dataesr/scientific-paragraphs-categorization huggingface dataset:
Samples For the SSC, SPARKv1 and SPARKv2 cohorts, we downloaded genetic and clinical data from SFARI Base (https://sfari. org/sfari-base). For the SSC cohort, we selected 10,141 individuals with both WES and single-nucleotide polymorphism (SNP) array data, who were not twins and did not show a high number of erroneous variant calls (families filtered out, 12958, 14572 and 11037). For the SPARKv1 cohort, we selected 19,671 individuals with both WES and SNP array data, who were not withdrawn, not twins and not showing excessive number of variants or abnormal age, and from families in which both parents were undiagnosed and had available genetic data. For the SPARKv2 cohort, we selected 5,970 individuals with both WES and SNP array data, who were not withdrawn and from families in which both parents were undiagnosed and had available genetic data.
For simplicity, the SPARKv1 and SPARKv2 samples were merged into one SPARK sample. For the UK Biobank cohort, we downloaded genetic, demographic and brain imaging data from the UK Biobank database (project 18584). We selected 200,428 individuals with both WES and SNP array data, not twins (kinship < 0. 4 from relationship file of UK Biobank) and who did not report autism-related symptoms (based on ICD10-F84 index or the autism diagnostic questionnaire). For the aggregated iPSYCH sample, we downloaded tabular files for each gene of interest from the Autism Sequencing Consortium website (https://asc. broadinstitute. org/) and calculated the maximum allele numbers per status for all variants, corresponding to 4,811 individuals with autism and 5,214 undiagnosed individuals.
Autism and constrained gene sets We focused on coding exons of 220 autism-associated genes: genes from the SFARI Gene database with a score of 1 (https://gene. sfari. org/ database/human-gene/), 102 genes from a recent case-control study of rare variations  8  and 157 genes robustly associated with autism in multiple independent studies and unrelated individuals by the SPARK committee (http://sparkforautism. org) (Supplementary Table  1 ). Constrained genes were defined based on suggested thresholds of the LoF observed/expected upper bound fraction < 0. 35 or the probability of LoF intolerance > 0. 9, both extracted from the gnomAD website (https://gnomad. broadinstitute. org)  13. The present study focused on autosomal genes and we filtered out the genes with an evidence of recessive type of inheritance  12.
Other neurodevelopmental and functional gene sets Cognitive impairment, epilepsy and neurodevelopmental disorder genes were extracted from our previous work  12. Briefly, cognitive impairment genes were those identified as 'primary' in the SysID database (https://sysid. cmbi. umcn. nl/), epilepsy genes extracted from six databases (The Lafora Gene Mutation Database, The Epilepsy Genetic Association Database, CarpeDB, EpilepsyGene, GenEpi and MeGene) and NDD genes from the Gene2Phenotype genes classified as associated with NDDs, restricted to those annotated as 'brain' or 'cognition'. Functional annotation of synaptic proteins were taken from SynGO  59  and transcription proteins were extracted from Gene Ontology term transcription, DNA templated  60. Microcephaly and macrocephaly genes were extracted from our previous work  61  and are available at https://genetrek.
SNP arrays For the SSC sample, the GRCh36-based SNP array data for the three different technologies (Illumina Omni1Mv1, n = 1,354; Omni1Mv3, n = 4,626; and Omni2. 5, n = 4,240) were downloaded from SFARI Base (https://sfari. org/sfari-base) and 15 individuals were removed because they were twins. Arrays from each technology were mapped onto the GRCh37 human genome version separately. We downloaded the preprocessed GRCh37-based genotyping files of 26,879 SPARKv1 and 15,904 SPARKv2 participants from SFARI Base. SSC and SPARK genotyping files were filtered from ambiguous SNPs (A/T and G/C SNPs if minor allele frequency (MAF) > 0. 4; SNPs with differing alleles; SNPs with >0. 2 allele frequency difference; and SNPs not in reference panel) and imputed on the Haplotype Reference Consortium panel v.
r1. 1 (ref. 62) on the Michigan servers with default parameters  63. GRCh37-based imputed genotyping files for 200,080 UK Biobank individuals were downloaded from the UK Biobank database (projects 51869 and 18584). After imputation we kept only variants with a r 2 ≥ 0. 8 and merged the three different SNP array technologies from the SSC sample keeping only SNPs shared between all three technologies.
