dataesr/scientific-paragraphs-categorization huggingface dataset:
Our strategy exploits the advantages provided by two approaches used in genetic association studies: firstly, the use of BVS to go beyond ''single SNP analyses'' in GWAS  [8] ; secondly, the joint modelling of multiple traits. This yields increased power and enhanced interpretability of the genetic associations, providing new insights into the underlying regulatory mechanisms. To the best of our knowledge, GUESS is the first integrated Bayesian computational tool that is able to perform both fast and efficient variable selection in large dimensional covariate space and association analyses with multiple continuous phenotypes. In a real case study of several blood lipid traits, we compared GUESS with two recently proposed Bayesian alternatives, namely the piMASS algorithm  [8]  and the Bayesian method that is included in the SNPTEST software  [9].
To make our BVS strategy feasible for a large number of covariates, we exploit Graphics Processing Unit (GPU) parallelization tools and accelerated linear algebra libraries  [17] , which enable efficient evaluation of the marginal likelihood of millions of alternative models during the search process. An R package R2GUESS, which implements GUESS, can be downloaded from http://www. bgx. org. uk/software/guess. html and will soon be available on CRAN. The SNPTEST package incorporates a Bayesian measure of association through the computation of a BF to quantify the evidence for association between a single explanatory variable and one or several continuous phenotypes. The benefits in terms of interpretability of using BFs rather than frequentist p-values in GWAS have been discussed in a number of papers  [18] ,  [19].
The name of the locus is also reported on the right of each branch of the two trees with the same colour code used in the table: black if the locus is associated with the phenotypes with FDR,5%, red if the locus is also in the top BMV with FDR,5%. doi:10. 1371/journal. pgen. 1003657. g001 are equally associated with rs629301 by GUESS analysis (Table  S1 ). This suggests that, besides the contribution of LDL to detect the genetic association with SORT1, our joint multi-trait analysis (including APOB) enhances the identification of the causal variant in this relatively small sample. Another example relates to LIPC which was detected in the TG-HDL combination (and also associated with the TG-HDL-LDL group, Figure  1C  and Table  S1 ) but not with any single trait. SNP rs261333 is located within the LIPC gene encoding hepatic lipase which hydrolyzes TG and catabolizes TG-enriched HDL  [32].
In the first step, we selected a 2Mb region centred at each identified variant (rs629301 and rs261333) and ran GUESS in each region with an adapted specification of the a priori expected model size (number of true associations) and standard deviation such that the prior model size is likely to range from 0 to 3. Table  S6  shows that for the selected phenotypic groups that were significantly associated with rs629301 and rs261333 in the original discovery dataset, the associations are confirmed in the two independent replication datasets. Remarkably, in CCHS and DESIR, GUESS detects the same causal variant originally identified (rs629301) for both phenotypic groups (TG-APOB and TG-LDL-APOB). The second SNP, rs261332 inside the LIPC gene, is not present in the CardioMetabochip  [39]  used for CCHS and DESIR.
The variants identified by GUESS for both phenotypic groups (TG-HDL and TG-HDL-LDL) are rs8034802 (CCHS) and rs1077834 (DESIR) with r 2 and D9 level equal to 0. 582 and 0. 979 between rs261332 and rs8034802 respectively, and 0. 838 and 0. 982 between rs261332 and rs1077834 respectively, in populations of European ancestry (1000 Genome project  [40] ). These results show that significant and novel multi-trait genome-wide associations obtained by GUESS are robust and reproducible in independent cohorts despite the relatively small size of the discovery dataset (n = 3,175). In the second step, we investigated whether we would find similarities between the order of association obtained previously between the causal SNPs and the phenotypic groups (Table  S1 ) and that obtained by applying our measure of association, RBF on the replication datasets.
Specifically, for all subsets of traits in the two trees, we calculated the RBF (see Material and Methods) for the SNPs identified in the first step as associated in each selected region (Table  S6 ). Table  S7  shows the results of this analysis for the two independent cohorts. Conditionally on rs629301, in CCHS the two phenotypic groups that receive higher RBF are TG-APOB and TG-LDL-APOB (Table  S7A ). The same analysis applied to the DESIR dataset (conditionally on the top BF hit SNP rs629301) provides similar results with TG-APOB ranked first (Table  S7B ), but with TG-LDL-APOB (ranked third) superseded by LDL-APOB. In both cases LDL is not the primary trait associated with the identified genetic variant, refining the suggested association found in  [2]. In summary, the results obtained in two independent cohorts are consistent to those seen in the discovery dataset (Table  S1 ) with the multi-trait group TG-APOB more tightly linked to the rs629301 genetic variant than any single trait.
