dataesr/scientific-paragraphs-categorization huggingface dataset:
54, P = 0. 07; Supplementary Data 14. 1). No scATAC-seq signal was detected in the cg09710440 region of LHFPL3, possibly due to proximity to a bivalent gene's TSS (232 bp). We examined whether the seven significant ATAC peaks identified a particular cell type subset. Due to the sparsity of scATAC-seq data, we determined the fraction of each cell group containing at least one of these regions. We found that stem cell-progenitor populations had a higher proportion of open chromatin at these sites than differentiated cells (mean of 14. 9% versus mean of 2. 9%; Fig. 8c ). This suggests that the observed age-related reduction of open chromatin states could be due to the loss (for example, death or differentiation) of progenitor cells in the tissue. We studied three cell groups: hematopoietic stem cells (HSCs), progenitor cells and differentiated cells.
scATAC-seq analysis in murine HSCs We tested whether our human HSC findings extended to murine HSCs by analyzing another public scATAC-seq dataset from murine HSCs with four replicates each in young (10-week) and old (20-month) mice  59. This dataset provided access to our age-related CpG sites in 4,492 young and 3,300 old HSCs. Of the top 35 positively age-related CpG sites, 33 overlapped with ATAC peaks (Supplementary Data 14. 2). We then calculated the proportion of HSCs in each age group with the respective peak. The proportion of old HSCs with a peak near Lhfpl4 was not significantly different from that of young HSCs (OR = 0. 94, P = 0. 7), implying no observable age-related chromatin compactification in murine HSCs. This was also true for the other 32 CpG sites and their associated peaks.
Three universal pan-mammalian clocks We applied elastic net regression models to establish three universal mammalian clocks for estimating chronological age across all tissues (n = 11,754 from 185 species) in eutherians (n = 11,439 from 174 species), marsupials (n = 210 from nine species) and monotremes (n = 15 from two species). The three elastic net regression models, implemented using the glmnet 4. 1-7 package in R, corresponded to different outcome measures described in the following: 1. log-transformed chronological age: log(Age + 2), where an offset of 2 years was added to avoid negative numbers in case of prenatal samples, 2. -log(-log(RelativeAge)) and 3. log-linear transformed age. DNAmAge estimates of each clock were computed via the respective inverse transformation. Age transformations used for building universal clocks 2 and 3 incorporated a selection of three species characteristics: gestational time (GestationT) , age at sexual maturity ( ASM) and maximum lifespan (MaxLifespan).
1d ). The proportionality constant c 1 controls the distribution of y. We chose the value of c 1 so that y follows approximately a normal distribution with mean zero. Because we wanted to define clock 3 without using MaxLifespan, we opted to use the ratio GestationT ASM as a surrogate for the oracle value m *. We achieved this approximation by fitting the following regression model with all mammalian species available in our AnAge database, log MaxLifespan + GestationT ASM + GestationT ≈ 2. 92 + 0. 38 × log GestationT ASM. ( 7 ) The two log variables in equation (  7 ) have moderate correlation (r = 0. 5). Subsequently, we defined m as follows: m = c 2 ( GestationT ASM ) 0. 38 , (8) where c 2 = c 1 e 2. 92. We chose c 2 = 5. 0 so that log-linear age termed y in equation (  5 ) follows approximately a normal distribution with mean zero (median = 9.
0 × 10 -4 , skewness = -0. 02; Extended Data Fig. 1f ). Setting x=RelativeAdultAge in equation (  5 ) results in f(RelativeAdultAge; m) = ⎧ ⎨ ⎩ RelativeAdultAge m -1, RelativeAdultAge ≥ m log RelativeAdultAge m , RelativeAdultAge < m. (9) Universal clock 3 predicts loglinearAge (denoted as y). To arrive at an age estimate, we employ both equations (  4 ) and (  6 ): DNAmAge = { m × (ASM + GestationT) × (y + 1) -GestationT , y ≥ 0 m× (ASM + GestationT) × e y -GestationT , y < 0. (10) Statistics for performance of model prediction. To validate our model, we used DNAmAge estimates from LOFO and LOSO analyses, respectively. At each type of estimate, we computed Pearson correlation coefficients and MAE between DNAm-based and observed variables across all samples. Correlation and MAE were also computed at the species level, limited to the subgroup with n ≥ 15 samples (within a species).
