dataesr/scientific-paragraphs-categorization huggingface dataset:
An overall picture of the region is represented in Figure  2  and Table  2  summarizes the correspondences between the formerly published and the predicted modules. The genomic positions of each described GMs and predicted modules are given in Supplementary Data file 'Klebsiella pneumoniae/Klebsiella modules. tsv'. Overall, our approach confirms the GMs that were functionally described by the authors, and provides novel insights for those that were uncharacterized. Only GM5 and GM7 were not predicted by panModule since they are composed of persistent genes. The predicted Module 12 is perfectly identical to the GM1 which codes for colibactin. Module 17 is extremely similar to GM6, only 2 genes which are likely unrelated to microcin biosynthesis were excluded from panModule predictions. Finding both GM2 (VirB) and GM3 (yersiniabactin) in the same predicted Module 13 indicates that they are conserved together in most K.
pneumoniae genomes and suggests that they were exchanged together in the evolution of this species. Only two genes involved in the VirB secretion system are absent from the predicted module because they are not found conserved with other VirB system genes in a large number of genomes. Among GMs that were described as unknown, GM4 and GM8 are composed of multiple modules predicted by panModule. Regarding GM4, we noticed that the genes that are in the same modules have similar annotations. No clear function could be inferred from Module 14. However, Module 15 contains mainly genes encoding enzymes and as such may be a metabolic pathway whereas Mod- Genomic modules (GM) corresponds to the modules that were characterized in the original publication. The panModule prediction column contains the correspondences between the predicted modules and their GMs.
Looking more broadly at the pangenome level, a total of 315 out of 566 K. pneumoniae genomes have a predicted variable region in the same integration spot as the one of GM1-GM6 region of the 1084 strain. It is a highly variable region, as there are 128 different organizations among the 315 genomes, with 108 different combinations of modules. The KPHPI208 organization appears only once in the pangenome as such, but 13 other genomes include all of those modules as well. A dynamic visualization of those 128 organizations with their module composition was generated by PPanGGOLiN and is available in Supplementary Data file 'Klebsiella pneumoniae/spot 19. html'.
Conclusion We presented a novel method, named panModule, which groups genes into modules among thousands of genomes. Our approach uses a partitioned pangenome graph, which makes large-scale comparisons easier to compute. We benchmarked it against a curated set of E. coli genomes which were expertly annotated and whose GIs were divided into modules. We showed that panModule predictions were quite reliable regarding those annotations even for incomplete genomes. We illustrated the usefulness of our approach by revisiting the curated annotation of a genomic island in K. pneumoniae 1084. Overall, we believe that panModule provides an original approach to identify conserved modules in the variable regions of genomes, which may help to determine their function but also to better understand their complex evolutionary history.
The panModule method is freely available and easily installable as part of the PPanGGOLiN software suite, and can therefore be coupled with the other tools provided by the software, such as the analysis of pangenome partitions, the detection of GIs and spots of insertion. A potential improvement of the method presented here could be to include information from the species phylogeny in the computation of modules. Indeed, the calculation of Jaccard similarity could be weighted by a phylogenetic distance to favor the grouping of genes from distant strains into modules.
Aims: Finerenone significantly reduced the risk of kidney and cardiovascular (CV) outcomes in patients with chronic kidney disease and type 2 diabetes in the FIDELIO-DKD trial (NCT02540993). This exploratory subgroup analysis investigates the effect of glucagon-like peptide-1 receptor agonist (GLP-1RA) use on the treatment effect of finerenone. Materials and Methods: Patients with type 2 diabetes, urine albumin-to-creatinine ratio (UACR) 30-5000 mg/g and estimated glomerular filtration rate 25-<75 ml/min per 1. 73 m 2 receiving optimized renin-angiotensin system blockade were randomized to finerenone or placebo.
| Study design and patient population The study design of FIDELIO-DKD (NCT02540993) has been described in detail previously (Figure  S1 ). 17, 24 Eligible patients were ≥18 years of age with T2D and a clinical diagnosis of CKD defined as either moderately increased albuminuria [urine albumin-to-creatinine ratio (UACR) ≥30 to <300 mg/g], an eGFR ≥25 to <60 ml/ min/1. 73 m 2 , and a history of diabetic retinopathy, or severely increased albuminuria (UACR ≥300 to ≤5000 mg/g) and an eGFR ≥25 to <75 ml/min/1. 73 m 2. Included patients received the maximum tolerated labelled dose of an angiotensin-converting enzyme inhibitor or an angiotensin receptor blocker for ≥4 weeks before the screening visit and had a serum potassium concentration of ≤4. 8 mmol/L at both the run-in and screening visits. Key exclusion criteria included glycated haemoglobin >12% (>108 mmol/L) and chronic symptomatic heart failure with reduced ejection fraction [New York Heart Association (NYHA) Class II-IV].
