Early childhood growth patterns are associated with adult health, yet the genetic factors and the developmental stages involved are not fully understood. Here, we combine genome-wide association studies with modeling of longitudinal growth traits to study the genetics of infant and child growth, followed by functional, pathway, genetic correlation, risk score, and colocalization analyses to determine how developmental timings, molecular pathways, and genetic determinants of these traits overlap with those of adult health. We found a robust overlap between the genetics of child and adult body mass index (BMI), with variants associated with adult BMI acting as early as 4 to 6 years old. However, we demonstrated a completely distinct genetic makeup for peak BMI during infancy, influenced by variation at the LEPR/LEPROT locus. These findings suggest that different genetic factors control infant and child BMI. In light of the obesity epidemic, these findings are important to inform the timing and targets of prevention strategies.

The present study set out to model sex-specific individual postnatal growth velocity and BMI curves in children using high-density longitudinal data collected from primary health care or clinical research visits. We first conducted a genome-wide association study (GWAS) on six harmonized early growth traits: peak height velocity (PHV), peak weight velocity (PWV), age at AP (Age-AP), BMI at AP (BMI-AP), age at AR (Age-AR), and BMI at AR (BMI-AR). We then analyzed the GWAS summary statistics for these six early growth traits to gain insights into the genes and molecular pathways involved and to assess the overlap between the genetic etiology of early growth traits and adult phenotypes. In particular, we tracked the changes in the genetic determinants of BMI occurring throughout infancy, later childhood, and adulthood.

To date, we have gained considerable insights into the shared genetic makeup of childhood and adult BMI ( 5 , 6 ). These previous studies were designed to identify genetic variants associated with BMI and obesity acting through the ages of 2 to 18 years. However, BMI does not remain constant, or follow a linear pattern throughout life, particularly not from birth until the age of adiposity rebound (AR) ( 7 , 8 ). On the contrary, the BMI trajectory in healthy individuals (fig. S1) encompasses three periods characterized by (i) a rapid increase in BMI up to the age of 9 months [adiposity peak (AP)], (ii) a rapid decline in BMI up to the age of 5 to 6 years [adiposity rebound timepoint (AR)], followed by (iii) a steady increase until early adulthood, when BMI growth rate decelerates. We have yet to determine whether changes in timing, velocity, or amplitude of this trajectory, during infancy and childhood, are influenced by specific genetic factors, acting at different developmental stages. The identification of genetic determinants of early growth traits is a fundamental step toward understanding the etiology of obesity and could be important in informing future strategies to prevent and treat it.

Childhood obesity and its relation to later adult health, social inequality, and psychosocial well-being remain one of the most important unsolved health concerns of the 21st century ( 1 ). Epidemiological studies have revealed unambiguous associations between alterations of childhood body mass index (BMI) trajectory and risk of adult obesity and multimorbidities, including type 2 diabetes ( 2 ) and other cardiometabolic diseases ( 3 ). From a life-course perspective, genetic and environmental factors driving child growth may have a lasting influence on maintaining health ( 4 ). Within this framework, identification of the genetic determinants of the critical periods in child development is important for understanding the mechanisms underpinning adult health and preventing disease development.

We estimated the chip SNP heritability (the proportion of variance explained by common SNPs) for the six early growth traits using LD score regression (LDSC) (see Methods). The heritability estimates for BMI-AR (h 2 snp = 0.38), Age-AR (h 2 snp = 0.36), PWV (h 2 snp = 0.32), and BMI-AP (h 2 snp = 0.29) were statistically significant (P < 0.05; Table 3 ). LDSC and SumHer ( 21 ) SNP heritability estimates (table S18) ranked these phenotypic heritabilities in a similar manner. The BMI-AP and BMI-AR estimates compared well with LDSC estimates for adult BMI (h 2 snp = 0.27) in a much larger sample of the UK Biobank (N = 152,736). Twin and family study heritability estimates for BMI-AP (h 2 = 0.75 to 0.78) ( 22 , 23 ) and BMI-AR (h 2 = 0.4 to 0.6) ( 24 , 25 ) were higher than the SNP heritability estimated here. However, the ratio of the SNP heritability obtained from LDSC and the total heritability obtained from family and twin studies suggests that a considerable (39 to 95%; see Methods) proportion of BMI heritability can be attributed to common variants. As the LDSC heritability estimates of BMI-AP, BMI-AR, and adult BMI are comparable, the differences in the genetic etiology observed in our study cannot be trivially attributed to large disparities in the variance explained by genetic factors. Hence, together, these data suggest that distinct, heritable developmental processes control the BMI trajectory at AP and AR.

