Abstract
Background
Sexual dimorphism is highly prominent in mammals with many
physiological and behavioral differences between male and female form
of the species. Accordingly, the fundamental social and cultural
stratification factors for humans is sex. The sex differences are
thought to emerge from a combination of genetic and environmental
factors. It distinguishes individuals most prominently on the
reproductive traits, but also affects many of the other related traits
and manifest in different disease susceptibilities and treatment
responses across sexes. Sex differences in brain have raised a lot of
controversy due to small and sometimes contradictory sex-specific
effects. Many studies have been published to identify sex-biased genes
in one or several brain regions, but the assessment of the robustness
of these studies is missing. We therefore collected huge amount of
publicly available transcriptomic data to first estimate whether
consistent sex differences exist and further explore their likely
origin and functional significance.
Results and conclusion
In order to systematically characterise sex-specific differences across
human brain regions, we collected transcription profiles for more than
16,000 samples from 46 datasets across 11 brain regions. By systematic
integration of the data from multiple studies, we identified robust
transcription level differences in human brain across to identify
male-biased and female-biased genes in each brain region. Firstly, both
male and female-biased genes were highly conserved across primates and
showed a high overlap with sex-biased genes in other species.
Female-biased genes were enriched for neuron-associated processes while
male-biased genes were enriched for membranes and nuclear structures.
Male-biased genes were enriched on the Y chromosome while female-biased
genes were enriched on the X chromosome, which included X chromosome
inactivation escapees explaining the origins of some sex differences.
Male-biased genes were enriched for mitotic processes while
female-biased genes were enriched for synaptic membrane and lumen.
Finally, sex-biased genes were enriched for drug-targets and more
female-biased genes were affected by adverse drug reactions than
male-biased genes. In summary, by building a comprehensive resource of
sex differences across human brain regions at gene expression level, we
explored their likely origin and functional significance. We have also
developed a web resource to make the entire analysis available for the
scientific community for further exploration, available at
[27]https://joshiapps.cbu.uib.no/SRB_app/
Supplementary Information
The online version contains supplementary material available at
10.1186/s13293-023-00515-w.
Keywords: Sex difference, Human brain, Gene regulation, Hormones, Data
integration, Conservation, Brain disorders, Drug response
Plain language summary
Sex and gender differences are present across many organs in humans and
have biological and social origins. The differences in brain raise a
lot controversy due to small and sometimes contradictory results and
its societal implications. In this study, we set out to discern the
consistency of sex differences in brain by collecting a huge amount of
publicly available transcriptomic data and further explore their likely
origin and functional significance. We identified robust sex-biased
genes in human brain with female-biased genes enriched for X chromosome
genes. We also noted that male- and female-biased genes were enriched
for distinct biological processes. Finally, sex-biased genes were
enriched for androgen response elements. In summary, our analysis
suggests sex-chromosomes and androgens as likely sources of sex
differences in brain. Finally, we noted that age affects gene
expression in brain more than sex.
Supplementary Information
The online version contains supplementary material available at
10.1186/s13293-023-00515-w.
Highlights
* By collecting and reanalyzing 16,000 samples from 46 datasets, we
identified robust male- and female-biased genes across 11 brain
regions in humans. Female-biased genes were significantly enriched
on the X chromosome and male-biased genes were enriched on the Y
chromosome. Robust sex-biased genes were highly conserved across
primates and other species.
* Sex-biased genes in the brain were enriched for brain-specific
genes, but not region-specific genes within the brain. Male-biased
genes across brain regions were enriched for astrocyte and
oligodendrocyte signature genes more than female-biased genes.
* We noted that both age and sex influence gene expression for most
genes. XIST was highly female-biased, while genes on the Y
chromosome were male-biased. Three genes were found to be age- and
sex-associated in both datasets, including FREM3, CHI3L1, and
SERPINA3, which have been associated with neurological disease.
Notably, CHI3L1 is highly expressed in female AD patients compared
to male AD samples.
* Over 80% of both male and female-biased genes were enriched for
either half or full androgen response element (ARE) sites across
brain regions, and sex-biased genes are also enriched for estrogen
response elements (ERE).
* Finally, we developed a web resource to make the entire analysis
available for the scientific community for further exploration,
available at [28]https://joshiapps.cbu.uib.no/SRB_app/
Supplementary Information
The online version contains supplementary material available at
10.1186/s13293-023-00515-w.
Background
Biological sex is one of the most prominent stratification factor for
the human population, with classical binary biological grouping into
male and female. The physiological and behavioral sexual dimorphism in
humans originates from both genetic and environmental constructs, and
can produce divergent sex-specific disease susceptibility. For example,
females carry a much higher burden of autoimmune diseases compared to
men, while men are more likely to suffer from schizophrenia.
Interestingly, the same alleles of the complement component 4 or C4
genes at the major histocompatibility complex (MHC) locus were shown to
increase risk for schizophrenia and reduce risk for autoimmune
disorders [[29]1]. Sex and gender terms have been used inter-changeably
in scientific literature. Sex is biologically determined by chromosomal
makeup, while gender is more behavioral in nature and also more
controversial as to how it is determined [[30]2]. Importantly, most
sexually dimorphic traits are likely to be a result of multiple,
independent sex-biasing factors where genetic and epigenetic factors
are manifested through sex-biased gene expression or hormonal control
[[31]3]. Such traits are defined as ’sex and gender’ or ’sex/gender
terms’ or simply as ’sex’. Hence forth, we will use the term ’sex’ for
simplicity.
Male and females have many differences, in physical appearance, social
behavior as well as in disease incidence, prevalence, morbidity and
mortality. Yet males have been predominantly used in basic and
pre-clinical research, due to female cyclic hormonal patterns and
importantly, a common belief that male and females mainly have only
reproductive difference [[32]4]. Historically clinical trials are
largely conducted on males only and unsurprisingly, females are more
likely to suffer from side effects from medications due to
under-representation in clinical trials [[33]5]. Despite this,
scientific publications in pharmacology field show a trend downward
with 29% of articles reporting the use of both sexes in 2019 compared
to 33% in 2009 [[34]6]. Studies of both males and females are essential
to understanding sex-specific human biology towards the advancement of
human health. There is growing scientific literature exploring sex
differences in healthy lifespan and aging. Transcriptomic studies allow
exploration of sex differences at genome-wide level providing clues for
the molecular basis of sex differences. GTEX consortium generated
transcriptome profiles across 53 human tissues using RNA-sequencing
data for 544 individuals (males and females). Several studies have used
this data to characterise sex differences across tissues
[[35]7–[36]10]. Sex-specific differences are noted in all organs and
these differences also affect tissues not specialized for reproduction,
including non-reproductive tissues. Sex influences gene expression
levels and cellular composition of tissue samples across the human
body, with a total of 37% of all genes exhibiting sex-biased expression
in at least one tissue [[37]10].
