This document summarizes grid-based expression QTL analysis. It discusses isolating susceptible or resistant strains of an organism to a disease, identifying genetic markers that differ between strains, generating offspring with mixed genotypes to score disease phenotypes, and using statistical methods to determine associations between markers and scores. It describes using dense genetic markers and interval mapping to localize where crossovers occur on chromosomes. The analysis can use gene expression levels as scores and link genetic loci to controlled genes and pathways. Distributed computation is proposed to handle the large amount of data generated from thousands of genes, genetic markers, individuals, and phenotypes.

1. Grid-based
expression QTL Analysis
Ann-Kristin Grimm, Steffen Möller
University of Lübeck
Institute for Neuro- and Bioinformatics
BOSC09
Stockholm

2. Statistical genetics for complex diseases
● Isolation of homozygous strains
● susceptible or
● resistant
to disease
● Identification of chromosomal markers that
differ between strains
● Generating offspring with mixed genotypes,
score the disease phenotypes of every
individual
● Determine statistical association of markers
with score BOSC09
Stockholm

3. Back-Cross: F1 x P
BOSC09
Stockholm

4. Analysis
● Markers sufficiently dense to spot all X-overs
● Interval-mapping: neighbouring markers
● Both homozygous: no X-over
● Both heterozygous: no X-over
● One hetero-, one homozygous: X-over in between
● Continuous refinements
● Steady (directed) increase of markers
● Additional phenotypes being investigated
● Additional mice being bread
– Stronger statistics
– More cross-overs
BOSC09
Stockholm

5. Peak: inferring ML of disease location
● A stretch between two markers
may be found to be influencing the
strong
score
Effect
● The position of the controlling
locus may be estimated from
weak
● the fraction of individuals with X-over
● that show the same effect
A/A A/B A/A
A/A A/B A/B
● The exact locus remains undefined Marker-Combination
● … but what if we have more molecular scores ...?
BOSC09
Stockholm

6. Expression QTL
● Use gene expression levels as scores
● Disease phenotypes + sex + mitochondrial
inheritance are covariates
● Every locus is described by
● genes that it controls
● pathways/GO terms/TFBS/miRNA these share
● Super-linear (epistatic) effects with other loci
● Conversely genes → genetic loci
● Direct effects from locus → gene
● Singular effects are of interest, even should the
QTG never be determined BOSC09
Stockholm

7. Huge amount of data
● Compute time so long you don't want to do this
twice.
30000 genes
x 200 markers (^2 for interactions)
x 150 individuals
x 20 phenotypes (^2)
● But for better insights biologists do – with
updated data.
BOSC09
Stockholm

8. Increased communication through
distributed computation
● Dynamic website to
● Ship raw data to
compute nodes
● Humanely present
(interim) results of
computations
● Grid jobs retrieve
series of work units
● Continuous input to
biological researchers
BOSC09
Stockholm

9. Please join in: http://eqtl.berlios.de
Acknowledgments
Programming: Ann-Kristin Grimm, Jan Kolbaum, Hajo Krabbenhöfft, Patrick Wernhoff
Data: Maja Jagodic, Mèlanie Thessèn-Hedreul, Dirk Koczan, Saleh Ibrahim
Computations: Olli Tourhunen and the NDGF BOSC09
Stockholm