the science of gender identity (part 3: psychology)

This is the third post in a multi-part series surveying the current science of gender identity, particularly with regard to the transgendered population. My first post on the subject covered proposed genetic associations and corresponding research. The second post on the matter discussed observed differences in brain anatomy between transgendered and cisgendered individuals. Here I survey a subset of the available the psychological science on the matter, starting by describing recent research on the stability of gender cognition in young transgendered children (it is stable). I then discuss research that strongly suggests that the psychological functioning of transgendered individuals improves with commencement of hormone replacement therapy. Finally, I briefly describe two studies addressing questions about whether personality differences exist between transgendered and cisgendered individuals.

Stable Gender Cognition in Transgendered Children

A parent challenged by the emergence of a transgendered child might reasonably hold skepticism, asking questions such as “is this a phase?”, “are they pretending?”, or “are they delayed in gender cognition growth?” Indeed therapists and medical providers struggle with these questions as well, as they severely impact decisions a transgendered child and their parents might want to make such as the application of puberty-blocking drugs and cross-sex hormone treatment. Unfortunately, there is very little research available to answer such questions, and existing research involves evidence dominated by children’s self-reporting of gender-identity. However, self-reporting would be unreliable under the conditions addressed by the questions above, so more precise measurement is needed.

A study [3] described below complements self-reporting (hereafter called an “explicit” measure of gender cognition) with two “implicit” measures of gender cognition. Such implicit measures are less susceptible to the possible bias discussed above. Particularly, implicit measures of identity and preferences were recorded and matched for comparison with the explicit measurements. The study recruited 32 transgendered pre-pubertal children and their cisgendered pre-pubertal siblings as one type of control. 32 additional control pre-pubertal cisgendered children were recruited and matched to the subjects by age and natal sex.

All transgendered study subjects were living in their preferred gender role, a matter I’ll discuss in more detail later. The participants then answered two implicit questions and three explicit questions:

Gender-preference implicit association test

This test evaluates a child’s implicit gender preferences. Subjects taking this test are asked to classify four categories of pictures (“male”, “female”, “good”, and “bad”) by forming key-value pairs such as (“female-good”, and “male-bad”). The pictures of male and female stimuli were photographs of children. The pictures of “good” and “bad” stimuli included things like puppies, ice-cream, snakes, and car accidents. The children then label their key-value sets with smiley faces or frowns for “good” and “bad”, and with additional photographs of a male and a female for gender (so they don’t have to read or write).

Such a design is considered more robust to the issues expressed at the top of this post involving explicit measurement. Scoring for each outcome results in more positive values when a test taker’s natal sex was associated with “good”, and negative values otherwise. In the case of the transgender subjects, they are scored twice, once with “good” associated with their gender identity and once with “good” associated with their natal sex.

Gender-identity implicit association test

This test evaluates a child’s implicit gender identity. The design is similar to the gender-preference implicit association test described above, except that “good” was replaced by “me” (“myself”, “I”, “mine”) and “bad” was replaced by “they” (“them”, “theirs”, “other”). As a result of this replacement some reading skills were required. The scoring followed the same pattern: More positive values indicate association of “myself” with stimuli related to the natal sex, negative values otherwise. Again, the transgendered subjects were scored both by natal sex and by gender identity.

Explicit gender peer preference

In a controlled manner carefully matching age and attractiveness, subjects were shown a series of pictures of boys and girls and asked which they’d most like to be friends with. Scores were coded in the controls by how many times a peer was selected of their own natal sex. For transgendered subjects, scores were calculated in this manner and in the opposite manner.

Explicit object preference

Participants were shown a series of images of children (varied by gender) and told that each had a preferred toy. Subjects were then asked which of the preferred toys they’d prefer playing with (testing for which gender “endorses” a toy). When the endorser and the participant are of the same natal sex among the controls, a count was added to the score. Transgendered subjects’ responses were recorded both by natal sex and by gender identity.

Results

The plots below, taken directly from the study paper [3], illustrate clearly that transgender subjects’ gender cognition follows that of their cisgendered peers when scored by gender identity for each of the four tests described above (p-values > 0.20). When scored by natal sex the departure is significant (p-values < 0.002):

The researchers were looking for three things in the data to answer questions about gender cognition in transgendered children. First, if the results for transgendered children looked confused, the conclusion might be that gender cognition in these children was not mature yet, or they were “confused”. However, the data revealed a clear direction in all measures.

Second, to assess whether the transgendered children were merely pretending, the researchers looked for the pattern of natal-sex congruent response on the implicit measures and gender identity response on the explicit measures. The results do not show this either.

