dataesr/scientific-paragraphs-categorization huggingface dataset:
Our expectation was conditioned on the following parameter estimates from G-PhoCs: N e for European/Boxer ancestral population (y 1 ), N e for European/Indian ancestral population (y 2 ), time of divergence for Europe and Boxer (P 0 ), time of divergence for Europe/India (P 2 ) and time of divergence for Europe-India/Asia (P 3 ) as well as the percentage of HXH that is made up of Asian admixture (a) from the f4-ratio analysis (Supplementary Table  23 ). Confidence intervals were estimated by resampling G-PhoCS parameters from their posterior distributions and finding predictedderived allele sharing ratios that were within a range determined for the observed data by a weighted jackknife resampling approach. See Supplementary Notes 12 and 13, Supplementary Figs 49-55 and Supplementary Table  23  for more details.
Coordinates of 30 putative 'domestication loci' were obtained from Axelsson et al. 30  and lifted over from CanFam2. 0 to CanFam3. 1 coordinates. Call set 1 SNPs within each window were extracted from the ancient samples and our genome sequence data set. Eigenstrat genotype file formats were generated per window using convertf from the EIGENSOFT package  52  and custom scripts were used to convert the genotype files into matrix formats for visualization using matrix2png 53 using a filtered subset of SNPs (minor allele frequencies between 0. 05 and 0. 49) for easing visualization of the matrices. NJ trees were estimated for each window with the full SNP set using the same methods as the whole-genome tree estimation (see above). Altogether, the haplotypes of the three ancient samples were classified as either dog or wolf-like for 18 matrices that showed clear distinction between dog and wild canid haplotypes based on average reference allele counts calculated per window.
Acknowledgements We thank  Dan Bradley  for his help obtaining the HXH specimen. We thank  Walter Eanes  and  Douglas Futuyma  for their comments on the manuscript,  Dorina Twigg  for the processing of canine copy number variation data,  Nick Patterson  for providing advanced access to the latest version of  Admixtools, Vida  for his thoughts, the  NYGC  for their assistance in the sequencing,  Valeria Mattiangeli  for performing initial Miseq sequencing on HXH,  Christian Sell  for his assistance with the raw data analysis pipeline used for the  CTC Miseq data , and the  Musee zoologique de la Ville de Strasbourg  for hosting the team of archaezoology. The Kidd Lab is supported by  NIH  Grant  R01GM103961  and A. L. P. is supported by  T32HG00040. A. S. and K. D. are supported by.
Data availability. Sequencing data are available from the NCBI sequence read archive (SRA) database under accession numbers SRS1407451 (CTC) and SRS1407453 (HXH), and mitochondrial genomes are available in GenBank under accessions KX379528 and KX379529, respectively. Code generated to call variants in the ancient samples is available at: https://github. com/kveeramah/ aDNA_GenoCaller.
Gas chromatography analyses were performed to achieve individual alkenone separation and subsequent quantification. Details on the analytical procedure can be found in ref. 55. The recent 100 years of the grain size and alkenone data were published in refs. 14, 17. Binning of IRD data younger than 1900 was done by calculating the average sand flux in the following time intervals 1898-1930, 1931-1962, 1963-1996, 1997-2008 (AD).
Acknowledgements We gratefully acknowledge funding for this study from the  VILLUM Foundation  to the project ' Past and future dynamics of the Greenland Ice Sheet : what is the ocean hiding?' (grant no. 10100 ). We thank the  Centre National de la Recherche Scientifique (CNRS)  for salary support. We also acknowledge support from the ice2ice project funded by the  European Research Council  ( ERC  grant no. 610055 ), the  Norwegian Research Council , and the  Centre for Climate Dynamics at the Bjerknes Centre. We thank  R. Fausto ,  A. Solgaard ,  T. Cronin  and the Ocean2k working group of the  Past Global Changes (PAGES)  for discussions and three anonymous reviewers for help in improving the manuscript.
Independent association signals at novel and known loci. To identify independent association signals within a known or novel locus, we performed joint conditional analysis of eGFRcrea based on aggregated study-specific statistics using the GCTA software  22. Among the combined 49 loci (39 known and 10 novel) attaining genome-wide significance, we uncovered eight independent signals, all among the previously reported loci, with p-values ranging from 2. 39 × 10 -8 to 2. 78 × 10 -17 after conditioning on the lead variants at each locus (Supplementary Table  11  and Supplementary Figure  3 ). We found that in all but one locus (DDX1), the previously reported lead variant was also genome-wide significant in our 1000 Genomes meta-analysis. A more detailed reasoning for the independent association signals is proposed in Supplementary Table  12.
