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Genetic risk prediction using a spatial autoregressive model with adaptive lasso.

Yalu Wen¹, Xiaoxi Shen², Qing Lu²

¹Department of Statistics, University of Auckland, Auckland, New Zealand.

Statistics in Medicine

|June 2, 2018

Summary

A new spatial autoregressive model with adaptive lasso (SARAL) improves risk prediction from high-dimensional sequencing data. SARAL effectively handles noise and rare variants, offering a powerful tool for precision medicine applications.

Keywords:

adaptive lasso high-dimensional sequencing data risk prediction spatial autoregressive model

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Area of Science:

Genomics
Statistical Genetics
Computational Biology

Background:

High-throughput sequencing technologies generate vast data for precision medicine.
Analyzing sequencing data for risk prediction faces challenges from noise and rare variants.

Purpose of the Study:

To develop a novel statistical model for risk prediction using high-dimensional sequencing data.
To address analytical challenges posed by noise and rare variants in genetic data.

Main Methods:

Proposed a spatial autoregressive model with adaptive lasso (SARAL).
SARAL is a set-based approach reducing data dimension and accumulating single-nucleotide variant (SNV) effects.
Implemented adaptive lasso to shrink noise SNV set effects, enhancing prediction accuracy.

Main Results:

SARAL demonstrated comparable or superior performance to the genomic best linear unbiased prediction (GBLUP) method in simulations.
The model effectively manages complex disease genetics with varying effect sizes across SNV sets.
Applied SARAL to Alzheimer's Disease Neuroimaging Initiative sequencing data.

Conclusions:

SARAL offers a robust and accurate method for risk prediction using high-dimensional sequencing data.
The model's set-based approach and adaptive lasso feature enhance its utility in precision medicine.
This method holds promise for analyzing complex diseases and improving disease risk prediction.