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Bioinformatics and Molecular Evolution Paul G. Higgs and Teresa K. Attwood ST 1 st Molecular evolution Jeong, Da Geum

Bioinformatics and Molecular Evolution Paul G. Higgs and Teresa K. Attwood

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UST 1 st Molecular evolution. Bioinformatics and Molecular Evolution Paul G. Higgs and Teresa K. Attwood. Jeong, Da Geum. Chapter 3 Molecular evolution and population genetics. Chapter Preview - mutation - natural selection - random drift - Coalescence theory - PowerPoint PPT Presentation

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Page 1: Bioinformatics and Molecular Evolution Paul G. Higgs and Teresa K. Attwood

Bioinformatics and Molecular EvolutionPaul G. Higgs and Teresa K. Attwood

UST 1st Molecular evolution

Jeong, Da Geum

Page 2: Bioinformatics and Molecular Evolution Paul G. Higgs and Teresa K. Attwood

Chapter 3Molecular evolution and population genetics

Chapter Preview

- mutation- natural selection- random drift - Coalescence theory- Fixation of new mutations in a population- Fixation probability- Adaptationist and Neutralist

Page 3: Bioinformatics and Molecular Evolution Paul G. Higgs and Teresa K. Attwood

3.1 What is Evolution?

Chapter 3Molecular evolution and population genetics

Life is a self-sustained chemical system capable of undergoing Darwinian evolution

Evolution- “Survival of the fittest”- “Change in frequency of genes in a population”- “Heritable changes in a population over many generations”

The Essential factors that define evolutioni) error-prone self-replicationii) variation in success at self-replication

Evolution Biology Life

Page 4: Bioinformatics and Molecular Evolution Paul G. Higgs and Teresa K. Attwood

Point i) error-prone self-replication

“Self-replication” – The ability to make copies of itselfDawkins(1976) – “replicator” – a thing that can self-replicate.fundmental replicator-> gene rather than organisms

“Error-prone” – Copies are not always identical to the originals.Error is essential for evolution.If there are too many errors, there will be no heredity.

Molecular evolution and population genetics3.1 What is Evolution?

Page 5: Bioinformatics and Molecular Evolution Paul G. Higgs and Teresa K. Attwood

Point ii) variation in success at self-replicationLimited size of populationPossibility of some aspects occurring

-Natural selection:Variants with higher fitness will increase in number relative to those with lower fitness

-Random drift (chance fluctuation):Change in gene frequency owing to chance effects

in a finite sized population rather than to natural selection

-Neutral evolution: a mutation whose fitness is equal or close to the fitness of the original sequence that the fate of the mutation is determined by random drift

How do gene sequences evolve?

Molecular evolution and population genetics3.1 What is Evolution?

Page 6: Bioinformatics and Molecular Evolution Paul G. Higgs and Teresa K. Attwood

Molecular evolution and population genetics3.2 MUTATIONS

Introduce some genetic terminology

Locus: A particular position on a chromosomea gene, a molecular marker

Alleles: Alternative sequence variants that occur at the same locus

polymorphism: having multiple alleles present in the population at a significant frequency ( usually <99%)

Haploid: Have a single copy of each locus( most prokaryotic organisms)Diploid: Have two copies of each locus( most eukaryotic organisms)

Page 7: Bioinformatics and Molecular Evolution Paul G. Higgs and Teresa K. Attwood

Molecular evolution and population genetics3.2 MUTATIONS

Introduce some genetic terminology

Homozygous : individual same allele at a locusHeterozygous: individual with different alleles

reference:www.geneticsandhealth.com/wp-content/allele.jpg

Mutation: Any change in a gene sequence that can be passed on to offspring (Result of some damages: ex. radiation)

DNABASE: A,G : purines C,T/U : pyrimidines

Transition: purine -> purine (A<->G) pyrimidine -> pyrimidine(C<->T)

Transversion: purine <-> pyrimidine ( A,G <-> C, T)

Page 8: Bioinformatics and Molecular Evolution Paul G. Higgs and Teresa K. Attwood

