Abstract
Background: White blood cell (WBC) traits and their subtypes such as
basophil count (Bas), eosinophil count (Eos), lymphocyte count (Lym),
monocyte count (Mon), and neutrophil counts (Neu) are known to be
associated with diseases such as stroke, peripheral arterial disease,
and coronary heart disease.
Methods: We meta-analyze summary statistics from genome-wide
association studies in 17,802 participants from the African Partnership
for Chronic Disease Research (APCDR) and African ancestry individuals
from the Blood Cell Consortium (BCX2) using GWAMA. We further carried
out a Bayesian fine mapping to identify causal variants driving the
association with WBC subtypes. To access the causal relationship
between WBC subtypes and asthma, we conducted a two-sample Mendelian
randomization (MR) analysis using summary statistics of the Consortium
on Asthma among African Ancestry Populations (CAAPA: n [cases] = 7,009,
n [control] = 7,645) as our outcome phenotype.
Results: Our metanalysis identified 269 loci at a genome-wide
significant value of (p = 5 × 10^−9) in a composite of the WBC subtypes
while the Bayesian fine-mapping analysis identified genetic variants
that are more causal than the sentinel single-nucleotide polymorphism
(SNP). We found for the first time five novel genes
(LOC126987/MTCO3P14, LINC01525, GAPDHP32/HSD3BP3, FLG-AS1/HMGN3P1, and
TRK-CTT13-1/MGST3) not previously reported to be associated with any
WBC subtype. Our MR analysis showed that Mon (IVW estimate = 0.38, CI:
0.221, 0.539, p < 0.001), Neu (IVW estimate = 0.189, CI: 0.133, 0.245,
p < 0.001), and WBCc (IVW estimate = 0.185, CI: 0.108, 0.262, p <
0.001) are associated with increased risk of asthma. However, there was
no evidence of causal relationship between Lym and asthma risk.
Conclusion: This study provides insight into the relationship between
some WBC subtypes and asthma and potential route in the treatment of
asthma and may further inform a new therapeutic approach.
Keywords: white blood cell traits, asthma, metanalysis, Mendelian
randomization, fine mapping
Introduction
Hematological cells play critical roles in protecting the host organism
against immune assault ([38]Li et al., 2013). Dysregulation or
aberration within the hematopoietic system has been implicated in
several diseases, and this could as well serve as prognostic markers.
For example, an aberration in leukocytes could be an indicator of
lymphoma, leukemia, heart failure, polycythemia, and hypertension
([39]Buttari et al., 2015). White blood cells (WBCs) play a major role
in both innate and adaptive immune systems, serving as the primary
defense system against foreign assaults. Due to the role they play in
defense and immunity, they are used as biomarkers for detecting
inflammation ([40]Gopal et al., 2012). High WBC count has been linked
to the pathogenesis of different disease conditions such as
cardiovascular disease and cancer ([41]Keller et al., 2014). WBCs are
categorized into five subtypes based on their functions and morphology:
basophils (Baso), eosinophils (Eos), lymphocytes (Lym), monocytes
(Mon), and neutrophils (Neu). WBC is an averagely heritable trait, with
h ^2 estimates between 0.14 and 0.40 across all the WBC types
([42]Chinchilla-Vargas et al., 2020).
Asthma is a major health problem in the world, and scientific advances
in the last two decades have improved our understanding and means of
managing it effectively ([43]Bateman et al., 2008). Studies have
estimated the global prevalence of asthma to be 1%–18% ([44]Masoli et
al., 2004; [45]Bateman et al., 2008; [46]To et al., 2012). Despite its
burden in Africa, asthma is classified as one of the neglected diseases
with an estimated average of 12% ([47]Asher et al., 2006; [48]Adeloye
et al., 2013; [49]Hodonsky et al., 2020).
Several genome-wide association studies that have been carried out on
WBC have reported more than 600 associated loci ([50]Yang et al., 2007;
[51]Ferreira et al., 2009; [52]Lo et al., 2011; [53]Okada et al., 2011;
[54]Reiner et al., 2011; [55]Keller et al., 2014; [56]Astle et al.,
2016). Despite the genetic diversity inherent within the African
population, most WBC GWAS have been carried out in European- or East
Asian-ancestry populations.
Mendelian randomization (MR) is a method that uses genetic proxies as
instrumental variables and has been employed to investigate causal
inference between an exposure and outcome phenotype ([57]Fall et al.,
2015).
