January 14, 2014

biomedicalephemera:

Fancy Pigeon (and English Carrier, top right) Breeds

Easily domesticated, with short generation times and friendly disposition, pigeons have long been ideal for “fancy” breeders - people who wanted to breed an animal based on looks, like the majority of modern dog and cat breeds.

Where the standard carrier pigeon is the simply-colored greyhound of the sky, fancy pigeons are everything from the problem-ridden, overly-droopy modern iteration of the basset hound, to the functional-but-fancy Cardigan Welsh corgi, to the ornamental-but-sound Maltese.

A fancy pigeon show is more like a cat show than a dog show, though. The breeds have largely been derived for their looks, though a few (such as the Maine Coon cat, or the Scandaroon pigeon) served additional purposes at some point in time. The animals are kept in cages, divided by color and type, and are most prized if they’re relaxed with handling, but still the type to “strut” and show off.

Read more about some of the most popular fancy pigeon breeds on Mental_floss.

Images:

Illustrirtes Mustertauben-Buch. Author unknown, 1880.

Darwin starts Origin with a consideration of pigeons.

(via notrare)

January 9, 2014
The Challenger Reports, Volume XXI, Plate I. The Hexactinellida, Euplectella aspergillum Owen. A glass sponge, popularly known as “Venus’s flower basket.” Collected in the Philippine Islands at a depth of 100 fathoms.

The Challenger Reports, Volume XXI, Plate I. The Hexactinellida, Euplectella aspergillum Owen. A glass sponge, popularly known as “Venus’s flower basket.” Collected in the Philippine Islands at a depth of 100 fathoms.

December 30, 2013
"

Natural selection of phenotypes cannot in itself produce cumulative change, because phenotypes are extremely temporary manifestations. They are the result of interactions between genotype and environment that produces what we recognize as an individual. Such an individual consists of genotypic information and information recorded since conception. Socrates consisted of the genes his parents gave him, the experiences they and his environment later provided, and the growth a development mediated by numerous meals. For all I know, he may have been very successful in the evolutionary sense of leaving numerous offspring. His phenotype, nevertheless, was utterly destroyed by the hemlock and has never since been duplicated. If the hemlock had not killed him, something else soon would have. So however natural selection may have been acting on Greek phenotypes in the forth century B.C., it did not of itself produce any cumulative effect.

The same argument holds also for genotypes. With Socrates’ death, not only did his phenotype disappear, but also his genotype. […] The loss of Socrates’ genotype is not assuaged by any consideration of how prolifically he may have reproduced. Socrates’ genes may be with us yet, but not his genotype, because meiosis and recombination destroy genotypes as surely as death.

"

— G. C. Williams, Adaptation and Natural Selection pp 23–24, via Graham Coop

December 23, 2013
Genetic and environmental deviations in GCSE

A twin study of GSCE results found that over half of the variance in grades could be attributed to genetic factors:

heritability was substantial for overall GCSE performance for compulsory core subjects (58%) as well as for each of them individually: English (52%), mathematics (55%) and science (58%). In contrast, the overall effects of shared environment, which includes all family and school influences shared by members of twin pairs growing up in the same family and attending the same school, accounts for about 36% of the variance of mean GCSE scores. The significance of these findings is that individual differences in educational achievement at the end of compulsory education are not primarily an index of the quality of teachers or schools: much more of the variance of GCSE scores can be attributed to genetics than to school or family environment.

While the shared environment is rather small relative to the genetic variance, we can consider how big a differences in scores you could make if you could magically change someone’s genes or environment but leave everything else about them the same.

