dataesr/scientific-paragraphs-categorization huggingface dataset:
10). GCTA-based genetic correlation analysis within those individuals having both handedness and brain imaging data (N = 32 774) showed similar results (Ps > 0. 10). This was also the case using twin-based co-heritability analysis in the HCP dataset, that is, no significant genetic correlations of handedness and brain skews (Ps > 0. 10). Brain size-related scaling factors showed high heritabilities in both the UK Biobank (ScalesAvg: h 2 = 72. 11%; ScalesX: h 2 = 56. 35%; ScalesY: h 2 = 46. 28%; and ScalesZ: h 2 = 60. 36%) and the HCP (ScalesAvg: h 2 = 92. 1%; ScalesX: h 2 = 85. 5%; ScalesY: h 2 = 85. 0%; and ScalesZ: h 2 = 86. 6%), which was expected because of the high heritability of brain size (UK Biobank: h 2 = 72. 3%; HCP: h 2 = 87. 8%). There were no significant genetic correlations between these scaling factors and brain skew measures (Ps > 0.
Discussion We carried out the largest-ever analysis of global brain shape asymmetry, that is, the horizontal and vertical asymmetry skews, in 3 independent datasets. The largest of the datasets comprised over 39 000 participants. At the population level, there was an anterior and dorsal skew of the right hemisphere, relative to the left. The population variances of these skews were largely independent, but both showed replicable associations with handedness, which establishes a link between lateralized structure and function of the human brain. The 2 skews also showed associations with multiple regional gray and white matter metrics, as well as various phenotypic variables including cognitive functions, sociodemographic physical, and mental health measures. The 2 skews showed SNP-based heritabilities of 4-13% depending on the method used to assess this, but no significant loci were found in GWASs, probably due to substantial polygenicity together with relatively low heritability.
Global Brain Asymmetry We measured the anterior-posterior and superior-inferior aspects of global left-right asymmetry in the human brain, using 3 population datasets and an automated, registration-based approach. The approach contrasts with older, manual methods for evaluating global asymmetry, or approaches using regionally restricted frontal and/or occipital hemispheric differences as proxies for overall torque (e. g. ,  LeMay 1976; Bear et al. 1986; Barrick et al. 2005; Van Essen et al. 2012; Maller et al. 2014; Fullard et al. 2019). Rather, the registration-based approach of the present study allowed an automatic and objective assessment of global asymmetry based on skewing transformation of the brain as a whole. This provides a truly global measure of left-right asymmetry in the human brain.
Given that global asymmetry measures correlated with both regional gray matter volumes and white matter properties, our analysis does not support a previous suggestion that brain torque is driven by white but not gray matter  (Allen et al. 2003). In addition, how much of the total variance of the global asymmetry could be explained by these regional measures together would be an interesting question to pursue in future studies, for example through machine learning with feature selection and cross-validation approaches. An aspect that was inconsistent across datasets in the present study was the relationship between individual differences in the horizontal and vertical components of global asymmetry. Specifically, a positive correlation between horizontal and vertical skew of 0. 074 was found in the UK Biobank, but a negative correlation of similar magnitude was observed in the HCP, and no significant correlation was present in the BIL&GIN.
Given that the UK Biobank was by far the largest of the 3 datasets, a low, positive correlation between the vertical and horizontal skews is likely to be the true population pattern. Regardless, it is clear that the 2 components of global brain asymmetry are largely independent in their variabilities. Therefore, consideration of these 2 distinct aspects of global brain asymmetry will be important in future studies of their functional significances and phenotypic associations. In general, our results are consistent with other literature which indicate that multiple different asymmetries of the brain can vary largely independently of each other  (Liu et al. 2009; Renteria 2012; Mazoyer et al. 2014; Kong et al. 2018).
Global Brain Asymmetry and Handedness We found significant associations of both horizontal and vertical skews with handedness, or the strength of hand preference. On average, left-handers showed relatively lower horizontal asymmetry scores than right-handers, that is, reduced asymmetry along the anterior-posterior axis, and higher vertical asymmetry scores, that is, increased asymmetry along the dorsal-ventral axis. The effect sizes were small, for example, in the UK Biobank: Cohen's d = 0. 09 for the handedness association with horizontal skew, and d = 0. 17 for vertical skew. However, particularly in the large UK Biobank dataset, the significance levels were unambiguous despite the subtle effects (P value as low as 5. 00e-20 for handedness with vertical skew). As regards the horizontal component, previous results with regard to handedness have been mixed, with some studies finding an association of handedness with brain torque  (LeMay 1976 (LeMay , 1977;; Galaburda et al.
