dataesr/scientific-paragraphs-categorization huggingface dataset:
Patients and molecular screening We first performed ES in three patients with features of the PDAC syndrome (patients 1 to 3). To identify potentially pathogenic variants in exomes, we filtered out synonymous variants, intronic variants other than those affecting the consensus splice sites, and variants with a minor allele frequency greater than 0. 5% in public (1000 Genomes, EVS, dbSNP138, GnomAD) and local (CHU Sainte-Justine Research Center exome dataset) databases. We next query variants affecting the same genes within the 3 screened individuals, as de novo, homozygous and compound heterozygous variants for each individual. Seventeen additional patients (patients 4 to 20) with features of the PDAC syndrome and no identified pathogenic variants in STRA6 and RARB were also screened by Sanger sequencing of all WNT7B coding exons using the primers listed in Table  S1  (patients 4 to 20).
3 grams; reference values: 20 +/-4 grams) with pyelocalyceal dilatation and medullary cysts involving distal and collecting tubules (Fig. 2 ). Dysmorphic features including tall forehead, frontal hirsutism, large anterior fontanel, small palpebral fissures, flat nasal bridge, microretrognathia. Neuropathological examination did not reveal any gross nor microscopic brain abnormalities. The parents were first cousins and originated from Tunisia. Because of the parental consanguinity in this family, we analyzed the ES dataset of the affected fetus for rare homozygous variants. We identified 11 variants meeting these criteria (Table  S3 ). Because of the presence of microphthalmia, we next focused our attention on variants in genes associated with eye development. By using the integrated Systems Tool for Eye gene discovery (iSyTE), we found that 2 of these variants (p.
(Arg98*) in WNT7B and p. (Arg1281Lys) in TRIOBP) were expressed during eye development. TRIOBP pathogenic variants have previously been   7  , and thus, the highest priority variant was the homozygous nonsense variant in WNT7B (c. 292C>T, p. (Arg98*)). Consistent with a recessive mode of inheritance, the c. 292C>T variant in WNT7B was further confirmed by Sanger sequencing to be homozygous in the proband and heterozygous in the parents (Fig. 1A ). The WNT7B gene includes 4 exons, which encode a 349 amino-acid growth factor that comprises N-and C-terminal domains connected by a linker (Fig  1B ). The c. 292C>T variant in WNT7B is absent from public SNP databases (1000 Genomes, EVS, dbSNP138, GnomAD) and from CHU Sainte-Justine Research Center exome dataset (n>1000). It is located in the second exon, which is present in all 5 known WNT7B isoforms.
WES in the first affected patient (P21) in Family 2 revealed the presence of compound heterozygous variants in WNT7B (c. 225C>G, p. (Tyr75*) and c. 562G>A, p. (Gly188Ser)). Both variants were found by Sanger sequencing in the second affected patient of this family (P22), and https://mc. manuscriptcentral. com/jmedgenet Journal of found to each harbour one of the two identified variants (Fig 1A). The nonsense p. (Tyr75*) is absent from public databases (1000 Genomes, EVS, dbSNP138, GnomAD) and is located in the second exon, as it was also the case for p. (Arg98Ter), found in Family 1. The c. 562G>A (p. (Gly188Ser)) is present heterozygous only once among 232162 alleles in GnomAD, and is predicted damaging by several in silico tools (SIFT [score: 0], MutationTaster [p-value:1], Polyphen-2 [HumDiv score: 1; HumVar score: 0.
Funding This work was supported by grants from the  Fondation Jeanne et Jean-Louis Lévesque (JLM) ,  Fondation Maladies Rares (NC) ,  Retina France (NC, LFT) ,  NHMRC  Grant  1099165  (RVJ) and  Costco and Luminesce Alliance (LGR). We acknowledge  Nicolas Fossat  for assistance with vector cloning. We thank the  Sequencing and Genotyping Core Facilities at KFSHRC  for their technical help. The authors extend their appreciation to the  King Salman Center For Disability Research  for funding this work through  Research Group  no  RG-2022-011. French patients were recruited through the  Rares Diseases Cohorts (RaDiCo) program  which is funded by the  French National Research Agency  under the  specific program  " Investments for the Future", Cohort  grant agreement  ANR-10-COHO-0003.
