CDC123, ADCY5, KLHDC5) the combination of fine-mapping genetic data and chromatin state enrichment maps, supplemented by allelic imbalance in chromatin accessibility pinpointed likely causal variants. The combination of increasingly-precise genetic and islet epigenomic information accelerates definition of causal mechanisms implicated in T2D pathogenesis.Human genetic studies have emphasised the dominant contribution of pancreatic islet dysfunction to development of Type 2 Diabetes (T2D). However, limited annotation of the islet epigenome has constrained efforts to define the molecular mechanisms mediating the, largely regulatory, signals revealed by Genome-Wide Association Studies (GWAS). We characterised patterns of chromatin accessibility  and DNA methylation  in human islets, generating high-resolution chromatin state maps through integration with established ChIP-seq marks. We found enrichment of GWAS signals for T2D and fasting glucose was concentrated in subsets of islet enhancers characterised by open chromatin and hypomethylation, with the former annotation predominant. At several loci (including  T2D is a complex disease characterised by insulin resistance and reduced beta cell function. Recent GWAS have identified a large number of T2D susceptibility loci , the majCharacterisation of the islet regulome has until now been limited in scope. The use of DNA methylation and open chromatin data to further annotate ChIP-seq derived chromatin states has successfully uncovered novel biology for other diseases . ExistinMost importantly, in part due to historical challenges in accessing human islet material or authentic human cellular models, reference annotations of the islet epigenome and transcriptome  have been largely absent. It is worth noting that islets constitute only ~1% of the pancreas, and islet epigenomes and transcriptomes cannot therefore be reliably assayed in analyses involving the entire organ. Previous islet epigenome studies have, therefore, had only limited ability to directly relate genetic variation to regulatory performance or to broadly characterise the role of DNA methylation in these processes.In this study, we set out to expand upon previous studies of the islet regulome in several ways. First, we explored the human islet methylome in unprecedented depth using Whole-Genome Bisulphite Sequencing (WGBS) applied to a set of 10 human islet preparations. Second, we explored both basal and genotype-dependent variation in chromatin accessibility through ATAC-seq in 17 human islet samples. Third, we integrated these genome-wide data with existing islet regulatory annotations to generate a high-resolution, epigenome map of this key tissue. Finally, we used this detailed map to interpret GWAS signals for T2D (and the related trait of fasting glucose) and deduce the molecular mechanisms through which some of these loci operate.To characterise the human islet methylome and characterise the role of DNA methylation with respect to T2D genetic risk, we performed WGBS (mean coverage 13X) in human pancreatic islet DNA samples isolated from 10 non-diabetic cadaveric donors of European descent. Methylation levels across the genome were highly correlated across individual donors  had use−16)  and weak enhancers , selected primarily because they are located near gene promoters and CpG-island regions. The focus of the 450 k array on these regions, which tend to be less variable in terms of methylation, explains the high 450 k array correlation levels between donors. In addition, this selective design results in marked differences in the distributions of genome-wide methylation values between WGBS and the 450 k array. Whilst the WGBS data revealed the expected pattern of widespread high methylation levels with hypomethylation (<50%) restricted to 11.2% (2.6M/23.3M CpG sites) of the genome, the array disproportionately interrogated those hypomethylated sites  (Kolmogorov–Smirnov (KS) test for difference, D = 0.40, p<2.2×10−16) . These d−16)   . We founIn terms of coverage, most chromatin states, apart from promoters, were poorly represented by CpG sites directly interrogated by the array: for example the array assayed only ~2.9% of CpG sites in strong enhancer states , CpG sites covered by the 450 k array interrogated less than 1% of the PPA of associated variants (vs. 99% captured by WGBS); the figure for the 850 k array would be 23%  and TFBS   and isle2FE = 1.0 and promoter-like UMRs log2FE = 1.1, 2FE = 0.4–1.0), but still significant (Bonferroni p<0.05) levels of enrichment with respect to all other islet regulatory annotations . In addition, our islet ATAC-Seq sites demonstrated substantial overlap with LMRs: 53% of LMRs overlapped 16% of all ATAC-seq peaks , transcribed and genic regions (H3K36me3), strong enhancers , weak enhancers (H3K4me1), insulators (CTCF) and repressed elements (H3K27me) . The addTo understand the relationship of these various state definitions to genetic variants influencing T2D risk, we applied the hierarchical modelling approach FGWAS  to the sIn single-feature analyses, we found significant enrichment  >0) limited to four enhancer states  , Table 12FE = 0.9, CI = −2.5 to 2.0, 2FE = 4.1, CI = 3.3 to 4.8 vs. closed strong enhancer joint log2FE = 2.4, CI = 0.5 to 3.3, We observed markedly different levels of enrichment for T2D association between and within open and closed enhancer states , Table 1Hypomethylation and open chromatin are highly correlated, but the observed difference in T2D enrichment between the weak enhancer states (particularly between ‘lowly-methylated’ and ‘open’ which differ markedly with respect to chromatin status) points to a primary role for open chromatin. To test this further, we regenerated chromatin state maps using different subsets of the data  for T2D association signals consistent with their co-localisation with distal regulatory elements, and displayed modestly increased enrichment compared to enhancer states derived from ChIP-seq alone  or PMDs . We also found no evidence that recently-described regions of T2D-associated differential methylation  were enriched for genome-wide T2D association signals   sequence into enhancer-like LMRs , promoter-like UMRs (7.5% of hypomethylated sequence), as well as PMDs (61% of hypomethylated sequence). The remaining 25% of hypomethylated sequence did not fit any category. LMRs were significantly (lower CI limit >0) enriched  .Finally, since the hypomethylation signal for T2D enrichment was concentrated in LMRs , we reraFGWAS analysis for FG corroborated the observations from T2D analysis. Despite reduced power of the FG GWAS data due to a lower number of significantly associated FG GWAS loci, both single feature and joint-model analyses of human islet epigenome data found significant enrichment in strong enhancer states with the strongest enrichment in enhancers with open chromatin and hypomethylation . In addiOverall, these analyses demonstrate that the addition of open chromatin and DNA methylation data to ChIP-seq marks enhances the resolution of regulatory annotation for human islets. In particular, it defines subsets of weak and strong enhancers that differ markedly with respect to the impact of genetic variation on T2D risk. Although DNA accessibility and hypomethylation status are strongly correlated and provide broadly similar enrichments, the effects of the former predominate. In line with the dominance of open chromatin status for T2D GWAS enrichment, we observed that T2D risk in relation to methylation status is primarily invested in hypomethylated LMRs (i.e. enhancers) rather than UMRs, dDMRs or PMDs.We next deployed the insights from the global FGWAS enrichment analyses to define the molecular mechanisms at individual T2D susceptibility loci, refining T2D causal variant localisation using the combination of genetic data (from fine-mapping) and the genome-wide patterns of epigenomic enrichment.−8, see Materials and methods for details). These corresponded to 49 known T2D associated regions representing that subset of the ~120 known T2D GWAS loci which passed those significance/filtering criteria in this European-only dataset. We then calculated reweighted PPAs for each variant within each segment and generated reweighted 99% credible sets. .Specifically, we applied FGWAS to the T2D DIAGRAM GWAS data  under thConsistent with the increased T2D GWAS enrichment of states including open chromatin and DNA methylation information, we found that analyses using enrichments from the augmented chromatin state model  were associated with smaller 99% credible sets (median of 17 SNPs) than those derived from FGWAS enrichment derived from ChIP-seq data alone (median 23). In parallel, the PPA for the best variant per locus increased (median 0.39 vs 0.31). Individual T2D GWAS locus results are shown in A subset of T2D GWAS signals are known to influence T2D risk through a primary effect on insulin secretion, whilst others act primarily through insulin resistance. We used previous categorisations of T2D GWAS loci based on the patterns of association with quantitative measurements of metabolic function and anthropometry , to defiCDC123 and CAMK1D, rs10842991 near KLHDC5 and rs11708067 at ADCY5)  a reweighted variant PPA >= 10% and (b) overlap with an enriched regulatory state within the FGWAS T2D joint-model . Of thest ADCY5) . All thrCDC123/CAMK1D locus, overlaps an ‘open strong enhancer’ region  at the ’ region  and the n islets . In expen islets , the T2DKLHDC5, no clear causal variant emerged based on genetic fine-mapping data alone as the credible set contained 23 variants in high mutual LD  to evaluate the interactions of 12 fragments covering the lead SNP rs11708067 and 15 SNPs in high LD (r2 >0.8) across a region of 47 kb. After adjusting for multiple testing using FDR correction, only two fragments yielded a significant normalised read number over background. This included the open-chromatin overlapping fragment containing rs11708067 and another fragment harbouring rs2877716, rs6798189, rs56371916 . We targeted the 11708067 . To resome peakC  , and liA key challenge in the quest to describe the molecular mechanisms through which GWAS signals influence traits of interest, involves the identification of the causal variants responsible and, given that most lie in non-coding sequence, the characterisation of the regulatory elements which they perturb. This underpins efforts to define the effector genes through which these variants operate and to reconstruct the biological networks that are central to disease pathogenesis.Genetic and physiological studies have highlighted the singular importance of pancreatic islet dysfunction in type 2 diabetes, but epigenomic characterisation of this tissue has been limited in large-scale community projects such as ENCODE and GTEx. The present study seeks to address this deficit by describing, in unprecedented detail, genome-wide patterns of methylation and chromatin accessibility in human islet material. We have combined these data with existing islet epigenomic marks to generate a refined regulatory map which, based on the evidence of improved enrichment for T2D association signals, offers more granular annotation of functional impact.ADCY5, CDC123 and KLHDC5.Our data show that, for DNA methylation, the signal of T2D predisposition is primarily associated with enhancer-like LMRs rather than other categories of methylation elements including UMRs, dDMRs or PMDs. We highlight the strong correlation between islet methylation status and chromatin accessibility but demonstrate that open chromatin predominantly contributes to defining the regulatory impact associated with genetic T2D risk. Finally, we demonstrate how these enhanced epigenomic annotations, when analysed in concert with genetic fine-mapping data and information from allelic imbalance in chromatin accessibility allow us to home in on likely causal variants at T2D association signals such as those near CDC123  variants on a single risk haplotype which jointly impact the phenotype. Analysis methods that combine functional fine-mapping with conditional analysis and consider LD and