Genetic correlation-guided mega-analysis of DO mice provides mechanistic insight and candidate genes for age-related pathologies

Abstract

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.

Fig 1. Performance of Haseman-Elston regression implementation and estimation of r_g among traits measured across diet and time point.

A, Comparison of h² estimates for 36 DO mouse traits derived from our implementation of Haseman-Elston regression to those from an orthogonal method, Residual Maximum Likelihood (REML). The line of identity is denoted by a solid line; the best-fit line is denoted by a dashed line. The Pearson correlation coefficient and accompanying p-value are reported in the upper-left of the plot area. Traits are colored by type: ‘AS’ - Acoustic Startle. ‘DX’ - DEXA scan. ‘EC’ - Echocardiogram. ‘GS’ - Grip Strength. ‘RR’ - Rotarod. ‘WR’ - Wheel Running. B, Comparison of r_g estimates among all pairs of 36 DO mouse traits derived from Haseman-Elston regression to those from REML. C, Mean intratrait r_g,t across time points for individual traits measured in the DRiDO study. Black bars denote the average mean intratrait r_g for traits of a particular type, and gray bars represent the mean intratrait r_g + /- the standard error of the mean. ‘AS’ - acoustic startle. ‘BW’ - body weight. ‘CBC’ - complete blood count. ‘FACS’ - immune cell composition via fluorescence activated cell sorting. ‘PIXI’ - body composition (lean, fat, bone density). D, Mean intratrait r_g,d dietary groups for individual traits measured in the DRiDO study. E, Mean intratrait r_g across diet and time point for each category of trait, with accompanying standard errors. F, Mean genetic correlations among traits measured at different time points in the Dietary Restriction in DO Mice (DRiDO) study. Size of the squares are inversely proportional to the standard error of each mean genetic correlation (left). G, Mean genetic correlations among traits measured in different dietary groups in the DRiDO study.

Fig 2. An atlas of r_g and r_pg estimates among complex phenotypes in DO mice.

A, The distribution of h², r_g, and r_pg, estimates for DO mouse phenotypes used in downstream analysis. The median, 25^th percentile, and 75^th percentile of the distribution are denoted via box and whisker plots. B, The relationship between the pairwise genetic correlation (r_g) and Pearson correlation of genetic correlations (r_pg) for each pair of traits in the dataset. Pearson and Spearman correlation coefficients are reported. C, A hierarchically clustered heatmap of r_g (lower triangle) and r_pg (upper triangle) estimates among 1,898 DO phenotypes. Phenotypes measured in individual dietary groups from the DRiDO study are excluded. The accompanying dendrogram shows euclidean distances among traits based on their r_pg estimates. D, The distribution of r_pg values among traits measured in the same study (intra-study, blue) or in different studies (inter-study, red). The median, 25^th percentile, and 75^th percentile of the distribution are denoted via box and whisker plots. E-F, Force-directed network diagrams of the 1,898 DO mouse phenotypes used in downstream analysis, with nodes corresponding to individual traits. Networks are colored by study (E) and trait type (F). Cal. Int. - Calico internal phenotypes. Sven - Svenson high fat diet study. Pazdro - Heart study. Ches - Chesler striatum study. Meta - meta analysis of lifespan. Attie - Pancreas and insulin secretion study. BW - bodyweight. DFI & BCS - Digital frailty index and body condition score. G, Plot of mean h² ratios among clusters of traits at different levels of hierarchical clustering (k). H, The location and LOD scores of the 884 QTL detected in meta-analyses of 383 clusters of genetically correlated traits (meta-traits). I, The number of QTL detected at LOD ≥ 6 in each of the 383 meta-traits plotted against the h² of each meta-trait. Points are colored by the percent of heritable trait variation explained by the detected QTL.

Fig 3. Genetic correlations involving measurements of lifespan in DO mice.

A, Heatmap of genetic correlations and heritability estimates among three independent measurements of lifespan in DO mice. The diagonal of the heatmap reports the h² of each lifespan trait. The lower triangle of the heatmap shows r_g among the lifespan traits and the upper triangle shows the r_pg among lifespan traits. Asterisks denote statistically significant h², r_g, or r_pg estimates. The dendrogram shows the results of hierarchical clustering of the lifespan traits based on euclidean distance derived from the r_pg values among each pair of traits. Clusters of traits that result in a maximal mean h² ratio when combined are represented by colors in the dendrogram as well as black boxes in the heatmap. B, Scatterplots of r_pg estimates between lifespan traits and the 380 other non-lifespan meta-traits. C, The strongest positive and negative r_pg estimates between meta-traits and combined lifespan data. Error bars correspond to the standard error of each r_pg estimate. Meta-trait number (cluster number) is shown on the left hand side of the plot and a brief summary of the phenotypes included in each meta-trait is shown on the right. D, Examples of trait clusters that, when aggregated into meta-traits, have high r_pg estimates with combined lifespan data. C_all 53 (top) comprises midlife (year 2) traits reflecting relative abundances of neutrophils and lymphocytes in the blood and includes frailty index and body condition scores. C_all 251 (bottom) comprises CD11 + B and chronic lymphoid leukemia-like (CLL) cell abundance traits from early and midlife (years 1 and 2). E, Manhattan plot of an additive whole-genome scan of a mega-analysis of lifespan consisting of data from the DRiDO, Harrison, and Shock lifespan studies. The red line indicates a genome-wide significance threshold of ⍺ = 0.05 based on 1,000 permutations of the data (see Methods). The dashed red line indicates a FDR-based significance threshold of LOD >= 6 (corresponding to an FDR of ~9%). QTL mapped at genome-wide significance in the mega-analysis or one of the individual studies comprising the analysis are denoted by asterisks. F, Volcano plot depicting the correlation coefficient and -log₁₀(p) of haplotype effects at lifespan loci with overlapping QTL from meta-analyses of other trait clusters. G, Expression of genes within the 2LOD support interval of the lifespan QTL on chr. 18 in human Natural Killer (NK) cells. H, Expression of genes within the 2LOD support interval of the lifespan QTL on chr. 12 in human NK, B, and T cells.

