Constructing a polygenic risk score for childhood obesity using functional data analysis
Jan 25, 2023·
SJC Craig, AM Kenney, J Lin, IM Paul, LL Birch, JS Savage, ME Marini, F Chiaromonte, ML Reimherr, KD Makova
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0 min read
Abstract
Obesity is a highly heritable condition that affects increasing numbers of adults and, concerningly, of children. However, only a small fraction of its heritability has been attributed to specific genetic variants. These variants are traditionally ascertained from genome-wide association studies (GWAS), which utilize samples with tens or hundreds of thousands of individuals for whom a single summary measurement (e.g., BMI) is collected. An alternative approach is to focus on a smaller, more deeply characterized sample in conjunction with advanced statistical models that leverage longitudinal phenotypes. Novel functional data analysis (FDA) techniques are used to capitalize on longitudinal growth information from a cohort of children between birth and three years of age. In an ultra-high dimensional setting, hundreds of thousands of single nucleotide polymorphisms (SNPs) are screened, and selected SNPs are used to construct two polygenic risk scores (PRS) for childhood obesity using a weighting approach that incorporates the dynamic and joint nature of SNP effects. These scores are significantly higher in children with (vs. without) rapid infant weight gain-a predictor of obesity later in life. Using two independent cohorts, it is shown that the genetic variants identified in very young children are also informative in older children and in adults, consistent with early childhood obesity being predictive of obesity later in life. In contrast, PRSs based on SNPs identified by adult obesity GWAS are not predictive of weight gain in the cohort of young children. This provides an example of a successful application of FDA to GWAS. This application is complemented with simulations establishing that a deeply characterized sample can be just as, if not more, effective than a comparable study with a cross-sectional response. Overall, it is demonstrated that a deep, statistically sophisticated characterization of a longitudinal phenotype can provide increased statistical power to studies with relatively small sample sizes; and shows how FDA approaches can be used as an alternative to the traditional GWAS.
Type
Publication
The Review of Economics and Statistics
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.
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.
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.
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.