1. Detection of the footprint of natural selection in the genome
Genomics
Noelia Pérez Pereira
Advanced Genetics

2. Contents Introduction
Natural selection
Modes of selection
Detecting natural selection
Detecting natural selection at macroevolutionary level
Gene-based methods
Other rate-based methods

3. INTRODUCTION

4. Mechanisms that explain adaptation of populations to their environment
Natural selection
Mechanisms that explain adaptation of populations to their environment
Beneficial traits (those that improve an individual’s chances to survive and reproduce) tend to become more frequent in populations over time.
Vitti et al. (2013)

5. Mechanisms that explain adaptation of populations to their environment
Natural selection
Mechanisms that explain adaptation of populations to their environment
Beneficial traits (those that improve an individual’s chances to survive and reproduce) tend to become more frequent in populations over time.
Vitti et al. (2013)

6. Mechanisms that explain adaptation of populations to their environment
Natural selection
Mechanisms that explain adaptation of populations to their environment
Beneficial traits (those that improve an individual’s chances to survive and reproduce) tend to become more frequent in populations over time.
Vitti et al. (2013)

7. Modes of selection
Modified from Quintana-Murci & Clark (2013)

8. Detecting natural selection
Microevolution
Polymorphism data
Divergence data
Macroevolution

9. Detecting selection at the MACROEVOLUTIONARY
level

10. Methods for macroevolution
Vitti et al. (2013)

11. Gene-based methods 1. Ka/Ks ratio
Synonymous changes are assumed to be neutral
non-synonymous changes
synonymous changes
Ka =
Ks =
non-synonymous sites
synonymous sites
Vitti et al. (2013)

12. Gene-based methods 1. Ka/Ks ratio λ > μ λ = μ λ < μ
Synonymous changes are assumed to be neutral
non-synonymous changes
synonymous changes
Ka =
Ks =
non-synonymous sites
synonymous sites
Ka / Ks > 1
Ka / Ks = 1
Ka / Ks < 1
Positive selection
Genetic drift
Purifying selection
λ > μ
λ = μ
λ < μ

13. Gene-based methods > 2. McDonald-Kreitman test (MKT) < = Pn Dn
Fixed difference
Polymorphic site
Positive selection
Null hypothesis
Purifying selection
Pn Dn
Ps Ds
Pn Dn
Ps Ds
Pn Dn
Ps Ds
>
< =
Egea et al. (2008)

14. Gene-based methods 2. McDonald-Kreitman test (MKT) Single-locus MKT
Single-locus MKT
Standard MKT: coding regions
Advanced MKT: closely linked regions
Multi-locus MKT
2x2 contingency table

15. Other rate-based methods
1. Hudson-Kreitman Aguadé (HKA)
Levels of polymorphism and divergence should be correlated (because both are primarily functions of the mutation rate) unless selection causes one to exceed the other
Change contributing to speciation was accelerated D P
Value
ratio
Diversity within the species is reduced
D: fixed interspecific differences
P: within-species polymorphisms
across loci
Value
Balancing selection between species
Vitti et al. (2013)

16. Other rate-based methods
2. Comparative genomic data
Regions that undergo accelerated change in one lineage but are conserved in related lineages are probable candidates for selection
Comparative analysis of 29 mammalian genomes
5.5% of the human genome: purifying selection
(∼1,2% exons)
3.6 millions of conserved elements

17. References
Vitti, J. J., Grossman, S. R., & Sabeti, P. C. (2013). Detecting natural selection in genomic data. Annual review of genetics. 47:
Quintana-Murci, L., & Clark, A. G. (2013). Population genetic tools for dissecting innate immunity in humans. Nature Reviews Immunology.13(4):
Hurst LD. (2002). The Ka/Ks ratio: diagnosing the form of sequence evolution. Trends Genet. 18(9):486.
Egea R, Casillas S, Barbadilla A. (2008). Standard and generalized McDonald-Kreitman test: a website to detect selection by comparing different classes of DNA sites. Nucleic Acids Res. 36:W157–62.
Lindblad-Toh, K., Garber, M., Zuk, O., Lin, M. F., Parker, B. J., Washietl, S., ... & Ward, L. D. (2011). A high-resolution map of human evolutionary constraint using 29 mammals. Nature. 478(7370):

18. Thank you for your attention
Genomics
Noelia Pérez Pereira
Advanced Genetics