Combined Associations of Genetic and Environmental Risk Factors in Age of Parkinson’s Disease Onset

doi:10.21203/rs.3.rs-3220001/v1

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Combined Associations of Genetic and Environmental Risk Factors in Age of Parkinson’s Disease Onset

https://doi.org/10.21203/rs.3.rs-3220001/v1

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published 02 Jan, 2024

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Substantial evidence has shown that the age at onset (AAO) of Parkinson’s disease (PD) is a major determinant of clinical heterogeneity. However, the mechanisms underlying heterogeneity in the AAO remain largely unknown. To investigate the independent association of genetic and environmental risk factors with the AAO of PD and their combined effects at a genome-wide level. A total of 3,156 patients with PD from the UK Biobank were included in this study. We evaluated the environmental risk factors associated with AAO using the Mann–Whitney U test and a generalized linear model. We further investigated the effects of genetic factors using linear regression analysis and their interactions with environmental risk factors using genome-wide by environment interaction studies. In addition to identifying previously reported environmental risk factors (smoking, non-steroidal anti-inflammatory drug intake, and family history of PD) associated with AAO, we found novel significant associations of physical activity (p < 0.0001) and excessive daytime sleepiness (p < 0.0001) with the AAO of PD. Individuals with a higher genetic risk had younger AAO (p = 3.91e-05). Additionally, we observed a nominally significant interaction between the polygenic risk scores and smoking for AAO (p = 0.0316). Specifically, several gene including ANGPT1 (p = 7.17e-07) and PLEKHA6 (p = 4.87e-06), suggestively significantly interacted with smoking to influence the AAO. Our data suggests that both genetic and environmental risk factors are associated with the AAO of PD and that there is a potential interaction between the two.

Parkinson’s disease

age at onset

genetic factors

environmental risk factors

genome-wide by environment interaction studies

Parkinson’s disease (PD) is a heterogeneous disorder caused by genetic, environmental, and aging factors^1–3. Substantial evidence has shown that age at onset (AAO) is a major determinant of clinical heterogeneity in PD patients^4–7. The incidence of late-onset PD (LOPD) is 5- to 10-fold higher than that of early onset PD (EOPD) and is similar between males and females⁸. Compared to LOPD, EOPD is characterized by slower progression, a decreased risk of developing dementia, reduced levels of biomarkers, a lower burden of non-motor symptoms, and an increased rate of dyskinesia in response to levodopa^9–13. The pathological underpinnings of EOPD and LOPD also differ^14,15. Therefore, the development of PD is closely associated with the AAO. However, the mechanisms underlying AAO heterogeneity remain largely unknown.

Substantial evidence has revealed a close relationship between genetic variants and AAO. For example, PD patients with monogenetically inherited variants often present with an early onset^16,17. Previous large genome-wide association studies (GWAS) and meta-analyses have identified several significant variants associated with AAO. Among them, rs356203 (SNCA), rs983361 (SNCA), rs34311866 (TMEM175), rs4698412 (BST1), and rs9356013 (PARK2) could reduce the AAO, whereas rs9783733 (NDN) could delay the AAO^18–21. Moreover, the polygenic risk score (PRS) constructed using cumulative small-effect variants from PD GWAS is thought to be negatively correlated with the AAO^22,23. Importantly, the heritability of the AAO is only approximately 0.11, which is much lower than the risk of PD¹⁸, suggesting that other factors may contribute to the AAO.

In addition to genetic factors, epidemiological studies have shown that environmental risk factors influence AAO²⁴. For instance, AAO has been reported to be delayed in patients with PD who smoke, consume coffee, and in those prescribed aspirin²⁵. Despite these findings, two published studies have demonstrated the potential influence of environmental risk factors on AAO in PD patients with the LRRK2 p.G2019S or GBA p.N370S mutation^26,27. However, previous studies on the AAO merely focused on the interaction effect of a special single nucleotide polymorphism (SNP) with environmental factors, usually without considering the interaction effect between them at a genome-wide level.