18were performed and analysed by FH, CN, NP and FSA. Sanger sequencing were performed by SJ, PC, and NC. Zebrafish studies were performed by SBou, SBoi, LFT, JMR, MM, CN, MD, and SE. In vitro analyses were performed by LGR, RG, DW, IP, and RJ. The first draft of the manuscript was written by SBou, JLM and NC and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript. Page 21 of 36 Journal of Medical Genetics Journal of Medical Genetics Journal of Medical Genetics Journal of Medical Genetics Journal of Medical Genetics Journal of Medical Genetics Supplemental Data Supplemental Data include five tables. C o C o C o Accession numbers The ClinVar (https://www. ncbi. nlm. nih. gov/clinvar/) accession numbers for the variants reported C o C o C o n f n f n f in this article are: SCV001976485 (c.
mental delay/intellectual disability, hypotonia, epilepsy, cardiomyopathy/congenital heart defect, brain abnormalities, hearing loss, eyes/vision problem, and short stature. The aim of our study was to (1) evaluate the incidence of the 1p36DS in the French population compared to 22q11. 2 deletion syndrome and trisomy 21; (2) review the postnatal phenotype related to microarray data, compared to previously publish prenatal data. Thanks to a collaboration with the ACLF (Association des Cytogénéticiens de Langue Française), we have collected data of 86 patients constituting, to the best of our knowledge, the second-largest cohort of 1p36DS patients in the literature. We estimated an average of at least 10 cases per year in France. 1p36DS seems to be much less frequent than 22q11. 2 deletion syndrome and trisomy 21.
Thanks to a collaboration with the Association des Cytogénéticiens de Langue Française (ACLF, the French-Speaking Cytogeneticists Association: www. eaclf. org), we have collected data of 86 patients postnatally diagnosed with 1p36DS. Data from the corresponding prenatal observations have been previously published  (Guterman et al. , 2019). To the best of our knowledge, we reported here the second-largest cohort of 1p36DS patients in the literature, performing mapping, and genotype-phenotype correlation. The aim of the present study was (1) try to assess all cases of 1p36 deletion diagnosed in France and with previously reported prenatal cases, try to evaluate its incidence; (2) review the phenotype, related to CMA data and help to establish a link between genotypes and phenotypes associated with this genetic syndrome to provide more information to families.
| Study design and population A multicenter, nationwide, retrospective study was set up to collect data on patients with 1p36DS with the cooperation of the ACLF. Ninety-one patients diagnosed postnatally between 1996 and 2018 using various diagnostic methods (conventional karyotyping, FISH analysis, Multiplex Ligation-dependent Probe Amplification (MLPA), or CMA) were reported by 18 French laboratories out of the 37 surveyed centers. The following data were requested by a questionnaire addressed to the referring geneticist: indication for genetic testing, clinical information, age at diagnosis, technique used for diagnosis, and mode of inheritance. Five patients were excluded from the clinical analysis: two patients for whom genetic and clinical information was lacking, one patient with a derivative chromosome one resulting from an unbalanced translocation (with 1p36 deletion associated with a 6 Mb chromosome 16 duplication), two other patients with very small deletions (41 and 166 kb) encompassing the GNB1 gene.
| Mapping Mapping data were obtained from either BAC-arrays, SNP-arrays, or array-CGH (aCGH) for most of them. A map of CMA results was generated using the University of California Santa Cruz (UCSC: https://genome. ucsc. edu/) genetic tools, UCSC Genome Browser (build GRCh37/hg19), with the creation of two groups according to deletion position. Distal and proximal 1p36 critical regions  (respectively chr1:1-6,289,973 and chr1:8,395,179-11,362,893)  were described in the literature  (Jordan et al. , 2015). The middle of these critical regions was used to create the two groups: Group A with distal deletions and Group B with proximal deletions. Deletions were considered apparently terminal when their boundary corresponded to the first or second probe of the array used and/or if a telomeric probe was deleted by FISH.
| Estimated incidence To estimate the number of cases diagnosed per year between 2012 and 2016, the prenatally diagnosed 1p36DS cases, previously published in the same geographic origin population by Guterman and colleagues, were used  (Guterman et al. , 2019). Since not all centers invited to collaborate participated, we focused on our region in France to estimate the incidence of 1p36DS assuming that the cases were exhaustively analyzed. We used the official data of live births per year in this region (Insee data: National Institute of statistics and national studies).
