dataesr/scientific-paragraphs-categorization huggingface dataset:
Migraine GWAS Overlap Based on the significant lead SNPs of the combined analysis, 3 of the 4 loci showed no association with migraine. This was confirmed using colocalization analysis, which showed that for the loci on chromosomes 1 and 2, H1 (only CH had a genetic association within the tested region) was most likely. However, the lead SNP (rs11153082) at the chromosome 6 locus showed overlap with migraine. We confirmed this signal resulting from the same causal variant as in the migraine GWAS from the UK Biobank GWAS database (posterior probability for shared causal variant at chr6q16 = 97. 4%). To exclude concurrent migraine driving this association, 655 UK patients with known migraine status (n = 195 had coexisting migraine) were reassessed. Fisher exact tests for the alternate allele frequency across the 2 groups, CH only compared to CH with concurrent migraine, in the lead SNP showed no significant differences (rs9386670: p = 0.
In combination with deep phenotyping, this has potential in providing genotype-phenotype correlations such as genotype and treatment response, which can lead to future tailor-made treatment options respective of CH subtypes, such as episodic versus chronic CH, which can improve diagnosis. This could offer unprecedented insights into the pathophysiological pathways underlying CH and novel targets for therapeutic intervention. Next, we combined the summary statistics from the 4 studies (Dutch, Norwegian, UK, Swedish) using inversevariance-weighted meta-analysis as implemented in METAL (with the "STDERR" option), after harmonizing the datasets using EasyQC. 53, 54 In total, 8,039,373 variants were analyzed. The association to CH remained significant for all the 8 index variants (in the 4 loci) reported in the 2 papers:.
Acknowledgments Sweden: This study was supported by the  Swedish Research Council  ( 2017-01096 ), the  Swedish Brain Foundation , and the  Mellby Gård Foundation  ( FO2018-0008 ),  Karolinska Institutet  Research Funds ( 2018-01738 ). We thank  F. Xiang  for excellent technical assistance and  A. -C. Karlsson  for help with patient recruitment. UK: E. O. is supported by  Brain Research UK. Patients were collected as part of the SYNaPS Study Group collaboration funded by the  Wellcome Trust and strategic award (Synaptopathies)  funding ( WT093205   MA  and  WT104033AIA ). J. V. is supported by  Health Education England and the Medical Research Council. L. S. is supported by the  Wellcome Trust Institutional Strategic Support Fund  ( 204809/Z/16/Z ) awarded to  St George's, University of London.
We thank  Dr. J. E. Burns  for his assistance in preparing the manuscript. The data handling was enabled by resources provided by the  Swedish National Infrastructure for Computing (SNIC)  at  Uppmax ,  Uppsala University  partially funded by the  Swedish Research Council  through grant agreement no. 2018-05973. [Correction added on July 26, 2021 after first online publication: The Acknowledgments section has been updated. ].
Animals and Experimental Pasture The present experiment was carried out in 2017 at Marcenat, INRAE's experimental farm. Other aspects and details of the experiment were described in Koczura et al. (24) , in which data obtained in the beginning of June 2017 had also been included. Twelve late-lactating dairy cows (four Ho, four Mo, and four Va) grazing on a biodiverse pasture since the beginning of June were monitored during two consecutive days in the end of June (end of first grazing cycle) and then in July (beginning of the second grazing cycle). Briefly, cows grazed (extensive continuous grazing conditions) a natural and highly biodiverse pasture (65 species; 48% of grasses, 13% of legumes, and 39% of forbs on ground cover), dominated by Festuca gr. rubra (18%), and Agrostis capillaris (15%).
Statistical Analysis Statistical analysis was performed with SAS software (version 8. 6, SAS Institute Inc. , Cary, NC). A repeated mixed model was applied, in which breed (Ho, Mo, Va), period (June, July) and their interaction were included as fixed factors. The repeated factor was the period, with the individual cow as subject. Results are presented as Least Square means and standard errors of the mean (SEM). In addition, Pearson's correlation coefficients between the variables describing grazing behavior and those of milk composition were calculated in order to be able to distinguish among individual cows.