haplotype patterns are likely to provide a more complete overview of the causal interactions at T2D GWAS loci.KLHDC5 rs10842991 variant through electrophoretic mobility shift assays. Our ongoing research efforts are now concentrated on improving the fine-mapping analysis and expanding these genomic enrichment analyses in larger numbers of human islet samples from healthy and diabetic islets. By coupling the integration of these data with empirical functional studies, we expect to provide an increasingly complete description of the causal interactions between DNA methylation, chromatin state, RNA expression and T2D susceptibility.In addition, while the present study characterises islet epigenome status and variability in chromatin accessibility in substantially larger numbers of islet samples than those previously reported , the numHuman islets were retrieved from deceased Caucasian non-diabetic donors from the Oxford DRWF Human Islet Isolation Facility (n = 34) and at the Alberta Diabetes Institute in Edmonton in Canada (n = 10). For the analysis only samples with a purity >70% were used as determined by dithizone labeling. The Human Research Ethics Board at the University of Alberta (Pro00001754), the University of Oxford's Oxford Tropical Research Ethics Committee (OxTREC Reference: 2–15), or the Oxfordshire Regional Ethics Committee B (REC reference: 09/H0605/2) approved the studies. All organ donors provided informed consent for use of pancreatic tissue in research.For all WGBS (n = 10) and a subset of 450 k array samples (n = 18) human pancreatic islet DNA was extracted from 100,000 to 150,000 islet cells using Trizol- as described previously . For theHuman pancreatic islets preparations (n = 18) were retrieved from 17 deceased non-diabetic donors of European descent from the Oxford DRWF Human Islet Isolation Facility and stored for 1–3 days in CMRL or UW media. The latter were reactivated in CMRL for 1 hr before processing them further. Approximately 50,000 islet cells per sample were hand-picked and immediately processed for ATAC-seq as described previously , however400 ng of DNA per human islet samples (n = 10) were sent as part of a collaborative effort to the Blizard Institute, Queen Mary University, London, UK and bisulphite- converted using the Ovation Ultralow Methyl-Seq DR Multiplex System 1–8  and purified using Agencourt AMPure beads (Beckman Coulter) as described previously .SCR_005604)(SCR_002105) in order to increase mapping efficiency. Coverage for the merged paired-end and realigned HiSeq read alignments was estimated for the human mappable genome  to obtain high-coverage read data. The obtained reads were trimmed using a customized python3 script (10 bp at the start and 15 bp at the end) and aligned to hg19 using the software Bismark . SpecifiR_005604) (versiond Encode ) using bCpG methylation levels were determined for each sample by calculating the ratio of unmodified C (methylated) and bisulphite converted T (unmethylated) alleles using BiFAST (first described here ). High-pSCR_006513) (Regulatory regions were identified using the R package methylseek (RRID:_006513) . After r_006513) , LMRs (<_006513) .TM Kit  and hybridised to the Illumina 450 k array and scanned with iScan (Illumina) according to the manufacturer’s protocol.In total, 41 samples were processed for the Illumina Infinium HumanMethylation450 BeadChip . Of these 18 samples were bisulphite-converted and processed as part of a collaboration at the UCL Cancer Institute, University College London, London, UK while the remaining 23 samples were processed in OCDEM, University of Oxford, Oxford, UK. The DNA was bisulphite converted using the EZ DNA MethylationSCR_012830) (SCR_001905). Specifically, CpG sites with a detection p-value>0.01 were removed from the analysis and samples with >5% of CpG sites failing this threshold (n = 9) were also removed from the analysis.The resulting data was analysed using the Package minfi (RRID:_012830)  and custFollowing separate quantile normalisation of signal intensities derived from methylated and unmethylated Type I probes and Type II probes, methylation levels (ß) were estimated, based on the intensities of the methylated (M) and unmethylated (U) signal in the following way: β = M/(M + U + 100). To correct for batch effects the ComBat function implemented in the sva  package ATAC-seq libraries were sequenced at the High-Throughput Genomics group which is part of the Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK. Samples were sequenced as 4-6plex libraries across 1–3 Hiseq2500 lanes with 50 bp paired-end read length.http://userweb.molbiol.ox.ac.uk/public/telenius/PipeSite.html). Specifically, library and sequencing quality was checked with FASTQC (RRID:SCR_014583) (http://www.bioinformatics.babraham.ac.uk/projects/fastqc) and reads were mapped to the human genome (hg19) via bowtie (SCR_005476) with default settings but -m 2, and maxins 2000 which allows mapping of reads with a maximum number of 2 alignments and a maximum insert size of 2000 bp. For reads that could not be aligned the first time, adapters were removed with Trim Galore at the three prime end  to enhance the chance of mapping. The resulting trimmed reads were then mapped again with bowtie. Any remaining unmapped and trimmed reads were processed with FLASH  which combines overlapping read pairs and reconstructs read pairs without overlap. These are then realigned a third time using bowtie. PCR duplicates are then removed from the mapped bam files using samtools rmdup function . The resulting reads were converted into bed format using the bedtools bamToBed function  and extended by 200 bp towards the 3’ end to account for fragment size.Human islet ChIP-seq histone mark and TFBS data were obtained from various sources: H3K4me1,  H3K27ac , H3K36me H3K27ac  and H3K2 H3K27ac . Availabfunction   and whole-genome CpG methylation status (hypermethylation/hypomethylation based on a threshold of 60% methylation) were binarised across 200 bp windows of the genome.These binarised 200 bp ChIP-seq, ATAC-seq and DNA methylation maps were combined and used to generate 3 sets of chromatin states derived from ChIP and DNA methylation data (ChIP +Meth), ChIP and ATAC-seq data (ChIP +ATAC) or ChIP, ATAC-seq and DNA methylation data (ChIP +ATAC + Meth) using the Learnmodel ChromHMM function . As suggADCY5 promoter in EndoC-ßH1 (RRID:CVCL_L909) cell lines (n = 3) .ADCY5 promoter and enriched using streptavidin bead pulldown. PCR amplified fragments were then sequenced through paired-end sequencing (Illumina Next-Seq). An in silico restriction enzyme digestion was performed on the set of reconstructed fragments (from paired-end sequenced reads) using the DpnII2E.pl script (https://github.com/Hughes-Genome-Group/captureC). Uncaptured reads and PCR duplicates were removed prior to mapping to the human genome (hg19) with Bowtie (ADCY5 promoter) or ‘reporter’ (outside the capture fragment on exclusion region) was performed with the CCanalyser2.pl wrapper (https://github.com/Hughes-Genome-Group/captureC). Unique mapped interactions were normalized to the total number of cis interactions (i.e. same chromosome) per 100,000 interactions. Significant chromatin interactions were determined from a rank-sum test implemented in the program peakC (https://github.com/deWitLab/peakC). Specifically, we evaluated interactions involving all SNPs in high LD (r2 >0.8) with the lead rs11708067. The lead variant (rs11708067) was in high LD with 15 SNPs (mapping to 12 DpnII fragments) that spanned a region of 47 kb. We applied the Benjamini-Hochberg correction to control the false discovery rate for the set of p-values corresponding to each restriction fragment within the 47 kb region at the ADCY5 locus.In brief, chromatin conformation capture (3C) libraries were generated by formaldehyde fixation prior to DpnII restriction enzyme digestion and subsequent DNA ligation. Following cross-link reversal, DNA extraction and sonication, sequencing adapters were added to sonicated fragments (~200 bp). Library fragments were subjected to a double capture through hybridisation with a biotinylated oligonucleotide probes encompassing the l script (https://h Bowtie (v 1.1.0) wrapper  and the data was analysed in the same way as described above for human islet samples.Endo-βH1 cells were obtained from Endocells and have been previously authenticated . In addiFOXA2, MAFB, NKX2.2, NKX6.1, PDX1), T2D-associated islet dDMRs .In addition, we also obtained the 850 k array manifest file to determine overlap of 850 k array CpG sites with GWAS credible set regions  and ATAC-seq open chromatin peaks (result section 2.3) in the aforementioned genomic annotations (method section 4.6) was determined through 100,000 random permutations. P-values and fold enrichment was determined by comparing the true overlap results to the permuted overlap results. The resulting P-values were multiple testing corrected using Bonferroni correction .FGWAS   and ENGAGE GWAS SNP summary level data.For each individual annotation, the model provided maximum likelihood enrichment parameters and annotations were considered as significantly enriched if the parameter estimate and 95% CI was above zero.To determine the maximum likelihood model the following approach suggested by  was usedInitially, similar enhancer chromatin states derived from the four different ChromHMM analyses  were compared. Similarity was determined based on shared histone chromatin marks according to the chromHMM emission parameters. Further comparisons between the ChIP-only and ChIP + ATAC + Meth model were performed based on the reweighted FGWAS maximum variant PPA and the number of reweighted 99% credible set variants per T2D locus (for details regarding FGWAS PPA see next section).However, considering that the chromatin states were derived from distinct sets of annotations across different analyses of ChromHMM, a direct comparison was not fully possible. Hence, a nested model approach was used to further dissect the contribution of open chromatin and DNA methylation to the enrichment. Specifically, an FGWAS analysis was performed that combined the ChIP-only chromHMM states with raw LMRs (representing DNA methylation) and ATAC-seq peaks (representing open chromatin). After determining the best maximum-likelihood cross-validation model  a nested model and log-likelihood ratio test were used to determine the contribution of each annotation to the model .The enrichment priors derived from the FGWAS maximum likelihood model were used as a basis for evaluating both the significance and functional impact of associated variants in GWAS regions; allowing variants that map into annotations that show global enrichment to be afforded extra weight.