Large scale genetic mapping studies seek to associate genetic markers, such as
SNPs
, of known location, with various quantitative and qualitative phenotypic
traits
. Both
Marker-Trait Association
and
SNP-Trait Association
processes were developed to address specific needs of these investigations.
Marker-Trait Association
is especially useful for studies involving
multi-allelic markers and some of the more complex modeling techniques. However, it is not particularly efficient at handling very large data sets.
Survey SNP-Trait Association
was specifically designed for very large genetic data sets, but it lacks some of the more complex options available in
Marker-Trait Association
. Both of these
procedures complement each other very well. However, n
either
Marker-Trait Association
nor
SNP-Trait Association
can accommodate complex survey designs.
Survey SNP-Trait Association
addresses this deficiency by testing for
association
between various types of traits and SNP
genotypes
or
alleles
from a single SNP at a time taking into account complex survey designs. Two types of analyses can be performed: an
ANOVA
based on SNP genotypes or a
regression
testing for a linear trend of SNP alleles. Adjustments can be made for quantitative
covariates
. Rao-Scott
chi-square
and
F
statistics can also be computed for non-continuous traits.
P-values
from these tests, with adjustments applied if requested, are plotted along the marker map.
See the
SURVEYFREQ
,
SURVEYLOGISTIC
, and
SURVEYREG
procedures in the SAS/STAT User's Guide for more information.
One
Input Data Set
, which contains all of the marker data, is needed for this process. The sample data set used in the following example, the
survey_genotype.sas7bdat
data set, was computer-generated and consists of 7000 rows of individuals with 38 columns corresponding to genetic data on these individuals. In this data set, genotypes are presented in the one-column format with numerical identities assigned based on the prevalence of each allele. Regardless of the actual identifier, individuals
homozygous
for the most common allele are identified with a
2
. Heterozygotes for the most common allele and the less common allele, are identified with a
1
. The six columns at the beginning of the data set (
PSU
,
Stratum
,
SampWeight
,
Age
,
Response
, and
BMI
) contain non-genetic information about the individuals. The first three are related to the survey sample design, containing the cluster identifier, stratum identifier, and sampling weights, respectively.
Age
and
BMI
(body mass index) are
variables
that can be used as covariates in the association analysis. The
Response
column contains the values for the trait that we are interested in mapping in the
genome
.
The
survey_genotype.sas7bdat
data set is partially shown. Note that this is a wide data set; markers are listed in columns, whereas individuals are listed in rows.
A second optional data set is the
Annotation Data Set
. This data set contains information, such as gene identity or chromosomal location, for each of the markers. This data set must be a tall data set; each row corresponds to a different marker.
Note
: The top-to-bottom order of the rows in the
annotation data set
must match the left-to-right order of the columns in the input data set. This correspondence is required for markers to be matched appropriately.
The
survey_genotype.sas7bdat
data set is included in the
Sample Data
folder that comes with JMP Genomics.
For detailed information about the files and data sets used or created by JMP Life Sciences software, see
Files and Data Sets
.
The output generated by this process is summarized in a Tabbed report. Refer to the
Survey SNP-Trait Association
output documentation for detailed descriptions and guides to interpreting your results.