In this study, we used data from a large-scale UK Biobank cohort to identify novel environmental risk factors for AAO in patients with PD. We further performed linear regression analysis to test the interaction effects of the PD PRS and environmental risk factors. Additionally, the possible interaction effect between genetic and environmental risk factors on AAO heterogeneity was identified using genome-wide by environment interaction studies (GWEIS).

Demographics of study samples

A total of 3,786 individuals with PD were included in this study. After excluding non-Europeans and related individuals, 3,156 of 3,768 PD cases remained, of which 3,063 PD cases had a Hospital Episode Statistics-coded diagnosis. As shown in Supplementary Table S1, 62.57% of the PD patients were male, the median AAR was 64 (interquartile range [IQR] = 60–67) years, the median AAO was 70 (IQR = 63–75) years, and the median Townsend Deprivation index was − 2.3007 (IQR = -3.7274–0.4774).

Environmental Risk Factors Associated with the AAO of PD

Based on simple group comparisons, patients with PD who reported smoking, coffee consumption ( > = 1 cup/day), moderate or vigorous physical activity ( > = 600 MET-min/week), and NSAIDs intake had a later AAO (Table 1). However, patients with PD with EDS or a family history of PD had a younger AAO. Although the positive association between coffee consumption and AAO was not significant, other results were supported by the multivariate linear regression models, which were further validated by including more covariates. The exclusion of self-reported PD cases did not alter the results (Supplementary Table S2).

Table 1

Association of environmental risk factors with AAO of PD.
			Yes	No	P	Adjusted_P
Normal BMI
		N	872	2257
		Median AAO (IQR)	70.0(62.0–75.0)	70.0(63.0–75.0)	0.0574	0.6744
Smoking
		N	1439	1702
		Median AAO (IQR)	71.0(65.0–76.0)	69.0(61.0–75.0)	< 0.0001*	0.0001*
Coffee consumption
		N	2315	835
		Median AAO (IQR)	71.0(63.0–75.0)	70.0(62.0–75.0)	0.0326*	0.4690
Tea consumption
		N	2628	518
		Median AAO (IQR)	71.0(63.0–75.0)	69.0(61.0–75.0)	0.0873	0.7509
Alcohol consumption
		N	2990	159
		Median AAO (IQR)	70.0(63.0–75.0)	70.0(63.0–76.0)	0.9843	0.3720
Cheese consumption
		N	2963	97
		Median AAO (IQR)	70.0(63.0–75.0)	71.0(66.0–75.0)	0.2742	0.1240
Milk consumption
		N	3056	97
		Median AAO (IQR)	70.0(63.0–75.0)	71.0(64.0–75.0)	0.7554	0.2740
Moderate or vigorous physical activity
		N	1742	771
		Median AAO (IQR)	71.0(64.0–75.0)	69.0(62.0–74.0)	< 0.0001*	< 0.0001*
NSAIDs intake
		N	1014	2097
		Median AAO (IQR)	72.0(65.0–76.0)	70.0(62.0–74.0)	< 0.0001*	0.0093*
Excessive daytime sleepiness
	N		1159	1980
	Median AAO (IQR)		69.0(61.0–75.0)	71.0(64.0–75.0)	< 0.0001*	< 0.0001*
Family history of PD
	N		280	2876
	Median AAO (IQR)		68.0(60.0–74.0)	71.0(63.0–75.0)	0.0001*	0.0001*

