1. Neutrality Test
First suggested by Kimura (1968) and King and Jukes (1969)
Shift to using neutrality as a null hypothesis in positive selection and selection sweep tests
Positive selection is when a new advantageous trait is segregating in a population
Selection sweep is when reduction of neutral allele diversity linked to a selected loci is fixed

2. Neutrality Test Neutrality tests allow us to:
Identify causes of species-specific phenotype differences
Identify regions currently under selection
Form hypotheses on function from genome data
Challenges in neutrality tests
Extracting the data
Identifying the loci under selection

3. Neutrality Test Two main classes of neutrality test
Allelic distribution and/or level of variability
Comparisons of divergence/variability between different mutation classes within a locus
The former relies on major assumptions on population demographics

4. Single-Locus Test Ewens Sampling Formula
Sampling probability under infinite allele model
Ewens-Watterson Test
Compare the expected homozygosity with the observed homozygosity
If larger than a threshold value, reject the null hypothesis

5. Single-Locus Test Tajima's D-Test Nucleotide data D = θπ-θω/Sθπ-θω
D is the scaled difference in the estimate of θ=4Νeμ
Θπ is an estimator of θ based on average number of pairwise differences
Θω is an estimator of θ based on number of segregating sites
Sθπ is an estimate of the standard error of the difference of the two estimates

6. Single-Locus Test D-Test
Difficulty in interpreting significant results
Useful for detecting bottlenecks and subdivision as well as selection sweeps

7. Multiple-Loci Test Lewontin-Krakauer test
Data from diallelic loci from multiple populations
F = σp2/[p(1-p)]
P and σp2 are the mean and variance of allele frequencies across populations
If F is too large, the neutral hypothesis is rejected

8. Multiple-Loci Test HKA Test
Variability between and within species is compared for two or more loci
Assumes that under neutrality
Expected number of segregating sites within species and expected number of fixed differences between species are proportional to mutation rate
Ratio of two expectations is constant among loci
Therefore if divergence:polymorphism ratio is too high, selection is at work

9. Multiple-Loci Test HKA Test
Challenge: Variance in segregating sites highly depends on demographics
Example: Immigration from unknown population
M = Immigration rate
CV = Standard deviation divided by mean in segregating sites number

10. Multiple-Loci Test Assumptions and Challenges Challenges
Selection will contrast target alleles/loci
Selection can be seen if significant difference in adherence to the neutral model between loci
Challenges
Our expected value and variance of D depends heavily on the demographic model

11. Multiple-Loci Test Example

12. Comparing Variability in Different Classes of Mutations
McDonald-Kreitman (MK) Type Tests
Traditionally used to detect and measure the amount of adaptive evolution within a species by determining whether adaptive evolution has occurred, and the proportion of substitutions that resulted from positive selection.
In general, the MK test compares the amount of species polymorphism and the divergence (substitutions) between species at neutral and non- neutral sites (advantageous or deleterious).

13. McDonald-Kreitman cont.
Setting up a MK test
Set up a two way contingency table show to the right
Term clarification:
Synonymous—a point mutation causing a silent mutation (phenotypically normal)—often used as a control
Nonsynonymous—mutation that causes a change in phenotype
Fixed
Polymorphic
Synonymous Ds Ps
Nonsynonymous Dn Pn
Ds: the number of synonymous substitutions per gene Dn: the number of nonsynonymous substitutions per gene Ps: the number of synonymous polymorphisms per gene Pn: the number of nonsynonymous polymorphisms per gene

14. McDonald-Kreitman cont.
First used with drosophila in 1991 and the ADH gene.
The test proposed a method to estimate the proportion of substitutions that are fixed by positive selection rather than by genetic drift.
The ratio of ns. to s. variation within a species is going to equal the ratio of ns. to s. variation between species:
Dn/Ds = Pn/Ps

15. McDonald-Kreitman cont.
When positive or negative selection influences ns. variation, the ratios will no longer be equal.
The ratio of ns. to s. between species is lower than the ratio of ns. to s. within species when negative selection is high and deleterious alleles strongly affect polymorphism: Dn/Ds < Pn/Ps
The ratio of ns. to s. within species is lower than the ratio of ns. to s. between species when positive selection is high. Dn/Ds > Pn/Ps These do not necessarily contribute to polymorphism but have an effect on divergence.

16. McDonald-Kreitman cont.
Possible shortcoming of the MK type tests:
It’s not always clear what type of selection is acting upon a gene
Ex—changes in pop size combined with weak selection against slightly deleterious mutation may either increase or decrease the number of ns polymorphisms
An increase in pop size will lead to excessive ns polymorphisms
Significant results from MK cannot be interpreted directly as evidence for positive selection

17. The Genomic Rate of Adaptive Evolution—Smith and Eyre-Walker
Additional work with MK tests by Smith and Eyre- Walker:
α = 1 – (DsPn)/(DnPs)
In the above equation, α = proportion of substitutions driven by positive selection.
See research handouts.

18. Test Based on Allelic Distribution in ns and s Sites
Some tests are done by examining different types of sites (non- protein coding sites)
Differences I the allelic distributions (frequency spectra) between s and ns polymorphisms.
Used for genomic sets in which large number of polymorphisms can be obtained.
Microsat data?
Nielsen and Weinreich performed frequency spectra analysis in the human genome. (1999)
Differences in the average age of ns and s mutations provided evidence for selection.

19. Tests Based on the dN/ds Ratio or ω
The most direct method for showing the presence of positive selection is to demonstrate that the number of ns substitutions per ns sites (dN) is much larger than the number of s substitutions per s sites (dS)

20. Definitions…The dN
dN (alternatively designated Ka) is a measure of the degree to which two homologous coding sequences differ with respect to amino-acid content.
Specifically, it indicates the degree to which two sequences differ at ns sites (substitution that changes the aa).
dN is the average number of nucleotide differences between the sequences per ns site.

21. More Definitions…The dS
dS (alternatively designated Ks) is a measure of the degree to which two homologous coding sequences differ with respect to silent nucleotide substitutions (substitutions that do not cause an amino-acid substitution).
It indicates the degree to which two sequences differ at s sites (substitution that does not change the aa).
dS is the average number of nucleotide differences between sequences per synonymous site.

22. Tests Based on the dN/ds Ratio or ω
A value of dN > dS implies that ns mutations are fixed with a higher P than neutral ones due to positive selection. If testing dN < dS (ω ≤ 1)for an entire gene is a very conservative test of neutrality. Purifying selection must occur frequently in functional genes to preserve function. Therefore, the average dN is expected to be much less than the average dS, even if positive selection is occurring in some sites.

23. Differences in MK and H0:ω≤1
ω≤1 is to date the only direct method available to provide data for detecting positive selection. ω>1 is to date the only direct method available for detecting positive selection from DNA sequence data. Limitations: they assume no recombination and the effect of strong codon bias on these methods have not been systematically explored (2001). **Have the above limitations been investigated yet?