Admixture We used the 1000 Genomes sequencing data of 2,504 individuals as a reference group of individuals of known ancestry  64. We selected the 1000 Genomes SNPs that were present in the SSC, SPARKv1 and SPARKv2 datasets to perform a combined admixture for SFARI Base samples and 1000 Genomes SNPs that were present in the UK Biobank dataset to perform a separate admixture, using the Admixture v. 1. 3. 0 tool  65  on one to eight clusters. SSC, SPARKv1 and SPARKv2 genotypes, as well as UK Biobank genotypes, were projected on the corresponding admixture models based on 1000 Genomes data and we selected five clusters for separating the individuals by ancestry, corresponding to a https://doi. org/10. 1038/s41591-023-02408-2 low cross-validation error in both admixture models (Supplementary Fig.
Whole-exome sequences We downloaded the GRCh37-aligned BAM files of 8,960 SSC participants from SFARI Base (https://sfari. org/sfari-base). We then called the variants using GATK v. 3. 8 following the Broad Institute Best Practices  66  and lifted over all variants to the GRCh38 human genome version. We downloaded the preprocessed GRCh38-based pVCF files of 27,270 SPARKv1 and 16,004 SPARKv2 participants from SFARI Base. All functional-equivalent GRCh38-based pVCF files for 200,642 UK Biobank participants were downloaded from the UK Biobank database (projects 51869 and 18584). All variants from SSC, SPARK and UK Biobank samples were filtered for call rate > 0. 9, genotype quality ≥ 30, depth > 20, allelic fraction ≥ 0. 25 (and ≤0. 75 for autosomal variants). Tabular lists of variants from the aggregated iPSYCH samples were downloaded from the Autism Sequencing Consortium website (https://asc.
We also performed further quality control for S-LoF annotation by visualizing the phase of variants for individuals carrying multiple nucleotide variants (MNVs) in the close vicinity of the originally reported S-LoF variants. Such MNVs, if in phase with the original S-LoF, could modify the effect of the variant on the encoded protein (changing from LoF to missense or synonymous variants). We filtered out 111 and 3,787 S-LoFs in autism-associated and constrained genes, representing 1. 9% and 3. 6% of the initial dataset, respectively. For the independent regression analyses on autism status in the iPSYCH sample, we performed additional quality control (QC) steps on the 236 S-LoFs in autism-associated genes and 1,345 S-LoFs in constrained genes. The initial QC steps for the iPSYCH Danish Blood Spot WES data have been described previously  69.
Briefly, after the first round of sample-level and variant-level QC, three call-rate filters were used subsequently, (1) remove variants with a call rate < 90%; (2) remove samples with a call rate < 95%; and (3) remove variants with a call rate < 95%. Between the sample call-rate filter and the final variant call-rate filter, one of each pair of related samples (relatedness as a pi-hat value ≥ 0. 2) was removed. Subsequently, we selected for this study the individuals diagnosed with autism no later than by the end of 2016. This gave us a study sample of 4,622 cases and 4,753 undiagnosed individuals. We defined rare variants as having an allele count no greater than five across our dataset (n = 9,375) and the non-Finnish Europeans from non-psychiatric exome subset of the gnomAD (n = 44,779).
Relative position on encoded protein and pext score We annotated the relative position of the variants on the encoded protein using the Loftee coding sequence (CDS) position when available or VEP CDS position otherwise and the CDS size for each transcript from BioMart (https://www. ensembl. org/biomart/martview/). To measure exon usage in different isoforms of each gene within brain tissues, we downloaded the base-level pext score from the gnomAD website (https://gnomad. broadinstitute. org)  15. Briefly, the pext score summarizes the isoform expression values across tissues and allows measurement of the expression status of exonic regions across tissues, at the exon level. For each exon of each gene, we selected the maximum value of the pext measures from 13 brain tissues (amygdala, anterior cingulate cortex BA24, caudate basal ganglia, cerebellar hemisphere, cerebellum, cortex, frontal cortex BA9, hippocampus, hypothalamus, nucleus accumbens basal ganglia, putamen basal ganglia, spinal cord and substantia nigra).
Developmental brain gene expression The developmental brain transcriptome data from 42 specimen and up to 16 brain structures were downloaded from the Allen Brain Atlas BrainSpan database (https://www. brainspan. org/). Only expression reads per kilobase of exon model per million mapped reads values >1 were considered for expression analysis. Values for each gene were https://doi. org/10. 1038/s41591-023-02408-2 averaged across four brain regions and eight developmental periods as previously described  23. Brain regions were defined as follows: R1, posterior inferior parietal cortex, primary auditory cortex, primary visual cortex, superior temporal cortex, inferior temporal cortex; R2, primary somatosensory cortex, primary motor cortex, orbital prefrontal cortex, dorsolateral prefrontal cortex, medial prefrontal cortex, ventrolateral prefrontal cortex; R3, striatum, hippocampus, amygdala; and R4, mediodorsal nucleus of the thalamus, cerebella cortex.
