text:
Fisher's Geometric Model for Dummies
Fisher's geometric model is still today one of the most important and fundamental model in evolutionary biology but it seems to me that most student in evolutionary biology don't really understand it (and I am one of these students). Such standard models are often found on wikipedia but in this case the wikipedia article (here) offers nothing more than a simple analogy.
According to Orr 2005:

Fisher's geometric model shows that the probability $P_a(x)$ that a random mutation of a given phenotypic size, $r$, is favorable is $1-\Phi(x)$, where $\Phi$ is the cumulative distribution function of a standard normal random variable is a standardized mutational size, $x=r\frac{\sqrt{n}}{2z}$, where $n$ is the number of characters and $z$ the distance to the optimum.

Can you please explain what the Fisher's geometric model and the math behind it (how are these functions calculated)?

If you haven't seen it, you might find this blog post from Dynamic Ecology interesting: The fitness landscape of beer: Fisher’s geometrical model vs. IPA.

Fishers Geometric Model (FGM) is a theoretical prediction about the adaptation process in traits. There are a number of things to establish before attempting comprehend FGM. Firstly, shifts in an adaptive landscape, in natural scenarios, are generally quite small. Because populations have been evolving for such a long time and the small shifts in adaptive peaks it means that most populations should be near or on a local fitness optimum within the relevant landscape. The adaptive landscape comes from S. Wright, discussed in the Orr paper, where he spoke of a field of possible gene combinations" each with a fitness value, some combinations are fitter than others. Adaptive peaks represent the fittest combinations of all traits.
We can picture all of this using a single trait:

Here there is a local optimum (which is also global, Fisher assumed that adaptive landscapes are less rugged than Sewall Wright and had only one optimum) for the trait. The population starts at point A (the red ball), just off the adaptive peak for the initial fitness distribution (blue trace). Then selection alters (red trace), initiating a new bout of adaptation, making the population (green ball) further from the optimum.

From here lets imagine this is two seperate populations, the green population (A) wants to move along the red line to the highest point, the red population (B) along the blue line also for its own optimum.
If a mutation which has a very small effect arises then there is 50% probability in both populations that it will improve fitness (assuming no neutral mutation) because it will either be recessive or deleterious. This value is shown in box 2 figure 1 of the Orr paper.

Now if a large effect mutation was to arise in then there is less chance of it being beneficial for population B than for population A, this is because B is closer to its phenotypic optimum so a large effect mutation is likely to "overshoot" the optimum. (The black line illustrates where either population would end up after a large effect mutation in the direction of the adaptive peak). You can see this mutation would put population A very close to its peak (the red line) and population B would have a reduced fitness. This is summed up nicely in the opening to the Orr paper.

"Precise adaptation is possible only if organisms can come to fit their environments by many minute adjustments"

So, descriptively, this is why we see a reduced probability of large effect beneficial mutations as we approach an optimum; we expect most bouts of adaptation to be due to small shifts in adaptive landscape and populations to start bouts near to the former adaptive peak.
The FGM expands this out beyond one trait (or, as Kauffman and Gillespie did, the sequence - with the latter seemingly being more successful than the former). As shown in figure 1 of the Orr paper,

This is a highly multidimensional space, with one dimension per trait, therefore more complex organisms by definition will have more complex spaces. The centre of this sphere is the phenotypic optimum, and each layer represents and adaptive movement, or substitution of the wild type as in the Maynard-Smith adaptive walks system. The red line shows the movement made by the population through this sphere.
And now for some math. (Refer to box two in the Orr paper). $P_a (\chi)$ is the probability that a mutation (of effect size $r$) is favourable. Thinking about what I discuss above, that is, $P_a (\chi)$ is the probability that a mutation will increase fitness, which must therefore be a product of the landscape complexity (the number of characters affecting the fitness), the distance from the population to the optimum, and the size of the effect. $\chi$ is the standardized mutation size, which accounts for the distance to optima, as $z$, the number of characters affecting fitness as $n$, and the effect size $r$. This means that $\chi$ = $r \sqrt{n} /2z$: therefore an increase in the distance to the phenotypic optimum ($z$) reduces the value of $\chi$ (thus increasing the probability that a mutation has a beneficial effect), whereas increases in $r$ or $n$ will decrease the probability that the mutation is beneficial (i.e. large effect mutations and high complexity). In the context of the above example, both populations have an $n$ of 1, and the values of $z$ vary - this means population A can tolerate a larger value of $r$, or in other words, population A is more able to make adaptive substitutions because it is further from peak fitness, thus more tolerant to large effects, than population B.

Difference between $r$ and $\chi$: $r$ is the raw value of effect size of a mutation, whereas $\chi$ is the standardized effect size. Assuming equal effect sizes the relative effect of mutations will depend on the number of traits affecting fitness (see the diagrams below), thus we have to correct for this.
How does complexity affect the probability of beneficial mutations? Look at the following diagram.

Each point represents a possible combination of traits when we have three traits affecting fitness, each with two possible incarnations (A or a [most fit and least fit respectively]). The outer green ring has one possible occupying combination (aaa) which is the least fit - it's the furthest from the optimal combination, the red point. On the green ring the probability of increasing fitness with a mutation is 3/3 (because there are 3 possible mutations all of which increase fitness). Note, assume all changes are equal effect. If we change one trait to the A version we can have 3 possible outcomes (Aaa, aAa, aaA) which occupy the yellow ring. From the yellow ring a further single mutation has four possible outcomes, of which 3 can increase fitness so the probability of improving fitness with a mutation is reduced (3/4). In the blue ring there is a 1/4 probability of increasing fitness. Jumping to the red point, where all traits are A, every possible mutation will reduce fitness (0/3).

Bearing this in mind, let's change n (the number of traits affecting fitness) from 3 to 4. The least fit starts in the red ring (aaaa), from here there is a 4/4 probability of increased fitness via mutation. From the green ring there is a 6/7 probability. From the yellow ring there is a 4/8 probability of increasing fitness, and from the blue a 1/7 chance. Compare this to when n=3, from the blue ring there was a 1/4 chance, when n=4 it is 1/7 - there are more possible combinations and still only one fittest combination
Put another way; if we consider it in terms of single point mutations in a sequence, a mutation which affects 1 trait affecting fitness (n=1) then the probability it will increase fitness is drawn from the distribution of probable fitness effects. If the point mutation affects 5 traits then the effect is drawn from the distribution 5 times, and because we assume most mutations are deleterious and beneficial ones are relatively constrained to being small, there is a greater probability of reducing fitness when n>1.
Fisher's geometric model
Below is some R code to generate the model's prediction that small effect mutations have a 50% chance of being favoured. The probability crashes rapidly as the effect of the mutation increases.
r = .01 # random mutation of phenotypic size r
n = 1 # number of characters
z = 1 # Distance to the optimum
standardized.mutational.size <- function(r, n, z) {
  (r * sqrt(n))/(2 * z)
}

# Get the standardized mutational size of various distances to the optimum
mutation.sizes = standardized.mutational.size(r, n, seq(0, 3 , by = .0001))
plot(x = mutation.sizes, y = 1 - pnorm(mutation.sizes),
     xlim = c(0, 3), ylim = c(0, 1),
     type = "l",
     main = "Fisher's geometric model of adaptation")
abline(h = c(0, .5), lty = 3)

Thanks a lot for your answer! The first part was very clear to me. I would rather think that complexity influences the number of traits $n$, therefore increasing $\chi$, though. Isn't it the way it should work? I still don't fully understand where does the $r\sqrt{n}/2z$ comes from. The formula would have been $2n\sqrt{r}/2z^2$ I would not be able to argue that the formula is wrong. Also I am a bit unsure of my understanding of the differences between $r$ and $\chi$.

edits made for you @Remi.b


text:
Book Recommendation: Complex Traits and Complex Genetic Architecture
I am looking for a book (or any good source of information) that offers an in-depth discussion and models about the evolution and analysis of complex traits and complex genetic architecture. Do you have any suggestions?
I'd define complex traits as those traits whose variance in the population is explained by quite a lot of various genetic loci and environmental factors.

You might like "The mathematical theory of selection, recombination, and mutation" by R Bürger

Lynch and Walsh's Genetics and Analysis of Quantitative Traits (http://www.sinauer.com/genetics-and-analysis-of-quantitative-traits.html) is pretty comprehensive (if not necessarily comprehensible).

Suggestions:
Evolutionary Genetics : Concepts and Case Studies: Concepts and Case Studies
Edited by Lexington Charles W. Fox Department of Entomology University of Kentucky, Faculty of Life Sciences University of Manchester Jason B. Wolf Lecturer
The mathematical theory of selection, recombination, and mutation
by R Bürger (as suggested by @rg255 in the comments)
Lynch and Walsh's Genetics and Analysis of Quantitative Traits
(as suggested by @kmm in the comments)
PS: Do tell us if you found a better one.

source: Stack Exchange

license: Creative Commons - Attribution Share-Alike - https://creativecommons.org/licenses/by-sa/3.0/

sort: votes


text:
Study case of the inheritance system of Oenothera
I've been told some interesting facts about oenothera. Apparently in this species some lineages have been through some translocations and in results to these translocations and in consequence, some chromosomes are inherited exclusively through the female function while some other chromosomes are inherited only through the male function.
Can you please provide a summary description of how things work in this species concerning the meiosis, the haplo-diplontic life cycle and the recombination?

I would recommend looking at this paper, and in particular the section "A SHORT INTRODUCTION TO OENOTHERA GENETICS":

The genetics of Oenothera is a genetics of entire haploid genomes (Renner complexes), which inherit as single units and are entitled with names such as hjohansen, Galbicans, or Gflavens (for terminology, see materials and methods). All loci of a Renner complex are in linkage disequilibrium.

Figure 1 from that paper shows what that looks like in meiosis, in terms of its impact on chromosome pairing- basically chromosome pairing forms rings of chromosomes linked by translocations.

Figure 1.
Additionally, it appears that plastid inheritance is biparental to some extent. These also contain pollen-lethal factors related to autosomes, such that cytonuclear incompatibilities kill pollen recombinants between a Renner complex and plastids (eggs are unaffected, Figure 2, 3):

Consequently, the F1 generation is identical to the parental generation without segregation of traits, since chromosomes separate as a set without intermixing.

