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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area under the curve
body mass index
effect allele frequency
epigenome-wide association study
forensic DNA phenotyping
- GBP surgery:
gastric bypass surgery
genome-wide association study
single base extension
single nucleotide polymorphism
World Health Organization
Kayser M (2015) Forensic DNA phenotyping: predicting human appearance from crime scene material for investigative purposes. Forensic Sci Int Genet 18:33–48. https://doi.org/10.1016/j.fsigen.2015.02.003
Herrera BM, Keildson S, Lindgren CM (2011) Genetics and epigenetics of obesity. Maturitas 69:41–49. https://doi.org/10.1016/j.maturitas.2011.02.018
Denham J, O’Brien BJ, Harvey JT, Charchar FJ (2015) Genome-wide sperm DNA methylation changes after 3 months of exercise training in humans. Epigenomics 7:717–731. https://doi.org/10.2217/epi.15.29
Donkin I, Versteyhe S, Ingerslev LR, Qian K, Mechta M, Nordkap L, Mortensen B, Appel EVR, Jørgensen N, Kristiansen VB, Hansen T, Workman CT, Zierath JR, Barrès R (2016) Obesity and bariatric surgery drive epigenetic variation of spermatozoa in humans. Cell Metab 23:369–378. https://doi.org/10.1016/j.cmet/2015.11.004
Rask-Andersen M, Karlsson T, Ek WE, Johansson A (2017) Gene-environment interaction study for BMI reveals interactions between genetic factors and physical activity, alcohol consumption and socioeconomic status. PLoS Genet 13:e1006977. https://doi.org/10.1371/journal.pgen.1006977
Locke AE, Kahali B, Berndt SI, Justice AE, Pers TH, Day FR et al (2015) Genetic studies of body mass index yield new insights for obesity biology. Nature 518:197–206. https://doi.org/10.1038/nature14177
Thorleifsson G, Walters GB, Gudbjartsson DF, Steinthorsdottir V, Sulem P, Helgadottir A et al (2009) Genome-wide association yields new sequence variants at seven loci that associated with measures of obesity. Nat Genet 41:18–24. https://doi.org/10.1038/ng.274
Speliotes EK, Willer CJ, Berndt SI, Monda KL, Thorleifsson G, Jackson AU et al (2010) Association analyses of 249,796 individuals reveal 18 new loci associated with body mass index. Nat Genet 42:937–948. https://doi.org/10.1038/ng.686
Wen W, Zheng W, Okada Y, Takeuchi F, Tabara Y, Hwang JY et al (2014) Meta-analysis of genome-wide association studies in East Asian-ancestry populations identifies four new loci for body mass index. Hum Mol Genet 23:5492–5504. https://doi.org/10.1093/hmg/ddu248
Lee HY, Lee SD, Shin KJ (2016) Forensic DNA methylation profiling from evidence material for investigative leads. BMB Rep 49:359–369. https://doi.org/10.5483/bmbrep.2016.49.7.070
Vidaki A, Kayser M (2017) From forensic epigenetics to forensic epigenomics: broadening DNA investigative intelligence. Genome Biol 18:238. https://doi.org/10.1186/s13059-017-1373-1
Goossens GH (2017) The metabolic phenotype in obesity: fat mass, body fat distribution, and adipose tissue function. Obes Facts 10:207–215. https://doi.org/10.1159/000471488
Wahl S, Drong A, Lehne B, Loh M, Scott WR, Kunze S (2017) Epigenome-wide association study of body mass index, and the adverse outcomes of adiposity. Nature 541:81–86. https://doi.org/10.1038/nature20784
Shah S, Bonder MJ, Marioni RE, Zhu Z, McRae AF, Zhernakova A et al (2015) Improving phenotypic prediction by combining genetic and epigenetic associations. Am J Hum Genet 97:75–85. https://doi.org/10.1016/j.ajhg.2015.05.014
McCartney DL, Hillary RF, Stevenson AJ, Ritchie SJ, Walker RM, Zhang Q et al (2018) Epigenetic prediction of complex traits and death. Genome Biol 19:136. https://doi.org/10.1186/s13059-018-1514-1
Elks CE, den Hoed M, Zhao JH, Sharp SJ, Wareham NJ, Loos RJ, Ong KK (2012) Variability in the heritability of body mass index: a systemic review and meta-regression. Front Endocrinol (Lausanne) 3:29. https://doi.org/10.3389/fendo.2012.00029
Willer CJ, Speliotes EK, Loos RJ, Li S, Lindgren CM, Heid IM et al (2009) Six new loci associated with body mass index highlight a neuronal influence on body weight regulation. Nat Genet 41:25–34. https://doi.org/10.1038/ng.287
