1. Gil McVean Department of Statistics
Bioinformatics
Gil McVean
Department of Statistics

2. What is it to be a human?

3. What is it to be an individual?
Species
Diversity (percent)
Humans
Chimpanzees
Drosophila simulans 2
E. coli 5
HIV1 30
Photos from UN photo gallery

4. Is it your genes?

5. Is it your transcripts?

6. Is it your proteins?

7. Is it your protein interactions?

8. Is it your systems?

9. Bioinformatics and genome biology
Bioinformatics is the analytical wing of genome biology
It concerns itself with large amounts of data (more than you can look at!)
It uses computers and efficient algorithms
It is
Data assembly
Data summary
Data modelling
Data analysis

10. The raw material

11. The output

12. Classical bioinformatics I: DNA and protein sequence alignment

13. Classical bioinformatics II: Genome assembly

14. Classical bioinformatics III: Gene finding

15. Classical bioinformatics IV: Protein structure prediction

16. Bioinformatics of genetic variation
An area of considerable current attention is human genetic variation
The aim of current experiments is to map the genetic basis of human phenotypic variation
Disease susceptibility
Normal variation
It is challenging because of
The scale of the data
The structure of the data
The underlying processes that shape variation
Bioinformatics is needed to
Assemble, collate, check and summarise data
Model the data
Make inferences

17. What does the data look like?
Single Nucleotide Polymorphisms (SNPs)
Insertion-Deletion Polymorphisms (INDELs)
TGCTTGGCAGGGCAGACTGACTGT
TGCTTGGCAGGGCAGACTGACTGT
TGCATGGCAGGGCAG-CTGACTGT
TGCATGGCAGGGCAG-CTGACTGT
TGCATGGCAGGGCAGACTGACTGT
TGCATGGCAGGGCAGACTGACTGT
SNP
INDEL

18. Collections of SNPs
HCB
JPT
YRI
CEU
SNP

19. Engineering challenges
Identifying SNPs
Working out which SNPs will work on a given platform
Controlling the genotyping work-flow
Controlling the output quality
Performing quality-assurance exercises
Identifying problems, gaps and inconsistencies

20. A Bioinformatics problem: How small is my P-value?
The basic idea of association studies is to look for genetic differences between groups
Cases (D)
It is easy to ask the question
“Is there a significant difference in the frequency of a mutation between groups?”
Controls (C)
Locus of interest

21. The problems
In a study of several hundred thousand mutations (or even millions) it is unlikely that we have actually typed the causal variant(s).
In a study of several hundred thousand mutations (or even millions), even if NONE of them are causal a lot of them will show significance at the 5%, 1% or even 0.01% level
Differences in the frequency of disease incidence between groups (for example African Americans and European Americans) will be associated with ANY genetic difference between them

22. What we really want to ask
“Does any of the genome show an association with disease over and above any effect I might expect from the correlation between genotype and environmental risk?”
“If so, what is the most likely position for the causal mutation(s)?”
Answering these questions is difficult, but a natural way to approach the problem is to model the process

23. Modelling genetic variation
Evolutionary parameters
Population
Sample
Stochastic
Evolutionary
process
Stochastic
Sampling
process
Selection
Mutation
Genetic drift
Recombination
Migration
ATGCATGGGCTATTGGACCT
ATGGATGGGCTATTGCACCT
MODEL
ATGCATGGGCAATTGCACCT
ATGCATGGGCAATTGGACCT
ATGGATGGGCTATTGCACCT
Inference

24. Genes in populations
Present day

25. Ancestry of current population
Present day

26. Ancestry of sample
Present day

27. The coalescent: samples in populations
Most recent common ancestor (MRCA)
coalescence
Ancestral lineages
Present day
time

28. How does this help us to think about mapping disease?
Individuals are related to each other through their genealogical history
Two nearby points on the genome will have similar genealogical histories, a result of which is that mutations at these positions will also be correlated
Understanding how genealogical history changes along the genome (through recombination) and between populations (through historical demography) will allow us to
Construct more powerful tests for disease association
Localise disease-associated mutations

29. The bioinformatics module
Genomic technologies
Annotating genomes
Modelling gene evolution
Mapping disease genes
Measuring gene and protein expression
Predicting protein structure