DOI: 10.5061/dryad.sj3tx96gd
title: Genetic correlation-guided mega-analysis of DO mice provides mechanistic insight and candidate genes for age-related pathologies

description: Diversity Outbred (DO) mice are a powerful model system for mapping
 complex traits due to their high genetic diversity and mapping
 resolution. However, while there are extensive tools available
 for standard genetic analysis in DO mice, fewer techniques have been
 implemented to facilitate integrated, cross-study analysis. Here, we
 implement Haseman-Elston regression to estimate genetic correlations among
 7,233 phenotypes measured across eleven independent DO mouse
 studies.  We used this network of genetic correlations to cluster
 phenotypes according to shared genetics, which enhanced the power to
 detect quantitative trait loci (QTL). This approach empowered the
 detection of 884 QTL for 383 meta-phenotypes, explaining an average of
 40.36% of the total genetic variance per mega-analysis. We leveraged this
 network for insights into specific areas of biology, including lifespan,
 frailty, immune composition, histological and functional lung phenotypes,
 and histological phenotypes of the aorta. We found the genetics of
 lifespan to share limited correlation with the genetics of frailty but
 stronger correlation with the genetics of immune cell composition.
 Additionally, mega-analyses driven by genetic correlations identified
 candidate genes (e.g., Cdkn2b) associated with degraded extracellular
 matrix in the aorta. Finally, an ensemble of genetic analyses implicated
 pulmonary neuroendocrine cell signaling and/or differentiation as a key
 driver of multiple lung pathophenotypes.
description: # Genetic correlation-guided mega-analysis of DO mice provides mechanistic
 insight and candidate genes for age-related pathologies Dataset DOI:
 [10.5061/dryad.sj3tx96gd](10.5061/dryad.sj3tx96gd) ## Description of the
 data and file structure The data in this repo correspond to a genetic
 analysis of Diversity Outbred (DO) mice conducted using internal data
 collected by Calico Life Sciences LLC and publicly available DO mouse
 datasets. This involved unifying several previously generated datasets,
 each of which involved distinct experimental efforts, for mega-analysis.
 ### Files and variables #### File: Table_S1_DO_Phenotypes_normalized.csv
 **Description:** Contains Mouse IDs, covariates used in phenotype
 processing, and z-score normalized phenotype values. ‘Mouse_ID’ is a
 unique identifier corresponding to each unique animal and includes study
 information. ‘Age’ is a covariate corresponding to the age of each animal
 at the time of phenotyping. For certain animals, different phenotypes were
 collected at different ages; for these animals, ‘Age’ is recorded as
 “variable”. ‘Sex’ corresponds to the sex of the animal (‘F’ for female and
 ‘M’ for male). ‘Diet’ corresponds to the dietary intervention group for
 each animal in the DRiDO study [(Di Francesco et al.
 2024)](https://www.zotero.org/google-docs/?broken=db5gaj), but may also
 correspond to the number of days on a high fat diet (animals from the
 Svenson high-fat diet study, denoted as ‘Sven_HFD’ [(Gatti et al.
 2017)](https://www.zotero.org/google-docs/?broken=siwecs)) or
 pharmacological intervention (e.g., lifespan data from [(Mullis et al.
 2025)](https://www.zotero.org/google-docs/?broken=mCsCeR)). ‘Genwave’
 corresponds to the DO generation of each animal. Additional columns
 correspond to various z-score normalized phenotypes after correction for
 the aforementioned covariates. Phenotype names contain a prefix
 corresponding to the study from which they were collected: ‘attie’
 (pancreatic phenotypes from (Keller et al. 2018)), ‘bone’ (bone strength
 phenotypes from (Al-Barghouthi et al. 2021)), ‘cal_cr’ (DRiDO phenotypes
 from [(Di Francesco et al.
