Divergence and Linked Background Selection

We have widely discussed the reduction in neutral diversity due to demography and linked selection effects (e.g. selective sweeps and hitchhiking, or background selection) in several previous posts (e.g see here, here, and here). However, how linked selection affects neutral divergence between species isn’t as clear, specifically due to difference in divergence times between species. For e.g. reduction in neutral diversity in the ancestor (e.g. due to background selection) theoretically would be expected to have negligible effect on current species (due to new mutations since the split) in long-diverged species. How then are patterns of divergence affected due to ancestral linked selection at linked neutral sites?  Phung et al. (2016) address this question using simulations and examining neutral divergence between species with a wide range of divergence times – humans versus chimpanzees, orangutans, and mice.


Models of the effects of linked background selection on species with different divergence times (short versus long). Red indicates coalescent times at neutral sites, and blue lines show divergence times at linked neutral sites affected by background selection. Image courtesy Phung et al. 2016 http://journals.plos.org/plosgenetics/article?id=info:doi/10.1371/journal.pgen.1006199

Testing the hypothesis that if natural selection indeed reduces divergence at linked neutral sites, then this effect would be greater at functional regions, Phung et al. 2016 find a negative correlation between divergence and functional content, and a positive correlation between human-primate neutral divergence and recombination rate. This indicates that neutral divergence is reduced at regions with greater functional constraint, and more tightly linked to those under the direct effect of selection (low recombination ~ low divergence, low divergence ~ greater functional constraint). A similar test between human and mouse genomes reveals the same negative correlation between divergence and functional content, and a positive correlation between divergence and strength of background selection. Phung et al. (2016) also recapitulate these patterns of correlation between divergence and recombination and background selection using simulations. Additionally, they also propose a simple two-locus model to explain these correlations between species with long divergence times (sensu humans and mice).

The authors however also point to alternate scenarios which can also contribute to the same/similar patterns of correlations:

  • Selective sweeps, and hitchhiking
  • Direct purifying selection effects
  • Variation in mutation rates across the genome
  • Biased gene conversion

The authors also echo the now-oft-cited sentiment about accounting for the effects of linked selection while using methods for demographic inference (also see my post on Schrider et al. 2016 here).

Our finding that background selection can increase the variance in coalescent times across the genome suggests these methods as well as other statistical methods which seek to infer demographic history from the distribution of coalescent times across the genome, such as the PSMC approach, should account for the increased variance in coalescent times across the genome due to background selection. Not accounting for background selection could result in inferring spurious demographic events to account for the additional variance in coalescent times across the genome as has recently been suggested for positive selection


Phung, Tanya N., Christian D. Huber, and Kirk E. Lohmueller. “Determining the Effect of Natural Selection on Linked Neutral Divergence across Species.” PLoS Genet 12.8 (2016): e1006199. DOI: http://dx.doi.org/10.1371/journal.pgen.1006199

Schrider, Daniel, Alexander G. Shanku, and Andrew D. Kern. “Effects of linked selective sweeps on demographic inference and model selection.”bioRxiv (2016): 047019. DOI: http://dx.doi.org/10.1101/047019

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How Molecular Ecologists Work: Matt Fujita on juggling personalities and buying a lonely PCR machine

Welcome to the final (!) installment in the How Molecular Ecologists Work series! We’ve received a great deal of positive feedback about these peeks into the lives of our colleagues, so we’d like to begin the planning for season 2 of “How Molecular Ecologists Work”. If you have ideas for questions, format, or scientists to nominate, contact Rob Denton <robert.d.denton@gmail.com>!

This entry is brought to us from Dr. Matt Fujita from the University of Texas at Arlington. Matt’s work uses genomic data to investigate the diversity of reptiles and amphibians around the world. Matt and his students have answered questions of species delimitation, genome structure, and the diversity of parthenogenetic lizards. This is how he works: Continue reading

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How Molecular Ecologists Work: Joel McGlothlin on self critique and the whereabouts of elusive emails

Welcome to the next installment in the How Molecular Ecologists Work series!

This entry is from Dr. Joel McGlothlin, assistant professor in the Department of Biological Sciences at Virginia Tech. Joel’s work spans across several areas of evolutionary biology, but you might know him best for his research on the evolution of toxin resistance in snakes that eat newts or quantifying/explaining genetic correlations between Caribbean anole species. Whether it is toxin resistance, social behavior, or quantitative genetics, here is how it gets done. Continue reading

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Building bridges across the chaos

In a new review, Eldon and co-authors (in press) attempt to build a bridge across the chaos of genetic patchiness in the sea. They i) describe the patterns characterized as chaotic genetic patchiness, ii) discuss the potential causes of these patterns and iii) outline issues and perspectives for the future.

The original pattern described by Johnson and Black (1982) is still relevant today in which spatial patterns of genetic differentiation do not fit the typical isolation by distance model whereby differentiation increases with distance. Rather, as is often the case with larval dispersers, spatial differentiation fluctuate rapidly across time, such as from one generation to the next.

