dataesr/scientific-paragraphs-categorization huggingface dataset:
Drought experiment Plant material and growth conditions In order to examine phenotypic plasticity related to water deficit, a subset of trees was chosen for a moderate drought glasshouse experiment in Southampton, UK. Six genotypes were selected from the P. nigra collection (see Table  S2  available as Supplementary Data at Tree Physiology Online): four from the extreme 'leaf size' genotypes (two Spanish 'small leaf', Sp1, Sp2; one Italian 'large leaf', Ita; and one from the Netherlands 'large leaf', NL) and two from the Drôme population in France (Fr1 and Fr2). These genotypes were chosen as typical 'small-leaf' genotypes from arid areas. The French genotypes were selected to represent a typical leaf morphology from a broad range in temperature and precipitation patterns. Cuttings were planted in John Innes No.
17, P < 0. 001) but no significant differences between genotypes (F 5,50 = 1. 06, P = 0. 392) and no genotype × treatment interaction effect (F 5,50 = 0. 82, P = 0. 543), meaning all the genotypes had their soil moisture decreased equally under drought (see Figure  3 , see Figures  S1  and S2  available as Supplementary Data at Tree Physiology Online). Physiological and growth measurements Biomass measurements were conducted on 1 September 2008 (0 day after drought (DAD)) and 17 September 2008 (16DAD). Measures included height, stem diameter measured using digital callipers at 10 cm from the stem base, the number of branches and the number of leaves. Growth was calculated as the difference between 0DAD and 16DAD for stem height and diameter, as well as number of branches and leaves. Leaves newly developed (NLN) during the experiment above the tag on the first emerging leaf were also counted at 16DAD and used with the total number of leaves at 0DAD and 16DAD to calculate the number of fallen leaves, as senescence = (NL 16DAD -NL 0DAD ) -NLN.
Affymetrix CEL files were imported into R software ( R Development Core Team 2014). Probe sets exhibiting no signal intensity were filtered out by a Present call procedure as described by  McClintock and Edenberg (2006). Briefly, CEL files were normalized using the MAS5 algorithm with default parameters (affy package, v1. 48. 0). MAS5 provides a detection call, Absent (A), Present (P) or Marginal (M), which indicates whether the specific transcript is detectable. For each probe set, the percentage of Present calls in each condition was calculated. Probe sets that exhibited a percentage of Present 912 Viger et al.
Tree Physiology Volume 36, 2016 Tree Physiology Online at http://www. treephys. oxfordjournals. org calls of 100% in at least one condition for both genotypes were kept. The other probe sets were removed from the analysis. This procedure also allowed probe sets that hybridized exclusively to one genotype to be discarded  ( Cohen et al. 2010). Finally, 31,084 validated probe sets were retained. In order to compute differential gene expression, CEL files were then normalized using the RMA algorithm with default parameters (affy package, v1. 48. 0). Differential expression was calculated as log 2 (fold change) between drought and control samples for the 31,084 validated probe sets. Statistical significance of differential expression was tested using moderated t-tests implemented in the eBayes function (limma package v3.
24. 12, Smyth 2004 ) and false discovery rate (FDR) corrections for multiple testing were applied. Thresholds of |log 2 (FC)| ≥ 1 and corrected P < 0. 05 were used to identify differentially expressed genes. Probe sets were annotated using the Poparray website (http://aspendb. uga. edu/poparray) and assigned to a Populus gene model (v. 3. 0) and its closest Arabidopsis homologue, and gene ontology (GO) biological process, cellular component and molecular function classifications. The software MapMan  ( Thimm et al. 2004 ) was used for pathway analysis. Statistics (Wilcoxon Rank Sum Test with a Benjamini-Hochberg FDR correction) were implemented in MapMan to reveal BINs (groups of functionally similar itemsgenes, enzyme activities, metabolites, used to construct pathways) exhibiting a significant difference in expression profile behaviour compared with the other BINs.
Gene ontology enrichment was also studied using the parametric analysis of gene set enrichment (PAGE) tool on the AgriGo website  ( Du et al. 2010 ) with default parameters using validated probe sets as the reference. Results of the microarray experiment were confirmed using quantitative real-time PCR (qPCR) for a set of differentially expressed candidate genes. Forward and reverse primers were designed, from the P. trichocarpa genome (v1. 2), specifically to each gene (see Table  S3  available as Supplementary Data at Tree Physiology Online). Reverse transcription of RNA to cDNA was performed using the ImProm-II Reverse Transcription kit (Promega UK, Southampton, UK) following the manufacturer's instructions. Each qPCR was composed of 5 μL 2× Precision-SY Master Mix (PrimerDesign Ltd, Southampton, UK), 5 pmol forward and reverse primers and 25 ng diluted cDNA.