Fig 4. Genetic correlations among frailty and lifespan phenotypes.

A, Pairwise (r_g, bottom triangle) and Pearson (r_pg, upper triangle), genetic correlations among Manual Frailty Index (MFI), Digital Frailty Index (DFI), and lifespan. Statistically significant genetic correlations are denoted with an asterisk. Box color represents the magnitude of the genetic correlation and box size is inversely correlated with the size of the standard error. B-C, Heritability and standard error of individual (B) and aggregate (C) frailty phenotypes. The dashed red line at h² = 0.1 is the threshold used to determine if phenotypes were included in downstream analysis. D, Hierarchically clustered heatmap showing r_g (lower triangle) and r_pg (upper triangle) among frailty phenotypes. The accompanying dendrogram shows euclidean distances among traits based on their r_pg estimates. Clusters of frailty traits (F₁ - F₁₀) that result in a maximal mean h² ratio when combined are represented by colors in the dendrogram as well as black boxes in the heatmap. r_g and r_pg among frailty and unclustered lifespan phenotypes are shown in the adjacent panels of the heatmap. E, Heritability and standard error of the meta-traits comprising clusters of genetically correlated frailty phenotypes. F, r_pg and standard error among frailty meta-traits and a meta-analysis of lifespan spanning three studies.

Fig 5. Genetic correlations among extracellular matrix traits in the aorta.

A, Hierarchically clustered heatmap showing r_g (lower triangle) and r_pg (upper triangle) among aorta phenotypes. The accompanying dendrogram shows euclidean distances among traits based on their r_pg estimates. Clusters of aorta phenotypes that result in a maximal mean h² ratio when combined are represented by colors in the dendrogram as well as black boxes in the heatmap. B, Manhattan plot of an additive whole-genome scan on aggregated aortic elastin thickness and pct. area phenotypes. The red line indicates a genome-wide significance threshold of ⍺ = 0.05 based on 1,000 permutations of the data (see Methods). The dashed red line indicates a nominal significance threshold of LOD >= 6. C, Manhattan plot of an additive whole-genome scan on aggregated aortic elastin breakage phenotypes. D, Manhattan plot of an additive whole-genome scan on aggregated aortic elastin layers phenotypes. E–I, Correlations of haplotype effects between the peak markers at QTL influencing elastin phenotypes and cis-eQTL in an external DO cardiac tissue transcriptomic dataset. Candidate genes are highlighted with red text.

Fig 6. Genetic correlations among lung structural and functional phenotypes.

A, Hierarchically clustered heatmap showing r_g (lower triangle) and r_pg (upper triangle) among lung phenotypes. The accompanying dendrogram shows euclidean distances among traits based on their r_pg estimates. Clusters of lung phenotypes that result in a maximal mean h² ratio when combined are represented by colors in the dendrogram as well as black boxes in the heatmap. B, Manhattan plot of an additive whole-genome scan on aggregated fibrosis volume phenotypes. The red line indicates a genome-wide significance threshold of ⍺ = 0.05 based on 1,000 permutations of the data (see Methods). The dashed red line indicates a nominal significance threshold of LOD >= 6. C–E, Allelic effects (BLUPs) and corresponding standard errors of the QTL influencing fibrosis volume in the lung (left), and variant association mapping of the 2 LOD support intervals around each QTL (right). Genes within the confidence intervals are shown in the bottom panel, and candidate genes are shown above variant association results. F, Manhattan plot of an additive whole-genome scan on aggregated fibrosis intensity phenotypes. G–I, Allelic effects (BLUPs) and corresponding standard errors of the QTL influencing fibrosis intensity in the lung (left), and variant association mapping of the 2 LOD support intervals around each QTL (right). J, Model depicting the cellular functions of pulmonary neuroendocrine cells (PNECs) and showcasing the roles of candidate genes may play in contributing to fibrotic µCT readouts in the lung. Candidate genes are colored by the whole-genome scan in which they were detected.