dataesr/scientific-paragraphs-categorization huggingface dataset:
Component 3, reflecting relatively better school grade performance in oral than in written exams, showed significant negative correlations with phoneme awareness and spelling. Lastly, component 4, reflecting relatively better school grade performance in Danish than in English, showed significant negative correlation with nonword repetition. The non-cognitive abilities involved in educational attainment identified in the GWAS-by-subtraction study  17  showed small but significant positive genetic correlations with word reading, nonword reading and nonword repetition. Genomic structural equation modelling (GenomicSEM)  19  was used to further investigate the shared genetic architecture of the five reading-and language-related traits, together with performance IQ, full-scale IQ and educational attainment (Supplemental Table  9 ).
Full results can be found in Supplemental Table  8. *Significant genetic correlation after correction for 18. 28 independent comparisons (p<2. 74x10 -  ); **p<2. 74x10 -4 ; ***p<2. 74x10 -. B: Three factor model fitted to the GenLang summary statistics for word reading, nonword reading, spelling, phoneme awareness and nonword repetition and performance IQ, and published GWAS summary statistics for full-scale IQ and educational attainment  16  using GenomicSEM  19. Black and grey paths represent factor loadings with p<0. 05 and p>0. 05, respectively. Standardized factor loadings are shown, with standard error in parentheses. The subscript g represents the genetic variables; the u variables represent the residual genetic variance not explained by the models. Unstandardized results and model fit indices are reported in Supplemental Table  9.
H3K4me1 is considered a marker for enhancer regions. 18. PCN: primary cultured neurospheres. The graph shows log-10 p-values on the y-axis and brain region-specific chromatin mark on the x-axis. The dashed line shows the p-value threshold for significant enrichment after Bonferroni correction for testing 489 annotations. Next, we used MAGMA gene property analysis  20  to study whether the multivariate reading results are enriched in a specific tissue or brain cell type, using tissue-specific and cell type-specific gene expression data in FUMA  31, 32. As MAGMA corrects for average expression, each comparison can only answer the question of whether the tissue or cell type is more related to the multivariate reading results than the average of the tissues or cell types in the dataset. After correction for 83 tissues (p<6.
02x10 -  ), no relation was found of the multivariate reading results with tissue-specific gene expression patterns of adult tissues from GTEx and brain tissues of a specific (developmental) time from Brainspan (Supplemental Figure  10  and Table  19 ). In the cell type-specific gene expression analysis, three single-cell RNA-sequencing datasets of embryonic, fetal and adult brain tissue were used. After correction for 142 cell types (p<3. 09x10 -  ), the multivariate reading results were significantly associated with one of the mature neurons from the fetal dataset: red nucleus neurons (Supplemental Figure  11  and Table  20 ). This association could reflect an association with this specific nucleus, or with the higher maturity of the neurons compared to the other cell types in the fetal dataset.
13 for nonword repetition to 0. 26 for nonword reading. These SNP-based heritabilities are similar to those of the related trait dyslexia (estimates range from 0. 15 to 0. 25 on a liability scale)  38, 39  , psychiatric traits such as ADHD symptoms in adults (0. 22)  40  , and brain imaging traits such as cortical surface area (range 0. 12-0. 33 for different cortex regions)  41  and cortical thickness (range 0. 08-0. 26) 41  and they are larger than that of psychiatric traits such as major depression (0. 08)  42  and alcohol dependence (0. 09)  43. So, despite highlighting the need for larger sample sizes to identify more than one genome-wide significant locus, our GenLang GWASs already allowed for multiple informative follow-up analyses based on the full dataset. Word reading, nonword reading, spelling, and phoneme awareness largely share the same genetic architecture, as is evident from genetic correlation analyses, as well as the three-factor model in which one factor explains most of the variation in these four traits.
