Thermo ebook on eQTL - 9

Genomic analysis using
Axiom PMR Array
The second step in identifying
eQTLs is to map SNP genotypes
of the same samples. Genotypes
from Axiom PMRA were called
using Axiom Analysis Suite; an
example of genotype calls is
shown in Figure 2. When multiple
probe sets were mapped to the
same SNP, the "best probe" SNP
was picked for eQTL analysis. On
Axiom Analysis Suite, "best probe
set" flag is 1 when it is the best or
the only probe set for a SNP, otherwise the flag is 0. Like gene expression data, SNP calls for each
of the probes were imported into
MatrixEQTL R for eQTL analysis.

Overlap of transcriptomic
and genomic results
Finally, discovery of potential
eQTLs involves examining the
correlation between genotype at
each SNP and the expression level of each of the assayed genes.
This correlation was examined on
MatrixEQTL R package using the
standard additive linear regression model.
In this model, the genotype is assumed to have an additive effect
on gene expression. Genotypes
were expressed as integer numbers (e.g., 0, 1, 2, etc.) based on
the number of the minor alleles
at each SNP. The statistical signifi-

Figure 2. Example of a SNP cluster plot for rs1409071. One point corresponds to one sample. Samples with genotypes AA and BB are red and
blue in color, respectively, while heterozygous samples are colored yellow.
Eclipses represent cluster boundaries. Samples for genotype calls were run
on Axiom PMRA arrays and analyzed on Axiom Analysis Suite.

Table 1. Statistically significant eQTLs as identified from microarray analysis.

Thermo ebook on eQTL

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