Areas of the brain function differently in females and males, and are
differentially affected by disease in the two sexes. For example, genes
associated with Parkinson’s disease and Alzheimer’s disease are
targeted by different sets of transcription factors in each sex
[[38]11]. Evaluating differences in male and female brains can
contribute to understanding sex differences in disease incidence,
manifestation, and outcome. Accordingly, several transcriptome studies
have focused specifically on the human brain regions to identify
sex-biased genes [[39]12–[40]14]. Sex differences in human brain have
nevertheless remained controversial due to small effects and
inconsistencies across studies as most of these studies have used
mostly one or in rare cases a few [[41]15] independent datasets making
it hard to estimate the reproducibility of their sex-biased gene lists.
Only a handful of studies have made a systematic effort, where the
experimental design revealed specific causal factors for future study
([[42]16], Table 1). As independent validation of genes from a single
study can be very expensive and time consuming, reproducible expression
across studies can also be used to identify reliable sex-biased genes.
Accordingly, we set out to investigate whether there are robust
sex-biased gene expression signatures in human brain by collecting and
systematically integrating vast amount of publicly available data.
Specifically, we collected transcription profiles for more than 16,000
samples from 46 datasets in human brain. By systematic integration of
the data from multiple studies, we identified robust transcription
level differences in human brain across 11 brain regions and classified
male-biased and female-biased genes, and their likely origin and
functional significance. We have also developed a web resource to make
the entire analysis available for the scientific community for further
exploration, available at [43]https://joshiapps.cbu.uib.no/SRB_app/
Methods
Data collections and differential expression analysis
Gene expression datasets analyzed in this study were collected from
several published brain studies (Fig. [44]1A and Additional file
[45]1). The raw or normalised quantification matrix deposited alongside
the original publication were re-processed and analyzed separately for
all datasets. For the data obtained from the Gene Expression Omnibus
(GEO) repository, the normalized gene expression were downloaded using
the R package GEOquery 2.54.1 [[46]17]. The microarray datasets with
raw expression values were normalized and log transformed using Robust
Multichip Average (RMA) method. Probes without a mapping gene were
removed. The average expression value of gene with multiple probe sets
was calculated. Differential expression analysis were performed
separately by 11 brain regions: amygdala (AMY), cerebellum (CBC),
frontal lobe (FC), hippocampus (HIP), medulla and spinal cord (MED),
occipital lobe (OC), basal ganglia (STR), temporal lobe (TC), thalamus
(THA), parietal lobe (PC), corpus callosum (CC). The empirical Bayes
differential expression analysis was performed by using limma 3.42.0. A
cutoff of fold-change at 1.2 and p-value of 0.05 were used to identify
genes as significant female-biased genes and male-biased genes. Then,
the female- and male-biased gene lists from each dataset were ranked by
log fold-change from the rank aggregation method. The schematic diagram
for methods in this study is shown in Additional file [47]4: Fig. S1.
We used the sex annotation provided by available metadata for the
samples in all datasets. We also performed principal component analysis
(PCA) of sex-chromosome gene expression to confirm the accuracy of
sex-labeling of samples. Additional file [48]4: Fig. S2 shows the
example PCA plot of Y-chromosome genes for six datasets ([49]GSE8397,
[50]GSE12649, [51]GSE17612, [52]GSE44456, [53]GSE30483 and
[54]GSE45642). There were very rare instances of disagreement. The
samples were omitted from the analysis in that case.
Fig. 1.
[55]Fig. 1
[56]Open in a new tab
A The number of datasets analyzed in each brain region. B A schematic
map of the brain regions studied. C The number of female-biased (pink)
and male-biased (blue) genes for each dataset before and after rank
aggregation. D The number sex-biased genes across brain regions.
E Fraction of sex-biased genes found at least in 4 primates. F Fraction
of sex-biased genes with sex-biased genes expression at least in 2
species. The number of genes on the sex chromosomes and autosomes in
female-biased G and male-biased H genes
Sex-biased gene prioritization by rank aggregation method
For each brain region, robust rank aggregation method (RRA) was used to
combine multiple female- and male-biased rank gene lists from all
datasets into a single prioritized female- and male-biased gene rank
list [[57]18]. For each gene, the status was assigned as a sex-biased
gene using combined RRA rank selected by p-value less than 0.05.
We also applied another pipeline to define female- and male-biased
genes. We firstly obtained differentially expressed genes by performing
empirical Bayes differential expression analysis by using limma
3.42.0.A. All genes in each dataset were ranked based on fold-change to
obtain male- and female-biased gene lists. The gene lists from each
dataset were then combined using RRA. For each brain region, combined
gene ranking from all gene ranks into one gene rank list using RRA and
FDR corrected P value
[MATH: <0.05 :MATH]
from RRA used to filter sex-biased genes, i.e., female- and male-biased
genes (Additional file [58]4: Fig. S27).
Correlations of sex-biased genes between brain regions were determined
using Spearman’s correlation coefficient. Enrichment of sex-biased
genes in X/Y-chromosomes and autosome were calculated using Fisher’s
exact test. A conserved human gene list in six primates (’Bolivian
squirrel monkey’, ’Chimpanzee’, ’Gorilla’, ’Gibbon’, ’Olive baboon’,
’Macaque’) from UCSC genome browser [[59]19] was used to investigate
the conservation of sex-biased genes. We used SAGD database [[60]8] to
check if sex-biased genes in human brain found in sex-biased genes of
other species.
Gene enrichment analysis and disease-related gene analysis
Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG)
pathways significantly enriched in sex-biased genes were implemented
using clusterprofiler (v.4.4.4) [[61]20] at adjusted p-value smaller
than 0.05 (corrected by the Benjamini–Hochberg method). Gene–disease
association and disease enrichment of sex-biased genes were identified
using DisGeNet2r package [[62]21]. CURATED and all database options
were used for disease enrichment analysis. The p-values resulting from
the multiple Fisher’s exact tests are corrected for false discovery
rate using the Benjamini–Hochberg method. The enrichment of Genome-Wide
Association Studies (GWAS) Catalog 2019 were performed by enrichr
[[63]22]. We also performed over-enrichment analysis of sex-biased
genes in a curated brain diseased and drug-target genes from BrainBase
database [[64]23] using Fisher’s exact test. To compare enriched terms
across brain regions, top five significantly enriched categories of
each brain regions were selected and plotted for visualizations for the
enrichment. The gene count denoted by the size of the circle and adjust
p-value denoted by the color. As enrichment analysis tools such as
enrichr [[65]22] do not allow user-defined background genes. We also
tested whether we observed brain-specific functional categories
enriched in DAVID online tool [[66]24, [67]25] using all genes
expressed in specific brain regions as a background.
