Does genetic predisposition modify the effect of lifestyle-related factors on DNA methylation?

ABSTRACT

Lifestyle-related phenotypes have been shown to be heritable and associated with DNA methylation. We aimed to investigate whether genetic predisposition to tobacco smoking, alcohol consumption, and higher body mass index (BMI) moderates the effect of these phenotypes on blood DNA methylation. We calculated polygenic scores (PGS) to quantify genetic predisposition to these phenotypes using training (N = 7,431) and validation (N = 4,307) samples. Using paired genetic-methylation data (N = 4,307), gene–environment interactions (i.e., PGS × lifestyle) were assessed using linear mixed-effects models with outcomes: 1) methylation at sites found to be strongly associated with smoking (1,061 CpGs), alcohol consumption (459 CpGs), and BMI (85 CpGs) and 2) two epigenetic ageing measures, PhenoAge and GrimAge. In the validation sample, PGS explained ~1.4% (P = 1 × 10⁻¹⁴), ~0.6% (P = 2 × 10⁻⁷), and ~8.7% (P = 7 × 10⁻⁸⁷) of variance in smoking initiation, alcohol consumption, and BMI, respectively. Nominally significant interaction effects (P < 0.05) were found at 61, 14, and 7 CpGs for smoking, alcohol consumption, and BMI, respectively. There was strong evidence that all lifestyle-related phenotypes were positively associated with PhenoAge and GrimAge, except for alcohol consumption with PhenoAge. There was weak evidence that the association of smoking with GrimAge was attenuated in participants genetically predisposed to smoking (interaction term: −0.022, standard error [SE] = 0.012, P = 0.058) and that the association of alcohol consumption with PhenoAge was attenuated in those genetically predisposed to drink alcohol (interaction term: −0.030, SE = 0.015, P = 0.041). In conclusion, genetic susceptibility to unhealthy lifestyles did not strongly modify the association between observed lifestyle behaviour and blood DNA methylation. Potential associations were observed for epigenetic ageing measures, which should be replicated in additional studies.

Introduction

Strong evidence shows that lifestyle-related factors influence epigenetic regulation mechanisms, such as DNA methylation [1–3]. For instance, tobacco use [4], alcohol consumption [5] and excess body weight [6] have been found to be strongly associated with blood DNA methylation changes. These lifestyle-related phenotypes are to some extent heritable [7–11]. Meta-analysis studies using imputed genotype data have estimated the genetic predisposition of an individual to smoke, drink alcohol, or become overweight or obese, with SNP-based heritability estimates of ~8% for smoking initiation [7], ~4% for alcohol drinks per week [7], and ~22% for body mass index (BMI) [8]. Therefore, a key question of interest is whether such genetic predisposition to unhealthy lifestyle-related factors modifies the relationship between such observed lifestyle-related phenotypes and DNA methylation.

One way to understand such a modification lies in examining gene–environment (i.e., gene–lifestyle) interactions in DNA methylation. Many gene–lifestyle interaction studies have focused on single genetic variants in candidate genes using Asian-ancestry samples [12–17]. For instance, the effects of alcohol intake and smoking on the risk of oesophageal and gastric cancer were reported to be strongly modified by risk alleles in alcohol dehydrogenase (ADH) and aldehyde dehydrogenase (ALDH) genes in large-scale population-based Japanese studies [12–14]. However, Ugai et al. found no evidence of a gene–environment interaction between the ALDH2 alcohol-consumption predisposition variant (rs671) and alcohol intake for breast cancer risk among Asian women from the Breast Cancer Association Consortium [17]. Thus, individual genetic variants, which have small effect sizes in association with complex lifestyle-related traits, may not always be informative for evaluating overall genetic susceptibility. Furthermore, these variants are typically very rare among European-ancestry populations compared to Asian-ancestry groups. For example, the minor allele frequency (MAF) of ALDH2 rs671 is 0% in TwinsUK registry, while it is 16%, 19%, and 21% for the Korean, Japanese, and Vietnamese populations, respectively [18]. Therefore, when using single variants, lack of power may limit the ability to detect interactions, especially for European-ancestry population-based data.

