analysis of stage-I GWAS summary statistics from the International Genomics of Alzheimer’s Project  IGAP; n. SNPs loAnalysis based on 66 tissue and cell tracks further refined the resolution of our enrichment study. Monocyte  and liver  annotation tracks were significantly enriched for LOAD associations . In factAPOE region  from the analysis , is associated with multiple pharmacogenomic traits related to neurological diseases, including response to cholinesterase inhibitor in AD [AIM1 is associated with stroke [We identified 15 candidate loci for pleiotropic effects , among wclearest . SLC9A9,or in AD , responsor in AD , responsor in AD , and mulor in AD , autism or in AD , and nonor in AD . Gene AIh stroke , human lh stroke , and immh stroke  and SLE h stroke .ANKRD33B [PRUNE2 is a gene associated with response to amphetamine [HBEGF is associated with AD in APOE ε4- population [PROK2 is a gene involved in Aβ-induced neurotoxicity [AXIN1 negatively affects phosphorylation of tau protein [CCDC158, PRSS16, and ZNF615, which are previously identified risk genes for PD, SCZ, and BIP, respectively [PRSS8 [HLA region. Interestingly, we also identified the surrounding region of MAPT, a gene that encodes the tau protein which is a critical component of both AD and PD pathologies [A few candidate loci pointed to clear gene candidates but showed unclear or distinct peaks of association . These iANKRD33B . PRUNE2 hetamine  and hipphetamine . HBEGF ipulation  and invopulation . PROK2 itoxicity . Additio protein . Other gectively –56. Somey [PRSS8  and the hologies , 57, 58.HLA loci from our analysis.Pathway enrichment analysis for genes in 15 pleiotropic candidate loci identified significant enrichment in immune-related pathways staphylococcus aureus infection  and systemic lupus erythematosus , and raSCIMP  were reported in a trans-ethnic GWAS meta-analysis for AD [PFDN1/HBEGF  and BZRAP1-AS1  matched with our top reprioritized SNPs  compared to the data in the discovery stage. Furthermore, data from IGAP stage-II cohort are not publicly available and we were limited to the inverse inference approach shown here. It is possible additional loci will replicate when IGAP stage-II summary or individual-level data are made available. However, all identified loci have been linked to AD or relevant phenotypes in the literature. echnique . Associad traits . Associareported . BZRAP1 ectively , 65. A pneration . Associaed tests . Finallys for AD , all of zed SNPs .Increasing evidence suggests that non-coding regulatory DNA elements may be the primary regions harboring risk variants in human complex diseases. In this work, we have substantially expanded our previously established GenoSkyline annotation by incorporating RNA-seq and DNA methylation into its framework, imputing incomplete epigenomic and transcriptomic annotation tracks, and extending it to more than 100 human tissue and cell types. With the help of integrative functional annotations, we identified strong enrichment for LOAD heritability in functional DNA elements related to innate immunity and liver tissue using hypothesis-free tissue-specific enrichment analysis. This enrichment was also found in immune-related DNA elements using PD data. Our analysis also clearly indicated that monocyte functional elements in particular appear to be highly relevant in explaining AD and PD heritability. Of note, we analyzed 45 complex diseases and traits in addition to AD and PD. The substantial enrichment for multiple psychiatric and neurological traits in the brain functional genome shows that the lack of brain enrichment in neurodegeneration is not due to poor quality of brain annotations. Further, the monocytes annotation track was the most significantly enriched for Crohn’s disease among the 45 GWAS, and was not ubiquitously enriched for a large number of traits. Consistent and biologically interpretable enrichment results on a large collection of complex traits demonstrate the effectiveness of our approach and increase the validity of novel findings.It is worth noting that multiple studies have highlighted the role of myeloid cells in the genetic susceptibility of neurodegenerative diseases . SeveralFurthermore, we successfully identified enrichment for shared genetic components between AD and PD in the monocyte functional genome, which hints at a shared neuroinflammation pathway between these two neurodegenerative diseases. We note that several candidate loci with potential pleiotropic effects showed fairly marginal associations with AD and PD, which explains why they have been missed in traditional SNP-based association analysis. Importantly, SNPs in immune-related DNA elements explain a large proportion of AD and PD heritability in total. These results suggest that weak but pervasive associations related with immunity still remain unidentified. Further evaluations of these relationships using GWAS with larger sample sizes may provide insights into the shared