Multiple regression analysis with age and sex as independent variable
To study the contribution of age and sex only two datasets in frontal
cortex had enough samples with a wide age range in both males and
females. Therefore, only samples from frontal cortex brain region from
[68]GSE11882 [[69]26] and [70]GSE53890 datasets [[71]27] were used for
sex–age-related gene analysis. We model linear regression of each gene
expression as linear combination of age, sex and (age*sex) variables as
shown in Eq. 1:
[MATH: expression=a
∗(age)+b∗(sex)+c∗(age∗
sex).
:MATH]
Sex was created as a binary variable. Variable standardization was
performed to reduce multicollinearity. Variance Inflation Factors (VIF)
was used to test multicollinearity of the third independent variable
with other independent variables. The cutoff of regression coefficient
of age and sex variables were used to identify age- or sex-related
genes (Additional file [72]4: Fig. S3).
Cell-type and tissue-specific enrichment analysis
Enrichment of our sex-biased genes in two cell type-specific gene lists
was calculated. First set of cell type-associated genes was from
McKenzie et al. [[73]28]. Fisher’s exact test was used to test for cell
type-specific tissue. The second cell-type gene list was from Dougherty
et al. [[74]29]. In this section, specific expression analysis across
cell types (CSEA) web tool was used to calculate Fisher’s exact test
with Benjamini–Hochberg correction of the overlap between our
sex-biased genes and their cell type-specific genes. In order to
investigate whether sex-biased genes are highly enriched or specific
expression in brain. Tissue-specific enrichment analysis (TSEA) was
performed using deTS package with GTEx panel [[75]30].
Androgen response element (ARE), estrogen response element (ERE) and motif
analysis
In order to determine the number of androgen (AR) and estrogen
receptors (ER) in sex-biased genes, genes with full and half ARE and
ERE binding sites from published studies [[76]31, [77]32] were used to
find an overlap genes between these receptor genes and sex-biased,
brain expressed and brain regionally elevated genes (from the human
protein atlas). Known motif enrichment analysis in the promoters of
sex-biased genes was performed by HOMER (v4.11) [[78]33].
Drug–target interactions and adverse drug response
We used drug–target interactions with 2118 drugs/chemicals from
BrainBase database [[79]23]. Fisher’s exact test was used to calculate
over-enrichment for drugs target in sex-biased genes. The enrich terms
with p-value less than 0.0001 were plotted across all regions. The
adverse drug reaction genes from Chen et al. [[80]34] was also used to
calculated overlap with sex-biased genes.
Results
Sex-biased gene expression across 11 human brain regions
Many studies have been published to identify sex-biased genes in one or
several brain regions, but assessment of the robustness of these
sex-biased genes is missing. In order to identify a robust sex-biased
gene signature, i.e., sex-biased genes supported by multiple studies,
we collected over 16,000 individual samples from 46 gene expression
datasets in human brain (Fig. [81]1A). The samples were grouped into 11
brain regions namely, amygdala (AMY), cerebellum (CBC), frontal lobe
(FC), hippocampus (HIP), medulla and spinal cord (MED), occipital lobe
(OC), basal ganglia (STR), temporal lobe (TC), thalamus (THA), parietal
lobe (PC), and corpus callosum (CC) (Fig. [82]1B). Individual datasets
consisted diverse human sample material, experimental protocols and
technology. We selected datasets with genome-wide expression data
generated using either microarray or RNA sequencing technologies and
with a minimum of ten samples of each sex. Technical and technological
divergence across datasets, complicated pooling of samples. We
therefore identified male-biased and female-biased genes in each
individual dataset using differential expression analysis (p-value
[MATH: <0.05 :MATH]
and 1.2 or more fold-change) and further used robust rank aggregation
method [[83]18] to combine multiple ranked lists of sex-biased genes
from different datasets (Additional file [84]4: Fig. S1), resulting
into a robust male and female-biased gene list in each brain region.
Fig. [85]1C (left box) shows the number of sex-biased genes (male—blue,
female—pink) in each individual dataset, whereas Fig. [86]1C (right
box) represents the number of sex-biased genes after the rank
aggregation in the FC brain region (Additional file [87]4: Figs. S4 and
S5 and Additional file [88]2 for female-biased genes and Addition file
[89]3 for male-biased genes). We noted that there were only a handful
of genes (
[MATH: <5 :MATH]
) detected as sex-biased across all studies in each brain region. This
is likely due to heterogeneity of data caused by multiple factors.
First and foremost, the heterogeneity across the human samples with
diverse demographic and socioeconomic traits as well as the
technological heterogeneity in the data including multiple platforms,
different experiment protocols, and unequal sample size. Most of our
sex-biased genes were identified as sex-biased in at least two
datasets. There were on average about a hundred sex-biased genes in
each of the 11 brain regions. THA, TC and FC with the most sex-biased
genes while AMY and CC had the lowest number of sex-biased genes. There
was no correlation between the number of sex-biased genes and the
number of available datasets or the total number of samples across
regions (Additional file [90]4: Fig. S6). Both male- and female-biased
genes were present across all brain regions with a very small bias for
male-biased genes than female-biased genes (Fig. [91]1D).
To estimate the conservation of sex-biased genes, we downloaded a list
of conserved human genes in six primates (‘Bolivian squirrel monkey’,
‘Chimpanzee’, ‘Gorilla’, ‘Gibbon’, ‘Olive baboon’, ‘Macaque’) from UCSC
genome browser [[92]19]. About 80% of both male and female-biased genes
were found in at least four primates compared to only about half of all
human genes (black) conserved in at least four primates (Fig. [93]1E).
Sex-biased genes in nearly all brain regions were therefore highly
conserved across primates. We also estimated the conservation of
sex-biased genes in higher eukaryotes from Ensembl. Lists of conserved
human genes in 198 species were downloaded from Ensembl genome browser.
