Our Research
One of the major aims of statistical genetics is to localise genes
influencing traits. This includes disease causing genes.
We can apply traditional statistical methods such as likelihood
modelling to identify likely regions of the genome that harbour genes
of interest. This then allows geneticists to examine the few remaining
candidates biologically for association with the trait. It is usually
not possible to check all 50,000 or so human genes as these methods may not reflect the DNA changes, either not at all or too weak to survive genome wide multiple testing adjustments. Microarrays
and SAGE (serial analysis of
gene expression) are alternative, new, technologies that try to do
exactly this.
The first step is to gather familial information such as
pedigrees (who is related to whom) and measurements of the trait
(quantitative or qualitative). Then we carry out a genome wide scan. In
other words we have no prior guesses (candidates) as to which gene may
be involved in influencing the trait. We use many hundreds of markers
in a genome wide scan which are approximately evenly spaced along the
human genome. Because we tend to inherit parts of chromosomes from our
ancestors we can follow the path of inheritance for these markers
through the pedigrees. If this path coincides with a pattern of the
trait, i.e. a particular form of a marker, called an allele, is always
associated with a high value of the trait then evidence mounts that
this particular marker is actually located close to a gene influencing
this trait.
This method, known as linkage analysis, comes in many different
flavours and has been successfully used for many decades to map genes
such as the gene for Huntingdon's disease, Muscular dystrophy and the
breast cancer genes BRCA1 and BRCA2.
The current marker of choice is the SNP (Single Nucleotide Polymorphism)
marker. An example would be
GTATGTTCAAC (maternally inherited DNA)
GTAAGTTCAAC (paternally inherited DNA)
Fortunately many hundereds of thousands of these exist throughout the human genome.
The process of
determining the form, or alleles, at each of these SNP markers for each individual is known as genotyping. In order to be able to genotype
individuals we usually need buccal swabs (mouth swabs) or a little bit
of blood, from which the DNA is extracted.
We now have access to high throughput genotyping technologies that allow the genotyping of ~1 million SNPs per person within 24 hrs for ~$1000 AUD.
These high throughput platforms were developed rapidly in the last 5 years to enable the hunt for common complex disease causes using
the phenomena of Linkage Disequilibrium (LD). This was also facilitated through massive projects such as the HAPMAP project.
The AGRF is a high throughput
genotyping facility which can genotype many markers for many
individuals in a single day. There is considerable technology involved
in the genotyping process. Further information is available on how a linkage
analysis should proceed.
The pedigree, trait and genotyping information is then
combined and analysed with probability models which measure the
significance of the linkage. This is a computationally
challenging problem since the possible number of ways the genotyping
and trait data could have been transmitted through the pedigree is
often very large. Hence we use fast computers with lots of memory to
carry out these calculations.
One can also use experimental animal models, such as mice, to map disease
genes. These also form pedigrees and we collect the trait data, also
known as phenotyping data, and genotyping data in a similar
way. We collaborate closely with the Molecular Medicine Division on murine mapping projects and with the AGRF (Australian Genome Research
Facility) on genotyping and quality control.
Selected Recent Publications
- Berkovic SF, Dibbens LM, Oshlack A, Silver JD, Katerelos M, Vears DF, Lüllmann-Rauch R, Blanz J, Zhang KW, Stankovich J, Kalnins RM, Dowling JP,
Andermann E, Andermann F, Faldini E, D'Hooge R, Vadlamudi L, Macdonell RA, Hodgson BL, Bayly MA, Savige J, Mulley JC, Smyth GK, Power DA, Saftig P, Bahlo M.
"Array-based gene discovery with three unrelated subjects shows SCARB2/LIMP-2 deficiency causes myoclonus epilepsy and glomerulosclerosis."
Am J Hum Genet. 2008 Mar;82(3):673-84
- Allen KJ, Gurrin LC, Constantine CC, Osborne NJ, Delatycki MB, Nicoll AJ, McLaren CE, Bahlo M, Nisselle AE, Vulpe CD, Anderson GJ,
Southey MC, Giles GG, English DR, Hopper JL, Olynyk JK, Powell LW, Gertig DM.
"Iron-overload-related disease in HFE hereditary hemochromatosis."
N Engl J Med. 2008 Jan 17;358(3):221-30.
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Silver JD, Hilton DJ, Bahlo M, Kile BT.
"Probabilistic analysis of recessive mutagenesis screen strategies."
Mamm Genome. 2007 Jan;18(1):5-22
- Bahlo M, Stankovich J, Speed TP, Rubio JP, Burfoot RK, Foote SJ.
"Detecting genome wide haplotype sharing using SNP or microsatellite haplotype data."
Hum Genet. 2006 Mar;119(1-2):38-50.
- Clarke NF, Smith RL, Bahlo M , North KN. "A novel X-linked form of congenital
fiber-type disproportion." Ann Neurol. Ann Neurol. 2005 Nov;58(5):767-72.(2005)
- Stankovich J, Bahlo M , Rubio JP, Wilkinson CR, Thomson R, Banks A, Ring M, Foote SJ, Speed TP.,
"Identifying nineteenth century genealogical links from genotypes" Hum Genet. (2005), 117 pp. 188-199
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