1. Ethnic variation in methylation of birth weight and length Presenter: Zahra Sohani Supervisor: Dr. Sonia Anand

2. OVERVIEW 1.Brief background 2.Research question 3.The approach 4.The approach – revised 5.Brief findings 6.Putting it all in context with South Asians

3. BACKGROUND Rates of type 2 diabetes mellitus differ between South Asians and Europeans; South Asians are at a 2-5 fold greater risk Numerous studies have pointed to differences in body composition As adults, South Asians have greater visceral adiposity and develop metabolic complications at lower BMI and younger age In adolescence, South Asians have greater levels of impaired fasting glucose, impaired glucose tolerance, and insulin resistance At birth, South Asians are smaller, but comparably adipose to Europeans

4. The relationship between birth weight and type 2 diabetes is well characterized A systematic review synthesized data from 6,090 diabetes patients in 31 populations and reported a 25% reduction in odds of T2DM per kilogram of weight gained (OR: 0.75, 95% CI 0.70-0.81) Another review classifying newborns into low birth weight (LBW) (<2.5 kg) compared with a birth weight of ≥ 2.5 kg found LBW to have a 32% increase in odds of T2DM (OR: 1.32, 95% CI 1.06-1.64)

5. As differences in body composition between these two groups are present at birth, the variation is likely a result of either altered genetic predisposition or the in-utero environment Since both genetic and environmental factors can independently and together alter methylation of genes, having downstream effects on the expression of genes, it has become a potentially important field of study to explain this ethnic variation

6. WHAT IS DNA METHYLATION? A methyl group to a nucleotide, commonly at the 5’ carbon of cytosine in CpG dinucleotides Methylation can transcriptionally regulate genes and miRNAs, control alternative promoter usage, and alternative splicing

7. THE QUESTION Are there differences in methylation of genes involved in birth weight and length among South Asians and white Caucasian newborns from the START and CHILD cohorts?

8. THE APPROACH STEP 1: Identify genes associated with birth weight and length in the literature

10. Table 1 – SNPs associated with birth weight and length at a genome wide threshold from the literature GeneSNPTraitstartposendpos LCORLrs724577brith length1799316017993660 PTCH1rs473902birth length9825598598256485 GPR126rs7763064birth length142797039142797539 HMGA2rs1351394birth length6635157666352076 DCST2rs905938birth length154991139154991639 SF3B4rs11205277birth length149892622149893122 PTPDC1rs1257763birth length9689369596894195 HHIPrs7689420birth length145568102145568602 ADAMTSL3rs11259936birth length8458033284580832 ZBTB38rs724016birth length141105320141105820 HMGA1rs2780226birth length3419884234199342 IGF1Rrs2871865birth length9919464699195146 GDF5rs143384birth length3402550634026006 DTLrs10863936birth length212237548212238048 JAZF1rs1708299birth length2818969628190196 ACBD4rs4986172birth length4321603143216531 ANKRD13Brs3110496birth length2791752127918021 PMLrs5742915birth length7433638374336883 CCNL1rs900400birth weight156798525156799025 CENPMrs5758511birth weight4233592242336422 ADCY5rs9883204birth weight123096570123097070 HMGA2rs1042725birth weight6635809766358597 CDKAL1rs6931514birth weight2070370220704202 CALCRrs7780752birth weight9324139093241890 ACTBL2rs4432842birth weight5717182857172328 LCORLrs724577birth weight1799316017993660 ADRB1rs1801253birth weight115804806115805306 SLC2A4rs5415birth weight71842317184731 TCF7L2rs7903146birth weight114758099114758599 HHEX-IDErs1111875birth weight9446263294463132

11. THE APPROACH STEP 2: Which methylation probe sites to investigate? Within 100 kilo base pairs of the SNP

12. 12 birth weight SNPs on 12 genes 332 probe sites in total from all 12 genes 222 probe sites within 100 kilo base pairs of the SNP 18 birth length SNPs on 18 genes 422 probe sites in total from all 18 genes 295 probe sites within 100 kilo base pairs of the SNP

14. THE APPROACH STEP 3: Is there variation in level of methylation by ethnicity? Equation 1: CpG probe site = β 0 + β 1 *ethnicity + ɛ Equation 2: For probe sites showing ethnic variation based on above: Birth weight = β 0 + β 1 *CpG probe site + covariates + ɛ in South Asians and Europeans, separately

