Abstract
Genome wide association studies (GWAS) have identified more than 180
variants associated with breast cancer risk, however the underlying
functional mechanisms and biological pathways which confer disease
susceptibility remain largely unknown. As gene expression traits are
under genetic regulation we hypothesise that differences in gene
expression variability may identify causal breast cancer susceptibility
genes. We performed variable expression quantitative trait loci (veQTL)
analysis using tissue-specific expression data from the Genotype-Tissue
Expression (GTEx) Common Fund Project. veQTL analysis identified 70
associations (p < 5 × 10^–8) consisting of 60 genes and 27 breast
cancer risk variants, including 55 veQTL that were observed in breast
tissue only. Pathway analysis of genes associated with breast-specific
veQTL revealed an enrichment of four genes (CYP11B1, CYP17A1 HSD3B2 and
STAR) involved in the C21-steroidal biosynthesis pathway that converts
cholesterol to breast-related hormones (e.g. oestrogen). Each of these
four genes were significantly more variable in individuals homozygous
for rs11075995 (A/A) breast cancer risk allele located in the FTO gene,
which encodes an RNA demethylase. The A/A allele was also found
associated with reduced expression of FTO, suggesting an
epi-transcriptomic mechanism may underlie the dysregulation of genes
involved in hormonal biosynthesis leading to an increased risk of
breast cancer. These findings provide evidence that genetic variants
govern high levels of expression variance in breast tissue, thus
building a more comprehensive insight into the underlying biology of
breast cancer risk loci.
Subject terms: Cancer genetics, Gene expression, Genetic interaction
Introduction
Genome wide association studies (GWAS) in breast cancer have identified
more than 180 common risk variants^[34]1–[35]3, however the causal
genes and biological mechanisms which confer disease susceptibility
remain largely unknown. Risk variants are often located in non-coding
regions making it difficult to determine pathogenic pathways.
Approximately 700 potential gene targets of breast cancer risk variants
have been identified using analytical methods that employ genomic data
from chromatin interactions, enhancer–promoter correlations,
transcription binding, topologically associated domains and gene
expression^[36]1,[37]3.
Gene expression traits are under genetic regulation and the
heritability of differences in genotypes have been extensively
described^[38]4. For example, identification of expression quantitative
trait loci (eQTL) has been a key approach for investigating
tissue-specific effects of breast cancer risk variants under the
hypothesis that non-breast tissue may be involved in breast cancer
risk^[39]5. Gene expression patterns are often explored assuming
genetic control of mean expression level, however the variability of
gene expression is also genetically controlled^[40]6–[41]9. Just as
differences in expression means have been associated with genotype so
too differences in expression variability can be associated with
genotype.
Gene expression variability has been described in a wide range of
organisms including prokaryotes^[42]10, yeast^[43]6,[44]7 and complex
multicellular organisms^[45]11–[46]13. Furthermore, gene expression
variability had been shown to be important in early human
development^[47]14, schizophrenia^[48]15 and cancer
subtypes^[49]12,[50]13. The effects of genetic variation on gene
expression variability has been recently described in human derived
lymphoblastoid cell lines from HapMap individuals^[51]8 and in the
TwinsUK cohort^[52]16,[53]17.
Breast cancer risk variants associated with eQTL, based on mean gene
expression, have been investigated in both breast tissue (tumour and
normal), and non-breast tissue^[54]5,[55]18,[56]19. However, the
mechanisms underlying breast cancer risk for the majority of variants
remains to be uncovered. Here, we demonstrate variable expression
quantitative trait loci (veQTL) as a method for testing the association
of variants with gene expression variability. We performed veQTL
analysis on 181 variants that have been previously associated with
breast cancer risk and identified 60 new candidate genes and pathways
associated with 27 breast cancer risk variants.
Methods
Data acquisition and processing
Genotype and expression data were acquired through the database of
Genotypes and Phenotypes (dbGaP) and the Genotype-Tissue Expression
(GTEx) Common Fund Project (release version phs000424.v7.p2.) under the
project title “Identification of variable expression quantitative trait
loci that are associated with cancer risk”. Datasets from breast,
ovarian, lung and kidney tissue used in this study were obtained
through the dbGaP approval number 17463.
Genotype data from 635 individuals acquired through GTEx were converted
to chromosome-specific matrices, where the genotypes were numbered by
the minor allele count. For tissue specific analysis, only genotypes
from individuals with tissue expression data in a given tissue (e.g.
breast, kidney, ovary and lung) were retained. Genotypes were filtered
so that only bi-allelic genotypes of at least 10 subjects with two or
more genotypes (AA, Aa, aa) were retained.