Universal chromatin state analysis To annotate our age-related CpG sites based on chromatin states, we assigned a state for all our mammalian CpG sites based on a recently published universal ChromHMM chromatin state annotation of the human genome  46. The underlying hidden Markov model was trained with over 1,000 datasets of 32 chromatin marks in more than 100 cell and tissue types. This model then produced a single chromatin state annotation per genomic position that is applicable across cell and tissue types, as opposed to producing an annotation that is specific to one cell or tissue type. A total of 100 distinct states were generated and categorized into 16 major groups according to the parameters of the model and external genome annotations  46  (described in Supplementary Data 8. 2).
GREAT enrichment analysis We applied the GREAT analysis software tool  51  to the top 1,000 positively age-related and the top 1,000 negatively age-related CpG sites from the EWAS of age. GREAT implemented foreground-background hypergeometric tests over genomic regions where we input all CpG sites of the mammalian array as background and the genomic regions of the 1,000 CpG sites as foreground. This approach yielded hypergeometric P values that were not confounded by the number of CpG sites within a gene (Supplementary Note 5).
Single-cell ATAC-seq of human bone marrow Recent advances have enabled the sequencing of ATAC profiles within single cells, enabling assessment of the proportion of cells containing an open chromatin region  58. We cross-referenced the top 35 CpG sites with positive age correlation across mammalian tissues with publicly available scATAC-seq data (Supplementary Table  3 ). We downloaded 10x Multiome count data in AnnData format as H5AD from the Gene Expression Omnibus (accession number GSE194122). The ATAC array data were managed using the Python package anndata  92. hg38 ATAC peak locations were extracted from the metadata '. var' section using anndata. Peak locations were overlapped with probe locations using GenomicRanges  93  for the top 35 CpG sites. The overlapping peaks were then used to extract the processed counts for each cell.
To confirm enrichment for the hypermethylated sites showing decrease in chromatin accessibility with age, we randomly selected 1,000 sets of 17 ATAC peaks and compared the mean correlation with age of the selected regions to the 1,000 sampled sets of regions. Mouse single-cell ATAC-seq in hematopoietic stem cells. We downloaded the publicly available data (H5, meta and fragment files of Illumina HiSeq 1500 array data) from Itokawa et al. 59.
(GSE162662). scATAC-seq data were profiled in four biological replicates in young (10-week) and old (20-month) mice. The ATAC-seq data were managed and analyzed with R Signac  94. We applied Fisher's exact test to ascertain whether locations with differential accessibility between young and old animals were enriched with the 33 top positively agerelated CpG sites (OR > 1 indicates a higher proportion in the old group). Further analytical details, including ATAC-seq data quality controls, are presented in Supplementary Note 5.
Reporting summary Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article. Extended Data Fig. 7  | Chromatin state analysis of age-related CpGs. The heatmap color-codes the hypergeometric overlap analysis between age-related CpGs (columns) and two groupings of CpGs (1) universal chromatin states analysis 1 and (2) binding by polycomb repressive complex 1 and 2 (PRC1, PRC2) defined based on ChIP-Seq datasets in ENCODE  53  , see the last two rows. The first column shows a bar plot that reports the proportion of CpGs that are known to be bounded by PRC2 that ranges from zero to one (PRC2). Note that chromatin states that contain a high proportion of PRC2 bound CpGs overlap significantly with the top 1,000 CpGs that increased with age across tissues and mammal species.
Acknowledgements This work was mainly supported by the  Paul G. Allen Frontiers Group  (S. H. ). Additional support was also provided by the  Open Philanthropy-Silicon Valley Fund  (S. H. and K. R. ). J. E. was supported  Nature Aging Resource  https://doi. org /10. 1038/s43587-023-00462-6  by  UCLA Jonsson Comprehensive Cancer Center  and  Eli  and  Edythe Broad Center of Regenerative Medicine and Stem Cell Research Ablon Scholars Program. J. A. M. and the NHP Core were supported by the  Intramural Research Program ,  National Institute on Aging ,  NIH. Plains zebra sample collection was supported by  National Geographic Society  grant  8941-11. We acknowledge the  Museum of Vertebrate Zoology  and  C. J. Conroy  from the  University of California, Berkeley. We acknowledge  R. Miller  and his laboratory (http://www.
richmillerlab. com/long-lived-mutants) for providing dwarf mice and controls (Snell dwarf mouse, GHRKO experiments). Lemur sample collections were supported by  Duke Lemur Center. N. C. B. was funded by a  DST-NRF SARChI chair of Mammalian Behavioural Ecology and Physiology  ( GUN 64756 ). V. N. G. , A. S. and V. G. were supported by  NIA  grants. V. G. was supported by the  Milky Way Research Foundation. D. T. O. was supported by  European Research Council  ( 788937 ) and  Cancer Research UK  ( 20412 ). The FHS is funded by  National Institutes of Health  contracts  N01-HC-25195  and  HHSN268201500001I. The laboratory work for this investigation was funded by the  Division of Intramural Research, National Heart, Lung, and Blood Institute, National Institutes of Health. The analytical component of this project was funded by the  Division of Intramural Research, National Heart, Lung, and Blood Institute  and the  Center for Information Technology, National Institutes of Health.