| Procedures and outcomes In FIDELIO-DKD, patients were randomized 1:1 to receive oral finerenone (10 or 20 mg) or matching placebo once daily (od). All participants and study personnel (except for the independent data monitoring committee) were masked to treatment allocation. Urinalysis was performed centrally during run-in and screening visits as well as at baseline, month 4, month 12 and then every 12 months thereafter. Central laboratory values, including serum potassium and serum creatinine, were obtained at all study visits. The key outcome assessed in this exploratory analysis was change in UACR from baseline to month 4. Composite kidney and CV outcomes were also evaluated; the primary composite kidney outcome of the FIDELIO-DKD trial was time to kidney failure [defined as end-stage kidney disease (initiation of chronic dialysis that lasts for ≥90 days or kidney transplantation) or sustained eGFR <15 ml/min/ 1.
X is the marker matrix, centered and scaled as: x ij ¼ ðw ij À 2f j Þ= ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ffi 2f j ð1 À f j Þ q , with w ij the number of copies of the reference allele f0,1,2g and f j the frequency of the reference allele. Parameters for this linear model were set as follows. For each simulated dataset we sampled from the available SNPs a set of n QTL QTLs and a set of n poly polygenic SNPs, as well as their corresponding genetic variances for each selected marker. To reduce the impact of extreme MAFs on genomic prediction  (Uemoto et al. 2015)  and QTL detection, we focused on frequent QTLs by drawing the n QTL and n poly SNPs from those with a MAF ! 0:15. In all simulations, we selected a total of n QTL ¼ 5 large QTLs, varying the corresponding proportion k of total genetic additive variance r 2 g as described below.
The phenotypic variance and mean were respectively set to r 2 y ¼ 100 and l ¼ 0, and SNP heritability h 2 ¼ r 2 g r 2 y was varied across simulation settings. We constructed 13 scenarios with different proportions k of genetic variance attributed to the QTLs, with 10 independent datasets created for each (Table  1 ). For the SNPs randomly selected as QTLs and polygenics SNPs, the corresponding effect b i for selected SNP i was set as follows: b i ¼ 1 2 u i ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 10 À4 r 2 g 2MAF i ð1 À MAF i Þ s if SNP i is polygenic 1 2 u i ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi kr 2 g 2MAF i ð1 À MAF i Þ s if SNP i is a QTL ; 8 > > > > > < > > > > > : where u i was drawn from a discrete UniformfÀ1; 1g distribution to allow nonnull effects to take on positive or negative values.
For unselected SNPs (i. e. , null SNPs), b i was set to 0. We varied the proportion of genetic variance attributed to each QTL between k ¼0. 725 and 5%, with a greater density of values evaluated between 0. 725 and 2%; we focused in particular on this range as it corresponds to more plausible QTL sizes and facilitated a study of the sensitivity of BayesR to small changes. For each value of k, the same n QTL ¼ 5 QTLs were used across scenarios, but the number (and thus the subset) of polygenic SNPs used varied (see Table  1 ). As the same 5 QTLs were simulated across scenarios for each of the 10 independent datasets, a total of 50 QTLs was considered. Finally, each scenario was run for four different levels of heritability h 2 ¼ f0:1; 0:3; 0:5; 0:8g, and we evaluated the performance of BayesR for two alternatives: (1) using genotype data that excludes the 5 known QTLs, resembling a classic 50k genotyping array ("50k data"); and (2) using genotype data that includes the 5 known QTLs, which mimics a custom 50k genotyping array ("50k custom data").
In total, this corresponds to 13 Â 10 Â 4 Â 2 ¼ 1040 simulated datasets. To investigate the sensitivity of BayesR to a different underlying genetic architecture, we also simulated a secondary set of simulations in an analogous manner with 5 large QTLs as well as 5 additional intermediate QTLs, whose percentage of genetic variance was set to 10% of that of the large QTLs. The settings for these additional simulations are described in Supplementary Table  S1.