The set of plastids ("plastome") will then break down in the F2 such that assortment can happen.

Selfing of appropriate flowers ensures that only plastome I is inherited to the next generation. However, in F2 the nuclear genome splits into the progenies CC-I and AC-I...

Since the ring of 14 chromosomes should inhibit free segregation of individual chromosomes and practically homologous recombination between haploid genomes in the hybrid AC, the coding potential of the A and C genomes is not mixed and a seemingly unchanged, homozygous CC genotype, now associated with plastome I, occurs in F2.

Figure 2.

Figure 3.
So it's rather complex. Hopefully that sheds some light on it, but it's understandable if it's still confusing!

all_licenses: Creative Commons - Attribution Share-Alike - https://creativecommons.org/licenses/by-sa/3.0/

all_licenses: Creative Commons - Attribution Share-Alike - https://creativecommons.org/licenses/by-sa/4.0/


text:
How important is the womb's environment?
- Background -
Talking about one phenotypic trait, the total phenotypic variance $V_p$ is decomposed into genetic and environmental variance for this trait, represented by the symbols $V_G$ and $V_E$ respectively. Also the covariance between genes and environment affect $V_p$ but we'll ignore it for the purpose of this question.
$$V_p = V_E + V_G + O(Cov(E,G))$$
The genetic variance $V_G$ can be further decomposed into dominance and additive variance. Similarly the environmental variance can be further decomposed into for example, for the mammals, the variance in the womb $V_W$ and the other environmental variance $V_{OE}$.
$$V_p = V_w + V_{OE} + V_A + V_D$$
$$h_N^2 = \frac{V_A}{V_p} = \frac{V_A}{V_w + V_{OE}}$$
One could measure $V_W$ by looking at correlation between dizygotic twins.
- Question -
What part of the environmental variance in phenotype is explained by the variance in wombs' environments? i.e. what are the estimates of $\frac{V_w}{V_e}$ or what are the estimates of $\frac{V_w}{V_p} = \frac{V_w}{V_w + V_{OE}}$?
Of course the answer will depends on the trait and on the species. I welcome either a general idea/estimation or any such measure for any specific trait.

Variance in womb is dependent on environmental variance. Some environmental effects are suppressed (some inflammatory modulators) whereas others are not. I am afraid your question is too broad. Moreover you are bringing in a lots of concepts together.

The womb environment depends on the non-womb environment (and probably on the genetics). Yes, there are covariance terms between $V_w$ and $V_E$ (and probably between $V_w$ and $V_G$) just like there are covariance terms between $V_E$ and $V_G$. It doesn't change the fact that there is a true $V_w$. My question might eventually be too broad because I don't specify a given phenotypic trait of interest. I edited my question to welcome answers about any phenotypic trait. I don't fully understand why my question mix up many different concepts? It sounds pretty straight forward to me. Thks

By "a lot of concepts" I mean that this question can be asked in a rather simple way. It would be straightforward to you because you posed it and you work on the area of population-biology. If I am not wrong this question has more to do with physiology (basically how well is a mammalian embryo protected against the environmental changes). Now it depends what those environmental changes are- temperature, chemicals (drugs), inflammation etc.

Now the second part is whether these changes that are relayed (with some attenuation?) to the embryo, will have an effect on some trait. So there are several environmental effects. You can break the question down that way; nonetheless you have to specify some environment variable else it gets too broad. Trait is still predictable if you fix the environmental factor.

Standard studies that aim at measuring the underlying genetic/environmental variances of variance in phenotypic trait do not look at the physiological mechanisms but only tries to determine the statistical associations. Also, typically these studies measure heritability through parent-offspring regression but does not make any consideration of specific environmental variables. You talk about "the second part of my question". Are there two parts in my question?

While I am defending my post, I have to accept that it has been asked 7 months ago and you are the first to even comment (Thank you) on it so you might somehow be right, or at least reflect why people did not answer.

Let us continue this discussion in chat.


text:
How does a test like the Natera Panorama distinguish the child's genome from the mother?
Blood tests on an expectant mother, like the Natera Panorama, are now being used regularly to screen fetuses for chromosome abnormalities. At my wife's recent prenatal visit, she wasn't really even asked if she wanted the test; apparently it's standard in that office. I understand some of the science of how such a test could be performed. When the placenta implants, some of the fetal cells get into the mother's bloodstream, and even though the chromosomes from those cells are breaking down, these pieces can be scanned for the markers for genetic disorders and gender. But I don't understand how the fetus's maternal chromosomes can be identified against the background of the mother's genome.
For example, one of the things that they can rule out with pretty high certainty is Monosomy X: only one X chromosome and no Y. I can see that after determining the mother's genome, it could be determined if there was no evidence of an X or Y from the father. But how would they know if an X was not contributed by the mother. If they are looking through decomposing pieces of DNA in the mother's bloodstream, are they not guaranteed to find each of her X chromosomes, whether or not the fetus has a copy of either one? How can they determine that any piece of a maternal fetal chromosome really came from the fetus rather than from the mother?

Disclaimer: I do not endorse Natera over any of their competitors. I mention them by name only because they are the only ones I am familiar with.

As you correctly discerned, distinguishing the fetal cell free DNA from the mothers cell free DNA in the blood stream is one of the major challenges in this type of diagnostics.
Consequently, the results that can be directly linked to the paternal DNA are the most reliable.
In the case of the example you give, the high accuracy stems from the fact that in case of Monosomy X, there is no indication of neither a paternal Y nor a paternal X chromosome. While the Y chromosome is the most easily discerned, a paternal X chromosome could also be detected because in that case the cell free DNA would contain fragments of three distinct X chromosomes.
This publication summarizes the issue nicely:

Once the cell-free DNA fragments have been purified, small differences
  between the fetal and maternal DNA sequences are exploited in order to
  make a specific fetal diagnosis. To date, the majority of studies have
  focused on the detection of paternally inherited sequences that are
  entirely absent from the maternal genotype, such as those on the Y
  chromosome of male fetuses. This target is particularly attractive as it
  comprises a large portion of DNA which is not otherwise present in
  women.
Variable regions of repeated DNA (short tandem repeats or
  STRs) can be used to identify paternally inherited sequences, by comparing
  the number of repeats present with the paternal and maternal sequences.
  Paternal alleles on the autosomal chromosomes can also be detected, if
  they are known to be absent in the maternal genome, but this requires
  detailed sequence knowledge of the paternal genotype of interest, as
  well as detection methods that can distinguish DNA sequences that
  might only differ by a single nucleotide. Importantly, all these methods
  rely upon the fetus inheriting a uniquely paternal sequence that is conveniently
  located for a particular diagnosis.

The reliability of these tests can apparently be improved by enriching the fetal cell free DNA:

Selective enrichment of fetal DNA, based on a difference in the
  average physical length of fetal and maternal DNA fragments, which
  can be exploited to increase the relative amount of cffDNA. Fetally
  derived DNA fragments are generally smaller than those that are
  maternally derived, being predominantly ,313 base-pairs in length
  (Chan et al., 2004). Therefore, by using standard size fractionation
  to select only DNA fragments ,300 base-pairs, circulating cffDNA
  can be enriched such that it comprises around 70% of the total cellfree
  DNA (Li et al., 2004b), prior to detection and identification by
  either PCR (Li et al., 2005) or mass spectrometry (Li et al., 2006).

Tell me if I have this right: If we see no sign of a paternal X, then obviously we have monosomy X. But if we do see it, then in the enriched cellfree DNA, because an inherited maternal X chromosome would be represented throughout the entire sample of enriched cellfree DNA while a non-inherited maternal X would only be represented in about 30% of it, we should  find approximately 3.3 times as much of one maternal X than the other to rule out maternal monosomy X. Or we would expect to see about the same amount of each maternal X if we have a problem. Is this about right? It's based on quantity?

I think in case of paternal chromosomes, the test is not only based on quantity but also on marker gene sequences (DNA fingerprints). From the blood sample, the DNA fingerprint of the maternal chromosomes are known from DNA contained in white blood cells. Now if the enriched cell free DNA contains DNA fingerprints of a X chromosome that does not belong to the mother, then its a girl, if we see DNA fingerprints of a Y chromosome, it's a boy and if we see neither, its a monosomy X.

However, excluding trisomy 21 has to rely to 50% on quantity of maternal chromosome 21 material because the trisomy could be caused by the child inheriting two chromosomes 21 from the mother which would only show up as an increase in chromosome 21 material in the cell free dna.

Oh. You're saying that monosomy X only happens paternally. It doesn't ever come from a missing X chromosome in the egg?

Honestly, I hadn't thought of that possibility. Of course, that could also happen...


text:
Nature vs Nurture in Rooster mating dance: Are roosters lame dancers because they never learned any better?
I've seen, read about, or heard several avian courtship dances, flights, songs, and other rituals. They are general very impressive, fancy, and beautiful. Roosters, by comparison, are quite lame. The link is a pretty good example of the sort of best effort I've seen or read about from a rooster. A lot of them seem to do quite a bit less than that.
Some time ago, I remember hearing about the various distinct finch calls, and the fact that these songs seem to be largely a learned behavior. In a modern factory style farm, chicks are usually hatched in an incubator and raised by humans in a brood of similar aged chicks. Some chicks, on more traditional farms perhaps, are raised in more natural settings with a mother hen present, and perhaps a rooster too, but even when a rooster grows up observing his father's behavior frequently, I wonder how often such a parent child example extends beyond just a few generations. In other words, I doubt that any chicken benefits from a long lineage of fine courtiers like their wild finch cousins.
My question then is primarily whether there is potentially a learned component of roosters' dancing, and secondarily whether a lack of sustained parental lineage stunts the development of proper dancing in a rooster. They certainly have some sort of instinct driving them, and they're beautiful birds, but their mojo falls flat compared with more wild relatives, like this pheasant. To avoid soliciting opinion-based answers, I want to focus this question on specific verifiable knowledge. For example:

Are there any actual studies on this topic with roosters or close relatives? Or is there any husbandry knowledge on the subject? For example are specific steps recommended to preserve the beautiful mating behavior of pheasants or pea fowl in captivity?
Is there any evidence of learned avian  mating behavior other than song?
Are there any notable recorded differences between the mating dance of an orphan rooster compared with roosters that have grow up with fathers?