Therefore, in this study, we performed an ancestry-specific metanalysis
of GWAS of WBC and the five subtypes, Baso, Eos, Lym, Mon, and Neu in
participants from two cohorts. The main aim of this study is to
identify novel loci and signals associated with WBC traits and assess
the causal relationships between these traits and asthma using SNPs as
genetic instruments. Findings from this study may provide some
biological and pathogenic insight into hematological disorders within
the African population.
Methods
Study Population
APCDR Cohort
The African Partnership for Chronic Disease Research (APCDR) is an
organization set out to advance collaboration of epidemiological and
genomic research of non-communicable diseases in sub-Saharan Africa.
The APCDR cohort comprises four studies: the Ugandan Genome Resource
(UGR), the Durban Diabetes Study (DDS), the Durban Case–Control Study
(DCC), and the Africa America Diabetes Mellitus Study (AADM).
DDS is a population-based study carried out among non-pregnant black
African individuals resident in eThekwini municipality in Durban South
Africa from November 2013 to December 2014 ([58]Hird et al., 2016). The
survey, which had 1,165 individuals, combined different socioeconomic
metrics with anthropometric measurements for infectious and
non-communicable diseases. A detailed description of this study
population and study design can be accessed in the paper ([59]Hird et
al., 2016).
The General Population Cohort (GPC) is a two-phase sample collection
study composed of UGWAS (the first sample collected) and UG2G (second
samples collected). GPC is a population-based study comprising
approximately 22,000 residents of Kyamulibwa located in the
southwestern part of Uganda. The goal of the study was to unravel the
epidemiology and genetic drivers of non-communicable and communicable
diseases using an Afrocentric population ([60]Gurdasani et al., 2019).
The Diabetes Case–Control study is a study consisting of individuals of
Zulu ancestry residing in KwaZulu-Natal, aged 40 and above that have
diabetes, recruited in a tertiary health facility in Durban. A total of
1,600 individuals were recruited for this study.
The AADM study is a genetic epidemiological study of individuals with
type 2 diabetes and associated diseases in Africans; this study has
extensively been described in other studies ([61]Rotimi et al., 2004;
[62]Adeyemo et al., 2015).
The Blood Cell Consortium Cohort
BCX2 is a consortium comprising of trans-ethnic data of blood cell
traits of 746,667 individuals from five different ancestries. From the
BCX2, we retrieved WBC traits from individuals with African ancestry
composed of data pulled from the BioMe™ BioBank Program, Cardiovascular
Health Study, Genetic Epidemiology Research on Adult Health and Aging,
Jackson Heart Study, The Multi-Ethnic Study of Atherosclerosis, and the
UK Biobank African ancestry. The same units were used across the WBC
traits in all the cohorts after inverse transformation [WBCc (10^9/L),
Mon (10^9/L), Neu (10^9/L), Eos (10^9/L), Baso (10^9/L), and Lym
(10^9/L)]. To obtain the pool effects of all the studies involved in
the BCX2, an inverse variance-weighted fixed-effect meta-analyses was
performed with the aid of GWAMA. For the study level association
analysis, an additive genetic model of association was used to
determine SNP association while a linear mixed-effect model was
employed to factor in cryptic relatedness.
Hematological Phenotype
White blood and red blood indices of all participants in the DDS cohort
were determined using a SYSMEX XT-2000i machine. For the UGR cohort, an
8.5-ml vacutainer was used to collect the venous blood while a 6-ml
EDTA bottle was used to collect whole blood. The collected blood
samples were stored at a temperature of 4–8°C. For hematological
analysis, a swing bucket centrifuge was used to centrifuge the samples
at 1,000–13,000 RCF (g) for 10 min.
Genotyping, Quality Control, and Imputation
Genotyping and quality control techniques have been described for each
population previously ([63]Gurdasani et al., 2019). Briefly, the DDS
samples were genotyped on Illumina HumanOmni Multi-Ethnic GWAS/Exome
Array employing the Infinium Assay. Illumina GenCall algorithm was used
for genotype calling. The 5,000 GPC samples were genotyped on the
Illumina HumanOmni 2.5M BeadChip array ([64]Table 1). Quality control
of the DDS cohort took into consideration the following criteria:
exclusion of SNPs with heterozygosity > 4 SD from the mean, called
proportion <97%, and sex check fails (F statistic >0.2 for women and
<0.8 for men). Likewise, SNP QC ensured that called proportion was
<−97%, relatedness (IBD >0.90), and Hardy–Weinberg disequilibrium (p <
10^−6). Imputation was done on pre-phased data with IMPUTE2 using a
merged reference label of the whole genome sequence data from the
African Genome Variation Project. Affymetrix Axiom PANAFR SNP array was
used to genotype the AADM data as described previously ([65]Adeyemo et
al., 2015). Genotyping, imputation, and quality control of the African
ancestry cohort of the BCX2 has been described somewhere else ([66]Chen
et al., 2020a).