Normalised Reads Per Kilobase of transcript, per Million mapped reads
(RPKM) counts for 56,203 unique Ensembl ([57]https://www.ensembl.org/)
gene ids were split into tissue-specific datasets. For each dataset,
only transcripts with RPKM > 0.1 in at least 10 samples were retained.
Subjects with multiple tissue-specific samples were collapsed by
calculating the average RPKM values. Linear regression models were used
to correct expression data for age and sex as covariates.
veQTL and eQTL analysis
Tissue specific veQTL were mapped for breast cancer risk variants that
passed the filtering criteria (Supplementary Table [58]S1). For each
gene, veQTL were mapped by testing for equal variance among individuals
of different genotypes using the Brown–Forsythe method^[59]20. Compared
to other analysis of variance methods, the Brown-Forsythe method is
more tolerant to non-normality with type I error^[60]21,[61]22. A
custom R script ([62]https://github.com/jfpuoc/veQTL) was used to
calculate Brown–Forsythe test-statistics (W, Eq. [63]1) on each
genotype and all transcripts. For a response variable y in j groups,
transformed to the median absolute deviation Z[ij] =|y[ij] – y[j.]|
where y[j.] is the median in group j, then W is defined by:
[MATH:
W=N-k
∑i=1kNi(Zi.-Z..)2k-1
∑i=1k∑i=1<
mi>Ni(Zij-Zi.)2Brown - Forsytheteststatistic :MATH]
1
where N is the number of samples, k is the number of different
genotypes (2 or 3), N[i] is the number of samples in group i, Z[i] is
the mean of the absolute deviation from the medians for group i and
Z[..] is the mean of the absolute deviations from all samples from
their respective group medians. The resulting W statistics follows the
F-distribution with degrees of freedom df1 = k – 1 and df2 = N –
k^[64]20.
veQTL analysis was performed using the residuals of the linear model
correcting for age and sex, and the genotypes that met the filtering
criteria. In instances where two genotypes were observed in more than
10 samples, and the third genotype was observed in less than 10
samples, the test statistic was only computed between groups with at
least 10 samples.
Tissue-specific eQTL analysis was performed in the same four tissue
datasets used for veQTL. The ultra-rapid MatrixEQTL package in R was
used to calculate p values for variant-gene pairs using a linear
regression model and correcting for age and sex as covariates^[65]23.
We limited proposed breast cancer susceptibility genes to those that
had: (i) significant (p < 5.0 × 10^–8) gene expression variability
associated with a breast cancer risk variants, (ii) the significant
veQTL association was only observed in breast tissue and (iii) the gene
was only associated with a change in expression variability (i.e.
veQTL) and not change in mean expression (i.e. eQTL).
Pathway enrichment analysis
Genes identified with altered expression by either veQTL or eQTL
analysis were annotated using their entrez identifier. Pathway analysis
was performed using the R packages clusterProfiler and
DOSE^[66]24,[67]25. Each candidate gene list was compared to the
background transcriptome for over representation of genes in pathways
annotated by GO terms.
Results
Identification of veQTLs and eQTLs
The GTEx dataset comprises 635 genotyped samples, of which tissue
samples from normal breast (n = 255), lung (n = 387), kidney (n = 41)
and ovary (n = 123) were used. A major proportion of breast cancer risk
variants are predicted to alter expression of cancer susceptibility
gene(s) in breast tissue. To identify veQTL that specifically increase
risk in breast tissue, even if the genes in the veQTL are ubiquitously
expressed in multiple tissues, we only considered veQTL that were
uniquely identified in breast tissue (i.e. breast-specific veQTL).
These assumptions, would however miss breast cancer susceptibility
genes whose expression variability is tolerated in other tissue but not
breast.
RNA-sequencing and genotype data were split into tissue-specific
datasets and filtered to remove low frequency genotypes and genes with
low expression. After pre-processing 33,059, 29,522, 25,026 and 35,137
transcripts were retained for the breast, ovary, kidney and lung,
respectively.
Large genome-wide association studies (GWAS) have identified variants
associated with breast cancer risk or subtype specific breast risk. In
total we identified 181 breast cancer risk variants in the literature
(Supplementary Table [68]S1), of which 152, 148, 106 and 152 breast
cancer risk variants were retained after filtering non-biallelic and
genotypes with few minor alleles (see methods) for the breast, ovary,
kidney and lung datasets, respectively (Fig. [69]1, Supplementary Table
[70]S1).