In contrast to candidate genes, polygenic scores (PGS), which summarize the estimated effects of many variants into a single value, have emerged as a powerful way to quantify an individual’s genetic predisposition to a phenotype [19]. In previous studies, PGS were found to account for approximately 4%, 2.5%, and 14% of variance in smoking initiation [7], alcohol consumption per week [7], and BMI [8], respectively, i.e., about half of the estimated SNP-based heritability of these phenotypes.

In this study, we hypothesized that genetic predisposition to unhealthy lifestyle-related phenotypes would moderate the effects of such observed lifestyle-related factors on some associated outcomes – for example, individuals genetically predisposed not to drink would likely drink less over their lifetime but might have a higher-than-average risk of associated disease. We used DNA methylation as an outcome variable in the analyses since many diseases have been shown to be associated with aberrant DNA methylation [20–26], including measures of epigenetic ageing [27–31]. Our aim in this study was therefore to investigate the interaction effects of observed tobacco smoking, alcohol consumption, and BMI with their respective PGS on DNA methylation in blood, for i) loci at which methylation changes with lifestyle and ii) two measures of epigenetic ageing, PhenoAge and GrimAge.

Materials and methods

Study participants

The Melbourne Collaborative Cohort Study (MCCS) is an Australian community-based study that recruited 41,513 European-ancestry participants in 1990–1994 [32]. Several nested case–control studies have been conducted to evaluate the associations between blood DNA methylation and the risk of eight types of cancer [33–36]. DNA was extracted from pre-diagnostic peripheral blood taken at recruitment (1990–1994) or at a subsequent follow-up visit (2003–2007) in cancer-free participants. Incident cases were matched to controls on age, sex, country of birth, and sample type (buffy coats, dried blood spots, and peripheral blood mononuclear cells) using incidence density sampling [32]. We used self-reported questionnaire-collected data on tobacco use, alcohol consumption, and measured height and weight to calculate BMI [4–6] for participants in the MCCS. The study was approved by the Cancer Council Victoria’s Human Research Ethics Committee, Melbourne, VIC, Australia, and all participants provided informed consent in accordance with the Declaration of Helsinki.

Genetic and DNA methylation data

Genome-wide genotyping was conducted on blood DNA samples from 12,584 MCCS participants using the Infinium OncoArray-500 K BeadChip (Illumina, San Diego, CA, USA) [32,37]. Following previous standardized protocols [38], we imputed autosomal genotypes using the Michigan imputation server [39] and IMPUTE version 2 [40] with the 1000 Genomes Project dataset (phase 3) as the reference panel. The genotype probabilities from imputation were used to hard-call (uncertainty <0.1) the genotypes for variants with an imputation info score >0.3. We then retained the hard-called variants with MAF > 0.1%, missing genotype rate <10% and Hardy-Weinberg equilibrium P-value > 10⁻⁶. To avoid bias due to confounding by shared environment among close relatives, individuals were removed based on relatedness by excluding one participant randomly selected from any pair with a genetic relationship >0.125 (third-degree or closer relationship) using the software GCTA [41]. After these quality control (QC) steps, 11,942 unrelated individuals with 9,355,361 genetic variants (including 8,578,993 SNPs) were retained for the follow-up analyses.