To combine information on the effects of common variants in biological pathways and networks underlying early growth, we applied a gene set enrichment analysis [Meta-Analysis Gene-set Enrichment of variaNT Associations (MAGENTA)] ( 19 ) to the discovery stage GWAS results (see Methods). We identified enrichment of gene sets (tables S16 and S17) but did not find evidence for overlap of enriched pathways and networks among early growth traits. Age-AR–associated regions are involved in the insulin-like growth factor 1 (IGF-1) signaling pathway (FDR < 0.05). The IGF-1 signaling pathway has a well-established role both in growth and in the regulation of energy metabolism through the activation of phosphatidylinositol 3-kinase (PI3K)/AKT pathway via either the insulin or the IGF-1 receptors ( 20 ).

Scatter plots show the effect size estimates (SD units) of the 97 adult BMI-associated SNP in GIANT consortium in the x axis and the corresponding effect size estimates (SD units) of the looked-up SNP of stage 1 meta-analysis GWAS on ( A ) BMI-AR and ( B ) Age-AR in the y axis. The effect size of the adult BMI increasing allele is plotted. The solid red line is the estimated effect of the GRS on the early growth phenotype, taking into account the uncertainty of the point estimates. The dashed line is the 95% CI of the predicted effect. Stage 1 meta-analysis GWAS SNPs with P < 0.05 are plotted with a solid circle and labeled with the nearest gene name. The scatter plots of the other early growth phenotypes are given in fig. S10.

To gain further insight into the observed genetic correlations with adult BMI and to understand the developmental timing of the adult BMI-associated variants, we constructed an adult BMI genetic risk score (GRS) based on the 97 adult BMI SNPs identified by the Genetic Investigation of Anthropometric Traits (GIANT) consortium ( 18 ) ( Fig. 4 and table S14) and applied it to the six early growth traits (see Methods). The adult BMI variants and the GRS were consistently and robustly associated with Age-AR (h 2 grs = 0.035, P = 2.6 × 10 −48 ) and BMI-AR (h 2 grs = 0.030, P = 1.7 × 10 −41 ) but not with other early growth traits ( Fig. 4 and table S15). In the remaining four early growth traits, the GRS explained a negligible proportion of variance (h 2 grs < 0.001), and the adult BMI variants had inconsistent genetic effects (fig. S10 and table S15). In particular, the adult BMI variant effects on BMI-AP and PWV were highly heterogeneous (P het < 2 × 10 −4 ), with evidence of horizontal pleiotropy (MR-PRESSO; P < 2 × 10 −4 ). This suggests that, in contrast with their effects on Age-AR and BMI-AR, the top loci associated with adult BMI do not have robust associations with the remaining four early growth traits. Thus, the underlying genetic determinants of adult BMI might differ from those influencing BMI-AP. Together, these data indicate that many GWAS variants associated with adult BMI have effects that begin in later childhood (4 to 6 years), as early as the Age-AR but not as early as AP (around 9 months).

Only a selected list of 37 phenotypes is represented on the correlation matrix. Genetic correlation results for all 72 phenotypes are given in table S16. Blue, positive genetic correlation; red, negative genetic correlation. The correlation matrix underneath represents the genetic correlation among the five early growth traits themselves. The size of the colored squares is proportional to the P value, where larger squares represent a smaller P value. Genetic correlations that are different from 0 at P < 0.05 are marked with an asterisk. The genetic correlations that are different from 0 at an FDR of 1% are marked with a circle. Genetic correlations estimated with stage 1 meta-analysis GWAS summary statistics from the current and literature studies using LD score regression.

