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Whiteboard http://cs273a.stanford.edu [Bejerano Winter 2020/21] - PPT Presentation

1 httpcs273astanfordedu Bejerano Winter 20 2021 2 Mon Wed 1130 AM 1250 on Zoom Prof Gill Bejerano CA Boyoung Bo Yoo Track class on Piazza CS273A Gill Lecture 9 Molecular Evolution Population Genetics ID: 908547

stanford cs273a winter bejerano cs273a stanford bejerano winter http 2020 genome evolution selection human population dna mutation genomics types

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Slide1

Whiteboard

http://cs273a.stanford.edu [Bejerano Winter 2020/21]

Slide2

http://cs273a.stanford.edu [Bejerano Winter 20

20/21

]

Mon, Wed 11:30 AM -

12:50, on Zoom*Prof: Gill BejeranoCA: Boyoung (Bo) Yoo* Track class on Piazza

CS273A

Gill Lecture 9: Molecular Evolution, Population Genetics

The

Human

Genome

Source

Code

Slide3

http://cs273a.stanford.edu [Bejerano Winter 2020/21]

Announcements

Remember thy honor code

Slide4

http://cs273a.stanford.edu [Bejerano Winter 2020/21]

Class Topics

(0) Genome context:

cells, DNA, central dogma

(1) Genome content / genome function:genes, gene regulation, epigenetics, repeats, SARS-CoV-2(2) Genome sequencing: technologies, assembly/analysis, technology dependence (3) Genome evolution: evolution = mutation + selection, main forces of evolution:Neutral evolution, Negative selection, Positive selection(4) Population genomics:Human migration, paternity testing, forensics, cryptogenomics(5) Genomics of human disease:personal genomics, GxE disease types, deep dive monogenics(6) Comparative Genomics :Genomics of amazing animal adaptations, ultraconservationgood morning!

