dataesr/scientific-paragraphs-categorization huggingface dataset:
Previous studies have suggested that altered asymmetry of the planum temporale (PT) is associated with neurodevelopmental disorders, including dyslexia, schizophrenia, and autism. Shared genetic factors have been suggested to link PT asymmetry to these disorders. In a dataset of unrelated subjects from the general population (UK Biobank, N ¼ 18,057), we found that PT volume asymmetry had a significant heritability of roughly 14%. In genome-wide association analysis, two loci were significantly associated with PT asymmetry, including a coding polymorphism within the gene ITIH5 that is predicted to affect the protein's function and to be deleterious (rs41298373, p ¼ 2. 01 Â 10e15), and a locus that affects the expression of the genes BOK and DTYMK (rs7420166, p ¼ 7. 54 Â 10-10). DTYMK showed left-right asymmetry of mRNA expression in post mortem PT tissue.
, 2011; Hirayasu et al. , 2000; Shapleske, Rossell, Woodruff, & David, 1999) ). A reduction of PT asymmetry in terms of area and thickness measures has also been observed in schizophrenia (SCZ)  (Ratnanather et al. , 2013). Auditory hallucinations can occur in SCZ  (Morch-Johnsen et al. , 2017) , which may conceivably relate to PT functions as secondary auditory cortex  (Crow, Ball, Bloom, & al, 1989). Average leftward PT asymmetry is established early in development, as evidenced by studies of fetuses  (Chi, Dooling, & Gilles, 1977) , preterm newborns  (Dubois, et al. , 2010)  and infants  (Li et al. , 2014; Wada, Clarke, & Hamm, 1975). A developmental program that results in population-level average asymmetry is likely to require a molecular genetic component that first creates a directional left-right axis early in development  (Francks, 2015; Postema, Carrion-Castillo, Fisher, Vingerhoets, & Francks, 2019; Reiter & Leroux, 2017; Willems, Van der Haegen, Fisher, & Francks, 2014).
, 2015). It has also been reported that foetal testosterone levels relate to the development of grey matter asymmetries of some cortical regions, including the PT  (Lombardo et al. , 2012). In fact, an extensive and influential theory was developed by Geschwind and Galaburda which involves sex hormones, brain laterality and disorder susceptibility  (Geschwind & Galaburda, 1985) , which still motivates current research, although definitive support is lacking  (Bryden, McManus, & Bulman-Fleming, 1994; Hollier, Maybery, Keelan, Hickey, & Whitehouse, 2014; Papadatou-Pastou & Martin, 2017). In the present study, we used the UK Biobank MRI dataset (N ¼ 18,057) to perform a more highly powered genetic analysis of PT asymmetry than has previously been possible. This sample size also permitted, for the first time, the estimation of SNP-based heritability of PT asymmetry, and its genetic correlations with other traits.
Dataset Data were obtained from the UK Biobank cohort (https://www. ukbiobank. ac. uk/using-the-resource/), as part of research application 16066, with Clyde Francks as the principal applicant. This is a general adult population cohort. The data collection in the UK Biobank, including the consent procedure, has been described elsewhere  (Sudlow et al. , 2015). Informed consent was obtained by the UK Biobank for all participants. For this study we used data from the October 2018 release of 22,392 participants' brain MRI data, together with the genomewide genetic data from the same participants. The age range of these participants was from 44 to 80 years (median 63. 9), and 11,269 were female, 10,551 were male.
PT volume and asymmetry measures The UK Biobank provided various derived brain imaging measures  (Miller et al. , 2016)  and documentation (https:// biobank. ctsu. ox. ac. uk/crystal/crystal/docs/brain_mri. pdf). Briefly, tissue-type segmentation  (Zhang, Brady, & Smith, 2001)  was performed from T1 images using FSL FAST  (Jenkinson, Beckmann, Behrens, Woolrich, & Smith, 2012; Smith et al. , 2004) , and 44 cerebral cortical regions were defined per hemisphere based on the HarvardeOxford probability atlas (distributed with the FSL software package; http:// fsl. fmrib. ox. ac. uk/fsl/). For the present study we focus on the PT as defined in the HarvardeOxford atlas (Fig. 1 ), which makes use of an anatomical definition close to that used in the original post mortem studies of this region (see Introduction).