ACKNOWLEDGMENTS The authors thank the staff of the  INRAE farm at Marcenat (UE Herbipôle ) with special thanks to  Carole Cirie ;  Agnès Cornu  for advice and help with the extraction of milk terpenes; and  Cyril Labonne ,  Jean-Baptiste Auger ,  Victoria Verdel ,  Précilia Bouchy ,  Laurent Lanore  and  Isabelle Constant  for their participation in the collection of behavioral observations data and treatment of the samples.
To achieve this, high coverage Illumina whole-genome sequencing of pooled samples was performed for 24 populations of European seabass and 27 populations of gilthead seabream. This resulted in a database of ~20 million SNPs per species, which were then filtered to identify high-quality variants and create the final set for the development of the 'MedFish' SNP array. The array was then tested by genotyping a subset of the discovery populations, highlighting a high conversion rate to functioning polymorphic assays on the array (92% in seabass; 89% in seabream) and repeatability (99. 4-99. 7%). The platform interrogates ~30 K markers in each species, includes features such as SNPs previously shown to be associated with performance traits, and is enriched for SNPs predicted to have high functional effects on proteins.
Introduction Modern aquaculture selective breeding programmes are embracing the availability of genomic technologies to sustainably increase genetic gain. Genomic tools can also facilitate improvements to methods for forming base populations for breeding programmes by computing wellcharacterized genetic variability and relationships, which is important for many aquaculture species still in the process of domestication  [1, 2]. To achieve these goals in target species typically requires the generation of genome-wide genetic marker data (usually SNP markers) across large numbers of individuals. When paired with trait recording on the genotyped individuals, such datasets can be applied to examine the genetic architecture of production traits of interest, including detection of quantitative trait loci (QTL) using genome-wide association studies (GWAS).
A 57 K SNP array was also recently developed for European seabass  [25]  and has been applied to assess the genetic basis of resistance to viral nervous necrosis. However, this array is only available on request from the GeneSea consortium. Therefore, from both an aquaculture and population genetics perspective, there is a need for a publicly available highthroughput genotyping platform for European seabass and gilthead seabream. Herein, an extensive and comprehensive SNP database was generated for European seabass and gilthead seabream across Europe by extensive sampling and pooled sequencing of ~25 populations per species from wild and aquaculture sites. From this SNP database, a subset of ~60 K SNPs was chosen based on several filtering criteria to give thorough coverage of each species' genome.
Bioinformatics analysis for SNP discovery The sequencing reads of the population pools -42 and 51 pools for the European seabass and gilthead seabream, respectively (see Table  1 ) were processed separately for each species using identical software and parameter values. These reads were filtered using the fastp software v 0. 20. 0  [27]. Reads with a minimum length of 80 bp for which <20% of their bases showed a BQ ≤ 20 were retained. Cleaned paired-end reads from each population pool were then aligned to either the European seabass  [13]  or the gilthead seabream  [14]  genome assemblies using BWA v 0. 7. 8  [28]. Only primary alignments to the relevant reference genome were retained for further analysis. PCR duplicates were removed from the alignment files using SAMtools v 1. 6  [29]. Variants were called separately for each species across all population pools using Freebayes v 1.
20  [30]  with GNU Parallel  [31]. Freebayes was set to call a variant if either (i) a minimum of 3 reads supporting the non-reference allele was observed, or (ii) the allele frequency in the pool was above 0. 05, after excluding alignments with a MQ < 20. This SNP calling pipeline led to the discovery of ~17 and 34 million putative polymorphisms in the European seabass and gilthead seabream genomes, respectively. This initial list of variants was then filtered using vcflib (htt ps://github. com/vcflib/vcflib) to keep bi-allelic SNPs that (i) showed supporting reads on both strands, (ii) a sequence coverage ranging from 17× to 90× for the European seabass and from 25× to 100× for the gilthead seabream, (iii) at least two reads 'balanced' to each side of the variant site, (iv) >90% of the observed alternate and reference alleles supported by properly paired reads, and (v) the ratio of mapping qualities between reference and alternate allele was between 0.