Plates were run on a Chrom4 Real-Time PCR Detection System (Bio-Rad Laboratories, Hercules, CA, USA). Reactions were incubated at 95 °C for 10 min and then 40 cycles of 15 s at 95 °C, 1 min at 60 °C and a plate read, followed by an incubation at 72 °C for 10 min. A melting curve was then performed from 60 to 95 °C with a read every 0. 2 °C and 1 s hold, in order to check for primer dimers, DNA contamination and secondary products. Values were exported with the software Opticon Monitor 3. 1 (Bio-Rad Laboratories). Amplification efficiency was measured following the equation from  Liu and Saint (2002) : E R R C C =       - - n A n B T A T B , , [ /( ) ] , , 1 1 where R n,A and R n,B are R n at arbitrary thresholds A and B in an individual curve, respectively, and C T,A and C T,B are the threshold cycles at these arbitrary thresholds  ( Liu and Saint 2002).
Ratios were calculated as: E E ( ) ( ) control drought target control drought reference - - Statistical analysis Data from the Belgium common garden experiment were analysed using the SPSS software package (SPSS, Chicago, IL, USA). Kolmogorov-Smirnov tests were used to test for normality, and transformation (log 10 ) was carried out when required. A general linear model (GLM) tested the effects of block and river population: Y ij i j = + + + µ α β ε where μ is the mean, Y ij is the phenotype in the ith block and in the jth river population, α i is the block effect, β j is the river population effect and ε is the residual error. A comparison of means was carried out between river populations using a Student-Newman-Keuls post hoc test. A GLM was also performed to test genotype effects: Y i i = + + µ α ε where Y i is the phenotype in the ith genotype, α i is the genotype effect and ε is the residual error.
Climatic data from the region of origin for genotypes were correlated with and among phenotypic traits as measured in the common garden was tested using Spearman's ρ in SPSS v19. 0 (SPSS). Data from the glasshouse experiment were also analysed for genotype and treatment effect (and their interaction) using the SPSS software package (SPSS). Normality (Kolmogorov-Smirnov test) and block effects were checked before performing an analysis of variance (ANOVA) GLM. Data were transformed using a natural log when required. A GLM tested the effects of genotype and treatment: Y ij i j = + + + µ α β ε where Y ij is the phenotype in the ith genotype and in the jth treatment, α i is the genotype effect, β j is the treatment effect and ε is the residual error. A comparison of means was carried out among genotypes using a Student-Newman-Keuls post hoc test.
Stomatal conductance and carbon isotope discrimination Stomatal conductance was measured during the progression of drought (see Table  S5  available as Supplementary Data at Tree Physiology Online, Figure  4g  and h ). Early after the onset of drought (5DAD, Figure  4g ), Spanish and French genotypes reacted quickly to water deficit with g s declining rapidly by -54 and -36%, respectively (genotype: F 5,96 = 5. 1, P < 0. 001, treatment: F 1,96 = 15. 9, P < 0. 001). In contrast, the Ita genotype Tree Physiology Online at http://www. treephys. oxfordjournals. org showed a small increase in g s in response to drought (3. 4%) and NL a moderate decline (-17%). After 15 days of drought ( Figure  4h ), these contrasting early responses of stomata to drought were no longer apparent and all genotypes showed a significant decline in g s (F 1,92 = 103.
Variation under well-watered conditions To elucidate constitutive differences in gene expression between the Spanish and Italian genotypes that are present in well-watered conditions, a comparison was also completed for the control data (see Table  S6  available as Supplementary Data at Tree Physiology Online). Two hundred and fifty-two up-regulated and 284 down-regulated transcripts were identified in Sp2 compared with Ita ( Figure  6c ). The AgriGO analysis showed enriched GO terms differentially expressed between Sp2 and Ita in well-watered conditions, and these were generally related to secondary metabolism. Also upregulated in the Spanish genotype were ERD1 (EARLY RESPONSE TO DEHYDRATION 1) and RD21 (RESPONSIVE TO DEHYDRA-TION 21). Real-time qPCR Microarray results were validated by real-time qPCR on four candidate genes selected after microarray analysis.