Genome wide association scanning After SNP-level quality control (see Supplementary Methods) there were 15,120,452 SNPs available for GWAS. GWAS was performed separately for each of the residualized PT AI, L and R measures, using imputed genotype dosages and an additive model, with BGENIE (v1. 2)  (Bycroft et al. , 2018). Sample sizes ranged from 18,037 to 18,053 depending on the specific measure (Table  S2 ). Manhattan plots of the results, and QQplots, were made using the "qqman" R (Turner, 2017) package, and regional association plots were made using LocusZoom v1. 4  (Pruim et al. , 2010). The summary statistics from the GWAS for PT AI were loaded into the annotation software FUMA  (Watanabe, Taskesen, van Bochoven, & Posthuma, 2017). Significantly associated SNPs were considered as those with pointwise p < 5 Â 10À8, to correct for multiple testing over the whole genome.
Gene-set enrichment analysis Gene-set enrichment analysis was applied to the GWAS results for the PT AI, using the software MAGMA v1. 06b (de  Leeuw, Mooij, Heskes, & Posthuma, 2015). The basis of this analysis is to first assign a single association score to each gene based on the GWAS data, and then test whether the genes in a given set show, on average, more evidence for association with the trait in question than the rest of the genes in the genome, for which scores could be calculated (hence this is sometimes called 'competitive' analysis). We tested for enrichment according to the Gene Ontology (GO) classification system  (Ashburner et al. , 2000; GO Consortium, 2017). A total of 5,875 GO sets (with a minimum of 10 genes per set) were tested, and Bonferroni multiple testing correction was applied to obtain corrected p-values.
Bioinformatic characterisation For rs41298373 and rs7420166, we assessed evolutionary conservation and expression QTL (eQTL) effects (see Supplementary Methods). These analyses would implicate BOK but also another nearby gene, DTYMK (see Results). We further queried the spatio-temporal expression patterns of the genes ITIH5, BOK, and DTYMK using publicly available data (Supplementary Methods). Using the Allen Brain Atlas microarray data, which are based on six post mortem brains (only two have data from both hemispheres), we also assessed whether the expression of ITIH5, BOK or DTYMK in the PT differed between hemispheres, by modelling expression as a function of probe and hemisphere, and including subject as a random factor [expression ~(1|subject) þ probe þ hemisphere], using the lmer function in the 'lme4' package in R.
9, resulting in N ¼ 18052 subjects for rs41298373 and N ¼ 16663 for rs7420166). Multiple comparison correction was performed on statistical maps using the 3dClustSim program implemented in AFNI  (version 16. 2. 19 ). Thresholds of voxel-level p <. 001 (Z ¼ 3. 29) and cluster-level p <. 01 (cluster size!40 voxels) were used based on Monte Carlo simulation in the left hemisphere mask. Results were mapped to left-hemisphere surface space for visualization of cerebral cortical effects, using BrainNet Viewer (v1. 63)  (Xia, Wang, & He, 2013 ) (the 'extremum voxel' option).
e. , do not require individual level data: LD score regression (LDSC, v. 1. 0. 0)  (Bulik-Sullivan et al. , 2015)  and SumHer  (Speed & Balding, 2019) , the latter using both the LDAK and GCTA models (see Supplementary Methods). We estimated the minimum power to detect genetic correlations across these pairs of traits and datasets at alpha <. 05 using the GCTA-GREML power calculator  (Visscher et al. , 2014) , given the sample sizes involved, and according to SNP-based heritability estimates obtained from LDSC and SumHer. Tables  S3  and S5  summarize all the parameters per trait that went into the power calculations. Since LD score regression  (Bulik-Sullivan et al. , 2015)  and SumHer  (Speed & Balding, 2019 ) utilize summary statistics while GCTA relies on the individual genotype data, the true power is likely to be slightly lower for the former two; however, the GCTA-GREML power calculator gives an indicative estimate.
Top SNPs and gene sets associated with PT asymmetry For three individually significant SNPs from the GWAS of PT AI in the UK Biobank (see Results), we looked up these SNPs in the publicly available GWAS results for ASD, SCZ, ADHD, intelligence and EA. In case the SNP was not present in the publicly available GWAS results, we identified proxy SNPs in high LD (r 2 >. 8) with them, as calculated within the UKB imaging dataset using plink (v1. 90).
