dataesr/scientific-paragraphs-categorization huggingface dataset:
annotation overrepresentation analysis We conducted a functional annotation overrepresentation analysis using DAVID  37. First, each independent variant considered was mapped to its closest gene using the closest function in BEDtools  64  , and the gencode release 44 annotation database  65  (Table  S6-7 ). Genes were submitted to DAVID as a gene list and fold enrichment was derived over the complete DAVID Knowledge-base. Overrepresentation analysis was applied to subset of variants from the ADHD and schizophrenia associated cluster which multitrait association p-value threshold. For each cluster, we report the top 10 annotation pathways showing overrepresentation significant after Bonferroni correction (Table  S8 -9). To assess the relevance of the enrichment we observed with the neurotrophins signaling pathway results in the schizophrenia cluster, we ran two other analyses using top independent variants of the cluster analysis without the variants of the cluster of interest, i) without threshold on the multivariate p-value and ii) with a multivariate p-value threshold of 5 x 10 -5.
Another solution is to search for differences in allele frequency between males and females caused by sex-specific mortality  (Rowe et al. , 2018). Several recent studies have used this approach to identify intralocus sexual conflicts in whole genome datasets  (Cheng & Kirkpatrick, 2016; Dutoit et al. , 2018; Lucotte et al. , 2016 Lucotte et al. , , 2022;; Wright et al. , 2018 Wright et al. , , 2019)). Recently,  Kasimatis et al. (2021)  failed to find signal of such conflict despite using dataset with much more statistical power (N = 409,406 human genomes). Confidently identifying signal of intralocus sexual conflict thus remains a challenging task. Since sexual reproduction erases genetic differences between males and females except on sex chromosomes, differentiation caused by sex-specific mortality is expected to be weak.
Moreover, substantial mortality in each sex is required to reach a significant detection level  (Kasimatis et al. , 2019 (Kasimatis et al. , , 2021)). It is unlikely that most intralocus sexual conflicts over survival are under strong selective pressure as this would represent too high a mortality cost for populations to persist. Given the low magnitude and detection power involved in intralocus sexual conflicts, separating signal from noise is a complex task, especially in large and complex whole genome sequencing datasets in which multiple testing allows detection of only the strongest signals. One way to overcome this challenge may be to search for cumulative signals of differentiation over several loci, as opposed to looking for significance at a single locus. This approach improves the detection power of intralocus sexual conflict in natural populations and makes detecting smaller selective effects possible  (Ruzicka et al.
g. , behavior, coloration, reproductive strategy;  (Barker & Milinski, 1993; Chellappa & Huntingford, 1989; Whoriskey et al. , 1986) ; (b) they produce large juvenile cohorts  (Craig & FitzGerald, 1982; Poulin & Fitzgerald, 1989) , thus having the potential for a high juvenile mortality load; (c) a very high-quality reference genome  (Nath et al. , 2021)  with a recently sequenced Y chromosome  (Peichel et al. , 2020)  is available, which mitigates the issue of duplicated genes on sex chromosomes and potential artifactual mapping of Y linked reads on autosomes. We performed whole-genome sequencing of 50 male and 50 female three-spined stickleback to search for signatures of intralocus sexual conflicts. After removing one low-quality male from the dataset, we used sex-specific variation in coverage and genetic variability to filter potentially duplicated variants on sex chromosomes.
Genome alignment and cleaning Raw reads were trimmed and quality filtered using fastsp version 0. 15. 0  (Chen et al. , 2018)  with default settings. We used bwa-mem version 0. 7. 17-r1188  (Li, 2013)  to align reads to the fifth version of the stickleback reference genome  (Nath et al. , 2021). Males and females were respectively mapped on a reference including or not the Y chromosome reference  (Peichel et al. , 2020) , available on https://stickleback. genetics. ugq. edu. The Y chromosome was trimmed of its pseudo-autosomal region (first 0. 34 Mb) as it was already present on the X reference (chrXIX). We used picardstools MarkDuplicates (1. 119, http://broadinstitute. github. io/picard/) to identify and trim PCR duplicates, and GATK RealignerTargetCreator and IndelRealiner  (DePristo et al.