First let’s look at the distribution of scores. I took numbers from main tables (XLS) in the 2013 results

EnglishGradeCount <- list(Astar = 20304, A = 68114, B = 126027, C = 174106, 
    D = 94935, E = 42310, F = 16836, G = 5138)
MathsGradeCount <- list(Astar = 37075, A = 69429, B = 110726, C = 188947, D = 58911, 
    E = 35678, F = 28273, G = 18912)

In the Shakeshaft et al study they coded the highest grade (A*) as 11 and and the lower (G) as 4

codings <- 11:4

plot(codings, unlist(EnglishGradeCount)/sum(unlist(EnglishGradeCount)), type = "h", 
    xlab = "GCSE English score", ylab = "Frequency")

Plot of English score distribution

plot(codings, unlist(MathsGradeCount)/sum(unlist(EnglishGradeCount)), type = "h", 
    xlab = "GCSE Maths score", ylab = "Frequency")

Plot of math score distribution

The results from the paper give the mean and SD of scores in their sample

englishMean = 8.93
englishSD = 1.17

mathsMean = 8.96
mathsSD = 1.4

and the heritabilities (called $h^2$ or $a^2$) and proportions of shared ($c^2$) and unique ($e^2$) environmental variance

englishA2 = 0.52
englishC2 = 0.31
englishE2 = 0.18

mathsA2 = 0.55
mathsC2 = 0.26
mathsE2 = 0.18

We can also calculate the weighted mean and SD from the 2013 data

EnglishScores <- cov.wt(matrix(codings), unlist(EnglishGradeCount))
MathsScores <- cov.wt(matrix(codings), unlist(MathsGradeCount))
sqrt(EnglishScores$cov)

##      [,1]
## [1,] 1.57

The combined genetic or shared environment effects are deviations from the mean with a standard deviation of $\sqrt{(v^2\sigma^2)}$ where $v^2$ is the proportion of variance and $\sigma^2$ is the variance (= the standard deviation squared). NB: I’m using the heritabilities from the study but the standard deviations and means from the 2013 results.

component_sd <- function(v2, sd) sqrt(v2 * sd^2)
# calculate genetic, shared environment, and unique environment standard
# deviations
englishASD <- component_sd(englishA2, sqrt(EnglishScores$cov))
englishCSD <- component_sd(englishC2, sqrt(EnglishScores$cov))
englishESD <- component_sd(englishE2, sqrt(EnglishScores$cov))

To make this concrete, think about drawing your GCSE scores from a genetic and environmental lottery. You start with the mean score, then pick a number from hats A, C, and E. The hats vary in size based on how much of the differences in scores each contributes. The bigger the hat (like the genetic hat, A), the farther from zero the numbers if yields will potentially be. With smaller hats, like the unique environment hat E, the numbers will be more clustered around 0. The numbers you get from the three hats are added or subtracted from the mean to produce your score.

set.seed(34450)
draw1 <- rnorm(3, mean = 0, sd = c(englishASD, englishCSD, englishESD))
draw1

## [1] -0.6979 -0.4289 -0.4988

EnglishScores$center + sum(draw1)

## [1] 6.507

So our first hypothetical person draws from the hat and gets 3 genetic, shared environment, and unique environment deviations that are below the mean (e.g., = ‘bad genes’, ‘poor schooling’, ‘bad experiences’). These add up to a GSCE score of 6.5 (between an E and a D)

draw2 <- rnorm(3, mean = 0, sd = c(englishASD, englishCSD, englishESD))
draw2

## [1]  1.1156 -0.5552  0.7212

EnglishScores$center + sum(draw2)

## [1] 9.414

The next person gets positive genetic and unique environment deviations but a negative shared environment deviation (= ‘good genes’, ‘poor school’, ‘good experience’). These factors combine together to result in a score of 9.4 (a B).

We can look at the distribution of deviations on each of these factors in comparison to the phenotypic variance (P)

scores <- seq(4, 11, by = 0.01)
englishADensity <- dnorm(scores, mean = EnglishScores$center, sd = englishASD)
englishCDensity <- dnorm(scores, mean = EnglishScores$center, sd = englishCSD)
englishEDensity <- dnorm(scores, mean = EnglishScores$center, sd = englishESD)
englishPDensity <- dnorm(scores, mean = EnglishScores$center, sd = component_sd(1, 
    sqrt(EnglishScores$cov)))
plot(codings, unlist(EnglishGradeCount)/sum(unlist(EnglishGradeCount)), type = "h", 
    xlab = "GCSE English score", ylab = "Frequency", ylim = c(0, 0.6))
lines(scores, englishADensity, col = "blue")
lines(scores, englishCDensity, col = "orange")
lines(scores, englishEDensity, col = "red")
lines(scores, englishPDensity, col = "purple")
legend("topright", c("A", "C", "E", "P"), col = c("blue", "orange", "red", "purple"), 
    lwd = 3)

Densities of variance components for English

The plot shows the distribution of deviations around the mean for each of the factors compared to the phenotypic variance (purple). If your genetic (blue) and shared environment (orange) factors are average (with values of 0), then your unique environment deviation could not move you very far from the mean. This is what the paper means when it says that the unique environment contributes very little to the variance. The genetic factor, in contrast, is more spread out, so having genes that contribute very positively or very negatively to your score can move you far away from the mean.

