dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2021). The ABGD approach sorts sequences into hypothetical species based on the barcode gap, which is modelled based on the pairwise genetic distance of the dataset in test and priors of intraspecific divergence  (Puillandre et al. , 2012a) ; the latter was adjusted to 0. 2 in this analysis. The ASAP method follows a hierarchical clustering algorithm based solely on pairwise genetic distances and species hypothesis are proposed without biological prior information of intraspecific diversity, thus providing unbiased estimates  (Puillandre et al. , 2021). Two tree-based approaches to species delimitation were also employed: the Bayesian implementation of the Poisson Tree Process model (bPTP;  Zhang et al. , 2013)  and the multi-rate PTP (mPTP;  Kapli et al. , 2017). In PTP approaches, speciation events are inferred based on the number of substitutions  (Zhang et al.
, 2013). As a recent improvement, the mPTP process incorporates intraspecific divergence caused by different evolutionary histories or uneven sampling of the species  (Kapli et al. , 2017). The bPTP analysis was conducted for 10 5 MCMC generations, a thinning of 100, and 25% burn-in. Default settings were applied to the mPTP approach. Finally, species hypotheses retained after the ABGD analysis were tested for their reciprocal monophyly by performing phylogenetic analyses on single-gene alignments (cox1, ITS2, and 28S) and on a concatenated dataset (cox1 + ITS2 + 28S). The determination of the best-fit partitioning schemes and models of molecular evolution, using Akaike's Information Criterion  (Akaike, 1973) , was conducted with PartitionFinder v1. 1. 1  (Lanfear et al. , 2017). To minimize the saturation effect and allow for heterogeneous rates of evolution in the cox1 marker  (Pond et al.
, 2009; Salemi, 2009) , data partitioning by codon was defined as follows: GTR-G (1 st partition), GTR + I + G (2 nd partition), and GTR + I (3 rd partition) in the dataset including H. variabilis; GTR + G (1st and 2nd partitions), GTR + I (3rd partitions) for the analyses without the outgroup. For the 28S dataset, the GTR model was set with the outgroup H. variabilis. The model HKY + I was set for the ITS2 dataset with Agathistoma viridulum as the outgroup, and GTR + I for ITS2 partition in the concatenated dataset. Bayesian Inference (BI) and Maximum Likelihood (ML) methodologies were used to reconstruct the phylogenies, using MrBayes v3. 2. 7  (Ronquist et al. , 2012)  and W-IQ-Tree web server  (Trifinopoulos et al. , 2016) , respectively. For the BI, two independent runs with four chains each for 2 × 10 7 generations, sampling of trees and parameters every 1000 generations, and heating parameter of 0.
Species delimitation and phylogenetic analyses The TCS network of the cox1 dataset (Fig. 2 ) distinguished a total of 11 haplotype networks at 95% confidence level. Samples morphologically assigned to T. miniata, T. tenuis, and T. speciosa, formed individual clusters as expected for each nominal species. Tricolia miniata was, however, separated in four subclusters, according to the geographic origin of the samples (Corsica, Malaga, Benidorm, and Alboran Island). Individuals from the Atlantic archipelagos, morphologically assigned to the nominal groups T. pullus azorica (Azores) and T. pullus canarica (Madeira, Selvagens, and Canaries), could not be distinguished, with most of the Azorean haplotypes displayed in a star-like shape. Samples from the Northern Portugal were assigned to two genetic lineages: Table  2  Marker, primer name and direction, sequence (5 ′ -3 ′ ), reference and cycling conditions used (temperature, time, and the number of cycles).