Don't feel like you need to address every point for a good answer. I'm just trying to provide a framework for avoiding opinion-based answers.

Maybe the hens think differently and do not see their roosters behaviour as lame? I mean, how is a human supposed to judge? Also, the hens have nothing better to compare to. So why the effort to improve?

@skymningen While I can certainly agree that chicken thought is very foreign territory, I do think there is a reasonable case that appreciation of beauty, music, and dance is somewhat shared between our species, the primary evidence being the fact that we do in fact appreciate most of these things in birds. Many have theorized that music and dance are features of bipedal existence. The other evidence is the seriously cool dance of close relatives to chickens.

@skymningen I think your point about having no need to improve is probably very accurate as most coops are limited to one rooster. But this goes right along with the point of my inquiry. That limit is part of the artificial bounds we set, and goes hand in hand with the lack of good example from older roosters.

I totally agree with you that the appreciation of beauty is shared, my idea is only that the perception of beauty might be completely different. Think of some animals that are attracted to smells or sights which for humans are disgusting.


text:
Book recommendation on selective breeding?
I am looking for a good book that gives a nice overview of the science and technics of selective breeding. I am particularly interested in the use of population genetics to support decision in this applied field. So do you have any suggestions?

Below are some examples of question that I would hope that book answers.

If the artificial selection is too strong, it is very likely that some beneficial alleles will disappear. How strong do we decide artificial selection to be?
How do we deal with various lifetime?
How do we deal with species that can have only few babies so that we are obliged to limit the strength of selection to keep a stable population size?
How do we make sure to not reduce the immunologic genetic diversity within the population to zero but only to reduce the diversity of genes that influence the trait of interest?
Is it better to wait to be able to measure the quantity of milk in the sisters of a bull in order to infer about the quality of the bull or is it wiser to directly allow the bull to reproduce to speed things up?
How are the estimations of heritability performed?
How do we measure the impact of various environment of the trait of interest?
Which cultural traits should we allow to spread and which should we stop?
How can we use a correlated trait to infer more simply or more rapidly of the amplitude of the trait of interest?
How much outbreeding should we allow?
When is mutation breeding preferable?

and more globally some questions of the kind:

What are the different solution to artificially inseminate?
Can we select on the spermatozoids?
What financial elements should be taken into before creating a breeding plan?
GWA's studies in selective breeding
What is the relative importance of selective breeding and GMO in current economy and in our current ability to improve strains for our purposes?

i would imagine that there are so many solutions amongst the scores of animals and plants that are bred selectively that such a single book does not exist.  selective breeding is a millenia old discipline depending on the culture and the strain and a lot of these genetic terminologies are relatively recent.  The differences between say a sheep and an orchid are so vast I'm not even certain that it would be worth the effort to try to cover all these topics in one volume.

I will Suggest:
Selective Breeding in Aquaculture: An Introduction
Authors: Trygve Gjedrem, Matthew Baranski
ISBN: 978-90-481-2772-6 (Print) 978-90-481-2773-3 (Online)
http://image.slidesharecdn.com/selectivebreedinginaquaculture-anintroductionreviews-methodsandtechnologiesinfishbiologyandfisheries-140725230642-phpapp01/95/selective-breeding-in-aquaculture-an-introduction-reviews-methods-and-technologies-in-fish-biology-and-fisheries-10-1-638.jpg?cb=1406347865

authors: Элл Нейгебауэр


text:
Why did Fair Meiosis evolve?
How and why did Fair Meiosis evolve?
I can hardly think that it provided a fitness advantage to the individual carrying the mutation. Why would it? Or did it evolve through lineage selection? Or was it some kind of pre-existing characteristic of meiosis that have no energy cost and therefore never disappear?
UPDATE:
I call fair meiosis the process of meiosis in which each chromosome has an equal chance of transmission.
I'm trying to make my questions clearer.
Is there any selection pressure in favour of alleles causing a fair meiosis? If yes, why? It doesn't seem to me to provide any advantage to the individual carrying this allele.

It would be helpful if you defined what you mean by 'Fair Meiosis'.

I do not understand what you mean with the last sentence.

I think you mean 'segment of a chromosome' if a chromosome's missing that's usually fatal in animals!

There's a more meta-level discussion on meiosis, which is that there is an advantage for genes which can get themselves selected in meiosis more often would prosper.  This is a prediction of the biologist Robert Trivers, working with Bill Hamilton in the late '60s, known also as the 'selfish gene'.  In fact the reason the selfish gene theory was so successful was that selfish genes - individual genes which propagated to offspring more often than 50% of the time. The Dawkins book 'The Selfish Gene' discusses this whole idea and its ramifications in great detail.
So at different times in evolutionary history, its hard to imagine that meiosis was completely 'fair' (50/50% change of any segment of DNA being chosen). In fact its pretty clear from genetic analysis now that some longer segments of DNA tend to be transmitted in one piece, though overall its still felt that the propagation if 50% likely for any given segment.
Over the long run, its been shown, mostly through game theory, that honesty is the best and most profitable policy.  The most famous of these experiments was the Prisoner's dilemma competition where all comers have been invited to submit computer programs which can either betray or cooperate.  Overall cooperative programs with some safegaurds which punished cheaters win overall.  Over time its been shown that systems tend to defend themselves from cheaters and that cooperation and 'fairness' do much better than mobs of cheaters running amok.
Cheaters and unfairness still continue to happen though - systems which are fair tend to invite in a small number of cheaters.  Say what you will about the income tax as a system!  there is variance over time as to how 'fair' a system can be.
Dawkins has even said he sometimes thinks he should have titled the 'selfish gene' something else instead: 'Nice Guys Finish First'.  I doubt that the book would quite have done as well though... we all should be fair, but it doesn't seem to invite book sales.

Thanks for your answer @shigeta. Now that I think of this question again, I think that something goes wrong in your answer.

I think the use of game theory doesn't bring much explaination for the fair meiosis. Whatever the game, altruistic behaviour may evolve thanks to genetic relatedness (Hamilton) or reciprocity (Trivers). For both of them it is important that the behaviour is not expressed toward any random individual/unit (therefore you need population structure, recognition or whatever). But such non-random interaction is not possible for alleles (at least under the assumption of random mating). Moreover, one might argue that the concept of relatedness does not stand when looking at two alleles. Am I right?

well the chromosome can be viewed as a highly structured community which plays a game every time it reproduces offspring.  that's the formulation that game theory might use look at meiosis. there are other ways, but this is I think a common view now.

I think that Fair meiosis (I assume that you are referring to that chromosomes have an equal chance of transmission) can be seen as a byproduct of recombination at meiosis, which makes every chromosome a mosaic of maternal and paternal chromosomes. Therefore, selection does not act on chromosomes as a single unit, and "unfair" meiosis becomes meaningless. The "value" of "fair meiosis" most likely lies in the adaptive value of recombination.
Addition: To expand on the value of recombination, it has been suggested that the specific value of recombination is to break up linkages and thereby prevent meiotic drive (Haig & Grafen 1991, also see Okasha 2006). Haig & Grafen 1991 also specifically discuss selection for recombination. For an interesting analogy between economy and fair meiosis (veil-of-ignorance vs fair meiosis) see Okasha. 2012. Okasha notes that:

In both, randomization is used to deprive self-interested agents (genes and individuals) of information about their identity, forcing them to adopt an impartial perspective.

In summary he also writes that:

A gene that distorts segregation in its favour can spread despite reducing its host’s
  fitness. This harms genes at unlinked loci, who are thus under selection to
  restore fair meiosis if they can. One effect of fair meiosis is to equalize the
  interests of all the genes, thus ensuring they work for the common good.
  One way of achieving this is to destroy linkage, given that segregation
  distortion typically involves linked genes working in concert.

Okasha's paper also has a nice and accessible description of the biological process, and of the selective forces that are operating to counterbalance meiotic drive/segregation disorders.

Thanks @fileunderwater! I updated my question, hoping to make it more understandable. Does your answer mean that there is no selection in favour of fair meiosis (as fairness is an inveitable consequence of meiosis)?

My point is that I think it is likely that the main "target" of selection is recombination, and that fair meiosis is a byproduct. However, I'm not a geneticist and there are maybe experiments showing selection for fair meiosis. Also, you should look into meiotic drive (preferential selection of certain gemetes), e.g. in sex-chromosomes; see http://www.plosbiology.org/article/info%3Adoi%2F10.1371%2Fjournal.pbio.0050293 and http://www.annualreviews.org/doi/abs/10.1146/annurev.ecolsys.32.081501.113958.


text:
$F_{ST}$ and the genetic variance in metapopulations
From this video (21'15''), the speaker gives the following formulae in order to calculate the between and among populations genetic variance from the $F_{ST}$:
$$V_{Among Pop} = 2 F_{ST}V_G$$
$$V_{Within Pop} = (1-F_{ST})V_G$$
, where $V_G$ is the total genetic variance of this population if it was well mixing. $V_{Among Pop}$ is the variance among populations and $V_{Within Pop}$ is the variance within populations.
In this same video, the speaker defines $F_{ST}$ as:
$$F_{ST} = \frac{\text{Var}(p)}{\bar p (1-\bar p)}$$
, where $p$ is a vector of frequencies of a given allele and $\bar p$ and $\text{Var}(p)$ are the mean and variance of this vector.
For example, consider a metapopulation made of 4 subpopulations. The allele frequencies in these 4 subpopulations are p=[0.2, 0.5, 0.8, 0.3]. $\bar p$ is the mean of $p$ ($\bar p = 0.45$) and $\text{Var}(p)$ is the variance of $p$ ($\text{Var}(p )=0.07 \space$).
Can you help me to make sense of the formulas for $V_{Among Pop}$ and $V_{Within Pop}$?
Can you prove these formulas? I would expect that $V_{Among Pop} + V_{Within Pop} = V_G$ but it doesn't! Maybe the issue has to do with the fact that $V_{Within Pop}$ as defined above fit to haploid population and not diploid populations. Then, I would expect that for diploid populations $V_{Within Pop}$ becomes $(1-2F_{ST})V_G$. Is that correct? Just in order to make it more general, how do you extrapolate these definitions for a tetraploid population? Also maybe I misunderstand the meaning of $V_G$. Is the variance within population the sum of the within populations variance or is it the average (or something else)? Thanks for your help!


text:
Anatomy suffixes?
Here are some suffixes I want to know their meaning:
-ium: trapezium (carpal bone)
-ius: trapezius  (column muscle)
-ous: Talous and calcanous (tarsal bone)
-alis: Acromialis, Ulnaris (shoulder proccess and bone of forearm)
-ate: Capitate, Hamate, lunate (carpal bones)
What is the difference between tuber and tubercle?
How to know where to use these suffixes, they are randomly given or it has to do with location or something?