Finally, the observed pattern of gender identity congruent responses on both the implicit and explicit measures strongly suggests that the transgendered children “know who they are”—that their gender identity is stable.

There is one major problem with this study however, but it is not a show-stopper: I mentioned above that the transgendered subjects were living according to their preferred gender identity. This meant they lived in supportive environments that permitted this. Therefore, it is unclear how well the results generalize to the total transgendered experience, given that this kind of familiar support is rare. That being said, this study still provides strong evidence that gender cognition is stable in young children.

Hormone Replacement Therapy and Psychological Functioning

Two studies investigated the impact of hormone replacement therapy on the psychological functioning and well-being of transgendered individuals [1, 2], both finding evidence that psychological functioning improves with hormone administration. The first study [1] administered the Social Anxiety and Distress Scale (SADS) and the Hospital Anxiety and Depression Scale (HADS) tests to 187 transsexual subjects, of which 64% where undergoing hormone replacement therapy while the remaining 36% had not started such treatment. The researchers controlled for covariates such as age, education level, duration of hormone treatment, and whether any cross-sex surgeries have occurred. 60% of the subjects were male-to-female transsexuals and 40% were female-to-male.

The SADS test measures social anxiety and social distress using a true/false questionnaire. Higher scores correlate with greater social anxiety. Both groups (with/without hormone treatment) fell within normal ranges, but the mean score for the group under hormone replacement therapy was significantly lower (p=0.038, F-test).

The HADS test quickly measures depression and anxiety using a questionnaire and is intended for use in non-psychiatric settings. It is not as comprehensive as the MMPI-2 (discussed below), but useful for initial diagnostics. It divides into two subscales: HAD-D for measuring depression and HAD-A for measuring anxiety. Mean HAD-D scores fell in the normal range for both groups, as did mean HAD-A for the group undergoing hormone replacement therapy. However, mean HAD-A for the non-treated group scored in the range suggesting possible mood disorder. The differences in means for both tests between the groups were significant: p=0.001 for HAD-A and p=0.002 for HAD-D (F-test).

There are a few limitations with this study, which the authors carefully describe. First, the subject pool came from Catalonia, which has a very developed transgender care infrastructure. Therefore the results may not generalize well to less accommodating regions. Second, there may be selection bias in that the most socially anxious or depressed may not seek out transition-related care. Finally, a longitudinal study is needed to firmly establish causality.

The second study [2] investigated the psychological improvement in transgendered men after starting testosterone therapy. Baseline measurement of psychological functioning for each study subject was established through administration of the Minnesota Multiphase Personality Inventory (MMPI-2) immediately prior to starting testosterone injections. The MMPI-2 was then re-administered to each participant three months after starting the testosterone treatment to assess changes. Because this was a longitudinal study design stronger conclusions about causality could be made.

The MMPI-2 is a popular, reliable, and well-studied tool for assessing psychopathology. It employs 567 true or false questions to examine clinical conditions such as depression, paranoia, and schizophrenia. Its scoring procedure involves comparison to norms by natal sex—the initial validation of the tool did not separate sex and gender—which is a problem for transgendered individuals. Therefore this study’s researchers employed the male scoring procedure when comparing results to those of male controls, and the female scoring procedure when comparing results to those of female controls. This enabled them to separate the effects of sex and gender in the analysis.

48 transgender men, 62 female controls, and 53 male controls were recruited for the study. The covariates of age, educational attainment, and employment status were controlled for.

At three months, after adjusting for baseline measurements, significant reductions in MMPI-2 scores for hypochondria, depression, hysteria, and paranoia occurred for the transgender subjects relative to the female controls. (Higher scores indicate greater pathology). A similar comparison to male controls yielded no statistically significant results. These two studies suggest that administration of hormone replacement therapy to transgendered individuals improves their psychological condition. However, more research into the matter would help.

Personality

Two studies [4, 5] explored personality differences between adult transsexuals and controls, each study applying a similar method but with different ethnic groups to investigate if findings are consistent across culture.

The first study [4] took place in Barcelona with 166 male-to-female transsexuals and 88 female-to-male transsexuals. There were 404 controls (division by natal sex unspecified). All subjects were mentally healthy. The covariates of age, educational attainment, and employment status were controlled for.

The researchers administered the Temperament and Character Inventory (TCI) to the subjects to characterize their personalities. The TCI is a 240 item questionnaire designed to measure each of seven general dimensions of personality: “novelty seeking”, “harm avoidance”, “reward dependence”, “persistence”, “self-directedness”, “cooperativeness”, and “self-transcendence”.

