Methylation clocks fail to generalize across genetically admixed individuals
- Biological and Medical Informatics Program, University of California, San Francisco, United States
- Bakar Computational Health Sciences Institute, University of California, San Francisco, United States
- Department of Epidemiology and Biostatistics, University of California, San Francisco, United States
- John P. Hussman Institute for Human Genomics, Miller School of Medicine, University of Miami, United States
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, United States
- The Dr. John T. Macdonald Foundation Department of Human Genetics, Miller School of Medicine, University of Miami, United States
- Neurogenetics Research Center, Instituto Nacional de Ciencias Neurologicas, Peru
- Department of Internal Medicine, Universidad Central Del Caribe, Puerto Rico
- Maya Angelou Center for Health Equity, Wake Forest University, United States
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, United States
Figures
Figure 1 with 2 supplements
Schematic of the workflow of the study.
We analyzed genome-wide methylation and genotyping data from blood samples from 621 AD and non-demented control individuals from the MAGENTA study. To replicate our findings, we analyzed data from 1394 healthy African Americans from the GENOA study, 422 healthy African Americans from the Grady Trauma Project, and 729 healthy Whites from the NSPHS study. We applied a set of first-, second-, and third-generation methylation clocks to the individuals and estimated their genetic ancestry. This enabled us to explore the performance of methylation clocks in individuals with different genetic ancestries.
Figure 1—figure supplement 1
The age distributions of the MAGENTA study cohorts do not differ systematically.
We compiled the chronological ages for the individuals in the MAGENTA study and compared their distributions, stratifying by their respective cohorts. The differences in age distributions for the cohorts, particularly between Whites, Puerto Ricans, and African Americans, do not explain the significant difference in clock accuracy between these cohorts.
Figure 1—figure supplement 2
Principal component analysis (PCA) of MAGENTA methylation data.
We performed a PCA of the normalized methylation data for all 621 MAGENTA individuals and calculated the variance explained by the first two PCs. PC1 explains 14.96% of the variance in the data, and PC2 explains 8.63% of the variance. Importantly, coloring the samples by cohort, sample center, sex, disease status, ethnicity, and sample plate did not show any outright stratification across the two PCs.
Figure 2 with 1 supplement
Methylation clock accuracy is lower in cohorts with substantial African genetic ancestry.
(A) Pearson correlation between chronological age and DNAm age predicted by the Horvath clock for controls in the White MAGENTA cohort. The correlation of is consistent with previous studies of similar age groups. (B) Pearson correlation between chronological age and DNAm age predicted by the Horvath clock for the genetically admixed cohorts in MAGENTA. For each cohort, the adjacent boxplots display the distribution of global ancestry proportions: European (CEU, red), African (YRI, green), and Amerindigenous (PEL, blue). The cohorts with substantial African ancestry—African Americans and Puerto Ricans—exhibit lower correlations ( and 0.45, respectively) compared to the Cubans () and Peruvians (). (C) Relative accuracy of the Horvath clock across cohorts compared to Whites. The bar plot shows the difference in Pearson correlation coefficients relative to the White cohort baseline (). Asterisks indicate a statistically significant difference from the baseline (* p < 0.05). Error bars denote 95% confidence intervals for relative correlation differences (Zou's method).
Figure 2—figure supplement 1
Combining cases and controls.
We calculated the correlation between Horvath DNAmAge and chronological age over combined AD cases and non-demented controls in the MAGENTA cohorts. The correlations did not change dramatically across the cohorts.
Figure 3 with 3 supplements
Lower methylation clock accuracy in African Americans holds across cohorts.
Scatter plots of chronological age versus Horvath DNAm age in three independent replication cohorts: Swedish Whites (NSPHS), GENOA Study African Americans, and Grady Trauma Project African Americans. Top Row: Age predictions for the full age range of each cohort. The Swedish White cohort exhibits the highest accuracy (, MAE = 3.04), while both African American cohorts show lower correlations ( and ) and higher error rates (MAE = 3.83 and 5.14, respectively). Bottom Row: Age predictions restricted to individuals ≥55 years old, similar to the demographics of the MAGENTA cohorts. In this age-restricted subset, the disparity in clock performance is consistent, with the Swedish White cohort maintaining a significantly higher correlation () compared to the GENOA () and Grady () African American cohorts.
Figure 3—figure supplement 1
Principal component (PC) Horvath clock applied to MAGENTA controls.
We evaluated the correlation between the PC Horvath DNAmAge and chronological age in non-demented controls from the MAGENTA cohorts. This version of the clock did not yield consistent improvements in age prediction accuracy or generalizability across MAGENTA cohorts.