1 as a putative species but it was consistently retrieved with ABGD and ASAP; bPTP and mPTP separated T. pullus pullus from the Western (Tunisia, Greece, Cyprus) and Eastern Mediterranean (Malaga, Corsica); mPTP clustered T. miniata from Malaga and Benidorm as the same taxon whereas the other three methodologies distinguished each of the four Spanish localities as unique taxa. We obtained phylogenetic reconstructions based on individual markers (Fig. 3 , Fig. S1-3 ) and on the concatenated datasets (Fig. 4 ). For each individual marker, BI and ML inferences produced comparable topologies for all the major splits. Ultrafast bootstrap values are more unbiased than the traditional bootstraps, and thus we have considered a clade to likely be real when SH-aLRT >= 80% and Ufboot >= 95%, as suggested by IQ-TREE authors  (Minh et al.
3%. Two genetic groups -Western Mediterranean and Central-East Mediterraneanwere present within T. pullus pullus with distances ranging from 0. 2% to 4. 6%. Finally, T. tenuis and T. speciosa diverge by 15. 1%, each forming a consistent group with low intra-specific divergence levels. The 28S dataset, reduced to 15 haplotypes (Table  S2b ), distinguished six genetic groups as follows: 1) Tricolia from the Atlantic archipelagos and Tricolia sp. 1; 2) T. pullus picta from Northern Portugal and the UK; 3) T. pullus pullus from the Mediterranean; 4) T. miniata; 5) T. tenuis, and 6) T. speciosa. Raw (p) distances ranged from intraspecific levels of 0% to interspecific divergence of 1. 5% between T. speciosa and T. miniata. For the ITS2 marker, nucleotide differences between 14 haplotypes ranged from 0 to 6.
4% (Table  S2c ). Insular Tricolia were undistinguishable and diverged from Tricolia sp. 1 by less than 0. 5%. Tricolia pullus picta and T. pullus pullus are only differentiated by 0. 5-0. 8%. Tricolia tenuis holds the most different ITS2 sequence, diverging 5. 2-6. 4% from the other congeners. Despite the low power of the nuclear markers to distinguish Tricolia taxa, nine parsimony informative sites were detected along the 809 bp 28S alignment and 32 were present in the 428 bp ITS2 dataset.
Species assignment and marker performance The specimens assayed genetically were initially assigned to five morphospecies, of which one not corresponding to a known nominal taxon (Tricolia sp. 1), and one split into four traditional subspecies (Tricolia pullus pullus, T. p. azorica, T. p. canarica, and T. p. picta). The species delimitation process adopted, while broadly confirmed the groups suggested by the morphological approach, led to significant redefinition of the status and relationships among them. The analysis of the cox1 dataset with the TCS program revealed 11 networks (Fig. 2 ) across the Mediterranean and NE Atlantic samples, at the 95% connection limit, which may represent a starting point in the identification of putative species  (Hart and Sunday, 2007; Chen et al. , 2010).
Considering the morphological distinction and the allopatric occurrence in distinct biogeographical units (sensu  Freitas et al. , 2019)  we hereby support the distinctiveness of Tricolia sp. 1 as an independent genetic lineage. A formal recognition as a species requires further characterization of its ecology and distribution. Interestingly, evidence for this "new" Tricolia genetic lineage had already been described but was probably overlooked. Borges et al. (2016)  found that their Tricolia specimens from Azores matched with two distinct BINs in BOLD Systems v3 database. One of the BINs corresponded to the sequence AM049358 from Wembury, UK (84. 6% similarity), which is a representative of T. picta as discussed before. The other BIN contained private sequences from Galicia (Spain) which differed only 5% from the Azorean specimens, a differentiation level which is compatible with the divergence found between T.
The phylogeographic break between the Western and Eastern Mediterranean basins (Fig. 3 ) is associated with the oceanographical phenomena located at the Sicily Channel (SC, Fig. 6 )  (Marzouk et al. , 2017; Sá-Pinto et al. , 2012, 2010 and references therein) , which hamper interpretations of the biogeographical affinities of populations in the area. In our dataset, Eastern Tunisian populations of T. pullus are closely related to Eastern Mediterranean localities (Greece and Cyprus), likely associated to local sea-surface circulation patterns and the potential role of the Aegean Front (AG; Fig. 6 )  (Sá-Pinto et al. , 2012; Marzouk et al. , 2017; Pascual et al. , 2017). In the case of T. miniata (Fig. 5E ), only samples from the Western Mediterranean area were analyzed (Fig. 6 ). Yet, our results suggested that this lineage comprises a species complex, as mitochondrial divergence between specimens ranged from 7 to 15% but differences were negligible at the nuclear level.