The study produced two findings: First, the personality differences between transsexuals and the controls were not clinically relevant—i.e., the transsexuals have “normal” personalities. Second, the researchers observed that the differences that did exist between female-to-male transsexuals and male-to-female transsexuals mirrored previously observed differences between cisgendered males and cisgendered females using the same measurement procedure. This means that male-to-female transsexual personalities are more congruent with cisgendered female personalities, and likewise for female-to-male transsexuals and cisgendered male controls.

The second study [5] administered a shortened form of the TCI to 187 female-to-male and 72 male-to-female Japanese transsexuals, along with 184 cisgendered male and 159 cisgendered female controls. All subjects were psychologically healthy. These researchers observed high and statistically significant values for the personality traits of “reward dependence” (relative to cisgendered males) and “cooperativeness” (relative to cisgendered males and male-to-female transsexuals) in the female-to-male subject group. They also observed a high and statistically significant value for trait of “self-transcendence” in the male-to-female group, relative to the values for each of the other three groups. These findings concur with [4] only in the “self-transcendence” observation for male-to-female transsexuals ([4] also conducted pair-wise comparisons, which I did not describe above since the differences observed were not considered “clinically relevant” by the authors). This difference in outcome might be explained by cultural differences or by differences in TCI version used (both language and length).

Related Posts

the science of gender identity (part 1: genetics)
the science of gender identity (part 2: brain anatomy)

References

  1. http://www.ncbi.nlm.nih.gov/pubmed/21937168
  2. http://www.ncbi.nlm.nih.gov/pubmed/25111431
  3. http://pss.sagepub.com/content/26/4/467
  4. http://www.ncbi.nlm.nih.gov/pubmed/23958334
  5. http://www.ncbi.nlm.nih.gov/pubmed/25130781

the science of gender identity (part 2: brain anatomy)

This is the second post in a mult-part series surveying the current science of gender identity, particularly with regard to the transgendered population. In my previous post I discussed the proposed genetic associations and corresponding research. A future post, if I can find sufficient data, will address neuropsychology research related to the transgender experience.

Here I discuss studies exploring differences in brain anatomy between transsexuals and cisgendered controls. The analysis is biased toward the male-to-female transsexual population versus the female-to-male population due to the availability of research data, which is unfortunate. As more research becomes available I will remedy this imbalance.

The research I describe below clearly points to structural differences in the brain between transsexual and cisgendered individuals. This is observational data—It still doesn’t answer why the structural differences emerge in the first place, though a predominant theory suggests variation in sex hormone uptake in the brain during fetal development is a cause [5]. My previous post discusses how this hypothesis connects to genetic features.

A Bit About the Words I’m Using

I’d prefer to use the umbrella term “transgender” to label the study participants described below. However, “transgender” is too broad, as the research I describe focused on those who particularly want to or have modified their bodies to become a member of a different sex, which not all transgendered individuals want to do. Therefore I use the medical term for this population: “transsexuals”.

Increased Putamen Volume

A comparison of MRIs from 24 male-to-female transsexuals, along MRIs from with 30 male and 30 female age-matched controls is reported in [1]. The transsexual study participants had not started hormone replacement therapy, therefore the research avoided a major possible confounding effect due to the possibility of hormone treatment altering brain anatomy [4]. However, the study did not control for the sexual orientation of subjects and controls, which has also been associated with brain anatomy [2, 3] and could confound the analysis. This decision was made because the sexual orientation of the controls was unknown since their MRIs came from a database that did not record that information.

The research found that male control and transsexual gray matter volumes were similar for all regions of the brain under investigation except the putamen, which was significantly larger for the transsexual group. The transsexual left and right putamen volume distributions were closer to that of the female controls, as shown in the following boxplots taken directly from the paper [1]:

A different view, also taken directly from the paper, displays the regions of the brain having significant volume difference between the transsexual subjects and male controls at p < 0.001 (FDR-corrected). Only the right putamen (in red) appears at this significance level.

These results suggest that male-to-female transsexuals carry a “feminized” putamen, which may help explain their differing gender identity compared to the male controls.

Increased Cortical Thickness

The authors of the study I described in the last section performed an additional study looking at cortical thickness differences between male-to-female transsexuals and cisgendered male controls [6]. As in the previous study, the subjects had not started hormone replacement therapy, and the researchers did not control for sexual orientation.

