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

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

💡 Research Summary

The paper tackles the persistent problem of low statistical power in genome‑wide association studies (GWAS) of obesity, especially when the sample size is modest (≈2,000 individuals in the Framingham Heart Study) but the number of single‑nucleotide polymorphisms (SNPs) exceeds half a million. Traditional GWAS, which regress each SNP individually on body‑mass index (BMI) and apply a stringent genome‑wide significance threshold (p < 5 × 10⁻⁸), fails to detect any loci under these conditions. Moreover, existing large‑scale meta‑analyses such as the GIANT consortium and the UK Biobank provide valuable prior information that is rarely incorporated into cohort‑specific analyses, and GWAS focus on mean BMI while clinical obesity concerns the upper tail of the BMI distribution.

To overcome these limitations, the authors propose Knowledge Integration Quantile Regression (KIQR), a two‑step penalized quantile regression framework that (i) integrates prior knowledge from external studies, (ii) targets high conditional quantiles of BMI (e.g., τ = 0.8, 0.9) which are more relevant to obesity risk, and (iii) performs simultaneous variable selection and coefficient estimation in an ultra‑high‑dimensional setting (d can grow exponentially with n).

Step 1 – Prior‑informed estimator: A conventional penalized quantile regression is fitted where SNPs identified in previous meta‑analyses (the prior set Sp) are left unpenalized. The resulting coefficient vector β̂p serves as a data‑driven prior prediction for each individual (xᵢᵀβ̂p).

Step 2 – KIQR estimation: The final objective function combines three components: (1) the quantile check loss for the observed BMI, (2) an SCAD penalty for sparsity, and (3) a second quantile check loss that measures the discrepancy between the model’s predicted BMI (xᵢᵀβ) and the prior prediction (xᵢᵀβ̂p). A tuning parameter ζ∈