PT asymmetry and its associations As expected, PT grey matter volume in the UK Biobank dataset of 18,049 healthy adult subjects, as measured using the HarvardeOxford atlas, was leftward asymmetrical at the population level (Figs. S1, S3 ), with males having a slightly more pronounced leftward asymmetry than females (t ¼ À11. 38, p-value ¼ 6. 17 Â 10 À30 , Fig. S4 ). The PT AI was not associated with handedness (Fig. S5 , Table  S4 ) or Fluid Intelligence (Table  S4 ), but was nominally associated with EduYears (unadjusted p ¼. 037, partial correlation À. 016) (Table  S4 ). This was due to EduYears being more strongly associated with the right PT volume than the left (Table  S4 ). As the Biobank's Fluid Intelligence measure is fairly crude (sum of 13 items), the lack of phenotypic association with this measure did not discount a possible genetic correlation of PT asymmetry with intelligence, when analysing GWAS data from other studies of intelligence.
Further characterisation of loci associated with PT asymmetry Rs41298373 on 10p14 is an exonic nonsynonymous SNP within exon 9 of ITIH5 (Inter-Alpha-Trypsin Inhibitor Heavy Chain Family Member 5) (transcript ¼ ENST00000397145). The minor allele results in a substitution from alanine to valine (p. A376V), within the von Willebrand factor type A (VWA) domain of the protein (pfam ¼ PF13768). The SNP is predicted to affect protein function by several bioinformatic tools (Polyphen ¼ probably damaging, SIFT ¼ deleterious, CADD ¼ 27. 9). The Genomic Evolutionary Rate Profiling (GERP) estimate for this genomic position is high (5. 23), reflecting that the ancestral allele G is conserved across mammals. G is also the allele at this site in the reference genomes of archaic hominins (two Neanderthals and one Denisovan)  (Green et al.
, 2010; Meyer et al. , 2012). The minor allele A has a frequency around 10% in most human populations, but is lower in African (2%) and East-Asian (. 7%) groups according to the gnomAD database  (Lek et al. , 2016). ITIH5 encodes a heavy chain component of one of the inter-alpha-trypsin inhibitor (ITI) family members  (Himmelfarb et al. , 2004). The ITI family contains multiple proteins (ITIP) that are made up of a light chain (bikunin) and a variable number of heavy chains (ITIH1-ITIH6), and are plasma serine protease inhibitors. They are involved in extracellular matrix stabilization and in prevention of tumor metastasis. ITIH5 is a known tumor suppressor gene, and it has been shown to be downregulated in breast cancer  (Himmelfarb et al. , 2004). The expression pattern of ITIH5 is broad, being most highly expressed in adipose, artery, colon and mammary tissue  (Consortium G.
Gene set enrichment analyses In gene set enrichment analysis of the GWAS results for PT AI, no gene sets were significantly enriched for association signals after correction for 5,875 tested GO sets. Since a previous GWAS of PT asymmetry in roughly 3,000 subjects had reported significant enrichment of association in two steroid-related gene sets  (Guadalupe et al. , 2015) , we also looked up the MAGMA enrichment results for these candidate sets as targeted hypotheses, but found no enrichment of association within 'steroid hormone receptor activity' (unadjusted p ¼. 68) nor 'steroid metabolic process' (unadjusted p ¼. 42). We also applied the same enrichment analysis software (Lee, OD;ushlaine, Thomas, & Purcell, 2012) for testing these two gene sets as had been used in the study by c o r t e x 1 2 4 ( 2 0 2 0 ) 1 3 7 e1 5 3 Fig.
Genetics of PT asymmetry Within the UK Biobank dataset, we showed that asymmetry of the PT has a modest but significant SNP-based heritability, estimated at 14%. In principle, a heritable component to PT asymmetry may arise because some genetic influences are quantitatively different on the left and right volumes of this structure, and we found evidence to support this, as the genetic correlation between left and right PT volumes was significantly less than 1. However, PT asymmetry may itself be considered a primary brain phenotype, not merely arising from different effects on left and right. This conception would be consistent with the early appearance of PT asymmetry in development in utero, and its association with disorders when disrupted. Thus PT asymmetry may be analogous to the placement of the visceral organs on the left-right body axis (heart, lungs etc.
Overall, the present study is both larger and uses more up-to-date analytic approaches (Table  S12 ). The present results in relation to steroid gene sets may therefore be taken to supersede the previous results, although the results of the previous study should also continue to be tested as available datasets expand. For example, it is possible that variation in PT asymmetry in younger adults (as included in the study by  Guadalupe et al. (2015) ) is more affected by steroid hormone influences than in older adults (as in the UK Biobank).
