Home
Welcome to the GEnetic Analysis Repository (GEAR) wiki!
GEAR is developed in Java, and requires java runtime environment 7 installed on a system.
The latest release of GEAR can be downloaded here.
Contact: Guo-Bo Chen, chenguobo@gmail.com
A quick tutorial for getting started
0. Data management options
1. EigenGWAS
- EigenGWAS: finding loci under selection in GWAS data
- Projected eigenvectors: a much easier way in generating eigenvectors for GWAS data
Citation: Heredity, 2016, 117:51-61, {EigenGWAS paper download}
2. Variance component analysis for GWAS data
Citations:
Front Genet, 2014, 5:107, {HE-IBS paper download}
Euro J Hum Genet, 2016, 24:1810-6, {HE structure paper download}
3. GWAS summary statistics
- Fst-derived PCA for cohorts without genotype data
- Meta-PCA for geographical inference for cohorts without genotype data
- LambdaMeta for testing overlapping samples between cohorts
- PPSR for pinpointing overlapping samples between cohorts
- Genome-wide meta-analysis
Citation:
Euro J Hum Genet, 2017, 25:137-146, {SumStat QC paper download}
4. Principal component analysis
5. Genomic profile risk score
Citation: Heredity, 2016, 117:51-61, {EigenGWAS paper download}
6. Open GWAS algoriTHm (OATH) for deep evaluation
- Step 1: Generating naive summary statistics
- Step 2: Synthesize naive summary statistics into various models
- Step 1&2 together: Synthesize various models using naive summary statistics
Citations: G3, 2017, 7:943-952, {G3 paper download}
7. Population genetics
- Genome-wide relatedness: IBS for whole-genome data
- Locus statistics: allele frequency and variance
- DNA fingerprint: find overlapping samples
8. Data simulation
- Polygenic model for quantitative traits
- Simulation for correlated traits
- Polygenic model for case-control data
- Discordant nuclear family simulation
- Experimental population simulation (BC, F2, DH, RIL, IF2)
- Simulation using real GWAS data