Abbreviations: Median AAO, median age at onset of PD; IQR, interquartile range; N, number of individuals; NA, not applicable; Normal BMI: yes = person with normal BMI, Normal BMI: no = person with abnormal BMI; Smoking: yes=person smoke currently or ever; no=person never smoke; Coffee consumption :yes=person drink at least one cup of coffee each day; no= person drink less than one cup of coffee each day; Tea consumption :yes=person drink at least one cup of tea each day; no= person drink less than one cup of tea each day; Alcohol consumption: yes=person currently or ever drink; no= person never drink; Cheese consumption: yes=person eat cheese; no=person don’t eat cheese; Milk consumption: yes=person drink milk; no=person don’t drink milk; Moderate or vigorous physical activity:yes=person with total metabolic equivalent task (MET) minutes per week more than or equal to 600; no= person with total metabolic equivalent task (MET) minutes per week less than 600; NSAIDs intake: yes=person regularly take aspirin or ibuprofen; no=person don’t take any aspirin or ibuprofen; P= two-sided exploratory P values from Mann-Whitney U-tests. Adjusted_P = two-sided exploratory P values from generalized linear models adjusted AAR, gender, and Townsend Deprivation index. Significant associations (p < 0.05) are in bold with star.

In the investigation of the dosage effects of smoking and physical activity on AAO, non-parametric Spearman’s correlation analysis showed that smoking duration (r = 0.140, p = 1.637e-09) and the intensity of physical activity (r = 0.094, p = 2.182e-06) were positively associated with the AAO (Fig. 1). After adjusting for AAR, sex, and Townsend Deprivation index, generalized linear regression showed similar significant results (smoking duration: β = 0.0517, p = 6.37e-07; physical activity: β = 1.531e-04, p = 0.0059).

High Polygenic Risk Scores Advanced the AAO of PD

The generalized linear regression model adjusted for AAR, sex, Townsend Deprivation index, and PCs1-4 revealed an inverse association between AAO and PRS. As shown in Fig. 2, for one standard deviation (SD) increase in the PRS, the AAO is approximately advanced about 0.5 years (β = -0.0567, SE = 0.0138, p = 3.91e-05).

The Effect of smoking on AAO of PD Depends on Genetic Risk Stratification

We selected environmental risk factors with adjusted P-values less than 0.10 (smoking, moderate or vigorous physical activity, NSAIDs intake, EDS, and family history of PD) for analyzing the interaction effect with PRS (Table 2). There was a nominally significant multiplicative interaction between PRS and smoking (β = 0.5300, P = 0.0316). There was also weaker evidence for a multiplicative interaction between a family history of PD (p = 0.0524) and PRS, as well as EDS (p = 0.0599). There were no interaction effect between the PRS and environmental risk factors in the other models. These results suggest that smoking is a potential protective factor against AAO among individuals with a higher genetic risk.

Table 2

Interaction effects of PRS and environmental risk factors on AAO of PD.
Model number	Variable	Estimate	SE	t value	P
1	Smoking	0.9681	0.2524	3.8350	0.0001
	PRS	-0.7556	0.1671	-4.5210	< 0.0001
	Smoking x PRS	0.5348	0.2487	2.1500	0.0316*
2	Moderate or vigorous physical activity	1.3308	0.2953	4.5060	< 0.0001
	PRS	-0.0906	0.2373	-0.3820	0.7027
	Moderate or vigorous physical activity x PRS	-0.5422	0.2897	-1.8710	0.0614
3	NSAID = s intake	0.6942	0.2687	2.5840	0.0098
	PRS	-0.5125	0.1516	-3.3800	0.0007
	NSAIDs intake x PRS	0.0045	0.2680	0.0170	0.9866
4	Excessive daytime sleepiness	-2.4980	0.2542	-9.8260	< 0.0001
	PRS	-0.3187	0.1580	-2.0170	0.0438
	Excessive daytime sleepiness x PRS	-0.4719	0.2507	-1.8820	0.0599
5	Family history of PD	-1.7108	0.4356	-3.9280	< 0.0001
	PRS	-0.5699	0.1295	-4.4010	< 0.0001
	Family history of PD x PRS	0.8614	0.4439	1.9410	0.0524
Abbreviations: P = two-sided exploratory P values from generalized linear models adjusted AAR, gender, Townsend Deprivation index, and P1-4. Significant associations (p < 0.05) are in bold with stars.