Figure 1.
[71]Figure 1
[72]Open in a new tab
Schematic of study rationale to identify veQTL and eQTL in four
different tissue types. Firstly, gene expression data from GTEx was
filtered to remove lowly expressed genes and samples with
tissue-specific expression data was matched with genotype calls.
Tissue-specific QTL analyses was performed on the breast cancer risk
variants identified by previous studies.
We tested for associations between breast cancer risk variants and gene
expression variability, correcting for sex and age, in four tissues.
These analyses identified significant (p < 5 × 10^–8) veQTL
interactions with breast cancer risk variants in the breast (70), ovary
(9) and lung (109) (Table [73]1, Supplementary Tables S2–S4). No
significant associations were observed in the kidney analysis. By
comparison, the number of observed eQTL in breast (155), ovary (19) and
lung (123) were greater, similarly there were no significant kidney
eQTL. The majority of veQTL and eQTL associations were trans and acted
over distances greater than 1 Mb or between chromosomes. Only 2/70,
5/109 and 2/9 significant association were cis-veQTL (+ /− 1 Mb) in the
breast, lung and ovary, respectively. A greater proportion of eQTL were
observed in cis compared to veQTL, with approximately 5% of veQTL and
13% eQTL acting in cis (Table [74]1).
Table 1.
Significant veQTL and eQTL breast cancer variants and associated genes
for each tissue.
veQTL eQTL
Variants Genes veQTL (cis) Variants Genes eQTL (cis)
Breast 27 60 70 (2) 16 139 155 (18)
Kidney 0 0 0 0 0 0
Lung 28 81 109 (5) 24 101 123 (17)
Ovary 5 9 9 (2) 7 19 19 (4)
[75]Open in a new tab
Classes of veQTLs
By assessing expression values associated with each genotype across the
four different tissues, we observed three classes of veQTL
(Fig. [76]2). Class I resembled a homozygous recessive phenotype, where
the presence of two minor alleles was associated with altered gene
expression variability. Class II showed a dominant phenotype where the
dosage of the minor allele correlated with the change in expression
variability. Class III resembled a heterozygous phenotype where the
presence of two different alleles altered gene expression variability.
Significant breast veQTL were largely Class I homozygous recessive
(56%), (Fig. [77]2), while the majority (9/11) of Class II veQTL were
also eQTL. In total, 21 veQTL (30%) were also eQTL. Seven breast cancer
risk variants that had significant veQTL, were unable to be classified
as no sample was homozygous for the minor allele. However in all seven
variants, gene expression variability was greater in heterozygous
samples, thus ruling out a Class I veQTL.
Figure 2.
[78]Figure 2
[79]Open in a new tab
Characteristics of gene expression variability in veQTLs. Three class
of veQTLs were observed with respect to the minor allele. Boxplots
represent genes at veQTL for each of the proposed classes. Each point
represent the expression of a single sample. The table (bottom)
presents the number of significant veQTL associations categorised by
class. Significant breast veQTLs were represented in all three classes
with the majority (39/70) class I.
Comparison of veQTL and eQTL
To estimate biases in dataset-specific veQTL analysis quantile–quantile
plots (q–q plots) were generated and genomic factors estimated for each
tissue (Fig. [80]3a). No substantial genomic inflation (λ < 1.1) was
observed for the veQTL analysis in the breast, lung or ovary (λ ranged
1.00–1.05). However, a larger genomic inflation factor of 1.15 was
observed for kidney tissue, implying a small underlying bias in the
analysis (Fig. [81]3a).
Figure 3.
[82]Figure 3
[83]Open in a new tab
Tissue-specific performance of veQTL and eQTL analysis. (a) Tissue
specific q–q plots and genomic inflation factors (λ) for the
associations of breast cancer risk variants and gene expression
variability, with observed p-values plotted as a function of expected
p-values under the null hypothesis of no association; red lines
indicate the a null distribution of p values. (b) Tissue-specific
p-value distribution for BC variants eQTLs (red) and veQTLs (blue). (c)
Tissue-specific correlations of –log10(p) for eQTL (x-axis) and veQTL
(y-axis).
Tissue specific p values distributions were similar between veQTL and
eQTL analyses (Fig. [84]3b). Three tissues (breast, lung and ovary)
displayed an anti-conservative distribution with a greater number of p
values tending towards zero. For the larger lung and breast datasets,
there was a greater number of p values near zero compared to ovary
tissue, suggesting a greater number of tests that reject the null
hypothesis of no difference in expression variability between groups.
