Population Genomics Module 3

class: center, middle, inverse, title-slide

.title[
# Population Genomics Module 3
]
.author[
### Jinliang Yang
]
.date[
### Oct. 19, 2023
]

---

# Syllabus

### Module 1: Introduction and popgen terminology
- Introduction of population genomics
- Basic principles of evolutionary processes

### Module 2: Diversity measurement
- Heterozygosity and diversity
- Population differentiation ( `\(F_{ST}\)` )

### __Module 3: Scan for direct and linked selection__
- __Direct selection__
  - The effects of selection on these mutation themselves
- __Linked selection__
  - SNPs themselves have no effect on fitness ( `\(s=0\)` ) but are affected by selection occurring nearby.

---

# Neutral theory of molecular evolution

The neutral theory asserts that the great majority of evolutionary changes at the molecular level are caused

- NOT by _Darwinian selection_

- But by _random drift of selectively neutral or nearly neutral mutants_

> Motoo Kimura (木村 資生), 1983 
> - Iowa State with Jay Lush and then University of Wisconsin with James Crow

#### __Core ideas of neutral theory__:

- #### Most mutations are not advantageous
  - Selectively (or effectively) neutral if `\(s < 1/(2N_e)\)`
- #### Most changes that are fixed over time are selectively neutral (fixed by drift)
   - Drift rather than selection predominates
   
---

# Neutral Theory

### What the neutral theory does not claim

- __Does NOT claim__ natural selection is unimportant in evolution

- In fact, most morphological adaptations are the result of natural selection

- It __does NOT deny__ that most mutations are (slightly) deleterious (it claims most of the variation _we see_ is neutral)

- Most of the deleterious mutations have been eliminated
  
  - Rare mutations have been fixed

### Selection counteracts drift

- `\(s > 1/(2N_e)\)`

`\begin{align*}
Pr(fix) = \frac{1 - e^{-2s}}{1-e^{-4N_es}}
\end{align*}`

---

```r
set.seed(12347)
Ne=20; A1=1; t=4*Ne
frq <- wright_fisher(N=Ne, A1=A1, t=t)
plot(frq, type="l", ylim=c(0,1), col=3, xlab="Generations", ylab="Freq")
    for(u in 1:100){
      frq <- wright_fisher(N=Ne, A1=A1, t=t)
      random <- sample(1:1000,1,replace=F)
      randomcolor <- colors()[random] 
      lines(frq, type="l", lwd=3, col=(randomcolor))
    }
```

---

# Expected allele frequencies distribution

On timescales shorter than those required for mutations to fix, selection will change the mean frequency of alleles in a population.

For new mutations, the density of polymorphisms found at frequency `\(q\)`, is

`\begin{align*}
f(q) & = \frac{2 \mu}{q(1-q)} \frac{1 - e^{(-4N_es)(1-q)}}{1 - e^{(-4N_e s)}} \\
\end{align*}`

> Wright, 1969

- Where `\(\mu\)` is the mutation rate.
- `\(s\)` is the fitness effect. 
  - Advantageous mutations have `\(s > 0\)` and deleterious mutations have `\(s <0\)`

---

# Types of selection

To find loci that are under selection we test for departures from the neutral theory