Both male and female-biased genes were more conserved across other
species than all genes (Additional file [94]4: Fig. S7). In order to
check whether human sex-biased genes in brain show sex-biased
expression in other species, we used SAGD database [[95]8]. The SAGD
database consists of sex-associated genes across organs in diverse
species. Our human brain sex-biased genes were indeed enriched for
sex-biased genes in other species. The fraction of male and
female-biased with sex-associated genes in at least two species
(excluding human) was significantly higher than all human genes (Fig.
[96]1F).
We further checked whether sex-biased genes were enriched in specific
genomic regions. Female-biased genes were significantly enriched on the
X chromosome (Fig. [97]1G) and male-biased genes were enriched on the Y
chromosome (Fig. [98]1H) as expected. For example, X chromosome
contains about 5% of human genes and about 20% of female-biased genes
in medulla were on the X chromosome. Similarly, Y chromosome contains
about 1% of human genes and about 40% of male-biased genes in amygdala
were on the Y chromosome. Furthermore, we checked whether the genes on
sex chromosomes belonged to the pseudoautosomal regions (PAR1 and PAR2)
of the human X and Y chromosomes which do recombine during meiosis. We
noted that male-biased genes on Y chromosome and female-biased genes on
X chromosome were not enriched for genes on pseudoautosomal regions
(Additional file [99]4: Fig. S9A). XIST, a long non-coding RNA
expressed from X chromosome ensures that one of the pair of X
chromosomes is transcriptionally silenced (X chromosome inactivation or
XCI) during early development in mammalian females. Many genes on X
chromosome evade this dosage equivalence providing a mechanism for
divergence between males and females, called XCI escape genes
[[100]35]. The female-biased genes on X chromosome highly overlapped
with XCI escape genes (Additional file [101]4: Fig. S10B). In summary,
we noted that sex-biased genes were enriched for sex chromosomes and
were located on the sex-specific part of each chromosome. We noted no
preference for autosomes for both male and female sex-biased genes.
To explore the functional relevance of sex-biased genes, we first
conducted the pathway enrichment analysis using Gene Ontology (GO) and
Kyoto Encyclopedia of Genes and Genomes pathway annotations (Fig.
[102]2, bigger figure shown in Additional file [103]4: Figs. S11–S14).
The functional enrichment of female-biased genes was brain region
specific with CBC and FC genes enriched for zinc and copper response
while OC genes for neuronal activity (Fig. [104]2A, B and Additional
file [105]4: Fig. S11). In cellular component enrichment analysis, the
most significant enrichment terms of female-biased genes were related
to postsynaptic and synaptic membrane in OC and PL and lumen in FC and
HIP (Fig. [106]2C and Additional file [107]4: Fig. S12A). While
male-biased genes were related to DNA packaging, spindle and nucleosome
in many brain regions (Fig. [108]2D and Additional file [109]4: Fig.
S12B). In summary, female-biased genes were enriched for
neuron-associated processes while male-biased genes were enriched for
nuclear structures. Enrichment of Disease-Related Genes in sex-biased
genes across brain regions were examined using BrainBase, DisGeNet
(curated) and GWAS catalog 2019 database (Fig. [110]2E, F, and
Additional file [111]4: Figs. S15, 16). Sex-biased genes were highly
enriched for genes related to glioma across many brain regions (Fig.
[112]2E, F). Alzheimer’s related genes were highly enriched for the
female-biased genes across four brain regions (Fig. [113]2E). SFARI
database ([114]https://gene.sfari.org/) [[115]36] contains about 1000
genes related to autism spectrum disorders (ASD). Female-biased genes
in several brain regions were enriched for ASD-related genes
(Additional file [116]4: Fig. S17).
Fig. 2 .
[117]Fig. 2
[118]Open in a new tab
The top 5 enriched terms for the gene ontology and disease enrichment
analysis across brain regions. A Biological process enrichment for
female-biased genes. B Biological process enrichment for male-biased
genes. C Cellular component enrichment for female-biased genes
D Cellular component enrichment for male-biased genes. E BrainBase
disease enrichment analysis for female-biased genes. F BrainBase
disease enrichment analysis for male-biased genes
Sex-biased gene overlap across brain regions
Given that functional enrichment showed high overlap across male-biased
genes, we hypothesized high overlap among male-biased gene sets
compared to female-biased gene sets across brain regions. Indeed, the
overlap of sex-biased genes showed that male-biased genes were more
shared with 14 genes were male-biased in all 11 brain regions (Fig.
[119]3B) compared to only three genes were female-biased in all brain
regions (Fig. [120]3A). Importantly, female- and male-biased genes
found in more than eight brain regions were located on X- and
Y-chromosomes, respectively.
Fig. 3.
[121]Fig. 3
[122]Open in a new tab
A The number of female-biased genes by number of regions. The color
grey, red and blue are shown bar graphs for proportions of genes mapped
into autosome, X-chromosome and Y-chromosome, respectively. B The
number of male-biased genes by number of regions. C The correlation
heatmap of female-biased genes. D The correlation heatmap of
male-biased genes. E DisGeNet (CURATED) enrichment of overlap
female-biased genes across FC, PL, TC and OC F DisGeNet (CURATED)
enrichment for overlap female-biased genes across FC, PL, TC and OC
We further calculated a pair-wise overlap of male and female-biased
genes across brain regions. Female-biased genes in each brain region
showed very little overlap with other brain regions (Fig. [123]3C),
while the male-biased genes grouped the brain regions in two core
clusters (Fig. [124]3D). Male-biased genes in AMY, CC and MED showed a
high overlap. We noted that sex-biased genes in PL, FC, TC and OC
showed a distinct signature in both males and females with a high
correlation between these brain regions (Fig. [125]3C, D). Overlap
sex-biased gene lists of PL, FC, TC and OC were further examined for
gene-disease association enrichment from databases GWAS catalog 2019
and DisGeNet (curated database) (Fig. [126]3E, F and Additional file
[127]4: Fig. S18). The female-biased genes in four regions were
enriched for Alzheimer’s disease progression (SYN3 and STK32B) and the
male-biased genes in four brain regions were enriched for neuroticism
(PAX6 and PLTP). The DisGeNet enrichment for female-biased genes in
four brain regions identified many mental disorders (Fig. [128]3E,
Additional file [129]4: Fig. S19A).