Figure 5. A “snapshot” of a simulation model of larval dispersal in the Santa Barbara Channel region. Land is indicated in gray, with Channel Islands National Park highlighted in light gray. In this illustrative example, larvae were released from the north shore of Santa Rosa Island, indicated by the star. Virtual larvae in the midst of dispersal and are represented by the dark circles; curved lines trace the dispersal path of each larva since their release. Note how most of the larvae were temporarily entrained in an oceanographic gyre (whirlpool) in the Santa Barbara Channel, then advected away by the southward-flowing California Current (see arrows in Figure 4). For visual clarity, only a fraction of the total larvae simulated in a formal analysis are shown here.

A “snapshot” of a simulation model of larval dispersal in the Santa Barbara Channel region taken from White (2010) The George White Forum 27: 280-291

Small scale patchiness isn’t in itself surprising, rather the

surprise and interest in chaotic genetic patchiness come[s] from the fact that genetic structure is observed at a scale where theory predicts that gene flow should completely homogenize neutral genetic variation across post-dispersal individuals.

But, what is the scale of gene flow and how do we start comparing across marine organisms?

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The almighty CRISPR-Cas9 technology: The future of conservation?

Source: Wikimedia Commons/ Ernesto del Aguila III, NHGRI

Source: Wikimedia Commons/ Ernesto del Aguila III, NHGRI

In the first post on CRISPR-Cas9, I’ve explained how bacteria and archaea create a “database” of infections and use it as a form of prokaryotic immunization. This time, I’m going to concentrate on how biotechnology turns this natural phenomenon into a powerful tool.

CRISPR-Cas9 is probably the most popular of CRISPR systems because of its simplicity. The natural effector complex consists of only three components – crRNA, tracrRNA and Cas9 endonuclease, but the synthetic form is even simpler, engineered as a two-component system by fusing the crRNA and tracrRNA into a single guide RNA (sgRNA or gRNA).

Engineering genomic regions of choice just by supplying two ingredients is a recipe any lab can handle. Directions according to Sander & Joung (2014):

“Twenty nucleotides at the 5′ end of the gRNA (corresponding to the protospacer portion of the crRNA) direct Cas9 to a specific target DNA site using standard RNA-DNA complementarity basepairing rules. These target sites must lie immediately 5′ of a PAM sequence that matches the canonical form 5′-NGG (although recognition at sites with alternate PAM sequences (e.g., 5′-NAG) has also been reported, albeit at less efficient rates). Thus, with this system, Cas9 nuclease activity can be directed to any DNA sequence of the form N20-NGG simply by altering the first 20 nt of the gRNA to correspond to the target DNA sequence.”
Naturally occurring (a) and engineered (b) CRISPR-Cas systems (Figure 3, Sander & Joung 2014)

Naturally occurring (a) and engineered (b)
CRISPR-Cas systems (Figure 3, Sander & Joung 2014)

CRISPR-based gene drive

Despite all the previously described magical properties of CRISPR-Cas9, its main strength comes with the connection to a gene drive. Gene drive is a genetic element that basically defies the Mendelian laws and via biased inheritance increases in frequency each generation (Champer et al. 2016).

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When your programming may be inadequate to the task: new options for metagenome analysis

Big Data, JD Hancock photos

Big Data, JD Hancock photos

There’s a lot of data in the form of metagenomes out there, and picking apart those mountains of data to uncover meaningful results is difficult. Recently, we received a suggestion from a reader to discuss a recent program (CLARK-S) developed to quickly and precisely classify short reads from metagenomic data sets. As a participant this July in the EarthCube Oceanography Geobiology Environment Omics (ECOGEO) workshop focused on training participants on the use of metagenomic tools, I figured I’d go for it. EarthCube was started by NSF with the goal of improving access to and analysis of all sorts of geoscience-related data, and if you’re interested, you can access the lectures and training material we used at ECOGEO here.

There are multiple programs available to help you sort through the oceans (quite literally) of metagenomic data available, and it seems like daily there’s another option for analysis, and basically, they all also have an acronym (seems like us biologists just love them!) I’ve had a chance to highlight a few recent tools already, here and here.

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The almighty CRISPR-Cas9 technology: How does it work?

CRISPR-Cas9 took the whole world of biology by storm. Selected Science’s 2015 Breakthrough of the Year, the CRISPR-Cas9 technology is revolutionizing science. Within five years of the official announcement (Jinek et al. 2012), it became the genome-editing technique of choice. The secret? It’s easy, cheap and precise.

Crystal Structure of Cas9 in Complex with Guide RNA and Target DNA (Nishimasu et al. 2014)

Crystal Structure of Cas9 in Complex with Guide RNA and Target DNA (Nishimasu et al. 2014)

Step by step, the CRISPR-Cas9 technology is infiltrating scientific fields. One of the fields where this technology might turn out to be a game-changer is conservation biology. But the future directions of CRISPR-Cas9 implementation to conservation practice have to be thoroughly considered and discussed. Only during the last twelve months, major scientific journals like Nature Review Genetics, Cell, Trends in Biotechnology, and PNAS published reviews on genome engineering for conservation purposes.

So, if phrases like gene drive, guide RNA, and protospacer make your head spin, this post is for you. If you didn’t know they existed, it’s for you too. And even for you who have no idea what the acronym CRISPR actually means. Like me until yesterday.

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