bioinformatics aps. founded in february 2002 with investment from biovision founders: staff working...

Bioinformatics ApS

Bioinformatics ApS

• Founded in February 2002 with investment from Biovision

• Founders: Staff working at the Bioinformatics Research Center, Aarhus University

• Employees: 2 software developers

Founders

• Leif Schauser, Ph.D. (CEO): Associate Professor, Molecular Biology, Bioinformatics Research Institute

• Jotun Hein, Ph.D. (Member of BoD): Professor in Bioinformatics at Oxford University

• Mikkel Schierup, Ph.D. (CSO): Associate Professor, Biology, Bioinformatics Research Institute

• Christian Storm, Ph.D. (CTO): Associate Professor, Computer Science, Bioinformatics Research Institute

Strategy

• Develop leading bioinformatics tools designed for association studies

• Branding the software

• Collaborations with pharmacogenomics industry

– Disease genes, adverse drug reactions

• Extend product suite to include other bioinformatics solutions (databases, comparative genomics)

Drug target hunting

Microarray analysis Disease gene mapping

100s of targets,not necessarilyrelevant

Mb of genomic sequence,Power dependentRequires high resolution

Problems:Tissues, controls,Limited by array

Problems:Sampling, modeling,penetrance

GeneRecon

• Association mapping using all markers at the same time and all other available information

• Fully probabilistic approach

Input:SNPs or microsattelites

Disease and control group

Output:Localisation of disease

GeneRecon - implementation

• C++ program (>10.000 lines of code)

• Multiplatform (Unix, Windows, MacOSX)

• Amenable to parallelization

• Bayesian MCMC approach

GeneRecon - implementation

• 10 million recalculations/hour

• Different models of disease transmission

• Diploid data with unknown phase

• Thorough tests for calculations

GeneRecon – output

• Disease gene location (full distribution)

• Disease-causing haplotypes

• Estimation of phenocopies

• Penetrance

• Date the origin of disease

GeneRecon - collaborations

• Scandinavian medium sized biotechnological companies– Proprietary dataset under analysis

• Danish University Hospital– Schizophrenia data are currently being collected

Disease mappingPedigree Analysis: Association Mapping:

Pedigree known

Few Meiosis (max 100s)

Many Generations

Resolution: cMorgans (Mbases)Pedigree sampled

Many Meiosis (>104)

Resolution: 10-4 Morgans (Kbases)

Tim

e

rM

D

rM

D

Linkage Disequilibrium (LD)

Haplotypes

Haplotypes:

SNPs:

A

T

G

C

C

A

{A,T} {C,G} {A,C}

2m-1

The Human Genome http://www.sanger.ac.uk/HGP/

1

2 3

4 56 7 8 9

X

Y15141312

10 11212019

181716

22

3 billion base pairs per haploid genome

30.000-40.000 genes

SNP facts

http://www.ncbi.nlm.nih.gov/SNP/

• For 2 complete haplotype genomes, there are about 3 million SNP differences (>1 SNP / kb).

• Currently 3 mio. SNPs in database

RefSNP with frequency with genotype

3.079.086 196.054 32.101

Large scale survey of LDReich et al. (2001)

Recent LD studies

• LD extends over considerable distance in most populations• African populations show less LD than European

populations• Small, isolated populations (e.g. Saami, Evenki) show

increased LD• Founder populations (e.g. Finland, Sardinia) do not always

show increased LS

• Evidence for heterogeneity in LD along chromosomes– Haplotype blocks– Recombination hotspots

Genetic Basis for DiseaseMonogenic

Cystic Fibrosis

Huntington’s Disease

Sickle Cell Anemia

Polygenic

Azheimer’s disease

Schizophrenia

Hereditaray Heart Disease

Astma

Cystic fibrosis: a case study

Traditional analysis Bayesian MCMC sampling

The market

• All major pharmaceutical and many biotech companies conduct genetic studies– Disease association (drug target identification)– Adverse drug response (pharmacogenomics)– Tailored drug administration

• Outsourcing of non-core activities

Timeline for drug discovery # Targets Discovery (5 yrs)

5000 Population study I

Pre-Clinical (1 yr)

50

Clinical (6 yrs)5 Population study II

Review (2 yrs)

1

Marketed

Cambridge Healthtech Institute: SNP-research market could reach

$1.2 billion by 2005• Annual expenditures on SNP research:

– $158 million in 2001 – $1.2 billion in 2005 (estimated): 7 fold growth

• Increasing interest in pharmacogenomics-or tailoring treatment to patients based on their genomic profiles-by pharmaceutical, biotechnology, and genomic tools companies.

Factors influencing SNP research

0

20

40

60

80

100

2000 2001 2002 2003

Price / SNP(Cents)

Identified SNPs(x100.000)

Investigations(x10)

Average size ofinvestigation(x20)

Example

• DeCode typed 10.000 markers in all Icelanders (250.000)

Needs of the market

• Detailed understanding of population biology

• Extract signals from noisy data (power)• Efficient algorithms that provide quick and precise answers

Comparative Genomics

• Gene finding (Correct annotation is crucial)• Identifying important residues in drug

targets (HIV, proteins etc.)• Identifying regulatory sequences, networks

Future

Disease gene finding:

GeneRecon

Databasesolutions Comparative Genomics

Haplotypes

Experimental methods of determining Haplotypes:

•Egg & Sperm Sequencing

•Cell Lines with Lost Chromosomes

•Sequencing Clones Spanning SNPs

These methods are very expensive so computational reconstruction of haplotypes from SNPs is preferable.

Haplotypes:

SNPs:

A

T

G

C

C

A

{A,T} {C,G} {A,C}

2m-1

ParametersBayesian Analysis, i.e. all parameters have assigned distributions.

Markov Chain Monte Carlo allows the calculation of posterior (post-data) calculation of parameters and quantities of interest.

The Shattered Coalescent(Morris, Whittaker & Balding,2002)

Advantages: Allows for multiple origins of the disease mutant + sporadic occurances of the disease without the mutation (phenocopies)