dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistical analysis: Normal distribution of samples was systematically assessed (Shapiro-Wilkinson normality test). Normal distributed samples were statistically compared using two-tailed Student's t test unless otherwise stated. When data distribution was not normal we used two-tailed Mann Whitney U-test. Compiled data are reported and presented as whisker box plots (the upper and lower whiskers representing the 90th and 10th percentiles, respectively, and the upper and lower boxes representing the 75th and 25th percentiles, respectively, and the horizontal line representing the median or the mean ± s. e. m. , with single data points plotted. Differences were considered significant if p<0. 05 (*p<0. 05, **p<0. 01, ***p<0. 001).
and  Maria del Mar Dierssen Sotos  for critically reading this manuscript. We also thank the ICM technical facilities PHENO-ICMICE and iGENSEQ. This work was supported by " Investissements d'avenir "  ANR-10-IAIHU-06 ,  BBT-MOCONET ;  Agence Nationale de la Recherche  ( ANR-12-EMMA-0010  ;  ANR-13-BSV4-0015-01 ;  ANR-16-CE16-0007-02 ;  ANR-17-CE16-0026-01 ;  ANR-18-CE16-0001-01 ),  Fondation Recherche Médicale (Equipe FRM   DEQ20150331684  and  EQU201903007860 ),  NARSAD  independent investigator grant and  Fondation Lejeune  (# 1790 ).
, eQTLs if the exposure is gene expression), while at the same time correcting for pleiotropic effects using genetic variants that are in LD with these IVs (cis-genetics) (Fig. 1 ). We assess the performance of MR-link using simulated data in 100 different scenarios that mimic the genetic architecture of gene The Biobank Integrative Omics Study (BIOS) cohort was used to identify expression quantitative trait loci (eQTLs) and characterize the genetic architecture of gene expression. Dashed outbox: Knowledge used in a simulation scheme that mimicked geneexpression traits, including linkage disequilibrium (LD) between eQTL single nucleotide polymorphism (SNPs). We used this simulation to assess the false positive rates and power for widely used Mendelian randomization (MR) methods. We applied our MR method, MR-link, to both the simulations and to individual-level data of low-density lipoprotein cholesterol (LDL-C) in 12,449 individuals (Lifelines) combined with BIOS and GTEx eQTL as well as protein quantitative trait loci (pQTL) summary statistics to identify gene-expression changes and protein level changes that are causally linked to LDL-C within or outside a genome-wide association study (GWAS) locus.
expression. We derive this information from eQTL association patterns in a large cohort of samples with genetic and transcriptomics data  13. Subsequently, we apply MR-link to individual-level data for LDL-C measurements in 12,449 individuals with four different eQTL summary statistic datasets: blood eQTLs identified in the BIOS cohort (Fig. 1 ) and eQTLs from blood, liver, and cerebellum from the GTEx Consortium  24  (Fig. 1 ). We further explore the performance of MR-link on another molecular layer, protein levels, through the application of MR-link on protein quantitative trait loci (pQTL) summary statistics from Sun et al. combined with our LDL-C measurements  25. Our results in simulated and real data show that MR-link can robustly identify causal relationships between molecular traitssuch as gene expression and protein levels-and an outcome (e.
We assessed the performance of MR-link under different scenarios and compared it to four other MR methods: Inverse variance weighting (IVW), which assumes the absence of LD and pleiotropy, and the pleiotropy-robust methods MR-Egger, LDA-MR-Egger, and MR-PRESSO (Table  1 )  [17] [18] [19] 28. In  ddition, we compared MR-link to the widely used Bayesian colocalization method coloc 29 , although this is not a formal test for assessing causal relationships, but rather a way to evaluate if two traits share the same causal variant(s) in a locus  29. We simulated causal relationships between an exposure and an outcome in a 5 Mb region, based on LD structure estimated for 403 European samples from the 1000 Genomes project  30  ("Methods"). All tested MR methods were assessed in 1500 simulated datasets for 100 different scenarios that varied with respect to the absence or presence of causality, the absence or presence of pleiotropy, and the number of causal eQTL variants.
