Using GWAS Data to Identify Copy Number Variants Contributing to Common Complex Diseases

TL;DR

This paper reviews methods for detecting copy number variants (CNVs) in human genomes, compares their effectiveness, especially for rare CNVs, and offers recommendations for identifying CNVs linked to complex diseases.

Contribution

It provides a comprehensive overview of CNV detection techniques, compares their power, and introduces alternative approaches for rare CNV identification in disease studies.

Findings

Hybridization intensity-based methods are commonly used for CNV detection.

Tag SNPs are effective for common CNVs but less so for rare variants.

De novo CNV detection methods can outperform case-control studies for disease association.

Abstract

Copy number variants (CNVs) account for more polymorphic base pairs in the human genome than do single nucleotide polymorphisms (SNPs). CNVs encompass genes as well as noncoding DNA, making these polymorphisms good candidates for functional variation. Consequently, most modern genome-wide association studies test CNVs along with SNPs, after inferring copy number status from the data generated by high-throughput genotyping platforms. Here we give an overview of CNV genomics in humans, highlighting patterns that inform methods for identifying CNVs. We describe how genotyping signals are used to identify CNVs and provide an overview of existing statistical models and methods used to infer location and carrier status from such data, especially the most commonly used methods exploring hybridization intensity. We compare the power of such methods with the alternative method of using tag SNPs to identify CNV carriers. As such methods are only powerful when applied to common CNVs, we describe two alternative approaches that can be informative for identifying rare CNVs contributing to disease risk. We focus particularly on methods identifying de novo CNVs and show that such methods can be more powerful than case-control designs. Finally we present some recommendations for identifying CNVs contributing to common complex disorders.