−8) or a regional FGWAS PPA >= 90% . The latter threshold is based on a recommendation from  and overlapping open enhancer states were prioritised for further follow-up. Genome-wide significance of loci was determined based on P-values  were tested for allelic imbalance.To avoid read-mapping and reference allele bias the software WASP Version   was applied to identify TF motifs that significantly (FDR < 0.05) matched the sequence overlapping a SNP variant showing allelic imbalance (20 bp up and downstream).The tool ‘Fimo’ implemenOverlap between genomic regulatory regions was performed using bedtools intersectBed function   was performed using R (version 3.0.2) including Spearman’s correlation analysis to compare the 450 k and WGBS array, the KS-test to compare 450 k and WGBS DNA methylation distributions, the binomial test to evaluate allelic imbalance and principal component analysis to identify batch effects in the 450 k data. Significance is defined as p<0.05 unless otherwise stated.All figures unless otherwise stated were generated using R (version 3.0.2) and/or ggplot2. Figure https://ega-archive.org/) and is accessible via the EGA accession numbers: EGAS00001002592, EGAD00001003946 and EGAD00001003947.ATAC-seq and WGBS sequencing data has been deposited at the EBI hosted European Genome-phenome Archive  The data presented starting in 2) Causal GWAS variants can be enriched in regions with other functional annotations, such as coding regions or regions with strong evolutionary conservation. Such regions have not been considered here. The authors chose to reweight their PPAs solely using chromatin data, but this would down-weight causal variants with other functional annotations in the same region.3) How much have the segmentations from the HMM improved with the additional data generated (without using GWAS enrichment as the benchmark)? It appears as though FGWAS enrichments for the actual annotations themselves shown in  are just4) Both the Bayes Factor and FGWAS approaches rely on the assumption that at most one SNP in each region is causal, which may not always be true. This limitation should be discussed.5) The addition of the Capture-C data in 6) Given the known advantages of Stratified LD Score Regression  over FGWAS to uncover true enrichments, have the authors considered using this approach?7) What proportion of the narrow sense heritability is captured by the PPA variants?8) The nomenclature used to define individual loci emerging from GWAS has followed the historical approach of using the closest gene to the associated SNPs. It has recently been recognized that this is often an imprecise approximation, as pointed out by reviewer 1. While there is no expectation that the authors would come up with a solution to this nomenclature conundrum, it was felt that the authors might address this issue on the paper, highlighting that the gene names associated with each locus may not accurately reflect the gene target of the association.9) It was also noted that, despite the depth and complexity of the analysis carried out in this work, key data are missing which would greatly enhance the value of this report. An example is provided by 10) Finally, we would suggest that the authors consider bringing up the possibility that the association signal of several of the GWAS loci analyzed, including the ones highlighted in this work , might result not from the phenotypic impact of a single causal variant, but rather by the combined effects of multiple variants within the associated haplotype. This is certainly a topic that will become more obvious as we perform more detailed and high throughput computational and experimental analysis of GWAS loci, and it would be important for this work to come out already pondering about this possibility. Below is a summary of essential points to be addressed:1) The data presented starting in Since, in the original analysis, only 1000 permutations had been performed, the lowest empirical P-value (0.001) did not lend itself to Bonferroni correction for the number of tests performed. Given the reviewers’ comment, we have increased the number of permutations to 100k to be able to estimate a more accurate empirical P-value and then applied a Bonferroni correction. This had no relevant impact on the findings since the empirical P-values calculated for most annotations were still less than 1/100,000 . All annotations that were significant using an FDR <0.05 remained significant using the Bonferroni correction (adjusted P<0.05) and the newly calculated enrichment estimates were in the same range as the original ones.As requested we also represented the P-values more appropriately by providing the P-values in We updated the results in section 2.2 and modified the Materials and methods section 4.9.1 as well as 2) Causal GWAS variants can be enriched in regions with other functional annotations, such as coding regions or regions with strong evolutionary conservation. Such regions have not been considered here. The authors chose to reweight their PPAs solely using chromatin data, but this would down-weight causal variants with other functional annotations in the same region.We agree with the reviewers’ comments that it is important to include a wide range of functional annotations. In fact, and contrary to the reviewer comments, we had already included coding regions in the original FGWAS analysis.In response to the reviewers’ suggestion, we have now repeated the analyses adding in conserved region annotation. We used conserved sequence annotations from Lindblad-Toh et al., 2011 and updated the results of the paper accordingly.not retained presumably because the signal arising from conserved sequence was adequately captured by the chromatin state and coding annotations. Therefore, the addition of conserved sequence annotation does not change the major findings and conclusions.Consistent with previous observations  we found that conserved sequence was significantly enriched for T2D association in the single-state analysis. However, in the joint model, conserved sequence annotation was To include the updated analysis results the manuscript has been revised as follows: We included the updated results in 3) How much have the segmentations from the HMM improved with the additional data generated (without using GWAS enrichment as the benchmark)? It appears as though FGWAS enrichments for the actual annotations themselves shown in  are justWhile high levels of enrichment do not necessarily reflect high levels of sensitivity across a large number of GWAS loci, we agree that this is an interesting question. To compare the effect of different annotations to the baseline model, we have generated the requested metrics (median number and distribution of 99% credible set SNPs and median top variant PPA and top variant PPA distributions) for the following datasets as suggested:- ChIP-only- ChIP+Meth- ChIP+ATAC- ChIP+ATAC+Meth- ATAC-only- LMR-onlyAs shown in the new reduction in top variant PPA compared to the baseline ChIP-only model. Credible set size increased when only DNA methylation (LMR-only) was included in the FGWAS fine-mapping analysis.In contrast, DNA methylation did not improve fine-mapping resolution: DNA methylation on its own (LMR-only) or in combination with ChIP-seq data, actually led to a Despite the opposing effects of ATAC-seq and DNA methylation, the combination of ChIP-seq, ATAC-seq and DNA methylation (ChIP+ATAC+Meth) also performed well. The ChIP+ATAC+Meth model showed the best results in terms of median credible set size, identified a higher number of significant segments/loci and was almost identical in performance to the ChIP+ATAC dataset in terms of median maximum variant PPA.Overall these results indicate that the chromatin accessibility data provided by ATAC-Seq (either in its own or in combination with islet ChIP-seq annotations) has the most value for refining fine-mapping and homing in on likely causal variants at T2D GWAS loci.These results are highly consistent with our original analysis described in section 2.3, which showed that the effect of open chromatin on genetic T2D risk predominates over the effects of DNA methylation. The additional results are shown in the new and updated “We also expanded the FGWAS PPA analysis to investigate open chromatin and DNA methylation effects on fine-mapping and found that the reduction in 99% credible set size and increase in maximum variant PPA was driven predominantly by open chromatin . This de4) Both the Bayes Factor and FGWAS approaches rely on the assumption that at most one SNP in each region is causal, which may not always be true. This limitation should be discussed.We agree with the reviewer. We have been working to implement additional analyses steps to circumvent this limitation. Specifically, we have been testing an adaptation to FGWAS that allows us to analyse regions with multiple signals, after conditional decomposition. This is something we hope to deploy in future analyses, but it is not yet ready for inclusion in this paper. Meantime, and in line with reviewers’ comment 10, we have included the following paragraph in the Discussion highlighting this limitation of FGWAS and how future studies could address this:“Although we provide highly detailed functional fine-mapping of T2D genetic variants to uncover causal variants, the FGWAS approach applied in this study is limited in its ability to determine the effect of multiple variants at individual loci. […] Analysis methods that combine functional fine-mapping with conditional analysis and consider LD and haplotype patterns are likely to provide a more complete overview of the causal interactions at T2D GWAS loci.”5) The addition of the Capture-C data in ADCY5 locus, the capture-C data we present demonstrate physical approximation between rs11708067 and the ADCY5 promoter in an appropriate cell type: this provides additional evidence (to be considered in tandem with other complementary data) to support their relationship. At ADCY5, we are able to push the interpretation a little further in terms of resolving the causal variant because rs11708067 is the sole variant in the enhancer region that shows this physical approximation to the promoter.This is correct. In our experience using Capture-C, it is typical that promoter “baits” capture multiple (enhancer) sequences in the adjacent region. This is not unexpected given the strong correlation of functional activity within adjacent sequence (as reflected in TAD structure for example). We agree, therefore, that it is generally not possible to use capture-C in isolation to definitively resolve the mechanistic interactions at a GWAS signal. At the ADCY5 region which contain rs11708067 or other close T2D-associated proxies. In this analysis, only two fragments (one of which contains rs11708067) are called as “peaks” on the basis of significant read number over background.To support more rigorous assessments of Capture-C data, we have been implementing a method for quantitative detection of Capture-C peaks which takes into account the background distribution of interaction “noise”. We have quantified the evidence for interaction for a total of 12 fragments across the These updated results are shown in the newly added “To resolve the significance of the interaction between the restriction fragment encompassing rs11708067 and the ADCY5 promoter, we used the programme peakC  (https://github.com/deWitLab/peakC) to evaluate the interactions of 12 fragments covering the lead SNP rs11708067 and 15 SNPs in high LD (r2 > 0.8) across a region of 47kb. […] These SNPs fall into a region that did not show evidence of open chromatin.”In addition, we also added the following paragraph to the Materials and methods section to include the additional analysis:“Significant chromatin interactions were determined from a rank-sum test implemented in the program peakC (https://github.com/deWitLab/peakC). […] We applied the Benjamini-Hochberg correction to control the false discovery rate for the set of p-values corresponding to each restriction fragment within the 47kb region at the ADCY5 locus.”6) Given the known advantages of Stratified LD Score Regression  over FGWAS to uncover true enrichments, have the authors considered using this approach?We agree with the authors that Stratified LD Score Regression could be a valuable method to run on our dataset, in particular, since it can include multiple causal signals at a locus. However, after considering the use of multiple methods  we ultimately decided to use FGWAS for the following reasons:- The FGWAS framework provides a stringent and straightforward way to determine a maximum likelihood model with multiple annotations using both forward and reverse mode selection.- The framework provides a simple way of calculating updated posterior probabilities based on the enrichment priors of the best maximum likelihood annotation model. Importantly, the joint maximum likelihood model provides relative enrichment estimates for each category that allow us to up-weight the PPA of variants overlapping a given annotation in a proportional way dependent on the annotation.- FGWAS is not dependent on reference datasets for LD-scores and can use all variants tested in the 1000G imputed DIAGRAM dataset including low frequency and rare variants.