Size at birth is known to be influenced by various fetal and maternal factors, including genetic effects. South Asians have a high burden of low birth weight and cardiometabolic diseases, yet studies of common genetic variations underpinning these phenotypes are lacking. We generated independent, weighted fetal genetic scores (fGSs) and maternal genetic scores (mGSs) from 196 birth weight–associated variants identified in Europeans and conducted an association analysis with various fetal birth parameters and anthropometric and cardiometabolic traits measured at different follow-up stages (5–6-year intervals) from seven Indian and Bangladeshi cohorts of South Asian ancestry. The results from these cohorts were compared with South Asians in UK Biobank and the Exeter Family Study of Childhood Health, a European ancestry cohort. Birth weight increased by 50.7 g and 33.6 g per SD of fGS (P = 9.1 × 10−11) and mGS (P = 0.003), respectively, in South Asians. A relatively weaker mGS effect compared with Europeans indicates possible different intrauterine exposures between Europeans and South Asians. Birth weight was strongly associated with body size in both childhood and adolescence (P = 3 × 10−5 to 1.9 × 10−51); however, fGS was associated with body size in childhood only (P < 0.01) and with head circumference, fasting glucose, and triglycerides in adults (P < 0.01). The substantially smaller newborn size in South Asians with comparable fetal genetic effect to Europeans on birth weight suggests a significant role of factors related to fetal growth that were not captured by the present genetic scores. These factors may include different environmental exposures, maternal body size, health and nutritional status, etc. Persistent influence of genetic loci on size at birth and adult metabolic syndrome in our study supports a common genetic mechanism that partly explains associations between early development and later cardiometabolic health in various populations, despite marked differences in phenotypic and environmental factors in South Asians.

Size at birth is a summary measure for intrauterine nutrition, growth, and development (1,2). It is influenced by genetic and environmental factors, and in clinical practice, it helps to predict neonatal well-being (3,4). Several longitudinal population-based studies both in higher- and lower- and middle-income countries, including India, have demonstrated a correlation between birth size (both small and large) and future risk of cardiometabolic diseases (1,2,58). This led to the fetal programming or developmental origins of health and disease hypothesis, which proposes that the intrauterine environment (meaning maternal diet, smoking, etc.) drives fetal growth and affects the development of metabolic organs, setting up later risk of disease (1,2). Up to one-third of South Asians living in the Indian subcontinent are born at a low birth weight (9). They also have a high prevalence of type 2 diabetes and cardiovascular diseases developed at a younger age and a lower BMI compared with Europeans (10). Understanding the genetic determinants of neonatal size and their association with later phenotypes may provide important insight into mechanisms of how fetal growth and development relate to later risk of cardiometabolic diseases in various ancestral groups with different environmental exposures.

Large-scale genome-wide association studies (GWAS), mostly in individuals of European ancestry, including participants from the Early Growth Genetics (EGG) consortium and the UK Biobank (UKBB), have identified several genetic variants associated with birth weight (1115). These genetic associations include 1) direct effects, where the fetus’s own genotype influences its birth weight; 2) indirect effects of the maternal genotype, which influence birth weight via the intrauterine environment; and 3) a combination of direct fetal and indirect maternal effects (11,15). A recent study in Europeans reported 209 conditionally independent GWAS-significant genetic variants at 190 independent loci that are associated with birth weight and explain 7% of birth weight variance (fetal genotype 6%, maternal genotype 2%, covariance −0.5%), further confirming the relatively weaker effect of maternal genetics than fetal genetics (15). The study further partitioned the genetic effects on birth weight into fetal and maternal effects using structure equation modeling and demonstrated their association with various cardiometabolic traits. Genetic risk score is one of the approaches used to summarize the genetic effects of multiple risk genes on a given trait, such as birth weight. On the basis of observations that fetal genetic score (fGS) for birth weight is negatively associated with adult blood pressure (BP), lipid, glucose, and insulin levels and insulin resistance, Warrington et al. (15) concluded that common genetic variants contribute to the observed associations between lower birth weight and later cardiometabolic disease. This conclusion supports the fetal insulin hypothesis first set out by Hatterseley et al. (16), which purports that the same genotype at a variant can influence birth weight and later, cardiometabolic risk.

The dual burden of low birth weight and cardiometabolic diseases in South Asians and the fact that South Asians, especially those living in lower- and middle-income countries, are not well represented in the majority of GWAS demand investigation of genetic variants associated with fetal development and how they relate to later cardiometabolic traits (1719). Here, we studied associations of the weighted genetic scores with birth size in ∼1,900 mother-offspring pairs from South Asian birth cohorts in India, Bangladesh, and the U.K. Association analysis was also conducted with body size and cardiometabolic traits among children, adolescents, and adults using available follow-up data from Indian cohorts. Overall, the study aimed to answer two questions: 1) Are fGSs and maternal genetic scores (mGSs) related to newborn size in South Asians in the same way as in Europeans? 2) Do the genetic scores related to birth weight influence cardiometabolic risk in a direction that would support a genetic contribution to the birth weight–cardiometabolic diseases link in the South Asian population?

Study Participants

The participants in this study were mother-child pairs from different prospective birth cohort studies from India, Bangladesh, and the U.K. The Indian cohorts comprise the Pune Maternal Nutrition Study (PMNS), Parthenon Study (PS), Mumbai Maternal Nutritional Project (MMNP), and Mysore Birth Records Cohort (MBRC). The individuals from PMNS and MMNP are Indo-Europeans, and those from the PS and MBRC are Dravidians, the two major ethnic populations on the Indian subcontinent (20,21). Informed consent was obtained from all participants on the basis of the guidelines of the Indian Council of Medical Research, Government of India, New Delhi. The Bangladeshi cohorts were from a substudy of the prospective multicenter European Union FP7 project GIFTS (Genomic and Lifestyle Predictors of Fetal Outcome Relevant to Diabetes and Obesity and Their Relevance to Prevention Strategies in South Asian People), consisting of work package 2 (WP2), WP3, and London, U.K., Bangladeshi (UK-Bang) cohorts that were conducted after appropriate institutional review board approval.

PMNS

The PMNS cohort, based in six rural villages near Pune in Western India, was established in 1993 to examine the relationship of maternal health and nutrition during pregnancy to fetal growth and development and future cardiometabolic risk (22). Women were recruited preconceptionally. A 75-g oral glucose tolerance test was carried out at 28 weeks’ gestation in pregnancy, and gestational diabetes mellitus (GDM) was diagnosed on the basis of then-prevalent World Health Organization (WHO) guidelines. Gestational age was based on last menstrual period date (recorded every month during the preconception period) unless it differed from early (<20 weeks’ gestation) ultrasound scan dating by ≥2 weeks, in which case the latter was used. Detailed newborn anthropometry was carried out by trained research staff within 72 h of birth. Multiple follow-up studies have been conducted starting from prepregnancy, during pregnancy, at birth, and during early childhood, adolescence, and young adulthood, and detailed anthropometric and biochemical data were collected. At 6 years of age, anthropometry, resting systolic and diastolic BP, plasma glucose and insulin (fasting and after an oral glucose load), and fasting lipids (triglycerides and LDL and HDL cholesterol) were measured. At 12 years of age, detailed anthropometry and measurements of BP, fasting glucose, insulin, and lipids were repeated. At both time points, the same measurements were carried out in both parents. We have used these data in the current study. The DNA samples isolated from the 6-year follow-up stage were used for genotyping.

PS

The PS was established in 1997–1998 in Mysore, South India, to examine the long-term effects of maternal glucose tolerance and nutritional status during pregnancy on cardiovascular risk factors and cognition in the offspring (23). Women (<32 weeks’ gestation) were recruited in the antenatal clinic of CSI Holdsworth Memorial Hospital. Gestational age was assessed using last menstrual period dates collected at recruitment. A 100-g oral glucose tolerance test was carried out at 28–32 weeks’ gestation, and GDM was diagnosed on the basis of Carpenter and Coustan criteria (24). Detailed newborn anthropometry was carried out by trained research staff within 72 h of birth. At 5 and 13.5 years of age, anthropometry, resting systolic and diastolic BP, plasma fasting glucose and insulin, and fasting lipids (triglycerides and LDL and HDL cholesterol) were measured. At 5 years, the same measurements were carried out in the mothers and only fasting glucose and insulin in the fathers. These data were used in this study. Genotyping was performed on the DNA samples isolated from blood samples at the 5-year follow-up stage.