In the second region centered on rs261332, we also replicated the order of association of the phenotypic groups with rs8034802 in CCHS and rs1077834 in DESIR (Table  S7A  and Table  S7B , respectively). In particular, in both datasets the TG-HDL-LDL and TG-HDL group receive substantially higher RBF than any other single and multiple traits group. Moreover the pattern of the RBF values is similar to that shown in the original discovery dataset (Table  S1 ) confirming that LDL does not increase power to detect the causal variant.
Power comparison (multiple and single-trait analyses) The real data analysis shows that SNPTEST has good power to detect the main variants but it includes several additional SNPs, possibly increasing the number of false positives. Using 273,294 SNPs from the GHS study (see Material and Methods) we carried out two simulation studies for single and multiple traits to quantify the power of SNPTEST and GUESS. In the multiple traits scenario, we also tested the performance of non-Bayesian multiple traits algorithms MANOVA  [10] , MLASSO  [11]  and SPLS  [12]  (see Material and Methods). We also tried a recently proposed generalised Group Fused LASSO  [41] , a multilocus sparse regression model which is designed to borrow information across correlated phenotypes. However the GFLASSO C++ implementation was not able to handle the whole GHS genotype dataset, requiring.
Discussion As large scale GWAS and meta-analyses of multiple continuous phenotypes are becoming increasingly common, there is a mounting need to develop models and computationally efficient algorithms for joint analysis of multi-SNP and multi-phenotype data. Current state-of-the-art Bayesian approaches have limitations either in the analysis of one SNP at a time  [9]  or in modelling single phenotypes with multiple SNPs  [8]. To address both these problems, we propose a powerful Bayesian statistical computational tool for analysing genome-wide scale datasets that deals with both multiple continuous traits and predictors, with a parallelized implementation. Our algorithm enables the identification of additive effects of many predictors on multiple combinations of traits as well as secondary genetic associations.
Our new finding of the association of LIPC with multiple traits rather than with a single phenotype is supported by recent data  [32]. We validated this finding in two independent datasets and, specifically, we were able to replicate the genetic association and reproduce the order of the strength of association of the genetic variant with the phenotypic groups. Beyond the application to lipid metabolism in GHS, the strategy we propose can be applied to any set of phenotypes where unsupervised clustering methods can be used to create informative groups of traits from which a ''tree-like'' structure can be derived. The increased power of GUESS shown in the real-case analysis was also demonstrated by an extensive simulation study, highlighting how alternative approaches, both Bayesian and non-Bayesian and in particular those specifically designed to deal with correlated predictors (MLASSO), are influenced by complex LD structures in the SNP data, and as a consequence have increased false positive association rates.
In contrast to existing methods, the flexible prior structure used for the regression coefficients adapts to any correlation structure of the predictors, which can be of a different nature. Therefore, GUESS can be employed for large-scale analysis of multiple continuous traits with both discrete and continuous predictors and their combinations. Beyond the straightforward application to GWAS of multiple traits, GUESS is particularly suitable for the analysis of diverse genomic datasets where complex dependencies in the predictor space are present (for instance, correlation between expression levels or methylation profiles).
Samples, genotyping and traits in the primary discovery dataset More details about the GHS study are provided in  [29]. The present study included 3,175 individuals of both sexes aged 35-74 years, who were successively enrolled into the GHS, a communitybased, prospective, observational single-center cohort study in the Rhein-Main region in western mid-Germany. Fasting Apolipoprotein A1 (APOA1) and B (APOB), HDL-cholesterol (HDL) and LDL-cholesterol (LDL) and Triglycerides (TG) were measured on an Architect c8000 by commercially available tests from Abbott (htpp://www. abbottdiagnostics. de). APOB is the primary apolipoprotein of LDL whereas APOA1 is the major protein component of HDL. Genotyping was performed using the Affymetrix Genome-Wide Human SNP Array 6. 0 and the Genome-Wide Human SNP NspI/StyI 5.
