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Applications of genome sequencing projects 1) Molecular Medicine 2) Energy sources and environmental applications 3) Risk assessment 4) Bioarchaeology,

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Presentation on theme: "Applications of genome sequencing projects 1) Molecular Medicine 2) Energy sources and environmental applications 3) Risk assessment 4) Bioarchaeology,"— Presentation transcript:

1 applications of genome sequencing projects 1) Molecular Medicine 2) Energy sources and environmental applications 3) Risk assessment 4) Bioarchaeology, anthropology, evolution, and human migration 5) DNA forensics 6) Agriculture, livestock breeding, and bioprocessing

2 Molecular medicine improved diagnosis of disease eearlier detection of genetic predisposition to disease rational drug design gene therapy and control systems for drugs ppharmacogenomics "custom drugs"

3 sequence variation within species Alleles –any variations in the genome at a particular location (locus) Polymorphic –two or more alleles at a locus Polymorphism – the particular variation DNA marker –polymorphic locus useful for mapping studies, disease diagnosis Anonymous locus –position on genome with no known function

4 DNA markers/polymorphisms RFLPs (restriction fragment length polymorphisms) - Size changes in fragments due to the loss or gain of a restriction site SSLPs (simple sequence length polymorphism) or microsatellite repeats. Copies of bi, tri or tetra nucleotide repeats of differing lengths e.g. 25 copies of a CA repeat can be detected using PCR analysis. SNPs (single nucleotide polymorphisms)-Sites resulting from a single change in individual bp.

5 RFLPs Fig. 11.7 – genetics/ Hartwell -Amplify fragment -Expose to restriction enzyme -Gel electrophoresis e.g., sickle-cell genotyping with a PCR based protocol

6 SSLPs Similar principles used in detection of RFLPs However, no change in restriction sites Changes in length of repeats

7 SNPs (single nucleotide polymorphisms) SNP detection using allele-specific oligonucleotides (ASOs) Very short probes (<21 bp) specific which hybridize to one allele or other Such probes are called ASOs Fig. 11.8 Sites resulting from a single change in individual bp

8 ASOs can determine genotype at any SNP locus Fig. 11.9 a-c

9 Hybridized and labeled with ASO for allele 1 Hybridized and labeled with ASO for allele 2 Fig. 11.9 d, e

10 Mendelian inheritance patterns Pappenheimer bodies in thalassemias Lungs affected in cystic fibrosis Huntington's Chorea

11 Complex traits Skin colour cancer

12 Incomplete penetrance – when a mutant genotype does not always cause a mutant phenotype No environmental factor associated with likelihood of breast cancer Positional cloning identified BRCA1 as one gene causing breast cancer. –Only 66% of women who carry BRCA1 mutation develop breast cancer by age 55 Incomplete penetrance hampers linkage mapping and positional cloning –Solution – exclude all nondisease individuals form analysis –Requires many more families for study

13 Variable expressivity - Expression of a mutant trait differs from person to person

14 Phenocopy –Disease phenotype is not caused by any inherited predisposing mutation –e.g. BRCA1 mutations 33% of women who do not carry BRCA1 mutation develop breast cancer by age 55

15 Genetic heterogeneity Mutations at more than one locus cause same phenotype e.g. thalassemias –Caused by mutations in either the or -globin genes. –Linkage analysis studies therefore always combine data from multiple families

16 Polygenic inheritance –Two or more genes interact in the expression of phenotype QTLs, or quantitative trait loci –Unlimited number of transmission patterns for QTLs »Discrete traits – penetrance may increase with number of mutant loci »Expressivity may vary with number of loci –Many other factors complicate analysis »Some mutant genes may have large effect »Mutations at some loci may be recessive while others are dominant or codominant

17 Polygenic inheritance E.g heart attacks or cholesterol levels Sudden cardiac death (SCD)

18 Haplotype association analysis specific combination of 2 or more DNA marker alleles situated close together on the same DNA molecule (homolog). SNPs most commonly used markers in haplotypes. series of closely linked mutations accumulate over time in the surviving generation derived from a common ancestor. powerful genetic tool for identifying ancient genetic relationships. Alleles at separate loci that are associated with each other at a frequency that is significantly higher than that expected by chance, are said to be in linkage disequilibrium

19 Formation of haplotypes over time

20 Ancient disease loci are associated with haplotypes Start with population genetically isolated for a long time such as Icelanders or Amish Collect DNA samples from subgroup with disease Also collect from equal number of people without disease Genotype each individual in subgroups for haplotypes throughout entire genome Look for association between haplotype and disease phenotype Association represents linkage disequilibrium If successful, provides high resolution to narrow parts of chromosomes