1978; Le May and Kido 1978) , and others not  (Kertesz and Geschwind 1971; Chiu and Damasio 1980 ; Koff et al. 1986 ; Narr et al. 2007). All 3 of our datasets showed the same direction of effect for the association between handedness and horizontal skew, although limited statistical power to detect this effect in the BIL&GIN dataset (N = 453) is likely to explain that the association was not significant in this specific dataset. For the vertical skew, the association with handedness was again consistent in direction across all 3 datasets, and also significant in all 3 datasets. The association of handedness with the vertical component of global brain asymmetry is a novel finding, as far as we are aware. Given the small effect sizes in our study, it is clear that inferring the handedness of individuals from their global brain asymmetry is unlikely to be possible.
,  Chiu and Damasio 1980; Koff et al. 1986; Narr et al. 2007). We found no significant genetic correlations between either aspect of global brain asymmetry and handedness in the UK Biobank or HCP datasets. This suggests that genetic factors influencing global brain asymmetry are largely dissociable from those affecting handedness, and therefore that environmental factors, such as early life experiences, may play a more predominant role in causing the associations of handedness with global brain asymmetry measures. Note that re-handed individuals could be present in the UK Biobank, as it comprises adults with average age over 60 years. No explicit data were collected on re-handing. However, we have previously reported how the rate of left-handedness decreases with increased age in the UK Biobank, which likely relates to more frequent re-handing in those born earlier  (de Kovel et al.
2009). Possible mechanisms may include inter-hemispheric differences in neural pruning  (Fullard et al. 2019 ), axon tension (Van Essen 1997) , and/or ventricular cerebrospinal fluid volume  (Maller et al. 2014 ) during neural development. Establishing the genetic contributions to brain asymmetry would help elucidate the developmental origins of this trait, as well as potentially its evolution, and the neural basis of functional lateralization. In the present study, we found heritabilities of 4-13% for the 2 global brain asymmetry measures in the UK Biobank and HCP datasets. This is lower than the 10-30% heritabilities reported in a previous study of vervet monkeys using a similar registration-based approach  (Fears et al. 2011) , which might suggest a species difference in the degree of genetic control of global brain asymmetry  (Xiang et al.
2019b). In addition, gene-set enrichment analysis of the GWAS results for vertical skew tentatively implicated genes involved in neuron projection guidance. This may relate, for example, to the establishment of interhemispheric connections via the corpus callosum. Consistent with this hypothesis, a recent GWAS analysis of publicly released, imaging-derived phenotypes in the UK Biobank  (Smith et al. 2020 ; https://open. win. ox. ac. uk/ukbio bank/big40/pheweb/, database queried on 1 August 2020) found that corpus callosum measures (e. g. , fractional anisotropy of the genu of the corpus callosum, and volume of the anterior part of middle corpus callosum) showed the most significant associations annotated to PLEC. As the heritabilities of both global asymmetry measures were low in our analyses, then nongenetic factors also seem likely to influence them.
2014; Fullard et al. 2019). Within the HCP dataset, there was a positive correlation between horizontal skew and oral reading recognition ability. Language-related cognition is well known to make use of lateralized functional networks  (Fedorenko and Thompson-Schill 2014; Kong et al. 2020a ). In addition, we found evidence for genetic correlation between horizontal brain skew and ASD, which may be consistent with the subtle but widespread alterations of regional cortical thickness asymmetry in ASD reported in a recent, largescale study  (Postema et al. 2019). The automated measurement of global brain asymmetry that we have employed here will be feasible for large-scale meta-analysis-based studies of brain disorders, such as those carried out within consortia such as ENIGMA (http://enigma. ini.