We initially evaluated two approaches to select QTL variants as IVs: GCTA-COJO (v1. 26. 0) and p value clumping ("Methods")  26, 31. We observed that GCTA-COJO was best suited for IV selection because: (i) the median number of IVs identified by GCTA-COJO better represented the number of simulated causal variants (Supplementary Data 2) and (ii) the false-positive rates (FPRs) in the MR analysis using the IVW method were lower (median FPR was 0. 057 using GCTA-COJO versus 0. 115 using clumping) (Supplementary Fig. 1  and Supplementary Data 2). We therefore selected IVs for the exposure using the GTCA-COJO approach in subsequent analyses. When we simulated pleiotropy through LD with no causal effect of the known exposure on the outcome (Figs. 2b, 3a , Supplementary Data 3 and "Methods"), all existing MR-methods showed inflated FPRs (up to 0.
Finally, we compared MR-link to the coloc package using the area under the receiver operator characteristic curve (AUC) metric as well as FPRs and power (calculated using coloc PP4 > 0. 9 as a threshold) ("Methods"). We used the AUC metric because coloc provides posterior probabilities of causal variant sharing and not p values ("Methods"). As coloc assumes that the exposure and the outcome share only one causal variant, we also included the recently implemented coloc variations (coloc-cond and coloc-masked) in our comparison. These variations are expected to perform better in scenarios with multiple causal variants  32. When comparing MR-link to the coloc variations through the AUC metric, we find that MR-link consistently outperforms coloc and coloc-masked in all scenarios, and coloccond in pleiotropic scenarios.
40) with a zero FPR (Supplementary Fig. 3a-c ) (Supplementary Data 6). In simulations of pleiotropy through LD, all coloc methods have increased FPRs (medians: 0. 026 for coloc, 0. 142 for coloc-cond, and 0. 0037 for coloc-masked) with a decrease in power relative to the nonpleiotropic simulations (max: 0. 37 for coloc, 0. 43 for coloc-cond, and 0. 14 for coloc-masked) (Supplementary Fig. 3d-f  and Supplementary Data 6). These patterns were even more apparent in cases of pleiotropy through overlap (Supplementary Fig. 3g-i  and Supplementary Data 6). This comparison through FPRs and power indicates again that MR-link has superior discriminative ability over coloc variations, especially in the presence of pleiotropy. MR-link identifies gene expression causal to LDL-C levels. We applied MR-link to four separate summary statistics-based eQTL datasets combined with individual-level genotype data and LDL-C measurements in 12,449 individuals from the Lifelines cohort  33  (Fig.
1 ). We assessed the causal effect of gene expression changes in (i) whole blood (using eQTLs from BIOS (n = 3503) and GTEx (n = 369)), (ii) liver as the main tissue important for cholesterol metabolism (using eQTLs from GTEx, n = 153), and (iii) cerebellum tissue (using eQTLs from GTEx, n = 154) as a tissue not involved in cholesterol metabolism but with similar sample size (and thus power) to liver tissue  24, 34. Transcriptome-wide application of MR-link to these eQTL datasets identified 24 significant genes whose variation in blood (18 using BIOS eQTLs, 2 using GTEx eQTLs) or liver (4 genes) was causally related to LDL-C (Tables  2, 3 , Supplementary Tables  1  and 2 ). No significant genes were found in the cerebellum (Supplementary Table  2 ). MR analysis that used whole-blood eQTLs from GTEx was, as expected, underpowered compared to the analysis using BIOS eQTLs.
002)  44. These results are in line with the wellknown causal relationship between increased ApoE plasma levels and LDL-C, and the widely described stronger impact of the E3 isoform compared to the E2 isoform  44. Interestingly, MR-link did not estimate BGAT, the protein product of ABO, to be significant in this dataset (SOMAmer ID: ABO. 9253. 52. 3, MR-link calibrated two-sided p = 0. 18) We compared the IVs identified for BGAT (rs9411463 and rs72775494) with those used in the ABO blood eQTL analysis and found that only one IV for the BGAT protein was in LD (rs9411463) with any of the four IVs for ABO expression in BIOS. This scenario is in line with the overall patterns observed in the proteomics study-only a small fraction of eQTLs in blood also affect protein levels, but our results could also reflect targeting of the SOMAmer to a specific ABO protein isoform  25.
Discussion Identification of genes whose changes in expression are causally linked to a phenotype is crucial for understanding the mechanisms behind complex traits. While several methods exist that infer causal relationships between two phenotypes, these rely on a set of assumptions that are often violated when gene expression is the exposure. Specifically, the presence of LD and pleiotropy between the genetic variants chosen as IVs are the main cause of violations of such assumptions  [17] [18] [19] 28. Her  we interrogated a large geneexpression dataset and showed that the eQTLs of a gene, which can be used as IVs, are very likely to be in LD, but not overlapping, with eQTLs of other genes, indicating that potential sources of pleiotropy in transcriptome-wide MR analyses are likely to come from variants in LD with the IVs.
