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Constructing a polygenic risk score for childhood obesity using functional data analysis.

Sarah J C Craig1,2, Ana M Kenney3, Junli Lin3

  • 1Department of Biology, Penn State University, University Park.

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|January 9, 2023
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Summary
This summary is machine-generated.

New genetic risk scores for childhood obesity were developed using functional data analysis (FDA) on longitudinal growth data. These scores predict rapid infant weight gain, outperforming traditional genome-wide association study (GWAS) methods in young children.

Keywords:
Feature screening and selectionFunctional Data AnalysisPolygenic Risk ScoreStatistical genomicsUltra-high dimensional statisticsUnder-sampling

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Area of Science:

  • Genetics
  • Pediatrics
  • Biostatistics

Background:

  • Obesity is a heritable condition with increasing prevalence in children, yet specific genetic variants explain only a small fraction of its heritability.
  • Traditional genome-wide association studies (GWAS) often use large samples with single measurements, limiting insights into complex genetic influences on obesity development.

Purpose of the Study:

  • To apply novel functional data analysis (FDA) techniques to longitudinal childhood growth data for identifying genetic variants associated with obesity.
  • To develop and validate polygenic risk scores (PRS) for childhood obesity using advanced statistical models that capture dynamic SNP effects.

Main Methods:

  • Utilized functional data analysis (FDA) on longitudinal growth data from birth to three years in a deep phenotyping cohort.
  • Screened hundreds of thousands of single nucleotide polymorphisms (SNPs) and constructed two PRS incorporating dynamic SNP effects.
  • Validated PRS in independent cohorts of children and adults, comparing their predictive power against adult-derived PRS.

Main Results:

  • The developed PRS were significantly higher in children exhibiting rapid infant weight gain, an early predictor of obesity.
  • Genetic variants identified in early childhood were informative for predicting obesity in older children and adults.
  • PRS derived from adult obesity GWAS were not predictive of weight gain in the young child cohort.

Conclusions:

  • Functional data analysis (FDA) offers a powerful alternative to traditional GWAS, enhancing statistical power in smaller, deeply characterized cohorts.
  • Longitudinal phenotyping combined with sophisticated statistical methods can improve the identification of genetic risk factors for childhood obesity.
  • Early childhood growth patterns and associated genetic markers are crucial for understanding and predicting long-term obesity risk.