MMNP

The MMNP was a randomized controlled trial set up in 2006 among women living in the slums of Mumbai, Western India, with the objective to test whether improving women’s dietary micronutrient quality before and during conception improves birth weight and other related outcomes (25). Women were recruited before conception. As in the PMNS, gestational age was assessed using a combination of last menstrual period dates (which were collected monthly during the preconception period) and ultrasound scans conducted before 20 weeks’ gestation. A 75-g oral glucose tolerance test was carried out at 28–32 weeks’ gestation, and GDM was diagnosed on the basis of the revised WHO 1999 guidelines. Trained research staff carried out newborn anthropometry within 10 days of birth. In the current study, we have used the child phenotype data at birth (anthropometry) and in early childhood (5–7-year follow-up), when detailed anthropometry, systolic and diastolic BP, fasting and postload glucose and insulin, and fasting LDL and HDL cholesterol and triglycerides were measured (26). Maternal anthropometry, BP, and fasting plasma glucose and insulin concentrations were also measured at this follow-up. Genomic DNA isolated from blood samples at the same stage were used for genotyping.

MBRC

The MBRC is a retrospective birth cohort of urban men and women born at the CSI Holdsworth Memorial Hospital during 1934–1955 (27). They were recruited for the first time as adults (mean age 47 years) in 1993–1995, and cardiometabolic risk factors were measured (7). Birth weight, length, and head circumference were obtained from the mothers’ obstetric records. We have included the anthropometric data at birth and cardiometabolic parameters measured between 40 and 70 years during 2013–2017. Gestational age was missing in the majority of subjects, and GDM status was not available. Since maternal DNA samples were not available, the analyses were restricted to the association of fGS and birth measurements and later life outcomes.

GIFTS Dhaka-WP2 and Dhaka-WP3

WP2 samples were collected between 2011 and 2012 in Dhaka, Bangladesh, from women attending the Maternal and Child Health Training Institute, a tertiary government hospital for antenatal care and registration in Dhaka. Primigravid pregnant women who were in the first trimester (≤14 weeks’ gestation) of a singleton pregnancy conceived naturally and who were willing to participate in the study were included in an observational study during pregnancy and immediately postpartum after written consent (28). GDM was diagnosed on the basis of revised WHO 1999 guidelines. Women with a previous history of type 2 diabetes, GDM, or pregnancy-induced hypertension were excluded. The aim of WP2 was to establish the methods and feasibility of recruitment and follow-up for an interventional study (Dhaka-WP3). WP3 samples were collected between 2014 and 2015 in Dhaka from pregnant women attending the Maternal and Child Health Training Institute who consented to an open-label micronutrient supplement trial of vitamin D and vitamin B12 supplementation (29). All consenting women eligible under the WP2 criteria were included in the study, and samples were collected from mother and baby under the same sampling frame as WP2. Women who were diagnosed later in pregnancy with GDM remained in the study.

UK-Bang

The UK-Bang cohort was set up between 2012 and 2015 as an exploratory observational study of GDM and its consequences on offspring. Pregnant women of Bangladeshi origin were recruited from the Royal London Hospital antenatal clinics at 28 weeks’ gestation at the time of 75-g oral glucose tolerance test. GDM was diagnosed on the basis of revised WHO 1999 guidelines. Women were recruited during routine antenatal care and enriched for the presence of GDM. Women with multiple pregnancies or preexisting or overt type 1 or type 2 diabetes were excluded. Gestational age was based on ultrasound scan dating. Detailed newborn anthropometry was carried out by trained research staff within 72 h of birth.

The Exeter Family Study of Childhood Health

The Exeter Family Study of Childhood Health (EFSOCH) is a prospective study of children born between 2000 and 2004 and their parents from a geographically defined region of Exeter, U.K. All women gave informed consent, and ethical approval was obtained from the North and East Devon Local Research Ethics Committee. Details of study protocol, including measurement of birth weight, are described in Knight et al. (30). Maternal and paternal DNA samples were extracted from parental blood samples obtained at the study visit (when the women were 28 weeks pregnant), and offspring DNA was obtained from cord blood at birth. Genotyping and imputation of EFSOCH samples have been described previously (31).

UKBB South Asian Study

The UKBB phenotype preparation has been described in detail elsewhere (15). Briefly, a total of 280,315 participants reported their own birth weight in kilograms, and 216,839 women reported the birth weight of their first child on at least one assessment center visit. Multiple births were excluded where reported. In the absence of gestational data, participants with birth weight values <2.5 kg or >4.5 kg were considered preterm births and excluded. In addition to the genotype quality control metrics performed centrally by the UKBB, we defined a subset of South Asian ancestry samples (32). To do this, we generated ancestry-informative principal components (PCs) in the 1000 Genomes Project samples. The UKBB samples were then projected into this PC space using the single nucleotide polymorphism (SNP) loadings obtained from the PC analysis using the 1000 Genomes Project samples. The UKBB participants’ ancestry was classified using K-means clustering centered on the three main 1000 Genomes Project populations (European, African, and South Asian). Those clustering with the South Asian population were classified as having South Asian ancestry.

Inclusion and Exclusion Criteria and Phenotype Measurements

In all the cohorts, the association analysis was restricted to individuals with both genotype and phenotype data available. The anthropometric measurements at birth were conducted within 72 h after birth, and babies with congenital defects were excluded from the analysis. Twins and babies born <37 weeks of gestational age (9–14%) were excluded from the association analysis at birth. For anthropometric and cardiometabolic analysis at follow-up stages during childhood and adolescence, we included all children with phenotype and genotype data available, irrespective of their gestational age at birth. For adults, phenotype data were taken from the following follow-up stages: PMNS mothers at 6 years, PMNS fathers at 12 years, PS mothers and fathers at 5 years, MMNP mothers at 7 years, and MBRC individuals at the latest follow-up during 2013–2017. Anthropometric measurements at birth and follow-up stages were conducted using standard methods. Body fat percentage was measured by whole-body DEXA scans. Biochemical measurements were conducted from fasting plasma samples using standard methods. Plasma glucose was measured by the glucose oxidase peroxidase method; plasma insulin was measured using the DELFIA technique. Insulin resistance was calculated using the HOMA of insulin resistance (HOMA-IR). Plasma lipid levels, including total cholesterol, triglycerides, HDL cholesterol, and LDL cholesterol were measured by standard enzymatic methods. Individuals with missing phenotype data were excluded from the analysis of the particular trait.

Genotyping and Imputation Quality Control

For the Indian cohorts, genome-wide genotyping was performed using Affymetrix Genome-Wide Human SNP Array 6.0 for fathers of the PMNS cohort; Illumina Infinium CoreExome-24 Kit for children and mothers of the PMNS and PS cohorts; and Illumina Infinium Global Screening Array-24 Kit for children and mothers of the MMNP, fathers of the PS, and individuals of the MBRC cohorts. Individuals with genotyping call rate ≤95% and SNPs with call rate ≤95% and Hardy Weinberg equilibrium P ≤ 10−6 were removed. Genome-wide imputation was performed using IMPUTE version 2 software (https://mathgen.stats.ox.ac.uk/impute/impute_v2.html) and 1000 Genomes Project phase 3 as the reference panel, and SNPs with imputation information score ≤0.4 were removed. The genome-wide genotyping for the children and mothers of all the Bangladeshi cohorts were performed using Illumina Infinium Global Screening Array-24 Kit, and genome-wide imputation was performed using the Haplotype Reference Consortium imputation panel.

Selection of Genetic Variants and Calculation of Weighted Genetic Scores

The scheme for selecting SNPs for the calculation of birth weight genetic score is shown in Fig. 1. Of the 205 autosomal SNPs reported as associated with birth weight in Warrington et al. (15), 9 SNPs were excluded because of either being missing or having an imputation information score <0.4 in at least one of the cohorts. Finally, 196 autosomal SNPs were used for generating the weighted fGS and mGS. Details of the 196 SNPs are provided in Supplementary Table 1. The SNP weights for generating fGS and mGS were taken from the structure equation model–adjusted effect estimates of the fetal and maternal effects, respectively, from the recent GWAS of birth weight from the EGG/UKBB consortium (15) (Supplementary Table 1). The structural equation model estimates associations of both maternal and fetal scores with birth weight while accounting for the relationship between fetal and maternal genotypes, thereby producing independent estimates of the fetal and maternal genetic effects on birth weight. The weighted genetic score was calculated using the following equation:
formula
where βn is the weight of SNPn taken from the EGG/UKBB birth weight GWAS, nSNPs are the number of SNPs available (n = 196), and Σβn is the sum total weight of all 196 SNPs. We identified independent genetic variants from the 196 SNPs used above by looking at pairwise linkage disequilibrium (r2 <0.01) in a window of 1,000 kb in the 1000 Genomes Project phase 3 reference panel and freshly conducted association analysis with birth weight.
Figure 1

Flowchart showing the overall study design, including SNP selection, generation of weighted fGSs and mGSs, association analysis, and final meta-analyses at different stages of follow-up. *Warrington et al. (15). info, information; SEM, structural equation model.

