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. 2019 Sep 4;5(9):eaaw3095.
doi: 10.1126/sciadv.aaw3095. eCollection 2019 Sep.

GWAS on Longitudinal Growth Traits Reveals Different Genetic Factors Influencing Infant, Child, and Adult BMI

Alexessander Couto Alves  1   2 N Maneka G De Silva  1 Ville Karhunen  1 Ulla Sovio  3   4 Shikta Das  1   5 H Rob Taal  6   7 Nicole M Warrington  8   9 Alexandra M Lewin  1   10 Marika Kaakinen  11   12   13 Diana L Cousminer  14   15   16 Elisabeth Thiering  17   18 Nicholas J Timpson  19   20 Tom A Bond  1 Estelle Lowry  21 Christopher D Brown  22 Xavier Estivill  23   24   25   26 Virpi Lindi  15 Jonathan P Bradfield  27 Frank Geller  28 Doug Speed  29   30 Lachlan J M Coin  1   31 Marie Loh  1   21   32 Sheila J Barton  33   34 Lawrence J Beilin  35 Hans Bisgaard  36 Klaus Bønnelykke  36 Rohia Alili  37 Ida J Hatoum  37   38   39 Katharina Schramm  40   41 Rufus Cartwright  1   42 Marie-Aline Charles  43 Vincenzo Salerno  1 Karine Clément  37   43 Annique A J Claringbould  44 BIOS ConsortiumCornelia M van Duijn  45 Elena Moltchanova  46 Johan G Eriksson  47   48   49 Cathy Elks  50 Bjarke Feenstra  28 Claudia Flexeder  17 Stephen Franks  42 Timothy M Frayling  51 Rachel M Freathy  51 Paul Elliott  1   52   53 Elisabeth Widén  16 Hakon Hakonarson  14   27   54   55 Andrew T Hattersley  51 Alina Rodriguez  1   56 Marco Banterle  10 Joachim Heinrich  17 Barbara Heude  43 John W Holloway  57 Albert Hofman  6   45 Elina Hyppönen  58   59   60 Hazel Inskip  33   34 Lee M Kaplan  38   39 Asa K Hedman  61   62 Esa Läärä  63 Holger Prokisch  40   41 Harald Grallert  64   65 Timo A Lakka  15   66   67 Debbie A Lawlor  19   20 Mads Melbye  28   68   69 Tarunveer S Ahluwalia  36 Marcella Marinelli  25   26   70 Iona Y Millwood  71   72 Lyle J Palmer  73 Craig E Pennell  8 John R Perry  50 Susan M Ring  19   20   74 Markku J Savolainen  75 Fernando Rivadeneira  45   76 Marie Standl  17 Jordi Sunyer  24   25   26   70 Carla M T Tiesler  17   18 Andre G Uitterlinden  45   76 William Schierding  77 Justin M O'Sullivan  77   78 Inga Prokopenko  11   13   61   79 Karl-Heinz Herzig  80   81   82   83 George Davey Smith  19   20 Paul O'Reilly  1   84 Janine F Felix  6   7   45 Jessica L Buxton  85 Alexandra I F Blakemore  86   87 Ken K Ong  50 Vincent W V Jaddoe  6   45 Struan F A Grant  14   27   54   55 Sylvain Sebert  1   21   80 Mark I McCarthy  61   79   88 Marjo-Riitta Järvelin  1   21   80   82   86   89 Early Growth Genetics (EGG) Consortium
Free PMC article

GWAS on Longitudinal Growth Traits Reveals Different Genetic Factors Influencing Infant, Child, and Adult BMI

Alexessander Couto Alves et al. Sci Adv. .
Free PMC article


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.


Fig. 1
Fig. 1. Regional association and forest plot of the novel genome-wide significant locus associated with BMI-AP.
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 r2 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 (I2 = 72.1%, P = 0.01) and combined stage (I2 = 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).
Fig. 2
Fig. 2. Tissue-specific posterior probabilities (PPs) of colocalization for LEPR and LEPROT.
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 –log10 P value eQTL direct lookup in the corresponding GTEx tissue of the GWAS SNP. LEPR and LEPROT eQTLs colocalized with BMI-AP variant rs9436303.
Fig. 3
Fig. 3. Genetic correlations between five early growth traits and a subset of 37 phenotypes.
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.
Fig. 4
Fig. 4. Adult BMI GRS analysis of early growth traits.
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.
Fig. 5
Fig. 5. Proposed model of child BMI suggesting the superimposition of two biological phenomena under the genetic control of different loci.
The schematic diagram shows the four genome-wide significant loci associated with early childhood growth traits and highlights the fact that only SNPs associated with phenotypes ascertained at AR are associated with adult BMI. The red curve represents the mean BMI trajectory from birth to puberty in the NFBC1966 cohort.

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