With sea-surface temperatures increased by 3-4 • C (Fig. 6 ), Malta's habitats are predicted to be unsuitable for Halopteris scoparia Sauvageau, 1904  (Gamliel et al. , 2020) , the preferential habitat of T. miniata  (Gofas, 1982). In fact, the only report of T. miniata in Maltese waters is from  Monterosato (1884)  and it is unlikely to occur there nowadays. Unfortunately, we did not have access to Atlantic T. miniata specimens, although their inclusion in future works will be of utmost importance to confirm whether they should be assigned to different nominal groups. Tricolia speciosa inhabits infralittoral Posidonia seagrasses in the Mediterranean (Fig. 5F ). In our dataset, it was represented by closely related specimens from Corsica, Italy, and Tunisia. The low levels of differentiation among localities matched the genetic similarity of Posidonia, its preferential habitat, from the Strait of Sicily and Western relatives  (Serra et al.
, 2010)  and can be related with the occurrence of rafting  (Manly, 1976; Ávila, 2013). Gene flow and consequent maintenance of genetic similarity between Corsican and Italian populations benefit from the geographical proximity to the Ligurian Sea, with no known oceanographical discontinuities in the area (Fig. 6 ). Notwithstanding the morphological resemblance of T. tenuis (Fig. 5G ) and T. pullus pullus and the hypothesis of incomplete speciation proposed by  Gofas (1982) , our current dataset did not support a close phylogenetic relationship between these two taxa. Tricolia tenuis is reported as the only representative of the genus Tricolia in Cabo Verde  (Rolán, 2005: fig. 143 ) but the distance, higher SSTs (~23-24 • C), and recent recognition of Cabo Verde as a separate biogeographic subprovince  (Freitas et al.
Conclusions This work is the first phylogenetic study of the genus Tricolia in the NE Atlantic and Mediterranean Sea, substantially advancing the knowledge regarding its genetic and taxonomic diversity and complementing previous revisions based only on morphological characters. Our dataset comprised seven of the currently recognized Tricolia species, widely distributed in the study area. A "new" Tricolia sp. 1 was found to occur from Northern Portugal up to Brittany (France), although the clarification of its status (as a distinct species or just as a differentiated lineage) requires future studies, namely its morphological and ecological characterization. Molecular data revealed that only one species, T. azorica (Dautzenberg, 1889), occurs in the NE Atlantic Archipelagos (Azores, Madeira, Selvagens, and Canaries).
Among large sized Tricolia species, T. tenuis and T. speciosa are the only obligate phanerogram dwellers, but their phylogenetic relationships remain unclear and would benefit from the inclusion of additional taxa and/or markers. With the current dataset, the high support of the cluster formed by T. miniata, T. azorica, Tricolia sp. 1, T. picta (algal dwellers), and T. pullus (phanerogram and algal dweller) suggests that phanerogram dwelling might constitute a plesiomorphic condition among large sized Tricolia in the NE Atlantic and Mediterranean. Less widespread Tricolia species were not included in this molecular study: T. algoidea  (Pallary, 1920)  and T. petiti  (Craven, 1882)  in the Atlantic, T. tingitana  Gofas, 1982 , T. nordsiecki (Talavera, 1978) , T. deschampsi Gofas, 1993, T. entomocheila Gofas, 1993, T.
