dataesr/scientific-paragraphs-categorization huggingface dataset:
01 from both the HMP, and correlation adjusted method (EBM for PrediXcan, or omnibus for TWAS) was chosen. Consensus between the 2 result sets (with 4 pvalue < 0. 01 thresholds) yielded the most robust findings, while the union of significant genes from the 2 result sets maximized sensitivity of discovery. For comparison of numeric outcomes, such as performance of predictive models or imputed gene expression between data sets or tissues, the distribution of correlation R 2 among multiple genes were compared to R 2 values derived from null distribution using Fisher's transformation through a modified R script originally from the Im lab (https://gist. github. com/hakyim/a925fea01b365a8c605e). Narrow-sense heritability (h 2 ) of phenotype from imputed GWAS data from unrelated patients was calculated using the GREML-LDMS method  [19]  from the Genome-wide Complex Trait Analysis (GCTA) software  [20] , v1.
93. 0beta. For hierarchical clustering, signed -log10p-value with sign of association beta coefficient as indicator of expression change direction were compiled for genes significantly associated to disease phenotype from multiple tissue data sets. Clustering heatmaps were generated using the Bioconductor R package, ComplexHeatmap  [21]  (additional details provided in the S1 Methods in S4 File). Manhattan plots of GWAS data and imputed gene expression phenotype associations were generated using the R package, qqman  [22] , and ggplot2  [23]. GWAS p-values of relevant SNPs were formatted as bedGraph files, and visualized on the UCSC genome browser (http://genome. ucsc. edu/) as custom annotation tracks against appropriate reference genomes. Pre-ranked Gene Set Enrichment Analysis  [24]  against several collection of gene sets and pathways were performed with both PrediXcan and TWAS platforms using the Bioconductor R package fgsea  [25].
Predictive models for genetic regulation of gene expression using training data from CF cohorts To build predictive models of genetic regulation of gene expression with training data from CF patients, we adapted the PredictDB_Pipeline for GTEx_v7 to work with CF genotype and gene expression data from both LCL  [11]  and nasal epithelial biopsy  [12]  data sets. The performance of the predictive models was evaluated by the correlations between predicted and observed gene expression, and genes were filtered at minimal performance suggested by Pre-dictDB. The number of imputable genes (as defined by prediction R 2 > 0. 01 and pvalue < 0. 05), including protein-coding, lincRNA, and pseudogenes, from nasal epithelial biopsy data set consisting of 132 training samples was 2,881; while that from 753 LCL data set was 5,299.
We investigated correlations of our CF LCL model predictions with those of GTEx on the same set of patients. The numbers of imputed genes that passed respective prediction filters are 5,299 from CF LCL, and 3,039 from GTEx Cells_EBV-transformed_lymphocytes (i. e. LCLs), with overlap of 1,623 genes by ENSEMBL gene_id. The correlation of the 1,623 genes between the 2 data sets were calculated and compared to expected R 2 distribution from null (S3 Fig in  S4 File ). The mean R 2 value among 1,623 genes is 0. 51, i. e. the two imputed gene expression data sets are highly correlated, suggesting similar genetic regulation of gene expression in the same cell type in independent training data sets. Also as reported, there is significant cross predictability of the models between different tissues  [9] , and the correlation between imputed gene expression from CF LCLs, and GTEx lung tissue, among 2,552 genes predicted in both data sets, are also significantly above null, with mean R 2 of 0.
Allele bias of gene expression estimation may confound interpretation of hyper-variable genes, such as HLAs Many HLA genes appear to be strongly regulated genetically, as reflected by variance explained or R 2 of the predictive models (S3, S4 Tables in S4 File) and HLA-dominated pathways are highly significant in our previous gene expression association studies  [11, 12]. However, since gene expression quantification relies on mapping of RNA-seq reads to genome/transcriptome sequences, expression levels may be biased towards the reference allele, especially for the hypermorphic HLA genes  [81, 82]. To assess influences of allele bias on gene expression quantification and trait association, we compared different strategies of RNA-seq read mapping from our nasal epithelial biopsy RNA-seq data set.