), confirming a population-level pattern of global asymmetry. For horizontal skew, the average pattern involved protrusions of the right frontal and left occipital regions (UK Biobank: N = 39 678, Mean = 0. 0110, Std = 0. 025; HCP: N = 1113, Mean= 0. 0054, Std = 0. 014; and BIL&GIN: N = 453, Mean = 0. 011, Std = 0. 017). One sample t-testing indicated a significant difference of each dataset mean from zero (UK Biobank: t(39 677) = 86. 78, P < 5. 00e-100, Cohen's d = 0. 44; HCP: t(1112) = 12. 56, P = 5. 96e-34, Cohen's d = 0. 38; and BIL&GIN: t(452) = 14. 14, P = 7. 68e-38, Cohen's d = 0. 67). The population average horizontal skew matches the widely-observed features of brain torque (e. g. , frontal/occipital petalia) in the human brain (see Introduction and e. g. , Toga and Thompson 2003).
Regarding the vertical asymmetry skew, the sample means were again all positive (UK Biobank: Mean = 0. 0103, Std = 0. 0158; HCP: Mean = 0. 011, Std = 0. 015; and BIL&GIN: Mean = 0. 0097, Std = 0. 012), indicating an average pattern involving downward skewing of the left hemisphere relative to the right. Again the means were significantly different from zero in each dataset Downloaded from https://academic. oup. com/cercor/article/31/9/4151/6218944 by Max-Planck-Institute for Psycholinguistics user on 16 August 2021.
Data and Code Availability For use of UK Biobank data, application must be made via http://www. ukbiobank. ac. uk/register-apply/. The HCP data are available via https://www. humanconnectome. org/. The BIL&GIN data sharing is based on a collaborative model: http://www. gin. cnrs. fr/BIL&GIN. All analyses were carried out as described in the methods. Software versions and relevant parameters are included in the corresponding methods sections. Scripts are available from the authors upon request.
While genome sequencing and assembly are now routine, we do not have a full, precise picture of polyploid genomes. No existing polyploid phasing method provides accurate and contiguous haplotype predictions. We developed nPhase, a ploidy agnostic tool that leverages long reads and accurate short reads to solve alignment-based phasing for samples of unspecified ploidy (https://github. com/ OmarOakheart/nPhase). nPhase is validated by tests on simulated and real polyploids. nPhase obtains on average over 95% accuracy and a contiguous 1. 25 haplotigs per haplotype to cover more than 90% of each chromosome (heterozygosity rate ≥ 0. 5%). nPhase allows population genomics and hybrid studies of polyploids.
Phasing pipeline and strategy We developed the nPhase pipeline, an alignment-based phasing method and associated algorithm that run using three inputs: highly accurate short reads, informative long reads, and a reference sequence. The pipeline takes the raw inputs and processes them into data usable by the nPhase algorithm. Unlike other existing methods, our algorithm is designed for ploidy agnostic phasing. It does not require the user to input a ploidy level and it does not contain any logic that attempts to estimate the ploidy of the input data. The idea at the core of the algorithm is that if you iteratively cluster the most similar long reads and groups of long reads together, you will naturally recreate the original haplotypes. For the first step of the pipeline, the long and short reads are aligned to the reference, then the aligned short reads are variant called to identify heterozygous positions and generate a high-quality dataset of variable positions (Fig.
1 ). Each long read is then reduced to its set of heterozygous SNPs according to the previously identified variable positions. We also collect long-read coverage information to allow the level of representation of a haplotype in the data to influence its likelihood of being properly phased (see the "Methods" section). The reduced long reads and the coverage information are then passed onto the nPhase algorithm, an iterative clustering method. On the first iteration, nPhase clusters Fig. 1  nPhase pipeline and verification process. a The nPhase pipeline requires three inputs: a long-read dataset, a short-read dataset, and a reference genome sequence. Both sequencing datasets are mapped to this reference genome; then, the short reads are variant called in order to identify heterozygous positions.
The long reads are reduced to only their heterozygous positions, and this set of linked heterozygous positions is phased by the nPhase algorithm and outputs phased haplotypes. b In parallel with running the virtual polyploids through the nPhase pipeline, we map the original strains to the same reference and variant call them to identify their haplotypes. This generates the true-positive dataset against which we will compare the haplotypes predicted by nPhase in order to assess the accuracy of our algorithm together the two most similar long reads, then it checks that the cluster identities are maintained, i. e. , it checks that merging these two long reads together does not significantly change the information they each contain individually, and finally, it generates a consensus sequence representative of the group of these two reads.