Acknowledgments This work was supported by  Fundação para a Ciência e Tecnologia, IP  (grant number  SFRH/BD/135918/2018  to L. B. ; research contract  IF/ 00465/2015  to S. P. A. ). Work supported by  National Funds through FCT-Fundação para a Ciência e a Tecnologia  in the scope of the project  UIDP/ 50027/2020  and by  FEDER  funds through the  Operational Programme for Competitiveness Factors -COMPETE  (projects  UIDB/50027/2020 ,  UID/BIA/50027/2013 ,  POCI-01-0145-FEDER-006821 ); by regional funds through  Direção Regional para a Ciência e Tecnologia  ( DRCT-M1. 1. a/005/Funcionamento-C-/2018 ; M3. 3. B/ORG. R. C. /005/2021 ,  M3. 3. B/ORG. R. C. /008/2022/EDIÇ ÃO 1, M3. 3. G/EXPEDIÇ ÕES CIENT ÍFICAS/005/2022  and  M3. 3. G/EXPEDIÇ ÕES CIENT ÍFICAS/004/ 2022  and  M3. 3. G/EXPEDIÇ ÕES CIENT ÍFICAS/005/2022 ).
Data availability The genetic data underlying this article is available in the GenBank Nucleotide Database (accession numbers ON970419-ON970472, ON971203-ON971219, and ON974897-ON974959 for the cox1; ON988103-ON988140 for the 28S; ON997565-ON997587 for the ITS2). The corresponding GenBank accession numbers can be accessed found in Supplementary Table  S1.
However, this approach showed some limits when biological samples and descriptive phenotypes are not available. To raise this drawback, a second approach called "bottom-up" or reverse genetic screen strategy was developed to specifically identify recessive lethal variants. This strategy, initially developed by VanRaden et al. [12] , is based on the exploitation of large number of genotyped animals easily available from genomic selection datasets to detect haplotypes showing deficit in homozygous animals, with a significant deviation from the Hardy-Weinberg equilibrium. Initially, this method was developed to detect embryonic lethal variant but the generalization of the method can also finemap deleterious variants leading to neonatal or juvenile lethality and morphological disorders. This reverse genetic screen has successfully identified numerous deficient homozygous haplotypes in several species, cattle  [12] [13] [14] [15] [16] [17] [18] [19] [20] [21] [22] [23] [24] [25] [26] [27] , pigs  [28, 29] , chicken  [30] , turkey  [31]  and horses  [32] , and the whole genome sequencing that followed has revealed the associated causative variants hosted by these regions  [13, 14, 18, 19, 21, [24] [25] [26] [27] [33] [34] [35] [36] [37] [38] [39] [40] [41].
MMUT and PKHD1 are involved in metabolic disorders such as "Methylmalonic aciduria" (OMIM 251000) and "Polycystic kidney disease" (OMIM 263200), respectively. TFAP2B is involved in bone defects and heart failure (OMIM 169100). Using WGS data from 22 MTR animals and among them, two MTRDHH1 heterozygous carriers, we were able to identify four candidate polymorphisms within MTRDHH1, but only one appeared as a strong functional candidate, a SNV (G>A) located in the MMUT gene at position g. 23,776,347 on OAR20. This SNV leads to a nonsense variation XM_004018875. 4: c. 1225C>T introducing a premature stop-codon (p. Gln409*). The genotyping of g. 23,776,347G>A in 714 animals with a known status at MTRDHH1 indicated an almost perfect association. The only 15 discordant animals were largely explained by shorter recombinant version of the MTRDHH1 haplotype (between 2 to 31 markers surrounding the SNV).
Diversity analysis have also revealed the segregation of MMUT SNV variant in Spanish Latxa Cara Rubia (LCR) breed. French MTR and Spanish LCR are very close populations. Since 1970s, many exchanges have occurred between these population across the border, first from Spain to France during the seventies, then the reverse since the nineties  [65]. The MMUT SNV evidenced in this study is not shared by other French sheep breeds and more largely by the individuals from the International Sheep Genome Consortium (dataset composed of 453 animals from 38 breeds all over the world, https://www. sheephapmap. org/). However, searching this dataset, we found another SNV located in ovine MMUT gene (rs1093255812) leading to a premature stop-gain (ENSOART00020022357. 1: p. Trp43*). This variant was identified at heterozygous state in the New Zealand Coopworth breed, and as in MTR, it could have the same deleterious impact on lamb viability.