Developmental periods were defined as follows: P1, early fetal; P2, early mid-fetal; P3, late mid-fetal; P4, late fetal; P5, infancy; P6, childhood; P7, adolescence; and P8, young adult. Note that only one individual was available for P1R4 in the BrainSpan database; the corresponding period/ region was therefore not investigated in this study. For the analysis of the correlation between gene expression and autism OR, we artificially replaced infinite autism OR values by the highest measurable autism OR in the gene set and the Pearson correlation test was performed in the log 10 space for both expression and OR of autism-associated genes. Autism polygenic score computation SSC, SPARKv1, SPARKv2 and UK Biobank imputed genotyping data were filtered separately from variants absent from >1% of individuals (geno001 parameter), then variants present in all four samples were merged with PLINK v.
1. 9 (ref. 70). The PGS for autism was computed by using the GWAS summary statistics from iPSYCH and the Psychiatric Genomics Consortium (PGC) 2. To exclude overlap in participants from the test and discovery data in the PGS analysis, the GWAS meta-analysis summary statistics reported  2  were recalculated with the SSC data excluded. We used the SBayesR 71 method of the GCTB tool v. 2. 02 with the banded linkage disequilibrium matrix and suggested options (https://cnsgenomics. com/software/gctb) on the PGC-ASD summary statistics to estimate the posterior statistics of SNP effects. We finally computed the autism PGS using PLINK v. 1. 9 based on SBayesR-derived statistics for common SNPs (MAF > 10%). We performed a principal-component analysis using PLINK v. 2. 0 and extracted the four first principal components to control for population structure when using the autism PGS in regression analyses.
We also calculated autism PGS values for subsets of genes. First, we selected the SNPs that fall in a window of ±20 kb from the minimum protein-coding transcript start and stop, to calculate the gene-specific autism PGS. Transcript start and stop positions were based on Ensembl annotation v. 107. Next, we further selected subsets of the protein-coding genes corresponding to those present in the lists of autism-associated genes, constrained genes, SynGO genes or micro-or macrocephaly genes. All numbers are reported in Supplementary Fig. 4. For the iPSYCH replication sample, we used our best genetic predictor as measure of common variant load, which is generated in part internally through a 50-fold cross-validation process, where the full iPSYCH2015 sample  72  was pruned for related individuals (at pi-hat 0.
The resulting IQ score bins were treated as continuous variables. The developmental milestones for SPARK samples were downloaded from SFARI Base (https://sfari. org/sfari-base) and were available for 4,722 probands. The number of developmental disorders was available for 6,910 SPARK individuals, including 5,630 individuals with autism. For the independent iPSYCH replication cohort, sex was extracted from the Danish registry database, corresponding to biological sex. The diagnoses of autism and cognitive impairment were conferred by the end of 2016 based on the psychiatric central register. We used the ICD10 codes F70-F79 for cognitive impairment diagnoses. There were 1,017 individuals diagnosed with both autism and cognitive impairment (with IQ < 70) and 3,605 individuals with autism only (with IQ ≥ 70).
Annual income was categorized by the UK Biobank sample in five bands (<£18,000, £18,000-30,999, £31,000-51,999, £52,000-100,000 and >£100,000) and was available for 162,968 participants. The Townsend deprivation index is a measure of material deprivation within a population, assigned to each individual as a score corresponding to the output area in which their postcode is located and was available for 188,630 individuals. For brain anatomy analyses, early-life trauma variables were downloaded from the UK Biobank database. Whether individuals were adopted with a yes/no answer was available for 188,443 individuals and whether individuals felt loved, felt hated, were physically abused by family or had someone to take them to doctor when needed as a child for 65,104 individuals. We excluded participants who responded 'do not know' or 'prefer not to answer' to these questions.
Phenome-wide association study in UK Biobank We performed a phenome-wide association study of 18,224 phenotypes present in the UK Biobank database (listed in Supplementary Table  6 ), for a total of 188,736 individuals. We used the PHESANT software (https://github. com/MRCIEU/PHESANT)  76 with default parameters and presence of a S-LoF in an autism-associated gene as a trait of interest (binary trait with 'genetic = TRUE' and 'standardize = FALSE' arguments). Each regression analysis used sex (National Health Service recorded or self-reported), age at recruitment and type of array (BiLEVE or Axiom) as covariates. We extracted the β coefficients from https://doi. org/10. 1038/s41591-023-02408-2 the combined result output, as well as P values that were further corrected for multiple testing using the FDR method.