0 Assay kit. Genotypes were called using the Affymetrix Birdseed-V2 calling algorithm. SNPs with a Minor Allele Frequency (MAF),0. 01 or deviating from Hardy-Weinberg equilibrium (p-value,10 24 ) were excluded. Only autosomal SNPs were considered for analysis. Missing values for each of the 22 autosomal chromosomes were imputed using FastPhase  [48] , allowing 20 random starts of the EM algorithm (-T20), 100 iterations of the EM algorithm for each random start (-C100), no haplotype estimation (-H-4), without the determination of the number of clusters (-K1). To reduce the number of SNPs prior to analysis, we performed tagging at r 2. 0. 80 level using an in-house method similar to  [49]. The original dataset consisting of 650,010 SNPs was reduced to 273,294 SNPs after tagging (57. 9% reduction).
Replication datasets The Copenhagen City Heart Study  [36] ,  [37]  (CCHS) is a prospective study of the Danish general population initiated in 1976-78 with follow-up examinations in 1981-83, 1991-94, and 2001-03. Individuals (n = 8,261) were selected based on the National Danish Civil Registration System to reflect the adult Danish population aged 20-100 years. Data were obtained from a questionnaire, a physical examination, and from blood samples including DNA extraction at the 1991-94 examination. A lipid profile was measured using standard hospital assays and genotyping was performed using customised version of the Illumina CardioMetabochip  [39]. For the replication, we selected a region centered at rs629301 (SORT1) and rs261333 (LIPC) comprising 543 and 204 SNPs, respectively. We also analyzed 4,663 subjects of European descent from the Data from an Epidemiological Study on the Insulin Resistance syndrome (DESIR) cohort.
GUESS implementation for large number of predictors The GUESS implementation extends the original ESS++ code  [50] , permitting an effective posterior exploration of model spaces of the size typically encountered in GWAS problems. Similarly to ESS++, GUESS simulates multiple Markov chains in parallel, with a different temperature attached to each chain. The different temperatures have the effect of flattening the log-Posterior (logmarginal likelihood6log-prior on the model space). The state of the chains is tentatively swapped at every iteration by a withinand between-chains probabilistic mechanism. This ensures that the posterior distribution is not trapped in any local mode and that the algorithm mixes efficiently since every chain constantly tries to transmit information about its state to the others.
By rewriting code in CUDA C/C++ parts of the algorithm can be redirected to the GPU rather than the CPU, often greatly speeding up a typical run  [17]. As detailed by  [7] , at each MCMC update of the ESS algorithm, it is necessary to evaluate the log-Posterior, which requires the expensive computation of the marginal likelihood. To increase stability, the marginal likelihood is calculated using the technique of QR matrix decomposition, as described in  [3]. For variable selection problems where the number of possible predictors in the model can be large, performing QR decomposition using regular CPU operations becomes prohibitively computationally expensive, resulting in infeasible run times. GUESS replaces core linear algebra operations, including the QR decomposition, with versions that exploit the GPU.
In the implementation used to produce the results described in this paper, we use version R11 of the proprietary CULA library (http://www. culatools. com/), which is freely available to academic users, directly replacing the GNU Scientific Library (http://www. gnu. org/software/gsl) versions of the relevant linear algebra routines present in the ESS++ code, with CUDA C/C++ equivalents from this library. Beyond the primary extension to ESS++, GUESS also implements a slight difference in the Metropolis-Hastings move type of the underling algorithm (see  [7] ). In particular, for the heated chains, the original move allowed the probability of proposing whether a particular variable was included or not to depend upon the temperature of the chain. We found that this encouraged too many proposals to models with a large number of variables (in the heated chains) which were very frequently rejected.
3, Pr(p c. T) = 12W(F)<0, so that the truncation has a negligible effect. The space of candidate models is reduced from 2 p to P T j~1 p j which is still considerably large. Finally, in GUESS we use the same hierarchical conjugate prior structure for the regression coefficients presented in  [7] , where the g-prior on the genetic effects, that replicates the covariance structure of the likelihood, is coupled with an inverse gamma hyper-prior on the selection coefficient g, giving rise to the socalled Zellner-Siow prior and a recommended heavy tailed distribution for the regression coefficients. The original GPU-enabled version of GUESS/ESS++ is freely available at http://www. bgx. org. uk/software/guess. html with an installation guide and an extensive description of the features of the algorithm.