Figure 1

Flowchart showing the overall study design, including SNP selection, generation of weighted fGSs and mGSs, association analysis, and final meta-analyses at different stages of follow-up. *Warrington et al. (15). info, information; SEM, structural equation model.

Close modal

Statistical Analysis and Power Calculation

Birth weight and other birth measures were transformed to standardized z-scores (z-score = [value – mean] / SD). Association analysis was performed by linear regression, using z-scores as the dependent variables and weighted genetic score as the independent variable, adjusted for the child’s sex and gestational age. The models were as follows: For the fetal analysis, birth weight z-score ∼ fGS + sex + gestational age and birth weight z-score ∼ fGS + sex + gestational age + mGS, and for the maternal analysis, birth weight z-score ∼ mGS + sex + gestational age and birth weight z-score ∼ mGS + sex + gestational age + fGS.

Power calculations were conducted to estimate the probable association observable in our analysis with a sample size of 2,693 individuals of South Asian ancestry. If the birth weight SNPs explained equal variance in South Asians to that explained in Europeans (6% and 2% for fGS and mGS, respectively) (15), we would have >99% power to see an association with the fGS and 98% power with the mGS at α = 0.05. However, it is likely that because of differing linkage disequilibrium between marker SNPs and underlying causal genetic variants, genetic variants identified in GWAS samples that were largely of European ancestry may explain less variation in non-European samples. Therefore, assuming that the genetic scores explained only 75% of the European ancestry variation in individuals with South Asian ancestry, we would still have 99% and 83% power for fGS and mGS, respectively, to detect an association with birth weight.

Association analysis of the anthropometric and cardiometabolic phenotype data acquired during follow-up at childhood and adolescence was performed by linear regression, using log10-transformed standardized z-scores as the dependent variables and weighted genetic score as an independent variable, adjusted for sex and age. Imputed genotype data from parents in the PMNS and PS, mothers in MMNP, and men and women in MBRC cohorts were used for investigating the effect of the genetic risk scores on adult anthropometric and cardiometabolic phenotypes. BMI was included as an additional covariate for the cardiometabolic traits. The models were as follows: For the anthropometric traits, log10-transformed z-score ∼ fGS + sex + age, and for the cardiometabolic traits, log10-transformed z-score ∼ fGS + sex + age + BMI.

The association analyses for birth weight and other birth measures and for anthropometric and cardiometabolic traits were conducted independently for each cohort, and fixed-effects inverse variance–weighted meta-analysis (using the metan command in Stata) was performed to combine the final results. A total of 57 tests in the three stages (childhood, adolescence, and adulthood) were conducted, and the significance level was set at P < 0.001 (α < 0.05/57 tests) to allow for multiple testing.

Data and Resource Availability

The data sets generated and/or analyzed during the current study are available upon reasonable request: Indian cohorts (PMNS, PS, MMNP, and MBRC) to author G.R.C., EFSOCH to the Exeter Clinical Research Facility (crf@exeter.ac.uk), GIFTS (Dhaka-WP2 and Dhaka-WP3) and UK-Bang to author G.A.H., and UKBB to https://www.ukbiobank.ac.uk/using-the-resource. No applicable resources were generated or analyzed during the current study.

Clinical and Demographic Characteristics of Study Participants

Newborn measurements, maternal details, and phenotypes at different follow-up stages are shown in Table 1 and Supplementary Tables 2–5. The mean birth weight of term babies in the different cohorts ranged from 2.64 to 3.12 kg. Within the cohorts of South Asian ancestry, babies born in India and Bangladesh were comparatively smaller, whereas Bangladeshi babies born in the U.K. from the UK-Bang and the UKBB South Asian Study (UKBB-SAS) were relatively larger (Supplementary Tables 2 and 3). Birth weight was much higher in the European babies as observed in the EFSOCH (Table 1). Boys were bigger than girls across all the cohorts. In contrast, the sum of skinfold thickness, a measure of adiposity, was greater in girls. Among all the cohorts, PMNS mothers living in rural India were the thinnest (mean BMI 18.0 kg/m2), whereas Bangladeshi mothers living in the U.K. (UK-Bang) were the heaviest (mean BMI 26.2 kg/m2). Mean BMI in the mothers from the other cohorts were in the normal range, between 20.3 and 23.6 kg/m2. The percentage of mothers with GDM was higher in the Bangladeshi cohorts (UK-Bang 50%, Dhaka-WP2 24.5%, Dhaka-WP3 25.8%), whereas in the Indian cohorts, it was 0.6%, 6.1%, and 6.9% in PMNS, PS, and MMNP, respectively. The UK-Bang cohort was positively selected to have higher rates of GDM than the underlying population, but the high rates of GDM in the Bangladeshi Dhaka-WP2 and Dhaka-WP3 cohorts represented the high rates of GDM in the community. The MBRC mothers were not tested for diabetes. PC analysis did not reveal any evidence of population stratification within the cohorts. (The data can be made available upon request.)

Table 1

Maternal and newborn details in the study cohorts, and fGSs and mGSs for the South Asian and European cohorts

