dataesr/scientific-paragraphs-categorization huggingface dataset:
g, representative electrocardiograms from conscious Ap4e1-mutant and C57bL/6N control mice with indication of eCG parameters and interval durations. Ap4e1 loss lowered heart rate and concurrently increased rr interval duration. Data are presented by Mouse Specifics software. which catalyzes the release of arachidonic acid from cell membrane phospholipids  11. Pla2g10 homozygous knockout mice had a lower heart rate with prolonged PR, PQ and QTc interval length, and females were predominantly affected (Fig. 1b ). Elmod1 encodes ELMO/CED-12 domain-containing 1, which acts as a GTPase-activating protein for the ARF family of small G proteins  12. Homozygous deletion of Elmod1 had no effect on heart rate but shortened the duration of PR, PQ and ST intervals. Masp1 encodes mannan-binding lectin serine protease 1, a serine protease that functions as a component of the lectin pathway of complement activation with an essential role in innate and adaptive immune responses  13.
Association of Gnao1 with VSD. To exploit the utility of our VSD dataset, we enriched our deep literature curation and the automated text mining tool 'Geneshot' (ref. 26 ) with human VSD data. This analysis identified genes known to be linked to VSDs (SIRT1 (ref. 27 ), SLC25A1 (ref. 28 ) and NXN  28  ) and genes with no direct human experimental evidence for VSDs (TMEM161B, WFDC2, SHOX2, GNAO1, UBR4 (ref. 29 ) and FURIN  30  ) apart from rare case studies that suggest association with structural heart defects for some of these genes. An example is one clinical report of a single patient with neurological disease and a STAMBP mutation with congenital malformations of the ventricular septum  31. Further evidence is found in hypermethylation of the WFDC2 promoter in fetal myocardia with VSDs 32 and association of an NKX2-5 variant, a gene suppressed by the regulator SHOX2, in a family with autosomal dominant inherited VSDs  33  and cardiac arrhythmia  34.
This analysis indicated that 178 of the 664 mouse-human orthologous genes (41 nonorthologous genes) were previously linked to heart disease, some of which functionally converge on skeletal and cardiac muscle structural integrity (for example, LDB3)  36  , cardiac development (BMP10)  37  , calcium signaling (CACNB2 and DSG2)  38, 39  , sodium channels (SCN2B)  40  and sarcomere contraction cycle (TNNC1 and MYH2)  30, 41. Notably, 486 orthologous genes had not been previously associated with a cardiac condition and likely represent unappreciated candidate genes for CVD and more yet for CHD (Fig. 3a ). Mouse genes associated with CHD phenotypes are often regulated by the transcription factor TBX20. To explore the regulatory elements most important for normal cardiogenesis and therefore likely to play a role in congenital heart defects, we conducted chromatin immunoprecipitation followed by sequencing (ChIP-seq) analysis (ChIP-X enrichment analysis (ChEA) 2016 dataset of publicly available ChIP-seq experiments  42  ).
More precisely, 40 genes with LOF mutations and one gene with a de novo mutation (UST) were aligned to data in the 100KGP, whereas 168 genes with LOF mutations and 55 genes with de novo mutations were found in the PCGC dataset (Fig. 4a ). Because of their rarity, detection of de novo LOF mutations can be a powerful way to discover unappreciated CHD risk genes 1. Here we present five representative genes causative of structural heart abnormalities in different stages of embryo cardiac development (Casz1, Dnajc18, Pde4dip, Rnf38 and Tmem161b; Fig. 4b ). The gene PDE4DIP  46  , encoding a scaffold protein, and CASZ1, a gene that encodes a zinc finger transcription factor essential for normal cardiovascular morphogenesis  47  , have been identified as both a de novo and an LOF variant in human studies and could be described as 'de novo LOF mutations'.
Of the mouse lines studied here, 65% were homozygous knockouts (corresponding to LOF in human) and 35% were heterozygous knockouts. This dataset included 248 of 705 lines (35%). Lines were confirmed to be lethal or subviable; we assessed cardiac development by analyzing three-dimensional micro-CT data obtained from iodine contrast-enhanced micro-CT that provides high spatial resolution of up to 3-14 μm per voxel from embryonic day (e)9. 5-e18. 5 embryos (http://www. mousephenotype. org/data/embryo). No embryo imaging was performed on viable knockout lines. embryo data were available for only a small subset of the total knockout genes used for this study. The VSD network included nine genes (Sirt1, Stambp, Casz1, Wfdc2, Tmem161b, Nxn, Dnajc18, Gnao1 and Slc25a) that, when LOF was induced, caused early mortality due to structural heart changes but most notably VSDs in the null mutant mice, experimentally shown by computed tomography microscopy.
