bioRxiv preprint doi: https://doi.org/10.1101/2021.06.23.449634; this version posted June 24, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. 1 Genetic Analyses of Epigenetic Predictors that Estimate Aging, 2 Metabolic Traits, and Lifespan 3 Khyobeni Mozhui 1, 2*, Ake T. Lu3, Caesar Z Li 3, Amin Haghani 4, Jose Vladimir Sandoval-Sierra 1, 4 Robert W. Williams 2, Steve Horvath3, 4 5 6 1 Department of Preventive Medicine, University of Tennessee Health Science Center, College of 7 Medicine, Memphis, Tennessee, USA 8 2 Department of Genetics, Genomics and Informatics, University of Tennessee Health Science 9 Center, College of Medicine, Memphis, Tennessee, USA 10 3 Department of Human Genetics, David Geffen School of Medicine, University of California Los 11 Angeles, Los Angeles, CA, USA 12 4 Department of Biostatistics, Fielding School of Public Health, University of California Los 13 Angeles, Los Angeles, CA, USA 14 15 *Correspondence: 16 Khyobeni Mozhui 17 [email protected] 1 bioRxiv preprint doi: https://doi.org/10.1101/2021.06.23.449634; this version posted June 24, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. 18 Abstract 19 DNA methylation (DNAm) clocks are accurate molecular biomarkers of aging. However, the 20 clock mechanisms remain unclear. Here, we used a pan-mammalian microarray to assay DNAm 21 in liver from 339 predominantly female mice belonging to the BXD family. We computed 22 epigenetic clocks and maximum lifespan predictor (predicted-maxLS), and examined 23 associations with DNAm entropy, diet, weight, metabolic traits, and genetic variation. The 24 epigenetic age acceleration (EAA) derived from the clocks, and predicted-maxLS were 25 correlated with lifespan of the BXD strains. Quantitative trait locus (QTL) analyses uncovered 26 significant QTLs on chromosome (Chr) 11 that encompasses the Erbb2/Her2 oncogenic region, 27 and on Chr19 that contains a cytochrome P450 cluster. Both loci harbor candidate genes 28 associated with EAA in humans (STXBP4, NKX2-3, CUTC). Transcriptome and proteome analyses 29 revealed enrichment in oxidation-reduction, metabolic, and mitotic genes. Our results highlight 30 loci that are concordant in human and mouse, and demonstrate intimate links between 31 metabolism, body weight, and epigenetic aging. 32 33 34 Keywords: epigenetic clock, lifespan, aging, DNA methylation, QTL, weight, diet 2 bioRxiv preprint doi: https://doi.org/10.1101/2021.06.23.449634; this version posted June 24, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. 35 Introduction 36 Epigenetic clocks are widely used molecular biomarkers of aging1. These biological clocks are 37 based on the methylation status across an ensemble of “clock CpGs” that are collectively used 38 to derive a DNA methylation (DNAm) based estimate of age (DNAmAge). This estimate tracks 39 closely, but not perfectly, with an individual’s chronological age. How much the DNAmAge 40 deviates from the known chronological age is a measure of the rate of biological aging. Denoted 41 as epigenetic age acceleration (EAA), a more accelerated measure (positive EAA) suggests an 42 older biological age. While DNAmAge predicts age, its age-adjusted counterpart, EAA, is 43 associated with health, fitness, exposure to stressors, body mass index (BMI), and even life 44 expectancy2-6. 45 DNAm clocks were initially reported for humans7,8. Since then, the age estimator has been 46 extended to model organisms9-11, and different variants of human clocks have also been 47 developed. Some clocks are tissue specific, others are pan-tissue, and others perform well at 48 predicting health and life expectancy5,8,12-14. 49 A new microarray platform was recently developed to profile CpGs that have high conservation 50 across mammalian clades. This pan-mammalian DNAm array (HorvathMammalMethylChip40) 51 provides a common platform to measure DNAm, and has been used to build universal 52 epigenetic clocks that can estimate age across a variety of tissues and mammalian species15,16. 53 Another remarkable development with this array is the novel lifespan predictor that can 54 estimate the maximum lifespan of over 190 mammals at high accuracy17. 