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

2021 ◽  
Vol 12 ◽  
Author(s):  
Veronika V. Odintsova ◽  
Valerie Rebattu ◽  
Fiona A. Hagenbeek ◽  
René Pool ◽  
Jeffrey J. Beck ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Birth Weight ◽  
Complex Traits ◽  
Maternal Smoking ◽  
Smoking Status ◽  
Association Studies ◽  
Methylation Data ◽  
Polygenic Scores ◽  
Prenatal Maternal Smoking ◽  
Variance Explained

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.

scholarly journals Mediation by Placental DNA Methylation of the Association of Prenatal Maternal Smoking and Birth Weight

2019 ◽  
Vol 188 (11) ◽  
pp. 1878-1886 ◽  
Author(s):  
Andres Cardenas ◽  
Sharon M Lutz ◽  
Todd M Everson ◽  
Patrice Perron ◽  
Luigi Bouchard ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Birth Weight ◽  
Maternal Smoking ◽  
Cell Types ◽  
Prenatal Smoking ◽  
Association Analyses ◽  
Lower Birth Weight ◽  
Cpg Sites ◽  
Prenatal Maternal Smoking ◽  
Infant Birth

Abstract Prenatal maternal smoking is a risk factor for lower birth weight. We performed epigenome-wide association analyses of placental DNA methylation (DNAm) at 720,077 cytosine-phosphate-guanine (CpG) sites and prenatal maternal smoking among 441 mother-infant pairs (2010–2014) and evaluated whether DNAm mediates the association between smoking and birth weight using mediation analysis. Mean birth weight was 3,443 (standard deviation, 423) g, and 38 mothers (8.6%) reported smoking at a mean of 9.4 weeks of gestation. Prenatal maternal smoking was associated with a 175-g lower birth weight (95% confidence interval (CI): −305.5, −44.8) and with differential DNAm of 71 CpGs in placenta, robust to latent-factor adjustment reflecting cell types (Bonferroni-adjusted P < 6.94 × 10−8). Of the 71 CpG sites, 7 mediated the association between prenatal smoking and birth weight (on MDS2, PBX1, CYP1A2, VPRBP, WBP1L, CD28, and CDK6 genes), and prenatal smoking × DNAm interactions on birth weight were observed for 5 CpG sites. The strongest mediator, cg22638236, was annotated to the PBX1 gene body involved in skeletal patterning and programming, with a mediated effect of 301-g lower birth weight (95% CI: −543, −86) among smokers but no mediated effect for nonsmokers (β = −38 g; 95% CI: −88, 9). Prenatal maternal smoking might interact with placental DNAm at specific loci, mediating the association with lower infant birth weight.

Placental Dna Methylation Is Associated With Birth Weight And Maternal Smoking: An Epigenome-Wide Association Study

2015 ◽  
Vol 2015 (1) ◽  
pp. 3156
Author(s):  
Johanna Lepeule ◽  
Emilie Abraham ◽  
Lise Giorgis-Allemand ◽  
Jorg Tost ◽  
Daniel Vaiman ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Birth Weight ◽  
Association Study ◽  
Maternal Smoking

scholarly journals Demographic history impacts stratification in polygenic scores

2020 ◽  
Author(s):  
Arslan A. Zaidi ◽  
Iain Mathieson
Keyword(s):  
Environmental Effects ◽  
Principal Components ◽  
Complex Traits ◽  
Population Stratification ◽  
Association Studies ◽  
Demographic History ◽  
Hybrid Approach ◽  
Genome Wide Association Studies ◽  
Common Variants ◽  
Polygenic Scores

AbstractLarge genome-wide association studies (GWAS) have identified many loci exhibiting small but statistically significant associations with complex traits and disease risk. However, control of population stratification continues to be a limiting factor, particularly when calculating polygenic scores where subtle biases can cumulatively lead to large errors. We simulated GWAS under realistic models of demographic history to study the effect of residual stratification in large GWAS. We show that when population structure is recent, it cannot be fully corrected using principal components based on common variants—the standard approach—because common variants are uninformative about recent demographic history. Consequently, polygenic scores calculated from such GWAS results are biased in that they recapitulate non-genetic environmental structure. Principal components calculated from rare variants or identity-by-descent segments largely correct for this structure if environmental effects are smooth. However, even these corrections are not effective for local or batch effects. While sibling-based association tests are immune to stratification, the hybrid approach of ascertaining variants in a standard GWAS and then re-estimating effect sizes in siblings reduces but does not eliminate bias. Finally, we show that rare variant burden tests are relatively robust to stratification. Our results demonstrate that the effect of population stratification on GWAS and polygenic scores depends not only on the frequencies of tested variants and the distribution of environmental effects but also on the demographic history of the population.