Given the evidence of a highly shared genetic architecture between word reading, nonword reading, spelling, and phoneme awareness, we performed a multivariate analysis with MTAG, to increase our power and reduce multiple-testing constraints for follow-up analyses of biological pathways. The multivariate word reading results showed genetic correlations with phenotypes related to education, eyesight, chronotype, wellbeing, lifestyle, physical health, exercise and socioeconomic status. These associations may in a large part reflect shared biology between the reading-/languagerelated traits and cognitive abilities, educational attainment and socioeconomic status. No significant genetic correlations were observed with neuropsychiatric disorders based on available data in LDhub. However, we note that summary statistics from the current investigation have also been used to investigate genetic overlaps with self-report of dyslexia diagnosis in an independent GWAS by 23andMe (~52k cases), yielding substantial negative genetic correlations between GenLang quantitative traits and dyslexia status (e.
Regarding functional implications of our findings, heritability partitioning analyses of the multivariate GenLang GWAS results identified enrichment in enhancer regions present in fetal brain tissue and the adult germinal matrix. The enhancer regions of the germinal matrix are highly similar to those of fetal brain tissues, and not the other adult brain tissues we studied, likely reflecting the neural stem cell population present in that tissue  65. In these analyses there was no specific association with one particular brain cell type. However, follow-up work with MAGMA, using single-cell RNA sequencing data from fetal, embryonic and adult brain, uncovered a significant association with fetal neurons from the Red nucleus, which may relate to the more adult state of these neurons compared to the other cell types in the fetal dataset  66.
The MAGMA analysis could not be used to test for replication of the association with (fetal) brain tissue of the LDSC heritability partitioning, as results in this case are corrected for the association with the average expression of the dataset  20, 31  , and the datasets with fetal data only included brain samples. We used the GWAS meta-analysis results to investigate evidence for association of previously reported candidate SNPs/genes and suggestive genome-wide screening results from prior studies of reading-/language-related traits and disorders. Out of the 54 candidate SNPs and 20 candidate genes that we assessed (none of which met genome-wide significance), only DCDC2 yielded an association that survived correction for multiple testing in the context of targeted replications. This locus showed association only at the gene-based level and with one trait: nonword reading.
No validated questions have yet been described that adequately capture interindividual variability in reading and language skills in the normal range, which still requires administration of psychometric tests. The GenLang Consortium was established as an international effort by multiple research teams with the aim of overcoming such difficulties through a range of strategies, and enabling large-scale well-powered investigations of genomic underpinnings of these important traits. This first wave of analysis from GenLang represents the largest GWAS meta-analyses for direct quantitative assessments of reading-and language-related abilities to date, including 22 cohorts with data available for at least one of the phenotypes. Nonetheless, although substantially increased over prior work in this area, sample sizes may still be considered relatively modest compared to the stateof-the-art for genetic association analyses of other complex traits.
While they captured a significant proportion of the genetic variation underlying each phenotype, yielding several novel insights into the associated biology, detection of genome-wide significant loci was still limited. In addition, a number of phenotypes of interest (such as those that tap into syntactic skills) could not (yet) be pursued due inadequate sample sizes, even when combining data available from multiple cohorts. We note that despite our best efforts at harmonizing the included datasets, and limited signs of heterogeneity in the results based on Cochran Q statistics, LDSC intercepts and genetic correlations between subsets of the data, we cannot fully exclude that heterogeneity is introduced by the inclusion of data from different assessment tools, languages, and ages. The choice of assessment tools for future collection of readingand language-related phenotypes for genomic studies, to increase the sample sizes of these GWAS meta-analyses and also to collect additional language-related phenotypes, should therefore be based at least partially on optimal matching with existing data.
The sample sizes per cohort range from 104 to 10,187 participants (104 to 5,080 participants of European ancestry, defined by principal component analyses (PCA)). For each cohort, an Institutional Review Board (IRB) or ethical committee approved the respective studies, and participants provided informed consent. Different measures for the reading-and language-related traits had been assessed in each cohort and could be included in the GWAS meta-analyses (see Supplemental Notes for details of each measure, and Supplemental Table  1  for an overview of the included measures and sample sizes for each cohort). Data from children, adolescents and young adults were included in the meta-analysis (age at time of assessment ranging from 5 years to 26 years). Outlier samples, based on the phenotype data (>4 SD), were removed for each phenotype separately.