| Statistical analysis and clinical data Clinical data were described using mean and median for quantitative variables and number and percentage for qualitative variables. Clinical data were studied between the two groups using univariate analyses (Fisher's exact tests). Statistical analyses were performed with Epi Info™ version 3. 5. 4 (CDC). For all tests, p < 0. 05 were considered to be statistically significant. Patients' photographs were analyzed using Face2gene (FDNA, Inc. : https://www. face2gene. com/) to evaluate facial features.
| Diagnostic methods and molecular cytogenetics findings 1p36DS diagnosis was made by CMA in 72 patients, FISH alone in 11 patients, MLPA in 1 patient, MLPA associated with FISH in 1 patient, and minisatellite probes associated with FISH in 1 patient. We found socalled terminal deletions (55), interstitial deletions (24), and derivative chromosomes (1). In five patients, CMA allowed to bring out complex rearrangements (1p36 deletion associated with other deletion or duplication on chromosome 1). In our cohort, minimum and maximum deletions sizes varied from 532 kb to 11. 8 Mb (Table  S1 ). Out of the 86 patients included in the study, CMA was performed on 72 patients. Among them, additional aberrations were reported in six patients and summarized in Table  S1. CMA data allowed us to align 67 deletions and to generate a map (Figure  1 ) using the UCSC Genome Browser (build GRCh37/hg19).
| Developmental delay or intellectual disability Data were available in 75 patients, and developmental delay or ID was reported in 100% of patients (75/75). The severity degree of this delay was unknown in most cases. Walking (data was available in 17 patients) was acquired for the earliest at 16 months and was not acquired at the time of the consultation for three patients at 3, 5, and 10 years, respectively. A regression of acquisitions was reported in four patients (details in Table  S3 ). Language delay was also a frequent feature (according to the available data, two patients have no language at 17 years and 5 years, respectively). One patient was described with mild ID (del61) and was autonomous in daily life. Thirty-three patients presented behavioral disorders (data was available in 44 patients), including aggressivity, self-injury/self-aggressivity, temper tantrums, low frustration tolerance, social interaction difficulties, and stereotypies.
| Dysmorphism Data were available in 78 patients with details in 69 patients. Facial dysmorphism was reported in 94% (73/78) (Figure  3 ) with: mostly horizontal and straight eyebrows (46%, 32/69), enophtalmia (39%, 27/69), blepharophimosis (13%, 9/69), epicanthal folds (12%, 8/69), low-set (27%, 19/69), and posteriorly rotated (13%, 9/69) ears, sometimes also stickyout ears (12%, 8/69), midface hypoplasia (16%, 11/69), short nose (12%, 8/69), microstomia (23%, 16/69) with thin lips (17%, 12/69), and brachycephaly (13%, 9/69). Nineteen patients' photographs were analyzed using Face2gene (FDNA, Inc. ), without clinical information. This tool proposed 1p36DS as the most likely diagnosis in 10 patients and in the top F I G U R E 1 Mapping of deletions diagnosed by CMA using UCSC genome browser (build GRCh37/hg19) and OMIM genes included in this region.
| CONCLUSION Thanks to a collaboration with the ACLF we have collected data of 86 patients constituting the second-largest cohort of 1p36DS patients in the literature to our knowledge. This study enables a continuum with prenatal published data. We estimated an average of, at least, 10 cases per year in France. 1p36DS seems to be much less frequent than 22q11. 2 deletion syndrome and trisomy 21. We observed that 1p36DS may be a life-threatening condition and can lead to early death in the most severe cases. Mapping and genotype-phenotype correlation allowed us to identify four critical regions responsible for ID and some phenotypic variability, particularly according to the deletion position. This study helps to refine the phenotype of 1p36DS, which is diagnosed more and more early, especially prenatally with the generalization of the CMA and more recently with genome analysis.
The "context" was worldwide (our search was not confined to a particular territory/geographic location). Based on a preliminary literature search, an a priori protocol was devised. MEDLINE, a major scholarly, electronic biomedical database, was accessed via PubMed, a freely available interface. No time or language restrictions were applied. The search string consisted of two major components: microbiome and swimmers, with synonyms/variants properly linked by using Boolean operators [(microbiome OR microbiota OR "bacterial community" OR "bacterial communities" OR "bacterial flora") AND (swimming OR swimmer)]. "Medical subject headings" (MeSH) terms and wild-card (truncated words) options were used. Extensive cross-referencing was carried out. Further, specific target journals were handsearched.