Cell-type and tissue specificity of sex-biased genes
So far, we identified robust sex-biased genes and noted that
male-biased genes across brain regions showed higher overlap in the
previous sections. To check whether sex-biased genes show
brain-specific gene expression, we performed tissue enrichment analysis
using deTS [[130]30]. The overlap of tissue-specific genes and
sex-biased genes revealed that both male and female-biased genes
significantly overlapped with brain-specific genes. Specifically, in
almost all brain regions, brain tissues were the only enriched tissues
out of a total of 48 body tissues for sex-biased genes (Additional file
[131]4: Fig. S20). However, the sex-biased genes were not enriched for
the genes specific to the individual brain regions, i.e., sex-biased
genes in hippocampus did not show highest enrichment for deTS
hippocampus genes. In summary, we observed that the sex-biased genes
were brain specific compared to other body tissues but not brain region
specific within the brain (Additional file [132]4: Fig. S20).
Given that sex-biased genes were enriched for brain-specific genes, we
further explored whether there was a cell-type specificity for
sex-biased genes in brain. We used brain cell signature gene lists for
five cell types (astrocytes, oligodendrocytes, microglia, neurons and
endothelial cells) from McKenzie et al. gene sets [[133]28] and
calculated significant overlap using different thresholds for both male
and female-biased genes (see "[134]Methods"). As expected, both male
and female-biased genes were enriched for many cell-type signature
genes across brain regions (Fig. [135]4A, B bigger figure shown in
Additional file [136]4: Fig. S21)). We noted that male-biased genes
across brain regions were enriched for astrocytes and oligodendrocyte
signature genes more than female-biased genes. A previous study
exploring genes exhibiting sex-biased expression in human fetal brain,
noted that the male-biased genes were enriched for expression in neural
progenitor cells, whereas female-biased genes are enriched for
expression in Cajal–Retzius cells and glia [[137]37]. This observation
was not supported in our analysis of adult brain regions. We also used
an independent resource of cell type-specific expression in human brain
[[138]38] to calculate cell type enrichment of male and female-biased
genes (Additional file [139]4: Fig. S22–S25). Indeed, male-biased gene
enrichment for astrocytes and oligodendrocytes in FC, OC, PL and TC is
supported by both the datasets (Fig. [140]4B).
Fig. 4.
[141]Fig. 4
[142]Open in a new tab
A Cell-type enrichment for female-biased genes. B Cell-type enrichment
for male-biased genes. C Transcription factor (TF) enrichment for
female-biased genes. D Transcription factor (TF) for male-biased genes.
E The percentage of overlap between androgen receptor element (ARE)
genes and sex-biased genes, brain expressed genes, and brain regionally
elevated genes. The colors grey, red, yellow and orange in bar graphs
represent the proportion of genes that not overlap, overlap with ARE
full sites genes, overlap with ARE half sites genes and overlap both in
ARE full and half sites, respectively. F The percentage of overlap
between estrogen receptor element (ERE) genes and sex-biased genes,
brain expressed genes and brain regionally elevated genes. The color of
grey and peach are shown the proportion of genes that not overlap and
overlap with ERE genes, respectively
Regulatory mechanisms behind sex-biased gene expression
To explore possible transcription regulatory mechanisms behind
sex-biased genes, we firstly performed known motif enrichment analysis
in the promoters of the sex-biased genes. The analysis did not identify
strong enrichment for motifs of specific transcription factors
(Additional file [143]4: Fig. S18) for both male and female-biased gene
sets. We then obtained a reconstructed transcription regulatory network
model in human brain by integrating brain-specific DNase footprinting
and TF-gene co-expression [[144]39]. This network consisted of over 700
transcription factor and their predicted targets. The enrichment
analysis of predicted transcription factor targets in male and
female-biased gene list identified many potential transcription factors
(Fig. [145]4C, D). Male-biased genes in TC, FC, OC and PL had a high
overlap. Accordingly, many transcription factors, notably SOX family
member targets were enriched in these four regions in male-biased genes
(Fig. [146]4D). SOX2 and SOX9 putative targets highly overlapped with
female-biased genes in two brain regions (HIP and THA) (Fig. [147]5C).
Interestingly, female-biased genes in HIP and THA had nearly no overlap
(Fig. [148]3C).
Fig. 5.
[149]Fig. 5
[150]Open in a new tab
A The scatter plot of the coefficients of age and gender variables from
the multiple linear regression from [151]GSE11882. B The scatter plot
of the coefficients of age and gender variables from the multiple
linear regression from [152]GSE53890 dataset. C Gene expression of XIST
gene in [153]GSE53890 dataset, labeled as red and blue for female and
male samples, respectively. D Gene expression of RPS4Y1 genes in
[154]GSE53890 dataset, colored red and blue for female and male
samples, respectively. E Gene expression of CALB1 genes in
[155]GSE53890 dataset, colored red and blue for female and male
samples, respectively. F Gene expression of FKBP5 genes in
[156]GSE53890 dataset, colored red and blue for female and male
samples, respectively. G Gene expression of FKBP5 genes in
[157]GSE53890 dataset, colored red and blue for female and male
samples, respectively. H Venn diagram of overlap genes between
sex-biased genes and age-biased genes from [158]GSE53890 and
[159]GSE11882 datasets
Sex-specific hormones can mediate sex-biased gene expression. We
therefore obtained genes enriched for the hormone response elements.
The overlap between our robust sex-biased genes and androgen response
elements (ARE) and estrogen response elements (ERE) was calculated. We
noted that over 80% of both male and female-biased genes were enriched
for either half or full ARE sites across brain regions (Fig. [160]4E).
This fraction is significantly higher than all human genes with about
50% genes with ARE half or full sites (Fig. [161]4E). We also obtained
gene lists with highly expressed genes in specific brain regions called
regionally elevated genes from Allan Brain Atlas. This genes showed
similar enrichment to sex-biased genes for ARE half and full sites
(Fig. [162]4E). The analysis of ERE binding sites provided with results
similar to ARE binding sites, i.e., sex-biased and regionally elevated
genes were enriched for ERE sites compared to all genes (Fig. [163]4F).
In summary, sex-biased genes are enriched for sex hormone response
elements.
Age and sex relationship in brain gene expression
We previously noted that one of the likely reasons for the low overlap
in sex-biased genes across different studies is the fact that brain
samples came from very diverse human cohorts with heterogeneity in many
socio-demographic traits including age. To dissect, sex and age
components, we selected datasets covering samples in a wide age range
for both sexes. Only two datasets from the human frontal cortex
provided sample variability in age to allow estimation of age and sex
effect on the gene expression. We therefore evaluated the effect the
sex and age on brain gene expression using two datasets; [164]GSE11882
[[165]26] and [166]GSE53890 [[167]27]. Multiple linear regression for
individual genes was performed using age, sex and age*sex as
independent terms in each dataset ([168]Methods for details). The
coefficients for the age and sex terms were used to select sex-biased
and age-biased genes (Fig. [169]4A, B). The regression coefficient for
age*sex term for about 50% genes was greater than individual age or sex
variable, demonstrating that both age and sex influence gene expression
for most genes.