The phenotype data were then adjusted for covariates (age, age 2 , sex and ancestry principal components; age-normed phenotypes were not adjusted for age and age 2 ). See Supplemental Table  2  for details on the included covariates per cohort. Phenotype data for word reading, nonword repetition and performance IQ were standardized to z-scores. Phenotype data for spelling, phoneme awareness and nonword reading were rank transformed to acquire normally distributed data for all cohorts. For follow-up analyses involving multiple phenotypes -the genetic correlation analysis with LDSC  71  and the multivariate GWAS analysis with MTAG  24  -a separate rank transformation was performed for word reading, nonword repetition and performance IQ to further harmonize the phenotype data processing. For male-and female-only association analyses, phenotype data were filtered, adjusted and transformed for male and female subsets separately.
The genotype data were subjected to stringent quality control according to a detailed analysis plan following standard procedures for GWAS, including SNP filters for minor allele frequency, call rate and Hardy-Weinberg equilibrium and sample filters including missingness and (for cohorts of unrelated individuals) relatedness. Cohort-specific details on quality control can be found in Supplemental Table  2. Individuals of European ancestry were identified using PCA-based analysis of genetic diversity. Individuals with non-European ancestry were excluded from all cohorts, with exception of ABCD, GenR, GRaD and PING. For the ABCD, GenR and PING cohorts, two association analyses were performed, one including and one excluding individuals of non-European ancestry. Results of datasets including individuals of non-European ancestry were excluded from follow-up analyses with LDSC  71  , MAGMA  20  and MTAG  24  , since these methods utilize LD information based on European-ancestry reference data when raw genotyping data is not available (as is the case for this GWAS meta-analysis).
The X chromosome was included by all cohorts except for NeuroDys. Genotype data were imputed using the Haplotype Reference Consortium version 1. 1 panel for 20 out of the 22 cohorts, and using the 1000 Genomes Project Phase 3 reference panel for the GRaD and SYS cohorts. Single variant association analyses were performed using linear regression methods with the imputed additive genotype dosages for the full dataset, and for males and females separately. For the X chromosome, males were treated as homozygous diploids. Descriptions for each cohort of the samples, phenotype measures, genotyping, quality control and analysis procedures can be found in Supplemental Table  1  and 2.
SNPs for which data were available from less than 5,000 individuals were excluded from the meta-analysis results. For the heritability and genetic correlation analyses with LDSC, separate meta-analyses without genomic control correction were performed, because the LDSC regression intercept can be used to estimate a more powerful and accurate correction factor than genomic control  71. Only data of individuals of the PCA-selected European subgroup were included, to allow use of pre-computed LD scores, as genotyping data of all cohorts was not available at a single site to allow the computation of LD-scores for the full partially admixed dataset. To accommodate the multiple-testing burden present in performing separate meta-analyses for the five reading-and language-related traits, while taking into account the high phenotypic correlations between them, we calculated the effective number of independent variables (VeffLi) from the metaanalysis results using PhenoSpD 74 (v1.
GenomicSEM To investigate the high genetic correlations between the reading-and language-related traits, and with cognitive performance and educational attainment, we used genomic structural equation modeling (GenomicSEM; version 0. 03)  19  to model the joint genetic architecture. Summary statistics of the five GenLang traits and performance IQ, and published GWAS summary statistics for cognitive performance and educational attainment from the Social Science Genetic Association Consortium (https://www. thessgac. org/data)  16 were used as input. GenomicSEM first runs multivariable LDSC to obtain genetic covariance and sampling covariance matrices. Next, exploratory factor analyses were run using a maximum-likelihood factor analysis, for models with one to four factors. Confirmatory factor analyses were then run in GenomicSEM for the exploratory model that explained the largest part of the variance in the data.
thessgac. org/data); the GWAS catalogue (noncognitive skills investigated with GWAS by subtraction  17  ; ftp://ftp. ebi. ac. uk/pub/databases/gwas/summary_statistics/GCST90011874), and through collaboration with the iPSYCH consortium (GWAS analysis of Danish school grades  18  ). PhenoSpD  74  was used to calculate the effective number of independent variables (VeffLi) to inform the multiple testing correction. A total of 18. 28 independent comparisons were performed in Figure  1 , the p-value threshold is therefore set to p=2. 74*10 -3. Publicly available GWAS summary statistics of neuroimaging traits were obtained via the Oxford Brain Imaging Genetics Server  75  (http://big. stats. ox. ac. uk/). Out of 3,144 brain imaging-derived traits with summary statistics available from the UK Biobank, a total of 58 neuroanatomical phenotypes were selected based on their relevance to language processing.
