dataesr/scientific-paragraphs-categorization huggingface dataset:
In the four cohorts, glucose-stimulated insulin secretion was evaluated by measurement of plasma glucose and serum insulin at 0, 30 and 120 minutes during a 75 g oral glucose tolerance test (OGTT). Insulinogenic index was calculated based on these as Insulinogenic index = (s-insulin at 30 minutes [pmol/l] -fasting s-insulin [pmol/l]) / p-glucose at 30 minutes (mmol/l). In these analyses, individuals with known diabetes were excluded. Two sample sets (Inter99 and Health2008) were then genotyped by Illumina OmniExpress array while the two other cohorts were genotyped by Illumina CoreExome array. Genotypes were called by Illumina GenCall algorithm. Genotype data were filtered for variants with call rate <98% and Hardy-Weinberg equilibrium P<10 -5. Samples were excluded if they were ethnic outliers, had mismatch between genetic and phenotypic sex or had a call rate <95%.
Related to Figure  6. To perform GWAS meta-analysis for HOMA-B and HOMA-IR, GWAS summary statistics were approximated using a recently developed approach, GWIS  77. We obtained summary statistics from the latest sex-specific and sex-differentiated GWAS meta-analysis of fasting glycemia (FG) and fasting insulin (FI) performed by the Meta-Analysis of Glucose and Insulin-related traits Consortium (MAGIC) in up to 88,320 and 64,090 individuals (that are comprised in 40 and 33 studies), respectively. To obtain summary statistics data that were appropriate for LD-score regression  78  analysis, individuals that were genotyped on the Metabochip array were excluded. FI was measured in pmol/l and natural log transformed, and FG was measured in mmol/l with a cut-off at 7 mmol/l, which were used to generate sex-specific summary statistics for meta-analysis of untransformed FG (up to 67,506 men and 73,089 women) and ln-transformed FI (up to 47,806 men and 50,404 women), without BMIadjustment.
Further details are explained in  79. We obtained GWAS summary statistics for T2D  35  , Insulinogenic index (see GWAS meta-analysis of Insulinogenic index from oral glucose tolerance test (OGTT) measures), HOMA-B and HOMA-IR (see GWAS meta-analysis of scores of the  ! coefficients) with the qvalue R package (Alan Dabney and John Storey, Department of Biostatistics, University of Washington) over 17 functional categories and 6 traits. FDR significance threshold was set at 0. 05. Polygenic risk scores (PRSs). Related to Figure  6. We created Polygenic risk scores (PRSs) based on T2D GWAS summary statistics from  35  (base dataset). UK Biobank individuals  84  were used as the target datasets, which comprised testing and validation datasets. To select markers used for PRS we first considered all genetic markers that were used as input for phasing and genotype imputation by UK Biobank investigators, and filtered for variants with MAF ≥ 5% and imputation quality score > 0.
8. We then reconciled the base and target datasets by looking at the variant overlap between summary statistics and the imputed UK Biobank data, discarding variants showing allele inconsistency between both datasets. We also removed those located in the MHC region, resulting in a final collection of 5,352,387 variants. Concerning UK Biobank samples, we first applied the following exclusions to the entire cohort: (i) individuals with excess of relatives (> 10 third-degree relatives according to the UK Biobank dataset), (ii) individuals without gender information, (iii) individuals with ICD10 codes E10 (insulin-dependent diabetes mellitus), E13 (other specified diabetes mellitus) and E14 (unspecified diabetes mellitus), (iv) individuals without body mass index (BMI) information, and (v) in pairs of individuals with thirddegree relatedness (as provided by UK Biobank investigators) we excluded subjects with the highest missing rate for a set of high-quality markers.
From these, only five signals, from which four were discovered in East Asian populations, do not show consistent direction of effect with the reference study (Supplementary Table  12 ). The entire dataset of 377,981 controls and 15,764 cases was divided in test and validation datasets. For the test dataset, we included only control subjects with age at recruitment ≥ 55 years and no family history of diabetes mellitus. The final sample size of the UK Biobank test dataset was 6,305 T2D cases and 73,922 controls. The remaining UK Biobank samples were used as a validation dataset, comprising 9,459 T2D cases and 226,777 controls. Control individuals in this validation dataset were included regardless of their age or family history. PRS models were calculated using the PRsice software  86  with default settings and the following clumping parameters (--clump-r2 0.
