Do we really need a new revamped theory of evolution?

A short answer to this meaty question is – NO?

But if you have been keeping a track of science news in the press recently, the half-baked articles all have this vague notion that an urgent extension is needed to the theory of evolution. And if you now ask why do we need such an extension, the answer invariably comes back as – EPIGENETICS.

In this summer we had a major controversy regarding the inadequate and completely wrong interpretation of epigenetics and gene regulation Siddarth Mukherjee’s article in The New Yorker based on his new book – The Gene: An Intimate History (Scribner, 2016). Apart from that, Royal Society conference hosted a conference on “New Trends in Evolutionary Biology: Biological, Philosophical, and Social Science Perspectives.” which harped on bringing a thorough revamp in evolutionary biology. And this was succeeded by a few articles like journalist Robby Berman’s Big Think,”How about a new theory of evolution with less natural selection?” and one by the eminent Carl Zimmer in Quanta Magazine as a longish essay, “Scientists seek to update evolution.”

So, what is this controversy about, who are the  “Third Way of Evolution” group and why despite all these claims evolutionary theory doesn’t require any sort of revision. This post discusses these issues and hopes that it would convince the readers that “All is well” with evolution.

The journos, and the “Third Way of Evolution” group  claim that the rising field of epigenetics is a way by which environment leads to long-lasting changes in the phenotype which can be inherited without altering the DNA sequences directly.epigenetic_mechanisms

These changes come somehow by the presence of methyl groups on a few nucleotides aka DNA methylation. Such changes can then be inherited by the next few generations, and so these folks claim such changes can be subjected to natural selection which leads to a form of evolutionary change similar to the old, bygone Lamarckian theory of inheritance of acquired characters. Apparently, traditional evolutionary theory doesn’t account for this mechanism and hence is in dire need for change!!

But some important aspects which people misread are the following:

  • This form of  neo-Lamarckian inheritance is not permanent and is wiped clean after a few generations. The most touted example of epigenetic changes inheritance in a plant lasted for 31 generations before being erased. And till now, no evidence has come up regarding epigenetic changes being inherited in a vertebrate.
  • When geneticists trace any adaptive changes being found on the genome, what is seen are the actual, real changes in the DNA sequence itself and not on the presence/absence of methyl markers on those nucleotides making up the sequence.
  • The increase in the frequency of DNA sequences which are susceptible to environmentally-induced methylation because them being adaptive is straight-forward natural selection and doesn’t require a revision of evolution.
  • Some methylation changes are indeed coded by the DNA, as in mediating parent-offspring conflict. But this form of evolution is not because of the environment but has happened due to normal natural selection.

Berman in his potpourri article used niche construction as an example of how epigenetics can work . But what he didn’t understand was that niche construction result more from DNA sequence changes which are adaptive to the novel environment the organism encounters and not due to the environment. As Jeffrey Coyne put it brilliantly – “Berman has no idea what he’s talking about here.”

In regards to the much-advertised meeting held by Royal Society – “New Trends in Evolutionary Biology: Biological, Philosophical, and Social Science Perspectives.” many of the attendees were sponsored by the Templeton Foundation which in recent years has led the highly stupid movement of bridging science and religion. It’s been the case that they have funded any project which is woozy and unscientific but sounds sciency/lofty in its aims. Check some of these woozy grants out –

So, this brings a huge doubt as to the impartial scientific motive behind this conference. Are these so-called revisionists simply “careerists” as Jeffrey Coyne puts it? Sadly, in this era of waning grants and increasing pressure to publish in high-impact journals (which itself is a crap idea to measure science) some people have come up with these half sciency, half baloney ideas which promise the moon all the while being blind.

This misunderstanding of epigenetics and the extent of its role is not restricted to these but even respected scientist/Pulitzer-winning author Siddhartha Mukherjee did a similar faux pas this summer with his article in The New Yorker. Nature even wrote an article collating the various viewpoints on the issue. His mistake was not claiming that evolution needs an overhaul but more nuanced, as he said that epigenetic markers play a huge role in gene regulation. What is now known to every biologist is that it’s transcription factors (proteins) which control the rate of transcription from DNA to messenger RNA, by binding to a specific DNA sequence. In turn, this helps to regulate the expression of genes near that sequence. Now, this coming from The New yorker and Mukherjee would convince layman about the role of epigenetic markers in gene regulation despite them not being true !! And the final nail in the coffin was when towards the end of the article he speculated that such inheritance of acquired characters via epigenetic markers (Lamarckism at its best !) could play a major role in evolution. As it’s been said, again and again there is simply no evidence for this and hence needless speculation based on shaky ground is harmful to science.