Compelling evidence of experimental human or rodent data is from relevant publications, primarily peer-reviewed 'small-scale experiment' literature used in the network analysis. eCHO, transthoracic echocardiography; eCG, electrocardiography; NONe, genes not analyzed so far in the IMPC; er, endoplasmic reticulum. heterozygous mutants were compelling, indicating that this may be a previously undiscovered CHD candidate gene. UK Biobank highlights potential mouse-to-human translational targets. We further aimed to identify genes with a role in healthy and abnormal cardiac function in mice that could inform about human cardiology and CVD. We used the UK Biobank and matched our genotype-phenotype information from ECG and TTE. Matching with structural heart defects is not applicable in this database because it is inclusive of healthy individuals.
Association testing for ECG parameters was performed based on extracted data from precomputed summary statistics of the pan-ancestry UK Biobank dataset (from the Neale laboratory, http://www. nealelab. is/data) for three phenotypes: ventricular rate (a surrogate of the heart rate), P duration and QRS duration (UK Biobank phenotype codes 12336, 12338 and 12340, respectively). For all genes, we assessed both strict boundaries of each gene (genome build 37) and a longer sequence of the same gene ±500 kb. The greatest significance was observed in variants of ARHGAP24, SMARCB1, ASPA, FAM53B and ABHD17B (Supplementary Table  3a ). Further analysis of the gene sequence alone identified variants in SETBP1 (for example, 18:42,424,594[C:T]; P = 6. 393 × 10 -17 ) and VTI1A (for example, 10:114,505,465[G:A]; P = 1.
Association tests for TTE parameters were carried out based on LV end-diastolic and systolic volumes (LVedv, LVesv), LV stroke volume (SV), SV index (SV adjusted for body surface area), cardiac output and cardiac index. In addition, three additional non-cardiac parameters were included such as body surface area, age and sex for genomes from the UK Biobank database with a minimum of five of the six selected TTE parameters. Five nonsynonymous coding variants were associated with a significance threshold of P < 1 × 10 -5 (Supplementary Table  3d ), three of which are 3′ and 5′ untranslated region variants for DNAJC18 (Supplementary Table  3e ). To assess noncoding consequences, we performed an overlap of qualifying variants associated with significant expression quantitative trait loci (eQTL) for the genes of interest at P < 1 × 10 -5 for each magnetic resonance imaging (MRI) feature in each tissue from the GTEx expression database (see Methods for details).
This set of 509 genes was further queried for newness using the Online Mendelian Inheritance in Man (OMIM) catalog (https://omim. org/) and Orphanet, a rare disease portal (https://www. orpha. net/) dataset. This secondary analysis confirmed that 486 of 509 genes were unknown candidate genes and the remaining 23 genes have been associated predominantly with pleiotropic neurodevelopmental disorders that include sporadic congenital heart defects or malformations. a, Alignment of 705 IMPC knockout genes using Pharos, OMIM and Orphanet classified them into genes with a previously known link (178 of 705) or unknown link (486 of 705) to CVD or poorly annotated genes (112 of 486); cardio, cardiovascular diseases and/or heart. b, Genes previously identified with abnormal cardiac function in knockout mice (positive genes) showed strong enrichment (P = 0.
004; odds ratio, 1. 31; combined score, 7. 2) for heart-specific targets of the transcription factor TbX20, a critical regulator of heart development  [65] [66] [67] 97  , associated with human CHD and adult cardiomyopathies  68, 70, 98, 99  , while the 3,189 nonsignificant IMPC knockout genes (negative genes) did not. Th  CheA 2016 dataset of publicly available ChIP-seq experiments was used for in silico analysis  42. Here 'stron ' enrichment was considered when the following parameters were met: combined score >5 and adjusted P value <0. 05; TF, transcription factor. c, Molecular properties of TbX20 target genes showed that 66 of 93 (~70%) have no previous known link to CVD or are poorly annotated genes (13 of 93, ~14%). This observation indicates that, while the role of TbX20 in heart physiology is not completely understood, our dataset sheds light on a previously unknown group of genes with potential relevance for heart development and function.