### Purifying selection: `\(N_e \times s < -1\)`
  - Deleterious mutations are eliminated

### Positive selection:   `\(N_e \times s > 1\)`
  - Opposite of purifying
  - Favorable mutations are selected

### Effectively netural: `\(-1 < N_e \times s < 1\)`

---

# The expected site frequency spectra

`\begin{align*}
f(q) & = \frac{2 \mu}{q(1-q)} \frac{1 - e^{(-4N_es)(1-q)}}{1 - e^{(-4N_e s)}} \\
\end{align*}`

```r
# expected freq spectra
f <- function(q, ns){
  frq = 2/(q*(1-q)) * (1 - exp(-4*ns*(1-q))) / (1 - exp(-4*ns))
  return(frq)}
q <- seq(from = 0.01, to =0.99, by=0.01)

## Ploting function
plot(q, f(q, ns=0.01), type="l", lty=1, lwd=3, xlab="Ns", 
     ylab="No. of polymorhpic sites", cex.lab=2)
lines(q, f(q, ns=-50), type="l", lty=1, lwd=3, col="red")
lines(q, f(q, ns=-5), type="l", lty=2, lwd=3, col="red")
lines(q, f(q, ns=5), type="l", lty=1, lwd=3, col="blue")
lines(q, f(q, ns=50), type="l", lty=2, lwd=3, col="blue")
legend(0.6, 200, title="Ne*s", legend=c("-50", "5", "0", "-5", "50"), 
       col=c("red", "red", "black", "blue", "blue"), 
       lty=c(1,2,1,1,2), cex=2, lwd=3)
```

---

# The expected distribution of `\(f(q)\)`

`\begin{align*}
f(q) & = \frac{2 \mu}{q(1-q)} \frac{1 - e^{(-4N_es)(1-q)}}{1 - e^{(-4N_e s)}} \\
\end{align*}`

.pull-left[
<img src="w3_files/figure-html/unnamed-chunk-4-1.png" width="100%" style="display: block; margin: auto;" />
]

.pull-right[
- #### Deleterious alleles => lower frequencies
 - most strongly deleterious mutations are immediately removed from the population
 
- #### Advantage alleles shifted toward higher frequencies
  - most strongly advantageous mutations fix very rapidly.
]

---

# Signature of negative selection

.pull-left[
### Site Freq Spectrum (SFS)

<div align="center">
<img src="negsel.png" height=300>
</div>
]

.pull-left[
- Comparison of expected and observed is __uneven__

- The rare alleles are at lower freq than expected

- Evidence of __negative selection__ (or __purifying selection__)

- However, confounded by population demographics (i.e., bottleneck effect)
]

---

# Signature of positive/balancing selection

.pull-left[
### Site Freq Spectrum (SFS)

<div align="center">
<img src="possel.png" height=300>
</div>
]

.pull-left[
- Comparison of expected and observed is __too even__

- The most common allele is more common than expected

- Evidence of __positive selection__ or __balancing selection__

- However, confounded by population demographics (i.e., population expansion)
]

---

# Diversity measurement

We now consider several statistics summarizing sequencing diversity that use information about __the frequency of derived alleles__
  - As these capture more information about our sequencing data.

Fu and Li (1993) defined a statistic, `\(\epsilon_1\)`, based on the number of __derived singletons__ in a sample.

`\begin{align*}
\epsilon_1 = S_1 \\
\end{align*}`

- Where `\(S_1\)` is the number of segregating site with derived alleles found on only one haplotype.

If we don't know the ancestral status, we can aslo define a statistic, `\(\eta_1\)`, based on __all singletons__ in a sample

`\begin{align*}
\eta_1 = S_1^*\frac{n-1}{n} \\
\end{align*}`

- Where `\(S_1^*\)` is all the singletons.

---
# Diversity measurement

A second summary statistic of diversity that uses ancestral state information is `\(\theta_H\)`:

`\begin{align*}
\theta_H = \frac{\sum_{i=1}^{n-1} i^2S_i}{n(n-1)/2} \\
\end{align*}`

- Where `\(S_i\)` is again the number of segregating sites where `\(i\)` haplotypes carry the derived allele (Fay and Wu, 2000).

---

# Summary of the `\(\theta\)` statistics

All of these statistics --- `\(\epsilon_1, \eta_1, \theta_H\)` --- are estimators of `\(\theta\)` 
- at __mutation-drift__ equilibrium 
- under an __infinite sites__ mutational model

Specifically,

`\begin{align*}
E(\epsilon_1) = E(\eta_1) = E(\theta_H)
\end{align*}`

These relationships arise because we know the expected shape of the allele frequency distribution under our standard neutral assumptions.

---

# Detecting selection using the SFS

## The effects of positive selection

.pull-left[
<div align="center">
<img src="sfs1.png" height=300>
</div>
> Hanh, 2020
]

.pull-right[
- After sweep ended, new mutations started to accumulate.

