dataesr/scientific-paragraphs-categorization huggingface dataset:
By contrast, the DB6 site was a fertile and managed beech forest, with a significantly higher efficiency conversion of photosynthates into biomass compared to less fertile and unmanaged sites  (Vicca et al. , 2012; Campioli et al. , 2015). The long term annual total NPP at the site was 780 g (C) m -2 yr -1 over the period 1992-2007, with a significant part allocated belowground  (Alberti et al. , 2015) , while heterotrophic respiration estimated at the site using either bomb-carbon  (Harrison et al. , 2000)  or 895 mineralization rates  (Persson et al. , 2000)  was around 200 g (C) m -2 yr -1 , resulting in similar NEP estimates by EC flux measurements versus biometric data combined with process studies. Fluxes Database Cluster (Table  1 ). Values of R eco and NEP were 82% larger and 40% lower, respectively, in Etzold et al.
(2011) compared with the default database values that do not explicitly correct for advection. However, the Etzold et al. (2011) mean EC-derived NEP was much closer to NEP values calculated from the net annual increment in the woody and non-woody biomass and soil C storage using four different biometric and modelling methods (range 307-514 g (C) m -2 yr -1 , mean 421 g (C) m -2 yr -1 ). The CSE obs value derived from  Etzold et al. (2011)  was 23%, and comparable to the value of 25% 905 that can be calculated from the decoupling-corrected EC budget computed by  Paul-Limoges et al. (2017)  for the same site for the years 2014-2015, in which the decoupling correction to account for undetected below-canopy fluxes doubled R eco and reduced NEP from 758 to 327 g (C) m -2 yr -1. These alternative CSE obs estimates were thus much lower than the default CSE obs value of 48% (Fig.
Forest net greenhouse gas balance dominated by carbon Based on the available data, the net GHG balance of the 31 forests investigated was generally not significantly affected by N 2 O or CH 4 (Fig. 7 ), with the caveat that these fluxes were not actually measured in situ everywhere, nor with the same intensity and duration as CO 2. Thus, the uncertainty in non-CO 2 GHG fluxes is much larger (possibly > 100%) than for 930 multi-annual EC-based CO 2 datasets, where a typical uncertainty is of the order of 10-30%  (Loescher et al. , 2006). Nonetheless, the N 2 O and CH 4 emissions observed by different methods in forest soils were typically two orders of magnitude smaller than the CO 2 sink (in GWP equivalents), which means that the quality of CO 2 estimates dominates the overall uncertainty in our forest GHG budgets.
This means adequate water supply as precipitation, reasonably low summertime evaporative demand, mild winters and temperate summers, long growing seasons. In other words, there are many gaps in the multi-dimensional variable space, which is incompletely explored by the available dataset. Thus, any regression analysis that would correlate 965 NEP and other C fluxes with N dep , without simultaneously accounting for climate, would be flawed, as  Sutton et al. (2008)  concluded from their re-analysis of the data used by  Magnani et al. (2007). A dC/dN slope calculated directly from a (linear or non-linear) mono-factorial regression analysis of GPP or NEP vs. N dep would misleadingly attribute the whole C flux variability to N dep while ignoring climate effects  (Fleischer et al. , 2013). In addition, a range of other potential explanatory variables such as soil type, especially the water holding capacity ( FC - WP ), soil fertility  (Vicca et al.
, 2012; Legout et al. , 970 2014) , tree species, stand age  (Besnard et al. , 2018) , are potentially needed to explain the observed variability  (Flechard et al. , 2019). Our attempts with more advanced forms of regression analyses (e. g. multiple, stepwise, mixed non-linear models, residual) did not prove successful at untangling the multiple inter-relationships, even when normalising GPP to climate proxies such as the length of the growing season, growing degree days, etc, due to the limited size and very large diversity of the dataset. This shows that a simple pattern to explain the coupling of carbon and nitrogen budgets with the available data 975 and knowledge is unlikely.
990 Another possible indicator of N saturation in the present dataset may be provided by the comparison of the relationships of C/N ratios of foliage and top soil (5 cm) to atmospheric N r deposition (Fig. 9A-B ). Since leaf N content was not only dependent on N r deposition but also on the ecosystem type (Fig. 4E ), C/N ratios are shown separately for the different vegetation classes in Fig. 9. There was a clear negative correlation of leaf C/N ratio to N dep for coniferous forests (ENF, spruce and pine pooled: exponential fit R 2 = 0. 86, p < 0. 01) and a similar but not significant trend for SN (linear R 2 = 0. 29) 995 (Fig. 9A ); for the other ecosystems (DBF, MF, EBF) there were not enough data to derive trends. In top soils (Fig. 9B ), there was also a broad downward trend of C/N ratios with increasing N dep within the ENF and SN classes, but only for N dep up to 2.
