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[Preprint]. 2024 Mar 16:2024.03.13.24304263.
doi: 10.1101/2024.03.13.24304263.

A framework for conducting time-varying genome-wide association studies: An application to body mass index across childhood in six multiethnic cohorts

Kimberley Burrows  1   2 , Anni Heiskala  3 , Jonathan P Bradfield  4   5 , Zhanna Balkhiyarova  6   7   8 , Lijiao Ning  9 , Mathilde Boissel  9 , Yee-Ming Chan  10   11 , Philippe Froguel  9   12 , Amelie Bonnefond  9   12 , Hakon Hakonarson  4 , Alessander Couto Alves  13 , Deborah A Lawlor  1   2 , Marika Kaakinen  6   7   14 , Marjo-Riitta Järvelin  3   15   16 , Struan F A Grant  4   17   18   19   20 , Kate Tilling  1 , Inga Prokopenko  6   7 , Sylvain Sebert  3 , Mickaël Canouil  9 , Nicole M Warrington  21   22
Affiliations

A framework for conducting time-varying genome-wide association studies: An application to body mass index across childhood in six multiethnic cohorts

Kimberley Burrows et al. medRxiv. .

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Abstract

Genetic effects on changes in human traits over time are understudied and may have important pathophysiological impact. We propose a framework that enables data quality control, implements mixed models to evaluate trajectories of change in traits, and estimates phenotypes to identify age-varying genetic effects in genome-wide association studies (GWASs). Using childhood body mass index (BMI) as an example, we included 71,336 participants from six cohorts and estimated the slope and area under the BMI curve within four time periods (infancy, early childhood, late childhood and adolescence) for each participant, in addition to the age and BMI at the adiposity peak and the adiposity rebound. GWAS on each of the estimated phenotypes identified 28 genome-wide significant variants at 13 loci across the 12 estimated phenotypes, one of which was novel (in DAOA) and had not been previously associated with childhood or adult BMI. Genetic studies of changes in human traits over time could uncover novel biological mechanisms influencing quantitative traits.

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Conflict of interest statement

Conflict of interest: DAL received support from Medtronic Ltd and Roche Diagnostics for research unrelated to that presented here. All other authors report no conflict of interest.

Figures

Figure 1:
Figure 1:. Population average BMI trajectories predicted by our final chosen model (cubic spline function in the fixed effects with cubic slope function in the random effects) for each of the six cohorts for males (A) and females (B).
BMI trajectories were predicted from 2 weeks to 17 years, which corresponds to the age range that the slopes and AUCs were predicted from, in all cohorts except OBE where they were predicted from 2 weeks to 16 years due to the lack of data after age 16. The year(s) of recruitment for each cohort are as follows: ALSPAC: 1991–1993, CHOP: 1988-present, NFBC1966: 1966, NFBC1986: 1985–1986, OBE: 1981–2001
Figure 2:
Figure 2:. Observed BMI measures versus fitted BMI values from our final chosen model (cubic spline function in the fixed effects with cubic slope function in the random effects) in males.
A) ALSPAC; B) CHOP European subset; C) CHOP African American subset; D) NFBC1966; E) NFBC1986; F) OBE. BMI is given in kg/m2
Figure 3:
Figure 3:. SNP-based heritability and genome-wide genetic correlation between the twelve estimated phenotypes summarising growth across early life.
SNP-based heritabilities, presented on the diagonals, and genetic correlations, presented on the off diagonals, were derived using linkage disequilibrium score regression. SNP-based heritabilities for AP age, infancy and adolescent slope are low, with high standard errors, and therefore the genetic correlations with these traits are unreliable but are shown for completeness. *estimates of genetic correlation were >1; given this is not possible we have set these to one.
Figure 4:
Figure 4:. Manhattan plots and quantile-quantile (QQ) plots of the meta-analyses for the area under the curve estimated phenotypes across infancy (0–0.5 years), early childhood (1.5–3.5 years), late childhood (6.5–10 years) and adolescence (12–17 years).
Loci are labelled with their nearest gene annotated by LocusZoom. The red dotted line corresponds to the genome-wide significance level of P<5×10−8.
Figure 5:
Figure 5:. Manhattan plots and quantile-quantile (QQ) plots of the meta-analyses for the slope estimated phenotypes across infancy (0–0.5 years), early childhood (1.5–3.5 years), late childhood (6.5–10 years) and adolescence (12–17 years).
Loci are labelled with their nearest gene annotated by LocusZoom. The red dotted line corresponds to the genome-wide significance level of P<5×10−8.
Figure 6:
Figure 6:. Manhattan plots and quantile-quantile (QQ) plots of the meta-analyses for the age and BMI at adiposity peak and adiposity rebound estimated phenotypes.
Loci are labelled with their nearest gene annotated by LocusZoom. The red dotted line corresponds to the genome-wide significance level of P<5×10−8.
See this image and copyright information in PMC

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