6 --clump-p 0. 01). We included 11 covariates in the analysis: the 7 principal components provided by UK Biobank investigators as well as BMI, age at recruitment, batch information, and sex. PRS models were built using the T2D GWAS summary statistics from  35  as the base dataset and the abovementioned subset of the UK Biobank as the test dataset (6,305 T2D cases and 73,922 controls). To examine islet enhancer hubs, we calculated PRS using variants overlapping all hub pcHi-C baits and enhancers. This generated a PRS model of 179 variants. In parallel, we generated a genome-wide PRS model based on the entire set of common genetic variants shared by the base and the validation dataset (~5M variants) that finally comprised 1,152 variants. As an additional control, we used genetic variants overlapping pseudo-enhancer hubs.
We generated pseudoenhancer hubs by shuffling all hub pcHi-C baits and their assigned enhancer fragments across the genome. We excluded pseudo-enhancer hubs that mapped to TADs containing real hubs, or that crossed TAD boundaries. Pseudo-interacting regions that mapped to blacklisted regions or had low mappability were excluded. We then built a PRS using variants overlapping pseudo-baits and pseudoenhancers. We generated 100 sets of pseudo-baits and pseudo-enhancers and the corresponding PRS models (median of 280 variants). We applied the resulting PRS models to the UK Biobank validation dataset (9,459 T2D cases and 226,777 controls) following the equation below provided by the PRSice authors 86 : 𝑃𝑅𝑆 = 𝑆𝑥𝐺 2 𝑀 where  corresponds to the summary statistic of the effective allele,  is the number of the effective allele and  is the number of SNPs.
To assess the predictive power of genome-wide, islet hub and pseudo-hub PRS, we examined the T2D frequency in the UK Biobank validation dataset across 40 bins, each one containing 2. 5% of individuals ranked by the PRS score. For analyses that considered PRS as a function of BMI and age of onset, we focused on all 226,777 control individuals and a subset of 6,127 T2D cases with age of diagnosis > 20. For each of the three PRS models (genome-wide, islet hub and 100 sets of pseudo-hubs), we calculated the ratio of T2D frequency in the highest and lowest PRS bins, each one containing 2. 5% of the entire population. We also used cox proportional hazard models, with age of onset (age at recruitment for controls) as the underlying time-scale, to analyse the effect of highest and lowest PRS values on the risk of T2D for the genome-wide, the islet-hub and 100 sets of pseudo-hubs PRS models.
The age of controls was censored at the time of recruitment. We also used cox proportional hazard models to show the HR for the top and bottom PRS bins according to the genome-wide and islet hub PRS models across three BMI categories (< 30, 30 -<35 and ≥ 35) and two categories of age of diagnosis (<60 and ≥ 60). Results are illustrated in forest plots with effect sizes and 95% CI. We further examined the predictive power of the PRS models by calculating the area-under-the curve (AUC) statistic. We addressed the performance of the PRS models in two ranges of BMI (< 35 and ≥ 35) and in two ranges of age of diagnosis for T2D (<60 and ≥ 60). Finally, we calculated the odds ratio for T2D in individuals with highest PRS scores (top 2. 5% bin) compared to the remainder UK Biobank individuals of the validation dataset in different age and BMI categories.
Acknowledgements This research was supported by the  National Institute for Health Research (NIHR) Imperial Biomedical Research Centre. Work was funded by grants from the  Wellcome Trust  ( WT101033  to J. F. and  WT205915  to I. P. ),  Horizon 2020 (Research and Innovation Programme 667191  to J. F. , 633595 to I. P. and  676556  to M. A. M-R;  Marie Sklodowska-Curie   658145  to I. M-E. , and  43062/ZENCODE  to G. A. ),  European Research Council  ( 609989  to M. A. M-R. ). Marató TV3 (201611, to J. F. , M. A. M-R. ), Ministerio de Economía y Competitividad  ( SAF2018  to J. F. , BFU2013-47736-P to M. A. M-R. , IJCI-2015-23352 to I. F. ),  AGAUR  (to M. A. M-R. ). UK Medical Research Council  ( MR/L007150/1  to P. F. ),  World Cancer Research Fund (WCRF UK  to I. P. ) and  World Cancer Research Fund International  ( 2017/1641 ),  Biobanking and Biomolecular Resources Research Infrastructure (BBMRI-NL ,  NWO 184.