Figure 3—figure supplement 2
Principal component (PC) Horvath clock applied to MAGENTA cases and controls.
We evaluated the correlation between the PC Horvath DNAmAge and chronological age in both cases and non-demented controls from the combined MAGENTA cohorts. This approach did not consistently improve the clock’s performance compared to the controls alone.
Figure 3—figure supplement 3
Principal component (PC) Horvath clock applied to replicate datasets.
We evaluated the correlation between the PC Horvath DNAm Age and chronological age in the three external, replication datasets of White Swedish individuals and African Americans. We observed results consistent with those obtained on the MAGENTA cohorts.
Figure 4
Lower methylation clock accuracy in cohorts with African ancestry holds across multiple clocks.
Pearson correlation coefficients between chronological age and DNAm age predicted by the Horvath (top), Hannum (middle), and Zhang_EN (bottom) clocks across all MAGENTA and replication (GENOA, Grady, Swedish) cohorts. The vertical dashed line in each panel represents the baseline correlation observed in the MAGENTA White cohort. Cohorts with substantial African ancestry (African American and Puerto Rican, red bars) consistently exhibit lower correlations compared to Whites, Peruvians, and Cubans across all three clock models. Asterisks indicate a statistically significant difference in correlation compared to the White MAGENTA cohort baseline (* p < 0.05).
Figure 5 with 2 supplements
Methylation clocks do not consistently identify accelerated aging in admixed Alzheimer’s cohorts.
(A) Comparison of the distributions of Horvath intrinsic age acceleration for AD patients and non-demented controls for each of the admixed cohorts in MAGENTA. AD patients do not show significantly higher age acceleration in any of the admixed cohorts. In contrast, the AD cases had significantly greater acceleration than controls in the white cohort (Figure 5—figure supplement 1). NS, not significant. (B) Median differences (in years) in intrinsic age acceleration between AD patients and non-demented controls for five methylation clocks for each cohort in MAGENTA. The clocks do not consistently identify accelerated aging in AD across cohorts, and the results also vary within cohorts. * p < 0.05.
Figure 5—figure supplement 1
The Horvath Clock accurately discerns between white AD patients and non-demented controls.
Horvath clock intrinsic age acceleration was significantly greater on average for white AD patients than matched, non-demented controls (median 1.7 vs. 1.5 years, p = 0.041), consistent with previous literature.
Figure 5—figure supplement 2
Combinations of DNAmAge and intrinsic age accelerations from multiple methylation clocks do not improve their performance.
We combined multiple methylation clocks and their respective predictions for all MAGENTA individuals but saw no increase in accuracy over individual clocks.
Figure 6 with 6 supplements
Genetic variants with different frequencies between ancestries associate with methylation levels at clock CpGs.
(A) Percentage of CpG sites in each methylation clock that exhibit differential methylation in whole blood between individuals of African vs. European ancestry. (B) Methylation levels at some clock CpG sites significantly associate with error across MAGENTA individuals CpGs for each first generation clock are sorted based on the multiple-testing-corrected significance of their association with clock error in MAGENTA. (C) The allele frequency distribution of the single nucleotide variants that disrupt CpG sites considered by the Horvath clock in 76,156 individuals from gnomAD (v3.0). Of the 353 clock CpG sites, 245 (69%) have at least one variant, but nearly all the variants are very rare. (D) Number of CpGs in each clock that are influenced by methylation quantitative trait loci (meQTLs). The Horvath clock contains 271 meQTL-associated CpGs, substantially more than the Hannum, EN, PhenoAge, or DunedinPACE clocks. (E) Clock meQTL have significantly higher allele frequency in individuals with African genetic ancestry from gnomAD than all other ancestry groups (median 0.068 for African vs. 0.004–0.046; p <3.85×10−25). (F) Frequency of meQTL variants in the MAGENTA cohorts. Non-differentiated meQTLs (left) have similar frequencies across cohorts, but African-differentiated meQTLs (right) are significantly more frequent in African Americans (AA) and Puerto Ricans (PR) compared to Peruvians (PER) and Cubans (CUB). (G) Venn diagrams illustrating the overlap between CpGs that contribute to clock prediction error (‘Error-associated’, blue) and those influenced by meQTLs (‘meQTL affected’, pink) for the first-generation clocks.
Figure 6—figure supplement 1
Allele frequencies for Horvath clock CpG-disrupting variants across each gnomAD v.3.0 ancestry.
The allele frequencies of the clock CpG-disrupting variants were very low, even after stratification by gnomAD ancestries. Note that the Amish had only two variants in the Horvath clock CpGs, but only one was common at 1% allele frequency.
Figure 6—figure supplement 2
Overlap of gnomAD version 4.1 variants and clock CpG sites across multiple clocks.