Discussion We have applied gene expression imputation to mine the CF gene modifier GWAS data set and extracted 379 potential and 52 consensus CF lung disease modifier candidates. The imputation techniques leveraged GTEx integrative training data sets from 48 human tissues  [5] , a large RNA-seq data set from whole-blood (DGN)  [13] , and our own CF gene expression data sets from nasal epithelial biopsy  [12]  and LCL  [11]  samples. Twenty eight of the 52 consensus genes are within 1 Mb of the 5 autosomal genome-wide significant loci  [1] , while 24 consensus modifier genes were not identified in GWAS. Overall, integration of GWAS with eQTL data through gene expression imputation highlighted some candidate modifier genes (   4 , blue triangles). Disease phenotype association testing of the imputed gene expression also predicted the direction of genetically regulated gene expression changes relative to CF lung disease severity, which provides guidance on mechanism of disease modification, and potential intervention strategies.
By using independently developed divergent approaches, we sought to balance sensitivity by combining the results from multiple tissues and platforms, and robustness by consensus of the findings between PrediXcan and TWAS. The consensus and potential CF lung disease modifier genes were then evaluated by biological context through literature review and gene set enrichment analyses. The usefulness of defining the relationship of SNP association to the imputed gene expression association to phenotype, deduced through independent eQTL data sets, can be illustrated at the chr11 locus ( Although EHF and APIP are the nearest genes to the intergenic chr11 GWAS locus with significant lung disease association p-values, PDHX is best predicted to be regulated by SNPs in the region based on current gene expression data.
g. significant interaction between cis-SNPs), which would not be accurately assessed by current predictive models. Furthermore, the number of genes whose expression can be reliably predicted from genetic variants varied among tissues, ranging from ~2,000 to ~10,000, which in large part can be attributed to training sample sizes  [10]  (S2 Fig in  S4 File ). With continued accumulation of tissue samples and improved data quality, e. g. from GTEx, as well as improvement of gene expression quantification, and machine learning techniques, we expect to discover more candidate modifier genes of CF lung disease, and other CF related traits. To estimate proportion of genetic influences on CF lung disease phenotype from GWAS and gene expression imputation, we calculated heritability (h 2 ) from the imputed GWAS data using the GREML-LDMS method  [19]  from the Genome-wide Complex Trait Analysis (GCTA) software  [20].
This impact should be investigated with more data sets to understand genetic regulation of true gene expression. In summary, we applied the technique of gene expression imputation, leveraging availability of CF and other eQTL data sets, to mine the CF GWAS data, and uncovered 52 consensus modifier genes for CF lung disease, which is substantially greater than identified by GWAS alone. Further, we identified an additional 327 potential candidate CF lung disease modifier genes. Some modifier candidates had been supported by independent studies, and functional annotations are consistent with our current knowledge of CF lung disease pathogenesis. These candidate modifiers provide potential targets for intervention of disease process in CF and for other airway diseases as well. Department of Human Genetics, Dr.
Acknowledgments We thank  Dr. Nancy J. Cox ,  Vanderbilt University , Division of Genetic Medicine,  Dr. Fred Wright ,  North Carolina State University, Bioinformatics Research Center , and  Dr. Ani W. Manichaikul ,  University of Virginia, Center for Public Health Genomics , for guidance, advisement, and discussion. We also like to thank  Dr. Hae Kyung Im  and  lab, University of Chicago ,.
Spatial comparisons of the different datasets over the Alpine and Pyrenean domains highlighted a better representation of the spatial variability of solar fluxes by DSSF and AROME than SAFRAN. We also showed that the altitudinal gradient of longwave irradiance is too strong for DSLFnew and too weak for SAFRAN. These datasets were then used as radiative forcing together with AROME near-surface forecasts to drive distributed snowpack simulations by the model Crocus in the French Alps and the Pyrenees. An evaluation against in situ snow depth measurements showed higher biases when using satellite-derived products, despite their quality. This effect is attributed to some error compensations in the atmospheric forcing and the snowpack model. However, satellite-derived irradiance products are judged beneficial for snowpack modelling in mountains, when the error compensations are solved.
, 2016; Vionnet et al. , 2016) , to describe the snow accumulation quantitative distribution  (Schirmer and Jamieson, 2015)  and to provide relevant high-resolution information for snowpack stability concerns  (Bellaire et al. , 2014; Horton et al. , 2015). The radiative forcing of these simulations relies on NWP forecasts of the SW↓ and LW↓ irradiances with no use of observations (in situ or from satellites). Vionnet et al. (2016)  made a preliminary evaluation of SW↓ and LW↓ irradiance forecasts by the NWP system AROME operating at 2. 5 km resolution over France. Through comparisons to ground-based measurements at two mountainous sites in the French Alps, they showed an overestimation of SW↓ and an underestimation of LW↓, linked to an underestimation of the cloud cover. Satellite-derived estimates of SW↓ and LW↓ irradiances are an alternative to NWP-based irradiance datasets in mountainous terrain.