We measured DNA methylation in blood samples from 4,511 of the 11,942 participants using the HumanMethylation450 BeadChip (Illumina, San Diego, CA, USA) applying methods described previously [32,42,43]. Among the 4,511 participants, DNA of 4,307 was extracted at baseline recruitment (1990–1994). QC details for measures of genome-wide DNA methylation have been reported previously [34–36,44]. Briefly, we removed probes with missing rate >20% and probes on Y-chromosome, and ultimately retained 484,431 CpG sites with their beta values for each sample. Of these, we focused on 1,061 CpGs, 459 CpGs, and 85 CpGs (Tables S2–S4) that were found to be strongly associated with smoking, alcohol consumption, and BMI, respectively, with P < 10⁻⁷ in both the MCCS and external data [4–6,34]. Methylation M-values, calculated as log₂(beta/(1-beta)), were used as these are thought to be more statistically valid for the detection of differential methylation [45].

A total of 7,431 individuals with genetic data but without DNA methylation data were used as the PGS training sample, and 4,307 individuals with paired genetic-methylation data were used for PGS validation and all other analyses of this study (Table 1).

Table 1.

Characteristics of the MCCS participants used in the study.

Sample characteristic	Training sample for PGS (N = 7,431)	Validation sample for PGS and for main analysis (N = 4,307)
Age at blood draw (median [IQR])	53.7 [47.1–60.8]	59.6 [52.7–64.8]
Sex: male, N (%) female, N (%)	2,655 (36%) 4,776 (64%)	2,541 (59%) 1,766 (41%)
Blood sample type: dried blood spots, N (%) peripheral blood mononuclear cells, N (%) buffy coats, N (%)		3,240 (75%) 993 (23%) 74 (2%)
Smoking status: current, N (%) former, N (%) never, N (%)	644 (9%) 2,235 (30%) 4,552 (61%)	477 (11%) 1,638 (38%) 2,192 (51%)
Alcohol consumption last week (g/day) *, median [IQR]	2.7 [0–14.7]	4.3 [0–17.1]
Body mass index (kg/m2), median [IQR]	26.1 [23.6–28.9]	26.6 [24.3–29.4]

*Note: Participants reported their alcohol intake on each day during the previous week, in terms of the number, measure, and type of drink (beer, wine, spirits). Grams per day (g/day) were then calculated based on average number of drinks a participant reported, as described previously [5,41].

PGS analyses

We considered three lifestyle-related phenotypes: tobacco use, alcohol consumption, and BMI. The largest published GWAS to date for smoking and alcohol consumption (~1,200,000 samples) [7] and for BMI (~700,000 samples) [8] was used as base data, which provided estimated effect and P-value for each genetic variant; we used the same phenotypic definitions and variable transformations as those used in these GWAS [7,8].

For tobacco use, we used smoking initiation [7] – a dichotomous phenotype for participants reporting ever being a regular smoker in their lifetime (current or former smokers), and those who reported never being a regular smoker. For alcohol consumption, participants reported their alcohol intake on each day during the previous week, in terms of the quantity and type of drink (beer, wine, spirits). Grams per day (g/day) were calculated based on the average number of drinks a participant reported, as described previously [5,46]. The variable was left-anchored at 1 and log-transformed to minimize the influence of potential outliers [7]. For BMI, we applied a rank-based inverse normal transformation to the raw values (in kg/m²) to better approximate the normal distribution [8].

The PGS is defined as the sum of an individual’s risk alleles weighted by effect sizes (${\hat{\beta }}_j$) taken from the published GWAS summary statistics, i.e.,

$$PGS=\sum _{j=1}^m{\hat{\beta }}_j\cdot X_j$$

where m is the number of SNPs used for calculating the PGS and $X_j$ is the risk allele number for the jth SNP (0, 1, or 2). Here, we calculated PGS using the PRSice software [47,48] with LD clumping parameters of R² > 0.25 over 250-kb sliding windows. We removed ambiguous SNPs with A/T or G/C alleles [48]. A total of 7,431 MCCS individuals were used as a training dataset (‘target data’ in PRSice), and the PGS with the P-value threshold (from 5 × 10⁻⁸ to 1 by increments of 5 × 10⁻⁵) found to explain most of the variance in the phenotypes in the training dataset was chosen as the optimal polygenic score. The phenotypic values were adjusted for age, sex, and first 20 ancestry principal components (PCs) to account for population structure. To assess if there was overfitting in the association of optimal PGS with phenotype [48], we used 4,307 MCCS individuals, which were unrelated to the training dataset, as an out-of-sample validation.