The study examined MRIs from 24 transsexuals and 24 age matched controls, comparing cortex thickness at thousands of points along the cortical surfaces. The statistical tests for difference at each point were corrected for multiple comparisons using false discovery rate. A map showing the significantly different regions, taken directly from the paper, is shown below [6]:

The following cortical regions were identified as different between the two groups: frontal cortex, orbito-frontal cortex, central sulcus, perisylvian regions, paracentral gyrus, pre/post-central gyrus, parietal cortex, temporal cortex, precuneus, fusiform, lingual, and orbito-frontal gyrus.

These findings strengthen the argument that brain anatomical differences exist between male-to-female transsexuals and cisgendered males.

Sex-Atypical Hypothalamus Activation

The research described in this section evaluated the hypothalamus activation of 12 male-to-female transsexuals when smelling two steroids known to elicit sex-differentiated responses: 4,16-androstadien-3-one (AND) and estra-1,3,5(10),16-tetraen-3-ol (EST) [7]. Data from a similar study by the researchers with an unspecified number of heterosexual male and female controls was available for comparison. The male-to-female transsexuals in the study were all heterosexual with regard to their birth sex; this eliminates the confounding effect of sexual orientation.

Both the transsexuals and the female controls experienced hypothalamus activation by AND, while the male controls experienced hypothalamus activation by EST. The activation heatmap image below, taken directly from the paper [7], illustrates the similar response to AND for male-to-female transsexuals (MFTR) and female controls (HeW), versus the response by the male controls (HeM). Furthermore, this image shows how the response to EST is distinct between transsexuals and control males.

This analysis suggests that transsexual hypothalamus activation by these steroids is birth-sex atypical.

White Matter Microstructure

[8] is the first analysis I found concerning female-to-male transsexualism. Unfortunately, I could only read the abstract since I couldn’t find the full article for free, but here is a summary of the findings:

Fractional anisotropy (FA) was performed on 18 female-to-male transsexuals, 24 male controls, and 19 female controls. The controls were heterosexual. The transsexual subjects had yet not started hormone replacement therapy. The FA procedure evaluated the white matter fibers of the whole brain.

FA values for the right superior longitudinal fasciculus, the forceps minor, and the corticospinal tract were compared between the groups. The values for the female-to-male transsexuals more closely resembled those of the control males than the control females. This lends support for the existence of brain structure differences between female-to-male transsexuals and cisgendered females.

Brain Anatomy More Congruent With Gender Identity Than Biological Sex
Another study [9] considered seven female-to-male transsexuals and ten male-to-female transsexuals simultaneously, along with age matched controls (eleven cis-females and seven cis-males). This seems small for a study, but the authors cite a limited subject pool. The subjects were given MRIs and brain regions distinguishable by the gender identity variable and the interaction between the gender identity variable and biological sex variable were identified.

The researchers identified four brain regions where gray matter volume of the transsexual subjects were identical to that of the controls sharing the subjects’ gender identity, but different from the controls sharing the subjects’ biological sex. The gray matter volume was higher in the right middle and inferior occipital gyri, the fusiform, the lingual gyri, and the right inferior temporal gyrus for male-to-female transsexuals and cis-female controls. In contrast, the gray matter volume was greater in the left pre-and postcentral gyri, the left posterior cingulate, the calcarine gyrus, and the precuneus in female-to-male transsexuals and cis-male controls.

To limit confounding effects, all transsexual recruits for the study were homosexual (with regard to birth sex) and had not started hormone replacement therapy. Nothing is stated in the paper about the sexual orientation of the controls.

Post Mortem Studies

Two post mortem studies are of note, though they are limited by small sample sizes and the fact that the male-to-female transsexual subjects involved had been treated by estrogen, which may impact brain plasticity [10].

The first study [11] observed that the size of male-to-female transsexuals’ bed nucleus of the stria terminalis was more typical of cis-female size. Similarly, [12] reported that male-to-female transsexuals’ volume and neuronal density of the interstial nucleus of the anterior hypothalamus was more cis-female typical.