Irradiance datasets Several irradiance datasets were used in this study: forecasts from the NWP model AROME, reanalyses from the SAFRAN analysis system, LSA SAF irradiance products derived from remotely sensed observations and a hybrid LW↓ irradiance product based on a combination of LSA SAF algo- rithms with AROME forecasts. An in situ observation dataset was built up for validation in mountains.
AROME (Application of Research to Operations at MEsoscale) is the meso-scale NWP system of Météo-France  (Seity et al. , 2011) , operating over France since December 2008 at 2. 5 km grid spacing (1. 3 km since 2015;  Brousseau et al. , 2016). It is a spectral and non-hydrostatic model. The physics and data assimilation schemes are detailed in  Seity et al. (2011). In particular, AROME uses the radiation parameterizations from the European Centre for Medium-Range Weather Forecasts (ECMWF), with the SW scheme from  Fouquart and Bonnel (1980)  and the LW scheme from  Mlawer et al. (1997). In this study, we built a continuous atmospheric forcing dataset to drive snowpack simulations using hourly AROME forecasts issued from the 00:00 UTC analysis time, from +6 to +29 h, extracted on a regular latitude-longitude grid with a 0.
LSA SAF products The LSA SAF is a project supported by the European Organisation for the Exploitation of Meteorological Satellites (EU-METSAT) and a consortium of European National Meteorological Services, with the purpose of using remotely sensed data to determine land surface variables  (Trigo et al. , 2011). In particular, it provides estimates of the downward surface shortwave flux (DSSF) and the downward surface longwave flux (DSLF), derived from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) radiometer on board the MSG geostationary satellite  (Schmetz et al. , 2002). They are gen-erated every 30 min, covering the MSG full disc with a 3 km resolution at nadir. They have been operationally disseminated since September 2005 (http://landsaf. ipma. pt, last access: 20 April 2020).
In situ irradiance observations To assess the distributed irradiance datasets, ground measurements of SW↓ and LW↓ were extracted from the Météo-France station network and additional automatic weather stations (AWSs). Stations with altitude higher than 1000 m were selected. As elevation influences incoming radiation  (Oliphant et al. , 2003) , stations were not used for evaluation if the difference between the station elevation and the elevation of the four closest AROME and LSA SAF grid points was higher than 300 m. As elevation differences up to 300 m may have an influence on the comparisons, the altitudes of the grid points associated with each station are listed in Table 1 and should be kept in mind when analysing the evaluation statistics. The resulting observation database, represented in Fig.
1 , includes 14 mountain SW↓ stations (8 in the French Alps, 1 in Jura and 5 in the Pyrenees) and 4 mountain LW↓ stations (3 in the French Alps and 1 in the Pyrenees). An additional station located on a plain at Carpentras (Fig. 1 ) has been included in the database since it is the ref-erence station for SW↓ and LW↓ measurements in France. These stations and their characteristics are listed in Table  1. Irradiance measurements are scarce in mountainous terrain and their quality is often lower than plain measurements, due to the difficulty of maintaining these stations and the possible occurrence of frost or snow on the sensors in winter  (Lapo et al. , 2015a). The pyranometers from Météo-France network (Kipp & Zonen CM5, CM6B and CM11) meet the quality standards of the World Meteorological Organization (WMO, 2014) and hence an uncertainty of hourly total SW↓ irradiance of ±10 %  (Leroy and Leches, 2014).
Snowpack model: Crocus Snowpack simulations driven by different irradiance datasets were performed with the detailed snow cover model Crocus  (Brun et al. , 1992; Vionnet et al. , 2012)  coupled with the ISBA land surface model within the SURFEX simulation platform  (Masson et al. , 2013) , to fully simulate the interactions between snowpack and soil. SURFEX/ISBA-Crocus (called Crocus hereafter) simulates the evolution of the snowpack physical properties along its stratigraphy, under given atmospheric forcing data (temperature and specific humidity at a given height above the surface, wind speed at a given height above the surface, SW↓ and LW↓ irradiance, solid and liquid precipitation). The simulations were carried out over the French Alps and Pyrenees domains (Fig. 1 ), on the AROME regular latitudelongitude grid at 0.