To the best of our knowledge, there is no additional published literature on this topic in pigs, although there are numerous examples in humans and a few in other livestock species  [10, 11]. Given these previous results, we hypothesized that microbiota information can help to predict and manage traits of interest in pigs. The first objective of the present study was to investigate the association of faecal microbial variants with feed efficiency (FCR and residual feed intake (RFI)) and other performance traits, including ADG, BFT, and daily feed intake (DFI). The second objective was to evaluate the accuracy of microbiome predictions of phenotypes of animals that do not (yet) have phenotypic information with the incorporation of a microbiome covariance matrix. We used data from two experimental pig lines that were divergently selected for RFI, which ensured sizeable variability of the traits of interest.
Microbial information The Quick-DNA ™ Faecal Microbe Miniprep Kit ™ (Zymo Research, Freiburg, Germany) was used to extract microbial DNA based on a 15-min bead-beating step at 30 Hz. PCR amplification of the V3-V4 region of the 16S rRNA gene obtained from diluted genomic DNA was carried out by using two primers: F343 (CTT TCC CTA CAC GAC GCT CTT CCG ATC TTA CGG RAG GCA GCA G) and R784 (GGA GTT CAG ACG TGT GCT CTT CCG ATC TTA CCA GGG TAT CTA ATCCT), in 30 cycles with an annealing temperature of 65 °C. To assemble pair-end sequences, the Flash software v1. 2. 6  [15]  was used with an overlap of at least 10-bp between the forward and reverse sequences and by allowing 10% mismatches. Single multiplexing was performed using an in-house 6-bp index, which was added to R784 during a second PCR with 12 cycles and the forward primer (AAT GAT ACG GCG ACC ACC GAG ATC TAC ACT CTT TCC CTA CAC GAC) and reverse primer (CAA GCA GAA GAC GGC ATA CGA GAT -index-GTG ACT GGA GTT CAG ACG TGT).
Then, the resulting PCR products were purified and loaded on the Illumina MiSeq cartridge according to the manufacturer's instructions. The quality of the runs was checked internally using PhiX, and each pairend sequence was assigned to its sample using the integrated index, with the bcl2fastq Illumina software. The sequences were submitted to the Bioproject database (https:// www. ncbi. nlm. nih. gov/ biopr oject/) with accession number PRJNA701065. Reads were filtered and trimmed for high-quality sequences using the DADA2 package in the R software  [16]  with the following parameters: maxN = 0, maxEE = 2, truncQ = 2, trimleft = 17. Chimera were removed with the consensus option in DADA2 to obtain the final OTU abundance table. Since no further clustering was applied, the OTU were equivalent to amplicon sequence variants (ASV) in this study.
Then, taxonomic annotation was predicted using the assignTaxonomy function of the DADA2 package with the Silva Dataset v132  [17]. To account for differences in sequencing depth across samples, a rarefaction step was applied: for all samples with more than 9000 reads, an equal number of 9000 reads were randomly selected. Sixteen samples had a smaller number of reads and were discarded for later analyses. After this rarefication step, the final abundance table contained 5689 OTU for 588 samples (295 LRFI and 293 HRFI). Finally, following Rothschild et al. [18] , OTU in the rarefied table were filtered for more than 1% nonzero values across all sampled animals, which decreased the number of OTU to 2630. Then, a constant value of 1 was added to the rarefied table, which allowed calculation of log values for OTU with zero abundances in the downstream analyses.
All models were fitted using the Bayesian framework using the GIBBSF90 software  [19]. In total, 100,000 samples were generated to obtain the posterior distributions of the parameters for each model, and a burnin period of 15,000 samples and thinning interval of 10 were considered. The convergence was verified through visual inspection of trace sample plots. Model comparisons were based on the deviance information criterion (DIC; Spiegelhalter et al. [20] ). As a further evaluation of models, correlations of phenotypes adjusted for fixed effects ( y * ) with the sum of solutions obtained for random terms (except residuals) for each individual were calculated and presented as r y * , m for Model (2), r y * , a for Model (3) and r y * , a + m for Model (4). (5) z 3ij = log P ij -log P ij j sd(log P ij ) j ,.