{Insert Figure  9  here} Following definition ii) of N saturation given above, the sum of inorganic N r losses, heavily dominated by DIN leaching at the upper end of the N dep range in our datasets (Fig. 3 ), may indicate various stages of N saturation in all forests with N dep > 1015 1-1. 5 g (N) m -2 yr -1. However, given the large uncertainties in the N budgets, a more confident threshold for an advanced saturation stage could be placed at 2-2. 5 g (N) m -2 yr -1. Such numbers are entirely consistent with the leaching risk classification of European forests proposed by  Dise and Wright (1995) , with low leaching risk at N dep <1 g (N) m -2 yr -1 , intermediate risk at N dep in the range 1-2. 5 g (N) m -2 yr -1 , and high risk at N dep > 2. 5 g (N) m -2 yr -1. The results are also in line with the review by De  Vries et al.
, 2016). This also means that the NO 3 -leaching flux is not necessarily a good proxy of the severity of N saturation, though this depends on which of the several definitions of N saturation is considered. The upper threshold of 2-2. 5 g (N) m -2 yr -1 happens to coincide with the levelling off of GPP, R eco and NEP, and the further 1030 reduction in C fluxes at higher N dep levels (Fig. 4A-C ). Whether this should be interpreted as a negative impact of advanced N saturation on soil processes and plant functioning and, hence, C sequestration potential, is not straightforward  (Binkley and Högberg, 2016). If the parallel effects of climate, soil fertility, other nutrient limitations, tree species traits, age and planting density are overlooked in a simplistic, first-order interpretation, the dataset hints at an "optimum" N dep level around 2 g (N) m -2 yr -1 , beyond which no further benefits (in carbon terms) could be gained from further atmospheric N r additions, 1035 which would be consistent with the 2-2.
Conclusion We provided estimates of carbon, nitrogen and greenhouse gas budgets for 40 flux tower sites over European forests and 1045 semi-natural vegetation, compiled from a large variability of state of the art methods that can be applied in such a network approach. The CO 2 budgets from well-established EC methods were the least uncertain, followed by GHG budgets of forests, then the CH 4 and DIC/DOC fluxes of wetlands; uncertainty levels were likely highest in the net N budgets, especially at the elevated N r deposition sites where NO 3 -leaching was almost of the same order as N dep. The uncertainty was still compounded by the lack of some data on biological N 2 fixation, N 2 loss by denitrification, and organic N r in rainwater, 1050 in dry deposition and in soil leaching, but some of these unknown terms would compensate mutually to some extent.
, within the REddyProc 850 algorithm embedded in the European Fluxes Database Cluster, was intended to ensure the independence of GPP and R eco estimates, since R eco was estimated from the intercept of the Michaelis-Menten light-response curve fitted to daytime measured NEE. This partitioning procedure should avoid the propagation into the GPP estimate of potential errors in nighttime R eco data, although it still assumes similar dependencies of day-and nighttime respiration to environmental factors, https://doi. org/10. 5194/bg-2019-333 Preprint. Discussion started: 11 September 2019 c Author(s) 2019. CC BY 4. 0 License.
Acknowledgements The authors gratefully acknowledge financial support by the  European Commission  through the two FP-6 integrated projects CarboEurope-IP (Project No. GOCE-CT-2003-505572 ) and NitroEurope-IP (Project No. 017841 ), the  FP-7 ECLAIRE 1095 project  (Grant Agreement No. 282910 ), and the  ABBA COST Action   ES0804. We are also thankful for funding from the  French GIP-ECOFOR  consortium under the  F-ORE-T forest observation and experimentation network , and from the  MDM-2017-0714 Spanish grant. We are grateful to  Christian Bernhofer ,  Robert Clement ,  Han Dolman ,  Axel Don ,  Eric Dufrêne ,  Damiano Gianelle ,  Ruediger Grote ,  Anders Lindroth ,  John Moncrieff ,  Dario Papale ,  Corinna Rebmann ,  Alex Vermeulen , for https://doi. org/10. 5194/bg-2019-333Preprint. Discussion started: 11 September 2019 c Author(s)  2019.
