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B1001
Title: Polygenic risk score based on weight gain trajectories is predictive of childhood obesity Authors:  Ana Kenney - Pennsylvania State University (United States) [presenting]
Matthew Reimherr - Pennsylvania State University (United States)
Francesca Chiaromonte - The Pennsylvania State University (United States)
Sarah Craig - Pennsylvania State University (United States)
Kateryna Makova - The Pennsylvania State University (United States)
Abstract: Obesity is highly heritable, yet 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 detailed phenotypes. We use novel functional data analysis (FDA) techniques to capitalize on longitudinal growth information and construct a polygenic risk score (PRS) for obesity in young children. This score is significantly higher in children with (vs. without) rapid infant weight gain. Using two independent cohorts, we show that genetic variants identified in early childhood 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 our cohort of children. Our research provides a strong example of a successful application of FDA to GWAS. We demonstrate that a deep, statistically sophisticated characterization of a longitudinal phenotype can provide increased statistical power to studies with relatively small sample sizes. This has the potential of shifting the existing paradigm in GWAS.