Brain structural anatomy Imaging-derived phenotype (IDP) data were downloaded from the UK Biobank database (projects 40980 and 18584). A total of 68 metrics for cortical regions and 16 metrics for subcortical regions, calculated using FreeSurfer and FSL software using the Desikan-Killiany Atlas, were provided for 21,040 individuals with genetic data. Details of the acquisition protocol and imaging processing toolbox are available on the UK Biobank website at https://biobank. ctsu. ox. ac. uk/crystal/ crystal/docs/brain_mri. pdf. Four global IDPs were investigated: total cortical volume, total cortical thickness, total cortical surface area and total subcortical volume. The total brain IDPs were obtained by summing left and right hemisphere global measures. Carriers of S-LoFs have a slightly lower age distribution compared to non-carriers in the subsample with imaging data available, although both are in the 40-70-year age range (P = 0.
Odds ratio (logistic regressions) and standardized beta values (linear regressions) associated to variant presence and autism PGS from multivariable regression analyses of socioeconomic traits and fluid intelligence, stratified by gene type and autism OR of genes carrying the variants, alternatively focusing on individuals within low and high range of values for each feature (Methods). For the Townsend index and fluid intelligence, the median of the distribution of values among S-LoF carriers was used to split the dataset (respectively z-scored reversed Townsend index of 0. 12092671 and fluid intelligence score of -0. 10951938). For income, we chose to split individuals below and above £31,000, and for qualification below and above A levels or equivalent. This procedure allowed to split individual carrying S-LoFs into two partitions of approximately the same size.
Error bars correspond to standard errors of the proportions. (e) Two-sided Pearson correlation coefficients and p values when comparing autism OR, pLI scores, LOEUF scores and fraction of LoFs among ClinVar pathogenic variants. P values were corrected for multiple testing using the FDR method. The ClinVar database (https://www. ncbi. nlm. nih. gov/clinvar/) was downloaded in July 2022, variants annotated as 'pathogenic' were extracted and separated between LoF ('nonsense', 'splice_acceptor_variant', 'frameshift_variant', 'splice_ donor_variant', 'stop_lost') and missense variants based on the consequence field. https://doi. org/10. 1038/s41591-023-02408-2 Extended Data Fig. 3 | Gene-level autism OR as a function of number of carrying individuals or families, average pext in brain tissues and relative position in encoded protein.
Acknowledgements This research has been conducted using the SSC and SPARK from the  Simons Foundation Autism Research Initiative. This research has been conducted using the  UK Biobank Resource  under application no. 18584. This work was supported by a grant from  SFARI  ( 240059 , to T. B. ). We are grateful to all of the families at the participating SSC sites, at the participating Simons Searchlight sites, the  SSC , as well as the principal investigators ( A. Beaudet ,  R. Bernier ,  J. Constantino ,  E. Cook ,  E. Fombonne ,  D. Geschwind ,  R. Goin-Kochel ,  E. Hanson ,  D. Grice ,  A. Klin ,  D. Ledbetter ,  C. Lord ,  C. Martin ,  D. Martin ,  R. Maxim ,  J. Miles ,  O. Ousley ,  K. Pelphrey ,  B. Peterson ,  J. Piggot ,  C. Saulnier ,  M. State ,  W. Stone ,  J. Sutcliffe ,  C. Walsh ,  Z.
This work was funded by  Institut Pasteur , the  Bettencourt-Schueller Foundation ,  Université de Paris , the  Conny-Maeva Charitable Foundation , the  Cognacq Jay Foundation , the  Eranet-Neuron (ALTRUISM)  and the  GenMed Labex ,  AIMS-2-TRIALS , which received support from the  Innovative Medicines Initiative 2 Joint Undertaking  under grant agreement no. 777394  and the  Inception program  ( Investissement d'Avenir  grant  ANR-16-CONV-0005 ). This project has received funding from the  Horizon Europe programs CANDY  and  R2D2-MH  under grant agreement nos. 847818  and  101057385. Views and opinions expressed are, however, those of the authors only and do not necessarily reflect those of the  European Union. Neither the  European Union  nor the granting authority can be held responsible for them.