Expression quantitative trait loci (eQTL) lookup. To explore potential functional implications of the novel loci, we interrogated published databases of cis eQTL in whole blood  25  for the significant SNPs or their proxy variants (r 2 > 0. 8 within a 1 MB window). At 2 novel loci, significant association (p-value < 0. 004) with gene expression were found: rs1111571 with SLC7A6, ZFP90, LYPLA3 and NFATC3, and for rs12144044 with RHOC and ST7L (Supplementary Table  15 ). We expanded our downstream analysis by annotating the significant variants with known and predicted regulatory elements using Regulome DB  26  : We confirmed rs1111571 and rs12144044 as significant associations with gene expression and found supporting evidence that these two variants show also evidence for transcription factor binding sites and DNase peaks.
To identify the contribution of rare variants (MAF < 1%) to eGFRcrea, large-scale sequencing data in addition to genomic chip data have been shown to be a promising approach  31. Third, these novel loci, missed by our previous analysis  7  , extend our knowledge of pathways underlying kidney function, which depicts the influence of kidney development, kidney structure, and metabolic activity on the development of CKD. The comparison of our 1000 Genomes meta-analysis with our previous HapMap meta-analysis is limited by several factors: the current analysis consists of a reduced number of samples and a slightly different study composition. Furthermore, different 1000 Genomes reference panels were used to impute genotypes and advances in imputation software and methodology must be acknowledged  32, 33.
Genome-wide association analysis. Each study performed GWAS according to a uniform analysis plan by regressing sex-and age-adjusted residuals of the natural logarithm of eGFRcrea and eGFRcys on the allelic dosage levels. When appropriate, adjustment for study-specific features such as study site or genetic principal components was included in the model. Family-based studies accounted for relatedness using mixed effect models. Details on the study-specific methods are reported in Supplementary Table  4. GWAS meta-analysis. All GWAS files underwent quality control using the GWAtoolbox package  40. GWAS meta-analyses for eGFRcrea and eGFRcys were performed using the software METAL  41  assuming fixed effects across studies and using inverse-variance weighting, excluding variants with imputation quality IQ ≤ 0.
Comparison of 1000 Genomes and HapMap results. For the variants available in both the 1000 Genomes and HapMap meta-analyses, we compared lead variants, effect sizes, imputation quality as well as the power that we had in the data to detect the respective effects. For this comparison, we also utilized the association results of our previous HapMap meta-analysis 7 in 50 studies including a maximum of 133,814 subjects. Power was calculated in R (www. r-project. org) for the approximate maximum number of subjects in the 1000 Genomes meta-analyses (n = 110,000) to identify the lead variants with an alpha of 5 × 10 -8. Further, effective power, which takes into account the imputation quality of the variant, was calculated based on the effective number of subjects, which is the number of subjects per variant multiplied by the median of the imputation quality across studies.
Pathway Analyses. Pathway analyses, comprised of pathway/gene set enrichment and tissue/cell type analyses, were performed by applying a software package called Data-Driven Expression Prioritized Integration for Complex Traits (DEPICT)  21. DEPICT performs gene set enrichment analyses by testing whether genes in GWAS-associated loci are enriched for reconstituted versions of known molecular pathways (jointly referred to as reconstituted gene sets). The reconstitution is accomplished by identifying genes that are co-regulated with other genes in a given gene set based on a panel of 77,840 gene expression microarrays  44. Genes that are found to be transcriptionally co-regulated with genes from the original gene set are added to the gene set, which results in the reconstitution. Several types of gene sets were reconstituted in DEPICT: 5,984 protein molecular pathways derived from 169,810 high-confidence experimentally derived protein-protein interactions  45  , 2,473 phenotypic gene sets derived from 211,882 gene-phenotype pairs from the Mouse Genetics Initiative  46  , 737 Reactome database pathways  47  , 184 Kyoto Encyclopedia of Genes and Genomes (KEGG) database pathways  48  and 5,083 Gene Ontology database terms  49.