Brain imaging traits encompassed surfacebased morphometric (SBM) and diffusion tensor imaging (DTI) phenotypes. For SBM, data were originally generated with Freesurfer by parcellation of the white surface (the surface area between the white and grey matter) using the Desikan-Killiany atlas. Both cortical surface area and mean cortical thickness were selected for brain areas that overlapped regions previously related to language processing, based on literature review  [53] [54] [55] [56]. For DTI, tracts spanning the extended language network  52  were selected, and fractional anisotropy values derived from both tract-based-spatial statistics and probabilistic tractography were used (both mean and weighted-mean fractional anisotropy). Again, PhenoSpd was used to calculate the effective number of independent comparisons.
All enrichments are controlled for the baseline LD v2 model  29, 84  , and heritability enrichment in human adult and fetal HGEs were additionally controlled for adult and fetal brain active regulatory elements. the participants and their families. Data analyses were supported by the St Andrews Bioinformatics Unit which is funded by Wellcome Trust ISSF awards (105621/Z/14/Z and 204821/Z/16/Z).
Bongiovanni, Laure Bricout, Camille Chabernaud, Yves Chaix, Isabelle Comte-Gervais, Florence Delteil-Pinton, Jean-François Démonet, Fabien Fauchereau, Florence George, Christophe-Loïc Gérard, Guillaume Huguet, Stéphanie Iannuzzi, Marie Lageat, Marie-France Leheuzey, Marie-Thérèse Lenormand, Marion Liébert, Emilie Longeras, Emilie Racaud, Isabelle Soares-Boucaud, Sylviane Valdois, Nadège Villiermet, and Johannes Ziegler. This project was funded by the EU FP6 grant to the Neurodys consortium, the SNSF grant 32-108130 and Austrian Science Fund (18351-B02). Netherlands Twin Register (NTR)Funding from the NWO and ZonMW is gratefully acknowledged: Twin family database for behavior genomics studies (NWO 480-04-004); Longitudinal data collection from teachers of Dutch twins and their siblings (481-08-011), Twin-family-study of individual differences in school achievement (NWO-FES, 056-32-010), Genotype-phenotype database for behavior genetic and genetic epidemiological studies (ZonMw Middelgroot 911-09-032); Genetic influences on stability and change in psychopathology from childhood to young adulthood (ZonMW 912-10-020), Why some children thrive (OCW_Gravity program -NWO-024.
Acknowledgements BM, BMM,  BSP , CF, EE, EV, GA, MvD and SEF are supported by the  Max Planck Society. AG and TFMA were supported by the  Munich Cluster for Systems Neurology (SyNergy) , and AG was supported by  Fondazione Umberto Veronesi. ATM is supported by the  National Health and Medical Research Council of Australia (NHMRC) Fellowships  ( 1105008 ;  1195955 ) and  Centre of Research Excellence grant  ( 1116976 ). AJOW is supported by an  Investigator Grant  from the  NHMRC  ( 1173896 ). BSP and MvD are supported by the  Simons Foundation Autism Research Initiative  grant ( 514787  to BSP). CYS works in the  Medical Research Council Integrative Epidemiology Unit at the University of Bristol  ( MC_UU_00011/3 ). DIB acknowledges the  Royal Netherlands Academy of Science Professor Award  ( PAH/6635 ).