For more:

  1. The Role of Methylation in Gene Expression
  2. Researcher under fire for New Yorker epigenetics article
  3. The Imprinter of All Maladies
  4. Once again: misguided calls for a thorough revamping of evolutionary biology
  5. The New Yorker screws up big time with science: researchers criticize the Mukherjee piece on epigenetics
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Population Genetics Undergrad Class

A nice bunch of notes for learning a wee bit of population genetics. Covers recent advances in pop gen too !

gcbias

We’re teaching Population and Quantitative Genetics (undergrad EVE102) this quarter. We’re posting our materials here, in case they are of interest.

A pdf of the popgen notes is here

The slide pdfs are linked to below

Lecture One [Introduction and HWE]. Reading  notes up to end of Section 1.2.

Week 1

lecture_2_rellys_inbreeding  [HWE, Relatedness (IBD), Inbreeding loops] Read Sections 1.3-1.5

lecture_3_population structure [Inbreeding, FST and population structure]

1/2 class Reading Discussion Simons Genome Diversity Project and Kreitman 1983 + 1/2 class on  lecture_4 [Other common approaches to population structure, Section 1.7 of Notes optional reading]

Week 2

lecture_5_ld_drift [Linkage Disequilibrium + Discussion of Neutral Polymorphism] Reading Section 1.8 of notes.

lecture_6_drift_loss_of_heterozygosity[Genetic Drift & mutation, effective population size. Read Chapter 2, up to end of Section 2.3]

Lecture 7. Finishing up lecture 6 & Discussion of Canid paper.

Week 3.

lecture_8_coalescent. [Pairwise Coalescent & n sample Coalescent…

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Evolution and cancer

In a 3 part series Prof. Mel Greaves, provides an excellent introduction to the evolution of cancer. Cancer is increasingly being looked through the evolutionary lens and is quite important to be done so. Like antibiotic resistance, we have to realise that evolution of metastasis from a single tumour cell is a dynamic process shaped by various selection pressures.

What has evolution got to do with cancer?

Darwin’s branching tree of evolutionary phylogeny

The principles of evolutionary natural selection in cancer

Our understanding of cancer is being transformed by exploring clonal diversity, drug resistance, and causation within an evolutionary framework. The therapeutic resilience of advanced cancer is a consequence of its character as a complex, dynamic, and adaptive ecosystem engendering robustness, underpinned by genetic diversity and epigenetic plasticity. The risk of mutation-driven escape by self-renewing cells is intrinsic to multicellularity but is countered by multiple restraints, facilitating increasing complexity and longevity of species. But our own species has disrupted this historical narrative by rapidly escalating intrinsic risk. Evolutionary principles illuminate these challenges and provide new avenues to explore for more effective control.

Lifetime risk of cancer now approximates to 50% in Western societies. And, despite many advances, the outcome for patients with disseminated disease remains poor, with drug resistance the norm. An evolutionary perspective may provide a clearer understanding of how cancer clones develop robustness and why, for us as a species, risk is now off the scale. And, perhaps, of what we might best do to achieve more effective control.

 

What to do when your Hessian matrix goes balmy !!!

So you ran some mixed models and got some balmy messages in return? Are these those messages?

“The Hessian (or G or D) Matrix is not positive definite. Convergence has stopped.”

OR

“The Model has not Converged. Parameter Estimates from the last iteration are displayed.”

Then this post is for you. First let’s try to understand right from the basics of matrix algebra itself. Before going into the Hessian matrix let’s take a detour into the murky world of mixed models and see what’s going on there and how come we get a thing called Hessian matrix !