Given the significant interaction between smoking and PRS in the regression analysis, GWEIS analysis was performed to investigate the potential interaction effect of SNPs and smoking on the AAO. We detected eight loci, harboring several candidate genes, such as ANGPT1 (P = 7.22458e-07) and PLEKHA6 (P = 4.87024e-06), which showed suggestive interactions with smoking in the AAO(Supplementary Table S3). A visualization of the results is shown in Fig. 3.

Functional annotations of the loci interacting with smoking

In the mining of the gene expression database, we found that some of the eight suggestive significant loci found in this GWEIS were highly significant expressed quantitative trait loci for genes in the substantia nigra, cortex, and basal ganglia (Supplementary Table S4). For example, the lead SNP (rs10093967) at 8q23 and its proxy are intronic variants in ANGPT1 and cis-eQTLs for ANGPT1 and OXR1 in the occipital and frontal cortices. We further searched for co-localization with the regulatory elements of the lead SNPs and their proxies. Remarkably, these lead sites and their proxies have been reported to be associated with PD or smoking, including DBX2, MEF2, and EGR-1, among others^28–30. The lead PLEKHA6 SNP and its proxy were mapped to transcription factor binding sites, promoter histone marks in the skeletal muscle, enhancer histone marks in the brain substantia nigra, and DNase I hypersensitivity sites in the fetal brain. Finally, an investigation of the Phenoscanner database revealed that lead SNPs and proxies were also associated with DNA methylation levels in the peripheral blood and cord blood. As shown in Supplementary Table S5, the G allele of rs4951256, the C allele of rs10093967, and the A allele of rs35262123 and rs113991597 were associated with lower levels of methylation, while the C allele of rs9934772 was associated with higher levels of methylation.

We analyzed the independent and combined effects of genetic and environmental risk factors on the AAO of PD in a large-scale European cohort. As a result, we not only found significant novel associations between physical activity and excessive daytime sleepiness with AAO, but also replicated the significant effects of smoking, NSAID intake, family history of PD, and PRS. We further demonstrated that the effect of smoking on the AAO depends on the genetic risk stratification. Notably, we identified several potential candidate genes that interact with smoking at the genome-wide level. Our results enhance our understanding of the mechanism of AAO heterogeneity in PD and provide novel perspectives on PD pathogenesis.

Most epidemiological studies have highlighted the protective role of smoking in PD. However, it is unclear whether smoking delays the AAO^25,31. A similar phenomenon has been observed in patients with a family history of PD ^32–34. This study provides strong evidence to explain the real-world effects of smoking and family history of AAO. Although NSAIDs have been found to delay the AAO in the United States population²⁵, our study is the first to demonstrate their protective effect in the European population. Notably, significant novel effects of physical activity and EDS on the AAO have been reported for the first time.

It is plausible that physical activity could delay the AAO either by relieving PD symptoms or by exerting neuroprotective effects. Physical activity can modify synaptic connectivity within the dopamine (DA)-depleted striatum³⁵, directly increasing stimulus-evoked DA release or decreasing DA decay, which improves behavioral outcomes in rodent neurotoxicant-induced models^36,37. In addition, physical activity has been shown to suppress oxidative stress and promote the expression of neurotrophic factors³⁸, thereby potentially attenuating dopaminergic neuron degeneration and contributing to the neuroplasticity and survival of dopaminergic neurons^39,40. In contrast, EDS is thought to be a potential prodromal symptom of PD with similar neuropathological lesions. On the one hand, according to Braak's theory⁴¹, EDS is associated with neuronal loss in areas closely involved in sleep and wake coordination, such as the hypothalamus, coeruleus, and ascending reticular activating system^41,42, occurring in stage II of PD before substantia nigra degeneration. In contrast, noradrenergic, serotonergic, and dopaminergic deficits may contribute to the development of EDS⁴³.