The consensus sequences that represent clusters are treated just like aligned long reads and continue to be clustered. Cluster identity maintenance: when all remaining clusters are very different from each other, they are not allowed to merge; this is to prevent the algorithm from always outputting only one cluster per region. The remaining clusters and their consensus sequences should correspond to the haplotypes present in the original dataset a tie, then we select a pair randomly. By default, the algorithm will not attempt to merge two sequences with less than 1% similarity. The pair that was selected now forms a cluster of two reads (Fig. 2b ). In order to continue this iterative algorithm, we need to define a way to calculate the similarity between a read and a cluster of reads, and the similarity between two clusters of reads.
Validation of the nPhase algorithm by combining reads of non-heterozygous individuals To test and validate the performance of nPhase, we decided to combine sequencing datasets of haploid and homozygous diploid organisms into virtual polyploid datasets. We selected four natural S. cerevisiae isolates as the basis for our virtual genomes: ACA, a haploid strain, and three homozygous diploid strains: CCN, BMB and CRL (Additional file 1: Table  S1 ). These four strains have different ecological and geographical origins and are sufficiently distinct from each other to allow us to evaluate the performance of nPhase at heterozygosity levels of up to 1% of the genome  [24]. We sequenced these strains using an Oxford Nanopore long-read sequencing strategy and obtained Illumina short-read data from our 1011 yeast genomes project  [24].
Since these strains do not have any heterozygosity, we could map their short reads to the Saccharomyces cerevisiae reference genome and variant call them to obtain their haplotypes (Fig. 1 ). We then used these haplotypes as a truth set to assess the performance of nPhase. With this truth set, we tested the influence of dataset characteristics: coverage, ploidy, heterozygosity level, and the inclusion or exclusion of reads that map to distant regions of the genome, hereafter described as split reads. We also investigated the influence of parameters that modulate the behavior of the nPhase algorithm: minimum similarity, minimum overlap, and maximum ID change (for a description of them, see the available parameters in the "Methods" section). To assess the influence of ploidy, we used the three constructions of the different virtual genomes previously mentioned.
We also randomly sampled 6250, 12,500, 62,500, and 125,000 heterozygous SNPs from each virtual genome to simulate datasets where 0. 05%, 0. 1%, 0. 5%, and 1% of the positions in the genome are heterozygous. This equates to three different ploidies and four heterozygosity levels, or 12 polyploid genomes to test. By running a total of 6000 validation tests on varying ploidy, heterozygosity, and coverage levels exploring the parameter space, we determined default parameters of nPhase (see the "Methods" section). According to these tests, the parameters that result in optimal results in terms of accuracy and contiguity are the following: 1% minimum similarity, 10% minimum overlap, and 5% maximum ID (see the "Identifying optimal parameters" section in the "Methods" section). We then ran nPhase with these default parameters on our previously described optimal datasets of varying ploidy (2n, 3n, and 4n) and heterozygosity levels (0.
The same representation is available for the other datasets of different ploidy and heterozygosity levels (Additional file 2: Fig. S1 ). Across the 12 phased genomes with variable ploidy and heterozygosity levels, we noted little variation in terms of contiguity as we obtained between 2. 4 and 4. 3 haplotigs per haplotype (Fig. 4a ). At a heterozygosity level of 0. 05%, the least contiguous genomes are observed with around 4 haplotigs per haplotype (Fig. 4a ). The triploid genomes decrease to around 3 haplotigs per haplotype for heterozygosity levels greater than 0. 1% (Fig. 4a ). The tetraploid tests continue the trend of higher ploidies becoming more stable and contiguous as the heterozygosity level increases, dropping to 3. 1 haplotigs per haplotype at the 0. 1% heterozygosity level and then stabilizing at 2.
