Polygenic risk score based on weight gain trajectories is a strong predictor of childhood obesity

Apr 11, 2019· SJC Craig, AM Kenney, J Lin, IM Paul, LL Birch, JS Savage, ME Marini, F Chiaromonte, M Reimherr, KD Makova · 0 min read
Abstract
Obesity is highly heritable, yet only a small fraction of its heritability has been attributed to specific genetic variants. Missing heritability is particularly pronounced for childhood obesity. Here we studied 226 children for whom we typed almost one million single-nucleotide polymorphisms (SNPs), and collected weight and length or height at eight time points between birth and the age of three years. Leveraging longitudinal weight gain trajectory information and novel functional data analysis (FDA) techniques, we constructed a polygenic risk score (PRS) comprised of 24 SNPs. This PRS explains 56% of the variability in weight gain trajectories among the studied children. Moreover, it is significantly higher in children with (vs. without) rapid infant weight gain—a predictor of obesity later in life. We validated the constructed PRS in populations of adolescents and adults—suggesting that some genetic variants predispose to obesity at both childhood and later life stages. In contrast, PRSs from genome-wide association studies (GWAS) of adult obesity were not predictive of weight gain in our cohort of children, and did not share SNPs with our PRS. Our research provides a strong example of a successful application of FDA to a GWAS. We demonstrate that a sophisticated characterization of a longitudinal phenotype can provide increased statistical power to studies with smaller sample sizes. This has the potential of shifting the existing paradigm in GWAS.
Type
Publication
bioRxiv
publication
Sarah J. Carnahan Craig
Authors
Research Assistant Professor
Sarah is interested in human evolution with a specific emphasis on genetic variation within and between species, gene duplication, and the evolution of regulatory elements. She found that the application of knowledge from these genetic investigations to explain (or help explain) phenotypic observations in humans (especially related to reproduction and human health) to be particularly exciting. Her project in the Makova Lab was focused on childhood obesity.
Ian Paul
Authors
Ian Paul, MD, MSc, graduated from Penn State College of Medicine and completed his residency training at Duke University Medical Center in Durham, N.C. In addition to his responsibilities as a general pediatrician at Penn State Pediatrics and in the newborn nursery, Dr. Paul spends much of his time working on a variety of research projects and clinical trials that seek to improve health care for children and their families. His current research includes studies to prevent obesity and help breastfeeding newborns.
Francesca Chiaromonte
Authors
Francesca is a statistician developing methods for the analysis of large, high-dimensional and complex data, and applying such methods in contemporary “Omics” sciences and other scientific fields – including Meteorology and Economics.
Matthew Reimherr
Authors
Matthew’s research interests include functional data analysis, longitudinal data analysis, and data privacy with applications to Omics data.
Kateryna Makova, Ph.D.
Authors
Professor
Kateryna is interested in genomics, evolution, and human genetics. Her lab studies mutations using both computational and experimental approaches. Additional topics of interest include sex chromosome evolution and genomics of childhood obesity.