Genome-wide association studies have reported 56 independently associated colorectal cancer (CRC) risk variants, most of which are non-coding and believed to exert their effects by modulating gene expression. The computational method PrediXcan uses cis-regulatory variant predictors to impute expression and perform gene-level association tests in GWAS without directly measured transcriptomes. In this study, we used reference datasets from colon (n = 169) and whole blood (n = 922) transcriptomes to test CRC association with genetically determined expression levels in a genome-wide analysis of 12,186 cases and 14,718 controls. Three novel associations were discovered from colon transverse models at FDR ≤ 0. 2 and further evaluated in an independent replication including 32,825 cases and 39,933 controls.
For instance, the Genotype-Tissue Expression (GTEx) Project (GTEx Consortium 2013) is building a biospecimen repository to comprehensively map tissue-specific eQTLs across human tissues, which currently includes transcriptomes from 169 colon transverse samples. These data provide a remarkable new resource for understanding function in non-coding regions that can be used to inform GWAS. We employed the computational method, PrediXcan  (Gamazon et al. 2015) , to perform a CRC transcriptomewide association study using reference datasets to 'impute' unobserved expression levels into GWAS datasets. Variant prediction models were developed using colon transverse transcriptomes (n = 169) from GTEx (GTEx Consortium 2013) and a larger whole blood transcriptome panel (n = 922) from the depression genes and networks (DGN)  (Battle et al.
2) that are associated with CRC in previous GWAS (P ≤ 5 × 10 -8 ) and were replicated in an independent dataset. We aimed to discover novel loci associated with CRC, and refine established regulatory risk loci by reducing the list of putative gene targets. Employing PrediXcan, we tested genetically regulated gene expression for association with CRC in a two-stage approach. In the discovery stage, up to 8277 gene sets were tested in 12,186 cases and 14,718 controls from the Genetics and Epidemiology of Colorectal Cancer Consortium (GECCO) and the Colon Cancer Family Registry (CCFR). This discovery set was also used to identify potential target genes in the 44 genetic regions harboring 56 known CRC risk variants. We attempted replication of three novel genes that were not positioned within 1 Mb of the 56 previously reported risk variants and with false discovery rate (FDR) ≤ 0.
Discovery of new CRC susceptibility genes In total, multivariate logistic regression was used to test the association of CRC with genetically impute gene expression for 4850 genes from colon transverse models and 8277 genes from whole blood models. We employed PrediXcan in 12,186 cases and 14,718 controls from 16 GWAS studies. Replication was attempted for associations meeting an FDR = 0. 2 threshold in the discovery phase if they were in a novel CRC region using an independent GWAS dataset comprised of 32,825 cases and 39,933 controls from the CORECT consortium, UK Biobank, and additional GWAS as described in Online Resource 1. In the discovery phase, colon transcriptome models identified CRC association with imputed genetically regulated gene expression in three putative novel regions. Two out of three genes tested in the replication dataset were significant after adjusting for multiple comparisons (α = 0.
05/3 = 0. 017) (Online Resource Fig S1, Table  1 ). In addition to being more than 1 Mb away from previously identified risk variants, we confirmed that none of the variant predictors used to impute gene expression for these three genes were in LD (r 2 ≤ 0. 1) with previously published CRC-risk variants. In the 7q22. 1 locus, increased expression of TRIM4 was associated with reduced CRC risk with an odds ratio (OR) of 0. 94 [95% confidence interval (CI) 0. 91-0. 97, discovery P = 2. 2 × 10 -4 ]. Reduced CRC risk was also statistically associated with increased genetically regulated gene expression of TRIM4 in the independent replication dataset (P = 0. 01). The second novel locus, 14q22. 1, was also found to be inversely associated, where increased genetically regulated gene expression of PYGL was associated with decreased CRC risk, showing an OR Models for colon transverse tissues and whole blood were used for imputation of expression into independent GWAS datasets for Colo-rectal Cancer (CRC).