15. 92% of the birth weight probe sites were statistically different between South Asians and European newborns

16. 92% of the birth length probe sites were statistically different between South Asians and European newborns

17. THE APPROACH What could be causing this large-scale variation by ethnicity? (1) Variation in cell type composition Definitionbeta-coefficientp-value South Asian Mean European Mean Lymphocytes: LYMPHS Absolute:-0.190790.6243454.634 4.825 Monocytes: MONOS Absolute:-0.266750.171641.213 1.479 Neutrophils: NEUTS Absolute:-0.941010.2150737.137 8.078 Nucleated Red Blood Cells: NRBC Absolute:-0.14970.7416991.107 1.257 Eosinophils: EOS Absolute:-0.1240.0635770.331 0.455 Basophils: BASOS Absolute:-0.002750.921940.098 0.101

18. (2) Is this difference restricted to birth weight genes or is this a global phenomenon? Meta-analysis of 1000 random sites from the epigenome in South Asians and Europeans to estimate global methylation levels in both groups Comparisonn_SAn_EUME Summary estimate (95% CI)Heterogeneity ME beta only CHILD mixed SA vs. CHILD EU10250 -0.00124 (-0.00161, -0.000876)X2 0, p= 1 -0.00124 CHILD mixed and pure SA vs. CHILD EU28250 -0.000821 (-0.00105, -0.000595)X2: 0, p= 0.988 -0.000821 CHILD pure SA vs. CHILD EU18250 -0.000567 (-0.000846, -0.000289)X2: 0, p= 0.927 -0.000567 START SA vs. CHILD EU2342500.0136 (0.0135, 0.0137)X2 = 0.00022, p= 0 0.0136

19. THE APPROACH - REVISED 1.Linear regression analysis of CpG probe sites on birth weight/length separately in CHILD and START birth weight = β 0 + β 1 *CpG probe site + covariates + ɛ 2.Meta-analysis of b-coefficients for probe sites from both cohorts Heterogeneity from meta-analysis to estimate ethnic heterogeneity

20. THE FINDINGS Table 2 – CpG probe sites showing ethnic variation in association with birth weight GeneCpG siteSouth Asian β-coefficient p-value European Caucasians β-coefficient p-value Summary estimate p-value heterogeneity TCF7L2cg090226071.051.70x10 -1 -3.701.51x10 -5 -1.102.72x10 -5 CALCRcg230611502.607.71x10 -3 -2.599.70 x10 -4 -0.562.82x10 -5 HMGA2cg248925715.699.55x10 -4 -1.957.87x10 -2 0.321.64x10 -4 HMGA2 cg24776736 0.792.40x10 -1 -2.221.97x10 -5 -1.113.43x10 -4 TCF7L2cg11748187 0.851.09x10 -1 -1.621.36x10 -3 -0.456.80x10 -4 CDKAL1cg06512263 0.493.06x10 -1 -1.726.40x10 -4 -0.561.31x10 -3

21. Table 3 – Association between methylation level and single nucleotide polymorphisms for birth weight GeneSNPcpg% methylationβ-coefficient [SE]P-valueSummary estimateHeterogeneity p-value TCF7L2rs7903146cg09022607GGGAGAAA START33.0533.1132.79 -0.0001 [0.0046]9.21 x10 -1 0.00214.74 x10 -1 CHILD28.9629.5129.60 0.0038 [0.0039]3.23 x10 -1 cg11748187GGGAGAAA START44.0144.2043.07 -0.0019 [0.0066]7.73 x10 -1 0.00822.99 x10 -2 CHILD36.3738.4239.84 0.0183 [0.0066]5.92 x10 -3 CALCRrs7780752cg23061150GGGAGAAA START86.1985.9685.52 -0.003 [0.0032]3.29 x10 -1 0.00001.24 x10 -1 CHILD81.3281.9382.19 0.0045 [0.0038]2.36 x10 -1 HMGA2rs1042725cg24892571GGGAGAAA START89.5289.6789.80 0.0014 [0.0020]4.97 x10 -1 0.00206.00 x10 -1 CHILD85.8785.8685.27 0.0032 [0.0028]2.61 x10 -1 cg24776736GGGAGAAA START62.4665.9766.15 -0.0111 [0.0052]3.25 x10 -2 -0.01403.86 x10 -1 CHILD56.6160.0960.44 -0.0181 [0.0062]3.79 x10 -3 CDKAL1rs6931514cg06512263CCCACAAA START34.5833.5434.86 -0.0043 [0.0076]5.72 x10 -1 -0.00637.33 x10 -1 CHILD28.1127.4526.32 -0.0079 [0.0070]2.62 x10 -1