TraitsPMNS (n = 515)PS (n = 511)MMNP (n = 466)MBRC (n = 684)Dhaka-WP2 (n = 53)Dhaka-WP3 (n = 314)UK-Bang (n = 150)UKBB-SAS* (n = 2,732)EFSOCH* (n = 674)
Birth weight (kg) 2.68 (0.34) 2.91 (0.41) 2.64 (0.37) 2.76 (0.42) 2.90 (0.38) 2.84 (0.42) 3.12 (0.45) 3.10 (0.68) 3.52 (0.47) 
Birth length (cm) 47.8 (1.97) 48.8 (2.11) 48.2 (2.26) 48.0 (2.95) 46.2 (2.56) 49.6 (2.60) 46.6 (2.03) NA 50.3 (2.12) 
Ponderal index (kg/m324.5 (2.44) 25.0 (2.75) 23.6 (2.60) 25.3 (4.85) 29.5 (4.42) 23.3 (3.50) 28.9 (4.27) NA 27.7 (2.58) 
Head circumference (cm) 33.1 (1.24) 33.9 (1.28) 33.2 (1.20) 35.6 (1.58) 33.4 (1.39) 33.0 (2.40) 33.6 (1.31) NA 35.2 (1.26) 
Chest circumference (cm) 31.2 (1.59) 32.0 (1.64) 30.9 (1.75) NA NA NA 33.4 (1.97) NA 34.2 (1.86) 
Abdomen circumference (cm) 28.7 (1.91) 30.0 (1.92) 28.4 (2.08) NA NA NA 31.4 (2.56) NA NA 
Mid-upper arm circumference (cm) 9.7 (0.88) 10.4 (0.92) 9.7 (0.82) NA 9.9 (0.71) 10.2 (2.09) 10.9 (2.13) NA 11.1 (0.90) 
Triceps skinfold (mm) 4.3 (0.87) 4.3 (0.90) 4.2 (1.05) NA NA NA 5.0 (1.93) NA 4.86 (1.08) 
Subscapular skinfold (mm) 4.2 (0.89) 4.5 (0.91) 4.2 (0.99) NA NA NA 5.3 (1.87) NA 4.87 (1.08) 
Gestational age (weeks) 39.0 (1.06) 39.5 (1.14) 39.3 (1.17) NA 40.3 (1.17) 39.2 (1.53) 40.0 (3.44) NA 40.1 (1.22) 
Maternal age (years) 21.4 (3.56) 23.8 (4.24) 24.8 (3.83) NA 19.9 (2.45) 22.7 (4.29) 29.7 (5.40) NA 30.5 (5.19) 
Maternal height (cm) 152.1(4.9) 154.5 (5.4) 151.3 (5.4) NA 151.1 (5.8) 150.9 (5.7) 156.0 (5.8) NA 165.0 (6.3) 
Maternal BMI (kg/m218.0 (1.9) 23.6 (3.55) 20.3 (3.67) NA 20.6 (3.40) 22.7 (4.03) 26.2 (4.34) NA 24.0 (4.34) 
Maternal GDM status, n (%) 3 (0.6) 31 (6.1) 32 (6.9) NA 13 (24.5) 81 (25.8) 75 (50.0) NA NA 
Year of birth 1994–1995 1998–1999 2006–2012 1934–1966 2011–2012 2015–2016 2011–2015 1934–1970 2000–2004 
fGS 191.0 (9.0) 191.0 (9.6) 189.0 (9.4) 189.0 (9.6) 191.0 (8.1) 188.0 (9.4) 188.0 (9.3) 192.0 (9.9) 192.0 (9.8) 
mGS 215.0 (10.3) 215.0 (10.4) 215.0 (10.5) NA 218.0 (10.2) 217.0 (10.2) 216.0 (9.3) 214.8 (11.0) 214.0 (10.8) 
TraitsPMNS (n = 515)PS (n = 511)MMNP (n = 466)MBRC (n = 684)Dhaka-WP2 (n = 53)Dhaka-WP3 (n = 314)UK-Bang (n = 150)UKBB-SAS* (n = 2,732)EFSOCH* (n = 674)
Birth weight (kg) 2.68 (0.34) 2.91 (0.41) 2.64 (0.37) 2.76 (0.42) 2.90 (0.38) 2.84 (0.42) 3.12 (0.45) 3.10 (0.68) 3.52 (0.47) 
Birth length (cm) 47.8 (1.97) 48.8 (2.11) 48.2 (2.26) 48.0 (2.95) 46.2 (2.56) 49.6 (2.60) 46.6 (2.03) NA 50.3 (2.12) 
Ponderal index (kg/m324.5 (2.44) 25.0 (2.75) 23.6 (2.60) 25.3 (4.85) 29.5 (4.42) 23.3 (3.50) 28.9 (4.27) NA 27.7 (2.58) 
Head circumference (cm) 33.1 (1.24) 33.9 (1.28) 33.2 (1.20) 35.6 (1.58) 33.4 (1.39) 33.0 (2.40) 33.6 (1.31) NA 35.2 (1.26) 
Chest circumference (cm) 31.2 (1.59) 32.0 (1.64) 30.9 (1.75) NA NA NA 33.4 (1.97) NA 34.2 (1.86) 
Abdomen circumference (cm) 28.7 (1.91) 30.0 (1.92) 28.4 (2.08) NA NA NA 31.4 (2.56) NA NA 
Mid-upper arm circumference (cm) 9.7 (0.88) 10.4 (0.92) 9.7 (0.82) NA 9.9 (0.71) 10.2 (2.09) 10.9 (2.13) NA 11.1 (0.90) 
Triceps skinfold (mm) 4.3 (0.87) 4.3 (0.90) 4.2 (1.05) NA NA NA 5.0 (1.93) NA 4.86 (1.08) 
Subscapular skinfold (mm) 4.2 (0.89) 4.5 (0.91) 4.2 (0.99) NA NA NA 5.3 (1.87) NA 4.87 (1.08) 
Gestational age (weeks) 39.0 (1.06) 39.5 (1.14) 39.3 (1.17) NA 40.3 (1.17) 39.2 (1.53) 40.0 (3.44) NA 40.1 (1.22) 
Maternal age (years) 21.4 (3.56) 23.8 (4.24) 24.8 (3.83) NA 19.9 (2.45) 22.7 (4.29) 29.7 (5.40) NA 30.5 (5.19) 
Maternal height (cm) 152.1(4.9) 154.5 (5.4) 151.3 (5.4) NA 151.1 (5.8) 150.9 (5.7) 156.0 (5.8) NA 165.0 (6.3) 
Maternal BMI (kg/m218.0 (1.9) 23.6 (3.55) 20.3 (3.67) NA 20.6 (3.40) 22.7 (4.03) 26.2 (4.34) NA 24.0 (4.34) 
Maternal GDM status, n (%) 3 (0.6) 31 (6.1) 32 (6.9) NA 13 (24.5) 81 (25.8) 75 (50.0) NA NA 
Year of birth 1994–1995 1998–1999 2006–2012 1934–1966 2011–2012 2015–2016 2011–2015 1934–1970 2000–2004 
fGS 191.0 (9.0) 191.0 (9.6) 189.0 (9.4) 189.0 (9.6) 191.0 (8.1) 188.0 (9.4) 188.0 (9.3) 192.0 (9.9) 192.0 (9.8) 
mGS 215.0 (10.3) 215.0 (10.4) 215.0 (10.5) NA 218.0 (10.2) 217.0 (10.2) 216.0 (9.3) 214.8 (11.0) 214.0 (10.8) 

Data are mean (SD) unless otherwise indicated. NA, not available.

*

Not used for meta-analysis. fGSs and mGSs were calculated from 196 birth weight–associated SNPs in children and mothers, respectively.

Association of Genetic Scores With Birth Weight and Other Birth Measures

The effect allele frequencies (EAFs) of 196 SNPs were similar in all seven South Asian cohorts, except two outliers, one each in the MBRC (rs2306547) and GIFTS (rs9851257) cohorts (Supplementary Fig. 1A and Supplementary Table 1). Although, the EAFs at several SNPs varied considerably between South Asians and the EGG/UKBB participants (Supplementary Fig. 1B and Supplementary Table 1), mean values for both fGS and mGS in the South Asian cohorts were similar to those in the EFSOCH European cohort (Table 1).

We noted that the fGS calculated from 196 SNPs was strongly associated with birth weight in South Asians (Table 2). The meta-analysis of the South Asian cohorts showed a 0.013-SD higher birth weight per 1-unit higher fGS, adjusted for the child’s sex and gestational age (P = 9.1 × 10−11) (Fig. 2A and Table 2). This is equivalent to 50.7 g of birth weight per SD unit of fGS (Fig. 2E). The strength of association was only partially attenuated after additional adjustment for the mGS (effect 0.015 SD, P = 1.1 × 10−10) (Fig. 2B and Table 2). The mGS was also directly associated with offspring birth weight, although compared with the fGS, the effect size was smaller (effect 0.006 SD, P = 0.003). This is equivalent to 33.6 g of birth weight per SD unit of mGS, and adjustment for fGS made little difference (effect 0.006 SD, P = 0.004) (Fig. 2C, D, and F and Table 2). Analyses of only Indians and only Bangladeshis showed consistent and overlapping effect sizes in the fGS association analysis, but the mGS association with birth weight was largely driven by the Bangladeshi cohorts (Supplementary Tables 8 and 9). Since GDM is associated with excess fetal growth, we repeated the association analysis after exclusion of offspring of women with GDM and observed similar associations (fGS effect 0.010, P = 5.1 × 10−8; mGS effect 0.005, P = 0.011) (Supplementary Tables 6 and 7). A plot of fGS versus birth weight showed that for each fGS, birth weight was substantially smaller in the South Asians (Fig. 3A and B). Similar observations were noted for the association of mGS with birth weight (Fig. 3C and D). The effect sizes of the fGS on birth weight in the South Asian cohorts was comparable to the same in EFSOCH (n = 674) and with the UKBB-SAS (n = 2,732; P = 0.17 and 0.23, respectively) (Fig. 2E). The association between mGS and offspring birth weight in our study was similar to that observed in the UKBB-SAS (P = 0.93). However, we noted a statistically significant smaller effect size of mGS among all the South Asian cohorts combined than in EFSOCH (P = 0.048) (Fig. 2F). The fGS was also positively associated with other birth measures; no associations were seen with the mGS (Table 3). Respective adjustments for mGS and fGS did not substantially change the strength of these associations (Supplementary Table 10). Furthermore, sensitivity analysis using 167 linkage disequilibrium–pruned SNPs (after exclusion of 29 SNPs with r2 >0.01 with other variants from the list of 196 SNPs) did not make any significant changes in the strength of association (Supplementary Tables 11–13).

Figure 2

Meta-analysis of associations of fGS with birth weight in South Asian populations and comparison with European cohorts. A: fGS adjusted for sex and gestational age. B: fGS adjusted for sex, gestational age, and mGS. C: mGS adjusted for sex and gestational age. D: mGS adjusted for sex, gestational age, and fGS. The x-axis indicates the effect size (ES) for standardized birth weight per unit of weighted genetic score. In MMNP, the allocation group was additionally adjusted for, and in MBRC, only sex was adjusted for, since gestational data were not available for the majority of the samples. E and F: Comparison between South Asians and European cohorts for weighted fGS (E) and weighted mGS (F). The x-axis indicates the ES for birth weight in grams per standardized weighted genetic score. Heterogeneity P value for fGS = 0.1777 and for mGS, 0.0046.

Figure 2

Meta-analysis of associations of fGS with birth weight in South Asian populations and comparison with European cohorts. A: fGS adjusted for sex and gestational age. B: fGS adjusted for sex, gestational age, and mGS. C: mGS adjusted for sex and gestational age. D: mGS adjusted for sex, gestational age, and fGS. The x-axis indicates the effect size (ES) for standardized birth weight per unit of weighted genetic score. In MMNP, the allocation group was additionally adjusted for, and in MBRC, only sex was adjusted for, since gestational data were not available for the majority of the samples. E and F: Comparison between South Asians and European cohorts for weighted fGS (E) and weighted mGS (F). The x-axis indicates the ES for birth weight in grams per standardized weighted genetic score. Heterogeneity P value for fGS = 0.1777 and for mGS, 0.0046.

