dataesr/scientific-paragraphs-categorization huggingface dataset:
Of these four methods, lassosum explained the highest proportion of variance (R 2 ) for maternal BMI in both ALSPAC and BiB (which we refer to as the target datasets); therefore, we used the lassosum PRS for subsequent MR analyses. Lassosum requires summary statistics from a genome-wide association study (GWAS), which we refer to as the base dataset. We conducted a GWAS in the UK Biobank (UKB), a prospective cohort of 502,628 volunteers (with 5% response rate of those invited), recruited from across the UK at age 40-69 years between 2006 and 2010  [58, 82]  (Additional file 1: Supplementary information S11). In order to avoid overfitting due to overlap between the base and target samples, we excluded attendees of the Bristol (where ALSPAC participants would have attended) or Leeds (where Born in Bradford participants would have attended) UKB assessment centres.
We meta-analysed the summary statistics from the UKB GWAS with a published BMI GWAS from the GIANT consortium  [60, 83] , giving a total base sample size of up to 756,048. We applied the lassosum algorithm to the meta-analysed base dataset; lassosum uses penalised regression to carry out shrinkage and selection on the base GWAS SNP effects and accounts for LD information from a reference panel. We used the ALSPAC or BiB datasets as the reference panels as per the authors' recommendations)  [53]. PLINK was used to calculate the PRS for ALSPAC and BiB individuals using the lassosum SNP weights for around 80,000 SNPs (see Table  2  for the exact number of SNPs for each cohort). We also calculated three PRS from fewer SNPs, to be used in sensitivity analyses to explore potential pleiotropic effects (we would expect that the risk of pleiotropic bias might decrease as fewer SNPs are included in the IV; see below).
We also tested for evidence of between-SNP MR estimate heterogeneity (Cochran's Q test) and used MR Egger regression  [74]  to investigate horizontal pleiotropy, for the analyses based on GWS SNPs. Finally, to investigate collider bias and bias due to assortative mating, we examined the association between the maternal and paternal lassosum BMI PRS, in the subset of ALSPAC participants with paternal genotype data available (N = 1325).
a Sex-adjusted percentile calculated using the WHO Child Growth Standards  [89]. All z scores and percentiles were calculated via the zanthro Stata package  [90]. Data for absolute values (as opposed to z scores) are presented in Additional file 1: Supplementary information S18 (Additional file 1: Supplementary information S17). Additional adjustment for gestational age at birth or 20 genetic PCs had a negligible effect (Additional file 1: Supplementary information S25, S26), and there was not a large difference when BMI or ponderal index at birth was substituted for BW in ALSPAC (Additional file 1: Supplementary information S27).
In linear regression analyses, we found moderate to strong evidence for associations between the maternal non-transmitted allele BMI PRS and offspring adolescent adiposity (including BMI and DXA-determined FMI). These observed maternal genetic effects merit further investigation in other datasets, particularly as previous studies have not found evidence for parental genetic effects on BMI in childhood  [96]  or adulthood  [97]. In ALSPAC and BiB White Europeans, we observed associations between the maternal BMI PRS and potential confounders, including parental occupation, education, age and maternal smoking. These results invite careful consideration of which of the ever-increasing number of GWS associated BMI SNPs are likely (in Fig. 4  Mean difference in offspring FMI (SD) per 1SD increase in maternal BMI, from MR models using different SNP sets and confounder adjusted multivariable regression (MV) models.
Strengths and limitations Our study has several key strengths. We studied two prospective birth cohorts with maternal and offspring genomewide genotype data, maternal BMI measurements and offspring adiposity outcomes available, allowing us to conduct mother-offspring MR analyses. We used state-of-the-art methods to calculate a powerful PRS from around 80,000 SNPs. This yielded a substantial increase in statistical power over previous MR studies, which analysed similar ALSPAC datasets to ours, but employed either a single SNP in the FTO gene  [14]  or allele scores calculated from up to 97 SNPs  [23, 24]  (similar to the "Speliotes" and "Locke" IVs in the present analysis). Our primary lassosum PRS explained 3-7% of maternal BMI variance, compared to ~1. 5% for the strongest IVs used previously (power calculations are given in Additional file 1: Supplementary information S34).