A linear mixed model looks like this (from Wikipedia):

\boldsymbol{y} = X \boldsymbol{\beta} + Z \boldsymbol{u} + \boldsymbol{\epsilon}

where

  • \boldsymbol{y} is a known vector of observations, with mean E(\boldsymbol{y}) = X \boldsymbol{\beta};
  • \boldsymbol{\beta} is an unknown vector of fixed effects;
  • \boldsymbol{u} is an unknown vector of random effects, with mean E(\boldsymbol{u})=\boldsymbol{0} and variance-covariance matrix \operatorname{var}(\boldsymbol{u})=G;
  • \boldsymbol{\epsilon} is an unknown vector of random errors, with mean E(\boldsymbol{\epsilon})=\boldsymbol{0} and variance \operatorname{var}(\boldsymbol{\epsilon})=R;
  • X and Z are known design matrices relating the observations \boldsymbol{y} to \boldsymbol{\beta} and \boldsymbol{u}, respectively.

Let’s focus on the variance-covariance matrix G or some software refer to it as the D. It is the a matrix of the variances and covariances of random effects. The variances are the diagonal elements and the off-diagonal ones are covariances. So if you have a mixed model with two random effects say, a random intercept as well as the random slope, then we would have a 2 X 2 G matrix. The variances of the intercept and slope terms would be in the diagonal whereas the off-diagonal would contain the covariances.

Remember this G matrix is a one which contains variances so mathematically speaking, the matrix should be positive definite (for a matrix to be so, diagonal elements should be positive). As variances are always positive, hence this makes sense.

The Hessian matrix referred to in the warning messages you got is actually based on this G matrix which is used to calculate the standard errors of the covariance parameters. So, the algorithms which calculate them would be stuck and won’t be able to find an optimised solution if the given Hessian matrix calculated for the model doesn’t have positive diagonal elements.

So, the whatever results you may get out of the mixed model wouldn’t be correct or trustworthy. What that means is that the model which you specified couldn’t estimate parameters etc with your data. Some might choose to ignore this warning and move ahead, but my request is please don’t !!! This warning is indeed important, and NO the software doesn’t have a vendetta against you/your project.

 The next step is obviously to ask what can you do in this circumstance and what might be the solution. One method might be to check the scaling of your predictor variables in the model. If they are highly different then that can be a good reason why the software has trouble in variance calculation. So, just a change in scaling of the predictors can solve your problem here.

Another method is when some covariance estimates are 0 or have no estimates at all or don’t produce the standard errors at all (SPSS usually does this, and produces blank estimates). Now don’t go on ignoring this variable, as something is fishy with the model itself. For if the best estimate of your variance is zero, this means there is zero variance within your data for the effect under consideration. For example, you have introduced a random slope for that effect, but in actuality the slopes do not differ across the subjects of your study in that effect and possibly a random intercept component might well explain all the variation.

So just remember when something like this happens, the best possible solution for you to do is to respecify the random components in your model and that could be about removing a random effect. Sometimes you might feel or have been told that a given random effect has to be introduced because of the design of the study, you wouldn’t find any variation in the data. Another thing, is that you could specify perhaps a simpler covariance structure which contains lesser number of unique parameters to be estimated.

Let me give an example to highlight this situation:

A researcher wants to understand the behavioural responses of rats living in their cages in a lab building by doing standard behavioural tests. Since the cages are situated in different floors, in different corners in the lab building, the researcher wanted to see if before experimentation is there any change in their responses to simple behavioural tests. Now let’s suppose there are 1000 rats in each floor and there are 10 floors in the building. That makes it 10000 rats which would be a huge number to study all of them individually. So, we take samples of rats within each floor and the design indicates including a random intercept component for each floor, to account for the fact that rats in the same floor may be more similar to each other than would be the case in a simple random sample. So, if this is true, we would likely want to estimate the variance of behavioural responses among floors.

But we know that modern animal facility guidelines calls for rigorous protocols to be followed and because of that rats are kept in similar cages with as similar conditions as possible. Then we can easily see here that there wouldn’t be much variance in the behavioural responses among the floors. This leads to the scenario i put up before, i.e., variance for floors = 0 and the model would be unable to uniquely estimate any variation from floor to floor, above and beyond the residual variance from one sampled rat to another.