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Figure 3

Scatter plot comparing the correlation between birth weight and fGS and mGS in South Asian and European cohorts. A and B: Absolute birth weight and fGS (A) and the same between cohort-specific birth weight z-scores and fGS (B). C and D: Absolute birth weight and mGS (C) and the same between cohort-specific birth weight z-scores and mGS (D). South Asian cohorts include PMNS, PS, MMNP, MBRC, Dhaka-WP2 and Dhaka-WP3 of GIFTS, and UK-Bang, while EFSOCH is the European cohort.

Figure 3

Scatter plot comparing the correlation between birth weight and fGS and mGS in South Asian and European cohorts. A and B: Absolute birth weight and fGS (A) and the same between cohort-specific birth weight z-scores and fGS (B). C and D: Absolute birth weight and mGS (C) and the same between cohort-specific birth weight z-scores and mGS (D). South Asian cohorts include PMNS, PS, MMNP, MBRC, Dhaka-WP2 and Dhaka-WP3 of GIFTS, and UK-Bang, while EFSOCH is the European cohort.

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Table 2

Associations of fGS with own birth weight and mGS with offspring birth weight in South Asian cohorts

CohortfGS adjusted for sex and GA*fGS adjusted for sex, GA, and mGSmGS adjusted for sex and GAǁmGS adjusted for sex, GA, and fGS
nEffectL95U95PnEffectL95U95PnEffectL95U95PnEffectL95U95P
PMNS 515 0.009 0.000 0.018 0.042 443 0.010 0.001 0.020 0.040 461 0.000 −0.008 0.008 0.976 443 0.001 −0.008 0.009 0.876 
PS 511 0.021 0.012 0.029 3.8 × 10−6 458 0.021 0.012 0.030 1.0 × 10−5 475 0.011 0.003 0.020 0.013 458 0.011 0.003 0.019 0.011 
MMNP 466 0.013 0.003 0.022 0.007 460 0.013 0.004 0.022 0.006 467 −0.001 −0.009 0.007 0.804 460 0.000 −0.009 0.008 0.957 
MBRC§ 684 0.006 −0.002 0.013 0.154 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA 
Dhaka-WP2 53 0.020 −0.015 0.055 0.277 53 0.019 −0.014 0.052 0.269 53 0.034 0.009 0.059 0.011 53 0.034 0.009 0.059 0.011 
Dhaka-WP3 314 0.013 0.003 0.024 0.015 314 0.013 0.002 0.023 0.022 314 0.010 0.001 0.020 0.040 314 0.009 0.000 0.019 0.060 
UK-Bang 150 0.024 0.008 0.040 0.004 150 0.021 0.004 0.037 0.015 150 0.016 0.001 0.032 0.041 150 0.012 −0.004 0.028 0.150 
Meta-analysis 2,693 0.013 0.009 0.017 9.1 × 10−11 1,878 0.015 0.01 0.020 1.1 × 10−10 1,920 0.006 0.002 0.010 0.003 1,878 0.006 0.002 0.010 0.004 
CohortfGS adjusted for sex and GA*fGS adjusted for sex, GA, and mGSmGS adjusted for sex and GAǁmGS adjusted for sex, GA, and fGS
nEffectL95U95PnEffectL95U95PnEffectL95U95PnEffectL95U95P
PMNS 515 0.009 0.000 0.018 0.042 443 0.010 0.001 0.020 0.040 461 0.000 −0.008 0.008 0.976 443 0.001 −0.008 0.009 0.876 
PS 511 0.021 0.012 0.029 3.8 × 10−6 458 0.021 0.012 0.030 1.0 × 10−5 475 0.011 0.003 0.020 0.013 458 0.011 0.003 0.019 0.011 
MMNP 466 0.013 0.003 0.022 0.007 460 0.013 0.004 0.022 0.006 467 −0.001 −0.009 0.007 0.804 460 0.000 −0.009 0.008 0.957 
MBRC§ 684 0.006 −0.002 0.013 0.154 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA 
Dhaka-WP2 53 0.020 −0.015 0.055 0.277 53 0.019 −0.014 0.052 0.269 53 0.034 0.009 0.059 0.011 53 0.034 0.009 0.059 0.011 
Dhaka-WP3 314 0.013 0.003 0.024 0.015 314 0.013 0.002 0.023 0.022 314 0.010 0.001 0.020 0.040 314 0.009 0.000 0.019 0.060 
UK-Bang 150 0.024 0.008 0.040 0.004 150 0.021 0.004 0.037 0.015 150 0.016 0.001 0.032 0.041 150 0.012 −0.004 0.028 0.150 
Meta-analysis 2,693 0.013 0.009 0.017 9.1 × 10−11 1,878 0.015 0.01 0.020 1.1 × 10−10 1,920 0.006 0.002 0.010 0.003 1,878 0.006 0.002 0.010 0.004 

Association analysis was performed by linear regression using birth weight transformed to standardized z-scores as dependent variable and adjusted for sex and gestational age for each cohort independently, and the final summary results were meta-analyzed. The effect size is in SD units of birth weight per unit change in genetic score. The SD of birth weight in all these cohorts ranged from 0.34 to 0.45 kg. GA, gestational age; L95, lower limit of 95% CI; NA, not available; U95, upper limit of 95% CI.

*

I2 = 32.8, P for heterogeneity = 0.177.

I2 = 0, P for heterogeneity = 0.643.

ǁ

I2 = 63.5, P for heterogeneity = 0.018.

I2 = 53.7, P for heterogeneity = 0.056.

In MMNP, allocation group was additionally adjusted for.

§

In MBRC, only sex was adjusted for since gestational age data were not available for the majority of the sample.

Table 3

Associations of fGSs and mGSs with other birth measures in South Asian populations

TraitfGS adjusted for sex and gestational age*mGS adjusted for sex and gestational age*
nEffectL95U95PI2Het-PnEffectL95U95PI2Het-P
Birth length 2,544 0.004 0.000 0.009 0.048 44.1 0.097 1,820 0.003 −0.002 0.008 0.153 42.5 0.122 
Ponderal index 2,517 0.009 0.004 0.013 2.1 × 10−4 28.3 0.213 1,796 0.000 −0.004 0.006 0.906 14.3 0.323 
Head circumference 2,564 0.005 0.000 0.009 0.030 48.0 0.073 1,844 0.002 −0.002 0.007 0.425 0.741 
Chest circumference 1,586 0.012 0.007 0.017 8.2 × 10−6 23.1 0.273 1,477 0.002 −0.002 0.007 0.383 3.7 0.374 
Abdominal circumference 1,586 0.014 0.008 0.019 3.4 × 10−7 68.5 0.023 1,477 0.002 −0.003 0.007 0.554 62.0 0.048 
Mid-upper arm circumference 1,953 0.014 0.009 0.019 1.3 × 10−7 0.485 1,844 0.005 0.000 0.010 0.045 0.982 
Triceps skinfold 1,564 0.013 0.007 0.018 3.6 × 10−6 44.6 0.144 1,455 0.003 −0.001 0.009 0.181 61.7 0.050 
Subscapular skinfold 1,563 0.012 0.006 0.017 2.4 × 10−5 42.3 0.158 1,454 0.003 −0.002 0.008 0.260 25.7 0.258 
TraitfGS adjusted for sex and gestational age*mGS adjusted for sex and gestational age*
nEffectL95U95PI2Het-PnEffectL95U95PI2Het-P
Birth length 2,544 0.004 0.000 0.009 0.048 44.1 0.097 1,820 0.003 −0.002 0.008 0.153 42.5 0.122 
Ponderal index 2,517 0.009 0.004 0.013 2.1 × 10−4 28.3 0.213 1,796 0.000 −0.004 0.006 0.906 14.3 0.323 
Head circumference 2,564 0.005 0.000 0.009 0.030 48.0 0.073 1,844 0.002 −0.002 0.007 0.425 0.741 
Chest circumference 1,586 0.012 0.007 0.017 8.2 × 10−6 23.1 0.273 1,477 0.002 −0.002 0.007 0.383 3.7 0.374 
Abdominal circumference 1,586 0.014 0.008 0.019 3.4 × 10−7 68.5 0.023 1,477 0.002 −0.003 0.007 0.554 62.0 0.048 
Mid-upper arm circumference 1,953 0.014 0.009 0.019 1.3 × 10−7 0.485 1,844 0.005 0.000 0.010 0.045 0.982 
Triceps skinfold 1,564 0.013 0.007 0.018 3.6 × 10−6 44.6 0.144 1,455 0.003 −0.001 0.009 0.181 61.7 0.050 
Subscapular skinfold 1,563 0.012 0.006 0.017 2.4 × 10−5 42.3 0.158 1,454 0.003 −0.002 0.008 0.260 25.7 0.258 

Association analysis was performed by linear regression using birth measures transformed to standardized z-scores as dependent variables and adjusted for sex and gestational age for each cohort independently, and the final summary results were meta-analyzed. The effect size is in SD units of the birth measure per unit change in genetic score. The South Asian populations include PMNS, PS, MMNP, MBRC, Dhaka-WP2 and Dhaka-WP3 of GIFTS, and UK-Bang. The n was different for each trait because of missingness of some phenotype data in MBRC and Dhaka-WP2 and Dhaka-WP3. Het-P, P for heterogeneity; L95, lower limit of 95% CI; U95, upper limit of 95% CI.