In total, 14,461 gene sets were assessed for enrichment in genes in associated regions. DEPICT also facilitates tissue and cell type enrichment analyses by testing whether the genes in associated regions are highly expressed in any of the 209 MeSH annotations for 37,427 microarrays on the Affymetrix U133 Plus 2. 0 Array platform. In our analysis, we used DEPICT version 1 rel194 and to be comparable to our previous analysis, included all variants reaching eGFRcrea association p-values < 1 × 10 -5 from HapMap and 1000 Genomes imputed data with genomic coordinates defined by genome build GRCh38 (https://genome. ucsc. edu/cgi-bin/hgLiftOver). Since 1000 Genomes imputed loci in the DEPICT analysis differed slightly from the HapMap imputed loci, our HapMap and 1000 Genomes input was created by adding all significant 1000 Genomes variants to all significant HapMap variants.
This process resulted in a total of 3,659 variants for HapMap, 7,894 variants for 1000 Genomes, and 9,270 variants for HapMap and 1000 Genomes analyses. Next, independent lead variants were identified with Plink 50 using ± 500 kb flanking regions and r 2 > 0. 01 with the 1000 Genomes data 14 as reference. Genomic intervals are generated consisting of all variants within r 2 > 0. 5 to each lead variant. If any of the 19,987 genes in the analysis overlaps or resides within a genomic interval, it is mapped to that interval. After merging of overlapping regions and excluding regions within the major histocompatibility complex on chromosome 6, base pairs 25,000,000-35,000,000, DEPICT analyses were conducted using the following parameters: 200 repetitions to compute FDR and 2,000 permutations to compute p-values adjusted for gene length by using 500 null GWAS.
For the enrichment analysis we used 10,968 reconstituted gene sets. For visualization, all novel significant gene sets were further merged into meta gene sets by running an affinity propagation  51  from Pythons scikit-learn package (http://scikit-learn. org/). The network was visualized with Cytoscape (http://cytoscape. org/). Identification of independent association signals with GCTA. We searched for independent association signals in the known and novel loci with a joint conditional analysis on the aggregated meta-analysis results using the GCTA-COJO method (conditional and joint genome-wide association analysis)  22, 52. The KORA-F4 GWAS data  53  were used to estimate the LD (r 2 ) in the joint conditional analysis, and to quantify the extent of coinheritance (D')  50. A potential independent association signal within a given locus was reported if the variant with the smallest conditional p-value was genome-wide significant (p-value < 5 × 10 -8 ) after conditioning on the previously reported variant in a locus.
| Var DBLα sequence analysis A custom pipeline was developed to demultiplex, denoise, and remove PCR and sequencing artefacts from the DBLα domain reads. The first part of the pipeline is available as the Multipass web server: http://www. cbs. dtu. dk/services/MultiPass-1. 0, and the following cleaning steps described below are implemented in a python script available here: https://github. com/454data/postprocess. The sfffiles obtained from each region on the 454-plate were divided into smaller isolate-specific sff-files by identification of reads with exact matching MID sequences in both ends using BioPython v1. 57 (Cock et al. , 2009). Ambiguous primer sites were then identified (exact match) and trimmed off the flowgrams, reverse reads were reverse complemented, and a dat-file (AmpliconNoise format) with the resulting flowgrams was created for each isolate, using BioPython v1.
57 (Cock et al. , 2009). By combining the forward and reverse reads, this method takes advantage of bidirectional amplicon sequencing, since the forward reads will have highest quality in the 5′-end of the target sequence, and the reverse reads will improve the 3′-end quality. Flowgram clustering was performed using PyroDist, FCluster, and PyroNoiseM from the AmpliconNoise package v1. 25  (Quince et al. , 2011). The flowgram clusters produced by AmpliconNoise were base called using Multipass to obtain the most likely DBLα sequences given a full length open reading frame (FRF) probability of p(FRF) =. 9979 as described in Rask et al. (Rask et al. , 2016) ; however, an alternate flow calibration was used. Control isolate flow value distributions for the longest homopolymers in this 454 run differed slightly from the previously described normal distributions  (Balzer et al.
| STRUCTURE analyses We used the Bayesian clustering method implemented in STRUCTURE v. 2. 1  (Pritchard, Stephens, & Donnelly, 2000)  to identify population structure. We ran models allowing for admixture, with the number of clusters or populations (K), ranging from K = 1 to the number of South American populations included (K = 5) (  (Raymond & Rousset, 1995; Anderson et al. , 2000) , depending on the dataset). All simulations used 100,000 Markov chain Monte Carlo (MCMC) generations in the burn-in phase and 100,000 generations in the data collection phase. Ten independent runs were performed for each specified K to verify convergence in the estimates of posterior probabilities. The optimal number of clusters was estimated using the method proposed in Evanno, Regnaut, and Goudet (2005)  (Evanno et al.
