Predicting Complex Traits and Exposures From Polygenic Scores and Blood and Buccal DNA Methylation Profiles

Abstract

We examined the performance of methylation scores (MS) and polygenic scores (PGS) for birth weight, BMI, prenatal maternal smoking exposure, and smoking status to assess the extent to which MS could predict these traits and exposures over and above the PGS in a multi-omics prediction model. MS may be seen as the epigenetic equivalent of PGS, but because of their dynamic nature and sensitivity of non-genetic exposures may add to complex trait prediction independently of PGS. MS and PGS were calculated based on genotype data and DNA-methylation data in blood samples from adults (Illumina 450 K; N = 2,431; mean age 35.6) and in buccal samples from children (Illumina EPIC; N = 1,128; mean age 9.6) from the Netherlands Twin Register. Weights to construct the scores were obtained from results of large epigenome-wide association studies (EWASs) based on whole blood or cord blood methylation data and genome-wide association studies (GWASs). In adults, MSs in blood predicted independently from PGSs, and outperformed PGSs for BMI, prenatal maternal smoking, and smoking status, but not for birth weight. The largest amount of variance explained by the multi-omics prediction model was for current vs. never smoking (54.6%) of which 54.4% was captured by the MS. The two predictors captured 16% of former vs. never smoking initiation variance (MS:15.5%, PGS: 0.5%), 17.7% of prenatal maternal smoking variance (MS:16.9%, PGS: 0.8%), 11.9% of BMI variance (MS: 6.4%, PGS 5.5%), and 1.9% of birth weight variance (MS: 0.4%, PGS: 1.5%). In children, MSs in buccal samples did not show independent predictive value. The largest amount of variance explained by the two predictors was for prenatal maternal smoking (2.6%), where the MSs contributed 1.5%. These results demonstrate that blood DNA MS in adults explain substantial variance in current smoking, large variance in former smoking, prenatal smoking, and BMI, but not in birth weight. Buccal cell DNA methylation scores have lower predictive value, which could be due to different tissues in the EWAS discovery studies and target sample, as well as to different ages. This study illustrates the value of combining polygenic scores with information from methylation data for complex traits and exposure prediction.

Study of the Combined Effect of Maternal Tobacco Smoking and Polygenic Risk Scores on Birth Weight and Body Mass Index in Childhood

“…The best PRSs were robustly associated with the phenotypic traits but only explained ∼4% of the phenotypic variation ( R 2 ). In HELIX, the variation explained by the PRS of BMI that included 60,993 SNPs ( R 2 = 4.7%) was in the range or slightly lower than previous estimations in children (R 2 = 3%, 2 M SNPs ( Odintsova et al, 2021 ); R 2 = 11%, 2.1 M SNPs ( Hüls et al, 2021 )), in adolescents ( R 2 = 6.5%, 941 SNPs; Xie et al, 2020 ), or in adult individuals ( R 2 = 2.9%, 97 SNPs ( Dashti et al, 2022 ); R 2 = 5.2%, 376 SNPs ( Sulc et al, 2020 ); R 2 = 6.7%, 2 M SNPs ( Odintsova et al, 2021 ); R 2 = 7.8%, 2.1 M SNPs ( Khera et al, 2019 )). In Khera et al, children in the 10th percentile of the PRS for BMI, which included 2.1 M SNPs, weighed 3.5 kg more than children in the lowest percentile.…”

“…In HELIX, the PRS with 3,316 SNPs derived from EGG explained 4.9% of the BW variance. Odintsova et al also computed a PRS for BW using the PanUK Biobank data ( Odintsova et al, 2021 ). Their PRS that included 9 M SNPs explained 1.4% of the variance of BW, similar to the PRS-PanUK with 18,563 SNPs that explain 2.5% of the variance in HELIX.…”

“…PRSs are applied to an individual’s disease risk prediction with the final goal of providing personalized preventive strategies and treatments ( Lewis and Vassos, 2020 ; Wray et al, 2021 ). Previous studies have shown that PRSs for BW and BMI, calculated using different statistical approaches and including different numbers of SNPs, explain between 2 and 11% of the phenotypic variation ( Horikoshi et al, 2016 ; Khera et al, 2019 ; Xie et al, 2020 ; Hüls et al, 2021 ; Odintsova et al, 2021 ). Despite this, PRSs are not deterministic.…”

Epigenome-wide association studies: current knowledge, strategies and recommendations

“…Simple MRSs, as well as more complex approaches, have been successfully implemented in the context of various complex diseases [ 85 – 87 ]. For guidance on constructing more complex MRSs, we recommend the recently published review by Hüls and Czamara [ 88 ].…”

A blood DNA methylation biomarker for predicting short-term risk of cardiovascular events

“…Emerging epidemiological evidence indicates that composite scores based on blood DNA methylation (DNAm) at different CpG sites are valuable biomarkers to predict complex traits and identify high-risk populations [1][2][3][4]. DNAm scores are usually built to model the association of CpG sites with the trait or disease of interest via epigenome-wide association studies (EWAS).…”