Abstract

Although many genome-wide association studies have been performed, the identification of disease polymorphisms remains important. It is now suspected that many rare disease variants induce the association signal of common variants in linkage disequilibrium (LD). Based on recent development of genetic models, the current study provides explanations of the existence of rare variants with high impacts and common variants with low impacts. Disease variants are neither necessary nor sufficient due to gene-gene or gene-environment interactions. A new method was developed based on theoretical aspects to identify both rare and common disease variants by their genotypes. Common disease variants were identified with relatively small odds ratios and relatively small sample sizes, except for specific situations in which the disease variants were in strong LD with a variant with a higher frequency. Rare disease variants with small impacts were difficult to identify without increasing sample sizes; however, the method was reasonably accurate for rare disease variants with high impacts. For rare variants, dominant variants generally showed better Type II error rates than recessive variants; however, the trend was reversed for common variants. Type II error rates increased in gene regions containing more than two disease variants because the more common variant, rather than both disease variants, was usually identified. The proposed method would be useful for identifying common disease variants with small impacts and rare disease variants with large impacts when disease variants have the same effects on disease presentation.

Highlights

  • Genome-wide association studies (GWAS) have been successful in revealing the existence of common disease variants; common variants identified using GWAS explain only small portions of heritability (Manolio et al, 2009)

  • Considering the importance of rare disease variants, the current study examined Type II error rates depending on various disease allele frequencies and odds ratios for the one-disease-variant model

  • The current study focused on rare variants, and variance corrections were partially based on the random sampling of the case samples

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Summary

Introduction

Genome-wide association studies (GWAS) have been successful in revealing the existence of common disease variants; common variants identified using GWAS explain only small portions of heritability (Manolio et al, 2009). This prompted efforts to find rare disease variants using re-sequencing to explain the remaining causes of heritability (Cirulli & Goldstein, 2010). The first report to identify actual functional variants through GWAS was for differential drug responses in patients with chronic hepatitis C (Fellay et al, 2010).

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