Population genomics in practice

What is population genomics?

Per Unneberg

NBIS

15-Nov-2023

Intended learning outcomes

Course

• Present minimum toolkit of methods that should be known to anyone starting out in population genomics
• Sufficiently small for one-week workshop

Lecture

• Present practical example of toolkit as applied in (Fuller et al., 2020)
• Briefly discuss baseline model (Johri et al., 2022)

Aim of lecture is to:

• present a practical application of commonly used methods in population genomics
• link population genomics to population genetics
• discuss statistical inference and the need of a baseline model with which to compare observations and conclusions

What is population genomics?

Points from (Hahn, 2019, pp. 249–250):

• whole-genome data instead of single loci - population genomics is population genetics for whole-genome sequences
  • if only this, not too exciting
• major promise: enables analyses not possible for single loci or that require genomic context
• addresses interactions between different forces, notably selection and demographic history

Some applications

• genome-wide scans for selection
  • selection vs demography (p. 251)
• methods for genome-wide scans (p. 258)

Caveats

• non-independence (p. 267)
  • different statistics rely on similar input
  • overlapping peaks from different statistics not independent

General points

(Hartl & Clark, 1997, pp. 469–470):

• more emphasis on differences within populations
• goal: understand differences among genomes -> requires complete sequence data from multiple individuals

(Li & Durbin, 2011, supplementary notes, p. 6) on the use of PSMC on autosomes:

“…highly consistent except for the very recent history, demonstrating the power of using whole-genome data.”

Example: Population genetics of the coral Acropora millepora

Motivation: corals are facing hard times and to prevent future losses of coral cover a better understanding of genetics is warranted.

Genome assembly and sampling

Motivation: most analyses require a reference sequence with which to compare resequenced samples

• Assemble high-quality reference genome
• Choice of populations, sampling locations

Figure 1: Genome assembly and sample collection.

Fuller et al. (2020)

(Fuller et al., 2020) is an example of a population genomics study that applies methods that could be seen as a basic foundation of population genomics. We believe these present a minimum toolkit of methods that should be known to anyone starting out in population genomics, and that is sufficiently small to be presented in a one-week workshop. At the end of this lecture, we will discuss some more advanced applications in population genomics.

Genome assembly and sampling

Why: most analyses require a reference sequence with which to compare resequenced samples

Points to consider:

• choice of reference individual
• the number of populations
• the number of samples (more sites better than many samples per population)
• the geographical distribution of samples
• sequencing depth (low-coverage often sufficient)

Example: Population genetics of the coral Acropora millepora

Motivation: corals are facing hard times and to prevent future losses of coral cover a better understanding of genetics is warranted.

Describe genetic structure and demographic history

Motivation:

• address basic question of why genetic structure looks the way it does
• demographic history may generate signals similar to selection

Figure 2: Variation and demographic history inferred from 44 resequenced individuals.

Fuller et al. (2020)

Variation and demographic history

Why: summarizing diversity provides (indirect) information on population size and more, as does the linkage structure. Estimate demographic history since fluctuating population size may produce signals similar to those of selection

• LD decay: important for imputation (e.g., stephens_AccountingDecayLinkage_2005) and setting window size for genome scans, where a common rule of thumb is to set the size larger than the genome background: this ensures windows are, in some sense, independent
• The extent of LD and its decay with genetic distance are useful parameters for determining the number of markers needed to successfully map a QTL, and the resolution with which the trait can be successfully mapped otyama_EvaluationLinkageDisequilibrium_2019
• 0.363% average pi, but large variation.
• many psmc plots show decline in population size, which could be an effect of bottleneck during pleistocene. Also population divergence (ghost ancestral populations, splits, extinction) can affect population size
• in aDNA studies missingness is common (i.e., heterozygotes are underestimated) and has to be accounted for since coalescence times are affected and may influence estimate of population size

Example: Population genetics of the coral Acropora millepora

Motivation: corals are facing hard times and to prevent future losses of coral cover a better understanding of genetics is warranted.

Characterize population structure

Motivation:

1. identify populations for contrasts in e.g. selection scans
2. identify admixed individuals that should be removed from analyses
3. identify barriers to gene flow etc

Figure 3: Characterizing population structure and gene flow across 12 refs

Fuller et al. (2020)

Population structure:

Why: many reasons: 1) identifying populations for contrasts in e.g. selection scans 2) identify admixed individuals that should be removed from analyses 3) identify barriers to gene flow etc

• no discernible relationship between geographic distance and genetic differentiation -> gene flow
  • for this reason, Fst between populations is low
• EEMS (Estimated Effective Migration Surfaces) models relationship between genetics and geography petkova_VisualizingSpatialPopulation_2016

Indicative of high connectivity among 12 sampled reefs.