Examination of the kidney dataset demonstrated a uniform distribution
of p-values, highlighting the limited effect for the selected variants
for both veQTL and eQTL analysis. Variant-gene pairs were ranked
according to eQTL significance and the rank correlation of p-values
between eQTL and veQTL analysis were calculated for each tissue
specific dataset. Correlations ranged from 0.052 in the kidney to 0.183
in the lung, suggesting the variant-gene ranks between veQTL and eQTL
analysis are different and veQTL analysis identified a novel set of
genes associated with risk variants (Fig. [85]3c).
Identification of potential target genes of breast cancer risk variants
The majority of breast cancer variants have no known associations with
other traits, however 25 variants have previously been associated with
a phenotype other than breast cancer risk ([86]www.gwascentral.org,
Supplementary Table [87]S5). Two variants (rs11571833 and rs17879961)
have been previously associated with lung cancer, while rs10069690 and
rs74911261 have been associated with ovarian and kidney cancers,
respectively. Interestingly, none of these variants were significantly
associated with differential variability in any genes in these tissues.
However, rs10069690 did have significant association with differential
variability in gene expression in each of the lung and breast analysis.
As the majority of the variants only show evidence for breast cancer
risk, we eliminated any veQTL that was observed in a non-breast tissue
(Fig. [88]4). Fifty-five of the 70 significant breast veQTL were
observed in breast tissue only. Pathway enrichment analysis of the
candidate genes associated with these breast-specific veQTL revealed
hormonal biosynthetic processes and collagen fibril organisation
pathways that were enriched (Fig. [89]4). The enrichment of the
hormonal pathways listed in Fig. [90]4 were driven by four genes
(CYP11B1, CYP17A1 HSD3B2 and STAR) all of which were associated with
the risk variant rs11075995. By comparison, the 88 veQTL that were
significant in lung tissue were not significantly enriched for any
pathway using pathway analysis (data not shown).
Figure 4.
[91]Figure 4
[92]Open in a new tab
Pathway enrichment of candidate breast cancer risk genes identified
through veQTL analysis. Fifty-five gene SNP pairs were observed only in
breast tissues, 47 of these were veQTL but not eQTL associations. The
candidate genes identified by these 47 associations were enriched for
pathways involved in C21-steroid hormone metabolic process. The
significance of pathway enrichment, − log10(p), are shown graphically
alongside a heatmap for genes involved (in blue) in the respective
pathways. Pathway analysis was performed in R using GO terms and using
the DOSE and ClusterProfiler packages.
rs11075995 alters expression of genes involved in C[21] steroid synthesis
The minor allele (A) of rs11075995, which is associated with ER
negative breast cancer risk, was found to be associated with increased
variability in expression of four genes by veQTL analysis (Fig. [93]5).
To connect the signals of veQTL analysis with the association of breast
cancer risk, we utilised the GWAS signals generated by Michailidou et
al.^[94]3 on the largest meta-analysis of breast cancer risk to date
and on veQTL signals generated using the GTEx data. Regional plots at
the rs11075995 locus for ER negative breast cancer risk associations or
trans-veQTL with candidate genes were visually examined to determine
likely casual variants (Fig. [95]5). Two signals were identified
associated with ER negative breast cancer risk, one of which was the
lead variant rs11075995 (Fig. [96]5a). The same variants (rs11075995)
produced the strongest signal for variable expression of all four
candidate genes involved in the C21-steroidal pathway (Fig. [97]5b).
Figure 5.
[98]Figure 5
[99]Open in a new tab
Co-localisation of ER negative breast cancer GWAS and trans-veQTL
signals. (a) Regional association plots for ER negative breast cancer
risk for rs11075995 from Michailidou et al.^[100]3. (b) Regional
association plots for trans-veQTL at rs11075995. Points indicate
individual SNPs at their chromosomal location and significance (−
log10(p value)) for either GWAS (a) or trans-veQTL (b). The blue line
represents the recombination rate and the colour of the points indicate
the strength of the LD with rs10075995 measured as r2 in the EUR
population from 1000 genomes (hg19). All plots were generated using
LocusZoom.