- These new mutations are by definition __singletons__
 - there is only one origin in the sample with each derived allele.
]

The SFS can be skewed toward an excess of low-frequency polymorphisms relative to the neutral spectrum.

---
# Detecting selection using the SFS

## The effects of balancing selection

Here we consider a simple scenario with a single biallelic site that has been under balancing selection for a long time.
- Variation within each allelic class has been able to __build up__ and __reach equilibrium__

.pull-left[
<div align="center">
<img src="sfs3.png" height=200>
</div>
> Bitarello et al., 2018
]

.pull-right[
- Neutral mutations has accumulated both within and between allelic classes

- Overall variation is higher

- SNPs at intermediate frequency show __a distinctive "bump"__ in the SFS.
]

---
# Detecting selection using SFS

A straightforward way would be test a difference between two SFSs.
- However, linkage among sites means that __SNPs at a locus are not independent__, which violates the assumptions made by almost all such test.

### Instead, we use `\(\theta\)` to detect deviations.

- `\(\theta_\pi\)`: pairwise necleotide diversity.

- `\(\theta_W\)`: Watterson's `\(\theta\)`, using total number of segregating sites

- `\(\epsilon_1 = S_1\)`: the number of derived singletons in a sample.
- `\(\eta_1\)`: based on all singletons in a sample.

Under the standard neutral model, all of these test statistics are expected to have a mean of 0.

---

# Tajima's D and related tests

Tajima (1989) constructed the first test to detect difference between the SFS.

His statistic, `\(D\)`, was defined as:

`\begin{align*}
D = \frac{\theta_\pi - \theta_W}{\sqrt{Var(\theta_\pi - \theta_W)}}
\end{align*}`

Fu and Li (1993) created similar statistics. These are known as Fu and Li's `\(D\)`, `\(F\)`, `\(D^*\)`, and `\(F^*\)`.

`\begin{align*}
D = \frac{\theta_\pi - \epsilon_1}{\sqrt{Var(\theta_\pi - \epsilon_1)}}
\end{align*}`

`\begin{align*}
F = \frac{\theta_W - \epsilon_1}{\sqrt{Var(\theta_W - \epsilon_1)}}
\end{align*}`

`\begin{align*}
D^* = \frac{\theta_\pi - \eta_1}{\sqrt{Var(\theta_\pi - \eta_1)}}
\end{align*}`

`\begin{align*}
F^* = \frac{\theta_W - \eta_1}{\sqrt{Var(\theta_W - \eta_1)}}
\end{align*}`

---

# Tajima's D and related tests

Tajima (1989) constructed the first test to detect difference between the SFS.

His statistic, `\(D\)`, was defined as:

`\begin{align*}
D = \frac{\theta_\pi - \theta_W}{\sqrt{Var(\theta_\pi - \theta_W)}}
\end{align*}`

Originally designed to fit a normal distribution, however, none of these test statistics fit a parametric distribution very well.

### Calculation

- __Only variable sites__ at each locus are needed

- The number of invariant sites do not figure into any calculations.

---

# Interpreting values of the test statistics

Tajima's `\(D\)`, Fu and Li's `\(D, F, D^*, F^*\)`:

`\begin{align*}
D = \frac{\theta_\pi - \theta_W}{\sqrt{Var(\theta_\pi - \theta_W)}}
\end{align*}`

- After a sweep, all SNPs are low in frequency, `\(\theta_\pi\)` will be much lower than expected.

- While statistics based on counts of segregating sites (like `\(\theta_W\)`) will be much closer to their expected values.

------

- All __negative__ when there has been a sweep

---

# Interpreting values of the test statistics

Tajima's `\(D\)`, Fu and Li's `\(D, F, D^*, F^*\)`:

`\begin{align*}
D = \frac{\theta_\pi - \theta_W}{\sqrt{Var(\theta_\pi - \theta_W)}}
\end{align*}`

Balancing selection lead to __an excess of intermediate frequency neutral variation__ surrounding a selected site.