For comparison with the optimal PGS, and to retain only SNPs having a strong effect on phenotypes of interest, we also calculated a ‘genome-wide significant PGS’ using only variants with a P-value < 5 × 10⁻⁸ and LD clumping parameters of R² > 0.25 over 250-kb sliding windows, and evaluated the association of this PGS with phenotype for both training and validation datasets.

Statistical analyses

Using 4,307 MCCS participants with paired genetic-methylation data, we examined the interaction effects of observed tobacco use, alcohol consumption, and BMI with their respective PGS on DNA methylation at individual CpG sites (1,061 CpGs, 85 CpGs, and 459 CpGs associated with smoking, alcohol consumption, and BMI, respectively [4–6,34]), using linear mixed-effects regression models with M-values as an outcome. The model was also adjusted for age, sex, first 20 ancestry PCs, sample type, white blood cell composition (percentage of CD4 + T cells, CD8 + T cells, B cells, NK cells, monocytes, and granulocytes estimated using the Houseman algorithm [49]) and the two other lifestyle-related phenotypes as fixed effects, and study, batch plate, and chip as random effects. Interaction effects were then assessed by examining the interaction term using the Wald test. It is noted that in these association analyses, we used a continuous comprehensive smoking index (CSI) variable [4,50] as it includes more information about smoking (cumulative lifetime exposure to tobacco smoke) than smoking initiation, which does not distinguish between current and former smokers or consider other smoking-related variables. No published GWAS to date has examined genetic associations with a comprehensive smoking index, thus we could not use this variable to generate the smoking PGS. For alcohol consumption and BMI, we used the phenotypic values defined in section ‘PGS analyses.’

We also used the same models to examine interaction effects of these lifestyle-related phenotypes with their PGS on epigenetic ageing measures, namely PhenoAge [27] and GrimAge [28], two composite predictors of mortality. These were calculated using the online calculator [51] and adjusted for age as described previously [31]. A flowchart showing the selection of participants of the Melbourne Collaborative Cohort Study and description of the analytical workflow is shown in Figure 1.

Figure 1.

The study flowchart indicating the selection of participants of MCCS and description of the analytical workflow.

Lastly, as a secondary analysis (not part of the original analysis plan), we investigated the same interaction effects (Bonferroni correction) for individual genome-wide significant SNPs associated with lifestyle factors (145 for smoking initiation, 79 for alcohol consumption, and 1,877 for BMI, Table 2).

Table 2.

PGS calculation for three lifestyle-related phenotypes in training and validation sample.

Lifestyle-related phenotype	Training sample (N = 7,431)
	Optimal PGS			Genome-wide significant PGS
	SNP number	R2	P	SNP number	R2	P
Smoking initiation	39,813	0.014	1.4 × 10−24	145	0.004	2.9 × 10−07
Alcohol consumption in previous week	151,167	0.006	5.3 × 10−12	79	0.002	4.8 × 10−4
BMI	156,400	0.085	2.4 × 10−146	1,877	0.041	8.5 × 10−70
Lifestyle-related phenotype	Validation sample (N = 4,307)
	Optimal PGS			Genome-wide significant PGS
	SNP number	R2	P	SNP number	R2	P
Smoking initiation	39,813	0.014	1.5 × 10−14	145	0.002	0.009
Alcohol consumption in previous week	151,167	0.006	2.0 × 10−7	79	0.005	1.4 × 10−6
BMI	156,400	0.087	6.6 × 10−87	1,877	0.039	3.9 × 10−39

Note: The SNPs included in optimal PGS were obtained using a P-value threshold (in base data, i.e., external GWAS) that maximized the variance explained in the phenotypes in the training sample, while the SNPs in ‘genome-wide significant PGS’ were obtained using a P-value threshold of 5 × 10⁻⁸. R² and P are the variance explained and strength of association between the phenotype and its PGS. The validation sample used the same SNPs as those retained in the optimal and genome-wide significant PGS from the training process.