Gene names are according to ENSEMBL GENES 96 database (human Genome build 37  correct for all potential sources of pleiotropy. To our knowledge, this approach has never been used in a causal inference method. We applied MR-link to real data by applying it to LDL-C cholesterol measurements and eQTLs derived from blood, cerebellum and liver. This identified known and previously unknown causal genes within and outside GWAS loci. For example, in liver we identified the well-known negative causal relationship between expression of SORT1 in liver and LDL-C  [41] [42] [43]. In liver, and suggestively in blood, we detected a causal effect for PVRL2, a gene located in the APOE locus. While a role for this gene is mostly known for immune and endothelial cells and in the context of atherosclerosis  45, 47  , our results indicate that regulation of expression of this gene in both blood and liver causally affects LDL-C levels.
MR-link has several advantages over other recent MR methods developed to overcome bias from LD and pleiotropy  17, 23. First, MR-link can model unobserved pleiotropy, whereas sources of pleiotropy need to be specified in multivariate MR methods. This is particularly important because sources of pleiotropy may be context-dependent and may arise from a phenotype other than those being measured in a cohort  14, 34. Second, MR-link can derive robust causal estimates even when only one or two IVs are available. The majority of genes tested in our large eQTL dataset have fewer than three IVs (68%), which makes it impossible for MR-PRESSO, MR-Egger, and LDA-MR-Egger to make causal estimates  [17] [18] [19]. One of the MR-link assumptions is that the IVs affect the outcome only through the exposure, conditional on the unmeasured pleiotropic effect.
This assumption is violated when the IVs of the exposure and of the pleiotropic effect are fully overlapping. This assumption must not be violated when a single IV is available, but can be relaxed when multiple IVs are used in the model, as the relative effects of the IVs help to discriminate between a true causal effect and a pleiotropic effect, similar to multivariable Mendelian randomization methods  22. In the case of multiple IVs that are fully overlapping, we have shown that MR-link has an increased FPR, yet still maintains higher power compared to other MR-methods and superior discriminative ability compared to coloc. The application of MR-link is not restricted to gene expression or proteomics datasets; it can also be applied to other molecular layers that are known to have a similar genetic architecture to gene expression, such as metabolites.
An informed consent form was obtained from all the participants. Genotyping was performed separately per cohort (see references). All combined genotypes were imputed to the Haplotype reference consortium dataset  61  using the Michigan imputation server  62. We retained only biallelic SNPs and confined our analyses to variants with minor allele frequency (MAF) > 0. 01, Hardy-Weinberg equilibrium (HWE) p value >10 -6 and an imputation quality RSQR > 0. 8. A genetic relationship matrix (GRM) was derived based on LD-pruned genotypes using the Plink 1. 9 command --indep 50 5 2, and one individual was kept from all pairs of individuals that had a GRM value > 0. 1 using the --rel-cutoff Plink 1. 9 command 31. Population outliers were identified using a principal component analysis of the GRM, and individuals more distant than three standard deviations from the mean of principal component 1 and principal component 2 were removed.
13. After genotype and expression quality control filters, 3503 individuals with expression data of 19,960 transcripts and genotype information of 7,838,327 SNPs were available for analyses. In this set, 57% were female and the average age was 52. 8 years (±16. 0 Stand. Dev. ). eQTL association analysis was performed for SNPs located ±1. 5 Mb of the transcript using Plink 1. 9 and the --assoc command  31. For 13,778 genes, at least one eQTL at p < 5 × 10 -8 was identified, and those genes were used for all the analyses described in this manuscript. We quantified how many genetic variants are necessary to explain gene expression using a conditional joint analysis approach. We identified jointly significant eQTLs by applying GCTA-COJO (v1. 26. 0)  26  to eQTL summary statistics, using the BIOS cohort as LD reference panel, and selecting jointly significant variants that showed a p < 5 × 10 -8 in this analysis step.