biology of these neurodegenerative conditions.Through multi-tier enrichment analyses on 45 GWAS, an in-depth case study of neurodegenerative diseases, and validation of known non-coding tissue-specific regulatory machinery, we have demonstrated the ability of GenoSkyline-Plus to provide unbiased, genome-wide insights into the genetic basis of human complex diseases. The analyzed GWAS represent a variety of human complex diseases and traits, highlighting the effectiveness of our method in different contexts and genetic architecture. However, while our non-coding validation study demonstrated that GenoSkyline-Plus annotations indeed captured tissue-specific activity in a variety of intergenic machinery, there is a need to develop a more statistically robust framework to identify new non-coding elements rather than validate existing ones. Our approach of identifying the functionally active proportion of all elements in aggregate is only able to identify tissue specificity while considering large groups of highly specific non-coding elements. The availability of over 100 different annotation tracks introduces many multiple-testing issues that should be addressed in the case of a statistically sound analysis for tissue-specificity. We have also demonstrated how GenoSkyline-Plus and its explanatory power improve with the addition of more data. Currently, functionality in 28% of exonic regions still remains to be identified. As the quantity and quality of high-throughput epigenomic data continue to grow, GenoSkyline-Plus has the potential to further evolve and provide even more comprehensive annotations of tissue-specific functionality in the human genome. We will update our annotations when data for new tissue and cell types from the Roadmap consortium become available. Finally, several recent papers have introduced novel models to integrate functional annotations in tissue-specific enrichment analysis , 74. Manhttp://egg2.wustl.edu/roadmap/). Specifically, ChIP-seq peak calls were collected for each epigenetic mark  in each Roadmap consolidated epigenome where available. Peak calls imputed using ChromImpute [Chromatin data were extracted from the Epigenomics Roadmap Project’s consolidated reference epigenomes database (omImpute  were usehttp://genome.ucsc.edu/), and unmethylated islands were those CpG islands with less than 0.5 fractionated methylation based on imputed methylation signal tracks in the Roadmap reference epigenomes database. Presence of an unmethylated CpG island was then encoded for each nucleotide as a binary variable. Finally, Roadmap’s RNA-seq data were dichotomized using an rpkm cutoff of 0.5 at 25-bp resolution and included in our annotations.DNA methylation data were also collected from the Roadmap’s reference epigenomes database. CpG islands were identified in each sample using the CpG Islands Track of the UCSC Genome Browser  algorithm. The posterior probability of functionality at a nucleotide, known as the GenoSkyline-Plus score, is then:With an assumed prior probability Giving us with 21 parameters for each annotation track:http://www.genome.gov/gwastudies/). 13,070 unique SNPs found to be significant in at least one published GWAS were expanded into 1kb bp intervals and formed a sampling covering 12,801,840 bp of the genome. This sampling method has been shown to be a robust representation of functional and non-functional regions along the genome [These parameters were estimated using the GWAS Catalog, downloaded from the NHGRI website  and mapped to hg19 using the UCSC liftover tool (http://genome.ucsc.edu/). lncRNA data was prepared similarly to miRNA. Expression data of 9,747 lncRNA transcripts based on GENCODE v3c annotation across 31 human tissues [Quantile-normalized expression values were downloaded for all mature miRNAs profiled in Ludwig et al . Due to http://enhancer.binf.ku.dk/presets/). Andersson et al. define their enhancer sets via bi-directional CAGE expression collected by the FANTOM consortium [Pre-defined enhancer differentially expressed cell facets  were downsortium . Cell faFor each of the three data validation sets, functional specificity is assessed by calculating the per-nucleotide functional proportion of all non-coding elements across a tissue. Functionality is defined by a Genoskyline-Plus score greater than 0.5 at that nucleotide. For Roadmap samples with multiple donors  we took the average GenoSkyline-Plus score at each nucleotide across the samples. For each set of non-coding elements we selected the top three tissues with the largest sample size that had matching annotations in Genoskyline-Plus. For example, we did not calculate scores for enhancers with maximal expressions in human testis because there is no corresponding Roadmap sample in which we would detect tissue-specific functionality.IL17A LCR in T-cell subsets, we extracted GenoSkyline-Plus scores for each nucleotide along the ~200 kilobase region between the genes PKHD1 and MCM3 [http://pipeline.lbl.gov/cgi-bin/gateway2). GenoSkyline-Plus scores in the 20 CNS sites and their 200-bp flanking regions were compared across different cell types.To examine cell-specific functionality of the and MCM3 . While sand MCM3  by takinhttp://web.pasteur-lille.fr/en/recherche/u744/igap/igap_download.php). GWAS summary statistics for PD was acquired from dbGap (accession: pha002868.1). Details for AD and PD studies have been previously reported [Summary statistics for 45 GWAS are publicly accessible. Details for these studies are summarized in reported .https://github.com/bulik/ldsc/). Annotation-stratified LD scores were estimated using dichotomized annotations, 1000 Genomes (1KG) samples with European ancestry [Heritability stratification and enrichment analyses were performed using LD score regression implemented in the LDSC software , representing common, physiologically related organ systems. Each category is defined as the union of functional regions in related tissue and cell types .GenoSkyline-Plus annotations for 66 selected tissue and cell types .The smoothing strategy for GenoCanyon improves its ability to identify general functionality in the human genome . GenoSkyA slightly different strategy was adopted when comparing the performance of different computation annotation tools. To make fair comparison, we dichotomized all annotation tracks using each score’s top 90% quantile calculated from SNPs with minor allele count greater than five in 1000 Genomes samples with European ancestry. We then followed the suggested protocol of LDSC and kept baseline annotations in the model while adding each annotation track one at a time.χ2 > 80), which leads to the removal of some SNPs in the APOE region in our analysis. Finally, we note that a recent method may potentially improve the heritability estimates based on LDSC [We calculated chromosome-by-chromosome heritability percentage through summing up and normalizing per-SNP heritability estimated using LD score regression and tier-3 annotation tracks. Of note, since only GWAS summary statistics were used as the input, popular heritability estimation tools such as GCTA  could no on LDSC .To evaluate enrichment of pleiotropic sites in the monocyte functional genome, we partition the genome into windows with length of 1M bases. Sex chromosomes and windows without SNPs are removed in our datasets. For each disease (i.e. AD and PD), we label a window 1 if the following criteria are met.There is at least one SNP with p-value < 1e-3 in the window.Among SNPs that meet condition 1, at least one is located in the monocyte-specific functional genome.Otherwise, the window is labeled 0. This labeling results in two binary vectors, one for each disease. A window marked as 1 for both AD and PD is a window of interest that suggests a possible association in monocytes-related DNA for both diseases in that region. We use a hypergeometric test to assess if such a pattern of local association appears more often than by chance. Windows marked as 1 for both diseases are subsequently curated to identify the association peaks that potentially have pleiotropic effects for AD and PD.There is a moderate overlap of control samples between IGAP AD GWAS and the PD GWAS . To account for the bias introduced by sample overlap and other confounding factors, we designed a permutation-based approach. In each permutation step, we shuffle the annotation status while keeping the total proportion of annotated regions, and then pick out windows that meet condition 2. We calculate the p-value through comparing the observed number of windows that meet conditions 1 and 2 for both diseases with the empirical distribution acquired in permutations.Of note, we also applied this approach using a window size of 500K bases. Results in all related tests remained similar.http://genocanyon.med.yale.edu/GenoSkyline). First, we identify three disjoint cases for SNPs in a given GWAS dataset.We briefly describe the SNP reprioritization approach implemented in the GenoWAP software available on our server .The SNP is in a genomic region that is functional for the given phenotype and tissue .The SNP is in a genomic region that is functional in the given tissue, but that region has no functionality for the phenotype (ZT = 0).The SNP is in a genomic region that is not functional in the given tissue . We can denote this probability using Bayes formula as follows:A useful metric for prioritizing SNPs is the conditional probability that the SNP is classified under case-1 given its p-value in the GWAS study, i.e. P(Case 3) = 1 − P(ZT = 1) can be directly identified using GenoSkyline-Plus scores. We partition all the SNPs into two subgroups based on a mean GenoSkyline-Plus score threshold of 0.1. Notably, these probabilities are not sensitive to changing threshold [f(p|Case 3) = f(p|ZT = 0) by applying a histogram approach on the SNP subgroup with low GenoSkyline-Plus scores.First, hreshold . In thisNext, we assume