 2024)](https://www.zotero.org/google-docs/?broken=KN4ANn)), ‘cal_int’
 (newly published frailty, aorta, and lung phenotypes), ‘catnap’ (metabolic
 cage data from (Z. Chen et al. 2022)), ‘ches_striatum’ (phenotypes from
 (Bagley et al. 2022)), ‘lifespan’ (lifespan data from independent lifespan
 studies analyzed in (Mullis et al. 2025); ‘shock’ and ‘har’ correspond to
 the Shock and Harrison studies, respectively), ‘pazdro_heart’ (cardiac
 phenotypes from (Starcher et al. 2021)), and ‘Sven_HFD’ (various
 phenotypes from (Gatti et al. 2017)). Phenotypes may also contain a suffix
 corresponding to dietary intervention groups from the DRiDO study: ‘AL’
 (*ad libitum*), ‘20’ (20% caloric restriction), ‘40’ (40% caloric
 restriction), ‘1D’ (one-day intermittent fasting), ‘2D’ (two-day
 intermittent fasting). Phenotypes from the DRiDO study collected annually
 or bi-annually will contain timepoint information corresponding to the
 year of the study in which they were collected (‘Y1’ - ‘Y4’). Biannual
 measurements are labeled with ‘A’ or ‘B’, corresponding to the first or
 second annual time point. ##### Variables * Mouse_ID: a unique identifier
 corresponding to each unique animal and includes study information. * Age:
 a covariate corresponding to the age of each animal at the time of
 phenotyping in days. For certain animals, different phenotypes were
 collected at different ages; for these animals, ‘Age’ is recorded as
 “variable”. * Sex: the sex of the animal (‘F’ for female and ‘M’ for
 male). * Diet: the dietary intervention group for each animal in the DRiDO
 study [(Di Francesco et al.
 2024)](https://www.zotero.org/google-docs/?broken=db5gaj), but may also
 correspond to the number of days on a high fat diet (animals from the
 Svenson high-fat diet study, denoted as ‘Sven_HFD’ [(Gatti et al.
 2017)](https://www.zotero.org/google-docs/?broken=siwecs)) or
 pharmacological intervention (e.g., lifespan data from [(Mullis et al.
 2025)](https://www.zotero.org/google-docs/?broken=mCsCeR)). * Genwave: the
 DO generation of each animal. * Additional columns: various z-score
 normalized phenotypes after correction for the aforementioned covariates.
 See above for details on phenotype prefixes and suffixes.  #### File:
 Table_S2_Study_summary.xlsx **Description:** Data sources for DO mouse
 phenotypes. Contains information on the studies from which phenotypes were
 obtained. This includes the number of animals used and phenotypes
 measured, the general focus of each study, the source of the phenotype and
 genotype data, the genotyping array used, and the prefix assigned to
 corresponding Mouse_ID and phenotype labels. ##### Variables * Study: the
 study from which data was collected. Corresponds to how each study is
 referred to in the accompanying manuscript. * No. mice: the number of mice
 used in the study. * No. phenotypes: the number of phenotypes collected in
 the study.  * Focus: the focus of the study, corresponding to broad
 aspects of physiology reflected in the in measured phenotypes.  * Source:
 the source of the data from the study. Can be an online database or a
 paper. 'N/A' means that the data was not previously available. 
 * Genotype array: the MUGA genotype array used to genotype the animals.  *
 Data prefix: the prefix used to tag phenotypes from each study.