Animal and genotyping data The total dataset is composed of 6,845 genotyped Manech Tête Rousse animals (82% males and 18% females) born between 1993 and 2021 (data description in S4 Fig). Genotyping was performed at the Labogena facility (http://www. labogena. fr/) in the framework of the French dairy sheep genomic selection  [66]. Both low density 15k SNP chip (SheepLD; n=2,956) and medium density 50k SNP chip (Ovine SNP50 BeadChip; n=3,889) were purchased from Illumina Inc. (San Diego, USA) (Table  5 ). Pedigree information was extracted from the official national database SIEOL (Système d'Information en Elevage Ovin Laitier, France).
Analysis of fertility traits Mating trait records of MTR between 2006 and 2019 were obtained from the national database SIEOL. Only artificial insemination success (AIS) and stillbirth rate (SBR) records from mating where both sire and maternal grand sire were genotyped (i. e. had a known status at each MTRDHH) were analyzed. AIS was coded "1" for success and "0" for failure based on lambing date according to the gestation length starting from the day of AI (151 ± 7 days; n=330,844 mating records). SBR was determined only in the AI success group, and coded "1" if there was at least one stillbirth in the litter or "0" if all lambs were born alive (n=201,637 mating records). We considered "at-risk mating" a mating between a carrier ram and a ewe from a carrier sire. We considered "safe mating" when the other combinations occurred: (i) non-carrier ram × ewe from a non-carrier sire, (ii) non-carrier ram × ewe from a carrier sire, (iii) carrier ram × ewe from a non-carrier sire.
A logistic threshold binary model with a logit link function was used to compare AIS and SBR between at-risk and safe mating (lsmeans estimate), using the GLIMMIX procedure in the SAS software (version 9. 4; SAS Institute Inc. , Cary, NC). The fixed effects for AIS and SBR were mating type (safe or at-risk), season of AI (spring or summer), and lactation number (L1, L2, L3 and L4+). For SBR only, prolificacy of the ewe (1, 2, 3+ lambs/litter) was added as a fixed effect. For AIS and SBR, the random effect was the interaction herd×year (n=313 herds between 2006 and 2019). Traits were considered to differ significantly when the mating type fixed effect had a P-value lower than 1. 0×10 -2 after Bonferroni correction for multiple testing with a level of significance α at 5%. This threshold was obtained by dividing the level of significance α by the number of tests corresponding to the number of independent haplotypes (n=5).
Each trait was tested by variance analysis comparing MTRDHH carrier and noncarrier rams using the GLM procedure in the SAS software (version 9. 4; SAS Institute Inc. , Cary, NC). The fixed effects were the genetic status (carrier, non-carrier) and year of birth (2000 to 2016) to correct for annual genetic gain. Traits were considered to differ significantly when the genetic status fixed effect had a P-value lower than 1. 0×10 -2 after Bonferroni correction for multiple testing with a level of significance α at 5%. This threshold was obtained as explained above for fertility traits (n=5 independent haplotypes). Total merit genomic index in dairy sheep (called "ISOLg", Index Synthétique des Ovins Laitiers) was extracted from the official genomic evaluation (GenEval, Jouy-en-Josas, France) for all the 714 genomic candidate lambs born in 2021.
, preweaning, premature death and decreased survival rate) or associated with mammalian genetic disorders were sorted using "biomaRt" R package (version 2. 52. 0, https://doi. org/doi:10. 18129/B9. bioc. biomaRt) extracted from Mouse Genome Informatics Publicly available data of 100 ovine short-read Illumina HiSeq/NovaSeq whole genome sequences (WGS) from 14 breeds generated in various INRAE and Teagasc research projects were used for variant calling. Among them, 22 WGS were obtained from MTR dairy sheep also genotyped with the MD SNP chip. A description of the different breeds and the accession numbers of sequencing raw data are available in S3 Table.