The non-synonymous SNP in ITIH5 was nominally associated with intelligence (unadjusted p ¼. 02) with a very small effect size (standard beta ¼. 011). Thus, it is possible that this locus does impact intelligence, although a p value of. 02 based on a study of 269,867 individuals means that this could only be clarified in a much larger, future dataset. Rs7420166 was not available in the intelligence and EA GWASes, but three SNPs in high LD with it (r 2 >. 8: rs114606940, rs73123528, rs77278412) were nominally associated with EA (unadjusted Ps <. 05). Again, these effects (beta ¼. 004) were very small, detectable only in a dataset of 766,345 individuals. As we found no phenotypic correlation of PT asymmetry with handedness, we did not pursue possible genetic correlation between these traits. A lack of correlation between PT asymmetry and handedness has also been reported by five previous, recent studies  (Guadalupe et al.
Limitations Limitations with regard to sample size, statistical power, and the lack of large scale GWAS for dyslexia have already been discussed above. As regards dyslexia, the phenotypic association with PT asymmetry has been reported to be limited to boys (see Introduction), which means that further sex-limited genetic correlation analysis may be warranted. For the present study, the brain imaging data in the UK Biobank would be reduced to groups of roughly 8,500e9,000 if split by sex, which is too low to perform well powered genetic correlation analysis with other datasets by use of summary statistics, for a trait such as PT asymmetry with only 14% heritability. Also, the GWAS summary statistics for SCZ, ASD, intelligence and EA were not available split by sex. Although sex-stratified GWAS summary statistics were available for ADHD, the power to detect genetic correlation of PT asymmetry with this disorder would not have been adequate in sex-split analysis.
In this study we controlled for sex as a covariate, and also found no evidence for a sex-specific effect of the top loci associated with PT asymmetry. The HarvardeOxford atlas, which we used to define the PT, was derived from manual segmentations of sets of reference brain images  (Destrieux, Fischl, Dale, & Halgren, 2010; Goldstein et al. , 1999 Goldstein et al. , , 2007)). It therefore contained asymmetrical definitions for regions that showed asymmetry in the reference dataset (including the PT). Accordingly, the measurement of average PT asymmetry in the UK Biobank dataset would partly reflect left-right differences present in the atlas. Nonetheless, the use of a 'real-world' asymmetrical atlas, rather than an artificially created symmetrical atlas, was appropriate for our primary purpose which was the GWAS of PT asymmetry in as large a sample as possible.
This is because regional identification was likely to be more accurate for structures that were asymmetrical both in the atlas and, on average, in the UK Biobank dataset, and the GWAS analysis is based on comparing relative levels of asymmetry between genotype groups, rather than measuring absolute asymmetry levels. In addition, we followed up the most significant SNP associations with PT asymmetry that arose in the GWAS, by performing brainwide grey matter asymmetry VBM association analysis, this time based on a symmetrized template and no regional atlas definitions, which produced results that were highly consistent with the GWAS analysis. We further investigated vertex-wise effects without regard to any regional atlas definitions, and using a co-registration approach to improve interhemispheric correspondence.
Acknowledgements This research was conducted using the UK Biobank Resource under Application Number  16066 , with  Clyde Francks  as the principal applicant. AC-C was funded by a grant to CF from the  Netherlands Organisation for Scientific Research (NWO)  ( 054-15-101 ), and B. M, N. T. M. , A. P. , and F. C by a grant from the  French National Research Agency (ANR , grant No. 15-HBPR-0001-03 ), as part of the  FLAG-ERA consortium project  ' MULTI-LATERAL  0 , a Partner Project to the  European Union's Flagship Human Brain Project. Additional support was from the  Max Planck Society (Germany).
To date, a few whole-genome investigations have been performed, which are mainly based on single nucleotide polymorphism (SNP) arrays and include only a few European local pig breeds  [19] [20] [21]. Most of these studies are based on small numbers of animals and provide preliminary information on their population structure. In the case of the Iberian and Casertana breeds, quantitative trait loci (QTL) and genome scans have identified genomic regions and mutations associated with morphological, production, meat and carcass traits  [9, 10, 16, 17, [22] [23] [24]. Other studies have compared SNP datasets from Italian autochthonous and commercial breeds to identify population-informative markers and analyse their inbreeding levels  [25, 26]. Muñoz et al. [27]  and Ribani et al. [28]  analysed candidate markers in major genes in  20  European local pig breeds and wild boar populations, which provided information on the segregation of relevant polymorphisms for breeding or traceability purposes  [29].