We clustered the sequences to a 97% similarity threshold without using singletons using UPARSE algorithm  (Edgar, 2010). This threshold represents the ITS barcode gap for the overwhelming majority of fungal species, including the subdivision Pucciniomycotina  (Schoch et al. , 2012). The forward and reverse Illumina reads were merged using the fastq_mergepairs command of USEARCH version 9. 0. 2132, and sequences with more than one expected error and less than 175 bp were removed. Ion Torrent sequences were only used if the forward and the reverse primer complement could be found within the sequence and if the sequence was at least 175 bp long. We discarded Ion Torrent sequences with more than two expected errors (EE). We set a higher EE threshold because the mean expected error rate of the Ion Torrent runs at the sequence length of 300 bp was two.
We trimmed non-biological (primer) sequences, allowing 10% bp mismatch using the Python tool "cutadapt" version 1. 13  (Martin, 2011)  if the forward primer or the reverse primer complement could be found at the sequence ends. Identical sequences were de-replicated. Illumina and Ion Torrent data were independently clustered to 97% similarity threshold without using singletons, using the UPARSE greedy clustering algorithm  (Edgar, 2013). We constructed a reference database from  UNITE and INSD (accessed 20. 11. 2016 ) and matched the representative sequence of each OTU to this database using BLAST version 2. 5. 0+ (Altschul et al. , 1997). We considered an OTU to represent the order Pucciniales if it matched Pucciniales sequences in the database >80% identity over at least 150 bp  (Nguyen et al.
| Statistical analyses We used R version 3. 4. 1 (R Core Team, 2017) for conducting analyses and creating graphs if not stated otherwise. To test whether a method detected more or fewer shared/unique rust fungal OTUs than expected by chance, we used the "permatswap" function of the R package "vegan" version 2. 0-7  (Oksanen et al. , 2017)  to create a null expectation. The simulated community matrices are based on Monte Carlo iterations, whereby the total number of OTUs per plot and total abundance within OTU were kept constant. We tested for differences in OTU abundances among methods using a generalized additive model (GAM) of the package "mgcv" version 1. 8-18  (Wood, 2001). A GAM was selected because: (a) it allows beta distribution for the response variable, which in this case was the appropriate distribution for the proportional abundance of each OTU found within a plot (to account for different sequencing depths); and (b) the approach allows testing for OTU and plot as random effects, and interaction between method and OTU.
To look for a taxonomic bias in detecting the different methods, we constructed a neighbor-net phylogeny  (Bryant & Moulton, 2004 ) using Splitstree 4. 0  (Huson, Kloepper, & Bryant, 2008)  and used chi-square test to test whether taxonomic clusters are independent of methods. We tested whether a possible difference is due to the detection of rare and dominant OTUs by rerunning all tests using the top and lower 50% of the rank abundance of each method. Species identities are based on the best BLAST match and were displayed as networks using the "igraph" package version 1. 0. 1  (Csardi & Nepusz, 2006)  with edge width representing relative species abundance within method.
, 2016; Zhan et al. , 2013). Next-generation sequencing metabarcoding seems appropriate for the large-scale detection of rust fungi and less appropriate for inferring absence of species. For example, the species Puccinia sorghi was initially present in the raw data of all three methods. However, only two sequences of this species were present in the Illumina raw data. These two sequences exhibited a point mutation or a possible sequencing error in their reverse sequence read and got treated as unique sequences (singletons) after merging. Hence, although initially present in the Illumina raw data, these two sequences could not form an OTU. This phenomenon of species getting lost during merging of paired-end sequencing has been noted earlier by  Nguyen, Smith, Peay, and Kennedy (2015)  and was generally caused by the usually poorer quality of reverse sequencing reads of the Illumina MiSeq platform.
, 2016). These studies suggest that if obstacles associated with PCR biases can be overcome, NGS metabarcoding holds promising potential not only for the detection but also for the quantification of species. Moreover, PCR-free techniques may remedy primer and amplification biases (including chimera formation) in the near future. Different gene copy numbers still pose a significant challenge for biomass estimates but could be overcome with the growing number of whole genome databases. Next-generation sequencing metabarcoding has been increasingly recognized as a promising tool for biomonitoring species and complex communities at large scales  (Holdaway et al. , 2017). In recent cases, it has been applied to plant pathogenic fungi  (Merges et al. , 2018)  and oomycetes  (Burgess et al. , 2017).
Mismatches are highlighted (bold and underlined). Sequences were selected from the National Center for Biotechnology Information (NCBI) to cover the gene region of cloning and metabarcoding primers when possible. Dot indicates no entry of base pair in the database. Accession numbers are given as footnotes. Accession numbers: a JN204183. 1 b HM022141. 1 c JN204182. 1 d KC466553. 1 e HQ317515. 1.
