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

http://www.ornl.gov/hgmis/project/benefits.html

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"

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

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.

RFLPs

Fig. 11.7 – genetics/ Hartwell

- Amplify fragment

- Expose to restriction enzyme

- Gel electrophoresis

e.g., sickle-cell genotyping with a PCR based protocol

SSLPs Similar principles used in detection of RFLPs However, no change in restriction sitesChanges in length of repeats

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

ASOs can determine

genotype at any SNP

locus

Fig. 11.9 a-c

Hybridized and labeled with ASO for allele 1

Hybridized and labeled with ASO for allele 2

Fig. 11.9 d, e

Mendelian inheritance patterns

Pappenheimer bodies in thalassemias

Lungs affected in cystic fibrosis

Huntington's Chorea

Complex traits

Skin colour cancer

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

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

• 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

Genetic heterogeneityMutations 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

• 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

Polygenic inheritance

E.g heart attacks or cholesterol levels

Sudden cardiac death (SCD)

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

Formation of haplotypes over time

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

Haplotype analysis provides high resolution gene mapping

How to identify disease genes

• Identify pathology

• Find families in which the disease is segregating

• Find ‘candidate gene’

• Screen for mutations in segregating families

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)

Position independent approach1) biochemical approach

Blood-clotting cascade in

which vessel damage causes a

cascade of inactive

factors to be converted to active factors

Blood tests determine if active form of each factor in the

cascade is present

Fig. 11.16 c

Techniques used to purify Factor VIII and clone the gene

Fig. 11.16 dFig. 11.16 d

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 approach2)        candidate gene approach3)        animal model approachB.            Position dependent approach (based on mapped position)reverse genetics / positional cloning

3) Animal model approachcompares 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.

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

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

B) Positional Cloning StepsStep 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 databaseStep 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 genesStep 5 – screen for mutations among affected families

• 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

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

Computational analysis of genomic sequences to identify candidate

genes

Fig. 11.19

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

Northern blot example showing SRY candidate for testes determining factor is

expressed in testes, but not lung, overy, or kidney

Fig. 11.20

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

Transgenic analysis can prove candidate gene is disease locus

Fig. 11.21

Northern blot analysis reveals only one of candidate genes is expressed in lungs and pancreas

Fig. 11.22 b

CF gene

ReadingHMG3 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