that SNPs that are functional in a tissue but not relevant to the phenotype will have the same p-value distribution to all other SNPs that are not relevant to the phenotype, which in turn behave similarly to SNPs that are not functional at all. We have previously demonstrated that this assumption is backed by empirical evidence . More fof(p|Z = 0) by using a similar approach to estimating f(p|ZT = 0), but partitioning SNPs using the general functionality GenoCanyon score instead of tissue-specific GenoSkyline-Plus score.We estimate the distribution ZT = 1) is the following mixture:Finally, all remaining terms in f(p|Case 2) has been estimated in f(p|Case 2) is assumed to follow a beta distribution, which guarantees a closed-form expression in the EM algorithm.Density function APOE region was removed in the SNP reprioritization analysis for LOAD.Notably, the http://web.pasteur-lille.fr/en/recherche/u744/igap/igap_download.php). We inferred z-scores from IGAP stage-II replication cohort using the following formula.Summary statistics from both IGAP stage-I GWAS and stage-I+II meta-analysis are publicly available . All annotation tracks can be visualized using UCSC genome browser. Web server g:Profiler was used to perform pathway enrichment analysis [GenoSkyline-Plus annotation tracks, tiers 1–3 LD score files, and scripts for generating GenoSkyline-Plus scores are freely available on the GenoSkyline server Click here for additional data file.S2 Fig(PNG)Click here for additional data file.S3 FigFor each locus, the upper and lower panels show associations for LOAD and PD, respectively. Monocyte functional regions are marked by red dots above gene names.(PNG)Click here for additional data file.S4 FigFor each locus, the upper and lower panels show associations for LOAD and PD, respectively. Monocyte functional regions are marked by red dots above gene names.(PNG)Click here for additional data file.S1 TableEpigenome ID and cell type list are acquired from Epigenomics Roadmap Project and ENCODE.(XLSX)Click here for additional data file.S2 Table(XLSX)Click here for additional data file.S3 Table(PDF)Click here for additional data file.S4 Table(XLSX)Click here for additional data file.S5 Table(XLSX)Click here for additional data file.S6 Table(XLSX)Click here for additional data file.S7 Table(XLSX)Click here for additional data file.S8 Table(XLSX)Click here for additional data file.S9 Table(PDF)Click here for additional data file.S10 TableSubscripts indicate stage-I discovery cohort, stage-II replication cohort, or stage-I+II meta-analysis. Each locus may contain multiple genes. SNP rs143560707 was not genotyped in the IGAP stage-II cohort.(XLSX)Click here for additional data file.


RADseq technology allows researchers to efficiently develop thousands of polymorphic loci across multiple individuals with little or no prior information on the genome. However, many questions remain about the biases inherent to this technology. Notably, sequence misalignments arising from paralogy may affect the development of single nucleotide polymorphism (SNP) markers and the estimation of genetic diversity. We evaluated the impact of putative paralog loci on genetic diversity estimation during the development of SNPs from a RADseq dataset for the nonmodel tree species Robinia pseudoacacia L. We sequenced nine genotypes and analyzed the frequency of putative paralogous RAD loci as a function of both the depth of coverage and the mismatch threshold allowed between loci. Putative paralogy was detected in a very variable number of loci, from 1% to more than 20%, with the depth of coverage having a major influence on the result. Putative paralogy artificially increased the observed degree of polymorphism and resulting estimates of diversity. The choice of the depth of coverage also affected diversity estimation and SNP validation: A low threshold decreased the chances of detecting minor alleles while a high threshold increased allelic dropout. SNP validation was better for the low threshold (4×) than for the high threshold (18×) we tested. Using the strategy developed here, we were able to validate more than 80% of the SNPs tested by means of individual genotyping, resulting in a readily usable set of 330 SNPs, suitable for use in population genetics applications.The  Seeds were disinfected, scratched with sandpaper to break dormancy, and placed in a growth chamber for germination. One seedling per population was grown in a greenhouse for 3 months. Its leaves were then harvested for DNA extraction, sequencing, and SNP validation by genotyping.2.2Solanum lycopersicum var. cerasiformae West Virginia 106) using a razor blade in a petri dish with 500 μl of Galbraith's nuclear‐isolation buffer, supplemented with 10 mmol/L sodium metabisulfite and 1% polyvinylpyrrolidone. The suspension was filtered through nylon mesh (pore size 50 μmol/L) and kept at 4°C. The nuclei were stained with 50 μg/ml propidium iodide after a 30‐min incubation