Gene expression was quantified for additional genotypes that were not included in the microarray experiment: Fr1 from France and NL from the Netherlands (see Figure  S3  available as Supplementary Data at Tree Physiology Online). Real-time qPCR values were expressed in response to drought for each genotype. SPEECHLESS expression ratios were lower in response to drought in both Ita and Sp2, although this response was greater in Ita (F 3,32 = 9. 311, P < 0. 001, see Figure  S3  available as Supplementary Data at Tree Physiology Online). The expression ratios of ERECTA were reduced in response to drought with no significant difference between genotypes (F 3,32 = 0. 845, P = 0. 48, see Figure  S3  available as Supplementary Data at Tree Physiology Online).
Acknowledgments The authors thank  P. J. Tricker ,  N. R. Street ,  A. M. Rae ,  C. Dixon ,  S. Milner  and  B. Street  for field and laboratory assistance,  K. Schamp  for field assistance,  M. J. Tallis  for preliminary data analysis and Forest Research for grinding the wood samples. work for  Research -Quality of Life and Management of the Living Resources Programme , contact No. QLK5-CT-2002-00953  (POPYOMICs), within the  Sixth Framework for Research as part of the Network of Excellence EVOLTREE , contract No. 016322  - Sustainable development, Global Change  and  Ecosystems Programme  and through the  Seventh Framework for Research, Food Agriculture and Fisheries, Biotechnology , within the project ENERGYPOPLAR, contract No. FP7-211917  and WATBIO, contract No. FP7-311929. H. T. was supported by a PhD studentship from  NERC.
Schizophrenia is a devastating psychiatric illness with high heritability. Brain structure and function differ, on average, between schizophrenia cases and healthy individuals. As common genetic associations are emerging for both schizophrenia and brain imaging phenotypes, we can now use genome-wide data to investigate genetic overlap. Here we integrated results from common variant studies of schizophrenia (33,636 cases, 43,008 controls) and volumes of several (mainly subcortical) brain structures (11,840 subjects). We did not find evidence of genetic overlap between schizophrenia risk and subcortical volume measures either at the level of common variant Franke et al.
, 11 With genome-wide association results available from large samples for schizophrenia and for MRI-based brain phenotypes, we can now use genomic approaches to evaluate the genetic link between disease risk and such brain measures. Findings of covariation would help us develop new hypotheses about the structures involved in the primary disease process of schizophrenia. In this proof-of-concept study, we created a roadmap for the analysis of genetic covariation using a battery of complementary methods. We evaluated the overlap of common genetic variation at the high level of genetic architecture as well as of individual genetic variants. We also evaluated common genetic variant effect sizes on neuroimaging phenotypes and schizophrenia. The data we analyzed are from large mega-analyses by the PGC (Psychiatric Genomics Consortium) for schizophrenia  3  and meta-analyses from the ENIGMA consortium (Enhancing NeuroImaging Genetics through Meta-Analysis) for eight MRI volumetric measures (amygdala, caudate nucleus, hippocampus, nucleus accumbens, pallidum, putamen, thalamus, and intracranial volume (ICV)).
Fifth, relatively small genetic correlations between schizophrenia and these brain volumes may have been masked by combining datasets in a meta-analytic framework (e. g. , heterogeneous sample characteristics such as age, sex, and technical noise resulting from different MRI scanners or acquisition sequences may remain). It is conceivable that this resulted in the lower than expected SNP-heritability for some of these measures. Mega-analysis could be an important way to improve control for heterogeneity. Sixth, we evaluated only common genetic variation. Although common genetic variation explains far more of the risk for schizophrenia than rare copy number variation or rare deleterious exonic variation, 2 rare genetic effects on brain structure could be salient for some cases of schizophrenia.
29 Brain volume heritability estimates from genome-wide data obtained using LDSR  14  were lower than observed in previous studies. 30 This was expected for the subcortical regions, as those were corrected for ICV. For ICV, a likely source of difference with previous studies is the removal of the extended MHC region from our analysis. Although we found no evidence for genetic correlation between subcortical volumes and schizophrenia, we also investigated whether effect sizes of genetic variants are larger for brain measures than for schizophrenia. This point has been debated with respect to "endophenotype" studies, which attempt to identify quantifiable brain measures or other biomarkers thought to be intermediate between genotype and the liability to a disorder.