The candidate genes (CYP11B1, CYP17A1 HSD3B2 and STAR) associated with
rs11075995 are all involved in the conversion of cholesterol to
hormones via the C21 steroidal biosynthesis pathway (Fig. [101]6). STAR
is involved in the transportation of free cholesterol into the
mitochondria where it is converted to pregnenolone. The remaining three
candidate genes all code for enzymes that catalyse the conversion of
multiple molecules and act in several pathways which produce different
hormones (Fig. [102]6).
Figure 6.
[103]Figure 6
[104]Open in a new tab
Schematic of part of C21-steroird biosynthesis pathway. Genes shown in
red were associated with a significant increase in variability in
individuals homozygous for the rs1105995 risk allele (A) in breast
tissue (i.e. 4 of the 70 breast-derived genes from Fig. [105]4).
The rs11075995 SNP is located in the second intron of the FTO gene
(Fig. [106]7)., a Fe^2+/2-oxoglutarate-dependent oxidative RNA
demethylases important in the demethylation of RNA methyladenosine
(m6A)^[107]26. Variants in this locus are associated with increased
body mass index (BMI), the mechanism of action has been linked to
expression changes of the neighbouring gene IRX3 in the human brain and
in particular the hypothalamus^[108]27,[109]28. Furthermore, there is
conflicting evidence of rs11075995 association with breast cancer risk.
Recent studies identified a loss of breast cancer risk association
after adjusting for BMI^[110]29. However, Garcia-Closas and colleagues
tested the association with ER negative breast cancer risk after
adjusting for BMI and observed no change^[111]30. We therefore explored
the effects of the rs11075995 on the expression of both FTO and IRX3 in
breast tissue. Neither FTO nor IRX3 had significant breast eQTL or
veQTL associations with rs11075995. However, FTO (p = 0.05), and not
IRX3 (p = 0.29), had decreased expression in the homozygous minor
allele individuals in breast tissue (Fig. [112]7b, Supplementary Fig.
S1).
Figure 7.
[113]Figure 7
[114]Open in a new tab
cis-effects of rs11075995 minor allele and FTO expression. (a) Ideogram
and chromosomal location of the rs11075995 variant within in the second
intron of the FTO gene. (b) Tissue-specific expression of FTO
stratified by genotypes at the rs11075995 location. T/T homozygous
major allele (Green), A/T heterozygous (Orange), A/A homozygous minor
allele (Blue).
Ethics approval and consent to participate
This research was approved by the University of Otago Ethics Committee.
Discussion
Tissue-specific veQTL datasets were generated for breast cancer
variants in four normal tissues dataset acquired from GTEx. To predict
candidate genes involved in breast cancer risk, significant
(p < 5 × 10^–8) veQTL unique to breast tissue were considered. This
approach identified 60 candidate genes that were associated with 27
variants. The majority of significant veQTL were class I and displayed
a homozygous recessive like phenotype (Fig. [115]2). Furthermore, veQTL
analysis identified distinctly different genes compared to eQTL
analysis (Fig. [116]3). Although, 30% of class II breast veQTL were
also eQTL, highlighting a small subset of genes that had both changes
in mean expression and variability associated with minor allele dosage.
Pathway analysis of the 60 candidate genes found several hormonal
biosynthetic pathways enriched along with monocyte chemotaxis and
collagen fibril organisation (Fig. [117]4). The enrichment of the
hormonal biosynthetic pathway was driven by the presence of four genes
(CYP11B1, CYP17A1, HSD3B2 and STAR) all of which were variable in
association with the risk allele of rs11075995. Furthermore, rs11075995
produced the strongest signal for variable expression for all four
candidate genes and was the most likely casual variant (Fig. [118]5).
Breast cancer development has been associated with exposure to steroid
hormones^[119]31. These hormones are typically synthesised in
non-breast tissues (e.g. ovary and adrenal gland) and are secreted into
the circulating system to act on distant tissues (e.g. breast). The
activation of local hormone biosynthesis, associated with the risk
allele of rs11075995, through the metabolism of cholesterol to
pregnenlone may lead to greater exposure and/or hormone imbalance in
breast tissues, which may drive tumourigenesis. Local steroidogenesis
and ultimately production of androgens has been observed in androgen
independent advance prostate cancers^[120]32. In prostate cancer, the
local production of androgens may explain the development of hormonal
treatment resistance in late-stage prostate cancers.
