introducing the bayesian posterior probability of your exam grade! you did about this well

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troducing the Bayesian posterior probabili of your exam grade! you did you did about about this this well well

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Page 1: Introducing the Bayesian posterior probability of your exam grade! you did about this well

introducing the Bayesian posterior probabilityof your exam grade!

you did you did aboutabout this this

wellwell

Page 2: Introducing the Bayesian posterior probability of your exam grade! you did about this well

more on mice•Darwin’s finches outstanding example

•want to continue with this because we have looked at 2 mice studies so far that have inferred gene regions associated with color, and have assumed selection

•Darwin’s postulates: the trait is variable, the trait is heritable...do we find out that survival is variable, and associated with inheritance of trait?

• lets make sure it is what we think it is!

Page 3: Introducing the Bayesian posterior probability of your exam grade! you did about this well

science isn’t always complicated

Page 4: Introducing the Bayesian posterior probability of your exam grade! you did about this well

recurring themes• how can a population better utilize available

resource? (some digestive enzyme)

• how can individuals better camouflage themselves? (fur or skin coloration)

• how does an individual maximize probability of offspring? (sperm motility)

• you can start to generate hypotheses for what could be ‘useful’ and then we recognize why some patterns recur

Page 5: Introducing the Bayesian posterior probability of your exam grade! you did about this well
Page 6: Introducing the Bayesian posterior probability of your exam grade! you did about this well

phylogenetics and biogeography

•previous slide uses phylogeny of genes unrelated to coat color to assess probability that coat color changes happen independently

•biogeography: the study of how and why organisms are distributed the way they are

•phylogeny can help us see whether species distributions represent descent from common ancestral range or population

Page 7: Introducing the Bayesian posterior probability of your exam grade! you did about this well

sticklebacks, redux

development of development of pelvic spines in pelvic spines in

marine marine populations populations

concomitant concomitant reduction in reduction in armoring in armoring in freshwater freshwater

populations populations

Page 8: Introducing the Bayesian posterior probability of your exam grade! you did about this well

genes responsible•formation of lateral bony plates: QTL

mapping led to ecdysoplasin (Eda) gene - involved with development of adult integument and teeth in vertebrates

•EdaLOW homozygotes have few lateral plates vs. EdaHIGH homozygotes

•alleles different enough that they have probably both existed for

Page 9: Introducing the Bayesian posterior probability of your exam grade! you did about this well

are you a geneious?

• informatics software makes it easier than ever to explore what sequence data exist, what they look like, how they are related to one another

• free version can do a lot; has tutorial for basic exploration and bioinformatics

•on wiki you might choose to explore allelic variation, diversity of a population or species, etc. (do so systematically, with purpose...)

Page 10: Introducing the Bayesian posterior probability of your exam grade! you did about this well
Page 11: Introducing the Bayesian posterior probability of your exam grade! you did about this well
Page 12: Introducing the Bayesian posterior probability of your exam grade! you did about this well

intron variation betweentwo allele types

(some heterozygotes)

Page 13: Introducing the Bayesian posterior probability of your exam grade! you did about this well

high Eda alleleshigh Eda alleles

low Eda alleleslow Eda alleles

Page 14: Introducing the Bayesian posterior probability of your exam grade! you did about this well

“marine” types show up repeatedly in freshwater

invasions; selection returns population to

less-armored phenotype

Page 15: Introducing the Bayesian posterior probability of your exam grade! you did about this well

agriculture

•human activities and behaviors have led to domestication of animals for meat and milk, crops for efficient nutrition

•know what the “green revolution” is?

•agriculture is a dramatic force of selection

Page 16: Introducing the Bayesian posterior probability of your exam grade! you did about this well
Page 17: Introducing the Bayesian posterior probability of your exam grade! you did about this well

dairy consumption(lactose tolerance,

lactase persistence)

more frequent in groups with

cultural tendency to drink milk

Page 18: Introducing the Bayesian posterior probability of your exam grade! you did about this well

nature.com

Page 19: Introducing the Bayesian posterior probability of your exam grade! you did about this well

signature of selection

•molecular evolution: analysis of how DNA sequences evolve in response to selection (and demographic) forces on phenotypes (populations)

•allele frequencies and linkage disequilibrium provide clues to the history of a genome region

Page 20: Introducing the Bayesian posterior probability of your exam grade! you did about this well

• how many segregating sites?