We intersected the genomic coordinates of all clock CpG sites across all clocks tested in our study with all variants in the latest version of the gnomAD database. Many variants overlap the clock CpG sites, but only a small number for each clock are common (AF>1%): Hannum (1), Zhang19_EN (5), PhenoAge (2), and DunedinPACE (1).
Figure 6—figure supplement 3
Allele frequencies of meQTL that affect Horvath clock CpG sites.
We compiled the reported allele frequencies for thousands of variants that affect the methylation levels of Horvath clock CpG sites. Most variants are reported to be common in their respective study populations. (A) Variants from EUR and SAS individuals; (B) Variants from African American individuals; (C) variants from EUR individuals in the UK.
Figure 6—figure supplement 4
Allele frequencies of meQTL that affect Horvath clock CpG sites, stratified by local ancestry blocks in gnomAD admixed individuals.
The allele frequency distribution of the 17,180 unique variants associated with methylation levels at Horvath clock CpGs found in gnomAD Latino admixed individuals. Clock meQTL have significantly higher allele frequencies in African local ancestry genomic segments among the 7612 Latino admixed individuals with varying proportions of European, American, and African ancestry from gnomAD v3.1.2.
Figure 6—figure supplement 5
Venn diagrams for overlap of clock CpG sites.
The four-way overlap includes sets of clock CpGs whose methylation levels are significantly associated with increased clock error in MAGENTA individuals, clock CpGs that are differentially methylated in whole blood samples of African ancestry individuals relative to whole blood samples of European ancestry individuals (adjusting for chronological age and sex), clock CpGs affected by meQTL, and clock CpGs affected by African ancestry-differentiated meQTL.
Figure 6—figure supplement 6
Distribution of unique meQTL affecting Horvath clock CpG sites.
We analyzed the distribution of the meQTL from EUR, SAS, and AFR individuals that affect Horvath clock CpG sites. Blue dashed lines indicate the maximum number of variants affecting a clock CpG site (1,699), orange dashed lines indicate the mean (108.3), and green dashed lines indicate the median (36).
Tables
Table 1
Demographics of the MAGENTA study cohorts.
| Alzheimer’s (N=313) | Control (N=308) | Overall (N=621) | |
|---|---|---|---|
| Cohort | |||
| African American | 98 (31.3%) | 107 (34.7%) | 205 (33.0%) |
| Cuban | 22 (7.0%) | 21 (6.8%) | 43 (6.9%) |
| White | 68 (21.7%) | 65 (21.1%) | 133 (21.4%) |
| Peruvian | 41 (13.1%) | 41 (13.3%) | 82 (13.2%) |
| Puerto Rican | 84 (26.8%) | 74 (24.0%) | 158 (25.4%) |
| Sex | |||
| Female | 206 (65.8%) | 213 (69.2%) | 419 (67.5%) |
| Male | 107 (34.2%) | 95 (30.8%) | 202 (32.5%) |
Appendix 1—table 1
Performance metrics for the Horvath Clock applied to non-demented controls in the MAGENTA cohorts.
| MAGENTA cohort | Sample size | Median absolute error (MAE) | Mean squared error (MSE) | R |
|---|---|---|---|---|
| Whites | 65 | 5.10 | 38.7 | 0.72 |
| African Americans | 107 | 5.38 | 46.7 | 0.51 |
| Puerto Ricans | 74 | 5.19 | 48.4 | 0.45 |
| Peruvians | 41 | 4.19 | 26.8 | 0.72 |
| Cubans | 21 | 5.60 | 53.6 | 0.68 |
Appendix 1—table 2
Summary of Horvath Clock performance across all evaluated cohorts (individuals ≥55 years old).
| Cohort | Sample size | Median absolute error (MAE) | Mean squared error (MSE) | R |
|---|---|---|---|---|
| MAGENTA Whites | 133 | 4.90 | 38.6 | 0.75 |
| MAGENTA African Americans | 205 | 5.62 | 60.4 | 0.58 |
| MAGENTA Puerto Ricans | 158 | 4.92 | 42.7 | 0.55 |
| MAGENTA Peruvians | 82 | 4.68 | 33.1 | 0.74 |
| MAGENTA Cubans | 43 | 5.18 | 42.7 | 0.74 |
| Grady Project African Americans | 62 | 5.49 | 53.4 | 0.57 |
| GENOA Study African Americans | 865 | 4.12 | 39.7 | 0.67 |
| White Swedish Individuals | 280 | 3.73 | 34.9 | 0.79 |
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Methylation clocks fail to generalize across genetically admixed individuals
eLife 14:RP105343.
https://doi.org/10.7554/eLife.105343.3