Mismatches are highlighted (bold and underlined). Sequences were selected from the National Center for Biotechnology Information (NCBI) to cover the gene region of cloning and metabarcoding primers when possible. Dot indicates no entry of base pair in the database. Accession numbers are given as footnotes. Accession numbers: a JN204183. 1 b HM022141. 1 c JN204182. 1 d KC466553. 1 e HQ317515. 1 Species Puccinia calcitrapae a Puccinia nishidana b Puccinia balsamorrhizae c Puccinia komarovii d Puccinia hieracii e metabarcoding primer seemed not to fall into a known taxonomic detected by cloning but not by metabarcoding Note. TA B L E 1 Metabarcoding primer mismatches to selected species that were cluster, like a subgenus (Van der Merwe, Ericson, Walker, Thrall, & 5'-fITS7 (forward primer) GTGARTCATCGAATCTTTG GTGAATCATTGAATCTTTG.
The genotyping of wild and cultivated accessions from a broad range of viticultural areas at two large grapevine repositories provided a significant dataset capable of elucidating relationships within and between the two subspecies at the global level  [10, 25]. Results from these studies suggest that grapevine spread from East-to-West after the first domestication process. The results also provide evidence of introgression from local sylvestris individuals with cultivated accessions  [25] , and the impact on genetic structure related to geographic origin and human use  [10]. A limitation of previous examinations of grape genetic diversity was unbalanced sampling resulting in a germplasm collection set that was limited to one or more countries and was not broadly representative. In addition, the sylvestris and wild germplasm from the Caucasus Mountains and Central Asia was poorly represented or not analyzed in these studies.
It would further allow us to determine the overall relationships of germplasm within the centers of domestication and with their wild progenitors. With these objectives, data were pooled from six previous studies {Laucou et al. [7] , De Andrès et al. [8] , Imazio et al. [12] , Riaz et al. [24] , Biagini et al. [28] , Zdunić et al. [29] } and new data were generated for wild accessions collected from Croatia, Georgia, Armenia and Azerbaijan, to develop a well-balanced set that represented both subspecies and provided maximum representation of key geographical regions [Mediterranean basin and Central Asia (Spain, France, Italy, Croatia, Georgia, Armenia, Azerbaijan, Iran, Turkmenistan and Pakistan)]. SSR data were analyzed to infer the genetic structure of populations in wild and cultivated grapevines and to determine the role of Central Asian grapevine germplasm in the diversification of the cultivated gene pool.
DNA extraction and genotyping Total genomic DNA was extracted from young leaves using DNeasy Plant Mini Kits (Qiagen, Valencia, CA, USA). Genotyping was carried out by amplifying 20 nuclear SSR loci: VMC1b11, VMC4f3. 1, VVIb01, VVIh54, VVIn16, VVIn73, VVIp31, VVIp60, VVIq52, VVIv37, VVIv67, VVMD21, VVMD24, VVMD25, VVMD27, VVMD28, VVMD32, VVMD5, VVMD7, VVS2  [7]. The amplifications were performed as reported in  [7]. The amplified loci were detected on an automated ABI 3500 Genetic Analyzer (Applied Biosystems, Life Technologies, Foster City, CA, USA). Allele sizes were scored using GeneMapper 4. 0 software (Applied Biosystems, Life Technologies) and recorded in base pairs.
Data analysis Determination of flower phenotype The flower phenotype of the V. vinifera subsp. sylvestris samples collected from Armenia, Azerbaijan, Georgia and Croatia was determined by a combination of a specifically designed marker from gene APT3 (adenine phosphoribosyl transferase) that is capable of distinguishing female plants from males or hermaphrodites  [24]. We also used a specific allele of the SSR marker VVIb23 that is closely linked with the sex locus on chromosome 2, and is capable of distinguishing hermaphrodites from female or male plants. The VVIb23 locus polymorphism has been detected and reported in  [30]. A total of 403 accessions were analyzed with these two markers to assign flower phenotype. The flower phenotypes of additional wild accessions from other countries were determined either during the time of collection or from plants maintainted in the germplasm repositories.
Genetic diversity In order to combine the fingerprint data of new genotypes with previous data sets  [7, 8, 12, 24, 28, 29] , genetic profiles of eight reference cultivars (Cabernet Sauvignon, Chardonnay, Dolcetto, Pinot noir, Riesling, Thompson Seedless, Zinfandel, and Sangiovese) were used as references to standardize the allele calls. The genetic diversity among groups and over all the groups was estimated. The normalized SSR genotyping data were used to determine the number of different alleles (Na), the effective number of alleles (Ne), Shannon's Information Index (I), observed heterozygosity (Ho) and expected heterozygosity (He;  [31] ). The parameters were estimated by GenAlEx 6. 5 software  [32]. Weir and Cockerham's F-statistics (F IS , F IT , F ST ;  [33] ) per locus and F IS values per each population were detected via FSTAT 2.
