Download Disease Identification

Genomics and
Disease Gene
Identification
Is the Disease Genetic or Environmental
How do we calculate
Twin Study:
Is an experiment that assess the genetic and
environmental influence on a trait
Using Monozygotic and Dizygotic twin pairs
• DZ twin share 50% of their gene and environment
• MZ twin share all their gene and environment
Monozygotic Twin (MZ)
Dizygotic Twin (DZ)
Disease
Manic Depressive psychosis
Cleft lip and palate
Rheumatoid arthritis
Asthma
Coronary artery disease
Diabetes mellitus
Concordance
MZ
67%
38%
34%
47%
19%
56%
DZ
5%
8%
7%
24%
9%
11%
Types of Genetic Disease
1.Mendelian/Monogenic Diseases : A mutation in just one of the genes ( 20,000-25,000) is responsible for
disease
i. Autosomal Recessive Single-Gene Diseases
ii. Autosomal Dominant Single-Gene Diseases
iii. X Chromosome–Linked Recessive Single-Gene Diseases
iv. X Chromosome–Linked Dominant Single-Gene Diseases
v. Y Chromosome–Linked Single-Gene Diseases
2. Polygenic Disorders: Mutations in more than one gene are responsible for disease .
3. Chromosomal Disease: Caused by alterations in chromosome structure or number.
i. Mosaicism
ii. Chromosomal Disorder
4. Complex Diseases: Most diseases are the result of multiple genetic changes as well as environmental
influences
Example of Different kind of Genetic Diseases
Genome-Wide Association Studies
Genome-wide association studies are a way for scientists to identify genes involved in human disease. This method
searches the genome for small variations, called single nucleotide polymorphisms or SNPs
Single Nucleotide Polymorphism:
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Most common class of genomic variation
Frequency is at least 1% in population
Occur every 100-300 Bases
~10 million SNPs in human genome
Occur both within gene and outside genes
Predispose to, rather than cause disease trait
How do we use SNPs to map disease gene:
If the X gene is diabetic?
If the answer is no 24,999 to look at
DNA microarrays (Gene Chips) are used to test
thousands of genetic variants simultaneously
Next generation sequencing (NGS) technology:
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Reduced the disease gene identification process from two-step
approach (positional mapping followed by Sanger sequencing) to
one-step approach (whole genome sequencing).
• The disease gene identification challenge shifted from the
identification to the interpretation phase
Whole Exome Sequencing (WES) is a technique
for sequencing all the expressed genes in an organism's
genome at one time.
Whole Genome Sequencing (WGS) s a laboratory process
determines the complete DNA sequence of an organism's
genome at a single time.
Why Do Whole Genome Sequencing:
• Making a diagnosis of a patient having a hereditary cause for a serious illness, developmental delay or a neurological
Disorder.
• Screening a couple for mutation that put a future child at a risk for serious hereditary disease
• Analyzing the genome of a tumor to provide information on prognosis and therapeutic options
Conclusion:
The 100,000 Genomes Project: The project will
sequence 100,000 genomes with rare disease. Their
aim is to create a new genomic medicine service .
https://www.youtube.com/watch?v=hxou7ayQSZQ
ENCODE Project: The Encyclopedia of DNA Elements is a public
research project launched by the US National Human Genome
Research Institute (NHGRI) in September 2003 to identify all
functional elements in the human genome.
Sources:
http://www.nature.com/scitable/ebooks/types-of-genetic-disease-16570291/contents
Belkadi, A., Bolze, A., Itan, Y., Cobat, A., Vincent, Q. B., Antipenko, A., ... & Abel, L. (2015). Whole-genome sequencing is more powerful than whole-exome
sequencing for detecting exome variants. Proceedings of the National Academy of Sciences, 112(17), 5473-5478.
Gilissen, C., Hoischen, A., Brunner, H. G., & Veltman, J. A. (2012). Disease gene identification strategies for exome sequencing. European Journal of Human
Genetics, 20(5), 490-497.
Huang, W., Wang, P., Liu, Z., & Zhang, L. (2009). Identifying disease associations via genome-wide association studies. BMC bioinformatics, 10(1), 1.
Voight, B. F., Scott, L. J., Steinthorsdottir, V., Morris, A. P., Dina, C., Welch, R. P., ... & McCulloch, L. J. (2010). Twelve type 2 diabetes susceptibility loci
identified through large-scale association analysis. Nature genetics, 42(7), 579-589.
Missier, P., Embury, S., Hedeler, C., Greenwood, M., Pennock, J., & Brass, A. (2007, June). Accelerating disease gene identification through integrated SNP
data analysis. In Data Integration in the Life Sciences (pp. 215-230). Springer Berlin Heidelberg.
https://www.genomicsengland.co.uk/the-100000-genomes-project/