Genome wide association studies (GWAS) are hypothesis-free methods for identifying associations between genetic regions and traits. GWAS analysis are usually used to identify genes involved in human disease.
By applying GWAS analysis to variant data we will be able to identify a given variant (or a set of variants) involved in a given phenotype or disorder. Based on a statistical test, GWAS analysis will provide a level of significance (or p-value) for each variant.
Input parameters
Output results
When running a GWAS analysis in OpenCGA, user will get a text file as output result. This text file consists of a header line (starting with #), and then one line per variant with the following 12-13 columns:
chromosome
Chromosome code
start
Start base-pair coordinate
end
End base-pair coordinate
strand
Strand
reference
Reference allele
alternate
Alternate allele
dbSNP
Variant identifier
gene
Gene name
biotype
Bioytpe
conseq. types
List of consequence types
chi square
Allelic test chi-square statistic. Not present with 'fisher' test.