Slide5

TTATATTGAATTTTCAAAAATTCTTACTTTTTTTTTGGATGGACGCAAAGAAGTTTAATAATCATATTACATGGCATTACCACCATATACATATCCATATCTAATCTTACTTATATGTTGTGGAAATGTAAAGAGCCCCATTATCTTAGCCTAAAAAAACCTTCTCTTTGGAACTTTCAGTAATACGCTTAACTGCTCATTGCTATATTGAAGTACGGATTAGAAGCCGCCGAGCGGGCGACAGCCCTCCGACGGAAGACTCTCCTCCGTGCGTCCTCGTCTTCACCGGTCGCGTTCCTGAAACGCAGATGTGCCTCGCGCCGCACTGCTCCGAACAATAAAGATTCTACAATACTAGCTTTTATGGTTATGAAGAGGAAAAATTGGCAGTAACCTGGCCCCACAAACCTTCAAATTAACGAATCAAATTAACAACCATAGGATGATAATGCGATTAGTTTTTTAGCCTTATTTCTGGGGTAATTAATCAGCGAAGCGATGATTTTTGATCTATTAACAGATATATAAATGGAAAAGCTGCATAACCACTTTAACTAATACTTTCAACATTTTCAGTTTGTATTACTTCTTATTCAAATGTCATAAAAGTATCAACAAAAAATTGTTAATATACCTCTATACTTTAACGTCAAGGAGAAAAAACTATAATGACTAAATCTCATTCAGAAGAAGTGATTGTACCTGAGTTCAATTCTAGCGCAAAGGAATTACCAAGACCATTGGCCGAAAAGTGCCCGAGCATAATTAAGAAATTTATAAGCGCTTATGATGCTAAACCGGATTTTGTTGCTAGATCGCCTGGTAGAGTCAATCTAATTGGTGAACATATTGATTATTGTGACTTCTCGGTTTTACCTTTAGCTATTGATTTTGATATGCTTTGCGCCGTCAAAGTTTTGAACGATGAGATTTCAAGTCTTAAAGCTATATCAGAGGGCTAAGCATGTGTATTCTGAATCTTTAAGAGTCTTGAAGGCTGTGAAATTAATGACTACAGCGAGCTTTACTGCCGACGAAGACTTTTTCAAGCAATTTGGTGCCTTGATGAACGAGTCTCAAGCTTCTTGCGATAAACTTTACGAATGTTCTTGTCCAGAGATTGACAAAATTTGTTCCATTGCTTTGTCAAATGGATCATATGGTTCCCGTTTGACCGGAGCTGGCTGGGGTGGTTGTACTGTTCACTTGGTTCCAGGGGGCCCAAATGGCAACATAGAAAAGGTAAAAGAAGCCCTTGCCAATGAGTTCTACAAGGTCAAGTACCCTAAGATCACTGATGCTGAGCTAGAAAATGCTATCATCGTCTCTAAACCAGCATTGGGCAGCTGTCTATATGAATTAGTCAAGTATACTTCTTTTTTTTACTTTGTTCAGAACAACTTCTCATTTTTTTCTACTCATAACTTTAGCATCACAAAATACGCAATAATAACGAGTAGTAACACTTTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAATGTTTTCAATGTAAGAGATTTCGATTATCCACAAACTTTAAAACACAGGGACAAAATTCTTGATATGCTTTCAACCGCTGCGTTTTGGATACCTATTCTTGACATGATATGACTACCATTTTGTTATTGTACGTGGGGCAGTTGACGTCTTATCATATGTCAAAGTTGCGAAGTTCTTGGCAAGTTGCCAACTGACGAGATGCAGTAACACTTTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAATGTTTTCAATGTAAGAGATTTCGATTATCCACAAACTTTAAAACACAGGGACAAAATTCTTGATATGCTTTCAACCGCTGCGTTTTGGATACCTATTCTTGACATGATATGACTACCATTTTGTTATTGTACGTGGGGCAGTTGACGTCTTATCATATGTCAAAGTCATTTGCGAAGTTCTTGGCAAGTTGCCAACTGACGAGATGCAGTTTCCTACGCATAATAAGAATAGGAGGGAATATCAAGCCAGACAATCTATCATTACATTTAAGCGGCTCTTCAAAAAGATTGAACTCTCGCCAACTTATGGAATCTTCCAATGAGACCTTTGCGCCAAATAATGTGGATTTGGAAAAAGAGTATAAGTCATCTCAGAGTAATATAACTACCGAAGTTTATGAGGCATCGAGCTTTGAAGAAAAAGTAAGCTCAGAAAAACCTCAATACAGCTCATTCTGGAAGAAAATCTATTATGAATATGTGGTCGTTGACAAATCAATCTTGGGTGTTTCTATTCTGGATTCATTTATGTACAACCAGGACTTGAAGCCCGTCGAAAAAGAAAGGCGGGTTTGGTCCTGGTACAATTATTGTTACTTCTGGCTTGCTGAATGTTTCAATATCAACACTTGGCAAATTGCAGCTACAGGTCTACAACTGGGTCTAAATTGGTGGCAGTGTTGGATAACAATTTGGATTGGGTACGGTTTCGTTGGTGCTTTTGTTGTTTTGGCCTCTAGAGTTGGATCTGCTTATCATTTGTCATTCCCTATATCATCTAGAGCATCATTCGGTATTTTCTTCTCTTTATGGCCCGTTATTAACAGAGTCGTCATGGCCATCGTTTGGTATAGTGTCCAAGCTTATATTGCGGCAACTCCCGTATCATTAATGCTGAAATCTATCTTTGGAAAAGATTTACAATGATTGTACGTGGGGCAGTTGACGTCTTATCATATGTCAAAGTCATTTGCGAAGTTCTTGGCAAGTTGCCAACTGACGAGATGCAGTAACACTTTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAATGTTTTCAATGTAAGAGATTTCGATTATCCACAAACTTTAAAACACAGGGACAAAATTCTTGATATGCTTTCAACCGCTGCGTTTTGGATACCTATTCTTGACATGATATGACTACCATTTTGTTATTGTTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAATGTTTTCAATGTAAGAGATTTCGATTATCCTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAATGTTTTCAATGTAAGAGATTTCGATTATCCTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAATGTTTTCAATGTAAGAGATTTCGATTATCCTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAATGTTTTCAATGTAAGAGATTTCGATTATCCTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAATGTTTTCAATGTAAGAGATTTCGATTATCCTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAATGTTTTCAATGTAAGAGATTTCGATTATCCTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAATGTTTTCAATGTAAGAGATTTCGATTATCCTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAATGTTTTCAATGTAAGAGATTTCGATTATCTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAATAAAG

Genome Evolution

http://cs273a.stanford.edu [Bejerano Winter 2020/21]

Slide6

Evolution

Vast & fascinating topic We’ll get a meaningful tasteInevitable in a class about the human genome, because “Nothing in Biology Makes Sense Except in the Light of Evolution

”

Theodosius DobzhanskyOne definition: “Changes in the proportions of biological types in a population over time” Stanford Encyclopedia of PhilosophyWe will mostly discussMutation, selection, neutral, negative and positive selectionWe’ll lightly mentionMigration, non-random mating, linkageWe’ll visit and revisit evolution for the remainder.http://cs273a.stanford.edu [Bejerano Winter 2020/21]

Slide7

http://cs273a.stanford.edu [Bejerano Winter 2020/21]

Mutation

Selection

 EvolutionMistakes can happen during DNA replication. Mistakes are oblivious to DNA segment function. But then selection kicks in.

...ACGTACGACTGACTAGCATCGACTACGA...

chicken

egg

...ACGTACGACTGACTAGCATCGACTACGA...

functional

junk

CAT

“anything

goes”

many changes

are

not

tolerated

chicken

This has bad implications – disease,

and good implications – adaptation.

Slide8

My Genome is like

A programming language?Not quite…An Operating System?Not quite…The disk of an operating system developed in place (for 3 billion years…)Quite.Developed HOW?!..

http://cs273a.stanford.edu [Bejerano Winter 2020/21]

Slide9

http://cs273a.stanford.edu [Bejerano Winter 2020/21]

Mutation

Slide10

Chromosomal (

ie big) MutationsFive types exist:DeletionInversionDuplicationTranslocation

(Nondisjunction)

Fusion/Fission

Slide11

Deletion

Due to breakageA piece of a chromosome is lost

Slide12

Inversion

Chromosome segment breaks offSegment flips around backwardsSegment

reattaches

This reverses and complements the sequence.

Slide13

Duplication

Occurs when a genomic region is repeated

Slide14

Translocation

Involves two chromosomes that aren’t homologousPart of one chromosome is transferred to another chromosomes

Slide15

Nondisjunction

Failure of chromosomes to separate during meiosisCauses gamete to have too many

too few chromosomesDisorders:Down Syndrome – three 21st chromosomesTurner Syndrome – single X chromosomeKlinefelter’s Syndrome – XXY chromosomes

Slide16

Whole Chromosome Fusion/Fission

http://cs273a.stanford.edu [Bejerano Winter 2020/21]

Human chromosome 2

Slide17

Genomic (

ie small) Mutations

Six types exist:

Substitution (

eg GT)DeletionInsertionInversion

DuplicationTranslocation

Slide18

Mutation

Mutation is random but not uniformly randomFor example, it depends a lot on local sequence contentSome examples we’ve met:

Mutation is however, oblivious to sequence function

That’s where selection kicks in…http://cs273a.stanford.edu [Bejerano Winter 2020/21]

18simple repeatsinterspersed repeatsrepeatmediateddeletion / inversion

Slide19

http://cs273a.stanford.edu [Bejerano Winter 2020/21]

Selection

Slide20

http://cs273a.stanford.edu [Bejerano Winter 2020/21]

Mutation + Selection

 Evolution

Mistakes can happen during DNA replication. Mistakes are oblivious to DNA segment function. But then selection kicks in.

...ACGTACGACTGACTAGCATCGACTACGA...

chicken

egg

...ACGTACGACTGACTAGCATCGACTACGA...

functional

junk

CAT

“anything

goes”

many changes

are

not

tolerated

chicken

This has bad implications – disease,

and good implications – adaptation.

Slide21

Example

Imagine a single UCA codon in a single exon coding gene.