 Corresponds to the phenotype prefixes used in Table_S1. #### File:
 Table_S3_h2_all_traits.txt **Description:** Heritability (*h^2^*) of
 individual traits.**** Individual heritability estimates for each of the
 phenotypes in the dataset, before filtering based on *h^2^*. #####
 Variables * Trait: a phenotype from Table_S1. * h2: the heritability of
 the trait as estimated by Haseman-Elston regression. * SE: the standard
 error of the trait. * p: the p-value of the *h2* estimate * n: the number
 of animals used in the estimate. #### File: Table_D1_rg_filtered.RData
 **Description:** Pairwise genetic correlation (*r~g~*) estimates for each
 of the phenotypes in the dataset, after filtering based on *h^2^*. #####
 Variables * Trait1: the first trait. * Trait2: the second trait. * rg: the
 genetic correlation between the two traits. 'NA' values may
 occur if the heritability estimate for one or more of the traits is
 negative.  * SE: the standard error of the genetic correlation estimate. *
 rg_p: the p-value associated with the Haseman-Elston (HE) regression
 coefficient. * r: the Pearson correlation coefficient of the two traits. *
 r_p: the p-value of the Pearson correlation coefficient. * h2_1: the
 heritability of the first trait via HE regression.  * h2_2: the
 heritability of the second trait via HE regression.  * n_1: the number of
 animals in which the first trait was measured. * n_2: the number of
 animals in which the second trait was measured.  #### File:
 Table_S4_rg_matrix_clustered.RData **Description:** Hierarchically
 clustered matrix of genetic correlations (*r~g~*).**** Row and column
 names correspond to the DO phenome (see Table_S1) after filtering by
 *h^2^.* This matrix includes DRiDO phenotypes split by diet group. ####
 File: Table_S5_rpg_matrix_clustered.RData **Description: Hierarchically
 clustered matrix of Pearson genetic correlations (*r~pg~*).** Row and
 column names correspond to the DO phenome (see Table_S1) after filtering
 by *h^2^.* This matrix includes DRiDO phenotypes split by diet group. ####
 File: Table_S6_trait_groups.txt **Description:** Meta-trait
 composition. Filtered by *h^2^* and excluding diet-specific DRiDO
 phenotypes. The first column (no column name) is an index.  #####
 Variables * Trait: corresponds to one of the phenotypes in Table S1. *
 Cluster: specifies the meta-trait each trait comprises. #### File:
 Table_S7_meta_phenotypes.txt **Description:** Meta-traits constructed from
 clustered individual phenotypes. Animal IDs are listed in the ‘Mouse_ID’
 column, with each subsequent column corresponding to a meta-trait. #####
 Variables * MouseID: The animal ID. * Additional columns: correspond to
 meta-traits constructed from clusters of genetically correlated
 phenotypes. If none of the traits comprising a meta-trait were measured in
 a particular animal, the trait value for that animal is ‘NA’. #### File:
 Table_S8_h2_meta_traits.txt **Description:** Heritability estimates for
 each of the meta-phenotypes in the dataset, excluding meta-traits composed
 of lifespan data from the Ellison study (prefixed with “lifespan_ell”,
 which were not significantly heritable and excluded from analysis). #####
 Variables * Trait: a meta-trait, which is composed of individual
 phenotypes listed in Table S8. * h2: the heritability of the meta-trait as
 estimated by Haseman-Elston regression. * SE: the standard error of the
 trait. * p: the p-value of the *h^2 ^*estimate. * n: the number of animals
 used in the estimate. #### File: Table_S9_rg_meta_traits.txt
 **Description:** Pairwise genetic correlation estimates for each of the
 meta-phenotypes in the dataset, excluding meta-traits composed of lifespan
 data from the Ellison study (prefixed with “lifespan_ell”, which were not
 significantly heritable and excluded from analysis). ##### Variables *
 Trait1: the first trait for which the genetic correlation (*rg*) is
 estimated. * Trait2: the second trait for which the *rg* is estimated. *
 rg: the genetic correlation estimate of Trait1 and Trait2. 'NA'
 values may occur if the heritability estimate for one or more of the
 traits is negative.  * SE: the standard error of the estimate.  * rg_p:
 the p-value associated with the Haseman-Elston regression.  * r: the
 Pearson correlation coefficient of Trait1 and Trait2. * r_p: the p-value
 of the Pearson correlation.  * h2_1: the heritability (*h2*) estimate for
 Trait1. * h2_2: the heritability (*h2*) estimate for Trait2. * n_1: the
 number of animals for which Trait1 was measured.  * n_2: the number of
 animals for which Trait2 was measured.  #### File:
 Table_S10_clustered_meta_traits_rg.txt **Description:** Hierarchically
 clustered matrix of genetic correlations among meta-traits. Row and column
 names correspond to the meta-trait after removing two meta-traits
 assembled from low *h^2^* lifespan estimates*.* #### File:
 Table_S11_clustered_meta_traits_rpg.txt **Description:** Hierarchically
 clustered matrix of Pearson genetic correlations among meta-traits. Row
 and column names correspond to the meta-trait after removing two
 meta-traits assembled from low *h^2^* lifespan estimates*.* #### File:
 Table_S12_qtl_alpha05.txt **Description:** QTL detected in meta-traits at
 permutation-based significance levels.**** ##### Variables * Trait:
 meta-trait associated with the locus. * Chr: chromosome on which the QTL
 was detected. * Pos: position info for the QTL. * LOD: the logarithm of
 the odds (LOD) score of the QTL.  * CI_start: the starting position of the
 2LOD support interval.  * CI_end: the ending position of the 2LOD support
 interval.  #### File: Table_S13_qtl_LOD6.txt **Description:** QTL detected
 in meta-traits at LOD ≥ 6. ##### Variables * Trait: meta-trait associated
 with the locus. * Chr: chromosome on which the QTL was detected. * Pos:
 position info for the QTL. * LOD: the logarithm of the odds (LOD) score of
 the QTL.  * CI_start: the starting position of the 2LOD support interval. 