PGC schizophrenia We mega-analyzed individual genotype data from 46 European-ancestry schizophrenia GWAS datasets (full details in reference  3  ). Briefly, quality control and imputation were performed by the PGC Statistical Analysis Group for each dataset separately. Genotype imputation was with the pre-phasing/imputation stepwise approach implemented in IMPUTE2/SHAPEIT (chunk size of 3 Mb and default parameters) using the 1000 Genomes Project dataset (phase 1, August 2012, URLs). After imputation, we identified autosomal SNPs with high imputation accuracy across all samples. For robust relatedness testing and population structure analysis, we evaluated a subset of SNPs following LD-pruning (r 2 > 0. 02) and frequency filtering (MAF > 0. 05). For association testing, we evaluated the 46 datasets separately using an additive logistic regression model including ancestry principal components as covariates, and then conducted a meta-analysis of the 52 sets of results using an inverse-weighted fixed effects model.
ENIGMA, sample with brain volume measures and assessment of endophenotype The data analyzed here are from the ENIGMA analysis of eight MRI volumetric measures (full details in reference  9  ). MRI brain scans and genome-wide genotype data were available for 11,840 subjects from 22 cohorts (Supplementary Table  1 ). Only cohorts without schizophrenia cases and controls overlapping with the PGC schizophrenia samples were included. Participants clustered with subjects of known European ancestry as verified by multidimensional scaling (MDS) analysis. Genomic data were imputed to a reference panel (1000 Genomes, v3 phase1) comprising only European samples and with monomorphic SNPs removed. Imputation was performed at each site using MaCH for phasing and minimac for imputation. 34 Only SNPs with an imputation score of RSQ > 0.
Linkage disequilibrium score regression (LDSR) For LDSR, each dataset underwent additional filtering. Only markers overlapping with HapMap Project Phase 3 SNPs and passing the following filters were included: INFO score > 0. 9 (where available), study missingness of 0, and MAF >1%. Indels and strandambiguous SNPs were removed. To remove a potential source of bias, all SNPs in the extended MHC region (chr6:25-35 Mb) were removed from all datasets. The schizophrenia analysis included only results from European studies were used (LDSR requires LD data from a comparable sample). For the ENIGMA amygdala results, the mean Χ 2 was too low (1. 0051) to reliably estimate heritability using LDSR. The analysis was conducted using a two-step procedure with the LD-scoring analysis package. 12 ,  14  An unconstrained regression was run to estimate the regression intercepts for each phenotype, followed by an analysis with regression intercepts constrained to those estimated in the first step and the covariance intercept defined as zero (note that we took steps to exclude overlapping samples).
Rank-rank-hypergeometric overlap test (RRHO) RRHO  17  tests the hypothesis that ordering of two lists (LD-pruned GWAS results for schizophrenia versus a brain structure phenotype) by the strength of their association is arbitrary. The number of independent SNPs in common between the two ordered lists is evaluated at specified step sizes. Two lists that show similar ordering of SNPs demonstrate a global pattern of similarity of associations. Independent SNPs were selected based on the 1000 Genomes European dataset for 200 SNP windows shifted at five SNP intervals using an r 2 threshold of 0. 25. SNPs found in both PGC and ENIGMA data with MAF ≥ 0. 01 were retained (172,652 SNPs). The SNPs were then ordered by the -log10(p-value) of association multiplied by the effect size. A two-sided RRHO test that allowed testing for either over-or under-enrichment was used with a step-size of 3000 SNPs.
SNP sign test in the top GWAS findings To investigate a potential accumulation of same or opposite direction effects of SNPs between PGC schizophrenia and ENIGMA, we counted the number of same direction effects for the top-findings from the schizophrenia dataset (94 LD-independent genomewide significant SNPs, 231 with P < 1×10 -6 ) in the different brain structure datasets and tested the significance of the result in a binomial test (n=14 tests for 7 effective ENIGMA phenotypes and 2 P-value thresholds).
Over 7. 5 million SNPs found in both the ENIGMA and PGC datasets with MAF ≥ 0. 01 were evaluated. A conjunction null hypothesis is the union of the individual null hypotheses, producing a 'composite null hypothesis'. In standard testing situations a "point null hypothesis" is used, meaning that there is exactly one configuration of the unknown parameters of interest that corresponds to the null. For example, "no gene-brain association, no case-control association" is a point null hypothesis. A composite null has multiple configurations. For example, both of these configurations fall into the conjunction null hypothesis: "true genebrain association, no case-control association"; "no gene-brain association; true case-control association". A valid conjunction test has to control false positive risk over all possible configurations in the conjunction null.