In such case, `\(\theta_\pi\)` will be greater than `\(\theta_W\)` and other statistics.

------

- All __negative__ when there has been a sweep

- All __positive__ when there is balancing selection

- Are usually __significant__ when the values  `\(> +2\)` or `\(< -2\)`
  - The exact thresholds depend on sample size, number of SNPs, etc.

---
# The power of the SFS

The time window for positive selection is limited.
- #### Too early during the sweep 
  - Signal will be not strong enough
  
--
  
- #### Too late after the sweep
  - Both levels and frequencies of variants will have returned to normal

Power also determined by the distance between our studied loci and the location of the selected site.
- Because of the effect of the recombination.
- Move far away enough and there will be no signal of selection at all.

---
class: center, middle

# Direct Selection

---
# The accumulation of sequence divergence

## Necleotide substituion rate ( `\(k\)` )

The variable `\(k\)` is defined as the substitution rate of __new alleles__
  - The rate of alleles that are fixed over long periods of time.
  - It determines how quickly two sequences are expected to diverge over time.

## Sequence divergence ( `\(d\)` )

We define `\(d\)` as the genetic distance between two orthologous sequences. 
- We generally calculate `\(d\)` by taking a single sequence from each species and counting the number of positions that differ between them, divided by the total number of aligned nucleotides.

---

# The accumulation of sequence divergence

The contribution of the __rate of substitution ( `\(k\)` )__ to the expected amount of __divergence ( `\(d\)` )__ can be seen in the following equation:

`\begin{align*}
E(d) = k2t + \theta_{Anc}
\end{align*}`

- Where `\(k\)` represents the allele substitution rate.

- `\(t\)` is the time since the species split
  - We use `\(2t\)` because substitutions can occur on both branches of the phylogenetic tree.
- `\(\theta_{Anc}\)`: average amount of nucleotide variation expected between two sequences in the ancestor.

- Because at the time of speciation there differences have already accumulated along the two linages.

Simplified as below if assuming divergence levels are much greater than the expected levels of polymorphism in the ancestral species,

`\begin{align*}
E(d) = k2t
\end{align*}`

---

# What affects `\(k\)`?

Two quantities determine the rate of substitution ( `\(k\)` ).

- The probability of fixation of any mutation ( `\(u\)` ).

- The total number of mutations that arise and can possibly be fixed.

---

# Fixation rate of new mutation

#### Neutral mutation ( `\(u_0\)` )

If a mutation has no effect on fitness, the probability of fixing is equal to __its current frequency__.

New mutations always begin at frequency `\(\frac{1}{2N}\)`, therefore,

`\begin{align*}
u_0 = \frac{1}{2N}
\end{align*}`

#### Advantageous mutations ( `\(u_a\)` )

For new, advantageous mutations ( `\(s > 0\)` ) and large effective population sizes, the probability of fixation is

`\begin{align*}
u_a \approx 2s_a
\end{align*}`

according to Haldane 1927; Fisher 1930; Wright 1931.

- `\(s_a\)` is the __selective advantage of the new allele in a heterozygote__ and `\(2s_a\)` in a homozygote.

---

# Fixation rate of new mutation

#### Deleterious mutations ( `\(u_d\)` )

For new, deleterious mutations ( `\(s < 0\)` ) that don't have large effects, the probability of fixation is (Kimura 1957):

`\begin{align*}
u_d \approx \frac{2s_d}{1 - e^{(-4N s_d)}}
\end{align*}`

- Here `\(s_d\)` is the __deleterious effect of the new allele in a heterozygote__ and `\(2s_d\)` is the effect in a homozygote.