A value of 0 for this parameter means that WhatsHap polyphase will generate the most contiguous results possible, and 5 means that it will generate the most accurate results possible. The performance of WhatsHap polyphase was measured in terms of switch error rate and N50 block lengths. Instead, we will talk about accuracy and average number of haplotigs per haplotype, two metrics that are more direct representations of the performance of the algorithms and answer two important questions: "How reliable are the results?", i. e. , what are the proportions of accurate, erroneous and missing calls? And "How informative are they?", i. e. , by how many haplotigs is each haplotype represented? nPhase and WhatsHap polyphase were both applied to our 20X test datasets of different ploidy and heterozygosity levels.
4 haplotigs per chromosome per haplotype (Fig. 5b ). The highly accurate WhatsHap polyphase sensitivity levels (5 and 4) output their results in a highly discontiguous 258. 7 and 88. 9 haplotigs per haplotype, respectively. In order to output results of similar contiguity to nPhase, WhatsHap polyphase must sacrifice accuracy and drop to a sensitivity level of 1 or 0, which output 2. 5 and 0. 9 haplotigs per chromosome per haplotype, respectively. This tradeoff between accuracy and contiguity performed by WhatsHap polyphase does not appear to have a useful middle ground and nPhase demonstrates that it is not necessary to make a choice given that it simultaneously achieves both. Fig. 5  Error types and number of haplotigs for nPhase and WhatsHap polyphase. nPhase and WhatsHap polyphase were both applied to our 20X test datasets of different ploidy and heterozygosity levels.
Running the nPhase algorithm on chromosome 2 of the potato plant species Solanum tuberosum We also tested nPhase on one chromosome of a larger, more repetitive plant genome. We used the autotetraploid Solanum tuberosum (potato) dataset generated for the WhatsHap polyphase paper  [20]. We used the latest version of the DM1-3 516 R44 assembly as a reference (v6. 1)  [25]. We chose to limit this section to phasing chromosome 2. At 46 Mb, it is the shortest chromosome in the reference assembly (chromosome 1 is the largest with an 88 Mb chromosome), and is 30× larger than the longest chromosome in S. cerevisiae (chromosome 4, 1. 5 Mb). We observe a 2. 4% heterozygosity level for chromosome 2 based on Illumina data, more than twice as high as our most heterozygous test case (1%). To reduce computation time, we used a randomly sampled subset of heterozygous variants, effectively phasing at a 0.
Discussion We developed nPhase, an algorithm that relies on a few intuitive rules to process an input dataset of long reads, reduced to heterozygous positions, outputting as few clusters as possible which we have shown correspond to the true haplotypes with > 90% accuracy. By not specifying the ploidy of the sample in any step, we allow nPhase to adapt to the particularities of the dataset and do not run the risk of forcing an incorrect result to fit such an arbitrary algorithmic constraint. We provide nPhase as part of a pipeline that enables anyone to use their short-and long-read sequences of the same sample as inputs and obtain a list of SNPs and a fastQ file for each predicted haplotig. Through our validation tests, we determined that there is a set of parameters for nPhase that performs optimally in nearly all of our test cases and that the algorithm performs well even with very low levels of genetic distance between haplotypes.
Context coverage Long reads are error-prone, but it is important not to perform any form of error correction to ensure that the heterozygosity is not incorrectly flattened or mis-assigned. The nPhase pipeline works with raw long reads. In order to minimize the influence of these errors, we consider that SNP coverage is a useful indicator of quality. We count the number of times each heterozygous SNP is present in a specific context in our dataset. We define context as being the closest flanking heterozygous SNPs (two heterozygous SNPs upstream and two heterozygous SNPs downstream). The context information will be used to better inform the nPhase algorithm and allow it to escape the situation where a sequencing error randomly converts a well-supported SNP to another SNP that is well-supported in another haplotype (Additional file 2: Fig.
Output results Once nPhase is done running it outputs several files: (i). A fastQ file for each haplotig containing all of the reads that have been clustered together for this haplotig, this file can then be used with a de novo assembly or alignment tool for further analysis. (ii). A tab separated file listing the consensus base for each heterozygous position contained within each haplotig. There are three columns: chromosome, position, and consensus base. If two different bases are equally represented for a given position and equally well supported within the cluster, they will both be represented in this file on separate lines. This file is sorted by position. (iii). A plot representing the different haplotigs along the reference genome, similar to the one displayed in Fig. 3  but lacking the haplotype color code since the ground truth is not known in a typical use case of nPhase.