Yet the environent can still make a big difference to test results if we were able to manipulate it. For example, if we took someone whose genetics and individual experience were average and moved them into a great family/school environment (2SD above the mean), we could potentially raise their English GCSE score from an 8 (a C) to a $8 + 2 \times \sqrt{.31 \times 1.6^2} = 9.8$ (which is almost an A). A student’s whose genes predispose them to the high intelligence and motivation that would result in an A* could be pulled down to a B by an unsupportive family/school environment. And someone’s whose genetic endowment destines them for a G could be potentially pulled up to a $4 + 2 \times \sqrt{.31 \times 1.6^2} + 2 * \sqrt{.18 \times 1.6^2} = 6.7$ (an E) with the right mix of interventions.

December 19, 2013
"The real issue, as I see it, is that we’re getting Bayes factors and posterior probabilities we don’t believe, because we’re assuming flat priors that don’t really make sense."

Andrew Gelman

November 22, 2013
Typesetting confidence intervals

When summarizing parameters from a Bayesian analysis I usually write, say if we had a parameter θ with a posterior mode of 2 and a coverage interval (CI) from 1 to 3

θ = 2 (CI = 1, 3)

The reason I use a’ comma ,’ instead of an n-dash ‘–’ is if the upper part of the range is negative. For example, if the CI was -3 to -1 versus -3 to +1.

θ = -2 (CI = -3–-1)
θ = -2 (CI = -3–1)

is typographically unclear but

θ = -2 (CI = -3, -1)
θ = -2 (CI = -3, 1)

is easier to distinguish. However using a comma doesn’t visually show that we are talking about a range. My first thought was to give more space around the n-dash

θ = -2 (CI = -3 – -1)
θ = -2 (CI = -3 – 1)

But that could still be confusing as the n-dash ‘—‘ and minus sign ‘-‘ look very similar. Could some other symbol, like a left right arrow ‘↔︎’ work?

θ = -2 (CI = -3 ↔ -1)
θ = -2 (CI = -3 ↔ 1)

[Update 12 Dec 2013] Alex Weiss suggests using ‘to’ as the separator:

θ = -2 (CI = -3 to -1)
θ = -2 (CI = -3 to 1)

November 21, 2013
"Models of data have a deep influence on the kinds of theorising that researchers do. A structural equation model with latent variables named Shifting, Updating, and Inhibition (Miyake et al. 2000) might suggest a view of the mind as inter-connected Gaussian distributed variables. These statistical constructs are driven by correlations between variables, rather than by the underlying cognitive processes – though the latter were used to select the measures used. Davelaar and Cooper (2010) argued, using a more cognitive-process-based mathematical model of the Stop Signal task and the Stroop task, that the inhibition part of the statistical model does not actually model inhibition, but rather models the strength of the pre-potent response channel. Returning to the older example introduced earlier of g (Spearman 1904), although the scores from a variety of tasks are positively correlated, this need not imply that the correlations are generated by a single cognitive (or social, or genetic, or whatever) process. The dynamical model proposed by van der Mass et al. (2006) shows that correlations can emerge due to mutually beneficial interactions between quite distinct processes."

— Fugard, A. J. B & Stenning, K. (2013). Statistical models as cognitive models of individual differences in reasoning. Argument & Computation, 4(1), 89–102 wherein @indicutivestep has a “brief whinge about two latent variable models often used in psychology

November 7, 2013
"

Psychology is an empiricist discipline; it has no core theory and so it leans heavily on it’s empirical results to prove that it’s doing something interesting.

The Bem experiments demonstrate how, without a theory, psychology is unable to deal rigorously with anomalous results.