Variant calling We used bcftools v1. 12 to call autosomal SNPs using mpileup (-a AD,DP,SP,ADF,ADR, -q 5, -d 20000) coupled with bcftools calls  (Li, 2011)  (-a GP, GQ, -G -). We kept biallelic SNPs with a good mapping quality (≥30), no mapping quality bias (p > 10 -3 ) or read position bias (p > 10 -3 ), and a good QUAL score (≥25). We also used custom python3 scripts to calculate Strand Odds Ratio as suggested by GATK (https://gatk. broadinstitute. org/hc/en-us/articles/360036361772-StrandOdds-Ratio) and discarded all SNPs with a value above 3. 5. We considered all genotypes calls with coverage < 4 (uncertain genotype) or > 35 (potential duplicates) as missing data and then removed SNPs with more than 20% missing data. We excluded individuals with more than 10% missing data and ran the process again on the remaining individuals.
Population structure After removing large genomic rearrangements, we searched for potential sex-specific structure that might lead to significant differences between males and females. We imputed our dataset using the most frequent genotype and ran a PCA to search for axes segregating males and females. We also compared the relatedness among males and females using the implementation of the KING equation in vcftools v1. 1. 16 (Danecek et al. , 2011; Manichaikul et al. , 2010)  to infer pairwise relationship coefficients among individuals.
For each SNP, we estimated intersex F ST as defined in  Cheng and Kirkpatrick (2016) , where F ST = (pm-P f ) 2 4 * p * (1-p) in which p m and p f represent the estimated allelic frequency at a biallelic locus in males and females while p is their average. We assessed its significance based on a null distribution of intersex F ST developed in  Ruzicka et al. (2020). We then tested if the number of significant SNPs was significantly higher than expected based on random permutations of our dataset. We applied this approach at three different levels (whole genome, per chromosome, and in 250 kb sliding windows with 50 kb overlap) to limit the effect of regions with lower-than-expected inter-sex divergence, which could reduce and cancel the signal of accumulation of significant SNPs in other regions.
For the chromosomal and sliding windows level, we estimated the FDR using the Benjamini-Hochberg procedure. We used 1,000 random permutation at the global and chromosomal level but, as we analyzed 8,000 windows, we increased the number of permutations to 20,000 to precisely estimate low p-value. As this method is based on calling individual SNPs significant, we applied this approach for three levels of F ST significance: 5%, 1%, and 0. 1% at the whole-genome or chromosomal scale, but only considered the 1% threshold for the window approach. Finally, we used RDA implemented in the R package vegan  (Oksanen et al. , 2022) , a multivariate ordination methods that have been successfully employed in genotype-environment association studies  (Capblancq & Forester, 2021; Laporte et al. , 2016)  to detect weak multilocus signals of selection.
GO enrichment and gene analysis To test if certain biological functions were associated with intralocus sexual conflict, we performed a Gene Ontology (GO) analysis on genes located within in 1 kb of significant SNPs in our dataset. The 1 kb distance was defined according to  Kratochwil & Meyer (2015)  as being in regions likely to contain cis-regulatory elements. To do so, we annotated a three-spined stickleback transcriptome from  Jones et al. (2012)  using the SWISS-PROT database and downloaded gene information from the UniProt database. We then used goatools  (Klopfenstein et al. , 2018)  to perform Fisher's exact tests and assess enrichment for biological processes using a 5% threshold for the q-value using the Benjamini-Hochberg procedure to control the false discovery rate. All scripts are available at https://github.
We estimated recombination rates using a linkage map from Rastas et al. (  2016 ) from which we used bwa-mem  (Li, 2013)  to lift over the coordinates of their SNPs to ours by aligning a 500 bp windows around each SNP to our version of the reference genome. Then, we used Mareymap (v 1. 3;  Rezvoy et al. , 2007)  to estimate the recombination rate by linear interpolation across the reference genome (loess interpolation span = 0. 2, degree = 2). With this, we classified each region of our dataset as low (≤1 cm/Mb), medium (between 1cM/Mb and 5cM/Mb), or high (>5cM/Mb) recombination rates and perform similar Wilcoxon rank sum test between pair of significant and nonsignificant windows of similar recombination rates. Finally, we wanted to test if some regions of potential intralocus sexual conflicts containing windows identified with cumulative F ST and RDA were associated to elevated Tajima's D.
