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BI420 – Introduction to Bioinformatics. Variation structure. Gabor T. Marth. Department of Biology, Boston College [email protected] Human variation structure is heterogeneous. chromosomal averages. polymorphism density along chromosomes. marker density. “dense”. “sparse”. allele frequency.

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Presentation Transcript
human variation structure is heterogeneous
Human variation structure is heterogeneous

chromosomal averages

polymorphism density along chromosomes

heterogeneity at the level of distributions

marker density

“dense”

“sparse”

allele frequency

“common”

“rare”

Heterogeneity at the level of distributions
what explains nucleotide diversity
What explains nucleotide diversity?

G+C nucleotide content

CpG di-nucleotide content

recombination rate

3’ UTR 5.00 x 10-4

5’ UTR 4.95 x 10-4

Exon, overall 4.20 x 10-4

Exon, coding 3.77 x 10-4

synonymous 366 / 653

non-synonymous 287 / 653

functional constraints

Variance is so high that these quantities are poor predictors of nucleotide diversity in local regions hence random processes are likely to govern the basic shape of the genome variation landscape  (random) genetic drift

components of drift genealogy
Components of drift: Genealogy

randomly mating population, genealogy evolves in a non-deterministic fashion

present generation

components of drift mutation
Components of drift: Mutation

mutation randomly “drift”: die out, go to higher frequency or get fixed

modulators changing population size
Modulators: Changing population size

mutation randomly “drift”: die out, go to higher frequency or get fixed

genetic bottleneck

modulators population subdivision
Modulators: Population subdivision

subdivision promotes private polymorphisms, and skews allele frequency

subdivision

modulators recombination
Modulators: Recombination

acagttatgcaga

acagttatgtaga

accgttatgcaga

accgttatgtaga

accgttatgcaga

acagttatgtaga

recombination

different nucleotide sites within the same DNA segment no longer share the same genealogy

modulators natural selection
Modulators: Natural selection

negative (purifying) selection

positive selection

the genealogy is no longer independent of (and hence cannot be decoupled from) the mutation process

modeling ancestral processes
Modeling ancestral processes

“forward simulations”

the “Coalescent” process

By focusing on a small sample, complexity of the relevant part of the ancestral process is greatly reduced. There are, however, limitations.

inferences from variation data
Inferences from variation data

larger mutation rate (μ) -> more mutations -> higher diversity (θ)

larger population size (N) -> more mutations -> higher diversity (θ)

higher diversity -> larger population size OR higher mutation rate

(θ = 4Nμ)

ancestral inference modeling
Ancestral inference: modeling

bottleneck

stationary

collapse

expansion

past

history

present

MD

(simulation)

AFS

(direct form)

ancestral inference model fitting
Ancestral inference: model fitting

modest but uninterrupted expansion

bottleneck

allelic association
Allelic association

acagttatgcaga

accgttatgcaga

acagttatgtaga

higher recombination rate (r)

accgttatgtaga

possible allele combinations (2-marker haplotypes)

allelic association ld
Allelic association: LD

measure of allelic association: “linkage disequilibrium (LD)”

haplotype structure
Haplotype structure

“haplotype block”

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