22. Table 4 – Variance explained Variables in the modelCHILDSTART HMGA2 cg248925710.04660.0130 + gestational age0.24530.3789 + rs10784502_G0.25090.3753 + sex0.25710.3794 cg247767360.07410.0060 + gestational age0.26580.3502 + rs10784502_G0.26640.3451 + sex0.27320.3526 TCF7L2 cg090226070.07610.0082 + gestational age0.25910.3511 + rs4132670_A0.27900.3451 + sex0.28910.3519 cg117481870.04240.0112 + gestational age0.24720.3508 + rs4132670_A0.27670.3449 + sex0.28500.3515 CDKAL1 cg065122630.04810.0046 + gestational age0.24870.3507 + rs9368222_A0.25340.3535 + sex0.25920.3616 CALCR cg230611500.04500.0306 + gestational age0.28140.3616 + rs6968642_A0.26830.3517 + sex0.27490.3612

23. IN CONTEXT WITH LITERATURE ON SOUTH ASIANS Overwhelming majority of studies in European populations Some candidate gene studies exploring birth weight in South Asians Busch et al found that among South Asians, those homozygous for PON2 A148/A148 had significantly lower birth weight (n=290) Is it possible that these genes are less important in governing birth weight in South Asians?

24. GeneLead SNPProxy SNPAlleleSouth AsianEuropean Caucasians Beta (SE)p-valueBeta (SE)p-value rs9883204 rs2877716A-0.017 (0.044)6.99E-010.042 (0.049)3.91E-01 rs7780752 rs6968642A-0.0003344 (0.03735)9.93E-01-0.015 (0.044)7.27E-01 rs1111875 rs10882099G0.007069 (0.03942)8.58E-010.008 (0.042)8.55E-01 TCF7L2rs7903146 rs4132670A0.04741 (0.04252)2.66E-010.101 (0.044)2.27E-02 rs1801253 rs740746G0.003531 (0.04451)9.37E-010.030 (0.048)5.30E-01 HMGA2rs1042725 rs10784502G0.07447 (0.04187)7.66E-020.063 (0.044)1.52E-01 rs900400 rs17451107G-0.03699 (0.04193)3.79E-01-0.040 (0.041)3.26E-01 rs724577 A0.08876 (0.04967)7.52E-020.062 (0.048)1.99E-01 CDKAL1rs6931514 rs9368222A-0.08865 (0.04288)3.98E-02-0.022 (0.047)6.46E-01

25. NEXT STEPS 1.Epigenome-wide search for cpgs associated with birth weight in South Asians 2.This analysis is conducted in only a subset of START / CHILD – use more data when available

26. Illumina Infinium HumanMethylation450 assay. (a) Infinium I assay. Each individual CpG is interrogated using two bead types: methylated (M) and unmethylated (U). The probe design assumes that all CpGs underlying the probe body have the same methylation status as the target CpG. Both bead types will incorporate the same labeled nucleotide for the same target CpG, thereby producing the same color fluorescence. The nucleotide that is added is determined by the base downstream of the 'C' of the target CpG. The proportion of methylation, β, can be calculated by comparing the intensities from the two different probes in the same color: β= M/(U + M). (b) Infinium II assay. Each target CpG is interrogated using a single bead type. A probe may have up to three underlying CpG sites, with a degenerate R base corresponding to the 'C' of each CpG. Methylation state is detected by single base extension at the position of the 'C' of the target CpG, which always results in the addition of a labeled 'G' or 'A' nucleotide, complementary to either the 'methylated' C or 'unmethylated' T, respectively. Each locus is detected in two colors, and methylation status is determined by comparing the two colors from the one position: β = Green (M)/(Red (U) + Green (M)).