Related Posts

the science of gender identity (part 1: genetics)

the science of gender identity (part 3: psychology)

References

  1. http://www.ncbi.nlm.nih.gov/pubmed/19341803
  2. http://www.ncbi.nlm.nih.gov/pubmed/17975723
  3. http://www.pnas.org/content/early/2008/06/13/0801566105.abstract
  4. http://www.eje-online.org/content/155/suppl_1/S107.full.pdf+html
  5. http://www.ncbi.nlm.nih.gov/pubmed/12492297
  6. http://www.ncbi.nlm.nih.gov/pubmed/23724358
  7. http://cercor.oxfordjournals.org/content/18/8/1900.full.pdf+html
  8. http://www.sciencedirect.com/science/article/pii/S0022395610001585
  9. http://www.ncbi.nlm.nih.gov/pubmed/24391851
  10. http://www.eje-online.org/content/155/suppl_1/S107.full.pdf+html
  11. http://www.nature.com/nature/journal/v378/n6552/abs/378068a0.html
  12. http://brain.oxfordjournals.org/content/131/12/3132

the science of gender identity (part 1: genetics)

This is the first in a multi-part series surveying the current science of gender identity, particularly with regard to the transgendered population. I intend to discuss the genetic, brain anatomic, and neuropsychological findings of recent studies on the matter. As always, I will incorporate my own statistical analysis of raw study data wherever possible.

Here I start by discussing four studies involving genetic variations thought to be correlated with transsexualism. Some of these studies show promising leads toward increasing our understanding, others report limited or no findings. Limited or no findings does not imply that no genetic factors relate to transsexualism, just that none were found for the particular gene variant examined by the study.

My only beef with these studies is that they consider only one or a few genetic variations at a time. This is a limitation of the technology used. As the cost of whole-genome sequencing decreases, we’ll be able to look for simultaneous genetic variations that play a role in concert with each other.

Code and data for the analyses presented below is attached.

A Bit About the Words I’m Using

Two words I use in this post bother me, so I thought I’d explain my choice to use them.

First, I’d prefer to use the umbrella term “transgender” to label the study participants described below. However, “transgender” is too broad, as the research I describe focused on those who particularly modify their bodies to become a member of a different sex, which not all transgendered individuals want to do. Therefore I use the medical term for this population: “transsexuals”.

Second, “nucleotide variation”, which I associate below through analysis with transsexualism, implies there is a “normal” non-variation. The word is used to indicate that the particular DNA sequence involved is not present in most individuals’ genome. More common DNA variations are those that result in blue eyes vs. the more frequent brown, and certainly nothing is pathological about have blue eyes. In the same vein, I assert that nothing is pathological about transsexualism; its hypothesized genetic component is simply part of our genetic diversity.

Gene Promoter Variation rs549669867

A nucleotide variation (rs549669867) in the promoter for the gene CYP17A1 associates with female-to-male transsexualism according to a study outlined in [1]. CYP17A1 is a key gene involved in steroid metabolism, and this particular variation causes carriers to possess higher concentrations of both testosterone and estrodiol in their bodies [1]. These findings are consistent with a prevailing theory that extra testosterone causes masculinization of the female brain during fetal development, thereby contributing to development of gender dysphoria.

Here I present independent statistical reasoning based on data obtained from the study paper, which supports the researchers’ conclusions. These conclusions do not fully explain the origins of female-to-male transsexualism, as there were non-transsexuals included in the study who had the nucleotide variation, and there were transsexuals in the study who did not. However, the difference in frequencies of the variation’s occurrence between the transsexual and non-transsexual study participant groups is statistically significant.

First I’ll discuss the nucleotide variation itself. The following screenshot from the UCSC Genome Browser [2] shows 50 nucleotides upstream and downstream from the start of gene CYP17A1 on chromosome 10 of the human genome:

The variation we are examining is shown in the lower left, 34 nucleotides before the start of CYP17A1 (this is inside the “promoter” region of the gene). For the genomic strand sequenced in the study (any of two could have been chosen), the normal nucleotide at this position is a “T” and the variation is a “C”. From analysis of 1000 Genomes Project data, this variation is expected to occur on one of an individual’s two copies of chromosome 10 with a frequency of 0.02% [3].

Now the statistical analysis:

The study recruited 49 female-to-male transsexuals and 913 female controls, then sequenced their DNA in the promoter region of gene CYP17A1 to determine their genotype. The genotype could be one of three outcomes: “TT”, indicating lack of the nucleotide variation on both copies of chromosome 10; “CT”, indicating the variation occurs on only one of the chromosome 10 copies; and “CC”, indicating the variation is present on both copies of chromosome 10. The genotypes and their frequencies by group are listed in the following table:

We make two comparisons: The number of recessive genotypes vs. non-recessive genotypes (CC vs. CT + TT), and the number of dominant genotypes vs. non-dominant genotypes (TT vs. CT + CC). A variation often has to be recessive (present on both copies of its chromosome) to be biologically active, though this is not always the case.

