Posted by The putrid stench of gnxp on Sat, 07 Apr 2007 20:27 | #

I don’t have the time to look this paper over as carefully as I would like, but note that ‘clustering’ can be viewed as a metric different from genetic similarity/differences.

Remember the Jorde paper from the Utah group discussed here recently.  The “w” measure of genetic similarity was a more stringent measurement than clustering, and is more in line with information need for determination of genetic interests.

Posted by James Bowery on Sat, 07 Apr 2007 22:04 | #

You are referring to the MR article “Genetic Similarities Within and Between Populations” wherein the academic paper by the same name by David J Witherspoon, Stephen Wooding, Alan R Rogers, Elizabeth E Marchani, W Scott Watkins, Mark A Batzer and Lynn B Jorde is discussed.

In that MR discussion you stated:

as the post states, “w” ignores structure

Could you present a definition of “structure” and how it differs from “clustering” if at all?

It sounds to me like you’re, perhaps indirectly, criticizing the use of correlation structure measures to strengthen the theory of EGI.

Posted by The putrid stench of gnxp on Sun, 08 Apr 2007 13:38 | #

“It sounds to me like you’re, perhaps indirectly, criticizing the use of correlation structure measures to strengthen the theory of EGI.”

That couldn’t be more mistaken.

Clustering, essentially, is based upon comparing an individual’s gene frequencies to the “centroid” of a particular population; that is, if Europeans are characterized by a set of gene frequencies, and Africans by another, how close is the individual to either set.

I would define structure as composing of those elements of genetic information above and beyond a one-by-one evaluation of gene frequencies and including as well genetic structures beyond that of the single allele.

Easy examples of “structure” would include copy number variation, insertions, deletions, inversions, etc.  But these are NOT the major determinants of structure, nor what we here usually refer to as structure.

That would be the coinheritance of particular alleles (or, even, gene sequences) between individuals and groups.  If one reads the “materials and methods” section of the Jorde paper, they are defining “w” as the average distance of each allele taken in turn - that is _not_ what we would consider structure.

The basic component of structure would thus be defined as the coinheritance of gene sequences/alleles.  Linkage disequilibrium is therefore one aspect of structure, and new LD patterns are established, not surprisingly, upon admixture.

To put it even more simply and to use a crude analogy (very crude), it is not the allele frequencies for, say, eye color, skin color, hair color, nose shape, skull shape, etc. each taken in turn and averaged.  Instead, structure concerns itself with the frequency that these various types of alleles happen to be co-inherited in the same individual or group, the frequency by which particular _combinations_ of the allele type show up in the specific set.

As far as I can see, this is not being measured by Jorde’s “w” distance metric, nor the “c” clustering metrics.

If I am mistaken, then I stand open for correction; perhaps Jorde et al., were not as clear in their paper, and I misinterpret their methodology.  I would, however, require clear and unmistakable evidence that such a misinterpretation has taken place.

Posted by The putrid stench of gnxp on Sun, 08 Apr 2007 13:47 | #

Note: I mistakenly placed the next two posts on this topic in the amygdala thread.

Posted by James Bowery on Sun, 08 Apr 2007 14:50 | #

The misplaced comments were:

From Jorde:

1. Methodology:

“Pairwise Genetic Distance
We use the “shared alleles” genetic distance (Bowcock et al. 1994; Chakraborty and Jin
1993; Mountain and Cavalli-Sforza 1997), which defines the distance between two individuals at
a locus as one minus half the number of alleles they share. The genetic distance between
individuals is the average of their per-locus distances. Pairs of individuals are classified as
“within-population” or “between-population” according to whether the individuals were sampled
from the same or different groups of populations as defined above.
8
Dissimilarity fraction ?ˆ
Let ? be the probability that a pair of individuals randomly chosen from different
populations are genetically more similar than an independent pair chosen from any single
population. We compute all possible pairwise genetic distances, classify them as within- or
between-population distances (the sets dW or dB, respectively), then calculate the frequency with
which dW > dB (that is, a within-population pair is more dissimilar than a between-population
pair.) This fraction, ?ˆ , is an estimator of ?. The expected value of ?ˆ ranges from 0 to 0.5
(regardless of the number of populations). At ?ˆ = 0, individuals are always more similar to
members of their own population than to members of other populations; at ?ˆ = 0.5, individuals
are as likely to be more similar to members of other populations as to members of their own. The
distributions of pairwise genetic distances implied here resemble the Common Ancestry Profiles
proposed by Mountain and Ramakrishnan (2005), who use a different measure of genetic
distance. The shared-alleles distance used here generally yields slightly lower values of ?ˆ .
Centroid misclassification rate CC
The centroid classification method is also based on pairwise genetic distances, with one
critical difference: every individual is compared to the centroid of each population, rather than to
every other individual. The centroid is the genetic average of a population, an individual whose
pseudo-genotypes at each locus are the frequencies of the genotypes in that population (not
including the individual being compared to the centroid). This genetic distance is equivalent to
the average of the genetic distances from an individual to all other individuals in the target
population. Each individual is then assigned to the population with the closest centroid, as in
Cornuet et al. (1999). These assignments are compared to the known populations of origin, and
the proportion of individuals misclassified is reported as CC. The expected classification error for
9
random assignment of individuals to populations is 1 – 1/n, where n is the number of
populations.

2. Problem with clustering:

“Nonetheless, it is instructive to consider the analogy using panel C as a guide.
For example, an African individual x with qx = 0.52 will be more similar to a European y with qy
= 0.55 than to another African z with qz = 0.4. Yet that individual x will still be closer to the
population mean trait value for Africans (qA ? 0.46, the African centroid) than to the mean value
of Europeans (qB ? 0.61).”
Posted by The putrid stench of gnxp on Sunday, April 8, 2007 at 12:44 PM | #

“The genetic distance between
individuals is the average of their per-locus distances.”

This is _not_ how I would define “structure.”
Posted by The putrid stench of gnxp on Sunday, April 8, 2007 at 12:46 PM | #

Posted by The putrid stench of gnxp on Mon, 09 Apr 2007 15:48 | #

A review of “On Genetic Interests” by van den Berghe, who considers himself a “political anarchist”:

http://lists.paleopsych.org/pipermail/paleopsych/2005-May/003227.html

One can contrast van den Berghe’s essentially fair review to the ludicrous “hatchet job” by David B of gnxp.  Of course, Dr. van den Berghe seems to be an independent entity, and not an extended phenotype of devious south asians.

Also, read the last paragraph of van den Berghe’s review.  Sound familiar?  “Life has no interests”, “who cares”, “toffee and Mahler.”

Posted by James Bowery on Fri, 23 Oct 2009 19:14 | #

My work on compression-as-intelligence has led me to the working belief that the best currently practical approach is to treat structure as algorithmic information.  What this means is you take a set of data—any data at all—and create the minimum length computer program that outputs that data. 

Its easy to state this as the goal but it is, in fact, provably uncomputable to come up with said minimum length program—although it is asymptotically computable with exponential computing resources.

There are other approaches involving generalization of relation arithmetic—hence quantum information systems—that may prove superior if or when quantum “computers” (a misnomer really) become a practical reality.

Posted by James Bowery on Fri, 23 Oct 2009 21:08 | #

There is an enormous amount of work on compression of genetic data.  Indeed, it may be the most active area of bioinformatics.

The primary value of what you call “existing distance measurements” is that they offer guidance as to what sort of a priori (or Bayesian prior) structures may offer optimal compression. 

The test is whether they do, in fact, enable the production of shorter programs that output the known data.

Posted by Guessedworker on Wed, 28 Oct 2009 00:40 | #

Thanks, Loriver.