Summary statistics of GWAS signals obtained through GWAS central
([121]www.gwascentral.org) identified significant associations of
rs11075995 with overall and ER negative breast cancer risk and with
body mass index (Supplementary Table [122]S5). No other trait was
reported to be associated at p < 0.001 with rs11075995. BMI is a known
dose-dependent risk factor for developing breast cancer in
post-menopausal women^[123]33. Interestingly, breast cancer risk
association studies that have adjusted for BMI have demonstrated a
dependence for variants at the rs11075995 locus on BMI status^[124]29.
However, an independent relationship was described for ER negative
breast cancer risk and BMI for rs11075995^[125]30, suggesting that
variants in the same locus may have disease-specific risk profiles.
The variant rs11075995 is located in intron 2 of the FTO gene.
Interestingly, we observed a marginally significant decrease in FTO
(p = 0.05) expression in breast tissue associated with individuals
homozygous for the rs11075995 risk allele. FTO is involved in
demethylation of RNA adenosine (m6A). Methylated adenosine are
post-transcriptional modifications which signals RNAs for processing,
including degradation and splicing^[126]34. The four genes associated
with rs11075995 all have the m6A target site (GGACU). RNA variability
may occur due to dysregulation of these pathways (mRNA degradation and
splicing) in response to decreased FTO expression.
Variants in intron 1 and 2 of FTO have been strongly associated with
obesity and changes in BMI^[127]27,[128]35, however these variants act
on the expression of the neighbouring gene IRX3 in the hypothalamus
region of the brain^[129]28. Iroquois homeobox protein 3 (IRX3) is a
highly conserved transcription factor typically expressed during neural
development^[130]36. The role of IRX3 in obesity is yet to be fully
elucidated with conflicting reports of body mass associated to
deficient Irx3. Smemo et al., described a 30% increase in body weight
of Irx3-deficient mice^[131]28. While in contrast the partial depletion
of Irx3 through a lentiviral system resulted in mice with greater body
mass^[132]37.
Intriguingly, both IRX3 and FTO are highly expressed in the
hypothalamus, a region of the brain important to hormonal
regulation^[133]28,[134]37. It is unknown whether risk variants, for
either BMI or breast cancer, directly disrupt the regulation of
hormonal control in the hypothalamus. Furthermore, it is unclear what
effect IRX3 expression would have on breast cancer risk and whether any
effect would be independent of the risk attributed to obesity alone. A
better understanding of the downstream transcriptional targets of IRX3
may identify pro-tumourgeneic pathways.
Our results are based on data from largely white, European derived
ancestry (GTEx is 85% white), hence extrapolation to more diverse
ancestry is a limitation of this study. However, these results are
consistent with the hypothesis that different variants in the FTO locus
may be associated with tissue-specific hormonal control and
subsequently different pathologies. Consequently, we would expect
differences in the regulation of C21 hormones in breast tissue for the
different rs11075995 genotypes. Furthermore, candidate genes identified
through veQTL analysis require functional validation. A major challenge
with assessment of intra-sample gene expression variability is the
limitation of single-point ‘grind and bind’ approaches. However,
approaches such as RNA hybridisation in situ and single cell
RNA-sequencing do provide the ability to detect expression variability.
It is of further importance to derive the mechanism of variability
which may be driven by interaction of genotypes with exposures or
epistasis.
Conclusions
In summary, breast cancer risk variants are associated with variable
expression of candidate breast cancer susceptibility genes. These
included genes involved in hormonal biosynthetic pathways that are
associated with a single variant (rs11075995). To our knowledge, this
is the first time gene expression variability has been used to identify
candidate cancer susceptibility genes.
Supplementary Information
[135]Supplementary Information^ (2.1MB, pdf)
Abbreviations
eQTL
Expression quantitative trait loci
veQTL
Variable expression quantitative trait loci
Author contributions
G.A.R.W., J.P. and L.C.W. conceived of the study. G.A.R.W., M.A.B.,
A.D., J.P. and L.C.W. designed and coordinated the study. G.A.R.W.
performed the bioinformatics and statistical analyses. T.R.M. provided
the resources from GTEx. G.A.R.W. drafted the manuscript. All authors
have read, contributed to and approved the final manuscript.
Data availability
The data that support the findings of this study are available from
GTEx and dbGaP but restrictions apply to the availability of these
data, which were used under license for the current study, and so are
not publicly available. Data are however available from the authors
upon reasonable request and with permission of GTEx and dbGaP.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher's note
Springer Nature remains neutral with regard to jurisdictional claims in
published maps and institutional affiliations.
These authors contributed equally: John F. Pearson and Logan C. Walker.
Supplementary Information
The online version contains supplementary material available at
10.1038/s41598-021-86690-5.
References