"

— from Golonka, S., & Wilson, A. D. Gibson’s ecological approach - a model for the benefits of a theory driven psychology and Replication will not save psychology

October 31, 2013
David Ward

Subject: Nerve and muscle thin section
Technique: Brightfield, Image Stacking
Magnification: 40x

via Wired

David Ward

Subject: Nerve and muscle thin section
Technique: Brightfield, Image Stacking
Magnification: 40x

via Wired

October 31, 2013
Zhong Hua

Subject: Peripheral nerves in E11.5 mouse embryo
Technique: Confocal
Magnification: 5x

via Wired

Zhong Hua

Subject: Peripheral nerves in E11.5 mouse embryo
Technique: Confocal
Magnification: 5x

via Wired

October 25, 2013
"Timeline for the fields of molecular and quantitative genetics. The figure illustrates how the new synthesis by Fisher during the early 20th century provided a unified theory for Mendelian and biometrical genetics, how several key discoveries within the fields facilitated the interdisciplinary connections leading to two of the most groundbreaking discoveries in genetics over the past decade, genetic mapping and genomic prediction, and why we believe a new synthesis is needed to provide a common theory that embraces the full width of these two fields. Abbreviations: QTL, quantitative trait locus; RFLP, restriction fragment length polymorphism; SNP, single nucleotide polymorphism."

Nelson, R. M., Pettersson, M. E., &amp; Carlborg, Ö. (2013). A century after Fisher: time for a new paradigm in quantitative genetics. Trends in Genetics. doi:10.1016/j.tig.2013.09.006

"Timeline for the fields of molecular and quantitative genetics. The figure illustrates how the new synthesis by Fisher during the early 20th century provided a unified theory for Mendelian and biometrical genetics, how several key discoveries within the fields facilitated the interdisciplinary connections leading to two of the most groundbreaking discoveries in genetics over the past decade, genetic mapping and genomic prediction, and why we believe a new synthesis is needed to provide a common theory that embraces the full width of these two fields. Abbreviations: QTL, quantitative trait locus; RFLP, restriction fragment length polymorphism; SNP, single nucleotide polymorphism."

October 23, 2013
Arthur CharpentierVisualization of the link function and variance in generalized linear models

Arthur Charpentier
Visualization of the link function and variance in generalized linear models

October 22, 2013
Wray&#8217;s favorite figure from Purcell (2009) Science paper is on page 44 of the Supplementary materials. #WCPG2013 http://t.co/EaVt2Ud9sl— Ravinesh A. Kumar (@RavineshKumar) October 21, 2013

The figure shows the predictive power of polygenic risk scores under different models (M1-M7) of genetic architecture. The text says &#8220;If these models are true, genotyping panels of hundreds (rather than tens of thousands) of SNPs may have the potential for use in genetic risk prediction&#8221;. If you are in favor of GWAS-based methods, you might be interested in the number of SNPs and sample-sizes necessary to explain variation in the prediction set. If you are wary of GWASs&#8217; utility, you&#8217;ll note that the R2 tops out at 0.3.

Purcell et al Common polygenic variation contributes to risk of schizophrenia and bipolar disorder. Nature. 2009.

The figure shows the predictive power of polygenic risk scores under different models (M1-M7) of genetic architecture. The text says “If these models are true, genotyping panels of hundreds (rather than tens of thousands) of SNPs may have the potential for use in genetic risk prediction”. If you are in favor of GWAS-based methods, you might be interested in the number of SNPs and sample-sizes necessary to explain variation in the prediction set. If you are wary of GWASs’ utility, you’ll note that the R2 tops out at 0.3.

October 21, 2013
"Open source…is not open science. Methods matter."

Lior Pachter

4:04pm  |   URL: http://tmblr.co/Z23PQyyFP8q5
Filed under: science open source 
October 17, 2013
"Taken together, these nine predictions represent a very stringent test of the theory, because all nine originate from a single theoretical framework. None of these predictions can be varied independently of the other eight in order to fit a particular set of data—the data must confirm them all, or the theory is ruled out."

Laura Mersini-Houghton, Beyond the Horizon of the Universe

Amazing how theories in physics fit together. To be able to murder hypotheses so definitively.