Imputed gene expression was then tested for association with case (ca. )-control (co. ) status in the discovery stage. Novel gene associations with a false discovery rate (FDR) = 0. 2 were assessed in an independent CRC GWAS dataset. As a secondary analysis, the association of genetically determined expression of genes in 44 GWAS-associated risk regions was examined of 0. 90 (95% CI 0. 85-0. 96) in the discovery dataset (discovery P = 2. 3 × 10 -4 ) as well as in the replication dataset (P = 7. 9 × 10 -4 ). Imputed genetically regulated gene expression for SLC22A31 was associated with increased CRC risk in the discovery phase (P = 1. 3 × 10 -4 ), but did not replicate in the independent dataset. We found no associations in novel regions using whole blood variant models that reached FDR = 0.
2 in the discovery phase. Colon Transverse PrediXcan analyses were repeated for TRIM4 and PYGL in the discovery dataset stratifying cases by proximal (n = 4454 cases), distal (n = 3580 cases), and rectal (n = 2936 cases) cancer sites. We excluded 1216 cases from the stratified analysis because the colon cancer site was unspecified. We found that for both genes the effects and p values were similar between the three sites. For TRIM4, the CRC association with genetically imputed gene expression had an OR of 0. 94 (95% CI 0. 90-0. 98, P = 3 × 10 -3 ) in proximal colon cases compared to an OR of 0. 95 (95% CI 0. 90-1. 0, P = 5 × 10 -2 ) in distal colon cases and an OR of 0. 93 (95% CI 0. 88-0. 98, P = 2 × 10 -2 ) in rectal cases. There was no significant difference in the effect estimates between these cancer sites for TRIM4 (Q-test for heterogeneity P = 1.
Refinement of known CRC GWAS-risk regions We first assembled a list of 56 previously reported independent (r 2 ≤ 0. 2) CRC GWAS risk variants and defined a distance-based region surrounding each variant as the chromosomal position of the first reported (index) variant ± 1 Mb (Online Resource 1 Table  S3 ). We then combined overlapping risk regions by taking the minimum and maximum chromosomal positions of all regions that overlapped, resulting in a total of 44 CRC risk regions harboring 1-4 independent CRC-risk variants. In these 44 regions, there was an average of 20 (SD ± 17) protein-coding genes per region annotated by the Consensus Coding Sequence Database (CCDS). The average number of protein-coding genes per region with imputed genetically regulated gene expression in the tissue-specific models was reduced to an average of 10 (SD ± 8) genes in colon transverse, and 14 (SD ± 11) genes in whole blood.
3-rs704017 P = 1 × 10 -4 and 10q24. 32-rs4919687 P = 1 × 10 -2 ). Another 2/10 GWAS risk variant did not replicate in our study (4q31. 1-rs60745952 P = 0. 8 and 16p13. 2-rs79900961 P = 0. 26). In the remaining 3/10 regions, we found that the index variants did not reach genome-wide significance, reflecting power limitations in our discovery dataset (4q32. 2-rs35509282 P = 6 × 10 -3 , 16q24. 1-rs16941835 P = 4 × 10 -3 , and 20p12. 3-rs961253 P = 4 × 10 -5 ). Among the 34 regions containing associated genes, we found that the most significant gene association in the Pre-diXcan analysis was often the strongest candidate based on either known CRC etiology and gene function or results from previous laboratory follow-up (e. g. COLCA2, LAMC1, POLD3, SMAD7, TGFB1). In addition to confirming suspected genes, new candidates were also identified.
These genes are of note due to their chemotherapeutic properties. Specifically, the CXCR inhibitor, Reparixin, is currently under investigation for progression free survival of metastatic triple negative breast cancer in a stage 2 clinical trial (NCT02370238). Interestingly, expression of CXCR1 and CXCR2 has been shown to be elevated in colon tumor epithelium relative to normal adjacent tissue (P < 0. 001). While there is still much to be learned, it is possible that this drug could also be useful for the treatment of CRC  (Dabkeviciene et al. 2015). This study had many strengths, most notably the use of reference transcriptome data to perform gene-level association testing in several large GWAS studies to both uncover novel associations and identify likely functional gene targets in known loci.
By integrating reference transcriptome data, this study focused on genes that are expressed in CRC-relevant tissues. Furthermore, this method provided biologically relevant sets to aggregate variants, thereby improving statistical power by reducing the burden of multiple comparisons. In addition, our study was quite large, being comprised of nearly 100,000 participants across the discovery and replication datasets. Our study had several limitations. For many genes, the predictive R 2 for genetic variant models was relatively low, indicating that a small proportion of the variance in gene expression was explained by these models. In a recent publication,  Su et al. (2018)  demonstrated through extensive simulations that while there is an attenuation of true signal as a results of this, the diminishment in power was less than anticipated and more importantly this does not increase type I error.