We used the 99th quantile (0. 0384) of the Tajima's D distribution to identify outlier windows associated with potential long term balancing selection. To refine the scale, we re-estimated Tajima's D for all those regions in smaller windows of 25 kb (5 kb sliding windows) following the same procedure. Since recent balancing selection can also lead to increased linkage disequilibrium (LD)  (Fijarczyk & Babik, 2015) , we used plink (v1. 90b5. 3, Chang et al. , 2015; Gaunt et al. , 2007)  to estimate LD between pairs of SNPs within these genomic regions and their surroundings (250 kb flanking regions). To simplify the representation of LD patterns, we used a custom python script to estimate the distribution of pairwise LD for windows of 25 kb and represented only the 99th quantile of this distribution.
85 to 8. 60 Mb), chrXVI (0. 95 Mb from 17. 1 to 18. 05 Mb), and chrXXI (0. 90 Mb from 1. 8 to 2. 7 Mb) that displayed characteristics consistent with polymorphic inversions and potential duplication (Supplementary Figures  S1-S3 , Supplementary Discussion). However, those structural variants showed neither sex bias nor inter-sex differentiation (Supplementary Discussion) and are thus unlikely candidates for intralocus sexual conflicts and were removed from the dataset. After that, no population structure or sex-specific structure was apparent on a PCA, confirming that our samples belong to a single panmictic population (Figure  1A , Supplementary Figure  S4 ). Investigating pairwise kin coefficients between samples, we confirmed that relatedness among individuals was equivalent between males and females (Figure  1B ) and should not influence inter-sexual differentiation.
To focus on current intralocus sexual conflicts rather than sex differentiation or resolved sexual conflicts, we targeted autosomal chromosomes only and implemented advanced filtering to avoid duplicated autosomal regions on sex chromosomes. We detected 22,825 SNPs with significant coverage bias between sexes and 430 SNPs that showed a significant difference in the number of surrounding SNPs between sexes (in a 500 SNP window surrounding each SNP) (Figure  1C ). Some of those SNPs were associated with a sex-specific increase in heterozygosity (Figure  1C ), with some alleles being only present in one sex and always in individuals that are heterozygous. Altogether, after filtering out low-quality SNPs and samples, the dataset includes 1,701,083 SNPs genotyped in 49 males and 50 females.
Redundancy analyses We then performed a RDA to search for multivariate associations between genotype and sex. We applied this method to identify groups of SNPs that covary with sex. Over the entire dataset, we found no evidence for intralocus sexual conflicts (p-value of. 447 and R 2 adjusted of 1. 101 × 10 -5 , Supplementary Table  S2 ). Running our RDA model independently for each chromosome identified two chromosomes in which genetic variation was significantly associated with inter-sex differentiation: chrIX (p-value of. 046 and an adjusted R 2 of 3. 735 × 10 -4 ) and chrXII (p-value of 0. 019 and an adjusted R 2 of 2. 271 × 10 -4 ) (Supplementary Table  S2 ). However, FDR associated to accepting these p-value were elevated (. 48 for accepting both, and. 38 if accepting only chr chrXII).
Intralocus sexual conflicts are not enriched in particular functions We use SNPs detected as potential targets for intralocus sexual conflict by our three methods to test for GO enrichment in our dataset, first using all detected SNPs (308 SNPs), then focusing on SNPs detected by at least two methods (49 SNPs, which represents higher confidence targets). Using all SNPs, 89 genes were identified, but showed no significant enrichment in particular function. SNPs shared among the three methods overlap with 11 genes, which is not enough to test for GO enrichment. Looking into SWISS-PROT annotation, two of these genes (slc6a4a on chrI, cadherin-12 on chrXXI) are associated to brain-specific functions, with slc6a4a being associated with serotonin transport. Two genes were associated with immune system response (LOC120825389, a predicted immunoglobulin on chrIX, and LOC120831298 on chrXIII, predicted to negatively regulate innate immune response).
oup. com/evolut/advance-article/doi/10. 1093/evolut/qpad075/7146184 by guest on 30 May 2023 heterogeneity in recombination rates along the genome is also worth considering before interpretation. While we did not find detection bias toward particular level of recombination for significant regions identified by cumulative F ST method (Wilcoxon rank sum test, p =. 15 or RDA [p =. 9]; Figure  4 ), it remains important to compare genetic diversity among regions of similar recombination rate, as recombination tends to be positively correlated with genetic diversity  (Payseur & Nachman, 2002). We performed our analyses again by comparing significant regions and other genomic regions with similar recombination rates. To do this, we divided our dataset into low (≤1 cm/Mb), medium ([1-5] cM/Mb), and high (>5 cM/Mb) recombination rates.