*

In MMNP, the allocation group was additionally adjusted for, and in MBRC, only sex was adjusted for since gestational age data were not available for the majority of the sample.

Associations of Birth Weight and fGS With Anthropometric and Cardiometabolic Traits in Follow-up Stages

The associations of birth weight and the fGS with later anthropometric and cardiometabolic traits in early childhood and early adolescence were investigated in the Indian cohorts only, since they had longitudinal follow-up data. Birth weight was strongly positively associated with all anthropometric traits in childhood (5–7 years, P = 3 × 10−5 to 1.9 × 10−51) and adolescence (11–14 years, P = 5.7 × 10−6 to 8.1 × 10−27) (Fig. 4A and Supplementary Table 14). It also showed strong evidence of a negative association with triglyceride levels in childhood (P = 9.8 × 10−4) and a weak association in adolescence (P = 0.002). We observed a negative association with systolic and diastolic BP and a positive association with fat percentage both in childhood and adolescence, but these did not pass the Bonferroni-corrected threshold of P < 0.001 (Fig. 4A and Supplementary Table 14). Similar to birth weight, a higher fGS was associated with larger body size in childhood (Table 3). We observed a strong positive association of the fGS with waist circumference (effect 0.01 SD per standard unit, P = 5.7 × 10−5), but the associations with other anthropometric parameters, including weight, height, BMI, head circumference, and mid-upper arm circumference were weaker (P = 0.017 to 0.001) and did not pass the multiple testing threshold of P < 0.001 (Table 4 and Fig. 4B). No evidence of associations between fGS and anthropometric traits were detected in adolescents. The fGS was not associated with any of the cardiometabolic parameters in children or adolescents (Table 4), and mGS had no association with any anthropometric and cardiometabolic parameters in children or adolescents (Supplementary Table 15).

Figure 4

Birth weight and fGS associations with various anthropometric and cardiometabolic traits at different follow-up stages in the Indian cohorts. A: Birth weight. B: fGS. The x-axis shows anthropometric and cardiometabolic traits at different stages of follow-up, including birth, early childhood, early adolescence, and adulthood. The y-axis indicates the effect size in SD units. Early childhood 5–7 years included children from PMNS at 6 years, PS at 5 years, and MMNP at 7 years of age, whereas adolescents from PMNS at 12 years and PS at 13.5 years formed the group early adolescence 12–14 years. Adults consisted of parents from PMNS and PS, mothers from MMNP, and individuals from MBRC. @P ≤ 0.001; #P ≤ 0.01; *P ≤ 0.05.

Figure 4

Birth weight and fGS associations with various anthropometric and cardiometabolic traits at different follow-up stages in the Indian cohorts. A: Birth weight. B: fGS. The x-axis shows anthropometric and cardiometabolic traits at different stages of follow-up, including birth, early childhood, early adolescence, and adulthood. The y-axis indicates the effect size in SD units. Early childhood 5–7 years included children from PMNS at 6 years, PS at 5 years, and MMNP at 7 years of age, whereas adolescents from PMNS at 12 years and PS at 13.5 years formed the group early adolescence 12–14 years. Adults consisted of parents from PMNS and PS, mothers from MMNP, and individuals from MBRC. @P ≤ 0.001; #P ≤ 0.01; *P ≤ 0.05.

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Table 4

Meta-analysis of associations of fGS with anthropometric and cardiometabolic traits in early childhood, adolescence, and adults in Indians

TraitChildrenAdolescentsAdults
nEffectPI2Het-PnEffectPI2Het-PnEffectPI2Het-P
Weight 1,866 0.008 0.001 0.830 1,120 0.002 0.592 0.641 3,311 0.002 0.341 0.698 
Height 1,865 0.006 0.017 0.846 1,120 0.002 0.437 0.889 3,307 0.003 0.037 0.574 
BMI 1,865 0.007 0.007 0.666 1,120 0.001 0.844 0.581 3,306 0.000 0.977 0.438 
Head circumference 1,866 0.007 0.003 0.999 1,115 0.004 0.223 0.633 3,256 0.006 5.5 × 10−4 32.2 0.194 
Waist circumference 1,864 0.010 5.5 × 10−5 0.463 1,096 0.004 0.254 0.918 3,251 0.001 0.528 13.8 0.326 
Hip circumference NA NA NA NA NA NA NA NA NA NA 3,256 0.001 0.456 0.680 
Waist-to-hip ratio NA NA NA NA NA NA NA NA NA NA 3,247 0.001 0.603 9.8 0.353 
Mid-upper arm circumference 1,865 0.005 0.032 0.705 1,112 0.000 0.976 0.595 3,258 0.000 0.852 0.645 
Triceps skinfold 1,865 0.002 0.511 0.760 1,114 0.002 0.487 0.790 3,259 0.001 0.748 0.725 
Subscapular skinfold 1,865 0.003 0.280 52.2 0.123 1,113 0.002 0.603 0.825 3,238 −0.001 0.673 0.926 
Fat percentage 1,860 0.003 0.254 50.3 0.133 1,085 0.002 0.475 45.8 0.174 NA NA NA NA NA 
Systolic BP* 1,847 −0.002 0.411 0.410 1,102 −0.005 0.112 88.6 0.003 3,081 0.000 0.801 0.454 
Diastolic BP* 1,848 0.000 0.989 0.765 1,102 0.000 0.904 92.4 0.000 3,082 0.000 0.922 0.467 
Fasting glucose* 1,840 −0.002 0.483 0.497 1,110 0.000 0.908 92.8 0.000 2,601 −0.006 9.3 × 10−4 30.5 0.218 
120-min glucose* 1,809 0.002 0.321 0.434 NA NA NA NA NA 1,320 0.000 0.905 0.707 
Fasting insulin* 1,831 0.002 0.369 18.9 0.291 1,111 0.002 0.463 47.7 0.167 2,596 −0.002 0.359 0.823 
HOMA-IR* 1,756 0.002 0.401 0.997 1,110 0.002 0.407 74.4 0.048 2,432 −0.005 0.022 0.802 
Total cholesterol* 1,838 −0.005 0.050 50.7 0.131 1,111 0.004 0.224 0.488 2,601 −0.003 0.118 0.968 
LDL cholesterol* 1,847 −0.003 0.280 52.9 0.119 1,111 0.006 0.070 0.676 2,600 −0.001 0.594 0.957 
HDL cholesterol* 1,849 −0.005 0.059 0.513 1,111 0.002 0.632 0.631 2,584 0.000 0.867 0.809 
Triglycerides* 1,838 −0.001 0.666 0.668 1,111 −0.002 0.440 37.8 0.205 2,601 −0.006 0.002 0.673 
TraitChildrenAdolescentsAdults
nEffectPI2Het-PnEffectPI2Het-PnEffectPI2Het-P
Weight 1,866 0.008 0.001 0.830 1,120 0.002 0.592 0.641 3,311 0.002 0.341 0.698 
Height 1,865 0.006 0.017 0.846 1,120 0.002 0.437 0.889 3,307 0.003 0.037 0.574 
BMI 1,865 0.007 0.007 0.666 1,120 0.001 0.844 0.581 3,306 0.000 0.977 0.438 
Head circumference 1,866 0.007 0.003 0.999 1,115 0.004 0.223 0.633 3,256 0.006 5.5 × 10−4 32.2 0.194 
Waist circumference 1,864 0.010 5.5 × 10−5 0.463 1,096 0.004 0.254 0.918 3,251 0.001 0.528 13.8 0.326 
Hip circumference NA NA NA NA NA NA NA NA NA NA 3,256 0.001 0.456 0.680 
Waist-to-hip ratio NA NA NA NA NA NA NA NA NA NA 3,247 0.001 0.603 9.8 0.353 
Mid-upper arm circumference 1,865 0.005 0.032 0.705 1,112 0.000 0.976 0.595 3,258 0.000 0.852 0.645 
Triceps skinfold 1,865 0.002 0.511 0.760 1,114 0.002 0.487 0.790 3,259 0.001 0.748 0.725 
Subscapular skinfold 1,865 0.003 0.280 52.2 0.123 1,113 0.002 0.603 0.825 3,238 −0.001 0.673 0.926 
Fat percentage 1,860 0.003 0.254 50.3 0.133 1,085 0.002 0.475 45.8 0.174 NA NA NA NA NA 
Systolic BP* 1,847 −0.002 0.411 0.410 1,102 −0.005 0.112 88.6 0.003 3,081 0.000 0.801 0.454 
Diastolic BP* 1,848 0.000 0.989 0.765 1,102 0.000 0.904 92.4 0.000 3,082 0.000 0.922 0.467 
Fasting glucose* 1,840 −0.002 0.483 0.497 1,110 0.000 0.908 92.8 0.000 2,601 −0.006 9.3 × 10−4 30.5 0.218 
120-min glucose* 1,809 0.002 0.321 0.434 NA NA NA NA NA 1,320 0.000 0.905 0.707 
Fasting insulin* 1,831 0.002 0.369 18.9 0.291 1,111 0.002 0.463 47.7 0.167 2,596 −0.002 0.359 0.823 
HOMA-IR* 1,756 0.002 0.401 0.997 1,110 0.002 0.407 74.4 0.048 2,432 −0.005 0.022 0.802 
Total cholesterol* 1,838 −0.005 0.050 50.7 0.131 1,111 0.004 0.224 0.488 2,601 −0.003 0.118 0.968 
LDL cholesterol* 1,847 −0.003 0.280 52.9 0.119 1,111 0.006 0.070 0.676 2,600 −0.001 0.594 0.957 
HDL cholesterol* 1,849 −0.005 0.059 0.513 1,111 0.002 0.632 0.631 2,584 0.000 0.867 0.809 
Triglycerides* 1,838 −0.001 0.666 0.668 1,111 −0.002 0.440 37.8 0.205 2,601 −0.006 0.002 0.673 