To infer how often eQTLs are shared between genes, we assessed the percentage of genes with top eQTLs (or jointly significant variants) that have LD r 2 > 0. 99. We used the r 2 > 0. 5 threshold to see how often eQTL variants were in LD with each other. We performed statistical fine-mapping of all genes using the FINEMAP v1. 3. 1 program  27. First, we searched for associated eQTL variants (p < 5 × 10 -8 ) in the cis-associated region. We then padded the associated regions with 100 kb and only looked for variants in this extended region. FINEMAP requires the same number of individuals across all variants, therefore we analyzed only the genes with the associated variants available in all subcohorts. We ran FINEMAP on these genes with the --sss option, using LD computed with Plink v1. 9, with the --r command.
Lifelines cohort genotype data and LDL-C levels. Lifelines is a multi-generational cohort study of 167,000 individuals from the north of The Netherlands. It was approved by the medical ethics committee of the University Medical Center Groningen and conducted in accordance with Helsinki Declaration Guidelines. All participants signed an informed consent form prior to enrollment. A subset of 13,436 Lifelines samples were genotyped with the cytoSNP array and underwent the quality control steps described in Scholtens et al. 33  : Genotyped variants were retained based on three criteria: MAF > 0. 001, HWE p > 10 -4 , and a genotyping call rate > 0. 95. After genotype quality control, samples were imputed using the Genome of the Netherlands reference panel  64  and Minimac version 2012. 10. 3  66.
7 years (±11. 5 Stand. Dev. )) and 7,336,374 variants remained for analyses. Association analysis for additive effects on LDL-C was performed using linear regression on standardized genotypes, e. g. , transforming genotypes into a distribution with mean 0 and variance 1. Summary statistics of this analysis were used to perform MR analyses using the existing MR methods listed in Table  1. GTEx download and analysis. We downloaded GTEx version 7 eQTL summary statistics, including non-significant results, from the GTEx website (https:// gtexportal. org/home/datasets/)  24. For every gene with at least one eQTL at p < 5 × 10 -8 , conditional analysis using GCTA-COJO was performed to select secondary variants at the same threshold, using the BIOS cohort as an LD reference. This resulted in 4028, 1557, and 1726 genes with at least one jointly significant eQTL for whole blood, liver, and brain (cerebellum) tissues, respectively.
pQTL summary statistics download and analysis. We downloaded the proteomics summary statistics of Sun et al. 25  from the GWAS catalog (ftp. ebi. ac. uk/ pub/databases/gwas/summary_statistics/SunBB_29875488_GCST005806). We isolated cis-regions by selecting variants within +/-1. 5 Mb from each transcript. These variants already passed the quality control steps of Sun et al. 25  : (i) INFO score > = 0. 7; (ii) minor allele count > = 8; (iii) Hardy-Weinberg equilibrium p > = 5 × 10 -6. For all these variants we used UK10K minor allele frequencies (ftp:// ngs. sanger. ac. uk/production/uk10k/UK10K_COHORT/REL-2012-06-02/UK10K_ COHORT. 20160215. sites. vcf. gz) as this information was not provided in the summary statistics but it is required for GCTA-COJO IV selection. We selected IVs using Lifelines genotypes as an LD reference  33.
Causal relationships were estimated with MR-link and four other existing methods: Inverse variance weighting (IVW)  28  , LDA-MR-Egger regression  17  , MR-Egger regression  18  , and MR-PRESSO  19. All methods were (re-)implemented in Python and compared to present equal results when compared with their original implementation. The corresponding code is available at https://github. com/adriaan-vd-graaf/genome_integration. The IVW method is a weighted meta-analysis of causal estimates from single IVs. Specifically, a causal estimate b i for an IV i is estimated as b 0 i ¼ MR-Egger regression adjusts for average pleiotropy by fitting a weighted linear regression between the exposure SNP-effects and the outcome SNP-effects  18. It assumes that <50% of the variants have a pleiotropic effect. MR-Egger can be applied when three or more instruments are available.
Colocalization analyses. We have run colocalization analyses on the simulated data using the R package coloc v4, git commit 6f3cbb1e5e90f07de772339d6e4a-f362140affc3, specifically its coloc. abf() function for the original coloc functionality and the coloc. signals() function for the masked (coloc-masked) and conditional (coloc-cond) estimates  29, 32. We used marginal effect sizes, standard errors and the MAFs as input that were calculated separately for the exposure and outcome. The LD for the conditional and masked coloc analysis was derived from the simulated reference cohort. For original coloc, we used the H4 test statistic of the coloc. abf() function as our result metric, which provides the posterior probability of sharing of the causal variants between the two traits being tested.