TABLE 1.
Total number of samples analyzed in APCDR and BCX2.
Traits APCDR BCX2 Total
WBC Count 2,741 15,061 17,802
Lymph count 2,681 13,477 16,158
Mono count 2,681 13,471 16,152
Eos count 2,671 11,615 14,286
Baso count 2,681 11,502 14,183
Neu count 2,671 13,476 16,147
[67]Open in a new tab
^*African Partnership for Chronic Disease Research (APCDR); Blood Cell
Consortium Cohort (BCX2); Mon, Monocytes; Neu, Neutrophil count; WBC,
White blood cell count (WBCC).
Meta-Analysis
Prior to metanalysis, all summary statistics data were manually checked
for integrity and accuracy (i.e., summary statistics downloaded have
the required variables and are appropriately labeled). Some quality
control measures were applied to the summary statistics, SNPs in each
cohort having MAF >0.05 were selected. The SNP association p values
from the summary statistics were meta-analyzed with the aid of GWAMA
(Genome-Wide Association Meta-Analysis) ([68]Morris, 2010). We further
applied genomic control, and Manhattan plots and quantile–quantile
plots were plotted for the meta-analyzed result.
Statistical Fine Mapping
Following our result output from meta-analysis, we used fine-mapping
analysis to pick out possible causal SNPs for the locus ±250 kb of all
the lead SNPs, using a Bayesian approach ([69]Maller et al., 2012). The
Z score was used to calculate the Bayes factor for each SNP denoted as
[MATH: BFi :MATH]
, given by
[MATH: BFi=e[Z∗Z−log(<
mi>K)2] :MATH]
Where K is the number of studies. The posterior probability of driving
the association for each SNP was computed by
[MATH:
Posteri
or prob<
/mi>ability = BFi∑jBFj
:MATH]
Where the summation in the denominator is over all SNPs at the locus.
Ninety-nine percent credible set sizes were derived by sorting all the
SNPs according to their posterior probability
[MATH: BFi :MATH]
at the locus from the highest to the lowest, and then counting the
number of SNPs needed to attain a cumulative posterior probability that
is greater than or equal to 0.99. Index SNPs accounting for more than
50% posterior probability of driving the WBC association at a given
signal were defined as high confidence.
Identification of Potential Novel Variants and Locus Definition
[70]Chen et al. (2020a) had previously performed a trans-ethnic and
ancestry-specific GWAS of blood cell traits using 746,667 individuals
([71]Chen et al., 2020b). To determine if the variants derived from our
study are novel, and perhaps identify novel variants, we checked if any
of our loci was reported or fall within ±250 kb window of those
identified by Chen et al. A locus is further defined as ±250 kb around
the significant SNPs (p < 5 × 10^−09).
Two-Sample Mendelian Randomization
We performed a two-sample Mendelian randomization analysis using the
R-based MR package Mendelian Randomization ([72]Yavorska and Burgess,
2017). To identify independent genetic instruments, we used significant
SNPs (p < 5 × 10^−09) in the summary statistics of Lym, Mon, Neu, and
WBCc derived from the metanalysis of APCDR and BCX2 summary statistics.
We ensured that the instruments selected were not in LD with each other
so that their impact on the exposure and outcome are uncorrelated. This
was achieved by using r ^2 < 0.001 and a 250-kb clumping upstream and
downstream of the lead SNPs. We used asthma as our outcome phenotype.
These data were selected from the Consortium on Asthma among African
Ancestry Populations (CAAPA; 7,009 cases and 7,645 controls) ([73]Daya
et al., 2019). Causal estimates were calculated based on IVW and
sensitivity analysis was carried out using MR-Egger and median-weighted
methods. For each trait, we used Q-statistics to account for
heterogeneity in the instruments and also excluded SNPs that may show
pleiotropy. Proxy SNP for each missing SNP was obtained from LDProxy
([74]Machiela and Chanock, 2015).