Study limitations. We generated and analyzed 3,984 IMPC single-gene-knockout mouse lines on a C57BL/6N inbred background. The study focus is to access, through systematic genotyping-phenotyping in mice, a landscape of previously unknown or poorly described genes playing a role in CHD with great translational potential. We have data on so-called proof-of-concept genes but by no means data on all known cardiac genes such as Ttn 53 and Scn5a  54. Analysis and description of the complete data quantity was beyond the feasible scope of the presented work. However, all production-genotype data and phenotyping results (raw and statistically analyzed) are freely available (http://ftp. ebi. ac. uk/pub/databases/impc/all-data-releases). Intersection of human and mouse genes was limited primarily due to the lack of CHD gene saturation in both reviewed human studies, the mouse data we generated and our selection of LOF and de novo variants.
NATURE CARDiOVASCUlAR RESEARCh alterations in QRS interval length in the UK Biobank matches the results of the enrichment analysis shown in 'Hairball' , where SIRT1 was directly associated with heart diseases alongside 11 other genes from our study. Overall, genes associated with TTE phenotypes in our dataset are associated with the same phenotypes in the UK Biobank and therefore strengthen our findings from the IMPC mouse models. We noted that the overlap analysis with human association data was performed only for single studies. With the release of large-scale whole-exome-sequencing data, such as UK Biobank whole-exome sequencing, gene-based datasets for rare variant analysis are beginning to emerge. However, these datasets are not yet easy to query (https://genebass. org/  73 ), which prevents efficient CVD has diverse manifestation.
analysis. Enabling programmatic access is a key driver of large dataset adoption and should be a priority for large biobank analysts. To date, over 500 human genes have been linked to CHD, including those encoding transcription factors, cell signaling molecules and structural proteins important for heart development. The large number of gene variants identified thus far clearly shows that diverse mechanisms lead to congenital heart defects  6. Nevertheless, many variants are still considered to be of unknown relevance (for example, VUR), and de novo variants are emerging. Inherited cardiac disease is a generic term covering a wide variety of relatively rare diseases of the heart, mainly divided into disorders that are primarily arrhythmogenic with no obvious changes in structure and diseases that are associated with structural change, such as dilated or hypertrophic cardiomyopathy  74.
Additionally, LOF is by far the best-studied mechanism in human patients with CHD with and without developmental disabilities. Currently available datasets from the field of human genetics have helped uncover several genes with heterozygous de novo LOF variants, which to date have shown the strongest association and effect size with syndromic and nonsyndromic CHD. Nevertheless, estimates of these studies in the field  69, 76  can only explain about 10-40% of cases, with a higher proportion in those with neurodevelopmental disorders. Although some of these studies have examined biallelic variants, current sample sizes are often too small to detect reliable associations. Detailed phenotypic studies are needed to further unravel the underlying mechanism and reveal milder and stronger phenotypic associations depending on the background allele.
The International Mouse Phenotyping Consortium. The IMPC is a collaborative, inter-institutional research initiative of 12 major research centers across Europe, North America and Asia  [77] [78] [79]  that aims to generate and phenotype a null mutant for every protein-coding gene in the mouse genome (https://www. mousephenotype. org/)  51, 77, 80. This IMPC program involves the systematic and standardized characterization of more than 20,000 knockout mice strains carried out under the uniform operating procedures detailed in the International Mouse Phenotyping Resource of Standardized Screens (IMPReSS), which was developed and validated during the pilot programs EUMORPHIA and EUMODIC  81, 82. IMPC gene nomination. Each IMPC center selects its genes freely; some make selections according to the preferences of funders, and others select based on scientific interest, a colocated receptor community and center-specific special focus.
Electrocardiography recording. As even modest handling of mice may induce alterations in heart rate  86  , each mouse was permitted to acclimatize on the ECG recording platform for 10 min before measurement. Furthermore, cage mates were placed on the adjacent platform unit to provide company. Electrocardiograms were recorded in a dim and quiet analysis room. To eliminate circadian influences, electrocardiograms were recorded at the same time of day. A disposable lead plate (Mouse Specifics) was embedded in the floor of the platform and spaced to provide contact between the electrodes and animals' paws, providing an ECG signal equivalent to that of Einthoven lead II. Only runs in which at least 15 ECG beats could be included in the analysis were chosen. Data were analyzed using standard protocols for ECG signal analysis by eMouse (Mouse Specifics).
