Introduction
- History and objectives of GWAS, inc motivation from heritability studies
- Missing heritability and genetic architecture
- Motivation for meta analysis
- Data sharing problems
- Comparison of meta vs mega analysis here?
Importance of imputation
- Doesn't necessarily improve power
- Does standardise set of variants across heterogeneous study designs
Methods commonly used for meta analysis
- Implementation
- Assessment of sources of heterogeneity
- Phenotypic
- Ancestrial
- Genotyping and imputation
Non-standard applications
- MetaCCA
- Rare variants
Ancilliary advantages of meta analysis
- Independent replication?
- Reduction of confounding due to population stratification
Source of genomic inflation: real genetic effects or cryptic stratification
- Double or single genomic control?
- Prediction using within family studies
- Examples - height / schizophrenia / BMI?
Where meta analysis is limited or not equivalent
- Linear mixed models or SNP h2
- Where summary data is too large to share
- Genetic interactions
- omicQTLs
Future prospects
- Datashield?