Functional Analysis
Functional analysis of SNPs identified by the metanalysis was carried
out using FUMA ([75]Watanabe et al., 2017). Independent SNPs in linkage
disequilibrium or within the same genomic location with the sentinel
SNPs were separated. A p-value cutoff of p < 5 × 10^−09 and 1,000 G
Phase3 AFR reference panel were used. We carried out other functional
analysis such as eQTL tissue expression, pathway enrichment analysis,
and biological process to get more insights into the functionality of
the loci identified. eQTL mapping was performed by mapping SNPs to
genes up to 1 Mb and using the Blood eQTL. We used GeneCards
([76]Stelzer et al., 2016) to determine the functions of the gene. GWAS
catalogue and Open Targets were used to identify any previously
associated phenotypes of the lead SNPs. Annotation of all the genes
identified in this study was done using NCBI’s Genome data viewer.
Pathway analysis of the identified loci was performed using Enrichr;
Enrichr is an integrative web-based server that facilitates the
visualization of the functional characteristics of a gene set. Enrichr
is available online at [77]http://amp.pharm.mssm.edu/Enrichr.
Results
Description of Study
The total samples analyzed for each WBC trait are shown in [78]Table 1.
WBCc is the trait with the highest number of individuals and Baso has
the lowest samples analyzed.
Metanalysis of WBC From APCDR and BCX2
For each of the WBC trait subtypes: Lym: 13 SNPs, Mon: 680 SNPs, Neu:
5,308 SNPs, and WBCc: 4,462 SNPs attained genome-wide significance (p <
5 × 10^−09) ([79]Figure 1). No SNPs in the Baso and Eos were
significant at p < 5 × 10^−09. Using a genomic distance of ±250 kb, we
identified 4, 34, 124, and 108 lead SNPs within different loci in Lym,
Mon, Neu, and WBCc, respectively, at p < 5 × 10^−09 [[80]Supplementary
Table S1 (ST1–ST4)]. We examined if the sentinel SNPs were within
±250 kb of SNPs reported in the Chen et al. study. When compared to
this, all the SNPs in Lym fall with ±250 kb of those previously
reported, while only eight SNPs in Mon, 38 SNPs in Neu, and 13 SNPs in
WBCc were unique. However, 4 SNPs out of the 8 SNPs unique in Mon, 3
out of 38 in Neu, and 1 out of 13 in WBCc have been previously
identified to be associated with WBC traits when queried on the GWAS
catalogue ([81]Buniello et al., 2019) and Open Targets ([82]Koscielny
et al., 2017). Two SNPs (rs1103700 and rs6693634 mapped near
RPS10P8/CD1A and UHMK1/UQCRBP2) are common in Mon and Neu.
FIGURE 1.
[83]FIGURE 1
[84]Open in a new tab
Manhattan plot of metanalysis results of Baso (A), Eos (B), Lym (C),
Mon (D), Neu (E), and WBC (F).
Fine Mapping
Fine mapping seeks to analyze a trait-associated region to determine
variants that are causal to a trait of interest. Fine mapping of loci
was carried out within 250 kb upstream and downstream genomic distance
of the sentinel variant identified via metanalysis. For each locus
fine-mapped, we established the 99% credible set of SNPs that jointly
make 99% of the posterior probability of driving the association
([85]Figure 2). The fine-mapping analysis revealed that some of the
lead variants such as rs369124352 (MAGI3), rs10918211 (LRRC52), and
rs10800292 (LINCO1363/POU2F1) accounted for more than 50% of the
posterior probability driving the association with these SNPs as the
only variant within the 99% credible set. Several other variants were
identified as the causal SNPs other than the sentinel SNP driving the
association in WBC trait subtypes. Summary of the 99% credible set of
variants driving the WBC trait can be found in [86]Supplementary Table
S2. We further went ahead and checked if these causal SNPs and their
corresponding genes have been previously reported using the GWAS
catalogue and Open Target; only five variants—rs11184898 (LOC126987,
MTCO3P14) ([87]Figure 3), rs907662 (LINC01525), rs7553527 (GAPDHP32,
HSD3BP3), rs1923504 (FLG-AS1, HMGN3P1), and rs10733045 (TRK-CTT13-1,
MGST3)—have not been reported to be associated with any WBC trait.
FIGURE 2.
[88]FIGURE 2
[89]Open in a new tab
99% credible set of SNPs that jointly make 99% of the posterior
probability of driving each WBC traits.
FIGURE 3.
