1. Genomics and Disease Gene Identification

2. Is the Disease Genetic or Environmental

3. 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%

4. 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

5. Example of Different kind of Genetic Diseases

6. 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 100-300 Bases ~10 million SNPs in human genome Occur both within gene and outside genes Predispose to, rather than cause disease trait

7. How do we use SNPs to map disease gene:

8. 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

10. 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.

11. 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

12. https://www.youtube.com/watch?v=hxou7ayQSZQ 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.

13. 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/