| Assembly of reads into var DBLα sequences Within each isolate, sequences were clustered into nonredundant DBLα sequences using the flowgram clustering method (Section 2). The method resulted in 5,699 DBLα sequences among the 128 South American isolates with 29. 8 sequence reads per DBLα sequence. Among the South American populations, between 352 and 2,048, nonredundant DBLα sequences were identified (Table  1 ) and represent the dataset on which the analyses were performed.
Concerning the Peruvian samples, the study protocol was approved by both the Ethical Review Committee of the Universidad Peruana Cayetano Heredia and the Institute of Tropical Medicine, Antwerp, Belgium. The research was performed in accordance with the ethical standards of the Peruvian Ministry of Health. The trial has been registered as an International Standard Randomised Controlled Trial, number NCT00373607 at http://www. clinicaltrials. gov. For samples collected in Venezuela, each patient gave written informed consent and ethical clearance was obtained from the Comité Ético Científico del Instituto de Medicina Tropical de la Universidad Central de Venezuela.
I G U R E 5 Isolation by distance between the South America populations. Pairwise geographic distances (km) between pairs of populations (indicated on the x-axis) were plotted against the indices of genetic distance for each dataset: PTD var DBLα types, Chao-Sørensen's QD var DBLα types, F ST SNP, F ST MS, and F ST DBLα types. The Pearson's correlation coefficients (r) were calculated between the various indices of genetic distance and were determined to be as follows: PTD var DBLα types (r = 0. 88, blue), Chao-Sørensen's QD var DBLα types (r = 0. 81, red), F ST SNP (r = 0. 77, green), F ST MS (r = 0. 88, purple), and F ST var DBLα types (r = 0. 76, orange) F I G U R E 6 Genetic relationship between South American populations, based on MS, SNPs, and var DBLα types. (a) Principal component analysis, where the colored dots represent the population isolates, and the colored ellipses represent 95% of the genetic variation within each population.
ACKNOWLEDGMENTS We would like to thank the participants for their willingness to be involved, as well as the field teams for their expertise and coordination. The Peruvian samples were collected through the  Institutional Collaboration Framework Agreement  from the  Belgian Directorate-General for Development (Project 95501 ) in collaboration with  Dr. Alejandro Llanos-Cuentas. Additionally , we would like to recognize the laboratory personnel at  New York University  for their assistance with the laboratory experiments. Finally, we thank everyone involved for their continued patience and understanding as this research was severely disrupted by  Hurricane Sandy  (New York, NY;  29 October 2012 ).
F I G U R E 5 Isolation by distance between the South America populations. Pairwise geographic distances (km) between pairs of populations (indicated on the x-axis) were plotted against the indices of genetic distance for each dataset: PTD var DBLα types, Chao-Sørensen's QD var DBLα types, F ST SNP, F ST MS, and F ST DBLα types. The Pearson's correlation coefficients (r) were calculated between the various indices of genetic distance and were determined to be as follows: PTD var DBLα types (r = 0. 88, blue), Chao-Sørensen's QD var DBLα types (r = 0. 81, red), F ST SNP (r = 0. 77, green), F ST MS (r = 0. 88, purple), and F ST var DBLα types (r = 0. 76, orange) F I G U R E 6 Genetic relationship between South American populations, based on MS, SNPs, and var DBLα types. (a) Principal component analysis, where the colored dots represent the population isolates, and the colored ellipses represent 95% of the genetic variation within each population.
Beyond the 10 contaminants identified in the negative controls (found in 29 occurrences), we identified 23 putative contaminants found in 37 occurrences (Fig. 3 , Supplementary Table  3 ), some already reported as reagents contaminants  19. Others were more unexpected such as Borrelia sp. (samples 103, 104, 108 and 110) or Rickettsia japonica (sample 117). Still, the taxonomic assignment of the contigs did not confirm the presence of those species and manual Blastn of reads against the NCBI nr database supported that they were likely in silico contaminants  19, 20. In summary, the mean percentage of reads assigned to bacteria that were considered as contaminants was 0. 7% (range 0. 01-5. 4%). Besides, we identified 25 species that could be due to a misclassification of reads to closely related bacteria, such as in samples 184 (where Corynebacterium striatum was found in culture, and some metagenomic reads were identified as Dermabacter sp.