Example: Population genetics of the coral Acropora millepora

Motivation: corals are facing hard times and to prevent future losses of coral cover a better understanding of genetics is warranted.

Genomic scans for selection

Motivation: identify loci associated with adaptation / selection

• little differentiation over reefs, however thermal regimes
• genomic scan for \(\pi\) (diversity) outliers

Figure 4: Genomic scans for local adaptation detect a signal at sacsin

Fuller et al. (2020)

Selection scan

Why: identify loci associated with adaptation / selection, which provides potential mechanisms for adaptation, as well as information that could be important for conservation strategies

Little differentiation across reefs -> little population structure over hundreds of kilometers. However, there are environmental differences (thermal regimes). Scan for pi outliers:

• points to sacsin gene
• h12 measures the frequency of the two most common haplotypes; red indicate 0.01% outlier genome-wide
• 4C: tree for central 1kb region in sacsin deeper than split from A.digitifera and A.tenuis
  • variation in sacsin has been maintained for long time
  • co-chaperone for heat-shock protein Hsp70

Example: Population genetics of the coral Acropora millepora

Study highlights common analyses in population genomics study:

1. Genome assembly, resequencing, variant calling and filtering
2. Description of variation (e.g., \(\pi\)) and genetic structure (LD)
3. Description of population structure (admixture, PCA)
4. Modelling of demographic history (PSMC)
5. Genome scans for adaptive traits

Population genetics

 

Mutation

Selection

 

Recombination

Drift

(Fuller et al., 2020) paper has population genetics in title -> population genetics is a key ingredient.

Population genetics focuses on the genetic basis of evolution. It is mainly a theoretical subject, owing to the slow changes of genetic variation. As such, it tries to explain the shape and structure of genetic variation from theoretical predictions and models.

From population genetics to population genomics

The variable sites at the Drosophila melanogaster ADH locus (Kreitman, 1983)

First study of natural population. However, limited to one locus.

• from locus-based studies (e.g., alcohol dehydrogenase in Drosophila (Kreitman, 1983)) to genome-wide (e.g., Drosophila population genomics (Begun et al., 2007)
• note: studied loci have often not been randomly chosen, which is another argument for whole-genome studies
• enabler: sequencing technology

(Fuller et al., 2020) paper has population genetics in title -> population genetics is a key ingredient.

Refer to Hahn’s points about learning something about global patterns:

• selection acts locally, demography globally
• the structure of genetic variation and how it depends on
  • recombination landscapes and linked selection
  • demographic changes
  • identification of neutral loci

So, not simply about applying 10000 selection tests for multiple loci

All of the points above point to the importance of statistics which implies mathematics / computational skills important

From population genetics to population genomics

Patterns of polymorphism and divergence (Begun et al., 2007)

Same system but genome-wide. Plots represent all chromosomes and the entire genome.

Begun et al. (2007) study: same system (Drosophila) but more individuals and whole genome. All of a sudden possible to ask questions about the general characteristics of diversity, not just limited to single loci.

From population genetics to population genomics

Numbers of polymorphic and fixed variants (Begun et al., 2007)

Novelty: now possible to do genome-wide characterization of variation in different functional contexts

Novelty: now possible to do genome-wide characterization of variation in different functional contexts

The technological revolution in sequencing and computing

Figure 5: Sequencing cost ($) per megabase (Wetterstrand, KA)

Moore’s law

Statistical inference in population genomics

The data deluge requires advanced statistical methods and models to do inference. Today data production outpaces theoretical advances. Therefore, take care not to attach too much faith to a test that explains data well.

A population genomics study should aim at generating a baseline model that takes into account the processes that shape genetic variation (Johri et al., 2022):

1. mutation
2. recombination
3. gene conversion
4. purifying selection acting on functional regions and its effects on linked variants (background selection)
5. genetic drift with demographic history and geographic structure

Caution against adaptationist storytelling; always compare to a baseline model that takes potential confounding factors into account

Applications of population genomics

conservation genomics (Webster et al., 2023)

speciation genomics (Stankowski et al., 2019)

disentangle forces that create variation (Rodrigues et al., 2023)

paleogenomics (aDNA) (van der Valk et al., 2021)

domestication (Barrera-Redondo et al., 2020)

ecology (Hohenlohe et al., 2019)

Bibliography

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