Testing recessive vs. non-recessive genotype counts by study group using a Chi-square test yields a p-value of 0.04034, indicating a statistically significant difference exists between the transsexual and non-transsexual groups with regard to presence or absence of the recessive genotype.

Testing dominant vs. non-dominant genotype counts by study group using a Chi-square test yields a p-value of 0.06322, which is just over the commonly used threshold for declaring statistical significance.

It follows from this data and analysis that we can conclude that the recessive genotype is associated with female-to-male transsexualism. Again, this association does not explain all cases, e.g., why some non-transsexuals also have the recessive genotype, but it contributes to scientific efforts to understand transsexualism’s origins.

Gene Variation rs743572

Nucleotide variation rs743572 also impacts gene CYP17A1. Rather than residing in the promoter region of the gene as did rs549669867, this variation lies within the gene itself.

In the my analysis of this variation’s study data discussed below [4], the association between the variation and transsexualism (comparing transsexuals vs. controls) is not significant. However, the difference in the frequency of the variation between female-to-male transsexuals and male-to-female transsexuals is significant according to the statistical test I conducted. (The study authors concluded the same thing, just with different p-values). Therefore I’m reporting this variation as notable with regard to our efforts to understand the genetic underpinnings of transsexualism. The difference between this variation’s frequency in female-to-male transsexuals vs. male-to-female transsexuals may lead to insight into the origin of each outcome separately (per nominal biological sex), rather than help provide a “one size fits all” explanation for transsexualism.

rs743572 resides 139 nucleotide positions from the start of gene CYP17A1. It occurs on one of individuals’ two copies of chromosome 10 with a frequency of 41% [5]. The fact that this variation is much more common than rs549669867 probably explains why the transsexualism vs. control association for the variation I investigate below does not prove statistically significant. The following screenshot from the UCSC Genome Browser [2] shows the variation on gene CYP17A1 within chromosome 10 of the human genome:

The study [4] whose data I analyze here recruited 151 male-to-female and 142 female-to-male transsexuals. The researchers also recruited 167 male and 168 female non-transsexuals. All were Spaniards with no possibly confounding health issues. Of these subjects, 36% of the male-to-female and 45% of the female-to-male transsexuals carried the variation. 39% of the male and 38% of the female non-transsexuals also carried the variation. Presence or absence of the variation was determined through DNA sequencing. From this data I constructed the following contingency table, rounding to get whole numbers:

Performing pairwise comparisons of the count proportions using a Chi-squared goodness of fit test yields the following p-values:

As mentioned above, the only significant difference in variation proportions is in the comparison of female-to-male vs. male-to-female transsexuals. Therefore this variation does not by itself seem a strong contributor to our effort to explain the transgendered experience in terms of genetics. However, a whole-genome comparison study on similar test subjects could elucidate whether this variation interacts with other variations to form a combined association with transsexualism.

Androgen Receptor Repeat Length Variation rs193922933

A study [6] correlated the androgen receptor (AR) gene’s CAG repeat length variation (rs193922933) with male-to-female transsexualism. I feel the researchers did not perform their statistical analysis correctly, and have remedhttp://rs193922933ied the situation below. However my conclusion was the same.

The AR gene’s CAG repeat length is highly variable between individuals. Each occurrence of the repeat appends an extra amino acid to the androgen receptor protein, as shown below. No information about the frequency distribution of this variation was readily available [7].

Longer CAG repeat lengths are known to diminish testosterone signaling, which impacts masculinization of the brain during development [6].

The study authors sequenced the CAG repeat region of 112 male-to-female transsexuals and 258 male controls. They report the length data in the following plot (but not their raw data) [6]:

Using the GNU Image Manipulation Program, I measured each bar to determine the percentages and reconstructed the source data, re-plotted as follows:

Here we see that the CAG repeat length medians between the transsexual subjects and the controls differ by one (with the transsexual group’s median being longer), and that the interquartile limits are identical. The control group has a heavier lower tail.

The researchers compared the means using a t-test, which I am uncomfortable with due to the skew in the male controls’ distribution. Therefore I performed a quasi-Poisson regression since this is underdispersed count data. That analysis reported a statistically significant difference between the two groups (p = 0.0269).

I could not find data on the practical significance of a median difference of one CAG repeat length.

Negative Results

Another study [8], found no association between CAG repeat length variation in the AR gene and transsexualism. Furthermore, it found no association between transsexualism and variations in four other sex hormone-related genes: estrogen receptors alpha and beta, aromatase CYP19, and progesterone receptor PGR.

