Types of genetic studies
| Type . | Alleles captured . | Advantages . | Limitations . |
|---|---|---|---|
| Targeted genotyping | Specific variants | Inexpensive, hypothesis driven | Constrained by current knowledge, cannot use genome to control for population effects |
| Genome-wide genotyping (GWAS) | Common; coding and noncoding | Affordable, comprehensive, agnostic, can control for population effects, streamlined analysis | Requires large sample sizes to detect modest effects at genome-wide statistical significance (P = 5 × 10−8) |
| Exome-wide genotyping | Common and low-frequency; coding | Affordable, comprehensive as far as genes are concerned, agnostic, can control for population effects, can conduct individual variant testing as well gene burden tests, easier interpretation of functional effects | Requires large sample sizes to detect modest effects at exome-wide statistical significance (P = 5 × 10−7 for single variants, P = 2.5 × 10−6 for gene-based tests of rare variant aggregation), only focuses on coding variation that is shared across populations |
| Whole-exome sequencing | Common, low-frequency, and rare; coding | Expensive; comprehensive as far as genes are concerned; agnostic; can control for population effects; can conduct individual variant testing as well gene burden tests; can discover novel variants in an individual, a family, or a group; easier interpretation of functional effects | Requires large sample sizes to detect modest effects at exome-wide statistical significance (P = 5 × 10−7 for single variants, P = 2.5 × 10−6 for gene-based tests of rare variant aggregation), capture of variation may be uneven across the genome |
| Whole-genome sequencing | Common, low-frequency, and rare; coding and noncoding | Very expensive, most comprehensive, agnostic, can control for population effects, can discover novel variants in an individual, a family, or a group | Unresolved threshold for statistical significance in the low-/rare frequency spectrum, challenging interpretation of functional effects |
| Type . | Alleles captured . | Advantages . | Limitations . |
|---|---|---|---|
| Targeted genotyping | Specific variants | Inexpensive, hypothesis driven | Constrained by current knowledge, cannot use genome to control for population effects |
| Genome-wide genotyping (GWAS) | Common; coding and noncoding | Affordable, comprehensive, agnostic, can control for population effects, streamlined analysis | Requires large sample sizes to detect modest effects at genome-wide statistical significance (P = 5 × 10−8) |
| Exome-wide genotyping | Common and low-frequency; coding | Affordable, comprehensive as far as genes are concerned, agnostic, can control for population effects, can conduct individual variant testing as well gene burden tests, easier interpretation of functional effects | Requires large sample sizes to detect modest effects at exome-wide statistical significance (P = 5 × 10−7 for single variants, P = 2.5 × 10−6 for gene-based tests of rare variant aggregation), only focuses on coding variation that is shared across populations |
| Whole-exome sequencing | Common, low-frequency, and rare; coding | Expensive; comprehensive as far as genes are concerned; agnostic; can control for population effects; can conduct individual variant testing as well gene burden tests; can discover novel variants in an individual, a family, or a group; easier interpretation of functional effects | Requires large sample sizes to detect modest effects at exome-wide statistical significance (P = 5 × 10−7 for single variants, P = 2.5 × 10−6 for gene-based tests of rare variant aggregation), capture of variation may be uneven across the genome |
| Whole-genome sequencing | Common, low-frequency, and rare; coding and noncoding | Very expensive, most comprehensive, agnostic, can control for population effects, can discover novel variants in an individual, a family, or a group | Unresolved threshold for statistical significance in the low-/rare frequency spectrum, challenging interpretation of functional effects |