 * CI_end: the ending position of the 2LOD support interval.  #### File:
 Table_S14_fdr_by_threshold_all_meta_traits.txt **Description:** False
 discovery rates (FDRs) for each phenome-wide meta-trait at LOD ≥ 6 and ⍺ =
 0.05 significance thresholds.**** ##### Variables * Trait: the meta-trait
 for which FDR was assessed.  * LOD6_FDR: the FDR at a significance
 threshold of LOD ≥ 6. * alpha_05_FDR: the FDR at a permutation-based
 significance threshold of ⍺ = 0.05.  #### File:
 Table_S15_frailty_clusters.txt **Description:** Frailty meta-trait
 composition. Lists each of the frailty phenotypes after filtering by *h2*.
 ##### Variables * Trait: corresponds to one of the phenotypes in Table_S1.
 * Cluster: specifies the frailty meta-trait each trait comprises. ####
 File: Table_S16_aorta_clusters.txt **Description:** Aorta meta-trait
 composition. Lists each of the aorta phenotypes after filtering by *h^2^*.
 ##### Variables * Trait: corresponds to one of the phenotypes in Table
 S1. Trait suffixes correspond to the type of stain from which the
 measurement was derived: H&E (‘*he’),* trichrome (‘_tc’), or
 Verhoeff-Van Gieson (VVG; ‘_vvg’). * Cluster: specifies the aorta
 meta-trait each trait comprises. #### File:
 Table_S17_fdr_by_threshold_aorta.txt **Description:** False discovery
 rates (FDRs) for each aorta meta-trait at LOD ≥ 6 and ⍺ = 0.05
 significance thresholds. ##### Variables * Trait: the meta-trait for which
 FDR was assessed.  * LOD6_FDR: the FDR at a significance threshold of
 LOD ≥ 6. * alpha_05_FDR: the FDR at a permutation-based significance
 threshold of ⍺ = 0.05.  #### File: Table_S18_aorta_qtl.txt
 **Description:** QTL detected in aorta mega-analysis at LOD ≥ 6. #####
 Variables * Cluster: the ID of the aorta meta-trait. * Trait: the name of
 the meta-trait associated with the locus. * Chr: chromosome. * Pos:
 position of the peak marker.  * LOD: LOD score.  * CI_start: the starting
 position of the 2LOD support interval.  * CI_end: the ending position of
 the 2LOD support interval.  #### File: Table_S19_lung_clusters.txt
 **Description:** Lung meta-trait composition. Lists each of the lung
 phenotypes after filtering by *h^2^*. ##### Variables * Trait: corresponds
 to one of the phenotypes in Table_S1. As in Table S16, trait suffixes
 correspond to the type of stain from which a phenotypic measurement was
 derived, when present. * Cluster: specifies the lung meta-trait each trait
 comprises. #### File: Table_S20_lung_qtl.txt **Description:** QTL detected
 in lung mega-analysis at LOD ≥ 6. ##### Variables * Cluster: the ID of the
 lung meta-trait. * Trait: the name of the meta-trait associated with the
 locus. * Chr: chromosome. * Pos: position of the peak marker.  * LOD: LOD
 score.  * CI_start: the starting position of the 2LOD support interval.  *
 CI_end: the ending position of the 2LOD support interval. #### File:
 Table_S21_fdr_by_threshold_lung.txt **Description:** False discovery rates
 (FDRs) for each lung meta-trait at LOD ≥ 6 and ⍺ = 0.05 significance
 thresholds. ##### Variables * Trait: the meta-trait for which FDR was
 assessed.  * LOD6_FDR: the FDR at a significance threshold of LOD ≥ 6. *
 alpha_05_FDR: the FDR at a permutation-based significance threshold of ⍺ =
 0.05.  #### File: Data_D1_all_8state_69k.RData **Description:** Haplotype
 probability information for the animals included in this study at ~69k
 genetic pseudomarkers. A list of three-dimensional matrices, with
 dimensions corresponding to animal, 8-state founder allele probability,
 and marker. The R object is titled 'apr' by default.  ##### List
 structure A list of 20 three-dimensional matrices, the first 19 of which