9. 3 and Arlequin 3. 5. 2. 2 softwares, respectively  [34, 35]  p-values were evaluated over 1000 permutations. Allelic richness (AR) and private allelic richness (PAR) for each population were estimated using the rarefaction method, which compensates for differences in sample size (i. e. rarified allelic richness) among populations as implemented in HP-Rare 1. 1  [36]. The effective number of migrants per generation (Nm) among the 12 grapevine populations and between the two subspecies was estimated using the private allele method of Barton and Slatkin  [37]  (1986) using GENEPOP 3. 4 software  [38].
Genetic relationships and differentiation Poppr  [39]  package implemented in R 3. 1 software  [40]  was used to design a phylogenetic tree with Neighbor-Joining. The distance matrix used in Poppr was calculated based on the Nei's distance  [41]. The unrooted dendrogram was plotted with the R package ape  [42]. To measure how well the hierarchical structure from the dendrogram represents the actual distances, the cophenetic correlation coefficient (CCC) has been calculated performing the cophenetic function implemented in R software. Hclust R function was used to perfume hierarchical clustering using a neighbor-joining agglomeration method. In order to elucidate the genetic relationships within and among geographic groups, principal coordinate analysis (PCoA) was performed on the multilocus microsatellite data, which was then arranged into geographic groups using the package adegenet implemented in R  [43].
Clustering validation and multivariate analysis was carried out using pairwise Nei's genetic distance  [44]  and pairwise F ST in GenAlEx 6. 5 software. Finally, an analysis of molecular variance (AMOVA,  [45] ) was performed to characterize the partition of the observed genetic variation among and within populations and genetic groups using Arlequin 3. 5. 2. 2 software. The significance test was performed over 1000 permutations.
Flower phenotype in the wild accessions Flower sex phenotype and seed morphology are key criteria normally used to differentiate subsp. sylvestris (dioecious vines, seeds with short beaks) from cultivated sativa forms (predominantly hermaphroditic flowers, seeds with larger beaks). The search for wild accessions was focused on collecting dioecious individuals because most cultivated genotypes are hermaphrodites. Flower phenotype data from the wild samples from Spain and Italy were recorded in the field and previously reported by Benito et al. [49]  and Biagini et al. [28, 50]. The sylvestris samples from France, Georgia (University of Milan repository) and Croatia were collected from natural habitats and flower phenotypes were recorded based on the presence of fruit (female) and flower rachis without fruit (male) during collection.
477 (VVIn73 locus) to 0. 803 (VVS2), with a mean value equal to 0. 678. While, the Ho values varied from 0. 535 (VVIn73) to 0. 845 (VVIp31) and the mean overall value was 0. 742. The locus with the lowest F value was VVIb01 (0. 021), while the highest was VVIq52 (0. 189). The mean F value for the dataset was 0. 088. Allelic profiles were used to calculate statistical indices and determine the genetic diversity of the cultivated and wild genotypes (Table  3 ). The number of alleles per locus (Na) was 9. 120 for sativa and 9. 164 for sylvestris samples. The Italian cultivars had the lowest Na value (4. 900) of the cultivated accessions and the highest Na value (12. 600) was detected in the Georgian cultivars. The number of alleles per locus for the wild accessions varied between 7. 050 (Armenia) and 12.
850 (Georgia). The Ne value over the whole dataset was 4. 441. The sativa accessions from Italy (3. 688) and sylvestris accessions from France (2. 792) had the lowest Ne values. The highest Ne values were detected in cultivated accessions (5. 751) and wild individuals (6. 016, Table  3 ) from Georgia. Within sativa, the allelic richness, adjusted to a minimum sample size of 42 genes, ranged from 6. 200 alleles for Spanish accessions to 9. 330 for Italian accessions, with an overall mean of 7. 848 alleles across loci. Within the sylvestris accessions, allelic richness ranged from 5. 870 for the Armenian group to 10. 200 for the Georgian group with an overall mean of 7. 089 across loci. The private allelic richness for sativa ranged from 0. 020 for the Spanish and French groups to 0. 520 for the Italian and Turkmenistan/Pakistani groups with an overall mean frequency of 0.