Data availability The individual-level data from the Mammalian Methylation Consortium can be accessed from several online locations. All data from the Mammalian Methylation Consortium are posted on Gene Expression Omnibus (complete dataset, GSE223748). Subsets of the datasets can also be downloaded from accession numbers GSE174758, GSE184211, GSE184213, GSE184215, GSE184216, GSE184218, GSE184220, GSE184221, GSE184224, GSE190660, GSE190661, GSE190662, GSE190663, GSE190664, GSE174544, GSE190665, GSE174767, GSE184222, GSE184223, GSE174777, GSE174778, GSE173330, GSE164127, GSE147002, GSE147003, GSE147004, GSE223943 and GSE223944. Additional details can be found in Supplementary Note 2. The mammalian data can also be downloaded from the Clock Foundation webpage: https://clockfoundation. org/MammalianMethylationConsortium.
Code availability The chip manifest files, genome annotations of CpG sites and the software code for universal pan-mammalian clocks can be found on GitHub  95  at https://github. com/shorvath/MammalianMethylation-Resource https://doi. org/10. 1038/s43587-023-00462-6 Resource https://doi. org/10. 1038/s43587-023-00462-6 Extended Data Fig. 7 | See next page for caption. Nature Aging Resource https://doi. org/10. 1038/s43587-023-00462-6.
6 Extended Data Fig. 1 | 61 Resource https://doi. org/10. 1038/s43587-023-00462-Transformed age in universal clocks. The plot displays transformed age in universal Clock 2 (a-c) and universal Clock 3 (d-f). (a, b) Loglog transformation of Relative Age (y-axis) versus age in universal Clock 2 and (d, e) log-linear age (y-axis) versus age in our universal Clock 3. Of the 969 mammalian species with available gestation time, age at sexual maturity and maximum lifespan in AnAge database, 339 species are available in our collection. We multiplied the reported maximum lifespan of non-human or non-mouse species by 1. 3. Transformed ages were calculated for all the 969 species with simulated age ranging from gestation time through the modified maximum lifespan. The columns (a, d) display all the 969 species with the simulated ages.
GREAT 2. TWAS 3. GWAS GREAT enrichment analysis Mouse phenotype Disease ontology GO biological process Human phenotype MSigDB pathway MSigDB perturbation GO molecular function TWAS category Human Monkey Mouse Rat GWAS category Behavioral phenotype DNAm biomarkers Fetal growth GIANT body fat distribution Longevity Metabolic outcomes and diseases Neurodegenerative disorder Reproductive aging Fig. 7 | , degeneration; AgeAccelGrim, epigenetic age acceleration derived from the mortality clock: GrimAge 23 ; DNAmGran, DNAm granulocyte (Supplementary Note 5); GIANT, Genetic Investigation of ANthropometric Traits; GTEx, Genotype-Tissue Expression; HD, Huntington's disease; hipp. , hippocampal; LTL, leukocyte telomere length; MSigDB, Molecular Signatures Database; mus. , muscle; OPCs, oligodendrocyte precursor cells; reg.
Each volume scan contained 128 cross-sectional images and was generated using a horizontal raster scanning protocol. To extract thickness information and to compress these 128 images into a single retinal 'thickness map', we developed an ANN algorithm involving a U-Net architecture  (Ronneberger 2015 ) (Fig. 1 ; Methods). The 31,135 left eye retinal thickness maps that we generated were then used as input to an autoencoder. This was trained end-to-end for 150 epochs utilising 2500 training and 500 test images. We explored various embedding dimensionalities and opted for a 64-dimensional vector (i. e. the latent space or 'bottleneck layer' contained 64 features) (Fig. 1 ; Methods). It has been previously shown that this autoencoder architecture can sufficiently represent datasets of similar complexity  (Schroff 2015; Song 2015).