e. , fat content, weight, tag weight and length to width ratio  [34]  and resistance to photobacteriosis  [11]. Importantly, if the aforementioned SNPs were not identified through our pool-sequencing experiment, they were not included directly on the platform. Instead, the economically relevant marker was substituted by a proxy SNP that was chosen by screening the surrounding region for the closest high quality variant present in our dataset. A second group of SNPs included in the MedFish SNP array is shared with other platforms that were developed in parallel by the GeneSea consortium  [25]. The purpose of including a subset of markers from the existing platforms was to facilitate backward compatibility and crossstudy comparison, especially via the use of genotype imputation. A third criterion for inclusion of SNPs on the MedFish platform was based on their predicted effect on protein-coding genes.
From the substantial SNP database generated in this study, markers were selected to achieve good coverage of the reference genomes of the European seabass and gilthead seabream following  [33]. In brief, markers were selected along each fish chromosome at a variable density depending on the estimated local nucleotide diversity (π), as in European seabass  [13]  and other fish species  [36]  a positive correlation between nucleotide diversity and recombination rate has been observed. For SNPs that were mapped to the "UN" chromosome of the European seabass, the synthetic chromosome was split into contigs that had been previously concatenated by 100 consecutive Ns. The contigs were isolated and the SNPs located within them were remapped with the contigs starting position set to 1. The genomes of both fish species were divided into 70 Kb (for European seabass) or 85 Kb (for gilthead seabream) nonoverlapping windows and local nucleotide diversity was estimated with VCFtools v 0.
To assess whether the SNP selection strategy followed for the development of the MedFish array affects population genetic inferences in European seabass and gilthead seabream, a commonly used summary statistic (F ST ) was estimated for pairs of populations (pairwise F ST ) based on the pooled whole-genome sequence data before ('non-ascertained' panel) and after SNP selection ('ascertained' panel). The 'non-ascertained' dataset (i. e. initial SNP discovery panel) reflects an unbiased representation of the 'true' genomic variation and allele frequency spectra present in the sampled populations of both fish species, as measured in this study. This dataset comprised ~1. 1 million high quality SNPs called across 24 European seabass population pools and ~9 million SNPs genotyped across 27 gilthead seabream population pools.
For populations for which two replicate pools were available (see Table  1 ), only one was selected for evaluation. The SNP QC filters applied to the variants called from the alignment files were the same as described in the 'Bioinformatics analysis for SNP discovery' section, except for the removal of markers with interfering SNPs in the flanking region. To generate the 'ascertained' datasets (one for each fish species), the SNP positions of the array markers were sub-sampled from the former ('non-ascertained') datasets using VCFtools v0. 1. 15 [37]. The four vcf files -'non-ascertained' and 'ascertained' SNP panels for both European seabass and gilthead seabreamwere imported to the R package poolfstat v1. 2. 0  [40]  for pairwise F ST estimation using the ANOVA method. For each species, the correlation between the pairwise F ST matrices generated from the two.
Population structure The combined species ~60 K MedFish array was tested by performing a metric multidimensional scaling (MDS) analysis of a wide range of Mediterranean (and a few Atlantic) European seabass and gilthead seabream farmed and wild populations typed with the platform. These individuals were part of the same set of samples used for the SNP discovery process from Pool-seq data (Table  3 ). A QC-filtered SNP dataset was created by applying the following filters in PLINK v2. 0  [42]. Bi-allelic SNPs were retained for analysis if they had (i) a call rate > 0. 95, (ii) MAF > 0. 01, (iii) HWE test p-value ≥1e-4 (estimated separately for each population) and (iv) no pairs with a squared LD correlation (r 2 ) > 0. 2 occurred within a 100 Kb window. For duplicated or related individuals with a kinship coefficient (KING-rob) > 0.
Analysis of haplotype sharing among farms To assess the ability of the SNP array to identify historical connections between farmed populations, a haplotype sharing analysis was performed on the farmed population samples (13 European seabass farms; 11 gilthead seabream farms). A SNP dataset in which all individual and SNP QC filters had been applied (see 'Population structure' section), except the removal of markers based on linkage disequilibrium (measured as r 2 ) was used for the analysis. Markers that were not located on the assembled chromosomes of the reference genome assemblies (i. e. those located on unplaced scaffolds) were removed from the dataset. Haplotypes were inferred for each individual using the software fast-PHASE v1. 4. 8  [43]. All individuals were phased together in a single analysis, taking into consideration their population labels during the model fitting procedure.