Finally, another option is to use a population averaged model instead of a linear mixed model. As population averaged models don’t have any random effects, but do contain the correlation of multiple responses by the sampled individuals.

For more, read these —

  1. West, B. T., Welch, K. B., & Galecki, A. T. (2007). Linear mixed models: A practical guide using statistical software. New York: Chapman & Hall/CRC
  2. Linear mixed models in R- http://www.r-bloggers.com/linear-mixed-models-in-r/
  3. Model Selection in Linear Mixed Models- http://arxiv.org/pdf/1306.2427v1.pdf
  4. Hessian matrix in statistics- http://www.slideshare.net/FerrisJumah/hessin

Why do we love? An empirical test…

Archetypal lovers Romeo and Juliet portrayed by Frank Dicksee

Yeah love is indeed a mysterious thing and has always captured our imaginations. One of the most famous tragic love stories was the Romeo and Juliet by William Shakespeare. Tragic in the sense that the main protagonists die at the altar of their own love. So, what makes love so special? Or indeed as a biologist i ask what is the need for love. Just look what goes into the love process. Endless dating games, elaborate preparations, endless flirtations, also many humiliations and finally if you are lucky the one acceptance.

But wouldn’t it be simpler to just think about procreation alone, i.e., reproduce for the sake of propagation?? Since, evolutionary struggles dictate that there exists differential reproduction and hence propagation of one’s own genes is the thing which ultimately matters. So, then why do we go for this protracted cycle?

To answer this question albeit in an indirect way authors – Malika Ihle, Bart Kempenaers and Wolfgang Forstmeier all from Department of Behavioral Ecology and Evolutionary Genetics, Max Planck Institute for Ornithology, Seewiesen, Germany conducted a remarkable experiment. The results of this experiment was published recently in PloS Biology – Fitness Benefits of Mate Choice for Compatibility in a Socially Monogamous Species.

As we know that to actually conduct a cost/benefit analysis of love is easier said than done and there would be innumerable ethical concerns regarding the bounds of experimentation with humans. This present study however, used a model animal in an elegant experiment which was designed to find the reproductive consequences of mate choice.

The Experiment

The model species used here was the –zebra finch (Taeniopygia guttata, a native bird of Australia).

Adult male at Dundee Wildlife Park, Murray Bridge, South Australia

Adult male at Dundee Wildlife Park, Murray Bridge, South Australia

They started off with a population of 160 birds that had recently been isolated from the wild, and then set them up on a sort of speed-dating session, with groups of 20 females to choose freely between 20 males (See figure 1 below). Once the birds had paired off, half of the couples (the “chosen” or C group) were allowed to live happily ever after. For the other half, however, the authors intervened like overbearing Indian parents, and split up the happy pair to forcibly pair them up with other broken-hearted individuals (the “non-chosen” or NC group). The bird couples of both C and NC groups were then left in aviaries to breed. The authors then measured the couple’s behaviour and the number and paternity of dead embryos, dead chicks and surviving offspring.

Experimental Design

Figure 1: Experimental Design

Results

Relative fitness estimates (mean ± SE) of males (n = 84) and females (n = 84) from chosen and non-chosen pairs

Figure 2: Relative fitness estimates (mean ± SE) of males (n = 84) and females (n = 84) from chosen (C) and non-chosen (NC)pairs

The first batch of results is elegantly shown in the figure above.The overall reproductive fitness (measured as the final number of surviving chicks) was 37% higher for individuals in chosen pairs than those in non-chosen pairs. But since reproductive fitness is the sum total of different effects which add up to the total number of offspring produced, it’s vital to look at those parameters and understand the mate choice in C group affected the fitness. To start off the authors noted that both the C and NC group laid similar number of eggs which suggests that their initial investment towards egg laying is not affected by the group they are in and also oblivious to the mismatched mate picked up by the authors. But the nests of NC group had almost three times as many unfertilized eggs as the chosen ones, and a greater number of eggs that were neglected (either buried or lost).