Association analysis was performed by linear regression using log10-transformed standardized z-scores as the dependent variable for each cohort independently, and the final summary results were meta-analyzed. Age and sex were included as covariates in the regression model for all traits. Allocation group was additionally adjusted for in MMNP. Meta-analysis for children included those from PMNS at 6 years, PS at 5 years, and MMNP at 7 years of age; for adolescents, from PMNS at 12 years and PS at 13.5 years of age; and for adults, from parents from PMNS and PS, mothers from MMNP, and individuals from MBRC. Those passing the Bonferroni-corrected P ≤ 0.001 were considered statistically significant. Het-P, P value for heterogeneity; NA, not available.

*

BMI was additionally included as a covariate for analysis of traits.

Using data on parents of children in the PMNS and PS, men and women in the MBRC, and mothers in the MMNP cohorts, we investigated the influence of fGS on anthropometric and cardiometabolic traits in adults (Fig. 4B and Table 4). The fGS showed a strong positive association with head circumference (effect 0.006, P = 5.5 × 10−4) and a statistically insignificant positive association with adult height (effect 0.002, P = 0.037) (Fig. 4B and Table 4). It was also negatively associated with fasting glucose (effect −0.006, P = 9.3 × 10−4) and showed a weak negative association with HOMA-IR and triglycerides (P = 0.022 and 2.0 × 10−3, respectively). The direction of associations was the same as the genome-wide correlations reported in Europeans (14) (P = 0.002 to 5.5 × 10−4) (Fig. 4B and Table 4). No evidence of association was noted between fGS and other anthropometric and cardiometabolic traits in adults (P > 0.05) (Table 4).

In this study that included four Indian and three Bangladeshi cohorts from both the Indian subcontinent and the U.K., we investigated whether the genetic variants identified in a GWAS of birth weight in Europeans also influence birth size in South Asians (15). We further investigated whether the same genetic variants (either fetal variants that directly influence birth weight or those in the mother that act indirectly via the intrauterine environment) were associated with anthropometric and cardiometabolic parameters measured during childhood, adolescence, and adulthood. We observed strong positive associations of fGS with birth weight and other birth measurements in these populations of South Asian ancestry, despite a large variation in maternal BMI and fetal birth weight. While birth weight positively predicted body size in both children and adolescents, fGS did so only in children but not in adolescents. We also noted a strong association of birth weight with plasma triglyceride levels in both children and adolescents, but fGS was not related to any of the child/adolescent cardiometabolic outcomes. However, fGS was inversely associated with plasma glucose and triglycerides in adults. mGS was weakly positively linked to birth weight and was unrelated to body size and cardiometabolic traits in both children and adolescents. Our study thus reports a strong association of fGS and relatively weak association of mGS with birth weight and other birth measures in a non-European population. Furthermore, the genetic constitution of the fetus at specific variants influences body size, and the data from the adults suggest that it contributes to future cardiometabolic risk in Indians. Overall, it provides support to the observational association between low birth size and noncommunicable diseases like type 2 diabetes and cardiovascular diseases in South Asians. Follow-up studies on a larger sample size will be required to answer our second research question (i.e., Is the birth weight-cardiometabolic risk association explained by shared genetic variants?) with confidence.

Most genetic studies associating early life parameters with future risk of cardiometabolic disorders have been conducted in Europeans. As far as we are aware, this is the first such analysis in South Asians. We found similar associations of fGS generated using weights from European studies with birth size in a consortium of seven birth cohorts of South Asian ancestry comprising Indian and Bangladeshi mother-child pairs. The replication of fGS association in South Asians was noted despite a wide variability in birth weight and maternal BMI within the South Asian cohorts and significant differences in the EAFs of many of the birth weight–associated variants between the EGG/UKBB and the South Asian participants. Despite similar fGS association with birth weight as in Europeans, the newborn size of South Asian babies was substantially smaller, indicating a significant role of factors not captured by the genetic score on fetal growth. These factors may include different environmental exposures, maternal body size, health and nutritional status, etc. We noted an increase of 50.7 g of birth weight per SD of fGS, which is consistent with the observation in the UKBB-SAS and is marginally smaller than in EFSOCH, examples of South Asian and European ancestry cohorts, respectively. The significant association of fGS with body size at birth persisted even after adjustment for mGS, indicating that the genetic effect is not significantly influenced by aspects of the intrauterine environment predicted by the genetic variants used in this study. This is further supported by a similar strength of association after exclusion of children born to mothers with GDM, which suggests that the fetal genetic effects are independent of maternal diabetes status during pregnancy. The similar association for fGS with birth weight observed between participants of South Asian and European ancestry in this study suggests that although it is difficult to conclude at an individual variant level, there are likely common genetic pathways for fetal growth and development in both ancestry groups. Although mGS was relatively weakly associated with fetal birth weight, the association was unaffected by the fetus’s own genotype, suggesting that the maternal genetic effect on birth weight was mediated through the intrauterine environment. The weaker association of mGS is not unexpected given the lower proportion of variance explained in birth weight by the mGS (∼2%) compared with fGS (∼6%). Thus, birth weight (body size) is an outcome of the baby’s genetic constitution and an influence of the intrauterine environment partly determined by the mother’s genotype. However, with the exception of a small number of variants that are known to influence fasting glucose levels, it is largely unclear which intrauterine exposures are influenced by which genetic variants used in the study, making it difficult to dissect their individual role. It was interesting to note that the influence of the mGS on birth weight varied considerably among the cohorts investigated in this study (heterogeneity P = 0.018). This heterogeneity in effect estimates could be driven by ethnicity, maternal BMI, height, nutritional status, socioeconomic status, and GDM status; this needs further investigation.

GWAS have established a robust association between fGS and later cardiometabolic risk, including glycemic and lipid parameters in Europeans (13,15). An important feature of our study is that we have been able to independently compare associations of birth weight and birth weight–associated genetic variants with later anthropometric and cardiometabolic traits. Birth weight showed a strong positive association with body composition and an inverse association with blood triglyceride concentrations in both childhood and adolescence. fGS explains only ∼6% of the variance in birth weight in European individuals (15), and considering equal effect of fGS on birth weight in South Asians as in Europeans, it is worth noting that a positive association with body size in childhood and height and head circumference in adults was observed. Effect estimates of fGS with other anthropometric traits was directionally consistent with the direct effect of birth weight; a lack of strong association may be due to a relatively smaller sample size and the smaller effect size compared with the birth weight itself. Absence of association between fGS and any of the traits during adolescence is consistent with findings from even larger studies that have found little evidence of influence of fetal birth weight variants on BMI beyond early childhood (33). Similar to our study, previous studies have demonstrated a pattern of positive genetic correlations with birth weight and with childhood and adulthood height (13,15). The fact that the fetus’s genotype and birth weight–associated genetic variants also influenced plasma glucose and triglycerides in adulthood is consistent with the fetal insulin hypothesis, which proposes that birth weight and later cardiometabolic risk are two effects of the same genotype (34). Our findings need to be replicated in larger independent studies of South Asian subjects. Further understanding of the link between birth weight and future cardiometabolic risk will be possible as we understand the exact role of each genetic variant, whether it operates directly or indirectly through its effects on the intrauterine environment.