Results

Sample characteristics of the MCCS participants used in this study are shown in Table 1.

We calculated optimal and genome-wide significant PGS of three lifestyle-related phenotypes for each participant, summarized in Table 2. In the validation sample, we found that the optimal PGS (derived from a training sample, see Figures S1–S3) explained ~1.4% (P = 1 × 10⁻¹⁴), ~0.6% (P = 2 × 10⁻⁷), and ~8.7% (P = 7 × 10⁻⁸⁷) of variance in smoking initiation, alcohol consumption in previous week and BMI, respectively. The genome-widesignificant PGS included substantially fewer SNPs and explained less variance. We also examined the genome-wide significant SNPs for each phenotype [7,8] individually, and found that the phenotypic variances explained by these single SNPs were very small in the validation sample compared with using the PGS (Table S1).

The interactions between the three lifestyle-related phenotypes and their respective optimal PGS in association with DNA methylation at 1,061 smoking-associated CpGs, 459 alcohol-consumption-associated CpGs, and 85 BMI-associated CpGs are shown in Tables S2–S4. Considering a nominal significance threshold of P < 0.05, the numbers of CpGs with significant interaction effects are shown in Table 3, none of them being greater than expected by chance. Considering a Bonferroni significance threshold for each of the three phenotypes (P < 0.05/1061 = 4.7 × 10⁻⁵, P < 0.05/459 = 1.1 × 10⁻⁴, and P < 0.05/85 = 5.9 × 10⁻⁴, respectively), we found a significant interaction for a BMI-associated CpG, cg11376147, chr11:57261198 (BMI main effect = −0.06, 95% CI: −0.08, −0.03, P = 8.7 × 10⁻⁷; interaction effect with PGS = −0.04, 95% CI: −0.06, −0.02, P = 3.8 × 10⁻⁴), Table S4.

Table 3.

CpG numbers of nominally significant interaction effects between three lifestyle-related phenotypes and their optimal PGS on DNA methylation at 1,061 smoking-associated CpGs, 459 alcohol-consumption-associated CpGs, and 85 BMI-associated CpGs.

Lifestyle-related phenotype	Total number of CpGs with interaction (P < 0.05)	Same direction with main effect	Opposite direction with main effect
Comprehensive smoking index	61	15	46
Alcohol consumption in previous week	14	10	4
Body mass index	7	5	2

Note: For BMI, one CpG had an interaction effect with a P-value less than the Bonferroni significance threshold (cg11376147, Table S4). Here same/opposite direction indicates the comparison between sign of estimated interaction effect term and sign of estimated main effect term in the models.

The interactions between the lifestyle-related phenotypes and their optimal PGS in association with PhenoAge and GrimAge are shown in Table 4. There was strong evidence that all lifestyle-related phenotypes were positively associated with PhenoAge and GrimAge, except for alcohol consumption with PhenoAge (P = 0.09). There was weak evidence that the association of CSI with GrimAge was attenuated in participants genetically predisposed to smoke with interaction term, −0.022 (95% CI: −0.046, 0.002), P = 0.06, and that the association of alcohol consumption with PhenoAge was attenuated in those genetically predisposed to drink alcohol with interaction term: −0.03 (95% CI: −0.059, −0.001), P = 0.04.

Table 4.

Interaction effects of lifestyle-related phenotypes and their optimal PGS on PhenoAge and GrimAge.