Next, we explored the association between AAO and PRS accumulated by known variant effects in PD using a generalized linear regression model. Consistent with previous findings^18,21, we found an inverse association between the PRS and AAO, suggesting that the PD risk polygenic profile can modify the AAO. Clinically, the PRS model may provide access to the early diagnosis and treatment of patients with potential PD. Although the known variants associated with the risk of PD can partially explain the heterogeneity of AAO, its genetic architecture remains unclear. Therefore, further studies are warranted to fully elucidate the underlying genetic architecture.

Previously, several studies have investigated gene-environment interactions in the AAO of PD, focusing on the modification effect of a single nucleotide variant or locus by environmental risk factors^26,27,44. These studies could partly explain the inconsistency in the effects of environmental risk factors on AAO but only considered the role of one SNP. In this study, we assessed the role of environmental risk factors that may contribute to the AAO in different strata of genetic risk according to the PRS. We observed that smoking had a greater protective effect against the AAO in those with a higher genetic risk.

As previous studies have reported, PD-associated genes could interact with smoking in PD or with its AAO^{45 46}, suggesting that there could be real differences in the protective effect of smoking on the AAO between different stratum of genetic risk. The association between smoking and delayed AAO in the high genetic risk group is biologically plausible. Nicotine in cigarettes can exert protective effects against damage to nigral dopaminergic neurons by: (1) stimulating the release of DA in different regions of the brain to increase DA levels ^47,48; (2) suppressing DA transporter activity⁴⁹; (3) reducing neuroinflammatory and cellular stresses via inhibition of SIRT6 expression⁵⁰; (4) preventing glutamate-induced neurotoxicity^51–53; (5) suppressing 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) induced dopaminergic neuron degeneration⁵⁴; (6) suppressing free-radical damage and monoamine oxidase B^55,56; and (7) suppressing the unfolded protein response⁵⁷. However, exclusion of the influence of other cigarette components requires further investigation. Based on the clinical diagnostic criteria developed by the Movement Disorder Society⁵⁸, the age at which PD patients first develop cardinal motor features is recorded as the AAO. However, only a loss of more than 50% of dopaminergic neurons results in the symptoms and manifestations of PD^59–61. Therefore, the neuroprotective effects of nicotine may delay the onset of PD symptoms and signs.

We identified eight loci that harbored relevant candidate genes that interacted with smoking in the AAO at the genome-wide level, with biological functions associated with PD or exposure to smoking. The lead SNP at 1q32 is located in an intron of PLEKHA6, playing an important role in regulating copper homeostasis, whereby disturbed copper homeostasis is related to PD⁶². An epigenome-wide association study confirmed that this gene co-localizes with smoking-related differentially methylated sites^63,64. The lead SNP at 8q23, located in an intron of ANGPT1, significantly decreased the expression level after exposure to cigarette smoke extract, but increased its expression in GBA-related PD^65,66. The lead SNP at the 2q31 locus is located near ITGA4, which shows upregulated gene expression after exposure to two known components of cigarettes, arsenic and BaP⁶⁷. Moreover, lead SNPs and their proxies were observed to co-localize with several transcription factor-binding sites that showed biologically relevant functions in PD. The dysregulation of MEF2 function may underlie PD pathogenesis and DNA methylation changes related to tobacco^29,68. EGR-1 has been shown to promote neuroinflammation and dopaminergic cell body loss in the substantia nigra pars compacta of the MPTP-PD mouse model⁶⁹. It has been confirmed that the expression of EGR-1 responded to cigarette smoke in pulmonary epithelial cells³⁰. Therefore, candidate genes at these loci are not only strongly related to PD but also to smoking, which confirms the reliability of our results. Furthermore, we observed differentially methylated CpG sites near the lead SNPs, some of which were associated with smoking. However, the relationship between genetic variants, gene expression, and methylation is complex and further research is required.