025 • resolution (Sect. 2. 2. 1) from 1 August 2010 to 31 July 2014. The effects of aspect and slope on incoming solar irradiance were not taken into account, because the snowpack is simulated over flat terrain, and the interactions with the vegetation and the parameterization of fractional snow cover were not activated, because the evaluation observations are located in flat and open fields. This configuration has already been used in  Vionnet et al. (2016)  and  Quéno et al. (2016). Except for incoming radiative fluxes, the atmospheric forcing of the snowpack simulations was built with AROME forecasts (Sect. 2. 2. 1). The radiative components of the forcings were extracted from the different irradiance datasets: (a) AROME irradiance forecasts (simulations named A-Cro hereafter), (b) SAFRAN irradiance reanalyses (simulations named AS-Cro hereafter), (c) DSSF and DSLFnew (simulations named AL-Cro hereafter), (d) DSSF and AROME LW↓ irradiance (simulations named AL SW -Cro hereafter), and (e) DSLFnew and AROME SW↓ irradiance (simulations named AL LW -Cro hereafter).
In situ snowpack observations To assess the quality of Crocus simulations, an observational dataset of snow depth measurements was constituted in the French Alps and the Pyrenees, within SAFRAN massifs. Only stations with less than 150 m elevation difference to the model topography were selected, in order to use the same dataset as  Quéno et al. (2016)  and  Vionnet et al. (2016). This dataset contains a total of 172 stations (89 in the French Alps and 83 in the Pyrenees) with daily manual measurements at ski resorts (at 06:00 UTC) and daily automatic measurements by ultra-sonic sensors at high-altitude sensors, as described in detail in  Vionnet et al. (2016)  for the French Alps and in  Quéno et al. (2016)  for the French and Spanish Pyrenees. 3 Evaluation of irradiance products over the Alps and the Pyrenees.
Overall, DSSF exhibits the best performance with a relative bias of -4 % and a relative RMSE of 33 %. SAFRAN has a relative bias of -7 % and a relative RMSE of 40 %. Finally, AROME exhibits the strongest relative bias (+12 %) and the highest relative RMSE (43 %). Figure  3  shows the biases and RMSEs of the different datasets of incoming LW↓ (DSLF, DSLFnew, AROME and SAFRAN) at the five LW↓ stations and the overall error statistics. In this figure, stations are ordered by altitude. In mountains, DSLF, DSLFnew and AROME have a negative bias, while SAFRAN bias tends to increase with altitude. At low elevation (Carpentras), the best performance is in favour of DSLFnew with a bias of +4 W m -2 (+1 %) and a RMSE of 16 W m -2 (5 %), which falls within the range of uncer- tainties of the sensor. At three mountain stations (Col de Porte, Bassiès and Argentière glacier), the lowest bias and RMSE are reached by SAFRAN, while AROME has the lowest RMSE at the Saint-Sorlin glacier.
Overall, AROME exhibits the strongest negative relative bias (-6 %) and the highest relative RMSE (12 %). DSLF and DSLFnew have equivalent error statistics with a relative bias of -3 % and a relative RMSE of 11 %. Finally, SAFRAN has a relative bias of +1 % and a relative RMSE of 11 %. These global error statistics are close to the sensor uncertainties in mountains, which does not enable "best product" to be chosen. However, some trends are identified, such as an underesti-mation of LW↓ by DSLF, DSLFnew and AROME. The performance of LSA SAF products and models is also clearly better in terms of LW↓ than SW↓, because of lower biases and RMSE. The yearly cycles of SW↓ irradiances are illustrated at Carpentras for reference (Fig. 4a ) and at Péone mountain station (Fig. 4b ). They show higher RMSE in spring and summer for each dataset, the lowest RMSE for DSSF and the highest RMSE for AROME during the whole year, except in December and January when the three products have equivalent RMSEs.
6g ), highlighting a poor representation of the orographic blocking as already noticed in  Quéno et al. (2016). The dependence of the different irradiance products on the altitude was further explored with the study of altitudinal gradients. Figure  7  represents the vertical evolution of the LW↓ and SW↓ averaged over the SAFRAN massifs of the French Alps and the Pyrenees by steps of 100 m of elevation over the whole study period, together with the associated standard deviations. The strong dependency of LW↓ irradiance on altitude is confirmed in Fig. 7a  for the French Alps and Fig. 7b  for the Pyrenees. As a reference, the altitudinal gradient for annual LW↓ means of -29 W m -2 km -1 found by  Marty et al. (2002)  in the Swiss Alps is plotted in dashed lines, while Table 3 lists the mean altitudinal gradient for each dataset in both domains.
