Table 1

Technologies and study designs for detecting rare variants

High-depth WGSCover nearly all rare variants with high confidence.
  1. High costs and computational challenges.

  2. Miss some coding variants as compared with WES.

Low-depth WGS and imputationCost-effective compared with high-depth WGS.
  1. Limited accuracy for rare variants.

  2. Decreasing accuracy with same number of subjects compared with high-depth WGS.

  1. Less expensive.

  2. Identify all variants resides in exomic regions.

  3. Easily interpreted.

Ignore non-coding regions which account for large proportion of genome.
Targeted sequencingCost-effective.Fail to identify disease-associated rare variants in some studies.
SNP-array genotyping with imputationLow costs.Lower accuracy for imputed rare variants.
Extreme phenotype samplingBoosts power to find rare variants.
  1. Requires statistical analysis to remove sampling bias.

  2. Difficult to generalise to the wider population.

  3. The results may be sensitive to outliers and sampling bias.

Population isolates
  1. Lacks phenotypic variability due to cultural and environmental homogeneity.

  2. Higher frequency of rare variants resulting from reduced genetic diversity and increased genetic drift.

Risk-conferring variants may be extremely rare and monomorphic due to lack of genetic diversity.
Family studies
  1.  Detect mutations that underlie Mendelian diseases successfully.

  2.  Improve statistical power significantly.

Less powerful than case-control designs for common diseases.
  • WES, whole-exome sequencing; WGS, whole-genome sequencing.