- The observed enrichment patterns (highest enrichment for active enhancer states) are highly consistent with previous studies .We will continue to review these different approaches and will seek to implement other methods – such as LD score regression – in future work.7) What proportion of the narrow sense heritability is captured by the PPA variants?We have estimated the narrow sense heritability for T2D using UK Biobank data from ~350k individuals. To avoid inflation of the heritability estimates by variants in high LD, we restricted the analysis to the (single) top PPA variant for each of the 52 segments that were significantly associated with T2D genome-wide. The narrow sense heritability for T2D was estimated at 1.8%. To provide a reference point, we also estimated that all currently published T2D GWAS variants  explain 5.7% of variation in T2D risk. We have also used the top associated FG variants at 20 genome-wide significant segments to calculate narrow sense heritability estimates for FG, using Oxford BioBank data from 3.6k individuals. The narrow sense heritability for FG was estimated at 1.9%.8) The nomenclature used to define individual loci emerging from GWAS has followed the historical approach of using the closest gene to the associated SNPs. It has recently been recognized that this is often an imprecise approximation, as pointed out by reviewer 1. While there is no expectation that the authors would come up with a solution to this nomenclature conundrum, it was felt that the authors might address this issue on the paper, highlighting that the gene names associated with each locus may not accurately reflect the gene target of the association.We agree! We have added the following sentence:“.”9) It was also noted that, despite the depth and complexity of the analysis carried out in this work, key data are missing which would greatly enhance the value of this report. An example is provided by - As requested we have created an additional table  that pro- Per variant, the following T2D/FGWAS information is provided: rsID; FGWAS PPA; T2D GWAS P-value; FGWAS segment number; T2D locus name; tested for allelic imbalance (Yes/No).- If available, the following eQTL information  is also provided:eQTL allele1 (effector), eQTL allele 2, eQTL q-value, eQTL effect and eQTL gene10) Finally, we would suggest that the authors consider bringing up the possibility that the association signal of several of the GWAS loci analyzed, including the ones highlighted in this work , might result not from the phenotypic impact of a single causal variant, but rather by the combined effects of multiple variants within the associated haplotype. This is certainly a topic that will become more obvious as we perform more detailed and high throughput computational and experimental analysis of GWAS loci, and it would be important for this work to come out already pondering about this possibility.In line with the reviewers’ comment 4 about the limitation of FGWAS to assume only single causal variants, we have added a paragraph in the Discussion describing the potential impact of multiple signals per locus and interacting causal variants on the phenotype and how this could be addressed. The part of the paragraph that highlights these effects is shown below:“Specifically, FGWAS relies on the assumption of a single causal variant within each region, which may not necessarily be true for all loci. […] Analysis methods that combine functional fine-mapping with conditional analysis and consider LD and haplotype patterns are likely to provide a more complete overview of the causal interactions at T2D GWAS loci.”


Purpose: Previously, we have shown that the SNP rs10932201 genotype of the cyclic AMP responsive element binding protein 1 gene (CREB1) contributes to individual differences in executive and memory function at the neural system and behavioral levels in healthy, young adults. However, longitudinal effects of CREB1 genotypes on cognition have not yet been addressed. Furthermore we were interested in replicating associations between CREB1 genotypes and human cognition in previous cross-sectional studies and explore whether APOE𝜀4 status might modify these relations.Materials and Methods: We investigated whether common, independent tag SNPs within CREB1  influence individual differences in age-related longitudinal change and level of executive function and memory performance independent of baseline age, sex, APOE𝜀4 status, and education. Our analysis included data from cognitively unimpaired older adults participating in the Baltimore Longitudinal Study of Aging. Eleven measures from six cognitive tests (sample sizes range 617–786) were analyzed using linear mixed effects and generalized estimating equations models. Mean baseline age ranged from 50 to 69 years and mean time of follow-up  ranged from 8 to 22 years.Results: We found significant effects of all three CREB1 SNPs on performance level and/or longitudinal change in performance based on eight measures assessing semantic memory, episodic memory, or both executive function and semantic memory. SNP rs10932201 showed the most significant and largest effect  on age-related longitudinal decline of semantic memory. Additionally, we show interactions between all three CREB1 SNPs and APOE𝜀4 status on age-related longitudinal declines and levels of memory and executive function.Conclusion: Our results suggest that CREB1 genotypes independently and by interactions with APOE𝜀4 status contribute to individual differences in cognitive aging. CREB1 gene (HGNC: 2345) are of interest because CREB signaling pathways, by regulating gene expression, contribute to the adaptation of neuronal properties in brain regions important for memory and executive functions  are community-dwelling volunteers. Continuous recruitment into the BLSA has occurred since 1958. Exclusion criteria at enrollment were past or present psychiatric, neurological, cardiovascular, severe gastrointestinal, kidney or liver disease, diabetes, birth defects, established genetic diseases, inability to perform daily self-care without assistance, inability to walk independently for at least 400 m without assistance and without developing symptoms, inability to perform normal activities of daily living without shortness of breath, active cancer (any activity in the last 10 years), except for locally limited basal cell cancer, clinically significant hormonal dysfunction, muscle-skeletal conditions due to diseases or traumas, any long-term medical treatment, important sensory deficits, having severe English language difficulties, physical illness and weight over 136 kg. Specifically for our analysis we restricted the sample to Caucasian Americans with data available for the e was ≥4 , the Clie was ≥4 , or concThe number of participants, time of follow-up and demographics varied depending on the cognitive measure  is a test of constructional and visuospatial ability . Three cLetter and category fluency (Fluencies) tests are measures of fluent language production and executive function. Participants were given 60 s to generate as many words as possible beginning with specific letters   and fromN = 786), this score was unavailable for 50 participants (6.4%) partly due to the fact that the CESD scale was unavailable before 1977.We used the Center for Epidemiological Studies Depression (CESD) scale  to screeCREB1 gene  were used for imputation . ImputatAPOE𝜀4 genotype status was determined by polymerase chain reaction amplification of leukocyte DNA followed by HhaI digestion and product characterization  and by tWe used the SPSS (IBM SPSS Statistics 23) and R (version 3.2.2) for Windows for our statistical analysis of genetic and behavioral data.R2 value of 0.6. Chi-squared tests were used to check HWE  and differences in genotype groups between sex . ANOVAs were used to analyze differences in age, education  and score for symptoms of depression (733 df) between SNP-based genotype groups. Only SNPs rs2253206, rs10932201, and rs6785 identified as independent were further analyzed because they were not in high LD defined as an R2 of ≥0.6.Bivariate Pearson’s correlation coefficients (two-tailed) were used to test pair-wise LD between SNPs. The cutoff for LD was set to an Z-score for the probability of hit [Z(p(hit))] minus Zscore for the probability of false alarm/incorrectly recognized as target [Z(p(FA))]. The CVLT-recognition response bias measures the degree of performance bias, which was calculated as Z-score for the probability of total hits plus Z-score for the probability of false alarms [-0.5∗Z(p(hit)) + Z(p(FA))]. Values closer to zero indicate less biased performance for the CVLT-recognition response bias measure, a value of zero indicates a completely unbiased or neutral response. Because of missing information on FA, the CVLT-recognition response bias and discriminability measures could not be calculated for one visit of a subject with multiple visits. Scores with zero total hits were excluded for nine visits from subjects with more than one visit and one subject with a single visit.Performance measures were based on the total sum of correct responses for the BNT, CLOCKs, CVLT free recall measures, Category and Letter Fluency and the total sum of errors for the BVRT. The CVLT-recognition correct was the sum of all correctly recognized targets . The CVLT-recognition discriminability is another performance accuracy measure, which was calculated as CREB1 SNPs, the genotype group homozygous for the global major allele was used as the reference genotype group . We modeled the genotype effect using either all three genotype groups or two groups by combining heterozygous and homozygous genotype groups for the global minor allele versus the genotype group homozygous for the global major allele (reference group).These 11 cognitive measures were used as dependent variables in our models. Baseline age, education, and time of follow-up  were continuous predictors, and sex, APOE𝜀4 status, and SNP were nominal predictors. There were 15 APOE𝜀4/4 carriers in our largest sample, which unfortunately is insufficient to carry out a subgroup specific analysis. Therefore APOE𝜀4 status was defined as APOE𝜀4 positive for participants with at least one APOE𝜀4 allele, while other participants were defined as APOE𝜀4 negative. For the We used generalized estimating equations (GEE) models with Poisson distribution for CLOCK based measures after their transformation  to model the counts. Accordingly, originally high performance scores were turned into low performance scores. These transformed scores were back-transformed for the graphical presentations. Linear mixed effects models (LME) with [unstructured covariance for the random effects] were used for all other cognitive measures. We included as fixed effects baseline age, sex, APOE𝜀4 status and education, time of follow-up , SNP and interactions of interest: baseline age × interval, sex × interval, APOE𝜀4 status × interval, and SNP × interval, into both the LME and GEE models. Intercept and interval for each subject were also included as random effects with the unstructured covariance in the LME models, and exchangeable covariance was used for repeated measures in the GEE models. We mean centered baseline age, sex, APOE𝜀4 status and education for simultaneous interpretation of main effects and interactions in the models. The main effect of interval is the slope for the mean of baseline age, sex, APOE𝜀4 status, and education for the SNP genotype reference group. Using these models allowed us to analyze cross-sectional effects  as well as interactions of interest with the longitudinal effect of interval simultaneously.We used Cohen’s d effect size definition implemented in LME models, where the group difference is estimated based on the beta coefficient from LME regression models and standard deviations of the data are estimated from the covariance structure of the random effects. For the GEE models, Cohen’s d effect size was calculated by using the estimates of variance-covariance matrix from the random effects in generalized linear mixed models.CREB1 SNPs and APOE𝜀4 status by adding to all our models the interaction between SNP and APOE𝜀4 status as well as the interaction between SNP, APOE𝜀4 status and interval. For those models showing significant interactions, we used post hoc test to