All datasets present a steady decrease in LW↓ with altitude and are close to each other below approximately 1200 m. For higher elevations, SAFRAN annual mean value is significantly stronger than AROME, DSLF and DSLFnew, due to a lower vertical gradient (Table  3 ). We showed in Sect. 3. 1 that AROME, DSLF and DSLFnew had a negative bias at the four mountain stations. This effect may come from a vertical gradient that is too strong (Table  3 ). DSLFnew is larger than AROME and DSLF at all altitudes below approx- imately 2900 m in the French Alps (Fig. 7a ) and 2200 m in the Pyrenees (Fig. 7b ). It gets lower at the highest altitudes due to a stronger vertical gradient. The stronger vertical gradient of DSLFnew compared to DSLF is the confirmation that the use of forecasts of higher resolution for the algorithm takes the topography into account more.
Impact of the irradiance products on snowpack simulations Snowpack simulations were performed over four winters from 2010 to 2014 to assess the impact of the different irradiance datasets as radiative forcing. DSSF and DSLFnew as radiative forcing (AL-Cro) increases the bias and the RMSE in the French Alps and the Pyrenees. On the contrary, the use of SAFRAN radiative forcing (AS-Cro) gives a lower bias and RMSE in both massifs. The highest biases and RMSE are reached at high altitudes (≥ 2200 m) by AL-Cro, because of the marked underestimation of DSSF and DSLFnew at these elevations. The use of SAFRAN irradiances (AS-Cro) tends to reduce the biases of A-Cro, particularly at the lowest elevations where the higher LW↓ increases the melting during the whole season. Above 1800 m, the RMSE is not reduced by the use of SAFRAN irradiances (except above 2200 m in the Alps), because the higher LW↓ enhances the melting in winter and the lower SW↓ reduces the melting in spring, which increases the dispersion around the annual bias.
AS-Cro exhibits lower SWE values at this date, due to its higher LW↓ irradiance. However, on 1 May 2013, both simulations exhibit higher SWE values than A-Cro almost everywhere, with differences mostly higher than 200 mm, locally reaching 400 mm, due to lower SW↓ irradiances. The impact of the radiative forcing on SWE simulations was further studied at two grid points in the French Pyrenees: one at low altitude (point A, 1359 m) and one at high altitude (point B, 2459 m), both located in Fig. 9. Contrary to snow depth, comparing SWE simulations enables the study of the impact of the different radiative forcing datasets on the snowpack mass balance with no additional uncertainty on snow compaction. Figure  10  represents the   2 ). At point A, melting occurs during the winter. Consequently, AS-Cro and AL LW -Cro simulations lead to lower values of SWE than A-Cro, since they both exhibit higher LW↓ than AROME (+8 W m -2 for DSLFnew and +9 W m -2 for SAFRAN).
Quality of irradiance datasets in mountainous terrain We presented an overview of the quality of several irradiance datasets through an in-depth assessment of the irradiance fields in mountainous terrain. In terms of SW↓ irradiances, DSSF exhibits the best metrics in mountains, particularly below 2000 m. Above 2000 m, its RMSE is similar to SAFRAN and AROME, due to a strong negative bias. AROME presents systematic and large overestimations of SW↓ irradiances, contrarily to SAFRAN's tendency to underestimate them. The spatial variations of SW↓ irradiances are better represented in DSSF and AROME than in SAFRAN. In terms of LW↓ irradiances, the obtained errors are comparable and it is difficult to identify the best product. The use of forecasts at higher spatial resolution to compute DSLFnew enhances the topographic dependence, which limits the underestimation of LW↓ irradiance at low and mid-altitudes found with DSLF but strengthens the negative bias at high altitude.
Since the TCWV is dependent on the elevation, the DSSF could be improved with AROME forecasts of TCWV at kilometric resolution, similarly to DSLFnew. Despite that, the DSSF still exhibits a better performance than AROME and SAFRAN in mountains. At sub-kilometric scale, the local topography strongly influences the solar and longwave irradiance variability. Oliphant et al. (2003)  identified the following surface characteristics as causes of radiative flux variability, by order of importance: slope aspect, slope angle, elevation, albedo, shading, sky view factor (SVF) and leaf area index. These local factors are not taken into account in AROME, SAFRAN and LSA SAF irradiance products. This study aims at assessing the practical benefits of different irradiance datasets to be used as radiative forcing for distributed snowpack simulations at 2.