Our study has several strengths and a few limitations. This study is the first to explore the influence of fetal and/or maternal genotype on birth size and their role in future cardiometabolic risk in South Asians. We combined diverse cohorts from India (including both Indo-European and Dravidian ethnicity) and from Bangladesh (local and immigrants to the U.K.); hence, the observations can be considered representative of South Asians. The greatest strength of the study is availability of mother-child pairs and anthropometric and cardiometabolic traits in early childhood and adolescence; thus, the conclusions drawn from these prospective cohorts are robust. The limitations of the study include a relatively small sample size, although assuming equal variance explained by SNPs in Europeans, our study in South Asians had >99% and 98% power to detect an association of fGS and mGS, respectively, with birth weight. Lack of adult phenotype data in children of these cohorts is another limitation, but we have partly circumvented this issue by using the genotype and phenotype data from parents of the children in the Indian cohorts. However, lack of birth size and maternal genotype data for these parents did not allow us to study the maternal influence in this group. The availability of a genetic score specific to individuals of South Asian ancestry would also allow us to further investigate the difference in association of mGS with birth weight compared with participants of European ancestry observed here, helping to disentangle environmental effects from those expected from a genetic score that may not capture the same underlying genetic associations in different ancestry groups.

The observations made in this study are important because the Indian subcontinent is facing the twin burden of poor fetal health and an emerging epidemic of type 2 diabetes and cardiovascular diseases (9,35,36). This has been linked to unique phenotypic features, environmental exposures, and a different genetic makeup of South Asians compared with Europeans (1721). However, this study suggests that the genetic contribution to birth size is largely similar to that in Europeans and that other factors may be responsible for the thin-fat phenotype of South Asians, which predisposes them to a higher risk of diabetes and related disorders compared with Caucasians. The validation of genetic associations with birth weight in populations of two ancestries, Europeans and South Asians, provides a hint about common pathways affecting fetal development that can be influenced by different environmental exposures.

To conclude, we report the associations of genetic scores identified in Europeans with size at birth in participants of South Asian ancestry. However, fGS is known to explain only ∼6% variability in birth weight in Europeans. Interestingly, despite similar association of fGSs with birth weight as in Europeans, South Asians have a considerably lower birth weight. This indicates a significant role of other factors on fetal growth, such as different environmental exposures that are not captured by the genetic variants included in the current study. These genetic loci also influenced early childhood body size and were associated with fasting glucose and triglyceride levels in adults, suggesting that common genetic variants explain part of the association between birth size and adult metabolic syndrome. The observed associations mentioned above not only support the fetal insulin hypothesis but also highlight an important interaction with environment (16,34). Lack of association between fGSs and cardiometabolic traits in children and adolescents deserves more exploration. Furthermore, birth weight–fetal genotype associations were consistent across all cohorts, and association of fetal birth weight with maternal genotype showed heterogeneity between cohorts. The heterogeneity in the association of maternal genotype with fetal birth weight may be related to differences in maternal size, glycemia, and socioeconomic status and need further research.

S.S.N. and R.N.B. are joint first authors.

C.H.D.F., C.S.Y., R.M.F., G.A.H., and G.R.C. are joint last authors.

This article contains supplementary material online at https://doi.org/10.2337/figshare.18624365.

Acknowledgments. The authors acknowledge the unflinching support of individuals who voluntarily participated in the study and continued to attend regular follow-ups. The authors also acknowledge the Genetic Laboratory of Internal Medicine, Erasmus MC, University Medical Centre Rotterdam, for the help with genotyping of the Bangladeshi cohorts. R.M.F. and R.N.B. acknowledge the use of the University of Exeter High-Performance Computing facility in carrying out this work.

Funding. S.S.N. and A.D. are grateful to the Council of Scientific and Industrial Research (CSIR Mission Mode Project HCP0008), and A.S. is grateful to the Indian Council for Medical Research (Centre for Advance Research GAP0504), Government of India, for its fellowship. Support from a U.K. Medical Research Council (MRC) Clinical Research Training Fellowship (G0800441) to S.F. is also acknowledged. The PMNS, PS, MMNP, and MBRC were funded by the MRC (U.K.), Wellcome Trust (U.K.), Parthenon Trust (Switzerland), and Newton Fund. The GIFTS and London Bangladeshi cohorts were supported by the MRC (clinical research training fellowship G0800441) and the European Union (FP7 EU grant 83599025). MMNP was supported by the Wellcome Trust, Parthenon Trust, ICICI Bank Ltd. (Mumbai), the MRC, and the U.K. Department for International Development (DFID) under the MRC/DFID concordat. Children’s follow-up was funded by MRC (MR/M005186/1). MBRC studies were also supported by an early career fellowship to M.K. by the Welcome Department of Biotechnology India Alliance. Genotyping for the MMNP mothers and children (The Epigenetic Mechanisms Linking Pre-conceptional Nutrition and Health Assessed in India and Sub-Saharan Africa [EMPHASIS] study) is jointly funded by MRC, DFID, and Department of Biotechnology, Ministry of Science and Technology, India, under the Newton Fund initiative (MRC grant MR/N006208/1 and Department of Biotechnology grant BT/IN/DBT-MRC/DFID/24/GRC/2015–16). High-throughput genotyping of the mother-child pairs from PMNS was funded through the GIFTS European Union (FP7 EU grant 83599025); for all other cohorts, the genetic analysis was funded by the CSIR, Ministry of Science and Technology, Government of India, through its network projects. R.M.F. and R.N.B. are supported by a Sir Henry Dale Fellowship (Wellcome Trust and Royal Society grant WT104150). This research has been conducted using the UKBB Resource under application number 7036. EFSOCH was supported by South West NHS Research and Development, Exeter NHS Research and Development, the Darlington Trust, and the Peninsula National Institute for Health Research (NIHR) Clinical Research Facility at the University of Exeter. This project is supported by NIHR in partnership between the University of Exeter Medical School College of Medicine and Health and Royal Devon and Exeter NHS Foundation Trust. Genotyping of the EFSOCH study samples was funded by the Wellcome Trust and Royal Society grant 104150/Z/14/Z. This research was funded, in part, by the University of Exeter (grant WT220390). A Creative Commons Attribution or equivalent license is applied to an author accepted manuscript arising from this submission in accordance with the grant’s open access conditions.

The views expressed in this article are those of the authors and not necessarily those of the NIHR or the Department of Health and Social Care. The funding bodies played no role in the design of the study and collection, analysis, interpretation of data, or writing of the manuscript.

Duality of Interest. No potential conflicts of interest relevant to this article were reported.

Author Contributions. S.S.N. and R.N.B. performed the central analysis and wrote the first draft of the manuscript. S.S.N. and A.D. performed high-throughput genotyping of the Indian cohorts. S.S.N., A.D., and A.S. cleaned the Indian cohorts’ genotype data and generated imputed genotypes. R.N.B. performed quality control and imputation of the Bangladeshi and EFSOCH cohort genotype data. A.R.W. defined the South Asian samples of the UKBB data set using ancestry PCs. B.-W.O., Z.H., T.M.F., and R.M.F. were responsible for preparing samples and genotyping in the Bangladeshi and EFSOCH cohorts. G.V.K., K.K., R.D.P., S.A.S., M.K., C.D.G., C.H.D.F., and C.S.Y. are coordinators for various Indian cohorts and played important roles in the follow-up and acquisition of phenotype data at different stages. I.D.M. provided technical support in DNA isolation and quality control analysis in the Indian cohorts. A.H. and A.K.A.K. managed the Bangladeshi cohort studies. B.W.B. oversaw data collection and phenotyping of subjects in the Bangladeshi cohorts. B.A.K. carried out sample collection and phenotyping in the EFSOCH cohort. S.F., C.H.D.F., C.S.Y., R.M.F., G.A.H., and G.R.C. conceptualized and contributed to the study design and collated and interpreted overall results from various cohorts in the study. S.F. and G.A.H. are the lead supervisors of the U.K. cohort. G.R.C. supervised the overall Indian cohort studies. All authors contributed to manuscript writing, provided critical inputs, and approved the final version of the manuscript. G.R.C. is the guarantor of this work and, as such, had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

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