identify group differences that underlie the overall effect.We also tested for putative interactions between the CREB1 gene are shown in Supplementary Table N = 786) SNP rs16839883 (MAF = 0.01) and rs2709393 (MAF = 0.02) were excluded from further analysis. Results of pairwise linkage disequilibrium analysis (Supplementary Table R2 = 0.6) as well as rs2551928, rs1045780, and rs6785. Therefore we selected rs2253206, rs10932201, and rs6785 (range of R2 = 0.1–0.2) for further analysis as independent tag SNPs. None of the CREB1 SNPs were in linkage disequilibrium with APOE𝜀4 genotype status . All three independent SNP genotypes were distributed according to HWE  in performance for all our measures of memory and executive function as well as lower cross-sectional performance levels and greater longitudinal declines in performance with increased baseline age for most of these measures  between rs2253206 genotype and APOE𝜀4 status for the decline in executive function and semantic memory performance, measured by CLOCK-3:25 . Following-up this overall interaction effect with post hoc tests (Supplementary Table p < 0.05) affected by the interaction between SNP rs6785 and APOE𝜀4 status . According to the post hoc tests (Supplementary Table We observed a significant interaction (p < 0.05) and short-delay (p < 0.01) episodic memory . Post hoc testing (Supplementary Table p < 0.05) and there was a tendency for similar effects for immediate and long-delay recall. In the other genotype group AA/GA, APOE𝜀4 positive compared to negative status was associated with significantly (p < 0.05) higher performance level for short-term episodic memory. No significant effects of APOE𝜀4 status were seen on performance levels for either immediate or long-term episodic memory in this genotype group.The interaction between the SNP rs10932201 genotype and APOE𝜀4 status significantly affected the performance level of immediate recall, long-delay .In a recent genetic neuroimaging study, we found an association between the n-making . More spCREB1 mRNA level in the prefrontal cortex of healthy Caucasian and African Americans . Previous research has linked this hannel 2 . Moreovehannel 2 .CREB1 SNPs, SNPs in the CREB1 binding protein gene (CREBBP), and another gene affecting the CREB1 pathway with episodic memory performance  The direction of genotype effects is the same across samples and measures. (3) The direction of genotype effects is the same across baseline performance and longitudinal decline. (4) The measures were not independent because there is an overlap between the measure-based samples and measures were selected to target two cognitive phenotypes (memory and executive function), with some measures being more correlated than others, e.g., measures for both memory and executive function versus measures for memory only. (5) Even if we use the Bonferroni corrected significance level of 0.01 according to three independent SNPs and two independent phenotypes (memory and executive function) we find multiple significant effects of CREB1 SNPs and APOE4-CREB1 interactions on cognition. There are several sources that may limit the generalizability of our findings. First we restricted the analysis to only Caucasians and unimpaired visits, which left us for our largest sample with only 6104 observations because 206 visits were excluded due to the presence of dementia, 96 visits due to MCI, 5 visits due to impairment but not MCI, and 21 visits due to unknown type of impairment. Furthermore the BLSA is a sample with good health, high education and socioeconomic status levels on average.Our current analyses were limited to a selection of only a few important factors that contribute to differences in human cognition during aging. Another concern is that we did not adjust for the number of SNPs and measures for executive function and memory tested. We argue that Bonferroni adjustment based on the number of analyses in our study is too conservative for the following reasons. (1) Our study was motivated by prior evidence for BACE1 promoter contains a potential CREB binding site (APP and TOMM40 (One potentially important interaction partner for CREB appears to be BACE1, which besides cleaving APP may also regulate synaptic plasticity and myelination . Upreguling site . Geneticing site . Other gd TOMM40  also mayd TOMM40 .Importantly, CREB1 also mediates interactions between genetic and environmental factors by regulating the transcription of a large number of neuroplasticity-related genes dependent on neuronal activation during, e.g., social and cognitive activities . Such exCREB1 gene variants contribute to individual differences in the level of memory and executive function performance in both young and older cognitively healthy adults. In addition to APOE𝜀4 status independent effects of CREB1 genotypes, we also provide evidence for interactive effects between APOE𝜀4 status and CREB1 genotypes on human cognition. The coexistence of APOE𝜀4 status dependent and independent effects of CREB1 genotypes is supported by studies in animals showing APOE dependent and independent CREB signaling pathways.We provide the first evidence for the influence of CREB on age-related longitudinal decline in memory and executive function in humans. The results from our previous  and the CREB1 and CREB1 target genes or individual differences in environment-related factors such as cognitive and social activities.In the future we will consider interactions between variants of CW designed the project, organized, analyzed and interpreted the genetic and behavioral data, and wrote the manuscript. YA, MB, and CG substantially contributed to the organization and statistical analysis of the data. TT carried out the genotyping. MK substantially contributed to the data collection. SR supervised the project. All authors critically revised and approved the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


DHCR7) and rs62389423  in addition to two previously identified loci (HLA-DQB1 and ANKRD55), which were all statistically validated using cross validation. DHCR7 encodes an enzyme important in cutaneous synthesis of vitamin D and DHCR7 mutations are believed to be important for early humans to adapt to Northern Europe where residents have reduced ultraviolet-B exposure and tend to have light skin color. IRF4 is a key locus responsible for skin color, with a vitamin D receptor-binding interval. These vGWAS results together suggest that vitamin D deficiency is potentially causal of sero-negative RA and provide new insights into the pathogenesis of the disorder.Sero-negative rheumatoid arthritis (RA) is a highly heterogeneous disorder with only a few additive loci identified to date. We report a genotypic variability-based genome-wide association study (vGWAS) of six cohorts of sero-negative RA recruited in Europe and the US that were genotyped with the Immunochip. A two-stage approach was used: (1) a mixed model to partition dichotomous phenotypes into an additive component and non-additive residuals on the liability scale and (2) the Levene’s test to assess equality of the residual variances across genotype groups. The vGWAS identified rs2852853 (P = 1.3e-08,  Sero-negative RA can be considered as a different disorder from sero-positive RA because of a distinct genetic background, e.g. a consistently weaker HLA-DRB1 association. The (narrow-sense) heritability of sero-negative RA (20%) is much lower than that in sero-positive RA (50%)2, suggesting a greater environmental component to susceptibility. Genome-wide association studies (GWASs) of sero-negative RA so far have identified only two loci reaching genome-wide significance (P < 5.0e-08): HLA-DRB1 and ANKRD554, each conferring relatively moderate effects5. It is, therefore, of interest to test for any potential non-additive effects such as gene–environment (GxE) and gene–gene (GxG) interactions that may contribute to the disease heterogeneity.Sero-negative rheumatoid arthritis (RA) is a complex and heterogeneous disorder where patients have no antibodies detected against citrullinated peptides8. One obvious reason for the limited findings is that testing for non-additive interactions is more complicated than testing for additive signals, e.g. requiring markers to tag the causal variant in GxG at both loci6, and thus requires much larger sample sizes than individual GWAS data offer. Ideally, datasets should also include measurements of environmental exposures of interest, but such data are not always available. Also the genetic markers and/or environmental variables under consideration do not necessarily represent the causal genetic variants and/or environmental factors leading to a reduced power of detection6. While causal genetic variants can be captured by deep sequencing data, causal environmental factors are particularly challenging because they are unknown in advance and thus unlikely readily available for GxE interaction tests9. Therefore, innovative approaches are needed.Thus far efforts in dissecting non-additive signals have yielded only a few convincing examples in human complex phenotypes, each carrying only moderate interaction effects, indicating that any uncovered interactions probably carry moderate/weak effects11. vGWAS can achieve this by considering substantial genotypic variability, i.e. differences of phenotypic variation across three SNP genotypes, as the potential aggregated interaction signatures tend to be weak when only additive effects are important but strong when non-additive effects such as GxE interactions are important13. Using unused genotypic variability information available in existing GWAS data, vGWAS could provide data complementary to GWAS. It is important to acknowledge, however, that vGWAS loci may not necessarily mark non-additive interaction because other factors such as overdominance and scaling (i.e. various transformations) could also generate apparent genotypic variability15. Additional explicit tests of GxE and/or GxG interactions would be needed but only for the identified vGWAS loci, leading to a power increase attributed to a much reduced multiple testing burden17. The genotypic association with phenotypic variability approach has been successfully applied to several quantitative traits and has identified interacting loci also carrying major additive effects (i.e. identified in previous GWASs)21.The genotypic variability based genome-wide association study (vGWAS) is a promising approach that can prioritize potentially interacting loci without requiring prior knowledge of interacting factors22. The vGWAS of sero-positive RA identified the major histocompatibility complex (MHC) as the key interacting locus in addition to the strongest additive signals in three sero-positive RA cohorts22. These results collectively indicate that vGWAS can be effective in providing additional insights into the major genetic loci (e.g. MHC) that not only act additively but also interact with other genes and/or environmental factors24. The vGWAS observations are also in line with recent GxE simulation studies showing that GxE interactions could be detected even when the environmental exposure was misclassified, if the polygenic risk (i.e. the aggregated additive effects) was used as the G variable26.We recently adapted vGWAS to analyze dichotomous disease phenotypes using a two-stage approach: firstly a mixed model to partition the dichotomous phenotypes into an additive component and non-additive environmental residuals on the liability scale and then the Levene’s test to assess equality of the residual variances across genotype groups27. The cohort samples were recruited in the United Kingdom (UK), Swedish Epidemiological Investigation of Rheumatoid Arthritis (SEE), Swedish Umea (SEU), Netherland (NL), Spain (ES), and United States of America (US), respectively.Nonetheless, vGWAS is yet to reach the potential of identifying loci carrying mainly non-additive effects and ‘novel’ to GWAS. One obvious reason is that vGWAS has not been widely explored in meta-analyses of multiple cohorts. Another possible reason is that such novel loci might be detected in phenotypes