A single bacteria or yeast was recovered for 104 out of 179 samples (58. 1%), the remaining yielding 2 (24/179, 13. 4%), 3 (26/179, 14. 5%), 4 (14/179, 7. 8%) or 5 (11/179, 6. 1%) bacteria and yeasts. Eventually, we sequenced two negative controls (the same process as samples but without any biological material). This study involved already existing, anonymous samples for which a further use was authorized by the Ethical Committee of the Lyon University Hospital (September 25, 2014). According to the French guidelines, as the exploitation of the samples and associated data is not performed in an interventional way, the consent of the patient is not needed. DNA manipulations. Tissue samples were cut into small pieces on a disposable Petri dish support using a scalpel while the swabs were thoroughly vortexed in 1 mL physiological solution.
bioinformatics. babraham. ac. uk/projects/fastqc/). The reads (fastq format) were processed with MetaPhlAn2 to get the taxonomic profile of the microbial community  23, 24. MetaPhlAn2 uses a database of clade-specific marker genes, and normalizes the abundances of the species with respect to the genome size of the bacteria to which reads are assigned. In order to filter out the human reads, we used Kraken  25  with default parameters and the -classified-out option on the miniKraken database, that embeds the genomes of bacteria, archaea and viruses, but noticeably not those of eukaryotes. Together with the seqtk subset command (https://github. com/lh3/seqtk), we were able to recover the pairs of reads that were found as from bacteria, archaea and viruses, and to assemble them using metaSPAdes  26  with default parameters.
The resulting contigs were annotated using PROKKA  27. The genome coverage was assessed by processing the metagenomic contigs by QUAST  28  against the reference genome of the species downloaded from the RefSeq database (http://www. ncbi. nlm. nih. gov/genome/). The total quality-filtered reads (fastq format) were mapped using Bowtie2 29 (using the-local argument for accepting soft clipping) onto a database made of the ARDs from the ResFinder database  30  (downloaded in July 2016) and ARDs from functional metagenomic studies  [31] [32] [33]  , eventually clustered at a 95% nucleic acid identity level (using CD-HIT  34  ). The mapped reads were then assembled with metaSPAdes with default parameters. The open reading frames (ORFs) and the amino acid sequences were obtained by Prodigal  35. The identification of ARDs was performed by blastp  36  (with a 10 -30 e-value) using the aforementioned ARD database, using a 80% amino acid identity threshold over 80% of the reference ARD sequence.
The same pipeline was applied to all samples after downsizing to 2 M paired reads using the seqtk sample command (https://github. com/lh3/ seqtk). The figures were drawn using R v3. 2. 3 and the ggplot2 package  40  , with colors occasionally chosen from colorbrewer2. org. The raw reads (just adaptor-trimmed) are available under the Bioproject PRJNA382079.
Results Susceptible individuals have an altered transcriptome in the non-infected state. We obtained whole blood samples from 25 healthy male Caucasian individuals (Supplementary Data S1). Six of the donors had recovered from active TB, and are thus putatively susceptible. The remaining 19 tested positive for latent TB without ever experiencing symptoms of active TB, and are thus putatively resistant. We isolated dendritic cells (DCs) and treated them with Mycobacterium tuberculosis (MTB) or a mock control for 18 hours. To measure genome-wide gene expression levels in infected and non-infected samples, we isolated and sequenced RNA using a processing pipeline designed to minimize the introduction of unwanted technical variation (Supplementary Fig. S1 ). We obtained a mean (±SEM) of 48 ± 6 million raw reads per sample.
We performed quality control analyses to remove non-expressed genes (Supplementary Fig. S2 ; Supplementary Data S2), identify and remove outliers (Supplementary Figs S3, S4 and S5), and check for confounding batch effects (Supplementary Figs  S6  and S7 ). Ultimately, data from 6 of the 50 samples (25 individuals × 2 treatments) failed the quality checks and were removed from all downstream analyses (Supplementary Fig. S5 ). The remaining 44 high-quality samples included 17 putatively resistant individuals and 5 putatively susceptible individuals. We performed a standard differential expression analysis using a linear modeling framework (Supplementary Data S3), defined in equation (1). As expected, there was a strong response to infection with MTB in both resistant and susceptible individuals (Supplementary Fig.