21 Haplotype analysis provides high resolution gene mapping

22 How to identify disease genes Identify pathology Find families in which the disease is segregating Find candidate gene Screen for mutations in segregating families

23 How to map candidate genes 2 broad strategies have been used A. Position independent approach (based on knowledge of gene function) 1) biochemical approach 2) candidate gene approach 3) animal model approach B. Position dependent approach (based on mapped position)

24 Position independent approach 1) biochemical approach Blood-clotting cascade in which vessel damage causes a cascade of inactive factors to be converted to active factors

25 Blood tests determine if active form of each factor in the cascade is present Fig. 11.16 c

26 Techniques used to purify Factor VIII and clone the gene Fig. 11.16 d

27 2) Candidate gene approach based on previously isolated human genes that may have a role in disease using expression array experiments (mRNA samples different in patients) Disease locus candidates identified based on possible role in disease physiology or map to the same chromosomal area & encode most likely protein e.g. hereditary retinal degeneration several genes encoding proteins involved in phototransduction identified choice of candidate - rhodopsin gene mutations identified in patients with retinitis pigmentosa Mapping candidate genes: 2 broad strategies have been used A. Position independent approach (based on knowledge of gene function) 1) biochemical approach 2) candidate gene approach 3) animal model approach B. Position dependent approach (based on mapped position) reverse genetics / positional cloning

28 3) Animal model approach compares animal mutant models in a phenotypically similar human disease. Identification of the SOX10 gene in human Waardenburg syndrome4 (WS4) Dom (dominant megacolon) mutant mice shared phenotypic traits (Hirschsprung disease, hearing loss and pigment abnormalities) similar to these human patients. WS4 patients screened for SOX10 mutations confirmed the role of this gene in WS4.

29 B) Positional dependent approach Positional cloning identifies a disease gene based on only approximate chromosomal location. It is used when nature of gene product / candidate genes is unknown. Candidate genes can be identified by a combination of their map position and expression, function or homology


31 B) Positional Cloning Steps Step 1 – Collect a large number of affected families as possible Step 2 - Identify a candidate region based on genetic mapping (~ 10Mb or more) Step 3- Establish a contig of clones across the region using ready- made contigs from the HG database Step 4 - Establish a transcript map, cataloguing all the genes in the region using either Combination of database searching and transcript mapping (Treacher Collins syndrome) Chromosomal aberrations (Duschenne muscular dystrophy) Linkage disequilibrium (Cystic fibrosis) Step 4 - Identify potential candidate genes Step 5 – screen for mutations among affected families


33 Once region of chromosome is identified, a high resolution mapping is performed with additional markers to narrow down region where gene may lie Fig. 11.17

34 Positional cloning – identifying candidate genes Once region of chromosome has been narrowed down by linkage analysis to 1000 kb or less, all genes within are identified Candidate genes –Usually about 17 genes per 1000 kb fragment –Identify coding regions Computational analysis to identify conserved sequences between species Computational analysis to identify exon-like sequences by looking for codon usage, ORFs, and splice sites Appearance on one or more EST clones derived from cDNA

35 Computational analysis of genomic sequences to identify candidate genes Fig. 11.19

36 Gene expression patterns can pinpoint candidate genes Look in public database of EST sequences representing certain tissues Northern blot –RNA transcripts in the cells of a particular tissue (e.g., with disease) separated by electrophoresis and probed with candidate gene sequence –Expression array analysis

37 Northern blot example showing SRY candidate for testes determining factor is expressed in testes, but not lung, overy, or kidney Fig. 11.20

38 Positional cloning – Find the gene responsible for the phenotype –Expression patterns RNA expression assayed by Northern blot or PCR amplification of cDNA with primers specific to candidate transcript Look for misexpression (no expression, underexpression, overexpression) –Sequence differences Missense mutations identified by sequencing coding region of candidate gene from normal and abnormal individuals –Transgenic modification of phenotype Insert the mutant gene into a model organism

39 Transgenic analysis can prove candidate gene is disease locus Fig. 11.21

40 Northern blot analysis reveals only one of candidate genes is expressed in lungs and pancreas Fig. 11.22 b CF gene


42 Reading HMG3 by T Strachan & AP Read : Chapter 14 AND/OR Genetics by Hartwell (2e) chapter 11 Optional Reading on Molecular medicine Nature (May2004) Vol 429 Insight series human genomics and medicine pp439 (editorial) predicting disease using medicine by John Bell pp 453-456. Mapping complex disease loci in whole genome studies by CS Carlson et al pp446-452

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