Genomics and Disease Gene Identification. Is the Disease Genetic or Environmental.

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Presentation transcript:

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 Monozygotic Twin (MZ) Dizygotic Twin (DZ) DZ twin share 50% of their gene and environment MZ twin share all their gene and environment Disease Concordance MZDZ Manic Depressive psychosis67%5% Cleft lip and palate38%8% Rheumatoid arthritis34%7% Asthma47%24% Coronary artery disease19%9% Diabetes mellitus56%11%

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 Types of Genetic Disease

Example of Different kind of Genetic Diseases

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 Genome-Wide Association Studies Single Nucleotide Polymorphism: Most common class of genomic variation Frequency is at least 1% in population Occur every 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: 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

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. Conclusion: 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: 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), 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), 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), 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 ). Springer Berlin Heidelberg.