More Research Needed

A search of DisGeNET (a database of disease*-gene annotations) [9] for the term “transsexualism” shows only five genes and five PubMed publications covering the subject. This reveals the dearth of research on the matter. The image below showing the genes and PubMed articles extracted from the search comes from my own implementation of DisGeNET’s data within a graph database, which I discuss here.

*I of course object to DisGeNET’s labeling of “transsexualism” as a disease, and to its connection with the MeSH term “mental disorders”. I’ve contacted DisGeNET and MeSH about this issue and will report back on their response shortly.

Related Posts

the science of gender identity (part 2: brain anatomy)

the science of gender identity (part 3: psychology)

Code and Data

code_and_data

References

  1. http://www.ncbi.nlm.nih.gov/pubmed/17765230
  2. https://genome.ucsc.edu/
  3. http://www.ncbi.nlm.nih.gov/SNP/snp_ref.cgi?type=rs&rs=rs549669867
  4. http://www.ncbi.nlm.nih.gov/pubmed/25929975
  5. http://www.ncbi.nlm.nih.gov/SNP/snp_ref.cgi?searchType=adhoc_search&type=rs&rs=rs743572
  6. http://www.ncbi.nlm.nih.gov/pubmed/18962445
  7. http://www.ncbi.nlm.nih.gov/SNP/snp_ref.cgi?type=rs&rs=rs193922933
  8. http://www.ncbi.nlm.nih.gov/pubmed/19604497
  9. http://www.disgenet.org/web/DisGeNET/menu

HRC Corporate Equality Index correlates with Fortune’s 50 most admired companies

The Human Right’s Campaign, one of America’s largest civil rights groups, scores companies in its yearly Corporate Equality Index (CEI) according to their treatment of lesbian, gay, bisexual, and transgender employees [1]. The companies automatically evaluated are the Fortune 1000 and American Lawyer’s top 200. Additionally, any sufficiently large private sector organization can request inclusion in the CEI [2].

Similarly, Fortune Magazine publishes an annual list of 50 of the world’s most admired companies [3]. Companies are rated by financial health, stock performance, leadership effectiveness, customer sentiment, scandals, and social responsibility.

I became curious whether CEI scores correlate with membership in Fortune’s most admired list, so I matched the two datasets and analyzed the outcome. The results (below) are striking. Code implementing the calculations, with the source data, is attached.

Results

Plotting the CEI score distributions by whether a company was included in Fortune’s list produced:

From this difference in distributions it is clear that the status of being “most admired” correlates with a high CEI score, though there are a few outliers. In the distribution on the left, we see that over 50% of the companies in Fortune’s list held the top CEI score of 100, whereas only 25% of the companies not contained in Fortune’s held the top score. The median score for the most admired group was 100 while for the companies not included in Fortune’s list it is about 80. Over 80% of the most admired companies scored 90 or above. The variance is much wider for the companies not included on the list. Statistical analysis comparing the two groups, detailed below, confirms the correlation.



While correlation does not imply causality, this analysis suggests two things: First, the type of leadership necessary to achieve a high CEI score is the same type of leadership that leads to inclusion in Fortune’s most admired companies group. Second, any company aspiring to membership in the most admired group might consider developing its CEI score.

There is one possible source of bias, but I don’t expect that it is large: “Social responsibility” is used in Fortune’s rankings, which may include CEI scores (I don’t know). However, Fortune’s emphasis on financial health and stock price probably trumps any contribution that the CEI would generate alone. Furthermore, in the CEI score distribution for the most admired companies, there are outliers containing extremely low scores. This suggests that the CEI played little if any role in the selection of most admired companies.

Method

I manually copied and pasted the company names and scores from the CEI online database [1]. Then I cleaned up the results to create a manageable CSV file. Similarly, I copied and pasted the Fortune 50 most admired company list [3] into another CSV file. After that, I matched the two datasets by hand. Perhaps I could have performed the match algorithmically, but I would have had to worry about different representations of company names between the two datasets, e.g. “3M Co.” vs. “3M”. There was only 50 cases so the manual match did not take long.

Two cases in Fortune’s list had to be excluded, BMW and Singapore Airlines, because they were not included in the CEI, possibly because they are based outside the USA. In the case of two other non-US companies in Fortune’s list, Toyota and Volkswagen, I matched to Toyota Motor Sales USA and Volkswagen Group of America, respectively.

Finally, I plotted the CEI score distributions shown above and performed the statistical analysis reported below using the attached Python code.