I cant even understand what you are asking here. Edit your question please.

These names originate from latin [1].

-ium is the second declension of a noun in accusative (trapezium refers to a bone thus is a noun)
-ius is a genitive adjective suffix (trapezius is the name of a muscle thus being an adjective)
you have written them wrong: talus and calcaneus is correct. They are both nominative nouns.
there is no -alis suffix; it is either -is or -lis. They are adjective suffixes.
there is no -ate suffix. Those are nouns.

Tuber is the latin word for tuberosity. It is the quality or condition of a tubercle (adjective) [2].
References:

Wikipedia, Latin declension
The Free Dictionary by Farlex, Tubercle


text:
Inbreeding Coefficient and Coefficient of Relationship
Wikipedia gives the following formula to calculate a "path of coefficient of relationship" between an ancestor $A$ and an offspring $O$:
$$\rho_{AO} = 2^{-n} \left( \frac{1+f_A}{1+f_O} \right)^{1/2} = \left( \frac{1}{2}\right)^n \sqrt { \frac{1+f_A}{1+f_O}}$$
, where $f_A$ and $f_O$ are the coefficient of inbreeding of the ancestor and the offspring respectively.

Question
Where does the term $\sqrt { \frac{1+f_A}{1+f_O}}$ comes from? Please explain why this multiplicative term is $\sqrt { \frac{1+f_A}{1+f_O}}$ and not something different such as $\frac{f_A f_O}{2}$ for example.

In his paper Wright presents this as an alternative to other (existing) formulas. The ultimate test is whether it predicts herd health well and because of broad assumptions underlying the model it may be hard to tell how good a formula is. In general we want something close to 1 unless there is strong homozygosity somewhere. Yours won't work because it would likely be very small.

Great Question! If you have solved this, please gives us the complete derivation.

No I haven't solved it yet. Thks a lot for coming back to this post. I'll read your answer in 48h as I won't have time before!


text:
How to name a case of convergent evolution where the same mutation is involved in both lineages?
Definition of Convergent evolution - from Wikipedia > Convergent Evolution

Convergent evolution is the independent evolution of similar features in species of different lineages. Convergent evolution creates analogous structures that have similar form or function, but that were not present in the last common ancestor of those groups.

Examples
-- Lactase --
Humans have evolved the ability to digest lactose at adult age twice; once in Europe and once in Africa. Different mutations are involved in European lineage and in African lineages.
Source: Tishkoff et al. (2007)
-- White fur --
Beach mice and mammoths have both evolved white fur independently. The same mutation is involved in both lineages.
Source: Römpler (2006)
Question
Is there a term to differentiate convergent evolution that involves the same mutation in different lineages from convergent evolution that involves different mutations in different lineages?

This is really a question of semantics, as there are not universally accepted definitions of these terms. The first example (lactase) is by the given Wikipedia definition not really "convergent evolution" since it states that it occurs "in species of different lineages" - not within one or very closely related species. This is a mutation that occurred twice in lineages of one species, and might be better termed genetic parallelism. I am not sure it is useful to speak in vague macroscopic terms like "convergent evolution" when discussing subtleties at a genetic or molecular level.

I have to say I really enjoyed this question, it was a new thought for me and it's great when biology SE gets to poke around in these sorts of very academic areas! Enjoyed reading for it and scouring literature for an answer - nice one @remi.b thanks for posting such a good question!

As you already know convergent evolution is when two separate lineages evolve the same (or similar) trait, and this is generally assumed to be a result of similar selection. For example, if flying helps both birds and bats catch insects to eat, then convergent evolution of flight is somewhat unsurprising. Other examples include those of mimicry where the convergence on a common colour patterning reduces predation rates.
The wikipedia page on convergent evolution does a pretty good job of explaining the subtle difference between parallel and convergent with its illustration. In convergent evolution, two lineages have dissimilar starting points and arrive independently at similar end points. In parallel evolution, two lineages have similar start points and arrive independently at similar end points.

"...a convergent change at an amino acid site refers to changes from different ancestral amino acids to the same descendant amino acid along independent evolutionary lineages ... It is distinguished from a parallel change, in which amino acid changes along independent lineages have occurred from the same ancestral amino acid (see fig. 1A for examples). Both convergent and parallel evolution, if verified, suggest adaptive evolution."

This, and other sources (page 113), suggests that convergent and parallel evolution/change/mutation are relevant terms. In your context you are looking for a term to differentiate two cases.

Case 1: convergent evolution of a trait, by different mutations; either different nucleotides at the same site, or mutations at different sites. This is seen in your lactase example.
Case 2: convergent evolution of a trait, by the same mutations; the same nucleotide changes at the same site(s). This is seen in your white fur example.

A key point is that your are looking at convergence at two different levels, phenotypic and molecular. In both cases you have convergent evolution at the phenotypic trait level. The cases differ at the molecular level where you have convergent and non-convergent mutation respectively. I have found one term being used in this paper.

"In some cases it has been shown that different loci are involved in phenotypic convergence (e.g. Refs [8,25,26]), demonstrating that similar phenotypes can be reached through alterations of distinct enzymes. However, other studies have traced phenotypic convergence to modifications of homologous genes (e.g. Refs [3,5,6,26,27]); in this paper such phenomena will be further referred to as convergent recruitment (Glossary)."

I think the term you are looking you are looking for would be  convergent recruitment to describe case 2, and some form of antonym* for case 1, perhaps you could say non-convergent recruitment. However, non-convergent could make it sound like no convergence is occurring so I'm not a fan and you should be careful in any papers to clarify. I'd say something like:

"Phenotypic convergence by the recruitment of different genes and/or mutations is herein referred to as non-convergent recruitment, in this case convergent evolution is occurring but through non-homologous changes at the molecular level."

You would then have to be consistent in your writing, talking about the phenotypic level as (non-)convergent evolution and molecular level as (non-)convergent recruitment. E.g.

"The convergent evolution of flight occurred as a result of non-convergent recruitment."

* I couldn't find an established antonym term, and as I suggest in the post, I don't think non-convergent recruitment is a perfect antonym, we could discuss it in chat or the comments to try to come up with some new terminology. Perhaps "non-homologous convergent evolution" or "non-genetic convergence" would work? In this paper they simply refer to it as "convergence through different mutations".

I'm not aware of a specific term for this. In the places I can find the concept discussed in the literature (here and here), the authors simply describe the pattern as "the same [...] mutation in two lineages" and "parallel mutation in two lineages".


text:
Data on Recombination rate and Gene Positions in Humans
I am looking for estimates of per base pair recombination rate in humans and indication on gene positions. The goal is to be a able to plot recombination rate in the y-axis and position (in bp) along all chromosomes in x-axis. Then add the genes on the graph.
I have never had to search for this kind of data. Can you give me some hints of where I could find these data into a simple format like the simple table presented below?
> Gene Positions
from       to
1145146    1147112
1568742    1570012
...

> Recombination rate
from   to       r
1      156787   1.2*1e-9
156788 256888   2*1e-8
....

A good baseline for this type of research in human genetics is Standards and guidelines for the interpretation of sequence variants from ACMG. It is a guideline for clinicians, and it gives a good sense of good variants data, bad variants data and setting up confidence level.
Try to consolidate data from:
Population databases

GWAS databases

Exome Aggregation Consortium
1000 Genomes Project (there is data in vcf format which is pretty much what you need)

SNV databases

dbSNP (it is uncurated so you got to be hypercritical)

Disease databases

ClinVar (be aware "12% of interpreted variants have ≥2 submitters in ClinVar , and 21% are interpreted differently")
OMIM
HGMD (it is a gold standard as it is curated, but you could only search for variants but not collect data because you could not download it unless you pay for it)


text:
What is this white stuff inside apples?
I live at Vancouver, BC, Canada and I often find this kind of white stuff inside the apples I buy

I used to live in Europe (Switzerland) and never saw that before. It very much look like a fungus. Is it? What species is it? What is the geographical range of this species?
When I cut my apples open, I just remove it and assume it did not lay too many mycotoxins in the rest of the apple but when I directly bite into the apple, it has happened to me to eat some of this white stuff. What are the health consequences of it?

I see it in Europe as well. I also suspect it is a fungus.

I am pretty sure that it is a moldy core rot which would be caused by a fungus that infects the apple during the flowering stage. The fungi is also referred to as "apple fuzz".

From SF Gate

fungus sometimes does enter apples at the blossom end and damage the core, especially if weather is wet, a condition called "moldy core,"

From PennStateExtension

Moldy core is caused by several different fungal pathogens. Many cultivars of apples are affected, including Delicious, which is very susceptible. Moldy core may develop into dry core rot if the pathogen penetrates into the core flesh, but the fungus is generally restricted to the core or carpel region.

The Disease cycle

The moldy core fungi colonize the flower parts as soon as the blossoms open. The fungi then enter the developing fruit through an opening in the calyx. Moldy core is primarily a problem during years with light fruit set or when dry weather in early summer is followed by heavy rains in late summer. In addition, wet weather during bloom may cause conditions favorable for the fungi to produce spores.