with relatively low heritability that are driven mainly by major environmental factors. We therefore performed a vGWAS meta-analysis in sero-negative RA using six independent cohorts from the Rheumatoid Arthritis Consortium International for Immunochip, each genotyped with the Immunochip that is an Illumina Infinium genotyping chip containing 195,806 SNPs for 186 loci known to be involved in any of 12 autoimmune diseasesHLA-DQB1 and HLA-DQA2) and rs2852853  at the genome-wide significance level of 5.0e-08, and rs71624119  and rs62389423  at the Immunochip-wide level of 2.5e-05  and 107,144 autosomal SNPs were included in the vGWAS meta-analysis of the combined data of the six sero-negative RA cohorts  whereas the IRF4 SNP was discovered only two times at the Immunochip-wide level; all the four loci had evidence of statistical validation (P < 0.05) in every iteration  as well as gender (P = 2.6e-03), and the HLA-DQB1 lead SNP rs9275428 interacted with cohort (P = 6.1e-04), where cohort and gender were both highly significant environmental factors in the logistic regression models based on Equation We explicitly tested GxG and GxE interactions using each of the identified vGWAS signals as the G and each available environmental factor as the E in the combined data. We found the DHCR7 signals were statistically validated and were involved in interactions with cohort and gender. In addition, the vGWAS identified HLA-DQB1, a well-known GWAS locus involved in GxE interactions with cohort and epistatic local interactions within the MHC as previously reported22. Such GxE and GxG interactions were not potent and thus unlikely to be identified from conventional locus-by-locus genome-wide scans for non-additive interactions because of the multiple testing burden. Our results therefore support vGWAS as an innovative tool to detect non-additive loci from widely available GWAS data.We show for the first time that vGWAS can identify novel non-additive loci in complex diseases. It is also the first time that vGWAS has proven genetically more informative than GWAS in a disease phenotype with low heritability. This was possible because of meta-analysis of multiple cohorts where cohorts represented geographical locations that might capture certain causal influence on sero-negative RA. The DHCR7 is a genome-wide significant locus associated with vitamin D deficiency and circulating vitamin D levels30. DHCR7 encodes an enzyme 7-dehydrocholesterol reductase that plays an important role in cutaneous synthesis of vitamin D31. DHCR7 mutations are believed to be one of the key factors allowing early humans to adapt to Northern latitudes  by generating additional cutaneous vitamin D331. Although Vitamin D deficiency is a common problem in RA patients, DHCR7 is moderately associated with sero-positive RA in the UK  when considering only additive effects32. Here we also found only a moderate association of DHCR7 additive effects with sero-negative RA. These GWAS results, together with our vGWAS results, clearly support the hypothesis that aspects of vitamin D metabolism are causal of RA.34. Of relevance, vGWAS of the combined data identified not only DHCR7 but also IRF4 which is a key locus responsible for skin color36 with a vitamin D receptor binding interval37. These vGWAS results together revealed potentially important biological insights into pathogenesis and heterogeneity of sero-negative RA at least in northern Europe.Latitude-dependent changes in cutaneous vitamin D3 levels may also be driven by pigment-based mechanisms (e.g. the lightest skin color and lack of ultraviolet-B exposure in northern Europe) and/or plausible mutations in other genesDHCR7. Dissection of such hidden factors would require further genetic and ecological epidemiological studies but may greatly improve understanding of the etiology of sero-negative RA. Second, is it time to consider modeling macro environmental factors (e.g. distinct geographic locations) in GxE interaction studies? Such macro environmental factors are likely causal of human adaptation and hence induce GxE interactions that can hardly be detected when considering only one environment but can become obvious when analyzing data from various environments together. Further studies will be required to address the questions above. In conclusion, we have demonstrated the potential of vGWAS analysis to identify novel loci for complex diseases.This study also raised questions. First, are there any hidden causal environmental factors jointly driving the vGWAS signals in sero-negative RA? Because the additive genetic effects and the effects of gender and cohort were removed at the first stage, the observed vGWAS signals could come only from unsolicited but associated environmental effects and non-additive effects. Given that only limited GxE interactions with cohort and gender were identified, our data seem to support existence of such causal factors and/or their GxE interactions with the vGWAS loci such as 3. Briefly, the cohorts were recruited in four European countries: the United Kingdom, Sweden, Netherland, Spain, and the United States of American respectively. All RA patients fulfilled the 1987 criteria of the American College of Rheumatology and were negative for anti-citrullinated peptide antibody tests. All participants provided written informed consent for participation. This study was approved by the North West Research Ethics Committee (MREC 99/8/84). All experiments (e.g. genotyping) were performed in accordance with relevant guidelines and regulations.The six sero-negative RA cohorts used in this study have been described in detail elsewhereSNPs on the sex chromosomes and samples of non-European origin were excluded. Quality control was conducted for each cohort to ensure: minor allele frequency > 0.01, SNP call rate > 0.95, sample call rate > 0.99, deviation from Hardy–Weinberg Equilibrium P < 1.0e-04. The six cohorts were merged into a combined data based on the common set of SNPs.22 was used to conduct vGWAS analyses for each individual cohort and the combined data. Stage one: a mixed model implemented in GCTA38 was used to compute the genetic relationship matrix (GRM) and subsequently the first ten principal components (PCs), and then to predict polygenic liability risk for each unrelated individual by imposing a GRM relatedness threshold of 0.15 recommended for the Immunochip39 while setting the disease prevalence as 0.01 and fitting gender and the first 10 PCs (and cohort in the combined data) as covariates in the mixed model. The parameters used in the GCTA command were: gcta--reml --grm-gz --pheno --mpheno --reml-pred-rand --grm-cutoff --prevalence --covar --thread-num --out.The two-stage approach previously developed for case-control disease phenotypes40:y; Zi = |yi − ỹgi| is the deviation of y of the ith sample (yi) and the median of y in samples with genotype g (ỹgi); N is the sample size and k is the total possible genotypes; nj is the number of samples with genotype j; Zj. is the mean deviation from the median for genotype j and Z.. is the mean deviation from the overall median. When N is large, T2 is an approximate χ2 test taking two degrees of freedom.Stage two: the resultant residuals were tested for variance heterogeneity across three SNP genotypes using the Levene’s (Brown-Forsythe) test implemented in an R package VariABEL22 is used as the Immunochip-wide suggestive threshold.The Levene’s (Brown-Forsythe) test requires no assumption of normally distributed phenotypes and hence is suitable for vGWAS of case-control disease phenotypes. For simplicity, the GWAS consensus threshold of 5.0e-08 is adopted as the genome-wide significance threshold, and a threshold of 2.5e-05 previously derived from permutationG from a cohort with N cases and M controls, the following steps were used to cross validate G:N and 50% M and split the cohort into two independent set1 and set2to randomly select 50% to perform vGWAS for both setsG and check whether it was discovered at either the genome-wide or Immunochip level and/or statistically validated (i.e. the same SNP with a vGWAS P < 5.0e-02)to record the P values of the vGWAS signal to repeat the above (a to c) steps 10 times and count successes of discovery and replicationGiven a vGWAS signal G:p is the probability of an individual being a case rather than a control in a population, µ is the model constant, β is the effects of fixed covariates (e.g. gender and/or cohort), βg is the effect of a vGWAS SNP G, βf is the effect of the interacting factor F that can be an environmental factor (i.e. testing GxE) or another SNP (i.e. testing GxG), βgf is effect of interactions between G and F, e is the random error.We used the following logistic regression model to test GxE or GxG interactions for an identified vGWAS SNP βgf = 0 hypothesis test took 2 degrees of freedom (df) for interactions between G and gender, 10 df between G and cohort in the combined data and 8 df in the reduced data, 4 df for GxG between G and another Immunochip SNP.The 28 was used to annotate the identified vGWAS SNPs and to calculate their conservation scores. Enlight41 was used to generate plots of vGWAS regions (50 kilobases flanking the lead SNP) of interest.ANNOVARThe datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.Supplementary Figures and Table


Burgeoning availability of genome-wide association study (GWAS) results and national biobank data has led to growing interest in performing multi-trait genetic analyses. Numerous multi-trait GWAS methods that exploit either summary statistics or individual-level data have been developed, but their relative performance is unclear. Here we develop a simulation framework to model the complex networks underlying multivariate genetic epidemiology, enabling the vast model space of genetic effects on multiple correlated traits to be explored systematically. We perform a comprehensive comparison of the leading multi-trait GWAS methods, finding: (1) method performance is highly sensitive to the specific combination of genetic effects and phenotypic correlations, (2) most of the current multivariate methods have remarkably similar statistical power, and (3) multivariate methods may offer a substantial increase in the discovery of genetic variants over the standard univariate approach. We believe our findings offer the clearest picture to date of the relative performance of multi-trait GWAS methods and act as a guide for method selection. We provide a web application and open-source software program implementing our simulation framework, for: (i) further benchmarking of multivariate GWAS methods, (ii) power calculations for multivariate genetic studies, and (iii) generating data for testing any multivariate method in genetic epidemiology. The early stages of the GWAS era were dominated by studies with a single phenotype as outcome1245789101112131415161718194212223P9, TATESHomHet91011121314We exploit our simulation framework to perform a comprehensive comparison of the current leading multi-trait GWAS methods. While some methods simultaneously model multiple SNPs and multiple traits78www.MultiTraitGWAS.kcl.ac.uk) that can reproduce all results in this paper, offering flexibility in output and providing interpolated results. A built-in tool generates simple multivariate genetic data sets instantaneously, while a downloadable command-line program can be used to simulate larger, more complex multivariate data and to extend our comparison study with novel methods or under different parameter settings. Finally, our web application acts as a power calculator for multivariate GWAS, which should aid with method selection given available data, and in budgeting proposed studies. Our simulation framework and associated software tool can help to guide the future development and direction of multivariate methodology in genetic epidemiology.We provide a web application  and v is the genetic effect vector of the variance in each of the K phenotypes explained by the genetic variant, and c models the phenotypic correlation matrix such that cs,t is the approximate (see below) correlation  between phenotype s and