We acknowledge several limitations of our study. First, although the results in BiB and ALSPAC were similar, replication in other cohorts with suitable data, and in particular with adolescent adiposity measures (which we could only examine in ALSPAC) would be valuable. A previous study meta-analysed data from ALSPAC and the Generation R cohort using 32 maternal SNPs  [23] , but we were unable to extend our approach to Generation R due to the unavailability of maternal genomewide SNP data. Additional file 1: Supplementary information S35 compares the present analysis to previous analyses of ALSPAC data. We have only studied UK participants. However, the similarity of findings between White European and South Asian BiB participants, and between BiB (a cohort with high levels of deprivation born during the obesity epidemic) and ALSPAC (more affluent than the UK average) suggest that our findings may be generalisable to other populations.
Second, BMI (especially self-reported BMI) is an imperfect proxy measure for adiposity. However, it has been shown previously in ALSPAC that self-reported pre-pregnancy BMI is strongly correlated with BMI measured in early pregnancy  [23] , and that any misreporting does not markedly differ by mean weight  [98]. There is also evidence that the correlation with directly measured adiposity is strong for child and adult BMI  [99, 100]  and moderate for neonatal weight  [101] ; furthermore, our results were similar for DXA derived FMI. Third, we assumed that causal relationships between exposures and outcomes were linear. Although our data provided no evidence for non-linearity, a slight plateauing of the observational association between maternal BMI and offspring child/adolescent BMI at higher maternal BMI levels was previously observed in a large meta-analysis  [6].
Availability of data and materials The data that support the findings of this study are available from the ALSP AC, BiB and UK Biobank executives, but restrictions apply to the availability of these data, which were used under licence for the current study, and so are not publicly available. The ALSPAC study website (http://www. bristol. ac. uk/ alspac/researchers/our-data/) contains details of all the data that are available through a fully searchable data dictionary and variable search tool. Scientists are encouraged and able to use BiB data. Data requests are made to the BiB executive using the form available from the study website: http://www. borninbradford. nhs. uk (please click on 'Science and Research' to access the form). Guidance for researchers and collaborators, the study protocol and the data collection schedule are all available via the website.
Acknowledgements We thank  Tom Palmer ,  Eleanor Sanderson  and  Stephen Burgess  for helpful discussions;  Mark Iles  for provision of BiB ethnicity variables; and  Amanda Hill  and  David Hughes  for support in delivery and management of the ALSPAC data. We gratefully acknowledge the  GIANT (Genetic Investigation of ANthropometric Traits) Consortium  for making GWAS summary statistics available. This publication is the work of the authors and may not reflect the views of those acknowledged here. Where authors are identified as personnel of the  International Agency for Research on Cancer / World Health Organization , the authors alone are responsible for the views expressed in this article and they do not necessarily represent the decisions, policy or views of the  International Agency for Research on Cancer / World Health Organization.
Further support for genome-wide data is from the  UK Medical Research Council  ( G0600705 ) and the  National Institute of Health Research  ( NF-SI-0611-10196 ). The work presented here was supported by the  US National Institute of Health  ( R01 DK10324 ), the  European Research Council  under the European Union's  Seventh Framework Programme  ( FP7/2007-2013) / ERC  grant agreement (669545) and the  British Heart Foundation  ( AA/18/7/34219 ). TAB is supported by the  Medical Research Council (MRC) (UK)  ( MR/K501281/1 ); TAB and DME are supported by the  NHMRC (Australia)  ( GNT1183074  and  GNT1157714 ); DAL, TAB,  MCB  and RCR work in/are affiliated with a unit that is supported by the  UK Medical Research Council  ( MC_UU_00011/1 & MC_UU_00011/6 ); and DAL is a  British Heart Foundation Chair  ( CH/F/20/90003 ) and  NIHR Senior Investigator  ( NF-0616-10102 ).
DAL,  MCB  and TAB are supported by the  British Heart Foundation   Accelerator Award  at the  University of Bristol  ( AA/18/7/34219 ). MCB's contribution to this work was supported by a  UK MRC   Skills Development Fellowship  ( MR/P014054/1 ). VK is funded by the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Sklodowska-Curie grant  ( 721567 ). MRJ is funded by  EU-H2020   LifeCycle Action  ( 733206 ) which also supports DAL's research,  EU-H2020 EDCMET (825762 ),  EU-H2020 EUCAN Connect (824989 ),  EU   H2020-MSCA-ITN-2016   CAPICE Marie Sklodowska-Curie grant  ( 721567 ) and the  MRC (UK)  ( MRC/ BBSRC MR/S03658X/1  [JPI HDHL]). RCR is a  de Pass Vice Chancellor's Research Fellow  at the  University of Bristol. AL and MRJ are supported by the  MRC (UK)  ( MR/M013138/1 ) and the  European Union Horizon   2020  programme.
