Identifying Genetic Variants for Obesity: A Knowledge Integration Quantile Regression (KIQR) Approach for Ultra-High-Dimensional Data

TL;DR

This paper introduces a novel quantile regression method that integrates prior knowledge to identify genetic variants associated with high BMI levels, addressing limitations of traditional GWAS.

Contribution

The study develops and applies a Knowledge Integration Quantile Regression (KIQR) approach for ultra-high-dimensional data, uncovering new genetic associations with obesity.

Findings

Identified novel genetic variants rs3798696, rs7070523, rs178260 linked to obesity.

Demonstrated that quantile regression with prior knowledge uncovers genes missed by GWAS.

Showed KIQR's effectiveness in analyzing high-dimensional genetic data.

Abstract

Obesity is widely recognized as a serious and pervasive health concern. We study obesity through body mass index (BMI), which is known to be highly heritable, and identify important genetic risk factors for BMI from hundreds of thousands of single nucleotide polymorphisms (SNPs) in the Framingham Study data. Several challenges arise when using traditional genome-wide association studies (GWAS): (1) They suffer from a low power due to a combination of a limited number of participants and the stringent genome-wide significance threshold; (2) existing prior knowledge from large meta-analyses may provide valuable guidance but is often underutilized; (3) the one-at-a-time univariate marginal regression framework ignores the joint and conditional nature of genetic effects; (4) GWAS focus solely on mean outcomes, whereas obesity inherently concerns abnormally high BMI levels. To address these challenges, we conduct the analysis by proposing and applying a novel Knowledge Integration Quantile Regression (KIQR) approach via simultaneous variable selection and estimation, focusing on the conditional high quantiles of BMI, which are most relevant to obesity risk, while integrating prior information from large-scale studies such as the GIANT consortium and UK Biobank. Notably, we identified promising novel associations: rs3798696 in \textit{TFAP2A}, rs7070523 in \textit{ITIH5}, and rs178260 in \textit{AIFM3}, which have not previously been reported in the GWAS literature. These findings provide new insights into the genetic architecture of obesity and demonstrate that quantile-based modeling with integrated prior knowledge can potentially uncover novel genes missed by traditional GWAS approaches. An R implementation and simulation scripts are available at: https://github.com/KIQR-submission/KIQR