Rare high-impact disease variants: properties and identifications

Abstract

SummaryAlthough 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.

Population-specific long-range linkage disequilibrium in the human genome and its influence on identifying common disease variants

“…Although false signals through LRLD might not be frequent, their potential existence must be considered in GWASs. Based on the disease model, if the disease allele frequency is high, the increments or decrements of the disease genotype frequencies are small, resulting in low odds ratios 42 . As shown in Fig.…”

“…At a gene frequency of 1 for the gene interacting with the environment (G E ), the population lifetime incidence (E + G E × E G ) would be 0.08. The proportion of gene-environment interaction was calculated for each disease variant for each population, and the derived genotype frequencies in the case population were calculated as described elsewhere 42 . Because dominant and recessive models showed similar results in a previous study 42 , a dominant model for one interacting gene was assumed.…”