For both fish species, the number of random starts of the EM algorithm (T) was set to 20, the number of iterations (C) was set to 35, and the number of haplotype clusters (K) to 8. The reference genomes of both species were divided into 1 Mb nonoverlapping windows using BEDTools v2. 25  [44]. SNP-based haplotype variants were defined for each window. The last window of each chromosome was excluded from the analysis. Since the number of haplotypes can be influenced by sample size, the same number of individuals were randomly chosen from each farmed population (6 individuals for the European seabass and 9 for the gilthead seabream). For each individual within a farm, the two haplotypes at any given locus were used to screen the whole dataset for an exact match. All matches with other individuals from a different farm were recorded.
Notably, and in comparison to the European seabass, a particularly high number of polymorphisms were initially discovered along the chromosomes of the gilthead seabream, particularly towards the terminal ends of the chromosome-level scaffolds. The most likely cause was that in this species the higher average sequencing depth of 63× (compared to 36× in the European seabass population pools) enabled the discovery of variants segregating at a lower frequency. Consequently, when the QC filter that removed SNPs with interfering markers in close proximity was applied to the gilthead seabream dataset, a substantial number of markers were filtered out from regions of the genome exhibiting higher levels of genetic polymorphisms. This led to fewer SNPs left to choose from for assay design in regions of the gilthead seabream genome that showed putative higher recombination (e.
The repeatability of the assays, after excluding loci with at least one missing value across replicates, was 99. 4% for the European seabass and 99. 8% for the gilthead seabream. Taken together, these metrics support the high quality and reliability of the genotype data generated by the MedFish SNP array. To evaluate whether the SNP selection strategy followed for the design of the MedFish array could bias F ST estimates, SNPs common to the array (~30 K for each species) were selected from the whole-genome sequence data of the population pools (consisting of 11-25 individuals each) to mimic array-derived genotype data. Pairwise F ST were calculated from this 'ascertained' dataset and then compared to those estimates obtained for the same population pools but by including all sites discovered through the re-sequencing experiment in the analysis (~1.
1 million SNPs in the European seabass; ~9 million SNPs in the gilthead seabream) ('non-ascertained' dataset). Overall, a high correlation between the pairwise F ST matrices calculated from the 'non-ascertained' and 'ascertained' SNP panels was observed, with an r = 0. 97 for both European seabass and gilthead seabream (Fig. 2A  and B ). Although for both fish species most of the pairwise F ST values were slightly higher when the estimations were based on the 'ascertained' dataset (Fig. S2 ), both 'ascertained' and 'non-ascertained' datasets yielded similar population clustering patterns, as observed across the first and second dimension of a PCoA (Fig. 2C  and D ).
To assess potential ascertainment bias arising in chip-derived genotype data, pairwise F ST was estimated for the same populations using a 'non-ascertained' and an 'ascretained' SNP panel for comparison. The patterns of pairwise F ST among populations were highly concordant when results based on the markers on the MedFish platform were compared to the full SNP discovery panel (Fig. 2 ), which supports the utility of the platform for population genetic studies. However, a slight upward bias of F ST values was observed in the dataset that mimics the array-derived markers and is likely caused by prioritizing common high-frequency markers, at the expense of SNPs with rarer alleles, in the SNP selection process. While the differences between F ST estimates between both datasets ('ascertained' versus 'nonascertained') were minor (Fig.
Given that the majority of the samples genotyped with the SNP array were also part of the SNP discovery process, metrics such as number and mean MAF of polymorphic markers reflected the performance of the SNP selection strategy. Despite the relatively small DNA pools  (11-25 individuals) , we were able to reliably identify and select informative markers for inclusion in our SNP array. The number of informative markers (MAF > 0) was high for both fish species. For the European seabass, 23,900 SNPs (99. 8%) of the validated markers were polymorphic, whereas for the gilthead seabream, this type of markers comprised 26,017 SNPs (99. 3%) of the data. The number of polymorphic markers was remarkably similar in wild and farmed populations of both species (90-99% across populations), demonstrating the efficacy of the SNP selection strategy for recovering highly informative markers in Pool-seq data sequenced at a high to moderate coverage across a wide range of different populations.