The strength of this study is that we used a large cohort to investigate the relationship between genetic factors, environmental risk factors, and their interactions with the AAO of PD in the European population. As many environmental risk factors are influenced by sex, age, and other factors, we adjusted for relevant confounding factors as much as possible. To avoid sample overlap, we used the standard PRS calculated by Thompson et al. using five GWAS datasets and excluding the UK Biobank samples. Simultaneously, a proportion of PD cases were self-reported, which may have led to an inaccurately reported AAO and the possibility of recall bias. Another important consideration is that a younger AAO correlates with a younger AAR. Although the results did not change after excluding self-reported cases in the sensitivity analysis and adjusting for AAR as a confounder, errors caused by recall bias could not be eliminated. Finally, whether the results of this study can be applied to other ethnic groups needs to be carefully considered, as we only included Europeans.

In conclusion, in addition to several known environmental factors, this study found new effects of physical activity and EDS on the AAO in patients with PD. At the same time, we found that the protective effect of smoking depended on the genetic risk, and several candidate genes, such as ANGPT1 and PLEKHA6, may play an important role in disease onset. This study provides strong evidence for a combined association between genetic and environmental risk factors and the AAO of PD.

Study Population

This research has been conducted using the UK Biobank Resource under approved application numbers 103082. The UK Biobank recruited more than 500,000 UK residents from 22 assessment centers between 2006 and 2010, ranging in age from 40 to 69 years. Detailed genetic, health, environmental, and lifestyle information was collected from the cohort. Details of the UK Biobank have been described previously⁷⁰. PD patients were defined using the International Statistical Classification of Diseases and Related Health Problems 10th Revision (ICD10) code G20 from data field 41202, and self-reported PD cases with code 1262 were identified from data field 20002. The AAO for PD was derived from the date of the first G20 report. One individual was randomly selected from a pair of related individuals with first-, second-, or third-degree relatives. Additionally, we excluded individuals of non-European ancestry to reduce bias due to demographic histories.

Environmental risk factors

We included several environmental risk factors reported to be associated with PD in the UK Biobank. Candidate factors included body mass index (BMI), smoking status, alcohol consumption status, coffee consumption, tea consumption, cheese consumption, milk consumption, physical activity, non-steroidal anti-inflammatory drug (NSAIDs) intake, excessive daytime sleepiness (EDS), and family history of PD. All those factors were obtained through a self-completed touchscreen questionnaire or physical measurements at baseline and were treated as binary variables in subsequent analysis, specific details are as follows:

BMI

BMI was calculated from the height and weight measured during the visit to the initial assessment center. Based on the World Health Organization’s criteria⁷¹, BMI was divided into two categories: normal (18.5–24.9 kg/m²) and abnormal (< 18.5 kg/m² or > = 25 kg/m²).

Smoking status

The participants were asked about their smoking status (never, previous, or current) and were defined as non-smokers (never smokers) or as smokers. For smokers, the duration of smoking was estimated according to the years from the start of smoking until smoking cessation, or from the age at starting smoking until the diagnosis of PD. For non-smokers, the duration of smoking was zero.

Alcohol consumption status

At baseline, participants reported their alcohol consumption status (never, previously, or currently). Participants were defined as non-drinkers if they had never consumed alcohol. The remaining participants were defined as drinkers.

Coffee or tea consumption

The participants reported their daily number of cups of coffee consumed. Coffee consumers were defined as those who drank at least one cup of coffee daily. The same classification was used for tea consumers.

NSAIDs intake

The participants answered questions regarding the details of their medication use. NSAIDs intake was defined as a regular intake of aspirin or ibuprofen.

Cheese consumption

The participants were asked about their frequency of cheese consumption. Cheese non-consumers were defined as those who never ate cheese, whereas the others were defined as cheese consumers.

Milk consumption

Based on the type of milk the participants drank, they were regarded as non-milk consumers if they reported never or rarely consuming milk.

Physical activity

The participants’ total physical activity was assessed using the International Physical Activity Questionnaire. Participants were divided into three groups based on the total metabolic equivalent (MET-min/week): low physical activity (< 600 MET-min/week), moderate physical activity (600–3000 MET-min/week), and high (≥ 3000 MET-min/week) physical activity⁷². The threshold at 600 MET-min/week was regarded as the recommended guideline for moderate physical activity.