Model parameters are then estimated using the posterior mean across iterations, after excluding the burn-in phase and thinning draws. Here, the Gibbs sampler was run for a total of 50,000 iterations, including 20,000 as a burn-in and a thinning rate of 10. In this study, we used the open source Fortran 90 code described in  Moser et al. (2015)  and available at https://github. com/ syntheke/bayesR. We made a few modifications to this code, notably adding the posterior variance of estimated SNP effects at each iteration to the output; our modified BayesR code may be found at https://github. com/fmollandin/BayesR_Simulations. Prediction accuracy for BayesR was quantified using the Pearson correlation between the true phenotypic values (y) and those estimated using BayesR (ŷ) in the validation set.
Modifying the proportion of genetic variance of the large effect class did not appear to have a strong impact on the validation correlation; nevertheless, we observed differences in correlation among the three prior values that reached up to 2. 6 and 1% for the 50k and 50k custom data respectively. However, the posterior mean of the number of SNPs assigned in each class and its associated posterior estimated variance do appear to be somewhat affected by this parameterization (Table  2 ). To assess the impact of the prior specification on per-SNP effect estimates, we calculated the Pearson correlation between the estimated posterior means bi across SNPs, simulated scenarios and datasets. Among the three prior specifications, the correlation of estimated SNP effects (between pairs of prior parameter settings for a given proportion of additive genetic variance) was between 97.
(2012). Predictive power of BayesR in varied simulation settings. We next sought to investigate the predictive power of BayesR across simulation scenarios, varying the contribution of QTLs to the additive genetic variance (which we refer to as scenarios below), heritability, and use of 50k or 50k custom genotype data. The mean validation correlation (over the ten independent datasets simulated for each) for each simulation scenario illustrates the expected drop in prediction quality for decreasing heritabilities, whether 50k or 50k custom data are used (Figure  1 ). For the former, the mean (6sd) validation correlation across scenarios is 0. 125 (60. 048), 0. 301 (60. 057), 0. 447 (60. 058), and 0. 650 (60. 049) for h 2 ¼ f0:1; 0:3; 0:5; 0:8g. For the latter, the inclusion of the true QTLs among the genotypes unsurprisingly leads to higher validation correlations, with mean (6sd) values across scenarios equal to 0.
Similar results are observed at this level of heritability regardless of the simulated effect size of the QTLs. However, for h 2 ¼ f0:3; 0:5; 0:8g, 50k custom data led to a nearly systematic gain in performance: the average increase in validation correlation was 0. 011 (60. 014), 0. 019 (60. 020), and 0. 031 (60. 030) across QTL effect size scenarios, and attained maximum values of 0. 076, 0. 112, and 0. 160, respectively. For a given heritability, Supplementary Figure  S1  also shows marked improvements in prediction when including QTLs simulated with large shares of additive genetic variance. QTL mapping using BayesR. A natural first tool to investigate for QTL mapping is the neighborhood PIP defined in Equation (  6 ). We focus on the behavior of the neighborhood PIPs for the true QTLs across scenarios (Figure  2 ), averaging over the 50 QTLs available for each (5 QTLs Â 10 independent datasets); note that as this is a window-based measure, this measure can be computed for the true QTLs whether the 50k or 50k custom data are used.
When h 2 ¼ f0:3; 0:5; 0:8g, as the simulated share of genetic variances for QTLs increases for both the 50k and 50k custom data, the null neighborhood PIP decreases and the large-effect neighborhood PIP increases. Across all simulated datasets and scenarios, the average (6sd) small-and medium-effect neighborhood PIPs are 0. 117 (60. 053) and 0. 058 (60. 040), respectively, illustrating that these two classes appear to be less often filled compared to the null and large classes (although all four classes do appear to be used outside of the lowest heritability setting). The neighborhood PIP results provide a preview of how QTLs are grouped into nonnull effect classes according to the neighborhood MAP rule [Equation (8); Figure  3 ]. In all simulation settings, no QTL neighborhoods were assigned to the small effect class using this criterion.