 correspond to each mouse autosome. The 20^th^ matrix corresponds to the X
 chromosome. Names of the matrices can be accessed via names(apr) in R. 
 **Matrix structure** For each matrix, there are three dimensions. *
 Animal: the animals in the dataset (*e.g.* apr[[1]][1,,] results in a
 dataframe of 8-state allele probabilities at all markers for the first
 mouse on chromosome 1).  * 8-state allele probability: a vector of 8
 probabilities corresponding to the DO founding strains
 (*e.g.* apr[[1]][,1,] results in a dataframe of probabilities for the
 first founder allele at all markers and for all mice on chromosome 1). The
 probability is the chance that the animal contains a particular founder
 strain's allele at a marker. The names along this dimension are A-H,
 each corresponding to a particular founding strain: A- A/J, B- C57BL/6J,
 C: 129S1/SvImJ, D: NOD/ShiLtJ, E: NZO/HlLtJ, F: CAST/EiJ, G: PWK/PhJ,
 H: WSB/EiJ. 
 See [https://www.jax.org/research-and-faculty/genetic-diversity-initiative/tools-data/diversity-outbred-reference-data](https://www.jax.org/research-and-faculty/genetic-diversity-initiative/tools-data/diversity-outbred-reference-data) for details.  * Marker: A genetic pseudomarker (interpolated marker data) at a particular location on the chromosome (*e.g.* str(apr[[1]][,,1]) results in a dataframe 8-state allele probabilities for all mice at the first marker on chromosome 1).  **Example** apr[[3]][1,5,8] would return a single value: the allele probability of the fifth founder allele (NZO/HlLtJ) at the 8^th^ pseudomarker on chromosome 3 for the first mouse in the dataset.  #### File: Data_D2_kinship_loco.Rdata **Description:** A list of 20 kinship matrices generated via the ‘qtl2’ packages in R. Each matrix corresponds to a chromosome, with the 20th matrix corresponding to the X chromosome. Rows and columns in the matrices correspond to animals in the dataset.  #### File: Data_S1_69k_grid_pgmap.rdata **Description:** Pseudomarkers used to interpolate genotype data across study. This is loaded into R as both a genetic map (named 'gmap' by default) and a physical map (named 'pmap' by default). Each map is a list of 20 vectors corresponding to chromosomes 1-19 and the X chromosome, respectively. Values in each vector correspond to the genetic position in centimorgans ('gmap') or physical position in megabases ('pmap'). The names of each vector correspond to the name of the pseudomarker using the naming convention "chromosome_position", where position is the position of the pseudomarker in bases.  #### File: Data_D3_meta_trait_gwas.zip **Description:** Genome-wide scan data for meta-traits. This .zip file contains a directory titled 'gwas', which contains 383 .Rdata files, each of which encodes a single-column *rqtl2::scan1() *dataframe titled 'scan' that contains GWAS results for a meta-trait in the dataset. The values in the 'scan' object are -log10(p) values for each of the ~69k pseudomarkers with rownames corresponding to the markers. The column name corresponds to the meta-trait that was analyzed.  #### File: Data_D4_variant_association_and_gene_annotations.zip **Description:** Variant association mapping for each phenome-wide meta-trait QTL, and lists of genome annotations within the confidence intervals of the QTL. The .zip file contains a directory titled 'finemapping', which contains ~1,700 files, each of which contains variant association data for a QTL detected in the GWAS of a meta-trait in our dataset. There are two files for each QTL: a variant association mapping file and a genes file. The variant association mapping files use the naming convention "meta_trait_k_385_cluster_X_chrY_posZ", where X is the meta-trait, Y is the chromosome, and Z is the position of the sentinel pseudomarker underlying the QTL. The genes file follow the same convention but have the suffix "_genes" at the end of the filename.  **Variant association files:** **Description:** Dataframes containing variant association mapping for a particular QTL.  **Variables:** * snp: the name of the variant-  contains chromosome, position, and nucleotide information.  * LOD: the LOD score of the variant.  * chr: the chromosome on which the variant is located. * pos: the physical position of the variant in megabases. * alleles: the alleles/nucleotides present at the variant.  * sdp: the strain distribution pattern of the alleles. This is a number/factor that encodes which of the 8 founder strains contains the major/minor allele.  * ensembl_gene: the Ensembl ID of the gene that a variant is present within. A blank value indicates that the variant is not within a gene.  * consequence: the function annotation associated with the variant.  * A_J - WSB_EiJ: factors specifying which allele is present in each of the founder strains.  * type: the type of variant. Can be "snp" (single nucleotide polymorphism), "indel" (insertion/deletion), or "SV" (structural variant, meaning a larger-scale genomic rearrangement). * on_map: TRUE/FALSE specifying whether the variant is in the physical/genetic map or not. Data for variants not on the map is imputed from genotype data at pseudomarkers during variant association.  **Gene files:** **Description:** A dataframe of genes within the region used for variant association mapping. Each row corresponds to a gene.  **Variables:** * chromosome: the chromosome on which the gene is located. * source: the database used to access gene information. * type: the type of annotation. Can either be "gene" or "pseudogene" * start: the start position of the gene in megabases. * end: the ending position of the gene in megabases.  * score: not used; always 'NA'. * strand: the strand of DNA on which the gene is encoded. Can be "+" or "-".  * phase: not used; always 'NA'.  * ID: the ID of the gene, taken from the 'source' database. Includes data on the database and the reference genome that was used.  * Name: the name of the gene.  * Parent: not used; always 'NA'.  * Dbxref: databases that were cross-referenced for gene data.  * gene_id: the ID of the gene, taken from the 'source' database. Only includes the source database and gene ID number (doesn't include ref genome information).  * mgi_type: the gene annotation in the source database.  * description: a functional description of the gene.  #### File: Data_S2_Aorta_Meta_Analyses.RData **Description:** Genome-wide scan data for aorta meta-traits. See description of Data_S4. #### File: Data_S3_Aorta_Variant_Association.zip **Description:** Variant association mapping for each aorta meta-trait QTL, and lists of genome annotations within the confidence intervals of the QTL. See description of Data_S5. #### File: Data_S4_Lung_Meta_Analyses.RData **Description:** Genome-wide scan data for lung meta-traits. See description of Data_S4. #### File: Data_S5_Lung_Variant_Association.zip **Description:** Variant association mapping for each lung meta-trait QTL, and lists of genome annotations within the confidence intervals of the QTL. See description of Data_S5. ## Code/software Analysis was conducted using R/qtl2_0.36.  Functions used to estimate *h^2^* and *r~g~* are available at: [https://github.com/calico/HE-regression](https://github.com/calico/HE-regression) ## Change log Second version: updates to file names. No data changed.