Several alternatives have been proposed in the literature to select significant MPPI either based on prediction considerations  [51]  or FDR principles  [52]. Here, we proposed a strategy similar to the ''Bayes/non-Bayes compromise'' described in  [53]. However, instead of deriving empirical p-values as the proportion of permuted datasets for which the MPPI exceeds the MPPI for the observed data, the permutation strategy allows us to define the MPPI threshold at a specific empirical FDR level. Specifically, for each group of phenotypes, we compute the MPPI for the observed data and, based on the same prior specification and the same parameters for the GUESS algorithm, the MPPI for artificial datasets created by permuting three times the rows (subjects) of the observed traits. Overall for each group of traits 819,882 (273,29463) observations from the null distribution were obtained using this procedure.
The MPPI threshold is then defined as the MPPI level for which the ratio between the number of declared associations in the shuffled datasets and the observed dataset is not greater than a specified FDR level. Since the sample size needs to be large to evaluate the tail of the MPPI distribution in the artificial datasets, we combined the MPPI of the null distributions for all the artificial groups of phenotypes with the same dimension (triplets, pairs and singleton). Table  S5  shows for each branch of the two trees the sample size (and number of artificial datasets) used to approximate the MPPI null distribution, the MPPI threshold and the number of MPPI declared significant at 5% empirical FDR.
piMASS analysis Each trait was normal transformed using the R function qqnorm. The effect of sex, age and body mass index was removed by performing standard multiple linear regression software and then running piMASS v0. 9 (www. bcm. edu/cnrc/mcmcmc/ pimass) on the residuals from this regression. In order to match the prior on the model size used in GUESS, we set the minimum and the maximum of p (the prior probability that a SNP is truly associated with the phenotype) equal to 1 and 56 out of the total number of SNPs (-pmin 1 -pmax 56), restricting the minimum and maximum number of SNPs in a model to be 1 and 104 (smin 1 -smax 104) without any constraint on the hyper-parameter h and no cut-off on MAF (-exclude-maf 0). We ran piMASS with 10 6 warm-up steps followed by 10 7 sampling steps (-w 1000000 -s 10000000), recording a sample every 10 steps (-num 10).
Sparse SPLS analysis We used the spls function from the mixOmics R package (http://cran. r-project. org/web/packages/mixOmics/index. html) [55] ,  [56]  to predict the multivariate outcome by a linear combination of SNPs. In accordance with the structure of the multiple traits simulated datasets, we only retained one axis (ncomp = 1) and investigated nine different values of the number of SNPs in this component (KeepX) ranging from 4 to 36. SNPs contributing to the component are defined as those with non-zero loadings coefficient. In this special case where only one component is retained for the regression model, SPLS corresponds to canonical regression  [12]. Building on the known structure of the multiple responses simulated dataset we were able to fix the number of components as well as the number of the SNPs contributing to each component.
Multivariate LASSO analysis We fitted a LASSO-type penalized multivariate linear regression model using glmnet (http://cran. r-project. org/web/ packages/glmnet/index. html) R package  [11]. The LASSO penalty used in this model generalizes the group LASSO penalty to account for potential correlation within the multivariate response. To accommodate for continuous multiple responses, the response type was set to family = mgaussian, and the LASSO penalty was enabled by setting alpha = 1. The penalty l was calibrated based on the first replicate of each multiple traits simulated dataset such that the number of retained SNPs by MLASSO is consistent with the sequence of values of KeepX defined in the SPLS analysis. The resulting set of nine values for l was used in all replicates of the two simulated multiple traits scenarios.