However, the enormous extent of this habitat and its remote location under several kilometers of water means that only a minute proportion has ever been sampled. Most previous studies have focused on morphological analyses of the macro-and mega-fauna, which typically show high levels of a diversity and small-scale faunal patchiness  (9) (10) (11) , and have recently been proposed as biological indicators for deep-ocean monitoring and conservation  (12). Less attention has been paid to the microbial and meiofaunal organisms that numerically dominate DOS communities  (13, 14)  but can hardly be identified using classical morphotaxonomic approaches. Studying planktonic organisms sinking to DOS is hampered by similar technical limitations related to great depths (resulting in poor spatial coverage of sinking plankton datasets) and limited morphological identification (but for shell-building taxa that can keep distinctive features once in the sediment).
Here, we tackle these problems by comparing a newly generated, global-scale DNA metabarcoding dataset of total eukaryotic diversity from deep oceanic surficial sediments (418 samples collected during 15 oceanographic cruises from 2010 to 2016; table  S1 ) to comparable published datasets from euphotic (1160 samples from the Tara Oceans expeditions)  (33, 34)  and aphotic (138 samples from the Tara Oceans and Malaspina expeditions)  (35)  zones across the world ocean. Together, these represent the first consistent molecular meta-dataset spanning the three main open-ocean realms (pelagic euphotic, pelagic aphotic, DOS) at a global scale across 447 sampling sites (Fig. 1A ). We assembled ~2. 42 billion DNA reads (table  S2 ), produced by polymerase chain reaction (PCR) amplification of the V9 region of the 18S ribosomal RNA gene, and processed them using the DADA2 workflow to infer amplicon sequence variants (ASVs).
RESULTS AND DISCUSSION Eukaryotic diversity from the ocean surface to the DOS We obtained a total of 242,465 eukaryotic ASVs represented by ~1. 95 billion DNA reads (Fig. 1B ). Only 3806 (1. 6%) of these ASVs were detected in all three realms, while 6382 pelagic ASVs were detected in DOS. These ASVs were assumed to correspond to sinking pelagic organisms, mainly plankton, although 29 ASVs (representing 1. 29% of the reads of these ASVs) could be ascribed to nekton (e. g. , dead vertebrates), which also contribute to the downward flux of organic matter. From the metazoan fraction of sinking pelagic organisms, we curated benthic animals with known meroplanktonic larvae (224 ASVs; see Materials and Methods). The number of ASVs found exclusively in DOS, here assumed to correspond to indigenous deep-sea benthic organisms, was comparable to that found in the pelagic realms, although there were 25 times more pelagic DNA reads in our meta-dataset (Fig.
1B ). To account for this variation in sequencing effort, we subsampled each aggregated dataset per realm 1000 times at identical sequencing depths (1 Mio reads) and analyzed the diversity of ASV, together with their distribution and abundances within and across the pelagic (euphotic and aphotic zones) and DOS (sinking pelagic and benthic organisms; fig. S1 ) realms. This indicated that, although nearly half of eukaryotic DNA reads represent sinking planktonic ASVs, the ASV richness in the DOS could be more than three times higher than in pelagic habitats, with more than 60% of ASVs being exclusively benthic. The unique size fractionation of the pelagic samples from the Tara Oceans dataset has a strong effect on a diversity (richness and evenness) and b diversity (compositional variation) measures (fig.
2A ; see fig. S4  for relative abundances and table  S3  for details). While diversity in the euphotic zone is dominated taxonomically by Alveolata (30. 8%), notably dinoflagellates (Dinophyceae, 19. 4%), the aphotic zone is extremely rich in Diplonemea [46. 5%; see  (37) (38) (39) ], mainly heterotrophic nanoflagellates in the family Eupelagonemidae (46. 2%). The taxonomic composition of the deep, exclusively benthic, eukaryotic assemblage is very different, comprising various groups that do not occur or rarely occur in the water column (fig. S5  and table  S3 ), e. g. , Dactylopodida amoebae (6. 5%), Chromadoria nematodes (5. 3%), Monothalamid foraminifera (4. 4%), and Oligohymenophorea ciliates (3. 7%). Nearly two-thirds (60. 1%) of the benthic eukaryotic ASVs (representing 47. 8% of the reads) could not be taxonomically annotated using current reference taxonomic databases and a similarity cutoff of 85%, and many of them matched a reference sequence with less than 80% similarity (Fig.