What kind of fungi is it?
Mycelium

Geographical range
The fungi is mostly found in the apple sorts “Delicious” and “Idared”. Idared's origin is from Idaho, and delicious is originally from Iowa, USA.

Is it dangerous to my health?
Not more dangerous than usual mold would be to your health. You can find more information here. On some forums online I read about people consuming the white fuzz, and did mostly not complain about any problems. However, I read about one case where the fungi evoked an allergic reaction.

Picture

Further reading

Principles of Forest Pathology page 558

The information from your first source is opposite to your conclusion: Dave Wilson fruit experts to whom he showed your photos agreed that the appearance of the cores is normal for Fuji apples, so the entire apple should be fine for jelly production. He said that fungus sometimes does enter apples at the blossom end and damage the core, especially if weather is wet, a condition called "moldy core," but your apples are free of fungus.

I don't have enough rep to post an opposing answer, so here's a comment explaining why I disagree with your conclusion. The picture that the OP posted is very similar to normal white fuzzy tufts present in some apple cores. From PennState Extension: "I’ve received some pictures of apples where the core has white fuzzy tufts (see pictures below). We’ve determined in the lab these “white fuzzy tufts” in the core are not fungal, but part of the apple tissue. Consequently, these apples are perfectly fine and disease-free."


text:
What is the convention for indicating genomic primer sites?
What is the most common notation for indicating genomic binding sites of primers relative to an ORF?
For instance, if I want to indicate a primer like so:
...tccgccGCCCGTCCACACCCGCCGCCagctcaccATGGATGATGATATCGCCGCGCTCGT...
         [ Primer           ]        [ Hs ACTB coding sequence   ]

Should the location of this primer be indicated as -8 or -28 (from ACTB start codon) or something else?

Personally, I would say something about the forward primer spanning nucleotides -28 to -8 from the ATG codon, and perhaps give a reference to the nucleotide positions in either the genomic sequence NC_000007.14 (ACTB is on the reverse strand) or the mRNA sequence NM_001101.3.

There is no such convention. People, mostly name their primers arbitrarily (or just FP and RP) and mention in the text about their binding sites.
In general, in the context of specific genes, the positions are described with respect to the transcription start site (TSS).


text:
What are Some Classical Examples of Local Adaptation?
Question
Can you please give a list of classical (textbook) examples of local adaptations?

How to answer
Examples don't necessarily need to include what evidence supports this specific example of local adaptation. A simple description of the local adaptation (e.g. coat colour changes from black on dark soil to white on light soil) and an brief explanation of the reason (e.g. because being nicely camouflaged prevents from predation from hawks) is enough.
I think a list of 10 or more such examples would be great.
Definition of local adaptation
Note that I define here local adaptation as differentially adapted subpopulation of a single species (with existing gene flow between subpopulations especially for sexually reproducing species).
Justification for the question
I found surprisingly complicated to find such list online. I think it could be a valuable post for many.
Examples
Examples of local adaptation (that you are free to add in your answer with a description) include beach mice camouflage, altitude adaptation in tibetans and peppered-moth camouflage.

Would you consider adaptation to be reversible i.e. just phenotypic adaptation to a condition. IMO, the term -- adaptation, also has multiple interpretations.

Adaptation is a change in a trait as a response to selection. As you ask for local adaptation I assume you want examples where sub-populations have either come under different selection and adapted differently, or cases where sub-populations have come under similar selection but not all have had the necessary genetic variation to evolve, i.e. selection has caused differentiation between sub-populations. Local adaptation can lead to varying degrees of divergence, so some for some examples it may be worth exploring speciation events. Here's some examples:
Galapagos Tortoises
There are two general shapes to the shell of tortoises on the Galapagos Islands. On islands with little low-lying vegetation the tortoises seem to have evolved long necks & limbs and different shell shapes which allow them to reach up more easily.

"The shell distortion and elongation of the limbs and neck in saddlebacks is probably an evolutionary compromise between the need for a small body size in dry conditions and a high vertical reach for dominance displays."

Silent Crickets
Marlene Zuk has some very famous work on crickets that evolved to be silent on a Hawaiian island where parasitic flies were using the call as a targeting method. It's a great example of rapid adaptation occurring under observation in the wild. During the 1990's the population of crickets was in decline as a result of parasitism. At some point a mutation arose where the crickets no longer produced song, and this allele spread rapidly and cricket numbers bounced back strongly.
Other possible examples. Some of these are not "classical examples" but serve as interesting examples nonetheless.

Peppered moth colouration in response to the industrial revolution
Sickle cell anaemia and Malaria
Artificial selection in crops, livestock, and domesticated animals
Darwin's Finches
Antibiotic resistance in diseases; resitance to pesticides and herbicides; resistance to diseases unique to/more prevelant in sub-populations
Seasonal and continuous breeding by species present in both temperate and tropical climates
The Stephens Island Wren adapted to life without predators, and then failed to adapt to life with predators
Barn owl colouration across Europe
Predator-prey co-evolutionary dynamics as demonstrated by Guppies in Trinidad and Tobago
Drosophila melanogaster and adaptation to starch and maltose based foods
Adaptation to different salinity across gradients in marine organisms


text:
Efficient algorithm to calculate various population divergence statistics
Intro and description of the data
I am simulating the evolution of very long DNA sequences. The model works well, is performant and will output data in the following kind of fasta format
>it0pop0ind0locus0
ATGTTG...
>it0pop0ind1locus0
ATGTTG...
>it0pop0ind2locus0
ATGTTG...
>it0pop0ind3locus0

, where it stands for iteration (replicate of the same simulation), pop stands for population (or subpopulation of the metapopulation if you prefer), ind stands for the individual chromosome that was sampled, locus stands for locus which is defined as a very big sequence (0.1GB or 1 GB maybe).
Issues
I never had to analyze genetic data (or only during my Bachelor degree). As I know nothing about the available algorithms that exist to make this kind of analysis, I first thought I would just make my own code (in Python). It turns out that I might have a lot of data to analyse and my Python code will be way too slow. I may also experience RAM-related issues
Moreover, I want to measure a whole series of different statistics of population divergence (Fst for each site, Fst averaged over many sites following Cochramm and Weir, Gsd, absolute number of fixed sites that differ, etc..), so the algorithm would need to be quite flexible.
Question
There are a bunch of existing efficient algorithms that would eventually fit my needs. Can you please give me some recommendations and make a few comparisons of what's available out there?
An efficient algorithm that takes in input:

a description of the naming convention in the fasta file, or a list of positions for each locus and each population in the fasta file.
A description of how I want the statistics to be calculated

and that outputs those statistics. That'd be great!

Have you looked at BioPython? Specifically the Genepop module (http://biopython.org/wiki/PopGen_Genepop) looks useful. If you've already got some Python experience, it might be the fastest way to get started.

Are these sequences of equal length?

I would suggest that instead of reading and outputting fasta files, you simply store the co-ordinates and the mutation locations as a numeric matrix. Use unsigned integers for storing co-ordinates. Use 32 bit (or if possible 16bit) float for storing other kinds of data such as frequency. If <1% variation in frequency won't matter to you then you can use unsigned short integers for saving them. Or if your sensitivity is 0.1% then multiply save them as per-thousand. In any case, even using regular floats should not impose a big load on memory.

@WYSIWYG Yes, the solution was pretty obvious, yet I didn't think of that at first. Do you want to make an answer out of your comment?


text:
Question about the expression of MHC
Is it possible to express both the MHC class-1 and MHC class-2 both in one cell?

B-lymphocytes and Antigen presenting cells, both have MHC-I and MHC-II. See wikipedia page on them

could u pls explain it? or give me the link where i could get the proper answer?(in details)

the question is a little short - the comment seems to cover it.  maybe expand the question?  good question -> good answer

see this

it is clear to me now that both can express in one cell... but is it possible to express both MHC-1 and MHC-2 on same cell at the same time? If yes then how they do function?

Sure generally any cell that expresses MHCII will also express MHCI. Most cells in the body express MHCI as a self-identifier and to present intracellular proteins to T cells as a sort of a status update i.e. the cell will constantly be presenting peptides (pieces of endogenous proteins) on its surface bound to MHCI, if the cell becomes infected it could also present viral proteins on its surface with MHCI, which can potentially be recognized by a T cell specific for that particular epitope. B cells and antigen presenting cells such as dendritic cells and macrophages express MHCII but also need to be able to be identified as self through their basal level of MHCI presentation of self-peptides. The two MHC molecules are recognized by different cell types, MHCI by CD8 T cells and MHCII by CD4 T cells. So you could consider them to work independently from one another. The source of antigens (peptides here) and process for presenting them via MHCI and MHCII are somewhat different from one another.

You should find a mechanism to make this possible - mechanism that controls the ignition of the expression of MHC-I and MHC-II at the same time.
In other words, parallelism is required by the cell.
Some think such parallelism exists in one process in childbirth, for instance.
I personally think that such processes are in series, than exact parallelism, because such machinery would require large amount of memory, which our cells do not have, and fine tuning machinery, which is would require too much energy to keep to maintain the control system.
In reality, the processes seem to be happening at the same time, because there is so many of them.
However, I think this is a paralysis, since different processes are occurring.
But to show that two processes happen in exactly the same time would require the fine tuning machinery which should be found first, and also the energy aspect would require too much energy to control it.
Third argument against such parallelism is in probability theory.
I take an example about the logic behind how to falsify your statement.
Statement: If something is a B-cell, then it can express MHC-I and MHC-II at exactly the same time.
Contraposition of this: This something cannot express MHC-I or MHC-II at exactly the same time, then it is not a B-cell. This is wrong because this something is still B-cell, although it cannot express MHC-I or MHC-II in all situations.
Another perspective, the probability that these two events happen at the same time in time space with one independent system is zero.
The probability that these two events happen at the same time in time space with two independent systems is zero still. You need to have three systems to make this possible: fine tuning machine and the machines that expresses this. However, I have not seen such machinery to exist.
Generally, one point in probability space is always zero.