---
# Fixation rate of new mutation

Probability of fixation, relative to a neutral allele, of new, selected mutations:

`\begin{align*}
u/u_0 \approx \frac{2s}{1 - e^{(-4N_e s)}} / \frac{1}{2N_e} = \frac{4N_e s}{1 - e^{(-4N_e s)}}
\end{align*}`

.pull-left[

```r
ns <- seq(from = -1, to =1, by=0.01)
plot(ns, 4*ns/(1 - exp(-4*ns)), xlab="Ns", ylab="")
abline(v=0, lty=2, lwd=2)
```

<img src="w3_files/figure-html/unnamed-chunk-5-1.png" width="80%" style="display: block; margin: auto;" />
]

.pull-right[
- `\(N_es=0\)`, neutral mutations

- `\(N_es > 0\)`, slightly advantageous mutations are not that much more likely to fix than neutral mutations

- `\(N_es < 0\)`, slightly deleterious mutations have some probability of fixing
]

---

# What affects `\(k\)`?

Two quantities determine the rate of substitution ( `\(k\)` ).

### The probability of fixation of any mutation ( `\(u\)` ).

`\begin{align*}
u_0 & = \frac{1}{2N_e} \\
u_a & \approx 2s_a \\
u_d & \approx \frac{2s_d}{1 - e^{(-4N_e s_d)}}
\end{align*}`

### The total number of mutations that arise and can possibly be fixed.

---

# The total number of mutations

If the probability of a mutation at a nucleotide in each generation is `\(\nu\)`, then in a population of `\(N\)` diploid individuals, there will be __ `\(2N\nu\)` new mutations per generation at a single site__.

- with `\(f_0\)` representing the fraction of neutral mutations.
  - __ `\(2N \nu f_0\)`__ will be neutral

- The remaining will be advantageous (__ `\(f_a\)` fraction__) and deleterious (__ `\(f_d\)` fraction__).
  - __ `\(2N \nu f_a\)`__ new advantageous mutations
  - __ `\(2N \nu f_d\)`__ new deleterious mutations

If advantageous and deleterious mutations have no contribution, then the substitution rate is a function of only _the total number_ of neutral mutations that arise and the _probability that each of them fixes_.

`\begin{align*}
k = (2N \nu f_0) \frac{1}{2N} = \nu f_0
\end{align*}`

---

# Advantageous and deleterious mutations

The rate of substitution for advantageous mutations:

`\begin{align*}
k = (2N_e \nu f_a) 2s_a = 4N_e \nu f_as_a
\end{align*}`

The rate of substitution for deleterious mutations:

`\begin{align*}
k & = (2N_e \nu f_d) \times \frac{2s_d}{1 - e^{(-4N_e s_d)}} \\
& = \frac{4N_e \nu f_d s_d}{1 - e^{(-4N_e s_d)}}
\end{align*}`

----------------

- The effective population size ( `\(N_e\)` ) plays an important role in the rate of substitution of selected mutations.

- More advantageous mutations will fix in larger populations than in smaller populations.

- More deleterious mutation will fix in smaller populations relative to larger populations.

---

# Detecting direct selection using divergence

In coding regions, we measure divergenece that is due to nonsynonymous and synonymous changes.

- `\(d_N\)` as the __number of nonsynonymous__ difference per nonsynonymous site

- `\(d_S\)` as the __number of synonymous__ differences per synonymous site

Note that natural selection has __a profound effect__ on the number of nonsynonymous mutations that are fixed.

`\begin{align*}
E(d_N) & = k2t \\
 & = 2t (\nu f_0 + 4N \nu f_as_a + \frac{4N_e \nu f_d s_d}{1 - e^{(-4N_e s_d)}}) \\
 & = \nu 2t  (f_0 + 4N_e f_as_a + \frac{4N_e f_d s_d}{1 - e^{(-4N_e s_d)}}) \\
\end{align*}`

The total nonsynonymous divergence in a region is due to all three types of mutations, therefore, our expression for `\(d_N\)` includes all three terms.