EGW is supported by  NICHD   P50 HD 27802. FR is supported by  Agence Nationale de la Recherche  ( ANR-06-NEURO-019-01 ,  ANR-17-EURE-0017   IEC ,  ANR-10-IDEX-0001-02   PSL ,  ANR-11-BSV4-014-01 ),  European Commission  ( LSHM-CT-2005-018696 ). HT is supported by a  VICI grant  of the  Netherlands Organization for Scientific Research (NWO)  and  The Netherlands Organisation for Health Research and Development (ZonMW)  ( 016. VICI. 170. 200 ). JCDF was supported by the  National Institute of Child Health and Human Development (NICHD)  ( P50 HD 27802 ). JJM, JBTo, and TK were supported by the  National Institutes of Health (NIH)  ( R01 DC014489 ). KP was supported by the  Hospital for Sick Children Research Training Program (Restracomp). KR is supported by a  Sir Henry Wellcome Postdoctoral Fellowship.
Data Source The measures analysed in the present research were collected at the end of an online study that examined the effect of energy labelling on virtual portion size selection  (27). A group of UK adults were recruited from the online panel provider Prolific Academic and recruitment was stratified by participant gender and highest education level to be broadly representative of the UK adult population. In the main study, participants provided demographic information, before making hypothetical food portion size selections in the presence vs. absence of energy labelling and completing measures of food choice motives and executive function. At the end of the study, participants completed additional measures in a randomized order for the purpose of the present research (see below). For detailed information about the larger online study and all measures included, see  (27).
We calculated the total number of metabolic equivalent task (MET) minutes per week. When time spent walking, doing moderate-intensity or vigorous-intensity activities was below 10 minutes it was recoded to be equal to 0 and when it was above 180 minutes to be equal to 180  (32). Because data collection took place during COVID-19 social lockdown in the UK (April-May, 2020), as part of the main study participants completed two measures about whether they suspected they currently or previously had COVID-19 (yes or no) and how worried they were about COVID-19 (Not at all worried to very worried). Participants also completed 3 attention checks (e. g. 'How many times have you visited the planet Mars?').
Main Planned Analyses Our pre-registered analysis plan and study data are available at https://osf. io/cszgd/. We planned to exclude participants who did not have complete data for all measures described above, failed one or more attention checks, completed the study more than once or reported implausible weight, height or BMI data; weight <30 kg or >250 kg, height <145 cm or >3m, BMI < 14 or BMI > 48, as in  (33). Because very low body weight and psychiatric conditions have both been associated with deficits in interoception  (34, 35) , we planned to exclude participants with a BMI < 18. 5 (underweight BMI category) or who reported a psychiatric condition. We first planned to conduct zero-order correlations (Pearson's r) between interoception, eating behaviour trait and BMI variables. Next, we examined whether interoceptive accuracy and interoceptive attention predicted BMI and eating behaviour traits when accounting for other variables.
Results Of the 1657 participants who passed all attention checks and had complete data for all variables, 34 were excluded on the basis of implausible weight, height or BMI values. A further 50 participants had BMIs in the underweight range (BMI < 18. 5) and were excluded. A further 392 (24. 9%) reported a previous psychiatric disorder diagnosis and were excluded from primary analyses. The final analytic sample was n=1181. Mean age was 37 years, 49% were female and the majority of participants were white (89%). Mean BMI was within the overweight BMI range (26. 3) and 53% of participants were classed as having overweight or obesity. Full sample characteristics are reported in Table  1.
Passive acoustic monitoring using autonomous recorders offers a modern, powerful and non-invasive alternative to traditional methods for studying wildlife presence, activity and distribution  (Gibb et al. 2019). Automated analysis of environmental recordings has been shown to be particularly useful in the case of remote and endangered seabird colonies  (Arneill et al. 2019)  because it minimizes the costs and the disturbance to the animals  (Buxton & Jones, 2012 , Borker et al. 2014 , Oppel et al. 2014) , while increasing the scale and efficacy of the detections  (Borker et al. 2015). Several ecoacoustic indices have been developed to extract and analyse the biophonic component (i. e. animal vocalizations, mostly occurring within the frequency range 0. 1-8 kHz) from large datasets of soundscape recordings  (Sueur et al.