• how many are unique?

• how many found in 2 individuals?

• how many in 3, 4?

• average # differences between sequences

dna: site frequencies and polymorphism

acctggctcgacgtctggctcaacacctagctcaatacctagcccaacacttggctcagcacttggcttaacacttggctcagcacttggctcagc

Page 21: Introducing the Bayesian posterior probability of your exam grade! you did about this well

calculate ∏ and why

• eventually gets us to estimating the population size, with assumptions...

• here, 3.03 is proportional to the number of individuals reproducing times the mutation rate (π=xNµ) thus (N=π/xµ)

acctggctcgacgtctggctcaacacctagctcaatacctagcccaacacttggctcagcacttggcttaacacttggctcagcacttggctcagc

Page 22: Introducing the Bayesian posterior probability of your exam grade! you did about this well

how does that work?

• briefly consider that there are N individuals in a population

• there is a rate µ at which a mutation arises at a locus

• if N individuals reproduce successfully, we can expect Nµ new mutations in next generation

• diversity is scaled by the number of copies of a locus in a reproductive event, e.g. there are 2 maternal and 2 paternal alleles that can have a mutation for a nuclear diploid locus... x=4, so that π = 4Nµ

Page 23: Introducing the Bayesian posterior probability of your exam grade! you did about this well

diversity at a locus

• however, we often find that our estimates of population size are far lower than the number we can count!! failure of theory?

• main cause of drift (coming after exam 1) is variance in reproductive success (spatial, temporal, and individual variance)

• the effective population size accounts for changes in population size, gender ratio, better nest sites, other causes for some individuals contributing disproportionately to next generation

Page 24: Introducing the Bayesian posterior probability of your exam grade! you did about this well

Effective population

• Several definitions, but Ne=N/(variance) works, where N is how many individuals

• If average individual has 1 offspring, most basic assumption is variance of 1 (normal distribution), so Ne=N

• If variance high, Ne (and diversity) goes down

Page 25: Introducing the Bayesian posterior probability of your exam grade! you did about this well

can also partition the data

• first codon position, second position, third position...

• in protein-coding regions, most variation is ‘silent’ or ‘synonymous’

• why?

Page 26: Introducing the Bayesian posterior probability of your exam grade! you did about this well

• π is math equivalent to heterozygosity

• patterns to be explored in this diversity

• more diversity in tropical species than polar?

• lower diversity following introduction?

proportion of

individuals

heterozygous at a locus

Page 27: Introducing the Bayesian posterior probability of your exam grade! you did about this well

Things in Athens, Georgia

Procambarus clarkii, Town Spring, π = 0.0 Notropis lutipinnis,

Oconee River, π = 2.1

Page 28: Introducing the Bayesian posterior probability of your exam grade! you did about this well

signature of selection

(time passes)

Page 29: Introducing the Bayesian posterior probability of your exam grade! you did about this well

we expect a classic signature

of “selective sweep” when

rare mutation quickly increases

in frequencywould require strong

selectionone 2004 study

estimated up to 19% more offspring surviving

to maturityBersaglieri, T. et al. Am. J. Hum. Genet. 74, 1111–1120 (2004).

width = length of homologous linked segments around allele

Page 30: Introducing the Bayesian posterior probability of your exam grade! you did about this well
Page 31: Introducing the Bayesian posterior probability of your exam grade! you did about this well

Nucleotide variation of studied regions on maize chromosome 10

Tian F et al. PNAS 2009;106:9979-9986

©2009 by National Academy of Sciences

Page 32: Introducing the Bayesian posterior probability of your exam grade! you did about this well

blueridgeimpressions.wordpress.com

Page 33: Introducing the Bayesian posterior probability of your exam grade! you did about this well
Page 34: Introducing the Bayesian posterior probability of your exam grade! you did about this well

resistance - ability to prevent damage

(herbivore, herbicide, toxin, pathogen)

tolerance - measure of fitness following damage

you should be expecting this answer by now!