The authors in their earlier studies had known this fact that embryo deaths happened mainly due to genetic incompatibility between the parents, however the egg hatching related deaths happened due to behavioural incompatibility. So, the next step was to compare these two phenomena in the two C and NC groups. They found that though the embryo mortality was similar in both the groups, however mortality of the hatched chicks was comparatively
higher in the NC couples. This suggests that its the behavioral incompatibility
between the non-chosen (NC) parents, and not genetic incompatibility which might be the driving factor behind the observed reduction in overall fitness (Fig. 3, below).

Embryo (A) and offspring (B) mortality rates (parameter estimates [mean ± SE]) in chosen and non-chosen pairs.

Figure 3: Embryo (A) and offspring (B) mortality rates (parameter estimates [mean ± SE]) in chosen (C) and non-chosen(NC) pairs.

So, the next question which the authors asked was if it’s the behavioural incompatibility which leads to greater hatchling death then can it be observed during the elaborate courtship rituals which happened before pairing? What they found was that although the NC and C couples spent almost similar time in courtship rituals the NC group females were far less receptive to NC males and also tended to copulate lesser compared to C group. Harmonious behaviour during courtship in zebra finches have been studied in detail and is taken as sum total of these: friendliness, mutual following, synchronous activity etc. So, a couple showing these behaviours in a greater amount would be termed as the ones who show behavioural compatibility and in anthropomorphic terms ”are in love”. An analysis of this behaviour among the C and NC couples showed that on an average the NC couples showed far less such behaviour than the ones in C group.Apart from these results, when the chicks hatched what was seen that greater proportion of males in NC group showed infidelity than in C group and the majority deaths of chicks which happened in the critical period of first 48 hours was due to lesser paternal care in NC group than in C. 

Discussions

The authors in the end ascribe this difference in reproductive fitness to the behavioural incompatibility between the two groups. They also mention – ‘‘The mechanisms behind such behavioural compatibility, in terms of willingness or ability to cooperate with certain individuals and in terms of coordination between partners need further study, in particular in the context of offspring provisioning.”

In humans, some studies suggest that individuals are more satisfied, more committed, and less likely to engage in domestic violence, when involved in a love-based rather than an arranged marriage (2,3,4). The challenge there is also to find out whether stable and happy marriages result from motivation to cooperate (and to identify what stimulates such feelings, see 5-8), or from congruence in terms of partners’ intrinsic behavioural types [9].

References:

  1. Ihle M, Kempenaers B, Forstmeier W. Fitness Benefits of Mate Choice for Compatibility in a Socially Monogamous Species. PLoS Biol. 2015; 13(9): e1002248. doi:10.1371/journal.pbio.1002248
  2. Xu XH, Whyte MK (1990) Love matches and arranged marriages—A Chinese replication. Journal of Marriage and the Family 52: 709–722.
  3. Sahin NH, Timur S, Ergin AB, Taspinar A, Balkaya NA, Cubukcu S (2010) Childhood trauma, type of marriage and self-esteem as correlates of domestic violence in married women in Turkey. Journal of Family Violence 25: 661–668.
  4. Regan PC, Lakhanpal S, Anguiano C (2012) Relationship outcomes in Indian-American love-based and arranged marriages. Psychological Reports 110: 915–924. PMID: 22897093
  5. Asendorpf JB, Penke L, Back MD (2011) From dating to mating and relating: predictors of initial and long-term outcomes of speed-dating in a community sample. European Journal of Personality 25: 16– 30.
  6. Honekopp J (2006) Once more: Is beauty in the eye of the beholder? Relative contributions of private and shared taste to judgments of facial attractiveness. Journal of Experimental Psychology-Human Perception and Performance 32: 199–209. PMID: 16634665
  7. Meltzer AL, McNulty JK (2014) “Tell me I’m sexy . . . and otherwise valuable”: Body valuation and relationship satisfaction. Personal Relationships 21: 68–87. PMID: 24683309
  8. Todd PM, Penke L, Fasolo B, Lenton AP (2007) Different cognitive processes underlie human mate choices and mate preferences. Proceedings of the National Academy of Sciences of the United States of America 104: 15011–15016. PMID: 17827279
  9. Rammstedt B, Spinath FM, Richter D, Schupp J (2013) Partnership longevity and personality congruence in couples. Personality and Individual Differences 54: 832–835.