---

# Detecting selection using divergence

`\begin{align*}
E(d_N) & = \nu 2t  (f_0 + 4N f_as_a + \frac{4N f_d s_d}{1 - e^{(-4N s_d)}}) \\
\end{align*}`

- A higher underlying mutation rate, `\(\nu\)`, and longer divergence times, `\(t\)`, will increase the amount of divergence

- The proportion of advantageous mutations fixed will be a function of the frequency at which they arise and their average selective effect

- The deleterious mutations can also contribute to divergence if selection is weak enough

---

# Synonymous mutations

Here we assume all synonymous changes are neutral.
- That is, `\(f_0 =1\)` and `\(f_a = f_d =0\)`

The total expected amount of synonymous divergence between two sequences is:

`\begin{align*}
E(d_S) & = \nu 2t \\
\end{align*}`

For neutral mutations, the substitution rate is simply equal to the mutation rate.

---

# The ratio of nonsynonymous to synonymous divergence

Because both `\(\nu\)` and `\(t\)` will be approximately the same of nonsynonymous and synonymous sites in the same gene, dividing above equations gives

`\begin{align*}
\frac{E(d_N)}{E(d_S)} & = f_0 + 4N_e f_as_a + \frac{4N_e f_d s_d}{1 - e^{(-4N_e s_d)}} \\
\end{align*}`

- Relative to synonymous divergence, the level of nonsynonymous divergence is again due to the fractions of mutations that are __neutral__, __advantageous__, and __deleterious__.

- Note that here, `\(f_0\)` represents only the __nonsynonymous mutations__.

---
# Some general guidelines

__ `\(d_N/d_S << 1\)`__
The vast majority of nonsynonymous mutations are deleterious, and negative (purifying) selection is predominant.

__ `\(d_N/d_S < 1\)`__
The majority of nonsynonymous mutations are deleterious, but here may be some unknown fraction of advantageous mutations.

__ `\(d_N/d_S = 1\)`__
This situation can occur in two cases:
- First, there is no selection and all nonsynoymous mutations are neutral.
- Second, there is simply a large number of neutral and advantageous mutations (as well as deleterious mutations).

__  `\(d_N/d_S > 1\)`__
There are many advantageous nonsynonymous mutations and positive selection is predominant, but there are still many deleterious mutations.

---
# `\(\pi_N/\pi_S\)`

Within a species, by analogy with the logic of the comparison of `\(d_N\)` and `\(d_S\)`, we can compare the average number of non-synonymous differences per nonsynoymous site ( `\(\pi_N\)` ) to the average number of synonymous differences per synonymous site ( `\(\pi_S\)` ).

- Combining the methods for calculating `\(\pi\)`
- With the methods for calculating nonsynonymous and synonymous changes.

### Interpretation of the ratio

- Values of `\(\pi_N/\pi_S\)` below 1 are again evidence for the predominance of purifying selection, and the vast majority of all coding loci show `\(\pi_N/\pi_S < 1\)`
- However, interpretation of `\(\pi_N/\pi_S > 1\)` is different.

---
# `\(\pi_N/\pi_S\)`

### Interpretation of the ratio

- Since positive selection will rapidly fix advantageous mutations, these adaptive changes will rarely be found in studies of polymorphism

- Instead, balancing selection will result in `\(\pi_N/\pi_S > 1\)`

- heterozygote advantage (heterosis)
  
  - Therefore `\(d_N/d_S > 1\)` for strong evidence of positive selection
  
  - `\(\pi_N/\pi_S > 1\)` is a very strict criterion for detecting balancing selection.
  
  - Single sites under very strong selection will never contribute enough to values of `\(\pi_N\)` to push `\(\pi_N/\pi_S\)` greater than 1.

---

# The _tb1_ gene example

.pull-left[
<div align="center">
<img src="tb1.png" height=250>
</div>

> [Studer et al., 2011, Nature Genetics](https://www.nature.com/articles/ng.942)
]

.pull-right[
<div align="center">
<img src="hka.png" height=250>
</div> 
]

---

# Join my lab as a PhD or a PostDoc

### Who we are looing for
- A master or PhD in plant genetics, genomics, or related field
- Be familiar with at least one coding language, R or python
- Can work independently and also a team player
- Have excellent communication skills

### What we can provide

- A stimulating and supportive international research environment
- Access to state-of-the art research infrastructure
- Living in a lovely city with a low cost and high life quality

### Learn more about us

https://jyanglab.com