6% of the reads) is much lower than for benthic diversity (47. 8% of the reads). Many of these unassigned pelagic ASVs may thus correspond to rare unknown eukaryotes or rare intraspecific/intragenomic variants of known eukaryotes with unusually high polymorphism  (41). Among the known taxa (39. 9% of benthic ASVs and 52. 2% of benthic reads), our data for selected typical deep benthic macrofaunal and meiofaunal groups show that some are relatively well represented in the current databases (e. g. , polychaetes and nemerteans; fig. S7 ), while others remain poorly represented (e. g. , foraminifera and nematodes).
Metakinetoplastina Diplonemidae 97. 7% 95. 7%   S3  for details). The number of ASVs and their relative abundance in the sediment are shown for ASVs of pelagic origin, as well as those derived from indigenous benthic taxa. (B) Number of ASVs (represented as density) as a function of their similarity with the best hit with a reference sequence in the PR 2 database. The peaks in density for each realm are highlighted on the plot, and the corresponding similarity level with reference sequences is indicated. (C) Cumulative proportion of ASVs and read abundance as a function of their similarity with the best hit with a reference sequence in PR 2. The red vertical lines indicate the similarity cutoff (85%) for taxonomic annotation used in this study. 13  taxonomic groups of the pelagic euphotic zone tend to be more geographically widespread, while benthic groups tend to be less widespread than expected by neutral models (fig.
Last, we attempted to correlate our global DOS plankton biodiversity dataset to yearly average POC export from the surface and the fraction of it reaching the DOS, as estimated, respectively, by thorium-derived export measurements, modeled at a global scale  (58) , and by evaluating the efficiency of POC transfer through the water column based on sediment trap POC flux data, net primary production estimates, and sea surface temperatures  (59). Overall, the pelagic ASVs detected in the DOS represent 21. 4% of the DNA reads obtained from sediment samples. The proportion of DNA reads of pelagic origin in the sediment follows an increasing trend from low to high latitudes (Fig. 5A ). This proportion also broadly approximates POC export from the surface (R 2 = 0. 23, P < 0. 001), despite the higher remineralization rates at productive high latitudes  (60).
Toward a holistic view of ocean biodiversity and ecosystem processes Our global molecular meta-dataset from the ocean surface to the DOS provides the first unified vision of eukaryotic biodiversity patterns across the three dimensions of the world ocean (Fig. 1 ). It shows that the DOS is an extremely rich and unique realm with a strong connection to the water masses above that is reflected in the pelagic DNA signature (Figs. 1, 4 , and 5 and fig. S11 ). Although focused on smaller-sized organisms (eukaryotic microbes and meiofauna), these DNA-based results are broadly consistent with morphological evidence from larger animals for high deep-sea benthic a diversities and small-scale patchiness (high local species turnover)  (9) (10) (11). The DOS appears to be much more diverse than oceanic waters (Fig.
15006165). PhiX DNA spike-in was adapted for some libraries (20% instead of 1%) to minimize the loss of data due to low nucleotide diversity at the beginning of the sequencing run. Libraries were sequenced on HiSeq4000 or HiSeq2500 instruments (Illumina) on a paired-end mode. The raw sediment sequencing data have been deposited to the European Nucleotide Archive (ENA) under project accessions PRJEB33873 (eDNAbyss) and PRJEB48517 (deep_sea).
Public 18S-V9 rDNA sequencing datasets We gathered published datasets (table  S2 ) targeting the V9 hypervariable region of the eukaryotic 18S rRNA gene and using the same PCR primers pair (1389F and 1510R) used here for the DOS samples. These datasets were produced by studies sampling the euphotic/ aphotic zones  (33) (34) (35) (67) (68) (69)  and the DOS  (30, 67).