In our study, Age-AR and BMI-AR have moderate to very strong genetic correlations with adult BMI and other adult adiposity-related phenotypes, but BMI-AP does not (see Methods, Fig. 3 , and table S12). Age-AR and BMI-AR had genetic correlations with multiple (more than four) adult complex phenotypes, including adult waist circumference (Age-AR r g = −0.62; BMI-AR r g = 0.48) and adult body fat percentage (Age-AR r g = −0.49; BMI-AR r g = 0.44). Adult BMI and adult obesity had strong genetic correlations with BMI-AR (r g = 0.64 and r g = 0.66) and Age-AR (r g = −0.72 and r g = −0.75) but weak correlation with BMI-AP (r g = 0.29 and r g = 0.33). The traits with genetic and phenotypic correlations that were directionally consistent (note S1 ) are reported in table S13. Genetic correlations of Age-AP with other traits could not be quantified because of low mean χ 2 of the GWAS summary statistics. In summary, genetic correlation analyses suggest that the genetic factors influencing adult BMI, body fat percentage, waist circumference, and obesity are also associated with BMI-AR and Age-AR, but their overlap with BMI-AP is either absent or weak.

PP of eQTL and GWAS SNP sharing a causal variant regulating the gene expression levels of (A) LEPR and (B) LEPROT. Colocalization reported for GTEX eQTLs data in 34 tissues that express at least one of the genes. Bar plot color-coded according to the –log 10 P value eQTL direct lookup in the corresponding GTEx tissue of the GWAS SNP. LEPR and LEPROT eQTLs colocalized with BMI-AP variant rs9436303.

To identify GWAS and expression quantitative trait loci (eQTLs) signals that share the same causal variants, we performed Bayesian colocalization analyses ( 14 ) using our stage 1 GWAS meta-analysis summary statistics and eQTL data from 44 postmortem tissues generated by the Genotype-Tissue Expression (GTEx) consortium (see Methods) ( 15 ). The lead GWAS variants with high (>95%) posterior probability (PP) of colocalization were followed-up in five separate studies (see Methods) using cis-eQTL data from five ex vivo tissues and combined with genomic annotation data (tables S9 and S10). In these analyses, we found high PPs of colocalization with local causal variants (>95%) driving the expression of LEPR and LEPROT ( Table 2 and fig. S7). The colocalization results for each gene are markedly tissue specific ( Fig. 2 and fig. S8). In ex vivo samples, the LEPR/LEPROT variant was in high LD with the top eQTLs of LEPR and LEPROT genes in omental fat, subcutaneous fat, and whole blood (table S9). Direct lookup of LEPR/LEPROT variant in eQTL data indicated that the G allele of this variant that raised BMI-AP in our GWAS up-regulated the NM017526 transcript of LEPROT and down-regulated the AK023598 transcript from the same gene in adult tissues (table S10). This observation was consistent across two different eQTL studies and four tissues, suggesting the involvement of alternative splicing of a cassette exon. The LEPR/LEPROT variant overlapped DNA binding motifs of transcription factors and regulatory regions, as well as enhancer and promoter histone marks in multiple tissues (fig. S9). In Avon Longitudinal Study of Parents and Children (ALSPAC), the same LEPR/LEPROT variant was associated with higher DNA methylation levels of a LEPR intron measured in blood samples taken from mother and offspring. In particular, associations were found during mother’s pregnancy and in offspring’s adolescence, but not at offspring’s birth, at childhood, or in mother’s middle age (table S11) ( 16 ). This observation might be consistent with the regulation of a constitutively expressed transcript, which is also supported by evidence that lower LEPR intron DNA methylation levels were associated with higher serum leptin concentrations ( 17 ). Together, these results suggest that shared causal variants in these loci regulate BMI trajectory at AP, orchestrate changes in gene expression in different tissues, and modulate methylation of the nearest genes during mother’s pregnancy and at specific developmental stages of the offspring.

The SNP rs9436303 overlaps a regulatory region in a LEPR intron and is downstream from a processed transcript of LEPROT gene (table S7). LEPROT and LEPR overlap and share the same promoter but encode distinct transcripts with specific biological functions ( 13 ). The known biological function and molecular mechanism of the proteins encoded by the nearest genes in the four loci discovered are given in table S8. However, as with most GWAS-identified loci, the expression of these genes may not necessarily be influenced by the underlying causal variant/s tagged by the GWAS SNP, so we sought further evidence that the BMI-AP–associated variants influence expression in the following section.