phenotype t, while the variance of each trait is 1.where Gi ∈{0, 1, 2} are generated according to Hardy-Weinberg Equilibrium (HWE) in the proportions {p2, 2pq, q2} where p is the major allele frequency and q is the minor allele frequency, with q = 1−p. The genotype-phenotype data are simulated according to the model:The SNP genotypes Yi = {Yi,1,…, Yi,K} denotes the phenotype data corresponding to K phenotypes for an individual vi, f denotes the regression coefficient corresponding to v phenotypic variance in Yi explained by the SNP genotypes Gi, and εI is the residual variance drawn from the multivariate normal distribution N . The regression coefficient vf is determined according to the following equation, under the assumption of additive genetic effects:where ve is the transformed phenotypic variance explained by the SNP, determined by:where ve = 0.005/0.995 = 0.005025 since the residual error variance is equal to 1. While this, our main data-generating model, does not consider indirect effects of genetic variants on tested traits via other tested traits, nor case/control phenotype data, we also simulate (see Methods) and investigate these.So, for example, to model a SNP that explains 0.5% phenotypic variance, then v and c take, genotype-phenotype data consistent with almost any underlying biological network  v and c are varied in a structured way, (S2) v and c sampled from uniform distributions, (S3) v and c reflect each other, (S4) v and c informed by real data. By varying the values that  network  and set While our simulation framework and associated software will be useful for multivariate methodology development and applications across genetic epidemiology, we exploit it here to perform a comprehensive comparison study of multi-trait GWAS methods.P9, TATESHomHet1214In this comparison study we compare single-SNP multi-trait methods, both those that use individual-level genotype-phenotype data: MANOVA, CCAv, and phenotypic correlations, c, are varied in a structured way. First we consider a case with only two phenotypes and three genetic effect vectors:In this scenario the genetic effects, v2 corresponds to a SNP that explains 0.5% variance in trait 1 and 0.1% variance in trait 2. The phenotypic correlations are varied between r = −0.9 and r = 0.9 in increments of 0.1. Data corresponding to 5,000 individuals are simulated according to P-value <5×10−8 or a log10 Bayes factor >6 for the Bayesian methods (see Methods). such that Hom, which has a different pattern in P and TATES methods – which have almost identical power – have lower power across much of the parameter space. In -P and TATES across the correlation range. Both methods perform univariate tests for association with each trait before correcting the smallest P-value to account for the number of tests performed (see Methods). When the traits are highly positively correlated the variability in the univariate P-values is small, resulting in similar P-values for each test. In contrast, when the traits are uncorrelated the two tests are independent and the variability in the univariate P-values is greater, increasing the probability that one is small. For negative phenotypic correlations, this variability is even greater, resulting in higher power. However, in P-value will almost certainly derive from the SNP with large effect and is thus invariant to the phenotypic correlation.Het method follows the same pattern as the other methods in this group, it has lower power for high positive and negative correlations. This is due to the trait sub-setting procedure of SHet (see Methods), which incurs a relatively strong multiple testing penalty. However, as a summary statistic method SHet can be applied to large publicly available GWAS results and thus may have a substantial boost in power for certain traits.The group of methods that follow a different pattern gain power via reducing the residual variance in analysing the traits jointly. When the genetic variant affects both traits equally and the traits are highly correlated , these mHom method, which performs a meta-analysis on the traits, generally performs best in pleiotropic scenarios. In Hom loses power over min-P and TATES here due to it relating to the average rather than maximum association. For highly positively correlated traits, the addition of the unaffected trait in the meta-analysis reduces the average effect size and thus power, but for highly negatively correlated traits the individual effect sizes are augmented, leading to increased power. While a standard meta-analysis would produce a different power curve to that of SHom in this scenario, the latter explicitly adjusts for the trait correlations, which provides a well-behaved statistic under the null. The same explanation applies to Hom is less pronounced due to the small genetic effect on the second trait.In While the number of qualitatively different genetic effect vectors is only three for two phenotypes , the number increases exponentially as more traits are considered. In this scenario and scenario S3 below, we consider 2, 4, 8, 20 and 48 phenotypes and for those with 4 or more we apply 10 genetic effect vectors, defined in v1, v4, v8, and v10 in relation to 4 phenotypes, and P and TATES, while SHom and SHet tend towards this leading group the more pleiotropic the scenario (e.g. v1). SHet has markedly higher power than the other summary statistic methods in most scenarios and often similar to the individual-level data methods. In the most pleiotropic scenario, v1, SHom performs best and min-P and TATES outperform the individual-level methods under high, positive phenotypic correlations . Here we perform simulations that model such an effect across two phenotypes (see Methods). We simulate the genetic variant as explaining 0.5%variance in the first phenotype, and the first phenotype explaining 1%, 5%, 10% and 20% variance in the second phenotype. The results from these simulations . Het and SHom, have almost identical power and distinctly higher than that of min-P and TATES, with the difference larger for a greater number of traits. These results are in broad agreement with those of scenario 1 (S1), where this leading group of methods generally outperforms min-P and TATES.In contrast to the structure of the S1 simulations, here we simulate data with genetic effects and phenotypic correlations sampled from uniform distributions. Data are re-simulated if phenotype correlation matrices are not positive-definite and genetic effect sizes are bounded between 0% and 0.5% phenotypic variance explained, but otherwise each of the 10,000 genotype-phenotype simulated datasets relate to a random combination of genetic effects and phenotypic correlations. Since genetic variants mostly explain <1% of phenotypic variance26Hom, however, are sensitive to the genetic effect vector, being the best or worst performing summary statistic method depending on the genetic effects, while the power of SHet and the individual-level data methods is greatly reduced for 20 and 48 traits. These results are in broad agreement with those of scenario 1 (S1) in which the genetic effects and phenotypic correlations are concordant. While the results for two phenotypes are mostly similar across all methods see , the sumReal data informed phenotype correlations. Here we draw the phenotypic correlations from a mixture of Gaussian distributions modelled on real phenotypic correlations corresponding to 16 phenotypes from the Northern Finland Birth Cohort 1966 (NFBC1966) ; in both cases the genetic effect vectors are independent of the phenotypic correlations, which optimises the statistical power of the individual-level data methods and SHet.Real data informed genetic effects and phenotype correlations Here we sample genetic effect sizes directly from reported genotype-phenotype associations from publicly released GWAS on 12 continuous phenotypes: height31 and HOMA-IR12CK combinations of the 12 phenotypes, and forming the corresponding genetic effect vectors and phenotype correlation matrices to simulate data.The final simulation scenario exploits real data from published GWAS results to simulate realistic values for the genetic effect and phenotype correlation parameters. This scenario is in two parts: (a) real data informed phenotype correlations, and (b) real data informed genetic effects and phenotype correlations.P, TATES and SHom, while SHet has similar power , SHet, SHom and the individual-level data methods show markedly higher power than TATES and min-P. This is consistent with a general tendency for the individual-level data methods to have greatest relative power when the genetic effects and phenotypic correlations are discordant. This is further supported by the results from scenario 3 (S3), where the genetic effects and phenotypic correlations reflect each other. Here the summary statistic methods tend to perform best, especially SHom in the scenarios that are most pleiotropic. In the final scenario (S4b), genetic effects and phenotypic correlations are based on real data, and in the results relating to 12 traits the individual-level methods provide twice the discovery of genetic variants over the summary statistic methods.In the structured simulations of scenario 1 (S1), the individual-level data methods and the meta-analysis approaches of SP < 5 × 10−8 and log10 Bayes factor >6 we have not ensured equal type 1 error rates of the methods throughout comparisons; therefore, the fine-scale power differences between the methods estimated here, in particular in comparing the Frequentist and Bayesian methods, should not be strongly interpreted. The choice of which individual-level method to use depends, in part, on the computational feasibility for the number of traits being analysed ; (ii) while the Combined-PC method shows almost equivalent power to the other individual-level data methods throughout, it has a marked departure in power for indirect genetic effects on the tested traits - this could provide a simple method for distinguishing direct and indirect effects; (iii) in many scenarios, SHet and SHom have similar power to the individual-level data methods, demonstrating the potential for summary statistics to provide as much information as individual-level multivariate data; (iv) most multi-trait methods are not optimised for identifying pleiotropic variantsHom, which is tailored to detect pleiotropic variants, performs poorly in the real data informed simulations relating to 12 traits, suggesting that tests for pleiotropic variants may not produce novel findings unless applied to phenotypes that have prior knowledge of shared genetic aetiology.In addition to providing a comprehensive guide to method choice in multi-trait GWAS, the extensive array of scenarios considered here expose several issues relating to the methods, not established in previous publications: (i) despite the sophisticated adjustment of univariate P and TATES to perform well, despite our overall findings that these methods appear to have sub-optimal power. However, such simulations are easily performed using our simulation software, which implements the scenarios considered here and allows flexibility in user choice over parameters such as the number of traits, genetic effects and phenotypic correlations in order to simulate new scenarios. The results of this highly non-pleiotropic scenario, where we simulate 48 traits with only one causal association, are presented in P and TATES do indeed perform well in this scenario, although the individual-level methods have greater power for higher phenotypic correlations. We also recognise that there are other multivariate approaches to genetic association studies that have not been considered here, such as linear mixed models78While we have assessed the performance of many of the leading multi-trait GWAS methods across a range of different scenarios, we acknowledge that there are many scenarios not considered here. For example, we did not consider the situation where only one of many traits is affected by the genetic variant; in this setting we may expect min-Multivariate genetic analyses are likely to expand dramatically