Statistical advances and challenges for analyzing correlated high dimensional SNP data in genomic study for complex diseases

TL;DR

This paper reviews recent statistical methods and challenges in analyzing high-dimensional, correlated SNP data for genomic studies of complex diseases, emphasizing feature reduction and interaction detection.

Contribution

It provides a comprehensive overview of advanced statistical techniques tailored for correlated SNP data analysis in complex disease research.

Findings

Review of feature reduction methods for high-dimensional data

Discussion of approaches for identifying interacting loci

Summary of statistical and machine learning techniques used in SNP analysis

Abstract

Recent advances of information technology in biomedical sciences and other applied areas have created numerous large diverse data sets with a high dimensional feature space, which provide us a tremendous amount of information and new opportunities for improving the quality of human life. Meanwhile, great challenges are also created driven by the continuous arrival of new data that requires researchers to convert these raw data into scientific knowledge in order to benefit from it. Association studies of complex diseases using SNP data have become more and more popular in biomedical research in recent years. In this paper, we present a review of recent statistical advances and challenges for analyzing correlated high dimensional SNP data in genomic association studies for complex diseases. The review includes both general feature reduction approaches for high dimensional correlated data and more specific approaches for SNPs data, which include unsupervised haplotype mapping, tag SNP selection, and supervised SNPs selection using statistical testing/scoring, statistical modeling and machine learning methods with an emphasis on how to identify interacting loci.