Page 35: Introducing the Bayesian posterior probability of your exam grade! you did about this well

pathways to resistance

• glyphosate attacks a highly conserved enzyme, EPSPS, involved in amino acid construction

• Monsanto developed transgenic crops that were glyphosate-resistant; spray crops and only weeds die?

• 1. some species have single amino acid difference in EPSPS that render glyphosate useless

• 2. others have duplicated the EPSPS region and thus produce more of this enzyme than glyphosate can block!

Page 36: Introducing the Bayesian posterior probability of your exam grade! you did about this well

interesting interpretation

• Bt is a toxin made by Bacillus thuringiensis; makes insects that eat crops sick

• spraying Bt on crops, or using transgenic crops that produce Bt, reduces insect damage

• but can insects evolve resistance? we would predict so

• text (p244) notes that maintaining non-Bt refuges limits evolution of resistance because resistance is costly (where there is no toxin, resistant insects have lower fitness than non-resistant insects)

• seems to be working; now it is law

• why wouldn’t (or would) same approach work for antibiotic resistance?

Page 37: Introducing the Bayesian posterior probability of your exam grade! you did about this well

contemporary problem

atlantic cod - Gadus morhuawe’re fishing the last 0.01% of this population...

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Page 38: Introducing the Bayesian posterior probability of your exam grade! you did about this well

• fisheries are large uncontrolled experiments in evolution

• rules put in place for what can be caught, where, what size, what species

• rules can change, may be ignored

• average 4-year-old cod is 10-15cm shorter now than 30 years ago: net size selected against fast growth

• heritability for rate of growth ~0.6

• if reproductive capacity proportional to body size, population is now smaller with a smaller potential rate of increase

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Page 39: Introducing the Bayesian posterior probability of your exam grade! you did about this well

bummer, man

Page 40: Introducing the Bayesian posterior probability of your exam grade! you did about this well

fewer fish...

smaller, younger fish...

Hutchings & Baum 2005

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Page 42: Introducing the Bayesian posterior probability of your exam grade! you did about this well

more on drift

Page 43: Introducing the Bayesian posterior probability of your exam grade! you did about this well
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Page 46: Introducing the Bayesian posterior probability of your exam grade! you did about this well
Page 47: Introducing the Bayesian posterior probability of your exam grade! you did about this well

what will happen?

• serial colonization and founders effects

• new habitats?

• what do you predict?

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Page 48: Introducing the Bayesian posterior probability of your exam grade! you did about this well
Page 49: Introducing the Bayesian posterior probability of your exam grade! you did about this well

change in allele frequency ∆p tells usabout the effective population size ina different way from diversity (π)

the larger the population that is beingsampled from, the smaller the changefrom one generation to next!

Page 50: Introducing the Bayesian posterior probability of your exam grade! you did about this well
Page 51: Introducing the Bayesian posterior probability of your exam grade! you did about this well

fixation

• if allele frequency goes to 0 or 1, there is no longer any variation, polymorphism, segregation.... it STAYS there

• until mutation causes new polymorphism

• probability that any given allele goes to fixation (frequency 1.0) is equal to its frequency–if allele A is at frequency p=0.7, then there is 70%

chance that allele will fix53

Page 52: Introducing the Bayesian posterior probability of your exam grade! you did about this well

Starts at high HETEROZYGOSITY

Ends with low HETEROZYGOSITY

And 50/50 fixation of types

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16

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9!

Page 55: Introducing the Bayesian posterior probability of your exam grade! you did about this well

effective population size

• Ne (effective population size) is lower than N (actual number of individuals) because of variance in reproductive success

• imbalance in males/females means not all of the more common gender get to reproduce

• “bottleneck” - a temporary reduction in population size - means that all the diversity descends from a smaller number of original individuals

• different habitat quality, etc.

Page 56: Introducing the Bayesian posterior probability of your exam grade! you did about this well

diversity is transient

• without mutation, in a finite population heterozygosity will decline every generation–Hg+1 = Hg[1-(1/2N)]

–decline is faster with small N

• selection can speed this loss (directional selection) or slow it down (balancing, frequency-dependent)

• migration, mating patterns also affect rate58

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Page 58: Introducing the Bayesian posterior probability of your exam grade! you did about this well
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