in future as an increasing number of GWAS results are released publicly and as individual-level multivariate panels are compiled by population-wide biobank studies. This makes our study extremely timely, and designing studies guided by its findings should lead to greater discovery of genuine associations. This could be especially significant for underpowered but extremely important phenotypes, such as depression33While our results provide a present snapshot of multi-trait GWAS method performance, our simulation framework offers a consistent platform from which future methods can be easily benchmarked via our web application and open-source software program. This should save researcher time and avoid repetition by guiding the development, application and publication of only those methods demonstrated as outperforming the alternatives. We believe that this study highlights the importance of systematic and comprehensive comparisons of competing methods of analysis, easily reproduced and extended via open-source software.The 10 methods used in the comparison study are briefly described below, and are summarised in et al.P-value from the group of K P-values corresponding to the K phenotypes under study is recorded for every SNP, using the published univariate GWAS results. Next the effective number of independent tests represented by the results on the K phenotypes is estimated using the correlation matrix of the phenotypes according to NyholtP-value is adjusted according to this number of tests in a standard Šidák correctionThis test was proposed in O’Reilly P but performs a more sophisticated correction for multiple testing across the different phenotype results. Here the results are ranked according to P-value and then the extended SIMES procedure of Li et al.P-value according to a scaling that is a function of the effective number of independent P-values.This testHom is that which can be most considered a ‘test for pleiotropy’, being equivalent to a meta-analysis of effect sizes across traits and cohorts with optimal power under fixed effects.This testHom but is designed to detect genetic variants that only affect a subset of the total number of traits under study. Only those traits with a corresponding Wald test statistic above some threshold are included in the calculation of a statistic equivalent to that of SHom. This is then recalculated across the range of possible threshold values with the maximum value obtained being the test statistic SHet. Since this SHet statistic does not follow a standard theoretical distribution, P-values are computed via simulation of a Gamma distribution.This testThe standard Multivariate Analysis of Variance statistical test, which is the multivariate extension of ANOVA, and equivalent to a reversed multiple linear regression with genetic variant as outcomeCanonical Correlation Analysis refers to a statistical procedure for identifying and testing the association of linear combinations of two sets of variables that maximise their correlation. While this approach could theoretically be applied to test for association between multiple genetic variants and traits jointly, in the context of multi-trait, single-SNP analyses, as incorporated into PLINK by Ferreira and PurcellThis testThis test10 Bayes factor threshold of 6, following Stephens and Baldinget al.This test10 Bayes factor quantifies the association between SNP and phenotypes. Statistical power is assessed using a log10 Bayes factor threshold of 6 . We model an indirect genetic effect from a SNP f(v)denotes the regression coefficient corresponding to v phenotypic variance in Yi,1 explained by the SNP, g(v′) denotes the regression coefficient corresponding to v phenotypic variance in Yi,2 explained by Yi,1, and ε~N. We only simulate downstream effects for two phenotypes, but the simulations can be easily extended to incorporate more phenotypes, as well as more complex interaction networks.where Equation 1, and then apply a liability threshold model of diseaseWe simulate and consider case/control data for scenario 1 (S1). We first simulate quantitative phenotype data as in prevk is the prevalence of the kth phenotype.where In scenario 4a (S4a), we fit a mixture Gaussian distribution to the observed NFBC1966 phenotype correlations on 16 metabolic traits:and sample the pairwise phenotypic correlations from this distribution.β (SNP effect size) estimates from published GWAS2282930Gi we take the effect size estimates across K phenotypes, say β1,…,βK, and apply a transformation so that the maximum effect size is 0.5% of phenotypic variance, while maintaining the relative effect sizes. The transformation factor di for SNP Gi is defined as follows:In scenario 4b (S4b), we use the where K phenotypes. The real data obtained beta coefficients for SNP Gi are then multiplied by di to generate the beta coefficients used to simulate the phenotype data as in Equation 1.where The real data simulations are performed on 12 phenotypes, as well as all possible subsets of 2, 4 and 8 phenotypes from these 12. In each case, we limit the number of SNPs associated with any one phenotype to a maximum of 20, then use this subset of SNPs to derive the betas as above. This aims to prevent bias towards phenotypes with a much larger number of associated SNPs.How to cite this article: Porter, H. F. and O’Reilly, P. F. Multivariate simulation framework reveals performance of multi-trait GWAS methods. Sci. Rep.7, 38837; doi: 10.1038/srep38837 (2017).Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.


Based on simulation, we show that type I error is controlled, and that POPFAM+ is more powerful than wQLS or PDT alone. In a real data application, we used POPFAM+ to analyze 43 genes of a hybrid epilepsy study containing 85 affected families, 80 independent cases, 234 controls, and 118 reference samples from the International HapMap Project. The results of our analysis identified a promising epilepsy candidate gene for follow-up sequencing: malic enzyme 2 .The efficient analysis of hybrid designs  is attractive because it should have increased power to detect associations between genetic variants and disease. However, the computational complexity of such an analysis is not trivial, especially when the data contain pedigrees of arbitrary size and structure. To address this concern, we developed a pragmatic test of association that summarizes all of the available evidence in certain hybrid designs, irrespective of pedigree size or structure. Under the null hypothesis of no association, our proposed test statistic (POPFAM+) is the quadratic form of two correlated tests: a population-based test , and a family-based test . We use the parametric bootstrap in conjunction with an estimate of the correlation to compute  To boost the power of genetic association studies, researchers are invariably compelled to increase sample size  study-specific subsets of the data separately ; Bourgain et al., Y ≡ Y; Martin et al., N under the null hypothesis of no association. Here, we illustrate our proposed test with wQLS and PDT because these two component tests are applicable to a wide variety of hybrid designs involving dichotomous traits with high heritability. Under the null hypothesis, the limiting distribution of X and Y is bivariate normal (BVN) with mean ′ = 0 and variance-covariance matrix Σ. Note that both components: X (which is population-based) and Y (which is family-based) are correlated because both depend on A—the genotypes in affected families. Furthermore, this correlation (denoted ρ) is estimated under the null hypothesis of no association by conventional gene dropping at the test SNP, where the only requirement for the simulation is a consistent estimate of the corresponding MAF .Let , …,  from a Note that POPFAM+ does not follow a χ2 test with 2 degrees of freedom, and the test that defines “more extreme” in terms of Euclidean distance from the origin , but this would require knowledge of the joint distribution of  under the alternative hypothesis of association. Because this information is never known, and because we found our results to be robust to different values of τ, we recommend setting τ = 0.5. In practice, this strikes a reasonable balance between a χ, but thi2 test with 2 degrees of freedom), let's consider a few instructive examples. In each example, we assume that the null hypothesis of no association is true, and that the sample is sufficiently large so that the finite sampling distribution of POPFAM+ is well approximated by its limiting distribution. First, if τ were 1, then the limiting distribution of POPFAM+ would be a χ2 distribution with 2 degrees of freedom. The contour of constant probability for this distribution is shown in Figure p-value ; summary statistics for the IGE families from this study are shown in Table When generating data under the null hypothesis of no association, D′—a measure of linkage disequilibrium between the test SNP and the disease gene, was fixed at zero. For all alternative hypotheses , D′ was fixed at 0.8. For all scenarios, the MAFs at the disease gene and test SNP were the same, and these frequencies were varied from 0.04 to 0.10. Prevalence of the disease was fixed at 5%, while penetrance, relative risk, and MAFs were modified accordingly to satisfy this prevalence constraint. The results for each scenario under both null and alternative hypotheses were evaluated on the basis of 1,000 replicates with τ set to 0.5.p-value based on 10,000 In a real data application, we analyzed 350 SNPs located in a 6 Mb region on chromosome 18 that was previously identified by cosegregation analysis .Furthermore, Figure 2 test with 2 degrees of freedom (data not shown); which is consistent with the finding of Joo et al. , the relative gains in power achieved by POPFAM+ are important because power is typically far from 1.0 in most genetic studies of common complex traits.To compare the power of POPFAM+, PDT, wQLS, and the max (denoted MAX), we simulated and analyzed data for relative risks (RRs) of 2 and 3 for dominant models, and 5 and 10 for recessive models. The power results are summarized in Figure o et al.  that MAXaka Genetic Generalized Epilepsy (GGE)]. For each SNP in the 6 Mb candidate region, p-values based on POPFAM+ were computed. The top 10 genes ranked by minimum p-value (denoted min p) are shown in Table p-value is the smallest p-value among the SNP-specific p-values across the gene. For comparison, we also show the rank of each gene based on the International League Against Epilepsy (ILAE) meta-analysis , set binding protein , Solute Carrier Family 14 (Urea Transporter), Member 2  and Zinc Finger and BTB Domain Containing 7C .The results of our analysis identified a promising epilepsy candidate gene for follow-up sequencing: malic enzyme 2 . This could be particularly useful for detecting variants with MAFs < 5%, where the statistical power could be low unless sample sizes or effect sizes happen to be large (Lee et al., 2 test with 2 degrees of freedom, and the test that defines “more extreme” as more distant from the origin (Figure For several reasons, we fixed the scaling factor τ at 0.5 for all analyses. First, when little is known about the distribution of genetic effects, fixing τ at 0.5 strikes a nice balance between a χn Figure . Second,n Figure  with τ =n > 2 lines of evidence, which for example, could facilitate the incorporation of gene expression data. Similarly, if researchers are concerned about the presence of population substructure then a program like STRUCTURE (Pritchard et al., p-values from each subset could be combined using, for example, Fisher's method. Therefore, as we continue to move toward integrative genomics, POPFAM+ should give researchers an important, and computationally tractable, tool for detecting coherent alternatives between genotypes and common complex human phenotypes.In principle, POPFAM+ could be extended to MW: Methodology development, data analysis, manuscript drafting, and revisions. WS: Methodology conception and development, data acquisition, manuscript drafting, and revisions.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.