EDS

The EDS of the participants was assessed based on the likelihood of unintentionally dozing off or falling asleep during the day. Participants were not considered to have EDS if they reported that they never or rarely dozed off or fell asleep during the daytime.

Family history of PD

Participants were asked if their first-degree relatives had PD. First-degree relatives include fathers, mothers, and siblings. Participants were classified as having a family history of PD if they reported that at least one first-degree relative had PD.

Polygenic risk score

Thompson et al. used data from five GWAS, excluding UK Biobank samples, to construct a standard PRS for individuals in the UK Biobank, and published the results in the UK Biobank PRS Release for all UK Biobank researchers to access⁷³. Additionally, genetic principal components were provided by the UK Biobank.

Statistical analysis

We excluded participants who answered "do not know" or "prefer not to answer," as well as those who had missing data. The Mann–Whitney U test was used to assess the different distributions of AAO among the different groups. Nonparametric Spearman’s correlation was performed to assess whether there was a dosage effect of the variables on the AAO. Generalized linear regression models were built to estimate independent and combined associations between environmental risk factors and PRS. In the analysis of the association between the PRS and AAO, the AAO was converted into Z-scores to make the analysis results more easily interpretable. We used inverse normal transformation to transform the PRS and regarded the PRS as a continuous variable in the interaction analysis. All results were adjusted for age at recruitment (AAR), sex, and Townsend Deprivation index. For the PRS and interaction analysis, four principal components were also included as covariates. All statistical analyses and diagrams were performed using R version 4.1.3.

GWEIS

To explore the interaction effect of SNPs and smoking on AAO, we used a generalized linear regression model in PLINK2.0. AAR, sex, and the first four principal components of the population structure were used as covariates. We excluded variants that deviated from the Hardy–Weinberg equilibrium (P < 1e-6), variants with a call rate < 95%, rare variants with a minor allele frequency < 1%, and low imputation quality (R² < 0.6). Individuals with more than 5% missing genotypes were excluded from the study. After filtering, 9,231,448 variants and 3,124 PD cases remained in the GWEIS. The significance threshold was set at p = 5.0 × 10^− 8. p = 5.0 × 10^− 6 was set as a suggestive significance threshold.

Functional annotations of the loci interacting with environmental risk factors

We evaluated the biological characteristics of these loci using GWEIS. For each locus, we integrated the most significant variant and its proxies with a strong linkage imbalance (r² > = 0.8). To explore the potential influence of the novel loci identified in this study on expression traits, we searched for cis-expression quantitative trait loci (eQTLs) in several gene expression databases containing different organizations, including brain (xQTL, PsychENCODE, BrainSeq, and UKBEC) ^74–78, blood (eQTLGen, BIOS, Bloodqtl, and FHS_eQTL)^79–82, and multiple tissues (GTEx)⁸³. To gain a deeper understanding of how genetic variants interact with environmental risk factors and explain how they functionally affect the AAO, we searched HaploReg version 4.1 to assess whether the SNPs were located in the regulatory elements⁸⁴. We also mined the Phenoscanner version 2 database to evaluate whether the SNPs were associated with DNA methylation⁸⁵.

Data availability

The UK Biobank data are available through the UK Biobank Access Management System https://www.ukbiobank.ac.uk/. We will return the derived data fields following UK Biobank policy; in due course, they will be available through the UK Biobank AccessManagement System. eQTLs data are available through the following websites: xQTL: http://mostafavilab.stat.ubc.ca/xQTLServe, PsychENCODE: http://development.psychencode.org/, BrainSeq: http://eqtl.brainseq.org/phase1/eqtl/, UKBEC: http://www.braineac.org/, eQTLGen: https://www.eqtlgen.org/, BIOS: http://genenetwork.nl/biosqtlbrowser, Bloodqtl: http://www.genenetwork.nl/bloodeqtlbrowser, FHS_eQTL: ftp://ftp.ncbi.nlm.nih.gov/eqtl/original_submissions/FHS_eQTL/, and GTEx: http://gtexportal.org.