Molecular evolution and population genetics3.2 MUTATIONS

Introduce some genetic terminology

Point mutation : A type of mutation that causes the replacement of a single base nucleotide with another nucleotide.

synonymous: a substitution at nucleic level that does not lead to change of the amino acid sequence of the proteinex) UUC(F) -> UUU(F)nonsynonymous: one that does change the amino acid

missense mutation: code for a different amino acid, vs silent mutationex)AUA(I) -> AUG(M)nonsense mutation: code for a stop, which can truncate the proteinex) TGG( trp) -> TAG ( stop)

Page 9: Bioinformatics and Molecular Evolution Paul G. Higgs and Teresa K. Attwood

Molecular evolution and population genetics3.2 MUTATIONS

Introduce some genetic terminology

Indels: insertions and deletions

Microsatellites : part of ’junk’ DNA. Also known as short sequence repeats,e.g. GCGCGCGCGC

Frameshift: insertion of deletion can distrupt the grouping of the codons.

Inverted: section of DNA can be reversed in direction.Translocated: cut out from one part of a genome and inserted into another

Page 10: Bioinformatics and Molecular Evolution Paul G. Higgs and Teresa K. Attwood

Molecular evolution and population genetics3.3 SEQUENCE VARIATION WITHIN AND BETWEEN SPECIES

Patterns of sequence variation

gene BRCA1(408 mutations)1/3 mutations: single nucleotide substitutions. small deletionssplice-site mutations

Deleterious: a mutation that causes the fitness of a gene to be reduced with respect to the original sequence

Advantageous: a mutation that increase the fitness of the sequence

Cytogenetic Location: 17q21Molecular Location on chromosome 17: base pairs 38,449,839 to 38,530,993

Page 11: Bioinformatics and Molecular Evolution Paul G. Higgs and Teresa K. Attwood

fig. 3.1: Part of the alignment of the DNA sequences of the BRCA1 gene

fig. 3.2 Alignment of the BRCA1 protein sequence for the same region of the gene as Fig. 3.1

Synonymous change

conservative: between a.a of similar chemical properties tend to be more frequent

transition -> transition (easy)

transition -> transversion (difficult)

A group of species known as Afrotheria

Page 12: Bioinformatics and Molecular Evolution Paul G. Higgs and Teresa K. Attwood

Molecular evolution and population genetics3.4 GENEALOGICAL TREES AND COALESCENCE

3.4.1 Adam and Eve

Coalescence : the merging of lineages in backwards time – To trace the lines of descent of a gene back through the generationmitochondrial DNA: is inherited through the maternal line (all)-mitochondria eve, 200000 years ago,-non african sequence : 52000 +/- 27000-“out of Africa” hypothesis: all descended from an African population-plate 3.1Y chromosome DNA: is inherited through the paternal line (male)-“Y-chromosome Adam”, 59000 years ago Thompson et al.(2000)No reason why Adam and Eve should have existed in the same time and same placePatterns of migration of men and women over time may also have been different

Fig3.3 Illustration of the coalescence process. Each circle represents one gene copy. Bold lines show the lines of descent of genes in the current generation. Thin lines show lines of descent that do not lead to the current generation. Shaded circles show the inheritance of two different mutation

Page 13: Bioinformatics and Molecular Evolution Paul G. Higgs and Teresa K. Attwood

Molecular evolution and population genetics3.4 GENEALOGICAL TREES AND COALESCENCE

3.4.2 A model of the coalescence process _

P(T) = (1-1/N)T-1 * 1/N

P(T) = 1/N * –T/N

Page 14: Bioinformatics and Molecular Evolution Paul G. Higgs and Teresa K. Attwood

Molecular evolution and population genetics3.5 THE SPREAD OF NEW MUTATIONS

3.5.1 Fixation of neutral mutations

P fix = 1/NU fix = Nu * P fix = u

Page 15: Bioinformatics and Molecular Evolution Paul G. Higgs and Teresa K. Attwood

3.5.2 Simulation of random drift and fixation

P(n) = CnN an(1-a) N-n

Molecular evolution and population genetics3.5 THE SPREAD OF NEW MUTATIONS

Fig. 3.4 Simulation of the spread of neutral mutations through a population under the influence of random drift