The BMI-AP–associated SNP rs9436303 ( Fig. 1 and Table 1 ) at the locus harboring LEPR/LEPROT (encoding the leptin receptor and the leptin receptor overlapping transcript) is novel. This novel variant is robustly associated with BMI-AP after applying a conservative bias-reducing correction for winner’s curse and a multiple testing correction for six phenotypes (α′ = 10 −8 ; see Methods and table S3). The risk allele (G) of this variant increases both BMI-AP and adult plasma soluble leptin receptor levels (P = 1.19 × 10 −9 ) (table S4) ( 11 ). The LEPR/LEPROT locus is in a chromosomal region, 1p31.3, that harbors another independent signal [ rs11208659: minor allele frequency (MAF) = 0.06; distance = 82.6 kilo–base pairs; R 2 = 0.01] associated with early-onset obesity ( 12 ), but our SNP rs9436303 is associated with BMI-AP independently of this variant [linkage disequilibrium (LD) R 2 = 0.01 and see conditional analysis in table S6]. There was some effect heterogeneity between studies for this variant (fig. S6, A and D), but excluding the two studies with inflated estimates eliminated heterogeneity (I 2 = 0) in the stage 1 + 2 meta-analysis (fig. S6, C and F) without a substantial impact on effect sizes or significance levels. This SNP explains 0.3% of variance in BMI-AP (see Methods).

Three of the four SNPs were associated with Age-AR and BMI-AR. These three variants were previously associated (P < 5 × 10 −8 ) with adult BMI and adult weight in the literature (table S4) and in the UK Biobank PheWAS (phenome-wide association study) ( 9 ) (table S5), as well as with several adiposity-related phenotypes in PhenoScanner ( 10 ) (see Methods). The lead SNPs at each of these three loci were the following: rs1421085 at the locus harboring FTO (encoding a 2-oxoglutarate–dependent demethylase) and rs2817419 at the locus harboring TFAP2B (encoding transcription factor AP-2β) associated with Age-AR, and rs10938397 near GNPDA2 (encoding adiposity regulating glucosamine-6-phosphate deaminase) locus associated with BMI-AR ( Table 1 and fig. S5). Each lead SNP (rs1421085, rs2817419, and rs10938397) associated with Age-AR and BMI-AR explains approximately 0.2% of variance in the relevant early growth trait (see Methods).

Purple diamond indicates the most significantly associated SNP in stage 1 meta-analysis, and circles represent the other SNPs in the region, with coloring from blue to red corresponding to r 2 values from 0 to 1 with the index SNP. The SNP position refers to the National Center for Biotechnology Information (NCBI) build 36. Estimated recombination rates are from HapMap build 36. Forest plots from the meta-analysis for each of the identified loci are plotted abreast. Effect size [95% confidence interval (CI)] in each individual study, discovery, follow-up, and combined meta-analysis stages is presented from fixed-effects models (heterogeneity of the SNP rs9436303 in LEPR/LEPROT; see fig. S6). At this locus, there was heterogeneity between the studies in discovery (I 2 = 72.1%, P = 0.01) and combined stage (I 2 = 59.3%, P = 0.002) fixed-effects meta-analyses that was mainly due to LISA-D, EDEN, and the larger well-defined NFBC1966 study (fig. S6, A and D). Removing the studies that showed inflated results from meta-analyses did not change the point estimates (fig. S6, C, F, and G). Both fixed- and random-effects models gave very similar results (fig. S6, B and E).

We conducted two-stage meta-analyses of GWASs on six early growth traits: PHV (in centimeters per month), PWV (in kilograms per month), Age-AP (in years), BMI-AP (in kilograms per square meter), Age-AR (in years), and BMI-AR (in kilograms per square meter). Figure S2 summarizes the study design, while participant characteristics, genotyping arrays, imputation and quality control for the discovery, and follow-up studies are presented in tables S1 and S2 and fig. S3. In the discovery stage (stage 1), we meta-analyzed GWAS from four population-based studies comprising between 6051 and 7215 term-born children of European ancestry that had both genetic and early growth trait data (stage 1; see Methods, table S1, and fig. S4). From the stage 1 inverse variance meta-analyses, we selected a total of eight loci with either P < 1 × 10 −7 or P < 1 × 10 −5 in/near genes known to be associated with obesity and metabolic traits in published GWAS or candidate gene studies. Table S3 shows selection criteria, false discovery rate (FDR), and bias-reduced effect size estimates for the selected single-nucleotide polymorphisms (SNPs). In stage 2 meta-analysis, we followed up these results in up to 16,550 term-born children from up to 11 additional studies (stage 2; see Methods and table S2). In the combined stage 1 + 2 meta-analysis of the discovery and follow-up studies (including up to 22,769 children), we identified a common variant in each of the four independent loci, associated at P < 5 × 10 −8 with one or more of the early growth traits ( Table 1 , Fig. 1 , and fig. S5).