Code availability

The related codes and scripts for the study will be made available upon reasonable request to the corresponding author.

Funding Sources: This work was supported by the National Key R&D Program of China (Grant No. 2021YFC2502100), Hunan Youth Science and Technology Innovation Talent Project (Grant No. 2020RC3060), National Natural Science Foundation of China (Grant No. 82001362), and Natural Science Foundation of Hunan Province, China (Grant No. 2021JJ31070).

Acknowledgements

We thank all study participants and their families and the investigators and members of the UK Biobank. We thank the members of the National Clinical Research Centre for Geriatric Disorders, Department of Geriatrics, Xiangya Hospital, Central South University. We are grateful for resources from the High-Performance Computing Center of Central South University.

Author information

Authors and Affiliations

National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, Hunan, China

Yuanfeng Huang, Qian Cheng, Zheng Wang, Yijing Wang, Qiao Zhou, Guihu Zhao, Beisha Tang, Bin Li and Jinchen Li

Department of Geriatrics, Xiangya Hospital, Central South University, Changsha, Hunan, China

Yuanfeng Huang, Qian Cheng, Yijing Wang, Qiao Zhou, Guihu Zhao, Bin Li and Jinchen Li

Department of Oncology, Xiangya Hospital, Central South University, Changsha, ,Hunan, China

Qian Cheng

Department of Neurology, Xiangya Hospital, Central South University, Changsha, Hunan, China

Guihu Zhao, Beisha Tang, Bin Li and Jinchen Li

National Health Commission Key Laboratory of Birth Defect Research and Prevention, Hunan Provincial Maternal and Child Health Care Hospital, Changsha, Hunan, China

Aojie Lian

Centre for Medical Genetics & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, Central South University, Changsha, Hunan, China

Kun Xia, Beisha Tang and Jinchen Li

Contributions

Y H. and Q C. were responsible for study design and primary analysis. Z W., Y W., A L., Q Z., G Z., B T. and K X. were responsible for assisting with study conception. Y H., Q C., B. L., and J L. were responsible for the original manuscript draft. All authors read and approved the final version of the manuscript. B. L., and J L. were responsible for the funding.

Corresponding authors

Correspondence to Bin Li or Jinchen Li

Ethics declarations

The authors declare that there are no commercial or financial relationships that represent a conflict of interest related to this study.

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SupplementaryFigureS1.tif
Supplementary figure S1 QQ plot of the results of the genome-wide by environment interaction study for AAO of PD Quantile-quantile (QQ) plot for the tests of interaction between individual SNPs and smoking on AAO of PD. The blue dots denote the distribution of observed –log₁₀(P) values against the expected –log₁₀(P) values from a theoretical chisquare distribution with one df. The straight line represents the theoretical distribution of expected –log₁₀(P) values under the null hypothesis of no association. There was no evidence of any systematic bias: the genomic inflation factor (λ) was equal to 0.972.
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Combined Associations of Genetic and Environmental Risk Factors in Age of Parkinson’s Disease Onset

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Journal Publication

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Abstract

Figures

INTRODUCTION

RESULTS

Demographics of study samples

Environmental Risk Factors Associated with the AAO of PD

High Polygenic Risk Scores Advanced the AAO of PD

The Effect of smoking on AAO of PD Depends on Genetic Risk Stratification

Functional annotations of the loci interacting with smoking

DISCUSSION

METHODS

Study Population

Environmental risk factors

BMI

Smoking status

Alcohol consumption status

Coffee or tea consumption

NSAIDs intake

Cheese consumption

Milk consumption

Physical activity

EDS

Family history of PD

Polygenic risk score

Statistical analysis

GWEIS

Functional annotations of the loci interacting with environmental risk factors

Declarations

References

Additional Declarations

Supplementary Files

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