scholarly journals Genomic prediction of cognitive traits in childhood and adolescence

2018 ◽  
Author(s):  
A.G. Allegrini ◽  
S. Selzam ◽  
K. Rimfeld ◽  
S. von Stumm ◽  
J.B. Pingault ◽  
...  
Keyword(s):  
Educational Attainment ◽  
Complex Traits ◽  
Predictive Power ◽  
Prediction Models ◽  
Educational Achievement ◽  
Association Studies ◽  
Occupational Status ◽  
Childhood And Adolescence ◽  
Genome Wide Association Studies ◽  
Polygenic Scores

AbstractRecent advances in genomics are producing powerful DNA predictors of complex traits, especially cognitive abilities. Here, we leveraged summary statistics from the most recent genome-wide association studies of intelligence and educational attainment to build prediction models of general cognitive ability and educational achievement. To this end, we compared the performances of multi-trait genomic and polygenic scoring methods. In a representative UK sample of 7,026 children at age 12 and 16, we show that we can now predict up to 11 percent of the variance in intelligence and 16 percent in educational achievement. We also show that predictive power increases from age 12 to age 16 and that genomic predictions do not differ for girls and boys. Multivariate genomic methods were effective in boosting predictive power and, even though prediction accuracy varied across polygenic scores approaches, results were similar using different multivariate and polygenic score methods. Polygenic scores for educational attainment and intelligence are the most powerful predictors in the behavioural sciences and exceed predictions that can be made from parental phenotypes such as educational attainment and occupational status.

scholarly journals DNA methylation links prenatal smoking exposure to later life health outcomes in offspring

2018 ◽  
Author(s):  
Petri Wiklund ◽  
Ville Karhunen ◽  
Rebecca C Richmond ◽  
Alina Rodriguez ◽  
Maneka De Silva ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Health Outcomes ◽  
Maternal Smoking ◽  
Psychiatric Morbidity ◽  
Later Life ◽  
Prenatal Smoking ◽  
Smoking During Pregnancy ◽  
Mediation Analyses ◽  
Increased Risk ◽  
Prenatal Maternal Smoking

AbstractMaternal smoking during pregnancy is associated with adverse offspring health outcomes across their life course. We hypothesize that DNA methylation is a potential mediator of this relationship. To test this, we examined the association of prenatal maternal smoking with DNA methylation in 2,821 individuals (age 16 to 48 years) from five prospective birth cohort studies and perform Mendelian randomization and mediation analyses to assess, whether methylation markers have causal effects on disease outcomes in the offspring. We identify 69 differentially methylated CpGs in 36 genomic regions (P < 1×10−7), and show that DNA methylation may represent a biological mechanism through which maternal smoking is associated with increased risk of psychiatric morbidity in the exposed offspring.

armDNA: A functional beta model for detecting age-related genomewide DNA methylation marks

2016 ◽  
Vol 27 (9) ◽  
pp. 2627-2640
Author(s):  
Chenyang Wang ◽  
Qi Shen ◽  
Li Du ◽  
Jinfeng Xu ◽  
Hong Zhang
Keyword(s):  
Dna Methylation ◽  
Association Studies ◽  
Rapid Development ◽  
Complex Diseases ◽  
Real Data ◽  
Wald Test ◽  
Methylation Data ◽  
Unknown Parameters ◽  
Diagnosis And Prognosis ◽  
Age Related

DNA methylation has been shown to play an important role in many complex diseases. The rapid development of high-throughput DNA methylation scan technologies provides great opportunities for genomewide DNA methylation-disease association studies. As methylation is a dynamic process involving time, it is quite plausible that age contributes to its variation to a large extent. Therefore, in analyzing genomewide DNA methylation data, it is important to identify age-related DNA methylation marks and delineate their functional relationship. This helps us to better understand the underlying biological mechanism and facilitate early diagnosis and prognosis analysis of complex diseases. We develop a functional beta model for analyzing DNA methylation data and detecting age-related DNA methylation marks on the whole genome by naturally taking sampling scheme into account and accommodating flexible age-methylation dynamics. We focus on DNA methylation data obtained through the widely used bisulfite conversion technique and propose to use a beta model to relate the DNA methylation level to the age. Adjusting for certain confounders, the functional age effect is left completely unspecified, offering great flexibility and allowing extra data dynamics. An efficient algorithm is developed for estimating unknown parameters, and the Wald test is used to detect age-related DNA methylation marks. Simulation studies and several real data applications were provided to demonstrate the performance of the proposed method.

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