text:
Genes that exist in old Affymetrix platform but not in the newer one
I am using two gene expression datasets from an Affy U95Av2 platform and an Affy U133 Plus 2.0 platform. When I map the Affy probe names to HUGO gene names, there are thousands of genes which exist in the newer Affy U133 Plus 2.0 dataset while not in the old Affy U95Av2 dataset, which is something expected. But there are also 97 genes which exist in the old Affy U95Av2 platform while not in Affy U133 Plus 2.0 platform. I would not expect that because Affy U133 Plus 2.0 is a much newer platform and I would expect it to contain all genes that were measured by Affy U95Av2. What does that mean? Should I understand that those 97 gene measurements in the Affy U95Av2 platform were not reliable and that's why they don't exist in Affy U133 Plus 2.0? Here are those 97 genes:
"ACSL4" "ACSM2A" "AP3S1" "AQP7" "ARPC3" "ATF4" "ATP5H" "BAK1" "BAK1P1" "CBX1" "CCL15" "CELP" "CFHR3" "CHEK2" "CLCNKA" "COL8A1" "CS" "CXorf40B" "CYP2D6" "DDI2" "EIF3F" "EIF3IP1" "EIF5AL1" "FCGR2A" "FCGR3A" "GBX1" "GPX1" "HAVCR1" "HBZ" "HIST1H2AH" "HIST1H2AI" "HIST1H2BC" "HIST1H2BJ" "HIST1H4I" "HOXA9" "HSPB1" "IFNA14" "IGF2" "IL9R" "ITGA1" "KAT7" "KRT33A" "KRTAP26-1" "LDHA" "MAGEA12" "MAP2K4P1" "MIA" "MKRN3" "MROH7" "MSX2P1" "MT1A" "MT1B" "NDUFV2" "OPHN1" "OR7E24" "PARP4" "PCDHA12" "PCDHA13" "PCDHGA12" "PCDHGB4" "PINK1-AS" "PMS2P3" "PSMC6" "PSME2" "RAB13" "RCN1" "RNF216P1" "RNF5" "RPL10A" "RPL18" "RPL27" "RPL35" "RPL37" "RPLP1" "RPS15A" "RPS26" "RPS29" "RPS5" "RPS9" "RSC1A1" "S100A7" "SAA1" "SAA4" "SNX29" "SPRR2D" "TOMM40" "UBC" "UBE2E3" "UBE2S" "UGT2B7" "UQCRFS1" "UQCRH" "VDAC2" "VENTXP7" "VOPP1" "XCL2" "ZNF799"

What annotation source are you using to map Affymetrix identifiers to gene names, NetAffx or ? If the annotation files are from different sources and / or generated at different times, then gene symbols may have changed in between and therefore fail to match. But in your list I clearly see well established gene symbols that have not changed for a long time, so something looks funky ...

I am using biomaRt (the same ensemble version) for mapping both. Here is my code: ensemble = useMart(host='dec2014.archive.ensembl.org', biomart='ENSEMBL_MART_ENSEMBL');  hsp = useDataset(mart=ensemble, dataset='hsapiens_gene_ensembl');  ids = getBM(filters='entrezgene', attributes=c('entrezgene','hgnc_symbol'), values=entrezgeneids, mart=hsp);

Well, I don't know where biomart in turn gets its information. I would recommend you track down the actual annotation source, or map identifiers via a trusted source like Affy's own annotations. I just did a quick query on NetAffx for ACSL4 (first in your list) and there are definitely probes targeting this gene on the U133. It's impossible to say why the results differ without knowing exactly how the annotations were done.

aI used to work at Affymetrix when most of these arrays were designed.  I was not on the design team itself, but I can maybe talk about this a bit more.
RNA Array designs were built to cover anything that might possibly be real transcript in the mix of EST collections, cDNA, in silico gene detections and miscellaneous entries in public databases.  There were a lot of different people trying to find genes as quickly as possible and a good deal of it was not real gene naturally. I'm sure there was a reasonable amount of contamination in the millions of transcripts we took in as well.
The team would find a good number of errors in the sequence database.  There is no way to submit this in a meaningful way to most bioinformatics databases by the way.  Just a note:)
When a new design came out the team would do auditing to see if any of the transcripts had fallen out of favor with the evidence and some of those 'genes' would be pared from the content.
This is useful because DNA hybridization tech is very high throughput for the dollar but it has a background noise and even a probeset with no correspondence in the RNA sample will give numbers that are non-zero.
RNAseq has similar issues from assemblies and sensitivity from limits of reads on the sample BTW.  There's no perfect solution as of yet.
BTW sometimes genes get renamed. I didn't get into your methods to see if this is a case, but something to keep in mind.

My experience is with Affymetrix probes for Drosophila, not H.sapiens, and only with one version. Nevertheless I'll describe the situation I encountered in case it is relevant to yours. Apologies if it is a red herring.
What I did with the Affymetrix data sheet was use it to construct my own SQL relational database containing probesetIDs and geneIDs (as well as the experimental data, of course). I was then able to make some ‘housekeeping’ queries to the database and was surprised (perhaps I shouldn’t have been) to find the following:

Some genes were picked up by more than one probeset. No great worry. Just had to choose the probeset that gave the highest signal, unless it fell into the second category.
Some probesets picked up more than one gene. This was a problem, and meant I had to classify the probesets as ambiguous or unambiguous. But an even greater problem was that for some genes there existed no unambigous probesets.

Obviously in designing the probesets Affymetrix thought they were producing unambigous gene-specific ones. When they updated the probesets to include new or corrected gene designations one imagines they would try to deal with this problem (assuming it also existed in the human gene sets). It seems hard to believe, but could the genes you mention be refractory to the preparation of unambigous probesets?

Thanks for the reply. I think what you explained is quite possible, and I think there are also two reasons: 1) (Thanks to mastal11 on Biostars) There are many Affy probe sets that are not all that reliable because they are based on ESTs. Also, the older arrays would have been based on older versions of the human genome sequence, and possibly some of the probesets on the older arrays don't map or don't map uniquely to the newer versions of the human genome sequence. Occasionally also, probe sets were based on GenBank sequences that were later withdrawn from GenBank. (cont. in the next comment)

Some of those 97 genes actually have mapping probes in the newer Affy platform, but the annotation source I am using to map the Affy probe sets to HUGO gene symbols, biomaRt, does not have the identifiers of those probes in its database, so cannot map them to the gene symbols. There are various annotation sources out there, and you may end up with a slightly different mapping depending on which you use.


text:
The reason why researchers usually use cell lines from "blast cells"?
What's the reason why researchers usually use cell lines from "blast cells" (so, immature, like lymphoblastoid cells) for measuring gene expression data? Is that they are growing up, which would make their expression data higher and more significant?

Some of the reasons why immature blast cells are studied:

They have self renewal ability
Can be differentiated to different types of cells
Serve as model for studying development: this is quite pertinent to the gene expression studies. Because it is important to know what changes take place during differentiation and simply looking at the differentiated state won't explain that process.


text:
Difference between "controlled release" vs "prolonged release"
My sister is using the epilepsy drug Tegretol which has both CR and retard (previous naming for prolonged release according to https://www.medicines.org.uk/emc/product/5932/smpc) versions in different countries. Given the active ingredient is the same (carbamazepine), would there be any difference between the effects of the two drugs in terms of preventing seizures?
Here are the boxes for the two versions:

For the sake of your sister's safety please consult with a pharmacist or other medical professional.

The first box contains Tegretol Retard, which is an old name for what is now known as Tegretol Prolonged Release (medicines.org.uk).
The second box contains Tegretol CR, which means controlled-release (PubMed).
Drugs.com says:

Your medicine is called Tegretol 200mg prolonged-release
  Tablets/Tegretol CR 200mg Tablets or Tegretol 400mg prolonged-release
  Tablets/Tegretol CR 400mg Tablets but will be referred to as Tegretol
  prolonged-release Tablets throughout this leaflet.

Concluding from that, Tegretol CR200 is the same as Tegretol Retard, which is now known as Tegretol Prolonged Release.
Saying that, it is a doctor who prescribes drugs and who can answer such questions.

license: Creative Commons - Attribution Share-Alike - https://creativecommons.org/licenses/by-sa/4.0/


text:
Confusion about a gene's description
I have a very basic biology question. I am reading the description of gene FAM166A here, and I have no idea what "sequence similarity 166" means. What does 166 stand for, what is this gene's sequence "similar to"? And what does member A mean?

Family membership is probably referring to something similar to this: https://www.ebi.ac.uk/training/online/course/introduction-protein-classification-ebi/protein-classification/what-are-protein-families. I'm not sure how the sequence similarity is scored, though.

Oh okay, then this gene named FAM166A is member A of the protein family with ID 166 where each family is determined based on sequence similarity. Thanks!

The FAM symbols are defined in the HUGO Gene Nomenclature Committee guidelines.
The FAM symbol is an anonymous and temporary identifier that is given to groups of poorly characterised genes which share more than 40% amino acid sequence identity (see Section 4.1).
There are two human genes/proteins in the sequence similarity 166 group; FAM166A and FAM166B. These have some sequence similarity (more than 40%) but  function of this similar sequence is currently unknown. Once further characterisation has been performed for any FAM group they will likely receive more meaningful names.


text:
Understanding F-statistics in population genetics
I am reading the classic Weir and Cockerham 1984 paper about $F_{ST}$ estimation. At the beginning (first page, right column), they define 3 statistics.

$F$ is the correlation of genes within individuals ("inbreeding")
$\theta$ is the correlation of genes of different individuals in the same population ("coancestry")
$f$ is the correlation of genes within individuals within populations.

They also state that the 3 statistics are related by
$$f = (F-\theta)(1-\theta)$$
I don't quite understand those 3 statistics and especially I don't understand why this relationship holds true. Can you help me with that?

I find the 1984 paper to be a bit dense in some places.  I don't have time at the moment to explicate this, but I found Bruce Weir's book very illuminating.  You don't need to read it straight through. There are two or three relevant chapters.  However, for your question, a good population genetics textbook such as Hartl and Clark might be sufficient.