Using the UniProt database, we identified potential function of genes located within 1 kb of a significant SNP in those regions. Concerning the region on chrVII, 4 SNPs fall within 1 kb of coding sequences, with notable genes identified inhibiting bacterial growth (OXLA), TRIM2, potentially involved in pathogen recognition and LEGC involved in the innate immune response and inflammatory response. Other genes are involved in cytoskeletal organization (SH319) and hook protein (FHI1A) On the chrXIII region, one SNP is found within 1 kb of the Msh3 gene involved in DNA repair mechanism. Checking coverage pattern of these genes, they do not present deviation from the 1:1 M:F ratio expected, although genes from the protocadherin alpha family showed high variance in coverage ratio (Fig. S8 ). They also feature a surprising pattern with a peak of coverage in female followed by an excess of coverage in male, located in the exon of protocadherin alpha-13 and alpha-2 (Supplementary Figure  S8 ).
Discussion Intralocus sexual conflicts have the potential to maintain genetic diversity in natural populations. However, because they leave only faint signals in genomic datasets, we still have a weak understanding of their genomic distribution, and thus of their role in the maintenance of genetic diversity. Using whole genome resequencing of 49 male and 50 female three-spined sticklebacks, our study provides a description of the genomic pattern of intralocus sexual conflicts in this species, and directly tests the association between intralocus sexual conflict and genetic diversity. We detected SNPs and regions with signature of putative intralocus sexual conflicts. Following stringent filtering of genomic regions duplicated on sex chromosomes, we combined three methods (SNP-by-SNP, cumulative F ST , and RDA) to detect signals of inter-sex differentiation.
Signature of intralocus sexual conflict as detected by a complementary set of methods Through differential mortality, intralocus sexual conflicts are predicted to translate into differential allelic frequency between sexes  (Cheng & Kirkpatrick, 2016; Lin et al. , 2022; Lucotte et al. , 2016) , which can be detected using various approaches such as inter-sex F ST  (Lin et al. , 2022; Wright et al. , 2018)  or genome-wide association studies (GWAS,  Kasimatis et al. , 2021; Ruzicka et al. , 2020). However, substantial mortality is required to generate significant intersex differentiation and statistical power to detect this signal is low, especially after accounting for multiple testing in genome-wide datasets. Since many loci under strong intralocus sexual conflict over mortality would likely represent an excessive mortality cost for natural populations, the search for intralocus sexual conflicts in genome-wide datasets mainly focus on finding small-effect variants.
Intralocus sexual conflicts are not associated with increased genetic diversity Theory predicts that intralocus sexual conflicts could play a significant role in maintaining genetic diversity in natural populations. This prediction is sometimes taken for granted and some studies have used signature of increased genetic diversity, such as elevated Tajima's D, to confirm the identification of intralocus sexual conflict  (Wright et al. , 2018). However, the extent to which this relationship is valid across the genome still lacks testing. Studies focusing on specific traits or genes have shown that intralocus sexual conflict can maintain genetic diversity in a variety of context  (Barson et al. , 2015; Foerster et al. , 2007; Hawkes et al. , 2016; Lonn et al. , 2017) , but only three studies have tested this relationship directly in large genomic datasets  (Dutoit et al.
Acknowledgments The authors are thankful to  I. Caza-Allard ,  E. Reni-Nolin ,  S. Delaive , and  F-A. Deschenes-Picard  for their help during fieldwork, as well as  S. Bernatchez  and  R. Bouchard  for their occasional help. We also thank  A. -L. Ferchaud  and  M. Laporte  for their advice on analysis,  Q. Rougemont  for its help in SNP calling and filtering,  C. Hernandez  and  A. Perreault  for their help with lab work, and  A. Xuereb  for proofreading the manuscript. We also thank  Nadia Aubin-Horth  for her advice on field and lab work, various exchanges, and in particular for her help around oral communications of this work. Sequencing was performed at the  Centre d'Expertise et de Services Genome Québec (Montréal, QC Canada ). The project is part of the  Ressources Aquatiques Québec (RAQ) research program.