DISCUSSION

There are few reports of studies investigating the genetic bases of these well-established growth and BMI trajectories (26, 27), and to our knowledge, our study is the largest genome-wide meta-analyses of early growth traits so far. In the present study, we identified four variants at four independent loci associated with three early growth traits, determined by modeling growth trajectories using high-density longitudinal data for height and weight. Our study provides insights into the developmental timings at which the genetic makeup of early and later measures of BMI overlaps or differs, and contributes to understanding the mechanisms and molecular pathways of early growth patterns.

The three common variants at FTO, TFAP2B, and GNPDA2, associated with timing of adiposity rebound and/or BMI-AR, are robustly associated with adult BMI and other adiposity traits. In contrast, the newly discovered variant at the LEPR/LEPROT locus associated with BMI-AP did not associate with other growth traits reported here, or in previous studies on childhood/adult BMI and obesity. This may indicate that genetic variants involved in adult BMI only start influencing BMI after AP and are robustly associated with child BMI by the time of AR. This is further corroborated by two additional lines of evidence provided by our study: (i) We observed strong genetic correlations of adult BMI, body fat percentage, and waist circumference with Age-AR and BMI-AR but not with Age-AP and BMI-AP, and (ii) the GRS constructed using adult BMI variants was robustly associated with Age-AR and BMI-AR but not with Age-AP and BMI-AP.

The difference in the genetic determinants of BMI-AP and BMI-AR and onward may be attributed to three factors: (i) BMI explains a relatively small proportion of body fat percentage (R2 < 0.3) in infancy (0 months < age ≤ 7 months) (28) but increasingly larger proportions (0.36 < R2 ≤ 0.8) in childhood (2 years ≤ age < 18 years) (29, 30) and adulthood (R2 ≈ 0.8; age, >18 years) (31); (ii) the genes involved in the regulation of BMI during infancy seem to differ from those acting in later childhood onward, which suggests distinct biological processes acting throughout these developmental stages; and (iii) sustained changes in the infant environment after weaning and onward may progressively unmask the effects of adult BMI variants. Consistent with this view, there is some evidence that infants’ and children’s environment modifies the effect of genetic factors. In particular, having been breastfed modifies the strength of association of the FTO variant with BMI (32) and with BMI growth trajectories (27). On the other hand, the adult risk alleles of the FTO and MC4R variants are not associated with increased infant BMI (26), but FTO’s strength of association with BMI progressively increases in later childhood (4 to 11 years) (24). Likewise, BMI heritability increases throughout childhood up to young adulthood (4 to 19 years) (22, 24, 25), as offspring BMI starts resembling adult BMI as an anthropometric marker of adiposity, and as the shared environment between adults and offspring progressively increases. Consistently, BMI heritability increased between AP and AR, and a considerable proportion of heritability was explained by common variants in our study. The increase in BMI heritability with age might be explained by correlations between genotype and environment. Small genetic differences are magnified as children progressively select, modify, and create environments correlated with their genetic propensities, which, in turn, unmask the effects of other genetic variants in a feedforward loop. These processes gradually may increase the phenotype variance explained by genetic factors and thereby increase BMI heritability. All in all, our study supports the accrual of shared genetic determinants between later childhood and adult BMI (5, 6), but not with infant BMI.

In our study, the IGF-1 pathway that links diet with growth was enriched for variants associated with Age-AR, but not Age-AP, in the MAGENTA analysis. Higher IGF-1 levels, via genetic and/or nutritional factors, can reduce growth hormone (GH) levels via a negative feedback (33). Subsequent lower circulating levels of GH can suppress lipolysis and contribute to fat accumulation (34), changing BMI trajectories and Age-AR, and, thereby, increasing risk of obesity and metabolic disorders. The regulation of the GH/IGF-1 axis is modulated by leptin and adiponectin levels, two hormones regulated by LEPR/LEPROT and TFAP2B genes, respectively (35).