Assume these three bases’

only role is to code for Ser.http://cs273a.stanford.edu [Bejerano Winter 2020/21]

21SerUC

Human genome

Slide22

If the 3rd position is under neutral

evolution

http://cs273a.stanford.edu [Bejerano Winter 2020/21]

SerUCC C

C A A A A A T A3rd position composition in population in generationtt+1t+2t+3…G

Slide23

If the 2nd position is under negative selection

http://cs273a.stanford.edu [Bejerano Winter 2020/21]

Ser

UC2nd position composition in population in generation

tt+1t+2t+3…A A A C C C C C T CG

Slide24

If the 1st position experiences positive selection

http://cs273a.stanford.edu [Bejerano Winter 2020/21]

Ser

UC1st position composition in population in generation

tt+1t+2t+3…C C C T T T T T A TG

Slide25

http://cs273a.stanford.edu [Bejerano Winter 2020/21]

Random Drift

Random drift acknowledges the fact that population size is in fact finite, and thus transmission becomes probabilistic.

For example under neutral evolution, imagine a population of size 10, 5 A and 5 T. Even though both have 50% chance of contributing each allele in the next generation, with probability 1/1024 all 10 alleles may be A next generation.

Similarly, random drift of finite size populations may “derail” negative & positive selection.

Slide26

http://cs273a.stanford.edu [Bejerano Winter 2020/21]

Biological Types

We cautiously defined evolution

as “Changes in the proportions of biological types in a population over time

”We talked about the evolution of a single basepair at a time.But the same hold for other biological types: gene, enhancer, pathway, individuals.(Aside: Also note that many other forms of evolution exist).

Slide27

http://cs273a.stanford.edu [Bejerano Winter 2020/21]

Genomic Transmission

For repeat copies to accumulate through human generations they must make it into the

germline

cells (eggs & sperms).Equally true for any genomic mutation.cell

genome =

all DNA

chicken ≈ 10

copies

(DNA) of egg (DNA)

chicken

egg

cell

division

DNA strings =

Chromosomes

Slide28

Human Mutation Rate

Recent sequencing analysis suggests ~40-60 new mutations in a child that were not present in either parent.~1 mutation per genome replicationMutations range from the smallest possible (single base pair change) to the largest – whole genome duplication (to be discussed).

Selection does not tolerate all of these mutation, but it sure does tolerate

many.http://cs273a.stanford.edu [Bejerano Winter 2020/21]28

chicken

egg

chicken

Slide29

http://cs273a.stanford.edu [Bejerano Winter 2020/21]

Class Topics

(0) Genome context:

cells, DNA, central dogma

Slide30

Population Genetics

http://cs273a.stanford.edu [Bejerano Winter 2020/21]

Slide31

We’re diploids

http://cs273a.stanford.edu [Bejerano Winter 2020/21]

Slide32

STRs identify individuals, paternity

http://cs273a.stanford.edu [Bejerano Winter 2020/21]

Every human is reduced

to a unique profile.Current U.S. forensics use a fixed 20 STR profile.A unique human being =20 number pairs.“up to monozygotic twins”

ShortTandemRepeats

Slide33

Most mutations are far from deleterious

We just said ~1 mutation per whole genome replication.Corollary: No two cells are identical.Corollary: Identical twins are not really identical.

http://cs273a.stanford.edu [Bejerano Winter

2020/21]33

Slide34

Identical twins are not really identical

Our technology is getting good enough to see this:http://cs273a.stanford.edu [Bejerano Winter

20/21]34

Slide35

Meiotic Crossover complicate things

http://cs273a.stanford.edu [Bejerano Winter 2020/21]

In humans:

1 crossover in ~100Mb

Slide36

.. but only quantitatively

http://cs273a.stanford.edu [Bejerano Winter 2020/21]36

Slide37

Identity by descent (IBD) stretches identify relatives

http://cs273a.stanford.edu [Bejerano Winter 2020/21]37

Slide38

http://cs273a.stanford.edu [Bejerano Winter 2020/21]

Slide39

Let’s go population-wide

Let’s keep >0 mutations per whole genome replication.But let’s forget sex, diploidity, and crossover.We can draw similar qualitative conclusions without themEvery human has a big genomeEvery offspring is almost identical to their single parent, except for a handful of mutations

http://cs273a.stanford.edu [Bejerano Winter

2020/21]

(not to scale: the genome is 3*10

bp, changes as small as a 1bp SNP!)