The probability of fixation is 1/N

Page 16: Bioinformatics and Molecular Evolution Paul G. Higgs and Teresa K. Attwood

3.5.3 Introducing selection

Molecular evolution and population genetics3.5 THE SPREAD OF NEW MUTATIONS

Fig. 3.5 Simulation of the spread of advantageous mutations througha population (a) For selection coefficient s = 0.05both selection and random drift are important. (b) For s = 0.2 selection dominates random drift. The dashed lines show the predictions of the deterministic theoryin Box 3.2

s = selection coefficient

Page 17: Bioinformatics and Molecular Evolution Paul G. Higgs and Teresa K. Attwood

Fig.3.6 Fixation probability in a population of N = 200 as a function ofselection coefficent s, for both advantageous and deleterious mutations.When Ns<<1, both types of mutation behave as nearly neutral mutations.

Extremely advantageouss >>1 pfix ≒ 1slightly advantageous mutations<<1, Ns>>1Nearly neutral mutationNs<<1

Molecular evolution and population genetics3.6 NEUTRAL EVOLUTION AND ADAPTATION

Page 18: Bioinformatics and Molecular Evolution Paul G. Higgs and Teresa K. Attwood

Molecular evolution and population genetics3.6 NEUTRAL EVOLUTION AND ADAPTATION

Natural selection – Positive selection (due to select new variants)-Adaptations at the sequence level

Adaptationists – people who argue that positive selection is the major driving force in molecular evolution

Neutralists – neutral evolution is a major role

Page 19: Bioinformatics and Molecular Evolution Paul G. Higgs and Teresa K. Attwood

Molecular evolution and population genetics3.6 NEUTRAL EVOLUTION AND ADAPTATION

NEW Technique for selection testAllozymes: Variant forms of an enzyme that are coded for by different alleles at the same locus -many protein loci are polymorphic-only detects a fraction of the sequence variation that is present

RFLPs (Restriction fragment length polymorphisms):Restriction enzymes will cut a long section of DNA into fragments that can be separated by electrophoresis-mitochondrial DNA-difference in fragment lengths: polymorphism in the DNA

Recent: DNA Sequencing

Page 20: Bioinformatics and Molecular Evolution Paul G. Higgs and Teresa K. Attwood

Adaptationists:Positive selection removes deleterious alleles, hence reduces the number of polymorphic loci

Selective scenarios for maintenance of polymorphisms1. advantageous and disadvantageous( maintenance of both alleles)2. Heterozygotes have a higher fitness than homozygotes

Molecular evolution and population genetics3.6 NEUTRAL EVOLUTION AND ADAPTATION

Page 21: Bioinformatics and Molecular Evolution Paul G. Higgs and Teresa K. Attwood

Neutralist:polymorphism is a constant process of - creation of new alleles by mutation - loss of old alleles due to random drift- measure of variability : heterozygosity

average heterozygosity: NuN: populatio sizeu: mutation rateNu>= 1: mean heterozygosity will be large and most loci will be polymorphicNu<<1mean heterozygosity will be lowfew polymorphic loci

Features of neutral theory-calculation using null hypothesis (selection or not)-large fluctuation in quantities

Molecular evolution and population genetics3.6 NEUTRAL EVOLUTION AND ADAPTATION

Page 22: Bioinformatics and Molecular Evolution Paul G. Higgs and Teresa K. Attwood

A selective sweep: the reduction or elimination of variationamong the nucleotides in neighbouring DNA of a mutation as the result of recent and strong natural selection.

stabilizing selection (purifying selection) : ①genetic diversity decreases as the population stabilizes on a particular trait value

Codon bias: Codon usagebecause of codon redundancy all but two amino acids are coded forby more than one triplet. Different organisms often show particular preferencesfor one of the several codons that encode the same given amino acid.

Molecular evolution and population genetics3.6 NEUTRAL EVOLUTION AND ADAPTATION

Page 23: Bioinformatics and Molecular Evolution Paul G. Higgs and Teresa K. Attwood

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