A follow-up study on these breeds included the analysis of linkage disequilibrium, F ST and effective population size using a medium-density SNP array  [30]. Other studies that involved a few Asian and European pig breeds investigated signatures of selection in the porcine genome using SNP chip or partial/reduced or wholegenome re-sequencing datasets and highlighted loci of economic importance  [7, [31] [32] [33] [34] [35] [36] [37] [38] [39] [40] [41] [42]. Thus, it is important to take further the analyses of the genome of autochthonous and cosmopolitan pig genetic resources, including unexplored and poorly investigated breeds, which were developed under different humandriven evolutionary conditions (i. e. selection programs) and production systems. In this study, we analysed the genome of 19 European autochthonous pig breeds from nine countries, three commercial Italian breeds and wild boars to identify signatures of selection by mining whole-genome sequencing data obtained by using a DNA-pool sequencing approach.
Quality controls and sequence alignment The obtained reads underwent several cleaning and filtering steps: removal of (i) adapters, (ii) reads containing more than 10% unknown bases (N), and (iii) reads containing low-quality bases (Q ≤ 5). FASTQ files were the inspected with the FASTQC v. 0. 11. 7 software (https :// www. bioin forma tics. babra ham. ac. uk/proje cts/fastq c/) that highlighted very high-quality reads. No other filtering procedures were carried out. Reads were mapped to the latest version of the Sus scrofa reference genome (Sscrofa11. 1) with the BWA tool 0. 7. 17  [45]  using the MEM function and the parameters for paired-end data. Picard v. 2. 1. 1 (https ://broad insti tute. githu b. io/picar d/) was used to remove duplicated reads. Whole-genome sequencing data statistics are in Additional file 1: Table  S2.
In spite of the low rate of false positives with CRISP  [46] , these procedures allow to filter out other possible false variants. In this step, we used the dbSNP v. 150 database (  [49] ; accessed on February 27 2018; number of variants = 64,535,988). Briefly, variants were initially annotated as reported in dbSNP ("in. dbSNP" class) or not ("novel" class). Then, these two classes were subdivided in "rare" and "common" variants. Rare variants were defined as variants that present a minor allele frequency (MAF) lower than 0. 0143. This value represents the "ideal" lower limit of detection (i. e. 1/70), since, in general, pools were composed of 35 diploid individuals (see Additional file 1: Table  S1 ). This is an approximated estimation that did not take the average sequencing depth into account.
Then, for the "rare" class of variants, we used the Kolmogorov-Smirnov (KS) test to compare the distributions of the quality score of the variants in the "in. dbSNP" and "novel" sub-classes. The KS test measures the similarity of the two distributions in a quantitative way via the D-statistics (a metric ranging from 0 to 1). Lower values of D indi-cate more similar distributions. Different cut-off values ranging from 0 to 50 with steps of 1, were tested. The CRISP quality score (Q CRISP ) that minimizes the D value was selected as the best score (Q CRISP = 21; (see Additional file 2: Figure  S1 ). • Step (3): to evaluate globally the quality of our dataset, we used the transition-to-transversion ratio (Ts/ Tv) as quality indicator  [50]. Variants on the mitochondrial genome (that can also be confused with differences in nuclear DNA sequences of mitochondrial origin or NUMTS;  [51] ) and variants on the sex chromosomes were discarded.
Variant detection in the wild boar DNA pool was carried out with Samtools v. 1. 7  [52]  by retaining the variants that were detected in the 22 pig DNA pools and considering an RD min of 3. Variants were annotated using the Variant Effect Predictor (VEP) v. 95. 0  [53] , by predicting their impact on the protein function with SIFT v. 5. 2. 2  [54]. Statistics about detected and annotated variants are Additional file 1: Tables  S3  and S4 , respectively. Pipelines were developed either in Python v. 2. 7. 12 or in R v. 3. 4. 4 ([55] ; the Kolmogorov-Smirnov test was carried out with the function "ks. test"). SNP allele frequencies (AF) were estimated by counting the number of reads that include the SNP position. To evaluate the reliability of the estimation of AF obtained by DNA-pool sequencing, we used SNP chip data from Muñoz et al.
e. 399). The H P index was computed for each window by using the formula described by Rubin et al. [6, 7]  and log2 transformed as proposed by Sun et al. [59]. The final H P value, related to each breed, was estimated as the overall mean of H P values. For each window, the F ST index was estimated according to the formula introduced by Karlsson et al. [57]. F ST was calculated for each breed or for groups of breeds in different comparisons. For a given breed, the F ST value of each genome window was computed as the average across the N -1 comparisons (N = 22; wild boar data were analysed separately) and then the final F ST value, related to the breed, was estimated as the overall mean of F ST values  [60]. Analysis of the wild boar population followed the same approach by considering N = 23.