I am a bit wobbly on the subject, but I think the most important bit of information is that they are re-parametrising Wright's (1951) hierarchical analysis of variation, "F-statistics," "hierarchical partitioning of variation," or "population parameters," depending on whom you ask. The parameters correspond as follows (on the bottom of p.1358): Fit=F, Fis=f, Fst=θ.
The relationship arises given some assumptions. Crucially here, if Fis (or f) is a measure of departure from Hardy-Weinberg Principle, and all populations identically depart from HWP, then Fit = 1 - Hi/Ht. It follows that, 1 - Fit = Hi/Ht. As well, we can rewrite this, so that, Hi/Ht = (Hi/Hs)(Hs/Ht).
Together, you can (maybe) see that, 1-Fit = (1−Fis)(1−Fst). Substituting, 1-F = (1-f)(1-θ).
(I realise that this is not a complete answer, but you can rearrange it with some algebra to get the Weir&Cockerham equation, I think).
[Update Oct 25, 2016]: it eventually yields f = (F-θ)/(1-θ). I think the posted question (above) contains a typo--specifically a missing division operator. Perhaps someone missed the stroke on a typewriter in the original paper?

Thanks for the answer. Solving $1-F(1-f)(1-\theta)$ for $f$ gives $f=\frac{\theta-F}{\theta-1}$.


text:
How to define "Quasifixation" in continuous approximation of finite population?
Background
Many models including the famous very first models derived by Sir Ronald Fisher in his early career, assume infinite population size. In an infinite population, an allele can rise in frequency (if its fitness is greater than the fitness of its counterpart(s)). This increase follows a logistic curve that never exactly reach a frequency of 1. Therefore, the term fixation does not apply to these model and in consequence the concept of quasifixation has been created.
Question
An allele is considered to be quasifixed if it reaches a frequency that is very close to 1. But how close? The decision is obviously arbitrary but when using a continuous approximation model in a finite population the question is necessarily raised and a decision needs to be made. How close should the frequency of an allele be to 1 so that it is considered to be quasifxed? The answer is probably a function of the population size $N$. What threshold do authors usually use in the theoretical population genetics literature? It would also be nice to have the definition of the author who first coined this term.

I think there is an answer in here: http://ac.els-cdn.com/0040580972900159/1-s2.0-0040580972900159-main.pdf?_tid=5ae1bc16-8049-11e4-96d2-00000aab0f01&acdnat=1418201507_114d91e34ce366129089c4c4ec02f468

Or this one might help as well: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1209652/pdf/280.pdf

Garrish and Lensky (1998) considered reaching fixation when $p > \dfrac{N-1}{N}$, where $p$ is the allele frequency and $N$ the population size.
Note that I ran simulations (unpublished) that well approximated the threshold chosen by Garrish and Lensky (1998).

As I haven't found any guideline, I just went with chosing a threshold at $p = 1-\frac{1}{4N}$ for a diploid population. Allele $A$ is therefore considered quasifixed if its frequency $p$ is greater than $1-\frac{1}{4N}$ i.e. the number of occurrences (count) or the other allele is lower than $\frac{1}{2}$.


text:
Does it make sense to classify all humans in a single species?
For what biological reasons do we consider that all human beings belong to the same species?

A Thai and a Nigerian share a common ancestor that is 140,000 years old (see Gravel et al. 2010 and this post). Would, for example, a mixed Thai - Nigerian baby suffer from any outbreeding depression?

humans have remarkably low genetic diversity especially given our population sizes. On top of that most ~85-92% of our genetic diversity is within population not across populations. Outbreeding depression is not going to happen. http://jorde-lab.genetics.utah.edu/elibrary/Jorde_2000a.pdf

For info: Motivation for the post
There had been a number of questions on whether it makes sense to classify all humans into a single species. Unfortunately, many of them where poorly phrased and therefore did not get much attention or even got closed. Here is my attempt to open a post that will be referenced to for future questions on the subject.

Are you sure this question is fit for Biology SE? To me it seems like it's more a match for some more "social" SE-e.g. it bear more on anthropology (in all of its dimensions), rather than to biology per se or the tag human-evolution.

The definition of "species" is pretty clear-cut.

@CarlWitthoft I disagree. Please have a look at this answer

Possible duplicate of: Outbreeding depression in humans

@theforestecologist This other post had received very little attention (no upvote, no answer, no comment and only 100 views in 7 months). I marked the other post as a possible duplicate of the current one as the current got a lot more attention in only 16 hours.

@theforestecologist Another candidate: Do humans have enough biological differences to be grouped into races or subspecies?

Neither of those two posts cited make any mention of outbreeding depression. Why then do they support the claim that there could be outbreeding depression between Nigerians and Thais? This seems speculative on the part of the OP.

@Will the reference is here to support the estimate divergence times and the SE post is here to offer a visualization of the tree of human lineages (this SE post is arguably not necessary). Sorry if you misunderstood the purpose of the citations. I reordered the sentence to prevent other misunderstanding. The post does not claim that there is outbreeding depression in humans but ask if there is any.

This question sounds quite racist....

@altroware While the question may bring up emotional reactions, the question is by its nature purely scientific. You will note by the way that the OP is the same as the one who wrote the top voted answer (and the same who wrote this comment :) ). You will also note that below the post I explained my motivations for asking this question.

Actually, we not only consider that all human beings belong to the same species (Homo sapiens) but even that we belong to the same subspecies (Homo sapiens sapiens). So, does it really makes sense?
Concept of species
First, please note that the concept of species is more arbitrary than the most layman would think. I wrote my opinion about the concept of species in this post.
Concept of species in sexually reproducing lineages
In sexual lineages, the existence of different species is generally defined by the existence of a reproductive barrier (see reproductive isolation and see UnderstandingEvolution > biological species concept).
A reproductive barrier can be caused by either pre-zygotic or post-zygotic isolation. I will mainly disregard potential case of pre-zygotic reproductive isolation in this answer because such isolation depends highly on the culture and not only on human genetics. I do not mean that pre-zygotic isolation does not matter or are irrelevant, I just decide to focus on post-zygotic isolation because, as being very independent of the culture, it makes its study much easier.
Reproductive isolation might be partial typically leading to cases of outbreeding depression (when it comes to post/zygotic isolation).
If we had many documented cases of outbreeding depression, it would suggest that we should reconsider the decision of considering all human being. So the question is really: Do we have documented cases of outbreeding depression?
Do we have documented cases of outbreeding depression?
Many cases of inbreeding depression have been documented in humans (McQuillan et al. 2012, Strauss et al. 2013, Lettic et al. 2008, Gellera et al. 1990) but cases of outbreeding depression seem much rarer if any!
I spent some time screening through the literature searching for potential evidence of outbreeding depression. The only paper I found is Udry et al. 2003.
Udry et al. 2003:

First, note that they do not place their results in the context of our present discussion and do not talk about outbreeding depression.
They report that children of "mixed-race" in US colleges report having more behavioral troubles. One would obviously note that there could well be true that "mixed-race" children experience a different environment (incl. social environment) than "non-mixed-race" children. Such results, therefore, does not suggest the existence of any outbreeding depression.
They also report more skin problem in mixed-race children. Unfortunately, 1) their p.value is only slightly significant and 2) they track tens of variables without correcting for multiple comparisons and only one came out significant causing the expected family-wise false positive rate to be higher than the observed family-wise positive rates (see false positive and related concept in statistics).

So, in short: No, there is little to no evidence of outbreeding depression in humans
Potential publication bias and taboo
It is not impossible that there is a publication bias in which researchers looked for inbreeding depression more than for outbreeding depression. One would note also that it would probably be quite taboo to attempt to suggest that there are several species of humans for social reasons and such taboo could cause a publication bias too.
I personally doubt it would be the case that such publication bias exists. Note also that if the evidence of outbreeding depression were obvious and common, then we would not have a problem telling that there are several species of humans. In essence, if there is a publication bias that is causing us to fail to see existing outbreeding depression, it must mean that existing outbreeding depression would be relatively rare and with small effect anyway.
Other definitions of species and taboo
Well, the issue is that there is no other commonly accepted definition of species. Typically, above I only considered outbreeding depression and I did not consider a potential pre-zygotic barrier that would result from geographic isolation or from cultural reasons (e.g. maybe on the ethnic group does not like the appearance of people of another ethnic group and vice versa). I don't doubt that for social reasons, we tend to be quite happy that our only definition of species support the idea that all humans belong to the same species.
It is hard (for me at least) to tell whether we might tend to be more permissive in other lineages and naming two sister lineages as belonging to different species.
Why same subspecies and not only species?
Under the biological species concept, H. sapiens and H. neanderthalis should belong to the same species. This is why we now rather call them H. sapiens sapiens and H. sapien neanderthalis. I suppose it would feel painful to many to not highlight our differences with H. neanderthalis and so, we decided to discriminate.
Again, it is hard (for me at least) to appreciate how this subspecies discrimination in the Homo lineage compares to discrimination among potential subspecies in other lineages.
Related posts

Do humans have enough biological differences to be grouped into races or subspecies?
Is there a consensus on whether or not race exists on a biological level?

I may possibly be prejudiced, but casual observation suggests that humans experience the opposite of outbreeding depression.  Don't have a clue as to where or how you'd find actual evidence, though.

The entire issue of inbreeding vs. outbreeding depression makes no sense in the description of the human population since it lives under very diverse natural conditions so something that can be classified as "fit" for some conditions (for example, better thermoregulation good for hotter climate) may actually be disadvantageous for other conditions. This makes the "fitness" of human rather an arbitrary category too broad to define out of particular social and/or geographic context and with the lack of such definition for fitness how can you say depression means anything,too?

@YordanYordanov Great points.  I was going to say that humans are highly adaptive to their environments, but I don't think that's the best way to describe it.  Humans have gotten very good at adapting our environment to us.  In many places in the world "natural conditions" are just the comfort standard we've adopted for day to day activities.  It's only if you're extremely unfit for conditions that you would really be able to tell.

You'll find in any lineage environmental variance for fitness and even eventually environmental variance for fitness in outbred individuals. It does not prevent the scientific community to come with arguments as to whether or not whether the lineage should be considered as a single species of as several.

Of course, the concept of species is largely undefined (see linked post in my answer), this is why I restricted the question to the existence of outbreeding depression. Any evidence of outbreeding depression (under any environment) would already suggest there are more than one species of humans. Failure to find such evidence would suggest that there is only one species of humans.