Leonardo Bottolo received funding from the  Wellcome Trust   Value-in-People award. Leonardo Bottolo  and  Sylvia Richardson  acknowledge support from the  Medical Research Council  (grant  G1002319 ). Marc Chadeau-Hyam  acknowledges support from  European Community 's  Seventh Framework Programme  ( FP7/ 2007-2013)  under grant agreement no. 226756  (Envirogenomarkers to SA Kyrtopoulos). Enrico Petretto acknowledges funding from  European Community 's  Seventh Framework Programme  ( FP7/2007-2013)  under grant agreement no. HEALTH-F4-2010-241504  (EURATRANS) and  Medical Research Council UK. David Tregouet  and  Enrico Petretto  acknowledge support from  PHC   ALLIANCE 2009  ( 19419PH ) grant. The  Gutenberg Health Study is funded through the government of Rheinland-Pfalz (''Stiftung Rheinland Pfalz fu ¨r Innovation '', contract number  AZ 961-386261/733 ), the  research programs ''Wissen schafft Zukunft'' and ''Schwerpunkt Vaskula ¨re Pra ¨vention'' of the Johannes Gutenberg-University of Mainz  and its contract with  Boehringer Ingelheim  and  PHILIPS Medical Systems  including an unrestricted grant for the  Gutenberg Health Study.
6 (Illumina) or by the Bowtie software  (29)  with either an exact 36 nt match or with a single or two mismatches. Reads mapped to multiple locations were discarded, and no more than two redundant reads having exactly the same genomic coordinates were used to identify enriched regions. Tag mapping in the large families of highly related ncRNA genes was performed with the Bowtie software, allowing two perfect matches in the genome. ChIP-Seq data were submitted to the Gene Expression Omnibus database and assigned the identifier GSE39263. Description of ChIP-Seq data processing and integrative analysis is available in Supplementary Materials and Methods (Supplementary data).
RNA-seq differential expression analysis Total RNA was isolated from HeLa-S3 and FLP143 cells treated as described earlier in the text. After selection of polyA+mRNA, RNA-seq libraries preparation was performed using Illumina TruSeq RNA Sample Preparation kit according to manufacturer protocol. All RNA samples were sequenced using the Illumina Genome Analyzer GAII and aligned to the hg19 reference genome using TopHat software  (31). Aligned reads were processed with Cuffdiff  (32)  to measure the differential expression using the RefSeq genes as reference annotation. Only the transcripts matching the genes potentially regulated by ZNF143 (TSS located at +/À2 kb of a ChIP-Seq peak summit) were analysed, and the expression value for each transcript was measured by a FPKM value (Fragments per kilobase of transcripts per million mapped tags).
We identified 476 A F G H E B C D Figure 1. Genome-wide characterization of ZNF143-binding events in human and mouse cells. (A) Correlation plots of ZNF143-binding events in four human cell lines. Grid of pairwise scatter plots with associated Pearson correlation coefficient obtained using Cistrome software  (33). The blue dots coordinates represent the normalized enrichments in reads for each cell lines at all the peaks coordinates (Supplementary Table  S1 ). The regression line is indicated in red and the Pearson correlation coefficients are indicated in the squares on the top right (B) Pie charts showing distribution of the ZNF143-binding events in different genomic regions. A promoter is defined as a region within À2 kb from the TSS of a gene; downstream, 2 kb downstream of the end of a gene; the distal intergenic are regions excluding a promoter, downstream, 5 0 UTR, 3'UTR, introns and (continued) ZNF143-binding events located at +/À2 kb from the TSS of a ncRNA gene belonging to four subsets (Supplementary Table  S6  and Supplementary Figure  S6 ).
Genome-wide analysis of ZNF76 binding in T-REx HEK293 cells reveals a total overlap with ZNF143 binding In mammalian cells, ZNF76 was characterized as the ZNF143 paralogue with an activator and repressor function  (16, 38). We asked whether the identified ZNF143 targets are recognized by ZNF76 in human T-Rex-HEK293 cells expressing ZNF76 (Supplementary Figure  S1D ). To facilitate ChIP, clonal cell lines expressing ZNF76 were generated (Supplementary Figure  S1A ), and we characterized the loci occupied by this factor by ChIP-Seq. The data were compared with those obtained by the anti-HA ChIP-Seq on FLP143-HA cells. An enrichment correlation coefficient of 0. 94 was observed between the two experiments for all of the binding events (Figure  1H ). Obviously, a de novo motif search showed that the ZNF76-binding events include the SBS1 or SBS2 motifs described earlier in the text.