[90]FIGURE 3
[91]Open in a new tab
Locus zoom regional association plot for rs1184898.
Causal Effects of WBC Traits on Asthma
We set out to evaluate the causal relationship between WBC traits (Lym,
Mon, Neu, and WBCc) and asthma. Our MR analysis identified strong
positive association of Mon (IVW estimate = 0.38, CI: 0.221, 0.539, p <
0.001), Neu (IVW estimate = 0.189, CI: 0.133, 0.245, p < 0.001), and
WBCc (IVW estimate = 0.185, CI: 0.108, 0.262, p < 0.001) with increased
risk of asthma. However, there were no evidence of causal relationship
between Lym and asthma risk (IVW estimate = −0.079, CI: −0.779, 0.621,
p = 0.825) ([92]Figure 4, [93]Supplementary Table S3), though there was
an inverse relationship between Lym and asthma risk. The causal
estimates of the weighted median and the MR-egger methods of Mon showed
a similar effect to the IVW method ([94]Supplementary Table S3).
FIGURE 4.
[95]FIGURE 4
[96]Open in a new tab
Forest plot for the association between Lym, Mon, Neu, and WBC with
asthma estimated using MR-IVW method.
Functional Analysis
MAGMA analysis using GTEx v8: 54 tissue types and GTEx v8: 54 general
tissue types showed significant expression in whole blood ([97]Figure
5E). The loci were also expressed in other tissues such as stomach and
the colon but not at a significant level. Lipid and atherosclerosis,
nitrogen metabolism, and FoxO signaling pathway are some of the
pathways enriched by these loci [[98]Figure 5A, [99]Supplementary Table
S4 (ST1)]. These loci were mapped at a significant level to
thrombocytopenia-absent radius syndrome, autoimmune lymphoproliferative
syndrome, lactose intolerance, central core myopathy etc. [[100]Figure
5B, [101]Supplementary Table S4 (ST2)]. They are also involved in the
regulation of cellular response to transforming growth factor beta
stimuli, regulation of transmembrane receptor protein serine/threonine
kinase signaling pathway, regulation of cellular biosynthetic process,
etc. ([102]Figure 5C), [103]Supplementary Table S4 (ST3)] and also in
exogenous lipid antigen binding molecular functions ([104]Figure 5D).
FIGURE 5.
[105]FIGURE 5
[106]Open in a new tab
KEGG pathway enrichment (A), Jensen disease enrichment (B), Biological
process (C), and Molecular function (D) of the genes associated with
WBC subtypes identified by metanalysis. Tissue expression of enriched
genes (E). Red bar in 3 E connotes tissues with significant enrichment
while blue bars indicate tissues with no significant enrichment.
Discussion
To the best of our knowledge, this is the first study to explore the
causal relationship of WBC traits and asthma in an African population.
Using metanalysis and Bayesian fine mapping, we identified 269
significant SNPs associated with WBC traits. Among these, five genes
(LOC126987/MTCO3P14, LINC01525, GAPDHP32/HSD3BP3, FLG-AS1, and
TRK-CTT13-1/MGST3) for the first time are reported to be associated
with WBC traits. We also found causal relationship between Mon, Neu,
and WBCc with asthma, while no causal relationship was seen between Lym
and Asthma.
FLG-AS1 (FLG Antisense RNA 1) is an RNA gene that is a member of the
long non-coding RNA (lncRNA). FLG-AS1 is associated with asthma
([107]Ferreira et al., 2019; [108]Johansson et al., 2019; [109]Pividori
et al., 2019; [110]Zhu et al., 2019; [111]Olafsdottir et al., 2020),
melanoma ([112]Rashkin et al., 2020), eczema ([113]Johansson et al.,
2019; [114]Kichaev et al., 2019), and acute myeloid leukemia ([115]Lv
et al., 2017). Microsomal glutathione S-transferase 3 encoded by MGST3
has been shown to help in cellular defense of host organisms against
lipid hydroperoxides, which may arise as a result of oxidative stress
([116]Jakobsson et al., 1997; [117]Stamova et al., 2013). There are
evidences of oxidative stress in asthma ([118]Misso and Thompson, 2005;
[119]Andrianjafimasy et al., 2017; [120]Sahiner et al., 2018), this may
explain the enrichment of this gene in the blood and causal association
with asthma.