When h 2 ¼ 0:1, without surprise, all QTLs were classified as null. For h 2 ¼ 0:5, a very small number of QTL neighborhoods were assigned to the medium effect class for the 50k data; increasing to h 2 ¼ 0:8 led to a larger number moving to this Figure  2  Neighborhood posterior inclusion probabilities across simulation settings. Panels represent combinations of heritability (columns; h 2 ¼ 0. 8 to 0. 1) and type of data used (rows; 50k or 50k custom). Bars represent average (across 5 QTLs Â 10 independent datasets) neighborhood PIP values for the four BayesR effect size classes: null (gray), small (blue), medium (yellow), and large (red). which increases to 22 for k ¼ 5%. However, including the causal markers in the genotype data enabled detection of QTL windows at k ! 1:3% for h 2 ¼ 0:3, with up to 30 correctly detected at k ¼ 5%.
Below this threshold, a large portion of QTL windows are not detected. Above this threshold, QTL window detection appears to become feasible once the simulated per-QTL percentage of genetic variance attains about k ¼ 2%. In the 50k custom data, QTL window detection does not however depend on the amount of LD, although we do note lower inclusion probabilities for QTLs in very high LD with their neighbors as compared to the 50k data. Similar to the 50k data, there is an effect size threshold at about k ¼ 1:8% at which QTL windows are more frequently detected. Because the same five QTLs are simulated in each independent dataset across effect size scenarios, Figure  5  also allows for their specific detection to be followed across configurations. Thus, it can be seen that some QTLs windows are not detected in any of the scenarios, while others are more easily detected, even for lower shares of the genetic variance.
That said, there are occasionally discontinuities in detection observed for increasing shares of the variance (i. e. , a QTL window correctly identified for k ¼ 0. 02 but not 0. 025). With the exception of h 2 ¼ 0:1, which had very weak detection in all scenarios and datasets, we found similar conclusions for h 2 ¼ 0:3 and 0. 8, with respectively slightly smaller and larger overall inclusion probabilities than those shown in Figure  5. Beyond the assignment of SNPs to effect classes using the neighborhood PIPs (and corresponding MAP rules), BayesR also provides posterior estimates of variability at several levels, including the additive genetic variance rg , the cumulative variance for each of the three nonnull effect classes, and the variance of each SNP. Before discussing the latter (arguably the most pertinent for QTL mapping), we verify the estimation quality of the additive genetic variance.
QTL windows that are represented by white to red cells are correctly detected using the neighborhood nonnull MAP. Genome-wide posterior estimate of the proportion of genetic variance per SNP for a single dataset with h 2 ¼ 0:5. Posterior estimates of the per-SNP proportion of genetic variance across all P ¼ 46, 178 SNPs for one of the simulated independent datasets. Panels represent a given simulation setting for percentage of genetic variance per QTL (columns; k ¼ f1%; 2:5%; 5%g) and data type (rows; 50k vs 50k custom). Points represent individual SNPs, and are colored according to their true effect class (null, polygenic, in the neighborhood of a true QTL, and true QTL). The same five QTLs appear in each panel; true QTLs are only present in the 50k custom data. which a cumulative integration of nonnull SNPs across the window provides additional information.
Because the CIP and estimated variance are both quantitative criteria, we sought to identify whether QTLs or their immediate neighborhoods tend to be more highly ranked than other SNPs using an Area Under the Receiver Operating Characteristic curve (AUROC). When considering genome-wide results, there is a considerable imbalance between positive and negative cases (e. g. , 5 Â 15 vs $40 k). As our focus was on the ranking of the top SNPs, we instead calculated AUROC values based on the 10 and 150 most highly ranked SNPs using V i and the CIP, respectively. AUROC values were averaged across the 10 datasets for each simulation setting, and undefined values (i. e. , cases where no positives were included among the top SNPs) were set to be zero. As expected, AUROC values were very small in cases of low heritability or small QTL effect sizes (Figure  7 ).
However, as heritability (h 2 > 0. 3) and QTL effect sizes increase (k > 2%), a marked increase in AUROC can be observed. For example, for sufficiently large values of heritability and QTL effect sizes, (h 2 ! 0. 5, k ! 2%) AUROC values attained nearly 0. 80 for the 50 k custom data using either the CIP or the posterior variance. Finally, we note a slight advantage to the CIP criterion compared to the posterior variances, both for 50k and 50k custom datasets. Taken together with the previous results shown in Figure  5 , these results suggest that the top rankings of SNPs provided by the CIP and posterior variance indeed tend to prioritize true positives, particularly in intermediate to favorable scenarios. Comparison of BayesR with BayesCp. Although BayesR has been our primary focus in this study, it is also of interest to compare its performance to that of a related widely used method, BayesCp  (Habier et al.
g. , h 2 ¼ 0:1), could provide insight on this point. Finally, when sampling SNPs to represent QTLs in our simulations, we chose to limit the choice to those with a MAF > 0. 15, thus excluding those with rare alleles. Although this allowed us to avoid edge cases that would arise with very low MAFs, making it easier to homogenize simulated datasets across different selections of QTLs, this however is an important consideration in QTL mapping.