I'm a bit annoyed that you place your personal pet-peeve of species definitions/concepts that upfront in an answer that is meant as a 'standard' reference for future use (on mostly another issue). I get that you take issue with the species concept (you have expressed this several times at BioSE), but this is hardly an uncontroversial point of view. For more info, you also link to your own answer on the issue, which is (in my opinion) quite unbalanced and biased. This answer would be better and more to the point without this section.

@fileunderwater Yes, I agree with the feeling it may give. However, in this answer (unlike in the comment response to CarlWitthoft), the link to my other answer is to further ones understanding. The answer does not rely on the other post. To clarify the independence of the current answer to my other answer, I added a link to Understanding Evolution that define the so-called biological concept of species to justify why I am focusing on evidence of outbreeding depression. Does it sound better now?

Also, outbreeding depression does not necessarily imply several species (which you seem to say in the comments and in the answer). If this is indeed what you claim, this is definitely not the standard definition of outbreeding depression, and your use of the term would differ drastically from standard use.

A reference to the Biological species concept is probably useful. What I take issue with is the description (and need for) the "concept of species" section.

I am not sure I fully understand the issue. I added "my opinion" somewhere in the "concept of species" section.

Coming from a layman (sorry), what about species inter-breeding? There's a theory that we may be something like 2% Neanderthal.

@JackR.Woods It is a very good remark. The answer is found in the section Concept of species and especially in the linked post (How could humans have interbred with Neanderthals if we're a different species?).

@Remi.b "I will mainly disregard potential case of pre-zygotic reproductive isolation in this answer because such isolation depends highly on the culture and not only on human genetics." I'm not sure how to interpret this, are you saying that you think that reproductive isolation only should be measured by the resulting general genetical fitness of cross-breeding? Isn't the variance resulting from all kind of barriers, even if they are purely cultural or geographical, highly relevant to be a part of a classification system that describes the artifacts of reproductive isolation?

Like what about the enforced reproductive barriers that are created by conscious selective breeding? Especially with pets like for example dogs, where the concept of outbreeding depression is quite irrelevant. Do the breeds of contemporary dogs have a place on a description of the tree of life? Should we only talk about wolfs, and say that the different "variations" of wolf, like bulldogs or poodles, are of no interest when we classify that branch of life?

@Alex That's a good point you are highlighting in your first comment. I added I do not mean that pre-zygotic isolation do not matter or are irrelevant, I just decide to focus on post-zygotic isolation because, as being very independent of the culture, it makes its study much easier. While I would happily down play the conceptual importance of geographical barrier in defining species, I would clearly not do so for other factors causing pre-zygotic isolation (such as not finding each others sexy)

@Alex I don't understand how dog breeds relate to the first comment. I do consider that domesticated species do belong to the tree of life and semantic difficulties of species definition also apply to them.

@Remi.b The comment about dogs was intended as an example to clarify what I perceived to be a problem with the approach of basing the classification of populations on evolutionary fitness. Dog breeds have clearly been under pre-zygotic reproductive isolation, and it's highly useful to categorize the different isolated populations regardless of the fact that they all could interbreed and it's arguably not relevant to talk about evolutionary fitness. Even when you don't care about statistical phenotypical traits.

I find it meaningful that a classification of populations would include significant historical de facto reproductive isolation, whatever the isolation is caused by, that may or may not have resulted in a divergent statistical genotype that you may or may not know about. I get the feeling that you find it more relevant to base the classification on future potential reproductive isolation based on genetical compatability? Not finding each others sexy could be both cultural and genetical, would you distinguish the hypothetical cases where it's 100% genetical from where it's 100% cultural?

Oh I have to apologize, I read this question when refreshing on the human races issue, and somehow fooled myself into thinking that this question and answer were concerned about the subspecies/race level. Seeing that this is actually about the species-level, I agree with you that is is reasonable to focus on genetic compatibility as you describe.


text:
How to seek for available genetic data relevant to ecology and evolution?
I had a quick look online. There seems to exist many different website of database archiving. Some data might be free of charge while some others might not be. I found things such as Dryad, TreeBase, DataOne or GeneBank. I think that several journals (American Naturalist, Molecular Ecology, Science and Nature for examples) force their authors to make their data available
General Question
Can you make a short review of what different database exist, what are their properties (what kind of data, what license and fees) and how to seek for data (are datasets associated to keywords or do we have to look for a paper and then look for their data).
Case study
Imagine for example, you are looking for SNP data of a spatially explicit population that recently underwent a range expansion (might be too accurate, you may want to drop down some specifications). How would you go about to look for this kind of data? (No need to pull out data but only to describe a procedure).

Please leave a comment when downvoting

I am working a lot with databases, and I have to say your question is too broad. There are heaps of different databases and thousands of ways to use them. Even describing GenBank in detail would make half a book. In you case, if you can describe the biological question so well, try looking for relevant publications, take the database identifiers they give for the data they used and look at what you get.

@skymningen Thanks for your comment. So, for the case study I am asking you would not try to look with keyword in a specific database but you would rather look for articles that may have potentially uploaded their datasets? Seems like it would take a long time to find relevant data. Would it be too broad to ask for a list of the different databases that exist along with their very basic feature such as broadly speaking, the nature of the data (genetic/phenotypic for example) or the field that have produced those data (ecology/biochemistry/evolutionary biology/...)?

That is still too many databases and I by far do not know all of them. I am specialised in -omics data, so I could mainly guide you there. The NCBI-databases are your best guess to get datasets, but the descriptions given there are often hard to understand if you did not read the publication. It is much easier to skim over a publication and decide if the data they used or produced is something that could help you. To help you with your case study I am already failing at the meaning of "spatially explicit", sorry.

@skymningen Ok. Well I guess you actually made the answer one could give through your two comments. Could make an answer out of your comments? Maybe something along the lines of: 1; One cannot summarize all data bases, there are too many of them 2; Because the datasets usually don't have enough metainformation (and eventually because tagging is not super efficient), your best bet is to look at the literature directly.

As a bioinformatician, one of my tasks is quite often to get information from databases or find the correct database to compare to. There are several big databases around, but even those are way too many for any single person to know.
There are databases specialising on genetic data, some specialising on taxonomic information, some on ecological information and then there are species specific databases which collect all kinds of data from a given species, genus or clade.
For me the biggest source of generalised information would be the NCBI databases, including GenBank for nucleotide sequence, their Protein database and maybe dbSNP for, well, SNPs. Then there are multiple 16S databases I have used, there is UCSC for the basic data about the human genome, flyBase for Drosophila,... I think the point should be clear. And those are only the ones that just sprang to my mind.
If you don't have a very exact question to answer like "Is there a gene similar to gene x from Drosophila present in any Bacteria?" it can be very hard to both choose the correct database and also find the important information in it efficiently.
Then there is the problem of metadata. I might be able to find sequencing data from the gut microbiome of a human treated with antibiotics quite easily. If I am looking for a specific antibiotic given over a minimal time of seven days to a different healthy humans, preferably including at least one breastfed child and it's mother (which was the one taking the antibiotics, not the child), then it's getting complicated. Here it is generally way easier to scan PubMed for some relevant publications on the matter and find out if they used published datasets or published their own.


text:
Function of the Auricle in heart
What is the function of the auricles (conical appendages through the atria) present in the heart?
Do they have any other function than increasing the volume of blood the heart can hold?

The left auricle is a decompression chamber when atrial blood pressure is high (in systole or in some pathological conditions) [1, 2]. Some research claims it also plays a role in cardiac regeneration because it contains cardiac progenitor cells [3].
Probably the right auricle has less important function; also there are fewer studies about it.

References:

Al-Saady NM, Obel OA, Camm AJ. Left atrial appendage: structure, function, and role in thromboembolism. Heart. 1999 Nov;82(5):547-54. PubMed PMID: 10525506.
Bansal M, Kasliwal RR. Echocardiography for left atrial appendage structure and function. Indian Heart J. 2012 Jul 27;64(5):469-75. doi: 10.1016/j.ihj.2012.07.020. PubMed PMID: 23102384.
Leinonen JV, Emanuelov AK, Platt Y, Helman Y, Feinberg Y, Lotan C, Beeri R. Left atrial appendages from adult hearts contain a reservoir of diverse cardiac progenitor cells. PLoS ONE. 2013 Mar 12;8(3):e59228. doi: 10.1371/journal.pone.0059228. PubMed PMID: 23555001.


text:
What would be the scientific name of this worm
I saw this pic while crawling online and it was a bit different for me to predict which worm is this.

Source: http://www.seafishingtackle4u.com/softbaits/ragworms-latex-lures/
It appears somewhat different form other annelids as parapodia or setae are somewhat transparent and very attractive too.
What worm is this?

That worm is plastic.

So, in summary, it is Nereis effigiei

@J.Musser i too think the same as i cant see any correct parapodia on its body

As Cornelius correctly pointed out, it's not a living worm :)
As to the question, what kind of worm they tried to imitate, I'd say, they had a nereid (errant, i.e. wandering) in mind and were likely inspired by the segmentation of Arenicola (sedentary), which together created this appearance, unrealistic for an errant polychaete: with the body segments outnumbering parapodia.
EDIT:
They also paid no attention to the head (a very important body part in polychaete systematics!), which seems to be absent. This feature could also be "inherited" from Arenicola, the head end of which is not prominent and bears no appendages.

what is the appandage like thing arising from mid part of its body

@agharehanabbas: hmm, you mean the hook?

yes can i get any details about its morphology anatomy and role in daily physiology?, i think it is special character of polycheate nerieds

The hook is actually part of the lure.

If you would have read the text from the source of that image (which by the way you didn't mention, I did a Google Image search and edited your question to include it), you would have known that what is seen in the picture is not a living thing, it is a latex fishing lure:

Product Description: Sandworm/Ragworm 8″ size, sure fire fish catching lures. Costing less than live worms, but lasting much longer, never be without worms again.

Source: http://www.seafishingtackle4u.com/softbaits/ragworms-latex-lures/