Additionally, for UK partners, the work was funded by  UK Research and Innovation  under the  UK government's Horizon Europe funding guarantee  (grant no. 10039383 ). The iPSYCH team was supported by grants from the  Lundbeck Foundation  ( R102-A9118 ,  R155-2014-1724  and  R248-2017-2003 ) and the  Universities and University Hospitals of Aarhus and Copenhagen. High-performance computer capacity for handling and statistical analysis of iPSYCH data on the GenomeDK HPC facility was provided by the  Center for Genomics and Personalized Medicine and the Centre for Integrative Sequencing , iSEQ,  Aarhus University, Denmark  (grant to A. D. B. ). S. B. C. received funding from the  Wellcome Trust   214322\Z\18\Z , support from the  European Union 's  Horizon 2020 research and innovation programme and EFPIA and AUTISM SPEAKS ,  Autistica ,  SFARI.
Data availability Researchers can obtain the whole-exome and SNP genotyping data from the SSC and SPARK cohorts used in this study by applying at https://base. sfari. org. The UK Biobank whole-exome, SNP genotyping, phenotypic and brain imaging data can be obtained by applying at the UK Biobank database (https://www. ukbiobank. ac. uk/). The human neurodevelopmental transcriptome dataset is available on the BrainSpan database (http://www. brainspan. org). Functional annotations can be obtained from SynGO (https://syngoportal. org/) and Gene Ontology (http://current. geneontology. org/annotations/goa_human. gaf. gz). Human reference genomes were obtained from https://www. ncbi.
The suggested LOEUF threshold of 0. 35 (a), pLI threshold of 0. 9 (b) and 50% of pathogenic variants that are LoF versus missense variants in ClinVar (c) are shown. (d) Fraction of autism-associated genes passing the thresholds for each metric. Error bars correspond to standard errors of the proportions. (e) Two-sided Pearson correlation coefficients and p values when comparing autism OR, pLI scores, LOEUF scores and fraction of LoFs among ClinVar pathogenic variants. P values were corrected for multiple testing using the FDR method. The ClinVar database (https://www. ncbi. nlm. nih. gov/clinvar/) was downloaded in July 2022, variants annotated as 'pathogenic' were extracted and separated between LoF ('nonsense', 'splice_acceptor_variant', 'frameshift_variant', 'splice_ donor_variant', 'stop_lost') and missense variants based on the consequence field.
Cuticle preparation Experiment has been done as previously described  (54). Laid embryos were allowed to age for 24 hours at 25°C. They were rinsed in water and then dechorionated using 8% sodium hypochlorite solution for 2 min. Embryos were rinsed again in water and the vitelline membrane was removed mechanically using a needle. Water was replaced by 1:1 lactic acid:Hoyer's-based medium. Embryos were transferred onto a glass microscope slide and placed under a cover slip. Slides were incubated at 60°C overnight. Phase contrast images were obtained using the ×20 objective of an Axio-ApoTome (Zeiss) and ZEN2 software.
Then, the mean of each sample ratio was used to compare the two conditions. Native RNA-IP followed by RT-qPCR For each sample, 80 pairs of ovaries from well-fed females (2 or 3 days) were manually dissected in cold 1× PBS, flash-frozen in liquid nitrogen and conserved at -80°C. S2 )  (56). For subsequent analyses, we used a cleaned version of the genome in which Y, U, and mitochondrial chromosomes were removed. This version, named "dm6_clean", has been deposited at the Gene Expression Omnibus (GEO) under accession number GSE203279. Sequence length distributions, sRNA mapping, and sRNA overlap signatures were generated from bowtie alignments using Python and R (www. r-project. org/) scripts, which were wrapped and run in Galaxy instance publicly from ARTbio platform available at http://mississippi.
fr (see also Supplementary Data). Tools and workflows used in this study may be downloaded from this Galaxy instance. For library comparisons, read counts were normalized to 1 million reads (table  S2 ). For sRNA mapping (Figs. 1  and 3  and fig. S2 ), we took into account only 23-to 29-nt RNA reads that uniquely aligned to reference sequences (unique mappers). Logos were calculated using Weblogo (57) from 3′ trimmed reads (23 nt long) matching P(lacW) (Fig. 1 , L and M) or selected genomic regions (Fig. 3C ). The percentage of reads containing a "U" at the first position was calculated with all 23-to 29-nt RNA matching the reference sequence. Distributions of piRNA overlaps were computed as first described in  (8)  and detailed in  (58). Thus, for each sequencing dataset, we collected all of the 23-to 29-nt RNA reads matching P(lacW) whose 5′ ends overlapped with another 23-to 29-nt RNA read on the opposite strand.