Description of study cohorts The discovery phase was comprised of 26,904 participants (12,186 CRC cases and 14,718 controls) of European ancestral heritage across 16 studies (described in methods and materials of Online Resource 1). Details of genotyping, QC and single-variant GWAS have been previously reported  (Peters et al. 2013; Schumacher et al. 2015). The replication phase included a total of 32,825 cases and 39,933 controls. In addition to previously published CRC GWAS studies from CORECT  (Schumacher et al. 2015)  we included UK Biobank (application number 8614) and new CRC GWAS from additional GWAS. A nested case-control dataset from the UK Biobank resource was constructed defining cases as subjects with primary invasive CRC diagnosed, or who died from CRC according to  ICD9 (1530 ICD9 ( -1534 ICD9 ( , 1536 ICD9 ( -1541) )  or ICD10 (C180, C182-C189, C19, C20) codes.
Whole-genome sequencing reference genotype imputation panel We performed low-pass whole-genome sequencing of 2192 samples (details in Online Resource 1) at the University of Washington Sequencing Center (Seattle, WA, USA). A detailed description is provided in the Online Resource 1. In brief, after sample QC and removal of samples with estimated DNA contamination > 3% (  16 ), duplicated samples (5) or related individuals (1), sex discrepancies (0), and samples with low concordance with genome-wide variant array data (11), there were a total of 1439 CRC cases and 720 controls of European ancestry available for subsequent imputation. These data were used as a reference imputation panel for the discovery and replication GWAS datasets.
Imputation of genetically regulated gene expression in study cohort Jointly measured genome variant data and transcriptome data sets were used by  Gamazon et al. to develop additive models of gene expression levels. The weights for the estimation were downloaded from the publicly available database (http://hakyi mlab. org/predi ctdb/). We used these models to estimate genetically regulated expression of genes in colon transverse, and whole blood. These estimates represent multi-variant prediction of tissue-specific gene expression levels. In-depth details of the reference cohort, datasets, and model building have previously been described  (Gamazon et al. 2015). To summarize, jointly measured genome-wide genotype data and RNA-seq data were obtained from two different projects: (1) the DGN cohort  (Battle et al.
The effect sizes (w k,g ) in Eq. (1) were estimated using the elastic net penalized approach. The summation in Eq. 1 is referred to as the genetically determined component of gene expression. The variant models (weights, w_k,g) were downloaded from the publicly available database (http://hakyi mlab. org/predi ctdb/). The heritability of gene expression was used to estimate how well the variant models predict gene expression levels. The narrow-sense heritability for each gene was calculated by  Gamazon et al. (2015) , using a variance-component model with a genetic relationship matrix (GRM) estimated from genotype data, as implemented in GCTA  (Yang et al. 2011). The proportion of the variance in gene expression explained by these local variants was calculated using a mixed-effects model  (Torres et al.
To address linkage disequilibrium among variant predictors,  Gamazon et al. (2015)  used the variable selection method to select a sparser set of (less correlated) of predictors. Specifically, variant weights (w k ) were derived using elastic net with the R package glmnet with α = 0. 5. These weights are available from http://hakyi mlab. org/predi ctdb/. Using Eq. 2, and the reference variant predictor weights (w k,g ), the (unobserved) genetically determined expression of each gene g (GReX) was estimated in our GWAS sample. For both transcriptome models, separate analyses were performed for genetically based expression of genes (up to 2 tests per gene). Genes with predictive R 2 > 0. 01 were tested for association with CRC in our cohort (colon transverse n = 4850 genes, and whole blood n = 8277 genes).
Discovery phase Statistical analyses of all data were conducted centrally at the GECCO coordinating center on individual-level data. Multivariate logistic regression models were adjusted for age, sex (when appropriate), center (when appropriate), and genotyping batch (ASTERISK) and the first four principal components to account for potential population substructure. Imputed genetically regulated gene expression (GREx), was treated as a continuous variable. All studies were analyzed together in a pooled dataset using logistic regression models to obtain odds ratios (ORs) and 95% confidence intervals (CIs). Quantile-quantile (Q-Q) plots were assessed to determine whether the distribution of the P was consistent with the null distribution (except for the extreme tail). All analyses were conducted using the R software (Version 3.