Park MJ, Lee HY, Yang WI, Shin KJ (2012) Understanding the Y chromosome variation in Korea—relevance of combined haplogroup and haplotype analyses. Int J Legal Med 126:589–599. https://doi.org/10.1007/s00414-012-0703-9
Quinlan JR (1986) Induction of decision trees. Mach Learn 1:81–106. https://doi.org/10.1007/BF00116251
World Health Organization. Regional Office for the Western Pacific (2000) The Asia-Pacific perspective: redefining obesity and its treatment. Sydney: Health Communications Australia. https://apps.who.int/iris/handle/10665/206936
Pan WH, Yeh WT (2008) How to define obesity? Evidence-based multiple action points for public awareness, screening, and treatment: an extension of Asian-Pacific recommendations. Asia Pac J Clin Nutr 17:370–374
Dick KJ, Nelson CP, Tsaprouni L, Sandling JK, Aïssi D, Wahl S et al (2014) DNA methylation and body-mass index: a genome-wide analysis. Lancet 383:1990–1998. https://doi.org/10.1016/S0140-6736(13)62674-4
Mendelson MM, Marioni RE, Joehanes R, Liu C, Hedman ÅK, Aslibekyan S et al (2017) Association of body mass index with DNA methylation and gene expression in blood cells and relations to cardiometabolic disease: a mendelian randomization approach. PLoS Med 14:e1002215. https://doi.org/10.1371/journal.pmed.1002215
Pan L, Mo MQ, Miao L, Zhang QH, Yang S, Gao H, Huang F, Pan SL, Yin RX (2018) Association of BDNF rs11030104 SNP and serum lipid levels in two Chinese ethnic groups. Int J Clin Exp Pathol 11:11466–11483
Hakanen M, Raitakari OT, Lehtimäki T, Peltonen N, Pahkala K, Sillanmäki L, Lagström H, Viikari J, Simell O, Rönnemaa T (2009) FTO genotype is associated with body mass index after the age of seven years but not with energy intake or leisure-time physical activity. J Clin Endocrinol Metab 94:1281–1287. https://doi.org/10.1210/jc.2008-1199
Hardy LM, Frisdal E, Le Goff W (2017) Critical role of the human ATP-binding cassette G1 transporter in cardiometabolic diseases. Int J Mol Sci 18:1892. https://doi.org/10.3390/ijms18091892
Almén MS, Jacobsson JA, Moschonis G, Benedict C, Chrousos GP, Fredriksson R, Schiöth HB (2012) Genome wide analysis reveals association of a FTO gene variant with epigenetic changes. Genomics 99:132–137. https://doi.org/10.1016/j.ygeno.2011.12.007
Merritt DC, Jamnik J, El-Sohemy A (2018) FTO genotype, dietary protein intake, and body weight in a multiethnic population of young adults: a cross-sectional study. Genes Nutr 13:4. https://doi.org/10.1186/s12263-018-0593-7
Hardy R, Wills AK, Wong A, Elks CE, Wareham NJ, Loos RJ, Kuh D, Ong KK (2010) Life course variations in the associations between FTO and MC4R gene variants and body size. Hum Mol Genet 19:545–552. https://doi.org/10.1093/hmg/ddp504
Mittelstraß K, Waldenberger M (2018) DNA methylation in human lipid metabolism and related diseases. Curr Opin Lipidol 29:116–124. https://doi.org/10.1097/MOL.0000000000000491
Pfeiffer L, Wahl S, Pilling LC, Reischl E, Sandling JK, Kunze S et al (2015) DNA methylation of lipid-related genes affects blood lipid levels. Circ Cardiovasc Genet 8:334–342. https://doi.org/10.1161/CIRCGENETICS.114.000804
Das M, Sha J, Hidalgo B, Aslibekyan S, Do AN, Zhi D, Sun D, Zhang T, Li S, Chen W, Srinivasan SR, Tiwari HK, Absher D, Ordovas JM, Berenson GS, Arnett DK, Irvin MR (2016) Association of DNA methylation at CPT1A locus with metabolic syndrome in the genetics of lipid lowering drugs and diet network (GOLDN) study. PLoS One 11:e0145789. https://doi.org/10.1371/journal.pone.0145789
Heyn H, Moran S, Esteller M (2013) Aberrant DNA methylation profiles in the premature aging disorders Hutchingson-Gilford Progeria and Werner syndrome. Epigenetics 8:28–33. https://doi.org/10.4161/epi.23366
Bradbury C, Köttgen A, Staubach F (2019) Off-target phenotypes in forensic DNA phenotyping and biogeographic ancestry inference: a resource. Forensic Sci Int Genet 38:93–104. https://doi.org/10.1016/j.fsigen.2018.10.010
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).
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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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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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
- DNA methylation
- Genetic variants
- Body mass index