For TTE examination of awake animals, mice were firmly held by the nape of the neck (in the supine position) in the palm of one hand with the tail held tightly between the last two fingers. Prewarmed ultrasound gel was placed on the chest at the area of imaging, and TTE recordings started in short-axis mode with papillary muscles being the point of reference. Images were obtained in parasternal long-axis B mode and short-axis views in M mode with at least three images per mode. Once imaging was complete, the animal was removed from the platform and allowed to recover on top of a heating pad. Qualitative and quantitative measurements were made offline using analytical software (VisualSonics). LVIDs, LVIDd, systolic and diastolic interventricular septum thickness (IVSs and IVSd), LVPWs and LVPWd were measured in three consecutive beats according to the American Society of Echocardiography leading-edge method  89  as a measure of the actual visualized thickness of the ventricular septum and other chamber dimensions as defined by the actual tissue-blood interface.
Importantly, each center records relevant metadata parameters including equipment manufacturer, equipment model, recording environment, anesthetic agent and anesthetic dose. Statistical methods. The workflow can be described in two major stages: (1) data collection (in Supplementary Methods) and (2) statistical analysis. The statistical analysis complies with the IMPC statistical pipeline and implementation in R 90 (version 3. 4. 0) and applies the R packages SmoothWin  91  and PhenStat  [92] [93] [94]. Outcomes of the statistical pipeline, including 57,500+ analyses and results, are then assigned a mammalian phenotype term using the standard definitions in IMPReSS (https://www. mousephenotype. org/impress/index). In brief, the Mammalian Phenotype Ontology is a community effort to provide standard terms for annotating phenotypic data (http://www.
informatics. jax. org/ vocab/mp_ontology). This browser can be used to view terms, definitions and term relationships in a hierarchical display (http://www. informatics. jax. org/userhelp/ VOCAB_mp_browser_help. shtml). Statistical results as well as the raw data and the assigned mammalian phenotype terms are available at https://zenodo. org. Windowed and standard (non-windowed  79  ) analyses for the IMPC data remain comparable on this web interface (https://zenodo. org). Human and mouse orthologs. To obtain human orthologs for mouse genes, we applied the protein-coding file from the HGNC website (20190617) and the HCOP file (human-to-mouse orthologs 20190617) (fixed for 12 unique inference methods; that is, methods that appear multiple times were counted only once). We considered good-quality orthologs as 'protCod_score ≥ 5_max' and defined ortholog-inference score ≥5 (that is, the inference is supported by five or more methods); both directions (mouse-to-human and human-to-mouse) with 'max' score (filtering out 'dup_max' and 'no_max' scores) had a total of 16,882 inferences, included one-to-one and one-to-many orthologs.
6. 9. 8, Bruker) software. Image-processing software CTVox (Bruker) and Slicer (https://www. slicer. org/) were used for rendering the reconstructed three-dimensional volume data and data analysis. Briefly, among lethal lines, approximately 50% of animals die before E9. 5, 15% are viable at E9. 5 and lethal before E15. 5 and the remaining 35% are usually lethal prenatally between E15. 5 and birth. Among our 486 genes of interest, 321 (66%) were viable and their corresponding lines will not be subjected to embryo phenotyping and 24% were lethal. Based on previous IMPC distributions, of the 77 lethal lines, we would expect 50% to be lethal before E9. 5, leaving 38 genes for which we would expect to obtain embryonic imaging data. Among these, we would expect data for six lines at E9. 5 (15% of lines viable at E9.
5 are then lethal at E12. 5 or E15. 5) and E15. 5 or E18. 5 data for the remaining 32 lines. For the subviable lines, we would expect data at E18. 5 for 29 genes. Thus, even when starting with a large collection of lines, only a limited set would have embryo data due to operational and biological reasons. UK Biobank database. We extracted data for LVedv, LVesv, SV, SV index (SV adjusted for body surface area), cardiac output and cardiac index. In addition, three additional non-cardiac parameters were examined such as body surface area, age and sex for genomes of 39,624 individuals in the UK Biobank database when at least one phenotype was not missing. We averaged values over the second and third visit to minimize missingness and adjusted MRI traits for age and sex as well as LVedv and LVesv for body surface area.
We restricted the analysis to complete cases only and excluded adjusted outlier values above 200, 4 and 10 for both end volumes, cardiac index and cardiac output, respectively. We performed step 1 of REGENIE 2 version 2. 2 using the 20 first genetic principal components on the directly typed version 2 release of UK Biobank filtered for MAF (0. 01), MAC (100), HWE (P > 1 × 10 -15 ) and a sample and genotyping rate of 0. 9. To build the set of qualifying variants for which association tests are performed, we queried the Ensembl REST API Homology endpoint and selected only human genes with a one-to-one homology status with the genes of interest. We extracted gene coordinates using the Ensembl REST API Lookup endpoint and extended them by 1 Mbp on either side. The resulting regions were merged using bedtools merge and extracted from the imputed version 2 dataset, restricted to the samples present in the phenotype file using plink version 2.
