Table 1

Technologies and study designs for detecting rare variants

Strategy	Advantages	Disadvantages
High-depth WGS	Cover nearly all rare variants with high confidence.	High costs and computational challenges. Miss some coding variants as compared with WES.
Low-depth WGS and imputation	Cost-effective compared with high-depth WGS.	Limited accuracy for rare variants. Decreasing accuracy with same number of subjects compared with high-depth WGS.
WES	Less expensive. Identify all variants resides in exomic regions. Easily interpreted.	Ignore non-coding regions which account for large proportion of genome.
Targeted sequencing	Cost-effective.	Fail to identify disease-associated rare variants in some studies.
SNP-array genotyping with imputation	Low costs.	Lower accuracy for imputed rare variants.
Extreme phenotype sampling	Boosts power to find rare variants.	Requires statistical analysis to remove sampling bias. Difficult to generalise to the wider population. The results may be sensitive to outliers and sampling bias.
Population isolates	Lacks phenotypic variability due to cultural and environmental homogeneity. 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	Detect mutations that underlie Mendelian diseases successfully. Improve statistical power significantly.	Less powerful than case-control designs for common diseases.