Acknowledgments We are grateful to the genotoul bioinformatics platform  Toulouse Occitanie  ( Bioinfo Genotoul , https://doi. org/10. 15454/1. 5572369328961167E12 )for providing help and/or computing and/or storage resources. The authors acknowledge the breeding confederations  CDEO (Centre Départemental de l'Elevage Ovin)  and  CONFELAC (Confederación de Asociaciones de Criadores de Ovino de Razas Latxa y Carranzana)  for providing access to private genomic data and/or biological samples. We thank also the breeders involved in the project and  Soline Szymczak  for help with genotyping.
Funding This research has received funding from the  European Union 's  Horizon 2020 research and innovation program  under the Grant Agreement No. 772787  (SMARTER) and  PRESAGE  project ( CASDAR n°20ART1532777 , the responsibility of the French ministry of Agriculture and Food cannot be engaged). MB was supported by a Ph. D. grant for the HOMLET program co-funded by  APIS-GENE  and  Région Occitanie.
0) [49,50]. S2 Fig shows the segregation of these Within the five MTRDHH genomic regions extended by 1Mb on each side, 408 protein coding variant among the 22 sequenced animals and their relationship with the status of MTRDHH4 genes are annotated (S2 Table). When available, information on mouse phenotypes (including and 5, but with no obvious association. This being made on a reduced number of animals, we lethal phenotypes) and association with mammalian genetic disorders were extracted for each specifically genotyped a larger set of animals (n=714 male lambs born in 2021) for the 1. 8kb gene using MGI, IMPC, OMIM and OMIA databases. Among the 408 genes, we highlighted insertion in RXFP2 and the two variants in ASIP. Using this cohort, we evidenced a partial 64 genes involved in lethal phenotypes in knock-out mice, and 45 genes associated with human association between MTRDHH4 and the 1.
, preweaning, premature death and decreased survival rate) or associated with mammalian genetic disorders were sorted using "biomaRt" R package (version 2. 52. 0, https://doi. org/doi:10. 18129/B9. bioc. biomaRt) extracted from Mouse Genome Informatics Publicly available data of 100 ovine short-read Illumina HiSeq/NovaSeq whole genome sequences (WGS) from 14 breeds generated in various INRAE and Teagasc research projects were used for variant calling. Among them, 22 WGS were obtained from MTR dairy sheep also genotyped with the MD SNP chip. A description of the different breeds and the accession numbers of sequencing raw data are available in S3 Table. (MGI, http://www. informatics. jax. org), International Mouse Phenotyping Consortium (IMPC, https://www. mousephenotype. org), Online Mendelian Inheritance in Man (OMIM, https://omim.
org) and Online Mendelian Inheritance in Animal (OMIA, https://omia. org) databases (last accession on 27 May 2022). Relevant candidate genes were presented as a heatmap using "pheatmap" R package (version 1. 0. 12). Whole genome sequencing data 1) using CLC export annotation function (QIAGEN CLC Main Workbench 7. 9). Genes with a known knock-out phenotype in mouse including mortality and aging (embryonic, prenatal, perinatal, neonatal, postnatal.
Results Discovery of 25 genome-wide significant seizure-associated CNVs regions We performed a meta-analysis of 16,109 individuals with epilepsy and 8545 population controls (the Epi25 Collaborative cohort) with 10,590 individuals with seizures (not explicitly meeting diagnostic criteria for epilepsy) and 483,779 population controls, derived from an aggregated CNV dataset of 17 cohorts (neuropsychiatric disorders cohort) (see all cohorts of this study in Supplementary Table  1 ). The genome was scanned using 267,237 genomic segments of 200 kb size in a 10 kb sliding window approach  26. After applying Bonferroni correction of the threshold for a significant association in the meta-analysis and finemapping, we identified 25 loci associated with seizures at genomewide significance (P ≤ 3. 74 × 10 -6 ).