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Validation of BMI genetic risk score and DNA methylation in a Korean population

Abstract

When DNA profiles obtained from biological evidence at a crime scene fail to match suspects or anyone in the database, forensic DNA phenotyping, which is the prediction of externally visible characteristics, can facilitate a traced search for an unknown suspect by limiting the search range. Therefore, age, trait, or lifestyle predictors, as well as the predictor for colorations, have been researched in the forensic field. In the present study, for the development of a prediction model for BMI or obesity, we investigated several previously reported BMI- or obesity-associated genetic and epigenetic markers that included four CpGs (cg06500161, cg00574958, cg12593793, and cg10505902 of the ABCG1, CPT1A, LMNA, and PDE4DIP genes, respectively), and eight SNPs (rs12463617, rs1558902, rs591166, rs11030104, rs11671664, rs6545814, rs16858082, and rs574367 near the TMEM18, FTO, MC4R, BDNF, GIPR/QPCTL, ADCY3/RBJ, GNPDA2, and SEC16B genes, respectively) in 700 Koreans within the BMI ranging from 16.1 to 40.6 (27.6 ± 4.5) kg/m2. Linear regression analysis showed that DNA methylation of the four CpG sites explained 10.9% total variance in BMI, and the model constructed using age information, genetic score from eight SNPs, and DNA methylation at four CpG sites could account for 17.4% of BMI variance. Using data mining techniques, i.e., decision tree (Entropy and Gini), random forest, and bagging, a total of eight models with BMI 31 or 32 as a cutoff value were also constructed based on the data obtained from 490 training samples with age and sex as a covariate. Among them, a random forest model with a cutoff value of 31 showed the best performance with 63.3% accuracy and the AUC value of 0.682 in 210 test set samples. In the present study, we could replicate the previous finding that DNA methylation contributes more to BMI than do genetic factors. In addition, although the accuracy for the prediction of BMI was not high, our study is meaningful in respect of the ability to use a small number of markers to achieve similar prediction accuracy to that obtained from a model composed of more than a thousand markers, which adds support to continued research to identify a small set of predictive markers for practical application in the forensic field.

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Data availability

All data generated or analyzed during this study are included in this published article and its supplementary information files.

Abbreviations

AUC:

area under the curve

BMI:

body mass index

EAF:

effect allele frequency

EWAS:

epigenome-wide association study

FDP:

forensic DNA phenotyping

GBP surgery:

gastric bypass surgery

GWAS:

genome-wide association study

SBE:

single base extension

SNP:

single nucleotide polymorphism

WHO:

World Health Organization

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Acknowledgements

We thank the National Biobank of Korea, the Centers for Disease Control and Prevention, Republic of Korea for providing bioresources for this study (KBN-2018-051).

Funding

This work was supported by Research Resettlement Fund for the new faculty of Seoul National University and a grant from the Bio & Medical Technology Development Program of the National Research Foundation of Korea (NRF) funded by the Korean government (NRF-2014M3A9E1069992).

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Contributions

HYL and EHL conceived and designed the experiments. EHL performed the experiments and analyzed the data. HK, JJA, SC, and MHS conducted statistical analyses. SC, JML, and HYL wrote the manuscript. All authors reviewed and approved the manuscript.

Corresponding author

Correspondence to Hwan Young Lee.

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Samples were collected from healthy volunteers with informed consents following the procedures approved by the Institutional Review Board of Severance Hospital, Yonsei University in Korea.

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The authors declare no competing interests.

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Cite this article

Cho, S., Lee, E.H., Kim, H. et al. Validation of BMI genetic risk score and DNA methylation in a Korean population. Int J Legal Med 135, 1201–1212 (2021). https://doi.org/10.1007/s00414-021-02517-y

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Keywords

  • DNA methylation
  • Genetic variants
  • Body mass index
  • prediction
  • Korean