We noted that XIST was highly sex specific and female biased (Fig.
[170]5C) and genes on Y chromosome—ribosomal protein S4 (RPS4Y1),
KDM5D, USP9Y and DDX3Y were male-biased, as expected (Fig. [171]5D). On
the other hand, some genes showed expression variability mainly through
aging. For example, calcium binding protein, CALB1 decreased during
aging (Fig. [172]5E), while immune regulatory gene FKBP5 increased
during aging (Fig. [173]5F) consistently in both males and females. We
noted that many female-biased genes decreased gene expression during
aging while many male-biased genes increased in gene expression. For
example, Cluster of differentiation 99 (CD99) expression was
male-biased and increased during aging (Fig. [174]5G). We identified
age and sex-associated genes in each dataset (see Methods) and
calculated overlap between them (Fig. [175]5H). Only 8 genes were sex
associated in both datasets (FREM3, DDX3Y, KDM5D, SERPINA, USP9Y, XIST,
CHI3L1, EIF1AY) and fourteen genes were age associated in both datasets
(CBLN4, FREM3, AKAP5, C11orf87, CRH, LINC00507, SERPINA3, CALB1, RGS4,
CHI3L1, AQP1, VIP, S100A8, NETO2). Age and sex-associated genes had a
high overlap in each dataset (Fig. [176]5H) and three genes were found
age- and sex- associated in both datasets. FREM3 was female-biased
while CHI3L1 and SERPINA3 genes were male-biased, and the expression of
CHI3L1 and SERPINA3 increased with age while the expression of FREM3
genes decreased with age. All these genes have been shown to be
associated with neurological disease [[177]40–[178]42]. FREM3 is
associated with depression and aging in human brain [[179]40]. Another
study also found sex-, age- and Alzheimer’s disease-related differences
in CHI3L1 expression in the brain. Interestingly, CHI3L1 is highly
expressed in female AD patients compared to male AD samples [[180]41].
Sex-biased drug response
After evaluating the likely regulatory factors of sex-biased genes, we
explored the clinical impact of sex-biased expression. It is well
documented that males and females have differential response to many
drugs. We used drug–target interactions covering 2118 drugs or
chemicals and 623 genes from BrainBase database [[181]23] to calculate
the enrichment for drugs in sex-biased genes. Many drug targets were
enriched for sex-biased genes (P value
[MATH: <0.0001 :MATH]
) in both males and females, particularly in FC, PL and TC brain
regions. More female-biased genes overlapped with drug targets than
male-biased genes. For example, midazolam target genes were
female-biased in FC and aspirin targets were male-biased in TH. Indeed,
midazolam, a sedative and anesthetic adjuvant, has demonstrated
sex-specific effects with deeper sedation in men compared with women
[[182]43] and sex difference in aspirin response is also well known
where women are 2.5 times more likely to be aspirin resistant than men
[[183]44]. Cisplatin targets were enriched in male-biased genes in
temporal and occipital CC. Cisplatin-related gender differences in
nephrotoxicity also showed greater damage in males than females
[[184]45]. Antipsychotic and antidepressant targets were enriched for
female-biased genes. There are known sex differences in pharmacodynamic
effects of many drugs. In women, they include greater sensitivity to
and enhanced effectiveness of beta blockers, opioids, selective
serotonin reuptake inhibitors, and typical antipsychotics.
Additionally, women are 50–75% more likely than men to experience an
adverse drug reaction [[185]46]. We therefore further explored whether
the genes associated with adverse drug reactions were sex-biased. We
obtained adverse drug reaction genes from Chen et al. [[186]34] and
calculated overlap with sex-biased genes. Both male and female-biased
genes overlapped with many adverse drug reaction phenotypes
(Fig. [187]6C, D, y axis). However, female-biased genes showed a higher
overlap of genes for most of adverse drug reaction phenotype
(Fig. [188]6C).
Fig. 6.
[189]Fig. 6
[190]Open in a new tab
A Over-enrichment of BrainBase drug targets for female-biased genes.
B Over-enrichment of BrainBase drug targets for male-biased genes.
C Number of genes overlapping between female-biased genes and adverse
drug reaction genes (left). Number of genes overlapping between
male-biased genes and adverse drug reaction genes (right)
A web resource of sex-biased gene expression analysis in human brain
We developed a publicly available web resource to provide access to the
key analysis of sex-biased genes. SexRankBrain is an R shiny
interactive tool [[191]47] to explore the sex-biased genes across
datasets from human brain. This web resource, in addition also allows
the robustness analysis of our findings as it allows users to change
different thresholds during the analysis. We utilized this feature of
the web resource to confirm that major finding noted in this study were
consistent at different thresholds. Users can set thresholds to obtain
sex-biased gene lists from all datasets for each brain regions in this
study. These lists can then be used in the web application for
calculating sex-biased gene rank using custom parameter from user and
create a result dashboard. There are three module tabs in the
application. The first and second tab allow users to explore the
functional features of sex-biased genes for individual brain regions as
well as a comparison of sex-biased genes across all brain regions,
respectively. The third tab contains information about web-application.
In the first tab, the web-app allows user to select their preference
cutoff for a specific brain region in three steps. First step is to
apply p-value and logFC cutoff for sex-biased genes filtering in all
datasets. Next, the web application performs a robust rank aggregation
(RRA) from all gene ranks and creates a combined sex-biased gene rank
[[192]18] for each brain region. Users can choose a custom RRA p-value
cutoff to filter significant sex-biased genes from an aggregate rank.
The last step is to perform diverse enrichment analyses of significant
sex-biased genes. Gene ontology (GO), Kyoto Encyclopedia of Genes and
Genomes (KEGG) pathways and DisGeNet [[193]21] enriched in sex-biased
genes are implemented using Enrichr [[194]22]. The web application has
a second tab for the comparison of sex-biased genes across brain
regions. Here, users can similarly choose parameters for the steps as
described in the first tab. The enrichment results for this tab are
implemented by compareCluster() function in clusterProfiler package
[[195]20]. For both tabs, the tables of custom filtered rank genes and
all individual figures can be downloaded. The open source code for the
shiny application is available on GitHub
([196]https://github.com/PattaWapee/SexRankBrain).