Interestingly, our result is consistent with the multivariable MR
analysis of [121]Astle et al. (2016), which showed a protective effect
of monocytes on Asthma. Furthermore, we also found a protective effect
of neutrophils against asthma, consistent with [122]Guyatt et al.
(2020), which did not find substantial evidence for a harmful effect of
neutrophils on asthma.
We found atherosclerosis to be significant in the KEGG pathway
enrichment, and atherosclerosis is a major risk factor of coronary
heart disease (CHD). Inflammation is an attribute of atherosclerosis;
hence, several inflammatory cells such as Mon, Lym., Eos, and Neu have
been implicated in CHD ([123]Prentice et al., 1982; [124]Madjid and
Fatemi, 2013). Most importantly, several epidemiological studies have
revealed that leukocyte count is an independent risk factor for CHD,
and a risk factor for future cardiovascular events in individuals who
do not have cardiovascular diseases ([125]Weijenberg et al., 1996;
[126]Madjid et al., 2004; [127]Chen et al., 2018). WBC count has also
been suggested as a risk factor for atherosclerotic vascular diseases
([128]Do LeeDo et al., 2001).
Significant enrichment of thrombocytopenia-absent radius syndrome
(TAR), which is a rare congenital disorder ([129]Greenhalgh et al.,
2002), suggests the involvement of some WBC trait genes in the
pathogenesis of this disease, as this disease is characterized by low
levels of platelets in the blood ([130]Greenhalgh et al., 2002).
Compared to other studies, one of the major strengths of this study is
the use of African-ancestry data and the increased power from
metanalysis. This enables the discovery of loci that have otherwise not
been reported by previous studies. In addition, the CAAPA summary
statistics is one of the latest cohorts of Asthma in Africa-admixed
population.
We also applied different sensitivity analysis in our MR analysis, and
we used strong instrumental variables by assessing the instrumental
validity.
This study has its limitation, which is characteristic of two-sample MR
analysis. Similar to any other non-experimental data that seek to make
causal inference, where some experimentally unverifiable assumptions
are made, our study is not an exception to this limitation.
Conclusively, we found evidence of causality between some WBC traits
and asthma, and though some observational and MR data support these
results, we believe more laboratorial experiments are needed to
understand the biological mechanism of this causality.
Key Messages
* • We carried out a well-powered meta-analysis genome-wide
association study of white blood cell (WBC) traits in African
populations.
* • We identified not previously known genes driving WBC traits in an
African population.
* • Bayesian fine mapping identified more credible variants with high
posterior probability associated with WBC subtypes.
* • Mendelian Randomization Analysis found a causal relationship
between monocyte count, neutrophil count, and white blood cell
count with asthma in African populations.
* • Findings from this study could provide more insight into the
roles WBC traits play in the pathogenesis of asthma and could as
well provide some directions in the treatment of asthma.
Data Availability Statement
The original contributions presented in the study are included in the
article/[131]Supplementary Material. Further inquiries can be directed
to the corresponding author.
Author Contributions
SF conceptualized the study. OS led the main analyses. CS, TM, TC, and
SF contributed to data analyses. OS wrote the first draft of the
manuscript. TM, TC, and SF reviewed the first draft. SF and TC
supervised the project. All the authors read and provided critical
feedback on the paper.
Funding
SF is an international intermediate fellow funded by the Wellcome Trust
grant (220740/Z/20/Z) at the MRC/UVRI and LSHTM. TC is an international
training fellow supported by the Wellcome Trust grant (214205/Z/18/Z).
MN acknowledges the support of Makerere University Non-Communicable
Diseases (MakNCD). TM is an RHDGen PhD fellow supported by the Wellcome
Trust, the University of Cape Town, and the inaugural Bongani Mayosi
UCT-PHRI Scholarship (McMaster University). ON and SF are funded in
part by the National Institutes of Health Common Fund to the H3ABioNet
Project grant number [132]5U24HG006941-09.
Conflict of Interest
The authors declare that the research was conducted in the absence of
any commercial or financial relationships that could be construed as a
potential conflict of interest.
Publisher’s Note
All claims expressed in this article are solely those of the authors
and do not necessarily represent those of their affiliated
organizations, or those of the publisher, the editors, and the
reviewers. Any product that may be evaluated in this article, or claim
that may be made by its manufacturer, is not guaranteed or endorsed by
the publisher.
Supplementary Material
The Supplementary Material for this article can be found online at:
[133]https://www.frontiersin.org/articles/10.3389/fgene.2021.749415/ful
l#supplementary-material
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References