Click on a button corresponding to a
genome-wide
association
studies (
GWAS
)
process. Refer to the table below and the
Comparison of Association Testing Processes
for guidance.
|
Quickly and easily performing association mapping of a
binary trait
(with two generic levels
case
and
control
) using a
chi-square
test on genetic marker data
|
|
|
Exploring and revealing
population
structure based on SNP
genotypes
for a sample; or, adjusting for
population stratification
or allele frequency variation due to ancestral differences in association tests
|
|
|
Testing association between various types of
traits
and individual
SNPs
, in a modeling framework allowing fixed and
random effect
adjustment, in large whole
genomes
or other
very large
data sets
Tip
: For studies involving multi-allelic markers, use
Marker-Trait Association
. Where SNP genotypes are imputed, use
Imputed SNP-Trait Association
. For complex survey designs, choose
Survey SNP-Trait Association
.
|
|
|
Testing association between various types of traits and individual SNPs, taking into account the uncertainty of SNP genotype
imputation
Note
: Unlike other genetic association processes, this process requires a
stacked
input data set.
|
|
|
Testing association between various types of traits and SNP genotypes or
alleles
from a single SNP at a time, taking into account
complex survey designs
|
|
|
Performing association analysis of family data, using one or more
quantitative
transmission disequilibrium tests
, to map
binary
and
quantitative
traits
|
|
|
Performing association analysis of family data, using one or more
family association
tests, to map
binary
traits, and to facilitate the analysis of
X-linked markers
|
|
|
Performing a
meta-analysis
of genome-wide association studies by combining
p-values
or effects for a SNP from multiple studies and calculating a combined
p
-value
|
See
Other Association Testing
for additional association testing processes.