Lifestyle-related phenotype	Outcome	Effect (SE) of phenotype	P	Effect (SE) of PGS	P	Effect (SE) of interaction	P
Comprehensive smoking index	PhenoAge	0.092 (0.015)	9.2 × 10−10	−0.012 (0.015)	0.41	−0.017 (0.014)	0.22
	GrimAge	0.506 (0.012)	<5 × 10−308	0.030 (0.012)	0.01	−0.022 (0.012)	0.06
Alcohol consumption in previous week	PhenoAge	0.025 (0.014)	0.09	−0.016 (0.015)	0.28	−0.030 (0.015)	0.04
	GrimAge	0.072 (0.012)	1.0 × 10−9	0.001 (0.012)	0.91	0.018 (0.012)	0.14
Body mass index	PhenoAge	0.069 (0.015)	5.7 × 10−6	0.049 (0.016)	0.002	0.006 (0.014)	0.68
	GrimAge	0.083 (0.012)	1.9 × 10−11	0.013 (0.013)	0.32	−0.014 (0.011)	0.22

The results using the genome-wide significant PGS at CpGs of interest are shown in Tables S2–S4 and summarized in Table S5. Using the Bonferroni correction, a potential interaction was detected at an alcohol-associated CpG cg02470690, chr6:27839548 (alcohol consumption main effect = −0.06, 95% CI: −0.09, −0.03, P = 3.5 × 10⁻⁵; interaction effect with PGS = 0.06, 95% CI: 0.03, 0.09, P = 6.7 × 10⁻⁵), but not for other lifestyle-related phenotypes or measures of epigenetic ageing (Table S6).

We found no significant interaction effect between the lifestyle-related phenotypes and individual genome-wide significant SNPs (Table 2) in association with methylation at CpGs related to these lifestyle factors and epigenetic ageing measures. The results for the most significant SNPs (rs7938812 for smoking initiation, rs141973904 for alcohol consumption, and rs10852521 for BMI, respectively) are shown in Tables S2–S4 and S6.

Discussion

This is to our knowledge the first study to examine gene–lifestyle interactions on DNA methylation. Interaction studies using PGS are likely to be more powerful than those based on individual variants or genes due to better quantifying the overall genetic predisposition. We expected that genetic predisposition to unhealthy lifestyle-related phenotypes would moderate their harmful effects; however, our results suggest that for CpGs associated with smoking, alcohol consumption, and BMI, genetic predisposition to unhealthy lifestyle-related factors does not strongly modify their effect on blood DNA methylation, i.e., there was no substantial evidence of interaction effects between observed lifestyle-related phenotypes and their respective PGS on DNA methylation at these loci. After the Bonferroni correction, an interaction was detected at a BMI-associated CpG (cg11376147); however, the interaction was in the same direction as the main effect, which was contrary to our hypothesis. Our results also suggest that the association of smoking with GrimAge was slightly attenuated in participants genetically predisposed to smoke and the association of alcohol consumption with PhenoAge was slightly attenuated in those genetically predisposed to drink alcohol; the evidence of interaction was nevertheless quite weak and not consistently observed across all lifestyle-related factors and epigenetic ageing measures. While epigenetic ageing measures provide a biological proxy for mortality, overall health, and many diseases such as cancer [31], future research should assess these outcomes directly for interactions between lifestyle and genetic predisposition, which we could not do in this study.