with RNase A 10 U/ml. DNA contents of isolated nuclei were determined using a Partec CyFlow Space cytometer equipped with a 488‐nm laser and filter 630LP. The 2C DNA value was calculated using the linear relationship between the fluorescence signals from the first population of isolated stained nuclei of R. pseudoacacia studied species (IRobinia) and the known internal S. lycopersicum standard (ISolanum) according to the following equation: 2CRobinia = IRobinia/ISolanum × 2CSolanum.The ploidy of the sequenced plants was studied using two approaches. First, ploidy and DNA content were estimated for eight of the nine plants used . Leaves or roots were chopped together with an internal standard –400 bp was carried out by agarose gel electrophoresis and fragment extraction from the gel. Single‐end sequencing was performed on an Illumina v3 chip with the 1 × 150 bp format. The reduced representation libraries were around 2 Mb and the sequencing output per sample about 50 Mb, resulting in a mean depth of coverage of 20–30×, depending on sample considered.Genomic DNA extraction and RAD library preparation were carried out in the laboratories of Ecogenics . Genomic DNA was extracted from dried leaves with the Qiagen  plant extraction kit following the manufacturer's protocol. Sequencing was carried out, and alignments were obtained with a double‐digest RAD approach. Genomic DNA was digested with fastqc version 0.11.4 software  and the maximum distance in nucleotides allowed between stacks (M), see below for tested values.Data were then analyzed with the program denovo_map.pl version 1.28 executing the bwa software version 0.7.12  and varying values of the maximum distance in nucleotides allowed between stacks, M (from 2 to 8 with a step of 2) for the stacks software. For all the following analyses, M was fixed to four , and th2 the paraclean LRT p‐value threshold. The nucleotide diversity was assessed globally on the nine fasta format files  obtained with reads2snp using the alnpi program from the fast toolbox .Two methods were used to estimate the sequencing error rate and the nucleotide diversity at the entire sequence level. First, mlrho was also used to estimate the global sequencing error rate and the nucleotide diversity for each individual separately  and maximum (–M) length of reads were fixed at 1 and 100, respectively, with a step (–S) of one .The sequencing error rate and diversity were calculated with both methods, both with and without paralogous RAD loci, over an increasing range of minimal coverage depths (2×–20×). In addition, the mean coverage of RAD loci identified as paralogous or nonparalogous was estimated for each sequenced individual using 2.5.2reads2snp, with two contrasting minimal depths of coverage (4× and 18×), for both paralogous and nonparalogous RAD loci. For each depth of coverage, we calculated the numbers of monomorphic RAD loci, loci carrying one or two SNPs, and those carrying more than two SNPs, as well as the number of bi‐allelic and multiallelic SNPs and the mean number of SNPs per sequence both for paralogous and nonparalogous RAD loci, respectively. For paralogous RAD loci, we also estimated the number of loci containing at least one SNP not detected as putative paralog  and the proportion of “pass” SNPs among the total number of SNPs detected in these loci.Single nucleotide polymorphisms were identified with 2.5.3HO and HS), and the inbreeding coefficient (FIS) following unbiased formula as found in Nei , observed and expected heterozygosity . Clustering and genotype calling were performed automatically with MassArray TyperAnalyser 4.0.22 software, with the autocluster option, corrected, if necessary, by visual inspection. Each SNP was then classified on the basis of the number of clusters observed . As the profile of class C could correspond to a plastid marker, we carried out a blast analysis with sequences containing these SNPs on the Chloroplast Genome Database  for the MassArray iPLEX genotyping assay (carried out according to the manufacturer's instructions). Products were detected in a MassArray mass spectrophotometer (Sequenom), and data were acquired in real time with MassArray RT software. All experiments were performed at the Bordeaux Genome Transcriptome Platform . Twelve multiplexes were designed for a total of 377 SNPs with MassArray assay design 4.1 software (Sequenom) and screened (Table S2). Focussing on RAD loci for which strictly more than six samples were genotyped in silico, the SNPs were designed from the consensus sequences given by the reads2snp were compared with the genotypes obtained with MassArray using python scripts.The genotypes obtained in silico with HO, HS, and FIS following unbiased formula as found in Nei  obtained by flow cytometry is shown in Fig. S1. This histogram depicts a sharp peak and a low variance. The DNA peak ratio measured in the two different tissues (roots and leaves) of a single plant was constant. The eight cultivars