[C] Left eye retinal thickness maps showing the difference in retinal structure between individuals with different inversion-related alleles. Left: mean depth (thickness) representation for reference:reference (no inversion) alleles. Middle: difference between image mean for reference:reference and image mean for reference:inversion (heterozygous inversion) genotypes. Right: difference between image mean for reference:reference and image mean for inversion:inversion (homozygous inversion) genotypes. A paracentral area of differential retinal thickness can only be visualised in the reference-to-homozygous difference map (in keeping with a recessive effect). [D] Phenome-wide associations for the inversion genotype against 454 ICD10 disease codes for which there were >1000 cases in the UK Biobank cohort (when only data obtained after the date of OCT image acquisition were considered); six codes (M16, G20, I84, M20, K60, J84) remained significant after Bonferroni correction; -log10 p-values are shown grouped by high-level ICD10 category.
We subsequently investigated the relationship between the 64 embedded features and a set of traits and disease codes (ICD10) that are available in the UK Biobank dataset. Unsurprisingly, most embeddings correlated with retinal layer thickness parameters (Supplementary Fig. 3 ). We then used a logistic regression approach (with sex, age, height and weight as covariates) and detected significant associations between specific embeddings and the following conditions: non-insulin-dependent diabetes, epilepsy, glaucoma and chronic ischaemic heart disease (Fig. 4B ). Two of these lead signals (epilespy and chronic ischaemic heart disease) are associated very specifically to only one embedding each (embedding no. 1 and no. 26 respectively). In contrast, glaucoma is associated with two different embeddings (no.
39 and no. 47) and diabetes to three sequential embeddings (no. 36-38) (Fig. 4C ). Reassuringly, MTAG analysis of embedding no. 37 (which factored in GWAS summary statistics from embeddings no. 36-38) revealed a statistically significant signal linked to the ADCY5 gene (Table  2 ). No other conventional or MTAG GWAS in this study detected an association with this gene which influences glucose metabolism and has been previously linked to non-insulin-dependent diabetes by multiple association studies  (Roman 2017). To understand which aspects of retinal morphology drove the association between the embedded features and the lead disease codes (non-insulin-dependent diabetes, epilepsy, glaucoma and chronic ischaemic heart disease) we inspected a set of retinal thickness difference maps. These compared retinal thickness in UK Biobank participants that had been assigned the relevant ICD10 code (after ophthalmic phenotyping) to those that have not (Fig.
Second, we only performed common-variant genetic association analyses of the obtained embedded features. The increasing availability of genome sequencing data in UK Biobank participants will allow us to more comprehensively look for genetic associations, including with rare variants and with copy number alterations in the future. Third, the fact that relationships were detected between embeddings and certain health outcomes does not necessarily imply causation. The main aim of this study was to assess if autoencoders can be utilised to produce biologically and clinically relevant phenotypes. In-depth confounder adjustment and causal inference studies were therefore not performed. Furthermore, the predictive models described here have a proof-of-concept nature and are not intended for implementation (especially as the data used for training and evaluation were highly homogeneous).
We performed quality control considering genetic and imaging parameters. First, to reduce the impact of population stratification and to increase the validity of the conducted genetic association studies, a subset of UK Biobank participants that contained little substructure (i. e. can be considered "genetically well-mixed") was selected. This was achieved by applying principal component analysis to UK Biobank genotypic data using standard, previously-implemented methods  (Currant 2023). Additional participants were excluded as their OCT scans failed to meet a set of previously-described, rigorous quality control criteria  (Patel 2016; Currant 2021; Currant 2023). Finally, participants were removed on the basis of being recommended for exclusion from genetic studies by the UK Biobank or because they were related to third degree or more.
The 128 images of each OCT scan were used to create a "thickness map", i. e. a single image displaying the retinal thickness throughout the imaged area. To achieve this, segmentation of all the scans in the dataset was performed using a U-Net based approach. The utilised U-Net method was first described in 2015  (Ronneberger 2015)  and involves a fully convolutional network that consists of a contracting path (that extracts features) and an expansive path (that localises objects). Initially, the inner-and outer-most limits of the retina (corresponding to the inner limiting membrane and the Bruch's membrane respectively) were manually identified in 100 randomly-selected OCT images using the https://www. makesense. ai tool. The original images and the generated 'ground truth' segmentation masks were subsequently utilised to train the U-Net.
Genome-wide association studies GWAS analyses of autoencoder-derived embedded features (64 embeddings and 25 embeddingrelated principal components) were performed using an additive linear model implemented in REGENIE v3. A degree of correlation was expected among autoencoder-derived embeddings and the summary statistics obtained from the GWAS analyses were used to perform a multi-trait meta-analysis. First, embeddings with a high genetic correlation (i. e. with Pearson correlation coefficient r > 0. 9) were identified. Then, the MTAG v1. 0. 8 tool (https://github. com/JonJala/mtag)  (Turley 2018 ) was used to conduct a single meta-analysis for every individual embedding, leveraging the findings from correlated embedded features and producing an updated set of GWAS summary statistics for each of these 64 variables.