Discussion
Males and females display a plethora of divergent physical and
behavioral patterns, affecting many life outcomes including disease
prevalence, symptoms, and progression rates. Funding agencies and
publishers are calling for greater attention to exploring basic
biological processes and disease mechanisms across the sexes and
genders. Besides the reproductive organs, most sex differences in the
body are quantitative, i.e., the distribution differ between the two
sexes but largely overlaps, as is the case with the height or the brain
volume, as well as many other physiological traits such as stress,
opioid sensitivity, and immune response [[197]16]. Accordingly, studies
exploring sex differences during development and disease in humans have
exploded in recent years. Nevertheless, most of them derived their
conclusions based on only single or a handful of datasets
[[198]7–[199]10]. A recent study [[200]10] noted sex differences across
many cell types in humans including brain, albeit only using one
dataset, unlike this study. The study [[201]10] did not focus
specifically on the brain but many of our findings were indeed
overlapping, e.g., enrichment of sex-biased genes on sex chromosomes.
The sex differences in brain are controversial mainly because of its
likely societal implications. Women have been culturally oppressed and
sex differences have been used as a justification for some of the
injustices. Nevertheless, the sex differences in the brain may explain
the differences seen in prevalence, symptomology and even treatment for
brain pathologies. It is therefore important to establish the validity
of observed sex differences across different studies. We therefore
combined a huge amount publicly available expression data to estimate
sex differences in human brain. Firstly and most importantly, we noted
that most of sex-biased genes obtained from one data were not
identified in other datasets. This is partially due to heterogeneity in
data, e.g., the impact of the age of individuals (explored in detail in
this paper), as well as other confounders, and technical and
technological differences across studies. By systematic data
integration, we obtained a robust sex-biased gene list for each of 11
brain regions. The robust sex-biased genes were highly conserved and
showed sex-biased gene expression in other species as well. This
allowed us to validate some of the findings obtained previously using
individual datasets as well as generated some novel hypotheses. We
first established that our findings are not sensitive to a specific
threshold of the analysis pipeline (analysis available as a web
resource). Furthermore, we used another independent pipeline to define
sex-biased genes (see [202]Methods for details and Additional file
[203]4: Fig. S27). Using this alternative pipeline (Additional file
[204]4: Fig. S27), we validated that main findings described in this
manuscript are not dependent on the specifics of the analysis pipeline
(Additional file [205]4: Figs. S27–30), providing additional confidence
in the findings.
Arnold proposed a general theory of mammalian sexual differentiation
whereby sex chromosome genes are the primary factors causing sexual
differentiation [[206]3]. The biggest genetic distinguishing factor
between two sexes is the presence of sex chromosome where x chromosome
causes sex differences in gene expression through XIST, X genes
escaping inactivation, and imprinted X genes [[207]3]. For example,
Kassam et al. [[208]9] observed that X-linked KAL1 gene had higher
expression in females than males in lung tissue. The biallelic
expression of KAL1 gene in lung tissue is an example of tissue-specific
escape from X-activation [[209]48]. Indeed, the sex-biased genes in
females were enriched on the X chromosome and particularly for the XCI
escapee genes. This suggests that a part of sex-biased gene expression
originates from the XCI escape mechanisms. Also, male-biased genes in
brain were enriched for Y chromosome. On the other hand, it was noted
that 90% of sex-biased genes across human tissues were mapped to
autosomes, thus it’s not restricted within sex chromosomes [[210]15].
This finding was partly supported in our study where most sex-biased
genes in all brain regions were expressed from autosomes, rather than
sex chromosomes. It is important to note that our robust sex-biased
genes contained a higher fraction of genes (15–40%) on sex chromosomes,
i.e., genes on sex chromosomes are more likely to be validated across
studies and across multiple tissues. Furthermore, sex-biased genes
found in more than eight brain regions were primarily on the sex
chromosomes. X chromosome is particularly enriched for genes involved
in brain-related functions. Many functional enrichment analysis tool
including enrichr do not allow user-defined background genes. We
therefore validated the brain-related functional enrichment in the
sex-biased genes using DAVID online tool [[211]24, [212]25] by
providing brain region specific background genes (Additional file
[213]4: Fig. S31).
Sex chromosomes are thought to regulate gene expression manifesting sex
differences in brain primarily through the steroid hormones.
Accordingly, we noted that both male and female-biased genes were
enriched for both androgen and estrogen receptor binding sites. These
findings departs from the traditional model of testosterone
masculinizes the brain of males away from a default female form and
supports a model where sex effects on the brain of both females and
males are exerted by genetic, hormonal, and environmental factors.
These factors act via multiple partly independent mechanisms that may
vary according to internal and external factors [[214]49].
We further tested the specific cell type enrichment for the robust
sex-biased genes. Our results are in somewhat agreement with the
previous reports that non-neuronal cells and inflammatory mediators
were found in greater number and at higher levels in male brains
[[215]50]. The higher baseline of inflammation is speculated to
increase male vulnerability to developmental neuropsychiatric disorders
that are triggered by inflammation [[216]50]. We noted a strong male
bias for astrocytes and oligodendrocytes but not microglia.
Nevertheless it is important to note that, gene expression is affected
many factors. Kang et al. [[217]51] studied the spatio-temporal
dynamics of the human brain transcriptome to note that age contributed
more to the global differences in gene expression than sex. For
example, in middle-aged women, the gene expression changes were higher
for astrocytes, endotheliocytes, and microglia compared to young women
[[218]52]. We performed a systematic analysis of the two datasets to
estimate the effect of age and sex on the gene expression to note that
for most genes sex and age both influence expression. Some age-related
traits are conserved across sexes, there is age-related activation of
immune- and inflammation-related genes in both male and female brains
[[219]26], while others are affected by both sex and age. Males showed
significantly more gene expression changes in brain through aging with
substantial gene change in the transition to the sixth and seventh
decades of life. In contrast, females showed the largest numbers of
genes responding in the eighth and ninth decades of life [[220]26].
Schizophrenia has a more severe course (negative symptoms as well as
cognitive impairment), experienced earlier in life in boys than in
girls [[221]53]. We explored the clinical implications the sex
differences and noted that female-biased genes showed a high overlap
with Alzheimer-related genes. Importantly, we also noted that more
female-biased genes are involved in adverse drug reactions. Despite
funders (e.g. NIH) pushing for female inclusion in clinical studies,
very few (less than 10 percent of studies) are examining health issues
related to females [[222]54].