of diploid The microsatellite analysis confirmed the ploidy level of the nine individuals used for the sequencing (Table S1), as each sample showed either one or two alleles for each of the 10 markers tested.3.2reads2snp were more dependent on the minimum depth of coverage than on the maximum distance in nucleotides allowed between stacks . For example, the number of loci was more than 70 times higher at m = 2 than at m = 20 (M = 2), but only 1.05 times higher at M = 2 than at M = 8 (m = 2). Similarly, the percentage of paralogy was more than 20 times higher at m = 20 than at m = 2 (M = 2), but only 1.12 times higher at M = 8 than at M = 2 (m = 2). For the following analyses, M was consequently set at 4, at a level for which we hypothesized that loci are neither too oversplit nor too merged. The impact of the minimum depth of coverage was, however, investigated in detail.Both the total number of RAD loci and the putative paralogy detected with 3.3Mean coverage was highly dependent on the minimal depth of coverage fixed for the analysis . It was also always higher for paralogous RAD loci than for nonparalogous ones (3.5 times higher at depth = 2 and 2.5 times higher at depth = 20).mlrho. When removing the effect of putative paralogy  than at 18× (20%). At 4×, only 17% of SNPs were detected on paralogous RAD loci, whereas at 18×, this proportion reached 66.5%. However, paralogous loci may contain at least one SNP not detected as paralogous by Higher levels of polymorphism were observed for 4× coverage than for 18× coverage . The test was, however, not significant at 18×. The depth of coverage also had a significant effect on distributions , when comparing the distributions at 4× and 18× for all loci, or when comparing the distributions at 4× and 18× excluding the paralogous loci.Figure FIS) is shown in Fig. FIS values of 0–0.05 and decreased the one with a low FIS value. The putative paralogy and the depth of coverage had both a significant effect on the FIS distributions, as revealed by significant Wilcoxon tests when comparing the distributions with and without paralogous or when comparing the distributions obtained at 4× and 18× .The impact of the removal of paralogous RAD loci on the inbreeding coefficient . A third of these SNPs showed a highly unbalanced allele frequency with the rare variant present in only one genotype. Across the 330 validated SNPs, more than 90% of the genotypes obtained in silico were on average similar to those obtained on MassArray analysis (Table S5). The validation of the genotypes read at 4× for SNPs detected at 18× was a bit lower (83%), and the percentage of absent data  was higher when the 18× threshold was considered. The percentage of validation was somewhat lower for some combinations of individual and threshold.The blast analysis carried out with the three sequences containing a class C SNP concluded that none of them resembled a chloroplast DNA sequence.3.7FIS .Minor allele frequency Fig. C and FIIS Fig. C were es44.1R. pseudoacacia. This hypothesis is congruent with the ancestral whole genome duplication events which occurred within the Fabaceae . Had we also included loci classified by the software as valid SNPs (“pass”), our estimate probably would have been somewhat lower. Nevertheless, this level of putative paralogy clearly exceeds previously reported values. Using the same approach, Gayral et al. , in line with but exceeding that of the depth of coverage. Consequently, neglecting paralogy when analyzing SNP data obtained with RADseq and mapped on a pseudo‐reference implies a major risk of bias in the estimates obtained . Although less, diversity estimates were also influenced by the depth of coverage. Lower theta values were observed at very low depths of coverage, in regardless of the presence or absence of paralogous RAD loci. This is because a low depth of coverage may lead to missing minor alleles, which would result in an underestimation of diversity. Finally, the small decrease in theta with increasing depth, when theta is estimated with reads2snp and mlrho) provide sequence‐based estimations of error rates, in a maximum‐likelihood framework  than at 18× (60%).4.3reads2snp to identify and exclude paralogous RAD loci. Consistent with our results, spurious SNPs due to putative paralogy can also be excluded by eliminating RAD loci with too many SNPs and markers deviating from Hardy–Weinberg equilibrium . Finally, paralogy should also be associated with overcoverage. In our study, the depth of coverage of paralogous RAD loci was roughly three times greater than that for nonparalogous. A strategy would therefore be to eliminate sequences with too high a coverage , Université de Liège  and European Community's Seventh Framework Programme (EU FP7) (Grant/Award Number: Trees4Future 284181).10.5061/dryad.qn4br.Raw sequencing data and quality reports are available on DRYAD at doi:None declared. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file.