A significant obstacle to the uptake of high-throughput genotyping technologies by the industry is the risk that a low fraction of a pre-built platform yields useful information. Indeed ascertainment bias is a common issue for genotyping arrays and can be caused when designing platforms based on a reduced number of individuals  [50]. Due to the fact that the MedFish 60 K array was developed based on the screening of genetic data derived from extensive sampling of dozens of Mediterranean fish populations and hundreds of fish of each species, it is tailored to maximize the retrieval of genetic information and provide an increased resolution for the analysis of farmed or wild stocks from this region.
Population structure and haplotype sharing analysis To test the MedFish SNP array, the genotyping data obtained from typing a diverse range of wild and farmed European seabass and gilthead seabream fish were used to perform a MDS and haplotype sharing analysis. Regarding the European seabass populations, the two first dimensions explained 26% of the genotypic variation. Interestingly, the wild Mediterranean populations span a continuum across the range of D1, but has a rather smaller dispersal across the D2 range. However, this continuum in D1 has gaps, and the wild populations seem to be divided into two clearly differentiated clusters located at each end of this major axis. These two clusters are represented by (i) distinct groups of wild individuals showing low levels of genetic differentiation, consistent with a sub-division (albeit weak) of the European seabass Mediterranean lineage  [54] ; and (ii) a wild population sampled from Morocco, which based on previous findings  [54]  is expected to cluster with Atlantic rather than Mediterranean populations.
Acknowledgements This study was made possible by the  EU projects MedAID  ( H2020  grant agreement No  727315 ) and  PerformFish  ( H2020  grant agreement No  727610 ). We are grateful to both projects' partners that contributed with samples for SNP discovery as well as the projects' coordinators ( A. López-Francos ,  B. Basurco ,  D. Furones  and  K. Moutou ) and the  H2020  project officer for enhancing a fruitful interaction between research consortia. The authors would also like to thank  Christos Palaiokostas  and  Sara Faggion  for providing additional information for traitassociated variants included in the SNP array. CP and RH are supported by funding from the  BBSRC   Institute Strategic Programme Grants   BB/P013759/1  and  BB/P013740/1. We also give thanks to the sequencing facilities  Edinburgh Genomics  ( University of Edinburgh, UK ) and the  Norwegian Sequencing Centre (University of Oslo, Norway).
Data availability Raw sequence reads from the European seabass and gilthead seabream population pools analyzed for SNP discovery have been deposited in NCBI's Sequence Read Archive (SRA) under accession number PRJEB40423. Details of the allele frequencies of the SNPs on the Med-Fish array can be found in the Mendeley Data Repository (https://doi. org/10. 17632/7w4cb4mdd4. 1).
Acknowledgements We acknowledge the  Centre Hospitalier Universitaire Vaudois  ( CHUV, Lausanne, Switzerland ) for providing the human skin samples. Finally, we acknowledge  Nicole Charrière  for her help in the skin permeation assays, and  Simon Deslarzes ,  Ferdinando Storti  and  Grégory Plateel  of the  Center universitaire romand de médecine légale  for their help in the chemical analyses.
No WNV viruses were detected in these mosquitoes by RT-qPCR after an individual extraction. The amount of excreta recovered on these traps was higher than it would be expected by considering that they would be expelled by these mosquitoes only, suggesting that a mosquito escape issue could have occurred for these traps. Culex and Culiseta mosquitoes from traps tested as negative for WNV and USUV based on excreta, were screened for these viruses based on an individual grinding and a pooled extraction and detection procedure. These viruses were not detected in any of the 241 pools, each made of 12 mosquitoes. We succeeded at sequencing a 341 bp WNV genomic region (GenBank accession number OK489805) located on the envelope gene on one excreta sample that was tested positive by RT-qPCR with the lowest Ct (sample F 2020/09/15).