Statistical Analysis

The extreme difference in variance between the two groups makes it impossible to compare medians using a non-parametric test, and the distribution of the CEI scores does not lend itself to a clean regression analysis. Therefore I built the following contingency table from the data:

The p-value for this table obtained from Fisher’s exact test is 4.53e-08, indicating that the proportions are significantly different.

References

  1. http://www.hrc.org/campaigns/corporate-equality-index
  2. http://www.hrc.org/resources/entry/corporate-equality-index-what-businesses-are-rated-and-how-to-participate
  3. http://fortune.com/worlds-most-admired-companies/

Code and Data

HRC_Fortune_data_and_code

frugal anarchy

Of all the systems that we seek freedom within and from, none pervades our lives as much as the “econosphere” we inhabit. By “econosphere”, I mean the global network of economic activity whose nodes are individuals and whose edges are trade relationships between individuals.

Even if we had no government, we’d still likely be trading goods and services. Therefore the econosphere may be more significant than the existence of government to those seeking freedom. Anarchists traditionally focus on the elimination of government as the means of increasing freedom. However, I propose that limited reliance on the econosphere is a more comprehensive goal for anarchistic thinking.

There are two paths to individual economic freedom in a free-market economy: The first is to be wealthy enough to afford whatever transactions one wants to make whenever one wants to make them. This is unfortunately out of reach for most people. The second path is voluntary frugality; limiting the transactions one makes to well thought out targets, such that utility and satisfaction of purchases is maximized and very few dollars are spent on things outside those targets.

This strategy of voluntary frugality limits individual reliance on the econosphere by limiting the amount of money that an individual needs to acquire and spend, thereby enhancing their freedom to choose their path in life. I cannot think of a more practical expression of anarchism within the “real world” that we inhabit today.

bias reinforcement through survey questionnaires

Today I play media theorist and examine how survey questionnaires reinforce survey designers’ biases:

The knowledge that biases emit from survey questionnaires is nothing new. The extreme case, “push-polling”, intentionally guides the questionnaire reader toward a viewpoint, without real interest in their prior opinion. Any survey writer willing to push-poll already understands my concerns about bias (because they are propagandists).

It is the unintended or “honest” biases that concern me here.



Consider for example the common belief that individuals can be categorized as a member of one out of four or five distinct racial groups, a belief reflected in many survey questionnaires that ask respondents to indicate which race they belong to. This is an example of what I call an “honestly” projected bias; the survey writer likely has limited awareness that there is even a problem, and does not expect their respondents to question the belief. In these cases, the bias enters the survey questionnaire through the questionnaire writers’ phrasing and provided options, and is confirmed when each respondent chooses one of the options.

Stepping back, we observe “bias in, bias out” where the belief itself gains strength across the survey process. It strengthens among the respondents as they accept the belief when answering the questions, and strengthens in the mind of the survey creator when they see tacit acceptance of the bias in the responses. At each step, neural pathways supporting the belief become stronger due to exercise.

I’ve mapped this process below, illustrating the cumulative bias amplification by degree of red in the arrows’ color:

While we cannot completely escape projecting our biases through our measurement instruments, I call on questionnaire writers to step back and consider what we might be propagating. We may have to become more creative to limit the damage. (For one example of a creative approach, see my post “a better way to ask about gender in survey questionnaires” for an idea on how to avoid propagating the binary sex/gender bias through survey questionnaires).

a better way to ask about gender in survey questionnaires

Survey questionnaires regularly ask respondents’ sex or gender, and mostly offer only the binary options:

When presented with such a survey on paper, I typically add and then select a third option: “Fuck you”. (Similarly, I do the same with race/ethnicity questions when asked to choose one out of four or five options).

However, we increasingly answer surveys online, making this write-in approach unavailable. Furthermore, scrawling profanities onto survey forms fails to positively address the very serious problem underlying my anger: that the binary sex/gender classification erases folks who, for a variety of reasons, do not fit within it.

In what I perceive as an honest attempt at inclusion—and I sincerely appreciate the effort—Google offers an “Other” option in its Google+ profile form:

But simply adding an “other” option still emphasizes the binary classification system; it reminds respondents that they either fit in, or don’t. Very few of those who don’t fit in enjoy that interjection when it involves something as fundamental as gender identity.



I recommend the following alternative for collecting gender and sex data from survey respondents:

Here the use of sliders reflects the continuous natures of sex and gender, while the division of the query into separate, orthogonal dimensions accounts for the distinctness of biology (sex) from social artifact (gender).

Certainly this scheme fails to capture all the nuances of gender identity, particularly its flux within individuals, but it reaches for a more honest and inclusive world.