Stimuli Stimulus generation in Experiment 1 (free-naming), Experiment 2 (18-AFC) and Experiment 3 (gloss ratings). We generated our stimulus set by computer rendering images of complex glossy objects under natural illumination fields. Each image depicted an object with the illumination field as the background, rendered at a resolution of 1,080 × 1,080 pixels. Object meshes were the Stanford Bunny and Dragon from the Stanford 3D Scanning Repository (Stanford University Computer Graphics Laboratory; http://graphics. stanford. edu/ data/3Dscanrep/). Wavefront. obj files of these models were imported into the open-source modelling software Blender (v. 2. 79) and rendered using the Cycles render engine, which is an unbiased, physically based path-tracing engine. Stimuli were illuminated by the 'kitchen' and 'campus' light fields from the Debevec Light Probe Image Gallery  57.
The saturation channel is also multiplied (by sSat or dSat). Both components are converted back to RGB and added together to yield the final image. We use an additional filter for the specific case of velvet/satin. It is applied to the specular component right after normal-based blurring. The specular component is multiplied by a first mask m 1 that is computed via nonlinear mapping of the luminance l of the specular component. We use the following formula: m 1 = f(0, l m /2, l) if l < l m /2, and m 1 = 1 - f(l m /2, l m , l) otherwise, where l m is the maximum luminance in the specular component image and f(a,b,x) is the 'smooth step' function defined in the OpenGL Shading Language that maps a ≤ x ≤ b to the [0,1] range (see https://www. khronos. org/registry/OpenGL-Refpages/ gl4/html/smoothstep.
Procedure Stimulus presentation. Stimulus presentation and data collection were controlled by a MATLAB script (release 2018b, Mathworks) using the Psychophysics Toolbox v. 3 (ref. 61). In the free-naming task, stimuli were projected onto a white wall in a classroom. Thick black cloth was used to block light from the windows, so that the only source of light came from the projected image. For all other experiments the stimuli were presented on a Sony OLED monitor running at a refresh rate of 120 Hz with a resolution of 1,920 × 1,080 pixels controlled by a Dell computer running Windows 10. Stimuli were viewed in a dark room at a viewing distance of approximately 60 cm. The only source of light was the monitor that displayed the stimuli. Task. Experiment 1 (free-naming) task. Fifteen participants gathered in a classroom and completed the task at the same time.
Two authors, A. S. C. and K. D. , were present in the room with the participants. This was a qualitative data collection session, and no specific hypothesis was considered at this stage. Participants viewed stimuli one at a time and were asked to classify the material of each stimulus, with no restrictions. They were provided with sheets of paper with space for each trial number to write down their answers. The experimenter controlled the stimulus presentation with a keyboard press. Each trial was presented for as long as it took for all participants to finish writing down their responses. A blank screen was shown for 1 second between each trial. The experiment took approximately 3 hours to complete, including breaks. The instructions, written on a sheet of paper in both English and German, were as follows: You will be shown 270 images, and your task is to write down your impressions of the material of each object; that is, what does it look like it is made of? Below are some suggestions of materials that might help prompt you.
Acknowledgements A. C. S. and K. D. are supported by a  Sofja Kovalevskaja Award  endowed by the  German Federal Ministry of Education , awarded to K. D. K. D. was also supported by The  Adaptive Mind , a research cluster funded by the  Hessian Ministry of Higher Education, Research, Science and the Arts, Germany. A. C. S. was also supported by a  Walter Benjamin Fellowship  funded by the  German Research Foundation  (DFG). P. B. is supported by  l'Agence nationale de la recherche (ANR)  project  VIDA  ( ANR-17-CE23-0017 ). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. We thank  M. Panayi ,  C. Baker ,  F. Schmidt  and  K. Gegenfurtner  for helpful feedback on earlier versions of this manuscript.
Data availability Psychophysics data and stimuli used in the experiments are available on Zendo: https://doi. org/10. 5281/zenodo. 5080227. The 3D meshes of the bunny and dragon objects were obtained from the Stanford 3D Scanning Repository and can be found under the following link: http:// graphics. stanford. edu/data/3Dscanrep/. Source data are provided with this paper.