In this study, we focused primarily on a best-fit PGS which explains the highest proportion of phenotypic variation, and also considered a PGS including only genome-wide significant variants. Although the latter accounts for less variance, it consists of a number of loci with stronger genetic effects on the lifestyle-related phenotypes, thus this PGS may be more specific to biological mechanisms explaining unhealthy lifestyle and could rule out some confounding effects due to pleiotropy with other traits. For instance, many of the 79 SNPs used for building genome-wide significant PGS of alcohol intake are in ADH and ALDH genes which are primary enzymes to metabolize alcohol in liver [52], whereas the majority of the 151,167 SNPs included in the optimal PGS may relate to many other traits such as mental health/disorders or educational attainment that are less likely to modify the effects of alcohol consumption on DNA methylation. It is noted that the variance explained (R²) in alcohol consumption in the validation sample reduced only a little, from 0.006 to 0.005, when using the optimal and genome-wide significant PGS. This implies that a large number of SNPs in the optimal PGS may not be relevant to this phenotypic trait. Consistent with this, we detected a significant interaction at an alcohol consumption-associated CpG (cg02470690) with the genome-wide significant PGS, with a direction consistent with our hypothesis (opposite to main effect). We also found nominally significant interactions (P < 0.05) that were in the opposite direction to main effect at 41 out of the 459 alcohol-related CpGs, which was substantially more than expected by chance (N = 11) and consistent with our previous finding using a PGS of 13 genetic variants associated with alcohol consumption [5].

For alcohol consumption, previous interaction studies were performed predominantly for the ADH and/or ALDH genes on Asian-ancestry samples [12–17]. In the present study, we used an Australian cohort of European ancestry, in which the allele frequencies of variants in these alcohol-metabolism genes were very low, most of them were <1%. For instance, the ALDH2 rs671 polymorphism, which has been widely studied in Asian groups [53–55], was not included in our analyses due to very low MAF (<0.1%). Therefore, the candidate gene approach focusing on single variants may not be appropriate in European-ancestry populations for this phenotypic trait, unless resources with a very large sample size are used.

There are several limitations in this study. First, the genotyping was conducted using the Infinium OncoArray-500 K BeadChip. Although we performed high-quality imputation to obtain a comprehensive genome-wide range of genotypes, the phenotypic variance explained by PGS we observed was somewhat smaller than using more general microarrays, e.g., R² reached ~10% for BMI using the same PGS calculation method but with an Illumina 610-Quadc1 array in another study [56]. Second, in this study, we applied the PRSice method (p-value thresholding and clumping) to calculate PGS, but other methods such as Bayesian approaches or regularized regression might have led to somewhat different PGS [57], which may have had some influence on our results. Third, although we included >4,300 participants with paired genetic-methylation data, this sample size might not be sufficient to detect associations at individual or aggregated CpG sites, because interaction effects are usually much smaller than main effects. Fourth, it is plausible that methylation measures at CpGs of interest may be associated with nearby SNPs. However, we found in our previous publications (4–6, 34) that this was true only for a small minority of CpGs, and these were still sufficiently variable to show strong associations with lifestyle factors, so this would not have affected our results. Finally, while many individual genetic variants that increase people’s predisposition to smoke tobacco or drink alcohol are associated with clear biological functions, such as better, e.g., nicotine or alcohol metabolism, or respiratory capacity, it is a limitation of using polygenic scores that has no clear biological interpretation. Future studies with large sample sizes could investigate individual SNPs or polygenic scores restricted to biologically relevant genes or pathways, particularly for BMI, which is a genetically complex disease.

In conclusion, genetic susceptibility to unhealthy lifestyles (smoking, drinking alcohol, and being overweight or obese) does not appear to strongly modify the association between observed lifestyle behaviour and blood DNA methylation based on our data. Potential interactions were observed for the composite measures of epigenetic ageing, which suggests that such genetic predisposition might moderate harmful effects of unhealthy lifestyles on biological ageing, but these findings should be replicated in additional studies.

Funding Statement

MCCS cohort recruitment was funded by VicHealth and Cancer Council Victoria. The MCCS was further supported by Australian NHMRC grants 209057, 251553, and 504711 and by infrastructure provided by Cancer Council Victoria. The nested case–control methylation studies were supported by the NHMRC grants 1011618, 1026892, 1027505, 1050198, 1043616, and 1074383. This work was further supported by NHMRC grant 1164455. M.C.S. is a recipient of a Senior Research Fellowship from the NHMRC (GTN1155163).

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

Supplementary material

Supplemental data for this article can be accessed online at https://doi.org/10.1080/15592294.2022.2088038