Environmental variables Although some environmental variables and sediment descriptors were collected during the oceanic expeditions from which we collected sediment samples, their heterogeneity led us to extract more homogeneous environmental layers from the Global Marine Environment Datasets (http://gmed. auckland. ac. nz) to standardize our concatenated dataset across multiple studies. These variables included the surface calcite (calcite, in mole per cubic meter), surface nitrate (nitrate, in micromole per liter), surface silicate (silicate, in micromole per liter), surface phosphate (phosphate, in micromole per liter), average photosynthetically active radiation (PAR_mean, in Einstein per square meter per day), surface pH, average sea surface temperature (sst_mean, in celcius), variation in sea surface temperature (sst_range, in celsius), average surface currents strength (srf_current, in meter per second), primary production (primprod, in mgC•m 2 /day/cell), average stock of particulate inorganic carbon (PIC_mean, in mole per cubic meter), average stock of POC (POC_ mean, in mole per cubic meter), total suspended matter (tsm_mean, in grams per cubic meter), seabed slope (slope, degree), seabed nitrate (sb_nitrate, in micromole per liter), seabed silicate (sb_silicate, in micromole per liter), seabed-dissolved oxygen (sb_o2dissolve, in milliliter per liter), seabed-utilized oxygen (sb_o2utilized, in milliliter per liter), seabed temperature (sb_temp, in celsius), seabed salinity [sb_salinity, practical salinity scale (PSS)], and average temperature in the water column (wat_col_temp, in celsius).
Raw sequencing data processing For the deep_sea dataset, the sequencing libraries were demultiplexed using Double Tag Demultiplexer (DTD) software (https://github. com/yoann-dufresne/DoubleTagDemultiplexer) to screen the R1 and R2 files of each library and retrieve unique tag-encoded primer combinations associated to each sample (allowing no mismatches). We thus produced pairs of fastq files for each sample. For all other illumina datasets (see table  S2 ), we obtained at least one pair of fastq files (paired-end) per sample (some samples were sequenced several times to obtain enough reads that were subsequently merged before statistical analysis). For the datasets produced with the 454 sequencing technology, we obtained one fastq file per sample. We used two R scripts (for paired-end illumina datasets and for 454 dataset, see the "rds_pipeline_illumina.
R" and "rds_pipeline_454. R" scripts) to process all the fastq files by batch of 10 samples per job on a High-Performance Computing cluster (Baobab, University of Geneva). The R scripts implemented the key steps of the DADA2 workflow  (71)  and additional quality filtering steps (see below). The scripts performed the quality filtering with the filterAndTrim function of the DADA2 v1. 12. 1 R package with default settings, the trimming of primers using the cutadapt v2. 4 software  (72) , the filtering of any read that still contain traces of primers (fastqFilterPrimersMatchs function in the fastqUtils. R script), the filtering of any read below 20 bp (fastqFilterWidth function in the fastqUtils. R script), the training of errors models using the learnErrors function of DADA2 with default settings, the inference of ASVs using the dada function with default settings (but for the 454 data, for which we used the HOMOPOLYMER_GAP_PENALTY = -1, BAND_SIZE = 32 options, as recommended by the DADA2 package developing team), and the merging for the overlapping paired-end reads using the mergePairs function with the option "trimOverhang.
Combining the datasets into a single ASV-to-sample table, taxonomic and functional annotations, matrix curation We reimported the rds files into R to build an ASV-to-sample table. We first produced an ASV table per dataset, filtered chimeric ASVs with the option "consensus" within each dataset, and aggregated the replicated libraries per biological sample for the deep_sea, tara, and tara_polar datasets. We also aggregated the reads obtained from DNA and RNA libraries generated for each deep_sea sediment samples, since a comparative analysis revealed that the diversity and biogeographic patterns of eukaryotic communities are mostly similar between DNA and RNA [fig. S12 ; in line with  (29, 73) ]. We filtered the ASVs detected in the negative controls of the eDNAbyss dataset across the eDNAbyss ASV table.
Last, we concatenated each datasetbased ASV-to-sample table into a single one and exported all the ASV sequences into a fasta file for taxonomic annotations. We used the "assignment-fasta-vsearch" module of the SLIM v0. 6 software (74) that wraps the vsearch v2. 2. 2 software  (75). The ASVs were compared to a custom version of PR 2 (33) that focus on the 18S V9 region and that include functional annotations (available at https:// doi. org/10. 5281/zenodo. 3768950) and with the SILVA v138 database  (76). Taxonomic annotations were the consensus among up to three candidate reference sequences that are above 85% similarity with the query or directly assigned to the reference sequence if the query had a similarity of at least 99%. We also performed another search without restricting a minimum similarity threshold to match an entry reference sequence in the custom V9 version of PR 2 , to identify non-18S V9 sequences.