55 Here, we examine these novel clocks, lifespan predictor, and methylome entropy in a cohort of 56 mice belonging to the BXD family that were maintained on either normal chow or high-fat diet 57 (HFD)18,19. The BXDs are a well-established mouse genetic reference panel that were first 58 created as a family of recombinant inbred (RI) strains by crossing two inbred progenitors: 59 C57BL/6J (B6) and DBA/2J (D2). The family has been expanded to ~150 fully sequenced progeny 60 strains20,21. Members of the BXD family vary greatly in their metabolic profiles, aging rates, and 61 natural life expectancy18,19,22-24. The genetic variation, and the availability of accompanying 62 deep -omic data make the BXDs a unique experimental population for dissecting the genetic 63 modulators of epigenetic aging. Previously, we explored the aging methylome in a small 64 number of BXD cases and found that HFD and higher body weight were associated with higher 65 age-dependent changes in methylation25. In the present work, our goals were to (1) test the 66 accuracy of the DNAm measures in predicting age, lifespan, and association with diet and 67 metabolic characteristics, and (2) apply quantitative trait locus (QTL) mapping and gene 68 expression analyses to uncover loci and genes that contribute to these DNAm biomarkers. 69 Our results are consistent with a faster clock for cases on HFD, and with higher body weight. 70 Both the DNAmAge and lifespan predictors were correlated with the genotype-dependent life 71 expectancy of female BXDs. We report QTLs on chromosomes (Chrs) 11 and 19. A strong 72 candidate gene in the chromosome (Chr) 11 interval (referred to as Eaaq11) is Stxbp4, a gene 73 that has been consistently associated with EAA by human genome-wide association studies 74 (GWAS)26-28. The Chr19 QTL (Eaaq19) also harbors strong contenders including Cyp26a1, Myof, 75 Cutc, and Nkx2–3, and the conserved genes in humans have been associated with longevity and 3 bioRxiv preprint doi: https://doi.org/10.1101/2021.06.23.449634; this version posted June 24, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. 76 EAA28-30. Eaaq19 may also have an effect on body weight change with age. We performed gene 77 expression analyses to clarify the physiology associated with the DNAm traits, and this, perhaps 78 unsurprisingly, highlighted metabolic networks as strong expression correlates of epigenetic 79 aging. 80 Results 81 Description of samples 82 The present study uses liver DNAm data from 339 predominantly female mice (18 males only) 83 belonging to 45 isogenic members of the BXD family, including F1 hybrids, and both parental 84 strains. Age ranged from 5.6 to 33.4 months. Mice were all weaned onto a normal chow 85 (control diet; CD) and a balanced subset of cases were then randomly assigned to the HFD (see 86 Roy et al for details 18). Tissues were collected at approximately six months intervals (see 87 Williams et al. 19). Individual-level data of cases used in this study are in Data S1. 88 Correlation with chronological age 89 For biological age prediction, three different types of mouse DNAm clocks were computed, 90 each as a pair: liver-specific, and pan-tissue (Table 1). These are: (1) a general DNAm clock 91 (referred to simply as DNAmAge): clock trained without pre-selecting for any specific CpG 92 subsets; (2) developmental clock (dev.DNAmAge): built from CpGs that change during 93 development; and (3) interventional clock (int.DNAmAge): built from CpGs that change in 94 response to aging related interventions such as caloric restriction, HFD, or dwarfing alleles9,11,25. 95 These clocks were trained either in an independent mouse dataset that did not include the 96 BXDs and were therefore unbiased to BXD characteristics (unbiased mouse clocks), or trained in 97 a subset of the BXD CD mice and used to estimate age in the full BXD cohort (BXD-biased Table 1. Epigenetic predictors and correlation with chronological age and entropy Training DNAmAge r with age r with entropy Clock type Tissue set1 name (n=339) (n=339) Standard unbiased DNAmAge pan 0.89 0.50 Standard unbiased DNAmAge liver 0.92 0.51 Developmental unbiased dev.DNAmAge pan 0.87 0.46 Developmental unbiased dev.DNAmAge liver 0.91 0.45 Interventional unbiased int.DNAmAge pan 0.85 0.38 Interventional unbiased int.DNAmAge liver 0.86 0.49 Standard BXD DNAmAge pan 0.93 0.55 Standard BXD DNAmAge liver 0.97 0.54 Developmental BXD dev.DNAmAge pan 0.95 0.51 Developmental BXD dev.DNAmAge liver 0.96 0.51 Interventional BXD int.DNAmAge pan 0.89 0.43 Interventional BXD int.DNAmAge liver 0.94 0.53 Universal unbiased univ.DNAmAge pan 0.92 0.59 Predicted- LiFespan unbiased pan -0.07 (ns) -0.13 (p=0.02) maxLS 1 Unbiased denotes that the clocks were trained in a completely independent dataset.