Under certain assumptions, the generated estimates will be more precise than those obtained from the input GWAS (Turley 2018). To refine the obtained association signals, further analyses were performed using the GCTA-COJO tool (https://yanglab. westlake. edu. cn/software/gcta/#COJO)  (Yang 2010 ). These analyses were conducted utilising linkage disequilibrium estimates from a reference sample  (Currant 2023 ) and summary statistics from: (i) the 64 embedding GWAS, (ii) the 25 embedding-related principal component GWAS, (iii) the 64 embedding MTAG-GWAS. Genetic variants in loci that were on different chromosomes or more than 10 Mb distant from each other were assumed to be uncorrelated. Each genetic change in the discovered associated variant set was manually annotated using Ensembl  (Cunningham 2022) , Open Targets  (Ochoa 2021 ) and GWAS Catalog (Sollis 2023) data.
3 ). Furthermore, a logistic regression approach was used to look for relationships between embeddings and a set of diseases (high-level ICD10 codes); only the 454 disease-related codes for which there were >1000 cases in the UK Biobank cohort were considered (when factoring in data obtained after the date of OCT image acquisition (2012)). Age, sex, height and weight were used as covariates and the statistical significance threshold was determined using Bonferroni correction.
ACKNOWLEDGEMENTS We acknowledge the following sources of funding: the  Wellcome Trust  ( 224643/Z/21/Z ,  Clinical Research Career Development Fellowship  to P. I. S. ); the  UK National Institute for Health Research (NIHR)   Clinical Lecturer Programme  ( CL-2017-06-001  to P. I. S. ); the  EMBL European Bioinformatics Institute (EMBL-EBI)  (A. D. , K. G. , E. B. , T. F. ). We also acknowledge the contribution of the  UK Biobank Eye and Vision Consortium. Members of this consortium include:  Naomi Allen ,  Tariq Aslam ,  Denize Atan ,  Sarah Barman ,  Jenny Barrett ,  Paul Bishop ,  Graeme Black ,  Tasanee Braithwaite ,  Roxana Carare ,  Usha Chakravarthy ,  Michelle Chan ,  Sharon Chua ,  Alexander Day ,  Parul Desai ,  Bal Dhillon ,  Andrew Dick ,  Alexander Doney ,  Cathy Egan ,  Sarah Ennis ,  Paul Foster ,  Marcus Fruttiger ,  John Gallacher ,  David Garway-Heath ,  Jane Gibson ,  Jeremy Guggenheim ,  Chris Hammond ,  Alison Hardcastle ,  Simon Harding ,  Ruth Hogg ,  Pirro Hysi ,  Pearse Keane ,  Peng Tee Khaw ,  Anthony Khawaja ,  Gerassimos Lascaratos ,  Thomas Littlejohns ,  Andrew Lotery ,  Robert Luben ,  Phil Luthert ,  Tom Macgillivray ,  Sarah Mackie ,  Savita Madhusudhan ,  Bernadette Mcguinness ,  Gareth Mckay ,  Martin Mckibbin ,  Tony Moore ,  James Morgan ,  Eoin O'Sullivan ,  Richard Oram ,  Chris Owen ,  Praveen Patel ,  Euan Paterson ,  Tunde Peto ,  Axel Petzold ,  Nikolas Pontikos ,  Jugnoo Rahi ,  Alicja Rudnicka ,  Naveed Sattar ,  Jay Self ,  Panagiotis Sergouniotis ,  Sobha Sivaprasad ,  David Steel ,  Irene Stratton ,  Nicholas Strouthidis ,  Cathie Sudlow ,  Zihan Sun ,  Robyn Tapp ,  Dhanes Thomas ,  Emanuele Trucco ,  Adnan Tufail ,  Ananth Viswanathan ,  Veronique Vitart ,  Mike Weedon ,  Cathy Williams ,  Katie Williams ,  Jayne Woodside ,  Max Yates ,  Jennifer Yip ,  Yalin Zheng.
Right: difference between image mean for reference:reference and image mean for inversion:inversion (homozygous inversion) genotypes. A paracentral area of differential retinal thickness can only be visualised in the reference-to-homozygous difference map (in keeping with a recessive effect). [D] Phenome-wide associations for the inversion genotype against 454 ICD10 disease codes for which there were >1000 cases in the UK Biobank cohort (when only data obtained after the date of OCT image acquisition were considered); six codes (M16, G20, I84, M20, K60, J84) remained significant after Bonferroni correction; -log10 p-values are shown grouped by high-level ICD10 category.