The papers published in this Special Issue “SNP arrays” (Single Nucleotide Polymorphism Arrays) focus on several perspectives associated with arrays of this type. The range of papers vary from a case report to reviews, thereby targeting wider audiences working in this field. The research focus of SNP arrays is often human cancers but this Issue expands that focus to include areas such as rare conditions, animal breeding and bioinformatics tools. Given the limited scope, the spectrum of papers is nothing short of remarkable and even from a technical point of view these papers will contribute to the field at a general level. Three of the papers published in this Special Issue focus on the use of various SNP array approaches in the analysis of three different cancer types. Two of the papers concentrate on two very different rare conditions, applying the SNP arrays slightly differently. Finally, two other papers evaluate the use of the SNP arrays in the context of genetic analysis of livestock. The findings reported in these papers help to close gaps in the current literature and also to give guidelines for future applications of SNP arrays. Single Nucleotide Polymorphism (SNP) microarrays are coming to an age as the first papers describing these arrays were published approximately 20 years ago. Since then developments in technology have made screening faster, with more coverage and lower economical cost . SNP mictumor protein p53 (TP53) and cyclin-dependent kinase inhibitors 2A/2B (CDKN2A/CDKN2B), which seem to be amongst the most common targets. This review also evaluated the current role of SNP arrays in regards to fluorescent in situ hybridization (FISH), NGS (Next Generation Sequencing) and comparative genomic hybridization (CGH). One of the advantages of SNP arrays is still the cost in comparison to NGS-based approaches. Three of the papers published in this Special Issue focus on the use of various SNP array approaches in the analysis of three different cancer types ,4,5. EteTP53 in general and the link between Janus kinase 2 (JAK2) V617F mutations in myeloproliferative neoplasms are well known, but the information provided gives an excellent overview of the specifics of expected alterations, suggesting clearly different pathways already from the initiation of these neoplasms. The authors also propose a diagnostic regime, including SNP array screening for patients with hematopoietic malignancies.The paper by Song et al. summarizes the SNP arrays in regard to hematopoietic malignancies in general, but with emphasis on the clinical variants which are of clinical significance . This reTP53 and Breast Cancer 1/2 (BRCA1/BRCA2). However, there seems to be a high variation and low reproducibility between studies of these areas. The authors of this review call for more detailed studies of the breakpoints using higher resolution technologies, such as NGS.Walker et al. provided an update on the current knowledge regarding the role of constitutional CNV in breast cancer . The rolSACS gene was discovered. This gene is on human chromosome 13 and was known as spastic ataxia of Charlevoix-Saguenay (sacsin); however, currently the approved full name is sacsin molecular chaperone. Both siblings were homozygous for this variant, demonstrating the effectiveness of this approach for identifying the underlying genetic phenomena in disorders like this. Another paper in this Special Issue also demonstrated the use of SNP microarrays in analyzing a rare condition, namely split-hand/foot malformation (SHFM) [beta-transducin repeat containing (BTRC) gene (at 10q24.32) to be critical for the split-hand/foot malformation type 3 (SHFM3) phenotype. High-density SNP analysis combined with Whole Exome Sequencing (WES) can be a powerful tool when looking for individual sequence variants. Nickerson et al. used this approach to analyze patients with a complex neurodegenerative disorder called autosomal recessive cerebellar ataxia (ARCA) . They sen (SHFM) . This coAs SNP arrays have become more affordable, a constantly growing amount of data has been generated from species other than human. However, due to variability of typing platforms, allele calling and file formats, the integration of information from various sources has been an obstacle for biological scientists, especially those without advanced bioinformatics skills. In this Special Issue of SNP microarrays, Nicolazzi et al. have concentrated on this problem and present an open source, multi-platform called the SNPConvert suite . BecauseThis selection of research papers in this Special Issue focuses on a variety of research areas linked to SNP arrays. These range from bioinformatics of cattle genetics to common human cancer. This Special Issue also gives insight into the diverse use of SNP microarrays. For example, how the SNP arrays can be utilized in tandem with WES, Lab-On-A-Chip and NGS, which are common methods currently used in systematic analysis of genetic alterations in both humans and cattle. Also, as a refined and accurate analysis of the data generated is crucial for the outcome, within this Issue, guidelines are provided for bioinformatics associated with these processes, together with open source analysis tools. In summary, the collection of these seven papers emphasizes the importance of understanding various aspects in the use of SNP arrays, with examples from a range of mammalian applications. These reports should also prove to be an asset for anyone intending to use SNP microarrays in the future.