The variant at LEPR/LEPROT colocalized with causal variants regulating the expression of LEPR and LEPROT in different tissues. LEPROT and the LEPR genes share the same promoter but encode distinct transcripts (13). LEPROT is cotranscribed with the LEPR, and both are expressed in multiple tissues with different functionalities. LEPR is widely distributed in peripheral tissues, shows signaling capability, and is thought to transport leptin across the blood-brain barrier (25). Some LEPR isoforms may function in leptin clearance or buffering (soluble LEPR). In our eQTL data, the G allele that raises BMI-AP up-regulates the NM017526 transcript of LEPROT but down-regulates AK023598 transcript from the same gene in adult tissues. This observation was consistent across the different eQTL studies and tissues, suggesting that this variant may regulate the alternative splicing of a cassette exon in adult blood and subcutaneous and omental adipose tissue. In addition, the LEPR/LEPROT variant was associated with methylation levels in the LEPR intron during mother’s pregnancy and at specific developmental stages of the offspring. Together, this functional analysis suggests that distinct molecular mechanisms in different tissues are involved in the expression regulation of these genes at different developmental stages.

LEPROT and the LEPR downstream mechanisms involved on the regulation of BMI are likely to be developmental stage dependent. In humans, loss-of-function mutations in the LEPR markedly increase weight of infants after birth that persists through adulthood (36). However, the regulatory elements of LEPROT and LEPR tagged by our GWAS SNP are not associated with BMI or any measure of adiposity in adults or in later childhood, despite being associated with BMI in infancy and involved in the control of the circulating levels of the soluble LEPR in adults. Hence, the regulatory variant identified is involved in the regulation of adult LEPR through a mechanism that does not alter BMI after later childhood (age, >4 years). More work is necessary to identify the impact of LEPROT mutations in weight gain and growth, as well as in the identification of the tissues and regulatory elements of the different LEPR isoforms.

Our study has limitations that should be taken into consideration when interpreting the data. First, dense longitudinal growth and GWAS data are only available in a few population studies worldwide, so we had limited power to detect genetic variants with smaller effects and/or low allele frequencies. Nevertheless, a post hoc power analysis showed that we are well powered to detect the reported effect sizes in the discovery sample (β = 0.065 SD units; power, 80%; significance level P < 5 × 10−8; see Methods). As a sex-stratified analysis would have halved the sample size, the analysis of sex-specific effects was left outside the scope of the paper. As in every joint meta-analysis GWAS, the final estimates may have suffered from winner’s curse (37). In our study, the follow-up sample is twice the size of the discovery sample. Consequently, the final joint analysis estimates are very close to the follow-up estimates and are thus potentially less biased. Second, it is noteworthy that these derived growth traits are likely to be influenced by a degree of measurement error and some heterogeneity, as some studies have fewer repeated measures around the time points being estimated. Ideally, regression would be weighted by the inverse variance of the phenotypes derived from the growth models. However, the variances for the derived outcomes could not be directly estimated because we used a model with random effects. The fact that we did not use inverse-weighted regression will increase SEs and decrease the power to detect associations. Despite this, we were still able to find genetic variants showing robust associations with these derived growth traits. Third, as the current approach implemented in MAGENTA focus on a fixed cutoff (the 95% percentile of the P value), our analysis has possibly missed enriched gene sets. Nevertheless, the top 10 gene sets that did not reach significance (FDR, >0.05) were reported. Last, we did not identify any variants associated with PHV, PWV, and Age-AP at genome-wide levels of significance, and this may be due to a combination of smaller genetic effects on growth at this stage of development, due to reduced statistical power because of smaller sample size, or because environmental factors masked the genetic influences at this age. The interplay between genetic variants, infant feeding, and other environmental factors also warrants additional research (27).

In conclusion, this longitudinal GWAS study, based on derived traits from growth modeling, has uncovered a completely new variant in LEPR/LEPROT locus that specifically associates with BMI at the peak of adiposity in infancy. The present study identified two BMI developmental stages in infancy and later childhood with distinct genetic makeup. Our results support the notion that genetic determinants of adult BMI progressively start acting in later childhood but not necessarily before the AP in infancy (5, 6). This finding may corroborate a model of BMI development consisting of the superimposition of two biological processes with distinct genetic drivers (Fig. 5), which, in turn, suggests that interventions in childhood aiming to modify BMI and achieve long-lasting reductions in the risk of adult obesity need to take into account the developmental stage. We believe that the identification of genetic factors underpinning the BMI trajectory is a fundamental step toward understanding the etiology of obesity and may inform strategies to prevent and treat it.