Acknowledgements We are very grateful for the altruistic donation of biological materials and questionnaire data by our generous study participants, without them this study would not be possible. In addition, we thank the  UMCG Genomics Coordination center  and the  UG Center for Information Technology , and their sponsors BBMRI-NL & TarGet, for storage and computing infrastructure. We thank  BBMRI-NL  for providing the transcriptome and genotyped data for the BIOS cohort. We thank  P. Visscher ,  N. Wray ,  J. Yang ,  E. Lopera-Maya ,  O. Bakker , and  N. de Klein  for valuable advice during the development and writing of this work. We also thank  K. McIntyre  for editorial assistance and  C. Benner  for support on running FINEMAP. This work is financed by the  Netherlands Organization for Scientific Research (NWO) :  NWO Spinoza Prize   SPI 92-266  (to C.
Data availability Individual-level data of Lifelines cohorts are available to all bona-fide researchers upon request to the Lifelines biobank (https://www. lifelines. nl/researcher). Individual-level data (genotypes and RNA-seq data) of the BIOS Consortium cohorts can be downloaded by researchers of Dutch Institutes, or analyzed (but not downloaded) by any non-Dutch researcher in a Cloud environment (https://www. bbmri. nl/acquisition-use-analyze/bios). GTEx summary statistics can be downloaded from the GTEx website (https://gtexportal. org/home/datasets). pQTLs summary statistics can be downloaded from GWAS Catalog (ftp. ebi. ac. uk/pub/databases/gwas/summary_statistics/SunBB_29875488_GCST005806/). Simulated data can be recreated using the code at the link provided in Code availability statement.
Gene names are according to ENSEMBL GENES 96 database (human Genome build 37). The causal effect estimate represents the changes in LDL-C (mg per dL) per standard deviation increase in gene expression. pvalues listed in this table are not adjusted for multiple testing (MR-link calibrated two-sided p value, see Supplementary Note 1). Full summary statistics of the genes are shown in Supplementary Table1. LDL-C low-density lipoprotein cholesterol, IV instrumental variable, HDL-C high-density lipoprotein cholesterol, GWAS genome-wide association study, siRNA small interfering RNA. ARTICLE NATURE COMMUNICATIONS | https://doi. org/10. 1038/s41467-020-18716-x 6 NATURE COMMUNICATIONS | (2020) 11:4930 | https://doi. org/10. 1038/s41467-020-18716-x | www. nature. com/naturecommunications.
We here present a reference database and three land use maps produced in 2017 over the Reunion island using a machine learning based methodology. These maps are the result of a satellite image analysis performed using the Moringa land cover processing chain developed in our laboratory. The input dataset for map production consists of a single very high spatial resolution Pleiades images, a time series of Sentinel-2 and Landsat-8 images, a Digital Terrain Model (DTM) and the aforementioned reference database. The Moringa chain adopts an object based approach: the Pleiades image provides spatial accuracy with the delineation of land samples via a segmentation process, the time series provides information on landscape and vegetation dynamics, the DTM provides information on topography and the reference database provides annotated samples (6256 polygons) for the supervised classification process and the validation of the results.
Data The data described in this data paper are of two different types related to land use on Reunion Island for the year 2017: A reference database consisting of GIS vector dataset in ESRI shapefile format composed of 6256 polygons representative of the diversity of land use on Reunion Island. This data is used in a Specifications Table  Subject  Computer Science, Earth Sciences, Social Sciences Specific subject area Remote Sensing, GIS, Land Cover Map Type of data Vector How data were acquired The reference database was created with the QGIS software (www. qgis. org) For the production of land use maps, the Moringa processing chain uses the Orfeo ToolBox software (www. orfeo-toolbox. org) with scripts written in Python language. The source code of the Moringa processing chain is available at https://gitlab.
irstea. fr/ raffaele. gaetano/moringa. git Data format Raw data (Shapefile, Esri) Parameters for data collection For the construction of the reference database, the objective of the choice of plots was (i) a good representativeness of the class and (ii) a homogeneous distribution over the territory Description of data collection To create the reference database, we used existing IGN databases but also field surveys acquired with a GPS and the knowledge of professionals specialized in their field. For the land use maps, we used a supervised classification method of satellite images (Sentinel2, Landsat8 and Pleiades) based on the Random Forest algorithm driven by the reference database mentioned above. We produced the three land use maps using the reference database and satellite image classifications as described below.