Figure3Neighborhood MAP rule for QTL mapping across simulation settings. Number of true QTL windows (out of 5 QTLs Â 10 independent datasets simulated for each scenario, corresponding to a total of 50) correctly assigned to the medium (yellow) and large (red) effect size class using the neighborhood MAP rule. Panels represent data type (columns; 50k and 50k custom) and heritability (rows; h 2 ¼ 0:8 to 0. 1). The small effect class is not represented because it was empty across all simulation configurations.
Figure5Genome-wide posterior estimate of the proportion of genetic variance per SNP for a single dataset with h 2 ¼ 0:5. Posterior estimates of the per-SNP proportion of genetic variance across all P ¼ 46, 178 SNPs for one of the simulated independent datasets. Panels represent a given simulation setting for percentage of genetic variance per QTL (columns; k ¼ f1%; 2:5%; 5%g) and data type (rows; 50k vs 50k custom). Points represent individual SNPs, and are colored according to their true effect class (null, polygenic, in the neighborhood of a true QTL, and true QTL). The same five QTLs appear in each panel; true QTLs are only present in the 50k custom data.
Average (across all simulation scenarios and independent datasets) of the posterior mean cardinality of each BayesR SNP effect class (null, small, medium, large) for three parameterizations of the prior large effect class variance Figure1BayesR predictive performance across simulation settings. For each setting (h 2 and percentage of genetic variance assigned to each QTL), points represent mean validation correlations across 10 independent datasets. Heritability values are represented by dark to light blue (h 2 ¼ 0:8 to 0. 1), and solid and dotted lines represent results for the 50k and 50k custom datasets, respectively. The error bars correspond to the Monte Carlo standard errors computed across the 10 datasets for each setting.
AUROC for the CIP and posterior variance across simulation settings. AUROC values vs heritability for the CIP (left column; based on top 150 values) and posterior variance (right column; based on top 10 values) by data type (rows; 50k vs 50k custom) for BayesR. Percentage of genetic variance per QTL is represented by different colored lines, and individual points represent averages across the 10 simulated datasets for each setting. The error bars correspond to the Monte Carlo standard errors computed across the 10 datasets for each setting.
5 40,783. 25 5,054. 51 300. 44 39. 80 0 25. 55 14. 89 9. 60 1 (default) 40,568. 94 5,256. 72 336. 12 16. 23 0 26. 65 6. 91 10. 15 2 40,501. 21 5,307. 33 361. 08 8. 38 0 26. 81 17. 97 5. 45 For a given dataset, each class size (#) is computed as the posterior mean of the number of SNPs assigned to each class across iterations, and V j is the posterior estimated cumulative variance of each class j. F. Mollandin, A. Rau, and P. Croiseau | 5 class for both the 50k and 50k custom data. When not assigned to the null class, it was much more common to attribute QTL neighborhoods to the large effect class; the number of correctly identified QTL neighborhoods increased with the simulated effect size and/or heritability, as well as when the causal markers were included among the genotypes; what's more, these gains tend to accumulate when taken together.
AUROC for the CIP and posterior variance across simulation settings. AUROC values vs heritability for the CIP (left column; based on top 150 values) and posterior variance (right column; based on top 10 values) by data type (rows; 50k vs 50k custom) for BayesR. Percentage of genetic variance per QTL is represented by different colored lines, and individual points represent averages across the 10 simulated datasets for each setting. The error bars correspond to the Monte Carlo standard errors computed across the 10 datasets for each setting. CIP top 150 Vi top 10 0. 9 0. 6 50k 0. 3 AUC 0. 0 0. 9 0. 6 50k custom 0. 3 0. 0 0. 2 0. 4 0. 6 0. 8 0. 2 0. 4 0. 6 0. 8 Heritability % of genetic 0. 01 0. 02 0. 03 0. 04 0. 05 variance per QTL Figure 7.