For the LR method, the phenotypes of the animals to be predicted were set to missing for each trait and their m were predicted ( m p , i. e. prediction using a partial dataset) with Models (  2 ) and (  4 ). For each replicate, prediction accuracy for each trait was evaluated based on the correlation between m p and predicted m using the full dataset ( m w , i. e. , prediction using the whole dataset) with the same model. The final criterion was the average of the correlations across replicates (r m p , m w ). Similarly, to evaluate the accuracy of microbiome predictions of phenotypes with Model (4), the average of part-whole correlations for the sum of predicted breeding values and microbial value was calculated, i. e. r ( a + m) p , ( a + m) w.
Single-OTU regression analysis Single-OTU regression analyses were applied to test the effect of the 2630 OTU, one at a time, and obtain the associated p-value, which is the most common approach  [10]. The model used for these analyses was the same as Model (3) except that a specific OTU was added as a fixed covariate. The model was fitted using the best linear unbiased prediction (BLUP) method of the AIREMLF90 software  [19]. The p-value of the estimate of the regression coefficient for the fitted OTU covariate was obtained by converting the estimate and its standard error to a Z-score and applying a Chisquared test.
Finally: Then, the Z-score for the estimate for each OTU j can be obtained from the diagonal elements of var OTU as: (6) Z 3 DZ ′ 3 = Mσ 2 m. (7) E(OTU) = OTU = DZ 3 ′ Z 3 DZ 3 ′ -1 m = 1 k Z 3 ′ M -1 m. ( 8 ) var OTU = var 1 k Z 3 ′ M -1 m = 1 k Z 3 ′ M -1 var m M -1 Z 3 1 k. ( 9 ) PEV m = var m -m = var(m) -var( m) = C mm σ 2 e ( 10 ) var m = var(m) -C mm σ 2 e = Mσ 2 m -C mm σ 2 e , (11) var OTU = 1 k Z 3 ′ M -1 Mσ 2 m -C mm σ 2 e M -1 Z 3 1 k. The corresponding p-values can then be calculated by applying a Chi-square test to these Z-scores. The back-solving method was run using a local script to construct and solve the mixed model equations based on the variance component estimates of Model (4) for each trait. It should be noted that both the single-OTU regression and the back-solving approach accounted for additive genetic effects in order to obtain comparable results.
These results are in line with the report of Khanal et al. [8] , who observed a higher predictive ability of models that included both genetic and microbiome effects for most of the traits. In addition, lower accuracy of microbiome predictions of phenotypes were obtained with the CG designs than with the random designs. This could be due to the high impact of the contemporary environment on the microbiome composition of animals. Vigors et al. [29]  reported that microbiome composition can vary between farms because of differences in management, season of the year, and sanitary status, and of the influence of the dam's diet. A preliminary non-metric multidimensional scaling analysis revealed that CG was the main driving factor of the microbiota composition in our dataset. Given that, in our study, the samples were collected on an experimental farm under a standardized management protocol, microbiota differences between CG should be limited compared to differences between farms.
Indeed, if variances deviated strongly from the actual values, biased MWAS results could be expected from the back-solving approach. The tests indicated that the abundance of some of the OTU may be associated with the variability of phenotypic traits. We have previously shown that some microbiota genera differ between lines  [12]. The aim of including the genetic covariance matrix in the model to run MWAS was to control the risk that the association of abundance of the significant OTU with phenotypes would be due to the divergent selection, since we could not conduct separate analyses for each pig line because their power would have been too limited. In a previous study at the genera level, using the same microbiome dataset but filtered for the most abundant genera, we showed significant genetic correlations of abundance of genera from the Lachnospiraceae, Ruminococcaceae, Prevotellaceae and Streptococcaceae families with RFI, DFI, and BFT  [12].
Conclusions We have shown that microbiota information can be used to better predict traits in pigs, especially feed efficiency traits. The sizable m 2 and the identification of some OTU with abundances that are associated with the phenotype traits indicate that some of the microbiota components are associated with the variability of production traits. In addition, the lower accuracy of microbiome predictions of phenotypes when none of the individuals from the contemporary group of the pigs were included in the training set suggests that connecting breeding conditions between the training and predicted datasets is needed. Altogether, prediction accuracies of phenotypes accounting for microbiome and genetic covariance between animals suggest that phenotypes of animals can be reliably predicted at mid-test.