Perspectives and significance
In summary, by integrating large amount of expression data, we
identified robust sex differences across human brain regions. We have
made entire analysis available as a web resource at
[223]https://joshiapps.cbu.uib.no/SRB_app/ for further exploration and
hypothesis generation. Sex, together with age and other factors,
affects brain function through human life span. Heterogeneity of human
samples in many gene expression cohorts therefore makes it challenging
to discern exactly the sex component. This indeed is a major
shortcoming of many studies including this one. The finding of this
study emphasized the importance of the need for greater attention to
exploring basic biological processes and disease mechanisms in a sex
and gender context. This study provided a foundation for future
research to further investigate the mechanisms and factors contributing
to sex and gender differences in the human brain.
Supplementary Information
[224]13293_2023_515_MOESM1_ESM.xlsx^ (14.4KB, xlsx)
Additional file 1. List of published datasets used in this study.
[225]13293_2023_515_MOESM2_ESM.xlsx^ (86.2KB, xlsx)
Additional file 2. The table of female-biased gene in each brain
regions with chromosome information and check-tick in all datasets used
in this study.
[226]13293_2023_515_MOESM3_ESM.xlsx^ (105.6KB, xlsx)
Additional file 3. The table of male-biased gene in each brain regions
with chromosome information and check-tick in all datasets used in this
study.
[227]13293_2023_515_MOESM4_ESM.pdf^ (22.9MB, pdf)
Additional file 4: Figure S1. Schematic diagram of the workflow
including rank aggregation for sex-biased genes detection in this
study. Figure S2. Principal component analysisplot of Y- chromosome
genes expression for GSE8397, GSE12649, GSE17612, GSE44456, GSE30483
and GSE45642. Figure S3. The cutoff for coefficient of age and sex
variables in section multiple regression analysis with age and sex as
independent variable. Figure S4. The number of sex-biased genes in
individual datasets, aggregated lists in AMY, CBC, CC, HIP, MED and OC.
Figure S5. The number of sex-biased genes in individual datasets,
aggregated lists in PL, STR, TC and THA. Figure S6. Plot of number of
sex-biased genes in different number of brain samples. Figure S7.
Fraction of sex-biased genes found at least in 40 species and 160
species. Figure S8. The number of overlapping sex-biased genes by
number of datasets for AMY, CBC, CC, FC, HIP and MED. Figure S9. The
number of overlapping sex-biased genes by number of datasets for OC,
PL, STR, TC and THA. Figure S10. The number of par1 genes in
male-biased genes and the number of XCI female-biased genes. Figure
S11. Gene ontology enrichment analysis of sex-biased genes for
biological process. The top 5 enrich terms across brain regions in
female-biased genes and male-biased genes. Figure S12. Gene ontology
enrichment analysis of sex-biased genes for cellular component. The top
5 enrich terms across brain regions in female-biased genes and
male-biased genes. Figure S13. Gene ontology enrichment analysis of
sex-biased genes for molecular functions. The top 5 enrich GO terms
across brain regions in female-biased genes and male-biased genes.
Figure S14. KEGG pathway enrichment analysis of sex-biased genes. The
top 5 enrich GO terms across brain regions in female-biased genes and
male-biased genes.Figure S15. GWAS catalog 2019 enrichment across brain
regions in female-biased genes and male-biased genes. Figure S16.
DisGeNET enrichment analysis of sex-biased genes across brain regions
in female-biased genes and male-biased genes. Figure S17. The number of
autism genes found in sex-biased genes. Autism genes come from SFARI
database. Figure S18. DisGeNetenrichment of overlap sex-biased genes
across FC,PL,TC and OC in female-biased genes and male-biasd genes.
GWAS catalog 2019 enrichment analysis of overlap sex-biased genes
across FC, PL, TC and OC in female-biased genes and male-biased genes.
Figure S19. Gene-disease class heatmap of DisGeNET for overlap
sex-biased gene lists of PL, FC, TC and OC. Diseases are grouped by the
their MeSH disease classes. The color scale is related to the
percentage of disease in each class. Figure S20. Tissue-specific
enrichment analysisof female-biased genes and male-biased genes. Figure
S21. Cell type enrichment of sex-biased genes from McKenzie et al gene
sets. Figure S22. Cell type specific expression analysisof female
biased genes in AMY, CBC, CC, FC, HIP and PL. Figure S23. Cell type
specific expression analysisof female biased genes in STR, TC and THA.
Figure S24. Cell type specific expression analysisof male biased genes
in CBC, CC, FC, HIP, MED and OC. Figure S25. Cell type specific
expression analysisof male biased genes in PL, STR, TC and THA. Figure
S26. Known motif enrichment analysis by HOMER in the promoters of the
female-biased genes and male-biased genes. Figure S27.Schematic diagram
of additional workflow to generate sex-biased gene list.The number of
sex-biased genes from the additional workflow. Figure S28.The number of
female-biased genes across brain regions using additional workflowThe
number of male-biased genes across brain regions using additional
workflow.The correlation heatmap of female-biased genes using
additional workflow.The correlation heatmap of male-biased genes using
additional workflow.The percentage of overlap between Androgen receptor
elementgenes and sex-biased genes, brain expressed genes, and brain
regionally elevated genes using additional workflow.The percentage of
overlap between Estrogen receptor elementgenes and sex-biased genes.
brain expressed genes and brain regionally elevated genes using
additional workflow. Figure S29.Fraction of sex-biased genes found at
least in 40 primates using additional workflow.Fraction of sex-biased
genes found at least in 160 primates using additional workflow.Gene
ontology enrichment analysis of female-biased genes for biological
process using additional workflow.Gene ontology enrichment analysis of
male-biased genes for biological process using additional workflow.Gene
ontology enrichment analysis of female-biased genes for molecular
function using additional workflow.Gene ontology enrichment analysis of
male- biased genes for molecular function using additional workflow.
Figure S30.Gene ontology enrichment analysis of female-biased genes for
cellular composition using additional workflow.Gene ontology enrichment
analysis of male-biased genes for molecular cellular composition using
additional workflow.DisGeNetenrichment of female-biased genes using
additional workflow.) DisGeNetenrichment of male- biased genes using
additional workflow. Figure S31. Gene ontology analysis of output from
DAVIDfor biological process.The top GO terms in female-biased genes in
THA region with background genes of THA gene expression data.The top GO
terms in male- biased genes in THA region with background genes of THA
gene expression data.
Acknowledgements