00a3LM AVX2 Intel (4 August 2021). Association was carried out on the resulting 13,193,809 variants using step 2 of REGENIE version 2. 2, and suggestively significant variants (log 10 (P > 5)) were extracted using R data. table (Supplementary Table  3 ). Exon coordinates were queried using the Ensembl API Lookup endpoint, and exonic variants were identified using bedtools intersect. The consequences of the resulting variants were assessed using the Ensembl REST API VEP endpoint. For eQTL mapping, we downloaded GTEx version 8, mapped IDs to build 37 coordinates using the '2017-06-05_v8' lookup table and subset the data to only include eQTL for the genes of interest. We excluded variants for which eQTL direction was not concordant across tissues for a given gene and merged these eQTL data with the REGENIE output.
Acknowledgements Hands-on mouse IMPC phenotyping has been carried out by a large number of laboratory staff with superb experimental skills and unsurpassed dedication. We profoundly apologize for not being able to name all these individuals due to space limitations. M. H. d. A. , V. G. -D. and H. F. thank all past and present GMC technicians and animal caretakers for their outstanding work. J. D. H. thanks the  French National Centre for Scientific Research (CNRS) , the  French National Institute of Health and Medical Research (INSERM) , the  University of Strasbourg  and all members of the  Mouse Clinical Institute (MCI-ICS)  for their help and helpful discussions during the project. R. E. B. , S. A. M. , J. K. W. and K. L. S. thank the members of the  Jackson Laboratory Research Animal Facility and Laboratory Informatics Team  for their outstanding support.
This research was made possible through access to the data and findings generated by the  100KGP. PCGC data were generated by the  PCGC  under the auspices of the  National Heart, Lung, and Blood Institute's Bench to Bassinet Program  (http://www. benchtobassinet. org/). The PCGC program is funded by the  National Heart, Lung, and Blood Institute , the  National Institutes of Health (NIH) ,  US Department of Health and Human Services  through grants  U01HL098123 ,  U01HL098147 ,  U01HL098153 ,  U01HL098162 ,  U01HL098163  and  U01HL098188. This text was not prepared in collaboration with investigators of the PCGC, has not been reviewed and/or approved by the  PCGC  and does not necessarily reflect the opinions of PCGC investigators or the  NHLBI. The IMPC has been supported by  NIH  grants  U54 HG006364 ,  NIH   U42 OD011175  and  NIH   UM1 OD023221 (K.
C. K. L. and  C. McKerlie );  NIH UM1OD023222  (J. M. W. , S. M. and  R. Braun ); MRC grant code  MC_A410  and  NIH UM1HG006370  (T. F. M. ,  H. Parkinson , P. F. , D. S. , J. M. , V. M. F. and H. M. ); MRC funding for the IMPC (reference 53658) (S. M. W. );  Genome Canada  and  Ontario Genomics  ( OGI-051 ) ( C. McKerlie  and S. D. M. B. ). The  Czech Centre for Phenogenomics  is supported by  RVO68378050  and projects  LM2015040 ,  LM2018126 ,  CZ. 1. 05/1. 1. 00/02. 0109 , CZ. 1. 05/2. 1. 00/19. 0395 , CZ. 02. 1. 01/0. 0/0. 0/16_013/0001789  and  CZ. 02. 1. 01/0. 0/0. 0/18_046/0015861 (R. S. ); the  French Agence Nationale de la Recherche  grants  ANR-10-IDEX-0002-02 ,  ANR-10-LABX-0030-INRT  and  ANR-10-INBS-07 PHENOMIN (Y. H. ) ;  European Union Horizon 2020  (IPAD-MD funding 653961); and the  German Center for Diabetes Research (DZD)  (M.
Data availability All data generated or analyzed during this study are included in this published article and its Supplementary Information. IMPC data are open access for public. For single-gene search, visit https://www. mousephenotype. org/data/search. For batch queries, please visit https://www. mousephenotype. org/data/batchQuery. To download a particular DR, visit http://ftp. ebi. ac. uk/pub/databases/impc/ all-data-releases/. Support for particular DR (here we used DR10. 1) download can be found at https://www. mousephenotype. org/help/programmatic-data-access/.