SNP meta-analysis We combined the association P-values of SNPs associated with schizophrenia with SNPs associated with the seven subcortical brain volumes and ICV from ENIGMA. Using METAL,  35  we conducted a sample size-weighted meta-analysis for schizophrenia (effective sample size 71,715) and ENIGMA (variable sample sizes per SNP ranging from 8,000-11,000). SNPs were excluded if they were not present in both datasets and for MAF < 1% (per analysis). The total number of SNPs present in the eight meta-analyses ranged from 7,847,762 to 7,945,194.
supported by the German Federal Ministry of Education and Research (BMBF) through the Integrated Genome Research Network (IG) MooDS (Systematic Investigation of the Molecular Causes of Major Mood Disorders and Schizophrenia; grant 01GS08144 to M. M. N. and S. C. , grant 01GS08147 to M. R. ), under the National Genome Research Network plus (NGFNplus), and the Integrated Network IntegraMent (Integrated Understanding of Causes and Mechanisms in Mental Disorders), under e:Med Programme (GSK control sample; Müller-Myhsok). This work has been funded by the Bavarian Ministry of Commerce and by the Federal Ministry of Education and Research in the framework of the National Genome Research Network, Förderkennzeichen 01GS0481 and the Bavarian Ministry of Commerce. M. M. N. is a member of the DFG-funded Excellence-Cluster ImmunoSensation.
We acknowledge use of DNA from The UK Blood Services collection of Common Controls (UKBS collection), funded by the Wellcome Trust grant 076113/CI04/Z, by the Juvenile Diabetes Research Foundation grant WT0618S8, and by the National Institute of Health Research of England. The Multicenter Genetics Studies of Schizophrenia and Molecular Genetics of Schizophrenia studies study were supported by NIMH grant R01 MH062276 (to DF Levinson, C Laurent, M Owen and D Wildenauer), grant R01 MH068922 (to PV Gejman), grant R01 MH068921 (to AE Pulver) and grant R01 MH068881 (to B Riley). D. F. L. was supported by the Walter E. Nichols, M. D. , Professorship in the School of Medicine, the Eleanor Nichols Endowment, the Walter F. & Rachael L. Nichols Endowment and the William and Mary McIvor Endowment, Stanford University.
Acknowledgements Psychiatric Genomics Consortium (PGC). The authors are grateful to the many family members who participated in the studies that recruited these samples, to the many clinicians who assisted in their recruitment, and to our team members without whom this study would have been impossible. Core funding for the PGC is from the  US National Institute of Mental Health  ( U01 MH094421 ). Statistical analyses were carried out on the Genetic Cluster Computer (http://www. geneticcluster. org)hosted by SURFsara and financially supported by the  Netherlands Scientific Organization  ( NWO 480-05-003 ) along with a supplement from the  Dutch Brain Foundation  and the  VU University Amsterdam. The GRAS data collection was supported by the  Max Planck Society , the  Max-Planck-Förderstiftung , and the  DFG Center for Nanoscale Microscopy & Molecular Physiology of the Brain (CNMPB), Göttingen, Germany.
The Boston CIDAR project was supported by the  NIMH  ( P50 MH080272 , RWM;  U01 MH081928 , LJS;  R01 MH092380 , TLP) and the  Massachusetts General Hospital Executive Committee on Research (TLP). P. H. L. is supported by  NIMH   K99 MH101367. ISC Portugal: CNP and MTP have been supported by  NIMH  grants  MH085548 ,  MH085542 ,  MH071681 ,  MH061884 ,  MH58693 , and  MH52618 , and the  NCRR   RR026075. CNP, MTP, and AHF have been supported by grants from the  Department of Veterans Affairs Merit Review Program. The Danish Aarhus study was supported by grants from  Lundbeck Foundation ,  Danish Strategic Research Council, Aarhus University, and Stanley Research Foundation. Work in Cardiff was supported by  MRC Centre  ( G0800509 ) and  MRC Programme  ( G0801418 ) Grants, the  European Community 's  Seventh Framework Programme  ( HEALTH-F2-2010-241909 , Project  EU-GEI) ) the  European Union Seventh Framework Programme  ( FP7/2007-2013)  under grant agreement  n°279227 , a fellowship to JW from the  MRC/Welsh Assembly Government  and the  Margaret Temple Award  from the  British Medical Association.