SNP =

Single

Nucleotide

Polymorphism

Slide40

Inferring population migration patterns

Now imagine people lived in one continent for a long time, then recently a handful of people left continent A, and successively populated continents B,C and D.What would the current genome pool in A,B,C,D look like?Lots more genomic variation in continent A than B,C,DBottleneck effect

Continent D shares private variation with continent C,

not seen in continents A or BEtc.That’s the essence of populationmigration pattern reconstructionfrom genomic datahttp://cs273a.stanford.edu [Bejerano Winter 2020/21]

40ABCD

Slide41

Population Sequencing –

1000 Genomes Project

Slide42

Why humans are so similar

Out of Africa

Oppenheimer S Phil. Trans. R. Soc. B 2012;367:770-784

Slide43

Low dimensional embedding

http://cs273a.stanford.edu [Bejerano Winter

20/21]

43Imagine you measured common SNPs in many Europeans.Any two individuals are separated by some SNP differences.Imagine you wanted to represent each person as a dot, and embed all dots into a 2-D space, such that the 2D distance between any pair was proportional to their SNP distance. What would the embedding look like?

Slide44

Global Ancestry Inference

Nature. 2008 November 6; 456(7218): 98–101.

Turns out:

PCA embedding the distances between genomes (SNPs) of many Europeans onto a 2D plane reconstructs the map of Europe!Why?

Travel was hard, dangerous and unpopular for the longest time.Corollary: People married (procreated) with people who lived nearby.

Slide45

Genome painting

http://cs273a.stanford.edu [Bejerano Winter 20

20/21

]45

If human populations are so well defined, we can sample many and define sets of SNPs that separate different ancestries from each other.E.g., DK variants not seen elsewhere in Europe or beyond.

Slide46

Identity by descent (IBD) stretches identify relatives

http://cs273a.stanford.edu [Bejerano Winter 2020/21]

What if you did IBD only on population level SNPs?

Slide47

Ancestry Painting

Danish

French

Spanish

Mexican

Slide48

Ancient DNA

Find ancient remains (e.g. bone, teeth). Sequence them. Fear degradation, contamination, post-mortem mutations. Look for similarity blocks to extant sequenced individuals. Consider individuals’ locations and known migrations. Revise best estimate of migration and mating patterns.Repeat.

Theoretically can sequence 0.4-1.5 million year old DNA.

DNA degrades faster in warmer places (Africa vs Europe)Human samples mostly 10-40,000 yr old.http://cs273a.stanford.edu [Bejerano Winter 2020/21]48

Slide49

The Neanderthal

Slide50

From bones, compared genomes of three different Neanderthals with five genomes from modern humans from different areas of the world

The Neanderthal Genome

Figure 1- R. E. Green et al., Science 328, 710-722 (2010)

Slide51

Neanderthal Genome

Slide52

Neanderthal heritage

http://cs273a.stanford.edu [Bejerano Winter 2020/21]52A major risk locus for COVID coincides with genomic variants some humans have inherited from Neanderthals.

Slide53

Denisovan

– Another human relative

Slide54

Coalescent Theory

http://cs273a.stanford.edu [Bejerano Winter 2020/21]54

The coalescent is a probability model for the tree underlying a sample of homologous DNA sequences drawn from a within-species population

.The focus of interest can be the underlying genealogical tree, mutation or recombination rates, or demographic parameters such as historic population sizes or migration rates.

Slide55

Human population migrations

Out of Africa, ReplacementSingle mother of all humans (Eve) ~190,000yrSingle father of all humans (Adam) ~340,000yr

Humans out of Africa

~50000 years ago replaced others (e.g., Neandertals)Multiregional EvolutionGenerally debunked, however,~5% of human genome in Europeans, Asians is Neanderthal, Denisova

Recent most likely migration & mating pattern estimates. They continue to be revised.

Slide56

Discoveries Continue

http://cs273a.stanford.edu [Bejerano Winter 2020/21]56

Slide57

http://cs273a.stanford.edu [Bejerano Winter 2020/21]

Class Topics

(0) Genome context:

cells, DNA, central dogma